ID,TITLE,ABSTRACT,Computer Science,Physics,Mathematics,Statistics,Quantitative Biology,Quantitative Finance
16778,Probing Primordial-Black-Hole Dark Matter with Gravitational Waves,"  Primordial black holes (PBHs) have long been suggested as a candidate for
making up some or all of the dark matter in the Universe. Most of the
theoretically possible mass range for PBH dark matter has been ruled out with
various null observations of expected signatures of their interaction with
standard astrophysical objects. However, current constraints are significantly
less robust in the 20 M_sun < M_PBH < 100 M_sun mass window, which has received
much attention recently, following the detection of merging black holes with
estimated masses of ~30 M_sun by LIGO and the suggestion that these could be
black holes formed in the early Universe. We consider the potential of advanced
LIGO (aLIGO) operating at design sensitivity to probe this mass range by
looking for peaks in the mass spectrum of detected events. To quantify the
background, which is due to black holes that are formed from dying stars, we
model the shape of the stellar-black-hole mass function and calibrate its
amplitude to match the O1 results. Adopting very conservative assumptions about
the PBH and stellar-black-hole merger rates, we show that ~5 years of aLIGO
data can be used to detect a contribution of >20 M_sun PBHs to dark matter down
to f_PBH<0.5 at >99.9% confidence level. Combined with other probes that
already suggest tension with f_PBH=1, the obtainable independent limits from
aLIGO will thus enable a firm test of the scenario that PBHs make up all of
dark matter.
",0,1,0,0,0,0
16779,Fundamental limits of low-rank matrix estimation: the non-symmetric case,"  We consider the high-dimensional inference problem where the signal is a
low-rank matrix which is corrupted by an additive Gaussian noise. Given a
probabilistic model for the low-rank matrix, we compute the limit in the large
dimension setting for the mutual information between the signal and the
observations, as well as the matrix minimum mean square error, while the rank
of the signal remains constant. This allows to locate the information-theoretic
threshold for this estimation problem, i.e. the critical value of the signal
intensity below which it is impossible to recover the low-rank matrix.
",0,0,1,0,0,0
16780,Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees,"  Gaussian processes (GPs) offer a flexible class of priors for nonparametric
Bayesian regression, but popular GP posterior inference methods are typically
prohibitively slow or lack desirable finite-data guarantees on quality. We
develop an approach to scalable approximate GP regression with finite-data
guarantees on the accuracy of pointwise posterior mean and variance estimates.
Our main contribution is a novel objective for approximate inference in the
nonparametric setting: the preconditioned Fisher (pF) divergence. We show that
unlike the Kullback--Leibler divergence (used in variational inference), the pF
divergence bounds the 2-Wasserstein distance, which in turn provides tight
bounds the pointwise difference of the mean and variance functions. We
demonstrate that, for sparse GP likelihood approximations, we can minimize the
pF divergence efficiently. Our experiments show that optimizing the pF
divergence has the same computational requirements as variational sparse GPs
while providing comparable empirical performance--in addition to our novel
finite-data quality guarantees.
",0,0,0,1,0,0
16781,Integral representations and asymptotic behaviours of Mittag-Leffler type functions of two variables,"  The paper explores various special functions which generalize the
two-parametric Mittag-Leffler type function of two variables. Integral
representations for these functions in different domains of variation of
arguments for certain values of the parameters are obtained. The asymptotic
expansions formulas and asymptotic properties of such functions are also
established for large values of the variables. This provides statements of
theorems for these formulas and their corresponding properties.
",0,0,1,0,0,0
16782,Efficient Mendler-Style Lambda-Encodings in Cedille,"  It is common to model inductive datatypes as least fixed points of functors.
We show that within the Cedille type theory we can relax functoriality
constraints and generically derive an induction principle for Mendler-style
lambda-encoded inductive datatypes, which arise as least fixed points of
covariant schemes where the morphism lifting is defined only on identities.
Additionally, we implement a destructor for these lambda-encodings that runs in
constant-time. As a result, we can define lambda-encoded natural numbers with
an induction principle and a constant-time predecessor function so that the
normal form of a numeral requires only linear space. The paper also includes
several more advanced examples.
",1,0,0,0,0,0
16783,Super Jack-Laurent Polynomials,"  Let $\mathcal{D}_{n,m}$ be the algebra of the quantum integrals of the
deformed Calogero-Moser-Sutherland problem corresponding to the root system of
the Lie superalgebra $\frak{gl}(n,m)$. The algebra $\mathcal{D}_{n,m}$ acts
naturally on the quasi-invariant Laurent polynomials and we investigate the
corresponding spectral decomposition. Even for general value of the parameter
$k$ the spectral decomposition is not simple and we prove that the image of the
algebra $\mathcal{D}_{n,m}$ in the algebra of endomorphisms of the generalised
eigen-space is $k[\varepsilon]^{\otimes r}$ where $k[\varepsilon]$ is the
algebra of the dual numbers the corresponding representation is the regular
representation of the algebra $k[\varepsilon]^{\otimes r}$.
",0,0,1,0,0,0
16784,A New Classification of Technologies,"  This study here suggests a classification of technologies based on taxonomic
characteristics of interaction between technologies in complex systems that is
not a studied research field in economics of technical change. The proposed
taxonomy here categorizes technologies in four typologies, in a broad analogy
with the ecology: 1) technological parasitism is a relationship between two
technologies T1 and T2 in a complex system S where one technology T1 benefits
from the interaction with T2, whereas T2 has a negative side from interaction
with T1; 2) technological commensalism is a relationship between two
technologies in S where one technology benefits from the other without
affecting it; 3) technological mutualism is a relationship in which each
technology benefits from the activity of the other within complex systems; 4)
technological symbiosis is a long-term interaction between two (or more)
technologies that evolve together in complex systems. This taxonomy
systematizes the typologies of interactive technologies within complex systems
and predicts their evolutionary pathways that generate stepwise coevolutionary
processes of complex systems of technology. This study here begins the process
of generalizing, as far as possible, critical typologies of interactive
technologies that explain the long-run evolution of technology. The theoretical
framework developed here opens the black box of the interaction between
technologies that affects, with different types of technologies, the
evolutionary pathways of complex systems of technology over time and space.
Overall, then, this new theoretical framework may be useful for bringing a new
perspective to categorize the gradient of benefit to technologies from
interaction with other technologies that can be a ground work for development
of more sophisticated concepts to clarify technological and economic change in
human society.
",1,0,0,0,0,0
16785,Potential functions on Grassmannians of planes and cluster transformations,"  With a triangulation of a planar polygon with $n$ sides, one can associate an
integrable system on the Grassmannian of 2-planes in an $n$-space. In this
paper, we show that the potential functions of Lagrangian torus fibers of the
integrable systems associated with different triangulations glue together by
cluster transformations. We also prove that the cluster transformations
coincide with the wall-crossing formula in Lagrangian intersection Floer
theory.
",0,0,1,0,0,0
16786,Physical properties of the first spectroscopically confirmed red supergiant stars in the Sculptor Group galaxy NGC 55,"  We present K-band Multi-Object Spectrograph (KMOS) observations of 18 Red
Supergiant (RSG) stars in the Sculptor Group galaxy NGC 55. Radial velocities
are calculated and are shown to be in good agreement with previous estimates,
confirming the supergiant nature of the targets and providing the first
spectroscopically confirmed RSGs in NGC 55. Stellar parameters are estimated
for 14 targets using the $J$-band analysis technique, making use of
state-of-the-art stellar model atmospheres. The metallicities estimated confirm
the low-metallicity nature of NGC 55, in good agreement with previous studies.
This study provides an independent estimate of the metallicity gradient of NGC
55, in excellent agreement with recent results published using hot massive
stars. In addition, we calculate luminosities of our targets and compare their
distribution of effective temperatures and luminosities to other RSGs, in
different environments, estimated using the same technique.
",0,1,0,0,0,0
16787,"Gas near a wall: a shortened mean free path, reduced viscosity, and the manifestation of a turbulent Knudsen layer in the Navier-Stokes solution of a shear flow","  For the gas near a solid planar wall, we propose a scaling formula for the
mean free path of a molecule as a function of the distance from the wall, under
the assumption of a uniform distribution of the incident directions of the
molecular free flight. We subsequently impose the same scaling onto the
viscosity of the gas near the wall, and compute the Navier-Stokes solution of
the velocity of a shear flow parallel to the wall. This solution exhibits the
Knudsen velocity boundary layer in agreement with the corresponding Direct
Simulation Monte Carlo computations for argon and nitrogen. We also find that
the proposed mean free path and viscosity scaling sets the second derivative of
the velocity to infinity at the wall boundary of the flow domain, which
suggests that the gas flow is formally turbulent within the Knudsen boundary
layer near the wall.
",0,1,0,0,0,0
16788,Greater data science at baccalaureate institutions,"  Donoho's JCGS (in press) paper is a spirited call to action for
statisticians, who he points out are losing ground in the field of data science
by refusing to accept that data science is its own domain. (Or, at least, a
domain that is becoming distinctly defined.) He calls on writings by John
Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that
statisticians have been dealing with data science for years, and encourages
acceptance of the direction of the field while also ensuring that statistics is
tightly integrated.
As faculty at baccalaureate institutions (where the growth of undergraduate
statistics programs has been dramatic), we are keen to ensure statistics has a
place in data science and data science education. In his paper, Donoho is
primarily focused on graduate education. At our undergraduate institutions, we
are considering many of the same questions.
",0,0,0,1,0,0
16789,Direct evidence of hierarchical assembly at low masses from isolated dwarf galaxy groups,"  The demographics of dwarf galaxy populations have long been in tension with
predictions from the Cold Dark Matter (CDM) paradigm. If primordial density
fluctuations were scale-free as predicted, dwarf galaxies should themselves
host dark matter subhaloes, the most massive of which may have undergone star
formation resulting in dwarf galaxy groups. Ensembles of dwarf galaxies are
observed as satellites of more massive galaxies, and there is observational and
theoretical evidence to suggest that these satellites at z=0 were captured by
the massive host halo as a group. However, the evolution of dwarf galaxies is
highly susceptible to environment making these satellite groups imperfect
probes of CDM in the low mass regime. We have identified one of the clearest
examples to date of hierarchical structure formation at low masses: seven
isolated, spectroscopically confirmed groups with only dwarf galaxies as
members. Each group hosts 3-5 known members, has a baryonic mass of ~4.4 x 10^9
to 2 x 10^10 Msun, and requires a mass-to-light ratio of <100 to be
gravitationally bound. Such groups are predicted to be rare theoretically and
found to be rare observationally at the current epoch and thus provide a unique
window into the possible formation mechanism of more massive, isolated
galaxies.
",0,1,0,0,0,0
16790,Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data,"  Risk prediction is central to both clinical medicine and public health. While
many machine learning models have been developed to predict mortality, they are
rarely applied in the clinical literature, where classification tasks typically
rely on logistic regression. One reason for this is that existing machine
learning models often seek to optimize predictions by incorporating features
that are not present in the databases readily available to providers and policy
makers, limiting generalizability and implementation. Here we tested a number
of machine learning classifiers for prediction of six-month mortality in a
population of elderly Medicare beneficiaries, using an administrative claims
database of the kind available to the majority of health care payers and
providers. We show that machine learning classifiers substantially outperform
current widely-used methods of risk prediction but only when used with an
improved feature set incorporating insights from clinical medicine, developed
for this study. Our work has applications to supporting patient and provider
decision making at the end of life, as well as population health-oriented
efforts to identify patients at high risk of poor outcomes.
",1,0,0,1,0,0
16791,R-C3D: Region Convolutional 3D Network for Temporal Activity Detection,"  We address the problem of activity detection in continuous, untrimmed video
streams. This is a difficult task that requires extracting meaningful
spatio-temporal features to capture activities, accurately localizing the start
and end times of each activity. We introduce a new model, Region Convolutional
3D Network (R-C3D), which encodes the video streams using a three-dimensional
fully convolutional network, then generates candidate temporal regions
containing activities, and finally classifies selected regions into specific
activities. Computation is saved due to the sharing of convolutional features
between the proposal and the classification pipelines. The entire model is
trained end-to-end with jointly optimized localization and classification
losses. R-C3D is faster than existing methods (569 frames per second on a
single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.
We further demonstrate that our model is a general activity detection framework
that does not rely on assumptions about particular dataset properties by
evaluating our approach on ActivityNet and Charades. Our code is available at
this http URL.
",1,0,0,0,0,0
16792,Regularization for Deep Learning: A Taxonomy,"  Regularization is one of the crucial ingredients of deep learning, yet the
term regularization has various definitions, and regularization methods are
often studied separately from each other. In our work we present a systematic,
unifying taxonomy to categorize existing methods. We distinguish methods that
affect data, network architectures, error terms, regularization terms, and
optimization procedures. We do not provide all details about the listed
methods; instead, we present an overview of how the methods can be sorted into
meaningful categories and sub-categories. This helps revealing links and
fundamental similarities between them. Finally, we include practical
recommendations both for users and for developers of new regularization
methods.
",1,0,0,1,0,0
16793,Re-Evaluating the Netflix Prize - Human Uncertainty and its Impact on Reliability,"  In this paper, we examine the statistical soundness of comparative
assessments within the field of recommender systems in terms of reliability and
human uncertainty. From a controlled experiment, we get the insight that users
provide different ratings on same items when repeatedly asked. This volatility
of user ratings justifies the assumption of using probability densities instead
of single rating scores. As a consequence, the well-known accuracy metrics
(e.g. MAE, MSE, RMSE) yield a density themselves that emerges from convolution
of all rating densities. When two different systems produce different RMSE
distributions with significant intersection, then there exists a probability of
error for each possible ranking. As an application, we examine possible ranking
errors of the Netflix Prize. We are able to show that all top rankings are more
or less subject to high probabilities of error and that some rankings may be
deemed to be caused by mere chance rather than system quality.
",1,0,0,0,0,0
16794,Infinite monochromatic sumsets for colourings of the reals,"  N. Hindman, I. Leader and D. Strauss proved that it is consistent that there
is a finite colouring of $\mathbb R$ so that no infinite sumset
$X+X=\{x+y:x,y\in X\}$ is monochromatic. Our aim in this paper is to prove a
consistency result in the opposite direction: we show that, under certain
set-theoretic assumptions, for any $c:\mathbb R\to r$ with $r$ finite there is
an infinite $X\subseteq \mathbb R$ so that $c$ is constant on $X+X$.
",0,0,1,0,0,0
16795,Mean-Field Games with Differing Beliefs for Algorithmic Trading,"  Even when confronted with the same data, agents often disagree on a model of
the real-world. Here, we address the question of how interacting heterogenous
agents, who disagree on what model the real-world follows, optimize their
trading actions. The market has latent factors that drive prices, and agents
account for the permanent impact they have on prices. This leads to a large
stochastic game, where each agents' performance criteria is computed under a
different probability measure. We analyse the mean-field game (MFG) limit of
the stochastic game and show that the Nash equilibria is given by the solution
to a non-standard vector-valued forward-backward stochastic differential
equation. Under some mild assumptions, we construct the solution in terms of
expectations of the filtered states. We prove the MFG strategy forms an
\epsilon-Nash equilibrium for the finite player game. Lastly, we present a
least-squares Monte Carlo based algorithm for computing the optimal control and
illustrate the results through simulation in market where agents disagree on
the model.
",0,0,0,0,0,1
16796,"Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors","  In addition to hardware wall-time restrictions commonly seen in
high-performance computing systems, it is likely that future systems will also
be constrained by energy budgets. In the present work, finite difference
algorithms of varying computational and memory intensity are evaluated with
respect to both energy efficiency and runtime on an Intel Ivy Bridge CPU node,
an Intel Xeon Phi Knights Landing processor, and an NVIDIA Tesla K40c GPU. The
conventional way of storing the discretised derivatives to global arrays for
solution advancement is found to be inefficient in terms of energy consumption
and runtime. In contrast, a class of algorithms in which the discretised
derivatives are evaluated on-the-fly or stored as thread-/process-local
variables (yielding high compute intensity) is optimal both with respect to
energy consumption and runtime. On all three hardware architectures considered,
a speed-up of ~2 and an energy saving of ~2 are observed for the high compute
intensive algorithms compared to the memory intensive algorithm. The energy
consumption is found to be proportional to runtime, irrespective of the power
consumed and the GPU has an energy saving of ~5 compared to the same algorithm
on a CPU node.
",1,1,0,0,0,0
16797,A Plane of High Velocity Galaxies Across the Local Group,"  We recently showed that several Local Group (LG) galaxies have much higher
radial velocities (RVs) than predicted by a 3D dynamical model of the standard
cosmological paradigm. Here, we show that 6 of these 7 galaxies define a thin
plane with root mean square thickness of only 101 kpc despite a widest extent
of nearly 3 Mpc, much larger than the conventional virial radius of the Milky
Way (MW) or M31. This plane passes within ${\sim 70}$ kpc of the MW-M31
barycentre and is oriented so the MW-M31 line is inclined by $16^\circ$ to it.
We develop a toy model to constrain the scenario whereby a past MW-M31 flyby
in Modified Newtonian Dynamics (MOND) forms tidal dwarf galaxies that settle
into the recently discovered planes of satellites around the MW and M31. The
scenario is viable only for a particular MW-M31 orbital plane. This roughly
coincides with the plane of LG dwarfs with anomalously high RVs.
Using a restricted $N$-body simulation of the LG in MOND, we show how the
once fast-moving MW and M31 gravitationally slingshot test particles outwards
at high speeds. The most distant such particles preferentially lie within the
MW-M31 orbital plane, probably because the particles ending up with the highest
RVs are those flung out almost parallel to the motion of the perturber. This
suggests a dynamical reason for our finding of a similar trend in the real LG,
something not easily explained as a chance alignment of galaxies with an
isotropic or mildly flattened distribution (probability $= {0.0015}$).
",0,1,0,0,0,0
16798,Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps,"  We present a method for scalable and fully 3D magnetic field simultaneous
localisation and mapping (SLAM) using local anomalies in the magnetic field as
a source of position information. These anomalies are due to the presence of
ferromagnetic material in the structure of buildings and in objects such as
furniture. We represent the magnetic field map using a Gaussian process model
and take well-known physical properties of the magnetic field into account. We
build local maps using three-dimensional hexagonal block tiling. To make our
approach computationally tractable we use reduced-rank Gaussian process
regression in combination with a Rao-Blackwellised particle filter. We show
that it is possible to obtain accurate position and orientation estimates using
measurements from a smartphone, and that our approach provides a scalable
magnetic field SLAM algorithm in terms of both computational complexity and map
storage.
",1,0,0,1,0,0
16799,K-means Algorithm over Compressed Binary Data,"  We consider a network of binary-valued sensors with a fusion center. The
fusion center has to perform K-means clustering on the binary data transmitted
by the sensors. In order to reduce the amount of data transmitted within the
network, the sensors compress their data with a source coding scheme based on
binary sparse matrices. We propose to apply the K-means algorithm directly over
the compressed data without reconstructing the original sensors measurements,
in order to avoid potentially complex decoding operations. We provide
approximated expressions of the error probabilities of the K-means steps in the
compressed domain. From these expressions, we show that applying the K-means
algorithm in the compressed domain enables to recover the clusters of the
original domain. Monte Carlo simulations illustrate the accuracy of the
obtained approximated error probabilities, and show that the coding rate needed
to perform K-means clustering in the compressed domain is lower than the rate
needed to reconstruct all the measurements.
",1,0,1,0,0,0
16800,Variational Inference for Gaussian Process Models with Linear Complexity,"  Large-scale Gaussian process inference has long faced practical challenges
due to time and space complexity that is superlinear in dataset size. While
sparse variational Gaussian process models are capable of learning from
large-scale data, standard strategies for sparsifying the model can prevent the
approximation of complex functions. In this work, we propose a novel
variational Gaussian process model that decouples the representation of mean
and covariance functions in reproducing kernel Hilbert space. We show that this
new parametrization generalizes previous models. Furthermore, it yields a
variational inference problem that can be solved by stochastic gradient ascent
with time and space complexity that is only linear in the number of mean
function parameters, regardless of the choice of kernels, likelihoods, and
inducing points. This strategy makes the adoption of large-scale expressive
Gaussian process models possible. We run several experiments on regression
tasks and show that this decoupled approach greatly outperforms previous sparse
variational Gaussian process inference procedures.
",1,0,0,1,0,0
16801,Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data,"  Diffusion MRI measurements using hyperpolarized gases are generally acquired
during patient breath hold, which yields a compromise between achievable image
resolution, lung coverage and number of b-values. In this work, we propose a
novel method that accelerates the acquisition of MR diffusion data by
undersampling in both spatial and b-value dimensions, thanks to incorporating
knowledge about the signal decay into the reconstruction (SIDER). SIDER is
compared to total variation (TV) reconstruction by assessing their effect on
both the recovery of ventilation images and estimated mean alveolar dimensions
(MAD). Both methods are assessed by retrospectively undersampling diffusion
datasets of normal volunteers and COPD patients (n=8) for acceleration factors
between x2 and x10. TV led to large errors and artefacts for acceleration
factors equal or larger than x5. SIDER improved TV, presenting lower errors and
histograms of MAD closer to those obtained from fully sampled data for
accelerations factors up to x10. SIDER preserved image quality at all
acceleration factors but images were slightly smoothed and some details were
lost at x10. In conclusion, we have developed and validated a novel compressed
sensing method for lung MRI imaging and achieved high acceleration factors,
which can be used to increase the amount of data acquired during a breath-hold.
This methodology is expected to improve the accuracy of estimated lung
microstructure dimensions and widen the possibilities of studying lung diseases
with MRI.
",1,1,0,0,0,0
16802,Rabi noise spectroscopy of individual two-level tunneling defects,"  Understanding the nature of two-level tunneling defects is important for
minimizing their disruptive effects in various nano-devices. By exploiting the
resonant coupling of these defects to a superconducting qubit, one can probe
and coherently manipulate them individually. In this work we utilize a phase
qubit to induce Rabi oscillations of single tunneling defects and measure their
dephasing rates as a function of the defect's asymmetry energy, which is tuned
by an applied strain. The dephasing rates scale quadratically with the external
strain and are inversely proportional to the Rabi frequency. These results are
analyzed and explained within a model of interacting standard defects, in which
pure dephasing of coherent high-frequency (GHz) defects is caused by
interaction with incoherent low-frequency thermally excited defects.
",0,1,0,0,0,0
16803,Learning rate adaptation for federated and differentially private learning,"  We propose an algorithm for the adaptation of the learning rate for
stochastic gradient descent (SGD) that avoids the need for validation set use.
The idea for the adaptiveness comes from the technique of extrapolation: to get
an estimate for the error against the gradient flow which underlies SGD, we
compare the result obtained by one full step and two half-steps. The algorithm
is applied in two separate frameworks: federated and differentially private
learning. Using examples of deep neural networks we empirically show that the
adaptive algorithm is competitive with manually tuned commonly used
optimisation methods for differentially privately training. We also show that
it works robustly in the case of federated learning unlike commonly used
optimisation methods.
",0,0,0,1,0,0
16804,Holomorphic Hermite polynomials in two variables,"  Generalizations of the Hermite polynomials to many variables and/or to the
complex domain have been located in mathematical and physical literature for
some decades. Polynomials traditionally called complex Hermite ones are mostly
understood as polynomials in $z$ and $\bar{z}$ which in fact makes them
polynomials in two real variables with complex coefficients. The present paper
proposes to investigate for the first time holomorphic Hermite polynomials in
two variables. Their algebraic and analytic properties are developed here.
While the algebraic properties do not differ too much for those considered so
far, their analytic features are based on a kind of non-rotational
orthogonality invented by van Eijndhoven and Meyers. Inspired by their
invention we merely follow the idea of Bargmann's seminal paper (1961) giving
explicit construction of reproducing kernel Hilbert spaces based on those
polynomials. ""Homotopic"" behavior of our new formation culminates in comparing
it to the very classical Bargmann space of two variables on one edge and the
aforementioned Hermite polynomials in $z$ and $\bar{z}$ on the other. Unlike in
the case of Bargmann's basis our Hermite polynomials are not product ones but
factorize to it when bonded together with the first case of limit properties
leading both to the Bargmann basis and suitable form of the reproducing kernel.
Also in the second limit we recover standard results obeyed by Hermite
polynomials in $z$ and $\bar{z}$.
",0,0,1,0,0,0
16805,"Equilibria, information and frustration in heterogeneous network games with conflicting preferences","  Interactions between people are the basis on which the structure of our
society arises as a complex system and, at the same time, are the starting
point of any physical description of it. In the last few years, much
theoretical research has addressed this issue by combining the physics of
complex networks with a description of interactions in terms of evolutionary
game theory. We here take this research a step further by introducing a most
salient societal factor such as the individuals' preferences, a characteristic
that is key to understand much of the social phenomenology these days. We
consider a heterogeneous, agent-based model in which agents interact
strategically with their neighbors but their preferences and payoffs for the
possible actions differ. We study how such a heterogeneous network behaves
under evolutionary dynamics and different strategic interactions, namely
coordination games and best shot games. With this model we study the emergence
of the equilibria predicted analytically in random graphs under best response
dynamics, and we extend this test to unexplored contexts like proportional
imitation and scale free networks. We show that some theoretically predicted
equilibria do not arise in simulations with incomplete Information, and we
demonstrate the importance of the graph topology and the payoff function
parameters for some games. Finally, we discuss our results with available
experimental evidence on coordination games, showing that our model agrees
better with the experiment that standard economic theories, and draw hints as
to how to maximize social efficiency in situations of conflicting preferences.
",1,1,0,0,0,0
16806,Scalable Generalized Dynamic Topic Models,"  Dynamic topic models (DTMs) model the evolution of prevalent themes in
literature, online media, and other forms of text over time. DTMs assume that
word co-occurrence statistics change continuously and therefore impose
continuous stochastic process priors on their model parameters. These dynamical
priors make inference much harder than in regular topic models, and also limit
scalability. In this paper, we present several new results around DTMs. First,
we extend the class of tractable priors from Wiener processes to the generic
class of Gaussian processes (GPs). This allows us to explore topics that
develop smoothly over time, that have a long-term memory or are temporally
concentrated (for event detection). Second, we show how to perform scalable
approximate inference in these models based on ideas around stochastic
variational inference and sparse Gaussian processes. This way we can train a
rich family of DTMs to massive data. Our experiments on several large-scale
datasets show that our generalized model allows us to find interesting patterns
that were not accessible by previous approaches.
",0,0,0,1,0,0
16807,Session Types for Orchestrated Interactions,"  In the setting of the pi-calculus with binary sessions, we aim at relaxing
the notion of duality of session types by the concept of retractable compliance
developed in contract theory. This leads to extending session types with a new
type operator of ""speculative selection"" including choices not necessarily
offered by a compliant partner. We address the problem of selecting successful
communicating branches by means of an operational semantics based on
orchestrators, which has been shown to be equivalent to the retractable
semantics of contracts, but clearly more feasible. A type system, sound with
respect to such a semantics, is hence provided.
",1,0,0,0,0,0
16808,An Agent-Based Approach for Optimizing Modular Vehicle Fleet Operation,"  Modularity in military vehicle designs enables on-base assembly, disassembly,
and reconfiguration of vehicles, which can be beneficial in promoting fleet
adaptability and life cycle cost savings. To properly manage the fleet
operation and to control the resupply, demand prediction, and scheduling
process, this paper illustrates an agent-based approach customized for highly
modularized military vehicle fleets and studies the feasibility and flexibility
of modularity for various mission scenarios. Given deterministic field demands
with operation stochasticity, we compare the performance of a modular fleet to
a conventional fleet in equivalent operation strategies and also compare fleet
performance driven by heuristic rules and optimization. Several indicators are
selected to quantify the fleet performance, including operation costs, total
resupplied resources, and fleet readiness.
When the model is implemented for military Joint Tactical Transport System
(JTTS) mission, our results indicate that fleet modularity can reduce total
resource supplies without significant losses in fleet readiness. The benefits
of fleet modularity can also be amplified through a real-time optimized
operation strategy. To highlight the feasibility of fleet modularity, a
parametric study is performed to show the impacts from working capacity on
modular fleet performance. Finally, we provide practical suggestions of modular
vehicle designs based on the analysis and other possible usage.
",1,0,0,0,0,0
16809,Delta-epsilon functions and uniform continuity on metric spaces,"  Under certain general conditions, an explicit formula to compute the greatest
delta-epsilon function of a continuous function is given. From this formula, a
new way to analyze the uniform continuity of a continuous function is given.
Several examples illustrating the theory are discussed.
",0,0,1,0,0,0
16810,Deterministic Dispersion of Mobile Robots in Dynamic Rings,"  In this work, we study the problem of dispersion of mobile robots on dynamic
rings. The problem of dispersion of $n$ robots on an $n$ node graph, introduced
by Augustine and Moses Jr. [1], requires robots to coordinate with each other
and reach a configuration where exactly one robot is present on each node. This
problem has real world applications and applies whenever we want to minimize
the total cost of $n$ agents sharing $n$ resources, located at various places,
subject to the constraint that the cost of an agent moving to a different
resource is comparatively much smaller than the cost of multiple agents sharing
a resource (e.g. smart electric cars sharing recharge stations). The study of
this problem also provides indirect benefits to the study of scattering on
graphs, the study of exploration by mobile robots, and the study of load
balancing on graphs.
We solve the problem of dispersion in the presence of two types of dynamism
in the underlying graph: (i) vertex permutation and (ii) 1-interval
connectivity. We introduce the notion of vertex permutation dynamism and have
it mean that for a given set of nodes, in every round, the adversary ensures a
ring structure is maintained, but the connections between the nodes may change.
We use the idea of 1-interval connectivity from Di Luna et al. [10], where for
a given ring, in each round, the adversary chooses at most one edge to remove.
We assume robots have full visibility and present asymptotically time optimal
algorithms to achieve dispersion in the presence of both types of dynamism when
robots have chirality. When robots do not have chirality, we present
asymptotically time optimal algorithms to achieve dispersion subject to certain
constraints. Finally, we provide impossibility results for dispersion when
robots have no visibility.
",1,0,0,0,0,0
16811,A brain signature highly predictive of future progression to Alzheimer's dementia,"  Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment
(MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI
individuals will progress to dementia, even when screened using biomarkers. We
propose here to identify a subset of individuals who share a common brain
signature highly predictive of oncoming dementia. This signature was composed
of brain atrophy and functional dysconnectivity and discovered using a machine
learning model in patients suffering from dementia. The model recognized the
same brain signature in MCI individuals, 90% of which progressed to dementia
within three years. This result is a marked improvement on the state-of-the-art
in prognostic precision, while the brain signature still identified 47% of all
MCI progressors. We thus discovered a sizable MCI subpopulation which
represents an excellent recruitment target for clinical trials at the prodromal
stage of Alzheimer's disease.
",0,0,0,1,0,0
16812,Deep scattering transform applied to note onset detection and instrument recognition,"  Automatic Music Transcription (AMT) is one of the oldest and most
well-studied problems in the field of music information retrieval. Within this
challenging research field, onset detection and instrument recognition take
important places in transcription systems, as they respectively help to
determine exact onset times of notes and to recognize the corresponding
instrument sources. The aim of this study is to explore the usefulness of
multiscale scattering operators for these two tasks on plucked string
instrument and piano music. After resuming the theoretical background and
illustrating the key features of this sound representation method, we evaluate
its performances comparatively to other classical sound representations. Using
both MIDI-driven datasets with real instrument samples and real musical pieces,
scattering is proved to outperform other sound representations for these AMT
subtasks, putting forward its richer sound representation and invariance
properties.
",1,0,0,1,0,0
16813,Gaschütz Lemma for Compact Groups,"  We prove the Gaschütz Lemma holds for all metrisable compact groups.
",0,0,1,0,0,0
16814,Driven flow with exclusion and spin-dependent transport in graphenelike structures,"  We present a simplified description for spin-dependent electronic transport
in honeycomb-lattice structures with spin-orbit interactions, using
generalizations of the stochastic non-equilibrium model known as the totally
asymmetric simple exclusion process. Mean field theory and numerical
simulations are used to study currents, density profiles and current
polarization in quasi- one dimensional systems with open boundaries, and
externally-imposed particle injection ($\alpha$) and ejection ($\beta$) rates.
We investigate the influence of allowing for double site occupancy, according
to Pauli's exclusion principle, on the behavior of the quantities of interest.
We find that double occupancy shows strong signatures for specific combinations
of rates, namely high $\alpha$ and low $\beta$, but otherwise its effects are
quantitatively suppressed. Comments are made on the possible relevance of the
present results to experiments on suitably doped graphenelike structures.
",0,1,0,0,0,0
16815,MOG: Mapper on Graphs for Relationship Preserving Clustering,"  The interconnected nature of graphs often results in difficult to interpret
clutter. Typically techniques focus on either decluttering by clustering nodes
with similar properties or grouping edges with similar relationship. We propose
using mapper, a powerful topological data analysis tool, to summarize the
structure of a graph in a way that both clusters data with similar properties
and preserves relationships. Typically, mapper operates on a given data by
utilizing a scalar function defined on every point in the data and a cover for
scalar function codomain. The output of mapper is a graph that summarize the
shape of the space. In this paper, we outline how to use this mapper
construction on an input graphs, outline three filter functions that capture
important structures of the input graph, and provide an interface for
interactively modifying the cover. To validate our approach, we conduct several
case studies on synthetic and real world data sets and demonstrate how our
method can give meaningful summaries for graphs with various complexities
",0,0,0,1,0,0
16816,"Variation Evolving for Optimal Control Computation, A Compact Way","  A compact version of the Variation Evolving Method (VEM) is developed for the
optimal control computation. It follows the idea that originates from the
continuous-time dynamics stability theory in the control field. The optimal
solution is analogized to the equilibrium point of a dynamic system and is
anticipated to be obtained in an asymptotically evolving way. With the
introduction of a virtual dimension, the variation time, the Evolution Partial
Differential Equation (EPDE), which describes the variation motion towards the
optimal solution, is deduced from the Optimal Control Problem (OCP), and the
equivalent optimality conditions with no employment of costates are
established. In particular, it is found that theoretically the analytic
feedback optimal control law does not exist for general OCPs because the
optimal control is related to the future state. Since the derived EPDE is
suitable to be solved with the semi-discrete method in the field of PDE
numerical calculation, the resulting Initial-value Problems (IVPs) may be
solved with mature Ordinary Differential Equation (ODE) numerical integration
methods.
",1,0,0,0,0,0
16817,Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection,"  Convolutional Neural Networks (CNNs) have become the state-of-the-art in
various computer vision tasks, but they are still premature for most sensor
data, especially in pervasive and wearable computing. A major reason for this
is the limited amount of annotated training data. In this paper, we propose the
idea of leveraging the discriminative power of pre-trained deep CNNs on
2-dimensional sensor data by transforming the sensor modality to the visual
domain. By three proposed strategies, 2D sensor output is converted into
pressure distribution imageries. Then we utilize a pre-trained CNN for transfer
learning on the converted imagery data. We evaluate our method on a gait
dataset of floor surface pressure mapping. We obtain a classification accuracy
of 87.66%, which outperforms the conventional machine learning methods by over
10%.
",1,0,0,0,0,0
16818,The Price of Differential Privacy For Online Learning,"  We design differentially private algorithms for the problem of online linear
optimization in the full information and bandit settings with optimal
$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
results demonstrate that $\epsilon$-differential privacy may be ensured for
free -- in particular, the regret bounds scale as
$O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear
optimization, and as a special case, for non-stochastic multi-armed bandits,
the proposed algorithm achieves a regret of
$\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known
best regret bound was
$\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
",1,0,0,1,0,0
16819,Simulation chain and signal classification for acoustic neutrino detection in seawater,"  Acoustic neutrino detection is a promising approach to extend the energy
range of neutrino telescopes to energies beyond $10^{18}$\,eV. Currently
operational and planned water-Cherenkov neutrino telescopes, most notably
KM3NeT, include acoustic sensors in addition to the optical ones. These
acoustic sensors could be used as instruments for acoustic detection, while
their main purpose is the position calibration of the detection units. In this
article, a Monte Carlo simulation chain for acoustic detectors will be
presented, covering the initial interaction of the neutrino up to the signal
classification of recorded events. The ambient and transient background in the
simulation was implemented according to data recorded by the acoustic set-up
AMADEUS inside the ANTARES detector. The effects of refraction on the neutrino
signature in the detector are studied, and a classification of the recorded
events is implemented. As bipolar waveforms similar to those of the expected
neutrino signals are also emitted from other sound sources, additional features
like the geometrical shape of the propagation have to be considered for the
signal classification. This leads to a large improvement of the background
suppression by almost two orders of magnitude, since a flat cylindrical
""pancake"" propagation pattern is a distinctive feature of neutrino signals. An
overview of the simulation chain and the signal classification will be
presented and preliminary studies of the performance of the classification will
be discussed.
",0,1,0,0,0,0
16820,Parameter Space Noise for Exploration,"  Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use parameter perturbations, but discard all temporal structure in
the process and require significantly more samples. Combining parameter noise
with traditional RL methods allows to combine the best of both worlds. We
demonstrate that both off- and on-policy methods benefit from this approach
through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
discrete action environments as well as continuous control tasks. Our results
show that RL with parameter noise learns more efficiently than traditional RL
with action space noise and evolutionary strategies individually.
",1,0,0,1,0,0
16821,Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network,"  We present Deep Illumination, a novel machine learning technique for
approximating global illumination (GI) in real-time applications using a
Conditional Generative Adversarial Network. Our primary focus is on generating
indirect illumination and soft shadows with offline rendering quality at
interactive rates. Inspired from recent advancement in image-to-image
translation problems using deep generative convolutional networks, we introduce
a variant of this network that learns a mapping from Gbuffers (depth map,
normal map, and diffuse map) and direct illumination to any global illumination
solution. Our primary contribution is showing that a generative model can be
used to learn a density estimation from screen space buffers to an advanced
illumination model for a 3D environment. Once trained, our network can
approximate global illumination for scene configurations it has never
encountered before within the environment it was trained on. We evaluate Deep
Illumination through a comparison with both a state of the art real-time GI
technique (VXGI) and an offline rendering GI technique (path tracing). We show
that our method produces effective GI approximations and is also
computationally cheaper than existing GI techniques. Our technique has the
potential to replace existing precomputed and screen-space techniques for
producing global illumination effects in dynamic scenes with physically-based
rendering quality.
",1,0,0,0,0,0
16822,Fraternal Dropout,"  Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In this paper we propose a simple technique called fraternal
dropout that takes advantage of dropout to achieve this goal. Specifically, we
propose to train two identical copies of an RNN (that share parameters) with
different dropout masks while minimizing the difference between their
(pre-softmax) predictions. In this way our regularization encourages the
representations of RNNs to be invariant to dropout mask, thus being robust. We
show that our regularization term is upper bounded by the expectation-linear
dropout objective which has been shown to address the gap due to the difference
between the train and inference phases of dropout. We evaluate our model and
achieve state-of-the-art results in sequence modeling tasks on two benchmark
datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
to performance improvement by a significant margin in image captioning
(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
",1,0,0,1,0,0
16823,Finite-sample bounds for the multivariate Behrens-Fisher distribution with proportional covariances,"  The Behrens-Fisher problem is a well-known hypothesis testing problem in
statistics concerning two-sample mean comparison. In this article, we confirm
one conjecture in Eaton and Olshen (1972), which provides stochastic bounds for
the multivariate Behrens-Fisher test statistic under the null hypothesis. We
also extend their results on the stochastic ordering of random quotients to the
arbitrary finite dimensional case. This work can also be seen as a
generalization of Hsu (1938) that provided the bounds for the univariate
Behrens-Fisher problem. The results obtained in this article can be used to
derive a testing procedure for the multivariate Behrens-Fisher problem that
strongly controls the Type I error.
",0,0,1,1,0,0
16824,Evidence for mixed rationalities in preference formation,"  Understanding the mechanisms underlying the formation of cultural traits,
such as preferences, opinions and beliefs is an open challenge. Trait formation
is intimately connected to cultural dynamics, which has been the focus of a
variety of quantitative models. Recently, some studies have emphasized the
importance of connecting those models to snapshots of cultural dynamics that
are empirically accessible. By analyzing data obtained from different sources,
it has been suggested that culture has properties that are universally present,
and that empirical cultural states differ systematically from randomized
counterparts. Hence, a question about the mechanism responsible for the
observed patterns naturally arises. This study proposes a stochastic structural
model for generating cultural states that retain those robust, empirical
properties. One ingredient of the model, already used in previous work, assumes
that every individual's set of traits is partly dictated by one of several,
universal ""rationalities"", informally postulated by several social science
theories. The second, new ingredient taken from the same theories assumes that,
apart from a dominant rationality, each individual also has a certain exposure
to the other rationalities. It is shown that both ingredients are required for
reproducing the empirical regularities. This key result suggests that the
effects of cultural dynamics in the real world can be described as an interplay
of multiple, mixing rationalities, and thus provides indirect evidence for the
class of social science theories postulating such mixing. The model should be
seen as a static, effective description of culture, while a dynamical, more
fundamental description is left for future research.
",1,1,0,0,0,0
16825,A Variance Maximization Criterion for Active Learning,"  Active learning aims to train a classifier as fast as possible with as few
labels as possible. The core element in virtually any active learning strategy
is the criterion that measures the usefulness of the unlabeled data based on
which new points to be labeled are picked. We propose a novel approach which we
refer to as maximizing variance for active learning or MVAL for short. MVAL
measures the value of unlabeled instances by evaluating the rate of change of
output variables caused by changes in the next sample to be queried and its
potential labelling. In a sense, this criterion measures how unstable the
classifier's output is for the unlabeled data points under perturbations of the
training data. MVAL maintains, what we refer to as, retraining information
matrices to keep track of these output scores and exploits two kinds of
variance to measure the informativeness and representativeness, respectively.
By fusing these variances, MVAL is able to select the instances which are both
informative and representative. We employ our technique both in combination
with logistic regression and support vector machines and demonstrate that MVAL
achieves state-of-the-art performance in experiments on a large number of
standard benchmark datasets.
",1,0,0,1,0,0
16826,"Polarization, plasmon, and Debye screening in doped 3D ani-Weyl semimetal","  We compute the polarization function in a doped three-dimensional
anisotropic-Weyl semimetal, in which the fermion energy dispersion is linear in
two components of the momenta and quadratic in the third. Through detailed
calculations, we find that the long wavelength plasmon mode depends on the
fermion density $n_e$ in the form $\Omega_{p}^{\bot}\propto n_{e}^{3/10}$
within the basal plane and behaves as $\Omega_{p}^{z}\propto n_{e}^{1/2}$ along
the third direction. This unique characteristic of the plasmon mode can be
probed by various experimental techniques, such as electron energy-loss
spectroscopy. The Debye screening at finite chemical potential and finite
temperature is also analyzed based on the polarization function.
",0,1,0,0,0,0
16827,Identifying Product Order with Restricted Boltzmann Machines,"  Unsupervised machine learning via a restricted Boltzmann machine is an useful
tool in distinguishing an ordered phase from a disordered phase. Here we study
its application on the two-dimensional Ashkin-Teller model, which features a
partially ordered product phase. We train the neural network with spin
configuration data generated by Monte Carlo simulations and show that distinct
features of the product phase can be learned from non-ergodic samples resulting
from symmetry breaking. Careful analysis of the weight matrices inspires us to
define a nontrivial machine-learning motivated quantity of the product form,
which resembles the conventional product order parameter.
",0,1,0,0,0,0
16828,A finite temperature study of ideal quantum gases in the presence of one dimensional quasi-periodic potential,"  We study the thermodynamics of ideal Bose gas as well as the transport
properties of non interacting bosons and fermions in a one dimensional
quasi-periodic potential, namely Aubry-André (AA) model at finite
temperature. For bosons in finite size systems, the effect of quasi-periodic
potential on the crossover phenomena corresponding to Bose-Einstein
condensation (BEC), superfluidity and localization phenomena at finite
temperatures are investigated. From the ground state number fluctuation we
calculate the crossover temperature of BEC which exhibits a non monotonic
behavior with the strength of AA potential and vanishes at the self-dual
critical point following power law. Appropriate rescaling of the crossover
temperatures reveals universal behavior which is studied for different
quasi-periodicity of the AA model. Finally, we study the temperature and flux
dependence of the persistent current of fermions in presence of a
quasi-periodic potential to identify the localization at the Fermi energy from
the decay of the current.
",0,1,0,0,0,0
16829,High-Frequency Analysis of Effective Interactions and Bandwidth for Transient States after Monocycle Pulse Excitation of Extended Hubbard Model,"  Using a high-frequency expansion in periodically driven extended Hubbard
models, where the strengths and ranges of density-density interactions are
arbitrary, we obtain the effective interactions and bandwidth, which depend
sensitively on the polarization of the driving field. Then, we numerically
calculate modulations of correlation functions in a quarter-filled extended
Hubbard model with nearest-neighbor interactions on a triangular lattice with
trimers after monocycle pulse excitation. We discuss how the resultant
modulations are compatible with the effective interactions and bandwidth
derived above on the basis of their dependence on the polarization of
photoexcitation, which is easily accessible by experiments. Some correlation
functions after monocycle pulse excitation are consistent with the effective
interactions, which are weaker or stronger than the original ones. However, the
photoinduced enhancement of anisotropic charge correlations previously
discussed for the three-quarter-filled organic conductor
$\alpha$-(bis[ethylenedithio]-tetrathiafulvalene)$_2$I$_3$
[$\alpha$-(BEDT-TTF)$_2$I$_3$] in the metallic phase is not fully explained by
the effective interactions or bandwidth, which are derived independently of the
filling.
",0,1,0,0,0,0
16830,"Fast binary embeddings, and quantized compressed sensing with structured matrices","  This paper deals with two related problems, namely distance-preserving binary
embeddings and quantization for compressed sensing . First, we propose fast
methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$,
associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and
we associate the cube with a pseudo-metric that approximates Euclidean distance
among points in $\mathcal{X}$. Our methods rely on quantizing fast
Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and
partial circulant ensembles, both of which admit fast transforms. Our
quantization methods utilize noise-shaping, and include Sigma-Delta schemes and
distributed noise-shaping schemes. The resulting approximation errors decay
polynomially and exponentially fast in $m$, depending on the embedding method.
This dramatically outperforms the current decay rates associated with binary
embeddings and Hamming distances. Additionally, it is the first such binary
embedding result that applies to fast Johnson-Lindenstrauss maps while
preserving $\ell_2$ norms.
Second, we again consider noise-shaping schemes, albeit this time to quantize
compressed sensing measurements arising from bounded orthonormal ensembles and
partial circulant matrices. We show that these methods yield a reconstruction
error that again decays with the number of measurements (and bits), when using
convex optimization for reconstruction. Specifically, for Sigma-Delta schemes,
the error decays polynomially in the number of measurements, and it decays
exponentially for distributed noise-shaping schemes based on beta encoding.
These results are near optimal and the first of their kind dealing with bounded
orthonormal systems.
",0,0,0,1,0,0
16831,The Many Faces of Link Fraud,"  Most past work on social network link fraud detection tries to separate
genuine users from fraudsters, implicitly assuming that there is only one type
of fraudulent behavior. But is this assumption true? And, in either case, what
are the characteristics of such fraudulent behaviors? In this work, we set up
honeypots (""dummy"" social network accounts), and buy fake followers (after
careful IRB approval). We report the signs of such behaviors including oddities
in local network connectivity, account attributes, and similarities and
differences across fraud providers. Most valuably, we discover and characterize
several types of fraud behaviors. We discuss how to leverage our insights in
practice by engineering strongly performing entropy-based features and
demonstrating high classification accuracy. Our contributions are (a)
instrumentation: we detail our experimental setup and carefully engineered data
collection process to scrape Twitter data while respecting API rate-limits, (b)
observations on fraud multimodality: we analyze our honeypot fraudster
ecosystem and give surprising insights into the multifaceted behaviors of these
fraudster types, and (c) features: we propose novel features that give strong
(>0.95 precision/recall) discriminative power on ground-truth Twitter data.
",1,0,0,0,0,0
16832,Diophantine approximation by special primes,"  We show that whenever $\delta>0$, $\eta$ is real and constants $\lambda_i$
satisfy some necessary conditions, there are infinitely many prime triples
$p_1,\, p_2,\, p_3$ satisfying the inequality $|\lambda_1p_1 + \lambda_2p_2 +
\lambda_3p_3+\eta|<(\max p_j)^{-1/12+\delta}$ and such that, for each
$i\in\{1,2,3\}$, $p_i+2$ has at most $28$ prime factors.
",0,0,1,0,0,0
16833,A Compositional Treatment of Iterated Open Games,"  Compositional Game Theory is a new, recently introduced model of economic
games based upon the computer science idea of compositionality. In it, complex
and irregular games can be built up from smaller and simpler games, and the
equilibria of these complex games can be defined recursively from the
equilibria of their simpler subgames. This paper extends the model by providing
a final coalgebra semantics for infinite games. In the course of this, we
introduce a new operator on games to model the economic concept of subgame
perfection.
",1,0,0,0,0,0
16834,Bayesian inference for spectral projectors of covariance matrix,"  Let $X_1, \ldots, X_n$ be i.i.d. sample in $\mathbb{R}^p$ with zero mean and
the covariance matrix $\mathbf{\Sigma^*}$. The classic principal component
analysis estimates the projector $\mathbf{P^*_{\mathcal{J}}}$ onto the direct
sum of some eigenspaces of $\mathbf{\Sigma^*}$ by its empirical counterpart
$\mathbf{\widehat{P}_{\mathcal{J}}}$. Recent papers [Koltchinskii, Lounici
(2017)], [Naumov et al. (2017)] investigate the asymptotic distribution of the
Frobenius distance between the projectors $\|
\mathbf{\widehat{P}_{\mathcal{J}}} - \mathbf{P^*_{\mathcal{J}}} \|_2$. The
problem arises when one tries to build a confidence set for the true projector
effectively. We consider the problem from Bayesian perspective and derive an
approximation for the posterior distribution of the Frobenius distance between
projectors. The derived theorems hold true for non-Gaussian data: the only
assumption that we impose is the concentration of the sample covariance
$\mathbf{\widehat{\Sigma}}$ in a vicinity of $\mathbf{\Sigma^*}$. The obtained
results are applied to construction of sharp confidence sets for the true
projector. Numerical simulations illustrate good performance of the proposed
procedure even on non-Gaussian data in quite challenging regime.
",0,0,1,1,0,0
16835,Handling Incomplete Heterogeneous Data using VAEs,"  Variational autoencoders (VAEs), as well as other generative models, have
been shown to be efficient and accurate to capture the latent structure of vast
amounts of complex high-dimensional data. However, existing VAEs can still not
directly handle data that are heterogenous (mixed continuous and discrete) or
incomplete (with missing data at random), which is indeed common in real-world
applications.
In this paper, we propose a general framework to design VAEs, suitable for
fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood
models for real-valued, positive real valued, interval, categorical, ordinal
and count data, and allows to estimate (and potentially impute) missing data
accurately. Furthermore, HI-VAE presents competitive predictive performance in
supervised tasks, outperforming super- vised models when trained on incomplete
data
",0,0,0,1,0,0
16836,Geometric mean of probability measures and geodesics of Fisher information metric,"  The space of all probability measures having positive density function on a
connected compact smooth manifold $M$, denoted by $\mathcal{P}(M)$, carries the
Fisher information metric $G$. We define the geometric mean of probability
measures by the aid of which we investigate information geometry of
$\mathcal{P}(M)$, equipped with $G$. We show that a geodesic segment joining
arbitrary probability measures $\mu_1$ and $\mu_2$ is expressed by using the
normalized geometric mean of its endpoints. As an application, we show that any
two points of $\mathcal{P}(M)$ can be joined by a geodesic. Moreover, we prove
that the function $\ell$ defined by $\ell(\mu_1, \mu_2):=2\arccos\int_M
\sqrt{p_1\,p_2}\,d\lambda$, $\mu_i=p_i\,\lambda$, $i=1,2$ gives the distance
function on $\mathcal{P}(M)$. It is shown that geodesics are all minimal.
",0,0,1,0,0,0
16837,On automorphism groups of Toeplitz subshifts,"  In this article we study automorphisms of Toeplitz subshifts. Such groups are
abelian and any finitely generated torsion subgroup is finite and cyclic. When
the complexity is non superlinear, we prove that the automorphism group is,
modulo a finite cyclic group, generated by a unique root of the shift. In the
subquadratic complexity case, we show that the automorphism group modulo the
torsion is generated by the roots of the shift map and that the result of the
non superlinear case is optimal. Namely, for any $\varepsilon > 0$ we construct
examples of minimal Toeplitz subshifts with complexity bounded by $C
n^{1+\epsilon}$ whose automorphism groups are not finitely generated. Finally,
we observe the coalescence and the automorphism group give no restriction on
the complexity since we provide a family of coalescent Toeplitz subshifts with
positive entropy such that their automorphism groups are arbitrary finitely
generated infinite abelian groups with cyclic torsion subgroup (eventually
restricted to powers of the shift).
",0,0,1,0,0,0
16838,How to Generate Pseudorandom Permutations Over Other Groups,"  Recent results by Alagic and Russell have given some evidence that the
Even-Mansour cipher may be secure against quantum adversaries with quantum
queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
the question as to whether or not other classical schemes may be generalized to
arbitrary groups and whether classical results still apply to those generalized
schemes. In this thesis, we generalize the Even-Mansour cipher and the Feistel
cipher. We show that Even and Mansour's original notions of secrecy are
obtained on a one-key, group variant of the Even-Mansour cipher. We generalize
the result by Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom,
to super pseudorandomness, also in the one-key, group case. Using a Slide
Attack we match the bound found above. After generalizing the Feistel cipher to
arbitrary groups we resolve an open problem of Patel, Ramzan, and Sundaram by
showing that the 3-round Feistel cipher over an arbitrary group is not super
pseudorandom. We generalize a result by Gentry and Ramzan showing that the
Even-Mansour cipher can be implemented using the Feistel cipher as the public
permutation. In this result, we also consider the one-key case over a group and
generalize their bound. Finally, we consider Zhandry's result on quantum
pseudorandom permutations, showing that his result may be generalized to hold
for arbitrary groups. In this regard, we consider whether certain card shuffles
may be generalized as well.
",1,0,1,0,0,0
16839,Measures of Tractography Convergence,"  In the present work, we use information theory to understand the empirical
convergence rate of tractography, a widely-used approach to reconstruct
anatomical fiber pathways in the living brain. Based on diffusion MRI data,
tractography is the starting point for many methods to study brain
connectivity. Of the available methods to perform tractography, most
reconstruct a finite set of streamlines, or 3D curves, representing probable
connections between anatomical regions, yet relatively little is known about
how the sampling of this set of streamlines affects downstream results, and how
exhaustive the sampling should be. Here we provide a method to measure the
information theoretic surprise (self-cross entropy) for tract sampling schema.
We then empirically assess four streamline methods. We demonstrate that the
relative information gain is very low after a moderate number of streamlines
have been generated for each tested method. The results give rise to several
guidelines for optimal sampling in brain connectivity analyses.
",0,0,0,1,1,0
16840,Network Flow Based Post Processing for Sales Diversity,"  Collaborative filtering is a broad and powerful framework for building
recommendation systems that has seen widespread adoption. Over the past decade,
the propensity of such systems for favoring popular products and thus creating
echo chambers have been observed. This has given rise to an active area of
research that seeks to diversify recommendations generated by such algorithms.
We address the problem of increasing diversity in recommendation systems that
are based on collaborative filtering that use past ratings to predicting a
rating quality for potential recommendations. Following our earlier work, we
formulate recommendation system design as a subgraph selection problem from a
candidate super-graph of potential recommendations where both diversity and
rating quality are explicitly optimized: (1) On the modeling side, we define a
new flexible notion of diversity that allows a system designer to prescribe the
number of recommendations each item should receive, and smoothly penalizes
deviations from this distribution. (2) On the algorithmic side, we show that
minimum-cost network flow methods yield fast algorithms in theory and practice
for designing recommendation subgraphs that optimize this notion of diversity.
(3) On the empirical side, we show the effectiveness of our new model and
method to increase diversity while maintaining high rating quality in standard
rating data sets from Netflix and MovieLens.
",1,0,0,0,0,0
16841,Lattice Model for Production of Gas,"  We define a lattice model for rock, absorbers, and gas that makes it possible
to examine the flow of gas to a complicated absorbing boundary over long
periods of time. The motivation is to deduce the geometry of the boundary from
the time history of gas absorption. We find a solution to this model using
Green's function techniques, and apply the solution to three absorbing networks
of increasing complexity.
",0,1,0,0,0,0
16842,Adaptive Representation Selection in Contextual Bandit,"  We consider an extension of the contextual bandit setting, motivated by
several practical applications, where an unlabeled history of contexts can
become available for pre-training before the online decision-making begins. We
propose an approach for improving the performance of contextual bandit in such
setting, via adaptive, dynamic representation learning, which combines offline
pre-training on unlabeled history of contexts with online selection and
modification of embedding functions. Our experiments on a variety of datasets
and in different nonstationary environments demonstrate clear advantages of our
approach over the standard contextual bandit.
",0,0,0,1,0,0
16843,Algebraic surfaces with zero-dimensional cohomology support locus,"  Using the theory of cohomology support locus, we give a necessary condition
for the Albanese map of a smooth projective surface being a submersion. More
precisely, assuming the cohomology support locus of any finite abelian cover of
a smooth projective surface consists of finitely many points, we prove that the
surface has trivial first Betti number, or is a ruled surface of genus one, or
is an abelian surface.
",0,0,1,0,0,0
16844,Insight into the temperature dependent properties of the ferromagnetic Kondo lattice YbNiSn,"  Analyzing temperature dependent photoemission (PE) data of the ferromagnetic
Kondo-lattice (KL) system YbNiSn in the light of the Periodic Anderson model
(PAM) we show that the KL behavior is not limited to temperatures below a
temperature T_K, defined empirically from resistivity and specificic heat
measurements. As characteristic for weakly hybridized Ce and Yb systems, the PE
spectra reveal a 4f-derived Fermi level peak, which reflects contributions from
the Kondo resonance and its crystal electric field (CEF) satellites. In YbNiSn
this peak has an unusual temperature dependence: With decreasing temperature a
steady linear increase of intensity is observed which extends over a large
interval ranging from 100 K down to 1 K without showing any peculiarities in
the region of T_K ~ TC= 5.6 K. In the light of the single-impurity Anderson
model (SIAM) this intensity variation reflects a linear increase of 4f
occupancy with decreasing temperature, indicating an onset of Kondo screening
at temperatures above 100 K. Within the PAM this phenomenon could be described
by a non-Fermi liquid like T- linear damping of the self-energy which accounts
phenomenologically for the feedback from the closely spaced CEF-states.
",0,1,0,0,0,0
16845,Some Ageing Properties of Dynamic Additive Mean Residual Life Model,"  Although proportional hazard rate model is a very popular model to analyze
failure time data, sometimes it becomes important to study the additive hazard
rate model. Again, sometimes the concept of the hazard rate function is
abstract, in comparison to the concept of mean residual life function. A new
model called `dynamic additive mean residual life model' where the covariates
are time-dependent has been defined in the literature. Here we study the
closure properties of the model for different positive and negative ageing
classes under certain condition(s). Quite a few examples are presented to
illustrate different properties of the model.
",0,0,1,1,0,0
16846,From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets,"  We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
for random running times. MCLV uses a stopping set built from the training data
and has maximum number of Markov chain steps K (referred as MCLV-K). We present
a MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence and
differences between LVS-K and Contrastive Divergence (CD-K), with LVS-K
significantly outperforming CD-K training RBMs over the MNIST dataset,
indicating MCLV to be a promising direction in learning generative models.
",1,0,0,1,0,0
16847,CD meets CAT,"  We show that if a noncollapsed $CD(K,n)$ space $X$ with $n\ge 2$ has
curvature bounded above by $\kappa$ in the sense of Alexandrov then $K\le
(n-1)\kappa$ and $X$ is an Alexandrov space of curvature bounded below by
$K-\kappa (n-2)$. We also show that if a $CD(K,n)$ space $Y$ with finite $n$
has curvature bounded above then it is infinitesimally Hilbertian.
",0,0,1,0,0,0
16848,Cost Models for Selecting Materialized Views in Public Clouds,"  Data warehouse performance is usually achieved through physical data
structures such as indexes or materialized views. In this context, cost models
can help select a relevant set ofsuch performance optimization structures.
Nevertheless, selection becomes more complex in the cloud. The criterion to
optimize is indeed at least two-dimensional, with monetary cost balancing
overall query response time. This paper introduces new cost models that fit
into the pay-as-you-go paradigm of cloud computing. Based on these cost models,
an optimization problem is defined to discover, among candidate views, those to
be materialized to minimize both the overall cost of using and maintaining the
database in a public cloud and the total response time ofa given query
workload. We experimentally show that maintaining materialized views is always
advantageous, both in terms of performance and cost.
",1,0,0,0,0,0
16849,Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes,"  Bayesian optimization (BO) methods are useful for optimizing functions that
are expensive to evaluate, lack an analytical expression and whose evaluations
can be contaminated by noise. These methods rely on a probabilistic model of
the objective function, typically a Gaussian process (GP), upon which an
acquisition function is built. This function guides the optimization process
and measures the expected utility of performing an evaluation of the objective
at a new point. GPs assume continous input variables. When this is not the
case, such as when some of the input variables take integer values, one has to
introduce extra approximations. A common approach is to round the suggested
variable value to the closest integer before doing the evaluation of the
objective. We show that this can lead to problems in the optimization process
and describe a more principled approach to account for input variables that are
integer-valued. We illustrate in both synthetic and a real experiments the
utility of our approach, which significantly improves the results of standard
BO methods on problems involving integer-valued variables.
",0,0,0,1,0,0
16850,Second-order constrained variational problems on Lie algebroids: applications to optimal control,"  The aim of this work is to study, from an intrinsic and geometric point of
view, second-order constrained variational problems on Lie algebroids, that is,
optimization problems defined by a cost functional which depends on
higher-order derivatives of admissible curves on a Lie algebroid. Extending the
classical Skinner and Rusk formalism for the mechanics in the context of Lie
algebroids, for second-order constrained mechanical systems, we derive the
corresponding dynamical equations. We find a symplectic Lie subalgebroid where,
under some mild regularity conditions, the second-order constrained variational
problem, seen as a presymplectic Hamiltonian system, has a unique solution. We
study the relationship of this formalism with the second-order constrained
Euler-Poincaré and Lagrange-Poincaré equations, among others. Our study is
applied to the optimal control of mechanical systems.
",0,0,1,0,0,0
16851,"The Galactic Cosmic Ray Electron Spectrum from 3 to 70 MeV Measured by Voyager 1 Beyond the Heliopause, What This Tells Us About the Propagation of Electrons and Nuclei In and Out of the Galaxy at Low Energies","  The cosmic ray electrons measured by Voyager 1 between 3-70 MeV beyond the
heliopause have intensities several hundred times those measured at the Earth
by PAMELA at nearly the same energies. This paper compares this new V1 data
with data from the earth-orbiting PAMELA experiment up to energies greater than
10 GeV where solar modulation effects are negligible. In this energy regime we
assume the main parameters governing electron propagation are diffusion and
energy loss and we use a Monte Carlo program to describe this propagation in
the galaxy. To reproduce the new Voyager electron spectrum, which is E-1.3,
together with that measured by PAMELA which is E-3.20 above 10 GeV, we require
a diffusion coefficient which is P 0.45 at energies above 0.5 GeV changing to a
P-1.00 dependence at lower rigidities. The entire electron spectrum observed at
both V1 and PAMELA from 3 MeV to 30 GeV can then be described by a simple
source spectrum, dj/dP P-2.25, with a spectral exponent that is independent of
rigidity. The change in exponent of the measured electron spectrum from -1.3 at
low energies to 3.2 at the highest energies can be explained by galactic
propagation effects related to the changing dependence of the diffusion
coefficient below 0.5 GeV, and the increasing importance above 0.5 GV of energy
loss from synchrotron and inverse Compton radiation, which are both E2, and
which are responsible for most of the changing spectral exponent above 1.0 GV.
As a result of the P-1.00 dependence of the diffusion coefficient below 0.5
GV that is required to fit the V1 electron spectrum, there is a rapid flow of
these low energy electrons out of the galaxy. These electrons in local IG space
are unobservable to us at any wave length and therefore form a dark energy
component which is 100 times the electrons rest energy.
",0,1,0,0,0,0
16852,Online Scheduling of Spark Workloads with Mesos using Different Fair Allocation Algorithms,"  In the following, we present example illustrative and experimental results
comparing fair schedulers allocating resources from multiple servers to
distributed application frameworks. Resources are allocated so that at least
one resource is exhausted in every server. Schedulers considered include DRF
(DRFH) and Best-Fit DRF (BF-DRF), TSF, and PS-DSF. We also consider server
selection under Randomized Round Robin (RRR) and based on their residual
(unreserved) resources. In the following, we consider cases with frameworks of
equal priority and without server-preference constraints. We first give typical
results of a illustrative numerical study and then give typical results of a
study involving Spark workloads on Mesos which we have modified and
open-sourced to prototype different schedulers.
",1,0,0,0,0,0
16853,On the representation dimension and finitistic dimension of special multiserial algebras,"  For monomial special multiserial algebras, which in general are of wild
representation type, we construct radical embeddings into algebras of finite
representation type. As a consequence, we show that the representation
dimension of monomial and self-injective special multiserial algebras is less
or equal to three. This implies that the finitistic dimension conjecture holds
for all special multiserial algebras.
",0,0,1,0,0,0
16854,Would You Like to Motivate Software Testers? Ask Them How,"  Context. Considering the importance of software testing to the development of
high quality and reliable software systems, this paper aims to investigate how
can work-related factors influence the motivation of software testers. Method.
We applied a questionnaire that was developed using a previous theory of
motivation and satisfaction of software engineers to conduct a survey-based
study to explore and understand how professional software testers perceive and
value work-related factors that could influence their motivation at work.
Results. With a sample of 80 software testers we observed that software testers
are strongly motivated by variety of work, creative tasks, recognition for
their work, and activities that allow them to acquire new knowledge, but in
general the social impact of this activity has low influence on their
motivation. Conclusion. This study discusses the difference of opinions among
software testers, regarding work-related factors that could impact their
motivation, which can be relevant for managers and leaders in software
engineering practice.
",1,0,0,0,0,0
16855,POMDP Structural Results for Controlled Sensing,"  This article provides a short review of some structural results in controlled
sensing when the problem is formulated as a partially observed Markov decision
process. In particular, monotone value functions, Blackwell dominance and
quickest detection are described.
",1,0,0,0,0,0
16856,Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption,"  We study a data model in which the data matrix D can be expressed as D = L +
S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
matrix whose non-zero columns are outlying data points. To date, robust PCA
algorithms have solely considered models with either S or C, but not both. As
such, existing algorithms cannot account for simultaneous element-wise and
column-wise corruptions. In this paper, a new robust PCA algorithm that is
robust to simultaneous types of corruption is proposed. Our approach hinges on
the sparse approximation of a sparsely corrupted column so that the sparse
expansion of a column with respect to the other data points is used to
distinguish a sparsely corrupted inlier column from an outlying data point. We
also develop a randomized design which provides a scalable implementation of
the proposed approach. The core idea of sparse approximation is analyzed
analytically where we show that the underlying ell_1-norm minimization can
obtain the representation of an inlier in presence of sparse corruptions.
",1,0,0,1,0,0
16857,Power-Sum Denominators,"  The power sum $1^n + 2^n + \cdots + x^n$ has been of interest to
mathematicians since classical times. Johann Faulhaber, Jacob Bernoulli, and
others who followed expressed power sums as polynomials in $x$ of degree $n+1$
with rational coefficients. Here we consider the denominators of these
polynomials, and prove some of their properties. A remarkable one is that such
a denominator equals $n+1$ times the squarefree product of certain primes $p$
obeying the condition that the sum of the base-$p$ digits of $n+1$ is at least
$p$. As an application, we derive a squarefree product formula for the
denominators of the Bernoulli polynomials.
",0,0,1,0,0,0
16858,A resource-frugal probabilistic dictionary and applications in bioinformatics,"  Indexing massive data sets is extremely expensive for large scale problems.
In many fields, huge amounts of data are currently generated, however
extracting meaningful information from voluminous data sets, such as computing
similarity between elements, is far from being trivial. It remains nonetheless
a fundamental need. This work proposes a probabilistic data structure based on
a minimal perfect hash function for indexing large sets of keys. Our structure
out-compete the hash table for construction, query times and for memory usage,
in the case of the indexation of a static set. To illustrate the impact of
algorithms performances, we provide two applications based on similarity
computation between collections of sequences, and for which this calculation is
an expensive but required operation. In particular, we show a practical case in
which other bioinformatics tools fail to scale up the tested data set or
provide lower recall quality results.
",1,0,0,0,0,0
16859,Fast learning rate of deep learning via a kernel perspective,"  We develop a new theoretical framework to analyze the generalization error of
deep learning, and derive a new fast learning rate for two representative
algorithms: empirical risk minimization and Bayesian deep learning. The series
of theoretical analyses of deep learning has revealed its high expressive power
and universal approximation capability. Although these analyses are highly
nonparametric, existing generalization error analyses have been developed
mainly in a fixed dimensional parametric model. To compensate this gap, we
develop an infinite dimensional model that is based on an integral form as
performed in the analysis of the universal approximation capability. This
allows us to define a reproducing kernel Hilbert space corresponding to each
layer. Our point of view is to deal with the ordinary finite dimensional deep
neural network as a finite approximation of the infinite dimensional one. The
approximation error is evaluated by the degree of freedom of the reproducing
kernel Hilbert space in each layer. To estimate a good finite dimensional
model, we consider both of empirical risk minimization and Bayesian deep
learning. We derive its generalization error bound and it is shown that there
appears bias-variance trade-off in terms of the number of parameters of the
finite dimensional approximation. We show that the optimal width of the
internal layers can be determined through the degree of freedom and the
convergence rate can be faster than $O(1/\sqrt{n})$ rate which has been shown
in the existing studies.
",1,0,1,1,0,0
16860,Closed-loop field development optimization with multipoint geostatistics and statistical assessment,"  Closed-loop field development (CLFD) optimization is a comprehensive
framework for optimal development of subsurface resources. CLFD involves three
major steps: 1) optimization of full development plan based on current set of
models, 2) drilling new wells and collecting new spatial and temporal
(production) data, 3) model calibration based on all data. This process is
repeated until the optimal number of wells is drilled. This work introduces an
efficient CLFD implementation for complex systems described by multipoint
geostatistics (MPS). Model calibration is accomplished in two steps:
conditioning to spatial data by a geostatistical simulation method, and
conditioning to production data by optimization-based PCA. A statistical
procedure is presented to assess the performance of CLFD. Methodology is
applied to an oil reservoir example for 25 different true-model cases.
Application of a single-step of CLFD, improved the true NPV in 64%--80% of
cases. The full CLFD procedure (with three steps) improved the true NPV in 96%
of cases, with an average improvement of 37%.
",1,0,0,1,0,0
16861,Reduction and regular $t$-balanced Cayley maps on split metacyclic 2-groups,"  A regular $t$-balanced Cayley map (RBCM$_t$ for short) on a group $\Gamma$ is
an embedding of a Cayley graph on $\Gamma$ into a surface with some special
symmetric properties. We propose a reduction method to study RBCM$_t$'s, and as
a first practice, we completely classify RBCM$_t$'s for a class of split
metacyclic 2-groups.
",0,0,1,0,0,0
16862,Perovskite Substrates Boost the Thermopower of Cobaltate Thin Films at High Temperatures,"  Transition metal oxides are promising candidates for thermoelectric
applications, because they are stable at high temperature and because strong
electronic correlations can generate large Seebeck coefficients, but their
thermoelectric power factors are limited by the low electrical conductivity. We
report transport measurements on Ca3Co4O9 films on various perovskite
substrates and show that reversible incorporation of oxygen into SrTiO3 and
LaAlO3 substrates activates a parallel conduction channel for p-type carriers,
greatly enhancing the thermoelectric performance of the film-substrate system
at temperatures above 450 °C. Thin-film structures that take advantage of
both electronic correlations and the high oxygen mobility of transition metal
oxides thus open up new perspectives for thermopower generation at high
temperature.
",0,1,0,0,0,0
16863,Motion of a rod pushed at one point in a weightless environment in space,"  We analyze the motion of a rod floating in a weightless environment in space
when a force is applied at some point on the rod in a direction perpendicular
to its length. If the force applied is at the centre of mass, then the rod gets
a linear motion perpendicular to its length. However, if the same force is
applied at a point other than the centre of mass, say, near one end of the rod,
thereby giving rise to a torque, then there will also be a rotation of the rod
about its centre of mass, in addition to the motion of the centre of mass
itself. If the force applied is for a very short duration, but imparting
nevertheless a finite impulse, like in a sudden (quick) hit at one end of the
rod, then the centre of mass will move with a constant linear speed and
superimposed on it will be a rotation of the rod with constant angular speed
about the centre of mass. However, if force is applied continuously, say by
strapping a tiny rocket at one end of the rod, then the rod will spin faster
and faster about the centre of mass, with angular speed increasing linearly
with time. As the direction of the applied force, as seen by an external
(inertial) observer, will be changing continuously with the rotation of the
rod, the acceleration of the centre of mass would also be not in one fixed
direction. However, it turns out that the locus of the velocity vector of the
centre of mass will describe a Cornu spiral, with the velocity vector reaching
a final constant value with time. The mean motion of the centre of mass will be
in a straight line, with superposed initial oscillations that soon die down.
",0,1,0,0,0,0
16864,Dimension theory and components of algebraic stacks,"  We prove some basic results on the dimension theory of algebraic stacks, and
on the multiplicities of their irreducible components, for which we do not know
a reference.
",0,0,1,0,0,0
16865,When is a polynomial ideal binomial after an ambient automorphism?,"  Can an ideal I in a polynomial ring k[x] over a field be moved by a change of
coordinates into a position where it is generated by binomials $x^a - cx^b$
with c in k, or by unital binomials (i.e., with c = 0 or 1)? Can a variety be
moved into a position where it is toric? By fibering the G-translates of I over
an algebraic group G acting on affine space, these problems are special cases
of questions about a family F of ideals over an arbitrary base B. The main
results in this general setting are algorithms to find the locus of points in B
over which the fiber of F
- is contained in the fiber of a second family F' of ideals over B;
- defines a variety of dimension at least d;
- is generated by binomials; or
- is generated by unital binomials.
A faster containment algorithm is also presented when the fibers of F are
prime. The big-fiber algorithm is probabilistic but likely faster than known
deterministic ones. Applications include the setting where a second group T
acts on affine space, in addition to G, in which case algorithms compute the
set of G-translates of I
- whose stabilizer subgroups in T have maximal dimension; or
- that admit a faithful multigrading by $Z^r$ of maximal rank r.
Even with no ambient group action given, the final application is an
algorithm to
- decide whether a normal projective variety is abstractly toric.
All of these loci in B and subsets of G are constructible; in some cases they
are closed.
",1,0,1,0,0,0
16866,Annihilators in $\mathbb{N}^k$-graded and $\mathbb{Z}^k$-graded rings,"  It has been shown by McCoy that a right ideal of a polynomial ring with
several indeterminates has a non-trivial homogeneous right annihilator of
degree 0 provided its right annihilator is non-trivial to begin with. In this
note, it is documented that any $\mathbb{N}$-graded ring $R$ has a slightly
weaker property: the right annihilator of a right ideal contains a homogeneous
non-zero element, if it is non-trivial to begin with. If $R$ is a subring of a
$\mathbb{Z}^k$ -graded ring $S$ satisfying a certain non-annihilation property
(which is the case if $S$ is strongly graded, for example), then it is possible
to find annihilators of degree 0.
",0,0,1,0,0,0
16867,q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators,"  We propose a new generic type of stochastic neurons, called $q$-neurons, that
considers activation functions based on Jackson's $q$-derivatives with
stochastic parameters $q$. Our generalization of neural network architectures
with $q$-neurons is shown to be both scalable and very easy to implement. We
demonstrate experimentally consistently improved performances over
state-of-the-art standard activation functions, both on training and testing
loss functions.
",0,0,0,1,0,0
16868,Liveness-Driven Random Program Generation,"  Randomly generated programs are popular for testing compilers and program
analysis tools, with hundreds of bugs in real-world C compilers found by random
testing. However, existing random program generators may generate large amounts
of dead code (computations whose result is never used). This leaves relatively
little code to exercise a target compiler's more complex optimizations.
To address this shortcoming, we introduce liveness-driven random program
generation. In this approach the random program is constructed bottom-up,
guided by a simultaneous structural data-flow analysis to ensure that the
generator never generates dead code.
The algorithm is implemented as a plugin for the Frama-C framework. We
evaluate it in comparison to Csmith, the standard random C program generator.
Our tool generates programs that compile to more machine code with a more
complex instruction mix.
",1,0,0,0,0,0
16869,Modeling and optimal control of HIV/AIDS prevention through PrEP,"  Pre-exposure prophylaxis (PrEP) consists in the use of an antiretroviral
medication to prevent the acquisition of HIV infection by uninfected
individuals and has recently demonstrated to be highly efficacious for HIV
prevention. We propose a new epidemiological model for HIV/AIDS transmission
including PrEP. Existence, uniqueness and global stability of the disease free
and endemic equilibriums are proved. The model with no PrEP is calibrated with
the cumulative cases of infection by HIV and AIDS reported in Cape Verde from
1987 to 2014, showing that it predicts well such reality. An optimal control
problem with a mixed state control constraint is then proposed and analyzed,
where the control function represents the PrEP strategy and the mixed
constraint models the fact that, due to PrEP costs, epidemic context and
program coverage, the number of individuals under PrEP is limited at each
instant of time. The objective is to determine the PrEP strategy that satisfies
the mixed state control constraint and minimizes the number of individuals with
pre-AIDS HIV-infection as well as the costs associated with PrEP. The optimal
control problem is studied analytically. Through numerical simulations, we
demonstrate that PrEP reduces HIV transmission significantly.
",0,0,1,0,0,0
16870,STARIMA-based Traffic Prediction with Time-varying Lags,"  Based on the observation that the correlation between observed traffic at two
measurement points or traffic stations may be time-varying, attributable to the
time-varying speed which subsequently causes variations in the time required to
travel between the two points, in this paper, we develop a modified Space-Time
Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags
for short-term traffic flow prediction. Particularly, the temporal lags in the
modified STARIMA change with the time-varying speed at different time of the
day or equivalently change with the (time-varying) time required to travel
between two measurement points. Firstly, a technique is developed to evaluate
the temporal lag in the STARIMA model, where the temporal lag is formulated as
a function of the spatial lag (spatial distance) and the average speed.
Secondly, an unsupervised classification algorithm based on ISODATA algorithm
is designed to classify different time periods of the day according to the
variation of the speed. The classification helps to determine the appropriate
time lag to use in the STARIMA model. Finally, a STARIMA-based model with
time-varying lags is developed for short-term traffic prediction. Experimental
results using real traffic data show that the developed STARIMA-based model
with time-varying lags has superior accuracy compared with its counterpart
developed using the traditional cross-correlation function and without
employing time-varying lags.
",1,0,1,0,0,0
16871,Electric Vehicle Charging Station Placement Method for Urban Areas,"  For accommodating more electric vehicles (EVs) to battle against fossil fuel
emission, the problem of charging station placement is inevitable and could be
costly if done improperly. Some researches consider a general setup, using
conditions such as driving ranges for planning. However, most of the EV growths
in the next decades will happen in the urban area, where driving ranges is not
the biggest concern. For such a need, we consider several practical aspects of
urban systems, such as voltage regulation cost and protection device upgrade
resulting from the large integration of EVs. Notably, our diversified objective
can reveal the trade-off between different factors in different cities
worldwide. To understand the global optimum of large-scale analysis, we add
constraint one-by-one to see how to preserve the problem convexity. Our
sensitivity analysis before and after convexification shows that our approach
is not only universally applicable but also has a small approximation error for
prioritizing the most urgent constraint in a specific setup. Finally, numerical
results demonstrate the trade-off, the relationship between different factors
and the global objective, and the small approximation error. A unique
observation in this study shows the importance of incorporating the protection
device upgrade in urban system planning on charging stations.
",1,0,0,0,0,0
16872,The Steinberg linkage class for a reductive algebraic group,"  Let G be a reductive algebraic group over a field of positive characteristic
and denote by C(G) the category of rational G-modules. In this note we
investigate the subcategory of C(G) consisting of those modules whose
composition factors all have highest weights linked to the Steinberg weight.
This subcategory is denoted ST and called the Steinberg component. We give an
explicit equivalence between ST and C(G) and we derive some consequences. In
particular, our result allows us to relate the Frobenius contracting functor to
the projection functor from C(G) onto ST .
",0,0,1,0,0,0
16873,Detection and Resolution of Rumours in Social Media: A Survey,"  Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to study how users share and discuss rumours, and to explore how natural
language processing and data mining techniques may be used to find ways of
determining their veracity. In this survey we introduce and discuss two types
of rumours that circulate on social media; long-standing rumours that circulate
for long periods of time, and newly-emerging rumours spawned during fast-paced
events such as breaking news, where reports are released piecemeal and often
with an unverified status in their early stages. We provide an overview of
research into social media rumours with the ultimate goal of developing a
rumour classification system that consists of four components: rumour
detection, rumour tracking, rumour stance classification and rumour veracity
classification. We delve into the approaches presented in the scientific
literature for the development of each of these four components. We summarise
the efforts and achievements so far towards the development of rumour
classification systems and conclude with suggestions for avenues for future
research in social media mining for detection and resolution of rumours.
",1,0,0,0,0,0
16874,On harmonic analysis of spherical convolutions on semisimple Lie groups,"  This paper contains a non-trivial generalization of the Harish-Chandra
transforms on a connected semisimple Lie group $G,$ with finite center, into
what we term spherical convolutions. Among other results we show that its
integral over the collection of bounded spherical functions at the identity
element $e \in G$ is a weighted Fourier transforms of the Abel transform at
$0.$ Being a function on $G,$ the restriction of this integral of its spherical
Fourier transforms to the positive-definite spherical functions is then shown
to be (the non-zero constant multiple of) a positive-definite distribution on
$G,$ which is tempered and invariant on $G=SL(2,\mathbb{R}).$ These results
suggest the consideration of a calculus on the Schwartz algebras of spherical
functions. The Plancherel measure of the spherical convolutions is also
explicitly computed.
",0,0,1,0,0,0
16875,Relaxation-based viscosity mapping for magnetic particle imaging,"  Magnetic Particle Imaging (MPI) has been shown to provide remarkable contrast
for imaging applications such as angiography, stem cell tracking, and cancer
imaging. Recently, there is growing interest in the functional imaging
capabilities of MPI, where color MPI techniques have explored separating
different nanoparticles, which could potentially be used to distinguish
nanoparticles in different states or environments. Viscosity mapping is a
promising functional imaging application for MPI, as increased viscosity levels
in vivo have been associated with numerous diseases such as hypertension,
atherosclerosis, and cancer. In this work, we propose a viscosity mapping
technique for MPI through the estimation of the relaxation time constant of the
nanoparticles. Importantly, the proposed time constant estimation scheme does
not require any prior information regarding the nanoparticles. We validate this
method with extensive experiments in an in-house magnetic particle spectroscopy
(MPS) setup at four different frequencies (between 250 Hz and 10.8 kHz) and at
three different field strengths (between 5 mT and 15 mT) for viscosities
ranging between 0.89 mPa.s to 15.33 mPa.s. Our results demonstrate the
viscosity mapping ability of MPI in the biologically relevant viscosity range.
",0,1,0,0,0,0
16876,Detecting Statistically Significant Communities,"  Community detection is a key data analysis problem across different fields.
During the past decades, numerous algorithms have been proposed to address this
issue. However, most work on community detection does not address the issue of
statistical significance. Although some research efforts have been made towards
mining statistically significant communities, deriving an analytical solution
of p-value for one community under the configuration model is still a
challenging mission that remains unsolved. To partially fulfill this void, we
present a tight upper bound on the p-value of a single community under the
configuration model, which can be used for quantifying the statistical
significance of each community analytically. Meanwhile, we present a local
search method to detect statistically significant communities in an iterative
manner. Experimental results demonstrate that our method is comparable with the
competing methods on detecting statistically significant communities.
",1,0,0,0,0,0
16877,On the effectivity of spectra representing motivic cohomology theories,"  Let k be an infinite perfect field. We provide a general criterion for a
spectrum in the stable homotopy category over k to be effective, i.e. to be in
the localizing subcategory generated by the suspension spectra of smooth
schemes. As a consequence, we show that two recent versions of generalized
motivic cohomology theories coincide.
",0,0,1,0,0,0
16878,Aggregated Momentum: Stability Through Passive Damping,"  Momentum is a simple and widely used trick which allows gradient-based
optimizers to pick up speed along low curvature directions. Its performance
depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
potentially deliver much larger speedups, but are prone to oscillations and
instability; hence one typically resorts to small values such as 0.5 or 0.9. We
propose Aggregated Momentum (AggMo), a variant of momentum which combines
multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
to implement, but significantly dampens oscillations, enabling it to remain
stable even for aggressive $\beta$ values such as 0.999. We reinterpret
Nesterov's accelerated gradient descent as a special case of AggMo and analyze
rates of convergence for quadratic objectives. Empirically, we find that AggMo
is a suitable drop-in replacement for other momentum methods, and frequently
delivers faster convergence.
",0,0,0,1,0,0
16879,On the Number of Bins in Equilibria for Signaling Games,"  We investigate the equilibrium behavior for the decentralized quadratic cheap
talk problem in which an encoder and a decoder, viewed as two decision makers,
have misaligned objective functions. In prior work, we have shown that the
number of bins under any equilibrium has to be at most countable, generalizing
a classical result due to Crawford and Sobel who considered sources with
density supported on $[0,1]$. In this paper, we refine this result in the
context of exponential and Gaussian sources. For exponential sources, a
relation between the upper bound on the number of bins and the misalignment in
the objective functions is derived, the equilibrium costs are compared, and it
is shown that there also exist equilibria with infinitely many bins under
certain parametric assumptions. For Gaussian sources, it is shown that there
exist equilibria with infinitely many bins.
",1,0,0,0,0,0
16880,The Dantzig selector for a linear model of diffusion processes,"  In this paper, a linear model of diffusion processes with unknown drift and
diagonal diffusion matrices is discussed. We will consider the estimation
problems for unknown parameters based on the discrete time observation in
high-dimensional and sparse settings. To estimate drift matrices, the Dantzig
selector which was proposed by Candés and Tao in 2007 will be applied. Then,
we will prove two types of consistency of the estimator of drift matrix; one is
the consistency in the sense of $l_q$ norm for every $q \in [1,\infty]$ and the
other is the variable selection consistency. Moreover, we will construct an
asymptotically normal estimator of the drift matrix by using the variable
selection consistency of the Dantzig selector.
",0,0,1,1,0,0
16881,A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data,"  Among the proposals for joint disease mapping, the shared component model has
become more popular. Another recent advance to strengthen inference of disease
data has been the extension of purely spatial models to include time and
space-time interaction. Such analyses have additional benefits over purely
spatial models. However, only a few proposed spatio-temporal models could
address analysing multiple diseases jointly.
In the proposed model, each component is shared by different subsets of
diseases, spatial and temporal trends are considered for each component, and
the relative weight of these trends for each component for each relevant
disease can be estimated. We present an application of the proposed method on
incidence rates of seven prevalent cancers in Iran. The effect of the shared
components on the individual cancer types can be identified. Regional and
temporal variation in relative risks is shown. We present a model which
combines the benefits of shared-components with spatio-temporal techniques for
multivariate data. We show, how the model allows to analyse geographical and
temporal variation among diseases beyond previous approaches.
",0,0,0,1,0,0
16882,The dynamo effect in decaying helical turbulence,"  We show that in decaying hydromagnetic turbulence with initial kinetic
helicity, a weak magnetic field eventually becomes fully helical. The sign of
magnetic helicity is opposite to that of the kinetic helicity - regardless of
whether or not the initial magnetic field was helical. The magnetic field
undergoes inverse cascading with the magnetic energy decaying approximately
like t^{-1/2}. This is even slower than in the fully helical case, where it
decays like t^{-2/3}. In this parameter range, the product of magnetic energy
and correlation length raised to a certain power slightly larger than unity, is
approximately constant. This scaling of magnetic energy persists over long time
scales. At very late times and for domain sizes large enough to accommodate the
growing spatial scales, we expect a cross-over to the t^{-2/3} decay law that
is commonly observed for fully helical magnetic fields. Regardless of the
presence or absence of initial kinetic helicity, the magnetic field experiences
exponential growth during the first few turnover times, which is suggestive of
small-scale dynamo action. Our results have applications to a wide range of
experimental dynamos and astrophysical time-dependent plasmas, including
primordial turbulence in the early universe.
",0,1,0,0,0,0
16883,Geometrically finite amalgamations of hyperbolic 3-manifold groups are not LERF,"  We prove that, for any two finite volume hyperbolic $3$-manifolds, the
amalgamation of their fundamental groups along any nontrivial geometrically
finite subgroup is not LERF. This generalizes the author's previous work on
nonLERFness of amalgamations of hyperbolic $3$-manifold groups along abelian
subgroups. A consequence of this result is that closed arithmetic hyperbolic
$4$-manifolds have nonLERF fundamental groups. Along with the author's previous
work, we get that, for any arithmetic hyperbolic manifold with dimension at
least $4$, with possible exceptions in $7$-dimensional manifolds defined by the
octonion, its fundamental group is not LERF.
",0,0,1,0,0,0
16884,"Dining Philosophers, Leader Election and Ring Size problems, in the quantum setting","  We provide the first quantum (exact) protocol for the Dining Philosophers
problem (DP), a central problem in distributed algorithms. It is well known
that the problem cannot be solved exactly in the classical setting. We then use
our DP protocol to provide a new quantum protocol for the tightly related
problem of exact leader election (LE) on a ring, improving significantly in
both time and memory complexity over the known LE protocol by Tani et. al. To
do this, we show that in some sense the exact DP and exact LE problems are
equivalent; interestingly, in the classical non-exact setting they are not.
Hopefully, the results will lead to exact quantum protocols for other important
distributed algorithmic questions; in particular, we discuss interesting
connections to the ring size problem, as well as to a physically motivated
question of breaking symmetry in 1D translationally invariant systems.
",1,0,0,0,0,0
16885,An Online Secretary Framework for Fog Network Formation with Minimal Latency,"  Fog computing is seen as a promising approach to perform distributed,
low-latency computation for supporting Internet of Things applications.
However, due to the unpredictable arrival of available neighboring fog nodes,
the dynamic formation of a fog network can be challenging. In essence, a given
fog node must smartly select the set of neighboring fog nodes that can provide
low-latency computations. In this paper, this problem of fog network formation
and task distribution is studied considering a hybrid cloud-fog architecture.
The goal of the proposed framework is to minimize the maximum computational
latency by enabling a given fog node to form a suitable fog network, under
uncertainty on the arrival process of neighboring fog nodes. To solve this
problem, a novel approach based on the online secretary framework is proposed.
To find the desired set of neighboring fog nodes, an online algorithm is
developed to enable a task initiating fog node to decide on which other nodes
can be used as part of its fog network, to offload computational tasks, without
knowing any prior information on the future arrivals of those other nodes.
Simulation results show that the proposed online algorithm can successfully
select an optimal set of neighboring fog nodes while achieving a latency that
is as small as the one resulting from an ideal, offline scheme that has
complete knowledge of the system. The results also show how, using the proposed
approach, the computational tasks can be properly distributed between the fog
network and a remote cloud server.
",1,0,0,0,0,0
16886,Computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model,"  We present a computer-assisted proof of heteroclinic connections in the
one-dimensional Ohta-Kawasaki model of diblock copolymers. The model is a
fourth-order parabolic partial differential equation subject to homogeneous
Neumann boundary conditions, which contains as a special case the celebrated
Cahn-Hilliard equation. While the attractor structure of the latter model is
completely understood for one-dimensional domains, the diblock copolymer
extension exhibits considerably richer long-term dynamical behavior, which
includes a high level of multistability. In this paper, we establish the
existence of certain heteroclinic connections between the homogeneous
equilibrium state, which represents a perfect copolymer mixture, and all local
and global energy minimizers. In this way, we show that not every solution
originating near the homogeneous state will converge to the global energy
minimizer, but rather is trapped by a stable state with higher energy. This
phenomenon can not be observed in the one-dimensional Cahn-Hillard equation,
where generic solutions are attracted by a global minimizer.
",1,0,1,0,0,0
16887,Dust and Gas in Star Forming Galaxies at z~3 - Extending Galaxy Uniformity to 11.5 Billion Years,"  We present millimetre dust emission measurements of two Lyman Break Galaxies
at z~3 and construct for the first time fully sampled infrared spectral energy
distributions (SEDs), from mid-IR to the Rayleigh-Jeans tail, of individually
detected, unlensed, UV-selected, main sequence (MS) galaxies at $z=3$. The SED
modelling of the two sources confirms previous findings, based on stacked
ensembles, of an increasing mean radiation field <U> with redshift, consistent
with a rapidly decreasing gas metallicity in z > 2 galaxies. Complementing our
study with CO[3-2] emission line observations, we measure the molecular gas
mass (M_H2) reservoir of the systems using three independent approaches: 1) CO
line observations, 2) the dust to gas mass ratio vs metallicity relation and 3)
a single band, dust emission flux on the Rayleigh-Jeans side of the SED. All
techniques return consistent M_H2 estimates within a factor of ~2 or less,
yielding gas depletion time-scales (tau_dep ~ 0.35 Gyrs) and gas-to-stellar
mass ratios (M_H2/M* ~ 0.5-1) for our z~3 massive MS galaxies. The overall
properties of our galaxies are consistent with trends and relations established
at lower redshifts, extending the apparent uniformity of star-forming galaxies
over the last 11.5 billion years.
",0,1,0,0,0,0
16888,Flow speed has little impact on propulsive characteristics of oscillating foils,"  Experiments are reported on the performance of a pitching and heaving
two-dimensional foil in a water channel in either continuous or intermittent
motion. We find that the thrust and power are independent of the mean
freestream velocity for two-fold changes in the mean velocity (four-fold in the
dynamic pressure), and for oscillations in the velocity up to 38\% of the mean,
where the oscillations are intended to mimic those of freely swimming motions
where the thrust varies during the flapping cycle. We demonstrate that the
correct velocity scale is not the flow velocity but the mean velocity of the
trailing edge. We also find little or no impact of streamwise velocity change
on the wake characteristics such as vortex organization, vortex strength, and
time-averaged velocity profile development---the wake is both qualitatively and
quantitatively unchanged. Our results suggest that constant velocity studies
can be used to make robust conclusions about swimming performance without a
need to explore the free-swimming condition.
",0,1,0,0,0,0
16889,"Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation","  In this paper, we consider the problem of attack-resilient state estimation,
that is to reliably estimate the true system states despite two classes of
attacks: (i) attacks on the switching mechanisms and (ii) false data injection
attacks on actuator and sensor signals, in the presence of unbounded stochastic
process and measurement noise signals. We model the systems under attack as
hidden mode stochastic switched linear systems with unknown inputs and propose
the use of a multiple-model inference algorithm to tackle these security
issues. Moreover, we characterize fundamental limitations to resilient
estimation (e.g., upper bound on the number of tolerable signal attacks) and
discuss the topics of attack detection, identification and mitigation under
this framework. Simulation examples of switching and false data injection
attacks on a benchmark system and an IEEE 68-bus test system show the efficacy
of our approach to recover resilient (i.e., asymptotically unbiased) state
estimates as well as to identify and mitigate the attacks.
",1,0,1,0,0,0
16890,Generating Sentence Planning Variations for Story Telling,"  There has been a recent explosion in applications for dialogue interaction
ranging from direction-giving and tourist information to interactive story
systems. Yet the natural language generation (NLG) component for many of these
systems remains largely handcrafted. This limitation greatly restricts the
range of applications; it also means that it is impossible to take advantage of
recent work in expressive and statistical language generation that can
dynamically and automatically produce a large number of variations of given
content. We propose that a solution to this problem lies in new methods for
developing language generation resources. We describe the ES-Translator, a
computational language generator that has previously been applied only to
fables, and quantitatively evaluate the domain independence of the EST by
applying it to personal narratives from weblogs. We then take advantage of
recent work on language generation to create a parameterized sentence planner
for story generation that provides aggregation operations, variations in
discourse and in point of view. Finally, we present a user evaluation of
different personal narrative retellings.
",1,0,0,0,0,0
16891,"Detection of planet candidates around K giants, HD 40956, HD 111591, and HD 113996","  Aims. The purpose of this paper is to detect and investigate the nature of
long-term radial velocity (RV) variations of K-type giants and to confirm
planetary companions around the stars.
Methods. We have conducted two planet search programs by precise RV
measurement using the 1.8 m telescope at Bohyunsan Optical Astronomy
Observatory (BOAO) and the 1.88 m telescope at Okayama Astrophysical
Observatory (OAO). The BOAO program searches for planets around 55 early K
giants. The OAO program is looking for 190 G-K type giants.
Results. In this paper, we report the detection of long-period RV variations
of three K giant stars, HD 40956, HD 111591, and HD 113996. We investigated the
cause of the observed RV variations and conclude the substellar companions are
most likely the cause of the RV variations. The orbital analyses yield P =
578.6 $\pm$ 3.3 d, $m$ sin $i$ = 2.7 $\pm$ 0.6 $M_{\rm{J}}$, $a$ = 1.4 $\pm$
0.1 AU for HD 40956; P = 1056.4 $\pm$ 14.3 d, $m$ sin $i$ = 4.4 $\pm$ 0.4
$M_{\rm{J}}$, $a$ = 2.5 $\pm$ 0.1 AU for HD 111591; P = 610.2 $\pm$ 3.8 d, $m$
sin $i$ = 6.3 $\pm$ 1.0 $M_{\rm{J}}$, $a$ = 1.6 $\pm$ 0.1 AU for HD 113996.
",0,1,0,0,0,0
16892,Perfect Half Space Games,"  We introduce perfect half space games, in which the goal of Player 2 is to
make the sums of encountered multi-dimensional weights diverge in a direction
which is consistent with a chosen sequence of perfect half spaces (chosen
dynamically by Player 2). We establish that the bounding games of Jurdziński
et al. (ICALP 2015) can be reduced to perfect half space games, which in turn
can be translated to the lexicographic energy games of Colcombet and
Niwiński, and are positionally determined in a strong sense (Player 2 can
play without knowing the current perfect half space). We finally show how
perfect half space games and bounding games can be employed to solve
multi-dimensional energy parity games in pseudo-polynomial time when both the
numbers of energy dimensions and of priorities are fixed, regardless of whether
the initial credit is given as part of the input or existentially quantified.
This also yields an optimal 2-EXPTIME complexity with given initial credit,
where the best known upper bound was non-elementary.
",1,0,0,0,0,0
16893,"Energy-efficient Analog Sensing for Large-scale, High-density Persistent Wireless Monitoring","  The research challenge of current Wireless Sensor Networks~(WSNs) is to
design energy-efficient, low-cost, high-accuracy, self-healing, and scalable
systems for applications such as environmental monitoring. Traditional WSNs
consist of low density, power-hungry digital motes that are expensive and
cannot remain functional for long periods on a single charge. In order to
address these challenges, a \textit{dumb-sensing and smart-processing}
architecture that splits sensing and computation capabilities among tiers is
proposed. Tier-1 consists of dumb sensors that only sense and transmit, while
the nodes in Tier-2 do all the smart processing on Tier-1 sensor data. A
low-power and low-cost solution for Tier-1 sensors has been proposed using
Analog Joint Source Channel Coding~(AJSCC). An analog circuit that realizes the
rectangular type of AJSCC has been proposed and realized on a Printed Circuit
Board for feasibility analysis. A prototype consisting of three Tier-1 sensors
(sensing temperature and humidity) communicating to a Tier-2 Cluster Head has
been demonstrated to verify the proposed approach. Results show that our
framework is indeed feasible to support large scale high density and persistent
WSN deployment.
",1,0,0,0,0,0
16894,Computation of ground-state properties in molecular systems: back-propagation with auxiliary-field quantum Monte Carlo,"  We address the computation of ground-state properties of chemical systems and
realistic materials within the auxiliary-field quantum Monte Carlo method. The
phase constraint to control the fermion phase problem requires the random walks
in Slater determinant space to be open-ended with branching. This in turn makes
it necessary to use back-propagation (BP) to compute averages and correlation
functions of operators that do not commute with the Hamiltonian. Several BP
schemes are investigated and their optimization with respect to the phaseless
constraint is considered. We propose a modified BP method for the computation
of observables in electronic systems, discuss its numerical stability and
computational complexity, and assess its performance by computing ground-state
properties for several substances, including constituents of the primordial
terrestrial atmosphere and small organic molecules.
",0,1,0,0,0,0
16895,Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification,"  Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the
process to quantify the uncertainties of random input parameters based on
experimental data. The introduction of model discrepancy term is significant
because ""over-fitting"" can theoretically be avoided. But it also poses
challenges in the practical applications. One of the mostly concerned and
unresolved problem is the ""lack of identifiability"" issue. With the presence of
model discrepancy, inverse UQ becomes ""non-identifiable"" in the sense that it
is difficult to precisely distinguish between the parameter uncertainties and
model discrepancy when estimating the calibration parameters. Previous research
to alleviate the non-identifiability issue focused on using informative priors
for the calibration parameters and the model discrepancy, which is usually not
a viable solution because one rarely has such accurate and informative prior
knowledge. In this work, we show that identifiability is largely related to the
sensitivity of the calibration parameters with regards to the chosen responses.
We adopted an improved modular Bayesian approach for inverse UQ that does not
require priors for the model discrepancy term. The relationship between
sensitivity and identifiability was demonstrated with a practical example in
nuclear engineering. It was shown that, in order for a certain calibration
parameter to be statistically identifiable, it should be significant to at
least one of the responses whose data are used for inverse UQ. Good
identifiability cannot be achieved for a certain calibration parameter if it is
not significant to any of the responses. It is also demonstrated that ""fake
identifiability"" is possible if model responses are not appropriately chosen,
or inaccurate but informative priors are specified.
",0,0,0,1,0,0
16896,The GENIUS Approach to Robust Mendelian Randomization Inference,"  Mendelian randomization (MR) is a popular instrumental variable (IV)
approach. A key IV identification condition known as the exclusion restriction
requires no direct effect of an IV on the outcome not through the exposure
which is unrealistic in most MR analyses. As a result, possible violation of
the exclusion restriction can seldom be ruled out in such studies. To address
this concern, we introduce a new class of IV estimators which are robust to
violation of the exclusion restriction under a large collection of data
generating mechanisms consistent with parametric models commonly assumed in the
MR literature. Our approach named ""MR G-Estimation under No Interaction with
Unmeasured Selection"" (MR GENIUS) may be viewed as a modification to Robins'
G-estimation approach that is robust to both additive unmeasured confounding
and violation of the exclusion restriction assumption. We also establish that
estimation with MR GENIUS may also be viewed as a robust generalization of the
well-known Lewbel estimator for a triangular system of structural equations
with endogeneity. Specifically, we show that unlike Lewbel estimation, MR
GENIUS is under fairly weak conditions also robust to unmeasured confounding of
the effects of the genetic IVs, another possible violation of a key IV
Identification condition. Furthermore, while Lewbel estimation involves
specification of linear models both for the outcome and the exposure, MR GENIUS
generally does not require specification of a structural model for the direct
effect of invalid IVs on the outcome, therefore allowing the latter model to be
unrestricted. Finally, unlike Lewbel estimation, MR GENIUS is shown to equally
apply for binary, discrete or continuous exposure and outcome variables and can
be used under prospective sampling, or retrospective sampling such as in a
case-control study.
",0,0,0,1,0,0
16897,Evaluation of Trace Alignment Quality and its Application in Medical Process Mining,"  Trace alignment algorithms have been used in process mining for discovering
the consensus treatment procedures and process deviations. Different alignment
algorithms, however, may produce very different results. No widely-adopted
method exists for evaluating the results of trace alignment. Existing
reference-free evaluation methods cannot adequately and comprehensively assess
the alignment quality. We analyzed and compared the existing evaluation
methods, identifying their limitations, and introduced improvements in two
reference-free evaluation methods. Our approach assesses the alignment result
globally instead of locally, and therefore helps the algorithm to optimize
overall alignment quality. We also introduced a novel metric to measure the
alignment complexity, which can be used as a constraint on alignment algorithm
optimization. We tested our evaluation methods on a trauma resuscitation
dataset and provided the medical explanation of the activities and patterns
identified as deviations using our proposed evaluation methods.
",1,0,0,0,0,0
16898,Size Constraints on Majorana Beamsplitter Interferometer: Majorana Coupling and Surface-Bulk Scattering,"  Topological insulator surfaces in proximity to superconductors have been
proposed as a way to produce Majorana fermions in condensed matter physics. One
of the simplest proposed experiments with such a system is Majorana
interferometry. Here, we consider two possibly conflicting constraints on the
size of such an interferometer. Coupling of a Majorana mode from the edge (the
arms) of the interferometer to vortices in the centre of the device sets a
lower bound on the size of the device. On the other hand, scattering to the
usually imperfectly insulating bulk sets an upper bound. From estimates of
experimental parameters, we find that typical samples may have no size window
in which the Majorana interferometer can operate, implying that a new
generation of more highly insulating samples must be explored.
",0,1,0,0,0,0
16899,Counting Arithmetical Structures on Paths and Cycles,"  Let $G$ be a finite, simple, connected graph. An arithmetical structure on
$G$ is a pair of positive integer vectors $\mathbf{d},\mathbf{r}$ such that
$(\mathrm{diag}(\mathbf{d})-A)\mathbf{r}=0$, where $A$ is the adjacency matrix
of $G$. We investigate the combinatorics of arithmetical structures on path and
cycle graphs, as well as the associated critical groups (the cokernels of the
matrices $(\mathrm{diag}(\mathbf{d})-A)$). For paths, we prove that
arithmetical structures are enumerated by the Catalan numbers, and we obtain
refined enumeration results related to ballot sequences. For cycles, we prove
that arithmetical structures are enumerated by the binomial coefficients
$\binom{2n-1}{n-1}$, and we obtain refined enumeration results related to
multisets. In addition, we determine the critical groups for all arithmetical
structures on paths and cycles.
",0,0,1,0,0,0
16900,From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior,"  The study of neuronal interactions is currently at the center of several
neuroscience big collaborative projects (including the Human Connectome, the
Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the
entire brain matrix. Under certain constraints, mathematical theory can advance
predictions of the expected neural dynamics based solely on the statistical
properties of such synaptic interaction matrix. This work explores the
application of free random variables (FRV) to the study of large synaptic
interaction matrices. Besides recovering in a straightforward way known results
on eigenspectra of neural networks, we extend them to heavy-tailed
distributions of interactions. More importantly, we derive analytically the
behavior of eigenvector overlaps, which determine stability of the spectra. We
observe that upon imposing the neuronal excitation/inhibition balance, although
the eigenvalues remain unchanged, their stability dramatically decreases due to
strong non-orthogonality of associated eigenvectors. It leads us to the
conclusion that the understanding of the temporal evolution of asymmetric
neural networks requires considering the entangled dynamics of both
eigenvectors and eigenvalues, which might bear consequences for learning and
memory processes in these models. Considering the success of FRV analysis in a
wide variety of branches disciplines, we hope that the results presented here
foster additional application of these ideas in the area of brain sciences.
",0,0,0,0,1,0
16901,Exploring cosmic origins with CORE: mitigation of systematic effects,"  We present an analysis of the main systematic effects that could impact the
measurement of CMB polarization with the proposed CORE space mission. We employ
timeline-to-map simulations to verify that the CORE instrumental set-up and
scanning strategy allow us to measure sky polarization to a level of accuracy
adequate to the mission science goals. We also show how the CORE observations
can be processed to mitigate the level of contamination by potentially worrying
systematics, including intensity-to-polarization leakage due to bandpass
mismatch, asymmetric main beams, pointing errors and correlated noise. We use
analysis techniques that are well validated on data from current missions such
as Planck to demonstrate how the residual contamination of the measurements by
these effects can be brought to a level low enough not to hamper the scientific
capability of the mission, nor significantly increase the overall error budget.
We also present a prototype of the CORE photometric calibration pipeline, based
on that used for Planck, and discuss its robustness to systematics, showing how
CORE can achieve its calibration requirements. While a fine-grained assessment
of the impact of systematics requires a level of knowledge of the system that
can only be achieved in a future study phase, the analysis presented here
strongly suggests that the main areas of concern for the CORE mission can be
addressed using existing knowledge, techniques and algorithms.
",0,1,0,0,0,0
16902,A non-ordinary peridynamics implementation for anisotropic materials,"  Peridynamics (PD) represents a new approach for modelling fracture mechanics,
where a continuum domain is modelled through particles connected via physical
bonds. This formulation allows us to model crack initiation, propagation,
branching and coalescence without special assumptions. Up to date, anisotropic
materials were modelled in the PD framework as different isotropic materials
(for instance, fibre and matrix of a composite laminate), where the stiffness
of the bond depends on its orientation. A non-ordinary state-based formulation
will enable the modelling of generally anisotropic materials, where the
material properties are directly embedded in the formulation. Other material
models include rocks, concrete and biomaterials such as bones. In this paper,
we implemented this model and validated it for anisotropic composite materials.
A composite damage criterion has been employed to model the crack propagation
behaviour. Several numerical examples have been used to validate the approach,
and compared to other benchmark solution from the finite element method (FEM)
and experimental results when available.
",1,1,0,0,0,0
16903,Discrete-attractor-like Tracking in Continuous Attractor Neural Networks,"  Continuous attractor neural networks generate a set of smoothly connected
attractor states. In memory systems of the brain, these attractor states may
represent continuous pieces of information such as spatial locations and head
directions of animals. However, during the replay of previous experiences,
hippocampal neurons show a discontinuous sequence in which discrete transitions
of neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation.
Here, we explored the underlying mechanisms of the discontinuous sequence
generation. We found that a continuous attractor neural network has several
phases depending on the interactions between external input and local
inhibitory feedback. The discrete-attractor-like behavior naturally emerges in
one of these phases without any discreteness assumption. We propose that the
dynamics of continuous attractor neural networks is the key to generate
discontinuous state changes phase-locked to the brain rhythm.
",0,0,0,0,1,0
16904,Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing,"  In this paper, we develop a framework for an innovative perceptive mobile
(i.e. cellular) network that integrates sensing with communication, and
supports new applications widely in transportation, surveillance and
environmental sensing. Three types of sensing methods implemented in the
base-stations are proposed, using either uplink or downlink multiuser
communication signals. The required changes to system hardware and major
technical challenges are briefly discussed. We also demonstrate the feasibility
of estimating sensing parameters via developing a compressive sensing based
scheme and providing simulation results to validate its effectiveness.
",1,0,0,0,0,0
16905,On the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$,"  We show that the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$ is
$\mathrm{PSL}_n(\mathbb{Z}/2\mathbb{Z}) = \mathrm{L}_n(2)$, thus confirming a
conjecture of Mecchia--Zimmermann. In the course of the proof we give an
exponential (in $n$) lower bound for the cardinality of a set on which
$\mathrm{SAut}(F_n)$, the unique index $2$ subgroup of $\mathrm{Aut}(F_n)$, can
act non-trivially. We also offer new results on the representation theory of
$\mathrm{SAut(F_n)}$ in small dimensions over small, positive characteristics,
and on rigidity of maps from $\mathrm{SAut}(F_n)$ to finite groups of Lie type
and algebraic groups in characteristic $2$.
",0,0,1,0,0,0
16906,Property Testing in High Dimensional Ising models,"  This paper explores the information-theoretic limitations of graph property
testing in zero-field Ising models. Instead of learning the entire graph
structure, sometimes testing a basic graph property such as connectivity, cycle
presence or maximum clique size is a more relevant and attainable objective.
Since property testing is more fundamental than graph recovery, any necessary
conditions for property testing imply corresponding conditions for graph
recovery, while custom property tests can be statistically and/or
computationally more efficient than graph recovery based algorithms.
Understanding the statistical complexity of property testing requires the
distinction of ferromagnetic (i.e., positive interactions only) and general
Ising models. Using combinatorial constructs such as graph packing and strong
monotonicity, we characterize how target properties affect the corresponding
minimax upper and lower bounds within the realm of ferromagnets. On the other
hand, by studying the detection of an antiferromagnetic (i.e., negative
interactions only) Curie-Weiss model buried in Rademacher noise, we show that
property testing is strictly more challenging over general Ising models. In
terms of methodological development, we propose two types of correlation based
tests: computationally efficient screening for ferromagnets, and score type
tests for general models, including a fast cycle presence test. Our correlation
screening tests match the information-theoretic bounds for property testing in
ferromagnets.
",0,0,1,1,0,0
16907,Stratification and duality for homotopical groups,"  In this paper, we show that the category of module spectra over
$C^*(B\mathcal{G},\mathbb{F}_p)$ is stratified for any $p$-local compact group
$\mathcal{G}$, thereby giving a support-theoretic classification of all
localizing subcategories of this category. To this end, we generalize Quillen's
$F$-isomorphism theorem, Quillen's stratification theorem, Chouinard's theorem,
and the finite generation of cohomology rings from finite groups to homotopical
groups. Moreover, we show that $p$-compact groups admit a homotopical form of
Gorenstein duality.
",0,0,1,0,0,0
16908,"Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE","  We present a family of Python modules for the numerical integration of
ordinary, delay, or stochastic differential equations. The key features are
that the user enters the derivative symbolically and it is
just-in-time-compiled, allowing the user to efficiently integrate differential
equations from a higher-level interpreted language. The presented modules are
particularly suited for large systems of differential equations such as used to
describe dynamics on complex networks. Through the selected method of input,
the presented modules also allow to almost completely automatize the process of
estimating regular as well as transversal Lyapunov exponents for ordinary and
delay differential equations. We conceptually discuss the modules' design,
analyze their performance, and demonstrate their capabilities by application to
timely problems.
",1,1,0,0,0,0
16909,Adaptive Diffusions for Scalable Learning over Graphs,"  Diffusion-based classifiers such as those relying on the Personalized
PageRank and the Heat kernel, enjoy remarkable classification accuracy at
modest computational requirements. Their performance however is affected by the
extent to which the chosen diffusion captures a typically unknown label
propagation mechanism, that can be specific to the underlying graph, and
potentially different for each class. The present work introduces a
disciplined, data-efficient approach to learning class-specific diffusion
functions adapted to the underlying network topology. The novel learning
approach leverages the notion of ""landing probabilities"" of class-specific
random walks, which can be computed efficiently, thereby ensuring scalability
to large graphs. This is supported by rigorous analysis of the properties of
the model as well as the proposed algorithms. Furthermore, a robust version of
the classifier facilitates learning even in noisy environments.
Classification tests on real networks demonstrate that adapting the diffusion
function to the given graph and observed labels, significantly improves the
performance over fixed diffusions; reaching -- and many times surpassing -- the
classification accuracy of computationally heavier state-of-the-art competing
methods, that rely on node embeddings and deep neural networks.
",0,0,0,1,0,0
16910,On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search,"  Active Learning (AL) methods have proven cost-saving against passive
supervised methods in many application domains. An active learner, aiming to
find some target hypothesis, formulates sequential queries to some oracle. The
set of hypotheses consistent with the already answered queries is called
version space. Several query selection measures (QSMs) for determining the best
query to ask next have been proposed. Assuming binaryoutcome queries, we
analyze various QSMs wrt. to the discrimination power of their selected queries
within the current version space. As a result, we derive superiority and
equivalence relations between these QSMs and introduce improved versions of
existing QSMs to overcome identified issues. The obtained picture gives a hint
about which QSMs should preferably be used in pool-based AL scenarios.
Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
these, we demonstrate how efficient heuristic search methods for optimal
queries in query synthesis AL scenarios can be devised.
",1,0,0,0,0,0
16911,Torchbearer: A Model Fitting Library for PyTorch,"  We introduce torchbearer, a model fitting library for pytorch aimed at
researchers working on deep learning or differentiable programming. The
torchbearer library provides a high level metric and callback API that can be
used for a wide range of applications. We also include a series of built in
callbacks that can be used for: model persistence, learning rate decay,
logging, data visualization and more. The extensive documentation includes an
example library for deep learning and dynamic programming problems and can be
found at this http URL. The code is licensed under the MIT
License and available at this https URL.
",0,0,0,1,0,0
16912,On estimation of contamination from hydrogen cyanide in carbon monoxide line intensity mapping,"  Line-intensity mapping surveys probe large-scale structure through spatial
variations in molecular line emission from a population of unresolved
cosmological sources. Future such surveys of carbon monoxide line emission,
specifically the CO(1-0) line, face potential contamination from a disjoint
population of sources emitting in a hydrogen cyanide emission line, HCN(1-0).
This paper explores the potential range of the strength of HCN emission and its
effect on the CO auto power spectrum, using simulations with an empirical model
of the CO/HCN--halo connection. We find that effects on the observed CO power
spectrum depend on modeling assumptions but are very small for our fiducial
model based on our understanding of the galaxy--halo connection, with the bias
in overall CO detection significance due to HCN expected to be less than 1%.
",0,1,0,0,0,0
16913,Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints,"  Classifiers can be trained with data-dependent constraints to satisfy
fairness goals, reduce churn, achieve a targeted false positive rate, or other
policy goals. We study the generalization performance for such constrained
optimization problems, in terms of how well the constraints are satisfied at
evaluation time, given that they are satisfied at training time. To improve
generalization performance, we frame the problem as a two-player game where one
player optimizes the model parameters on a training dataset, and the other
player enforces the constraints on an independent validation dataset. We build
on recent work in two-player constrained optimization to show that if one uses
this two-dataset approach, then constraint generalization can be significantly
improved. As we illustrate experimentally, this approach works not only in
theory, but also in practice.
",0,0,0,1,0,0
16914,Automated Website Fingerprinting through Deep Learning,"  Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.
",1,0,0,0,0,0
16915,"DataCite as a novel bibliometric source: Coverage, strengths and limitations","  This paper explores the characteristics of DataCite to determine its
possibilities and potential as a new bibliometric data source to analyze the
scholarly production of open data. Open science and the increasing data sharing
requirements from governments, funding bodies, institutions and scientific
journals has led to a pressing demand for the development of data metrics. As a
very first step towards reliable data metrics, we need to better comprehend the
limitations and caveats of the information provided by sources of open data. In
this paper, we critically examine records downloaded from the DataCite's OAI
API and elaborate a series of recommendations regarding the use of this source
for bibliometric analyses of open data. We highlight issues related to metadata
incompleteness, lack of standardization, and ambiguous definitions of several
fields. Despite these limitations, we emphasize DataCite's value and potential
to become one of the main sources for data metrics development.
",1,0,0,0,0,0
16916,Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise,"  We obtain strong consistency and asymptotic normality of a least squares
estimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck
processes disturbed by fractional noise, extending the result of Y. Hu and D.
Nualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special
2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality
and complex fourth moment theorems. The main ingredients of this paper are the
sample path regularity of a multiple Wiener-Ito integral and two equivalent
conditions of complex fourth moment theorems in terms of the contractions of
integral kernels and complex Malliavin derivatives.
",0,0,1,1,0,0
16917,E-learning Information Technology Based on an Ontology Driven Learning Engine,"  In the article, proposed is a new e-learning information technology based on
an ontology driven learning engine, which is matched with modern pedagogical
technologies. With the help of proposed engine and developed question database
we have conducted an experiment, where students were tested. The developed
ontology driven system of e-learning facilitates the creation of favorable
conditions for the development of personal qualities and creation of a holistic
understanding of the subject area among students throughout the educational
process.
",1,0,0,0,0,0
16918,Global regularity for 1D Eulerian dynamics with singular interaction forces,"  The Euler-Poisson-Alignment (EPA) system appears in mathematical biology and
is used to model, in a hydrodynamic limit, a set agents interacting through
mutual attraction/repulsion as well as alignment forces. We consider
one-dimensional EPA system with a class of singular alignment terms as well as
natural attraction/repulsion terms. The singularity of the alignment kernel
produces an interesting effect regularizing the solutions of the equation and
leading to global regularity for wide range of initial data. This was recently
observed in the paper by Do, Kiselev, Ryzhik and Tan. Our goal in this paper is
to generalize the result and to incorporate the attractive/repulsive potential.
We prove that global regularity persists for these more general models.
",0,0,1,0,0,0
16919,A $q$-generalization of the para-Racah polynomials,"  New bispectral orthogonal polynomials are obtained from an unconventional
truncation of the Askey-Wilson polynomials. In the limit $q \to 1$, they reduce
to the para-Racah polynomials which are orthogonal with respect to a quadratic
bi-lattice. The three term recurrence relation and q-difference equation are
obtained through limits of those of the Askey-Wilson polynomials. An explicit
expression in terms of hypergeometric series and the orthogonality relation are
provided. A $q$-generalization of the para-Krawtchouk polynomials is obtained
as a special case. Connections with the $q$-Racah and dual-Hahn polynomials are
also presented.
",0,0,1,0,0,0
16920,Data Poisoning Attack against Unsupervised Node Embedding Methods,"  Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have
attracted growing interests given their simplicity and effectiveness. However,
although these methods have been proved effective in a variety of applications,
none of the existing work has analyzed the robustness of them. This could be
very risky if these methods are attacked by an adversarial party. In this
paper, we take the task of link prediction as an example, which is one of the
most fundamental problems for graph analysis, and introduce a data positioning
attack to node embedding methods. We give a complete characterization of
attacker's utilities and present efficient solutions to adversarial attacks for
two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed
attack model on multiple real-world graphs. Experimental results show that our
proposed model can significantly affect the results of link prediction by
slightly changing the graph structures (e.g., adding or removing a few edges).
We also show that our proposed model is very general and can be transferable
across different embedding methods. Finally, we conduct a case study on a
coauthor network to better understand our attack method.
",1,0,0,0,0,0
16921,Entanglement properties of the two-dimensional SU(3) AKLT state,"  Two-dimensional (spin-$2$) Affleck-Kennedy-Lieb-Tasaki (AKLT) type valence
bond solids on the square lattice are known to be symmetry protected
topological (SPT) gapped spin liquids [Shintaro Takayoshi, Pierre Pujol, and
Akihiro Tanaka Phys. Rev. B ${\bf 94}$, 235159 (2016)]. Using the projected
entangled pair state (PEPS) framework, we extend the construction of the AKLT
state to the case of $SU(3)$, relevant for cold atom systems. The entanglement
spectrum is shown to be described by an alternating $SU(3)$ chain of ""quarks""
and ""antiquarks"", subject to exponentially decaying (with distance) Heisenberg
interactions, in close similarity with its $SU(2)$ analog. We discuss the SPT
feature of the state.
",0,1,0,0,0,0
16922,Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms,"  Efficient management of low blood pressure (BP) in preterm neonates remains
challenging with a considerable variability in clinical practice. The ability
to assess preterm wellbeing during episodes of low BP will help to decide when
and whether hypotension treatment should be initiated. This work aims to
investigate the relationship between heart rate variability (HRV), BP and the
short-term neurological outcome in preterm infants less than 32 weeks
gestational age (GA). The predictive power of common HRV features with respect
to the outcome is assessed and shown to improve when HRV is observed during
episodes of low mean arterial pressure (MAP) - with a single best feature
leading to an AUC of 0.87. Combining multiple features with a boosted decision
tree classifier achieves an AUC of 0.97. The work presents a promising step
towards the use of multimodal data in building an objective decision support
tool for clinical prediction of short-term outcome in preterms who suffer
episodes of low BP.
",0,0,0,1,0,0
16923,Understanding News Outlets' Audience-Targeting Patterns,"  The power of the press to shape the informational landscape of a population
is unparalleled, even now in the era of democratic access to all information
outlets. However, it is known that news outlets (particularly more traditional
ones) tend to discriminate who they want to reach, and who to leave aside. In
this work, we attempt to shed some light on the audience targeting patterns of
newspapers, using the Chilean media ecosystem. First, we use the gravity model
to analyze geography as a factor in explaining audience reachability. This
shows that some newspapers are indeed driven by geographical factors (mostly
local news outlets) but some others are not (national-distribution outlets).
For those which are not, we use a regression model to study the influence of
socioeconomic and political characteristics in news outlets adoption. We
conclude that indeed larger, national-distribution news outlets target
populations based on these factors, rather than on geography or immediacy.
",1,0,0,0,0,0
16924,Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings,"  Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodology to derive a representative vector for ontology classes whose
instances were converted to the vector space. We start by deriving five
candidate vectors which are then used to train a machine learning model that
would calculate a representative vector for the class. We show that our
methodology out-performs the traditional mean and median vector
representations.
",1,0,0,0,0,0
16925,The ESA Gaia Archive: Data Release 1,"  ESA Gaia mission is producing the more accurate source catalogue in astronomy
up to now. That represents a challenge on the archiving area to make accessible
this information to the astronomers in an efficient way. Also, new astronomical
missions have reinforced the change on the development of archives. Archives,
as simple applications to access the data are being evolving into complex data
center structures where computing power services are available for users and
data mining tools are integrated into the server side. In the case of astronomy
science that involves the use of big catalogues, as in Gaia (or Euclid to
come), the common ways to work on the data need to be changed to a new paradigm
""move code close to the data"", what implies that data mining functionalities
are becoming a must to allow the science exploitation. To enable these
capabilities, a TAP+ interface, crossmatch capabilities, full catalogue
histograms, serialisation of intermediate results in cloud resources like
VOSpace, etc have been implemented for the Gaia DR1, to enable the exploitation
of these science resources by the community without the bottlenecks on the
connection bandwidth. We present the architecture, infrastructure and tools
already available in the Gaia Archive Data Release 1
(this http URL) and we describe capabilities and
infrastructure.
",0,1,0,0,0,0
16926,Efficient algorithm for large spectral partitions,"  We present an amelioration of current known algorithms for optimal spectral
partitioning problems. The idea is to use the advantage of a representation
using density functions while decreasing the computational time. This is done
by restricting the computation to neighbourhoods of regions where the
associated densities are above a certain threshold. The algorithm extends and
improves known methods in the plane and on surfaces in dimension 3. It also
makes possible to make some of the first computations of volumic 3D spectral
partitions on sufficiently large discretizations.
",0,0,1,0,0,0
16927,A Martian Origin for the Mars Trojan Asteroids,"  Seven of the nine known Mars Trojan asteroids belong to an orbital cluster
named after its largest member 5261 Eureka. Eureka is likely the progenitor of
the whole cluster, which formed at least 1 Gyr ago. It was suggested that the
thermal YORP effect spun-up Eureka resulting with fragments being ejected by
the rotational-fission mechanism. Eureka's spectrum exhibits a broad and deep
absorption band around 1 {\mu}m, indicating an olivine-rich composition. Here
we show evidence that the Trojan Eureka cluster progenitor could have
originated as impact debris excavated from the Martian mantle. We present new
near-infrared observations of two Trojans (311999 2007 NS2 and 385250 2001
DH47) and find that both exhibit an olivine-rich reflectance spectrum similar
to Eureka's. These measurements confirm that the progenitor of the cluster has
an achondritic composition. Olivine-rich reflectance spectra are rare amongst
asteroids but are seen around the largest basins on Mars. They are also
consistent with some Martian meteorites (e.g. Chassigny), and with the material
comprising much of the Martian mantle. Using numerical simulations, we show
that the Mars Trojans are more likely to be impact ejecta from Mars than
captured olivine-rich asteroids transported from the main belt. This result
links directly specific asteroids to debris from the forming planets.
",0,1,0,0,0,0
16928,Far-infrared metallicity diagnostics: Application to local ultraluminous infrared galaxies,"  The abundance of metals in galaxies is a key parameter which permits to
distinguish between different galaxy formation and evolution models. Most of
the metallicity determinations are based on optical line ratios. However, the
optical spectral range is subject to dust extinction and, for high-z objects (z
> 3), some of the lines used in optical metallicity diagnostics are shifted to
wavelengths not accessible to ground based observatories. For this reason, we
explore metallicity diagnostics using far-infrared (IR) line ratios which can
provide a suitable alternative in such situations. To investigate these far-IR
line ratios, we modeled the emission of a starburst with the photoionization
code CLOUDY. The most sensitive far-IR ratios to measure metallicities are the
[OIII]52$\mu$m and 88$\mu$m to [NIII]57$\mu$m ratios. We show that this ratio
produces robust metallicities in the presence of an AGN and is insensitive to
changes in the age of the ionizing stellar. Another metallicity sensitive ratio
is the [OIII]88$\mu$m/[NII]122$\mu$m ratio, although it depends on the
ionization parameter. We propose various mid- and far-IR line ratios to break
this dependency. Finally, we apply these far-IR diagnostics to a sample of 19
local ultraluminous IR galaxies (ULIRGs) observed with Herschel and Spitzer. We
find that the gas-phase metallicity in these local ULIRGs is in the range 0.7 <
Z_gas/Z_sun < 1.5, which corresponds to 8.5 < 12 + log (O/H) < 8.9. The
inferred metallicities agree well with previous estimates for local ULIRGs and
this confirms that they lie below the local mass-metallicity relation.
",0,1,0,0,0,0
16929,Quantum communication by means of collapse of the wave function,"  We show that quantum communication by means of collapse of the wave function
is possible. In this study, quantum communication does not mean quantum
teleportation or quantum cryptography, but transmission of information itself.
Because of consistency with special relativity, the possibility of the quantum
communication leads to another conclusion that the collapse of the wave
function must propagate at the speed of light or slower.
We show this requirement is consistent with nonlocality in quantum mechanics.
We also demonstrate that the Einstein-Podolsky-Rosen experiment does not
disprove our conclusion.
",0,1,0,0,0,0
16930,DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning,"  Terramechanics plays a critical role in the areas of ground vehicles and
ground mobile robots since understanding and estimating the variables
influencing the vehicle-terrain interaction may mean the success or the failure
of an entire mission. This research applies state-of-the-art algorithms in deep
learning to two key problems: estimating wheel slip and classifying the terrain
being traversed by a ground robot. Three data sets collected by ground robotic
platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
Fitorobot) are employed in order to compare the performance of traditional
machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
Networks (CNNs). This work also shows the impact that certain tuning parameters
and the network architecture (MLP, DNN and CNN) play on the performance of
those methods. This paper also contributes a deep discussion with the lessons
learned in the implementation of DNNs and CNNs and how these methods can be
extended to solve other problems.
",1,0,0,0,0,0
16931,"Characterizations of multinormality and corresponding tests of fit, including for Garch models","  We provide novel characterizations of multivariate normality that incorporate
both the characteristic function and the moment generating function, and we
employ these results to construct a class of affine invariant, consistent and
easy-to-use goodness-of-fit tests for normality. The test statistics are
suitably weighted $L^2$-statistics, and we provide their asymptotic behavior
both for i.i.d. observations as well as in the context of testing that the
innovation distribution of a multivariate GARCH model is Gaussian. We also
study the finite-sample behavior of the new tests and compare the new criteria
with alternative existing tests.
",0,0,1,1,0,0
16932,Grouped Gaussian Processes for Solar Power Prediction,"  We consider multi-task regression models where the observations are assumed
to be a linear combination of several latent node functions and weight
functions, which are both drawn from Gaussian process priors. Driven by the
problem of developing scalable methods for forecasting distributed solar and
other renewable power generation, we propose coupled priors over groups of
(node or weight) processes to exploit spatial dependence between functions. We
estimate forecast models for solar power at multiple distributed sites and
ground wind speed at multiple proximate weather stations. Our results show that
our approach maintains or improves point-prediction accuracy relative to
competing solar benchmarks and improves over wind forecast benchmark models on
all measures. Our approach consistently dominates the equivalent model without
coupled priors, achieving faster gains in forecast accuracy. At the same time
our approach provides better quantification of predictive uncertainties.
",0,0,0,1,0,0
16933,Modeling epidemics on d-cliqued graphs,"  Since social interactions have been shown to lead to symmetric clusters, we
propose here that symmetries play a key role in epidemic modeling. Mathematical
models on d-ary tree graphs were recently shown to be particularly effective
for modeling epidemics in simple networks [Seibold & Callender, 2016]. To
account for symmetric relations, we generalize this to a new type of networks
modeled on d-cliqued tree graphs, which are obtained by adding edges to regular
d-trees to form d-cliques. This setting gives a more realistic model for
epidemic outbreaks originating, for example, within a family or classroom and
which could reach a population by transmission via children in schools.
Specifically, we quantify how an infection starting in a clique (e.g. family)
can reach other cliques through the body of the graph (e.g. public places).
Moreover, we propose and study the notion of a safe zone, a subset that has a
negligible probability of infection.
",1,0,0,0,1,0
16934,On the K-theory stable bases of the Springer resolution,"  Cohomological and K-theoretic stable bases originated from the study of
quantum cohomology and quantum K-theory. Restriction formula for cohomological
stable bases played an important role in computing the quantum connection of
cotangent bundle of partial flag varieties. In this paper we study the
K-theoretic stable bases of cotangent bundles of flag varieties. We describe
these bases in terms of the action of the affine Hecke algebra and the twisted
group algebra of Kostant-Kumar. Using this algebraic description and the method
of root polynomials, we give a restriction formula of the stable bases. We
apply it to obtain the restriction formula for partial flag varieties. We also
build a relation between the stable basis and the Casselman basis in the
principal series representations of the Langlands dual group. As an
application, we give a closed formula for the transition matrix between
Casselman basis and the characteristic functions.
",0,0,1,0,0,0
16935,Recency Bias in the Era of Big Data: The Need to Strengthen the Status of History of Mathematics in Nigerian Schools,"  The amount of information available to the mathematics teacher is so enormous
that the selection of desirable content is gradually becoming a huge task in
itself. With respect to the inclusion of elements of history of mathematics in
mathematics instruction, the era of Big Data introduces a high likelihood of
Recency Bias, a hitherto unconnected challenge for stakeholders in mathematics
education. This tendency to choose recent information at the expense of
relevant older, composite, historical facts stands to defeat the aims and
objectives of the epistemological and cultural approach to mathematics
instructional delivery. This study is a didactic discourse with focus on this
threat to the history and pedagogy of mathematics, particularly as it affects
mathematics education in Nigeria. The implications for mathematics curriculum
developers, teacher-training programmes, teacher lesson preparation, and
publication of mathematics instructional materials were also deeply considered.
",1,0,1,0,0,0
16936,Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings,"  This paper presents a convergence analysis of kernel-based quadrature rules
in misspecified settings, focusing on deterministic quadrature in Sobolev
spaces. In particular, we deal with misspecified settings where a test
integrand is less smooth than a Sobolev RKHS based on which a quadrature rule
is constructed. We provide convergence guarantees based on two different
assumptions on a quadrature rule: one on quadrature weights, and the other on
design points. More precisely, we show that convergence rates can be derived
(i) if the sum of absolute weights remains constant (or does not increase
quickly), or (ii) if the minimum distance between design points does not
decrease very quickly. As a consequence of the latter result, we derive a rate
of convergence for Bayesian quadrature in misspecified settings. We reveal a
condition on design points to make Bayesian quadrature robust to
misspecification, and show that, under this condition, it may adaptively
achieve the optimal rate of convergence in the Sobolev space of a lesser order
(i.e., of the unknown smoothness of a test integrand), under a slightly
stronger regularity condition on the integrand.
",1,0,0,1,0,0
16937,The toric Frobenius morphism and a conjecture of Orlov,"  We combine the Bondal-Uehara method for producing exceptional collections on
toric varieties with a result of the first author and Favero to expand the set
of varieties satisfying Orlov's Conjecture on derived dimension.
",0,0,1,0,0,0
16938,Friendship Maintenance and Prediction in Multiple Social Networks,"  Due to the proliferation of online social networks (OSNs), users find
themselves participating in multiple OSNs. These users leave their activity
traces as they maintain friendships and interact with other users in these
OSNs. In this work, we analyze how users maintain friendship in multiple OSNs
by studying users who have accounts in both Twitter and Instagram.
Specifically, we study the similarity of a user's friendship and the evenness
of friendship distribution in multiple OSNs. Our study shows that most users in
Twitter and Instagram prefer to maintain different friendships in the two OSNs,
keeping only a small clique of common friends in across the OSNs. Based upon
our empirical study, we conduct link prediction experiments to predict missing
friendship links in multiple OSNs using the neighborhood features, neighborhood
friendship maintenance features and cross-link features. Our link prediction
experiments shows that un- supervised methods can yield good accuracy in
predicting links in one OSN using another OSN data and the link prediction
accuracy can be further improved using supervised method with friendship
maintenance and others measures as features.
",1,1,0,0,0,0
16939,Learning to Generate Music with BachProp,"  As deep learning advances, algorithms of music composition increase in
performance. However, most of the successful models are designed for specific
musical structures. Here, we present BachProp, an algorithmic composer that can
generate music scores in many styles given sufficient training data. To adapt
BachProp to a broad range of musical styles, we propose a novel representation
of music and train a deep network to predict the note transition probabilities
of a given music corpus. In this paper, new music scores generated by BachProp
are compared with the original corpora as well as with different network
architectures and other related models. We show that BachProp captures
important features of the original datasets better than other models and invite
the reader to a qualitative comparison on a large collection of generated
songs.
",1,0,0,0,0,0
16940,How to Quantize $n$ Outputs of a Binary Symmetric Channel to $n-1$ Bits?,"  Suppose that $Y^n$ is obtained by observing a uniform Bernoulli random vector
$X^n$ through a binary symmetric channel with crossover probability $\alpha$.
The ""most informative Boolean function"" conjecture postulates that the maximal
mutual information between $Y^n$ and any Boolean function $\mathrm{b}(X^n)$ is
attained by a dictator function. In this paper, we consider the ""complementary""
case in which the Boolean function is replaced by
$f:\left\{0,1\right\}^n\to\left\{0,1\right\}^{n-1}$, namely, an $n-1$ bit
quantizer, and show that $I(f(X^n);Y^n)\leq (n-1)\cdot\left(1-h(\alpha)\right)$
for any such $f$. Thus, in this case, the optimal function is of the form
$f(x^n)=(x_1,\ldots,x_{n-1})$.
",1,0,1,0,0,0
16941,Semi-Supervised Deep Learning for Monocular Depth Map Prediction,"  Supervised deep learning often suffers from the lack of sufficient training
data. Specifically in the context of monocular depth map prediction, it is
barely possible to determine dense ground truth depth images in realistic
dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
present in the distance measurements, the calibration between sensors cannot be
perfect, and the measurements are typically much sparser than the camera
images. In this paper, we propose a novel approach to depth map prediction from
monocular images that learns in a semi-supervised way. While we use sparse
ground-truth depth for supervised learning, we also enforce our deep network to
produce photoconsistent dense depth maps in a stereo setup using a direct image
alignment loss. In experiments we demonstrate superior performance in depth map
prediction from single images compared to the state-of-the-art methods.
",1,0,0,0,0,0
16942,Approximation Schemes for Clustering with Outliers,"  Clustering problems are well-studied in a variety of fields such as data
science, operations research, and computer science. Such problems include
variants of centre location problems, $k$-median, and $k$-means to name a few.
In some cases, not all data points need to be clustered; some may be discarded
for various reasons.
We study clustering problems with outliers. More specifically, we look at
Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with
outliers, we have to open some centres, discard up to $z$ points of $\cal X$
and assign every other point to the nearest open centre, minimizing the total
assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have
to open up to $k$ centres but there are no opening costs. In $k$-Means, the
cost of assigning $j$ to $i$ is $\delta^2(j,i)$. We present several results.
Our main focus is on cases where $\delta$ is a doubling metric or is the
shortest path metrics of graphs from a minor-closed family of graphs. For
uniform-cost UFL with outliers on such metrics we show that a multiswap simple
local search heuristic yields a PTAS. With a bit more work, we extend this to
bicriteria approximations for the $k$-Median and $k$-Means problems in the same
metrics where, for any constant $\epsilon > 0$, we can find a solution using
$(1+\epsilon)k$ centres whose cost is at most a $(1+\epsilon)$-factor of the
optimum and uses at most $z$ outliers. We also show that natural local search
heuristics that do not violate the number of clusters and outliers for
$k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics.
Furthermore, we show how our analysis can be extended to general metrics for
$k$-Means with outliers to obtain a $(25+\epsilon,1+\epsilon)$ bicriteria.
",1,0,0,0,0,0
16943,Order preserving pattern matching on trees and DAGs,"  The order preserving pattern matching (OPPM) problem is, given a pattern
string $p$ and a text string $t$, find all substrings of $t$ which have the
same relative orders as $p$. In this paper, we consider two variants of the
OPPM problem where a set of text strings is given as a tree or a DAG. We show
that the OPPM problem for a single pattern $p$ of length $m$ and a text tree
$T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are
drawn from an integer alphabet of polynomial size. The time complexity becomes
$O(m \log m + N)$ if the pattern $p$ is over a general ordered alphabet. We
then show that the OPPM problem for a single pattern and a text DAG is
NP-complete.
",1,0,0,0,0,0
16944,Categorical Probabilistic Theories,"  We present a simple categorical framework for the treatment of probabilistic
theories, with the aim of reconciling the fields of Categorical Quantum
Mechanics (CQM) and Operational Probabilistic Theories (OPTs). In recent years,
both CQM and OPTs have found successful application to a number of areas in
quantum foundations and information theory: they present many similarities,
both in spirit and in formalism, but they remain separated by a number of
subtle yet important differences. We attempt to bridge this gap, by adopting a
minimal number of operationally motivated axioms which provide clean
categorical foundations, in the style of CQM, for the treatment of the problems
that OPTs are concerned with.
",0,0,1,0,0,0
16945,Maximal polynomial modulations of singular integrals,"  Let $K$ be a standard Hölder continuous Calderón--Zygmund kernel on
$\mathbb{R}^{\mathbf{d}}$ whose truncations define $L^2$ bounded operators. We
show that the maximal operator obtained by modulating $K$ by polynomial phases
of a fixed degree is bounded on $L^p(\mathbb{R}^{\mathbf{d}})$ for $1 < p <
\infty$. This extends Sjölin's multidimensional Carleson theorem and Lie's
polynomial Carleson theorem.
",0,0,1,0,0,0
16946,Effects of Hubbard term correction on the structural parameters and electronic properties of wurtzite Zn,"  The effects of including the Hubbard on-site Coulombic correction to the
structural parameters and valence energy states of wurtzite ZnO was explored.
Due to the changes in the structural parameters caused by correction of
hybridization between Zn d states and O p states, suitable parameters of
Hubbard terms have to be determined for an accurate prediction of ZnO
properties. Using the LDA+${U}$ method by applying Hubbard corrections $U_p$ to
Zn 3d states and $U_p$ to O 2p states, the lattice constants were
underestimated for all tested Hubbard parameters. The combination of both $U_d$
and $U_p$ correction terms managed to widen the band gap of wurtzite ZnO to the
experimental value. Pairs of $U_p$ and $U_p$ parameters with the correct
positioning of d-band and accurate bandwidths were selected, in addition to
predicting an accurate band gap value. Inspection of vibrational properties,
however, revealed mismatches between the estimated gamma phonon frequencies and
experimental values. The selection of Hubbard terms based on electronic band
properties alone cannot ensure an accurate vibrational description in LDA+${U}$
calculation.
",0,1,0,0,0,0
16947,A multi-scale Gaussian beam parametrix for the wave equation: the Dirichlet boundary value problem,"  We present a construction of a multi-scale Gaussian beam parametrix for the
Dirichlet boundary value problem associated with the wave equation, and study
its convergence rate to the true solution in the highly oscillatory regime. The
construction elaborates on the wave-atom parametrix of Bao, Qian, Ying, and
Zhang and extends to a multi-scale setting the technique of Gaussian beam
propagation from a boundary of Katchalov, Kurylev and Lassas.
",0,0,1,0,0,0
16948,Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes,"  A functional risk curve gives the probability of an undesirable event as a
function of the value of a critical parameter of a considered physical system.
In several applicative situations, this curve is built using phenomenological
numerical models which simulate complex physical phenomena. To avoid cpu-time
expensive numerical models, we propose to use Gaussian process regression to
build functional risk curves. An algorithm is given to provide confidence
bounds due to this approximation. Two methods of global sensitivity analysis of
the models' random input parameters on the functional risk curve are also
studied. In particular, the PLI sensitivity indices allow to understand the
effect of misjudgment on the input parameters' probability density functions.
",0,0,1,1,0,0
16949,Global optimization for low-dimensional switching linear regression and bounded-error estimation,"  The paper provides global optimization algorithms for two particularly
difficult nonconvex problems raised by hybrid system identification: switching
linear regression and bounded-error estimation. While most works focus on local
optimization heuristics without global optimality guarantees or with guarantees
valid only under restrictive conditions, the proposed approach always yields a
solution with a certificate of global optimality. This approach relies on a
branch-and-bound strategy for which we devise lower bounds that can be
efficiently computed. In order to obtain scalable algorithms with respect to
the number of data, we directly optimize the model parameters in a continuous
optimization setting without involving integer variables. Numerical experiments
show that the proposed algorithms offer a higher accuracy than convex
relaxations with a reasonable computational burden for hybrid system
identification. In addition, we discuss how bounded-error estimation is related
to robust estimation in the presence of outliers and exact recovery under
sparse noise, for which we also obtain promising numerical results.
",1,0,0,1,0,0
16950,An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction,"  During visuomotor tasks, robots must compensate for temporal delays inherent
in their sensorimotor processing systems. Delay compensation becomes crucial in
a dynamic environment where the visual input is constantly changing, e.g.,
during the interacting with a human demonstrator. For this purpose, the robot
must be equipped with a prediction mechanism for using the acquired perceptual
experience to estimate possible future motor commands. In this paper, we
present a novel neural network architecture that learns prototypical visuomotor
representations and provides reliable predictions on the basis of the visual
input. These predictions are used to compensate for the delayed motor behavior
in an online manner. We investigate the performance of our method with a set of
experiments comprising a humanoid robot that has to learn and generate visually
perceived arm motion trajectories. We evaluate the accuracy in terms of mean
prediction error and analyze the response of the network to novel movement
demonstrations. Additionally, we report experiments with incomplete data
sequences, showing the robustness of the proposed architecture in the case of a
noisy and faulty visual sensor.
",1,0,0,0,0,0
16951,A CutFEM method for two-phase flow problems,"  In this article, we present a cut finite element method for two-phase
Navier-Stokes flows. The main feature of the method is the formulation of a
unified continuous interior penalty stabilisation approach for, on the one
hand, stabilising advection and the pressure-velocity coupling and, on the
other hand, stabilising the cut region. The accuracy of the algorithm is
enhanced by the development of extended fictitious domains to guarantee a well
defined velocity from previous time steps in the current geometry. Finally, the
robustness of the moving-interface algorithm is further improved by the
introduction of a curvature smoothing technique that reduces spurious
velocities. The algorithm is shown to perform remarkably well for low capillary
number flows, and is a first step towards flexible and robust CutFEM algorithms
for the simulation of microfluidic devices.
",1,0,0,0,0,0
16952,Learning under selective labels in the presence of expert consistency,"  We explore the problem of learning under selective labels in the context of
algorithm-assisted decision making. Selective labels is a pervasive selection
bias problem that arises when historical decision making blinds us to the true
outcome for certain instances. Examples of this are common in many
applications, ranging from predicting recidivism using pre-trial release data
to diagnosing patients. In this paper we discuss why selective labels often
cannot be effectively tackled by standard methods for adjusting for sample
selection bias, even if there are no unobservables. We propose a data
augmentation approach that can be used to either leverage expert consistency to
mitigate the partial blindness that results from selective labels, or to
empirically validate whether learning under such framework may lead to
unreliable models prone to systemic discrimination.
",0,0,0,1,0,0
16953,Opacity limit for supermassive protostars,"  We present a model for the evolution of supermassive protostars from their
formation at $M_\star \simeq 0.1\,\text{M}_\odot$ until their growth to
$M_\star \simeq 10^5\,\text{M}_\odot$. To calculate the initial properties of
the object in the optically thick regime we follow two approaches: based on
idealized thermodynamic considerations, and on a more detailed one-zone model.
Both methods derive a similar value of $n_{\rm F} \simeq 2 \times 10^{17}
\,\text{cm}^{-3}$ for the density of the object when opacity becomes important,
i.e. the opacity limit. The subsequent evolution of the growing protostar is
determined by the accretion of gas onto the object and can be described by a
mass-radius relation of the form $R_\star \propto M_\star^{1/3}$ during the
early stages, and of the form $R_\star \propto M_\star^{1/2}$ when internal
luminosity becomes important. For the case of a supermassive protostar, this
implies that the radius of the star grows from $R_\star \simeq 0.65 \,{\rm AU}$
to $R_\star \simeq 250 \,{\rm AU}$ during its evolution. Finally, we use this
model to construct a sub-grid recipe for accreting sink particles in numerical
simulations. A prime ingredient thereof is a physically motivated prescription
for the accretion radius and the effective temperature of the growing protostar
embedded inside it. From the latter, we can conclude that photo-ionization
feedback can be neglected until very late in the assembly process of the
supermassive object.
",0,1,0,0,0,0
16954,Learning to Imagine Manipulation Goals for Robot Task Planning,"  Prospection is an important part of how humans come up with new task plans,
but has not been explored in depth in robotics. Predicting multiple task-level
is a challenging problem that involves capturing both task semantics and
continuous variability over the state of the world. Ideally, we would combine
the ability of machine learning to leverage big data for learning the semantics
of a task, while using techniques from task planning to reliably generalize to
new environment. In this work, we propose a method for learning a model
encoding just such a representation for task planning. We learn a neural net
that encodes the $k$ most likely outcomes from high level actions from a given
world. Our approach creates comprehensible task plans that allow us to predict
changes to the environment many time steps into the future. We demonstrate this
approach via application to a stacking task in a cluttered environment, where
the robot must select between different colored blocks while avoiding
obstacles, in order to perform a task. We also show results on a simple
navigation task. Our algorithm generates realistic image and pose predictions
at multiple points in a given task.
",1,0,0,0,0,0
16955,On the Combinatorial Power of the Weisfeiler-Lehman Algorithm,"  The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a
powerful graph invariant. It is at least as powerful in its ability to
distinguish non-isomorphic graphs as the most prominent algebraic graph
invariants. It determines not only the spectrum of a graph, and the angles
between standard basis vectors and the eigenspaces, but even the angles between
projections of standard basis vectors into the eigenspaces. Here, we
investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k]
determines all combinatorial properties of a graph. Many traditionally used
combinatorial invariants are determined by WL[k] for small k. We focus on two
fundamental invariants, the num- ber of cycles Cp of length p, and the number
of cliques Kp of size p. We show that WL[2] determines the number of cycles of
lengths up to 6, but not those of length 8. Also, WL[2] does not determine the
number of 4-cliques.
",1,0,0,0,0,0
16956,Learning to Generate Reviews and Discovering Sentiment,"  We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment analysis. These representations, learned in an unsupervised
manner, achieve state of the art on the binary subset of the Stanford Sentiment
Treebank. They are also very data efficient. When using only a handful of
labeled examples, our approach matches the performance of strong baselines
trained on full datasets. We also demonstrate the sentiment unit has a direct
influence on the generative process of the model. Simply fixing its value to be
positive or negative generates samples with the corresponding positive or
negative sentiment.
",1,0,0,0,0,0
16957,Designing magnetism in Fe-based Heusler alloys: a machine learning approach,"  Combining material informatics and high-throughput electronic structure
calculations offers the possibility of a rapid characterization of complex
magnetic materials. Here we demonstrate that datasets of electronic properties
calculated at the ab initio level can be effectively used to identify and
understand physical trends in magnetic materials, thus opening new avenues for
accelerated materials discovery. Following a data-centric approach, we utilize
a database of Heusler alloys calculated at the density functional theory level
to identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.
The hybridization of Fe with the nearest neighbour $X$ ion is found to cause
redistribution of the on-site Fe charge and a net increase of its magnetic
moment proportional to the valence of $X$. Thus, late transition metals are
ideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the
same time a thermodynamic stability analysis is found to restrict $Z$ to main
group elements. Machine learning regressors, trained to predict magnetic moment
and volume of Heusler alloys, are used to determine the magnetization for all
materials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,
and in particular Co$_2$FeSi, maximize the magnetization, which reaches values
up to 1.2T. This is in good agreement with both ab initio and experimental
data. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective
materials class, offering a magnetization of approximately 0.65T.
",0,1,0,0,0,0
16958,On a diffuse interface model for tumour growth with non-local interactions and degenerate mobilities,"  We study a non-local variant of a diffuse interface model proposed by
Hawkins--Darrud et al. (2012) for tumour growth in the presence of a chemical
species acting as nutrient. The system consists of a Cahn--Hilliard equation
coupled to a reaction-diffusion equation. For non-degenerate mobilities and
smooth potentials, we derive well-posedness results, which are the non-local
analogue of those obtained in Frigeri et al. (European J. Appl. Math. 2015).
Furthermore, we establish existence of weak solutions for the case of
degenerate mobilities and singular potentials, which serves to confine the
order parameter to its physically relevant interval. Due to the non-local
nature of the equations, under additional assumptions continuous dependence on
initial data can also be shown.
",0,0,1,0,0,0
16959,Gradient Descent Can Take Exponential Time to Escape Saddle Points,"  Although gradient descent (GD) almost always escapes saddle points
asymptotically [Lee et al., 2016], this paper shows that even with fairly
natural random initialization schemes and non-pathological functions, GD can be
significantly slowed down by saddle points, taking exponential time to escape.
On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et
al., 2017] is not slowed down by saddle points - it can find an approximate
local minimizer in polynomial time. This result implies that GD is inherently
slower than perturbed GD, and justifies the importance of adding perturbations
for efficient non-convex optimization. While our focus is theoretical, we also
present experiments that illustrate our theoretical findings.
",1,0,1,1,0,0
16960,Spectral parameter power series for arbitrary order linear differential equations,"  Let $L$ be the $n$-th order linear differential operator $Ly = \phi_0y^{(n)}
+ \phi_1y^{(n-1)} + \cdots + \phi_ny$ with variable coefficients. A
representation is given for $n$ linearly independent solutions of $Ly=\lambda r
y$ as power series in $\lambda$, generalizing the SPPS (spectral parameter
power series) solution which has been previously developed for $n=2$. The
coefficient functions in these series are obtained by recursively iterating a
simple integration process, begining with a solution system for $\lambda=0$. It
is shown how to obtain such an initializing system working upwards from
equations of lower order. The values of the successive derivatives of the power
series solutions at the basepoint of integration are given, which provides a
technique for numerical solution of $n$-th order initial value problems and
spectral problems.
",0,0,1,0,0,0
16961,Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social,"  The traditional humanism of the twentieth century, inspired by the culture of
the book, systematically distanced itself from the new society of digital
information; the Internet and tools of information processing revolutionized
the world, society during this period developed certain adaptive
characteristics based on coexistence (Human - Machine), this transformation
sets based on the impact of three technology segments: devices, applications
and infrastructure of social communication, which are involved in various
physical, behavioural and cognitive changes of the human being; and the
emergence of new models of influence and social control through the new
ubiquitous communication; however in this new process of conviviality new
models like the ""collaborative thinking"" and ""InfoSharing"" develop; managing
social information under three Human ontological dimensions (h) - Information
(i) - Machine (m), which is the basis of a new physical-cyber ecosystem, where
they coexist and develop new social units called ""virtual communities "". This
new communication infrastructure and social management of information given
discovered areas of vulnerability ""social perspective of risk"", impacting all
social units through massive impact vector (i); The virtual environment ""H + i
+ M""; and its components, as well as the life cycle management of social
information allows us to understand the path of integration ""Techno - Social""
and setting a new contribution to cybernetics, within the convergence of
technology with society and the new challenges of coexistence, aimed at a new
holistic and not pragmatic vision, as the human component (h) in the virtual
environment is the precursor of the future and needs to be studied not as an
application, but as the hub of a new society.
",1,0,0,0,0,0
16962,A Deterministic Approach to Avoid Saddle Points,"  Loss functions with a large number of saddle points are one of the main
obstacles to training many modern machine learning models. Gradient descent
(GD) is a fundamental algorithm for machine learning and converges to a saddle
point for certain initial data. We call the region formed by these initial
values the ""attraction region."" For quadratic functions, GD converges to a
saddle point if the initial data is in a subspace of up to n-1 dimensions. In
this paper, we prove that a small modification of the recently proposed
Laplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317]
contributes to avoiding saddle points without sacrificing the convergence rate
of GD. In particular, we show that the dimension of the LSGD's attraction
region is at most floor((n-1)/2) for a class of quadratic functions which is
significantly smaller than GD's (n-1)-dimensional attraction region.
",1,0,0,1,0,0
16963,Automatic Generation of Typographic Font from a Small Font Subset,"  This paper addresses the automatic generation of a typographic font from a
subset of characters. Specifically, we use a subset of a typographic font to
extrapolate additional characters. Consequently, we obtain a complete font
containing a number of characters sufficient for daily use. The automated
generation of Japanese fonts is in high demand because a Japanese font requires
over 1,000 characters. Unfortunately, professional typographers create most
fonts, resulting in significant financial and time investments for font
generation. The proposed method can be a great aid for font creation because
designers do not need to create the majority of the characters for a new font.
The proposed method uses strokes from given samples for font generation. The
strokes, from which we construct characters, are extracted by exploiting a
character skeleton dataset. This study makes three main contributions: a novel
method of extracting strokes from characters, which is applicable to both
standard fonts and their variations; a fully automated approach for
constructing characters; and a selection method for sample characters. We
demonstrate our proposed method by generating 2,965 characters in 47 fonts.
Objective and subjective evaluations verify that the generated characters are
similar to handmade characters.
",1,0,0,0,0,0
16964,The Second Postulate of Euclid and the Hyperbolic Geometry,"  The article deals with the connection between the second postulate of Euclid
and non-Euclidean geometry. It is shown that the violation of the second
postulate of Euclid inevitably leads to hyperbolic geometry. This eliminates
misunderstandings about the sums of some divergent series. The connection
between hyperbolic geometry and relativistic computations is noted.
",0,0,1,0,0,0
16965,Transkernel: An Executor for Commodity Kernels on Peripheral Cores,"  Modern mobile and embedded platforms see a large number of ephemeral tasks
driven by background activities. In order to execute such a task, the OS kernel
wakes up the platform beforehand and puts it back to sleep afterwards. In doing
so, the kernel operates various IO devices and orchestrates their power state
transitions. Such kernel execution phases are lengthy, having high energy cost,
and yet difficult to optimize. We advocate for relieving the CPU from these
kernel phases by executing them on a low-power, microcontroller-like core,
dubbed peripheral core, hence leaving the CPU off. Yet, for a peripheral core
to execute phases in a complex commodity kernel (e.g. Linux), existing
approaches either incur high engineering effort or high runtime overhead. We
take a radical approach with a new executor model called transkernel. Running
on a peripheral core, a transkernel executes the binary of the commodity kernel
through cross-ISA, dynamic binary translation (DBT). The transkernel translates
stateful kernel code while emulating a small set of stateless kernel services;
it sets a narrow, stable binary interface for emulated services; it specializes
for kernel's beaten paths; it exploits ISA similarities for low DBT cost. With
a concrete implementation on a heterogeneous ARM SoC, we demonstrate the
feasibility and benefit of transkernel. Our result contributes a new OS
structure that combines cross-ISA DBT and emulation for harnessing a
heterogeneous SoC. Our result demonstrates that while cross-ISA DBT is
typically used under the assumption of efficiency loss, it can be used for
efficiency gain, even atop off-the-shelf hardware.
",1,0,0,0,0,0
16966,No iterated identities satisfied by all finite groups,"  We show that there is no iterated identity satisfied by all finite groups.
For $w$ being a non-trivial word of length $l$, we show that there exists a
finite group $G$ of cardinality at most $\exp(l^C)$ which does not satisfy the
iterated identity $w$. The proof uses the approach of Borisov and Sapir, who
used dynamics of polynomial mappings for the proof of non residual finiteness
of some groups.
",0,0,1,0,0,0
16967,Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning,"  The goal of this tutorial is to introduce key models, algorithms, and open
questions related to the use of optimization methods for solving problems
arising in machine learning. It is written with an INFORMS audience in mind,
specifically those readers who are familiar with the basics of optimization
algorithms, but less familiar with machine learning. We begin by deriving a
formulation of a supervised learning problem and show how it leads to various
optimization problems, depending on the context and underlying assumptions. We
then discuss some of the distinctive features of these optimization problems,
focusing on the examples of logistic regression and the training of deep neural
networks. The latter half of the tutorial focuses on optimization algorithms,
first for convex logistic regression, for which we discuss the use of
first-order methods, the stochastic gradient method, variance reducing
stochastic methods, and second-order methods. Finally, we discuss how these
approaches can be employed to the training of deep neural networks, emphasizing
the difficulties that arise from the complex, nonconvex structure of these
models.
",1,0,0,1,0,0
16968,A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data,"  Partial Least Squares (PLS) methods have been heavily exploited to analyse
the association between two blocs of data. These powerful approaches can be
applied to data sets where the number of variables is greater than the number
of observations and in presence of high collinearity between variables.
Different sparse versions of PLS have been developed to integrate multiple data
sets while simultaneously selecting the contributing variables. Sparse
modelling is a key factor in obtaining better estimators and identifying
associations between multiple data sets. The cornerstone of the sparsity
version of PLS methods is the link between the SVD of a matrix (constructed
from deflated versions of the original matrices of data) and least squares
minimisation in linear regression. We present here an accurate description of
the most popular PLS methods, alongside their mathematical proofs. A unified
algorithm is proposed to perform all four types of PLS including their
regularised versions. Various approaches to decrease the computation time are
offered, and we show how the whole procedure can be scalable to big data sets.
",0,0,0,1,0,0
16969,Asymptotic behaviour of the fifth Painlevé transcendents in the space of initial values,"  We study the asymptotic behaviour of the solutions of the fifth Painlevé
equation as the independent variable approaches zero and infinity in the space
of initial values. We show that the limit set of each solution is compact and
connected and, moreover, that any solution with the essential singularity at
zero has an infinite number of poles and zeroes, and any solution with the
essential singularity at infinity has infinite number of poles and takes value
$1$ infinitely many times.
",0,1,1,0,0,0
16970,Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane,"  Internet-wide scans are a common active measurement approach to study the
Internet, e.g., studying security properties or protocol adoption. They involve
probing large address ranges (IPv4 or parts of IPv6) for specific ports or
protocols. Besides their primary use for probing (e.g., studying protocol
adoption), we show that - at the same time - they provide valuable insights
into the Internet control plane informed by ICMP responses to these probes - a
currently unexplored secondary use. We collect one week of ICMP responses
(637.50M messages) to several Internet-wide ZMap scans covering multiple TCP
and UDP ports as well as DNS-based scans covering > 50% of the domain name
space. This perspective enables us to study the Internet's control plane as a
by-product of Internet measurements. We receive ICMP messages from ~171M
different IPs in roughly 53K different autonomous systems. Additionally, we
uncover multiple control plane problems, e.g., we detect a plethora of outdated
and misconfigured routers and uncover the presence of large-scale persistent
routing loops in IPv4.
",1,0,0,0,0,0
16971,Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms,"  We present a method for metric optimization in the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced
Riemannian metric on the space of diffeomorphisms as a kernel in a machine
learning context. For simplicity, we choose the kernel Fischer Linear
Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters
in an Expectation-Maximization framework, we define model fidelity via the
hinge loss of the decision function. The resulting algorithm optimizes the
parameters of the LDDMM norm-inducing differential operator as a solution to a
group-wise registration and classification problem. In practice, this may lead
to a biology-aware registration, focusing its attention on the predictive task
at hand such as identifying the effects of disease. We first tested our
algorithm on a synthetic dataset, showing that our parameter selection improves
registration quality and classification accuracy. We then tested the algorithm
on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our
Schizpohrenia-Control predictive model showed significant improvement in ROC
AUC compared to baseline parameters.
",0,0,0,1,0,0
16972,On a direct algorithm for constructing recursion operators and Lax pairs for integrable models,"  We suggested an algorithm for searching the recursion operators for nonlinear
integrable equations. It was observed that the recursion operator $R$ can be
represented as a ratio of the form $R=L_1^{-1}L_2$ where the linear
differential operators $L_1$ and $L_2$ are chosen in such a way that the
ordinary differential equation $(L_2-\lambda L_1)U=0$ is consistent with the
linearization of the given nonlinear integrable equation for any value of the
parameter $\lambda\in \textbf{C}$. For constructing the operator $L_1$ we use
the concept of the invariant manifold which is a generalization of the
symmetry. Then for searching $L_2$ we take an auxiliary linear equation
connected with the linearized equation by the Darboux transformation.
Connection of the invariant manifold with the Lax pairs and the
Dubrovin-Weierstrass equations is discussed.
",0,1,0,0,0,0
16973,Network Classification and Categorization,"  To the best of our knowledge, this paper presents the first large-scale study
that tests whether network categories (e.g., social networks vs. web graphs)
are distinguishable from one another (using both categories of real-world
networks and synthetic graphs). A classification accuracy of $94.2\%$ was
achieved using a random forest classifier with both real and synthetic
networks. This work makes two important findings. First, real-world networks
from various domains have distinct structural properties that allow us to
predict with high accuracy the category of an arbitrary network. Second,
classifying synthetic networks is trivial as our models can easily distinguish
between synthetic graphs and the real-world networks they are supposed to
model.
",1,0,0,1,0,0
16974,A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks,"  Many complex systems in biology, physics, and engineering include a large
number of state-variables, and measuring the full state of the system is often
impossible. Typically, a set of sensors is used to measure part of the
state-variables. A system is called observable if these measurements allow to
reconstruct the entire state of the system. When the system is not observable,
an important and practical problem is how to add a \emph{minimal} number of
sensors so that the system becomes observable. This minimal observability
problem is practically useful and theoretically interesting, as it pinpoints
the most informative nodes in the system. We consider the minimal observability
problem for an important special class of Boolean networks, called conjunctive
Boolean networks (CBNs). Using a graph-theoretic approach, we provide a
necessary and sufficient condition for observability of a CBN with $n$
state-variables, and an efficient~$O(n^2)$-time algorithm for solving the
minimal observability problem. We demonstrate the usefulness of these results
by studying the properties of a class of random CBNs.
",1,0,1,0,0,0
16975,The Description and Scaling Behavior for the Inner Region of the Boundary Layer for 2-D Wall-bounded Flows,"  A second derivative-based moment method is proposed for describing the
thickness and shape of the region where viscous forces are dominant in
turbulent boundary layer flows. Rather than the fixed location sublayer model
presently employed, the new method defines thickness and shape parameters that
are experimentally accessible without differentiation. It is shown
theoretically that one of the new length parameters used as a scaling parameter
is also a similarity parameter for the velocity profile. In fact, we show that
this new length scale parameter removes one of the theoretical inconsistencies
present in the traditional Prandtl Plus scalings. Furthermore, the new length
parameter and the Prandtl Plus scaling parameters perform identically when
operating on experimental datasets. This means that many of the past successes
ascribed to the Prandtl Plus scaling also apply to the new parameter set but
without one of the theoretical inconsistencies. Examples are offered to show
how the new description method is useful in exploring the actual physics of the
boundary layer.
",0,1,0,0,0,0
16976,Completely Sidon sets in $C^*$-algebras (New title),"  A sequence in a $C^*$-algebra $A$ is called completely Sidon if its span in
$A$ is completely isomorphic to the operator space version of the space
$\ell_1$ (i.e. $\ell_1$ equipped with its maximal operator space structure).
The latter can also be described as the span of the free unitary generators in
the (full) $C^*$-algebra of the free group $\F_\infty$ with countably
infinitely many generators. Our main result is a generalization to this context
of Drury's classical theorem stating that Sidon sets are stable under finite
unions. In the particular case when $A=C^*(G)$ the (maximal) $C^*$-algebra of a
discrete group $G$, we recover the non-commutative (operator space) version of
Drury's theorem that we recently proved. We also give several non-commutative
generalizations of our recent work on uniformly bounded orthonormal systems to
the case of von Neumann algebras equipped with normal faithful tracial states.
",0,0,1,0,0,0
16977,Conflict-Free Coloring of Planar Graphs,"  A conflict-free k-coloring of a graph assigns one of k different colors to
some of the vertices such that, for every vertex v, there is a color that is
assigned to exactly one vertex among v and v's neighbors. Such colorings have
applications in wireless networking, robotics, and geometry, and are
well-studied in graph theory. Here we study the natural problem of the
conflict-free chromatic number chi_CF(G) (the smallest k for which
conflict-free k-colorings exist). We provide results both for closed
neighborhoods N[v], for which a vertex v is a member of its neighborhood, and
for open neighborhoods N(v), for which vertex v is not a member of its
neighborhood.
For closed neighborhoods, we prove the conflict-free variant of the famous
Hadwiger Conjecture: If an arbitrary graph G does not contain K_{k+1} as a
minor, then chi_CF(G) <= k. For planar graphs, we obtain a tight worst-case
bound: three colors are sometimes necessary and always sufficient. We also give
a complete characterization of the computational complexity of conflict-free
coloring. Deciding whether chi_CF(G)<= 1 is NP-complete for planar graphs G,
but polynomial for outerplanar graphs. Furthermore, deciding whether
chi_CF(G)<= 2 is NP-complete for planar graphs G, but always true for
outerplanar graphs. For the bicriteria problem of minimizing the number of
colored vertices subject to a given bound k on the number of colors, we give a
full algorithmic characterization in terms of complexity and approximation for
outerplanar and planar graphs.
For open neighborhoods, we show that every planar bipartite graph has a
conflict-free coloring with at most four colors; on the other hand, we prove
that for k in {1,2,3}, it is NP-complete to decide whether a planar bipartite
graph has a conflict-free k-coloring. Moreover, we establish that any general}
planar graph has a conflict-free coloring with at most eight colors.
",1,0,1,0,0,0
16978,Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms,"  We study the problem of utility maximization from terminal wealth in which an
agent optimally builds her portfolio by investing in a bond and a risky asset.
The asset price dynamics follow a diffusion process with regime-switching
coefficients modeled by a continuous-time finite-state Markov chain. We
consider an investor with a Constant Relative Risk Aversion (CRRA) utility
function. We deduce the associated Hamilton-Jacobi-Bellman equation to
construct the solution and the optimal trading strategy and verify optimality
by showing that the value function is the unique constrained viscosity solution
of the HJB equation. By means of a Laplace transform method, we show how to
explicitly compute the value function and illustrate the method with the two-
and three-states cases. This method is interesting in its own right and can be
adapted in other applications involving hybrid systems and using other types of
transforms with basic properties similar to the Laplace transform.
",0,0,0,0,0,1
16979,Spectroscopic study of the elusive globular cluster ESO452-SC11 and its surroundings,"  Globular clusters (GCs) are amongst the oldest objects in the Galaxy and play
a pivotal role in deciphering its early history. We present the first
spectroscopic study of the GC ESO452-SC11 using the AAOmega spectrograph at
medium resolution. Given the sparsity of this object and high degree of
foreground contamination due to its location toward the bulge, few details are
known for this cluster: there is no consensus of its age, metallicity, or its
association with the disk or bulge. We identify 5 members based on radial
velocity, metallicity, and position within the GC. Using spectral synthesis,
accurate abundances of Fe and several $\alpha$-, Fe-peak, neutron-capture
elements (Si,Ca,Ti,Cr,Co,Ni,Sr,Eu) were measured. Two of the 5 cluster
candidates are likely non-members, as they have deviant Fe abundances and
[$\alpha$/Fe] ratios. The mean radial velocity is 19$\pm$2 km s$^{-1}$ with a
low dispersion of 2.8$\pm$3.4 km s$^{-1}$, in line with its low mass. The mean
Fe-abundance from spectral fitting is $-0.88\pm0.03$, with a spread driven by
observational errors. The $\alpha$-elements of the GC candidates are marginally
lower than expected for the bulge at similar metallicities. As spectra of
hundreds of stars were collected in a 2 degree field around ESO452-SC11,
detailed abundances in the surrounding field were measured. Most non-members
have higher [$\alpha$/Fe] ratios, typical of the nearby bulge population. Stars
with measured Fe-peak abundances show a large scatter around Solar values,
though with large uncertainties. Our study provides the first systematic
measurement of Sr in a Galactic bulge GC. The Eu and Sr abundances of the GC
candidates are consistent with a disk or bulge association. Our calculations
place ESO452 on an elliptical orbit in the central 3 kpc of the bulge. We find
no evidence of extratidal stars in our data. (Abridged)
",0,1,0,0,0,0
16980,The Fundamental Infinity-Groupoid of a Parametrized Family,"  Given an infinity-category C, one can naturally construct an
infinity-category Fam(C) of families of objects in C indexed by
infinity-groupoids. An ordinary categorical version of this construction was
used by Borceux and Janelidze in the study of generalized covering maps in
categorical Galois theory. In this paper, we develop the homotopy theory of
such ""parametrized families"" as generalization of the classical homotopy theory
of spaces. In particular, we study homotopy-theoretical constructions that
arise from the fundamental infinity-groupoids of families in an
infinity-category. In the same spirit, we show that Fam(C) admits a
Grothendieck topology which generalizes the canonical/epimorphism topology on
the infinity-topos of infinity-groupoids in the sense of Carchedi.
",0,0,1,0,0,0
16981,Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation,"  Training 3D object detectors for autonomous driving has been limited to small
datasets due to the effort required to generate annotations. Reducing both task
complexity and the amount of task switching done by annotators is key to
reducing the effort and time required to generate 3D bounding box annotations.
This paper introduces a novel ground truth generation method that combines
human supervision with pretrained neural networks to generate per-instance 3D
point cloud segmentation, 3D bounding boxes, and class annotations. The
annotators provide object anchor clicks which behave as a seed to generate
instance segmentation results in 3D. The points belonging to each instance are
then used to regress object centroids, bounding box dimensions, and object
orientation. Our proposed annotation scheme requires 30x lower human annotation
time. We use the KITTI 3D object detection dataset to evaluate the efficiency
and the quality of our annotation scheme. We also test the the proposed scheme
on previously unseen data from the Autonomoose self-driving vehicle to
demonstrate generalization capabilities of the network.
",0,0,0,1,0,0
16982,Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination,"  We study the changes of opinions about vaccination together with the
evolution of a disease. In our model we consider a multiplex network consisting
of two layers. One of the layers corresponds to a social network where people
share their opinions and influence others opinions. The social model that rules
the dynamic is the M-model, which takes into account two different processes
that occurs in a society: persuasion and compromise. This two processes are
related through a parameter $r$, $r<1$ describes a moderate and committed
society, for $r>1$ the society tends to have extremist opinions, while $r=1$
represents a neutral society. This social network may be of real or virtual
contacts. On the other hand, the second layer corresponds to a network of
physical contacts where the disease spreading is described by the SIR-Model. In
this model the individuals may be in one of the following four states:
Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A
Susceptible individual can: i) get vaccinated, if his opinion in the other
layer is totally in favor of the vaccine, ii) get infected, with probability
$\beta$ if he is in contact with an infected neighbor. Those $I$ individuals
recover after a certain period $t_r=6$. Vaccinated individuals have an
extremist positive opinion that does not change. We consider that the vaccine
has a certain effectiveness $\omega$ and as a consequence vaccinated nodes can
be infected with probability $\beta (1 - \omega)$ if they are in contact with
an infected neighbor. In this case, if the infection process is successful, the
new infected individual changes his opinion from extremist positive to totally
against the vaccine. We find that depending on the trend in the opinion of the
society, which depends on $r$, different behaviors in the spread of the
epidemic occurs. An epidemic threshold was found.
",0,1,0,0,0,0
16983,CASP Solutions for Planning in Hybrid Domains,"  CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.
",1,0,0,0,0,0
16984,Primordial black holes from inflaton and spectator field perturbations in a matter-dominated era,"  We study production of primordial black holes (PBHs) during an early
matter-dominated phase. As a source of perturbations, we consider either the
inflaton field with a running spectral index or a spectator field that has a
blue spectrum and thus provides a significant contribution to the PBH
production at small scales. First, we identify the region of the parameter
space where a significant fraction of the observed dark matter can be produced,
taking into account all current PBH constraints. Then, we present constraints
on the amplitude and spectral index of the spectator field as a function of the
reheating temperature. We also derive constraints on the running of the
inflaton spectral index, ${\rm d}n/{\rm d}{\rm ln}k \lesssim -0.002$, which are
comparable to those from the Planck satellite for a scenario where the
spectator field is absent.
",0,1,0,0,0,0
16985,State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning,"  Typical reinforcement learning (RL) agents learn to complete tasks specified
by reward functions tailored to their domain. As such, the policies they learn
do not generalize even to similar domains. To address this issue, we develop a
framework through which a deep RL agent learns to generalize policies from
smaller, simpler domains to more complex ones using a recurrent attention
mechanism. The task is presented to the agent as an image and an instruction
specifying the goal. This meta-controller guides the agent towards its goal by
designing a sequence of smaller subtasks on the part of the state space within
the attention, effectively decomposing it. As a baseline, we consider a setup
without attention as well. Our experiments show that the meta-controller learns
to create subgoals within the attention.
",1,0,0,1,0,0
16986,Robust Imitation of Diverse Behaviors,"  Deep generative models have recently shown great promise in imitation
learning for motor control. Given enough data, even supervised approaches can
do one-shot imitation learning; however, they are vulnerable to cascading
failures when the agent trajectory diverges from the demonstrations. Compared
to purely supervised methods, Generative Adversarial Imitation Learning (GAIL)
can learn more robust controllers from fewer demonstrations, but is inherently
mode-seeking and more difficult to train. In this paper, we show how to combine
the favourable aspects of these two approaches. The base of our model is a new
type of variational autoencoder on demonstration trajectories that learns
semantic policy embeddings. We show that these embeddings can be learned on a 9
DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a
resulting smooth interpolation of reaching behavior. Leveraging these policy
representations, we develop a new version of GAIL that (1) is much more robust
than the purely-supervised controller, especially with few demonstrations, and
(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own
does not. We demonstrate our approach on learning diverse gaits from
demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics
environment.
",1,0,0,0,0,0
16987,Efficient Measurement of the Vibrational Rogue Waves by Compressive Sampling Based Wavelet Analysis,"  In this paper we discuss the possible usage of the compressive sampling based
wavelet analysis for the efficient measurement and for the early detection of
one dimensional (1D) vibrational rogue waves. We study the construction of the
triangular (V-shaped) wavelet spectra using compressive samples of rogue waves
that can be modeled as Peregrine and Akhmediev-Peregrine solitons. We show that
triangular wavelet spectra can be sensed by compressive measurements at the
early stages of the development of vibrational rogue waves. Our results may
lead to development of the efficient vibrational rogue wave measurement and
early sensing systems with reduced memory requirements which use the
compressive sampling algorithms. In typical solid mechanics applications,
compressed measurements can be acquired by randomly positioning single sensor
and multisensors.
",0,0,1,0,0,0
16988,SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes,"  This paper presents SceneCut, a novel approach to jointly discover previously
unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
joint reasoning over scene semantics and geometry allows a robot to detect and
segment object instances in complex scenes where modern deep learning-based
methods either fail to separate object instances, or fail to detect objects
that were not seen during training. SceneCut automatically decomposes a scene
into meaningful regions which either represent objects or scene surfaces. The
decomposition is qualified by an unified energy function over objectness and
geometric fitting. We show how this energy function can be optimized
efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
a pre-trained convolutional oriented boundary network to predict accurate
boundaries from images, which are used to construct high-quality region
hierarchies. We evaluate SceneCut on several different indoor environments, and
the results show that SceneCut significantly outperforms all the existing
methods.
",1,0,0,0,0,0
16989,An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions,"  In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is inspired by the strong evidence in the applied perception
community that humans perform recognition in a foveated manner, that is they
recognize events or objects by only focusing on visually significant aspects.
An important contribution of our method is that the computation of the weights
assigned to body parts is invariant to viewing directions and camera parameters
in the input data. We have performed extensive experiments to validate the
proposed approach and demonstrate its significance. In particular, results show
that considerable improvement in performance is gained by taking into account
the relative importance of different body parts as defined by our approach.
",1,0,0,0,0,0
16990,Forecasting Crime with Deep Learning,"  The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The crime counts are broken into 10 bins and our model predicts
the most likely bin for a each spatial region at a daily level. We train this
data using increasingly complex neural network structures, including variations
that are suited to the spatial and temporal aspects of the crime prediction
problem. With our best model we are able to predict the correct bin for overall
crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
respectively. The results show the efficacy of neural networks for the
prediction problem and the value of using external datasets in addition to
standard crime data.
",0,0,0,1,0,0
16991,A family of compact semitoric systems with two focus-focus singularities,"  About 6 years ago, semitoric systems were classified by Pelayo & Vu Ngoc by
means of five invariants. Standard examples are the coupled spin oscillator on
$\mathbb{S}^2 \times \mathbb{R}^2$ and coupled angular momenta on $\mathbb{S}^2
\times \mathbb{S}^2$, both having exactly one focus-focus singularity. But so
far there were no explicit examples of systems with more than one focus-focus
singularity which are semitoric in the sense of that classification. This paper
introduces a 6-parameter family of integrable systems on $\mathbb{S}^2 \times
\mathbb{S}^2$ and proves that, for certain ranges of the parameters, it is a
compact semitoric system with precisely two focus-focus singularities. Since
the twisting index (one of the semitoric invariants) is related to the
relationship between different focus-focus points, this paper provides systems
for the future study of the twisting index.
",0,0,1,0,0,0
16992,Mixed Threefolds Isogenous to a Product,"  In this paper we study \emph{threefolds isogenous to a product of mixed type}
i.e. quotients of a product of three compact Riemann surfaces $C_i$ of genus at
least two by the action of a finite group $G$, which is free, but not diagonal.
In particular, we are interested in the systematic construction and
classification of these varieties. Our main result is the full classification
of threefolds isogenous to a product of mixed type with $\chi(\mathcal O_X)=-1$
assuming that any automorphism in $G$, which restricts to the trivial element
in $Aut(C_i)$ for some $C_i$, is the identity on the product. Since the
holomorphic Euler-Poincaré-characteristic of a smooth threefold of general
type with ample canonical class is always negative, these examples lie on the
boundary, in the sense of threefold geography. To achieve our result we use
techniques from computational group theory. Indeed, we develop a MAGMA
algorithm to classify these threefolds for any given value of $\chi(\mathcal
O_X)$.
",0,0,1,0,0,0
16993,Discriminatory Transfer,"  We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show they both suffer discriminatory transfer on the real-world Communities and
Crime data set. The presented case study introduces an interaction between
fairness and transfer learning, as an extension of existing fairness studies
that focus on single task learning.
",1,0,0,1,0,0
16994,Ultrafast relaxation of hot phonons in Graphene-hBN Heterostructures,"  Fast carrier cooling is important for high power graphene based devices.
Strongly Coupled Optical Phonons (SCOPs) play a major role in the relaxation of
photoexcited carriers in graphene. Heterostructures of graphene and hexagonal
boron nitride (hBN) have shown exceptional mobility and high saturation
current, which makes them ideal for applications, but the effect of the hBN
substrate on carrier cooling mechanisms is not understood. We track the cooling
of hot photo-excited carriers in graphene-hBN heterostructures using ultrafast
pump-probe spectroscopy. We find that the carriers cool down four times faster
in the case of graphene on hBN than on a silicon oxide substrate thus
overcoming the hot phonon (HP) bottleneck that plagues cooling in graphene
devices.
",0,1,0,0,0,0
16995,Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables,"  Pattern matching is a powerful tool which is part of many functional
programming languages as well as computer algebra systems such as Mathematica.
Among the existing systems, Mathematica offers the most expressive pattern
matching. Unfortunately, no open source alternative has comparable pattern
matching capabilities. Notably, these features include support for associative
and/or commutative function symbols and sequence variables. While those
features have individually been subject of previous research, their
comprehensive combination has not yet been investigated. Furthermore, in many
applications, a fixed set of patterns is matched repeatedly against different
subjects. This many-to-one matching can be sped up by exploiting similarities
between patterns. Discrimination nets are the state-of-the-art solution for
many-to-one matching. In this thesis, a generalized discrimination net which
supports the full feature set is presented. All algorithms have been
implemented as an open-source library for Python. In experiments on real world
examples, significant speedups of many-to-one over one-to-one matching have
been observed.
",1,0,0,0,0,0
16996,Pair Background Envelopes in the SiD Detector,"  The beams at the ILC produce electron positron pairs due to beam-beam
interactions. This note presents for the first time a study of these processes
in a detailed simulation, which shows that these pair background particles
appear at angles that extend to the inner layers of the detector. The full data
set of pairs produced in one bunch crossing was used to calculate the helix
tracks, which the particles form in the solenoid field of the SiD detector. The
results suggest to further study the reduction of the beam pipe radius and
therefore to either add another SiD vertex detector layer, or reduce the radius
of the existing vertex detector layers, without increasing the detector
occupancy significantly. This has to go along with additional studies whether
the improvement in physics reconstruction methods, like c-tagging, is worth the
increased background level at smaller radii.
",0,1,0,0,0,0
16997,Expansion of percolation critical points for Hamming graphs,"  The Hamming graph $H(d,n)$ is the Cartesian product of $d$ complete graphs on
$n$ vertices. Let $m=d(n-1)$ be the degree and $V = n^d$ be the number of
vertices of $H(d,n)$. Let $p_c^{(d)}$ be the critical point for bond
percolation on $H(d,n)$. We show that, for $d \in \mathbb N$ fixed and $n \to
\infty$,
\begin{equation*}
p_c^{(d)}= \dfrac{1}{m} + \dfrac{2d^2-1}{2(d-1)^2}\dfrac{1}{m^2}
+ O(m^{-3}) + O(m^{-1}V^{-1/3}),
\end{equation*} which extends the asymptotics found in
\cite{BorChaHofSlaSpe05b} by one order. The term $O(m^{-1}V^{-1/3})$ is the
width of the critical window. For $d=4,5,6$ we have $m^{-3} =
O(m^{-1}V^{-1/3})$, and so the above formula represents the full asymptotic
expansion of $p_c^{(d)}$. In \cite{FedHofHolHul16a} \st{we show that} this
formula is a crucial ingredient in the study of critical bond percolation on
$H(d,n)$ for $d=2,3,4$. The proof uses a lace expansion for the upper bound and
a novel comparison with a branching random walk for the lower bound. The proof
of the lower bound also yields a refined asymptotics for the susceptibility of
a subcritical Erdős-Rényi random graph.
",0,0,1,0,0,0
16998,Efficiently Manifesting Asynchronous Programming Errors in Android Apps,"  Android, the #1 mobile app framework, enforces the single-GUI-thread model,
in which a single UI thread manages GUI rendering and event dispatching. Due to
this model, it is vital to avoid blocking the UI thread for responsiveness. One
common practice is to offload long-running tasks into async threads. To achieve
this, Android provides various async programming constructs, and leaves
developers themselves to obey the rules implied by the model. However, as our
study reveals, more than 25% apps violate these rules and introduce
hard-to-detect, fail-stop errors, which we term as aysnc programming errors
(APEs). To this end, this paper introduces APEChecker, a technique to
automatically and efficiently manifest APEs. The key idea is to characterize
APEs as specific fault patterns, and synergistically combine static analysis
and dynamic UI exploration to detect and verify such errors. Among the 40
real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51
are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs
than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces
testing time from half an hour to a few minutes. On a specific type of APEs,
APEChecker confirms 5X more errors than the data race detection tool,
EventRacer, with very few false alarms.
",1,0,0,0,0,0
16999,AI Challenges in Human-Robot Cognitive Teaming,"  Among the many anticipated roles for robots in the future is that of being a
human teammate. Aside from all the technological hurdles that have to be
overcome with respect to hardware and control to make robots fit to work with
humans, the added complication here is that humans have many conscious and
subconscious expectations of their teammates - indeed, we argue that teaming is
mostly a cognitive rather than physical coordination activity. This introduces
new challenges for the AI and robotics community and requires fundamental
changes to the traditional approach to the design of autonomy. With this in
mind, we propose an update to the classical view of the intelligent agent
architecture, highlighting the requirements for mental modeling of the human in
the deliberative process of the autonomous agent. In this article, we outline
briefly the recent efforts of ours, and others in the community, towards
developing cognitive teammates along these guidelines.
",1,0,0,0,0,0
17000,"Generalizing the MVW involution, and the contragredient","  For certain quasi-split reductive groups $G$ over a general field $F$, we
construct an automorphism $\iota_G$ of $G$ over $F$, well-defined as an element
of ${\rm Aut}(G)(F)/jG(F)$ where $j:G(F) \rightarrow {\rm Aut}(G)(F)$ is the
inner-conjugation action of $G(F)$ on $G$. The automorphism $\iota_G$
generalizes (although only for quasi-split groups) an involution due to
Moeglin-Vigneras-Waldspurger in [MVW] for classical groups which takes any
irreducible admissible representation $\pi$ of $G(F)$ for $G$ a classical group
and $F$ a local field, to its contragredient $\pi^\vee$. The paper also
formulates a conjecture on the contragredient of an irreducible admissible
representation of $G(F)$ for $G$ a reductive algebraic group over a local field
$F$ in terms of the (enhanced) Langlands parameter of the representation.
",0,0,1,0,0,0
17001,Miraculous cancellations for quantum $SL_2$,"  In earlier work, Helen Wong and the author discovered certain ""miraculous
cancellations"" for the quantum trace map connecting the Kauffman bracket skein
algebra of a surface to its quantum Teichmueller space, occurring when the
quantum parameter $q$ is a root of unity. The current paper is devoted to
giving a more representation theoretic interpretation of this phenomenon, in
terms of the quantum group $U_q(sl_2)$ and its dual Hopf algebra $SL_2^q$.
",0,0,1,0,0,0
17002,Energy and time measurements with high-granular silicon devices,"  This note is a short summary of the workshop on ""Energy and time measurements
with high-granular silicon devices"" that took place on the 13/6/16 and the
14/6/16 at DESY/Hamburg in the frame of the first AIDA-2020 Annual Meeting.
This note tries to put forward trends that could be spotted and to emphasise in
particular open issues that were addressed by the speakers.
",0,1,0,0,0,0
17003,Action Tubelet Detector for Spatio-Temporal Action Localization,"  Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
i.e., sequences of bounding boxes with associated scores. The same way
state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
based on anchor cuboids. We build upon the SSD framework. Convolutional
features are extracted for each frame, while scores and regressions are based
on the temporal stacking of these features, thus exploiting information from a
sequence. Our experimental results show that leveraging sequences of frames
significantly improves detection performance over using individual frames. The
gain of our tubelet detector can be explained by both more accurate scores and
more precise localization. Our ACT-detector outperforms the state-of-the-art
methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
particular at high overlap thresholds.
",1,0,0,0,0,0
17004,Significance of Side Information in the Graph Matching Problem,"  Percolation based graph matching algorithms rely on the availability of seed
vertex pairs as side information to efficiently match users across networks.
Although such algorithms work well in practice, there are other types of side
information available which are potentially useful to an attacker. In this
paper, we consider the problem of matching two correlated graphs when an
attacker has access to side information, either in the form of community labels
or an imperfect initial matching. In the former case, we propose a naive graph
matching algorithm by introducing the community degree vectors which harness
the information from community labels in an efficient manner. Furthermore, we
analyze a variant of the basic percolation algorithm proposed in literature for
graphs with community structure. In the latter case, we propose a novel
percolation algorithm with two thresholds which uses an imperfect matching as
input to match correlated graphs.
We evaluate the proposed algorithms on synthetic as well as real world
datasets using various experiments. The experimental results demonstrate the
importance of communities as side information especially when the number of
seeds is small and the networks are weakly correlated.
",1,1,0,0,0,0
17005,Extended Gray-Wyner System with Complementary Causal Side Information,"  We establish the rate region of an extended Gray-Wyner system for 2-DMS
$(X,Y)$ with two additional decoders having complementary causal side
information. This extension is interesting because in addition to the
operationally significant extreme points of the Gray-Wyner rate region, which
include Wyner's common information, G{á}cs-K{ö}rner common information and
information bottleneck, the rate region for the extended system also includes
the K{ö}rner graph entropy, the privacy funnel and excess functional
information, as well as three new quantities of potential interest, as extreme
points. To simplify the investigation of the 5-dimensional rate region of the
extended Gray-Wyner system, we establish an equivalence of this region to a
3-dimensional mutual information region that consists of the set of all triples
of the form $(I(X;U),\,I(Y;U),\,I(X,Y;U))$ for some $p_{U|X,Y}$. We further
show that projections of this mutual information region yield the rate regions
for many settings involving a 2-DMS, including lossless source coding with
causal side information, distributed channel synthesis, and lossless source
coding with a helper.
",1,0,1,0,0,0
17006,Learning Powers of Poisson Binomial Distributions,"  We introduce the problem of simultaneously learning all powers of a Poisson
Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of
$n$ mutually independent Bernoulli random variables $X_i$, where
$\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a
range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each
Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
The learning algorithm can query any power $P_k$ several times and succeeds in
learning all powers in the range, if with probability at least $1- \delta$:
given any $k \in [m]$, it returns a probability distribution $Q_k$ with total
variation distance from $P_k$ at most $\epsilon$. We provide almost matching
lower and upper bounds on query complexity for this problem. We first show a
lower bound on the query complexity on PBD powers instances with many distinct
parameters $p_i$ which are separated, and we almost match this lower bound by
examining the query complexity of simultaneously learning all the powers of a
special class of PBD's resembling the PBD's of our lower bound. We study the
fundamental setting of a Binomial distribution, and provide an optimal
algorithm which uses $O(1/\epsilon^2)$ samples. Diakonikolas, Kane and Stewart
[COLT'16] showed a lower bound of $\Omega(2^{1/\epsilon})$ samples to learn the
$p_i$'s within error $\epsilon$. The question whether sampling from powers of
PBDs can reduce this sampling complexity, has a negative answer since we show
that the exponential number of samples is inevitable. Having sampling access to
the powers of a PBD we then give a nearly optimal algorithm that learns its
$p_i$'s. To prove our two last lower bounds we extend the classical minimax
risk definition from statistics to estimating functions of sequences of
distributions.
",1,0,1,1,0,0
17007,Geometry of simplices in Minkowski spaces,"  There are many problems and configurations in Euclidean geometry that were
never extended to the framework of (normed or) finite dimensional real Banach
spaces, although their original versions are inspiring for this type of
generalization, and the analogous definitions for normed spaces represent a
promising topic. An example is the geometry of simplices in non-Euclidean
normed spaces. We present new generalizations of well known properties of
Euclidean simplices. These results refer to analogues of circumcenters, Euler
lines, and Feuerbach spheres of simplices in normed spaces. Using duality, we
also get natural theorems on angular bisectors as well as in- and exspheres of
(dual) simplices.
",0,0,1,0,0,0
17008,DLR : Toward a deep learned rhythmic representation for music content analysis,"  In the use of deep neural networks, it is crucial to provide appropriate
input representations for the network to learn from. In this paper, we propose
an approach to learn a representation that focus on rhythmic representation
which is named as DLR (Deep Learning Rhythmic representation). The proposed
approach aims to learn DLR from the raw audio signal and use it for other music
informatics tasks. A 1-dimensional convolutional network is utilised in the
learning of DLR. In the experiment, we present the results from the source task
and the target task as well as visualisations of DLRs. The results reveals that
DLR provides compact rhythmic information which can be used on multi-tagging
task.
",1,0,0,0,0,0
17009,Phylogeny-based tumor subclone identification using a Bayesian feature allocation model,"  Tumor cells acquire different genetic alterations during the course of
evolution in cancer patients. As a result of competition and selection, only a
few subgroups of cells with distinct genotypes survive. These subgroups of
cells are often referred to as subclones. In recent years, many statistical and
computational methods have been developed to identify tumor subclones, leading
to biologically significant discoveries and shedding light on tumor
progression, metastasis, drug resistance and other processes. However, most
existing methods are either not able to infer the phylogenetic structure among
subclones, or not able to incorporate copy number variations (CNV). In this
article, we propose SIFA (tumor Subclone Identification by Feature Allocation),
a Bayesian model which takes into account both CNV and tumor phylogeny
structure to infer tumor subclones. We compare the performance of SIFA with two
other commonly used methods using simulation studies with varying sequencing
depth, evolutionary tree size, and tree complexity. SIFA consistently yields
better results in terms of Rand Index and cellularity estimation accuracy. The
usefulness of SIFA is also demonstrated through its application to whole genome
sequencing (WGS) samples from four patients in a breast cancer study.
",0,0,0,1,1,0
17010,Confidence-based Graph Convolutional Networks for Semi-Supervised Learning,"  Predicting properties of nodes in a graph is an important problem with
applications in a variety of domains. Graph-based Semi-Supervised Learning
(SSL) methods aim to address this problem by labeling a small subset of the
nodes as seeds and then utilizing the graph structure to predict label scores
for the rest of the nodes in the graph. Recently, Graph Convolutional Networks
(GCNs) have achieved impressive performance on the graph-based SSL task. In
addition to label scores, it is also desirable to have confidence scores
associated with them. Unfortunately, confidence estimation in the context of
GCN has not been previously explored. We fill this important gap in this paper
and propose ConfGCN, which estimates labels scores along with their confidences
jointly in GCN-based setting. ConfGCN uses these estimated confidences to
determine the influence of one node on another during neighborhood aggregation,
thereby acquiring anisotropic capabilities. Through extensive analysis and
experiments on standard benchmarks, we find that ConfGCN is able to outperform
state-of-the-art baselines. We have made ConfGCN's source code available to
encourage reproducible research.
",1,0,0,1,0,0
17011,Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network,"  This paper studies the daily connectivity time series of a wind
speed-monitoring network using multifractal detrended fluctuation analysis. It
investigates the long-range fluctuation and multifractality in the residuals of
the connectivity time series. Our findings reveal that the daily connectivity
of the correlation-based network is persistent for any correlation threshold.
Further, the multifractality degree is higher for larger absolute values of the
correlation threshold
",0,0,0,1,0,0
17012,The Dynamics of Norm Change in the Cultural Evolution of Language,"  What happens when a new social convention replaces an old one? While the
possible forces favoring norm change - such as institutions or committed
activists - have been identified since a long time, little is known about how a
population adopts a new convention, due to the difficulties of finding
representative data. Here we address this issue by looking at changes occurred
to 2,541 orthographic and lexical norms in English and Spanish through the
analysis of a large corpora of books published between the years 1800 and 2008.
We detect three markedly distinct patterns in the data, depending on whether
the behavioral change results from the action of a formal institution, an
informal authority or a spontaneous process of unregulated evolution. We
propose a simple evolutionary model able to capture all the observed behaviors
and we show that it reproduces quantitatively the empirical data. This work
identifies general mechanisms of norm change and we anticipate that it will be
of interest to researchers investigating the cultural evolution of language
and, more broadly, human collective behavior.
",0,0,0,0,1,0
17013,Bayesian Joint Spike-and-Slab Graphical Lasso,"  In this article, we propose a new class of priors for Bayesian inference with
multiple Gaussian graphical models. We introduce fully Bayesian treatments of
two popular procedures, the group graphical lasso and the fused graphical
lasso, and extend them to a continuous spike-and-slab framework to allow
self-adaptive shrinkage and model selection simultaneously. We develop an EM
algorithm that performs fast and dynamic explorations of posterior modes. Our
approach selects sparse models efficiently with substantially smaller bias than
would be induced by alternative regularization procedures. The performance of
the proposed methods are demonstrated through simulation and two real data
examples.
",0,0,0,1,0,0
17014,Variations on the theme of the uniform boundary condition,"  The uniform boundary condition in a normed chain complex asks for a uniform
linear bound on fillings of null-homologous cycles. For the $\ell^1$-norm on
the singular chain complex, Matsumoto and Morita established a characterisation
of the uniform boundary condition in terms of bounded cohomology. In
particular, spaces with amenable fundamental group satisfy the uniform boundary
condition in every degree. We will give an alternative proof of statements of
this type, using geometric F{\o}lner arguments on the chain level instead of
passing to the dual cochain complex. These geometric methods have the advantage
that they also lead to integral refinements. In particular, we obtain
applications in the context of integral foliated simplicial volume.
",0,0,1,0,0,0
17015,revisit: a Workflow Tool for Data Science,"  In recent years there has been widespread concern in the scientific community
over a reproducibility crisis. Among the major causes that have been identified
is statistical: In many scientific research the statistical analysis (including
data preparation) suffers from a lack of transparency and methodological
problems, major obstructions to reproducibility. The revisit package aims
toward remedying this problem, by generating a ""software paper trail"" of the
statistical operations applied to a dataset. This record can be ""replayed"" for
verification purposes, as well as be modified to enable alternative analyses.
The software also issues warnings of certain kinds of potential errors in
statistical methodology, again related to the reproducibility issue.
",1,0,0,1,0,0
17016,Programmatically Interpretable Reinforcement Learning,"  We present a reinforcement learning framework, called Programmatically
Interpretable Reinforcement Learning (PIRL), that is designed to generate
interpretable and verifiable agent policies. Unlike the popular Deep
Reinforcement Learning (DRL) paradigm, which represents policies by neural
networks, PIRL represents policies using a high-level, domain-specific
programming language. Such programmatic policies have the benefits of being
more easily interpreted than neural networks, and being amenable to
verification by symbolic methods. We propose a new method, called Neurally
Directed Program Search (NDPS), for solving the challenging nonsmooth
optimization problem of finding a programmatic policy with maximal reward. NDPS
works by first learning a neural policy network using DRL, and then performing
a local search over programmatic policies that seeks to minimize a distance
from this neural ""oracle"". We evaluate NDPS on the task of learning to drive a
simulated car in the TORCS car-racing environment. We demonstrate that NDPS is
able to discover human-readable policies that pass some significant performance
bars. We also show that PIRL policies can have smoother trajectories, and can
be more easily transferred to environments not encountered during training,
than corresponding policies discovered by DRL.
",1,0,0,1,0,0
17017,Kinetic Simulation of Collisional Magnetized Plasmas with Semi-Implicit Time Integration,"  Plasmas with varying collisionalities occur in many applications, such as
tokamak edge regions, where the flows are characterized by significant
variations in density and temperature. While a kinetic model is necessary for
weakly-collisional high-temperature plasmas, high collisionality in colder
regions render the equations numerically stiff due to disparate time scales. In
this paper, we propose an implicit-explicit algorithm for such cases, where the
collisional term is integrated implicitly in time, while the advective term is
integrated explicitly in time, thus allowing time step sizes that are
comparable to the advective time scales. This partitioning results in a more
efficient algorithm than those using explicit time integrators, where the time
step sizes are constrained by the stiff collisional time scales. We implement
semi-implicit additive Runge-Kutta methods in COGENT, a finite-volume
gyrokinetic code for mapped, multiblock grids and test the accuracy,
convergence, and computational cost of these semi-implicit methods for test
cases with highly-collisional plasmas.
",1,1,0,0,0,0
17018,VC-dimension of short Presburger formulas,"  We study VC-dimension of short formulas in Presburger Arithmetic, defined to
have a bounded number of variables, quantifiers and atoms. We give both lower
and upper bounds, which are tight up to a polynomial factor in the bit length
of the formula.
",1,0,1,0,0,0
17019,Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree,"  Traffic accident data are usually noisy, contain missing values, and
heterogeneous. How to select the most important variables to improve real-time
traffic accident risk prediction has become a concern of many recent studies.
This paper proposes a novel variable selection method based on the Frequent
Pattern tree (FP tree) algorithm. First, all the frequent patterns in the
traffic accident dataset are discovered. Then for each frequent pattern, a new
criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is
calculated. This ROPR is added to the importance score of the variables that
differentiate one frequent pattern from the others. To test the proposed
method, a dataset was compiled from the traffic accidents records detected by
only one detector on interstate highway I-64 in Virginia in 2005. This dataset
was then linked to other variables such as real-time traffic information and
weather conditions. Both the proposed method based on the FP tree algorithm, as
well as the widely utilized, random forest method, were then used to identify
the important variables or the Virginia dataset. The results indicate that
there are some differences between the variables deemed important by the FP
tree and those selected by the random forest method. Following this, two
baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network)
were developed to predict accident risk based on the variables identified by
both the FP tree method and the random forest method. The results show that the
models based on the variable selection using the FP tree performed better than
those based on the random forest method for several versions of the k-NN and
Bayesian network models.The best results were derived from a Bayesian network
model using variables from FP tree. That model could predict 61.11% of
accidents accurately while having a false alarm rate of 38.16%.
",1,0,0,1,0,0
17020,Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration,"  Third-party library reuse has become common practice in contemporary software
development, as it includes several benefits for developers. Library
dependencies are constantly evolving, with newly added features and patches
that fix bugs in older versions. To take full advantage of third-party reuse,
developers should always keep up to date with the latest versions of their
library dependencies. In this paper, we investigate the extent of which
developers update their library dependencies. Specifically, we conducted an
empirical study on library migration that covers over 4,600 GitHub software
projects and 2,700 library dependencies. Results show that although many of
these systems rely heavily on dependencies, 81.5% of the studied systems still
keep their outdated dependencies. In the case of updating a vulnerable
dependency, the study reveals that affected developers are not likely to
respond to a security advisory. Surveying these developers, we find that 69% of
the interviewees claim that they were unaware of their vulnerable dependencies.
Furthermore, developers are not likely to prioritize library updates, citing it
as extra effort and added responsibility. This study concludes that even though
third-party reuse is commonplace, the practice of updating a dependency is not
as common for many developers.
",1,0,0,0,0,0
17021,Is Smaller Better: A Proposal To Consider Bacteria For Biologically Inspired Modeling,"  Bacteria are easily characterizable model organisms with an impressively
complicated set of capabilities. Among their capabilities is quorum sensing, a
detailed cell-cell signaling system that may have a common origin with
eukaryotic cell-cell signaling. Not only are the two phenomena similar, but
quorum sensing, as is the case with any bacterial phenomenon when compared to
eukaryotes, is also easier to study in depth than eukaryotic cell-cell
signaling. This ease of study is a contrast to the only partially understood
cellular dynamics of neurons. Here we review the literature on the strikingly
neuron-like qualities of bacterial colonies and biofilms, including ion-based
and hormonal signaling, and action potential-like behavior. This allows them to
feasibly act as an analog for neurons that could produce more detailed and more
accurate biologically-based computational models. Using bacteria as the basis
for biologically feasible computational models may allow models to better
harness the tremendous ability of biological organisms to make decisions and
process information. Additionally, principles gleaned from bacterial function
have the potential to influence computational efforts divorced from biology,
just as neuronal function has in the abstract influenced countless machine
learning efforts.
",1,0,0,0,0,0
17022,A Bayesian Data Augmentation Approach for Learning Deep Models,"  Data augmentation is an essential part of the training process applied to
deep learning models. The motivation is that a robust training process for deep
learning models depends on large annotated datasets, which are expensive to be
acquired, stored and processed. Therefore a reasonable alternative is to be
able to automatically generate new annotated training samples using a process
known as data augmentation. The dominant data augmentation approach in the
field assumes that new training samples can be obtained via random geometric or
appearance transformations applied to annotated training samples, but this is a
strong assumption because it is unclear if this is a reliable generative model
for producing new training samples. In this paper, we provide a novel Bayesian
formulation to data augmentation, where new annotated training points are
treated as missing variables and generated based on the distribution learned
from the training set. For learning, we introduce a theoretically sound
algorithm --- generalised Monte Carlo expectation maximisation, and demonstrate
one possible implementation via an extension of the Generative Adversarial
Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the
better performance of our proposed method compared to the current dominant data
augmentation approach mentioned above --- the results also show that our
approach produces better classification results than similar GAN models.
",1,0,0,0,0,0
17023,Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach,"  Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt ""pedagogical approaches"" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.
",0,0,0,1,0,0
17024,Parameterized complexity of machine scheduling: 15 open problems,"  Machine scheduling problems are a long-time key domain of algorithms and
complexity research. A novel approach to machine scheduling problems are
fixed-parameter algorithms. To stimulate this thriving research direction, we
propose 15 open questions in this area whose resolution we expect to lead to
the discovery of new approaches and techniques both in scheduling and
parameterized complexity theory.
",1,0,0,0,0,0
17025,"Potential Conditional Mutual Information: Estimators, Properties and Applications","  The conditional mutual information I(X;Y|Z) measures the average information
that X and Y contain about each other given Z. This is an important primitive
in many learning problems including conditional independence testing, graphical
model inference, causal strength estimation and time-series problems. In
several applications, it is desirable to have a functional purely of the
conditional distribution p_{Y|X,Z} rather than of the joint distribution
p_{X,Y,Z}. We define the potential conditional mutual information as the
conditional mutual information calculated with a modified joint distribution
p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We
develop K nearest neighbor based estimators for this functional, employing
importance sampling, and a coupling trick, and prove the finite k consistency
of such an estimator. We demonstrate that the estimator has excellent practical
performance and show an application in dynamical system inference.
",1,0,0,1,0,0
17026,"A new approach to divergences in quantum electrodynamics, concrete examples","  An interesting attempt for solving infrared divergence problems via the
theory of generalized wave operators was made by P. Kulish and L. Faddeev. Our
method of using the ideas from the theory of generalized wave operators is
essentially different. We assume that the unperturbed operator $A_0$ is known
and that the scattering operator $S$ and the unperturbed operator $A_0$ are
permutable. (In the Kulish-Faddeev theory this basic property is not
fulfilled.) The permutability of $S$ and $A_0$ gives us an important
information about the structure of the scattering operator. We show that the
divergences appeared because the deviations of the initial and final waves from
the free waves were not taken into account. The approach is demonstrated on
important examples.
",0,0,1,0,0,0
17027,Indefinite boundary value problems on graphs,"  We consider the spectral structure of indefinite second order boundary-value
problems on graphs. A variational formulation for such boundary-value problems
on graphs is given and we obtain both full and half-range completeness results.
This leads to a max-min principle and as a consequence we can formulate an
analogue of Dirichlet-Neumann bracketing and this in turn gives rise to
asymptotic approximations for the eigenvalues.
",0,0,1,0,0,0
17028,Integral curvatures of Finsler manifolds and applications,"  In this paper, we study the integral curvatures of Finsler manifolds. Some
Bishop-Gromov relative volume comparisons and several Myers type theorems are
obtained. We also establish a Gromov type precompactness theorem and a
Yamaguchi type finiteness theorem. Furthermore, the isoperimetric and Sobolev
constants of a closed Finsler manifold are estimated by integral curvature
bounds.
",0,0,1,0,0,0
17029,L-functions and sharp resonances of infinite index congruence subgroups of $SL_2(\mathbb{Z})$,"  For convex co-compact subgroups of SL2(Z) we consider the ""congruence
subgroups"" for p prime. We prove a factorization formula for the Selberg zeta
function in term of L-functions related to irreducible representations of the
Galois group SL2(Fp) of the covering, together with a priori bounds and
analytic continuation. We use this factorization property combined with an
averaging technique over representations to prove a new existence result of
non-trivial resonances in an effective low frequency strip.
",0,0,1,0,0,0
17030,An Enhanced Lumped Element Electrical Model of a Double Barrier Memristive Device,"  The massive parallel approach of neuromorphic circuits leads to effective
methods for solving complex problems. It has turned out that resistive
switching devices with a continuous resistance range are potential candidates
for such applications. These devices are memristive systems - nonlinear
resistors with memory. They are fabricated in nanotechnology and hence
parameter spread during fabrication may aggravate reproducible analyses. This
issue makes simulation models of memristive devices worthwhile.
Kinetic Monte-Carlo simulations based on a distributed model of the device
can be used to understand the underlying physical and chemical phenomena.
However, such simulations are very time-consuming and neither convenient for
investigations of whole circuits nor for real-time applications, e.g. emulation
purposes. Instead, a concentrated model of the device can be used for both fast
simulations and real-time applications, respectively. We introduce an enhanced
electrical model of a valence change mechanism (VCM) based double barrier
memristive device (DBMD) with a continuous resistance range. This device
consists of an ultra-thin memristive layer sandwiched between a tunnel barrier
and a Schottky-contact. The introduced model leads to very fast simulations by
using usual circuit simulation tools while maintaining physically meaningful
parameters.
Kinetic Monte-Carlo simulations based on a distributed model and experimental
data have been utilized as references to verify the concentrated model.
",1,1,0,0,0,0
17031,Non-perturbative positive Lyapunov exponent of Schrödinger equations and its applications to skew-shift,"  We first study the discrete Schrödinger equations with analytic potentials
given by a class of transformations. It is shown that if the coupling number is
large, then its logarithm equals approximately to the Lyapunov exponents. When
the transformation becomes the skew-shift, we prove that the Lyapunov exponent
is week Hölder continuous, and the spectrum satisfies Anderson Localization
and contains large intervals. Moreover, all of these conclusions are
non-perturbative.
",0,0,1,0,0,0
17032,Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees,"  Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe
(FW) algorithms regained popularity in recent years due to their simplicity,
effectiveness and theoretical guarantees. MP and FW address optimization over
the linear span and the convex hull of a set of atoms, respectively. In this
paper, we consider the intermediate case of optimization over the convex cone,
parametrized as the conic hull of a generic atom set, leading to the first
principled definitions of non-negative MP algorithms for which we give explicit
convergence rates and demonstrate excellent empirical performance. In
particular, we derive sublinear ($\mathcal{O}(1/t)$) convergence on general
smooth and convex objectives, and linear convergence ($\mathcal{O}(e^{-t})$) on
strongly convex objectives, in both cases for general sets of atoms.
Furthermore, we establish a clear correspondence of our algorithms to known
algorithms from the MP and FW literature. Our novel algorithms and analyses
target general atom sets and general objective functions, and hence are
directly applicable to a large variety of learning settings.
",1,0,0,1,0,0
17033,Probing the accretion disc structure by the twin kHz QPOs and spins of neutron stars in LMXBs,"  We analyze the relation between the emission radii of twin kilohertz
quasi-periodic oscillations (kHz QPOs) and the co-rotation radii of the 12
neutron star low mass X-ray binaries (NS-LMXBs) which are simultaneously
detected with the twin kHz QPOs and NS spins. We find that the average
co-rotation radius of these sources is r_co about 32 km, and all the emission
positions of twin kHz QPOs lie inside the corotation radii, indicating that the
twin kHz QPOs are formed in the spin-up process. It is noticed that the upper
frequency of twin kHz QPOs is higher than NS spin frequency by > 10%, which may
account for a critical velocity difference between the Keplerian motion of
accretion matter and NS spin that is corresponding to the production of twin
kHz QPOs. In addition, we also find that about 83% of twin kHz QPOs cluster
around the radius range of 15-20 km, which may be affected by the hard surface
or the local strong magnetic field of NS. As a special case, SAX J1808.4-3658
shows the larger emission radii of twin kHz QPOs of r about 21-24 km, which may
be due to its low accretion rate or small measured NS mass (< 1.4 solar mass).
",0,1,0,0,0,0
17034,Can scientists and their institutions become their own open access publishers?,"  This article offers a personal perspective on the current state of academic
publishing, and posits that the scientific community is beset with journals
that contribute little valuable knowledge, overload the community's capacity
for high-quality peer review, and waste resources. Open access publishing can
offer solutions that benefit researchers and other information users, as well
as institutions and funders, but commercial journal publishers have influenced
open access policies and practices in ways that favor their economic interests
over those of other stakeholders in knowledge creation and sharing. One way to
free research from constraints on access is the diamond route of open access
publishing, in which institutions and funders that produce new knowledge
reclaim responsibility for publication via institutional journals or other open
platforms. I argue that research journals (especially those published for
profit) may no longer be fit for purpose, and hope that readers will consider
whether the time has come to put responsibility for publishing back into the
hands of researchers and their institutions. The potential advantages and
challenges involved in a shift away from for-profit journals in favor of
institutional open access publishing are explored.
",1,0,0,0,0,0
17035,Character sums for elliptic curve densities,"  If $E$ is an elliptic curve over $\mathbb{Q}$, then it follows from work of
Serre and Hooley that, under the assumption of the Generalized Riemann
Hypothesis, the density of primes $p$ such that the group of
$\mathbb{F}_p$-rational points of the reduced curve $\tilde{E}(\mathbb{F}_p)$
is cyclic can be written as an infinite product $\prod \delta_\ell$ of local
factors $\delta_\ell$ reflecting the degree of the $\ell$-torsion fields,
multiplied by a factor that corrects for the entanglements between the various
torsion fields. We show that this correction factor can be interpreted as a
character sum, and the resulting description allows us to easily determine
non-vanishing criteria for it. We apply this method in a variety of other
settings. Among these, we consider the aforementioned problem with the
additional condition that the primes $p$ lie in a given arithmetic progression.
We also study the conjectural constants appearing in Koblitz's conjecture, a
conjecture which relates to the density of primes $p$ for which the cardinality
of the group of $\mathbb{F}_p$-points of $E$ is prime.
",0,0,1,0,0,0
17036,A monolithic fluid-structure interaction formulation for solid and liquid membranes including free-surface contact,"  A unified fluid-structure interaction (FSI) formulation is presented for
solid, liquid and mixed membranes. Nonlinear finite elements (FE) and the
generalized-alpha scheme are used for the spatial and temporal discretization.
The membrane discretization is based on curvilinear surface elements that can
describe large deformations and rotations, and also provide a straightforward
description for contact. The fluid is described by the incompressible
Navier-Stokes equations, and its discretization is based on stabilized
Petrov-Galerkin FE. The coupling between fluid and structure uses a conforming
sharp interface discretization, and the resulting non-linear FE equations are
solved monolithically within the Newton-Raphson scheme. An arbitrary
Lagrangian-Eulerian formulation is used for the fluid in order to account for
the mesh motion around the structure. The formulation is very general and
admits diverse applications that include contact at free surfaces. This is
demonstrated by two analytical and three numerical examples exhibiting strong
coupling between fluid and structure. The examples include balloon inflation,
droplet rolling and flapping flags. They span a Reynolds-number range from
0.001 to 2000. One of the examples considers the extension to rotation-free
shells using isogeometric FE.
",1,1,0,0,0,0
17037,Different Non-extensive Models for heavy-ion collisions,"  The transverse momentum ($p_T$) spectra from heavy-ion collisions at
intermediate momenta are described by non-extensive statistical models.
Assuming a fixed relative variance of the temperature fluctuating event by
event or alternatively a fixed mean multiplicity in a negative binomial
distribution (NBD), two different linear relations emerge between the
temperature, $T$, and the Tsallis parameter $q-1$. Our results qualitatively
agree with that of G.~Wilk. Furthermore we revisit the ""Soft+Hard"" model,
proposed recently by G.~G.~Barnaföldi \textit{et.al.}, by a $T$-independent
average $p_T^2$ assumption. Finally we compare results with those predicted by
another deformed distribution, using Kaniadakis' $\kappa$ parametrization.
",0,1,0,0,0,0
17038,Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees,"  Toxicity prediction of chemical compounds is a grand challenge. Lately, it
achieved significant progress in accuracy but using a huge set of features,
implementing a complex blackbox technique such as a deep neural network, and
exploiting enormous computational resources. In this paper, we strongly argue
for the models and methods that are simple in machine learning characteristics,
efficient in computing resource usage, and powerful to achieve very high
accuracy levels. To demonstrate this, we develop a single task-based chemical
toxicity prediction framework using only 2D features that are less compute
intensive. We effectively use a decision tree to obtain an optimum number of
features from a collection of thousands of them. We use a shallow neural
network and jointly optimize it with decision tree taking both network
parameters and input features into account. Our model needs only a minute on a
single CPU for its training while existing methods using deep neural networks
need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better
performance on several toxicity benchmark tasks. We also develop a cumulative
feature ranking method which enables us to identify features that can help
chemists perform prescreening of toxic compounds effectively.
",1,0,0,1,0,0
17039,Minmax Hierarchies and Minimal Surfaces in Manifolds,"  We introduce a general scheme that permits to generate successive min-max
problems for producing critical points of higher and higher indices to
Palais-Smale Functionals in Banach manifolds equipped with Finsler structures.
We call the resulting tree of minmax problems a minmax hierarchy. Using the
viscosity approach to the minmax theory of minimal surfaces introduced by the
author in a series of recent works, we explain how this scheme can be deformed
for producing smooth minimal surfaces of strictly increasing area in arbitrary
codimension. We implement this scheme to the case of the $3-$dimensional
sphere. In particular we are giving a min-max characterization of the Clifford
Torus and conjecture what are the next minimal surfaces to come in the $S^3$
hierarchy. Among other results we prove here the lower semi continuity of the
Morse Index in the viscosity method below an area level.
",0,0,1,0,0,0
17040,Nonseparable Multinomial Choice Models in Cross-Section and Panel Data,"  Multinomial choice models are fundamental for empirical modeling of economic
choices among discrete alternatives. We analyze identification of binary and
multinomial choice models when the choice utilities are nonseparable in
observed attributes and multidimensional unobserved heterogeneity with
cross-section and panel data. We show that derivatives of choice probabilities
with respect to continuous attributes are weighted averages of utility
derivatives in cross-section models with exogenous heterogeneity. In the
special case of random coefficient models with an independent additive effect,
we further characterize that the probability derivative at zero is proportional
to the population mean of the coefficients. We extend the identification
results to models with endogenous heterogeneity using either a control function
or panel data. In time stationary panel models with two periods, we find that
differences over time of derivatives of choice probabilities identify utility
derivatives ""on the diagonal,"" i.e. when the observed attributes take the same
values in the two periods. We also show that time stationarity does not
identify structural derivatives ""off the diagonal"" both in continuous and
multinomial choice panel models.
",0,0,0,1,0,0
17041,Corona limits of tilings : Periodic case,"  We study the limit shape of successive coronas of a tiling, which models the
growth of crystals. We define basic terminologies and discuss the existence and
uniqueness of corona limits, and then prove that corona limits are completely
characterized by directional speeds. As an application, we give another proof
that the corona limit of a periodic tiling is a centrally symmetric convex
polyhedron (see [Zhuravlev 2001], [Maleev-Shutov 2011]).
",0,0,1,0,0,0
17042,The Spatial Range of Conformity,"  Properties of galaxies like their absolute magnitude and their stellar mass
content are correlated. These correlations are tighter for close pairs of
galaxies, which is called galactic conformity. In hierarchical structure
formation scenarios, galaxies form within dark matter halos. To explain the
amplitude and the spatial range of galactic conformity two--halo terms or
assembly bias become important. With the scale dependent correlation
coefficients the amplitude and the spatial range of conformity are determined
from galaxy and halo samples. The scale dependent correlation coefficients are
introduced as a new descriptive statistic to quantify the correlations between
properties of galaxies or halos, depending on the distances to other galaxies
or halos. These scale dependent correlation coefficients can be applied to the
galaxy distribution directly. Neither a splitting of the sample into
subsamples, nor an a priori clustering is needed. This new descriptive
statistic is applied to galaxy catalogues derived from the Sloan Digital Sky
Survey III and to halo catalogues from the MultiDark simulations. In the galaxy
sample the correlations between absolute Magnitude, velocity dispersion,
ellipticity, and stellar mass content are investigated. The correlations of
mass, spin, and ellipticity are explored in the halo samples. Both for galaxies
and halos a scale dependent conformity is confirmed. Moreover the scale
dependent correlation coefficients reveal a signal of conformity out to 40Mpc
and beyond. The halo and galaxy samples show a differing amplitude and range of
conformity.
",0,1,0,0,0,0
17043,Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis,"  Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of
Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is
added to an unbiased estimate of the gradient at each iteration. This modest
change allows SGLD to escape local minima and suffices to guarantee asymptotic
convergence to global minimizers for sufficiently regular non-convex objectives
(Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis
in the context of non-convex learning problems, giving finite-time guarantees
for SGLD to find approximate minimizers of both empirical and population risks.
As in the asymptotic setting, our analysis relates the discrete-time SGLD
Markov chain to a continuous-time diffusion process. A new tool that drives the
results is the use of weighted transportation cost inequalities to quantify the
rate of convergence of SGLD to a stationary distribution in the Euclidean
$2$-Wasserstein distance.
",1,0,1,1,0,0
17044,Multilevel preconditioner of Polynomial Chaos Method for quantifying uncertainties in a blood pump,"  More than 23 million people are suffered by Heart failure worldwide. Despite
the modern transplant operation is well established, the lack of heart
donations becomes a big restriction on transplantation frequency. With respect
to this matter, ventricular assist devices (VADs) can play an important role in
supporting patients during waiting period and after the surgery. Moreover, it
has been shown that VADs by means of blood pump have advantages for working
under different conditions. While a lot of work has been done on modeling the
functionality of the blood pump, but quantifying uncertainties in a numerical
model is a challenging task. We consider the Polynomial Chaos (PC) method,
which is introduced by Wiener for modeling stochastic process with Gaussian
distribution. The Galerkin projection, the intrusive version of the generalized
Polynomial Chaos (gPC), has been densely studied and applied for various
problems. The intrusive Galerkin approach could represent stochastic process
directly at once with Polynomial Chaos series expansions, it would therefore
optimize the total computing effort comparing with classical non-intrusive
methods. We compared different preconditioning techniques for a steady state
simulation of a blood pump configuration in our previous work, the comparison
shows that an inexact multilevel preconditioner has a promising performance. In
this work, we show an instationary blood flow through a FDA blood pump
configuration with Galerkin Projection method, which is implemented in our open
source Finite Element library Hiflow3. Three uncertainty sources are
considered: inflow boundary condition, rotor angular speed and dynamic
viscosity, the numerical results are demonstrated with more than 30 Million
degrees of freedom by using supercomputer.
",0,1,0,0,0,0
17045,Superradiant Mott Transition,"  The combination of strong correlation and emergent lattice can be achieved
when quantum gases are confined in a superradiant Fabry-Perot cavity. In
addition to the discoveries of exotic phases, such as density wave ordered Mott
insulator and superfluid, a surprising kink structure is found in the slope of
the cavity strength as a function of the pumping strength. In this Letter, we
show that the appearance of such a kink is a manifestation of a liquid-gas like
transition between two superfluids with different densities. The slopes in the
immediate neighborhood of the kink become divergent at the liquid-gas critical
points and display a critical scaling law with a critical exponent 1 in the
quantum critical region. Our predictions could be tested in current
experimental set-up.
",0,1,0,0,0,0
17046,Communication via FRET in Nanonetworks of Mobile Proteins,"  A practical, biologically motivated case of protein complexes (immunoglobulin
G and FcRII receptors) moving on the surface of mastcells, that are common
parts of an immunological system, is investigated. Proteins are considered as
nanomachines creating a nanonetwork. Accurate molecular models of the proteins
and the fluorophores which act as their nanoantennas are used to simulate the
communication between the nanomachines when they are close to each other. The
theory of diffusion-based Brownian motion is applied to model movements of the
proteins. It is assumed that fluorophore molecules send and receive signals
using the Forster Resonance Energy Transfer. The probability of the efficient
signal transfer and the respective bit error rate are calculated and discussed.
",0,0,0,0,1,0
17047,"Multivariate generalized Pareto distributions: parametrizations, representations, and properties","  Multivariate generalized Pareto distributions arise as the limit
distributions of exceedances over multivariate thresholds of random vectors in
the domain of attraction of a max-stable distribution. These distributions can
be parametrized and represented in a number of different ways. Moreover,
generalized Pareto distributions enjoy a number of interesting stability
properties. An overview of the main features of such distributions are given,
expressed compactly in several parametrizations, giving the potential user of
these distributions a convenient catalogue of ways to handle and work with
generalized Pareto distributions.
",0,0,1,1,0,0
17048,Invertibility of spectral x-ray data with pileup--two dimension-two spectrum case,"  In the Alvarez-Macovski method, the line integrals of the x-ray basis set
coefficients are computed from measurements with multiple spectra. An important
question is whether the transformation from measurements to line integrals is
invertible. This paper presents a proof that for a system with two spectra and
a photon counting detector, pileup does not affect the invertibility of the
system. If the system is invertible with no pileup, it will remain invertible
with pileup although the reduced Jacobian may lead to increased noise.
",0,1,0,0,0,0
17049,Steinberg representations and harmonic cochains for split adjoint quasi-simple groups,"  Let $G$ be an adjoint quasi-simple group defined and split over a
non-archimedean local field $K$. We prove that the dual of the Steinberg
representation of $G$ is isomorphic to a certain space of harmonic cochains on
the Bruhat-Tits building of $G$. The Steinberg representation is considered
with coefficients in any commutative ring.
",0,0,1,0,0,0
17050,Lorentzian surfaces and the curvature of the Schmidt metric,"  The b-boundary is a mathematical tool used to attach a topological boundary
to incomplete Lorentzian manifolds using a Riemaniann metric called the Schmidt
metric on the frame bundle. In this paper, we give the general form of the
Schmidt metric in the case of Lorentzian surfaces. Furthermore, we write the
Ricci scalar of the Schmidt metric in terms of the Ricci scalar of the
Lorentzian manifold and give some examples. Finally, we discuss some
applications to general relativity.
",0,0,1,0,0,0
17051,Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System,"  Lattice Quantum Chromodynamics (Lattice QCD) is a quantum field theory on a
finite discretized space-time box so as to numerically compute the dynamics of
quarks and gluons to explore the nature of subatomic world. Solving the
equation of motion of quarks (quark solver) is the most compute-intensive part
of the lattice QCD simulations and is one of the legacy HPC applications. We
have developed a mixed-precision quark solver for a large Intel Xeon Phi (KNL)
system named ""Oakforest-PACS"", employing the $O(a)$-improved Wilson quarks as
the discretized equation of motion. The nested-BiCGSTab algorithm for the
solver was implemented and optimized using mixed-precision,
communication-computation overlapping with MPI-offloading, SIMD vectorization,
and thread stealing techniques. The solver achieved 2.6 PFLOPS in the
single-precision part on a $400^3\times 800$ lattice using 16000 MPI processes
on 8000 nodes on the system.
",0,1,0,0,0,0
17052,A spectral/hp element MHD solver,"  A new MHD solver, based on the Nektar++ spectral/hp element framework, is
presented in this paper. The velocity and electric potential quasi-static MHD
model is used. The Hartmann flow in plane channel and its stability, the
Hartmann flow in rectangular duct, and the stability of Hunt's flow are
explored as examples. Exponential convergence is achieved and the resulting
numerical values were found to have an accuracy up to $10^{-12}$ for the state
flows compared to an exact solution, and $10^{-5}$ for the stability
eigenvalues compared to independent numerical results.
",0,1,0,0,0,0
17053,"Journalists' information needs, seeking behavior, and its determinants on social media","  We describe the results of a qualitative study on journalists' information
seeking behavior on social media. Based on interviews with eleven journalists
along with a study of a set of university level journalism modules, we
determined the categories of information need types that lead journalists to
social media. We also determined the ways that social media is exploited as a
tool to satisfy information needs and to define influential factors, which
impacted on journalists' information seeking behavior. We find that not only is
social media used as an information source, but it can also be a supplier of
stories found serendipitously. We find seven information need types that expand
the types found in previous work. We also find five categories of influential
factors that affect the way journalists seek information.
",1,0,0,0,0,0
17054,Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models,"  The problem of low rank matrix completion is considered in this paper. To
exploit the underlying low-rank structure of the data matrix, we propose a
hierarchical Gaussian prior model, where columns of the low-rank matrix are
assumed to follow a Gaussian distribution with zero mean and a common precision
matrix, and a Wishart distribution is specified as a hyperprior over the
precision matrix. We show that such a hierarchical Gaussian prior has the
potential to encourage a low-rank solution. Based on the proposed hierarchical
prior model, a variational Bayesian method is developed for matrix completion,
where the generalized approximate massage passing (GAMP) technique is embedded
into the variational Bayesian inference in order to circumvent cumbersome
matrix inverse operations. Simulation results show that our proposed method
demonstrates superiority over existing state-of-the-art matrix completion
methods.
",1,0,0,1,0,0
17055,Emergence of superconductivity in the canonical heavy-electron metal YbRh2Si2,"  We report magnetic and calorimetric measurements down to T = 1 mK on the
canonical heavy-electron metal YbRh2Si2. The data reveal the development of
nuclear antiferromagnetic order slightly above 2 mK. The latter weakens the
primary electronic antiferromagnetism, thereby paving the way for
heavy-electron superconductivity below Tc = 2 mK. Our results demonstrate that
superconductivity driven by quantum criticality is a general phenomenon.
",0,1,0,0,0,0
17056,Obtaining a Proportional Allocation by Deleting Items,"  We consider the following control problem on fair allocation of indivisible
goods. Given a set $I$ of items and a set of agents, each having strict linear
preference over the items, we ask for a minimum subset of the items whose
deletion guarantees the existence of a proportional allocation in the remaining
instance; we call this problem Proportionality by Item Deletion (PID). Our main
result is a polynomial-time algorithm that solves PID for three agents. By
contrast, we prove that PID is computationally intractable when the number of
agents is unbounded, even if the number $k$ of item deletions allowed is small,
since the problem turns out to be W[3]-hard with respect to the parameter $k$.
Additionally, we provide some tight lower and upper bounds on the complexity of
PID when regarded as a function of $|I|$ and $k$.
",1,0,0,0,0,0
17057,DeepFace: Face Generation using Deep Learning,"  We use CNNs to build a system that both classifies images of faces based on a
variety of different facial attributes and generates new faces given a set of
desired facial characteristics. After introducing the problem and providing
context in the first section, we discuss recent work related to image
generation in Section 2. In Section 3, we describe the methods used to
fine-tune our CNN and generate new images using a novel approach inspired by a
Gaussian mixture model. In Section 4, we discuss our working dataset and
describe our preprocessing steps and handling of facial attributes. Finally, in
Sections 5, 6 and 7, we explain our experiments and results and conclude in the
following section. Our classification system has 82\% test accuracy.
Furthermore, our generation pipeline successfully creates well-formed faces.
",1,0,0,0,0,0
17058,High quality mesh generation using cross and asterisk fields: Application on coastal domains,"  This paper presents a method to generate high quality triangular or
quadrilateral meshes that uses direction fields and a frontal point insertion
strategy. Two types of direction fields are considered: asterisk fields and
cross fields. With asterisk fields we generate high quality triangulations,
while with cross fields we generate right-angled triangulations that are
optimal for transformation to quadrilateral meshes. The input of our algorithm
is an initial triangular mesh and a direction field calculated on it. The goal
is to compute the vertices of the final mesh by an advancing front strategy
along the direction field. We present an algorithm that enables to efficiently
generate the points using solely information from the base mesh. A
multi-threaded implementation of our algorithm is presented, allowing us to
achieve significant speedup of the point generation. Regarding the
quadrangulation process, we develop a quality criterion for right-angled
triangles with respect to the local cross field and an optimization process
based on it. Thus we are able to further improve the quality of the output
quadrilaterals. The algorithm is demonstrated on the sphere and examples of
high quality triangular and quadrilateral meshes of coastal domains are
presented.
",1,0,0,0,0,0
17059,ELFI: Engine for Likelihood-Free Inference,"  Engine for Likelihood-Free Inference (ELFI) is a Python software library for
performing likelihood-free inference (LFI). ELFI provides a convenient syntax
for arranging components in LFI, such as priors, simulators, summaries or
distances, to a network called ELFI graph. The components can be implemented in
a wide variety of languages. The stand-alone ELFI graph can be used with any of
the available inference methods without modifications. A central method
implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference
(BOLFI), which has recently been shown to accelerate likelihood-free inference
up to several orders of magnitude by surrogate-modelling the distance. ELFI
also has an inbuilt support for output data storing for reuse and analysis, and
supports parallelization of computation from multiple cores up to a cluster
environment. ELFI is designed to be extensible and provides interfaces for
widening its functionality. This makes the adding of new inference methods to
ELFI straightforward and automatically compatible with the inbuilt features.
",1,0,0,1,0,0
17060,Boosting Adversarial Attacks with Momentum,"  Deep neural networks are vulnerable to adversarial examples, which poses
security concerns on these algorithms due to the potentially severe
consequences. Adversarial attacks serve as an important surrogate to evaluate
the robustness of deep learning models before they are deployed. However, most
of existing adversarial attacks can only fool a black-box model with a low
success rate. To address this issue, we propose a broad class of momentum-based
iterative algorithms to boost adversarial attacks. By integrating the momentum
term into the iterative process for attacks, our methods can stabilize update
directions and escape from poor local maxima during the iterations, resulting
in more transferable adversarial examples. To further improve the success rates
for black-box attacks, we apply momentum iterative algorithms to an ensemble of
models, and show that the adversarially trained models with a strong defense
ability are also vulnerable to our black-box attacks. We hope that the proposed
methods will serve as a benchmark for evaluating the robustness of various deep
models and defense methods. With this method, we won the first places in NIPS
2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack
competitions.
",1,0,0,1,0,0
17061,Information spreading during emergencies and anomalous events,"  The most critical time for information to spread is in the aftermath of a
serious emergency, crisis, or disaster. Individuals affected by such situations
can now turn to an array of communication channels, from mobile phone calls and
text messages to social media posts, when alerting social ties. These channels
drastically improve the speed of information in a time-sensitive event, and
provide extant records of human dynamics during and afterward the event.
Retrospective analysis of such anomalous events provides researchers with a
class of ""found experiments"" that may be used to better understand social
spreading. In this chapter, we study information spreading due to a number of
emergency events, including the Boston Marathon Bombing and a plane crash at a
western European airport. We also contrast the different information which may
be gleaned by social media data compared with mobile phone data and we estimate
the rate of anomalous events in a mobile phone dataset using a proposed anomaly
detection method.
",1,1,0,0,0,0
17062,Large-Scale Plant Classification with Deep Neural Networks,"  This paper discusses the potential of applying deep learning techniques for
plant classification and its usage for citizen science in large-scale
biodiversity monitoring. We show that plant classification using near
state-of-the-art convolutional network architectures like ResNet50 achieves
significant improvements in accuracy compared to the most widespread plant
classification application in test sets composed of thousands of different
species labels. We find that the predictions can be confidently used as a
baseline classification in citizen science communities like iNaturalist (or its
Spanish fork, Natusfera) which in turn can share their data with biodiversity
portals like GBIF.
",1,0,0,1,0,0
17063,Deep Reinforcement Learning for General Video Game AI,"  The General Video Game AI (GVGAI) competition and its associated software
framework provides a way of benchmarking AI algorithms on a large number of
games written in a domain-specific description language. While the competition
has seen plenty of interest, it has so far focused on online planning,
providing a forward model that allows the use of algorithms such as Monte Carlo
Tree Search.
In this paper, we describe how we interface GVGAI to the OpenAI Gym
environment, a widely used way of connecting agents to reinforcement learning
problems. Using this interface, we characterize how widely used implementations
of several deep reinforcement learning algorithms fare on a number of GVGAI
games. We further analyze the results to provide a first indication of the
relative difficulty of these games relative to each other, and relative to
those in the Arcade Learning Environment under similar conditions.
",0,0,0,1,0,0
17064,Purely infinite labeled graph $C^*$-algebras,"  In this paper, we consider pure infiniteness of generalized Cuntz-Krieger
algebras associated to labeled spaces $(E,\mathcal{L},\mathcal{E})$. It is
shown that a $C^*$-algebra $C^*(E,\mathcal{L},\mathcal{E})$ is purely infinite
in the sense that every nonzero hereditary subalgebra contains an infinite
projection (we call this property (IH)) if $(E, \mathcal{L},\mathcal{E})$ is
disagreeable and every vertex connects to a loop. We also prove that under the
condition analogous to (K) for usual graphs,
$C^*(E,\mathcal{L},\mathcal{E})=C^*(p_A, s_a)$ is purely infinite in the sense
of Kirchberg and R{\o}rdam if and only if every generating projection $p_A$,
$A\in \mathcal{E}$, is properly infinite, and also if and only if every
quotient of $C^*(E,\mathcal{L},\mathcal{E})$ has the property (IH).
",0,0,1,0,0,0
17065,From safe screening rules to working sets for faster Lasso-type solvers,"  Convex sparsity-promoting regularizations are ubiquitous in modern
statistical learning. By construction, they yield solutions with few non-zero
coefficients, which correspond to saturated constraints in the dual
optimization formulation. Working set (WS) strategies are generic optimization
techniques that consist in solving simpler problems that only consider a subset
of constraints, whose indices form the WS. Working set methods therefore
involve two nested iterations: the outer loop corresponds to the definition of
the WS and the inner loop calls a solver for the subproblems. For the Lasso
estimator a WS is a set of features, while for a Group Lasso it refers to a set
of groups. In practice, WS are generally small in this context so the
associated feature Gram matrix can fit in memory. Here we show that the
Gauss-Southwell rule (a greedy strategy for block coordinate descent
techniques) leads to fast solvers in this case. Combined with a working set
strategy based on an aggressive use of so-called Gap Safe screening rules, we
propose a solver achieving state-of-the-art performance on sparse learning
problems. Results are presented on Lasso and multi-task Lasso estimators.
",1,0,1,1,0,0
17066,Exoplanet Radius Gap Dependence on Host Star Type,"  Exoplanets smaller than Neptune are numerous, but the nature of the planet
populations in the 1-4 Earth radii range remains a mystery. The complete Kepler
sample of Q1-Q17 exoplanet candidates shows a radius gap at ~ 2 Earth radii, as
reported by us in January 2017 in LPSC conference abstract #1576 (Zeng et al.
2017). A careful analysis of Kepler host stars spectroscopy by the CKS survey
allowed Fulton et al. (2017) in March 2017 to unambiguously show this radius
gap. The cause of this gap is still under discussion (Ginzburg et al. 2017;
Lehmer & Catling 2017; Owen & Wu 2017). Here we add to our original analysis
the dependence of the radius gap on host star type.
",0,1,0,0,0,0
17067,Mapping the Invocation Structure of Online Political Interaction,"  The surge in political information, discourse, and interaction has been one
of the most important developments in social media over the past several years.
There is rich structure in the interaction among different viewpoints on the
ideological spectrum. However, we still have only a limited analytical
vocabulary for expressing the ways in which these viewpoints interact.
In this paper, we develop network-based methods that operate on the ways in
which users share content; we construct \emph{invocation graphs} on Web domains
showing the extent to which pages from one domain are invoked by users to reply
to posts containing pages from other domains. When we locate the domains on a
political spectrum induced from the data, we obtain an embedded graph showing
how these interaction links span different distances on the spectrum. The
structure of this embedded network, and its evolution over time, helps us
derive macro-level insights about how political interaction unfolded through
2016, leading up to the US Presidential election. In particular, we find that
the domains invoked in replies spanned increasing distances on the spectrum
over the months approaching the election, and that there was clear asymmetry
between the left-to-right and right-to-left patterns of linkage.
",1,0,0,0,0,0
17068,Collective decision for open set recognition,"  In open set recognition (OSR), almost all existing methods are designed
specially for recognizing individual instances, even these instances are
collectively coming in batch. Recognizers in decision either reject or
categorize them to some known class using empirically-set threshold. Thus the
threshold plays a key role, however, the selection for it usually depends on
the knowledge of known classes, inevitably incurring risks due to lacking
available information from unknown classes. On the other hand, a more realistic
OSR system should NOT just rest on a reject decision but should go further,
especially for discovering the hidden unknown classes among the reject
instances, whereas existing OSR methods do not pay special attention. In this
paper, we introduce a novel collective/batch decision strategy with an aim to
extend existing OSR for new class discovery while considering correlations
among the testing instances. Specifically, a collective decision-based OSR
framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet
process (HDP). Thanks to the HDP, our CD-OSR does not need to define the
specific threshold and can automatically reserve space for unknown classes in
testing, naturally resulting in a new class discovery function. Finally,
extensive experiments on benchmark datasets indicate the validity of CD-OSR.
",0,0,0,1,0,0
17069,HARPS-N high spectral resolution observations of Cepheids I. The Baade-Wesselink projection factor of δ Cep revisited,"  The projection factor p is the key quantity used in the Baade-Wesselink (BW)
method for distance determination; it converts radial velocities into pulsation
velocities. Several methods are used to determine p, such as geometrical and
hydrodynamical models or the inverse BW approach when the distance is known. We
analyze new HARPS-N spectra of delta Cep to measure its cycle-averaged
atmospheric velocity gradient in order to better constrain the projection
factor. We first apply the inverse BW method to derive p directly from
observations. The projection factor can be divided into three subconcepts: (1)
a geometrical effect (p0); (2) the velocity gradient within the atmosphere
(fgrad); and (3) the relative motion of the optical pulsating photosphere with
respect to the corresponding mass elements (fo-g). We then measure the fgrad
value of delta Cep for the first time. When the HARPS-N mean cross-correlated
line-profiles are fitted with a Gaussian profile, the projection factor is
pcc-g = 1.239 +/- 0.034(stat) +/- 0.023(syst). When we consider the different
amplitudes of the radial velocity curves that are associated with 17 selected
spectral lines, we measure projection factors ranging from 1.273 to 1.329. We
find a relation between fgrad and the line depth measured when the Cepheid is
at minimum radius. This relation is consistent with that obtained from our best
hydrodynamical model of delta Cep and with our projection factor decomposition.
Using the observational values of p and fgrad found for the 17 spectral lines,
we derive a semi-theoretical value of fo-g. We alternatively obtain fo-g =
0.975+/-0.002 or 1.006+/-0.002 assuming models using radiative transfer in
plane-parallel or spherically symmetric geometries, respectively. The new
HARPS-N observations of delta Cep are consistent with our decomposition of the
projection factor.
",0,1,0,0,0,0
17070,Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US,"  The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag between demographic changes and their appearance in the ACS can
exceed half a decade. As digital imagery becomes ubiquitous and machine vision
techniques improve, automated data analysis may provide a cheaper and faster
alternative. Here, we present a method that determines socioeconomic trends
from 50 million images of street scenes, gathered in 200 American cities by
Google Street View cars. Using deep learning-based computer vision techniques,
we determined the make, model, and year of all motor vehicles encountered in
particular neighborhoods. Data from this census of motor vehicles, which
enumerated 22M automobiles in total (8% of all automobiles in the US), was used
to accurately estimate income, race, education, and voting patterns, with
single-precinct resolution. (The average US precinct contains approximately
1000 people.) The resulting associations are surprisingly simple and powerful.
For instance, if the number of sedans encountered during a 15-minute drive
through a city is higher than the number of pickup trucks, the city is likely
to vote for a Democrat during the next Presidential election (88% chance);
otherwise, it is likely to vote Republican (82%). Our results suggest that
automated systems for monitoring demographic trends may effectively complement
labor-intensive approaches, with the potential to detect trends with fine
spatial resolution, in close to real time.
",1,0,0,0,0,0
17071,Gaussian Process Neurons Learn Stochastic Activation Functions,"  We propose stochastic, non-parametric activation functions that are fully
learnable and individual to each neuron. Complexity and the risk of overfitting
are controlled by placing a Gaussian process prior over these functions. The
result is the Gaussian process neuron, a probabilistic unit that can be used as
the basic building block for probabilistic graphical models that resemble the
structure of neural networks. The proposed model can intrinsically handle
uncertainties in its inputs and self-estimate the confidence of its
predictions. Using variational Bayesian inference and the central limit
theorem, a fully deterministic loss function is derived, allowing it to be
trained as efficiently as a conventional neural network using mini-batch
gradient descent. The posterior distribution of activation functions is
inferred from the training data alongside the weights of the network.
The proposed model favorably compares to deep Gaussian processes, both in
model complexity and efficiency of inference. It can be directly applied to
recurrent or convolutional network structures, allowing its use in audio and
image processing tasks.
As an preliminary empirical evaluation we present experiments on regression
and classification tasks, in which our model achieves performance comparable to
or better than a Dropout regularized neural network with a fixed activation
function. Experiments are ongoing and results will be added as they become
available.
",1,0,0,1,0,0
17072,The short-term price impact of trades is universal,"  We analyze a proprietary dataset of trades by a single asset manager,
comparing their price impact with that of the trades of the rest of the market.
In the context of a linear propagator model we find no significant difference
between the two, suggesting that both the magnitude and time dependence of
impact are universal in anonymous, electronic markets. This result is important
as optimal execution policies often rely on propagators calibrated on anonymous
data. We also find evidence that in the wake of a trade the order flow of other
market participants first adds further copy-cat trades enhancing price impact
on very short time scales. The induced order flow then quickly inverts, thereby
contributing to impact decay.
",0,1,0,0,0,0
17073,Questions on mod p representations of reductive p-adic groups,"  This is a list of questions raised by our joint work arXiv:1412.0737 and its
sequels.
",0,0,1,0,0,0
17074,Filamentary superconductivity in semiconducting policrystalline ZrSe2 compound with Zr vacancies,"  ZrSe2 is a band semiconductor studied long time ago. It has interesting
electronic properties, and because its layers structure can be intercalated
with different atoms to change some of the physical properties. In this
investigation we found that Zr deficiencies alter the semiconducting behavior
and the compound can be turned into a superconductor. In this paper we report
our studies related to this discovery. The decreasing of the number of Zr atoms
in small proportion according to the formula ZrxSe2, where x is varied from
about 8.1 to 8.6 K, changing the semiconducting behavior to a superconductor
with transition temperatures ranging between 7.8 to 8.5 K, it depending of the
deficiencies. Outside of those ranges the compound behaves as semiconducting
with the properties already known. In our experiments we found that this new
superconductor has only a very small fraction of superconducting material
determined by magnetic measurements with applied magnetic field of 10 Oe. Our
conclusions is that superconductivity is filamentary. However, in one studied
sample the fraction was about 10.2 %, whereas in others is only about 1 % or
less. We determined the superconducting characteristics; the critical fields
that indicate a type two superonductor with Ginzburg-Landau ? parameter of the
order about 2.7. The synthesis procedure is quite normal fol- lowing the
conventional solid state reaction. In this paper are included, the electronic
characteristics, transition temperature, and evolution with temperature of the
critical fields.
",0,1,0,0,0,0
17075,Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time,"  In this study we map out the large-scale structure of citation networks of
science journals and follow their evolution in time by using stochastic block
models (SBMs). The SBM fitting procedures are principled methods that can be
used to find hierarchical grouping of journals into blocks that show similar
incoming and outgoing citations patterns. These methods work directly on the
citation network without the need to construct auxiliary networks based on
similarity of nodes. We fit the SBMs to the networks of journals we have
constructed from the data set of around 630 million citations and find a
variety of different types of blocks, such as clusters, bridges, sources, and
sinks. In addition we use a recent generalization of SBMs to determine how much
a manually curated classification of journals into subfields of science is
related to the block structure of the journal network and how this relationship
changes in time. The SBM method tries to find a network of blocks that is the
best high-level representation of the network of journals, and we illustrate
how these block networks (at various levels of resolution) can be used as maps
of science.
",1,1,0,0,0,0
17076,Limits on the anomalous speed of gravitational waves from binary pulsars,"  A large class of modified theories of gravity used as models for dark energy
predict a propagation speed for gravitational waves which can differ from the
speed of light. This difference of propagations speeds for photons and
gravitons has an impact in the emission of gravitational waves by binary
systems. Thus, we revisit the usual quadrupolar emission of binary system for
an arbitrary propagation speed of gravitational waves and obtain the
corresponding period decay formula. We then use timing data from the
Hulse-Taylor binary pulsar and obtain that the speed of gravitational waves can
only differ from the speed of light at the percentage level. This bound places
tight constraints on dark energy models featuring an anomalous propagations
speed for the gravitational waves.
",0,1,0,0,0,0
17077,Central limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications,"  The notion of entropy-regularized optimal transport, also known as Sinkhorn
divergence, has recently gained popularity in machine learning and statistics,
as it makes feasible the use of smoothed optimal transportation distances for
data analysis. The Sinkhorn divergence allows the fast computation of an
entropically regularized Wasserstein distance between two probability
distributions supported on a finite metric space of (possibly) high-dimension.
For data sampled from one or two unknown probability distributions, we derive
the distributional limits of the empirical Sinkhorn divergence and its centered
version (Sinkhorn loss). We also propose a bootstrap procedure which allows to
obtain new test statistics for measuring the discrepancies between multivariate
probability distributions. Our work is inspired by the results of Sommerfeld
and Munk (2016) on the asymptotic distribution of empirical Wasserstein
distance on finite space using unregularized transportation costs. Incidentally
we also analyze the asymptotic distribution of entropy-regularized Wasserstein
distances when the regularization parameter tends to zero. Simulated and real
datasets are used to illustrate our approach.
",0,0,1,1,0,0
17078,Inference for Stochastically Contaminated Variable Length Markov Chains,"  In this paper, we present a methodology to estimate the parameters of
stochastically contaminated models under two contamination regimes. In both
regimes, we assume that the original process is a variable length Markov chain
that is contaminated by a random noise. In the first regime we consider that
the random noise is added to the original source and in the second regime, the
random noise is multiplied by the original source. Given a contaminated sample
of these models, the original process is hidden. Then we propose a two steps
estimator for the parameters of these models, that is, the probability
transitions and the noise parameter, and prove its consistency. The first step
is an adaptation of the Baum-Welch algorithm for Hidden Markov Models. This
step provides an estimate of a complete order $k$ Markov chain, where $k$ is
bigger than the order of the variable length Markov chain if it has finite
order and is a constant depending on the sample size if the hidden process has
infinite order. In the second estimation step, we propose a bootstrap Bayesian
Information Criterion, given a sample of the Markov chain estimated in the
first step, to obtain the variable length time dependence structure associated
with the hidden process. We present a simulation study showing that our
methodology is able to accurately recover the parameters of the models for a
reasonable interval of random noises.
",0,0,0,1,0,0
17079,Variable-Length Resolvability for General Sources and Channels,"  We introduce the problem of variable-length source resolvability, where a
given target probability distribution is approximated by encoding a
variable-length uniform random number, and the asymptotically minimum average
length rate of the uniform random numbers, called the (variable-length)
resolvability, is investigated. We first analyze the variable-length
resolvability with the variational distance as an approximation measure. Next,
we investigate the case under the divergence as an approximation measure. When
the asymptotically exact approximation is required, it is shown that the
resolvability under the two kinds of approximation measures coincides. We then
extend the analysis to the case of channel resolvability, where the target
distribution is the output distribution via a general channel due to the fixed
general source as an input. The obtained characterization of the channel
resolvability is fully general in the sense that when the channel is just the
identity mapping, the characterization reduces to the general formula for the
source resolvability. We also analyze the second-order variable-length
resolvability.
",1,0,0,0,0,0
17080,Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging,"  3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological
brain sections by measuring their birefringence. This study investigates
another effect caused by the optical anisotropy of brain tissue -
diattenuation. Based on numerical and experimental studies and a complete
analytical description of the optical system, the diattenuation was determined
to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation
effect has negligible impact on the fiber orientations derived by 3D-PLI. The
diattenuation signal, however, was found to highlight different anatomical
structures that cannot be distinguished with current imaging techniques, which
makes Diattenuation Imaging a promising extension to 3D-PLI.
",0,1,0,0,0,0
17081,Higgs Modes in the Pair Density Wave Superconducting State,"  The pair density wave (PDW) superconducting state has been proposed to
explain the layer- decoupling effect observed in the compound
La$_{2-x}$Ba$_x$CuO$_4$ at $x=1/8$ (Phys. Rev. Lett. 99, 127003). In this state
the superconducting order parameter is spatially modulated, in contrast with
the usual superconducting (SC) state where the order parameter is uniform. In
this work, we study the properties of the amplitude (Higgs) modes in a
unidirectional PDW state. To this end we consider a phenomenological model of
PDW type states coupled to a Fermi surface of fermionic quasiparticles. In
contrast to conventional superconductors that have a single Higgs mode,
unidirectional PDW superconductors have two Higgs modes. While in the PDW state
the Fermi surface largely remains gapless, we find that the damping of the PDW
Higgs modes into fermionic quasiparticles requires exceeding an energy
threshold. We show that this suppression of damping in the PDW state is due to
kinematics. As a result, only one of the two Higgs modes is significantly
damped. In addition, motivated by the experimental phase diagram, we discuss
the mixing of Higgs modes in the coexistence regime of the PDW and uniform SC
states. These results should be observable directly in a Raman spectroscopy, in
momentum resolved electron energy loss spectroscopy, and in resonant inelastic
X-ray scattering, thus providing evidence of the PDW states.
",0,1,0,0,0,0
17082,A Serverless Tool for Platform Agnostic Computational Experiment Management,"  Neuroscience has been carried into the domain of big data and high
performance computing (HPC) on the backs of initiatives in data collection and
an increasingly compute-intensive tools. While managing HPC experiments
requires considerable technical acumen, platforms and standards have been
developed to ease this burden on scientists. While web-portals make resources
widely accessible, data organizations such as the Brain Imaging Data Structure
and tool description languages such as Boutiques provide researchers with a
foothold to tackle these problems using their own datasets, pipelines, and
environments. While these standards lower the barrier to adoption of HPC and
cloud systems for neuroscience applications, they still require the
consolidation of disparate domain-specific knowledge. We present Clowdr, a
lightweight tool to launch experiments on HPC systems and clouds, record rich
execution records, and enable the accessible sharing of experimental summaries
and results. Clowdr uniquely sits between web platforms and bare-metal
applications for experiment management by preserving the flexibility of
do-it-yourself solutions while providing a low barrier for developing,
deploying and disseminating neuroscientific analysis.
",1,0,0,0,0,0
17083,Traveling-wave parametric amplifier based on three-wave mixing in a Josephson metamaterial,"  We have developed a recently proposed Josephson traveling-wave parametric
amplifier with three-wave mixing [A. B. Zorin, Phys. Rev. Applied 6, 034006,
2016]. The amplifier consists of a microwave transmission line formed by a
serial array of nonhysteretic one-junction SQUIDs. These SQUIDs are flux-biased
in a way that the phase drops across the Josephson junctions are equal to 90
degrees and the persistent currents in the SQUID loops are equal to the
Josephson critical current values. Such a one-dimensional metamaterial
possesses a maximal quadratic nonlinearity and zero cubic (Kerr) nonlinearity.
This property allows phase matching and exponential power gain of traveling
microwaves to take place over a wide frequency range. We report the
proof-of-principle experiment performed at a temperature of T = 4.2 K on Nb
trilayer samples, which has demonstrated that our concept of a practical
broadband Josephson parametric amplifier is valid and very promising for
achieving quantum-limited operation.
",0,1,0,0,0,0
17084,Measuring LDA Topic Stability from Clusters of Replicated Runs,"  Background: Unstructured and textual data is increasing rapidly and Latent
Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods
for it. Past work suggests that instability of LDA topics may lead to
systematic errors. Aim: We propose a method that relies on replicated LDA runs,
clustering, and providing a stability metric for the topics. Method: We
generate k LDA topics and replicate this process n times resulting in n*k
topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k
clusters now represent the original LDA topics and we present them like normal
LDA topics showing the ten most probable words. For the clusters, we try
multiple stability metrics, out of which we recommend Rank-Biased Overlap,
showing the stability of the topics inside the clusters. Results: We provide an
initial validation where our method is used for 270,000 Mozilla Firefox commit
messages with k=20 and n=20. We show how our topic stability metrics are
related to the contents of the topics. Conclusions: Advances in text mining
enable us to analyze large masses of text in software engineering but
non-deterministic algorithms, such as LDA, may lead to unreplicable
conclusions. Our approach makes LDA stability transparent and is also
complementary rather than alternative to many prior works that focus on LDA
parameter tuning.
",1,0,0,0,0,0
17085,Continuum Foreground Polarization and Na~I Absorption in Type Ia SNe,"  We present a study of the continuum polarization over the 400--600 nm range
of 19 Type Ia SNe obtained with FORS at the VLT. We separate them in those that
show Na I D lines at the velocity of their hosts and those that do not.
Continuum polarization of the sodium sample near maximum light displays a broad
range of values, from extremely polarized cases like SN 2006X to almost
unpolarized ones like SN 2011ae. The non--sodium sample shows, typically,
smaller polarization values. The continuum polarization of the sodium sample in
the 400--600 nm range is linear with wavelength and can be characterized by the
mean polarization (P$_{\rm{mean}}$). Its values span a wide range and show a
linear correlation with color, color excess, and extinction in the visual band.
Larger dispersion correlations were found with the equivalent width of the Na I
D and Ca II H & K lines, and also a noisy relation between P$_{\rm{mean}}$ and
$R_{V}$, the ratio of total to selective extinction. Redder SNe show stronger
continuum polarization, with larger color excesses and extinctions. We also
confirm that high continuum polarization is associated with small values of
$R_{V}$.
The correlation between extinction and polarization -- and polarization
angles -- suggest that the dominant fraction of dust polarization is imprinted
in interstellar regions of the host galaxies.
We show that Na I D lines from foreground matter in the SN host are usually
associated with non-galactic ISM, challenging the typical assumptions in
foreground interstellar polarization models.
",0,1,0,0,0,0
17086,Toward Faultless Content-Based Playlists Generation for Instrumentals,"  This study deals with content-based musical playlists generation focused on
Songs and Instrumentals. Automatic playlist generation relies on collaborative
filtering and autotagging algorithms. Autotagging can solve the cold start
issue and popularity bias that are critical in music recommender systems.
However, autotagging remains to be improved and cannot generate satisfying
music playlists. In this paper, we suggest improvements toward better
autotagging-generated playlists compared to state-of-the-art. To assess our
method, we focus on the Song and Instrumental tags. Song and Instrumental are
two objective and opposite tags that are under-studied compared to genres or
moods, which are subjective and multi-modal tags. In this paper, we consider an
industrial real-world musical database that is unevenly distributed between
Songs and Instrumentals and bigger than databases used in previous studies. We
set up three incremental experiments to enhance automatic playlist generation.
Our suggested approach generates an Instrumental playlist with up to three
times less false positives than cutting edge methods. Moreover, we provide a
design of experiment framework to foster research on Songs and Instrumentals.
We give insight on how to improve further the quality of generated playlists
and to extend our methods to other musical tags. Furthermore, we provide the
source code to guarantee reproducible research.
",1,0,0,0,0,0
17087,Direct observation of the band gap transition in atomically thin ReS$_2$,"  ReS$_2$ is considered as a promising candidate for novel electronic and
sensor applications. The low crystal symmetry of the van der Waals compound
ReS$_2$ leads to a highly anisotropic optical, vibrational, and transport
behavior. However, the details of the electronic band structure of this
fascinating material are still largely unexplored. We present a
momentum-resolved study of the electronic structure of monolayer, bilayer, and
bulk ReS$_2$ using k-space photoemission microscopy in combination with
first-principles calculations. We demonstrate that the valence electrons in
bulk ReS$_2$ are - contrary to assumptions in recent literature - significantly
delocalized across the van der Waals gap. Furthermore, we directly observe the
evolution of the valence band dispersion as a function of the number of layers,
revealing a significantly increased effective electron mass in single-layer
crystals. We also find that only bilayer ReS$_2$ has a direct band gap. Our
results establish bilayer ReS$_2$ as a advantageous building block for
two-dimensional devices and van der Waals heterostructures.
",0,1,0,0,0,0
17088,Lattice embeddings between types of fuzzy sets. Closed-valued fuzzy sets,"  In this paper we deal with the problem of extending Zadeh's operators on
fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2
(T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs,
whose membership degrees are singletons is not order-preserving. We then
describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if
the former singleton viewpoint is required, we reformulate the intersection on
hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets.
This new type of fuzzy sets extends standard union and intersection on FSs. In
addition, it allows handling together membership degrees of different nature
as, for instance, closed intervals and finite sets. Finally, all these
constructions are viewed as T2FSs forming a chain of lattices.
",1,0,0,0,0,0
17089,Coupling of Magneto-Thermal and Mechanical Superconducting Magnet Models by Means of Mesh-Based Interpolation,"  In this paper we present an algorithm for the coupling of magneto-thermal and
mechanical finite element models representing superconducting accelerator
magnets. The mechanical models are used during the design of the mechanical
structure as well as the optimization of the magnetic field quality under
nominal conditions. The magneto-thermal models allow for the analysis of
transient phenomena occurring during quench initiation, propagation, and
protection. Mechanical analysis of quenching magnets is of high importance
considering the design of new protection systems and the study of new
superconductor types. We use field/circuit coupling to determine temperature
and electromagnetic force evolution during the magnet discharge. These
quantities are provided as a load to existing mechanical models. The models are
discretized with different meshes and, therefore, we employ a mesh-based
interpolation method to exchange coupled quantities. The coupling algorithm is
illustrated with a simulation of a mechanical response of a standalone
high-field dipole magnet protected with CLIQ (Coupling-Loss Induced Quench)
technology.
",1,1,0,0,0,0
17090,Converging expansions for Lipschitz self-similar perforations of a plane sector,"  In contrast with the well-known methods of matching asymptotics and
multiscale (or compound) asymptotics, the "" functional analytic approach "" of
Lanza de Cristoforis (Analysis 28, 2008) allows to prove convergence of
expansions around interior small holes of size $\epsilon$ for solutions of
elliptic boundary value problems. Using the method of layer potentials, the
asymptotic behavior of the solution as $\epsilon$ tends to zero is described
not only by asymptotic series in powers of $\epsilon$, but by convergent power
series. Here we use this method to investigate the Dirichlet problem for the
Laplace operator where holes are collapsing at a polygonal corner of opening
$\omega$. Then in addition to the scale $\epsilon$ there appears the scale
$\eta = \epsilon^{\pi/\omega}$. We prove that when $\pi/\omega$ is irrational,
the solution of the Dirichlet problem is given by convergent series in powers
of these two small parameters. Due to interference of the two scales, this
convergence is obtained, in full generality, by grouping together integer
powers of the two scales that are very close to each other. Nevertheless, there
exists a dense subset of openings $\omega$ (characterized by Diophantine
approximation properties), for which real analyticity in the two variables
$\epsilon$ and $\eta$ holds and the power series converge unconditionally. When
$\pi/\omega$ is rational, the series are unconditionally convergent, but
contain terms in log $\epsilon$.
",0,0,1,0,0,0
17091,A Viral Timeline Branching Process to study a Social Network,"  Bio-inspired paradigms are proving to be useful in analyzing propagation and
dissemination of information in networks. In this paper we explore the use of
multi-type branching processes to analyse viral properties of content in a
social network, with and without competition from other sources. We derive and
compute various virality measures, e.g., probability of virality, expected
number of shares, or the rate of growth of expected number of shares etc. They
allow one to predict the emergence of global macro properties (e.g., viral
spread of a post in the entire network) from the laws and parameters that
determine local interactions. The local interactions, greatly depend upon the
structure of the timelines holding the content and the number of friends (i.e.,
connections) of users of the network. We then formulate a non-cooperative game
problem and study the Nash equilibria as a function of the parameters. The
branching processes modelling the social network under competition turn out to
be decomposable, multi-type and continuous time variants. For such processes
types belonging to different sub-classes evolve at different rates and have
different probabilities of extinction etc. We compute content provider wise
extinction probability, rate of growth etc. We also conjecture the
content-provider wise growth rate of expected shares.
",1,0,0,0,0,0
17092,Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence,"  Viral zoonoses have emerged as the key drivers of recent pandemics. Human
infection by zoonotic viruses are either spillover events -- isolated
infections that fail to cause a widespread contagion -- or species jumps, where
successful adaptation to the new host leads to a pandemic. Despite expensive
bio-surveillance efforts, historically emergence response has been reactive,
and post-hoc. Here we use machine inference to demonstrate a high accuracy
predictive bio-surveillance capability, designed to pro-actively localize an
impending species jump via automated interrogation of massive sequence
databases of viral proteins. Our results suggest that a jump might not purely
be the result of an isolated unfortunate cross-infection localized in space and
time; there are subtle yet detectable patterns of genotypic changes
accumulating in the global viral population leading up to emergence. Using tens
of thousands of protein sequences simultaneously, we train models that track
maximum achievable accuracy for disambiguating host tropism from the primary
structure of surface proteins, and show that the inverse classification
accuracy is a quantitative indicator of jump risk. We validate our claim in the
context of the 2009 swine flu outbreak, and the 2004 emergence of H5N1
subspecies of Influenza A from avian reservoirs; illustrating that
interrogation of the global viral population can unambiguously track a near
monotonic risk elevation over several preceding years leading to eventual
emergence.
",0,0,0,1,1,0
17093,Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward,"  Operationalizing machine learning based security detections is extremely
challenging, especially in a continuously evolving cloud environment.
Conventional anomaly detection does not produce satisfactory results for
analysts that are investigating security incidents in the cloud. Model
evaluation alone presents its own set of problems due to a lack of benchmark
datasets. When deploying these detections, we must deal with model compliance,
localization, and data silo issues, among many others. We pose the problem of
""attack disruption"" as a way forward in the security data science space. In
this paper, we describe the framework, challenges, and open questions
surrounding the successful operationalization of machine learning based
security detections in a cloud environment and provide some insights on how we
have addressed them.
",1,0,0,0,0,0
17094,SOI RF Switch for Wireless Sensor Network,"  The objective of this research was to design a 0-5 GHz RF SOI switch, with
0.18um power Jazz SOI technology by using Cadence software, for health care
applications. This paper introduces the design of a RF switch implemented in
shunt-series topology. An insertion loss of 0.906 dB and an isolation of 30.95
dB were obtained at 5 GHz. The switch also achieved a third order distortion of
53.05 dBm and 1 dB compression point reached 50.06dBm. The RF switch
performance meets the desired specification requirements.
",1,0,0,0,0,0
17095,The Pentagonal Inequality,"  Given a positive linear combination of five (respectively seven) cosines,
where the angles are positive and sum to pi, the aim of this article is to
express the sharp bound of the combination as a Positive Real Fraction in the
coefficients (hence cosine-free). The method uses algebraic and arithmetic
manipulations with judicious transformations.
",0,0,1,0,0,0
17096,The Landscape of Deep Learning Algorithms,"  This paper studies the landscape of empirical risk of deep neural networks by
theoretically analyzing its convergence behavior to the population risk as well
as its stationary points and properties. For an $l$-layer linear neural
network, we prove its empirical risk uniformly converges to its population risk
at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training
sample size of $n$, the total weight dimension of $d$ and the magnitude bound
$r$ of weight of each layer. We then derive the stability and generalization
bounds for the empirical risk based on this result. Besides, we establish the
uniform convergence of gradient of the empirical risk to its population
counterpart. We prove the one-to-one correspondence of the non-degenerate
stationary points between the empirical and population risks with convergence
guarantees, which describes the landscape of deep neural networks. In addition,
we analyze these properties for deep nonlinear neural networks with sigmoid
activation functions. We prove similar results for convergence behavior of
their empirical risks as well as the gradients and analyze properties of their
non-degenerate stationary points.
To our best knowledge, this work is the first one theoretically
characterizing landscapes of deep learning algorithms. Besides, our results
provide the sample complexity of training a good deep neural network. We also
provide theoretical understanding on how the neural network depth $l$, the
layer width, the network size $d$ and parameter magnitude determine the neural
network landscapes.
",1,0,1,1,0,0
17097,"The effect of the environment on the structure, morphology and star-formation history of intermediate-redshift galaxies","  With the aim of understanding the effect of the environment on the star
formation history and morphological transformation of galaxies, we present a
detailed analysis of the colour, morphology and internal structure of cluster
and field galaxies at $0.4 \le z \le 0.8$. We use {\em HST} data for over 500
galaxies from the ESO Distant Cluster Survey (EDisCS) to quantify how the
galaxies' light distribution deviate from symmetric smooth profiles. We
visually inspect the galaxies' images to identify the likely causes for such
deviations. We find that the residual flux fraction ($RFF$), which measures the
fractional contribution to the galaxy light of the residuals left after
subtracting a symmetric and smooth model, is very sensitive to the degree of
structural disturbance but not the causes of such disturbance. On the other
hand, the asymmetry of these residuals ($A_{\rm res}$) is more sensitive to the
causes of the disturbance, with merging galaxies having the highest values of
$A_{\rm res}$. Using these quantitative parameters we find that, at a fixed
morphology, cluster and field galaxies show statistically similar degrees of
disturbance. However, there is a higher fraction of symmetric and passive
spirals in the cluster than in the field. These galaxies have smoother light
distributions than their star-forming counterparts. We also find that while
almost all field and cluster S0s appear undisturbed, there is a relatively
small population of star-forming S0s in clusters but not in the field. These
findings are consistent with relatively gentle environmental processes acting
on galaxies infalling onto clusters.
",0,1,0,0,0,0
17098,Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning,"  Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper, we propose a novel recommender system with the
capability of continuously improving its strategies during the interactions
with users. We model the sequential interactions between users and a
recommender system as a Markov Decision Process (MDP) and leverage
Reinforcement Learning (RL) to automatically learn the optimal strategies via
recommending trial-and-error items and receiving reinforcements of these items
from users' feedback. Users' feedback can be positive and negative and both
types of feedback have great potentials to boost recommendations. However, the
number of negative feedback is much larger than that of positive one; thus
incorporating them simultaneously is challenging since positive feedback could
be buried by negative one. In this paper, we develop a novel approach to
incorporate them into the proposed deep recommender system (DEERS) framework.
The experimental results based on real-world e-commerce data demonstrate the
effectiveness of the proposed framework. Further experiments have been
conducted to understand the importance of both positive and negative feedback
in recommendations.
",0,0,0,1,0,0
17099,Accumulated Gradient Normalization,"  This work addresses the instability in asynchronous data parallel
optimization. It does so by introducing a novel distributed optimizer which is
able to efficiently optimize a centralized model under communication
constraints. The optimizer achieves this by pushing a normalized sequence of
first-order gradients to a parameter server. This implies that the magnitude of
a worker delta is smaller compared to an accumulated gradient, and provides a
better direction towards a minimum compared to first-order gradients, which in
turn also forces possible implicit momentum fluctuations to be more aligned
since we make the assumption that all workers contribute towards a single
minima. As a result, our approach mitigates the parameter staleness problem
more effectively since staleness in asynchrony induces (implicit) momentum, and
achieves a better convergence rate compared to other optimizers such as
asynchronous EASGD and DynSGD, which we show empirically.
",1,0,0,1,0,0
17100,An Optimal Algorithm for Changing from Latitudinal to Longitudinal Formation of Autonomous Aircraft Squadrons,"  This work presents an algorithm for changing from latitudinal to longitudinal
formation of autonomous aircraft squadrons. The maneuvers are defined
dynamically by using a predefined set of 3D basic maneuvers. This formation
changing is necessary when the squadron has to perform tasks which demand both
formations, such as lift off, georeferencing, obstacle avoidance and landing.
Simulations show that the formation changing is made without collision. The
time complexity analysis of the transformation algorithm reveals that its
efficiency is optimal, and the proof of correction ensures its longitudinal
formation features.
",1,0,0,0,0,0
17101,Ideal Cluster Points in Topological Spaces,"  Given an ideal $\mathcal{I}$ on $\omega$, we show that a sequence in a
topological space $X$ is $\mathcal{I}$-convergent if and only if there exists a
""big"" $\mathcal{I}$-convergent subsequence. In addition, we study several
properties of $\mathcal{I}$-cluster points. As a consequence, the underlying
topology $\tau$ coincides with the topology generated by the pair
$(\tau,\mathcal{I})$. Then, we obtain two characterizations of the set of
$\mathcal{I}$-cluster points as classical cluster points of a filters on $X$
and as the smallest closed set containing ""almost all"" the sequence.
",0,0,1,0,0,0
17102,Spin Hall effect of gravitational waves,"  Gravitons possess a Berry curvature due to their helicity. We derive the
semiclassical equations of motion for gravitons taking into account the Berry
curvature. We show that this quantum correction leads to the splitting of the
trajectories of right- and left-handed gravitational waves in curved space, and
that this correction can be understood as a topological phenomenon. This is the
spin Hall effect (SHE) of gravitational waves. We find that the SHE of
gravitational waves is twice as large as that of light. Possible future
observations of the SHE of gravitational waves can potentially test the quantum
nature of gravitons beyond the classical general relativity.
",0,1,0,0,0,0
17103,Many-Goals Reinforcement Learning,"  All-goals updating exploits the off-policy nature of Q-learning to update all
possible goals an agent could have from each transition in the world, and was
introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work
this was mostly explored in small-state RL problems that allowed tabular
representations and where all possible goals could be explicitly enumerated and
learned separately. In this paper we empirically explore 3 different extensions
of the idea of updating many (instead of all) goals in the context of RL with
deep neural networks (or DeepRL for short). First, in a direct adaptation of
Kaelbling's approach we explore if many-goals updating can be used to achieve
mastery in non-tabular visual-observation domains. Second, we explore whether
many-goals updating can be used to pre-train a network to subsequently learn
faster and better on a single main task of interest. Third, we explore whether
many-goals updating can be used to provide auxiliary task updates in training a
network to learn faster and better on a single main task of interest. We
provide comparisons to baselines for each of the 3 extensions.
",0,0,0,1,0,0
17104,Localized Structured Prediction,"  Key to structured prediction is exploiting the problem structure to simplify
the learning process. A major challenge arises when data exhibit a local
structure (e.g., are made by ""parts"") that can be leveraged to better
approximate the relation between (parts of) the input and (parts of) the
output. Recent literature on signal processing, and in particular computer
vision, has shown that capturing these aspects is indeed essential to achieve
state-of-the-art performance. While such algorithms are typically derived on a
case-by-case basis, in this work we propose the first theoretical framework to
deal with part-based data from a general perspective. We derive a novel
approach to deal with these problems and study its generalization properties
within the setting of statistical learning theory. Our analysis is novel in
that it explicitly quantifies the benefits of leveraging the part-based
structure of the problem with respect to the learning rates of the proposed
estimator.
",0,0,0,1,0,0
17105,Routing in FRET-based Nanonetworks,"  Nanocommunications, understood as communications between nanoscale devices,
is commonly regarded as a technology essential for cooperation of large groups
of nanomachines and thus crucial for development of the whole area of
nanotechnology. While solutions for point-to-point nanocommunications have been
already proposed, larger networks cannot function properly without routing. In
this article we focus on the nanocommunications via Forster Resonance Energy
Transfer (FRET), which was found to be a technique with a very high signal
propagation speed, and discuss how to route signals through nanonetworks. We
introduce five new routing mechanisms, based on biological properties of
specific molecules. We experimentally validate one of these mechanisms.
Finally, we analyze open issues showing the technical challenges for signal
transmission and routing in FRET-based nanocommunications.
",0,0,0,0,1,0
17106,"FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why","  Winograd-based convolution has quickly gained traction as a preferred
approach to implement convolutional neural networks (ConvNet) on various
hardware platforms because it requires fewer floating point operations than
FFT-based or direct convolutions.
This paper compares three highly optimized implementations (regular FFT--,
Gauss--FFT--, and Winograd--based convolutions) on modern multi-- and
many--core CPUs. Although all three implementations employed the same
optimizations for modern CPUs, our experimental results with two popular
ConvNets (VGG and AlexNet) show that the FFT--based implementations generally
outperform the Winograd--based approach, contrary to the popular belief.
To understand the results, we use a Roofline performance model to analyze the
three implementations in detail, by looking at each of their computation phases
and by considering not only the number of floating point operations, but also
the memory bandwidth and the cache sizes. The performance analysis explains
why, and under what conditions, the FFT--based implementations outperform the
Winograd--based one, on modern CPUs.
",1,0,0,0,0,0
17107,The application of selection principles in the study of the properties of function spaces,"  In this paper we investigate the properties of function spaces using the
selection principles.
",0,0,1,0,0,0
17108,Progressive Neural Architecture Search,"  We propose a new method for learning the structure of convolutional neural
networks (CNNs) that is more efficient than recent state-of-the-art methods
based on reinforcement learning and evolutionary algorithms. Our approach uses
a sequential model-based optimization (SMBO) strategy, in which we search for
structures in order of increasing complexity, while simultaneously learning a
surrogate model to guide the search through structure space. Direct comparison
under the same search space shows that our method is up to 5 times more
efficient than the RL method of Zoph et al. (2018) in terms of number of models
evaluated, and 8 times faster in terms of total compute. The structures we
discover in this way achieve state of the art classification accuracies on
CIFAR-10 and ImageNet.
",1,0,0,1,0,0
17109,Lower Bounds for Maximum Gap in (Inverse) Cyclotomic Polynomials,"  The maximum gap $g(f)$ of a polynomial $f$ is the maximum of the differences
(gaps) between two consecutive exponents that appear in $f$. Let $\Phi_{n}$ and
$\Psi_{n}$ denote the $n$-th cyclotomic and $n$-th inverse cyclotomic
polynomial, respectively. In this paper, we give several lower bounds for
$g(\Phi_{n})$ and $g(\Psi_{n})$, where $n$ is the product of odd primes. We
observe that they are very often exact. We also give an exact expression for
$g(\Psi_{n})$ under a certain condition. Finally we conjecture an exact
expression for $g(\Phi_{n})$ under a certain condition.
",0,0,1,0,0,0
17110,"Dynamical Analysis of Cylindrically Symmetric Anisotropic Sources in $f(R,T)$ Gravity","  In this paper, we have analyzed the stability of cylindrically symmetric
collapsing object filled with locally anisotropic fluid in $f(R,T)$ theory,
where $R$ is the scalar curvature and $T$ is the trace of stress-energy tensor
of matter. Modified field equations and dynamical equations are constructed in
$f(R,T)$ gravity. Evolution or collapse equation is derived from dynamical
equations by performing linear perturbation on them. Instability range is
explored in both Newtonian and post-Newtonian regimes with the help of
adiabetic index, which defines the impact of physical parameters on the
instability range. Some conditions are imposed on physical quantities to secure
the stability of the gravitating sources.
",0,1,0,0,0,0
17111,Trace and Kunneth formulas for singularity categories and applications,"  We present an $\ell$-adic trace formula for saturated and admissible
dg-categories over a base monoidal dg-category. Moreover, we prove Künneth
formulas for dg-category of singularities, and for inertia-invariant vanishing
cycles. As an application, we prove a version of Bloch's Conductor Conjecture
(stated by Spencer Bloch in 1985), under the additional hypothesis that the
monodromy action of the inertia group is unipotent.
",0,0,1,0,0,0
17112,A Redshift Survey of the Nearby Galaxy Cluster Abell 2199: Comparison of the Spatial and Kinematic Distributions of Galaxies with the Intracluster Medium,"  We present the results from an extensive spectroscopic survey of the central
region of the nearby galaxy cluster Abell 2199 at $z=0.03$. By combining 775
new redshifts from the MMT/Hectospec observations with the data in the
literature, we construct a large sample of 1624 galaxies with measured
redshifts at $R<30^\prime$, which results in high spectroscopic completeness at
$r_{\rm petro,0}<20.5$ (77%). We use these data to study the kinematics and
clustering of galaxies focusing on the comparison with those of the
intracluster medium (ICM) from Suzaku X-ray observations. We identify 406
member galaxies of A2199 at $R<30^\prime$ using the caustic technique. The
velocity dispersion profile of cluster members appears smoothly connected to
the stellar velocity dispersion profile of the cD galaxy. The luminosity
function is well fitted with a Schechter function at $M_r<-15$. The radial
velocities of cluster galaxies generally agree well with those of the ICM, but
there are some regions where the velocity difference between the two is about a
few hundred kilometer per second. The cluster galaxies show a hint of global
rotation at $R<5^\prime$ with $v_{\rm rot}=300{-}600\,\textrm{km s}^{-1}$, but
the ICM in the same region do not show such rotation. We apply a
friends-of-friends algorithm to the cluster galaxy sample at $R<60^\prime$ and
identify 32 group candidates, and examine the spatial correlation between the
galaxy groups and X-ray emission. This extensive survey in the central region
of A2199 provides an important basis for future studies of interplay among the
galaxies, the ICM and the dark matter in the cluster.
",0,1,0,0,0,0
17113,A trapped field of 13.4 T in a stack of HTS tapes with 30 μm substrate,"  Superconducting bulk (RE)Ba$_2$Cu$_3$O$_{7-x}$ materials (RE-rare earth
elements) have been successfully used to generate magnetic flux densities in
excess of 17 T. This work investigates an alternative approach by trapping flux
in stacks of second generation high temperature superconducting tape from
several manufacturers using field cooling and pulsed field magnetisation
techniques. Flux densities of up to 13.4 T were trapped by field cooling at ~5
K between two 12 mm square stacks, an improvement of 70% over previous value
achieved in an HTS tape stack. The trapped flux approaches the record values in
(RE)BCO bulks and reflects the rapid developments still being made in the HTS
tape performance.
",0,1,0,0,0,0
17114,Exact Simulation of the Extrema of Stable Processes,"  We exhibit an exact simulation algorithm for the supremum of a stable process
over a finite time interval using dominated coupling from the past (DCFTP). We
establish a novel perpetuity equation for the supremum (via the representation
of the concave majorants of Lévy processes) and apply it to construct a
Markov chain in the DCFTP algorithm. We prove that the number of steps taken
backwards in time before the coalescence is detected is finite.
",0,0,0,1,0,0
17115,Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient,"  We consider a nonparametric Bayesian approach to estimate the diffusion
coefficient of a stochastic differential equation given discrete time
observations over a fixed time interval. As a prior on the diffusion
coefficient, we employ a histogram-type prior with piecewise constant
realisations on bins forming a partition of the time interval. Specifically,
these constants are realizations of independent inverse Gamma distributed
randoma variables. We justify our approach by deriving the rate at which the
corresponding posterior distribution asymptotically concentrates around the
data-generating diffusion coefficient. This posterior contraction rate turns
out to be optimal for estimation of a Hölder-continuous diffusion coefficient
with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to
implement, as the posterior distributions turn out to be inverse Gamma again,
and leads to good practical results in a wide range of simulation examples.
Finally, we apply our method on exchange rate data sets.
",0,0,1,1,0,0
17116,Dirac State in a Centrosymmetric Superconductor alpha-PdBi2,"  Topological superconductor (TSC) hosting Majorana fermions has been
established as a milestone that may shift our scientific trajectory from
research to applications in topological quantum computing. Recently,
superconducting Pd-Bi binaries have attracted great attention as a possible
medium for the TSC phase as a result of their large spin-orbit coupling
strength. Here, we report a systematic high-resolution angle-resolved
photoemission spectroscopy (ARPES) study on the normal state electronic
structure of superconducting alpha-PdBi2 (Tc = 1.7 K). Our results show the
presence of Dirac states at higher-binding energy with the location of the
Dirac point at 1.26 eV below the chemical potential at the zone center.
Furthermore, the ARPES data indicate multiple band crossings at the chemical
potential, consistent with the metallic behavior of alpha-PdBi2. Our detailed
experimental studies are complemented by first-principles calculations, which
reveal the presence of surface Rashba states residing in the vicinity of the
chemical potential. The obtained results provide an opportunity to investigate
the relationship between superconductivity and topology, as well as explore
pathways to possible future platforms for topological quantum computing.
",0,1,0,0,0,0
17117,Hölder and Lipschitz continuity of functions definable over Henselian rank one valued fields,"  Consider a Henselian rank one valued field $K$ of equicharacteristic zero
with the three-sorted language $\mathcal{L}$ of Denef--Pas. Let $f: A \to K$ be
a continuous $\mathcal{L}$-definable (with parameters) function on a closed
bounded subset $A \subset K^{n}$. The main purpose is to prove that then $f$ is
Hölder continuous with some exponent $s\geq 0$ and constant $c \geq 0$, a
fortiori, $f$ is uniformly continuous. Further, if $f$ is locally Lipschitz
continuous with a constant $c$, then $f$ is (globally) Lipschitz continuous
with possibly some larger constant $d$. Also stated are some problems
concerning continuous and Lipschitz continuous functions definable over
Henselian valued fields.
",0,0,1,0,0,0
17118,Bistability of Cavity Magnon Polaritons,"  We report the first observation of the magnon-polariton bistability in a
cavity magnonics system consisting of cavity photons strongly interacting with
the magnons in a small yttrium iron garnet (YIG) sphere. The bistable behaviors
are emerged as sharp frequency switchings of the cavity magnon-polaritons
(CMPs) and related to the transition between states with large and small number
of polaritons. In our experiment, we align, respectively, the [100] and [110]
crystallographic axes of the YIG sphere parallel to the static magnetic field
and find very different bistable behaviors (e.g., clockwise and
counter-clockwise hysteresis loops) in these two cases. The experimental
results are well fitted and explained as being due to the Kerr nonlinearity
with either positive or negative coefficient. Moreover, when the magnetic field
is tuned away from the anticrossing point of CMPs, we observe simultaneous
bistability of both magnons and cavity photons by applying a drive field on the
lower branch.
",0,1,0,0,0,0
17119,Errors and secret data in the Italian research assessment exercise. A comment to a reply,"  Italy adopted a performance-based system for funding universities that is
centered on the results of a national research assessment exercise, realized by
a governmental agency (ANVUR). ANVUR evaluated papers by using 'a dual system
of evaluation', that is by informed peer review or by bibliometrics. In view of
validating that system, ANVUR performed an experiment for estimating the
agreement between informed review and bibliometrics. Ancaiani et al. (2015)
presents the main results of the experiment. Baccini and De Nicolao (2017)
documented in a letter, among other critical issues, that the statistical
analysis was not realized on a random sample of articles. A reply to the letter
has been published by Research Evaluation (Benedetto et al. 2017). This note
highlights that in the reply there are (1) errors in data, (2) problems with
'representativeness' of the sample, (3) unverifiable claims about weights used
for calculating kappas, (4) undisclosed averaging procedures; (5) a statement
about 'same protocol in all areas' contradicted by official reports. Last but
not least: the data used by the authors continue to be undisclosed. A general
warning concludes: many recently published papers use data originating from
Italian research assessment exercise. These data are not accessible to the
scientific community and consequently these papers are not reproducible. They
can be hardly considered as containing sound evidence at least until authors or
ANVUR disclose the data necessary for replication.
",1,0,0,0,0,0
17120,Exploring Features for Predicting Policy Citations,"  In this study we performed an initial investigation and evaluation of
altmetrics and their relationship with public policy citation of research
papers. We examined methods for using altmetrics and other data to predict
whether a research paper is cited in public policy and applied receiver
operating characteristic curve on various feature groups in order to evaluate
their potential usefulness. From the methods we tested, classifying based on
tweet count provided the best results, achieving an area under the ROC curve of
0.91.
",1,0,0,0,0,0
17121,Recovery guarantees for compressed sensing with unknown errors,"  From a numerical analysis perspective, assessing the robustness of
l1-minimization is a fundamental issue in compressed sensing and sparse
regularization. Yet, the recovery guarantees available in the literature
usually depend on a priori estimates of the noise, which can be very hard to
obtain in practice, especially when the noise term also includes unknown
discrepancies between the finite model and data. In this work, we study the
performance of l1-minimization when these estimates are not available,
providing robust recovery guarantees for quadratically constrained basis
pursuit and random sampling in bounded orthonormal systems. Several
applications of this work are approximation of high-dimensional functions,
infinite-dimensional sparse regularization for inverse problems, and fast
algorithms for non-Cartesian Magnetic Resonance Imaging.
",0,0,1,0,0,0
17122,Synthetic Homology in Homotopy Type Theory,"  This paper defines homology in homotopy type theory, in the process stable
homotopy groups are also defined. Previous research in synthetic homotopy
theory is relied on, in particular the definition of cohomology. This work lays
the foundation for a computer checked construction of homology.
",1,0,1,0,0,0
17123,Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors Over Long Distances,"  We investigate a time-dependent spatial vector-host epidemic model with
non-coincident domains for the vector and host populations. The host population
resides in small non-overlapping sub-regions, while the vector population
resides throughout a much larger region. The dynamics of the populations are
modeled by a reaction-diffusion-advection compartmental system of partial
differential equations. The disease is transmitted through vector and host
populations in criss-cross fashion. We establish global well-posedness and
uniform a prior bounds as well as the long-term behavior. The model is applied
to simulate the outbreak of bluetongue disease in sheep transmitted by midges
infected with bluetongue virus. We show that the long-range directed movement
of the midge population, due to wind-aided movement, enhances the transmission
of the disease to sheep in distant sites.
",0,0,0,0,1,0
17124,Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation,"  Recently, the principal component pursuit has received increasing attention
in signal processing research ranging from source separation to video
surveillance. So far, all existing formulations are real-valued and lack the
concept of phase, which is inherent in inputs such as complex spectrograms or
color images. Thus, in this letter, we extend principal component pursuit to
the complex and quaternionic cases to account for the missing phase
information. Specifically, we present both complex and quaternionic proximity
operators for the $\ell_1$- and trace-norm regularizers. These operators can be
used in conjunction with proximal minimization methods such as the inexact
augmented Lagrange multiplier algorithm. The new algorithms are then applied to
the singing voice separation problem, which aims to separate the singing voice
from the instrumental accompaniment. Results on the iKala and MSD100 datasets
confirmed the usefulness of phase information in principal component pursuit.
",0,0,0,1,0,0
17125,Light axion-like dark matter must be present during inflation,"  Axion-like particles (ALPs) might constitute the totality of the cold dark
matter (CDM) observed. The parameter space of ALPs depends on the mass of the
particle $m$ and on the energy scale of inflation $H_I$ , the latter being
bound by the non-detection of primordial gravitational waves. We show that the
bound on HI implies the existence of a mass scale $m_\chi = 10 {\rm \,neV}
÷ 0.5 {\rm \,peV}$, depending on the ALP susceptibility $\chi$, such that
the energy density of ALPs of mass smaller than $m_\chi$ is too low to explain
the present CDM budget, if the ALP field has originated after the end of
inflation. This bound affects Ultra-Light Axions (ULAs), which have recently
regained popularity as CDM candidates. Light ($m < m_\chi$) ALPs can then be
CDM candidates only if the ALP field has already originated during the
inflationary period, in which case the parameter space is constrained by the
non-detection of axion isocurvature fluctuations. We comment on the effects on
these bounds from additional physics beyond the Standard Model, besides ALPs.
",0,1,0,0,0,0
17126,Boolean function analysis meets stochastic optimization: An approximation scheme for stochastic knapsack,"  The stochastic knapsack problem is the stochastic variant of the classical
knapsack problem in which the algorithm designer is given a a knapsack with a
given capacity and a collection of items where each item is associated with a
profit and a probability distribution on its size. The goal is to select a
subset of items with maximum profit and violate the capacity constraint with
probability at most $p$ (referred to as the overflow probability). While
several approximation algorithms have been developed for this problem, most of
these algorithms relax the capacity constraint of the knapsack. In this paper,
we design efficient approximation schemes for this problem without relaxing the
capacity constraint.
(i) Our first result is in the case when item sizes are Bernoulli random
variables. In this case, we design a (nearly) fully polynomial time
approximation scheme (FPTAS) which only relaxes the overflow probability. (ii)
Our second result generalizes the first result to the case when all the item
sizes are supported on a (common) set of constant size. (iii) Our third result
is in the case when item sizes are so-called ""hypercontractive"" random
variables i.e., random variables whose second and fourth moments are within
constant factors of each other. In other words, the kurtosis of the random
variable is upper bounded by a constant.
Crucially, all of our algorithms meet the capacity constraint exactly, a
result which was previously known only when the item sizes were Poisson or
Gaussian random variables. Our results rely on new connections between Boolean
function analysis and stochastic optimization. We believe that these ideas and
techniques may prove to be useful in other stochastic optimization problems as
well.
",1,0,0,0,0,0
17127,Nonparanormal Information Estimation,"  We study the problem of using i.i.d. samples from an unknown multivariate
probability distribution $p$ to estimate the mutual information of $p$. This
problem has recently received attention in two settings: (1) where $p$ is
assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large
nonparametric smoothness class. Estimators proposed for the Gaussian case
converge in high dimensions when the Gaussian assumption holds, but are
brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for
the nonparametric case fail to converge with realistic sample sizes except in
very low dimensions. As a result, there is a lack of robust mutual information
estimators for many realistic data. To address this, we propose estimators for
mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian
copula) model, a semiparametric compromise between Gaussian and nonparametric
extremes. Using theoretical bounds and experiments, we show these estimators
strike a practical balance between robustness and scaling with dimensionality.
",1,0,1,1,0,0
17128,Controlling a population,"  We introduce a new setting where a population of agents, each modelled by a
finite-state system, are controlled uniformly: the controller applies the same
action to every agent. The framework is largely inspired by the control of a
biological system, namely a population of yeasts, where the controller may only
change the environment common to all cells. We study a synchronisation problem
for such populations: no matter how individual agents react to the actions of
the controller, the controller aims at driving all agents synchronously to a
target state. The agents are naturally represented by a non-deterministic
finite state automaton (NFA), the same for every agent, and the whole system is
encoded as a 2-player game. The first player (Controller) chooses actions, and
the second player (Agents) resolves non-determinism for each agent. The game
with m agents is called the m -population game. This gives rise to a
parameterized control problem (where control refers to 2 player games), namely
the population control problem: can Controller control the m-population game
for all m in N whatever Agents does?
",1,0,0,0,0,0
17129,Finite scale local Lyapunov exponents distribution in fully developed homogeneous isotropic turbulence,"  The present work analyzes the distribution function of the finite scale local
Lyapunov exponent of a pair fluid particles trajectories in fully developed
incompressible homogeneous isotropic turbulence. According to the hypothesis of
fully developed chaos, this PDF is reasonably estimated by maximizing the
entropy associated to such distribution, resulting to be an uniform
distribution function in a proper interval of variation of the local Lyapunov
exponents. From this PDF, we determine the relationship between the average and
maximum Lyapunov exponents and the longitudinal velocity correlation function.
This link, which leads to the closure of von Kàrmàn--Howarth and Corrsin
equations, agrees with the relation obtained in the previous work, supporting
the proposed PDF calculation, at least for the purposes of the energy cascade
effect estimation. Furthermore, through the property that the Lyapunov vectors
tend to align to the direction of the maximum growth rate of trajectories
distance, we obtain the link between maximum and average Lyapunov exponents in
line with the previous result.
",0,1,0,0,0,0
17130,Improved Power Decoding of One-Point Hermitian Codes,"  We propose a new partial decoding algorithm for one-point Hermitian codes
that can decode up to the same number of errors as the Guruswami--Sudan
decoder. Simulations suggest that it has a similar failure probability as the
latter one. The algorithm is based on a recent generalization of the power
decoding algorithm for Reed--Solomon codes and does not require an expensive
root-finding step. In addition, it promises improvements for decoding
interleaved Hermitian codes.
",1,0,0,0,0,0
17131,Finite numbers of initial ideals in non-Noetherian polynomial rings,"  In this article, we generalize the well-known result that ideals of
Noetherian polynomial rings have only finitely many initial ideals to the
situation of ascending ideal chains in non-Noetherian polynomial rings. More
precisely, we study ideal chains in the polynomial ring $R=K[x_{i,j}\,|\,1\leq
i\leq c,j\in N]$ that are invariant under the action of the monoid $Inc(N)$ of
strictly increasing functions on $N$, which acts on $R$ by shifting the second
variable index. We show that for every such ideal chain, the number of initial
ideal chains with respect to term orders on $R$ that are compatible with the
action of $Inc(N)$ is finite. As a consequence of this, we will see that
$Inc(N)$-invariant ideals of $R$ have only finitely many initial ideals with
respect to $Inc(N)$-compatible term orders. The article also addresses the
question of how many such term orders exist. We give a complete list of the
$Inc(N)$-compatible term orders for the case $c=1$ and show that there are
infinitely many for $c >1$. This answers a question by Hillar, Kroner, Leykin.
",0,0,1,0,0,0
17132,The Diederich-Fornaess Index and Good Vector Fields,"  We consider the relationship between two sufficient conditions for regularity
of the Bergman Projection on smooth, bounded, pseudoconvex domains. We show
that if the set of infinite type points is reasonably well-behaved, then the
existence of a family of good vector fields in the sense of Boas and Straube
implies that the Diederich-Fornaess Index of the domain is equal to one.
",0,0,1,0,0,0
17133,Complete Minors of Self-Complementary Graphs,"  We show that any self-complementary graph with $n$ vertices contains a
$K_{\lfloor \frac{n+1}{2}\rfloor}$ minor. We derive topological properties of
self-complementary graphs.
",0,0,1,0,0,0
17134,Statistical estimation of the Oscillating Brownian Motion,"  We study the asymptotic behavior of estimators of a two-valued, discontinuous
diffusion coefficient in a Stochastic Differential Equation, called an
Oscillating Brownian Motion. Using the relation of the latter process with the
Skew Brownian Motion, we propose two natural consistent estimators, which are
variants of the integrated volatility estimator and take the occupation times
into account. We show the stable convergence of the renormalized errors'
estimations toward some Gaussian mixture, possibly corrected by a term that
depends on the local time. These limits stem from the lack of ergodicity as
well as the behavior of the local time at zero of the process. We test both
estimators on simulated processes, finding a complete agreement with the
theoretical predictions.
",0,0,1,1,0,0
17135,Competing Ferromagnetic and Anti-Ferromagnetic interactions in Iron Nitride $ζ$-Fe$_2$N,"  The paper discusses the magnetic state of zeta phase of iron nitride viz.
$\zeta$-Fe$_2$N on the basis of spin polarized first principles electronic
structure calculations together with a review of already published data.
Results of our first principles study suggest that the ground state of
$\zeta$-Fe$_2$N is ferromagnetic (FM) with a magnetic moment of 1.528
$\mu_\text{B}$ on the Fe site. The FM ground state is lower than the
anti-ferromagnetic (AFM) state by 8.44 meV and non-magnetic(NM) state by 191
meV per formula unit. These results are important in view of reports which
claim that $\zeta$-Fe$_2$N undergoes an AFM transition below 10K and others
which do not observe any magnetic transition up to 4.2K. We argue that the
experimental results of AFM transition below 10K are inconclusive and we
propose the presence of competing FM and AFM superexchange interactions between
Fe sites mediated by nitrogen atoms, which are consistent with
Goodenough-Kanamori-Anderson rules. We find that the anti-ferromagnetically
coupled Fe sites are outnumbered by ferromagnetically coupled Fe sites leading
to a stable FM ground state. A Stoner analysis of the results also supports our
claim of a FM ground state.
",0,1,0,0,0,0
17136,A cautionary tale: limitations of a brightness-based spectroscopic approach to chromatic exoplanet radii,"  Determining wavelength-dependent exoplanet radii measurements is an excellent
way to probe the composition of exoplanet atmospheres. In light of this, Borsa
et al. (2016) sought to develop a technique to obtain such measurements by
comparing ground-based transmission spectra to the expected brightness
variations during an exoplanet transit. However, we demonstrate herein that
this is not possible due to the transit light curve normalisation necessary to
remove the effects of the Earth's atmosphere on the ground-based observations.
This is because the recoverable exoplanet radius is set by the planet-to-star
radius ratio within the transit light curve; we demonstrate this both
analytically and with simulated planet transits, as well as through a
reanalysis of the HD 189733b data.
",0,1,0,0,0,0
17137,Generating Memorable Mnemonic Encodings of Numbers,"  The major system is a mnemonic system that can be used to memorize sequences
of numbers. In this work, we present a method to automatically generate
sentences that encode a given number. We propose several encoding models and
compare the most promising ones in a password memorability study. The results
of the study show that a model combining part-of-speech sentence templates with
an $n$-gram language model produces the most memorable password
representations.
",1,0,0,0,0,0
17138,De-excitation spectroscopy of strongly interacting Rydberg gases,"  We present experimental results on the controlled de-excitation of Rydberg
states in a cold gas of Rb atoms. The effect of the van der Waals interactions
between the Rydberg atoms is clearly seen in the de-excitation spectrum and
dynamics. Our observations are confirmed by numerical simulations. In
particular, for off-resonant (facilitated) excitation we find that the
de-excitation spectrum reflects the spatial arrangement of the atoms in the
quasi one-dimensional geometry of our experiment. We discuss future
applications of this technique and implications for detection and controlled
dissipation schemes.
",0,1,0,0,0,0
17139,Hyperplane Clustering Via Dual Principal Component Pursuit,"  We extend the theoretical analysis of a recently proposed single subspace
learning algorithm, called Dual Principal Component Pursuit (DPCP), to the case
where the data are drawn from of a union of hyperplanes. To gain insight into
the properties of the $\ell_1$ non-convex problem associated with DPCP, we
develop a geometric analysis of a closely related continuous optimization
problem. Then transferring this analysis to the discrete problem, our results
state that as long as the hyperplanes are sufficiently separated, the dominant
hyperplane is sufficiently dominant and the points are uniformly distributed
inside the associated hyperplanes, then the non-convex DPCP problem has a
unique global solution, equal to the normal vector of the dominant hyperplane.
This suggests the correctness of a sequential hyperplane learning algorithm
based on DPCP. A thorough experimental evaluation reveals that hyperplane
learning schemes based on DPCP dramatically improve over the state-of-the-art
methods for the case of synthetic data, while are competitive to the
state-of-the-art in the case of 3D plane clustering for Kinect data.
",1,0,0,1,0,0
17140,Utilizing Domain Knowledge in End-to-End Audio Processing,"  End-to-end neural network based approaches to audio modelling are generally
outperformed by models trained on high-level data representations. In this
paper we present preliminary work that shows the feasibility of training the
first layers of a deep convolutional neural network (CNN) model to learn the
commonly-used log-scaled mel-spectrogram transformation. Secondly, we
demonstrate that upon initializing the first layers of an end-to-end CNN
classifier with the learned transformation, convergence and performance on the
ESC-50 environmental sound classification dataset are similar to a CNN-based
model trained on the highly pre-processed log-scaled mel-spectrogram features.
",1,0,0,1,0,0
17141,Testing Microfluidic Fully Programmable Valve Arrays (FPVAs),"  Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
the next-generation flow-based microfluidic biochips. This 2D-array consists of
regularly-arranged valves, which can be dynamically configured by users to
realize microfluidic devices of different shapes and sizes as well as
interconnections. Additionally, the regularity of the underlying structure
renders FPVAs easier to integrate on a tiny chip. However, these arrays may
suffer from various manufacturing defects such as blockage and leakage in
control and flow channels. Unfortunately, no efficient method is yet known for
testing such a general-purpose architecture. In this paper, we present a novel
formulation using the concept of flow paths and cut-sets, and describe an
ILP-based hierarchical strategy for generating compact test sets that can
detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
the proposed method in detecting manufacturing faults with only a small number
of test vectors.
",1,0,0,0,0,0
17142,"Fundamental groups, slalom curves and extremal length","  We define the extremal length of elements of the fundamental group of the
twice punctured complex plane and give upper and lower bounds for this
invariant. The bounds differ by a multiplicative constant. The main motivation
comes from $3$-braid invariants and their application.
",0,0,1,0,0,0
17143,Topology and stability of the Kondo phase in quark matter,"  We investigate properties of the ground state of a light quark matter with
heavy quark impurities. This system exhibits the ""QCD Kondo effect"" where the
interaction strength between a light quark near the Fermi surface and a heavy
quark increases with decreasing energy of the light quark towards the Fermi
energy, and diverges at some scale near the Fermi energy, called the Kondo
scale. Around and below the Kondo scale, we must treat the dynamics
nonperturbatively. As a typical nonperturbative method to treat the strong
coupling regime, we adopt a mean-field approach where we introduce a
condensate, the Kondo condensate, representing a mixing between a light quark
and a heavy quark, and determine the ground state in the presence of the Kondo
condensate. We show that the ground state is a topologically non-trivial state
and the heavy quark spin forms the hedgehog configuration in the momentum
space. We can define the Berry phase for the ground-state wavefunction in the
momentum space which is associated with a monopole at the position of a heavy
quark. We also investigate fluctuations around the mean field in the
random-phase approximation, and show the existence of (exciton-like) collective
excitations made of a hole $h$ of a light quark and a heavy quark $Q$.
",0,1,0,0,0,0
17144,Second order nonlinear gyrokinetic theory : From the particle to the gyrocenter,"  A gyrokinetic reduction is based on a specific ordering of the different
small parameters characterizing the background magnetic field and the
fluctuating electromagnetic fields. In this tutorial, we consider the following
ordering of the small parameters: $\epsilon\_B=\epsilon\_\delta^2$ where
$\epsilon\_B$ is the small parameter associated with spatial inhomogeneities of
the background magnetic field and $\epsilon\_\delta$ characterizes the small
amplitude of the fluctuating fields. In particular, we do not make any
assumption on the amplitude of the background magnetic field. Given this choice
of ordering, we describe a self-contained and systematic derivation which is
particularly well suited for the gyrokinetic reduction, following a two-step
procedure. We follow the approach developed in [Sugama, Physics of Plasmas 7,
466 (2000)]:In a first step, using a translation in velocity, we embed the
transformation performed on the symplectic part of the gyrocentre reduction in
the guiding-centre one. In a second step, using a canonical Lie transform, we
eliminate the gyroangle dependence from the Hamiltonian. As a consequence, we
explicitly derive the fully electromagnetic gyrokinetic equations at the second
order in $\epsilon\_\delta$.
",0,1,0,0,0,0
17145,On the Classification and Algorithmic Analysis of Carmichael Numbers,"  In this paper, we study the properties of Carmichael numbers, false positives
to several primality tests. We provide a classification for Carmichael numbers
with a proportion of Fermat witnesses of less than 50%, based on if the
smallest prime factor is greater than a determined lower bound. In addition, we
conduct a Monte Carlo simulation as part of a probabilistic algorithm to detect
if a given composite number is Carmichael. We modify this highly accurate
algorithm with a deterministic primality test to create a novel, more efficient
algorithm that differentiates between Carmichael numbers and prime numbers.
",0,0,1,0,0,0
17146,Phase reduction and synchronization of a network of coupled dynamical elements exhibiting collective oscillations,"  A general phase reduction method for a network of coupled dynamical elements
exhibiting collective oscillations, which is applicable to arbitrary networks
of heterogeneous dynamical elements, is developed. A set of coupled adjoint
equations for phase sensitivity functions, which characterize phase response of
the collective oscillation to small perturbations applied to individual
elements, is derived. Using the phase sensitivity functions, collective
oscillation of the network under weak perturbation can be described
approximately by a one-dimensional phase equation. As an example, mutual
synchronization between a pair of collectively oscillating networks of
excitable and oscillatory FitzHugh-Nagumo elements with random coupling is
studied.
",0,1,0,0,0,0
17147,Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs,"  Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, that can be used to induce new edges in the graph, i.e., link
prediction in knowledge graphs. Learning these representations relies on
contrasting positive instances with negative ones. Knowledge graphs include
only positive relation instances, leaving the door open for a variety of
methods for selecting negative examples. In this paper we present an empirical
study on the impact of negative sampling on the learned embeddings, assessed
through the task of link prediction. We use state-of-the-art knowledge graph
embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
benchmark datasets -- FB15k and WN18. We compare well known methods for
negative sampling and additionally propose embedding based sampling methods. We
note a marked difference in the impact of these sampling methods on the two
datasets, with the ""traditional"" corrupting positives method leading to best
results on WN18, while embedding based methods benefiting the task on FB15k.
",1,0,0,0,0,0
17148,Reverse Curriculum Generation for Reinforcement Learning,"  Many relevant tasks require an agent to reach a certain state, or to
manipulate objects into a desired configuration. For example, we might want a
robot to align and assemble a gear onto an axle or insert and turn a key in a
lock. These goal-oriented tasks present a considerable challenge for
reinforcement learning, since their natural reward function is sparse and
prohibitive amounts of exploration are required to reach the goal and receive
some learning signal. Past approaches tackle these problems by exploiting
expert demonstrations or by manually designing a task-specific reward shaping
function to guide the learning agent. Instead, we propose a method to learn
these tasks without requiring any prior knowledge other than obtaining a single
state in which the task is achieved. The robot is trained in reverse, gradually
learning to reach the goal from a set of start states increasingly far from the
goal. Our method automatically generates a curriculum of start states that
adapts to the agent's performance, leading to efficient training on
goal-oriented tasks. We demonstrate our approach on difficult simulated
navigation and fine-grained manipulation problems, not solvable by
state-of-the-art reinforcement learning methods.
",1,0,0,0,0,0
17149,Mutually touching infinite cylinders in the 3D world of lines,"  Recently we gave arguments that only two unique topologically different
configurations of 7 equal all mutually touching round cylinders (the
configurations being mirror reflections of each other) are possible in 3D,
although a whole world of configurations is possible already for round
cylinders of arbitrary radii. It was found that as many as 9 round cylinders
(all mutually touching) are possible in 3D while the upper bound for arbitrary
cylinders was estimated to be not more than 14 under plausible arguments. Now
by using the chirality and Ring matrices that we introduced earlier for the
topological classification of line configurations, we have given arguments that
the maximal number of mutually touching straight infinite cylinders of
arbitrary cross-section (provided that its boundary is a smooth curve) in 3D
cannot exceed 10. We generated numerically several configurations of 10
cylinders, restricting ourselves with elliptic cylinders. Configurations of 8
and 9 equal elliptic cylinders (all in mutually touching) are generated
numerically as well. A possibility and restriction of continuous
transformations from elliptic into round cylinder configurations are discussed.
Some curious results concerning the properties of the chirality matrix (which
coincides with Seidel's adjacency matrix important for the Graph theory) are
presented.
",0,0,1,0,0,0
17150,Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields,"  This paper is a tutorial on Formal Concept Analysis (FCA) and its
applications. FCA is an applied branch of Lattice Theory, a mathematical
discipline which enables formalisation of concepts as basic units of human
thinking and analysing data in the object-attribute form. Originated in early
80s, during the last three decades, it became a popular human-centred tool for
knowledge representation and data analysis with numerous applications. Since
the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics
include Information Retrieval with a focus on visualisation aspects, Machine
Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
",1,0,0,1,0,0
17151,Fully stripped? The dynamics of dark and luminous matter in the massive cluster collision MACSJ0553.4$-$3342,"  We present the results of a multiwavelength investigation of the very X-ray
luminous galaxy cluster MACSJ0553.4-3342 ($z = 0.4270$; hereafter MACSJ0553).
Combining high-resolution data obtained with the Hubble Space Telescope and the
Chandra X-ray Observatory with ground-based galaxy spectroscopy, our analysis
establishes the system unambiguously as a binary, post-collision merger of
massive clusters. Key characteristics include perfect alignment of luminous and
dark matter for one component, a separation of almost 650 kpc (in projection)
between the dark-matter peak of the other subcluster and the second X-ray peak,
extremely hot gas (k$T > 15$ keV) at either end of the merger axis, a potential
cold front in the east, an unusually low gas mass fraction of approximately
0.075 for the western component, a velocity dispersion of $1490_{-130}^{+104}$
km s$^{-1}$, and no indication of significant substructure along the line of
sight. We propose that the MACSJ0553 merger proceeds not in the plane of the
sky, but at a large inclination angle, is observed very close to turnaround,
and that the eastern X-ray peak is the cool core of the slightly less massive
western component that was fully stripped and captured by the eastern
subcluster during the collision. If correct, this hypothesis would make
MACSJ0553 a superb target for a competitive study of ram-pressure stripping and
the collisional behaviour of luminous and dark matter during cluster formation.
",0,1,0,0,0,0
17152,When intuition fails in assessing conditional risks: the example of the frog riddle,"  Recently, the educational initiative TED-Ed has published a popular brain
teaser coined the 'frog riddle', which illustrates non-intuitive implications
of conditional probabilities. In its intended form, the frog riddle is a
reformulation of the classic boy-girl paradox. However, the authors alter the
narrative of the riddle in a form, that subtly changes the way information is
conveyed. The presented solution, unfortunately, does not take this point into
full account, and as a consequence, lacks consistency in the sense that
different parts of the problem are treated on unequal footing. We here review,
how the mechanism of receiving information matters, and why this is exactly the
reason that such kind of problems challenge intuitive thinking. Subsequently,
we present a generalized solution, that accounts for the above difficulties,
and preserves full logical consistency. Eventually, the relation to the
boy-girl paradox is discussed.
",0,1,0,0,0,0
17153,Tuning parameter selection rules for nuclear norm regularized multivariate linear regression,"  We consider the tuning parameter selection rules for nuclear norm regularized
multivariate linear regression (NMLR) in high-dimensional setting.
High-dimensional multivariate linear regression is widely used in statistics
and machine learning, and regularization technique is commonly applied to deal
with the special structures in high-dimensional data. As we know, how to select
the tuning parameter is an essential issue for regularization approach and it
directly affects the model estimation performance. To the best of our
knowledge, there are no rules about the tuning parameter selection for NMLR
from the point of view of optimization. In order to establish such rules, we
study the duality theory of NMLR. Then, we claim the choice of tuning parameter
for NMLR is based on the sample data and the solution of NMLR dual problem,
which is a projection on a nonempty, closed and convex set. Moreover, based on
the (firm) nonexpansiveness and the idempotence of the projection operator, we
build four tuning parameter selection rules PSR, PSRi, PSRfn and PSR+.
Furthermore, we give a sequence of tuning parameters and the corresponding
intervals for every rule, which states that the rank of the estimation
coefficient matrix is no more than a fixed number for the tuning parameter in
the given interval. The relationships between these rules are also discussed
and PSR+ is the most efficient one to select the tuning parameter. Finally, the
numerical results are reported on simulation and real data, which show that
these four tuning parameter selection rules are valuable.
",0,0,1,1,0,0
17154,Understanding the evolution of multimedia content in the Internet through BitTorrent glasses,"  Today's Internet traffic is mostly dominated by multimedia content and the
prediction is that this trend will intensify in the future. Therefore, main
Internet players, such as ISPs, content delivery platforms (e.g. Youtube,
Bitorrent, Netflix, etc) or CDN operators, need to understand the evolution of
multimedia content availability and popularity in order to adapt their
infrastructures and resources to satisfy clients requirements while they
minimize their costs. This paper presents a thorough analysis on the evolution
of multimedia content available in BitTorrent. Specifically, we analyze the
evolution of four relevant metrics across different content categories: content
availability, content popularity, content size and user's feedback. To this end
we leverage a large-scale dataset formed by 4 snapshots collected from the most
popular BitTorrent portal, namely The Pirate Bay, between Nov. 2009 and Feb.
2012. Overall our dataset is formed by more than 160k content that attracted
more than 185M of download sessions.
",1,0,0,0,0,0
17155,The weak rate of convergence for the Euler-Maruyama approximation of one-dimensional stochastic differential equations involving the local times of the unknown process,"  In this paper, we consider the weak convergence of the Euler-Maruyama
approximation for one dimensional stochastic differential equations involving
the local times of the unknown process. We use a transformation in order to
remove the local time from the stochastic differential equations and we provide
the approximation of Euler-maruyama for the stochastic differential equations
without local time. After that, we conclude the approximation of Euler-maruyama
for one dimensional stochastic differential equations involving the local times
of the unknown process , and we provide the rate of weak convergence for any
function G in a certain class.
",0,0,1,0,0,0
17156,Study of deteriorating semiopaque turquoise lead-potassium glass beads at different stages of corrosion using micro-FTIR spectroscopy,"  Nowadays, a problem of historical beadworks conservation in museum
collections is actual more than ever because of fatal corrosion of the 19th
century glass beads. Vibrational spectroscopy is a powerful method for
investigation of glass, namely, of correlation of the structure-chemical
composition. Therefore, Fourier-transform infrared spectroscopy was used for
examination of degradation processes in cloudy turquoise glass beads, which in
contrast to other color ones deteriorate especially strongly. Micro-X-ray
fluorescence spectrometry of samples has shown that lead-potassium glass
PbO-K$_2$O-SiO$_2$ with small amount of Cu and Sb was used for manufacture of
cloudy turquoise beads. Fourier-transform infrared spectroscopy study of the
beads at different stages of glass corrosion was carried out in the range from
200 to 4000 cm$^{-1}$ in the attenuated total reflection mode. In all the
spectra, we have observed shifts of two major absorption bands to low-frequency
range (~1000 and ~775 cm$^{-1}$) compared to ones typical for amorphous SiO2
(~1100 and 800 cm$^{-1}$, respectively). Such an effect is connected with
Pb$^{2+}$ and K$^+$ appending to the glass network. The presence of a weak band
at ~1630 cm$^{-1}$ in all the spectra is attributed to the adsorption of
H$_2$O. After annealing of the beads, the band disappeared completely in less
deteriorated samples and became significantly weaker in more destroyed ones.
Based on that we conclude that there is adsorbed molecular water on the beads.
However, products of corrosion (e.g., alkali in the form of white crystals or
droplets of liquid alkali) were not observed on the glass surface. We have also
observed glass depolymerisation in the strongly degraded beads, which is
exhibited in domination of the band peaked at ~1000 cm$^{-1}$.
",0,1,0,0,0,0
17157,Stratified surgery and K-theory invariants of the signature operator,"  In work of Higson-Roe the fundamental role of the signature as a homotopy and
bordism invariant for oriented manifolds is made manifest in how it and related
secondary invariants define a natural transformation between the
(Browder-Novikov-Sullivan-Wall) surgery exact sequence and a long exact
sequence of C*-algebra K-theory groups.
In recent years the (higher) signature invariants have been extended from
closed oriented manifolds to a class of stratified spaces known as L-spaces or
Cheeger spaces. In this paper we show that secondary invariants, such as the
rho-class, also extend from closed manifolds to Cheeger spaces. We revisit a
surgery exact sequence for stratified spaces originally introduced by
Browder-Quinn and obtain a natural transformation analogous to that of
Higson-Roe. We also discuss geometric applications.
",0,0,1,0,0,0
17158,Weighted Tensor Decomposition for Learning Latent Variables with Partial Data,"  Tensor decomposition methods are popular tools for learning latent variables
given only lower-order moments of the data. However, the standard assumption is
that we have sufficient data to estimate these moments to high accuracy. In
this work, we consider the case in which certain dimensions of the data are not
always observed---common in applied settings, where not all measurements may be
taken for all observations---resulting in moment estimates of varying quality.
We derive a weighted tensor decomposition approach that is computationally as
efficient as the non-weighted approach, and demonstrate that it outperforms
methods that do not appropriately leverage these less-observed dimensions.
",0,0,0,1,0,0
17159,Sparse Bounds for Discrete Quadratic Phase Hilbert Transform,"  Consider the discrete quadratic phase Hilbert Transform acting on $\ell^{2}$
finitely supported functions $$ H^{\alpha} f(n) : = \sum_{m \neq 0} \frac{e^{2
\pi i\alpha m^2} f(n - m)}{m}. $$ We prove that, uniformly in $\alpha \in
\mathbb{T}$, there is a sparse bound for the bilinear form $\langle H^{\alpha}
f , g \rangle$. The sparse bound implies several mapping properties such as
weighted inequalities in an intersection of Muckenhoupt and reverse Hölder
classes.
",0,0,1,0,0,0
17160,Fast Distributed Approximation for TAP and 2-Edge-Connectivity,"  The tree augmentation problem (TAP) is a fundamental network design problem,
in which the input is a graph $G$ and a spanning tree $T$ for it, and the goal
is to augment $T$ with a minimum set of edges $Aug$ from $G$, such that $T \cup
Aug$ is 2-edge-connected.
TAP has been widely studied in the sequential setting. The best known
approximation ratio of 2 for the weighted case dates back to the work of
Frederickson and JáJá, SICOMP 1981. Recently, a 3/2-approximation was
given for the unweighted case by Kortsarz and Nutov, TALG 2016, and recent
breakthroughs by Adjiashvili, SODA 2017, and by Fiorini et al., 2017, give
approximations better than 2 for bounded weights.
In this paper, we provide the first fast distributed approximations for TAP.
We present a distributed $2$-approximation for weighted TAP which completes in
$O(h)$ rounds, where $h$ is the height of $T$. When $h$ is large, we show a
much faster 4-approximation algorithm for the unweighted case, completing in
$O(D+\sqrt{n}\log^*{n})$ rounds, where $n$ is the number of vertices and $D$ is
the diameter of $G$.
Immediate consequences of our results are an $O(D)$-round 2-approximation
algorithm for the minimum size 2-edge-connected spanning subgraph, which
significantly improves upon the running time of previous approximation
algorithms, and an $O(h_{MST}+\sqrt{n}\log^{*}{n})$-round 3-approximation
algorithm for the weighted case, where $h_{MST}$ is the height of the MST of
the graph. Additional applications are algorithms for verifying
2-edge-connectivity and for augmenting the connectivity of any connected
spanning subgraph to 2.
Finally, we complement our study with proving lower bounds for distributed
approximations of TAP.
",1,0,0,0,0,0
17161,Generative Adversarial Source Separation,"  Generative source separation methods such as non-negative matrix
factorization (NMF) or auto-encoders, rely on the assumption of an output
probability density. Generative Adversarial Networks (GANs) can learn data
distributions without needing a parametric assumption on the output density. We
show on a speech source separation experiment that, a multi-layer perceptron
trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders
trained with maximum likelihood, and variational auto-encoders in terms of
source to distortion ratio.
",1,0,0,1,0,0
17162,Bootstrapping Generalization Error Bounds for Time Series,"  We consider the problem of finding confidence intervals for the risk of
forecasting the future of a stationary, ergodic stochastic process, using a
model estimated from the past of the process. We show that a bootstrap
procedure provides valid confidence intervals for the risk, when the data
source is sufficiently mixing, and the loss function and the estimator are
suitably smooth. Autoregressive (AR(d)) models estimated by least squares obey
the necessary regularity conditions, even when mis-specified, and simulations
show that the finite- sample coverage of our bounds quickly converges to the
theoretical, asymptotic level. As an intermediate step, we derive sufficient
conditions for asymptotic independence between empirical distribution functions
formed by splitting a realization of a stochastic process, of independent
interest.
",0,0,1,1,0,0
17163,Sign reversal of magnetoresistance and p to n transition in Ni doped ZnO thin film,"  We report the magnetoresistance and nonlinear Hall effect studies over a wide
temperature range in pulsed laser deposited Ni0.07Zn0.93O thin film. Negative
and positive contributions to magnetoresistance at high and low temperatures
have been successfully modeled by the localized magnetic moment and two band
conduction process involving heavy and light hole subbands, respectively.
Nonlinearity in the Hall resistance also agrees well with the two channel
conduction model. A negative Hall voltage has been found for T $\gte 50 K$,
implying a dominant conduction mainly by electrons whereas positive Hall
voltage for T less than 50 K shows hole dominated conduction in this material.
Crossover in the sign of magnetoresistance from negative to positive reveals
the spin polarization of the charge carriers and hence the applicability of Ni
doped ZnO thin film for spintronic applications.
",0,1,0,0,0,0
17164,Proceedings of the Third Workshop on Formal Integrated Development Environment,"  This volume contains the proceedings of F-IDE 2016, the third international
workshop on Formal Integrated Development Environment, which was held as an FM
2016 satellite event, on November 8, 2016, in Limassol (Cyprus). High levels of
safety, security and also privacy standards require the use of formal methods
to specify and develop compliant software (sub)systems. Any standard comes with
an assessment process, which requires a complete documentation of the
application in order to ease the justification of design choices and the review
of code and proofs. Thus tools are needed for handling specifications, program
constructs and verification artifacts. The aim of the F-IDE workshop is to
provide a forum for presenting and discussing research efforts as well as
experience returns on design, development and usage of formal IDE aiming at
making formal methods ""easier"" for both specialists and non-specialists.
",1,0,0,0,0,0
17165,A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment,"  Animals (especially humans) have an amazing ability to learn new tasks
quickly, and switch between them flexibly. How brains support this ability is
largely unknown, both neuroscientifically and algorithmically. One reasonable
supposition is that modules drawing on an underlying general-purpose sensory
representation are dynamically allocated on a per-task basis. Recent results
from neuroscience and artificial intelligence suggest the role of the general
purpose visual representation may be played by a deep convolutional neural
network, and give some clues how task modules based on such a representation
might be discovered and constructed. In this work, we investigate module
architectures in an embodied two-dimensional touchscreen environment, in which
an agent's learning must occur via interactions with an environment that emits
images and rewards, and accepts touches as input. This environment is designed
to capture the physical structure of the task environments that are commonly
deployed in visual neuroscience and psychophysics. We show that in this
context, very simple changes in the nonlinear activations used by such a module
can significantly influence how fast it is at learning visual tasks and how
suitable it is for switching to new tasks.
",1,0,0,1,0,0
17166,3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation,"  In this paper, we present a new approach to visual servoing for robotics,
referred to as 3D Move to See (3DMTS), based on the principle of finding the
next best view using a 3D camera array and a robotic manipulator to obtain
multiple samples of the scene from different perspectives. The method uses
semantic vision and an objective function applied to each perspective to sample
a gradient representing the direction of the next best view. The method is
demonstrated within simulation and on a real robotic platform containing a
custom 3D camera array for the challenging scenario of robotic harvesting in a
highly occluded and unstructured environment. It was shown on a real robotic
platform that by moving the end effector using the gradient of an objective
function leads to a locally optimal view of the object of interest, even
amongst occlusions. The overall performance of the 3DMTS method obtained a mean
increase in target size by 29.3% compared to a baseline method using a single
RGB-D camera, which obtained 9.17%. The results demonstrate qualitatively and
quantitatively that the 3DMTS method performed better in most scenarios, and
yielded three times the target size compared to the baseline method. The
increased target size in the final view will improve the detection of key
features of the object of interest for further manipulation, such as grasping
and harvesting.
",1,0,0,0,0,0
17167,Modeling influenza-like illnesses through composite compartmental models,"  Epidemiological models for the spread of pathogens in a population are
usually only able to describe a single pathogen. This makes their application
unrealistic in cases where multiple pathogens with similar symptoms are
spreading concurrently within the same population. Here we describe a method
which makes possible the application of multiple single-strain models under
minimal conditions. As such, our method provides a bridge between theoretical
models of epidemiology and data-driven approaches for modeling of influenza and
other similar viruses.
Our model extends the Susceptible-Infected-Recovered model to higher
dimensions, allowing the modeling of a population infected by multiple viruses.
We further provide a method, based on an overcomplete dictionary of feasible
realizations of SIR solutions, to blindly partition the time series
representing the number of infected people in a population into individual
components, each representing the effect of a single pathogen.
We demonstrate the applicability of our proposed method on five years of
seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We
demonstrate that our method describes, on average, 44\% of the variance in the
ILI time series. The individual infectious components derived from our model
are matched to known viral profiles in the populations, which we demonstrate
matches that of independently collected epidemiological data. We further show
that the basic reproductive numbers ($R0$) of the matched components are in
range known for these pathogens.
Our results suggest that the proposed method can be applied to other
pathogens and geographies, providing a simple method for estimating the
parameters of epidemics in a population.
",1,1,0,0,0,0
17168,Notes on the Polish Algorithm,"  We study, with the help of a computer program, the Polish Algorithm for
finite terms satisfying various algebraic laws, e.g., left distributivity a(bc)
= (ab)(ac). While the termination of the algorithm for left distributivity
remains open in general, we can establish some partial results, which might be
useful towards a positive solution. In contrast, we show the divergence of the
algorithm for the laws a(bc) = (ab)(cc) and a(bc) = (ab)(a(ac)).
",0,0,1,0,0,0
17169,Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection,"  We introduce a new class of mean regression estimators -- penalized maximum
tangent likelihood estimation -- for high-dimensional regression estimation and
variable selection. We first explain the motivations for the key ingredient,
maximum tangent likelihood estimation (MTE), and establish its asymptotic
properties. We further propose a penalized MTE for variable selection and show
that it is $\sqrt{n}$-consistent, enjoys the oracle property. The proposed
class of estimators consists penalized $\ell_2$ distance, penalized exponential
squared loss, penalized least trimmed square and penalized least square as
special cases and can be regarded as a mixture of minimum Kullback-Leibler
distance estimation and minimum $\ell_2$ distance estimation. Furthermore, we
consider the proposed class of estimators under the high-dimensional setting
when the number of variables $d$ can grow exponentially with the sample size
$n$, and show that the entire class of estimators (including the aforementioned
special cases) can achieve the optimal rate of convergence in the order of
$\sqrt{\ln(d)/n}$. Finally, simulation studies and real data analysis
demonstrate the advantages of the penalized MTE.
",0,0,0,1,0,0
17170,A minimally-dissipative low-Mach number solver for complex reacting flows in OpenFOAM,"  Large eddy simulation (LES) has become the de-facto computational tool for
modeling complex reacting flows, especially in gas turbine applications.
However, readily usable general-purpose LES codes for complex geometries are
typically academic or proprietary/commercial in nature. The objective of this
work is to develop and disseminate an open source LES tool for low-Mach number
turbulent combustion using the OpenFOAM framework. In particular, a
collocated-mesh approach suited for unstructured grid formulation is provided.
Unlike other fluid dynamics models, LES accuracy is intricately linked to
so-called primary and secondary conservation properties of the numerical
discretization schemes. This implies that although the solver only evolves
equations for mass, momentum, and energy, the implied discrete equation for
kinetic energy (square of velocity) should be minimally-dissipative. Here, a
specific spatial and temporal discretization is imposed such that this kinetic
energy dissipation is minimized. The method is demonstrated using manufactured
solutions approach on regular and skewed meshes, a canonical flow problem, and
a turbulent sooting flame in a complex domain relevant to gas turbines
applications.
",0,1,0,0,0,0
17171,Surface energy of strained amorphous solids,"  Surface stress and surface energy are fundamental quantities which
characterize the interface between two materials. Although these quantities are
identical for interfaces involving only fluids, the Shuttleworth effect
demonstrates that this is not the case for most interfaces involving solids,
since their surface energies change with strain. Crystalline materials are
known to have strain dependent surface energies, but in amorphous materials,
such as polymeric glasses and elastomers, the strain dependence is debated due
to a dearth of direct measurements. Here, we utilize contact angle measurements
on strained glassy and elastomeric solids to address this matter. We show
conclusively that interfaces involving polymeric glasses exhibit strain
dependent surface energies, and give strong evidence for the absence of such a
dependence for incompressible elastomers. The results provide fundamental
insight into our understanding of the interfaces of amorphous solids and their
interaction with contacting liquids.
",0,1,0,0,0,0
17172,Biaxial magnetic field setup for angular magnetic measurements of thin films and spintronic nanodevices,"  The biaxial magnetic-field setup for angular magnetic measurements of thin
film and spintronic devices is designed and presented. The setup allows for
application of the in-plane magnetic field using a quadrupole electromagnet,
controlled by power supply units and integrated with an electromagnet biaxial
magnetic field sensor. In addition, the probe station is equipped with a
microwave circuitry, which enables angle-resolved spin torque oscillation
measurements. The angular dependencies of magnetoresistance and spin diode
effect in a giant magnetoresistance strip are shown as an operational
verification of the experimental setup. We adapted an analytical macrospin
model to reproduce both the resistance and spin-diode angular dependency
measurements.
",0,1,0,0,0,0
17173,Active matter invasion of a viscous fluid: unstable sheets and a no-flow theorem,"  We investigate the dynamics of a dilute suspension of hydrodynamically
interacting motile or immotile stress-generating swimmers or particles as they
invade a surrounding viscous fluid. Colonies of aligned pusher particles are
shown to elongate in the direction of particle orientation and undergo a
cascade of transverse concentration instabilities, governed at small times by
an equation which also describes the Saffman-Taylor instability in a Hele-Shaw
cell, or Rayleigh-Taylor instability in two-dimensional flow through a porous
medium. Thin sheets of aligned pusher particles are always unstable, while
sheets of aligned puller particles can either be stable (immotile particles),
or unstable (motile particles) with a growth rate which is non-monotonic in the
force dipole strength. We also prove a surprising ""no-flow theorem"": a
distribution initially isotropic in orientation loses isotropy immediately but
in such a way that results in no fluid flow everywhere and for all time.
",0,0,0,0,1,0
17174,Interplay of synergy and redundancy in diamond motif,"  The formalism of partial information decomposition provides independent or
non-overlapping components constituting total information content provided by a
set of source variables about the target variable. These components are
recognised as unique information, synergistic information and, redundant
information. The metric of net synergy, conceived as the difference between
synergistic and redundant information, is capable of detecting synergy,
redundancy and, information independence among stochastic variables. And it can
be quantified, as it is done here, using appropriate combinations of different
Shannon mutual information terms. Utilisation of such a metric in network
motifs with the nodes representing different biochemical species, involved in
information sharing, uncovers rich store for interesting results. In the
current study, we make use of this formalism to obtain a comprehensive
understanding of the relative information processing mechanism in a diamond
motif and two of its sub-motifs namely bifurcation and integration motif
embedded within the diamond motif. The emerging patterns of synergy and
redundancy and their effective contribution towards ensuring high fidelity
information transmission are duly compared in the sub-motifs and independent
motifs (bifurcation and integration). In this context, the crucial roles played
by various time scales and activation coefficients in the network topologies
are especially emphasised. We show that the origin of synergy and redundancy in
information transmission can be physically justified by decomposing diamond
motif into bifurcation and integration motif.
",0,1,0,0,0,0
17175,Hardy-Sobolev equations with asymptotically vanishing singularity: Blow-up analysis for the minimal energy,"  We study the asymptotic behavior of a sequence of positive solutions
$(u_{\epsilon})_{\epsilon >0}$ as $\epsilon \to 0$ to the family of equations
\begin{equation*} \left\{\begin{array}{ll} \Delta
u_{\epsilon}+a(x)u_{\epsilon}=
\frac{u_{\epsilon}^{2^*(s_{\epsilon})-1}}{|x|^{s_{\epsilon}}}& \hbox{ in
}\Omega\\ u_{\epsilon}=0 & \hbox{ on }\partial\Omega. \end{array}\right.
\end{equation*} where $(s_{\epsilon})_{\epsilon >0}$ is a sequence of positive
real numbers such that $\lim \limits_{\epsilon \rightarrow 0} s_{\epsilon}=0$,
$2^{*}(s_{\epsilon}):= \frac{2(n-s_{\epsilon})}{n-2}$ and $\Omega \subset
\mathbb{R}^{n}$ is a bounded smooth domain such that $0 \in \partial \Omega$.
When the sequence $(u_{\epsilon})_{\epsilon >0}$ is uniformly bounded in
$L^{\infty}$, then upto a subsequence it converges strongly to a minimizing
solution of the stationary Schrödinger equation with critical growth. In
case the sequence blows up, we obtain strong pointwise control on the blow up
sequence, and then using the Pohozaev identity localize the point of
singularity, which in this case can at most be one, and derive precise blow up
rates. In particular when $n=3$ or $a\equiv 0$ then blow up can occur only at
an interior point of $\Omega$ or the point $0 \in \partial \Omega$.
",0,0,1,0,0,0
17176,"Some results on Ricatti Equations, Floquet Theory and Applications","  In this paper, we present two new results to the classical Floquet theory,
which provides the Floquet multipliers for two classes of the planar periodic
system. One these results provides the Floquet multipliers independently of the
solution of system. To demonstrate the application of these analytical results,
we consider a cholera epidemic model with phage dynamics and seasonality
incorporated.
",0,0,1,0,0,0
17177,"Size, Shape, and Phase Control in Ultrathin CdSe Nanosheets","  Ultrathin two-dimensional nanosheets raise a rapidly increasing interest due
to their unique dimensionality-dependent properties. Most of the
two-dimensional materials are obtained by exfoliation of layered bulk materials
or are grown on substrates by vapor deposition methods. To produce
free-standing nanosheets, solution-based colloidal methods are emerging as
promising routes. In this work, we demonstrate ultrathin CdSe nanosheets with
controllable size, shape and phase. The key of our approach is the use of
halogenated alkanes as additives in a hot-injection synthesis. Increasing
concentrations of bromoalkanes can tune the shape from sexangular to
quadrangular to triangular and the phase from zinc blende to wurtzite. Geometry
and crystal structure evolution of the nanosheets take place in the presence of
halide ions, acting as cadmium complexing agents and as surface X-type ligands,
according to mass spectrometry and X-ray photoelectron spectroscopies. Our
experimental findings show that the degree of these changes depends on the
molecular structure of the halogen alkanes and the type of halogen atom.
",0,1,0,0,0,0
17178,Transfer of magnetic order and anisotropy through epitaxial integration of 3$d$ and 4$f$ spin systems,"  Resonant x-ray scattering at the Dy $M_5$ and Ni $L_3$ absorption edges was
used to probe the temperature and magnetic field dependence of magnetic order
in epitaxial LaNiO$_3$-DyScO$_3$ superlattices. For superlattices with 2 unit
cell thick LaNiO$_3$ layers, a commensurate spiral state develops in the Ni
spin system below 100 K. Upon cooling below $T_{ind} = 18$ K, Dy-Ni exchange
interactions across the LaNiO$_3$-DyScO$_3$ interfaces induce collinear
magnetic order of interfacial Dy moments as well as a reorientation of the Ni
spins to a direction dictated by the strong magneto-crystalline anisotropy of
Dy. This transition is reversible by an external magnetic field of 3 T.
Tailored exchange interactions between rare-earth and transition-metal ions
thus open up new perspectives for the manipulation of spin structures in
metal-oxide heterostructures and devices.
",0,1,0,0,0,0
17179,Exotic limit sets of Teichmüller geodesics in the HHS boundary,"  We answer a question of Durham, Hagen, and Sisto, proving that a
Teichmüller geodesic ray does not necessarily converge to a unique point in
the hierarchically hyperbolic space boundary of Teichmüller space. In fact,
we prove that the limit set can be almost anything allowed by the topology.
",0,0,1,0,0,0
17180,"Decoupling ""when to update"" from ""how to update""","  Deep learning requires data. A useful approach to obtain data is to be
creative and mine data from various sources, that were created for different
purposes. Unfortunately, this approach often leads to noisy labels. In this
paper, we propose a meta algorithm for tackling the noisy labels problem. The
key idea is to decouple ""when to update"" from ""how to update"". We demonstrate
the effectiveness of our algorithm by mining data for gender classification by
combining the Labeled Faces in the Wild (LFW) face recognition dataset with a
textual genderizing service, which leads to a noisy dataset. While our approach
is very simple to implement, it leads to state-of-the-art results. We analyze
some convergence properties of the proposed algorithm.
",1,0,0,0,0,0
17181,Advection of potential temperature in the atmosphere of irradiated exoplanets: a robust mechanism to explain radius inflation,"  The anomalously large radii of strongly irradiated exoplanets have remained a
major puzzle in astronomy. Based on a 2D steady state atmospheric circulation
model, the validity of which is assessed by comparison to 3D calculations, we
reveal a new mechanism, namely the advection of the potential temperature due
to mass and longitudinal momentum conservation, a process occuring in the
Earth's atmosphere or oceans. At depth, the vanishing heating flux forces the
atmospheric structure to converge to a hotter adiabat than the one obtained
with 1D calculations, implying a larger radius for the planet. Not only do the
calculations reproduce the observed radius of HD209458b, but also the observed
correlation between radius inflation and irradiation for transiting planets.
Vertical advection of potential temperature induced by non uniform atmospheric
heating thus provides a robust mechanism explaining the inflated radii of
irradiated hot Jupiters.
",0,1,0,0,0,0
17182,Band depths based on multiple time instances,"  Bands of vector-valued functions $f:T\mapsto\mathbb{R}^d$ are defined by
considering convex hulls generated by their values concatenated at $m$
different values of the argument. The obtained $m$-bands are families of
functions, ranging from the conventional band in case the time points are
individually considered (for $m=1$) to the convex hull in the functional space
if the number $m$ of simultaneously considered time points becomes large enough
to fill the whole time domain. These bands give rise to a depth concept that is
new both for real-valued and vector-valued functions.
",0,0,1,1,0,0
17183,On Biased Correlation Estimation,"  In general, underestimation of risk is something which should be avoided as
far as possible. Especially in financial asset management, equity risk is
typically characterized by the measure of portfolio variance, or indirectly by
quantities which are derived from it. Since there is a linear dependency of the
variance and the empirical correlation between asset classes, one is compelled
to control or to avoid the possibility of underestimating correlation
coefficients. In the present approach, we formalize common practice and
classify these approaches by computing their probability of underestimation. In
addition, we introduce a new estimator which is characterized by having the
advantage of a constant and controllable probability of underestimation. We
prove that the new estimator is statistically consistent.
",0,0,1,1,0,0
17184,Atomic Swaptions: Cryptocurrency Derivatives,"  The atomic swap protocol allows for the exchange of cryptocurrencies on
different blockchains without the need to trust a third-party. However, market
participants who desire to hold derivative assets such as options or futures
would also benefit from trustless exchange. In this paper I propose the atomic
swaption, which extends the atomic swap to allow for such exchanges. Crucially,
atomic swaptions do not require the use of oracles. I also introduce the margin
contract, which provides the ability to create leveraged and short positions.
Lastly, I discuss how atomic swaptions may be routed on the Lightning Network.
",0,0,0,0,0,1
17185,An arbitrary order scheme on generic meshes for miscible displacements in porous media,"  We design, analyse and implement an arbitrary order scheme applicable to
generic meshes for a coupled elliptic-parabolic PDE system describing miscible
displacement in porous media. The discretisation is based on several
adaptations of the Hybrid-High-Order (HHO) method due to Di Pietro et al.
[Computational Methods in Applied Mathematics, 14(4), (2014)]. The equation
governing the pressure is discretised using an adaptation of the HHO method for
variable diffusion, while the discrete concentration equation is based on the
HHO method for advection-diffusion-reaction problems combined with numerically
stable flux reconstructions for the advective velocity that we have derived
using the results of Cockburn et al. [ESAIM: Mathematical Modelling and
Numerical Analysis, 50(3), (2016)]. We perform some rigorous analysis of the
method to demonstrate its $L^2$ stability under the irregular data often
presented by reservoir engineering problems and present several numerical tests
to demonstrate the quality of the results that are produced by the proposed
scheme.
",1,0,0,0,0,0
17186,"Parametricity, automorphisms of the universe, and excluded middle","  It is known that one can construct non-parametric functions by assuming
classical axioms. Our work is a converse to that: we prove classical axioms in
dependent type theory assuming specific instances of non-parametricity. We also
address the interaction between classical axioms and the existence of
automorphisms of a type universe. We work over intensional Martin-Löf
dependent type theory, and in some results assume further principles including
function extensionality, propositional extensionality, propositional
truncation, and the univalence axiom.
",1,0,1,0,0,0
17187,Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity,"  We extend the classic convergence rate theory for subgradient methods to
apply to non-Lipschitz functions. For the deterministic projected subgradient
method, we present a global $O(1/\sqrt{T})$ convergence rate for any convex
function which is locally Lipschitz around its minimizers. This approach is
based on Shor's classic subgradient analysis and implies generalizations of the
standard convergence rates for gradient descent on functions with Lipschitz or
Hölder continuous gradients. Further, we show a $O(1/\sqrt{T})$ convergence
rate for the stochastic projected subgradient method on convex functions with
at most quadratic growth, which improves to $O(1/T)$ under either strong
convexity or a weaker quadratic lower bound condition.
",1,0,0,0,0,0
17188,A quantum dynamics method for excited electrons in molecular aggregate system using a group diabatic Fock matrix,"  We introduce a practical calculation scheme for the description of excited
electron dynamics in molecular aggregated systems within a locally group
diabatic Fock representation. This scheme makes it easy to analyze the
interacting time-dependent excitations of local sites in complex systems. In
addition, light-electron couplings are considered. The present scheme is
intended for investigations on the migration dynamics of excited electrons in
light-energy conversion systems. The scheme was applied to two systems: a
naphthalene(NPTL)-tetracyanoethylene(TCNE) dimer and a 20-mer circle of
ethylene molecules. Through local group analyses of the dynamical electrons, we
obtained an intuitive understanding of the electron transfers between the
monomers.
",0,1,0,0,0,0
17189,Products of topological groups in which all closed subgroups are separable,"  We prove that if $H$ is a topological group such that all closed subgroups of
$H$ are separable, then the product $G\times H$ has the same property for every
separable compact group $G$.
Let $c$ be the cardinality of the continuum. Assuming $2^{\omega_1} = c$, we
show that there exist:
(1) pseudocompact topological abelian groups $G$ and $H$ such that all closed
subgroups of $G$ and $H$ are separable, but the product $G\times H$ contains a
closed non-separable $\sigma$-compact subgroup;
(2) pseudocomplete locally convex vector spaces $K$ and $L$ such that all
closed vector subspaces of $K$ and $L$ are separable, but the product $K\times
L$ contains a closed non-separable $\sigma$-compact vector subspace.
",0,0,1,0,0,0
17190,Low rank solutions to differentiable systems over matrices and applications,"  Differentiable systems in this paper means systems of equations that are
described by differentiable real functions in real matrix variables. This paper
proposes algorithms for finding minimal rank solutions to such systems over
(arbitrary and/or several structured) matrices by using the Levenberg-Marquardt
method (LM-method) for solving least squares problems. We then apply these
algorithms to solve several engineering problems such as the low-rank matrix
completion problem and the low-dimensional Euclidean embedding one. Some
numerical experiments illustrate the validity of the approach.
On the other hand, we provide some further properties of low rank solutions
to systems linear matrix equations. This is useful when the differentiable
function is linear or quadratic.
",0,0,1,0,0,0
17191,A Simple and Realistic Pedestrian Model for Crowd Simulation and Application,"  The simulation of pedestrian crowd that reflects reality is a major challenge
for researches. Several crowd simulation models have been proposed such as
cellular automata model, agent-based model, fluid dynamic model, etc. It is
important to note that agent-based model is able, over others approaches, to
provide a natural description of the system and then to capture complex human
behaviors. In this paper, we propose a multi-agent simulation model in which
pedestrian positions are updated at discrete time intervals. It takes into
account the major normal conditions of a simple pedestrian situated in a crowd
such as preferences, realistic perception of environment, etc. Our objective is
to simulate the pedestrian crowd realistically towards a simulation of
believable pedestrian behaviors. Typical pedestrian phenomena, including the
unidirectional and bidirectional movement in a corridor as well as the flow
through bottleneck, are simulated. The conducted simulations show that our
model is able to produce realistic pedestrian behaviors. The obtained
fundamental diagram and flow rate at bottleneck agree very well with classic
conclusions and empirical study results. It is hoped that the idea of this
study may be helpful in promoting the modeling and simulation of pedestrian
crowd in a simple way.
",1,1,0,0,0,0
17192,Local connectivity modulates multi-scale relaxation dynamics in a metallic glass-forming system,"  The structural description for the intriguing link between the fast
vibrational dynamics and slow diffusive dynamics in glass-forming systems is
one of the most challenging issues in physical science. Here, in a model of
metallic supercooled liquid, we find that local connectivity as an atomic-level
structural order parameter tunes the short-time vibrational excitations of the
icosahedrally coordinated particles and meanwhile modulates their long-time
relaxation dynamics changing from stretched to compressed exponentials,
denoting a dynamic transition from subdiffusive to hyperdiffusive motions of
such particles. Our result indicates that long-time dynamics has an
atomic-level structural origin which is related to the short-time dynamics,
thus suggests a structural bridge to link the fast vibrational dynamics and the
slow structural relaxation in glassy materials.
",0,1,0,0,0,0
17193,SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks,"  Purpose: This paper focuses on an automated analysis of surgical motion
profiles for objective skill assessment and task recognition in robot-assisted
surgery. Existing techniques heavily rely on conventional statistic measures or
shallow modelings based on hand-engineered features and gesture segmentation.
Such developments require significant expert knowledge, are prone to errors,
and are less efficient in online adaptive training systems. Methods: In this
work, we present an efficient analytic framework with a parallel deep learning
architecture, SATR-DL, to assess trainee expertise and recognize surgical
training activity. Through an end-to-end learning technique, abstract
information of spatial representations and temporal dynamics is jointly
obtained directly from raw motion sequences. Results: By leveraging a shared
high-level representation learning, the resulting model is successful in the
recognition of trainee skills and surgical tasks, suturing, needle-passing, and
knot-tying. Meanwhile, we explore the use of ensemble in classification at the
trial level, where the SATR-DL outperforms state-of-the-art performance by
achieving accuracies of 0.960 and 1.000 in skill assessment and task
recognition, respectively. Conclusion: This study highlights the potential of
SATR-DL to provide improvements for an efficient data-driven assessment in
intelligent robotic surgery.
",1,0,0,0,0,0
17194,Equivalence of weak and strong modes of measures on topological vector spaces,"  A strong mode of a probability measure on a normed space $X$ can be defined
as a point $u$ such that the mass of the ball centred at $u$ uniformly
dominates the mass of all other balls in the small-radius limit. Helin and
Burger weakened this definition by considering only pairwise comparisons with
balls whose centres differ by vectors in a dense, proper linear subspace $E$ of
$X$, and posed the question of when these two types of modes coincide. We show
that, in a more general setting of metrisable vector spaces equipped with
measures that are finite on bounded sets, the density of $E$ and a uniformity
condition suffice for the equivalence of these two types of modes. We
accomplish this by introducing a new, intermediate type of mode. We also show
that these modes can be inequivalent if the uniformity condition fails. Our
results shed light on the relationships between among various notions of
maximum a posteriori estimator in non-parametric Bayesian inference.
",0,0,1,1,0,0
17195,Algebras of Quasi-Plücker Coordinates are Koszul,"  Motivated by the theory of quasi-determinants, we study non-commutative
algebras of quasi-Plücker coordinates. We prove that these algebras provide
new examples of non-homogeneous quadratic Koszul algebras by showing that their
quadratic duals have quadratic Gröbner bases.
",0,0,1,0,0,0
17196,Doubly-Attentive Decoder for Multi-modal Neural Machine Translation,"  We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.
",1,0,0,0,0,0
17197,prDeep: Robust Phase Retrieval with a Flexible Deep Network,"  Phase retrieval algorithms have become an important component in many modern
computational imaging systems. For instance, in the context of ptychography and
speckle correlation imaging, they enable imaging past the diffraction limit and
through scattering media, respectively. Unfortunately, traditional phase
retrieval algorithms struggle in the presence of noise. Progress has been made
recently on more robust algorithms using signal priors, but at the expense of
limiting the range of supported measurement models (e.g., to Gaussian or coded
diffraction patterns). In this work we leverage the regularization-by-denoising
framework and a convolutional neural network denoiser to create prDeep, a new
phase retrieval algorithm that is both robust and broadly applicable. We test
and validate prDeep in simulation to demonstrate that it is robust to noise and
can handle a variety of system models.
A MatConvNet implementation of prDeep is available at
this https URL.
",0,0,0,1,0,0
17198,Novel Exotic Magnetic Spin-order in Co5Ge3 Nano-size Materials,"  The Cobalt-germanium (Co-Ge) is a fascinating complex alloy system that has
unique structure and exhibit range of interesting magnetic properties which
would change when reduce to nanoscale dimension. At this experimental work, the
high-aspect-ratio Co5Ge3 nanoparticle with average size of 8nm was synthesized
by gas aggregation-type cluster-deposition technology. The nanostructure
morphology of the as-made binary Co5Ge3 nanoparticles demonstrate excellent
single-crystalline hexagonal structure with mostly preferable growth along
(110) and (102) directions. In contrast the bulk possess Pauli paramagnetic
spin-order at all range of temperature, here we discover size-driven new
magnetic ordering of as-synthesized Co5Ge3 nanoparticles exhibiting
ferromagnetism at room temperature with saturation magnetization of Ms = 32.2
emu/cm3. This is first report of observing such new magnetic spin ordering in
this kind of material at nano-size which the magnetization has lower
sensitivity to thermal energy fluctuation and exhibit high Curie temperature
close to 850 K. This ferromagnetic behavior along with higher Curie temperature
at Co5Ge3 nanoparticles are attributes to low-dimension and quantum-confinement
effect which imposes strong spin coupling and provides a new set of size-driven
spin structures in Co5Ge3 nanoparticle which no such magnetic behavior being
present in the bulk of same material. This fundamental scientific study
provides important insights into the formation, structural, and the magnetic
property of sub 10nm Co5Ge3 nanostructure which shall lead to promising
practical versatile applications for magneto- germanide based nano-devices.
",0,1,0,0,0,0
17199,Toward Multimodal Image-to-Image Translation,"  Many image-to-image translation problems are ambiguous, as a single input
image may correspond to multiple possible outputs. In this work, we aim to
model a \emph{distribution} of possible outputs in a conditional generative
modeling setting. The ambiguity of the mapping is distilled in a
low-dimensional latent vector, which can be randomly sampled at test time. A
generator learns to map the given input, combined with this latent code, to the
output. We explicitly encourage the connection between output and the latent
code to be invertible. This helps prevent a many-to-one mapping from the latent
code to the output during training, also known as the problem of mode collapse,
and produces more diverse results. We explore several variants of this approach
by employing different training objectives, network architectures, and methods
of injecting the latent code. Our proposed method encourages bijective
consistency between the latent encoding and output modes. We present a
systematic comparison of our method and other variants on both perceptual
realism and diversity.
",1,0,0,1,0,0
17200,On the Sample Complexity of the Linear Quadratic Regulator,"  This paper addresses the optimal control problem known as the Linear
Quadratic Regulator in the case when the dynamics are unknown. We propose a
multi-stage procedure, called Coarse-ID control, that estimates a model from a
few experimental trials, estimates the error in that model with respect to the
truth, and then designs a controller using both the model and uncertainty
estimate. Our technique uses contemporary tools from random matrix theory to
bound the error in the estimation procedure. We also employ a recently
developed approach to control synthesis called System Level Synthesis that
enables robust control design by solving a convex optimization problem. We
provide end-to-end bounds on the relative error in control cost that are nearly
optimal in the number of parameters and that highlight salient properties of
the system to be controlled such as closed-loop sensitivity and optimal control
magnitude. We show experimentally that the Coarse-ID approach enables efficient
computation of a stabilizing controller in regimes where simple control schemes
that do not take the model uncertainty into account fail to stabilize the true
system.
",1,0,0,1,0,0
17201,Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification,"  Deep neural networks (DNNs) have transformed several artificial intelligence
research areas including computer vision, speech recognition, and natural
language processing. However, recent studies demonstrated that DNNs are
vulnerable to adversarial manipulations at testing time. Specifically, suppose
we have a testing example, whose label can be correctly predicted by a DNN
classifier. An attacker can add a small carefully crafted noise to the testing
example such that the DNN classifier predicts an incorrect label, where the
crafted testing example is called adversarial example. Such attacks are called
evasion attacks. Evasion attacks are one of the biggest challenges for
deploying DNNs in safety and security critical applications such as
self-driving cars. In this work, we develop new methods to defend against
evasion attacks. Our key observation is that adversarial examples are close to
the classification boundary. Therefore, we propose region-based classification
to be robust to adversarial examples. For a benign/adversarial testing example,
we ensemble information in a hypercube centered at the example to predict its
label. In contrast, traditional classifiers are point-based classification,
i.e., given a testing example, the classifier predicts its label based on the
testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets
demonstrate that our region-based classification can significantly mitigate
evasion attacks without sacrificing classification accuracy on benign examples.
Specifically, our region-based classification achieves the same classification
accuracy on testing benign examples as point-based classification, but our
region-based classification is significantly more robust than point-based
classification to various evasion attacks.
",1,0,0,1,0,0
17202,Partition-free families of sets,"  Let $m(n)$ denote the maximum size of a family of subsets which does not
contain two disjoint sets along with their union. In 1968 Kleitman proved that
$m(n) = {n\choose m+1}+\ldots +{n\choose 2m+1}$ if $n=3m+1$. Confirming the
conjecture of Kleitman, we establish the same equality for the cases $n=3m$ and
$n=3m+2$, and also determine all extremal families. Unlike the case $n=3m+1$,
the extremal families are not unique. This is a plausible reason behind the
relative difficulty of our proofs. We completely settle the case of several
families as well.
",1,0,0,0,0,0
17203,Measuring the Galactic Cosmic Ray Flux with the LISA Pathfinder Radiation Monitor,"  Test mass charging caused by cosmic rays will be a significant source of
acceleration noise for space-based gravitational wave detectors like LISA.
Operating between December 2015 and July 2017, the technology demonstration
mission LISA Pathfinder included a bespoke monitor to help characterise the
relationship between test mass charging and the local radiation environment.
The radiation monitor made in situ measurements of the cosmic ray flux while
also providing information about its energy spectrum. We describe the monitor
and present measurements which show a gradual 40% increase in count rate
coinciding with the declining phase of the solar cycle. Modulations of up to
10% were also observed with periods of 13 and 26 days that are associated with
co-rotating interaction regions and heliospheric current sheet crossings. These
variations in the flux above the monitor detection threshold (approximately 70
MeV) are shown to be coherent with measurements made by the IREM monitor
on-board the Earth orbiting INTEGRAL spacecraft. Finally we use the measured
deposited energy spectra, in combination with a GEANT4 model, to estimate the
galactic cosmic ray differential energy spectrum over the course of the
mission.
",0,1,0,0,0,0
17204,Thread-Modular Static Analysis for Relaxed Memory Models,"  We propose a memory-model-aware static program analysis method for accurately
analyzing the behavior of concurrent software running on processors with weak
consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
our method is a unified framework for deciding the feasibility of inter-thread
interferences to avoid propagating spurious data flows during static analysis
and thus boost the performance of the static analyzer. We formulate the
checking of interference feasibility as a set of Datalog rules which are both
efficiently solvable and general enough to capture a range of hardware-level
memory models. Compared to existing techniques, our method can significantly
reduce the number of bogus alarms as well as unsound proofs. We implemented the
method and evaluated it on a large set of multithreaded C programs. Our
experiments showthe method significantly outperforms state-of-the-art
techniques in terms of accuracy with only moderate run-time overhead.
",1,0,0,0,0,0
17205,Bidirectional Evaluation with Direct Manipulation,"  We present an evaluation update (or simply, update) algorithm for a
full-featured functional programming language, which synthesizes program
changes based on output changes. Intuitively, the update algorithm retraces the
steps of the original evaluation, rewriting the program as needed to reconcile
differences between the original and updated output values. Our approach,
furthermore, allows expert users to define custom lenses that augment the
update algorithm with more advanced or domain-specific program updates.
To demonstrate the utility of evaluation update, we implement the algorithm
in Sketch-n-Sketch, a novel direct manipulation programming system for
generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style
functional program to generate HTML output. When the user directly manipulates
the output using a graphical user interface, the update algorithm reconciles
the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by
authoring ten examples comprising approximately 1400 lines of code in total.
These examples demonstrate how a variety of HTML documents and applications can
be developed and edited interactively in Sketch-n-Sketch, mitigating the
tedious edit-run-view cycle in traditional programming environments.
",1,0,0,0,0,0
17206,Extreme Event Statistics in a Drifting Markov Chain,"  We analyse extreme event statistics of experimentally realized Markov chains
with various drifts. Our Markov chains are individual trajectories of a single
atom diffusing in a one dimensional periodic potential. Based on more than 500
individual atomic traces we verify the applicability of the Sparre Andersen
theorem to our system despite the presence of a drift. We present detailed
analysis of four different rare event statistics for our system: the
distributions of extreme values, of record values, of extreme value occurrence
in the chain, and of the number of records in the chain. We observe that for
our data the shape of the extreme event distributions is dominated by the
underlying exponential distance distribution extracted from the atomic traces.
Furthermore, we find that even small drifts influence the statistics of extreme
events and record values, which is supported by numerical simulations, and we
identify cases in which the drift can be determined without information about
the underlying random variable distributions. Our results facilitate the use of
extreme event statistics as a signal for small drifts in correlated
trajectories.
",0,1,0,0,0,0
17207,On a Possibility of Self Acceleration of Electrons in a Plasma,"  The self-consistent nonlinear interaction of a monoenergetic bunch with cold
plasma is considered. It is shown that under certain conditions a
self-acceleration of the bunch tail electrons up to high energies is possible.
",0,1,0,0,0,0
17208,An Adaptive Strategy for Active Learning with Smooth Decision Boundary,"  We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters.
",1,0,0,1,0,0
17209,Towards Adversarial Retinal Image Synthesis,"  Synthesizing images of the eye fundus is a challenging task that has been
previously approached by formulating complex models of the anatomy of the eye.
New images can then be generated by sampling a suitable parameter space. In
this work, we propose a method that learns to synthesize eye fundus images
directly from data. For that, we pair true eye fundus images with their
respective vessel trees, by means of a vessel segmentation technique. These
pairs are then used to learn a mapping from a binary vessel tree to a new
retinal image. For this purpose, we use a recent image-to-image translation
technique, based on the idea of adversarial learning. Experimental results show
that the original and the generated images are visually different in terms of
their global appearance, in spite of sharing the same vessel tree.
Additionally, a quantitative quality analysis of the synthetic retinal images
confirms that the produced images retain a high proportion of the true image
set quality.
",1,0,0,1,0,0
17210,Tailoring the SiC surface - a morphology study on the epitaxial growth of graphene and its buffer layer,"  We investigate the growth of the graphene buffer layer and the involved step
bunching behavior of the silicon carbide substrate surface using atomic force
microscopy. The formation of local buffer layer domains are identified to be
the origin of undesirably high step edges in excellent agreement with the
predictions of a general model of step dynamics. The applied polymer-assisted
sublimation growth method demonstrates that the key principle to suppress this
behavior is the uniform nucleation of the buffer layer. In this way, the
silicon carbide surface is stabilized such that ultra-flat surfaces can be
conserved during graphene growth on a large variety of silicon carbide
substrate surfaces. The analysis of the experimental results describes
different growth modes which extend the current understanding of epitaxial
graphene growth by emphasizing the importance of buffer layer nucleation and
critical mass transport processes.
",0,1,0,0,0,0
17211,Polarization of the Vaccination Debate on Facebook,"  Vaccine hesitancy has been recognized as a major global health threat. Having
access to any type of information in social media has been suggested as a
potential powerful influence factor to hesitancy. Recent studies in other
fields than vaccination show that access to a wide amount of content through
the Internet without intermediaries resolved into major segregation of the
users in polarized groups. Users select the information adhering to theirs
system of beliefs and tend to ignore dissenting information. In this paper we
assess whether there is polarization in Social Media use in the field of
vaccination. We perform a thorough quantitative analysis on Facebook analyzing
2.6M users interacting with 298.018 posts over a time span of seven years and 5
months. We used community detection algorithms to automatically detect the
emergent communities from the users activity and to quantify the cohesiveness
over time of the communities. Our findings show that content consumption about
vaccines is dominated by the echo-chamber effect and that polarization
increased over years. Communities emerge from the users consumption habits,
i.e. the majority of users only consumes information in favor or against
vaccines, not both. The existence of echo-chambers may explain why social-media
campaigns providing accurate information may have limited reach, may be
effective only in sub-groups and might even foment further polarization of
opinions. The introduction of dissenting information into a sub-group is
disregarded and can have a backfire effect, further reinforcing the existing
opinions within the sub-group.
",1,0,0,0,0,0
17212,"Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors","  Personal electronic devices including smartphones give access to behavioural
signals that can be used to learn about the characteristics and preferences of
individuals. In this study, we explore the connection between demographic and
psychological attributes and the digital behavioural records, for a cohort of
7,633 people, closely representative of the US population with respect to
gender, age, geographical distribution, education, and income. Along with the
demographic data, we collected self-reported assessments on validated
psychometric questionnaires for moral traits and basic human values and
combined this information with passively collected multi-modal digital data
from web browsing behaviour and smartphone usage. A machine learning framework
was then designed to infer both the demographic and psychological attributes
from the behavioural data. In a cross-validated setting, our models predicted
demographic attributes with good accuracy as measured by the weighted AUROC
score (Area Under the Receiver Operating Characteristic), but were less
performant for the moral traits and human values. These results call for
further investigation since they are still far from unveiling individuals'
psychological fabric. This connection, along with the most predictive features
that we provide for each attribute, might prove useful for designing
personalised services, communication strategies, and interventions, and can be
used to sketch a portrait of people with a similar worldview.
",1,0,0,0,0,0
17213,Isotonic regression in general dimensions,"  We study the least squares regression function estimator over the class of
real-valued functions on $[0,1]^d$ that are increasing in each coordinate. For
uniformly bounded signals and with a fixed, cubic lattice design, we establish
that the estimator achieves the minimax rate of order
$n^{-\min\{2/(d+2),1/d\}}$ in the empirical $L_2$ loss, up to poly-logarithmic
factors. Further, we prove a sharp oracle inequality, which reveals in
particular that when the true regression function is piecewise constant on $k$
hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of
convergence of $(k/n)^{\min(1,2/d)}$, again up to poly-logarithmic factors.
Previous results are confined to the case $d \leq 2$. Finally, we establish
corresponding bounds (which are new even in the case $d=2$) in the more
challenging random design setting. There are two surprising features of these
results: first, they demonstrate that it is possible for a global empirical
risk minimisation procedure to be rate optimal up to poly-logarithmic factors
even when the corresponding entropy integral for the function class diverges
rapidly; second, they indicate that the adaptation rate for shape-constrained
estimators can be strictly worse than the parametric rate.
",0,0,1,1,0,0
17214,Extraction of Schottky barrier height insensitive to temperature via forward currentvoltage- temperature measurements,"  The thermal stability of most electronic and photo-electronic devices
strongly depends on the relationship between Schottky Barrier Height (SBH) and
temperature. In this paper, the possible of thermionic current depicted via
correct and reliability relationship between forward current and voltage is
consequently discussed, the intrinsic SBH insensitive to temperature can be
calculated by modification on Richardson- Dushman`s formula suggested in this
paper. The results of application on four hetero-junctions prove that the
method proposed is credible in this paper, this suggests that the I/V/T method
is a feasible alternative to characterize these heterojunctions.
",0,1,0,0,0,0
17215,Emergent Open-Endedness from Contagion of the Fittest,"  In this paper, we study emergent irreducible information in populations of
randomly generated computable systems that are networked and follow a
""Susceptible-Infected-Susceptible"" contagion model of imitation of the fittest
neighbor. We show that there is a lower bound for the stationary prevalence (or
average density of ""infected"" nodes) that triggers an unlimited increase of the
expected local emergent algorithmic complexity (or information) of a node as
the population size grows. We call this phenomenon expected (local) emergent
open-endedness. In addition, we show that static networks with a power-law
degree distribution following the Barabási-Albert model satisfy this lower
bound and, thus, display expected (local) emergent open-endedness.
",1,0,0,0,0,0
17216,Incompressible Limit of isentropic Navier-Stokes equations with Navier-slip boundary,"  This paper concerns the low Mach number limit of weak solutions to the
compressible Navier-Stokes equations for isentropic fluids in a bounded domain
with a Navier-slip boundary condition. In \cite{DGLM99}, it has been proved
that if the velocity is imposed the homogeneous Dirichlet boundary condition,
as the Mach number goes to 0, the velocity of the compressible flow converges
strongly in $L^2$ under the geometrical assumption (H) on the domain. We
justify the same strong convergence when the slip length in the Navier
condition is the reciprocal of the square root of the Mach number.
",0,0,1,0,0,0
17217,On Some Exponential Sums Related to the Coulter's Polynomial,"  In this paper, the formulas of some exponential sums over finite field,
related to the Coulter's polynomial, are settled based on the Coulter's
theorems on Weil sums, which may have potential application in the construction
of linear codes with few weights.
",1,0,0,0,0,0
17218,Distribution-Preserving k-Anonymity,"  Preserving the privacy of individuals by protecting their sensitive
attributes is an important consideration during microdata release. However, it
is equally important to preserve the quality or utility of the data for at
least some targeted workloads. We propose a novel framework for privacy
preservation based on the k-anonymity model that is ideally suited for
workloads that require preserving the probability distribution of the
quasi-identifier variables in the data. Our framework combines the principles
of distribution-preserving quantization and k-member clustering, and we
specialize it to two variants that respectively use intra-cluster and Gaussian
dithering of cluster centers to achieve distribution preservation. We perform
theoretical analysis of the proposed schemes in terms of distribution
preservation, and describe their utility in workloads such as covariate shift
and transfer learning where such a property is necessary. Using extensive
experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
the merits of our algorithms over standard k-anonymization for a hallmark
health care application where an insurance company wishes to understand the
risk in entering a new market. Furthermore, by empirically quantifying the
reidentification risk, we also show that the proposed approaches indeed
maintain k-anonymity.
",1,0,0,1,0,0
17219,Using controlled disorder to probe the interplay between charge order and superconductivity in NbSe2,"  The interplay between superconductivity and charge density waves (CDW) in
$H$-NbSe2 is not fully understood despite decades of study. Artificially
introduced disorder can tip the delicate balance between two competing forms of
long-range order, and reveal the underlying interactions that give rise to
them. Here we introduce disorders by electron irradiation and measure in-plane
resistivity, Hall resistivity, X-ray scattering, and London penetration depth.
With increasing disorder, $T_{\textrm{c}}$ varies nonmonotonically, whereas
$T_{\textrm{CDW}}$ monotonically decreases and becomes unresolvable above a
critical irradiation dose where $T_{\textrm{c}}$ drops sharply. Our results
imply that CDW order initially competes with superconductivity, but eventually
assists it. We argue that at the transition where the long-range CDW order
disappears, the cooperation with superconductivity is dramatically suppressed.
X-ray scattering and Hall resistivity measurements reveal that the short-range
CDW survives above the transition. Superconductivity persists to much higher
dose levels, consistent with fully gapped superconductivity and moderate
interband pairing.
",0,1,0,0,0,0
17220,A training process for improving the quality of software projects developed by a practitioner,"  Background: The quality of a software product depends on the quality of the
software process followed in developing the product. Therefore, many higher
education institutions (HEI) and software organizations have implemented
software process improvement (SPI) training courses to improve the software
quality. Objective: Because the duration of a course is a concern for HEI and
software organizations, we investigate whether the quality of software projects
will be improved by reorganizing the activities of the ten assignments of the
original personal software process (PSP) course into a modified PSP having
fewer assignments (i.e., seven assignments). Method: The assignments were
developed by following a modified PSP with fewer assignments but including the
phases, forms, standards, and logs suggested in the original PSP. The
measurement of the quality of the software assignments was based on defect
density. Results: When the activities in the original PSP were reordered into
fewer assignments, as practitioners progress through the PSP training, the
defect density improved with statistical significance. Conclusions: Our
modified PSP could be applied in academy and industrial environments which are
concerned in the sense of reducing the PSP training time
",1,0,0,0,0,0
17221,Gaia Data Release 1. Cross-match with external catalogues - Algorithm and results,"  Although the Gaia catalogue on its own will be a very powerful tool, it is
the combination of this highly accurate archive with other archives that will
truly open up amazing possibilities for astronomical research. The advanced
interoperation of archives is based on cross-matching, leaving the user with
the feeling of working with one single data archive. The data retrieval should
work not only across data archives, but also across wavelength domains. The
first step for seamless data access is the computation of the cross-match
between Gaia and external surveys. The matching of astronomical catalogues is a
complex and challenging problem both scientifically and technologically
(especially when matching large surveys like Gaia). We describe the cross-match
algorithm used to pre-compute the match of Gaia Data Release 1 (DR1) with a
selected list of large publicly available optical and IR surveys. The overall
principles of the adopted cross-match algorithm are outlined. Details are given
on the developed algorithm, including the methods used to account for position
errors, proper motions, and environment; to define the neighbours; and to
define the figure of merit used to select the most probable counterpart.
Statistics on the results are also given. The results of the cross-match are
part of the official Gaia DR1 catalogue.
",0,1,0,0,0,0
17222,"Masses of Kepler-46b, c from Transit Timing Variations","  We use 16 quarters of the \textit{Kepler} mission data to analyze the transit
timing variations (TTVs) of the extrasolar planet Kepler-46b (KOI-872). Our
dynamical fits confirm that the TTVs of this planet (period
$P=33.648^{+0.004}_{-0.005}$ days) are produced by a non-transiting planet
Kepler-46c ($P=57.325^{+0.116}_{-0.098}$ days). The Bayesian inference tool
\texttt{MultiNest} is used to infer the dynamical parameters of Kepler-46b and
Kepler-46c. We find that the two planets have nearly coplanar and circular
orbits, with eccentricities $\simeq 0.03$ somewhat higher than previously
estimated. The masses of the two planets are found to be
$M_{b}=0.885^{+0.374}_{-0.343}$ and $M_{c}=0.362^{+0.016}_{-0.016}$ Jupiter
masses, with $M_{b}$ being determined here from TTVs for the first time. Due to
the precession of its orbital plane, Kepler-46c should start transiting its
host star in a few decades from now.
",0,1,0,0,0,0
17223,Recovering water wave elevation from pressure measurements,"  The reconstruction of water wave elevation from bottom pressure measurements
is an important issue for coastal applications, but corresponds to a difficult
mathematical problem. In this paper we present the derivation of a method which
allows the elevation reconstruction of water waves in intermediate and shallow
waters. From comparisons with numerical Euler solutions and wave-tank
experiments we show that our nonlinear method provides much better results of
the surface elevation reconstruction compared to the linear transfer function
approach commonly used in coastal applications. More specifically, our
methodaccurately reproduces the peaked and skewed shape of nonlinear wave
fields. Therefore, it is particularly relevant for applications on extreme
waves and wave-induced sediment transport.
",0,1,0,0,0,0
17224,Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization,"  The Schatten quasi-norm was introduced to bridge the gap between the trace
norm and rank function. However, existing algorithms are too slow or even
impractical for large-scale problems. Motivated by the equivalence relation
between the trace norm and its bilinear spectral penalty, we define two
tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove
that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms,
respectively. By applying the two defined Schatten quasi-norms to various rank
minimization problems such as MC and RPCA, we only need to solve much smaller
factor matrices. We design two efficient linearized alternating minimization
algorithms to solve our problems and establish that each bounded sequence
generated by our algorithms converges to a critical point. We also provide the
restricted strong convexity (RSC) based and MC error bounds for our algorithms.
Our experimental results verified both the efficiency and effectiveness of our
algorithms compared with the state-of-the-art methods.
",0,0,0,1,0,0
17225,Spectral Radii of Truncated Circular Unitary Matrices,"  Consider a truncated circular unitary matrix which is a $p_n$ by $p_n$
submatrix of an $n$ by $n$ circular unitary matrix by deleting the last $n-p_n$
columns and rows. Jiang and Qi (2017) proved that the maximum absolute value of
the eigenvalues (known as spectral radius) of the truncated matrix, after
properly normalized, converges in distribution to the Gumbel distribution if
$p_n/n$ is bounded away from $0$ and $1$. In this paper we investigate the
limiting distribution of the spectral radius under one of the following four
conditions: (1). $p_n\to\infty$ and $p_n/n\to 0$ as $n\to\infty$; (2).
$(n-p_n)/n\to 0$ and $(n-p_n)/(\log n)^3\to\infty$ as $n\to\infty$; (3).
$n-p_n\to\infty$ and $(n-p_n)/\log n\to 0$ as $n\to\infty$ and (4). $n-p_n=k\ge
1$ is a fixed integer. We prove that the spectral radius converges in
distribution to the Gumbel distribution under the first three conditions and to
a reversed Weibull distribution under the fourth condition.
",0,0,1,1,0,0
17226,Information Assisted Dictionary Learning for fMRI data analysis,"  In this paper, the task-related fMRI problem is treated in its matrix
factorization formulation. The focus of the reported work is on the dictionary
learning (DL) matrix factorization approach. A major novelty of the paper lies
in the incorporation of well-established assumptions associated with the GLM
technique, which is currently in use by the neuroscientists. These assumptions
are embedded as constraints in the DL formulation. In this way, our approach
provides a framework of combining well-established and understood techniques
with a more ``modern'' and powerful tool. Furthermore, this paper offers a way
to relax a major drawback associated with DL techniques; that is, the proper
tuning of the DL regularization parameter. This parameter plays a critical role
in DL-based fMRI analysis since it essentially determines the shape and
structures of the estimated functional brain networks. However, in actual fMRI
data analysis, the lack of ground truth renders the a priori choice of the
regularization parameter a truly challenging task. Indeed, the values of the DL
regularization parameter, associated with the $\ell_1$ sparsity promoting norm,
do not convey any tangible physical meaning. So it is practically difficult to
guess its proper value. In this paper, the DL problem is reformulated around a
sparsity-promoting constraint that can directly be related to the minimum
amount of voxels that the spatial maps of the functional brain networks occupy.
Such information is documented and it is readily available to neuroscientists
and experts in the field.
The proposed method is tested against a number of other popular techniques
and the obtained performance gains are reported using a number of synthetic
fMRI data. Results with real data have also been obtained in the context of a
number of experiments and will be soon reported in a different publication.
",0,0,0,1,0,0
17227,"Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models","  Motivated by the task of clustering either $d$ variables or $d$ points into
$K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W)
$K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been
shown in the literature to have good statistical properties in a variety of
settings, but remains intractable to solve in practice. To this end we propose
FORCE, a new algorithm to solve this SDP relaxation. Compared to the naive
interior point method, our method reduces the computational complexity of
solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to
$\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an
$\epsilon$-optimal solution. Our method combines a primal first-order method
with a dual optimality certificate search, which when successful, allows for
early termination of the primal method. We show for certain variable clustering
problems that, with high probability, FORCE is guaranteed to find the optimal
solution to the SDP relaxation and provide a certificate of exact optimality.
As verified by our numerical experiments, this allows FORCE to solve the P-W
SDP with dimensions in the hundreds in only tens of seconds. For a variation of
the P-W SDP where $K$ is not known a priori a slight modification of FORCE
reduces the computational complexity of solving this problem as well: from
$\tilde{O}(d^7\log\epsilon^{-1})$ using a standard SDP solver to
$\tilde{O}(d^{4}\epsilon^{-1})$.
",0,0,0,1,0,0
17228,Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data,"  We present wide-field (167 deg$^2$) weak lensing mass maps from the Hyper
Supreme-Cam Subaru Strategic Program (HSC-SSP). We compare these weak lensing
based dark matter maps with maps of the distribution of the stellar mass
associated with luminous red galaxies. We find a strong correlation between
these two maps with a correlation coefficient of $\rho=0.54\pm0.03$ (for a
smoothing size of $8'$). This correlation is detected even with a smaller
smoothing scale of $2'$ ($\rho=0.34\pm 0.01$). This detection is made uniquely
possible because of the high source density of the HSC-SSP weak lensing survey
($\bar{n}\sim 25$ arcmin$^{-2}$). We also present a variety of tests to
demonstrate that our maps are not significantly affected by systematic effects.
By using the photometric redshift information associated with source galaxies,
we reconstruct a three-dimensional mass map. This three-dimensional mass map is
also found to correlate with the three-dimensional galaxy mass map.
Cross-correlation tests presented in this paper demonstrate that the HSC-SSP
weak lensing mass maps are ready for further science analyses.
",0,1,0,0,0,0
17229,A Time-spectral Approach to Numerical Weather Prediction,"  Finite difference methods are traditionally used for modelling the time
domain in numerical weather prediction (NWP). Time-spectral solution is an
attractive alternative for reasons of accuracy and efficiency and because time
step limitations associated with causal, CFL-like critera are avoided. In this
work, the Lorenz 1984 chaotic equations are solved using the time-spectral
algorithm GWRM. Comparisons of accuracy and efficiency are carried out for both
explicit and implicit time-stepping algorithms. It is found that the efficiency
of the GWRM compares well with these methods, in particular at high accuracy.
For perturbative scenarios, the GWRM was found to be as much as four times
faster than the finite difference methods. A primary reason is that the GWRM
time intervals typically are two orders of magnitude larger than those of the
finite difference methods. The GWRM has the additional advantage to produce
analytical solutions in the form of Chebyshev series expansions. The results
are encouraging for pursuing further studies, including spatial dependence, of
the relevance of time-spectral methods for NWP modelling.
",0,1,0,0,0,0
17230,Trivial Constraints on Orbital-free Kinetic Energy Density Functionals,"  Kinetic energy density functionals (KEDFs) are central to orbital-free
density functional theory. Limitations on the spatial derivative dependencies
of KEDFs have been claimed from differential virial theorems. We point out a
central defect in the argument: the relationships are not true for an arbitrary
density but hold only for the minimizing density and corresponding chemical
potential. Contrary to the claims therefore, the relationships are not
constraints and provide no independent information about the spatial derivative
dependencies of approximate KEDFs. A simple argument also shows that validity
for arbitrary $v$-representable densities is not restored by appeal to the
density-potential bijection.
",0,1,0,0,0,0
17231,The Multi-layer Information Bottleneck Problem,"  The muti-layer information bottleneck (IB) problem, where information is
propagated (or successively refined) from layer to layer, is considered. Based
on information forwarded by the preceding layer, each stage of the network is
required to preserve a certain level of relevance with regards to a specific
hidden variable, quantified by the mutual information. The hidden variables and
the source can be arbitrarily correlated. The optimal trade-off between rates
of relevance and compression (or complexity) is obtained through a
single-letter characterization, referred to as the rate-relevance region.
Conditions of successive refinabilty are given. Binary source with BSC hidden
variables and binary source with BSC/BEC mixed hidden variables are both proved
to be successively refinable. We further extend our result to Guassian models.
A counterexample of successive refinability is also provided.
",1,0,0,1,0,0
17232,A geometric approach to non-linear correlations with intrinsic scatter,"  We propose a new mathematical model for $n-k$-dimensional non-linear
correlations with intrinsic scatter in $n$-dimensional data. The model is based
on Riemannian geometry, and is naturally symmetric with respect to the measured
variables and invariant under coordinate transformations. We combine the model
with a Bayesian approach for estimating the parameters of the correlation
relation and the intrinsic scatter. A side benefit of the approach is that
censored and truncated datasets and independent, arbitrary measurement errors
can be incorporated. We also derive analytic likelihoods for the typical
astrophysical use case of linear relations in $n$-dimensional Euclidean space.
We pay particular attention to the case of linear regression in two dimensions,
and compare our results to existing methods. Finally, we apply our methodology
to the well-known $M_\text{BH}$-$\sigma$ correlation between the mass of a
supermassive black hole in the centre of a galactic bulge and the corresponding
bulge velocity dispersion. The main result of our analysis is that the most
likely slope of this correlation is $\sim 6$ for the datasets used, rather than
the values in the range $\sim 4$-$5$ typically quoted in the literature for
these data.
",0,1,1,1,0,0
17233,Computing simplicial representatives of homotopy group elements,"  A central problem of algebraic topology is to understand the homotopy groups
$\pi_d(X)$ of a topological space $X$. For the computational version of the
problem, it is well known that there is no algorithm to decide whether the
fundamental group $\pi_1(X)$ of a given finite simplicial complex $X$ is
trivial. On the other hand, there are several algorithms that, given a finite
simplicial complex $X$ that is simply connected (i.e., with $\pi_1(X)$
trivial), compute the higher homotopy group $\pi_d(X)$ for any given $d\geq 2$.
%The first such algorithm was given by Brown, and more recently, Čadek et
al.
However, these algorithms come with a caveat: They compute the isomorphism
type of $\pi_d(X)$, $d\geq 2$ as an \emph{abstract} finitely generated abelian
group given by generators and relations, but they work with very implicit
representations of the elements of $\pi_d(X)$. Converting elements of this
abstract group into explicit geometric maps from the $d$-dimensional sphere
$S^d$ to $X$ has been one of the main unsolved problems in the emerging field
of computational homotopy theory.
Here we present an algorithm that, given a~simply connected space $X$,
computes $\pi_d(X)$ and represents its elements as simplicial maps from a
suitable triangulation of the $d$-sphere $S^d$ to $X$. For fixed $d$, the
algorithm runs in time exponential in $size(X)$, the number of simplices of
$X$. Moreover, we prove that this is optimal: For every fixed $d\geq 2$, we
construct a family of simply connected spaces $X$ such that for any simplicial
map representing a generator of $\pi_d(X)$, the size of the triangulation of
$S^d$ on which the map is defined, is exponential in $size(X)$.
",1,0,1,0,0,0
17234,Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling,"  Optimization is becoming a crucial element in industrial applications
involving sustainable alternative energy systems. During the design of such
systems, the engineer/decision maker would often encounter noise factors (e.g.
solar insolation and ambient temperature fluctuations) when their system
interacts with the environment. In this chapter, the sizing and design
optimization of the solar powered irrigation system was considered. This
problem is multivariate, noisy, nonlinear and multiobjective. This design
problem was tackled by first using the Fuzzy Type II approach to model the
noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
context of a weighted sum framework) was employed to solve this multiobjective
fuzzy design problem. This method was then used to construct the approximate
Pareto frontier as well as to identify the best solution option in a fuzzy
setting. Comprehensive analyses and discussions were performed on the generated
numerical results with respect to the implemented solution methods.
",1,0,0,0,0,0
17235,Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture,"  In this paper, we study convergence properties of the gradient
Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian
Mixture Models for general number of clusters and mixing coefficients. We
derive the convergence rate depending on the mixing coefficients, minimum and
maximum pairwise distances between the true centers and dimensionality and
number of components; and obtain a near-optimal local contraction radius. While
there have been some recent notable works that derive local convergence rates
for EM in the two equal mixture symmetric GMM, in the more general case, the
derivations need structurally different and non-trivial arguments. We use
recent tools from learning theory and empirical processes to achieve our
theoretical results.
",1,0,1,1,0,0
17236,The Gravitational-Wave Physics,"  The direct detection of gravitational wave by Laser Interferometer
Gravitational-Wave Observatory indicates the coming of the era of
gravitational-wave astronomy and gravitational-wave cosmology. It is expected
that more and more gravitational-wave events will be detected by currently
existing and planned gravitational-wave detectors. The gravitational waves open
a new window to explore the Universe and various mysteries will be disclosed
through the gravitational-wave detection, combined with other cosmological
probes. The gravitational-wave physics is not only related to gravitation
theory, but also is closely tied to fundamental physics, cosmology and
astrophysics. In this review article, three kinds of sources of gravitational
waves and relevant physics will be discussed, namely gravitational waves
produced during the inflation and preheating phases of the Universe, the
gravitational waves produced during the first-order phase transition as the
Universe cools down and the gravitational waves from the three phases:
inspiral, merger and ringdown of a compact binary system, respectively. We will
also discuss the gravitational waves as a standard siren to explore the
evolution of the Universe.
",0,1,0,0,0,0
17237,Multivariant Assertion-based Guidance in Abstract Interpretation,"  Approximations during program analysis are a necessary evil, as they ensure
essential properties, such as soundness and termination of the analysis, but
they also imply not always producing useful results. Automatic techniques have
been studied to prevent precision loss, typically at the expense of larger
resource consumption. In both cases (i.e., when analysis produces inaccurate
results and when resource consumption is too high), it is necessary to have
some means for users to provide information to guide analysis and thus improve
precision and/or performance. We present techniques for supporting within an
abstract interpretation framework a rich set of assertions that can deal with
multivariance/context-sensitivity, and can handle different run-time semantics
for those assertions that cannot be discharged at compile time. We show how the
proposed approach can be applied to both improving precision and accelerating
analysis. We also provide some formal results on the effects of such assertions
on the analysis results.
",1,0,0,0,0,0
17238,An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems,"  Through the development of efficient algorithms, data structures and
preprocessing techniques, real-world shortest path problems in street networks
are now very fast to solve. But in reality, the exact travel times along each
arc in the network may not be known. This lead to the development of robust
shortest path problems, where all possible arc travel times are contained in a
so-called uncertainty set of possible outcomes.
Research in robust shortest path problems typically assumes this set to be
given, and provides complexity results as well as algorithms depending on its
shape. However, what can actually be observed in real-world problems are only
discrete raw data points. The shape of the uncertainty is already a modelling
assumption. In this paper we test several of the most widely used assumptions
on the uncertainty set using real-world traffic measurements provided by the
City of Chicago. We calculate the resulting different robust solutions, and
evaluate which uncertainty approach is actually reasonable for our data. This
anchors theoretical research in a real-world application and allows us to point
out which robust models should be the future focus of algorithmic development.
",1,0,1,0,0,0
17239,An experimental comparison of velocities underneath focussed breaking waves,"  Nonlinear wave interactions affect the evolution of steep wave groups, their
breaking and the associated kinematic field. Laboratory experiments are
performed to investigate the effect of the underlying focussing mechanism on
the shape of the breaking wave and its velocity field. In this regard, it is
found that the shape of the wave spectrum plays a substantial role. Broader
underlying wave spectra leads to energetic plungers at a relatively low
amplitude. For narrower spectra waves break at a higher amplitudes but with a
less energetic spiller. Comparison with standard engineering methods commonly
used to predict the velocity underneath extreme waves shows that, under certain
conditions, the measured velocity profile strongly deviates from engineering
predictions.
",0,1,0,0,0,0
17240,Full Momentum and Energy Resolved Spectral Function of a 2D Electronic System,"  The single-particle spectral function measures the density of electronic
states (DOS) in a material as a function of both momentum and energy, providing
central insights into phenomena such as superconductivity and Mott insulators.
While scanning tunneling microscopy (STM) and other tunneling methods have
provided partial spectral information, until now only angle-resolved
photoemission spectroscopy (ARPES) has permitted a comprehensive determination
of the spectral function of materials in both momentum and energy. However,
ARPES operates only on electronic systems at the material surface and cannot
work in the presence of applied magnetic fields. Here, we demonstrate a new
method for determining the full momentum and energy resolved electronic
spectral function of a two-dimensional (2D) electronic system embedded in a
semiconductor. In contrast with ARPES, the technique remains operational in the
presence of large externally applied magnetic fields and functions for
electronic systems with zero electrical conductivity or with zero electron
density. It provides a direct high-resolution and high-fidelity probe of the
dispersion and dynamics of the interacting 2D electron system. By ensuring the
system of interest remains under equilibrium conditions, we uncover delicate
signatures of many-body effects involving electron-phonon interactions,
plasmons, polarons, and a novel phonon analog of the vacuum Rabi splitting in
atomic systems.
",0,1,0,0,0,0
17241,Complete parallel mean curvature surfaces in two-dimensional complex space-forms,"  The purpose of this article is to determine explicitly the complete surfaces
with parallel mean curvature vector, both in the complex projective plane and
the complex hyperbolic plane. The main results are as follows: When the
curvature of the ambient space is positive, there exists a unique such surface
up to rigid motions of the target space. On the other hand, when the curvature
of the ambient space is negative, there are `non-trivial' complete parallel
mean curvature surfaces generated by Jacobi elliptic functions and they exhaust
such surfaces.
",0,0,1,0,0,0
17242,Parallelized Linear Classification with Volumetric Chemical Perceptrons,"  In this work, we introduce a new type of linear classifier that is
implemented in a chemical form. We propose a novel encoding technique which
simultaneously represents multiple datasets in an array of microliter-scale
chemical mixtures. Parallel computations on these datasets are performed as
robotic liquid handling sequences, whose outputs are analyzed by
high-performance liquid chromatography. As a proof of concept, we chemically
encode several MNIST images of handwritten digits and demonstrate successful
chemical-domain classification of the digits using volumetric perceptrons. We
additionally quantify the performance of our method with a larger dataset of
binary vectors and compare the experimental measurements against predicted
results. Paired with appropriate chemical analysis tools, our approach can work
on increasingly parallel datasets. We anticipate that related approaches will
be scalable to multilayer neural networks and other more complex algorithms.
Much like recent demonstrations of archival data storage in DNA, this work
blurs the line between chemical and electrical information systems, and offers
early insight into the computational efficiency and massive parallelism which
may come with computing in chemical domains.
",0,0,0,0,1,0
17243,Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification,"  Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because spatial annotations of the labels are generally not provided.
In this paper, we propose a unified deep neural network that exploits both
semantic and spatial relations between labels with only image-level
supervisions. Given a multi-label image, our proposed Spatial Regularization
Network (SRN) generates attention maps for all labels and captures the
underlying relations between them via learnable convolutions. By aggregating
the regularized classification results with original results by a ResNet-101
network, the classification performance can be consistently improved. The whole
deep neural network is trained end-to-end with only image-level annotations,
thus requires no additional efforts on image annotations. Extensive evaluations
on 3 public datasets with different types of labels show that our approach
significantly outperforms state-of-the-arts and has strong generalization
capability. Analysis of the learned SRN model demonstrates that it can
effectively capture both semantic and spatial relations of labels for improving
classification performance.
",1,0,0,0,0,0
17244,The critical binary star separation for a planetary system origin of white dwarf pollution,"  The atmospheres of between one quarter and one half of observed single white
dwarfs in the Milky Way contain heavy element pollution from planetary debris.
The pollution observed in white dwarfs in binary star systems is, however, less
clear, because companion star winds can generate a stream of matter which is
accreted by the white dwarf. Here we (i) discuss the necessity or lack thereof
of a major planet in order to pollute a white dwarf with orbiting minor planets
in both single and binary systems, and (ii) determine the critical binary
separation beyond which the accretion source is from a planetary system. We
hence obtain user-friendly functions relating this distance to the masses and
radii of both stars, the companion wind, and the accretion rate onto the white
dwarf, for a wide variety of published accretion prescriptions. We find that
for the majority of white dwarfs in known binaries, if pollution is detected,
then that pollution should originate from planetary material.
",0,1,0,0,0,0
17245,A quantum Mirković-Vybornov isomorphism,"  We present a quantization of an isomorphism of Mirković and Vybornov which
relates the intersection of a Slodowy slice and a nilpotent orbit closure in
$\mathfrak{gl}_N$ , to a slice between spherical Schubert varieties in the
affine Grassmannian of $PGL_n$ (with weights encoded by the Jordan types of the
nilpotent orbits). A quantization of the former variety is provided by a
parabolic W-algebra and of the latter by a truncated shifted Yangian. Building
on earlier work of Brundan and Kleshchev, we define an explicit isomorphism
between these non-commutative algebras, and show that its classical limit is a
variation of the original isomorphism of Mirković and Vybornov. As a
corollary, we deduce that the W-algebra is free as a left (or right) module
over its Gelfand-Tsetlin subalgebra, as conjectured by Futorny, Molev, and
Ovsienko.
",0,0,1,0,0,0
17246,Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach,"  This paper is concerned with a multi-asset mean-variance portfolio selection
problem under model uncertainty. We develop a continuous time framework for
taking into account ambiguity aversion about both expected return rates and
correlation matrix of the assets, and for studying the effects on portfolio
diversification. We prove a separation principle for the associated robust
control problem, which allows to reduce the determination of the optimal
dynamic strategy to the parametric computation of the minimal risk premium
function. Our results provide a justification for under-diversification, as
documented in empirical studies. We explicitly quantify the degree of
under-diversification in terms of correlation and Sharpe ratio ambiguity. In
particular, we show that an investor with a poor confidence in the expected
return estimation does not hold any risky asset, and on the other hand, trades
only one risky asset when the level of ambiguity on correlation matrix is
large. This extends to the continuous-time setting the results obtained by
Garlappi, Uppal and Wang [13], and Liu and Zeng [24] in a one-period model. JEL
Classification: G11, C61 MSC Classification: 91G10, 91G80, 60H30
",0,0,0,0,0,1
17247,TensorLayer: A Versatile Library for Efficient Deep Learning Development,"  Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.
",1,0,0,1,0,0
17248,Effects of excess carriers on native defects in wide bandgap semiconductors: illumination as a method to enhance p-type doping,"  Undesired unintentional doping and doping limits in semiconductors are
typically caused by compensating defects with low formation energies. Since the
formation energy of a charged defect depends linearly on the Fermi level,
doping limits can be especially pronounced in wide bandgap semiconductors where
the Fermi level can vary substantially. Introduction of non-equilibrium carrier
concentrations during growth or processing alters the chemical potentials of
band carriers and thus provides the possibility of modifying populations of
charged defects in ways impossible at thermal equilibrium. Herein we
demonstrate that, for an ergodic system with excess carriers, the rates of
carrier capture and emission involving a defect charge transition level
rigorously determine the admixture of electron and hole quasi-Fermi levels
determining the formation energy of non-zero charge states of that defect type.
To catalog the range of possible responses to excess carriers, we investigate
the behavior of a single donor-like defect as functions of extrinsic doping and
energy of the charge transition level. The technologically most important
finding is that excess carriers will increase the formation energy of
compensating defects for most values of the charge transition level in the
bandgap. Thus, it may be possible to overcome limitations on doping imposed by
native defects. Cases also exist in wide bandgap semiconductors in which the
concentration of defects with the same charge polarity as the majority dopant
is either left unchanged or actually increases. The causes of these various
behaviors are rationalized in terms of the capture and emission rates and
guidelines for carrying out experimental tests of this model are given.
",0,1,0,0,0,0
17249,LATTE: Application Oriented Social Network Embedding,"  In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the ""appLicAtion
orienTed neTwork Embedding"" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node ""diffusive proximity"" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.
",1,0,0,0,0,0
17250,Giant paramagnetism induced valley polarization of electrons in charge-tunable monolayer MoSe2,"  For applications exploiting the valley pseudospin degree of freedom in
transition metal dichalcogenide monolayers, efficient preparation of electrons
or holes in a single valley is essential. Here, we show that a magnetic field
of 7 Tesla leads to a near-complete valley polarization of electrons in MoSe2
monolayer with a density 1.6x10^{12} cm^{-2}; in the absence of exchange
interactions favoring single-valley occupancy, a similar degree of valley
polarization would have required a pseudospin g-factor exceeding 40. To
investigate the magnetic response, we use polarization resolved
photoluminescence as well as resonant reflection measurements. In the latter,
we observe gate voltage dependent transfer of oscillator strength from the
exciton to the attractive-Fermi-polaron: stark differences in the spectrum of
the two light helicities provide a confirmation of valley polarization. Our
findings suggest an interaction induced giant paramagnetic response of MoSe2,
which paves the way for valleytronics applications.
",0,1,0,0,0,0
17251,Highrisk Prediction from Electronic Medical Records via Deep Attention Networks,"  Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the high risky vascular disease from symbolic medical histories
sequence of hypertension patients such as ICD-10 and pharmacy codes only,
Medical History-based Prediction using Attention Network (MeHPAN). We
demonstrate two types of attention models based on 1) bidirectional gated
recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
with respect to precision, recall, f1-measure and area under the curve (AUC).
Experimental results show that our MeHPAN methods outperform standard
classification models. Comparing two MeHPANs, R-MeHPAN provides more better
discriminative capability with respect to all metrics while C-MeHPAN presents
much shorter training time with competitive accuracy.
",1,0,0,1,0,0
17252,Agent based simulation of the evolution of society as an alternate maximization problem,"  Understanding the evolution of human society, as a complex adaptive system,
is a task that has been looked upon from various angles. In this paper, we
simulate an agent-based model with a high enough population tractably. To do
this, we characterize an entity called \textit{society}, which helps us reduce
the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We
propose a very realistic setting, where we design a joint alternate
maximization step algorithm to maximize a certain \textit{fitness} function,
which we believe simulates the way societies develop. Our key contributions
include (i) proposing a novel protocol for simulating the evolution of a
society with cheap, non-optimal joint alternate maximization steps (ii)
providing a framework for carrying out experiments that adhere to this
joint-optimization simulation framework (iii) carrying out experiments to show
that it makes sense empirically (iv) providing an alternate justification for
the use of \textit{society} in the simulations.
",1,0,0,1,0,0
17253,Can a heart rate variability biomarker identify the presence of autism spectrum disorder in eight year old children?,"  Autonomic nervous system (ANS) activity is altered in autism spectrum
disorder (ASD). Heart rate variability (HRV) derived from electrocardiogram
(ECG) has been a powerful tool to identify alterations in ANS due to a plethora
of pathophysiological conditions, including psychological ones such as
depression. ECG-derived HRV thus carries a yet to be explored potential to be
used as a diagnostic and follow-up biomarker of ASD. However, few studies have
explored this potential. In a cohort of boys (ages 8 - 11 years) with (n=18)
and without ASD (n=18), we tested a set of linear and nonlinear HRV measures,
including phase rectified signal averaging (PRSA), applied to a segment of ECG
collected under resting conditions for their predictive properties of ASD. We
identified HRV measures derived from time, frequency and geometric
signal-analytical domains which are changed in ASD children relative to peers
without ASD and correlate to psychometric scores (p<0.05 for each). Receiver
operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite
being a small cohort lacking external validation, these promising preliminary
results warrant larger prospective validation studies.
",0,0,0,0,1,0
17254,Semantic Entity Retrieval Toolkit,"  Unsupervised learning of low-dimensional, semantic representations of words
and entities has recently gained attention. In this paper we describe the
Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our
previously published entity representation models. The toolkit provides a
unified interface to different representation learning algorithms, fine-grained
parsing configuration and can be used transparently with GPUs. In addition,
users can easily modify existing models or implement their own models in the
framework. After model training, SERT can be used to rank entities according to
a textual query and extract the learned entity/word representation for use in
downstream algorithms, such as clustering or recommendation.
",1,0,0,0,0,0
17255,Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection,"  In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each
associated with an unknown reward distribution. We are required to identify the
$k$ arms with the largest means by taking as few samples as possible. In this
paper, we make progress towards a complete characterization of the
instance-wise sample complexity bounds for the Best-$k$-Arm problem. On the
lower bound side, we obtain a novel complexity term to measure the sample
complexity that every Best-$k$-Arm instance requires. This is derived by an
interesting and nontrivial reduction from the Best-$1$-Arm problem. We also
provide an elimination-based algorithm that matches the instance-wise lower
bound within doubly-logarithmic factors. The sample complexity of our algorithm
strictly dominates the state-of-the-art for Best-$k$-Arm (module constant
factors).
",1,0,0,1,0,0
17256,Dimensions of equilibrium measures on a class of planar self-affine sets,"  We study equilibrium measures (Käenmäki measures) supported on
self-affine sets generated by a finite collection of diagonal and anti-diagonal
matrices acting on the plane and satisfying the strong separation property. Our
main result is that such measures are exact dimensional and the dimension
satisfies the Ledrappier-Young formula, which gives an explicit expression for
the dimension in terms of the entropy and Lyapunov exponents as well as the
dimension of the important coordinate projection of the measure. In particular,
we do this by showing that the Käenmäki measure is equal to the sum of (the
pushforwards) of two Gibbs measures on an associated subshift of finite type.
",0,0,1,0,0,0
17257,Hubble PanCET: An isothermal day-side atmosphere for the bloated gas-giant HAT-P-32Ab,"  We present a thermal emission spectrum of the bloated hot Jupiter HAT-P-32Ab
from a single eclipse observation made in spatial scan mode with the Wide Field
Camera 3 (WFC3) aboard the Hubble Space Telescope (HST). The spectrum covers
the wavelength regime from 1.123 to 1.644 microns which is binned into 14
eclipse depths measured to an averaged precision of 104 parts-per million. The
spectrum is unaffected by a dilution from the close M-dwarf companion
HAT-P-32B, which was fully resolved. We complemented our spectrum with
literature results and performed a comparative forward and retrieval analysis
with the 1D radiative-convective ATMO model. Assuming solar abundance of the
planet atmosphere, we find that the measured spectrum can best be explained by
the spectrum of a blackbody isothermal atmosphere with Tp = 1995 +/- 17K, but
can equally-well be described by a spectrum with modest thermal inversion. The
retrieved spectrum suggests emission from VO at the WFC3 wavelengths and no
evidence of the 1.4 micron water feature. The emission models with temperature
profiles decreasing with height are rejected at a high confidence. An
isothermal or inverted spectrum can imply a clear atmosphere with an absorber,
a dusty cloud deck or a combination of both. We find that the planet can have
continuum of values for the albedo and recirculation, ranging from high albedo
and poor recirculation to low albedo and efficient recirculation. Optical
spectroscopy of the planet's day-side or thermal emission phase curves can
potentially resolve the current albedo with recirculation degeneracy.
",0,1,0,0,0,0
17258,One Model To Learn Them All,"  Deep learning yields great results across many fields, from speech
recognition, image classification, to translation. But for each problem,
getting a deep model to work well involves research into the architecture and a
long period of tuning. We present a single model that yields good results on a
number of problems spanning multiple domains. In particular, this single model
is trained concurrently on ImageNet, multiple translation tasks, image
captioning (COCO dataset), a speech recognition corpus, and an English parsing
task. Our model architecture incorporates building blocks from multiple
domains. It contains convolutional layers, an attention mechanism, and
sparsely-gated layers. Each of these computational blocks is crucial for a
subset of the tasks we train on. Interestingly, even if a block is not crucial
for a task, we observe that adding it never hurts performance and in most cases
improves it on all tasks. We also show that tasks with less data benefit
largely from joint training with other tasks, while performance on large tasks
degrades only slightly if at all.
",1,0,0,1,0,0
17259,Porcupine Neural Networks: (Almost) All Local Optima are Global,"  Neural networks have been used prominently in several machine learning and
statistics applications. In general, the underlying optimization of neural
networks is non-convex which makes their performance analysis challenging. In
this paper, we take a novel approach to this problem by asking whether one can
constrain neural network weights to make its optimization landscape have good
theoretical properties while at the same time, be a good approximation for the
unconstrained one. For two-layer neural networks, we provide affirmative
answers to these questions by introducing Porcupine Neural Networks (PNNs)
whose weight vectors are constrained to lie over a finite set of lines. We show
that most local optima of PNN optimizations are global while we have a
characterization of regions where bad local optimizers may exist. Moreover, our
theoretical and empirical results suggest that an unconstrained neural network
can be approximated using a polynomially-large PNN.
",1,0,0,1,0,0
17260,Configuration Path Integral Monte Carlo Approach to the Static Density Response of the Warm Dense Electron Gas,"  Precise knowledge of the static density response function (SDRF) of the
uniform electron gas (UEG) serves as key input for numerous applications, most
importantly for density functional theory beyond generalized gradient
approximations. Here we extend the configuration path integral Monte Carlo
(CPIMC) formalism that was previously applied to the spatially uniform electron
gas to the case of an inhomogeneous electron gas by adding a spatially periodic
external potential. This procedure has recently been successfully used in
permutation blocking path integral Monte Carlo simulations (PB-PIMC) of the
warm dense electron gas [Dornheim \textit{et al.}, Phys. Rev. E in press,
arXiv:1706.00315], but this method is restricted to low and moderate densities.
Implementing this procedure into CPIMC allows us to obtain exact finite
temperature results for the SDRF of the electron gas at \textit{high to
moderate densities} closing the gap left open by the PB-PIMC data. In this
paper we demonstrate how the CPIMC formalism can be efficiently extended to the
spatially inhomogeneous electron gas and present the first data points.
Finally, we discuss finite size errors involved in the quantum Monte Carlo
results for the SDRF in detail and present a solution how to remove them that
is based on a generalization of ground state techniques.
",0,1,0,0,0,0
17261,Superzone gap formation and low lying crystal electric field levels in PrPd$_2$Ge$_2$ single crystal,"  The magnetocrystalline anisotropy exhibited in PrPd$_2$Ge$_2$ single crystal
has been investigated by measuring the magnetization, magnetic susceptibility,
electrical resistivity and heat capacity. PrPd$_2$Ge$_2$ crystallizes in the
well known ThCr$_2$Si$_2$\--type tetragonal structure. The antiferromagnetic
ordering is confirmed as 5.1~K with the [001]-axis as the easy axis of
magnetization. A superzone gap formation is observed from the electrical
resistivity measurement when the current is passed along the [001] direction.
The crystal electric field (CEF) analysis on the magnetic susceptibility,
magnetization and the heat capacity measurements confirms a doublet ground
state with a relatively low over all CEF level splitting. The CEF level
spacings and the Zeeman splitting at high fields become comparable and lead to
metamagnetic transition at 34~T due to the CEF level crossing.
",0,1,0,0,0,0
17262,Adaptive Real-Time Software Defined MIMO Visible Light Communications using Spatial Multiplexing and Spatial Diversity,"  In this paper, we experimentally demonstrate a real-time software defined
multiple input multiple output (MIMO) visible light communication (VLC) system
employing link adaptation of spatial multiplexing and spatial diversity.
Real-time MIMO signal processing is implemented by using the Field Programmable
Gate Array (FPGA) based Universal Software Radio Peripheral (USRP) devices.
Software defined implantation of MIMO VLC can assist in enabling an adaptive
and reconfigurable communication system without hardware changes. We measured
the error vector magnitude (EVM), bit error rate (BER) and spectral efficiency
performance for single carrier M-QAM MIMO VLC using spatial diversity and
spatial multiplexing. Results show that spatial diversity MIMO VLC improves
error performance at the cost of spectral efficiency that spatial multiplexing
should enhance. We propose the adaptive MIMO solution that both modulation
schema and MIMO schema are dynamically adapted to the changing channel
conditions for enhancing the error performance and spectral efficiency. The
average error-free spectral efficiency of adaptive 2x2 MIMO VLC achieved 12
b/s/Hz over 2 meters indoor dynamic transmission.
",1,0,1,0,0,0
17263,Maximum Principle Based Algorithms for Deep Learning,"  The continuous dynamical system approach to deep learning is explored in
order to devise alternative frameworks for training algorithms. Training is
recast as a control problem and this allows us to formulate necessary
optimality conditions in continuous time using the Pontryagin's maximum
principle (PMP). A modification of the method of successive approximations is
then used to solve the PMP, giving rise to an alternative training algorithm
for deep learning. This approach has the advantage that rigorous error
estimates and convergence results can be established. We also show that it may
avoid some pitfalls of gradient-based methods, such as slow convergence on flat
landscapes near saddle points. Furthermore, we demonstrate that it obtains
favorable initial convergence rate per-iteration, provided Hamiltonian
maximization can be efficiently carried out - a step which is still in need of
improvement. Overall, the approach opens up new avenues to attack problems
associated with deep learning, such as trapping in slow manifolds and
inapplicability of gradient-based methods for discrete trainable variables.
",1,0,0,1,0,0
17264,Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations,"  With the popularity of Linked Open Data (LOD) and the associated rise in
freely accessible knowledge that can be accessed via LOD, exploiting LOD for
recommender systems has been widely studied based on various approaches such as
graph-based or using different machine learning models with LOD-enabled
features. Many of the previous approaches require construction of an additional
graph to run graph-based algorithms or to extract path-based features by
combining user- item interactions (e.g., likes, dislikes) and background
knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
based on particularly lightweight LOD-enabled features which can be directly
obtained via a public SPARQL Endpoint without any additional effort to
construct a graph. Firstly, we aim to study whether using FM with these
lightweight LOD-enabled features can provide competitive performance compared
to a learning-to-rank approach leveraging LOD as well as other well-established
approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
out to what extent each set of LOD-enabled features contributes to the
recommendation performance. Experimental evaluation on a standard dataset shows
that our proposed approach using FM with lightweight LOD-enabled features
provides the best performance compared to other approaches in terms of five
evaluation metrics. In addition, the study of the recommendation performance
based on different sets of LOD-enabled features indicate that property-object
lists and PageRank scores of items are useful for improving the performance,
and can provide the best performance through using them together for FM. We
observe that subject-property lists of items does not contribute to the
recommendation performance but rather decreases the performance.
",1,0,0,0,0,0
17265,Wave propagation and homogenization in 2D and 3D lattices: a semi-analytical approach,"  Wave motion in two- and three-dimensional periodic lattices of beam members
supporting longitudinal and flexural waves is considered. An analytic method
for solving the Bloch wave spectrum is developed, characterized by a
generalized eigenvalue equation obtained by enforcing the Floquet condition.
The dynamic stiffness matrix is shown to be explicitly Hermitian and to admit
positive eigenvalues. Lattices with hexagonal, rectangular, tetrahedral and
cubic unit cells are analyzed. The semi-analytical method can be asymptotically
expanded for low frequency yielding explicit forms for the Christoffel matrix
describing wave motion in the quasistatic limit.
",0,1,0,0,0,0
17266,Waring's problem for unipotent algebraic groups,"  In this paper, we formulate an analogue of Waring's problem for an algebraic
group $G$. At the field level we consider a morphism of varieties $f\colon
\mathbb{A}^1\to G$ and ask whether every element of $G(K)$ is the product of a
bounded number of elements $f(\mathbb{A}^1(K)) = f(K)$. We give an affirmative
answer when $G$ is unipotent and $K$ is a characteristic zero field which is
not formally real.
The idea is the same at the integral level, except one must work with
schemes, and the question is whether every element in a finite index subgroup
of $G(\mathcal{O})$ can be written as a product of a bounded number of elements
of $f(\mathcal{O})$. We prove this is the case when $G$ is unipotent and
$\mathcal{O}$ is the ring of integers of a totally imaginary number field.
",0,0,1,0,0,0
17267,Spreading of localized attacks in spatial multiplex networks,"  Many real-world multilayer systems such as critical infrastructure are
interdependent and embedded in space with links of a characteristic length.
They are also vulnerable to localized attacks or failures, such as terrorist
attacks or natural catastrophes, which affect all nodes within a given radius.
Here we study the effects of localized attacks on spatial multiplex networks of
two layers. We find a metastable region where a localized attack larger than a
critical size induces a nucleation transition as a cascade of failures spreads
throughout the system, leading to its collapse. We develop a theory to predict
the critical attack size and find that it exhibits novel scaling behavior. We
further find that localized attacks in these multiplex systems can induce a
previously unobserved combination of random and spatial cascades. Our results
demonstrate important vulnerabilities in real-world interdependent networks and
show new theoretical features of spatial networks.
",1,1,0,0,0,0
17268,Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree,"  The reconstruction of sparse signals requires the solution of an
$\ell_0$-norm minimization problem in Compressed Sensing. Previous research has
focused on the investigation of a single candidate to identify the support
(index of nonzero elements) of a sparse signal. To ensure that the optimal
candidate can be obtained in each iteration, we propose here an iterative
greedy reconstruction algorithm (GSRA). First, the intersection of the support
sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit
(SP) is set as the initial support set. Then, a hope-tree is built to expand
the set. Finally, a developed decreasing subspace pursuit method is used to
rectify the candidate set. Detailed simulation results demonstrate that GSRA is
more accurate than other typical methods in recovering Gaussian signals, 0--1
sparse signals, and synthetic signals.
",1,0,1,0,0,0
17269,Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems,"  In this paper, we propose a fault detection and isolation based attack-aware
multi-sensor integration algorithm for the detection of cyberattacks in
autonomous vehicle navigation systems. The proposed algorithm uses an extended
Kalman filter to construct robust residuals in the presence of noise, and then
uses a parametric statistical tool to identify cyberattacks. The parametric
statistical tool is based on the residuals constructed by the measurement
history rather than one measurement at a time in the properties of
discrete-time signals and dynamic systems. This approach allows the proposed
multi-sensor integration algorithm to provide quick detection and low false
alarm rates for applications in dynamic systems. An example of INS/GNSS
integration of autonomous navigation systems is presented to validate the
proposed algorithm by using a software-in-the-loop simulation.
",1,0,0,0,0,0
17270,"Turbulence, cascade and singularity in a generalization of the Constantin-Lax-Majda equation","  We study numerically a Constantin-Lax-Majda-De Gregorio model generalized by
Okamoto, Sakajo and Wunsch, which is a model of fluid turbulence in one
dimension with an inviscid conservation law. In the presence of the viscosity
and two types of the large-scale forcings, we show that turbulent cascade of
the inviscid invariant, which is not limited to quadratic quantity, occurs and
that properties of this model's turbulent state are related to singularity of
the inviscid case by adopting standard tools of analyzing fluid turbulence.
",0,1,0,0,0,0
17271,Fitting phase--type scale mixtures to heavy--tailed data and distributions,"  We consider the fitting of heavy tailed data and distribution with a special
attention to distributions with a non--standard shape in the ""body"" of the
distribution. To this end we consider a dense class of heavy tailed
distributions introduced recently, employing an EM algorithm for the the
maximum likelihood estimates of its parameters. We present methods for fitting
to observed data, histograms, censored data, as well as to theoretical
distributions. Numerical examples are provided with simulated data and a
benchmark reinsurance dataset. We empirically demonstrate that our model can
provide excellent fits to heavy--tailed data/distributions with minimal
assumptions
",0,0,1,1,0,0
17272,Deep Incremental Boosting,"  This paper introduces Deep Incremental Boosting, a new technique derived from
AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
the required training time and improves generalisation. We draw inspiration
from Transfer of Learning approaches to reduce the start-up time to training
each incremental Ensemble member. We show a set of experiments that outlines
some preliminary results on some common Deep Learning datasets and discuss the
potential improvements Deep Incremental Boosting brings to traditional Ensemble
methods in Deep Learning.
",1,0,0,1,0,0
17273,Empirical Likelihood for Linear Structural Equation Models with Dependent Errors,"  We consider linear structural equation models that are associated with mixed
graphs. The structural equations in these models only involve observed
variables, but their idiosyncratic error terms are allowed to be correlated and
non-Gaussian. We propose empirical likelihood (EL) procedures for inference,
and suggest several modifications, including a profile likelihood, in order to
improve tractability and performance of the resulting methods. Through
simulations, we show that when the error distributions are non-Gaussian, the
use of EL and the proposed modifications may increase statistical efficiency
and improve assessment of significance.
",0,0,0,1,0,0
17274,Grassmannian flows and applications to nonlinear partial differential equations,"  We show how solutions to a large class of partial differential equations with
nonlocal Riccati-type nonlinearities can be generated from the corresponding
linearized equations, from arbitrary initial data. It is well known that
evolutionary matrix Riccati equations can be generated by projecting linear
evolutionary flows on a Stiefel manifold onto a coordinate chart of the
underlying Grassmann manifold. Our method relies on extending this idea to the
infinite dimensional case. The key is an integral equation analogous to the
Marchenko equation in integrable systems, that represents the coodinate chart
map. We show explicitly how to generate such solutions to scalar partial
differential equations of arbitrary order with nonlocal quadratic
nonlinearities using our approach. We provide numerical simulations that
demonstrate the generation of solutions to
Fisher--Kolmogorov--Petrovskii--Piskunov equations with nonlocal
nonlinearities. We also indicate how the method might extend to more general
classes of nonlinear partial differential systems.
",0,1,1,0,0,0
17275,The Reinhardt Conjecture as an Optimal Control Problem,"  In 1934, Reinhardt conjectured that the shape of the centrally symmetric
convex body in the plane whose densest lattice packing has the smallest density
is a smoothed octagon. This conjecture is still open. We formulate the
Reinhardt Conjecture as a problem in optimal control theory. The smoothed
octagon is a Pontryagin extremal trajectory with bang-bang control. More
generally, the smoothed regular $6k+2$-gon is a Pontryagin extremal with
bang-bang control. The smoothed octagon is a strict (micro) local minimum to
the optimal control problem. The optimal solution to the Reinhardt problem is a
trajectory without singular arcs. The extremal trajectories that do not meet
the singular locus have bang-bang controls with finitely many switching times.
Finally, we reduce the Reinhardt problem to an optimization problem on a
five-dimensional manifold. (Each point on the manifold is an initial condition
for a potential Pontryagin extremal lifted trajectory.) We suggest that the
Reinhardt conjecture might eventually be fully resolved through optimal control
theory. Some proofs are computer-assisted using a computer algebra system.
",0,0,1,0,0,0
17276,Deep submillimeter and radio observations in the SSA22 field. I. Powering sources and Lyα escape fraction of Lyα blobs,"  We study the heating mechanisms and Ly{\alpha} escape fractions of 35
Ly{\alpha} blobs (LABs) at z = 3.1 in the SSA22 field. Dust continuum sources
have been identified in 11 of the 35 LABs, all with star formation rates (SFRs)
above 100 Msun/yr. Likely radio counterparts are detected in 9 out of 29
investigated LABs. The detection of submm dust emission is more linked to the
physical size of the Ly{\alpha} emission than to the Ly{\alpha} luminosities of
the LABs. A radio excess in the submm/radio detected LABs is common, hinting at
the presence of active galactic nuclei. Most radio sources without X-ray
counterparts are located at the centers of the LABs. However, all X-ray
counterparts avoid the central regions. This may be explained by absorption due
to exceptionally large column densities along the line-of-sight or by LAB
morphologies, which are highly orientation dependent. The median Ly{\alpha}
escape fraction is about 3\% among the submm-detected LABs, which is lower than
a lower limit of 11\% for the submm-undetected LABs. We suspect that the large
difference is due to the high dust attenuation supported by the large SFRs, the
dense large-scale environment as well as large uncertainties in the extinction
corrections required to apply when interpreting optical data.
",0,1,0,0,0,0
17277,Modeling temporal constraints for a system of interactive scores,"  In this chapter we explain briefly the fundamentals of the interactive scores
formalism. Then we develop a solution for implementing the ECO machine by
mixing petri nets and constraints propagation. We also present another solution
for implementing the ECO machine using concurrent constraint programming.
Finally, we present an extension of interactive score with conditional
branching.
",1,0,0,0,0,0
17278,Electronic structure of ThRu2Si2 studied by angle-resolved photoelectron spectroscopy: Elucidating the contribution of U 5f states in URu2Si2,"  The electronic structure of ThRu2Si2 was studied by angle-resolved
photoelectron spectroscopy (ARPES) with incident photon energies of hn=655-745
eV. Detailed band structure and the three-dimensional shapes of Fermi surfaces
were derived experimentally, and their characteristic features were mostly
explained by means of band structure calculations based on the density
functional theory. Comparison of the experimental ARPES spectra of ThRu2Si2
with those of URu2Si2 shows that they have considerably different spectral
profiles particularly in the energy range of 1 eV from the Fermi level,
suggesting that U 5f states are substantially hybridized in these bands. The
relationship between the ARPES spectra of URu2Si2 and ThRu2Si2 is very
different from the one between the ARPES spectra of CeRu2Si2 and LaRu2Si2,
where the intrinsic difference in their spectra is limited only in the very
vicinity of the Fermi energy. The present result suggests that the U 5f
electrons in URu2Si2 have strong hybridization with ligand states and have an
essentially itinerant character.
",0,1,0,0,0,0
17279,Non-zero constant curvature factorable surfaces in pseudo-Galilean space,"  Factorable surfaces, i.e. graphs associated with the product of two functions
of one variable, constitute a wide class of surfaces. Such surfaces in the
pseudo-Galilean space with zero Gaussian and mean curvature were obtained in
[1]. In this study, we provide new classification results relating to the
factorable surfaces with non-zero Gaussian and mean curvature.
",0,0,1,0,0,0
17280,Darboux and Binary Darboux Transformations for Discrete Integrable Systems. II. Discrete Potential mKdV Equation,"  The paper presents two results. First it is shown how the discrete potential
modified KdV equation and its Lax pairs in matrix form arise from the
Hirota-Miwa equation by a 2-periodic reduction. Then Darboux transformations
and binary Darboux transformations are derived for the discrete potential
modified KdV equation and it is shown how these may be used to construct exact
solutions.
",0,1,0,0,0,0
17281,Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates,"  Sequential change-point detection when the distribution parameters are
unknown is a fundamental problem in statistics and machine learning. When the
post-change parameters are unknown, we consider a set of detection procedures
based on sequential likelihood ratios with non-anticipating estimators
constructed using online convex optimization algorithms such as online mirror
descent, which provides a more versatile approach to tackle complex situations
where recursive maximum likelihood estimators cannot be found. When the
underlying distributions belong to a exponential family and the estimators
satisfy the logarithm regret property, we show that this approach is nearly
second-order asymptotically optimal. This means that the upper bound for the
false alarm rate of the algorithm (measured by the average-run-length) meets
the lower bound asymptotically up to a log-log factor when the threshold tends
to infinity. Our proof is achieved by making a connection between sequential
change-point and online convex optimization and leveraging the logarithmic
regret bound property of online mirror descent algorithm. Numerical and real
data examples validate our theory.
",1,0,1,1,0,0
17282,Algorithms in the classical Néron Desingularization,"  We give algorithms to construct the Néron Desingularization and the easy
case from \cite{KK} of the General Néron Desingularization.
",0,0,1,0,0,0
17283,Recent Advances in Neural Program Synthesis,"  In recent years, deep learning has made tremendous progress in a number of
fields that were previously out of reach for artificial intelligence. The
successes in these problems has led researchers to consider the possibilities
for intelligent systems to tackle a problem that humans have only recently
themselves considered: program synthesis. This challenge is unlike others such
as object recognition and speech translation, since its abstract nature and
demand for rigor make it difficult even for human minds to attempt. While it is
still far from being solved or even competitive with most existing methods,
neural program synthesis is a rapidly growing discipline which holds great
promise if completely realized. In this paper, we start with exploring the
problem statement and challenges of program synthesis. Then, we examine the
fascinating evolution of program induction models, along with how they have
succeeded, failed and been reimagined since. Finally, we conclude with a
contrastive look at program synthesis and future research recommendations for
the field.
",1,0,0,0,0,0
17284,Generator Reversal,"  We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we propose instead to use more flexible code distributions. These
distributions are estimated non-parametrically by reversing the generator map
during training. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
",1,0,0,1,0,0
17285,Finite model reasoning over existential rules,"  Ontology-based query answering (OBQA) asks whether a Boolean conjunctive
query is satisfied by all models of a logical theory consisting of a relational
database paired with an ontology. The introduction of existential rules (i.e.,
Datalog rules extended with existential quantifiers in rule-heads) as a means
to specify the ontology gave birth to Datalog+/-, a framework that has received
increasing attention in the last decade, with focus also on decidability and
finite controllability to support effective reasoning. Five basic decidable
fragments have been singled out: linear, weakly-acyclic, guarded, sticky, and
shy. Moreover, for all these fragments, except shy, the important property of
finite controllability has been proved, ensuring that a query is satisfied by
all models of the theory iff it is satisfied by all its finite models. In this
paper we complete the picture by demonstrating that finite controllability of
OBQA holds also for shy ontologies, and it therefore applies to all basic
decidable Datalog+/- classes. To make the demonstration, we devise a general
technique to facilitate the process of (dis)proving finite controllability of
an arbitrary ontological fragment. This paper is under consideration for
acceptance in TPLP.
",1,0,0,0,0,0
17286,On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization,"  Despite their popularity, the practical performance of asynchronous
stochastic gradient descent methods (ASGD) for solving large scale machine
learning problems are not as good as theoretical results indicate. We adopt and
analyze a synchronous K-step averaging stochastic gradient descent algorithm
which we call K-AVG. We establish the convergence results of K-AVG for
nonconvex objectives and explain why the K-step delay is necessary and leads to
better performance than traditional parallel stochastic gradient descent which
is a special case of K-AVG with $K=1$. We also show that K-AVG scales better
than ASGD. Another advantage of K-AVG over ASGD is that it allows larger
stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD
implementations and achieves better accuracies and faster convergence for
\cifar dataset.
",1,0,0,1,0,0
17287,Adversarial Neural Machine Translation,"  In this paper, we study a new learning paradigm for Neural Machine
Translation (NMT). Instead of maximizing the likelihood of the human
translation as in previous works, we minimize the distinction between human
translation and the translation given by an NMT model. To achieve this goal,
inspired by the recent success of generative adversarial networks (GANs), we
employ an adversarial training architecture and name it as Adversarial-NMT. In
Adversarial-NMT, the training of the NMT model is assisted by an adversary,
which is an elaborately designed Convolutional Neural Network (CNN). The goal
of the adversary is to differentiate the translation result generated by the
NMT model from that by human. The goal of the NMT model is to produce high
quality translations so as to cheat the adversary. A policy gradient method is
leveraged to co-train the NMT model and the adversary. Experimental results on
English$\rightarrow$French and German$\rightarrow$English translation tasks
show that Adversarial-NMT can achieve significantly better translation quality
than several strong baselines.
",1,0,0,1,0,0
17288,Surface group amalgams that (don't) act on 3-manifolds,"  We determine which amalgamated products of surface groups identified over
multiples of simple closed curves are not fundamental groups of 3-manifolds. We
prove each surface amalgam considered is virtually the fundamental group of a
3-manifold. We prove that each such surface group amalgam is abstractly
commensurable to a right-angled Coxeter group from a related family. In an
appendix, we determine the quasi-isometry classes among these surface amalgams
and their related right-angled Coxeter groups.
",0,0,1,0,0,0
17289,Shading Annotations in the Wild,"  Understanding shading effects in images is critical for a variety of vision
and graphics problems, including intrinsic image decomposition, shadow removal,
image relighting, and inverse rendering. As is the case with other vision
tasks, machine learning is a promising approach to understanding shading - but
there is little ground truth shading data available for real-world images. We
introduce Shading Annotations in the Wild (SAW), a new large-scale, public
dataset of shading annotations in indoor scenes, comprised of multiple forms of
shading judgments obtained via crowdsourcing, along with shading annotations
automatically generated from RGB-D imagery. We use this data to train a
convolutional neural network to predict per-pixel shading information in an
image. We demonstrate the value of our data and network in an application to
intrinsic images, where we can reduce decomposition artifacts produced by
existing algorithms. Our database is available at
this http URL.
",1,0,0,0,0,0
17290,Koszul cycles and Golod rings,"  Let $S$ be the power series ring or the polynomial ring over a field $K$ in
the variables $x_1,\ldots,x_n$, and let $R=S/I$, where $I$ is proper ideal
which we assume to be graded if $S$ is the polynomial ring. We give an explicit
description of the cycles of the Koszul complex whose homology classes generate
the Koszul homology of $R=S/I$ with respect to $x_1,\ldots,x_n$. The
description is given in terms of the data of the free $S$-resolution of $R$.
The result is used to determine classes of Golod ideals, among them proper
ordinary powers and proper symbolic powers of monomial ideals. Our theory is
also applied to stretched local rings.
",0,0,1,0,0,0
17291,PacGAN: The power of two samples in generative adversarial networks,"  Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little diversity, even when trained on diverse datasets. This phenomenon,
known as mode collapse, has been the main focus of several recent advances in
GANs. Yet there is little understanding of why mode collapse happens and why
existing approaches are able to mitigate mode collapse. We propose a principled
approach to handling mode collapse, which we call packing. The main idea is to
modify the discriminator to make decisions based on multiple samples from the
same class, either real or artificially generated. We borrow analysis tools
from binary hypothesis testing---in particular the seminal result of Blackwell
[Bla53]---to prove a fundamental connection between packing and mode collapse.
We show that packing naturally penalizes generators with mode collapse, thereby
favoring generator distributions with less mode collapse during the training
process. Numerical experiments on benchmark datasets suggests that packing
provides significant improvements in practice as well.
",1,0,0,1,0,0
17292,Stein-like Estimators for Causal Mediation Analysis in Randomized Trials,"  Causal mediation analysis aims to estimate the natural direct and indirect
effects under clearly specified assumptions. Traditional mediation analysis
based on Ordinary Least Squares (OLS) relies on the absence of unmeasured
causes of the putative mediator and outcome. When this assumption cannot be
justified, Instrumental Variables (IV) estimators can be used in order to
produce an asymptotically unbiased estimator of the mediator-outcome link.
However, provided that valid instruments exist, bias removal comes at the cost
of variance inflation for standard IV procedures such as Two-Stage Least
Squares (TSLS). A Semi-Parametric Stein-Like (SPSL) estimator has been proposed
in the literature that strikes a natural trade-off between the unbiasedness of
the TSLS procedure and the relatively small variance of the OLS estimator.
Moreover, the SPSL has the advantage that its shrinkage parameter can be
directly estimated from the data. In this paper, we demonstrate how this
Stein-like estimator can be implemented in the context of the estimation of
natural direct and natural indirect effects of treatments in randomized
controlled trials. The performance of the competing methods is studied in a
simulation study, in which both the strength of hidden confounding and the
strength of the instruments are independently varied. These considerations are
motivated by a trial in mental health evaluating the impact of a primary
care-based intervention to reduce depression in the elderly.
",0,0,0,1,0,0
17293,Structure-Based Subspace Method for Multi-Channel Blind System Identification,"  In this work, a novel subspace-based method for blind identification of
multichannel finite impulse response (FIR) systems is presented. Here, we
exploit directly the impeded Toeplitz channel structure in the signal linear
model to build a quadratic form whose minimization leads to the desired channel
estimation up to a scalar factor. This method can be extended to estimate any
predefined linear structure, e.g. Hankel, that is usually encountered in linear
systems. Simulation findings are provided to highlight the appealing advantages
of the new structure-based subspace (SSS) method over the standard subspace
(SS) method in certain adverse identification scenarii.
",1,0,0,1,0,0
17294,On Certain Analytical Representations of Cellular Automata,"  We extend a previously introduced semi-analytical representation of a
decomposition of CA dynamics in arbitrary dimensions and neighborhood schemes
via the use of certain universal maps in which CA rule vectors are derivable
from the equivalent of superpotentials. The results justify the search for
alternative analog models of computation and their possible physical
connections.
",0,1,0,0,0,0
17295,Strong consistency and optimality for generalized estimating equations with stochastic covariates,"  In this article we study the existence and strong consistency of GEE
estimators, when the generalized estimating functions are martingales with
random coefficients. Furthermore, we characterize estimating functions which
are asymptotically optimal.
",0,0,1,1,0,0
17296,Synthesis and electronic properties of Ruddlesden-Popper strontium iridate epitaxial thin films stabilized by control of growth kinetics,"  We report on the selective fabrication of high-quality Sr$_2$IrO$_4$ and
SrIrO$_3$ epitaxial thin films from a single polycrystalline Sr$_2$IrO$_4$
target by pulsed laser deposition. Using a combination of X-ray diffraction and
photoemission spectroscopy characterizations, we discover that within a
relatively narrow range of substrate temperature, the oxygen partial pressure
plays a critical role in the cation stoichiometric ratio of the films, and
triggers the stabilization of different Ruddlesden-Popper (RP) phases. Resonant
X-ray absorption spectroscopy measurements taken at the Ir $L$-edge and the O
$K$-edge demonstrate the presence of strong spin-orbit coupling, and reveal the
electronic and orbital structures of both compounds. These results suggest that
in addition to the conventional thermodynamics consideration, higher members of
the Sr$_{n+1}$Ir$_n$O$_{3n+1}$ series can possibly be achieved by kinetic
control away from the thermodynamic limit. These findings offer a new approach
to the synthesis of ultra-thin films of the RP series of iridates and can be
extended to other complex oxides with layered structure.
",0,1,0,0,0,0
17297,A proof on energy gap for Yang-Mills connection,"  In this note, we prove an ${L^{\frac{n}{2}}}$-energy gap result for
Yang-Mills connections on a principal $G$-bundle over a compact manifold
without using Lojasiewicz-Simon gradient inequality (arXiv:1502.00668).
",0,0,1,0,0,0
17298,Realisability of Pomsets via Communicating Automata,"  Pomsets are a model of concurrent computations introduced by Pratt. They can
provide a syntax-oblivious description of semantics of coordination models
based on asynchronous message-passing, such as Message Sequence Charts (MSCs).
In this paper, we study conditions that ensure a specification expressed as a
set of pomsets can be faithfully realised via communicating automata. Our main
contributions are (i) the definition of a realisability condition accounting
for termination soundness, (ii) conditions for global specifications with
""multi-threaded"" participants, and (iii) the definition of realisability
conditions that can be decided directly over pomsets. A positive by-product of
our approach is the efficiency gain in the verification of the realisability
conditions obtained when restricting to specific classes of choreographies
characterisable in term of behavioural types.
",1,0,0,0,0,0
17299,Complex pattern formation driven by the interaction of stable fronts in a competition-diffusion system,"  The ecological invasion problem in which a weaker exotic species invades an
ecosystem inhabited by two strongly competing native species is modelled by a
three-species competition-diffusion system. It is known that for a certain
range of parameter values competitor-mediated coexistence occurs and complex
spatio-temporal patterns are observed in two spatial dimensions. In this paper
we uncover the mechanism which generates such patterns. Under some assumptions
on the parameters the three-species competition-diffusion system admits two
planarly stable travelling waves. Their interaction in one spatial dimension
may result in either reflection or merging into a single homoclinic wave,
depending on the strength of the invading species. This transition can be
understood by studying the bifurcation structure of the homoclinic wave. In
particular, a time-periodic homoclinic wave (breathing wave) is born from a
Hopf bifurcation and its unstable branch acts as a separator between the
reflection and merging regimes. The same transition occurs in two spatial
dimensions: the stable regular spiral associated to the homoclinic wave
destabilizes, giving rise first to an oscillating breathing spiral and then
breaking up producing a dynamic pattern characterized by many spiral cores. We
find that these complex patterns are generated by the interaction of two
planarly stable travelling waves, in contrast with many other well known cases
of pattern formation where planar instability plays a central role.
",0,0,0,0,1,0
17300,Solitons with rings and vortex rings on solitons in nonlocal nonlinear media,"  Nonlocality is a key feature of many physical systems since it prevents a
catastrophic collapse and a symmetry-breaking azimuthal instability of intense
wave beams in a bulk self-focusing nonlinear media. This opens up an intriguing
perspective for stabilization of complex topological structures such as
higher-order solitons, vortex rings and vortex ring-on-line complexes. Using
direct numerical simulations, we find a class of cylindrically-symmetric $n$-th
order spatial solitons having the intensity distribution with a central bright
spot surrounded by $n$ bright rings of varying size. We investigate dynamical
properties of these higher-order solitons in a media with thermal nonlocal
nonlinear response. We show theoretically that a vortex complex of vortex ring
and vortex line, carrying two independent winding numbers, can be created by
perturbation of the stable optical vortex soliton in nonlocal nonlinear media.
",0,1,0,0,0,0
17301,Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection,"  We introduce Deep-HiTS, a rotation invariant convolutional neural network
(CNN) model for classifying images of transients candidates into artifacts or
real sources for the High cadence Transient Survey (HiTS). CNNs have the
advantage of learning the features automatically from the data while achieving
high performance. We compare our CNN model against a feature engineering
approach using random forests (RF). We show that our CNN significantly
outperforms the RF model reducing the error by almost half. Furthermore, for a
fixed number of approximately 2,000 allowed false transient candidates per
night we are able to reduce the miss-classified real transients by
approximately 1/5. To the best of our knowledge, this is the first time CNNs
have been used to detect astronomical transient events. Our approach will be
very useful when processing images from next generation instruments such as the
Large Synoptic Survey Telescope (LSST). We have made all our code and data
available to the community for the sake of allowing further developments and
comparisons at this https URL.
",1,1,0,0,0,0
17302,"On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL","  In various approaches to learning, notably in domain adaptation, active
learning, learning under covariate shift, semi-supervised learning, learning
with concept drift, and the like, one often wants to compare a baseline
classifier to one or more advanced (or at least different) strategies. In this
chapter, we basically argue that if such classifiers, in their respective
training phases, optimize a so-called surrogate loss that it may also be
valuable to compare the behavior of this loss on the test set, next to the
regular classification error rate. It can provide us with an additional view on
the classifiers' relative performances that error rates cannot capture. As an
example, limited but convincing empirical results demonstrates that we may be
able to find semi-supervised learning strategies that can guarantee performance
improvements with increasing numbers of unlabeled data in terms of
log-likelihood. In contrast, the latter may be impossible to guarantee for the
classification error rate.
",1,0,0,1,0,0
17303,Do Reichenbachian Common Cause Systems of Arbitrary Finite Size Exist?,"  The principle of common cause asserts that positive correlations between
causally unrelated events ought to be explained through the action of some
shared causal factors. Reichenbachian common cause systems are probabilistic
structures aimed at accounting for cases where correlations of the aforesaid
sort cannot be explained through the action of a single common cause. The
existence of Reichenbachian common cause systems of arbitrary finite size for
each pair of non-causally correlated events was allegedly demonstrated by
Hofer-Szabó and Rédei in 2006. This paper shows that their proof is
logically deficient, and we propose an improved proof.
",1,1,0,1,0,0
17304,Co-evolution of nodes and links: diversity driven coexistence in cyclic competition of three species,"  When three species compete cyclically in a well-mixed, stochastic system of
$N$ individuals, extinction is known to typically occur at times scaling as the
system size $N$. This happens, for example, in rock-paper-scissors games or
conserved Lotka-Volterra models in which every pair of individuals can interact
on a complete graph. Here we show that if the competing individuals also have a
""social temperament"" to be either introverted or extroverted, leading them to
cut or add links respectively, then long-living state in which all species
coexist can occur when both introverts and extroverts are present. These states
are non-equilibrium quasi-steady states, maintained by a subtle balance between
species competition and network dynamcis. Remarkably, much of the phenomena is
embodied in a mean-field description. However, an intuitive understanding of
why diversity stabilizes the co-evolving node and link dynamics remains an open
issue.
",0,0,0,0,1,0
17305,Online Learning with an Almost Perfect Expert,"  We study the multiclass online learning problem where a forecaster makes a
sequence of predictions using the advice of $n$ experts. Our main contribution
is to analyze the regime where the best expert makes at most $b$ mistakes and
to show that when $b = o(\log_4{n})$, the expected number of mistakes made by
the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe
an adversary strategy showing that this bound is tight and that the worst case
is attained for binary prediction.
",0,0,0,1,0,0
17306,Actively Learning what makes a Discrete Sequence Valid,"  Deep learning techniques have been hugely successful for traditional
supervised and unsupervised machine learning problems. In large part, these
techniques solve continuous optimization problems. Recently however, discrete
generative deep learning models have been successfully used to efficiently
search high-dimensional discrete spaces. These methods work by representing
discrete objects as sequences, for which powerful sequence-based deep models
can be employed. Unfortunately, these techniques are significantly hindered by
the fact that these generative models often produce invalid sequences. As a
step towards solving this problem, we propose to learn a deep recurrent
validator model. Given a partial sequence, our model learns the probability of
that sequence occurring as the beginning of a full valid sequence. Thus this
identifies valid versus invalid sequences and crucially it also provides
insight about how individual sequence elements influence the validity of
discrete objects. To learn this model we propose an approach inspired by
seminal work in Bayesian active learning. On a synthetic dataset, we
demonstrate the ability of our model to distinguish valid and invalid
sequences. We believe this is a key step toward learning generative models that
faithfully produce valid discrete objects.
",1,0,0,1,0,0
17307,Symmetries and conservation laws of Hamiltonian systems,"  In this paper we study the infinitesimal symmetries, Newtonoid vector fields,
infinitesimal Noether symmetries and conservation laws of Hamiltonian systems.
Using the dynamical covariant derivative and Jacobi endomorphism on the
cotangent bundle we find the invariant equations of infinitesimal symmetries
and Newtonoid vector fields and prove that the canonical nonlinear connection
induced by a regular Hamiltonian can be determined by these symmetries.
Finally, an example from optimal control theory is given.
",0,0,1,0,0,0
17308,Fractional differential and fractional integral modified-Bloch equations for PFG anomalous diffusion and their general solutions,"  The studying of anomalous diffusion by pulsed field gradient (PFG) diffusion
technique still faces challenges. Two different research groups have proposed
modified Bloch equation for anomalous diffusion. However, these equations have
different forms and, therefore, yield inconsistent results. The discrepancy in
these reported modified Bloch equations may arise from different ways of
combining the fractional diffusion equation with the precession equation where
the time derivatives have different derivative orders and forms. Moreover, to
the best of my knowledge, the general PFG signal attenuation expression
including finite gradient pulse width (FGPW) effect for time-space fractional
diffusion based on the fractional derivative has yet to be reported by other
methods. Here, based on different combination strategy, two new modified Bloch
equations are proposed, which belong to two significantly different types: a
differential type based on the fractal derivative and an integral type based on
the fractional derivative. The merit of the integral type modified Bloch
equation is that the original properties of the contributions from linear or
nonlinear processes remain unchanged at the instant of the combination. The
general solutions including the FGPW effect were derived from these two
equations as well as from two other methods: a method observing the signal
intensity at the origin and the recently reported effective phase shift
diffusion equation method. The relaxation effect was also considered. It is
found that the relaxation behavior influenced by fractional diffusion based on
the fractional derivative deviates from that of normal diffusion. The general
solution agrees perfectly with continuous-time random walk (CTRW) simulations
as well as reported literature results. The new modified Bloch equations is a
valuable tool to describe PFG anomalous diffusion in NMR and MRI.
",0,1,0,0,0,0
17309,Change of the vortex core structure in two-band superconductors at impurity-scattering-driven $s_\pm/s_{++}$ crossover,"  We report a nontrivial transition in the core structure of vortices in
two-band superconductors as a function of interband impurity scattering. We
demonstrate that, in addition to singular zeros of the order parameter, the
vortices there can acquire a circular nodal line around the singular point in
one of the superconducting components. It results in the formation of the
peculiar ""moat""-like profile in one of the superconducting gaps. The moat-core
vortices occur generically in the vicinity of the impurity-induced crossover
between $s_{\pm}$ and $s_{++}$ states.
",0,1,0,0,0,0
17310,Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit,"  Multi-armed bandit (MAB) is a class of online learning problems where a
learning agent aims to maximize its expected cumulative reward while repeatedly
selecting to pull arms with unknown reward distributions. We consider a
scenario where the reward distributions may change in a piecewise-stationary
fashion at unknown time steps. We show that by incorporating a simple
change-detection component with classic UCB algorithms to detect and adapt to
changes, our so-called M-UCB algorithm can achieve nearly optimal regret bound
on the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps,
$K$ is the number of arms, and $M$ is the number of stationary segments.
Comparison with the best available lower bound shows that our M-UCB is nearly
optimal in $T$ up to a logarithmic factor. We also compare M-UCB with the
state-of-the-art algorithms in numerical experiments using a public Yahoo!
dataset to demonstrate its superior performance.
",0,0,0,1,0,0
17311,An initial-boundary value problem of the general three-component nonlinear Schrodinger equation with a 4x4 Lax pair on a finite interval,"  We investigate the initial-boundary value problem for the general
three-component nonlinear Schrodinger (gtc-NLS) equation with a 4x4 Lax pair on
a finite interval by extending the Fokas unified approach. The solutions of the
gtc-NLS equation can be expressed in terms of the solutions of a 4x4 matrix
Riemann-Hilbert (RH) problem formulated in the complex k-plane. Moreover, the
relevant jump matrices of the RH problem can be explicitly found via the three
spectral functions arising from the initial data, the Dirichlet-Neumann
boundary data. The global relation is also established to deduce two distinct
but equivalent types of representations (i.e., one by using the large k of
asymptotics of the eigenfunctions and another one in terms of the
Gelfand-Levitan-Marchenko (GLM) method) for the Dirichlet and Neumann boundary
value problems. Moreover, the relevant formulae for boundary value problems on
the finite interval can reduce to ones on the half-line as the length of the
interval approaches to infinity. Finally, we also give the linearizable
boundary conditions for the GLM representation.
",0,1,1,0,0,0
17312,Deep Learning Microscopy,"  We demonstrate that a deep neural network can significantly improve optical
microscopy, enhancing its spatial resolution over a large field-of-view and
depth-of-field. After its training, the only input to this network is an image
acquired using a regular optical microscope, without any changes to its design.
We blindly tested this deep learning approach using various tissue samples that
are imaged with low-resolution and wide-field systems, where the network
rapidly outputs an image with remarkably better resolution, matching the
performance of higher numerical aperture lenses, also significantly surpassing
their limited field-of-view and depth-of-field. These results are
transformative for various fields that use microscopy tools, including e.g.,
life sciences, where optical microscopy is considered as one of the most widely
used and deployed techniques. Beyond such applications, our presented approach
is broadly applicable to other imaging modalities, also spanning different
parts of the electromagnetic spectrum, and can be used to design computational
imagers that get better and better as they continue to image specimen and
establish new transformations among different modes of imaging.
",1,1,0,0,0,0
17313,Effects of pressure impulse and peak pressure of a shock wave on microjet velocity and the onset of cavitation in a microchannel,"  The development of needle-free injection systems utilizing high-speed
microjets is of great importance to world healthcare. It is thus crucial to
control the microjets, which are often induced by underwater shock waves. In
this contribution from fluid-mechanics point of view, we experimentally
investigate the effect of a shock wave on the velocity of a free surface
(microjet) and underwater cavitation onset in a microchannel, focusing on the
pressure impulse and peak pressure of the shock wave. The shock wave used had a
non-spherically-symmetric peak pressure distribution and a spherically
symmetric pressure impulse distribution [Tagawa et al., J. Fluid Mech., 2016,
808, 5-18]. First, we investigate the effect of the shock wave on the jet
velocity by installing a narrow tube and a hydrophone in different
configurations in a large water tank, and measuring the shock wave pressure and
the jet velocity simultaneously. The results suggest that the jet velocity
depends only on the pressure impulse of the shock wave. We then investigate the
effect of the shock wave on the cavitation onset by taking measurements in an
L-shaped microchannel. The results suggest that the probability of cavitation
onset depends only on the peak pressure of the shock wave. In addition, the jet
velocity varies according to the presence or absence of cavitation. The above
findings provide new insights for advancing a control method for high-speed
microjets.
",0,1,0,0,0,0
17314,Clustering with Noisy Queries,"  In this paper, we initiate a rigorous theoretical study of clustering with
noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to
recover the true clustering by asking minimum number of pairwise queries to an
oracle. Oracle can answer queries of the form : ""do elements $u$ and $v$ belong
to the same cluster?"" -- the queries can be asked interactively (adaptive
queries), or non-adaptively up-front, but its answer can be erroneous with
probability $p$. In this paper, we provide the first information theoretic
lower bound on the number of queries for clustering with noisy oracle in both
situations. We design novel algorithms that closely match this query complexity
lower bound, even when the number of clusters is unknown. Moreover, we design
computationally efficient algorithms both for the adaptive and non-adaptive
settings. The problem captures/generalizes multiple application scenarios. It
is directly motivated by the growing body of work that use crowdsourcing for
{\em entity resolution}, a fundamental and challenging data mining task aimed
to identify all records in a database referring to the same entity. Here crowd
represents the noisy oracle, and the number of queries directly relates to the
cost of crowdsourcing. Another application comes from the problem of {\em sign
edge prediction} in social network, where social interactions can be both
positive and negative, and one must identify the sign of all pair-wise
interactions by querying a few pairs. Furthermore, clustering with noisy oracle
is intimately connected to correlation clustering, leading to improvement
therein. Finally, it introduces a new direction of study in the popular {\em
stochastic block model} where one has an incomplete stochastic block model
matrix to recover the clusters.
",1,0,0,1,0,0
17315,Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation,"  Classical reverse-mode automatic differentiation (AD) imposes only a small
constant-factor overhead in operation count over the original computation, but
has storage requirements that grow, in the worst case, in proportion to the
time consumed by the original computation. This storage blowup can be
ameliorated by checkpointing, a process that reorders application of classical
reverse-mode AD over an execution interval to tradeoff space \vs\ time.
Application of checkpointing in a divide-and-conquer fashion to strategically
chosen nested execution intervals can break classical reverse-mode AD into
stages which can reduce the worst-case growth in storage from linear to
sublinear. Doing this has been fully automated only for computations of
particularly simple form, with checkpoints spanning execution intervals
resulting from a limited set of program constructs. Here we show how the
technique can be automated for arbitrary computations. The essential innovation
is to apply the technique at the level of the language implementation itself,
thus allowing checkpoints to span any execution interval.
",1,0,0,0,0,0
17316,Bow Ties in the Sky II: Searching for Gamma-ray Halos in the Fermi Sky Using Anisotropy,"  Many-degree-scale gamma-ray halos are expected to surround extragalactic
high-energy gamma ray sources. These arise from the inverse Compton emission of
an intergalactic population of relativistic electron/positron pairs generated
by the annihilation of >100 GeV gamma rays on the extragalactic background
light. These are typically anisotropic due to the jetted structure from which
they originate or the presence of intergalactic magnetic fields. Here we
propose a novel method for detecting these inverse-Compton gamma-ray halos
based upon this anisotropic structure. Specifically, we show that by stacking
suitably defined angular power spectra instead of images it is possible to
robustly detect gamma-ray halos with existing Fermi Large Area Telescope (LAT)
observations for a broad class of intergalactic magnetic fields. Importantly,
these are largely insensitive to systematic uncertainties within the LAT
instrumental response or associated with contaminating astronomical sources.
",0,1,0,0,0,0
17317,Gain-loss-driven travelling waves in PT-symmetric nonlinear metamaterials,"  In this work we investigate a one-dimensional parity-time (PT)-symmetric
magnetic metamaterial consisting of split-ring dimers having gain or loss.
Employing a Melnikov analysis we study the existence of localized travelling
waves, i.e. homoclinic or heteroclinic solutions. We find conditions under
which the homoclinic or heteroclinic orbits persist. Our analytical results are
found to be in good agreement with direct numerical computations. For the
particular nonlinearity admitting travelling kinks, numerically we observe
homoclinic snaking in the bifurcation diagram. The Melnikov analysis yields a
good approximation to one of the boundaries of the snaking profile.
",0,1,0,0,0,0
17318,CapsuleGAN: Generative Adversarial Capsule Network,"  We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional
neural networks (CNNs) as discriminators within the generative adversarial
network (GAN) setting, while modeling image data. We provide guidelines for
designing CapsNet discriminators and the updated GAN objective function, which
incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
that CapsuleGAN outperforms convolutional-GAN at modeling image data
distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
adversarial metric and at semi-supervised image classification.
",0,0,0,1,0,0
17319,sourceR: Classification and Source Attribution of Infectious Agents among Heterogeneous Populations,"  Zoonotic diseases are a major cause of morbidity, and productivity losses in
both humans and animal populations. Identifying the source of food-borne
zoonoses (e.g. an animal reservoir or food product) is crucial for the
identification and prioritisation of food safety interventions. For many
zoonotic diseases it is difficult to attribute human cases to sources of
infection because there is little epidemiological information on the cases.
However, microbial strain typing allows zoonotic pathogens to be categorised,
and the relative frequencies of the strain types among the sources and in human
cases allows inference on the likely source of each infection. We introduce
sourceR, an R package for quantitative source attribution, aimed at food-borne
diseases. It implements a fully joint Bayesian model using strain-typed
surveillance data from both human cases and source samples, capable of
identifying important sources of infection. The model measures the force of
infection from each source, allowing for varying survivability, pathogenicity
and virulence of pathogen strains, and varying abilities of the sources to act
as vehicles of infection. A Bayesian non-parametric (Dirichlet process)
approach is used to cluster pathogen strain types by epidemiological behaviour,
avoiding model overfitting and allowing detection of strain types associated
with potentially high 'virulence'.
sourceR is demonstrated using Campylobacter jejuni isolate data collected in
New Zealand between 2005 and 2008. It enables straightforward attribution of
cases of zoonotic infection to putative sources of infection by epidemiologists
and public health decision makers. As sourceR develops, we intend it to become
an important and flexible resource for food-borne disease attribution studies.
",0,0,0,1,0,0
17320,Low resistive edge contacts to CVD-grown graphene using a CMOS compatible metal,"  The exploitation of the excellent intrinsic electronic properties of graphene
for device applications is hampered by a large contact resistance between the
metal and graphene. The formation of edge contacts rather than top contacts is
one of the most promising solutions for realizing low ohmic contacts. In this
paper the fabrication and characterization of edge contacts to large area
CVD-grown monolayer graphene by means of optical lithography using CMOS
compatible metals, i.e. Nickel and Aluminum is reported. Extraction of the
contact resistance by Transfer Line Method (TLM) as well as the direct
measurement using Kelvin Probe Force Microscopy demonstrates a very low width
specific contact resistance.
",0,1,0,0,0,0
17321,Uniqueness of planar vortex patch in incompressible steady flow,"  We investigate a steady planar flow of an ideal fluid in a bounded simple
connected domain and focus on the vortex patch problem with prescribed
vorticity strength. There are two methods to deal with the existence of
solutions for this problem: the vorticity method and the stream function
method. A long standing open problem is whether these two entirely different
methods result in the same solution. In this paper, we will give a positive
answer to this problem by studying the local uniqueness of the solutions.
Another result obtained in this paper is that if the domain is convex, then the
vortex patch problem has a unique solution.
",0,0,1,0,0,0
17322,An Equivalence of Fully Connected Layer and Convolutional Layer,"  This article demonstrates that convolutional operation can be converted to
matrix multiplication, which has the same calculation way with fully connected
layer. The article is helpful for the beginners of the neural network to
understand how fully connected layer and the convolutional layer work in the
backend. To be concise and to make the article more readable, we only consider
the linear case. It can be extended to the non-linear case easily through
plugging in a non-linear encapsulation to the values like this $\sigma(x)$
denoted as $x^{\prime}$.
",1,0,0,1,0,0
17323,Critical Points of Neural Networks: Analytical Forms and Landscape Properties,"  Due to the success of deep learning to solving a variety of challenging
machine learning tasks, there is a rising interest in understanding loss
functions for training neural networks from a theoretical aspect. Particularly,
the properties of critical points and the landscape around them are of
importance to determine the convergence performance of optimization algorithms.
In this paper, we provide full (necessary and sufficient) characterization of
the analytical forms for the critical points (as well as global minimizers) of
the square loss functions for various neural networks. We show that the
analytical forms of the critical points characterize the values of the
corresponding loss functions as well as the necessary and sufficient conditions
to achieve global minimum. Furthermore, we exploit the analytical forms of the
critical points to characterize the landscape properties for the loss functions
of these neural networks. One particular conclusion is that: The loss function
of linear networks has no spurious local minimum, while the loss function of
one-hidden-layer nonlinear networks with ReLU activation function does have
local minimum that is not global minimum.
",1,0,0,1,0,0
17324,When the Annihilator Graph of a Commutative Ring Is Planar or Toroidal?,"  Let $R$ be a commutative ring with identity, and let $Z(R)$ be the set of
zero-divisors of $R$. The annihilator graph of $R$ is defined as the undirected
graph $AG(R)$ with the vertex set $Z(R)^*=Z(R)\setminus\{0\}$, and two distinct
vertices $x$ and $y$ are adjacent if and only if $ann_R(xy)\neq ann_R(x)\cup
ann_R(y)$. In this paper, all rings whose annihilator graphs can be embed on
the plane or torus are classified.
",0,0,1,0,0,0
17325,Econometric modelling and forecasting of intraday electricity prices,"  In the following paper we analyse the ID$_3$-Price on German Intraday
Continuous Electricity Market using an econometric time series model. A
multivariate approach is conducted for hourly and quarter-hourly products
separately. We estimate the model using lasso and elastic net techniques and
perform an out-of-sample very short-term forecasting study. The model's
performance is compared with benchmark models and is discussed in detail.
Forecasting results provide new insights to the German Intraday Continuous
Electricity Market regarding its efficiency and to the ID$_3$-Price behaviour.
The supplementary materials are available online.
",0,0,0,0,0,1
17326,Matrix-Based Characterization of the Motion and Wrench Uncertainties in Robotic Manipulators,"  Characterization of the uncertainty in robotic manipulators is the focus of
this paper. Based on the random matrix theory (RMT), we propose uncertainty
characterization schemes in which the uncertainty is modeled at the macro
(system) level. This is different from the traditional approaches that model
the uncertainty in the parametric space of micro (state) level. We show that
perturbing the system matrices rather than the state of the system provides
unique advantages especially for robotic manipulators. First, it requires only
limited statistical information that becomes effective when dealing with
complex systems where detailed information on their variability is not
available. Second, the RMT-based models are aware of the system state and
configuration that are significant factors affecting the level of uncertainty
in system behavior. In this study, in addition to the motion uncertainty
analysis that was first proposed in our earlier work, we also develop an
RMT-based model for the quantification of the static wrench uncertainty in
multi-agent cooperative systems. This model is aimed to be an alternative to
the elaborate parametric formulation when only rough bounds are available on
the system parameters. We discuss that how RMT-based model becomes advantageous
when the complexity of the system increases. We perform experimental studies on
a KUKA youBot arm to demonstrate the superiority of the RMT-based motion
uncertainty models. We show that how these models outperform the traditional
models built upon Gaussianity assumption in capturing real-system uncertainty
and providing accurate bounds on the state estimation errors. In addition, to
experimentally support our wrench uncertainty quantification model, we study
the behavior of a cooperative system of mobile robots. It is shown that one can
rely on less demanding RMT-based formulation and yet meets the acceptable
accuracy.
",1,0,0,1,0,0
17327,Good Similar Patches for Image Denoising,"  Patch-based denoising algorithms like BM3D have achieved outstanding
performance. An important idea for the success of these methods is to exploit
the recurrence of similar patches in an input image to estimate the underlying
image structures. However, in these algorithms, the similar patches used for
denoising are obtained via Nearest Neighbour Search (NNS) and are sometimes not
optimal. First, due to the existence of noise, NNS can select similar patches
with similar noise patterns to the reference patch. Second, the unreliable
noisy pixels in digital images can bring a bias to the patch searching process
and result in a loss of color fidelity in the final denoising result. We
observe that given a set of good similar patches, their distribution is not
necessarily centered at the noisy reference patch and can be approximated by a
Gaussian component. Based on this observation, we present a patch searching
method that clusters similar patch candidates into patch groups using Gaussian
Mixture Model-based clustering, and selects the patch group that contains the
reference patch as the final patches for denoising. We also use an unreliable
pixel estimation algorithm to pre-process the input noisy images to further
improve the patch searching. Our experiments show that our approach can better
capture the underlying patch structures and can consistently enable the
state-of-the-art patch-based denoising algorithms, such as BM3D, LPCA and PLOW,
to better denoise images by providing them with patches found by our approach
while without modifying these algorithms.
",1,0,0,0,0,0
17328,Ginzburg - Landau expansion in strongly disordered attractive Anderson - Hubbard model,"  We have studied disordering effects on the coefficients of Ginzburg - Landau
expansion in powers of superconducting order - parameter in attractive Anderson
- Hubbard model within the generalized $DMFT+\Sigma$ approximation. We consider
the wide region of attractive potentials $U$ from the weak coupling region,
where superconductivity is described by BCS model, to the strong coupling
region, where superconducting transition is related with Bose - Einstein
condensation (BEC) of compact Cooper pairs formed at temperatures essentially
larger than the temperature of superconducting transition, and the wide range
of disorder - from weak to strong, where the system is in the vicinity of
Anderson transition. In case of semi - elliptic bare density of states disorder
influence upon the coefficients $A$ and $B$ before the square and the fourth
power of the order - parameter is universal for any value of electron
correlation and is related only to the general disorder widening of the bare
band (generalized Anderson theorem). Such universality is absent for the
gradient term expansion coefficient $C$. In the usual theory of ""dirty""
superconductors the $C$ coefficient drops with the growth of disorder. In the
limit of strong disorder in BCS limit the coefficient $C$ is very sensitive to
the effects of Anderson localization, which lead to its further drop with
disorder growth up to the region of Anderson insulator. In the region of BCS -
BEC crossover and in BEC limit the coefficient $C$ and all related physical
properties are weakly dependent on disorder. In particular, this leads to
relatively weak disorder dependence of both penetration depth and coherence
lengths, as well as of related slope of the upper critical magnetic field at
superconducting transition, in the region of very strong coupling.
",0,1,0,0,0,0
17329,Reallocating and Resampling: A Comparison for Inference,"  Simulation-based inference plays a major role in modern statistics, and often
employs either reallocating (as in a randomization test) or resampling (as in
bootstrapping). Reallocating mimics random allocation to treatment groups,
while resampling mimics random sampling from a larger population; does it
matter whether the simulation method matches the data collection method?
Moreover, do the results differ for testing versus estimation? Here we answer
these questions in a simple setting by exploring the distribution of a sample
difference in means under a basic two group design and four different
scenarios: true random allocation, true random sampling, reallocating, and
resampling. For testing a sharp null hypothesis, reallocating is superior in
small samples, but reallocating and resampling are asymptotically equivalent.
For estimation, resampling is generally superior, unless the effect is truly
additive. Moreover, these results hold regardless of whether the data were
collected by random sampling or random allocation.
",0,0,1,1,0,0
17330,An Efficient Algorithm for Bayesian Nearest Neighbours,"  K-Nearest Neighbours (k-NN) is a popular classification and regression
algorithm, yet one of its main limitations is the difficulty in choosing the
number of neighbours. We present a Bayesian algorithm to compute the posterior
probability distribution for k given a target point within a data-set,
efficiently and without the use of Markov Chain Monte Carlo (MCMC) methods or
simulation - alongside an exact solution for distributions within the
exponential family. The central idea is that data points around our target are
generated by the same probability distribution, extending outwards over the
appropriate, though unknown, number of neighbours. Once the data is projected
onto a distance metric of choice, we can transform the choice of k into a
change-point detection problem, for which there is an efficient solution: we
recursively compute the probability of the last change-point as we move towards
our target, and thus de facto compute the posterior probability distribution
over k. Applying this approach to both a classification and a regression UCI
data-sets, we compare favourably and, most importantly, by removing the need
for simulation, we are able to compute the posterior probability of k exactly
and rapidly. As an example, the computational time for the Ripley data-set is a
few milliseconds compared to a few hours when using a MCMC approach.
",1,0,0,1,0,0
17331,In search of a new economic model determined by logistic growth,"  In this paper we extend the work by Ryuzo Sato devoted to the development of
economic growth models within the framework of the Lie group theory. We propose
a new growth model based on the assumption of logistic growth in factors. It is
employed to derive new production functions and introduce a new notion of wage
share. In the process it is shown that the new functions compare reasonably
well against relevant economic data. The corresponding problem of maximization
of profit under conditions of perfect competition is solved with the aid of one
of these functions. In addition, it is explained in reasonably rigorous
mathematical terms why Bowley's law no longer holds true in post-1960 data.
",0,0,1,0,0,0
17332,Limits on light WIMPs with a 1 kg-scale germanium detector at 160 eVee physics threshold at the China Jinping Underground Laboratory,"  We report results of a search for light weakly interacting massive particle
(WIMP) dark matter from the CDEX-1 experiment at the China Jinping Underground
Laboratory (CJPL). Constraints on WIMP-nucleon spin-independent (SI) and
spin-dependent (SD) couplings are derived with a physics threshold of 160 eVee,
from an exposure of 737.1 kg-days. The SI and SD limits extend the lower reach
of light WIMPs to 2 GeV and improve over our earlier bounds at WIMP mass less
than 6 GeV.
",0,1,0,0,0,0
17333,"A stellar census of the nearby, young 32 Orionis group","  The 32 Orionis group was discovered almost a decade ago and despite the fact
that it represents the first northern, young (age ~ 25 Myr) stellar aggregate
within 100 pc of the Sun ($d \simeq 93$ pc), a comprehensive survey for members
and detailed characterisation of the group has yet to be performed. We present
the first large-scale spectroscopic survey for new (predominantly M-type)
members of the group after combining kinematic and photometric data to select
candidates with Galactic space motion and positions in colour-magnitude space
consistent with membership. We identify 30 new members, increasing the number
of known 32 Ori group members by a factor of three and bringing the total
number of identified members to 46, spanning spectral types B5 to L1. We also
identify the lithium depletion boundary (LDB) of the group, i.e. the luminosity
at which lithium remains unburnt in a coeval population. We estimate the age of
the 32 Ori group independently using both isochronal fitting and LDB analyses
and find it is essentially coeval with the {\beta} Pictoris moving group, with
an age of $24\pm4$ Myr. Finally, we have also searched for circumstellar disc
hosts utilising the AllWISE catalogue. Although we find no evidence for warm,
dusty discs, we identify several stars with excess emission in the WISE W4-band
at 22 {\mu}m. Based on the limited number of W4 detections we estimate a debris
disc fraction of $32^{+12}_{-8}$ per cent for the 32 Ori group.
",0,1,0,0,0,0
17334,A High-Level Rule-based Language for Software Defined Network Programming based on OpenFlow,"  This paper proposes XML-Defined Network policies (XDNP), a new high-level
language based on XML notation, to describe network control rules in Software
Defined Network environments. We rely on existing OpenFlow controllers
specifically Floodlight but the novelty of this project is to separate
complicated language- and framework-specific APIs from policy descriptions.
This separation makes it possible to extend the current work as a northbound
higher level abstraction that can support a wide range of controllers who are
based on different programming languages. By this approach, we believe that
network administrators can develop and deploy network control policies easier
and faster.
",1,0,0,0,0,0
17335,Domain Objects and Microservices for Systems Development: a roadmap,"  This paper discusses a roadmap to investigate Domain Objects being an
adequate formalism to capture the peculiarity of microservice architecture, and
to support Software development since the early stages. It provides a survey of
both Microservices and Domain Objects, and it discusses plans and reflections
on how to investigate whether a modeling approach suited to adaptable
service-based components can also be applied with success to the microservice
scenario.
",1,0,0,0,0,0
17336,Stabilization of prethermal Floquet steady states in a periodically driven dissipative Bose-Hubbard model,"  We discuss the effect of dissipation on heating which occurs in periodically
driven quantum many body systems. We especially focus on a periodically driven
Bose-Hubbard model coupled to an energy and particle reservoir. Without
dissipation, this model is known to undergo parametric instabilities which can
be considered as an initial stage of heating. By taking the weak on-site
interaction limit as well as the weak system-reservoir coupling limit, we find
that parametric instabilities are suppressed if the dissipation is stronger
than the on-site interaction strength and stable steady states appear. Our
results demonstrate that periodically-driven systems can emit energy, which is
absorbed from external drivings, to the reservoir so that they can avoid
heating.
",0,1,0,0,0,0
17337,Compressed Sensing using Generative Models,"  The goal of compressed sensing is to estimate a vector from an
underdetermined system of noisy linear measurements, by making use of prior
knowledge on the structure of vectors in the relevant domain. For almost all
results in this literature, the structure is represented by sparsity in a
well-chosen basis. We show how to achieve guarantees similar to standard
compressed sensing but without employing sparsity at all. Instead, we suppose
that vectors lie near the range of a generative model $G: \mathbb{R}^k \to
\mathbb{R}^n$. Our main theorem is that, if $G$ is $L$-Lipschitz, then roughly
$O(k \log L)$ random Gaussian measurements suffice for an $\ell_2/\ell_2$
recovery guarantee. We demonstrate our results using generative models from
published variational autoencoder and generative adversarial networks. Our
method can use $5$-$10$x fewer measurements than Lasso for the same accuracy.
",1,0,0,1,0,0
17338,Two-part models with stochastic processes for modelling longitudinal semicontinuous data: computationally efficient inference and modelling the overall marginal mean,"  Several researchers have described two-part models with patient-specific
stochastic processes for analysing longitudinal semicontinuous data. In theory,
such models can offer greater flexibility than the standard two-part model with
patient-specific random effects. However, in practice the high dimensional
integrations involved in the marginal likelihood (i.e. integrated over the
stochastic processes) significantly complicates model fitting. Thus
non-standard computationally intensive procedures based on simulating the
marginal likelihood have so far only been proposed. In this paper, we describe
an efficient method of implementation by demonstrating how the high dimensional
integrations involved in the marginal likelihood can be computed efficiently.
Specifically, by using a property of the multivariate normal distribution and
the standard marginal cumulative distribution function identity, we transform
the marginal likelihood so that the high dimensional integrations are contained
in the cumulative distribution function of a multivariate normal distribution,
which can then be efficiently evaluated. Hence maximum likelihood estimation
can be used to obtain parameter estimates and asymptotic standard errors (from
the observed information matrix) of model parameters. We describe our proposed
efficient implementation procedure for the standard two-part model
parameterisation and when it is of interest to directly model the overall
marginal mean. The methodology is applied on a psoriatic arthritis data set
concerning functional disability.
",0,0,0,1,0,0
17339,Progressive Image Deraining Networks: A Better and Simpler Baseline,"  Along with the deraining performance improvement of deep networks, their
structures and learning become more and more complicated and diverse, making it
difficult to analyze the contribution of various network modules when
developing new deraining networks. To handle this issue, this paper provides a
better and simpler baseline deraining network by considering network
architecture, input and output, and loss functions. Specifically, by repeatedly
unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take
advantage of recursive computation. A recurrent layer is further introduced to
exploit the dependencies of deep features across stages, forming our
progressive recurrent network (PReNet). Furthermore, intra-stage recursive
computation of ResNet can be adopted in PRN and PReNet to notably reduce
network parameters with graceful degradation in deraining performance. For
network input and output, we take both stage-wise result and original rainy
image as input to each ResNet and finally output the prediction of {residual
image}. As for loss functions, single MSE or negative SSIM losses are
sufficient to train PRN and PReNet. Experiments show that PRN and PReNet
perform favorably on both synthetic and real rainy images. Considering its
simplicity, efficiency and effectiveness, our models are expected to serve as a
suitable baseline in future deraining research. The source codes are available
at this https URL.
",1,0,0,0,0,0
17340,Optimal Nonparametric Inference under Quantization,"  Statistical inference based on lossy or incomplete samples is of fundamental
importance in research areas such as signal/image processing, medical image
storage, remote sensing, signal transmission. In this paper, we propose a
nonparametric testing procedure based on quantized samples. In contrast to the
classic nonparametric approach, our method lives on a coarse grid of sample
information and are simple-to-use. Under mild technical conditions, we
establish the asymptotic properties of the proposed procedures including
asymptotic null distribution of the quantization test statistic as well as its
minimax power optimality. Concrete quantizers are constructed for achieving the
minimax optimality in practical use. Simulation results and a real data
analysis are provided to demonstrate the validity and effectiveness of the
proposed test. Our work bridges the classical nonparametric inference to modern
lossy data setting.
",1,0,1,1,0,0
17341,Nearest neighbor imputation for general parameter estimation in survey sampling,"  Nearest neighbor imputation is popular for handling item nonresponse in
survey sampling. In this article, we study the asymptotic properties of the
nearest neighbor imputation estimator for general population parameters,
including population means, proportions and quantiles. For variance estimation,
the conventional bootstrap inference for matching estimators with fixed number
of matches has been shown to be invalid due to the nonsmoothness nature of the
matching estimator. We propose asymptotically valid replication variance
estimation. The key strategy is to construct replicates of the estimator
directly based on linear terms, instead of individual records of variables. A
simulation study confirms that the new procedure provides valid variance
estimation.
",0,0,0,1,0,0
17342,Time-delay signature suppression in a chaotic semiconductor laser by fiber random grating induced distributed feedback,"  We demonstrate that a semiconductor laser perturbed by the distributed
feedback from a fiber random grating can emit light chaotically without the
time delay signature. A theoretical model is developed based on the
Lang-Kobayashi model in order to numerically explore the chaotic dynamics of
the laser diode subjected to the random distributed feedback. It is predicted
that the random distributed feedback is superior to the single reflection
feedback in suppressing the time-delay signature. In experiments, a massive
number of feedbacks with randomly varied time delays induced by a fiber random
grating introduce large numbers of external cavity modes into the semiconductor
laser, leading to the high dimension of chaotic dynamics and thus the
concealment of the time delay signature. The obtained time delay signature with
the maximum suppression is 0.0088, which is the smallest to date.
",0,1,0,0,0,0
17343,SAFS: A Deep Feature Selection Approach for Precision Medicine,"  In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others.
",1,0,0,1,0,0
17344,Deep Reasoning with Multi-scale Context for Salient Object Detection,"  To detect and segment salient objects accurately, existing methods are
usually devoted to designing complex network architectures to fuse powerful
features from the backbone networks. However, they put much less efforts on the
saliency inference module and only use a few fully convolutional layers to
perform saliency reasoning from the fused features. However, should feature
fusion strategies receive much attention but saliency reasoning be ignored a
lot? In this paper, we find that weakness of the saliency reasoning unit limits
salient object detection performance, and claim that saliency reasoning after
multi-scale convolutional features fusion is critical. To verify our findings,
we first extract multi-scale features with a fully convolutional network, and
then directly reason from these comprehensive features using a deep yet
light-weighted network, modified from ShuffleNet, to fast and precisely predict
salient objects. Such simple design is shown to be capable of reasoning from
multi-scale saliency features as well as giving superior saliency detection
performance with less computation cost. Experimental results show that our
simple framework outperforms the best existing method with 2.3\% and 3.6\%
promotion for F-measure scores, 2.8\% reduction for MAE score on PASCAL-S,
DUT-OMRON and SOD datasets respectively.
",1,0,0,0,0,0
17345,On Estimation of $L_{r}$-Norms in Gaussian White Noise Models,"  We provide a complete picture of asymptotically minimax estimation of
$L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over
Nikolskii-Besov spaces. In this regard, we complement the work of Lepski,
Nemirovski and Spokoiny (1999), who considered the cases of $r=1$ (with
poly-logarithmic gap between upper and lower bounds) and $r$ even (with
asymptotically sharp upper and lower bounds) over Hölder spaces. We
additionally consider the case of asymptotically adaptive minimax estimation
and demonstrate a difference between even and non-even $r$ in terms of an
investigator's ability to produce asymptotically adaptive minimax estimators
without paying a penalty.
",1,0,1,1,0,0
17346,Secure communications with cooperative jamming: Optimal power allocation and secrecy outage analysis,"  This paper studies the secrecy rate maximization problem of a secure wireless
communication system, in the presence of multiple eavesdroppers. The security
of the communication link is enhanced through cooperative jamming, with the
help of multiple jammers. First, a feasibility condition is derived to achieve
a positive secrecy rate at the destination. Then, we solve the original secrecy
rate maximization problem, which is not convex in terms of power allocation at
the jammers. To circumvent this non-convexity, the achievable secrecy rate is
approximated for a given power allocation at the jammers and the approximated
problem is formulated into a geometric programming one. Based on this
approximation, an iterative algorithm has been developed to obtain the optimal
power allocation at the jammers. Next, we provide a bisection approach, based
on one-dimensional search, to validate the optimality of the proposed
algorithm. In addition, by assuming Rayleigh fading, the secrecy outage
probability (SOP) of the proposed cooperative jamming scheme is analyzed. More
specifically, a single-integral form expression for SOP is derived for the most
general case as well as a closed-form expression for the special case of two
cooperative jammers and one eavesdropper. Simulation results have been provided
to validate the convergence and the optimality of the proposed algorithm as
well as the theoretical derivations of the presented SOP analysis.
",1,0,1,0,0,0
17347,Stochastic Calculus with respect to Gaussian Processes: Part I,"  Stochastic integration \textit{wrt} Gaussian processes has raised strong
interest in recent years, motivated in particular by its applications in
Internet traffic modeling, biomedicine and finance. The aim of this work is to
define and develop a White Noise Theory-based anticipative stochastic calculus
with respect to all Gaussian processes that have an integral representation
over a real (maybe infinite) interval. Very rich, this class of Gaussian
processes contains, among many others, Volterra processes (and thus fractional
Brownian motion) as well as processes the regularity of which varies along the
time (such as multifractional Brownian motion).A systematic comparison of the
stochastic calculus (including It{ô} formula) we provide here, to the ones
given by Malliavin calculus in
\cite{nualart,MV05,NuTa06,KRT07,KrRu10,LN12,SoVi14,LN12}, and by It{ô}
stochastic calculus is also made. Not only our stochastic calculus fully
generalizes and extends the ones originally proposed in \cite{MV05} and in
\cite{NuTa06} for Gaussian processes, but also the ones proposed in
\cite{ell,bosw,ben1} for fractional Brownian motion (\textit{resp.} in
\cite{JLJLV1,JL13,LLVH} for multifractional Brownian motion).
",0,0,1,0,0,0
17348,Path-like integrals of lenght on surfaces of constant curvature,"  We naturally associate a measurable space of paths to a couple of orthogonal
vector fields over a surface and we integrate the length function over it. This
integral is interpreted as a natural continuous generalization of indirect
influences on finite graphs and can be thought as a tool to capture geometric
information of the surface. As a byproduct we calculate volumes in different
examples of spaces of paths.
",0,0,1,0,0,0
17349,Automated Synthesis of Divide and Conquer Parallelism,"  This paper focuses on automated synthesis of divide-and-conquer parallelism,
which is a common parallel programming skeleton supported by many
cross-platform multithreaded libraries. The challenges of producing (manually
or automatically) a correct divide-and-conquer parallel program from a given
sequential code are two-fold: (1) assuming that individual worker threads
execute a code identical to the sequential code, the programmer has to provide
the extra code for dividing the tasks and combining the computation results,
and (2) sometimes, the sequential code may not be usable as is, and may need to
be modified by the programmer. We address both challenges in this paper. We
present an automated synthesis technique for the case where no modifications to
the sequential code are required, and we propose an algorithm for modifying the
sequential code to make it suitable for parallelization when some modification
is necessary. The paper presents theoretical results for when this {\em
modification} is efficiently possible, and experimental evaluation of the
technique and the quality of the produced parallel programs.
",1,0,0,0,0,0
17350,"Nikol'ski\uı, Jackson and Ul'yanov type inequalities with Muckenhoupt weights","  In the present work we prove a Nikol'ski inequality for trigonometric
polynomials and Ul'yanov type inequalities for functions in Lebesgue spaces
with Muckenhoupt weights. Realization result and Jackson inequalities are
obtained. Simultaneous approximation by polynomials is considered. Some uniform
norm inequalities are transferred to weighted Lebesgue space.
",0,0,1,0,0,0
17351,CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks,"  Inferring model parameters from experimental data is a grand challenge in
many sciences, including cosmology. This often relies critically on high
fidelity numerical simulations, which are prohibitively computationally
expensive. The application of deep learning techniques to generative modeling
is renewing interest in using high dimensional density estimators as
computationally inexpensive emulators of fully-fledged simulations. These
generative models have the potential to make a dramatic shift in the field of
scientific simulations, but for that shift to happen we need to study the
performance of such generators in the precision regime needed for science
applications. To this end, in this work we apply Generative Adversarial
Networks to the problem of generating weak lensing convergence maps. We show
that our generator network produces maps that are described by, with high
statistical confidence, the same summary statistics as the fully simulated
maps.
",1,1,0,0,0,0
17352,Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data,"  This paper establishes an upper bound for the Kolmogorov distance between the
maximum of a high-dimensional vector of smooth Wiener functionals and the
maximum of a Gaussian random vector. As a special case, we show that the
maximum of multiple Wiener-Itô integrals with common orders is
well-approximated by its Gaussian analog in terms of the Kolmogorov distance if
their covariance matrices are close to each other and the maximum of the fourth
cumulants of the multiple Wiener-Itô integrals is close to zero. This may be
viewed as a new kind of fourth moment phenomenon, which has attracted
considerable attention in the recent studies of probability. This type of
Gaussian approximation result has many potential applications to statistics. To
illustrate this point, we present two statistical applications in
high-frequency financial econometrics: One is the hypothesis testing problem
for the absence of lead-lag effects and the other is the construction of
uniform confidence bands for spot volatility.
",0,0,1,1,0,0
17353,A Kronecker-type identity and the representations of a number as a sum of three squares,"  By considering a limiting case of a Kronecker-type identity, we obtain an
identity found by both Andrews and Crandall. We then use the Andrews-Crandall
identity to give a new proof of a formula of Gauss for the representations of a
number as a sum of three squares. From the Kronecker-type identity, we also
deduce Gauss's theorem that every positive integer is representable as a sum of
three triangular numbers.
",0,0,1,0,0,0
17354,DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction,"  In this paper, we consider the temporal pattern in traffic flow time series,
and implement a deep learning model for traffic flow prediction. Detrending
based methods decompose original flow series into trend and residual series, in
which trend describes the fixed temporal pattern in traffic flow and residual
series is used for prediction. Inspired by the detrending method, we propose
DeepTrend, a deep hierarchical neural network used for traffic flow prediction
which considers and extracts the time-variant trend. DeepTrend has two stacked
layers: extraction layer and prediction layer. Extraction layer, a fully
connected layer, is used to extract the time-variant trend in traffic flow by
feeding the original flow series concatenated with corresponding simple average
trend series. Prediction layer, an LSTM layer, is used to make flow prediction
by feeding the obtained trend from the output of extraction layer and
calculated residual series. To make the model more effective, DeepTrend needs
first pre-trained layer-by-layer and then fine-tuned in the entire network.
Experiments show that DeepTrend can noticeably boost the prediction performance
compared with some traditional prediction models and LSTM with detrending based
methods.
",1,0,0,0,0,0
17355,A new approach to Kaluza-Klein Theory,"  We propose in this paper a new approach to the Kaluza-Klein idea of a five
dimensional space-time unifying gravitation and electromagnetism, and extension
to higher-dimensional space-time. By considering a natural geometric definition
of a matter fluid and abandoning the usual requirement of a Ricci-flat five
dimensional space-time, we show that a unified geometrical frame can be set for
gravitation and electromagnetism, giving, by projection on the classical
4-dimensional space-time, the known Einstein-Maxwell-Lorentz equations for
charged fluids. Thus, although not introducing new physics, we get a very
aesthetic presentation of classical physics in the spirit of general
relativity. The usual physical concepts, such as mass, energy, charge,
trajectory, Maxwell-Lorentz law, are shown to be only various aspects of the
geometry, for example curvature, of space-time considered as a Lorentzian
manifold; that is no physical objects are introduced in space-time, no laws are
given, everything is only geometry.
We then extend these ideas to more than 5 dimensions, by considering
spacetime as a generalization of a $(S^1\times W)$-fiber bundle, that we named
multi-fibers bundle, where $S^1$ is the circle and $W$ a compact manifold. We
will use this geometric structure as a possible way to model or encode
deviations from standard 4-dimensional General Relativity, or ""dark"" effects
such as dark matter or energy.
",0,0,1,0,0,0
17356,Density of orbits of dominant regular self-maps of semiabelian varieties,"  We prove a conjecture of Medvedev and Scanlon in the case of regular
morphisms of semiabelian varieties. That is, if $G$ is a semiabelian variety
defined over an algebraically closed field $K$ of characteristic $0$, and
$\varphi\colon G\to G$ is a dominant regular self-map of $G$ which is not
necessarily a group homomorphism, we prove that one of the following holds:
either there exists a non-constant rational fibration preserved by $\varphi$,
or there exists a point $x\in G(K)$ whose $\varphi$-orbit is Zariski dense in
$G$.
",0,0,1,0,0,0
17357,Asymptotic coverage probabilities of bootstrap percentile confidence intervals for constrained parameters,"  The asymptotic behaviour of the commonly used bootstrap percentile confidence
interval is investigated when the parameters are subject to linear inequality
constraints. We concentrate on the important one- and two-sample problems with
data generated from general parametric distributions in the natural exponential
family. The focus of this paper is on quantifying the coverage probabilities of
the parametric bootstrap percentile confidence intervals, in particular their
limiting behaviour near boundaries. We propose a local asymptotic framework to
study this subtle coverage behaviour. Under this framework, we discover that
when the true parameters are on, or close to, the restriction boundary, the
asymptotic coverage probabilities can always exceed the nominal level in the
one-sample case; however, they can be, remarkably, both under and over the
nominal level in the two-sample case. Using illustrative examples, we show that
the results provide theoretical justification and guidance on applying the
bootstrap percentile method to constrained inference problems.
",0,0,1,1,0,0
17358,Correlations and enlarged superconducting phase of $t$-$J_\perp$ chains of ultracold molecules on optical lattices,"  We compute physical properties across the phase diagram of the $t$-$J_\perp$
chain with long-range dipolar interactions, which describe ultracold polar
molecules on optical lattices. Our results obtained by the density-matrix
renormalization group (DMRG) indicate that superconductivity is enhanced when
the Ising component $J_z$ of the spin-spin interaction and the charge component
$V$ are tuned to zero, and even further by the long-range dipolar interactions.
At low densities, a substantially larger spin gap is obtained. We provide
evidence that long-range interactions lead to algebraically decaying
correlation functions despite the presence of a gap. Although this has recently
been observed in other long-range interacting spin and fermion models, the
correlations in our case have the peculiar property of having a small and
continuously varying exponent. We construct simple analytic models and
arguments to understand the most salient features.
",0,1,0,0,0,0
17359,MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks,"  We introduce MinimalRNN, a new recurrent neural network architecture that
achieves comparable performance as the popular gated RNNs with a simplified
structure. It employs minimal updates within RNN, which not only leads to
efficient learning and testing but more importantly better interpretability and
trainability. We demonstrate that by endorsing the more restrictive update
rule, MinimalRNN learns disentangled RNN states. We further examine the
learning dynamics of different RNN structures using input-output Jacobians, and
show that MinimalRNN is able to capture longer range dependencies than existing
RNN architectures.
",1,0,0,1,0,0
17360,Boolean quadric polytopes are faces of linear ordering polytopes,"  Let $BQP(n)$ be a boolean quadric polytope, $LOP(m)$ be a linear ordering
polytope. It is shown that $BQP(n)$ is linearly isomorphic to a face of
$LOP(2n)$.
",1,0,0,0,0,0
17361,Sparse Matrix Code Dependence Analysis Simplification at Compile Time,"  Analyzing array-based computations to determine data dependences is useful
for many applications including automatic parallelization, race detection,
computation and communication overlap, verification, and shape analysis. For
sparse matrix codes, array data dependence analysis is made more difficult by
the use of index arrays that make it possible to store only the nonzero entries
of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence
analysis is often stymied by such indirect array accesses due to the values of
the index array not being available at compile time. Consequently, many
dependences cannot be proven unsatisfiable or determined until runtime.
Nonetheless, index arrays in sparse matrix codes often have properties such as
monotonicity of index array elements that can be exploited to reduce the amount
of runtime analysis needed. In this paper, we contribute a formulation of array
data dependence analysis that includes encoding index array properties as
universally quantified constraints. This makes it possible to leverage existing
SMT solvers to determine whether such dependences are unsatisfiable and
significantly reduces the number of dependences that require runtime analysis
in a set of eight sparse matrix kernels. Another contribution is an algorithm
for simplifying the remaining satisfiable data dependences by discovering
equalities and/or subset relationships. These simplifications are essential to
make a runtime-inspection-based approach feasible.
",1,0,0,0,0,0
17362,ICA based on the data asymmetry,"  Independent Component Analysis (ICA) - one of the basic tools in data
analysis - aims to find a coordinate system in which the components of the data
are independent. Most of existing methods are based on the minimization of the
function of fourth-order moment (kurtosis). Skewness (third-order moment) has
received much less attention.
In this paper we present a competitive approach to ICA based on the Split
Gaussian distribution, which is well adapted to asymmetric data. Consequently,
we obtain a method which works better than the classical approaches, especially
in the case when the underlying density is not symmetric, which is a typical
situation in the color distribution in images.
",0,0,1,1,0,0
17363,Solid hulls of weighted Banach spaces of analytic functions on the unit disc with exponential weights,"  We study weighted $H^\infty$ spaces of analytic functions on the open unit
disc in the case of non-doubling weights, which decrease rapidly with respect
to the boundary distance. We characterize the solid hulls of such spaces and
give quite explicit representations of them in the case of the most natural
exponentially decreasing weights.
",0,0,1,0,0,0
17364,Line bundles defined by the Schwarz function,"  Cauchy and exponential transforms are characterized, and constructed, as
canonical holomorphic sections of certain line bundles on the Riemann sphere
defined in terms of the Schwarz function. A well known natural connection
between Schwarz reflection and line bundles defined on the Schottky double of a
planar domain is briefly discussed in the same context.
",0,0,1,0,0,0
17365,Collisional excitation of NH3 by atomic and molecular hydrogen,"  We report extensive theoretical calculations on the rotation-inversion
excitation of interstellar ammonia (NH3) due to collisions with atomic and
molecular hydrogen (both para- and ortho-H2). Close-coupling calculations are
performed for total energies in the range 1-2000 cm-1 and rotational cross
sections are obtained for all transitions among the lowest 17 and 34
rotation-inversion levels of ortho- and para-NH3, respectively. Rate
coefficients are deduced for kinetic temperatures up to 200 K. Propensity rules
for the three colliding partners are discussed and we also compare the new
results to previous calculations for the spherically symmetrical He and para-H2
projectiles. Significant differences are found between the different sets of
calculations. Finally, we test the impact of the new rate coefficients on the
calibration of the ammonia thermometer. We find that the calibration curve is
only weakly sensitive to the colliding partner and we confirm that the ammonia
thermometer is robust.
",0,1,0,0,0,0
17366,Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data,"  We consider the multi-view data completion problem, i.e., to complete a
matrix $\mathbf{U}=[\mathbf{U}_1|\mathbf{U}_2]$ where the ranks of
$\mathbf{U},\mathbf{U}_1$, and $\mathbf{U}_2$ are given. In particular, we
investigate the fundamental conditions on the sampling pattern, i.e., locations
of the sampled entries for finite completability of such a multi-view data
given the corresponding rank constraints. In contrast with the existing
analysis on Grassmannian manifold for a single-view matrix, i.e., conventional
matrix completion, we propose a geometric analysis on the manifold structure
for multi-view data to incorporate more than one rank constraint. We provide a
deterministic necessary and sufficient condition on the sampling pattern for
finite completability. We also give a probabilistic condition in terms of the
number of samples per column that guarantees finite completability with high
probability. Finally, using the developed tools, we derive the deterministic
and probabilistic guarantees for unique completability.
",1,0,1,0,0,0
17367,Grid-forming Control for Power Converters based on Matching of Synchronous Machines,"  We consider the problem of grid-forming control of power converters in
low-inertia power systems. Starting from an average-switch three-phase inverter
model, we draw parallels to a synchronous machine (SM) model and propose a
novel grid-forming converter control strategy which dwells upon the main
characteristic of a SM: the presence of an internal rotating magnetic field. In
particular, we augment the converter system with a virtual oscillator whose
frequency is driven by the DC-side voltage measurement and which sets the
converter pulse-width-modulation signal, thereby achieving exact matching
between the converter in closed-loop and the SM dynamics. We then provide a
sufficient condition assuring existence, uniqueness, and global asymptotic
stability of equilibria in a coordinate frame attached to the virtual
oscillator angle. By actuating the DC-side input of the converter we are able
to enforce this sufficient condition. In the same setting, we highlight strict
incremental passivity, droop, and power-sharing properties of the proposed
framework, which are compatible with conventional requirements of power system
operation. We subsequently adopt disturbance decoupling techniques to design
additional control loops that regulate the DC-side voltage, as well as AC-side
frequency and amplitude, while in the end validating them with numerical
experiments.
",0,0,1,0,0,0
17368,Characterizing Dust Attenuation in Local Star-Forming Galaxies: Near-Infrared Reddening and Normalization,"  We characterize the near-infrared (NIR) dust attenuation for a sample of
~5500 local (z<0.1) star-forming galaxies and obtain an estimate of their
average total-to-selective attenuation $k(\lambda)$. We utilize data from the
United Kingdom Infrared Telescope (UKIRT) and the Two Micron All-Sky Survey
(2MASS), which is combined with previously measured UV-optical data for these
galaxies. The average attenuation curve is slightly lower in the far-UV than
local starburst galaxies, by roughly 15%, but appears similar at longer
wavelengths with a total-to-selective normalization at V-band of
$R_V=3.67\substack{+0.44 \\ -0.35}$. Under the assumption of energy balance,
the total attenuated energy inferred from this curve is found to be broadly
consistent with the observed infrared dust emission ($L_{\rm{TIR}}$) in a small
sample of local galaxies for which far-IR measurements are available. However,
the significant scatter in this quantity among the sample may reflect large
variations in the attenuation properties of individual galaxies. We also derive
the attenuation curve for sub-populations of the main sample, separated
according to mean stellar population age (via $D_n4000$), specific star
formation rate, stellar mass, and metallicity, and find that they show only
tentative trends with low significance, at least over the range which is probed
by our sample. These results indicate that a single curve is reasonable for
applications seeking to broadly characterize large samples of galaxies in the
local Universe, while applications to individual galaxies would yield large
uncertainties and is not recommended.
",0,1,0,0,0,0
17369,Sequential Checking: Reallocation-Free Data-Distribution Algorithm for Scale-out Storage,"  Using tape or optical devices for scale-out storage is one option for storing
a vast amount of data. However, it is impossible or almost impossible to
rewrite data with such devices. Thus, scale-out storage using such devices
cannot use standard data-distribution algorithms because they rewrite data for
moving between servers constituting the scale-out storage when the server
configuration is changed. Although using rewritable devices for scale-out
storage, when server capacity is huge, rewriting data is very hard when server
constitution is changed. In this paper, a data-distribution algorithm called
Sequential Checking is proposed, which can be used for scale-out storage
composed of devices that are hardly able to rewrite data. Sequential Checking
1) does not need to move data between servers when the server configuration is
changed, 2) distribute data, the amount of which depends on the server's
volume, 3) select a unique server when datum is written, and 4) select servers
when datum is read (there are few such server(s) in most cases) and find out a
unique server that stores the newest datum from them. These basic
characteristics were confirmed through proofs and simulations. Data can be read
by accessing 1.98 servers on average from a storage comprising 256 servers
under a realistic condition. And it is confirmed by evaluations in real
environment that access time is acceptable. Sequential Checking makes selecting
scale-out storage using tape or optical devices or using huge capacity servers
realistic.
",1,0,0,0,0,0
17370,Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels,"  Noisy PN learning is the problem of binary classification when training
examples may be mislabeled (flipped) uniformly with noise rate rho1 for
positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
to solve noisy PN learning and the open problem of estimating the noise rates,
i.e. the fraction of wrong positive and negative labels. Unlike prior
solutions, RP is time-efficient and general, requiring O(T) for any
unrestricted choice of probabilistic classifier with T fitting time. We prove
RP has consistent noise estimation and equivalent expected risk as learning
with uncorrupted labels in ideal conditions, and derive closed-form solutions
when conditions are non-ideal. RP achieves state-of-the-art noise estimation
and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the
amount of noise and performs similarly impressively when a large portion of
training examples are noise drawn from a third distribution. To highlight, RP
with a CNN classifier can predict if an MNIST digit is a ""one""or ""not"" with
only 0.25% error, and 0.46 error across all digits, even when 50% of positive
examples are mislabeled and 50% of observed positive labels are mislabeled
negative examples.
",1,0,0,1,0,0
17371,code2vec: Learning Distributed Representations of Code,"  We present a neural model for representing snippets of code as continuous
distributed vectors (""code embeddings""). The main idea is to represent a code
snippet as a single fixed-length $\textit{code vector}$, which can be used to
predict semantic properties of the snippet. This is performed by decomposing
code to a collection of paths in its abstract syntax tree, and learning the
atomic representation of each path $\textit{simultaneously}$ with learning how
to aggregate a set of them. We demonstrate the effectiveness of our approach by
using it to predict a method's name from the vector representation of its body.
We evaluate our approach by training a model on a dataset of 14M methods. We
show that code vectors trained on this dataset can predict method names from
files that were completely unobserved during training. Furthermore, we show
that our model learns useful method name vectors that capture semantic
similarities, combinations, and analogies. Comparing previous techniques over
the same data set, our approach obtains a relative improvement of over 75%,
being the first to successfully predict method names based on a large,
cross-project, corpus. Our trained model, visualizations and vector
similarities are available as an interactive online demo at
this http URL. The code, data, and trained models are available at
this https URL.
",1,0,0,1,0,0
17372,Learning a Local Feature Descriptor for 3D LiDAR Scans,"  Robust data association is necessary for virtually every SLAM system and
finding corresponding points is typically a preprocessing step for scan
alignment algorithms. Traditionally, handcrafted feature descriptors were used
for these problems but recently learned descriptors have been shown to perform
more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
scans. The descriptor is learned using a Convolutional Neural Network (CNN).
Our proposed architecture consists of a Siamese network for learning a feature
descriptor and a metric learning network for matching the descriptors. We also
present a method for estimating local surface patches and obtaining
ground-truth correspondences. In extensive experiments, we compare our learned
feature descriptor with existing 3D local descriptors and report highly
competitive results for multiple experiments in terms of matching accuracy and
computation time. \end{abstract}
",1,0,0,0,0,0
17373,Dynamical tides in exoplanetary systems containing Hot Jupiters: confronting theory and observations,"  We study the effect of dynamical tides associated with the excitation of
gravity waves in an interior radiative region of the central star on orbital
evolution in observed systems containing Hot Jupiters. We consider WASP-43,
Ogle-tr-113, WASP-12, and WASP-18 which contain stars on the main sequence
(MS). For these systems there are observational estimates regarding the rate of
change of the orbital period. We also investigate Kepler-91 which contains an
evolved giant star. We adopt the formalism of Ivanov et al. for calculating the
orbital evolution.
For the MS stars we determine expected rates of orbital evolution under
different assumptions about the amount of dissipation acting on the tides,
estimate the effect of stellar rotation for the two most rapidly rotating stars
and compare results with observations. All cases apart from possibly WASP-43
are consistent with a regime in which gravity waves are damped during their
propagation over the star. However, at present this is not definitive as
observational errors are large. We find that although it is expected to apply
to Kepler-91, linear radiative damping cannot explain this dis- sipation regime
applying to MS stars. Thus, a nonlinear mechanism may be needed.
Kepler-91 is found to be such that the time scale for evolution of the star
is comparable to that for the orbit. This implies that significant orbital
circularisation may have occurred through tides acting on the star.
Quasi-static tides, stellar winds, hydrodynamic drag and tides acting on the
planet have likely played a minor role.
",0,1,0,0,0,0
17374,Metastability versus collapse following a quench in attractive Bose-Einstein condensates,"  We consider a Bose-Einstein condensate (BEC) with attractive two-body
interactions in a cigar-shaped trap, initially prepared in its ground state for
a given negative scattering length, which is quenched to a larger absolute
value of the scattering length. Using the mean-field approximation, we compute
numerically, for an experimentally relevant range of aspect ratios and initial
strengths of the coupling, two critical values of quench: one corresponds to
the weakest attraction strength the quench to which causes the system to
collapse before completing even a single return from the narrow configuration
(""perihelion"") in its breathing cycle. The other is a similar critical point
for the occurrence of collapse before completing two returns. In the latter
case, we also compute the limiting value, as we keep increasing the strength of
the post-quench attraction towards its critical value, of the time interval
between the first two perihelia. We also use a Gaussian variational model to
estimate the critical quenched attraction strength below which the system is
stable against the collapse for long times. These time intervals and critical
attraction strengths---apart from being fundamental properties of nonlinear
dynamics of self-attractive BECs---may provide clues to the design of upcoming
experiments that are trying to create robust BEC breathers.
",0,1,0,0,0,0
17375,A similarity criterion for sequential programs using truth-preserving partial functions,"  The execution of sequential programs allows them to be represented using
mathematical functions formed by the composition of statements following one
after the other. Each such statement is in itself a partial function, which
allows only inputs satisfying a particular Boolean condition to carry forward
the execution and hence, the composition of such functions (as a result of
sequential execution of the statements) strengthens the valid set of input
state variables for the program to complete its execution and halt succesfully.
With this thought in mind, this paper tries to study a particular class of
partial functions, which tend to preserve the truth of two given Boolean
conditions whenever the state variables satisfying one are mapped through such
functions into a domain of state variables satisfying the other. The existence
of such maps allows us to study isomorphism between different programs, based
not only on their structural characteristics (e.g. the kind of programming
constructs used and the overall input-output transformation), but also the
nature of computation performed on seemingly different inputs. Consequently, we
can now relate programs which perform a given type of computation, like a loop
counting down indefinitely, without caring about the input sets they work on
individually or the set of statements each program contains.
",1,0,0,0,0,0
17376,Subsampling large graphs and invariance in networks,"  Specify a randomized algorithm that, given a very large graph or network,
extracts a random subgraph. What can we learn about the input graph from a
single subsample? We derive laws of large numbers for the sampler output, by
relating randomized subsampling to distributional invariance: Assuming an
invariance holds is tantamount to assuming the sample has been generated by a
specific algorithm. That in turn yields a notion of ergodicity. Sampling
algorithms induce model classes---graphon models, sparse generalizations of
exchangeable graphs, and random multigraphs with exchangeable edges can all be
obtained in this manner, and we specialize our results to a number of examples.
One class of sampling algorithms emerges as special: Roughly speaking, those
defined as limits of random transformations drawn uniformly from certain
sequences of groups. Some known pathologies of network models based on graphons
are explained as a form of selection bias.
",0,0,1,1,0,0
17377,Taylor coefficients of non-holomorphic Jacobi forms and applications,"  In this paper, we prove modularity results of Taylor coefficients of certain
non-holomorphic Jacobi forms. It is well-known that Taylor coefficients of
holomorphic Jacobi forms are quasimoular forms. However recently there has been
a wide interest for Taylor coefficients of non-holomorphic Jacobi forms for
example arising in combinatorics. In this paper, we show that such coefficients
still inherit modular properties. We then work out the precise spaces in which
these coefficients lie for two examples.
",0,0,1,0,0,0
17378,Beamspace SU-MIMO for Future Millimeter Wave Wireless Communications,"  For future networks (i.e., the fifth generation (5G) wireless networks and
beyond), millimeter-wave (mmWave) communication with large available unlicensed
spectrum is a promising technology that enables gigabit multimedia
applications. Thanks to the short wavelength of mmWave radio, massive antenna
arrays can be packed into the limited dimensions of mmWave transceivers.
Therefore, with directional beamforming (BF), both mmWave transmitters (MTXs)
and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G
networks. However, for the transmission between an MTX and an MRX, most works
have only considered a single beam, which means that they do not make full
potential use of mmWave. Furthermore, the connectivity of single beam
transmission can easily be blocked. In this context, we propose a single-user
multi-beam concurrent transmission scheme for future mmWave networks with
multiple reflected paths. Based on spatial spectrum reuse, the scheme can be
described as a multiple-input multiple-output (MIMO) technique in beamspace
(i.e., in the beam-number domain). Moreover, this study investigates the
challenges and potential solutions for implementing this scheme, including
multibeam selection, cooperative beam tracking, multi-beam power allocation and
synchronization. The theoretical and numerical results show that the proposed
beamspace SU-MIMO can largely improve the achievable rate of the transmission
between an MTX and an MRX and, meanwhile, can maintain the connectivity.
",1,0,0,0,0,0
17379,Learning Robust Visual-Semantic Embeddings,"  Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more robust multi-modal representations across domains. The proposed method
combines representation learning models (i.e., auto-encoders) together with
cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn
joint embeddings for semantic and visual features. A novel technique of
unsupervised-data adaptation inference is introduced to construct more
comprehensive embeddings for both labeled and unlabeled data. We evaluate our
method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with
a wide range of applications, including zero and few-shot image recognition and
retrieval, from inductive to transductive settings. Empirically, we show that
our framework improves over the current state of the art on many of the
considered tasks.
",1,0,0,0,0,0
17380,Quantitative estimates of the surface habitability of Kepler-452b,"  Kepler-452b is currently the best example of an Earth-size planet in the
habitable zone of a sun-like star, a type of planet whose number of detections
is expected to increase in the future. Searching for biosignatures in the
supposedly thin atmospheres of these planets is a challenging goal that
requires a careful selection of the targets. Under the assumption of a
rocky-dominated nature for Kepler-452b, we considered it as a test case to
calculate a temperature-dependent habitability index, $h_{050}$, designed to
maximize the potential presence of biosignature-producing activity (Silva et
al.\ 2016). The surface temperature has been computed for a broad range of
climate factors using a climate model designed for terrestrial-type exoplanets
(Vladilo et al.\ 2015). After fixing the planetary data according to the
experimental results (Jenkins et al.\ 2015), we changed the surface gravity,
CO$_2$ abundance, surface pressure, orbital eccentricity, rotation period, axis
obliquity and ocean fraction within the range of validity of our model. For
most choices of parameters we find habitable solutions with $h_{050}>0.2$ only
for CO$_2$ partial pressure $p_\mathrm{CO_2} \lesssim 0.04$\,bar. At this
limiting value of CO$_2$ abundance the planet is still habitable if the total
pressure is $p \lesssim 2$\,bar. In all cases the habitability drops for
eccentricity $e \gtrsim 0.3$. Changes of rotation period and obliquity affect
the habitability through their impact on the equator-pole temperature
difference rather than on the mean global temperature. We calculated the
variation of $h_{050}$ resulting from the luminosity evolution of the host star
for a wide range of input parameters. Only a small combination of parameters
yield habitability-weighted lifetimes $\gtrsim 2$\,Gyr, sufficiently long to
develop atmospheric biosignatures still detectable at the present time.
",0,1,0,0,0,0
17381,Design and implementation of dynamic logic gates and R-S flip-flop using quasiperiodically driven Murali-Lakshmanan-Chua circuit,"  We report the propagation of a square wave signal in a quasi-periodically
driven Murali-Lakshmanan-Chua (QPDMLC) circuit system. It is observed that
signal propagation is possible only above a certain threshold strength of the
square wave or digital signal and all the values above the threshold amplitude
are termed as 'region of signal propagation'. Then, we extend this region of
signal propagation to perform various logical operations like AND/NAND/OR/NOR
and hence it is also designated as the 'region of logical operation'. Based on
this region, we propose implementing the dynamic logic gates, namely
AND/NAND/OR/NOR, which can be decided by the asymmetrical input square waves
without altering the system parameters. Further, we show that a single QPDMLC
system will produce simultaneously two outputs which are complementary to each
other. As a result, a single QPDMLC system yields either AND as well as NAND or
OR as well as NOR gates simultaneously. Then we combine the corresponding two
QPDMLC systems in a cross-coupled way and report that its dynamics mimics that
of fundamental R-S flip-flop circuit. All these phenomena have been explained
with analytical solutions of the circuit equations characterizing the system
and finally the results are compared with the corresponding numerical and
experimental analysis.
",0,1,0,0,0,0
17382,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects","  We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep
generative model for videos of moving objects. It can reliably discover and
track objects throughout the sequence of frames, and can also generate future
frames conditioning on the current frame, thereby simulating expected motion of
objects. This is achieved by explicitly encoding object presence, locations and
appearances in the latent variables of the model. SQAIR retains all strengths
of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016),
including learning in an unsupervised manner, and addresses its shortcomings.
We use a moving multi-MNIST dataset to show limitations of AIR in detecting
overlapping or partially occluded objects, and show how SQAIR overcomes them by
leveraging temporal consistency of objects. Finally, we also apply SQAIR to
real-world pedestrian CCTV data, where it learns to reliably detect, track and
generate walking pedestrians with no supervision.
",0,0,0,1,0,0
17383,Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks,"  We present a new local descriptor for 3D shapes, directly applicable to a
wide range of shape analysis problems such as point correspondences, semantic
segmentation, affordance prediction, and shape-to-scan matching. The descriptor
is produced by a convolutional network that is trained to embed geometrically
and semantically similar points close to one another in descriptor space. The
network processes surface neighborhoods around points on a shape that are
captured at multiple scales by a succession of progressively zoomed out views,
taken from carefully selected camera positions. We leverage two extremely large
sources of data to train our network. First, since our network processes
rendered views in the form of 2D images, we repurpose architectures pre-trained
on massive image datasets. Second, we automatically generate a synthetic dense
point correspondence dataset by non-rigid alignment of corresponding shape
parts in a large collection of segmented 3D models. As a result of these design
choices, our network effectively encodes multi-scale local context and
fine-grained surface detail. Our network can be trained to produce either
category-specific descriptors or more generic descriptors by learning from
multiple shape categories. Once trained, at test time, the network extracts
local descriptors for shapes without requiring any part segmentation as input.
Our method can produce effective local descriptors even for shapes whose
category is unknown or different from the ones used while training. We
demonstrate through several experiments that our learned local descriptors are
more discriminative compared to state of the art alternatives, and are
effective in a variety of shape analysis applications.
",1,0,0,0,0,0
17384,Alternating minimization for dictionary learning with random initialization,"  We present theoretical guarantees for an alternating minimization algorithm
for the dictionary learning/sparse coding problem. The dictionary learning
problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an
appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$.
Our algorithm is a simple alternating minimization procedure that switches
between $\ell_1$ minimization and gradient descent in alternate steps.
Dictionary learning and specifically alternating minimization algorithms for
dictionary learning are well studied both theoretically and empirically.
However, in contrast to previous theoretical analyses for this problem, we
replace the condition on the operator norm (that is, the largest magnitude
singular value) of the true underlying dictionary $A^*$ with a condition on the
matrix infinity norm (that is, the largest magnitude term). This not only
allows us to get convergence rates for the error of the estimated dictionary
measured in the matrix infinity norm, but also ensures that a random
initialization will provably converge to the global optimum. Our guarantees are
under a reasonable generative model that allows for dictionaries with growing
operator norms, and can handle an arbitrary level of overcompleteness, while
having sparsity that is information theoretically optimal. We also establish
upper bounds on the sample complexity of our algorithm.
",1,0,0,1,0,0
17385,Optimal Transmission Line Switching under Geomagnetic Disturbances,"  In recent years, there have been increasing concerns about how geomagnetic
disturbances (GMDs) impact electrical power systems. Geomagnetically-induced
currents (GICs) can saturate transformers, induce hot spot heating and increase
reactive power losses. These effects can potentially cause catastrophic damage
to transformers and severely impact the ability of a power system to deliver
power. To address this problem, we develop a model of GIC impacts to power
systems that includes 1) GIC thermal capacity of transformers as a function of
normal Alternating Current (AC) and 2) reactive power losses as a function of
GIC. We use this model to derive an optimization problem that protects power
systems from GIC impacts through line switching, generator redispatch, and load
shedding. We employ state-of-the-art convex relaxations of AC power flow
equations to lower bound the objective. We demonstrate the approach on a
modified RTS96 system and the UIUC 150-bus system and show that line switching
is an effective means to mitigate GIC impacts. We also provide a sensitivity
analysis of optimal switching decisions with respect to GMD direction.
",1,0,0,0,0,0
17386,Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks,"  In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.
",1,0,0,0,0,0
17387,The QKP limit of the quantum Euler-Poisson equation,"  In this paper, we consider the derivation of the Kadomtsev-Petviashvili (KP)
equation for cold ion-acoustic wave in the long wavelength limit of the
two-dimensional quantum Euler-Poisson system, under different scalings for
varying directions in the Gardner-Morikawa transform. It is shown that the
types of the KP equation depend on the scaled quantum parameter $H>0$. The
QKP-I is derived for $H>2$, QKP-II for $0<H<2$ and the dispersive-less KP (dKP)
equation for the critical case $H=2$. The rigorous proof for these limits is
given in the well-prepared initial data case, and the norm that is chosen to
close the proof is anisotropic in the two directions, in accordance with the
anisotropic structure of the KP equation as well as the Gardner-Morikawa
transform. The results can be generalized in several directions.
",0,0,1,0,0,0
17388,Variational Implicit Processes,"  This paper introduces the variational implicit processes (VIPs), a Bayesian
nonparametric method based on a class of highly flexible priors over functions.
Similar to Gaussian processes (GPs), in implicit processes (IPs), an implicit
multivariate prior (data simulators, Bayesian neural networks, etc.) is placed
over any finite collections of random variables. A novel and efficient
variational inference algorithm for IPs is derived using wake-sleep updates,
which gives analytic solutions and allows scalable hyper-parameter learning
with stochastic optimization. Experiments on real-world regression datasets
demonstrate that VIPs return better uncertainty estimates and superior
performance over existing inference methods for GPs and Bayesian neural
networks. With a Bayesian LSTM as the implicit prior, the proposed approach
achieves state-of-the-art results on predicting power conversion efficiency of
molecules based on raw chemical formulas.
",0,0,0,1,0,0
17389,Silicon Micromachined High-contrast Artificial Dielectrics for Millimeter-wave Transformation Optics Antennas,"  Transformation optics methods and gradient index electromagnetic structures
rely upon spatially varied arbitrary permittivity. This, along with recent
interest in millimeter-wave lens-based antennas demands high spatial resolution
dielectric variation. Perforated media have been used to fabricate gradient
index structures from microwaves to THz but are often limited in contrast. We
show that by employing regular polygon unit-cells (hexagon, square, and
triangle) on matched lattices we can realize very high contrast permittivity
ranging from 0.1-1.0 of the background permittivity. Silicon micromachining
(Bosch process) is performed on high resistivity Silicon wafers to achieve a
minimum permittivity of 1.25 (10% of Silicon) in the WR28 waveguide band,
specifically targeting the proposed 39 GHz 5G communications band. The method
is valid into the THz band.
",0,1,0,0,0,0
17390,Pseudogap and Fermi surface in the presence of spin-vortex checkerboard for 1/8-doped lanthanum cuprates,"  Lanthanum family of high-temperature cuprate superconductors is known to
exhibit both spin and charge electronic modulations around doping level 1/8. We
assume that these modulations have the character of two-dimensional spin-vortex
checkerboard and investigate whether this assumption is consistent with the
Fermi surface and the pseudogap measured by angle-resolved photo-emission
spectroscopy. We also explore the possibility of observing quantum oscillations
of transport coefficients in such a background. These investigations are based
on a model of non-interacting spin-1/2 fermions hopping on a square lattice and
coupled through spins to a magnetic field imitating spin-vortex checkerboard.
The main results of this article include (i) calculation of Fermi surface
containing Fermi arcs at the positions in the Brillouin zone largely consistent
with experiments; (ii) identification of factors complicating the observations
of quantum oscillations in the presence of spin modulations; and (iii)
investigation of the symmetries of the resulting electronic energy bands,
which, in particular, indicates that each band is double-degenerate and, in
addition, has at least one conical point, where it touches another
double-degenerate band. We discuss possible implications these cones may have
for the transport properties and the pseudogap.
",0,1,0,0,0,0
17391,Revealing the cluster of slow transients behind a large slow slip event,"  Capable of reaching similar magnitudes to large megathrust earthquakes
($M_w>7$), slow slip events play a major role in accommodating tectonic motion
on plate boundaries. These slip transients are the slow release of built-up
tectonic stress that are geodetically imaged as a predominantly aseismic
rupture, which is smooth in both time and space. We demonstrate here that large
slow slip events are in fact a cluster of short-duration slow transients. Using
a dense catalog of low-frequency earthquakes as a guide, we investigate the
$M_w7.5$ slow slip event that occurred in 2006 along the subduction interface
40~km beneath Guerrero, Mexico. We show that while the long-period surface
displacement as recorded by GPS suggests a six month duration, motion in the
direction of tectonic release only sporadically occurs over 55 days and its
surface signature is attenuated by rapid relocking of the plate interface.These
results demonstrate that our current conceptual model of slow and continuous
rupture is an artifact of low-resolution geodetic observations of a
superposition of small, clustered slip events. Our proposed description of slow
slip as a cluster of slow transients implies that we systematically
overestimate the duration $T$ and underestimate the moment magnitude $M$ of
large slow slip events.
",0,1,0,0,0,0
17392,General $N$-solitons and their dynamics in several nonlocal nonlinear Schrödinger equations,"  General $N$-solitons in three recently-proposed nonlocal nonlinear
Schrödinger equations are presented. These nonlocal equations include the
reverse-space, reverse-time, and reverse-space-time nonlinear Schrödinger
equations, which are nonlocal reductions of the Ablowitz-Kaup-Newell-Segur
(AKNS) hierarchy. It is shown that general $N$-solitons in these different
equations can be derived from the same Riemann-Hilbert solutions of the AKNS
hierarchy, except that symmetry relations on the scattering data are different
for these equations. This Riemann-Hilbert framework allows us to identify new
types of solitons with novel eigenvalue configurations in the spectral plane.
Dynamics of $N$-solitons in these equations is also explored. In all the three
nonlocal equations, a generic feature of their solutions is repeated
collapsing. In addition, multi-solitons can behave very differently from
fundamental solitons and may not correspond to a nonlinear superposition of
fundamental solitons.
",0,1,0,0,0,0
17393,Revisiting wireless network jamming by SIR-based considerations and Multiband Robust Optimization,"  We revisit the mathematical models for wireless network jamming introduced by
Commander et al.: we first point out the strong connections with classical
wireless network design and then we propose a new model based on the explicit
use of signal-to-interference quantities. Moreover, to address the intrinsic
uncertain nature of the jamming problem and tackle the peculiar right-hand-side
(RHS) uncertainty of the problem, we propose an original robust cutting-plane
algorithm drawing inspiration from Multiband Robust Optimization. Finally, we
assess the performance of the proposed cutting plane algorithm by experiments
on realistic network instances.
",1,0,1,0,0,0
17394,New models for symbolic data analysis,"  Symbolic data analysis (SDA) is an emerging area of statistics based on
aggregating individual level data into group-based distributional summaries
(symbols), and then developing statistical methods to analyse them. It is ideal
for analysing large and complex datasets, and has immense potential to become a
standard inferential technique in the near future. However, existing SDA
techniques are either non-inferential, do not easily permit meaningful
statistical models, are unable to distinguish between competing models, and are
based on simplifying assumptions that are known to be false. Further, the
procedure for constructing symbols from the underlying data is erroneously not
considered relevant to the resulting statistical analysis. In this paper we
introduce a new general method for constructing likelihood functions for
symbolic data based on a desired probability model for the underlying classical
data, while only observing the distributional summaries. This approach resolves
many of the conceptual and practical issues with current SDA methods, opens the
door for new classes of symbol design and construction, in addition to
developing SDA as a viable tool to enable and improve upon classical data
analyses, particularly for very large and complex datasets. This work creates a
new direction for SDA research, which we illustrate through several real and
simulated data analyses.
",0,0,0,1,0,0
17395,Soft Methodology for Cost-and-error Sensitive Classification,"  Many real-world data mining applications need varying cost for different
types of classification errors and thus call for cost-sensitive classification
algorithms. Existing algorithms for cost-sensitive classification are
successful in terms of minimizing the cost, but can result in a high error rate
as the trade-off. The high error rate holds back the practical use of those
algorithms. In this paper, we propose a novel cost-sensitive classification
methodology that takes both the cost and the error rate into account. The
methodology, called soft cost-sensitive classification, is established from a
multicriteria optimization problem of the cost and the error rate, and can be
viewed as regularizing cost-sensitive classification with the error rate. The
simple methodology allows immediate improvements of existing cost-sensitive
classification algorithms. Experiments on the benchmark and the real-world data
sets show that our proposed methodology indeed achieves lower test error rates
and similar (sometimes lower) test costs than existing cost-sensitive
classification algorithms. We also demonstrate that the methodology can be
extended for considering the weighted error rate instead of the original error
rate. This extension is useful for tackling unbalanced classification problems.
",1,0,0,0,0,0
17396,Raman LIDARs and atmospheric calibration for the Cherenkov Telescope Array,"  The Cherenkov Telescope Array (CTA) is the next generation of Imaging
Atmospheric Cherenkov Telescopes. It will reach a sensitivity and energy
resolution never obtained until now by any other high energy gamma-ray
experiment. Understanding the systematic uncertainties in general will be a
crucial issue for the performance of CTA. It is well known that atmospheric
conditions contribute particularly in this aspect.Within the CTA consortium
several groups are currently building Raman LIDARs to be installed on the two
sites. Raman LIDARs are devices composed of a powerful laser that shoots into
the atmosphere, a collector that gathers the backscattered light from molecules
and aerosols, a photo-sensor, an optical module that spectrally selects
wavelengths of interest, and a read--out system.Unlike currently used elastic
LIDARs, they can help reduce the systematic uncertainties of the molecular and
aerosol components of the atmosphere to <5% so that CTA can achieve its energy
resolution requirements of<10% uncertainty at 1 TeV.All the Raman LIDARs in
this work have design features that make them different than typical Raman
LIDARs used in atmospheric science and are characterized by large collecting
mirrors (2.5m2) and reduced acquisition time.They provide both multiple elastic
and Raman read-out channels and custom made optics design.In this paper, the
motivation for Raman LIDARs, the design and the status of advance of these
technologies are described.
",0,1,0,0,0,0
17397,Generalized notions of sparsity and restricted isometry property. Part II: Applications,"  The restricted isometry property (RIP) is a universal tool for data recovery.
We explore the implication of the RIP in the framework of generalized sparsity
and group measurements introduced in the Part I paper. It turns out that for a
given measurement instrument the number of measurements for RIP can be improved
by optimizing over families of Banach spaces. Second, we investigate the
preservation of difference of two sparse vectors, which is not trivial in
generalized models. Third, we extend the RIP of partial Fourier measurements at
optimal scaling of number of measurements with random sign to far more general
group structured measurements. Lastly, we also obtain RIP in infinite dimension
in the context of Fourier measurement concepts with sparsity naturally replaced
by smoothness assumptions.
",0,0,0,1,0,0
17398,Mellin-Meijer-kernel density estimation on $\mathbb{R}^+$,"  Nonparametric kernel density estimation is a very natural procedure which
simply makes use of the smoothing power of the convolution operation. Yet, it
performs poorly when the density of a positive variable is to be estimated
(boundary issues, spurious bumps in the tail). So various extensions of the
basic kernel estimator allegedly suitable for $\mathbb{R}^+$-supported
densities, such as those using Gamma or other asymmetric kernels, abound in the
literature. Those, however, are not based on any valid smoothing operation
analogous to the convolution, which typically leads to inconsistencies. By
contrast, in this paper a kernel estimator for $\mathbb{R}^+$-supported
densities is defined by making use of the Mellin convolution, the natural
analogue of the usual convolution on $\mathbb{R}^+$. From there, a very
transparent theory flows and leads to new type of asymmetric kernels strongly
related to Meijer's $G$-functions. The numerous pleasant properties of this
`Mellin-Meijer-kernel density estimator' are demonstrated in the paper. Its
pointwise and $L_2$-consistency (with optimal rate of convergence) is
established for a large class of densities, including densities unbounded at 0
and showing power-law decay in their right tail. Its practical behaviour is
investigated further through simulations and some real data analyses.
",0,0,1,1,0,0
17399,Gene Ontology (GO) Prediction using Machine Learning Methods,"  We applied machine learning to predict whether a gene is involved in axon
regeneration. We extracted 31 features from different databases and trained
five machine learning models. Our optimal model, a Random Forest Classifier
with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than
the baseline score. We concluded that our models have some predictive
capability. Similar methodology and features could be applied to predict other
Gene Ontology (GO) terms.
",1,0,0,1,0,0
17400,Dimension Spectra of Lines,"  This paper investigates the algorithmic dimension spectra of lines in the
Euclidean plane. Given any line L with slope a and vertical intercept b, the
dimension spectrum sp(L) is the set of all effective Hausdorff dimensions of
individual points on L. We draw on Kolmogorov complexity and geometrical
arguments to show that if the effective Hausdorff dimension dim(a, b) is equal
to the effective packing dimension Dim(a, b), then sp(L) contains a unit
interval. We also show that, if the dimension dim(a, b) is at least one, then
sp(L) is infinite. Together with previous work, this implies that the dimension
spectrum of any line is infinite.
",1,0,0,0,0,0
17401,Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search,"  Inference in log-linear models scales linearly in the size of output space in
the worst-case. This is often a bottleneck in natural language processing and
computer vision tasks when the output space is feasibly enumerable but very
large. We propose a method to perform inference in log-linear models with
sublinear amortized cost. Our idea hinges on using Gumbel random variable
perturbations and a pre-computed Maximum Inner Product Search data structure to
access the most-likely elements in sublinear amortized time. Our method yields
provable runtime and accuracy guarantees. Further, we present empirical
experiments on ImageNet and Word Embeddings showing significant speedups for
sampling, inference, and learning in log-linear models.
",1,0,0,1,0,0
17402,Temperature Dependence of Magnetic Excitations: Terahertz Magnons above the Curie Temperature,"  When an ordered spin system of a given dimensionality undergoes a second
order phase transition the dependence of the order parameter i.e. magnetization
on temperature can be well-described by thermal excitations of elementary
collective spin excitations (magnons). However, the behavior of magnons
themselves, as a function of temperature and across the transition temperature
TC, is an unknown issue. Utilizing spin-polarized high resolution electron
energy loss spectroscopy we monitor the high-energy (terahertz) magnons,
excited in an ultrathin ferromagnet, as a function of temperature. We show that
the magnons' energy and lifetime decrease with temperature. The
temperature-induced renormalization of the magnons' energy and lifetime depends
on the wave vector. We provide quantitative results on the temperature-induced
damping and discuss the possible mechanism e.g., multi-magnon scattering. A
careful investigation of physical quantities determining the magnons'
propagation indicates that terahertz magnons sustain their propagating
character even at temperatures far above TC.
",0,1,0,0,0,0
17403,On stable solitons and interactions of the generalized Gross-Pitaevskii equation with PT-and non-PT-symmetric potentials,"  We report the bright solitons of the generalized Gross-Pitaevskii (GP)
equation with some types of physically relevant parity-time-(PT-) and
non-PT-symmetric potentials. We find that the constant momentum coefficient can
modulate the linear stability and complicated transverse power-flows (not
always from the gain toward loss) of nonlinear modes. However, the varying
momentum coefficient Gamma(x) can modulate both unbroken linear PT-symmetric
phases and stability of nonlinear modes. Particularly, the nonlinearity can
excite the unstable linear mode (i.e., broken linear PT-symmetric phase) to
stable nonlinear modes. Moreover, we also find stable bright solitons in the
presence of non-PT-symmetric harmonic-Gaussian potential. The interactions of
two bright solitons are also illustrated in PT-symmetric potentials. Finally,
we consider nonlinear modes and transverse power-flows in the three-dimensional
(3D) GP equation with the generalized PT-symmetric Scarf-II potential
",0,1,1,0,0,0
17404,Mechanical properties of borophene films: A reactive molecular dynamics investigation,"  The most recent experimental advances could provide ways for the fabrication
of several atomic thick and planar forms of boron atoms. For the first time, we
explore the mechanical properties of five types of boron films with various
vacancy ratios ranging from 0.1 to 0.15, using molecular dynamics simulations
with ReaxFF force field. It is found that the Young's modulus and tensile
strength decrease with increasing the temperature. We found that boron sheets
exhibit an anisotropic mechanical response due to the different arrangement of
atoms along the armchair and zigzag directions. At room temperature, 2D Young's
modulus and fracture stress of these five sheets appear in the range 63 N/m and
12 N/m, respectively. In addition, the strains at tensile strength are in the
ranges of 9, 11, and 10 percent at 1, 300, and 600 K, respectively. This
investigation not only reveals the remarkable stiffness of 2D boron, but
establishes relations between the mechanical properties of the boron sheets to
the loading direction, temperature and atomic structures.
",0,1,0,0,0,0
17405,Stronger selection can slow down evolution driven by recombination on a smooth fitness landscape,"  Stronger selection implies faster evolution---that is, the greater the force,
the faster the change. This apparently self-evident proposition, however, is
derived under the assumption that genetic variation within a population is
primarily supplied by mutation (i.e.\ mutation-driven evolution). Here, we show
that this proposition does not actually hold for recombination-driven
evolution, i.e.\ evolution in which genetic variation is primarily created by
recombination rather than mutation. By numerically investigating population
genetics models of recombination, migration and selection, we demonstrate that
stronger selection can slow down evolution on a perfectly smooth fitness
landscape. Through simple analytical calculation, this apparently
counter-intuitive result is shown to stem from two opposing effects of natural
selection on the rate of evolution. On the one hand, natural selection tends to
increase the rate of evolution by increasing the fixation probability of fitter
genotypes. On the other hand, natural selection tends to decrease the rate of
evolution by decreasing the chance of recombination between immigrants and
resident individuals. As a consequence of these opposing effects, there is a
finite selection pressure maximizing the rate of evolution. Hence, stronger
selection can imply slower evolution if genetic variation is primarily supplied
by recombination.
",0,1,0,0,0,0
17406,Differences Among Noninformative Stopping Rules Are Often Relevant to Bayesian Decisions,"  L.J. Savage once hoped to show that ""the superficially incompatible systems
of ideas associated on the one hand with [subjective Bayesianism] and on the
other hand with [classical statistics]...lend each other mutual support and
clarification."" By 1972, however, he had largely ""lost faith in the devices"" of
classical statistics. One aspect of those ""devices"" that he found objectionable
is that differences among the ""stopping rules"" that are used to decide when to
end an experiment which are ""noninformative"" from a Bayesian perspective can
affect decisions made using a classical approach. Two experiments that produce
the same data using different stopping rules seem to differ only in the
intentions of the experimenters regarding whether or not they would have
carried on if the data had been different, which seem irrelevant to the
evidential import of the data and thus to facts about what actions the data
warrant.
I argue that classical and Bayesian ideas about stopping rules do in fact
""lend each other"" the kind of ""mutual support and clarification"" that Savage
had originally hoped to find. They do so in a kind of case that is common in
scientific practice, in which those who design an experiment have different
interests from those who will make decisions in light of its results. I show
that, in cases of this kind, Bayesian principles provide qualified support for
the classical statistical practice of ""penalizing"" ""biased"" stopping rules.
However, they require this practice in a narrower range of circumstances than
classical principles do, and for different reasons. I argue that classical
arguments for this practice are compelling in precisely the class of cases in
which Bayesian principles also require it, and thus that we should regard
Bayesian principles as clarifying classical statistical ideas about stopping
rules rather than the reverse.
",0,0,1,1,0,0
17407,CNNs are Globally Optimal Given Multi-Layer Support,"  Stochastic Gradient Descent (SGD) is the central workhorse for training
modern CNNs. Although giving impressive empirical performance it can be slow to
converge. In this paper we explore a novel strategy for training a CNN using an
alternation strategy that offers substantial speedups during training. We make
the following contributions: (i) replace the ReLU non-linearity within a CNN
with positive hard-thresholding, (ii) reinterpret this non-linearity as a
binary state vector making the entire CNN linear if the multi-layer support is
known, and (iii) demonstrate that under certain conditions a global optima to
the CNN can be found through local descent. We then employ a novel alternation
strategy (between weights and support) for CNN training that leads to
substantially faster convergence rates, nice theoretical properties, and
achieving state of the art results across large scale datasets (e.g. ImageNet)
as well as other standard benchmarks.
",1,0,0,0,0,0
17408,The Kontsevich integral for bottom tangles in handlebodies,"  The Kontsevich integral is a powerful link invariant, taking values in spaces
of Jacobi diagrams. In this paper, we extend the Kontsevich integral to
construct a functor on the category of bottom tangles in handlebodies. This
functor gives a universal finite type invariant of bottom tangles, and refines
a functorial version of the Le-Murakami-Ohtsuki 3-manifold invariant for
Lagrangian cobordisms of surfaces.
",0,0,1,0,0,0
17409,Commutativity theorems for groups and semigroups,"  In this note we prove a selection of commutativity theorems for various
classes of semigroups. For instance, if in a separative or completely regular
semigroup $S$ we have $x^p y^p = y^p x^p$ and $x^q y^q = y^q x^q$ for all
$x,y\in S$ where $p$ and $q$ are relatively prime, then $S$ is commutative. In
a separative or inverse semigroup $S$, if there exist three consecutive
integers $i$ such that $(xy)^i = x^i y^i$ for all $x,y\in S$, then $S$ is
commutative. Finally, if $S$ is a separative or inverse semigroup satisfying
$(xy)^3=x^3y^3$ for all $x,y\in S$, and if the cubing map $x\mapsto x^3$ is
injective, then $S$ is commutative.
",0,0,1,0,0,0
17410,Content-based Approach for Vietnamese Spam SMS Filtering,"  Short Message Service (SMS) spam is a serious problem in Vietnam because of
the availability of very cheap pre-paid SMS packages. There are some systems to
detect and filter spam messages for English, most of which use machine learning
techniques to analyze the content of messages and classify them. For
Vietnamese, there is some research on spam email filtering but none focused on
SMS. In this work, we propose the first system for filtering Vietnamese spam
SMS. We first propose an appropriate preprocessing method since existing tools
for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
experiment with vector representations and classifiers to find the best model
for this problem. Our system achieves an accuracy of 94% when labelling spam
messages while the misclassification rate of legitimate messages is relatively
small, about only 0.4%. This is an encouraging result compared to that of
English and can be served as a strong baseline for future development of
Vietnamese SMS spam prevention systems.
",1,0,0,0,0,0
17411,Measuring Software Performance on Linux,"  Measuring and analyzing the performance of software has reached a high
complexity, caused by more advanced processor designs and the intricate
interaction between user programs, the operating system, and the processor's
microarchitecture. In this report, we summarize our experience about how
performance characteristics of software should be measured when running on a
Linux operating system and a modern processor. In particular, (1) We provide a
general overview about hardware and operating system features that may have a
significant impact on timing and how they interact, (2) we identify sources of
errors that need to be controlled in order to obtain unbiased measurement
results, and (3) we propose a measurement setup for Linux to minimize errors.
Although not the focus of this report, we describe the measurement process
using hardware performance counters, which can faithfully reflect the real
bottlenecks on a given processor. Our experiments confirm that our measurement
setup has a large impact on the results. More surprisingly, however, they also
suggest that the setup can be negligible for certain analysis methods.
Furthermore, we found that our setup maintains significantly better performance
under background load conditions, which means it can be used to improve
software in high-performance applications.
",1,0,0,0,0,0
17412,A general method for calculating lattice Green functions on the branch cut,"  We present a method for calculating the complex Green function $G_{ij}
(\omega)$ at any real frequency $\omega$ between any two sites $i$ and $j$ on a
lattice. Starting from numbers of walks on square, cubic, honeycomb,
triangular, bcc, fcc, and diamond lattices, we derive Chebyshev expansion
coefficients for $G_{ij} (\omega)$. The convergence of the Chebyshev series can
be accelerated by constructing functions $f(\omega)$ that mimic the van Hove
singularities in $G_{ij} (\omega)$ and subtracting their Chebyshev coefficients
from the original coefficients. We demonstrate this explicitly for the square
lattice and bcc lattice. Our algorithm achieves typical accuracies of 6--9
significant figures using 1000 series terms.
",0,1,0,0,0,0
17413,"Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems","  Literature on the modeling and simulation of complex adaptive systems (cas)
has primarily advanced vertically in different scientific domains with
scientists developing a variety of domain-specific approaches and applications.
However, while cas researchers are inher-ently interested in an
interdisciplinary comparison of models, to the best of our knowledge, there is
currently no single unified framework for facilitating the development,
comparison, communication and validation of models across different scientific
domains. In this thesis, we propose first steps towards such a unified
framework using a combination of agent-based and complex network-based modeling
approaches and guidelines formulated in the form of a set of four levels of
usage, which allow multidisciplinary researchers to adopt a suitable framework
level on the basis of available data types, their research study objectives and
expected outcomes, thus allowing them to better plan and conduct their
respective re-search case studies.
",1,1,0,0,0,0
17414,Recover Fine-Grained Spatial Data from Coarse Aggregation,"  In this paper, we study a new type of spatial sparse recovery problem, that
is to infer the fine-grained spatial distribution of certain density data in a
region only based on the aggregate observations recorded for each of its
subregions. One typical example of this spatial sparse recovery problem is to
infer spatial distribution of cellphone activities based on aggregate mobile
traffic volumes observed at sparsely scattered base stations. We propose a
novel Constrained Spatial Smoothing (CSS) approach, which exploits the local
continuity that exists in many types of spatial data to perform sparse recovery
via finite-element methods, while enforcing the aggregated observation
constraints through an innovative use of the ADMM algorithm. We also improve
the approach to further utilize additional geographical attributes. Extensive
evaluations based on a large dataset of phone call records and a demographical
dataset from the city of Milan show that our approach significantly outperforms
various state-of-the-art approaches, including Spatial Spline Regression (SSR).
",1,0,0,0,0,0
17415,The Power Allocation Game on Dynamic Networks: Subgame Perfection,"  In the game theory literature, there appears to be little research on
equilibrium selection for normal-form games with an infinite strategy space and
discontinuous utility functions. Moreover, many existing selection methods are
not applicable to games involving both cooperative and noncooperative scenarios
(e.g., ""games on signed graphs""). With the purpose of equilibrium selection,
the power allocation game developed in \cite{allocation}, which is a static,
resource allocation game on signed graphs, will be reformulated into an
extensive form. Results about the subgame perfect Nash equilibria in the
extensive-form game will be given. This appears to be the first time that
subgame perfection based on time-varying graphs is used for equilibrium
selection in network games. This idea of subgame perfection proposed in the
paper may be extrapolated to other network games, which will be illustrated
with a simple example of congestion games.
",1,0,0,0,0,0
17416,MIMO Graph Filters for Convolutional Neural Networks,"  Superior performance and ease of implementation have fostered the adoption of
Convolutional Neural Networks (CNNs) for a wide array of inference and
reconstruction tasks. CNNs implement three basic blocks: convolution, pooling
and pointwise nonlinearity. Since the two first operations are well-defined
only on regular-structured data such as audio or images, application of CNNs to
contemporary datasets where the information is defined in irregular domains is
challenging. This paper investigates CNNs architectures to operate on signals
whose support can be modeled using a graph. Architectures that replace the
regular convolution with a so-called linear shift-invariant graph filter have
been recently proposed. This paper goes one step further and, under the
framework of multiple-input multiple-output (MIMO) graph filters, imposes
additional structure on the adopted graph filters, to obtain three new (more
parsimonious) architectures. The proposed architectures result in a lower
number of model parameters, reducing the computational complexity, facilitating
the training, and mitigating the risk of overfitting. Simulations show that the
proposed simpler architectures achieve similar performance as more complex
models.
",0,0,0,1,0,0
17417,An Edge Driven Wavelet Frame Model for Image Restoration,"  Wavelet frame systems are known to be effective in capturing singularities
from noisy and degraded images. In this paper, we introduce a new edge driven
wavelet frame model for image restoration by approximating images as piecewise
smooth functions. With an implicit representation of image singularities sets,
the proposed model inflicts different strength of regularization on smooth and
singular image regions and edges. The proposed edge driven model is robust to
both image approximation and singularity estimation. The implicit formulation
also enables an asymptotic analysis of the proposed models and a rigorous
connection between the discrete model and a general continuous variational
model. Finally, numerical results on image inpainting and deblurring show that
the proposed model is compared favorably against several popular image
restoration models.
",1,0,1,0,0,0
17418,An exact algorithm exhibiting RS-RSB/easy-hard correspondence for the maximum independent set problem,"  A recently proposed exact algorithm for the maximum independent set problem
is analyzed. The typical running time is improved exponentially in some
parameter regions compared to simple binary search. The algorithm also
overcomes the core transition point, where the conventional leaf removal
algorithm fails, and works up to the replica symmetry breaking (RSB) transition
point. This suggests that a leaf removal core itself is not enough for typical
hardness in the random maximum independent set problem, providing further
evidence for RSB being the obstacle for algorithms in general.
",1,1,0,0,0,0
17419,Flexible Support for Fast Parallel Commutative Updates,"  Privatizing data is a useful strategy for increasing parallelism in a shared
memory multithreaded program. Independent cores can compute independently on
duplicates of shared data, combining their results at the end of their
computations. Conventional approaches to privatization, however, rely on
explicit static or dynamic memory allocation for duplicated state, increasing
memory footprint and contention for cache resources, especially in shared
caches. In this work, we describe CCache, a system for on-demand privatization
of data manipulated by commutative operations. CCache garners the benefits of
privatization, without the increase in memory footprint or cache occupancy.
Each core in CCache dynamically privatizes commutatively manipulated data,
operating on a copy. Periodically or at the end of its computation, the core
merges its value with the value resident in memory, and when all cores have
merged, the in-memory copy contains the up-to-date value. We describe a
low-complexity architectural implementation of CCache that extends a
conventional multicore to support on-demand privatization without using
additional memory for private copies. We evaluate CCache on several high-value
applications, including random access key-value store, clustering, breadth
first search and graph ranking, showing speedups upto 3.2X.
",1,0,0,0,0,0
17420,Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms,"  Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
the 3D visualization of cellular structure and organization at submolecular
resolution. It enables analyzing the native structures of macromolecular
complexes and their spatial organization inside single cells. However, due to
the high degree of structural complexity and practical imaging limitations,
systematic macromolecular structural recovery inside CECT images remains
challenging. Particularly, the recovery of a macromolecule is likely to be
biased by its neighbor structures due to the high molecular crowding. To reduce
the bias, here we introduce a novel 3D convolutional neural network inspired by
Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
segmentation of macromolecules of interest in subtomograms. The tests of our
models on realistically simulated CECT data demonstrate that our new approach
has significantly improved segmentation performance compared to our baseline
approach. Also, we demonstrate that the proposed model has generalization
ability to segment new structures that do not exist in training data.
",0,0,0,1,1,0
17421,CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks,"  Large-scale deep convolutional neural networks (CNNs) are widely used in
machine learning applications. While CNNs involve huge complexity, VLSI (ASIC
and FPGA) chips that deliver high-density integration of computational
resources are regarded as a promising platform for CNN's implementation. At
massive parallelism of computational units, however, the external memory
bandwidth, which is constrained by the pin count of the VLSI chip, becomes the
system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of
the flexibility to be reconfigured for the various parameters of CNNs. This
paper presents CNN-MERP to address these issues. CNN-MERP incorporates an
efficient memory hierarchy that significantly reduces the bandwidth
requirements from multiple optimizations including on/off-chip data allocation,
data flow optimization and data reuse. The proposed 2-level reconfigurability
is utilized to enable fast and efficient reconfiguration, which is based on the
control logic and the multiboot feature of FPGA. As a result, an external
memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower
than prior arts. Under limited DRAM bandwidth, a system throughput of
1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times
higher than the state-of-the-art FPGA implementations.
",1,0,0,0,0,0
17422,Enstrophy Cascade in Decaying Two-Dimensional Quantum Turbulence,"  We report evidence for an enstrophy cascade in large-scale point-vortex
simulations of decaying two-dimensional quantum turbulence. Devising a method
to generate quantum vortex configurations with kinetic energy narrowly
localized near a single length scale, the dynamics are found to be
well-characterised by a superfluid Reynolds number, $\mathrm{Re_s}$, that
depends only on the number of vortices and the initial kinetic energy scale.
Under free evolution the vortices exhibit features of a classical enstrophy
cascade, including a $k^{-3}$ power-law kinetic energy spectrum, and steady
enstrophy flux associated with inertial transport to small scales. Clear
signatures of the cascade emerge for $N\gtrsim 500$ vortices. Simulating up to
very large Reynolds numbers ($N = 32, 768$ vortices), additional features of
the classical theory are observed: the Kraichnan-Batchelor constant is found to
converge to $C' \approx 1.6$, and the width of the $k^{-3}$ range scales as
$\mathrm{Re_s}^{1/2}$. The results support a universal phenomenology
underpinning classical and quantum fluid turbulence.
",0,1,0,0,0,0
17423,Contextual Multi-armed Bandits under Feature Uncertainty,"  We study contextual multi-armed bandit problems under linear realizability on
rewards and uncertainty (or noise) on features. For the case of identical noise
on features across actions, we propose an algorithm, coined {\em NLinRel},
having $O\left(T^{\frac{7}{8}} \left(\log{(dT)}+K\sqrt{d}\right)\right)$ regret
bound for $T$ rounds, $K$ actions, and $d$-dimensional feature vectors. Next,
for the case of non-identical noise, we observe that popular linear hypotheses
including {\em NLinRel} are impossible to achieve such sub-linear regret.
Instead, under assumption of Gaussian feature vectors, we prove that a greedy
algorithm has $O\left(T^{\frac23}\sqrt{\log d}\right)$ regret bound with
respect to the optimal linear hypothesis. Utilizing our theoretical
understanding on the Gaussian case, we also design a practical variant of {\em
NLinRel}, coined {\em Universal-NLinRel}, for arbitrary feature distributions.
It first runs {\em NLinRel} for finding the `true' coefficient vector using
feature uncertainties and then adjust it to minimize its regret using the
statistical feature information. We justify the performance of {\em
Universal-NLinRel} on both synthetic and real-world datasets.
",1,0,0,1,0,0
17424,Asymmetric Variational Autoencoders,"  Variational inference for latent variable models is prevalent in various
machine learning problems, typically solved by maximizing the Evidence Lower
Bound (ELBO) of the true data likelihood with respect to a variational
distribution. However, freely enriching the family of variational distribution
is challenging since the ELBO requires variational likelihood evaluations of
the latent variables. In this paper, we propose a novel framework to enrich the
variational family by incorporating auxiliary variables to the variational
family. The resulting inference network doesn't require density evaluations for
the auxiliary variables and thus complex implicit densities over the auxiliary
variables can be constructed by neural networks. It can be shown that the
actual variational posterior of the proposed approach is essentially modeling a
rich probabilistic mixture of simple variational posterior indexed by auxiliary
variables, thus a flexible inference model can be built. Empirical evaluations
on several density estimation tasks demonstrates the effectiveness of the
proposed method.
",1,0,0,1,0,0
17425,Dynamics of homogeneous shear turbulence: A key role of the nonlinear transverse cascade in the bypass concept,"  To understand the self-sustenance of subcritical turbulence in spectrally
stable shear flows, we performed direct numerical simulations of homogeneous
shear turbulence for different aspect ratios of the flow domain and analyzed
the dynamical processes in Fourier space. There are no exponentially growing
modes in such flows and the turbulence is energetically supported only by the
linear growth of perturbation harmonics due to the shear flow non-normality.
This non-normality-induced, or nonmodal growth is anisotropic in spectral
space, which, in turn, leads to anisotropy of nonlinear processes in this
space. As a result, a transverse (angular) redistribution of harmonics in
Fourier space appears to be the main nonlinear process in these flows, rather
than direct or inverse cascades. We refer to this type of nonlinear
redistribution as the nonlinear transverse cascade. It is demonstrated that the
turbulence is sustained by a subtle interplay between the linear nonmodal
growth and the nonlinear transverse cascade that exemplifies a well-known
bypass scenario of subcritical turbulence. These two basic processes mainly
operate at large length scales, comparable to the domain size. Therefore, this
central, small wave number area of Fourier space is crucial in the
self-sustenance; we defined its size and labeled it as the vital area of
turbulence. Outside the vital area, the nonmodal growth and the transverse
cascade are of secondary importance. Although the cascades and the
self-sustaining process of turbulence are qualitatively the same at different
aspect ratios, the number of harmonics actively participating in this process
varies, but always remains quite large. This implies that the self-sustenance
of subcritical turbulence cannot be described by low-order models.
",0,1,0,0,0,0
17426,A taxonomy of learning dynamics in 2 x 2 games,"  Learning would be a convincing method to achieve coordination on an
equilibrium. But does learning converge, and to what? We answer this question
in generic 2-player, 2-strategy games, using Experience-Weighted Attraction
(EWA), which encompasses many extensively studied learning algorithms. We
exhaustively characterize the parameter space of EWA learning, for any payoff
matrix, and we understand the generic properties that imply convergent or
non-convergent behaviour in 2 x 2 games.
Irrational choice and lack of incentives imply convergence to a mixed
strategy in the centre of the strategy simplex, possibly far from the Nash
Equilibrium (NE). In the opposite limit, in which the players quickly modify
their strategies, the behaviour depends on the payoff matrix: (i) a strong
discrepancy between the pure strategies describes dominance-solvable games,
which show convergence to a unique fixed point close to the NE; (ii) a
preference towards profiles of strategies along the main diagonal describes
coordination games, with multiple stable fixed points corresponding to the NE;
(iii) a cycle of best responses defines discoordination games, which commonly
yield limit cycles or low-dimensional chaos.
While it is well known that mixed strategy equilibria may be unstable, our
approach is novel from several perspectives: we fully analyse EWA and provide
explicit thresholds that define the onset of instability; we find an emerging
taxonomy of the learning dynamics, without focusing on specific classes of
games ex-ante; we show that chaos can occur even in the simplest games; we make
a precise theoretical prediction that can be tested against data on
experimental learning of discoordination games.
",0,1,0,0,0,0
17427,Dynamic attitude planning for trajectory tracking in underactuated VTOL UAVs,"  This paper addresses the trajectory tracking control problem for
underactuated VTOL UAVs. According to the different actuation mechanisms, the
most common UAV platforms can achieve only a partial decoupling of attitude and
position tasks. Since position tracking is of utmost importance for
applications involving aerial vehicles, we propose a control scheme in which
position tracking is the primary objective. To this end, this work introduces
the concept of attitude planner, a dynamical system through which the desired
attitude reference is processed to guarantee the satisfaction of the primary
objective: the attitude tracking task is considered as a secondary objective
which can be realized as long as the desired trajectory satisfies specific
trackability conditions. Two numerical simulations are performed by applying
the proposed control law to a hexacopter with and without tilted propellers,
which accounts for unmodeled dynamics and external disturbances not included in
the control design model.
",1,0,0,0,0,0
17428,The JCMT Transient Survey: Data Reduction and Calibration Methods,"  Though there has been a significant amount of work investigating the early
stages of low-mass star formation in recent years, the evolution of the mass
assembly rate onto the central protostar remains largely unconstrained.
Examining in depth the variation in this rate is critical to understanding the
physics of star formation. Instabilities in the outer and inner circumstellar
disk can lead to episodic outbursts. Observing these brightness variations at
infrared or submillimetre wavelengths sets constraints on the current accretion
models. The JCMT Transient Survey is a three-year project dedicated to studying
the continuum variability of deeply embedded protostars in eight nearby
star-forming regions at a one month cadence. We use the SCUBA-2 instrument to
simultaneously observe these regions at wavelengths of 450 $\mu$m and 850
$\mu$m. In this paper, we present the data reduction techniques, image
alignment procedures, and relative flux calibration methods for 850 $\mu$m
data. We compare the properties and locations of bright, compact emission
sources fitted with Gaussians over time. Doing so, we achieve a spatial
alignment of better than 1"" between the repeated observations and an
uncertainty of 2-3\% in the relative peak brightness of significant, localised
emission. This combination of imaging performance is unprecedented in
ground-based, single dish submillimetre observations. Finally, we identify a
few sources that show possible and confirmed brightness variations. These
sources will be closely monitored and presented in further detail in additional
studies throughout the duration of the survey.
",0,1,0,0,0,0
17429,"Synchronization Strings: Explicit Constructions, Local Decoding, and Applications","  This paper gives new results for synchronization strings, a powerful
combinatorial object that allows to efficiently deal with insertions and
deletions in various communication settings:
$\bullet$ We give a deterministic, linear time synchronization string
construction, improving over an $O(n^5)$ time randomized construction.
Independently of this work, a deterministic $O(n\log^2\log n)$ time
construction was just put on arXiv by Cheng, Li, and Wu. We also give a
deterministic linear time construction of an infinite synchronization string,
which was not known to be computable before. Both constructions are highly
explicit, i.e., the $i^{th}$ symbol can be computed in $O(\log i)$ time.
$\bullet$ This paper also introduces a generalized notion we call
long-distance synchronization strings that allow for local and very fast
decoding. In particular, only $O(\log^3 n)$ time and access to logarithmically
many symbols is required to decode any index.
We give several applications for these results:
$\bullet$ For any $\delta<1$ and $\epsilon>0$ we provide an insdel correcting
code with rate $1-\delta-\epsilon$ which can correct any $O(\delta)$ fraction
of insdel errors in $O(n\log^3n)$ time. This near linear computational
efficiency is surprising given that we do not even know how to compute the
(edit) distance between the decoding input and output in sub-quadratic time. We
show that such codes can not only efficiently recover from $\delta$ fraction of
insdel errors but, similar to [Schulman, Zuckerman; TransInf'99], also from any
$O(\delta/\log n)$ fraction of block transpositions and replications.
$\bullet$ We show that highly explicitness and local decoding allow for
infinite channel simulations with exponentially smaller memory and decoding
time requirements. These simulations can be used to give the first near linear
time interactive coding scheme for insdel errors.
",1,0,0,0,0,0
17430,Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures,"  We apply both distance-based (Jin and Matteson, 2017) and kernel-based
(Pfister et al., 2016) mutual dependence measures to independent component
analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA,
minimizing empirical dependence measures as an objective function in both
deflation and parallel manners. Solving this minimization problem, we introduce
Latin hypercube sampling (LHS) (McKay et al., 2000), and a global optimization
method, Bayesian optimization (BO) (Mockus, 1994) to improve the initialization
of the Newton-type local optimization method. The performance of MDMICA is
evaluated in various simulation studies and an image data example. When the ICA
model is correct, MDMICA achieves competitive results compared to existing
approaches. When the ICA model is misspecified, the estimated independent
components are less mutually dependent than the observed components using
MDMICA, while they are prone to be even more mutually dependent than the
observed components using other approaches.
",0,0,0,1,0,0
17431,The local geometry of testing in ellipses: Tight control via localized Kolmogorov widths,"  We study the local geometry of testing a mean vector within a
high-dimensional ellipse against a compound alternative. Given samples of a
Gaussian random vector, the goal is to distinguish whether the mean is equal to
a known vector within an ellipse, or equal to some other unknown vector in the
ellipse. Such ellipse testing problems lie at the heart of several
applications, including non-parametric goodness-of-fit testing, signal
detection in cognitive radio, and regression function testing in reproducing
kernel Hilbert spaces. While past work on such problems has focused on the
difficulty in a global sense, we study difficulty in a way that is localized to
each vector within the ellipse. Our main result is to give sharp upper and
lower bounds on the localized minimax testing radius in terms of an explicit
formula involving the Kolmogorov width of the ellipse intersected with a
Euclidean ball. When applied to particular examples, our general theorems yield
interesting rates that were not known before: as a particular case, for testing
in Sobolev ellipses of smoothness $\alpha$, we demonstrate rates that vary from
$(\sigma^2)^{\frac{4 \alpha}{4 \alpha + 1}}$, corresponding to the classical
global rate, to the faster rate $(\sigma^2)^{\frac{8
\alpha}{8 \alpha + 1}}$, achievable for vectors at favorable locations within
the ellipse. We also show that the optimal test for this problem is achieved by
a linear projection test that is based on an explicit lower-dimensional
projection of the observation vector.
",0,0,1,1,0,0
17432,A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe,"  Two of the most fundamental prototypes of greedy optimization are the
matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
view on both classes of methods, leading to the first explicit convergence
rates of matching pursuit methods in an optimization sense, for general sets of
atoms. We derive sublinear ($1/t$) convergence for both classes on general
smooth objectives, and linear convergence on strongly convex objectives, as
well as a clear correspondence of algorithm variants. Our presented algorithms
and rates are affine invariant, and do not need any incoherence or sparsity
assumptions.
",1,0,0,1,0,0
17433,Subsampled Rényi Differential Privacy and Analytical Moments Accountant,"  We study the problem of subsampling in differential privacy (DP), a question
that is the centerpiece behind many successful differentially private machine
learning algorithms. Specifically, we provide a tight upper bound on the
Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms
that: (1) subsample the dataset, and then (2) applies a randomized mechanism M
to the subsample, in terms of the RDP parameters of M and the subsampling
probability parameter. Our results generalize the moments accounting technique,
developed by Abadi et al. (2016) for the Gaussian mechanism, to any subsampled
RDP mechanism.
",0,0,0,1,0,0
17434,"Concurrency and Probability: Removing Confusion, Compositionally","  Assigning a satisfactory truly concurrent semantics to Petri nets with
confusion and distributed decisions is a long standing problem, especially if
one wants to fully replace nondeterminism with probability distributions and no
stochastic structure is desired/allowed. Here we propose a general solution
based on a recursive, static decomposition of (finite, occurrence) nets in loci
of decision, called structural branching cells (s-cells). Each s-cell exposes a
set of alternatives, called transactions, that can be equipped with a general
probabilistic distribution. The solution is formalised as a transformation from
a given Petri net to another net whose transitions are the transactions of the
s-cells and whose places are the places of the original net, with some
auxiliary structure for bookkeeping. The resulting net is confusion-free,
namely if a transition is enabled, then all its conflicting alternatives are
also enabled. Thus sets of conflicting alternatives can be equipped with
probability distributions, while nonintersecting alternatives are purely
concurrent and do not introduce any nondeterminism: they are Church-Rosser and
their probability distributions are independent. The validity of the
construction is witnessed by a tight correspondence result with the recent
approach by Abbes and Benveniste (AB) based on recursively stopped
configurations in event structures. Some advantages of our approach over AB's
are that: i) s-cells are defined statically and locally in a compositional way,
whereas AB's branching cells are defined dynamically and globally; ii) their
recursively stopped configurations correspond to possible executions, but the
existing concurrency is not made explicit. Instead, our resulting nets are
equipped with an original concurrency structure exhibiting a so-called complete
concurrency property.
",1,0,0,0,0,0
17435,Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration,"  We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks.
",1,0,0,0,0,0
17436,Dynamic Word Embeddings,"  We present a probabilistic language model for time-stamped text data which
tracks the semantic evolution of individual words over time. The model
represents words and contexts by latent trajectories in an embedding space. At
each moment in time, the embedding vectors are inferred from a probabilistic
version of word2vec [Mikolov et al., 2013]. These embedding vectors are
connected in time through a latent diffusion process. We describe two scalable
variational inference algorithms--skip-gram smoothing and skip-gram
filtering--that allow us to train the model jointly over all times; thus
learning on all data while simultaneously allowing word and context vectors to
drift. Experimental results on three different corpora demonstrate that our
dynamic model infers word embedding trajectories that are more interpretable
and lead to higher predictive likelihoods than competing methods that are based
on static models trained separately on time slices.
",0,0,0,1,0,0
17437,Multiple scattering effect on angular distribution and polarization of radiation by relativistic electrons in a thin crystal,"  The multiple scattering of ultra relativistic electrons in an amorphous
matter leads to the suppression of the soft part of radiation spectrum (the
Landau-Pomeranchuk-Migdal effect), and also can change essentially the angular
distribution of the emitted photons. A similar effect must take place in a
crystal for the coherent radiation of relativistic electron. The results of the
theoretical investigation of angular distributions and polarization of
radiation by a relativistic electron passing through a thin (in comparison with
a coherence length) crystal at a small angle to the crystal axis are presented.
The electron trajectories in crystal were simulated using the binary collision
model which takes into account both coherent and incoherent effects at
scattering. The angular distribution of radiation and polarization were
calculated as a sum of radiation from each electron. It is shown that there are
nontrivial angular distributions of the emitted photons and their polarization
that are connected to the superposition of the coherent scattering of electrons
by atomic rows (""doughnut scattering"" effect) and the suppression of radiation
(similar to the Landau-Pomeranchuk-Migdal effect in an amorphous matter). It is
also shown that circular polarization of radiation in the considered case is
identically zero.
",0,1,0,0,0,0
17438,Branched coverings of $CP^2$ and other basic 4-manifolds,"  We give necessary and sufficient conditions for a 4-manifold to be a branched
covering of $CP^2$, $S^2\times S^2$, $S^2 \mathbin{\tilde\times} S^2$ and $S^3
\times S^1$, which are expressed in terms of the Betti numbers and the
intersection form of the 4-manifold.
",0,0,1,0,0,0
17439,Instantaneous effects of photons on electrons in semiconductors,"  The photoelectric effect established by Einstein is well known, which
indicates that electrons on lower energy levels can jump up to higher levels by
absorbing photons, or jump down from higher levels to lower levels and give out
photons1-3. However, how do photons act on electrons and further on atoms have
kept unknown up to now. Here we show the results that photons collide on
electrons with energy-transmission in semiconductors and pass their momenta to
electrons, which make the electrons jump up from lower energy levels to higher
levels. We found that (i) photons have rest mass of 7.287exp(-38) kg and
2.886exp(-35) kg, in vacuum and silicon respectively; (ii) excited by photons
with energy of 1.12eV, electrons in silicon may jump up from the top of valance
band to the bottom of conduction band with initial speed of 2.543exp(3) m/s and
taking time of 4.977exp(-17) s; (iii) acted by photons with energy of 4.6eV,
the atoms who lose electrons may be catapulted out of the semiconductors by the
extruded neighbor atoms, and taking time of 2.224exp(-15) s. These results make
reasonable explanation to rapid thermal annealing, laser ablation and laser
cutting.
",0,1,0,0,0,0
17440,Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model,"  We apply sequence-to-sequence model to mitigate the impact of speech
recognition errors on open domain end-to-end dialog generation. We cast the
task as a domain adaptation problem where ASR transcriptions and original text
are in two different domains. In this paper, our proposed model includes two
individual encoders for each domain data and make their hidden states similar
to ensure the decoder predict the same dialog text. The method shows that the
sequence-to-sequence model can learn the ASR transcriptions and original text
pair having the same meaning and eliminate the speech recognition errors.
Experimental results on Cornell movie dialog dataset demonstrate that the
domain adaption system help the spoken dialog system generate more similar
responses with the original text answers.
",1,0,0,0,0,0
17441,Simultaneous 183 GHz H2O Maser and SiO Observations Towards Evolved Stars Using APEX SEPIA Band 5,"  We investigate the use of 183 GHz H2O masers for characterization of the
physical conditions and mass loss process in the circumstellar envelopes of
evolved stars. We used APEX SEPIA Band 5 to observe the 183 GHz H2O line
towards 2 Red Supergiant and 3 Asymptotic Giant Branch stars. Simultaneously,
we observed lines in 28SiO v0, 1, 2 and 3, and for 29SiO v0 and 1. We detected
the 183 GHz H2O line towards all the stars with peak flux densities greater
than 100 Jy, including a new detection from VY CMa. Towards all 5 targets, the
water line had indications of being due to maser emission and had higher peak
flux densities than for the SiO lines. The SiO lines appear to originate from
both thermal and maser processes. Comparison with simulations and models
indicate that 183 GHz maser emission is likely to extend to greater radii in
the circumstellar envelopes than SiO maser emission and to similar or greater
radii than water masers at 22, 321 and 325 GHz. We speculate that a prominent
blue-shifted feature in the W Hya 183 GHz spectrum is amplifying the stellar
continuum, and is located at a similar distance from the star as mainline OH
maser emission. From a comparison of the individual polarizations, we find that
the SiO maser linear polarization fraction of several features exceeds the
maximum fraction allowed under standard maser assumptions and requires strong
anisotropic pumping of the maser transition and strongly saturated maser
emission. The low polarization fraction of the H2O maser however, fits with the
expectation for a non-saturated maser. 183 GHz H2O masers can provide strong
probes of the mass loss process of evolved stars. Higher angular resolution
observations of this line using ALMA Band 5 will enable detailed investigation
of the emission location in circumstellar envelopes and can also provide
information on magnetic field strength and structure.
",0,1,0,0,0,0
17442,What does the free energy principle tell us about the brain?,"  The free energy principle has been proposed as a unifying theory of brain
function. It is closely related, and in some cases subsumes, earlier unifying
ideas such as Bayesian inference, predictive coding, and active learning. This
article clarifies these connections, teasing apart distinctive and shared
predictions.
",0,0,0,0,1,0
17443,Learning with Changing Features,"  In this paper we study the setting where features are added or change
interpretation over time, which has applications in multiple domains such as
retail, manufacturing, finance. In particular, we propose an approach to
provably determine the time instant from which the new/changed features start
becoming relevant with respect to an output variable in an agnostic
(supervised) learning setting. We also suggest an efficient version of our
approach which has the same asymptotic performance. Moreover, our theory also
applies when we have more than one such change point. Independent post analysis
of a change point identified by our method for a large retailer revealed that
it corresponded in time with certain unflattering news stories about a brand
that resulted in the change in customer behavior. We also applied our method to
data from an advanced manufacturing plant identifying the time instant from
which downstream features became relevant. To the best of our knowledge this is
the first work that formally studies change point detection in a distribution
independent agnostic setting, where the change point is based on the changing
relationship between input and output.
",1,0,0,1,0,0
17444,Estimation Considerations in Contextual Bandits,"  Contextual bandit algorithms are sensitive to the estimation method of the
outcome model as well as the exploration method used, particularly in the
presence of rich heterogeneity or complex outcome models, which can lead to
difficult estimation problems along the path of learning. We study a
consideration for the exploration vs. exploitation framework that does not
arise in multi-armed bandits but is crucial in contextual bandits; the way
exploration and exploitation is conducted in the present affects the bias and
variance in the potential outcome model estimation in subsequent stages of
learning. We develop parametric and non-parametric contextual bandits that
integrate balancing methods from the causal inference literature in their
estimation to make it less prone to problems of estimation bias. We provide the
first regret bound analyses for contextual bandits with balancing in the domain
of linear contextual bandits that match the state of the art regret bounds. We
demonstrate the strong practical advantage of balanced contextual bandits on a
large number of supervised learning datasets and on a synthetic example that
simulates model mis-specification and prejudice in the initial training data.
Additionally, we develop contextual bandits with simpler assignment policies by
leveraging sparse model estimation methods from the econometrics literature and
demonstrate empirically that in the early stages they can improve the rate of
learning and decrease regret.
",1,0,0,1,0,0
17445,Using solar and load predictions in battery scheduling at the residential level,"  Smart solar inverters can be used to store, monitor and manage a home's solar
energy. We describe a smart solar inverter system with battery which can either
operate in an automatic mode or receive commands over a network to charge and
discharge at a given rate. In order to make battery storage financially viable
and advantageous to the consumers, effective battery scheduling algorithms can
be employed. Particularly, when time-of-use tariffs are in effect in the region
of the inverter, it is possible in some cases to schedule the battery to save
money for the individual customer, compared to the ""automatic"" mode. Hence,
this paper presents and evaluates the performance of a novel battery scheduling
algorithm for residential consumers of solar energy. The proposed battery
scheduling algorithm optimizes the cost of electricity over next 24 hours for
residential consumers. The cost minimization is realized by controlling the
charging/discharging of battery storage system based on the predictions for
load and solar power generation values. The scheduling problem is formulated as
a linear programming problem. We performed computer simulations over 83
inverters using several months of hourly load and PV data. The simulation
results indicate that key factors affecting the viability of optimization are
the tariffs and the PV to Load ratio at each inverter. Depending on the tariff,
savings of between 1% and 10% can be expected over the automatic approach. The
prediction approach used in this paper is also shown to out-perform basic
""persistence"" forecasting approaches. We have also examined the approaches for
improving the prediction accuracy and optimization effectiveness.
",1,0,0,0,0,0
17446,Towards Large-Pose Face Frontalization in the Wild,"  Despite recent advances in face recognition using deep learning, severe
accuracy drops are observed for large pose variations in unconstrained
environments. Learning pose-invariant features is one solution, but needs
expensively labeled large-scale data and carefully designed feature learning
algorithms. In this work, we focus on frontalizing faces in the wild under
various head poses, including extreme profile views. We propose a novel deep 3D
Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial
Network (GAN), termed as FF-GAN, to generate neutral head pose face images. Our
framework differs from both traditional GANs and 3DMM based modeling.
Incorporating 3DMM into the GAN structure provides shape and appearance priors
for fast convergence with less training data, while also supporting end-to-end
training. The 3DMM-conditioned GAN employs not only the discriminator and
generator loss but also a new masked symmetry loss to retain visual quality
under occlusions, besides an identity loss to recover high frequency
information. Experiments on face recognition, landmark localization and 3D
reconstruction consistently show the advantage of our frontalization method on
faces in the wild datasets.
",1,0,0,0,0,0
17447,Managing the Public to Manage Data: Citizen Science and Astronomy,"  Citizen science projects recruit members of the public as volunteers to
process and produce datasets. These datasets must win the trust of the
scientific community. The task of securing credibility involves, in part,
applying standard scientific procedures to clean these datasets. However,
effective management of volunteer behavior also makes a significant
contribution to enhancing data quality. Through a case study of Galaxy Zoo, a
citizen science project set up to generate datasets based on volunteer
classifications of galaxy morphologies, this paper explores how those involved
in running the project manage volunteers. The paper focuses on how methods for
crediting volunteer contributions motivate volunteers to provide higher quality
contributions and to behave in a way that better corresponds to statistical
assumptions made when combining volunteer contributions into datasets. These
methods have made a significant contribution to the success of the project in
securing trust in these datasets, which have been well used by other
scientists. Implications for practice are then presented for citizen science
projects, providing a list of considerations to guide choices regarding how to
credit volunteer contributions to improve the quality and trustworthiness of
citizen science-produced datasets.
",1,1,0,0,0,0
17448,Modular representations in type A with a two-row nilpotent central character,"  We study the category of representations of $\mathfrak{sl}_{m+2n}$ in
positive characteristic, whose p-character is a nilpotent whose Jordan type is
the two-row partition (m+n,n). In a previous paper with Anno, we used
Bezrukavnikov-Mirkovic-Rumynin's theory of positive characteristic localization
and exotic t-structures to give a geometric parametrization of the simples
using annular crossingless matchings. Building on this, here we give
combinatorial dimension formulae for the simple objects, and compute the
Jordan-Holder multiplicities of the simples inside the baby Vermas (in special
case where n=1, i.e. that a subregular nilpotent, these were known from work of
Jantzen). We use Cautis-Kamnitzer's geometric categorification of the tangle
calculus to study the images of the simple objects under the [BMR] equivalence.
The dimension formulae may be viewed as a positive characteristic analogue of
the combinatorial character formulae for simple objects in parabolic category O
for $\mathfrak{sl}_{m+2n}$, due to Lascoux and Schutzenberger.
",0,0,1,0,0,0
17449,Knowledge Acquisition: A Complex Networks Approach,"  Complex networks have been found to provide a good representation of the
structure of knowledge, as understood in terms of discoverable concepts and
their relationships. In this context, the discovery process can be modeled as
agents walking in a knowledge space. Recent studies proposed more realistic
dynamics, including the possibility of agents being influenced by others with
higher visibility or by their own memory. However, rather than dealing with
these two concepts separately, as previously approached, in this study we
propose a multi-agent random walk model for knowledge acquisition that
incorporates both concepts. More specifically, we employed the true self
avoiding walk alongside a new dynamics based on jumps, in which agents are
attracted by the influence of others. That was achieved by using a Lévy
flight influenced by a field of attraction emanating from the agents. In order
to evaluate our approach, we use a set of network models and two real networks,
one generated from Wikipedia and another from the Web of Science. The results
were analyzed globally and by regions. In the global analysis, we found that
most of the dynamics parameters do not significantly affect the discovery
dynamics. The local analysis revealed a substantial difference of performance
depending on the network regions where the dynamics are occurring. In
particular, the dynamics at the core of networks tend to be more effective. The
choice of the dynamics parameters also had no significant impact to the
acquisition performance for the considered knowledge networks, even at the
local scale.
",1,1,0,0,0,0
17450,Frequent flaring in the TRAPPIST-1 system - unsuited for life?,"  We analyze short cadence K2 light curve of the TRAPPIST-1 system. Fourier
analysis of the data suggests $P_\mathrm{rot}=3.295\pm0.003$ days. The light
curve shows several flares, of which we analyzed 42 events, these have
integrated flare energies of $1.26\times10^{30}-1.24\times10^{33}$ ergs.
Approximately 12% of the flares were complex, multi-peaked eruptions. The
flaring and the possible rotational modulation shows no obvious correlation.
The flaring activity of TRAPPIST-1 probably continuously alters the atmospheres
of the orbiting exoplanets, making these less favorable for hosting life.
",0,1,0,0,0,0
17451,Playing Pairs with Pepper,"  As robots become increasingly prevalent in almost all areas of society, the
factors affecting humans trust in those robots becomes increasingly important.
This paper is intended to investigate the factor of robot attributes, looking
specifically at the relationship between anthropomorphism and human development
of trust. To achieve this, an interaction game, Matching the Pairs, was
designed and implemented on two robots of varying levels of anthropomorphism,
Pepper and Husky. Participants completed both pre- and post-test questionnaires
that were compared and analyzed predominantly with the use of quantitative
methods, such as paired sample t-tests. Post-test analyses suggested a positive
relationship between trust and anthropomorphism with $80\%$ of participants
confirming that the robots' adoption of facial features assisted in
establishing trust. The results also indicated a positive relationship between
interaction and trust with $90\%$ of participants confirming this for both
robots post-test
",1,0,0,0,0,0
17452,Wild theories with o-minimal open core,"  Let $T$ be a consistent o-minimal theory extending the theory of densely
ordered groups and let $T'$ be a consistent theory. Then there is a complete
theory $T^*$ extending $T$ such that $T$ is an open core of $T^*$, but every
model of $T^*$ interprets a model of $T'$. If $T'$ is NIP, $T^*$ can be chosen
to be NIP as well. From this we deduce the existence of an NIP expansion of the
real field that has no distal expansion.
",0,0,1,0,0,0
17453,Objective Bayesian inference with proper scoring rules,"  Standard Bayesian analyses can be difficult to perform when the full
likelihood, and consequently the full posterior distribution, is too complex
and difficult to specify or if robustness with respect to data or to model
misspecifications is required. In these situations, we suggest to resort to a
posterior distribution for the parameter of interest based on proper scoring
rules. Scoring rules are loss functions designed to measure the quality of a
probability distribution for a random variable, given its observed value.
Important examples are the Tsallis score and the Hyvärinen score, which allow
us to deal with model misspecifications or with complex models. Also the full
and the composite likelihoods are both special instances of scoring rules.
The aim of this paper is twofold. Firstly, we discuss the use of scoring
rules in the Bayes formula in order to compute a posterior distribution, named
SR-posterior distribution, and we derive its asymptotic normality. Secondly, we
propose a procedure for building default priors for the unknown parameter of
interest that can be used to update the information provided by the scoring
rule in the SR-posterior distribution. In particular, a reference prior is
obtained by maximizing the average $\alpha-$divergence from the SR-posterior
distribution. For $0 \leq |\alpha|<1$, the result is a Jeffreys-type prior that
is proportional to the square root of the determinant of the Godambe
information matrix associated to the scoring rule. Some examples are discussed.
",0,0,1,1,0,0
17454,Large Area X-ray Proportional Counter (LAXPC) Instrument on AstroSat,"  Large Area X-ray Proportional Counter (LAXPC) is one of the major AstroSat
payloads. LAXPC instrument will provide high time resolution X-ray observations
in 3 to 80 keV energy band with moderate energy resolution. A cluster of three
co-aligned identical LAXPC detectors is used in AstroSat to provide large
collection area of more than 6000 cm2 . The large detection volume (15 cm
depth) filled with xenon gas at about 2 atmosphere pressure, results in
detection efficiency greater than 50%, above 30 keV. With its broad energy
range and fine time resolution (10 microsecond), LAXPC instrument is well
suited for timing and spectral studies of a wide variety of known and transient
X-ray sources in the sky. We have done extensive calibration of all LAXPC
detectors using radioactive sources as well as GEANT4 simulation of LAXPC
detectors. We describe in brief some of the results obtained during the payload
verification phase along with LXAPC capabilities.
",0,1,0,0,0,0
17455,"A Counterexample to the Vector Generalization of Costa's EPI, and Partial Resolution","  We give a counterexample to the vector generalization of Costa's entropy
power inequality (EPI) due to Liu, Liu, Poor and Shamai. In particular, the
claimed inequality can fail if the matix-valued parameter in the convex
combination does not commute with the covariance of the additive Gaussian
noise. Conversely, the inequality holds if these two matrices commute.
",1,0,0,0,0,0
17456,Models for Predicting Community-Specific Interest in News Articles,"  In this work, we ask two questions: 1. Can we predict the type of community
interested in a news article using only features from the article content? and
2. How well do these models generalize over time? To answer these questions, we
compute well-studied content-based features on over 60K news articles from 4
communities on reddit.com. We train and test models over three different time
periods between 2015 and 2017 to demonstrate which features degrade in
performance the most due to concept drift. Our models can classify news
articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0
ROC AUC. However, while we can predict the community-specific popularity of
news articles with high accuracy, practitioners should approach these models
carefully. Predictions are both community-pair dependent and feature group
dependent. Moreover, these feature groups generalize over time differently,
with some only degrading slightly over time, but others degrading greatly.
Therefore, we recommend that community-interest predictions are done in a
hierarchical structure, where multiple binary classifiers can be used to
separate community pairs, rather than a traditional multi-class model. Second,
these models should be retrained over time based on accuracy goals and the
availability of training data.
",0,0,0,1,0,0
17457,On subfiniteness of graded linear series,"  Hilbert's 14th problem studies the finite generation property of the
intersection of an integral algebra of finite type with a subfield of the field
of fractions of the algebra. It has a negative answer due to the counterexample
of Nagata. We show that a subfinite version of Hilbert's 14th problem has a
confirmative answer. We then establish a graded analogue of this result, which
permits to show that the subfiniteness of graded linear series does not depend
on the function field in which we consider it. Finally, we apply the
subfiniteness result to the study of geometric and arithmetic graded linear
series.
",0,0,1,0,0,0
17458,Natasha 2: Faster Non-Convex Optimization Than SGD,"  We design a stochastic algorithm to train any smooth neural network to
$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
More broadly, it finds $\varepsilon$-approximate local minima of any smooth
nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to
stochastic gradients.
",1,0,0,1,0,0
17459,Evidence for a Dayside Thermal Inversion and High Metallicity for the Hot Jupiter WASP-18b,"  We find evidence for a strong thermal inversion in the dayside atmosphere of
the highly irradiated hot Jupiter WASP-18b (T$_{eq}=2411K$, $M=10.3M_{J}$)
based on emission spectroscopy from Hubble Space Telescope secondary eclipse
observations and Spitzer eclipse photometry. We demonstrate a lack of water
vapor in either absorption or emission at 1.4$\mu$m. However, we infer emission
at 4.5$\mu$m and absorption at 1.6$\mu$m that we attribute to CO, as well as a
non-detection of all other relevant species (e.g., TiO, VO). The most probable
atmospheric retrieval solution indicates a C/O ratio of 1 and a high
metallicity (C/H=$283^{+395}_{-138}\times$ solar). The derived composition and
T/P profile suggest that WASP-18b is the first example of both a planet with a
non-oxide driven thermal inversion and a planet with an atmospheric metallicity
inconsistent with that predicted for Jupiter-mass planets at $>2\sigma$. Future
observations are necessary to confirm the unusual planetary properties implied
by these results.
",0,1,0,0,0,0
17460,$α$-$β$ and $β$-$γ$ phase boundaries of solid oxygen observed by adiabatic magnetocaloric effect,"  The magnetic-field-temperature phase diagram of solid oxygen is investigated
by the adiabatic magnetocaloric effect (MCE) measurement with pulsed magnetic
fields. Relatively large temperature decrease with hysteresis is observed at
just below the $\beta$-$\gamma$ and $\alpha$-$\beta$ phase transition
temperatures owing to the field-induced transitions. The magnetic field
dependences of these phase boundaries are obtained as
$T_\mathrm{\beta\gamma}(H)=43.8-1.55\times10^{-3}H^2$ K and
$T_\mathrm{\alpha\beta}(H)=23.9-0.73\times10^{-3}H^2$ K. The magnetic
Clausius-Clapeyron equation quantitatively explains the $H$ dependence of
$T_\mathrm{\beta\gamma}$, meanwhile, does not $T_\mathrm{\alpha\beta}$. The MCE
curve at $T_\mathrm{\beta\gamma}$ is of typical first-order, while the curve at
$T_\mathrm{\alpha\beta}$ seems to have both characteristics of first- and
second-order transitions. We discuss the order of the $\alpha$-$\beta$ phase
transition and propose possible reasons for the unusual behavior.
",0,1,0,0,0,0
17461,Localization Algorithm with Circular Representation in 2D and its Similarity to Mammalian Brains,"  Extended Kalman filter (EKF) does not guarantee consistent mean and
covariance under linearization, even though it is the main framework for
robotic localization. While Lie group improves the modeling of the state space
in localization, the EKF on Lie group still relies on the arbitrary Gaussian
assumption in face of nonlinear models. We instead use von Mises filter for
orientation estimation together with the conventional Kalman filter for
position estimation, and thus we are able to characterize the first two moments
of the state estimates. Since the proposed algorithm holds a solid
probabilistic basis, it is fundamentally relieved from the inconsistency
problem. Furthermore, we extend the localization algorithm to fully circular
representation even for position, which is similar to grid patterns found in
mammalian brains and in recurrent neural networks. The applicability of the
proposed algorithms is substantiated not only by strong mathematical foundation
but also by the comparison against other common localization methods.
",1,0,0,0,1,0
17462,"Lusin-type approximation of Sobolev by Lipschitz functions, in Gaussian and $RCD(K,\infty)$ spaces","  We establish new approximation results, in the sense of Lusin, of Sobolev
functions by Lipschitz ones, in some classes of non-doubling metric measure
structures. Our proof technique relies upon estimates for heat semigroups and
applies to Gaussian and $RCD(K, \infty)$ spaces. As a consequence, we obtain
quantitative stability for regular Lagrangian flows in Gaussian settings.
",0,0,1,0,0,0
17463,Distributed Coordination for a Class of Nonlinear Multi-agent Systems with Regulation Constraints,"  In this paper, a multi-agent coordination problem with steady-state
regulation constraints is investigated for a class of nonlinear systems. Unlike
existing leader-following coordination formulations, the reference signal is
not given by a dynamic autonomous leader but determined as the optimal solution
of a distributed optimization problem. Furthermore, we consider a global
constraint having noisy data observations for the optimization problem, which
implies that reference signal is not trivially available with existing
optimization algorithms. To handle those challenges, we present a
passivity-based analysis and design approach by using only local objective
function, local data observation and exchanged information from their
neighbors. The proposed distributed algorithms are shown to achieve the optimal
steady-state regulation by rejecting the unknown observation disturbances for
passive nonlinear agents, which are persuasive in various practical problems.
Applications and simulation examples are then given to verify the effectiveness
of our design.
",1,0,1,0,0,0
17464,Intermodulation distortion of actuated MEMS capacitive switches,"  For the first time, intermodulation distortion of micro-electromechanical
capacitive switches in the actuated state was analyzed both theoretically and
experimentally. The distortion, although higher than that of switches in the
suspended state, was found to decrease with increasing bias voltage but to
depend weakly on modulation frequencies between 55 kHz and 1.1 MHz. This
dependence could be explained by the orders-of-magnitude increase of the spring
constant when the switches were actuated. Additionally, the analysis suggested
that increasing the spring constant and decreasing the contact roughness could
improve the linearity of actuated switches. These results are critical to
micro-electromechanical capacitive switches used in tuners, filters, phase
shifters, etc. where the linearity of both suspended and actuated states are
critical.
",0,1,0,0,0,0
17465,Parasitic Bipolar Leakage in III-V FETs: Impact of Substrate Architecture,"  InGaAs-based Gate-all-Around (GAA) FETs with moderate to high In content are
shown experimentally and theoretically to be unsuitable for low-leakage
advanced CMOS nodes. The primary cause for this is the large leakage penalty
induced by the Parasitic Bipolar Effect (PBE), which is seen to be particularly
difficult to remedy in GAA architectures. Experimental evidence of PBE in
In70Ga30As GAA FETs is demonstrated, along with a simulation-based analysis of
the PBE behavior. The impact of PBE is investigated by simulation for
alternative device architectures, such as bulk FinFETs and
FinFETs-on-insulator. PBE is found to be non-negligible in all standard InGaAs
FET designs. Practical PBE metrics are introduced and the design of a substrate
architecture for PBE suppression is elucidated. Finally, it is concluded that
the GAA architecture is not suitable for low-leakage InGaAs FETs; a bulk FinFET
is better suited for the role.
",0,1,0,0,0,0
17466,Properties of Ultra Gamma Function,"  In this paper we study the integral of type
\[_{\delta,a}\Gamma_{\rho,b}(x)
=\Gamma(\delta,a;\rho,b)(x)=\int_{0}^{\infty}t^{x-1}e^{-\frac{t^{\delta}}{a}-\frac{t^{-\rho}}{b}}dt.\]
Different authors called this integral by different names like ultra gamma
function, generalized gamma function, Kratzel integral, inverse Gaussian
integral, reaction-rate probability integral, Bessel integral etc. We prove
several identities and recurrence relation of above said integral, we called
this integral as Four Parameter Gamma Function. Also we evaluate relation
between Four Parameter Gamma Function, p-k Gamma Function and Classical Gamma
Function. With some conditions we can evaluate Four Parameter Gamma Function in
term of Hypergeometric function.
",0,0,1,0,0,0
17467,Further remarks on liftings of crossed modules,"  In this paper we define the notion of pullback lifting of a lifting crossed
module over a crossed module morphism and interpret this notion in the category
of group-groupoid actions as pullback action. Moreover, we give a criterion for
the lifting of homotopic crossed module morphisms to be homotopic, which will
be called homotopy lifting property for crossed module morphisms. Finally, we
investigate some properties of derivations of lifting crossed modules according
to base crossed module derivations.
",0,0,1,0,0,0
17468,Submodular Maximization through the Lens of Linear Programming,"  The simplex algorithm for linear programming is based on the fact that any
local optimum with respect to the polyhedral neighborhood is also a global
optimum. We show that a similar result carries over to submodular maximization.
In particular, every local optimum of a constrained monotone submodular
maximization problem yields a $1/2$-approximation, and we also present an
appropriate extension to the non-monotone setting. However, reaching a local
optimum quickly is a non-trivial task. Moreover, we describe a fast and very
general local search procedure that applies to a wide range of constraint
families, and unifies as well as extends previous methods. In our framework, we
match known approximation guarantees while disentangling and simplifying
previous approaches. Moreover, despite its generality, we are able to show that
our local search procedure is slightly faster than previous specialized
methods. Furthermore, we resolve an open question on the relation between
linear optimization and submodular maximization; namely, whether a linear
optimization oracle may be enough to obtain strong approximation algorithms for
submodular maximization. We show that this is not the case by providing an
example of a constraint family on a ground set of size $n$ for which, if only
given a linear optimization oracle, any algorithm for submodular maximization
with a polynomial number of calls to the linear optimization oracle will have
an approximation ratio of only $O ( \frac{1}{\sqrt{n}} \cdot \frac{\log
n}{\log\log n} )$.
",1,0,0,0,0,0
17469,Channel Estimation for Diffusive MIMO Molecular Communications,"  In diffusion-based communication, as for molecular systems, the achievable
data rate is very low due to the slow nature of diffusion and the existence of
severe inter-symbol interference (ISI). Multiple-input multiple-output (MIMO)
technique can be used to improve the data rate. Knowledge of channel impulse
response (CIR) is essential for equalization and detection in MIMO systems.
This paper presents a training-based CIR estimation for diffusive MIMO (D-MIMO)
channels. Maximum likelihood and least-squares estimators are derived, and the
training sequences are designed to minimize the corresponding Cramér-Rao
bound. Sub-optimal estimators are compared to Cramér-Rao bound to validate
their performance.
",1,0,0,1,0,0
17470,Multi-stage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods,"  Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two
popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance
sampling. As in the HMC case, the bulk of the computational cost of MHMC
algorithms lies in the numerical integration of a Hamiltonian system of
differential equations. We suggest novel integrators designed to enhance
accuracy and sampling performance of MHMC methods. The novel integrators belong
to families of splitting algorithms and are therefore easily implemented. We
identify optimal integrators within the families by minimizing the energy error
or the average energy error. We derive and discuss in detail the modified
Hamiltonians of the new integrators, as the evaluation of those Hamiltonians is
key to the efficiency of the overall algorithms. Numerical experiments show
that the use of the new integrators may improve very significantly the sampling
performance of MHMC methods, in both statistical and molecular dynamics
problems.
",1,0,0,0,0,0
17471,Proposal for a High Precision Tensor Processing Unit,"  This whitepaper proposes the design and adoption of a new generation of
Tensor Processing Unit which has the performance of Google's TPU, yet performs
operations on wide precision data. The new generation TPU is made possible by
implementing arithmetic circuits which compute using a new general purpose,
fractional arithmetic based on the residue number system.
",1,0,0,0,0,0
17472,DICOD: Distributed Convolutional Sparse Coding,"  In this paper, we introduce DICOD, a convolutional sparse coding algorithm
which builds shift invariant representations for long signals. This algorithm
is designed to run in a distributed setting, with local message passing, making
it communication efficient. It is based on coordinate descent and uses locally
greedy updates which accelerate the resolution compared to greedy coordinate
selection. We prove the convergence of this algorithm and highlight its
computational speed-up which is super-linear in the number of cores used. We
also provide empirical evidence for the acceleration properties of our
algorithm compared to state-of-the-art methods.
",1,0,0,1,0,0
17473,On uniqueness results for Dirichlet problems of elliptic systems without DeGiorgi-Nash-Moser regularity,"  We study uniqueness of Dirichlet problems of second order divergence-form
elliptic systems with transversally independent coefficients on the upper
half-space in absence of regularity of solutions. To this end, we develop a
substitute for the fundamental solution used to invert elliptic operators on
the whole space by means of a representation via abstract single layer
potentials. We also show that such layer potentials are uniquely determined.
",0,0,1,0,0,0
17474,(LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ as a model system for unconventional charge transfer and polar metallicity,"  At interfaces between oxide materials, lattice and electronic reconstructions
always play important roles in exotic phenomena. In this study, the density
functional theory and maximally localized Wannier functions are employed to
investigate the (LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ magnetic superlattices. The
electron transfer from Ti$^{3+}$ to V$^{3+}$ is predicted, which violates the
intuitive band alignment based on the electronic structures of LaTiO$_3$ and
LaVO$_3$. Such unconventional charge transfer quenches the magnetism of
LaTiO$_3$ layer mostly and leads to metal-insulator transition in the $n=1$
superlattice when the stacking orientation is altered. In addition, the
compatibility among the polar structure, ferrimagnetism, and metallicity is
predicted in the $n=2$ superlattice.
",0,1,0,0,0,0
17475,Modeling sorption of emerging contaminants in biofilms,"  A mathematical model for emerging contaminants sorption in multispecies
biofilms, based on a continuum approach and mass conservation principles is
presented. Diffusion of contaminants within the biofilm is described using a
diffusion-reaction equation. Binding sites formation and occupation are modeled
by two systems of hyperbolic partial differential equations are mutually
connected through the two growth rate terms. The model is completed with a
system of hyperbolic equations governing the microbial species growth within
the biofilm; a system of parabolic equations for substrates diffusion and
reaction and a nonlinear ordinary differential equation describing the free
boundary evolution. Two real special cases are modelled. The first one
describes the dynamics of a free sorbent component diffusing and reacting in a
multispecies biofilm. In the second illustrative case, the fate of two
different contaminants has been modelled.
",0,1,0,0,0,0
17476,Stabilizing Training of Generative Adversarial Networks through Regularization,"  Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping support between the model distribution and the data
distribution, causing their density ratio and the associated f-divergence to be
undefined. We overcome this fundamental limitation and propose a new
regularization approach with low computational cost that yields a stable GAN
training procedure. We demonstrate the effectiveness of this regularizer across
several architectures trained on common benchmark image generation tasks. Our
regularization turns GAN models into reliable building blocks for deep
learning.
",1,0,0,1,0,0
17477,Spurious Vanishing Problem in Approximate Vanishing Ideal,"  Approximate vanishing ideal, which is a new concept from computer algebra, is
a set of polynomials that almost takes a zero value for a set of given data
points. The introduction of approximation to exact vanishing ideal has played a
critical role in capturing the nonlinear structures of noisy data by computing
the approximate vanishing polynomials. However, approximate vanishing has a
theoretical problem, which is giving rise to the spurious vanishing problem
that any polynomial turns into an approximate vanishing polynomial by
coefficient scaling. In the present paper, we propose a general method that
enables many basis construction methods to overcome this problem. Furthermore,
a coefficient truncation method is proposed that balances the theoretical
soundness and computational cost. The experiments show that the proposed method
overcomes the spurious vanishing problem and significantly increases the
accuracy of classification.
",1,0,0,1,0,0
17478,Opportunities for Two-color Experiments at the SASE3 undulator line of the European XFEL,"  X-ray Free Electron Lasers (XFELs) have been proven to generate short and
powerful radiation pulses allowing for a wide class of novel experiments. If an
XFEL facility supports the generation of two X-ray pulses with different
wavelengths and controllable delay, the range of possible experiments is
broadened even further to include X-ray-pump/X-ray-probe applications. In this
work we discuss the possibility of applying a simple and cost-effective method
for producing two-color pulses at the SASE3 soft X-ray beamline of the European
XFEL. The technique is based on the installation of a magnetic chicane in the
baseline undulator and can be accomplished in several steps. We discuss the
scientific interest of this upgrade for the Small Quantum Systems (SQS)
instrument, in connection with the high-repetition rate of the European XFEL,
and we provide start-to-end simulations up to the radiation focus on the
sample, proving the feasibility of our concept.
",0,1,0,0,0,0
17479,Braiding errors in interacting Majorana quantum wires,"  Avenues of Majorana bound states (MBSs) have become one of the primary
directions towards a possible realization of topological quantum computation.
For a Y-junction of Kitaev quantum wires, we numerically investigate the
braiding of MBSs while considering the full quasi-particle background. The two
central sources of braiding errors are found to be the fidelity loss due to the
incomplete adiabaticity of the braiding operation as well as the hybridization
of the MBS. The explicit extraction of the braiding phase in the low-energy
Majorana sector from the full many-particle Hilbert space allows us to analyze
the breakdown of the independent-particle picture of Majorana braiding.
Furthermore, we find nearest-neighbor interactions to significantly affect the
braiding performance to the better or worse, depending on the sign and
magnitude of the coupling.
",0,1,0,0,0,0
17480,Machine Learning for Structured Clinical Data,"  Research is a tertiary priority in the EHR, where the priorities are patient
care and billing. Because of this, the data is not standardized or formatted in
a manner easily adapted to machine learning approaches. Data may be missing for
a large variety of reasons ranging from individual input styles to differences
in clinical decision making, for example, which lab tests to issue. Few
patients are annotated at a research quality, limiting sample size and
presenting a moving gold standard. Patient progression over time is key to
understanding many diseases but many machine learning algorithms require a
snapshot, at a single time point, to create a usable vector form. Furthermore,
algorithms that produce black box results do not provide the interpretability
required for clinical adoption. This chapter discusses these challenges and
others in applying machine learning techniques to the structured EHR (i.e.
Patient Demographics, Family History, Medication Information, Vital Signs,
Laboratory Tests, Genetic Testing). It does not cover feature extraction from
additional sources such as imaging data or free text patient notes but the
approaches discussed can include features extracted from these sources.
",1,0,0,0,0,0
17481,Hidden order and symmetry protected topological states in quantum link ladders,"  We show that whereas spin-1/2 one-dimensional U(1) quantum-link models (QLMs)
are topologically trivial, when implemented in ladder-like lattices these
models may present an intriguing ground-state phase diagram, which includes a
symmetry protected topological (SPT) phase that may be readily revealed by
analyzing long-range string spin correlations along the ladder legs. We propose
a simple scheme for the realization of spin-1/2 U(1) QLMs based on
single-component fermions loaded in an optical lattice with s- and p-bands,
showing that the SPT phase may be experimentally realized by adiabatic
preparation.
",0,1,0,0,0,0
17482,The effect of prudence on the optimal allocation in possibilistic and mixed models,"  In this paper two portfolio choice models are studied: a purely possibilistic
model, in which the return of a risky asset is a fuzzy number, and a mixed
model in which a probabilistic background risk is added. For the two models an
approximate formula of the optimal allocation is computed, with respect to the
possibilistic moments associated with fuzzy numbers and the indicators of the
investor risk preferences (risk aversion, prudence).
",0,0,0,0,0,1
17483,On universal operators and universal pairs,"  We study some basic properties of the class of universal operators on Hilbert
space, and provide new examples of universal operators and universal pairs.
",0,0,1,0,0,0
17484,Interleaving Lattice for the APS Linac,"  To realize and test advanced accelerator concepts and hardware, a beamline is
being reconfigured in the Linac Extension Area (LEA) of APS linac. A
photo-cathode RF gun installed at the beginning of the APS linac will provide a
low emittance electron beam into the LEA beamline. The thermionic RF gun beam
for the APS storage ring, and the photo-cathode RF gun beam for LEA beamline
will be accelerated through the linac in an interleaved fashion. In this paper,
the design studies for interleaving lattice realization in APS linac is
described with initial experiment result
",0,1,0,0,0,0
17485,\textit{Ab Initio} Study of the Magnetic Behavior of Metal Hydrides: A Comparison with the Slater-Pauling Curve,"  We investigated the magnetic behavior of metal hydrides FeH$_{x}$, CoH$_{x}$
and NiH$_{x}$ for several concentrations of hydrogen ($x$) by using Density
Functional Theory calculations. Several structural phases of the metallic host:
bcc ($\alpha$), fcc ($\gamma$), hcp ($\varepsilon$), dhcp ($\varepsilon'$),
tetragonal structure for FeH$_{x}$ and $\varepsilon$-$\gamma$ phases for
CoH$_{x}$, were studied. We found that for CoH$_{x}$ and NiH$_{x}$ the magnetic
moment ($m$) decreases regardless the concentration $x$. However, for FeH$_{x}$
systems, $m$ increases or decreases depending on the variation in $x$. In order
to find a general trend for these changes of $m$ in magnetic metal hydrides, we
compare our results with the Slater-Pauling curve for ferromagnetic metallic
binary alloys. It is found that the $m$ of metal hydrides made of Fe, Co and Ni
fits the shape of the Slater-Pauling curve as a function of $x$. Our results
indicate that there are two main effects that determine the $m$ value due to
hydrogenation: an increase of volume causes $m$ to increase, and the addition
of an extra electron to the metal always causes it to decrease. We discuss
these behaviors in detail.
",0,1,0,0,0,0
17486,Secret Sharing for Cloud Data Security,"  Cloud computing helps reduce costs, increase business agility and deploy
solutions with a high return on investment for many types of applications.
However, data security is of premium importance to many users and often
restrains their adoption of cloud technologies. Various approaches, i.e., data
encryption, anonymization, replication and verification, help enforce different
facets of data security. Secret sharing is a particularly interesting
cryptographic technique. Its most advanced variants indeed simultaneously
enforce data privacy, availability and integrity, while allowing computation on
encrypted data. The aim of this paper is thus to wholly survey secret sharing
schemes with respect to data security, data access and costs in the
pay-as-you-go paradigm.
",1,0,0,0,0,0
17487,Design and Analysis of a Secure Three Factor User Authentication Scheme Using Biometric and Smart Card,"  Password security can no longer provide enough security in the area of remote
user authentication. Considering this security drawback, researchers are trying
to find solution with multifactor remote user authentication system. Recently,
three factor remote user authentication using biometric and smart card has
drawn a considerable attention of the researchers. However, most of the current
proposed schemes have security flaws. They are vulnerable to attacks like user
impersonation attack, server masquerading attack, password guessing attack,
insider attack, denial of service attack, forgery attack, etc. Also, most of
them are unable to provide mutual authentication, session key agreement and
password, or smart card recovery system. Considering these drawbacks, we
propose a secure three factor user authentication scheme using biometric and
smart card. Through security analysis, we show that our proposed scheme can
overcome drawbacks of existing systems and ensure high security in remote user
authentication.
",1,0,0,0,0,0
17488,Real-World Modeling of a Pathfinding Robot Using Robot Operating System (ROS),"  This paper presents a practical approach towards implementing pathfinding
algorithms on real-world and low-cost non- commercial hardware platforms. While
using robotics simulation platforms as a test-bed for our algorithms we easily
overlook real- world exogenous problems that are developed by external factors.
Such problems involve robot wheel slips, asynchronous motors, abnormal sensory
data or unstable power sources. The real-world dynamics tend to be very painful
even for executing simple algorithms like a Wavefront planner or A-star search.
This paper addresses designing techniques that tend to be robust as well as
reusable for any hardware platforms; covering problems like controlling
asynchronous drives, odometry offset issues and handling abnormal sensory
feedback. The algorithm implementation medium and hardware design tools have
been kept general in order to present our work as a serving platform for future
researchers and robotics enthusiast working in the field of path planning
robotics.
",1,0,0,0,0,0
17489,Understanding Convolution for Semantic Segmentation,"  Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical value. First, we design dense upsampling convolution
(DUC) to generate pixel-level prediction, which is able to capture and decode
more detailed information that is generally missing in bilinear upsampling.
Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
phase. This framework 1) effectively enlarges the receptive fields (RF) of the
network to aggregate global information; 2) alleviates what we call the
""gridding issue"" caused by the standard dilated convolution operation. We
evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
state-of-art result of 80.1% mIOU in the test set at the time of submission. We
also have achieved state-of-the-art overall on the KITTI road estimation
benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
found at this https URL .
",1,0,0,0,0,0
17490,Mechanical Failure in Amorphous Solids: Scale Free Spinodal Criticality,"  The mechanical failure of amorphous media is a ubiquitous phenomenon from
material engineering to geology. It has been noticed for a long time that the
phenomenon is ""scale-free"", indicating some type of criticality. In spite of
attempts to invoke ""Self-Organized Criticality"", the physical origin of this
criticality, and also its universal nature, being quite insensitive to the
nature of microscopic interactions, remained elusive. Recently we proposed that
the precise nature of this critical behavior is manifested by a spinodal point
of a thermodynamic phase transition. Moreover, at the spinodal point there
exists a divergent correlation length which is associated with the
system-spanning instabilities (known also as shear bands) which are typical to
the mechanical yield. Demonstrating this requires the introduction of an ""order
parameter"" that is suitable for distinguishing between disordered amorphous
systems, and an associated correlation function, suitable for picking up the
growing correlation length. The theory, the order parameter, and the
correlation functions used are universal in nature and can be applied to any
amorphous solid that undergoes mechanical yield. Critical exponents for the
correlation length divergence and the system size dependence are estimated. The
phenomenon is seen at its sharpest in athermal systems, as is explained below;
in this paper we extend the discussion also to thermal systems, showing that at
sufficiently high temperatures the spinodal phenomenon is destroyed by thermal
fluctuations.
",0,1,0,0,0,0
17491,Moment conditions in strong laws of large numbers for multiple sums and random measures,"  The validity of the strong law of large numbers for multiple sums $S_n$ of
independent identically distributed random variables $Z_k$, $k\leq n$, with
$r$-dimensional indices is equivalent to the integrability of
$|Z|(\log^+|Z|)^{r-1}$, where $Z$ is the typical summand. We consider the
strong law of large numbers for more general normalisations, without assuming
that the summands $Z_k$ are identically distributed, and prove a multiple sum
generalisation of the Brunk--Prohorov strong law of large numbers. In the case
of identical finite moments of irder $2q$ with integer $q\geq1$, we show that
the strong law of large numbers holds with the normalisation $\|n_1\cdots
n_r\|^{1/2}(\log n_1\cdots\log n_r)^{1/(2q)+\varepsilon}$ for any
$\varepsilon>0$. The obtained results are also formulated in the setting of
ergodic theorems for random measures, in particular those generated by marked
point processes.
",0,0,1,0,0,0
17492,Connecting Clump Sizes in Turbulent Disk Galaxies to Instability Theory,"  In this letter we study the mean sizes of Halpha clumps in turbulent disk
galaxies relative to kinematics, gas fractions, and Toomre Q. We use 100~pc
resolution HST images, IFU kinematics, and gas fractions of a sample of rare,
nearby turbulent disks with properties closely matched to z~1.5-2 main-sequence
galaxies (the DYNAMO sample). We find linear correlations of normalized mean
clump sizes with both the gas fraction and the velocity dispersion-to-rotation
velocity ratio of the host galaxy. We show that these correlations are
consistent with predictions derived from a model of instabilities in a
self-gravitating disk (the so-called ""violent disk instability model""). We also
observe, using a two-fluid model for Q, a correlation between the size of
clumps and self-gravity driven unstable regions. These results are most
consistent with the hypothesis that massive star forming clumps in turbulent
disks are the result of instabilities in self-gravitating gas-rich disks, and
therefore provide a direct connection between resolved clump sizes and this in
situ mechanism.
",0,1,0,0,0,0
17493,Anomalous slowing down of individual human activity due to successive decision-making processes,"  Motivated by a host of empirical evidences revealing the bursty character of
human dynamics, we develop a model of human activity based on successive
switching between an hesitation state and a decision-realization state, with
residency times in the hesitation state distributed according to a heavy-tailed
Pareto distribution. This model is particularly reminiscent of an individual
strolling through a randomly distributed human crowd. Using a stochastic model
based on the concept of anomalous and non-Markovian Lévy walk, we show
exactly that successive decision-making processes drastically slow down the
progression of an individual faced with randomly distributed obstacles.
Specifically, we prove exactly that the average displacement exhibits a
sublinear scaling with time that finds its origins in: (i) the intrinsically
non-Markovian character of human activity, and (ii) the power law distribution
of hesitation times.
",0,1,0,0,0,0
17494,"The spectrum, radiation conditions and the Fredholm property for the Dirichlet Laplacian in a perforated plane with semi-infinite inclusions","  We consider the spectral Dirichlet problem for the Laplace operator in the
plane $\Omega^{\circ}$ with double-periodic perforation but also in the domain
$\Omega^{\bullet}$ with a semi-infinite foreign inclusion so that the
Floquet-Bloch technique and the Gelfand transform do not apply directly. We
describe waves which are localized near the inclusion and propagate along it.
We give a formulation of the problem with radiation conditions that provides a
Fredholm operator of index zero. The main conclusion concerns the spectra
$\sigma^{\circ}$ and $\sigma^{\bullet}$ of the problems in $\Omega^{\circ}$ and
$\Omega^{\bullet},$ namely we present a concrete geometry which supports the
relation $\sigma^{\circ}\varsubsetneqq\sigma^{\bullet}$ due to a new non-empty
spectral band caused by the semi-infinite inclusion called an open waveguide in
the double-periodic medium.
",0,0,1,0,0,0
17495,On the Semantics and Complexity of Probabilistic Logic Programs,"  We examine the meaning and the complexity of probabilistic logic programs
that consist of a set of rules and a set of independent probabilistic facts
(that is, programs based on Sato's distribution semantics). We focus on two
semantics, respectively based on stable and on well-founded models. We show
that the semantics based on stable models (referred to as the ""credal
semantics"") produces sets of probability models that dominate infinitely
monotone Choquet capacities, we describe several useful consequences of this
result. We then examine the complexity of inference with probabilistic logic
programs. We distinguish between the complexity of inference when a
probabilistic program and a query are given (the inferential complexity), and
the complexity of inference when the probabilistic program is fixed and the
query is given (the query complexity, akin to data complexity as used in
database theory). We obtain results on the inferential and query complexity for
acyclic, stratified, and cyclic propositional and relational programs,
complexity reaches various levels of the counting hierarchy and even
exponential levels.
",1,0,0,0,0,0
17496,Fitting Probabilistic Index Models on Large Datasets,"  Recently, Thas et al. (2012) introduced a new statistical model for the
probability index. This index is defined as $P(Y \leq Y^*|X, X^*)$ where Y and
Y* are independent random response variables associated with covariates X and
X* [...] Crucially to estimate the parameters of the model, a set of
pseudo-observations is constructed. For a sample size n, a total of $n(n-1)/2$
pairwise comparisons between observations is considered. Consequently for large
sample sizes, it becomes computationally infeasible or even impossible to fit
the model as the set of pseudo-observations increases nearly quadratically. In
this dissertation, we provide two solutions to fit a probabilistic index model.
The first algorithm consists of splitting the entire data set into unique
partitions. On each of these, we fit the model and then aggregate the
estimates. A second algorithm is a subsampling scheme in which we select $K <<
n$ observations without replacement and after B iterations aggregate the
estimates. In Monte Carlo simulations, we show how the partitioning algorithm
outperforms the latter [...] We illustrate the partitioning algorithm and the
interpretation of the probabilistic index model on a real data set (Przybylski
and Weinstein, 2017) of n = 116,630 where we compare it against the ordinary
least squares method. By modelling the probabilistic index, we give an
intuitive and meaningful quantification of the effect of the time adolescents
spend using digital devices such as smartphones on self-reported mental
well-being. We show how moderate usage is associated with an increased
probability of reporting a higher mental well-being compared to random
adolescents who do not use a smartphone. On the other hand, adolescents who
excessively use their smartphone are associated with a higher probability of
reporting a lower mental well-being than randomly chosen peers who do not use a
smartphone.[...]
",0,0,0,1,0,0
17497,BICEP2 / Keck Array IX: New Bounds on Anisotropies of CMB Polarization Rotation and Implications for Axion-Like Particles and Primordial Magnetic Fields,"  We present the strongest constraints to date on anisotropies of CMB
polarization rotation derived from $150$ GHz data taken by the BICEP2 & Keck
Array CMB experiments up to and including the 2014 observing season (BK14). The
definition of polarization angle in BK14 maps has gone through self-calibration
in which the overall angle is adjusted to minimize the observed $TB$ and $EB$
power spectra. After this procedure, the $QU$ maps lose sensitivity to a
uniform polarization rotation but are still sensitive to anisotropies of
polarization rotation. This analysis places constraints on the anisotropies of
polarization rotation, which could be generated by CMB photons interacting with
axion-like pseudoscalar fields or Faraday rotation induced by primordial
magnetic fields. The sensitivity of BK14 maps ($\sim 3\mu$K-arcmin) makes it
possible to reconstruct anisotropies of polarization rotation angle and measure
their angular power spectrum much more precisely than previous attempts. Our
data are found to be consistent with no polarization rotation anisotropies,
improving the upper bound on the amplitude of the rotation angle spectrum by
roughly an order of magnitude compared to the previous best constraints. Our
results lead to an order of magnitude better constraint on the coupling
constant of the Chern-Simons electromagnetic term $f_a \geq 1.7\times
10^2\times (H_I/2\pi)$ ($2\sigma$) than the constraint derived from uniform
rotation, where $H_I$ is the inflationary Hubble scale. The upper bound on the
amplitude of the primordial magnetic fields is 30nG ($2\sigma$) from the
polarization rotation anisotropies.
",0,1,0,0,0,0
17498,Unsupervised Object Discovery and Segmentation of RGBD-images,"  In this paper we introduce a system for unsupervised object discovery and
segmentation of RGBD-images. The system models the sensor noise directly from
data, allowing accurate segmentation without sensor specific hand tuning of
measurement noise models making use of the recently introduced Statistical
Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the
system is able to apply probabilistic inference, enabling reliable segmentation
in previously challenging scenarios. In addition, we introduce new methods for
filtering out false positives, significantly improving the signal to noise
ratio. We show that the system significantly outperform state-of-the-art in on
a challenging real-world dataset.
",1,0,0,0,0,0
17499,Enabling large-scale viscoelastic calculations via neural network acceleration,"  One of the most significant challenges involved in efforts to understand the
effects of repeated earthquake cycle activity are the computational costs of
large-scale viscoelastic earthquake cycle models. Computationally intensive
viscoelastic codes must be evaluated thousands of times and locations, and as a
result, studies tend to adopt a few fixed rheological structures and model
geometries, and examine the predicted time-dependent deformation over short
(<10 yr) time periods at a given depth after a large earthquake. Training a
deep neural network to learn a computationally efficient representation of
viscoelastic solutions, at any time, location, and for a large range of
rheological structures, allows these calculations to be done quickly and
reliably, with high spatial and temporal resolution. We demonstrate that this
machine learning approach accelerates viscoelastic calculations by more than
50,000%. This magnitude of acceleration will enable the modeling of
geometrically complex faults over thousands of earthquake cycles across wider
ranges of model parameters and at larger spatial and temporal scales than have
been previously possible.
",0,1,0,0,0,0
17500,Speaker identification from the sound of the human breath,"  This paper examines the speaker identification potential of breath sounds in
continuous speech. Speech is largely produced during exhalation. In order to
replenish air in the lungs, speakers must periodically inhale. When inhalation
occurs in the midst of continuous speech, it is generally through the mouth.
Intra-speech breathing behavior has been the subject of much study, including
the patterns, cadence, and variations in energy levels. However, an often
ignored characteristic is the {\em sound} produced during the inhalation phase
of this cycle. Intra-speech inhalation is rapid and energetic, performed with
open mouth and glottis, effectively exposing the entire vocal tract to enable
maximum intake of air. This results in vocal tract resonances evoked by
turbulence that are characteristic of the speaker's speech-producing apparatus.
Consequently, the sounds of inhalation are expected to carry information about
the speaker's identity. Moreover, unlike other spoken sounds which are subject
to active control, inhalation sounds are generally more natural and less
affected by voluntary influences. The goal of this paper is to demonstrate that
breath sounds are indeed bio-signatures that can be used to identify speakers.
We show that these sounds by themselves can yield remarkably accurate speaker
recognition with appropriate feature representations and classification
frameworks.
",1,0,0,1,0,0
17501,Vector valued maximal Carleson type operators on the weighted Lorentz spaces,"  In this paper, by using the idea of linearizing maximal op-erators originated
by Charles Fefferman and the TT* method of Stein-Wainger, we establish a
weighted inequality for vector valued maximal Carleson type operators with
singular kernels proposed by Andersen and John on the weighted Lorentz spaces
with vector-valued functions.
",0,0,1,0,0,0
17502,Rigidity of square-tiled interval exchange transformations,"  We look at interval exchange transformations defined as first return maps on
the set of diagonals of a flow of direction $\theta$ on a square-tiled surface:
using a combinatorial approach, we show that, when the surface has at least one
true singularity both the flow and the interval exchange are rigid if and only
if tan $\theta$ has bounded partial quotients. Moreover, if all vertices of the
squares are singularities of the flat metric, and tan $\theta$ has bounded
partial quotients, the square-tiled interval exchange transformation T is not
of rank one. Finally, for another class of surfaces, those defined by the
unfolding of billiards in Veech triangles, we build an uncountable set of rigid
directional flows and an uncountable set of rigid interval exchange
transformations.
",0,0,1,0,0,0
17503,Global Marcinkiewicz estimates for nonlinear parabolic equations with nonsmooth coefficients,"  Consider the parabolic equation with measure data \begin{equation*} \left\{
\begin{aligned} &u_t-{\rm div} \mathbf{a}(D u,x,t)=\mu&\text{in}& \quad
\Omega_T, &u=0 \quad &\text{on}& \quad \partial_p\Omega_T, \end{aligned}\right.
\end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^n$,
$\Omega_T=\Omega\times (0,T)$, $\partial_p\Omega_T=(\partial\Omega\times
(0,T))\cup (\Omega\times\{0\})$, and $\mu$ is a signed Borel measure with
finite total mass. Assume that the nonlinearity ${\bf a}$ satisfies a small
BMO-seminorm condition, and $\Omega$ is a Reifenberg flat domain. This paper
proves a global Marcinkiewicz estimate for the SOLA (Solution Obtained as
Limits of Approximation) to the parabolic equation.
",0,0,1,0,0,0
17504,Can Who-Edits-What Predict Edit Survival?,"  As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.
",1,0,0,1,0,0
17505,Introduction to intelligent computing unit 1,"  This brief note highlights some basic concepts required toward understanding
the evolution of machine learning and deep learning models. The note starts
with an overview of artificial intelligence and its relationship to biological
neuron that ultimately led to the evolution of todays intelligent models.
",1,0,0,1,0,0
17506,Spatial heterogeneities shape collective behavior of signaling amoeboid cells,"  We present novel experimental results on pattern formation of signaling
Dictyostelium discoideum amoeba in the presence of a periodic array of
millimeter-sized pillars. We observe concentric cAMP waves that initiate almost
synchronously at the pillars and propagate outwards. These waves have higher
frequency than the other firing centers and dominate the system dynamics. The
cells respond chemotactically to these circular waves and stream towards the
pillars, forming periodic Voronoi domains that reflect the periodicity of the
underlying lattice. We performed comprehensive numerical simulations of a
reaction-diffusion model to study the characteristics of the boundary
conditions given by the obstacles. Our simulations show that, the obstacles can
act as the wave source depending on the imposed boundary condition.
Interestingly, a critical minimum accumulation of cAMP around the obstacles is
needed for the pillars to act as the wave source. This critical value is lower
at smaller production rates of the intracellular cAMP which can be controlled
in our experiments using caffeine. Experiments and simulations also show that
in the presence of caffeine the number of firing centers is reduced which is
crucial in our system for circular waves emitted from the pillars to
successfully take over the dynamics. These results are crucial to understand
the signaling mechanism of Dictyostelium cells that experience spatial
heterogeneities in its natural habitat.
",0,0,0,0,1,0
17507,Yamabe Solitons on three-dimensional normal almost paracontact metric manifolds,"  The purpose of the paper is to study Yamabe solitons on three-dimensional
para-Sasakian, paracosymplectic and para-Kenmotsu manifolds. Mainly, we proved
that *If the semi-Riemannian metric of a three-dimensional para-Sasakian
manifold is a Yamabe soliton, then it is of constant scalar curvature, and the
flow vector field V is Killing. In the next step, we proved that either
manifold has constant curvature -1 and reduces to an Einstein manifold, or V is
an infinitesimal automorphism of the paracontact metric structure on the
manifold. *If the semi-Riemannian metric of a three-dimensional
paracosymplectic manifold is a Yamabe soliton, then it has constant scalar
curvature. Furthermore either manifold is $\eta$-Einstein, or Ricci flat. *If
the semi-Riemannian metric on a three-dimensional para-Kenmotsu manifold is a
Yamabe soliton, then the manifold is of constant sectional curvature -1,
reduces to an Einstein manifold. Furthermore, Yamabe soliton is expanding with
$\lambda$=-6 and the vector field V is Killing. Finally, we construct examples
to illustrate the results obtained in previous sections.
",0,0,1,0,0,0
17508,Clustering and Hitting Times of Threshold Exceedances and Applications,"  We investigate exceedances of the process over a sufficiently high threshold.
The exceedances determine the risk of hazardous events like climate
catastrophes, huge insurance claims, the loss and delay in telecommunication
networks.
Due to dependence such exceedances tend to occur in clusters. The cluster
structure of social networks is caused by dependence (social relationships and
interests) between nodes and possibly heavy-tailed distributions of the node
degrees. A minimal time to reach a large node determines the first hitting
time. We derive an asymptotically equivalent distribution and a limit
expectation of the first hitting time to exceed the threshold $u_n$ as the
sample size $n$ tends to infinity. The results can be extended to the second
and, generally, to the $k$th ($k> 2$) hitting times. Applications in
large-scale networks such as social, telecommunication and recommender systems
are discussed.
",0,0,1,1,0,0
17509,Classification of Local Field Potentials using Gaussian Sequence Model,"  A problem of classification of local field potentials (LFPs), recorded from
the prefrontal cortex of a macaque monkey, is considered. An adult macaque
monkey is trained to perform a memory-based saccade. The objective is to decode
the eye movement goals from the LFP collected during a memory period. The LFP
classification problem is modeled as that of classification of smooth functions
embedded in Gaussian noise. It is then argued that using minimax function
estimators as features would lead to consistent LFP classifiers. The theory of
Gaussian sequence models allows us to represent minimax estimators as finite
dimensional objects. The LFP classifier resulting from this mathematical
endeavor is a spectrum based technique, where Fourier series coefficients of
the LFP data, followed by appropriate shrinkage and thresholding, are used as
features in a linear discriminant classifier. The classifier is then applied to
the LFP data to achieve high decoding accuracy. The function classification
approach taken in the paper also provides a systematic justification for using
Fourier series, with shrinkage and thresholding, as features for the problem,
as opposed to using the power spectrum. It also suggests that phase information
is crucial to the decision making.
",0,0,0,1,0,0
17510,A few explicit examples of complex dynamics of inertia groups on surfaces - a question of Professor Igor Dolgachev,"  We give a few explicit examples which answer an open minded question of
Professor Igor Dolgachev on complex dynamics of the inertia group of a smooth
rational curve on a projective K3 surface and its variants for a rational
surface and a non-projective K3 surface.
",0,0,1,0,0,0
17511,Ancestral inference from haplotypes and mutations,"  We consider inference about the history of a sample of DNA sequences,
conditional upon the haplotype counts and the number of segregating sites
observed at the present time. After deriving some theoretical results in the
coalescent setting, we implement rejection sampling and importance sampling
schemes to perform the inference. The importance sampling scheme addresses an
extension of the Ewens Sampling Formula for a configuration of haplotypes and
the number of segregating sites in the sample. The implementations include both
constant and variable population size models. The methods are illustrated by
two human Y chromosome data sets.
",0,0,1,1,0,0
17512,Ellipsoid Method for Linear Programming made simple,"  In this paper, ellipsoid method for linear programming is derived using only
minimal knowledge of algebra and matrices. Unfortunately, most authors first
describe the algorithm, then later prove its correctness, which requires a good
knowledge of linear algebra.
",1,0,0,0,0,0
17513,Collective strong coupling of cold atoms to an all-fiber ring cavity,"  We experimentally demonstrate a ring geometry all-fiber cavity system for
cavity quantum electrodynamics with an ensemble of cold atoms. The fiber cavity
contains a nanofiber section which mediates atom-light interactions through an
evanescent field. We observe well-resolved, vacuum Rabi splitting of the cavity
transmission spectrum in the weak driving limit due to a collective enhancement
of the coupling rate by the ensemble of atoms within the evanescent field, and
we present a simple theoretical model to describe this. In addition, we
demonstrate a method to control and stabilize the resonant frequency of the
cavity by utilizing the thermal properties of the nanofiber.
",0,1,0,0,0,0
17514,Robust Model-Based Clustering of Voting Records,"  We explore the possibility of discovering extreme voting patterns in the U.S.
Congressional voting records by drawing ideas from the mixture of contaminated
normal distributions. A mixture of latent trait models via contaminated normal
distributions is proposed. We assume that the low dimensional continuous latent
variable comes from a contaminated normal distribution and, therefore, picks up
extreme patterns in the observed binary data while clustering. We consider in
particular such model for the analysis of voting records. The model is applied
to a U.S. Congressional Voting data set on 16 issues. Note this approach is the
first instance within the literature of a mixture model handling binary data
with possible extreme patterns.
",0,0,0,1,0,0
17515,The Quest for Solvable Multistate Landau-Zener Models,"  Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ)
theory were proposed [1]. They describe common properties of all known solved
systems with linearly time-dependent Hamiltonians. Here we show that ICs enable
efficient computer assisted search for new solvable MLZ models that span
complexity range from several interacting states to mesoscopic systems with
many-body dynamics and combinatorially large phase space. This diversity
suggests that nontrivial solvable MLZ models are numerous. In addition, we
refine the formulation of ICs and extend the class of solvable systems to
models with points of multiple diabatic level crossing.
",0,1,1,0,0,0
17516,Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility,"  We consider the estimation of the multi-period optimal portfolio obtained by
maximizing an exponential utility. Employing Jeffreys' non-informative prior
and the conjugate informative prior, we derive stochastic representations for
the optimal portfolio weights at each time point of portfolio reallocation.
This provides a direct access not only to the posterior distribution of the
portfolio weights but also to their point estimates together with uncertainties
and their asymptotic distributions. Furthermore, we present the posterior
predictive distribution for the investor's wealth at each time point of the
investment period in terms of a stochastic representation for the future wealth
realization. This in turn makes it possible to use quantile-based risk measures
or to calculate the probability of default. We apply the suggested Bayesian
approach to assess the uncertainty in the multi-period optimal portfolio by
considering assets from the FTSE 100 in the weeks after the British referendum
to leave the European Union. The behaviour of the novel portfolio estimation
method in a precarious market situation is illustrated by calculating the
predictive wealth, the risk associated with the holding portfolio, and the
default probability in each period.
",0,0,1,1,0,0
17517,Reconstructing the gravitational field of the local universe,"  Tests of gravity at the galaxy scale are in their infancy. As a first step to
systematically uncovering the gravitational significance of galaxies, we map
three fundamental gravitational variables -- the Newtonian potential,
acceleration and curvature -- over the galaxy environments of the local
universe to a distance of approximately 200 Mpc. Our method combines the
contributions from galaxies in an all-sky redshift survey, halos from an N-body
simulation hosting low-luminosity objects, and linear and quasi-linear modes of
the density field. We use the ranges of these variables to determine the extent
to which galaxies expand the scope of generic tests of gravity and are capable
of constraining specific classes of model for which they have special
significance. Finally, we investigate the improvements afforded by upcoming
galaxy surveys.
",0,1,0,0,0,0
17518,Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation,"  By mapping the most advanced elements of the contemporary social
interactions, the world scientific collaboration network develops an extremely
involved and heterogeneous organization. Selected characteristics of this
heterogeneity are studied here and identified by focusing on the scientific
collaboration community of H. Eugene Stanley - one of the most prolific world
scholars at the present time. Based on the Web of Science records as of March
28, 2016, several variants of networks are constructed. It is found that the
Stanley #1 network - this in analogy to the Erdős # - develops a largely
consistent hierarchical organization and Stanley himself obeys rules of the
same hierarchy. However, this is seen exclusively in the weighted network
representation. When such a weighted network is evolving, an existing relevant
model indicates that the spread of weight gets stimulation to the
multiplicative bursts over the neighbouring nodes, which leads to a balanced
growth of interconnections among them. While not exclusive to Stanley, such a
behaviour is not a rule, however. Networks of other outstanding scholars
studied here more often develop a star-like form and the central hubs
constitute the outliers. This study is complemented by a spectral analysis of
the normalised Laplacian matrices derived from the weighted variants of the
corresponding networks and, among others, it points to the efficiency of such a
procedure for identifying the component communities and relations among them in
the complex weighted networks.
",1,1,0,0,0,0
17519,Rational links and DT invariants of quivers,"  We prove that the generating functions for the colored HOMFLY-PT polynomials
of rational links are specializations of the generating functions of the
motivic Donaldson-Thomas invariants of appropriate quivers that we naturally
associate with these links. This shows that the conjectural links-quivers
correspondence of Kucharski-Reineke-Stošić-Su{\l}kowski as well as the
LMOV conjecture hold for rational links. Along the way, we extend the
links-quivers correspondence to tangles and, thus, explore elements of a skein
theory for motivic Donaldson-Thomas invariants.
",0,0,1,0,0,0
17520,G2-structures for N=1 supersymmetric AdS4 solutions of M-theory,"  We study the N=1 supersymmetric solutions of D=11 supergravity obtained as a
warped product of four-dimensional anti-de-Sitter space with a
seven-dimensional Riemannian manifold M. Using the octonion bundle structure on
M we reformulate the Killing spinor equations in terms of sections of the
octonion bundle on M. The solutions then define a single complexified
G2-structure on M or equivalently two real G2-structures. We then study the
torsion of these G2-structures and the relationships between them.
",0,0,1,0,0,0
17521,A Neural Stochastic Volatility Model,"  In this paper, we show that the recent integration of statistical models with
deep recurrent neural networks provides a new way of formulating volatility
(the degree of variation of time series) models that have been widely used in
time series analysis and prediction in finance. The model comprises a pair of
complementary stochastic recurrent neural networks: the generative network
models the joint distribution of the stochastic volatility process; the
inference network approximates the conditional distribution of the latent
variables given the observables. Our focus here is on the formulation of
temporal dynamics of volatility over time under a stochastic recurrent neural
network framework. Experiments on real-world stock price datasets demonstrate
that the proposed model generates a better volatility estimation and prediction
that outperforms mainstream methods, e.g., deterministic models such as GARCH
and its variants, and stochastic models namely the MCMC-based model
\emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol},
on average negative log-likelihood.
",1,0,0,1,0,0
17522,A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds,"  This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.
",1,0,0,0,0,0
17523,Mass-to-Light versus Color Relations for Dwarf Irregular Galaxies,"  We have determined new relations between $UBV$ colors and mass-to-light
ratios ($M/L$) for dwarf irregular (dIrr) galaxies, as well as for transformed
$g^\prime - r^\prime$. These $M/L$ to color relations (MLCRs) are based on
stellar mass density profiles determined for 34 LITTLE THINGS dwarfs from
spectral energy distribution fitting to multi-wavelength surface photometry in
passbands from the FUV to the NIR. These relations can be used to determine
stellar masses in dIrr galaxies for situations where other determinations of
stellar mass are not possible. Our MLCRs are shallower than comparable MLCRs in
the literature determined for spiral galaxies. We divided our dwarf data into
four metallicity bins and found indications of a steepening of the MLCR with
increased oxygen abundance, perhaps due to more line blanketing occurring at
higher metallicity.
",0,1,0,0,0,0
17524,Insight into High-order Harmonic Generation from Solids: The Contributions of the Bloch Wave-packets Moving on the Group and Phase Velocities,"  We study numerically the Bloch electron wavepacket dynamics in periodic
potentials to simulate laser-solid interactions. We introduce a new perspective
in the coordinate space combined with the motion of the Bloch electron
wavepackets moving at group and phase velocities under the laser fields. This
model interprets the origins of the two contributions (intra- and interband
transitions) of the high-order harmonic generation (HHG) by investigating the
local and global behavior of the wavepackets. It also elucidates the underlying
physical picture of the HHG intensity enhancement by means of carrier-envelope
phase (CEP), chirp and inhomogeneous fields. It provides a deep insight into
the emission of high-order harmonics from solids. This model is instructive for
experimental measurements and provides a new avenue to distinguish mechanisms
of the HHG from solids in diffrent laser fields.
",0,1,0,0,0,0
17525,Using deterministic approximations to accelerate SMC for posterior sampling,"  Sequential Monte Carlo has become a standard tool for Bayesian Inference of
complex models. This approach can be computationally demanding, especially when
initialized from the prior distribution. On the other hand, deter-ministic
approximations of the posterior distribution are often available with no
theoretical guaranties. We propose a bridge sampling scheme starting from such
a deterministic approximation of the posterior distribution and targeting the
true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of
distributions that is determined in an adaptive way. We illustrate the
robustness and the efficiency of the methodology on a large simulation study.
When applied to network datasets, SBS inference leads to different statistical
conclusions from the one supplied by the standard variational Bayes
approximation.
",0,0,0,1,0,0
17526,Evaluating (and improving) the correspondence between deep neural networks and human representations,"  Decades of psychological research have been aimed at modeling how people
learn features and categories. The empirical validation of these theories is
often based on artificial stimuli with simple representations. Recently, deep
neural networks have reached or surpassed human accuracy on tasks such as
identifying objects in natural images. These networks learn representations of
real-world stimuli that can potentially be leveraged to capture psychological
representations. We find that state-of-the-art object classification networks
provide surprisingly accurate predictions of human similarity judgments for
natural images, but fail to capture some of the structure represented by
people. We show that a simple transformation that corrects these discrepancies
can be obtained through convex optimization. We use the resulting
representations to predict the difficulty of learning novel categories of
natural images. Our results extend the scope of psychological experiments and
computational modeling by enabling tractable use of large natural stimulus
sets.
",1,0,0,0,0,0
17527,An approach to nonsolvable base change and descent,"  We present a collection of conjectural trace identities and explain why they
are equivalent to base change and descent of automorphic representations of
$\mathrm{GL}_n(\mathbb{A}_F)$ along nonsolvable extensions (under some
simplifying hypotheses). The case $n=2$ is treated in more detail and
applications towards the Artin conjecture for icosahedral Galois
representations are given.
",0,0,1,0,0,0
17528,Towards Attack-Tolerant Networks: Concurrent Multipath Routing and the Butterfly Network,"  Targeted attacks against network infrastructure are notoriously difficult to
guard against. In the case of communication networks, such attacks can leave
users vulnerable to censorship and surveillance, even when cryptography is
used. Much of the existing work on network fault-tolerance focuses on random
faults and does not apply to adversarial faults (attacks). Centralized networks
have single points of failure by definition, leading to a growing popularity in
decentralized architectures and protocols for greater fault-tolerance. However,
centralized network structure can arise even when protocols are decentralized.
Despite their decentralized protocols, the Internet and World-Wide Web have
been shown both theoretically and historically to be highly susceptible to
attack, in part due to emergent structural centralization. When single points
of failure exist, they are potentially vulnerable to non-technological (i.e.,
coercive) attacks, suggesting the importance of a structural approach to
attack-tolerance. We show how the assumption of partial trust transitivity,
while more realistic than the assumption underlying webs of trust, can be used
to quantify the effective redundancy of a network as a function of trust
transitivity. We also prove that the effective redundancy of the wrap-around
butterfly topology increases exponentially with trust transitivity and describe
a novel concurrent multipath routing algorithm for constructing paths to
utilize that redundancy. When portions of network structure can be dictated our
results can be used to create scalable, attack-tolerant infrastructures. More
generally, our results provide a theoretical formalism for evaluating the
effects of network structure on adversarial fault-tolerance.
",1,0,0,0,0,0
17529,PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making,"  Reinforcement learning and symbolic planning have both been used to build
intelligent autonomous agents. Reinforcement learning relies on learning from
interactions with real world, which often requires an unfeasibly large amount
of experience. Symbolic planning relies on manually crafted symbolic knowledge,
which may not be robust to domain uncertainties and changes. In this paper we
present a unified framework {\em PEORL} that integrates symbolic planning with
hierarchical reinforcement learning (HRL) to cope with decision-making in a
dynamic environment with uncertainties.
Symbolic plans are used to guide the agent's task execution and learning, and
the learned experience is fed back to symbolic knowledge to improve planning.
This method leads to rapid policy search and robust symbolic plans in complex
domains. The framework is tested on benchmark domains of HRL.
",0,0,0,1,0,0
17530,Structure of Native Two-dimensional Oxides on III--Nitride Surfaces,"  When pristine material surfaces are exposed to air, highly reactive broken
bonds can promote the formation of surface oxides with structures and
properties differing greatly from bulk. Determination of the oxide structure,
however, is often elusive through the use of indirect diffraction methods or
techniques that probe only the outer most layer. As a result, surface oxides
forming on widely used materials, such as group III-nitrides, have not been
unambiguously resolved, even though critical properties can depend sensitively
on their presence. In this work, aberration corrected scanning transmission
electron microscopy reveals directly, and with depth dependence, the structure
of native two--dimensional oxides that form on AlN and GaN surfaces. Through
atomic resolution imaging and spectroscopy, we show that the oxide layers are
comprised of tetrahedra--octahedra cation--oxygen units, similar to bulk
$\theta$--Al$_2$O$_3$ and $\beta$--Ga$_2$O$_3$. By applying density functional
theory, we show that the observed structures are more stable than previously
proposed surface oxide models. We place the impact of these observations in the
context of key III-nitride growth, device issues, and the recent discovery of
two-dimnesional nitrides.
",0,1,0,0,0,0
17531,Hydrodynamic charge and heat transport on inhomogeneous curved spaces,"  We develop the theory of hydrodynamic charge and heat transport in strongly
interacting quasi-relativistic systems on manifolds with inhomogeneous spatial
curvature. In solid-state physics, this is analogous to strain disorder in the
underlying lattice. In the hydrodynamic limit, we find that the thermal and
electrical conductivities are dominated by viscous effects, and that the
thermal conductivity is most sensitive to this disorder. We compare the effects
of inhomogeneity in the spatial metric to inhomogeneity in the chemical
potential, and discuss the extent to which our hydrodynamic theory is relevant
for experimentally realizable condensed matter systems, including suspended
graphene at the Dirac point.
",0,1,0,0,0,0
17532,Simulation assisted machine learning,"  Predicting how a proposed cancer treatment will affect a given tumor can be
cast as a machine learning problem, but the complexity of biological systems,
the number of potentially relevant genomic and clinical features, and the lack
of very large scale patient data repositories make this a unique challenge.
""Pure data"" approaches to this problem are underpowered to detect
combinatorially complex interactions and are bound to uncover false
correlations despite statistical precautions taken (1). To investigate this
setting, we propose a method to integrate simulations, a strong form of prior
knowledge, into machine learning, a combination which to date has been largely
unexplored. The results of multiple simulations (under various uncertainty
scenarios) are used to compute similarity measures between every pair of
samples: sample pairs are given a high similarity score if they behave
similarly under a wide range of simulation parameters. These similarity values,
rather than the original high dimensional feature data, are used to train
kernelized machine learning algorithms such as support vector machines, thus
handling the curse-of-dimensionality that typically affects genomic machine
learning. Using four synthetic datasets of complex systems--three biological
models and one network flow optimization model--we demonstrate that when the
number of training samples is small compared to the number of features, the
simulation kernel approach dominates over no-prior-knowledge methods. In
addition to biology and medicine, this approach should be applicable to other
disciplines, such as weather forecasting, financial markets, and agricultural
management, where predictive models are sought and informative yet approximate
simulations are available. The Python SimKern software, the models (in MATLAB,
Octave, and R), and the datasets are made freely available at
this https URL .
",0,0,0,1,1,0
17533,Rechargeable redox flow batteries: Maximum current density with electrolyte flow reactant penetration in a serpentine flow structure,"  Rechargeable redox flow batteries with serpentine flow field designs have
been demonstrated to deliver higher current density and power density in medium
and large-scale stationary energy storage applications. Nevertheless, the
fundamental mechanisms involved with improved current density in flow batteries
with flow field designs have not been understood. Here we report a maximum
current density concept associated with stoichiometric availability of
electrolyte reactant flow penetration through the porous electrode that can be
achieved in a flow battery system with a ""zero-gap""serpentine flow field
architecture. This concept can explain a higher current density achieved within
allowing reactions of all species soluble in the electrolyte. Further
validations with experimental data are confirmed by an example of a vanadium
flow battery with a serpentine flow structure over carbon paper electrode.
",0,1,0,0,0,0
17534,Open problems in mathematical physics,"  We present a list of open questions in mathematical physics. After a
historical introduction, a number of problems in a variety of different fields
are discussed, with the intention of giving an overall impression of the
current status of mathematical physics, particularly in the topical fields of
classical general relativity, cosmology and the quantum realm. This list is
motivated by the recent article proposing 42 fundamental questions (in physics)
which must be answered on the road to full enlightenment. But paraphrasing a
famous quote by the British football manager Bill Shankly, in response to the
question of whether mathematics can answer the Ultimate Question of Life, the
Universe, and Everything, mathematics is, of course, much more important than
that.
",0,1,1,0,0,0
17535,Stochastic Bandit Models for Delayed Conversions,"  Online advertising and product recommendation are important domains of
applications for multi-armed bandit methods. In these fields, the reward that
is immediately available is most often only a proxy for the actual outcome of
interest, which we refer to as a conversion. For instance, in web advertising,
clicks can be observed within a few seconds after an ad display but the
corresponding sale --if any-- will take hours, if not days to happen. This
paper proposes and investigates a new stochas-tic multi-armed bandit model in
the framework proposed by Chapelle (2014) --based on empirical studies in the
field of web advertising-- in which each action may trigger a future reward
that will then happen with a stochas-tic delay. We assume that the probability
of conversion associated with each action is unknown while the distribution of
the conversion delay is known, distinguishing between the (idealized) case
where the conversion events may be observed whatever their delay and the more
realistic setting in which late conversions are censored. We provide
performance lower bounds as well as two simple but efficient algorithms based
on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
conversion rates are low, is based on a Poissonization argument, of independent
interest in other settings where aggregation of Bernoulli observations with
different success probabilities is required.
",1,0,0,0,0,0
17536,Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions,"  The goal of population spectral synthesis (PSS) is to decipher from the
spectrum of a galaxy the mass, age and metallicity of its constituent stellar
populations. This technique has been established as a fundamental tool in
extragalactic research. It has been extensively applied to large spectroscopic
data sets, notably the SDSS, leading to important insights into the galaxy
assembly history. However, despite significant improvements over the past
decade, all current PSS codes suffer from two major deficiencies that inhibit
us from gaining sharp insights into the star-formation history (SFH) of
galaxies and potentially introduce substantial biases in studies of their
physical properties (e.g., stellar mass, mass-weighted stellar age and specific
star formation rate). These are i) the neglect of nebular emission in spectral
fits, consequently, ii) the lack of a mechanism that ensures consistency
between the best-fitting SFH and the observed nebular emission characteristics
of a star-forming (SF) galaxy. In this article, we present FADO (Fitting
Analysis using Differential evolution Optimization): a conceptually novel,
publicly available PSS tool with the distinctive capability of permitting
identification of the SFH that reproduces the observed nebular characteristics
of a SF galaxy. This so-far unique self-consistency concept allows us to
significantly alleviate degeneracies in current spectral synthesis. The
innovative character of FADO is further augmented by its mathematical
foundation: FADO is the first PSS code employing genetic differential evolution
optimization. This, in conjunction with other unique elements in its
mathematical concept (e.g., optimization of the spectral library using
artificial intelligence, convergence test, quasi-parallelization) results in
key improvements with respect to computational efficiency and uniqueness of the
best-fitting SFHs.
",0,1,0,0,0,0
17537,A Combinatoric Shortcut to Evaluate CHY-forms,"  In \cite{Chen:2016fgi} we proposed a differential operator for the evaluation
of the multi-dimensional residues on isolated (zero-dimensional) poles.In this
paper we discuss some new insight on evaluating the (generalized)
Cachazo-He-Yuan (CHY) forms of the scattering amplitudes using this
differential operator. We introduce a tableau representation for the
coefficients appearing in the proposed differential operator. Combining the
tableaux with the polynomial forms of the scattering equations, the evaluation
of the generalized CHY form becomes a simple combinatoric problem. It is thus
possible to obtain the coefficients arising in the differential operator in a
straightforward way. We present the procedure for a complete solution of the
$n$-gon amplitudes at one-loop level in a generalized CHY form. We also apply
our method to fully evaluate the one-loop five-point amplitude in the maximally
supersymmetric Yang-Mills theory; the final result is identical to the one
obtained by Q-Cut.
",0,0,1,0,0,0
17538,ART: adaptive residual--time restarting for Krylov subspace matrix exponential evaluations,"  In this paper a new restarting method for Krylov subspace matrix exponential
evaluations is proposed. Since our restarting technique essentially employs the
residual, some convergence results for the residual are given. We also discuss
how the restart length can be adjusted after each restart cycle, which leads to
an adaptive restarting procedure. Numerical tests are presented to compare our
restarting with three other restarting methods. Some of the algorithms
described in this paper are a part of the Octave/Matlab package expmARPACK
available at this http URL.
",1,0,0,0,0,0
17539,Nil extensions of simple regular ordered semigroup,"  In this paper, nil extensions of some special type of ordered semigroups,
such as, simple regular ordered semigroups, left simple and right regular
ordered semigroup. Moreover, we have characterized complete semilattice
decomposition of all ordered semigroups which are nil extension of ordered
semigroup.
",0,0,1,0,0,0
17540,The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings,"  We examine a class of embeddings based on structured random matrices with
orthogonal rows which can be applied in many machine learning applications
including dimensionality reduction and kernel approximation. For both the
Johnson-Lindenstrauss transform and the angular kernel, we show that we can
select matrices yielding guaranteed improved performance in accuracy and/or
speed compared to earlier methods. We introduce matrices with complex entries
which give significant further accuracy improvement. We provide geometric and
Markov chain-based perspectives to help understand the benefits, and empirical
results which suggest that the approach is helpful in a wider range of
applications.
",0,0,0,1,0,0
17541,The Bayesian optimist's guide to adaptive immune receptor repertoire analysis,"  Probabilistic modeling is fundamental to the statistical analysis of complex
data. In addition to forming a coherent description of the data-generating
process, probabilistic models enable parameter inference about given data sets.
This procedure is well-developed in the Bayesian perspective, in which one
infers probability distributions describing to what extent various possible
parameters agree with the data. In this paper we motivate and review
probabilistic modeling for adaptive immune receptor repertoire data then
describe progress and prospects for future work, from germline haplotyping to
adaptive immune system deployment across tissues. The relevant quantities in
immune sequence analysis include not only continuous parameters such as gene
use frequency, but also discrete objects such as B cell clusters and lineages.
Throughout this review, we unravel the many opportunities for probabilistic
modeling in adaptive immune receptor analysis, including settings for which the
Bayesian approach holds substantial promise (especially if one is optimistic
about new computational methods). From our perspective the greatest prospects
for progress in probabilistic modeling for repertoires concern ancestral
sequence estimation for B cell receptor lineages, including uncertainty from
germline genotype, rearrangement, and lineage development.
",0,0,0,0,1,0
17542,Predictive Indexing,"  There has been considerable research on automated index tuning in database
management systems (DBMSs). But the majority of these solutions tune the index
configuration by retrospectively making computationally expensive physical
design changes all at once. Such changes degrade the DBMS's performance during
the process, and have reduced utility during subsequent query processing due to
the delay between a workload shift and the associated change. A better approach
is to generate small changes that tune the physical design over time, forecast
the utility of these changes, and apply them ahead of time to maximize their
impact.
This paper presents predictive indexing that continuously improves a
database's physical design using lightweight physical design changes. It uses a
machine learning model to forecast the utility of these changes, and
continuously refines the index configuration of the database to handle evolving
workloads. We introduce a lightweight hybrid scan operator with which a DBMS
can make use of partially-built indexes for query processing. Our evaluation
shows that predictive indexing improves the throughput of a DBMS by 3.5--5.2x
compared to other state-of-the-art indexing approaches. We demonstrate that
predictive indexing works seamlessly with other lightweight automated physical
design tuning methods.
",1,0,0,0,0,0
17543,Making Deep Q-learning methods robust to time discretization,"  Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust
to hyperparameterization, implementation details, or small environment changes
(Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key
to making DRL applicable to real world problems. In this paper, we identify
sensitivity to time discretization in near continuous-time environments as a
critical factor; this covers, e.g., changing the number of frames per second,
or the action frequency of the controller. Empirically, we find that
Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and
Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small
time steps. Formally, we prove that Q-learning does not exist in continuous
time. We detail a principled way to build an off-policy RL algorithm that
yields similar performances over a wide range of time discretizations, and
confirm this robustness empirically.
",1,0,0,1,0,0
17544,Anomalous current in diffusive ferromagnetic Josephson junctions,"  We demonstrate that in diffusive superconductor/ferromagnet/superconductor
(S/F/S) junctions a finite, {\it anomalous}, Josephson current can flow even at
zero phase difference between the S electrodes. The conditions for the
observation of this effect are non-coplanar magnetization distribution and a
broken magnetization inversion symmetry of the superconducting current. The
latter symmetry is intrinsic for the widely used quasiclassical approximation
and prevent previous works, based on this approximation, from obtaining the
Josephson anomalous current. We show that this symmetry can be removed by
introducing spin-dependent boundary conditions for the quasiclassical equations
at the superconducting/ferromagnet interfaces in diffusive systems. Using this
recipe we considered generic multilayer magnetic systems and determine the
ideal experimental conditions in order to maximize the anomalous current.
",0,1,0,0,0,0
17545,Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates,"  Although a majority of the theoretical literature in high-dimensional
statistics has focused on settings which involve fully-observed data, settings
with missing values and corruptions are common in practice. We consider the
problems of estimation and of constructing component-wise confidence intervals
in a sparse high-dimensional linear regression model when some covariates of
the design matrix are missing completely at random. We analyze a variant of the
Dantzig selector [9] for estimating the regression model and we use a
de-biasing argument to construct component-wise confidence intervals. Our first
main result is to establish upper bounds on the estimation error as a function
of the model parameters (the sparsity level s, the expected fraction of
observed covariates $\rho_*$, and a measure of the signal strength
$\|\beta^*\|_2$). We find that even in an idealized setting where the
covariates are assumed to be missing completely at random, somewhat
surprisingly and in contrast to the fully-observed setting, there is a
dichotomy in the dependence on model parameters and much faster rates are
obtained if the covariance matrix of the random design is known. To study this
issue further, our second main contribution is to provide lower bounds on the
estimation error showing that this discrepancy in rates is unavoidable in a
minimax sense. We then consider the problem of high-dimensional inference in
the presence of missing data. We construct and analyze confidence intervals
using a de-biased estimator. In the presence of missing data, inference is
complicated by the fact that the de-biasing matrix is correlated with the pilot
estimator and this necessitates the design of a new estimator and a novel
analysis. We also complement our mathematical study with extensive simulations
on synthetic and semi-synthetic data that show the accuracy of our asymptotic
predictions for finite sample sizes.
",0,0,0,1,0,0
17546,Applications of an algorithm for solving Fredholm equations of the first kind,"  In this paper we use an iterative algorithm for solving Fredholm equations of
the first kind. The basic algorithm is known and is based on an EM algorithm
when involved functions are non-negative and integrable. With this algorithm we
demonstrate two examples involving the estimation of a mixing density and a
first passage time density function involving Brownian motion. We also develop
the basic algorithm to include functions which are not necessarily non-negative
and again present illustrations under this scenario. A self contained proof of
convergence of all the algorithms employed is presented.
",0,0,1,1,0,0
17547,Fully symmetric kernel quadrature,"  Kernel quadratures and other kernel-based approximation methods typically
suffer from prohibitive cubic time and quadratic space complexity in the number
of function evaluations. The problem arises because a system of linear
equations needs to be solved. In this article we show that the weights of a
kernel quadrature rule can be computed efficiently and exactly for up to tens
of millions of nodes if the kernel, integration domain, and measure are fully
symmetric and the node set is a union of fully symmetric sets. This is based on
the observations that in such a setting there are only as many distinct weights
as there are fully symmetric sets and that these weights can be solved from a
linear system of equations constructed out of row sums of certain submatrices
of the full kernel matrix. We present several numerical examples that show
feasibility, both for a large number of nodes and in high dimensions, of the
developed fully symmetric kernel quadrature rules. Most prominent of the fully
symmetric kernel quadrature rules we propose are those that use sparse grids.
",1,0,1,1,0,0
17548,Conditional Accelerated Lazy Stochastic Gradient Descent,"  In this work we introduce a conditional accelerated lazy stochastic gradient
descent algorithm with optimal number of calls to a stochastic first-order
oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
over the projection-free, Online Frank-Wolfe based stochastic gradient descent
of Hazan and Kale [2012] with convergence rate
$O\left(\frac{1}{\varepsilon^4}\right)$.
",1,0,0,1,0,0
17549,MMD GAN: Towards Deeper Understanding of Moment Matching Network,"  Generative moment matching network (GMMN) is a deep generative model that
differs from Generative Adversarial Network (GAN) by replacing the
discriminator in GAN with a two-sample test based on kernel maximum mean
discrepancy (MMD). Although some theoretical guarantees of MMD have been
studied, the empirical performance of GMMN is still not as competitive as that
of GAN on challenging and large benchmark datasets. The computational
efficiency of GMMN is also less desirable in comparison with GAN, partially due
to its requirement for a rather large batch size during the training. In this
paper, we propose to improve both the model expressiveness of GMMN and its
computational efficiency by introducing adversarial kernel learning techniques,
as the replacement of a fixed Gaussian kernel in the original GMMN. The new
approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN.
The new distance measure in MMD GAN is a meaningful loss that enjoys the
advantage of weak topology and can be optimized via gradient descent with
relatively small batch sizes. In our evaluation on multiple benchmark datasets,
including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN
significantly outperforms GMMN, and is competitive with other representative
GAN works.
",1,0,0,1,0,0
17550,Multipartite entanglement after a quantum quench,"  We study the multipartite entanglement of a quantum many-body system
undergoing a quantum quench. We quantify multipartite entanglement through the
quantum Fisher information (QFI) density and we are able to express it after a
quench in terms of a generalized response function. For pure state initial
conditions and in the thermodynamic limit, we can express the QFI as the
fluctuations of an observable computed in the so-called diagonal ensemble. We
apply the formalism to the dynamics of a quantum Ising chain after a quench in
the transverse field. In this model the asymptotic state is, in almost all
cases, more than two-partite entangled. Moreover, starting from the
ferromagnetic phase, we find a divergence of multipartite entanglement for
small quenches closely connected to a corresponding divergence of the
correlation length.
",0,1,0,0,0,0
17551,Thermal properties of graphene from path-integral simulations,"  Thermal properties of graphene monolayers are studied by path-integral
molecular dynamics (PIMD) simulations, which take into account the quantization
of vibrational modes in the crystalline membrane, and allow one to consider
anharmonic effects in these properties. This system was studied at temperatures
in the range from 12 to 2000~K and zero external stress, by describing the
interatomic interactions through the LCBOPII effective potential. We analyze
the internal energy and specific heat and compare the results derived from the
simulations with those yielded by a harmonic approximation for the vibrational
modes. This approximation turns out to be rather precise up to temperatures of
about 400~K. At higher temperatures, we observe an influence of the elastic
energy, due to the thermal expansion of the graphene sheet. Zero-point and
thermal effects on the in-plane and ""real"" surface of graphene are discussed.
The thermal expansion coefficient $\alpha$ of the real area is found to be
positive at all temperatures, in contrast to the expansion coefficient
$\alpha_p$ of the in-plane area, which is negative at low temperatures, and
becomes positive for $T \gtrsim$ 1000~K.
",0,1,0,0,0,0
17552,Measuring Affectiveness and Effectiveness in Software Systems,"  The summary presented in this paper highlights the results obtained in a
four-years project aiming at analyzing the development process of software
artifacts from two points of view: Effectiveness and Affectiveness. The first
attribute is meant to analyze the productivity of the Open Source Communities
by measuring the time required to resolve an issue, while the latter provides a
novel approach for studying the development process by analyzing the
affectiveness ex-pressed by developers in their comments posted during the
issue resolution phase. Affectivenes is obtained by measuring Sentiment,
Politeness and Emotions. All the study presented in this summary are based on
Jira, one of the most used software repositories.
",1,0,0,0,0,0
17553,Intertangled stochastic motifs in networks of excitatory-inhibitory units,"  A stochastic model of excitatory and inhibitory interactions which bears
universality traits is introduced and studied. The endogenous component of
noise, stemming from finite size corrections, drives robust inter-nodes
correlations, that persist at large large distances. Anti-phase synchrony at
small frequencies is resolved on adjacent nodes and found to promote the
spontaneous generation of long-ranged stochastic patterns, that invade the
network as a whole. These patterns are lacking under the idealized
deterministic scenario, and could provide novel hints on how living systems
implement and handle a large gallery of delicate computational tasks.
",0,1,0,0,0,0
17554,Accurate Computation of the Distribution of Sums of Dependent Log-Normals with Applications to the Black-Scholes Model,"  We present a new Monte Carlo methodology for the accurate estimation of the
distribution of the sum of dependent log-normal random variables. The
methodology delivers statistically unbiased estimators for three distributional
quantities of significant interest in finance and risk management: the left
tail, or cumulative distribution function, the probability density function,
and the right tail, or complementary distribution function of the sum of
dependent log-normal factors. In all of these three cases our methodology
delivers fast and highly accurate estimators in settings for which existing
methodology delivers estimators with large variance that tend to underestimate
the true quantity of interest. We provide insight into the computational
challenges using theory and numerical experiments, and explain their much wider
implications for Monte Carlo statistical estimators of rare-event
probabilities. In particular, we find that theoretically strongly-efficient
estimators should be used with great caution in practice, because they may
yield inaccurate results in the pre-limit. Further, this inaccuracy may not be
detectable from the output of the Monte Carlo simulation, because the
simulation output may severely underestimate the true variance of the
estimator.
",0,0,0,1,0,0
17555,"The complete unitary dual of non-compact Lie superalgebra su(p,q|m) via the generalised oscillator formalism, and non-compact Young diagrams","  We study the unitary representations of the non-compact real forms of the
complex Lie superalgebra sl(n|m). Among them, only the real form su(p,q|m)
(p+q=n) admits nontrivial unitary representations, and all such representations
are of the highest-weight type (or the lowest-weight type). We extend the
standard oscillator construction of the unitary representations of non-compact
Lie superalgebras over standard Fock spaces to generalised Fock spaces which
allows us to define the action of oscillator determinants raised to non-integer
powers. We prove that the proposed construction yields all the unitary
representations including those with continuous labels. The unitary
representations can be diagrammatically represented by non-compact Young
diagrams. We apply our general results to the physically important case of
four-dimensional conformal superalgebra su(2,2|4) and show how it yields
readily its unitary representations including those corresponding to
supermultiplets of conformal fields with continuous (anomalous) scaling
dimensions.
",0,0,1,0,0,0
17556,DeepPainter: Painter Classification Using Deep Convolutional Autoencoders,"  In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessing or manual feature extraction. We first train a deep convolutional
autoencoder on a dataset of paintings, and subsequently use it to initialize a
supervised convolutional neural network for the classification phase.
The proposed approach substantially outperforms previous methods, improving
the previous state-of-the-art for the 3-painter classification problem from
90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
reduction in error rate.
",1,0,0,1,0,0
17557,"Sharp gradient estimate for heat kernels on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will establish an elliptic local Li-Yau gradient estimate
for weak solutions of the heat equation on metric measure spaces with
generalized Ricci curvature bounded from below. One of its main applications is
a sharp gradient estimate for the logarithm of heat kernels. These results seem
new even for smooth Riemannian manifolds.
",0,0,1,0,0,0
17558,Thermodynamic properties of diatomic molecules systems under anharmonic Eckart potential,"  Due to one of the most representative contributions to the energy in diatomic
molecules being the vibrational, we consider the generalized Morse potential
(GMP) as one of the typical potential of interaction for one-dimensional
microscopic systems, which describes local anharmonic effects. From Eckart
potential (EP) model, it is possible to find a connection with the GMP model,
as well as obtain the analytical expression for the energy spectrum because it
is based on $S\,O\left(2,1\right)$ algebras. In this work we find the
macroscopic properties such as vibrational mean energy $U$, specific heat $C$,
Helmholtz free energy $F$ and entropy $S$ for a heteronuclear diatomic system,
along with the exact partition function and its approximation for the high
temperature region. Finally, we make a comparison between the graphs of some
thermodynamic functions obtained with the GMP and the Morse potential (MP) for
$H\,Cl$ molecules.
",0,1,0,0,0,0
17559,Effects of ultrasound waves intensity on the removal of Congo red color from the textile industry wastewater by Fe3O4@TiO2 core-shell nanospheres,"  Environmental pollutants, such as colors from the textile industry, affect
water quality indicators like color, smell, and taste. These substances in the
water cause the obstruction of filters and membranes and thereby reduce the
efficiency of advanced water treatment processes. In addition, they are harmful
to human health because of reaction with disinfectants and production of
by-products. Iron oxide nanoparticles are considered effective absorbents for
the removal of pollutants from aqueous environments. In order to increase the
stability and dispersion, nanospheres with iron oxide core and titanium dioxide
coating were used in this research and their ability to absorb Congo red color
was evaluated. Iron oxide-titanium oxide nanospheres were prepared based on the
coprecipitation method and then their physical properties were determined using
a tunneling electron microscope (TEM) and an X-ray diffraction device.
Morphological investigation of the absorbent surface showed that iron
oxide-titanium oxide nanospheres sized about 5 to 10 nm. X-ray dispersion
survey also suggested the high purity of the sample. In addition, the
absorption rate was measured in the presence of ultrasound waves and the
results indicated that the capacity of the synthesized sample to absorb Congo
red is greatly dependent on the intensity power of ultrasound waves, as the
absorption rate reaches 100% at powers above 30 watts.
",0,1,0,0,0,0
17560,Enumeration of Tree-like Maps with Arbitrary Number of Vertices,"  This paper provides the generating series for the embedding of tree-like
graphs of arbitrary number of vertices, accourding to their genus. It applies
and extends the techniques of Chan, where it was used to give an alternate
proof of the Goulden and Slofstra formula. Furthermore, this greatly
generalizes the famous Harer-Zagier formula, which computes the Euler
characteristic of the moduli space of curves, and is equivalent to the
computation of one vertex maps.
",0,0,1,0,0,0
17561,Depth resolved chemical speciation of a superlattice structure,"  We report results of simultaneous x-ray reflectivity and grazing incidence
x-ray fluorescence measurements in combination with x-ray standing wave
assisted depth resolved near edge x-ray absorption measurements to reveal new
insights on chemical speciation of W in a W-B4C superlattice structure.
Interestingly, our results show existence of various unusual electronic states
for the W atoms especially those sitting at the surface and interface boundary
of a thin film medium as compared to that of the bulk. These observations are
found to be consistent with the results obtained using first principles
calculations. Unlike the conventional x-ray absorption measurements the present
approach has an advantage that it permits the determination of depth resolved
chemical nature of an element in the thin layered materials at atomic length
scale resolutions.
",0,1,0,0,0,0
17562,Optospintronics in graphene via proximity coupling,"  The observation of micron size spin relaxation makes graphene a promising
material for applications in spintronics requiring long distance spin
communication. However, spin dependent scatterings at the contact/graphene
interfaces affect the spin injection efficiencies and hence prevent the
material from achieving its full potential. While this major issue could be
eliminated by nondestructive direct optical spin injection schemes, graphenes
intrinsically low spin orbit coupling strength and optical absorption place an
obstacle in their realization. We overcome this challenge by creating sharp
artificial interfaces between graphene and WSe2 monolayers. Application of a
circularly polarized light activates the spin polarized charge carriers in the
WSe2 layer due to its spin coupled valley selective absorption. These carriers
diffuse into the superjacent graphene layer, transport over a 3.5 um distance,
and are finally detected electrically using BN/Co contacts in a non local
geometry. Polarization dependent measurements confirm the spin origin of the
non local signal.
",0,1,0,0,0,0
17563,Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors,"  Advanced gravitational-wave detectors such as the Laser Interferometer
Gravitational-Wave Observatories (LIGO) require an unprecedented level of
isolation from the ground. When in operation, they are expected to observe
changes in the space-time continuum of less than one thousandth of the diameter
of a proton. Strong teleseismic events like earthquakes disrupt the proper
functioning of the detectors, and result in a loss of data until the detectors
can be returned to their operating states. An earthquake early-warning system,
as well as a prediction model have been developed to help understand the impact
of earthquakes on LIGO. This paper describes a control strategy to use this
early-warning system to reduce the LIGO downtime by 30%. It also presents a
plan to implement this new earthquake configuration in the LIGO automation
system.
",0,1,0,0,0,0
17564,Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO,"  The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.
",1,1,0,0,0,0
17565,Types and unitary representations of reductive p-adic groups,"  We prove that for every Bushnell-Kutzko type that satisfies a certain
rigidity assumption, the equivalence of categories between the corresponding
Bernstein component and the category of modules for the Hecke algebra of the
type induces a bijection between irreducible unitary representations in the two
categories. This is a generalization of the unitarity criterion of Barbasch and
Moy for representations with Iwahori fixed vectors.
",0,0,1,0,0,0
17566,Average values of L-functions in even characteristic,"  Let $k = \mathbb{F}_{q}(T)$ be the rational function field over a finite
field $\mathbb{F}_{q}$, where $q$ is a power of $2$. In this paper we solve the
problem of averaging the quadratic $L$-functions $L(s, \chi_{u})$ over
fundamental discriminants. Any separable quadratic extension $K$ of $k$ is of
the form $K = k(x_{u})$, where $x_{u}$ is a zero of $X^2+X+u=0$ for some $u\in
k$. We characterize the family $\mathcal I$ (resp. $\mathcal F$, $\mathcal F'$)
of rational functions $u\in k$ such that any separable quadratic extension $K$
of $k$ in which the infinite prime $\infty = (1/T)$ of $k$ ramifies (resp.
splits, is inert) can be written as $K = k(x_{u})$ with a unique $u\in\mathcal
I$ (resp. $u\in\mathcal F$, $u\in\mathcal F'$). For almost all $s\in\mathbb C$
with ${\rm Re}(s)\ge \frac{1}2$, we obtain the asymptotic formulas for the
summation of $L(s,\chi_{u})$ over all $k(x_{u})$ with $u\in \mathcal I$, all
$k(x_{u})$ with $u\in \mathcal F$ or all $k(x_{u})$ with $u\in \mathcal F'$ of
given genus. As applications, we obtain the asymptotic mean value formulas of
$L$-functions at $s=\frac{1}2$ and $s=1$ and the asymptotic mean value formulas
of the class number $h_{u}$ or the class number times regulator $h_{u} R_{u}$.
",0,0,1,0,0,0
17567,"Decoupled molecules with binding polynomials of bidegree (n,2)","  We present a result on the number of decoupled molecules for systems binding
two different types of ligands. In the case of $n$ and $2$ binding sites
respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
molecules with the same binding polynomial. For molecules with more binding
sites for the second ligand, we provide computational results.
",1,1,0,0,0,0
17568,Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization,"  Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks.
",1,0,0,1,0,0
17569,McDiarmid Drift Detection Methods for Evolving Data Streams,"  Increasingly, Internet of Things (IoT) domains, such as sensor networks,
smart cities, and social networks, generate vast amounts of data. Such data are
not only unbounded and rapidly evolving. Rather, the content thereof
dynamically evolves over time, often in unforeseen ways. These variations are
due to so-called concept drifts, caused by changes in the underlying data
generation mechanisms. In a classification setting, concept drift causes the
previously learned models to become inaccurate, unsafe and even unusable.
Accordingly, concept drifts need to be detected, and handled, as soon as
possible. In medical applications and emergency response settings, for example,
change in behaviours should be detected in near real-time, to avoid potential
loss of life. To this end, we introduce the McDiarmid Drift Detection Method
(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
The MDDM approach proceeds by sliding a window over prediction results, and
associate window entries with weights. Higher weights are assigned to the most
recent entries, in order to emphasize their importance. As instances are
processed, the detection algorithm compares a weighted mean of elements inside
the sliding window with the maximum weighted mean observed so far. A
significant difference between the two weighted means, upper-bounded by the
McDiarmid inequality, implies a concept drift. Our extensive experimentation
against synthetic and real-world data streams show that our novel method
outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
delays as well as lower false negative rates, while maintaining high
classification accuracies.
",1,0,0,1,0,0
17570,Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth,"  It has recently been shown that yield in amorphous solids under oscillatory
shear is a dynamical transition from asymptotically periodic to asymptotically
chaotic, diffusive dynamics. However, the type and universality class of this
transition are still undecided. Here we show that the diffusive behavior of the
vector of coordinates of the particles comprising an amorphous solid when
subject to oscillatory shear, is analogous to that of a particle diffusing in a
percolating lattice, the so-called ""ant in the labyrinth"" problem, and that
yield corresponds to a percolation transition in the lattice. We explain this
as a transition in the connectivity of the energy landscape, which affects the
phase-space regions accessible to the coordinate vector for a given maximal
strain amplitude. This transition provides a natural explanation to the
observed limit-cycles, periods larger than one and diverging time-scales at
yield.
",0,1,0,0,0,0
17571,Statistical methods in astronomy,"  We present a review of data types and statistical methods often encountered
in astronomy. The aim is to provide an introduction to statistical applications
in astronomy for statisticians and computer scientists. We highlight the
complex, often hierarchical, nature of many astronomy inference problems and
advocate for cross-disciplinary collaborations to address these challenges.
",0,1,0,1,0,0
17572,A note on some algebraic trapdoors for block ciphers,"  We provide sufficient conditions to guarantee that a translation based cipher
is not vulnerable with respect to the partition-based trapdoor. This trapdoor
has been introduced, recently, by Bannier et al. (2016) and it generalizes that
introduced by Paterson in 1999. Moreover, we discuss the fact that studying the
group generated by the round functions of a block cipher may not be sufficient
to guarantee security against these trapdoors for the cipher.
",1,0,1,0,0,0
17573,Bi-Demographic Changes and Current Account using SVAR Modeling,"  The paper, as a new contribution, aims to explore the impacts of
bi-demographic structure on the current account and growth. By using a SVAR
modeling, we track the dynamic impacts between the underlying variables of the
Saudi economy. New insights have been developed to study the interrelations
between population growth, current account and economic growth inside the
neoclassical theory of population. The long-run net impact on economic growth
of the bi-population growth is negative, due to the typically lower skill sets
of the immigrant labor population. Besides, the negative long-run contribution
of immigrant workers to the current account growth largely exceeds that of
contributions from the native population, because of the increasing levels of
remittance outflows from the country. We find that a positive shock in
immigration leads to a negative impact on native active age ratio. Thus, the
immigrants appear to be more substitutes than complements for native workers.
",0,0,0,0,0,1
17574,Supervised Machine Learning for Signals Having RRC Shaped Pulses,"  Classification performances of the supervised machine learning techniques
such as support vector machines, neural networks and logistic regression are
compared for modulation recognition purposes. The simple and robust features
are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
having root-raised-cosine shaped pulses are simulated in extreme noisy
conditions having joint impurities of block fading, lack of symbol and sampling
synchronization, carrier offset, and additive white Gaussian noise. The
features are based on sample mean and sample variance of the imaginary part of
the product of two consecutive complex signal values.
",1,0,0,0,0,0
17575,End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks,"  We propose an autoencoding sequence-based transceiver for communication over
dispersive channels with intensity modulation and direct detection (IM/DD),
designed as a bidirectional deep recurrent neural network (BRNN). The receiver
uses a sliding window technique to allow for efficient data stream estimation.
We find that this sliding window BRNN (SBRNN), based on end-to-end deep
learning of the communication system, achieves a significant bit-error-rate
reduction at all examined distances in comparison to previous block-based
autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
increase of the transmission distance. We also compare the end-to-end SBRNN
with a state-of-the-art IM/DD solution based on two level pulse amplitude
modulation with an FFNN receiver, simultaneously processing multiple received
symbols and approximating nonlinear Volterra equalization. Our results show
that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
learning-based systems for communication over nonlinear channels with memory,
such as the optical IM/DD fiber channel.
",1,0,0,1,0,0
17576,Effective difference elimination and Nullstellensatz,"  We prove effective Nullstellensatz and elimination theorems for difference
equations in sequence rings. More precisely, we compute an explicit function of
geometric quantities associated to a system of difference equations (and these
geometric quantities may themselves be bounded by a function of the number of
variables, the order of the equations, and the degrees of the equations) so
that for any system of difference equations in variables $\mathbf{x} = (x_1,
\ldots, x_m)$ and $\mathbf{u} = (u_1, \ldots, u_r)$, if these equations have
any nontrivial consequences in the $\mathbf{x}$ variables, then such a
consequence may be seen algebraically considering transforms up to the order of
our bound. Specializing to the case of $m = 0$, we obtain an effective method
to test whether a given system of difference equations is consistent.
",0,0,1,0,0,0
17577,A note on primitive $1-$normal elements over finite fields,"  Let $q$ be a prime power of a prime $p$, $n$ a positive integer and $\mathbb
F_{q^n}$ the finite field with $q^n$ elements. The $k-$normal elements over
finite fields were introduced and characterized by Huczynska et al (2013).
Under the condition that $n$ is not divisible by $p$, they obtained an
existence result on primitive $1-$normal elements of $\mathbb F_{q^n}$ over
$\mathbb F_q$ for $q>2$. In this note, we extend their result to the excluded
case $q=2$.
",0,0,1,0,0,0
17578,Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons,"  Zebrafish pretectal neurons exhibit specificities for large-field optic flow
patterns associated with rotatory or translatory body motion. We investigate
the hypothesis that these specificities reflect the input statistics of natural
optic flow. Realistic motion sequences were generated using computer graphics
simulating self-motion in an underwater scene. Local retinal motion was
estimated with a motion detector and encoded in four populations of
directionally tuned retinal ganglion cells, represented as two signed input
variables. This activity was then used as input into one of two learning
networks: a sparse coding network (competitive learning) and backpropagation
network (supervised learning). Both simulations develop specificities for optic
flow which are comparable to those found in a neurophysiological study (Kubo et
al. 2014), and relative frequencies of the various neuronal responses are best
modeled by the sparse coding approach. We conclude that the optic flow neurons
in the zebrafish pretectum do reflect the optic flow statistics. The predicted
vectorial receptive fields show typical optic flow fields but also ""Gabor"" and
dipole-shaped patterns that likely reflect difference fields needed for
reconstruction by linear superposition.
",0,0,0,0,1,0
17579,Revisiting the problem of audio-based hit song prediction using convolutional neural networks,"  Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.
",1,0,0,1,0,0
17580,Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations,"  We train multi-task autoencoders on linguistic tasks and analyze the learned
hidden sentence representations. The representations change significantly when
translation and part-of-speech decoders are added. The more decoders a model
employs, the better it clusters sentences according to their syntactic
similarity, as the representation space becomes less entangled. We explore the
structure of the representation space by interpolating between sentences, which
yields interesting pseudo-English sentences, many of which have recognizable
syntactic structure. Lastly, we point out an interesting property of our
models: The difference-vector between two sentences can be added to change a
third sentence with similar features in a meaningful way.
",0,0,0,1,0,0
17581,Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data,"  Time series forecasting is a crucial component of many important
applications, ranging from forecasting the stock markets to energy load
prediction. The high-dimensionality, velocity and variety of the data collected
in these applications pose significant and unique challenges that must be
carefully addressed for each of them. In this work, a novel Temporal Logistic
Neural Bag-of-Features approach, that can be used to tackle these challenges,
is proposed. The proposed method can be effectively combined with deep neural
networks, leading to powerful deep learning models for time series analysis.
However, combining existing BoF formulations with deep feature extractors pose
significant challenges: the distribution of the input features is not
stationary, tuning the hyper-parameters of the model can be especially
difficult and the normalizations involved in the BoF model can cause
significant instabilities during the training process. The proposed method is
capable of overcoming these limitations by a employing a novel adaptive scaling
mechanism and replacing the classical Gaussian-based density estimation
involved in the regular BoF model with a logistic kernel. The effectiveness of
the proposed approach is demonstrated using extensive experiments on a
large-scale financial time series dataset that consists of more than 4 million
limit orders.
",1,0,0,1,0,1
17582,Extended Bose Hubbard model for two leg ladder systems in artificial magnetic fields,"  We investigate the ground state properties of ultracold atoms with long range
interactions trapped in a two leg ladder configuration in the presence of an
artificial magnetic field. Using a Gross-Pitaevskii approach and a mean field
Gutzwiller variational method, we explore both the weakly interacting and
strongly interacting regime, respectively. We calculate the boundaries between
the density-wave/supersolid and the Mott-insulator/superfluid phases as a
function of magnetic flux and uncover regions of supersolidity. The mean-field
results are confirmed by numerical simulations using a cluster mean field
approach.
",0,1,0,0,0,0
17583,Insensitivity of The Distance Ladder Hubble Constant Determination to Cepheid Calibration Modeling Choices,"  Recent determination of the Hubble constant via Cepheid-calibrated supernovae
by \citet{riess_2.4_2016} (R16) find $\sim 3\sigma$ tension with inferences
based on cosmic microwave background temperature and polarization measurements
from $Planck$. This tension could be an indication of inadequacies in the
concordance $\Lambda$CDM model. Here we investigate the possibility that the
discrepancy could instead be due to systematic bias or uncertainty in the
Cepheid calibration step of the distance ladder measurement by R16. We consider
variations in total-to-selective extinction of Cepheid flux as a function of
line-of-sight, hidden structure in the period-luminosity relationship, and
potentially different intrinsic color distributions of Cepheids as a function
of host galaxy. Considering all potential sources of error, our final
determination of $H_0 = 73.3 \pm 1.7~{\rm km/s/Mpc}$ (not including systematic
errors from the treatment of geometric distances or Type Ia Supernovae) shows
remarkable robustness and agreement with R16. We conclude systematics from the
modeling of Cepheid photometry, including Cepheid selection criteria, cannot
explain the observed tension between Cepheid-variable and CMB-based inferences
of the Hubble constant. Considering a `model-independent' approach to relating
Cepheids in galaxies with known distances to Cepheids in galaxies hosting a
Type Ia supernova and finding agreement with the R16 result, we conclude no
generalization of the model relating anchor and host Cepheid magnitude
measurements can introduce significant bias in the $H_0$ inference.
",0,1,0,0,0,0
17584,Phrase-based Image Captioning with Hierarchical LSTM Model,"  Automatic generation of caption to describe the content of an image has been
gaining a lot of research interests recently, where most of the existing works
treat the image caption as pure sequential data. Natural language, however
possess a temporal hierarchy structure, with complex dependencies between each
subsequence. In this paper, we propose a phrase-based hierarchical Long
Short-Term Memory (phi-LSTM) model to generate image description. In contrast
to the conventional solutions that generate caption in a pure sequential
manner, our proposed model decodes image caption from phrase to sentence. It
consists of a phrase decoder at the bottom hierarchy to decode noun phrases of
variable length, and an abbreviated sentence decoder at the upper hierarchy to
decode an abbreviated form of the image description. A complete image caption
is formed by combining the generated phrases with sentence during the inference
stage. Empirically, our proposed model shows a better or competitive result on
the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the
art models. We also show that our proposed model is able to generate more novel
captions (not seen in the training data) which are richer in word contents in
all these three datasets.
",1,0,0,0,0,0
17585,The three-dimensional structure of swirl-switching in bent pipe flow,"  Swirl-switching is a low-frequency oscillatory phenomenon which affects the
Dean vortices in bent pipes and may cause fatigue in piping systems. Despite
thirty years worth of research, the mechanism that causes these oscillations
and the frequencies that characterise them remain unclear. Here we show that a
three-dimensional wave-like structure is responsible for the low-frequency
switching of the dominant Dean vortex. The present study, performed via direct
numerical simulation, focuses on the turbulent flow through a 90 \degree pipe
bend preceded and followed by straight pipe segments. A pipe with curvature 0.3
(defined as ratio between pipe radius and bend radius) is studied for a bulk
Reynolds number Re = 11 700, corresponding to a friction Reynolds number
Re_\tau \approx 360. Synthetic turbulence is generated at the inflow section
and used instead of the classical recycling method in order to avoid the
interference between recycling and swirl-switching frequencies. The flow field
is analysed by three-dimensional proper orthogonal decomposition (POD) which
for the first time allows the identification of the source of swirl-switching:
a wave-like structure that originates in the pipe bend. Contrary to some
previous studies, the flow in the upstream pipe does not show any direct
influence on the swirl-switching modes. Our analysis further shows that a
three- dimensional characterisation of the modes is crucial to understand the
mechanism, and that reconstructions based on 2D POD modes are incomplete.
",0,1,0,0,0,0
17586,InfoVAE: Information Maximizing Variational Autoencoders,"  A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoencoders can lead to inaccurate
amortized inference distributions and, in some cases, improving the objective
provably degrades the inference quality. In addition, it has been observed that
variational autoencoders tend to ignore the latent variables when combined with
a decoding distribution that is too flexible. We again identify the cause in
existing training criteria and propose a new class of objectives (InfoVAE) that
mitigate these problems. We show that our model can significantly improve the
quality of the variational posterior and can make effective use of the latent
features regardless of the flexibility of the decoding distribution. Through
extensive qualitative and quantitative analyses, we demonstrate that our models
outperform competing approaches on multiple performance metrics.
",1,0,0,1,0,0
17587,Between-class Learning for Image Classification,"  In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC learning has the ability to impose constraints on the shape of
the feature distributions, and thus the generalization ability is improved. BC
learning is originally a method developed for sounds, which can be digitally
mixed. Mixing two image data does not appear to make sense; however, we argue
that because convolutional neural networks have an aspect of treating input
data as waveforms, what works on sounds must also work on images. First, we
propose a simple mixing method using internal divisions, which surprisingly
proves to significantly improve performance. Second, we propose a mixing method
that treats the images as waveforms, which leads to a further improvement in
performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
ImageNet-1K and CIFAR-10, respectively.
",1,0,0,1,0,0
17588,Girsanov reweighting for path ensembles and Markov state models,"  The sensitivity of molecular dynamics on changes in the potential energy
function plays an important role in understanding the dynamics and function of
complex molecules.We present a method to obtain path ensemble averages of a
perturbed dynamics from a set of paths generated by a reference dynamics. It is
based on the concept of path probability measure and the Girsanov theorem, a
result from stochastic analysis to estimate a change of measure of a path
ensemble. Since Markov state models (MSM) of the molecular dynamics can be
formulated as a combined phase-space and path ensemble average, the method can
be extended toreweight MSMs by combining it with a reweighting of the Boltzmann
distribution. We demonstrate how to efficiently implement the Girsanov
reweighting in a molecular dynamics simulation program by calculating parts of
the reweighting factor ""on the fly"" during the simulation, and we benchmark the
method on test systems ranging from a two-dimensional diffusion process to an
artificial many-body system and alanine dipeptide and valine dipeptide in
implicit and explicit water. The method can be used to study the sensitivity of
molecular dynamics on external perturbations as well as to reweight
trajectories generated by enhanced sampling schemes to the original dynamics.
",0,1,0,0,0,0
17589,Coordination of multi-agent systems via asynchronous cloud communication,"  In this work we study a multi-agent coordination problem in which agents are
only able to communicate with each other intermittently through a cloud server.
To reduce the amount of required communication, we develop a self-triggered
algorithm that allows agents to communicate with the cloud only when necessary
rather than at some fixed period. Unlike the vast majority of similar works
that propose distributed event- and/or self-triggered control laws, this work
doesn't assume agents can be ""listening"" continuously. In other words, when an
event is triggered by one agent, neighboring agents will not be aware of this
until the next time they establish communication with the cloud themselves.
Using a notion of ""promises"" about future control inputs, agents are able to
keep track of higher quality estimates about their neighbors allowing them to
stay disconnected from the cloud for longer periods of time while still
guaranteeing a positive contribution to the global task. We prove that our
self-triggered coordination algorithm guarantees that the system asymptotically
reaches the set of desired states. Simulations illustrate our results.
",1,0,1,0,0,0
17590,PatternListener: Cracking Android Pattern Lock Using Acoustic Signals,"  Pattern lock has been widely used for authentication to protect user privacy
on mobile devices (e.g., smartphones and tablets). Given its pervasive usage,
the compromise of pattern lock could lead to serious consequences. Several
attacks have been constructed to crack the lock. However, these approaches
require the attackers to either be physically close to the target device or be
able to manipulate the network facilities (e.g., WiFi hotspots) used by the
victims. Therefore, the effectiveness of the attacks is significantly impacted
by the environment of mobile devices. Also, these attacks are not scalable
since they cannot easily infer unlock patterns of a large number of devices.
Motivated by an observation that fingertip motions on the screen of a mobile
device can be captured by analyzing surrounding acoustic signals on it, we
propose PatternListener, a novel acoustic attack that cracks pattern lock by
analyzing imperceptible acoustic signals reflected by the fingertip. It
leverages speakers and microphones of the victim's device to play imperceptible
audio and record the acoustic signals reflected by the fingertip. In
particular, it infers each unlock pattern by analyzing individual lines that
compose the pattern and are the trajectories of the fingertip. We propose
several algorithms to construct signal segments according to the captured
signals for each line and infer possible candidates of each individual line
according to the signal segments. Finally, we map all line candidates into grid
patterns and thereby obtain the candidates of the entire unlock pattern. We
implement a PatternListener prototype by using off-the-shelf smartphones and
thoroughly evaluate it using 130 unique patterns. The real experimental results
demonstrate that PatternListener can successfully exploit over 90% patterns
within five attempts.
",1,0,0,0,0,0
17591,Marginal Release Under Local Differential Privacy,"  Many analysis and machine learning tasks require the availability of marginal
statistics on multidimensional datasets while providing strong privacy
guarantees for the data subjects. Applications for these statistics range from
finding correlations in the data to fitting sophisticated prediction models. In
this paper, we provide a set of algorithms for materializing marginal
statistics under the strong model of local differential privacy. We prove the
first tight theoretical bounds on the accuracy of marginals compiled under each
approach, perform empirical evaluation to confirm these bounds, and evaluate
them for tasks such as modeling and correlation testing. Our results show that
releasing information based on (local) Fourier transformations of the input is
preferable to alternatives based directly on (local) marginals.
",1,0,0,0,0,0
17592,A possible flyby anomaly for Juno at Jupiter,"  In the last decades there have been an increasing interest in improving the
accuracy of spacecraft navigation and trajectory data. In the course of this
plan some anomalies have been found that cannot, in principle, be explained in
the context of the most accurate orbital models including all known effects
from classical dynamics and general relativity. Of particular interest for its
puzzling nature, and the lack of any accepted explanation for the moment, is
the flyby anomaly discovered in some spacecraft flybys of the Earth over the
course of twenty years. This anomaly manifest itself as the impossibility of
matching the pre and post-encounter Doppler tracking and ranging data within a
single orbit but, on the contrary, a difference of a few mm$/$s in the
asymptotic velocities is required to perform the fitting.
Nevertheless, no dedicated missions have been carried out to elucidate the
origin of this phenomenon with the objective either of revising our
understanding of gravity or to improve the accuracy of spacecraft Doppler
tracking by revealing a conventional origin.
With the occasion of the Juno mission arrival at Jupiter and the close flybys
of this planet, that are currently been performed, we have developed an orbital
model suited to the time window close to the perijove. This model shows that an
anomalous acceleration of a few mm$/$s$^2$ is also present in this case. The
chance for overlooked conventional or possible unconventional explanations is
discussed.
",0,1,0,0,0,0
17593,Crossover between various initial conditions in KPZ growth: flat to stationary,"  We conjecture the universal probability distribution at large time for the
one-point height in the 1D Kardar-Parisi-Zhang (KPZ) stochastic growth
universality class, with initial conditions interpolating from any one of the
three main classes (droplet, flat, stationary) on the left, to another on the
right, allowing for drifts and also for a step near the origin. The result is
obtained from a replica Bethe ansatz calculation starting from the KPZ
continuum equation, together with a ""decoupling assumption"" in the large time
limit. Some cases are checked to be equivalent to previously known results from
other models in the same class, which provides a test of the method, others
appear to be new. In particular we obtain the crossover distribution between
flat and stationary initial conditions (crossover from Airy$_1$ to Airy$_{\rm
stat}$) in a simple compact form.
",0,1,1,0,0,0
17594,Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool,"  We carry out a study of the statistical distribution of rainfall
precipitation data for 20 cites in India. We have determined the best-fit
probability distribution for these cities from the monthly precipitation data
spanning 100 years of observations from 1901 to 2002. To fit the observed data,
we considered 10 different distributions. The efficacy of the fits for these
distributions was evaluated using four empirical non-parametric goodness-of-fit
tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike
information criterion, and Bayesian Information criterion. Finally, the
best-fit distribution using each of these tests were reported, by combining the
results from the model comparison tests. We then find that for most of the
cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution
most adequately fits the observed data.
",0,1,0,1,0,0
17595,Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing,"  Bin Packing problems have been widely studied because of their broad
applications in different domains. Known as a set of NP-hard problems, they
have different vari- ations and many heuristics have been proposed for
obtaining approximate solutions. Specifically, for the 1D variable sized bin
packing problem, the two key sets of optimization heuristics are the bin
assignment and the bin allocation. Usually the performance of a single static
optimization heuristic can not beat that of a dynamic one which is tailored for
each bin packing instance. Building such an adaptive system requires modeling
the relationship between bin features and packing perform profiles. The primary
drawbacks of traditional AI machine learnings for this task are the natural
limitations of feature engineering, such as the curse of dimensionality and
feature selection quality. We introduce a deep learning approach to overcome
the drawbacks by applying a large training data set, auto feature selection and
fast, accurate labeling. We show in this paper how to build such a system by
both theoretical formulation and engineering practices. Our prediction system
achieves up to 89% training accuracy and 72% validation accuracy to select the
best heuristic that can generate a better quality bin packing solution.
",1,0,0,1,0,0
17596,Surges of collective human activity emerge from simple pairwise correlations,"  Human populations exhibit complex behaviors---characterized by long-range
correlations and surges in activity---across a range of social, political, and
technological contexts. Yet it remains unclear where these collective behaviors
come from, or if there even exists a set of unifying principles. Indeed,
existing explanations typically rely on context-specific mechanisms, such as
traffic jams driven by work schedules or spikes in online traffic induced by
significant events. However, analogies with statistical mechanics suggest a
more general mechanism: that collective patterns can emerge organically from
fine-scale interactions within a population. Here, across four different modes
of human activity, we show that the simplest correlations in a
population---those between pairs of individuals---can yield accurate
quantitative predictions for the large-scale behavior of the entire population.
To quantify the minimal consequences of pairwise correlations, we employ the
principle of maximum entropy, making our description equivalent to an Ising
model whose interactions and external fields are notably calculated from past
observations of population activity. In addition to providing accurate
quantitative predictions, we show that the topology of learned Ising
interactions resembles the network of inter-human communication within a
population. Together, these results demonstrate that fine-scale correlations
can be used to predict large-scale social behaviors, a perspective that has
critical implications for modeling and resource allocation in human
populations.
",1,0,0,0,0,0
17597,Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection,"  Although timely sepsis diagnosis and prompt interventions in Intensive Care
Unit (ICU) patients are associated with reduced mortality, early clinical
recognition is frequently impeded by non-specific signs of infection and
failure to detect signs of sepsis-induced organ dysfunction in a constellation
of dynamically changing physiological data. The goal of this work is to
identify patient at risk of life-threatening sepsis utilizing a data-centered
and machine learning-driven approach. We derive a mortality risk predictive
dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and
compare the predictive accuracy of the derived DBN with the Sepsis-related
Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the
Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning
Score (MEWS) tools.
A customized sepsis ontology was used to derive the DBN node structure and
semantically characterize temporal features derived from both structured
physiological data and unstructured clinical notes. We assessed the performance
in predicting mortality risk of the DBN predictive model and compared
performance to other models using Receiver Operating Characteristic (ROC)
curves, area under curve (AUROC), calibration curves, and risk distributions.
The derived dataset consists of 24,506 ICU stays from 19,623 patients with
evidence of suspected infection, with 2,829 patients deceased at discharge. The
DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA
(0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net
Reclassification Index and Integrated Discrimination Improvement analysis
supported the superiority DBN. Compared with conventional rule-based risk
scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved
performance for predicting mortality of infected patients in ICUs.
",0,0,0,1,0,0
17598,Hölder regularity of viscosity solutions of some fully nonlinear equations in the Heisenberg group,"  In this paper we prove the Hölder regularity of bounded, uniformly
continuous, viscosity solutions of some degenerate fully nonlinear equations in
the Heisenberg group.
",0,0,1,0,0,0
17599,Normal form for transverse instability of the line soliton with a nearly critical speed of propagation,"  There exists a critical speed of propagation of the line solitons in the
Zakharov-Kuznetsov (ZK) equation such that small transversely periodic
perturbations are unstable for line solitons with larger-than-critical speeds
and orbitally stable for those with smaller-than-critical speeds. The normal
form for transverse instability of the line soliton with a nearly critical
speed of propagation is derived by means of symplectic projections and
near-identity transformations. Justification of this normal form is provided
with the energy method. The normal form predicts a transformation of the
unstable line solitons with larger-than-critical speeds to the orbitally stable
transversely modulated solitary waves.
",0,1,1,0,0,0
17600,The CLaC Discourse Parser at CoNLL-2016,"  This paper describes our submission ""CLaC"" to the CoNLL-2016 shared task on
shallow discourse parsing. We used two complementary approaches for the task. A
standard machine learning approach for the parsing of explicit relations, and a
deep learning approach for non-explicit relations. Overall, our parser achieves
an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
explicit relations and 0.1219 for non-explicit relations) on the blind
CoNLL-2016 test set.
",1,0,0,0,0,0
17601,Ordinary differential equations in algebras of generalized functions,"  A local existence and uniqueness theorem for ODEs in the special algebra of
generalized functions is established, as well as versions including parameters
and dependence on initial values in the generalized sense. Finally, a Frobenius
theorem is proved. In all these results, composition of generalized functions
is based on the notion of c-boundedness.
",0,0,1,0,0,0
17602,Interesting Paths in the Mapper,"  The Mapper produces a compact summary of high dimensional data as a
simplicial complex. We study the problem of quantifying the interestingness of
subpopulations in a Mapper, which appear as long paths, flares, or loops.
First, we create a weighted directed graph G using the 1-skeleton of the
Mapper. We use the average values at the vertices of a target function to
direct edges (from low to high). The difference between the average values at
vertices (high-low) is set as the edge's weight. Covariation of the remaining h
functions (independent variables) is captured by a h-bit binary signature
assigned to the edge. An interesting path in G is a directed path whose edges
all have the same signature. We define the interestingness score of such a path
as a sum of its edge weights multiplied by a nonlinear function of their ranks
in the path.
Second, we study three optimization problems on this graph G. In the problem
Max-IP, we seek an interesting path in G with the maximum interestingness
score. We show that Max-IP is NP-complete. For the special case when G is a
directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial
time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G.
In the more general problem IP, the goal is to find a collection of
edge-disjoint interesting paths such that the overall sum of their
interestingness scores is maximized. We also study a variant of IP termed k-IP,
where the goal is to identify a collection of edge-disjoint interesting paths
each with k edges, and their total interestingness score is maximized. While
k-IP can be solved in polynomial time for k <= 2, we show k-IP is NP-complete
for k >= 3 even when G is a DAG. We develop polynomial time heuristics for IP
and k-IP on DAGs.
",1,0,1,0,0,0
17603,On a three dimensional vision based collision avoidance model,"  This paper presents a three dimensional collision avoidance approach for
aerial vehicles inspired by coordinated behaviors in biological groups. The
proposed strategy aims to enable a group of vehicles to converge to a common
destination point avoiding collisions with each other and with moving obstacles
in their environment. The interaction rules lead the agents to adapt their
velocity vectors through a modification of the relative bearing angle and the
relative elevation. Moreover the model satisfies the limited field of view
constraints resulting from individual perception sensitivity. From the proposed
individual based model, a mean-field kinetic model is derived. Simulations are
performed to show the effectiveness of the proposed model.
",0,0,1,0,0,0
17604,Algorithms for Covering Multiple Barriers,"  In this paper, we consider the problems for covering multiple intervals on a
line. Given a set $B$ of $m$ line segments (called ""barriers"") on a horizontal
line $L$ and another set $S$ of $n$ horizontal line segments of the same length
in the plane, we want to move all segments of $S$ to $L$ so that their union
covers all barriers and the maximum movement of all segments of $S$ is
minimized. Previously, an $O(n^3\log n)$-time algorithm was given for the case
$m=1$. In this paper, we propose an $O(n^2\log n\log \log n+nm\log m)$-time
algorithm for a more general setting with any $m\geq 1$, which also improves
the previous work when $m=1$. We then consider a line-constrained version of
the problem in which the segments of $S$ are all initially on the line $L$.
Previously, an $O(n\log n)$-time algorithm was known for the case $m=1$. We
present an algorithm of $O(m\log m+n\log m \log n)$ time for any $m\geq 1$.
These problems may have applications in mobile sensor barrier coverage in
wireless sensor networks.
",1,0,0,0,0,0
17605,Shattering the glass ceiling? How the institutional context mitigates the gender gap in entrepreneurship,"  We examine how the institutional context affects the relationship between
gender and opportunity entrepreneurship. To do this, we develop a multi-level
model that connects feminist theory at the micro-level to institutional theory
at the macro-level. It is hypothesized that the gender gap in opportunity
entrepreneurship is more pronounced in low-quality institutional contexts and
less pronounced in high-quality institutional contexts. Using data from the
Global Entrepreneurship Monitor (GEM) and regulation data from the economic
freedom of the world index (EFW), we test our predictions and find evidence in
support of our model. Our findings suggest that, while there is a gender gap in
entrepreneurship, these disparities are reduced as the quality of the
institutional context improves.
",0,0,0,0,0,1
17606,An Automated Text Categorization Framework based on Hyperparameter Optimization,"  A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning problem, whereas others are specifically designed to tackle a
particular task, using complex and computational expensive processes such as
lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
propose a minimalistic and wide system able to tackle text classification tasks
independent of domain and language, namely microTC. It is composed by some easy
to implement text transformations, text representations, and a supervised
learning algorithm. These pieces produce a competitive classifier even in the
domain of informally written text. We provide a detailed description of microTC
along with an extensive experimental comparison with relevant state-of-the-art
methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
microTC obtained the best performance in 20 datasets while achieves competitive
results in the remaining 10. The compared datasets include several problems
like topic and polarity classification, spam detection, user profiling and
authorship attribution. Furthermore, it is important to state that our approach
allows the usage of the technology even without knowledge of machine learning
and natural language processing.
",1,0,0,1,0,0
17607,Abdominal aortic aneurysms and endovascular sealing: deformation and dynamic response,"  Endovascular sealing is a new technique for the repair of abdominal aortic
aneurysms. Commercially available in Europe since~2013, it takes a
revolutionary approach to aneurysm repair through minimally invasive
techniques. Although aneurysm sealing may be thought as more stable than
conventional endovascular stent graft repairs, post-implantation movement of
the endoprosthesis has been described, potentially leading to late
complications. The paper presents for the first time a model, which explains
the nature of forces, in static and dynamic regimes, acting on sealed abdominal
aortic aneurysms, with references to real case studies. It is shown that
elastic deformation of the aorta and of the endoprosthesis induced by static
forces and vibrations during daily activities can potentially promote undesired
movements of the endovascular sealing structure.
",0,1,0,0,0,0
17608,Affinity Scheduling and the Applications on Data Center Scheduling with Data Locality,"  MapReduce framework is the de facto standard in Hadoop. Considering the data
locality in data centers, the load balancing problem of map tasks is a special
case of affinity scheduling problem. There is a huge body of work on affinity
scheduling, proposing heuristic algorithms which try to increase data locality
in data centers like Delay Scheduling and Quincy. However, not enough attention
has been put on theoretical guarantees on throughput and delay optimality of
such algorithms. In this work, we present and compare different algorithms and
discuss their shortcoming and strengths. To the best of our knowledge, most
data centers are using static load balancing algorithms which are not efficient
in any ways and results in wasting the resources and causing unnecessary delays
for users.
",1,0,0,0,0,0
17609,Multivariate Regression with Gross Errors on Manifold-valued Data,"  We consider the topic of multivariate regression on manifold-valued output,
that is, for a multivariate observation, its output response lies on a
manifold. Moreover, we propose a new regression model to deal with the presence
of grossly corrupted manifold-valued responses, a bottleneck issue commonly
encountered in practical scenarios. Our model first takes a correction step on
the grossly corrupted responses via geodesic curves on the manifold, and then
performs multivariate linear regression on the corrected data. This results in
a nonconvex and nonsmooth optimization problem on manifolds. To this end, we
propose a dedicated approach named PALMR, by utilizing and extending the
proximal alternating linearized minimization techniques. Theoretically, we
investigate its convergence property, where it is shown to converge to a
critical point under mild conditions. Empirically, we test our model on both
synthetic and real diffusion tensor imaging data, and show that our model
outperforms other multivariate regression models when manifold-valued responses
contain gross errors, and is effective in identifying gross errors.
",1,0,1,1,0,0
17610,Computing an Approximately Optimal Agreeable Set of Items,"  We study the problem of finding a small subset of items that is
\emph{agreeable} to all agents, meaning that all agents value the subset at
least as much as its complement. Previous work has shown worst-case bounds,
over all instances with a given number of agents and items, on the number of
items that may need to be included in such a subset. Our goal in this paper is
to efficiently compute an agreeable subset whose size approximates the size of
the smallest agreeable subset for a given instance. We consider three
well-known models for representing the preferences of the agents: ordinal
preferences on single items, the value oracle model, and additive utilities. In
each of these models, we establish virtually tight bounds on the approximation
ratio that can be obtained by algorithms running in polynomial time.
",1,0,0,0,0,0
17611,3D Sketching using Multi-View Deep Volumetric Prediction,"  Sketch-based modeling strives to bring the ease and immediacy of drawing to
the 3D world. However, while drawings are easy for humans to create, they are
very challenging for computers to interpret due to their sparsity and
ambiguity. We propose a data-driven approach that tackles this challenge by
learning to reconstruct 3D shapes from one or more drawings. At the core of our
approach is a deep convolutional neural network (CNN) that predicts occupancy
of a voxel grid from a line drawing. This CNN provides us with an initial 3D
reconstruction as soon as the user completes a single drawing of the desired
shape. We complement this single-view network with an updater CNN that refines
an existing prediction given a new drawing of the shape created from a novel
viewpoint. A key advantage of our approach is that we can apply the updater
iteratively to fuse information from an arbitrary number of viewpoints, without
requiring explicit stroke correspondences between the drawings. We train both
CNNs by rendering synthetic contour drawings from hand-modeled shape
collections as well as from procedurally-generated abstract shapes. Finally, we
integrate our CNNs in a minimal modeling interface that allows users to
seamlessly draw an object, rotate it to see its 3D reconstruction, and refine
it by re-drawing from another vantage point using the 3D reconstruction as
guidance. The main strengths of our approach are its robustness to freehand
bitmap drawings, its ability to adapt to different object categories, and the
continuum it offers between single-view and multi-view sketch-based modeling.
",1,0,0,0,0,0
17612,Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier,"  We study the problem of ranking a set of items from nonactively chosen
pairwise preferences where each item has feature information with it. We
propose and characterize a very broad class of preference matrices giving rise
to the Feature Low Rank (FLR) model, which subsumes several models ranging from
the classic Bradley-Terry-Luce (BTL) (Bradley and Terry 1952) and Thurstone
(Thurstone 1927) models to the recently proposed blade-chest (Chen and Joachims
2016) and generic low-rank preference (Rajkumar and Agarwal 2016) models. We
use the technique of matrix completion in the presence of side information to
develop the Inductive Pairwise Ranking (IPR) algorithm that provably learns a
good ranking under the FLR model, in a sample-efficient manner. In practice,
through systematic synthetic simulations, we confirm our theoretical findings
regarding improvements in the sample complexity due to the use of feature
information. Moreover, on popular real-world preference learning datasets, with
as less as 10% sampling of the pairwise comparisons, our method recovers a good
ranking.
",1,0,0,1,0,0
17613,SAGA and Restricted Strong Convexity,"  SAGA is a fast incremental gradient method on the finite sum problem and its
effectiveness has been tested on a vast of applications. In this paper, we
analyze SAGA on a class of non-strongly convex and non-convex statistical
problem such as Lasso, group Lasso, Logistic regression with $\ell_1$
regularization, linear regression with SCAD regularization and Correct Lasso.
We prove that SAGA enjoys the linear convergence rate up to the statistical
estimation accuracy, under the assumption of restricted strong convexity (RSC).
It significantly extends the applicability of SAGA in convex and non-convex
optimization.
",0,0,0,1,0,0
17614,Characterization of Traps at Nitrided SiO$_2$/SiC Interfaces near the Conduction Band Edge by using Hall Effect Measurements,"  The effects of nitridation on the density of traps at SiO$_2$/SiC interfaces
near the conduction band edge were qualitatively examined by a simple, newly
developed characterization method that utilizes Hall effect measurements and
split capacitance-voltage measurements. The results showed a significant
reduction in the density of interface traps near the conduction band edge by
nitridation, as well as the high density of interface traps that was not
eliminated by nitridation.
",0,1,0,0,0,0
17615,Response theory of the ergodic many-body delocalized phase: Keldysh Finkel'stein sigma models and the 10-fold way,"  We derive the finite temperature Keldysh response theory for interacting
fermions in the presence of quenched disorder, as applicable to any of the 10
Altland-Zirnbauer classes in an Anderson delocalized phase with at least a U(1)
continuous symmetry. In this formulation of the interacting Finkel'stein
nonlinear sigma model, the statistics of one-body wave functions are encoded by
the constrained matrix field, while physical correlations follow from the
hydrodynamic density or spin response field, which decouples the interactions.
Integrating out the matrix field first, we obtain weak (anti)localization and
Altshuler-Aronov quantum conductance corrections from the hydrodynamic response
function. This procedure automatically incorporates the correct infrared
physics, and in particular gives the Altshuler-Aronov-Khmelnitsky (AAK)
equations for dephasing of weak (anti)localization due to electron-electron
collisions. We explicate the method by deriving known quantum corrections in
two dimensions for the symplectic metal class AII, as well as the spin-SU(2)
invariant superconductor classes C and CI. We show that conductance corrections
due to the special modes at zero energy in nonstandard classes are
automatically cut off by temperature, as previously expected, while the
Wigner-Dyson class Cooperon modes that persist to all energies are cut by
dephasing. We also show that for short-ranged interactions, the standard
self-consistent solution for the dephasing rate is equivalent to a diagrammatic
summation via the self-consistent Born approximation. This should be compared
to the AAK solution for long-ranged Coulomb interactions, which exploits the
Markovian noise correlations induced by thermal fluctuations of the
electromagnetic field. We discuss prospects for exploring the many-body
localization transition from the ergodic side as a dephasing catastrophe in
short-range interacting models.
",0,1,0,0,0,0
17616,Classification via Tensor Decompositions of Echo State Networks,"  This work introduces a tensor-based method to perform supervised
classification on spatiotemporal data processed in an echo state network.
Typically when performing supervised classification tasks on data processed in
an echo state network, the entire collection of hidden layer node states from
the training dataset is shaped into a matrix, allowing one to use standard
linear algebra techniques to train the output layer. However, the collection of
hidden layer states is multidimensional in nature, and representing it as a
matrix may lead to undesirable numerical conditions or loss of spatial and
temporal correlations in the data.
This work proposes a tensor-based supervised classification method on echo
state network data that preserves and exploits the multidimensional nature of
the hidden layer states. The method, which is based on orthogonal Tucker
decompositions of tensors, is compared with the standard linear output weight
approach in several numerical experiments on both synthetic and natural data.
The results show that the tensor-based approach tends to outperform the
standard approach in terms of classification accuracy.
",1,0,0,1,0,0
17617,Inference on Breakdown Frontiers,"  Given a set of baseline assumptions, a breakdown frontier is the boundary
between the set of assumptions which lead to a specific conclusion and those
which do not. In a potential outcomes model with a binary treatment, we
consider two conclusions: First, that ATE is at least a specific value (e.g.,
nonnegative) and second that the proportion of units who benefit from treatment
is at least a specific value (e.g., at least 50\%). For these conclusions, we
derive the breakdown frontier for two kinds of assumptions: one which indexes
relaxations of the baseline random assignment of treatment assumption, and one
which indexes relaxations of the baseline rank invariance assumption. These
classes of assumptions nest both the point identifying assumptions of random
assignment and rank invariance and the opposite end of no constraints on
treatment selection or the dependence structure between potential outcomes.
This frontier provides a quantitative measure of robustness of conclusions to
relaxations of the baseline point identifying assumptions. We derive
$\sqrt{N}$-consistent sample analog estimators for these frontiers. We then
provide two asymptotically valid bootstrap procedures for constructing lower
uniform confidence bands for the breakdown frontier. As a measure of
robustness, estimated breakdown frontiers and their corresponding confidence
bands can be presented alongside traditional point estimates and confidence
intervals obtained under point identifying assumptions. We illustrate this
approach in an empirical application to the effect of child soldiering on
wages. We find that sufficiently weak conclusions are robust to simultaneous
failures of rank invariance and random assignment, while some stronger
conclusions are fairly robust to failures of rank invariance but not
necessarily to relaxations of random assignment.
",0,0,0,1,0,0
17618,Sequential Detection of Three-Dimensional Signals under Dependent Noise,"  We study detection methods for multivariable signals under dependent noise.
The main focus is on three-dimensional signals, i.e. on signals in the
space-time domain. Examples for such signals are multifaceted. They include
geographic and climatic data as well as image data, that are observed over a
fixed time horizon. We assume that the signal is observed as a finite block of
noisy samples whereby we are interested in detecting changes from a given
reference signal. Our detector statistic is based on a sequential partial sum
process, related to classical signal decomposition and reconstruction
approaches applied to the sampled signal. We show that this detector process
converges weakly under the no change null hypothesis that the signal coincides
with the reference signal, provided that the spatial-temporal partial sum
process associated to the random field of the noise terms disturbing the
sampled signal con- verges to a Brownian motion. More generally, we also
establish the limiting distribution under a wide class of local alternatives
that allows for smooth as well as discontinuous changes. Our results also cover
extensions to the case that the reference signal is unknown. We conclude with
an extensive simulation study of the detection algorithm.
",0,0,1,1,0,0
17619,T-Branes at the Limits of Geometry,"  Singular limits of 6D F-theory compactifications are often captured by
T-branes, namely a non-abelian configuration of intersecting 7-branes with a
nilpotent matrix of normal deformations. The long distance approximation of
such 7-branes is a Hitchin-like system in which simple and irregular poles
emerge at marked points of the geometry. When multiple matter fields localize
at the same point in the geometry, the associated Higgs field can exhibit
irregular behavior, namely poles of order greater than one. This provides a
geometric mechanism to engineer wild Higgs bundles. Physical constraints such
as anomaly cancellation and consistent coupling to gravity also limit the order
of such poles. Using this geometric formulation, we unify seemingly different
wild Hitchin systems in a single framework in which orders of poles become
adjustable parameters dictated by tuning gauge singlet moduli of the F-theory
model.
",0,0,1,0,0,0
17620,Space-time crystal and space-time group,"  Crystal structures and the Bloch theorem play a fundamental role in condensed
matter physics. We extend the static crystal to the dynamic ""space-time""
crystal characterized by the general intertwined space-time periodicities in
$D+1$ dimensions, which include both the static crystal and the Floquet crystal
as special cases. A new group structure dubbed ""space-time"" group is
constructed to describe the discrete symmetries of space-time crystal. Compared
to space and magnetic groups, space-time group is augmented by ""time-screw""
rotations and ""time-glide"" reflections involving fractional translations along
the time direction. A complete classification of the 13 space-time groups in
1+1D is performed. The Kramers-type degeneracy can arise from the glide
time-reversal symmetry without the half-integer spinor structure, which
constrains the winding number patterns of spectral dispersions. In 2+1D,
non-symmorphic space-time symmetries enforce spectral degeneracies, leading to
protected Floquet semi-metal states. Our work provides a general framework for
further studying topological properties of the $D+1$ dimensional space-time
crystal.
",0,1,0,0,0,0
17621,On the Performance of Zero-Forcing Processing in Multi-Way Massive MIMO Relay Networks,"  We consider a multi-way massive multiple-input multiple-output relay network
with zero-forcing processing at the relay. By taking into account the
time-division duplex protocol with channel estimation, we derive an analytical
approximation of the spectral efficiency. This approximation is very tight and
simple which enables us to analyze the system performance, as well as, to
compare the spectral efficiency with zero-forcing and maximum-ratio processing.
Our results show that by using a very large number of relay antennas and with
the zero-forcing technique, we can simultaneously serve many active users in
the same time-frequency resource, each with high spectral efficiency.
",1,0,1,0,0,0
17622,Motivic rational homotopy type,"  In this paper we introduce and study motives for rational homotopy types.
",0,0,1,0,0,0
17623,Preconditioner-free Wiener filtering with a dense noise matrix,"  This work extends the Elsner & Wandelt (2013) iterative method for efficient,
preconditioner-free Wiener filtering to cases in which the noise covariance
matrix is dense, but can be decomposed into a sum whose parts are sparse in
convenient bases. The new method, which uses multiple messenger fields,
reproduces Wiener filter solutions for test problems, and we apply it to a case
beyond the reach of the Elsner & Wandelt (2013) method. We compute the Wiener
filter solution for a simulated Cosmic Microwave Background map that contains
spatially-varying, uncorrelated noise, isotropic $1/f$ noise, and large-scale
horizontal stripes (like those caused by the atmospheric noise). We discuss
simple extensions that can filter contaminated modes or inverse-noise filter
the data. These techniques help to address complications in the noise
properties of maps from current and future generations of ground-based
Microwave Background experiments, like Advanced ACTPol, Simons Observatory, and
CMB-S4.
",0,1,0,0,0,0
17624,"Order-unity argument for structure-generated ""extra"" expansion","  Self-consistent treatment of cosmological structure formation and expansion
within the context of classical general relativity may lead to ""extra""
expansion above that expected in a structureless universe. We argue that in
comparison to an early-epoch, extrapolated Einstein-de Sitter model, about
10-15% ""extra"" expansion is sufficient at the present to render superfluous the
""dark energy"" 68% contribution to the energy density budget, and that this is
observationally realistic.
",0,1,0,0,0,0
17625,The least unramified prime which does not split completely,"  Let $K/F$ be a finite extension of number fields of degree $n \geq 2$. We
establish effective field-uniform unconditional upper bounds for the least norm
of a prime ideal of $F$ which is degree 1 over $\mathbb{Q}$ and does not ramify
or split completely in $K$. We improve upon the previous best known general
estimates due to X. Li when $F = \mathbb{Q}$ and Murty-Patankar when $K/F$ is
Galois. Our bounds are the first when $K/F$ is not assumed to be Galois and $F
\neq \mathbb{Q}$.
",0,0,1,0,0,0
17626,Crosscorrelation of Rudin-Shapiro-Like Polynomials,"  We consider the class of Rudin-Shapiro-like polynomials, whose $L^4$ norms on
the complex unit circle were studied by Borwein and Mossinghoff. The polynomial
$f(z)=f_0+f_1 z + \cdots + f_d z^d$ is identified with the sequence
$(f_0,f_1,\ldots,f_d)$ of its coefficients. From the $L^4$ norm of a
polynomial, one can easily calculate the autocorrelation merit factor of its
associated sequence, and conversely. In this paper, we study the
crosscorrelation properties of pairs of sequences associated to
Rudin-Shapiro-like polynomials. We find an explicit formula for the
crosscorrelation merit factor. A computer search is then used to find pairs of
Rudin-Shapiro-like polynomials whose autocorrelation and crosscorrelation merit
factors are simultaneously high. Pursley and Sarwate proved a bound that limits
how good this combined autocorrelation and crosscorrelation performance can be.
We find infinite families of polynomials whose performance approaches quite
close to this fundamental limit.
",1,0,1,0,0,0
17627,An Application of Rubi: Series Expansion of the Quark Mass Renormalization Group Equation,"  We highlight how Rule-based Integration (Rubi) is an enhanced method of
symbolic integration which allows for the integration of many difficult
integrals not accomplished by other computer algebra systems. Using Rubi, many
integration techniques become tractable. Integrals are approached using
step-wise simplification, hence distilling an integral (if the solution is
unknown) into composite integrals which highlight yet undiscovered integration
rules. The motivating example we use is the derivation of the updated series
expansion of the quark mass renormalization group equation (RGE) to five-loop
order. This series provides the relation between a light quark mass in the
modified minimal subtraction ($\overline{\text{MS}}$) scheme defined at some
given scale, e.g. at the tau-lepton mass scale, and another chosen energy
scale, $s$. This relation explicitly depicts the renormalization scheme
dependence of the running quark mass on the scale parameter, $s$, and is
important in accurately determining a light quark mass at a chosen scale. The
five-loop QCD $\beta(a_s)$ and $\gamma(a_s)$ functions are used in this
determination.
",1,0,0,0,0,0
17628,On the Performance of Wireless Powered Communication With Non-linear Energy Harvesting,"  In this paper, we analyze the performance of a time-slotted multi-antenna
wireless powered communication (WPC) system, where a wireless device first
harvests radio frequency (RF) energy from a power station (PS) in the downlink
to facilitate information transfer to an information receiving station (IRS) in
the uplink. The main goal of this paper is to provide insights and guidelines
for the design of practical WPC systems. To this end, we adopt a recently
proposed parametric non-linear RF energy harvesting (EH) model, which has been
shown to accurately model the end-to-end non-linearity of practical RF EH
circuits. In order to enhance the RF power transfer efficiency, maximum ratio
transmission is adopted at the PS to focus the energy signals on the wireless
device. Furthermore, at the IRS, maximum ratio combining is used. We analyze
the outage probability and the average throughput of information transfer,
assuming Nakagami-$m$ fading uplink and downlink channels. Moreover, we study
the system performance as a function of the number of PS transmit antennas, the
number of IRS receive antennas, the transmit power of the PS, the fading
severity, the transmission rate of the wireless device, and the EH time
duration. In addition, we obtain a fixed point equation for the optimal
transmission rate and the optimal EH time duration that maximize the asymptotic
throughput for high PS transmit powers. All analytical results are corroborated
by simulations.
",1,0,0,0,0,0
17629,Realizing polarization conversion and unidirectional transmission by using a uniaxial crystal plate,"  We show that polarization states of electromagnetic waves can be manipulated
easily using a single thin uniaxial crystal plate. By performing a rotational
transformation of the coordinates and controlling the thickness of the plate,
we can achieve a complete polarization conversion between TE wave and TM wave
in a spectral band. We show that the off-diagonal element of the permittivity
is the key for polarization conversion. Our analysis can explain clearly the
results found in experiments with metamaterials. Finally, we propose a simple
device to realize unidirectional transmission based on polarization conversion
and excitation of surface plasmon polaritons.
",0,1,0,0,0,0
17630,When Should You Adjust Standard Errors for Clustering?,"  In empirical work in economics it is common to report standard errors that
account for clustering of units. Typically, the motivation given for the
clustering adjustments is that unobserved components in outcomes for units
within clusters are correlated. However, because correlation may occur across
more than one dimension, this motivation makes it difficult to justify why
researchers use clustering in some dimensions, such as geographic, but not
others, such as age cohorts or gender. It also makes it difficult to explain
why one should not cluster with data from a randomized experiment. In this
paper, we argue that clustering is in essence a design problem, either a
sampling design or an experimental design issue. It is a sampling design issue
if sampling follows a two stage process where in the first stage, a subset of
clusters were sampled randomly from a population of clusters, while in the
second stage, units were sampled randomly from the sampled clusters. In this
case the clustering adjustment is justified by the fact that there are clusters
in the population that we do not see in the sample. Clustering is an
experimental design issue if the assignment is correlated within the clusters.
We take the view that this second perspective best fits the typical setting in
economics where clustering adjustments are used. This perspective allows us to
shed new light on three questions: (i) when should one adjust the standard
errors for clustering, (ii) when is the conventional adjustment for clustering
appropriate, and (iii) when does the conventional adjustment of the standard
errors matter.
",0,0,1,1,0,0
17631,Approximate homomorphisms on lattices,"  We prove two results concerning an Ulam-type stability problem for
homomorphisms between lattices. One of them involves estimates by quite general
error functions; the other deals with approximate (join) homomorphisms in terms
of certain systems of lattice neighborhoods. As a corollary, we obtain a
stability result for approximately monotone functions.
",0,0,1,0,0,0
17632,Learning Latent Events from Network Message Logs: A Decomposition Based Approach,"  In this communication, we describe a novel technique for event mining using a
decomposition based approach that combines non-parametric change-point
detection with LDA. We prove theoretical guarantees about sample-complexity and
consistency of the approach. In a companion paper, we will perform a thorough
evaluation of our approach with detailed experiments.
",0,0,0,1,0,0
17633,Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,"  Despite significant advances in artificial intelligence (AI) for computer
vision, its application in medical imaging has been limited by the burden and
limits of expert-generated labels. We used images from optical coherence
tomography angiography (OCTA), a relatively new imaging modality that measures
perfusion of the retinal vasculature, to train an AI algorithm to generate
vasculature maps from standard structural optical coherence tomography (OCT)
images of the same retinae, both exceeding the ability and bypassing the need
for expert labeling. Deep learning was able to infer perfusion of
microvasculature from structural OCT images with similar fidelity to OCTA and
significantly better than expert clinicians (P < 0.00001). OCTA suffers from
need of specialized hardware, laborious acquisition protocols, and motion
artifacts; whereas our model works directly from standard OCT which are
ubiquitous and quick to obtain, and allows unlocking of large volumes of
previously collected standard OCT data both in existing clinical trials and
clinical practice. This finding demonstrates a novel application of AI to
medical imaging, whereby subtle regularities between different modalities are
used to image the same body part and AI is used to generate detailed and
accurate inferences of tissue function from structure imaging.
",0,0,0,1,0,0
17634,Varieties with Ample Tangent Sheaves,"  This paper generalises Mori's famous theorem about ""Projective manifolds with
ample tangent bundles"" to normal projective varieties in the following way:
A normal projective variety over $\mathbb{C}$ with ample tangent sheaf is
isomorphic to the complex projective space.
",0,0,1,0,0,0
17635,Water sub-diffusion in membranes for fuel cells,"  We investigate the dynamics of water confined in soft ionic nano-assemblies,
an issue critical for a general understanding of the multi-scale
structure-function interplay in advanced materials. We focus in particular on
hydrated perfluoro-sulfonic acid compounds employed as electrolytes in fuel
cells. These materials form phase-separated morphologies that show outstanding
proton-conducting properties, directly related to the state and dynamics of the
absorbed water. We have quantified water motion and ion transport by combining
Quasi Elastic Neutron Scattering, Pulsed Field Gradient Nuclear Magnetic
Resonance, and Molecular Dynamics computer simulation. Effective water and ion
diffusion coefficients have been determined together with their variation upon
hydration at the relevant atomic, nanoscopic and macroscopic scales, providing
a complete picture of transport. We demonstrate that confinement at the
nanoscale and direct interaction with the charged interfaces produce anomalous
sub-diffusion, due to a heterogeneous space-dependent dynamics within the ionic
nanochannels. This is irrespective of the details of the chemistry of the
hydrophobic confining matrix, confirming the statistical significance of our
conclusions. Our findings turn out to indicate interesting connections and
possibilities of cross-fertilization with other domains, including biophysics.
They also establish fruitful correspondences with advanced topics in
statistical mechanics, resulting in new possibilities for the analysis of
Neutron scattering data.
",0,1,0,0,0,0
17636,Global Strong Solution of a 2D coupled Parabolic-Hyperbolic Magnetohydrodynamic System,"  The main objective of this paper is to study the global strong solution of
the parabolic-hyperbolic incompressible magnetohydrodynamic (MHD) model in two
dimensional space. Based on Agmon, Douglis and Nirenberg's estimates for the
stationary Stokes equation and the Solonnikov's theorem of
$L^p$-$L^q$-estimates for the evolution Stokes equation, it is shown that the
mixed-type MHD equations exist a global strong solution.
",0,0,1,0,0,0
17637,Subcritical thermal convection of liquid metals in a rapidly rotating sphere,"  Planetary cores consist of liquid metals (low Prandtl number $Pr$) that
convect as the core cools. Here we study nonlinear convection in a rotating
(low Ekman number $Ek$) planetary core using a fully 3D direct numerical
simulation. Near the critical thermal forcing (Rayleigh number $Ra$),
convection onsets as thermal Rossby waves, but as the $Ra$ increases, this
state is superceded by one dominated by advection. At moderate rotation, these
states (here called the weak branch and strong branch, respectively) are
smoothly connected. As the planetary core rotates faster, the smooth transition
is replaced by hysteresis cycles and subcriticality until the weak branch
disappears entirely and the strong branch onsets in a turbulent state at $Ek <
10^{-6}$. Here the strong branch persists even as the thermal forcing drops
well below the linear onset of convection ($Ra=0.7Ra_{crit}$ in this study). We
highlight the importance of the Reynolds stress, which is required for
convection to subsist below the linear onset. In addition, the Péclet number
is consistently above 10 in the strong branch. We further note the presence of
a strong zonal flow that is nonetheless unimportant to the convective state.
Our study suggests that, in the asymptotic regime of rapid rotation relevant
for planetary interiors, thermal convection of liquid metals in a sphere onsets
through a subcritical bifurcation.
",0,1,0,0,0,0
17638,Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour ?,"  Followership is generally defined as a strategy that evolved to solve social
coordination problems, and particularly those involved in group movement.
Followership behaviour is particularly interesting in the context of
road-crossing behaviour because it involves other principles such as
risk-taking and evaluating the value of social information. This study sought
to identify the cognitive mechanisms underlying decision-making by pedestrians
who follow another person across the road at the green or at the red light in
two different countries (France and Japan). We used agent-based modelling to
simulate the road-crossing behaviours of pedestrians. This study showed that
modelling is a reliable means to test different hypotheses and find the exact
processes underlying decision-making when crossing the road. We found that two
processes suffice to simulate pedestrian behaviours. Importantly, the study
revealed differences between the two nationalities and between sexes in the
decision to follow and cross at the green and at the red light. Japanese
pedestrians are particularly attentive to the number of already departed
pedestrians and the number of waiting pedestrians at the red light, whilst
their French counterparts only consider the number of pedestrians that have
already stepped off the kerb, thus showing the strong conformism of Japanese
people. Finally, the simulations are revealed to be similar to observations,
not only for the departure latencies but also for the number of crossing
pedestrians and the rates of illegal crossings. The conclusion suggests new
solutions for safety in transportation research.
",0,0,0,0,1,0
17639,Driven by Excess? Climatic Implications of New Global Mapping of Near-Surface Water-Equivalent Hydrogen on Mars,"  We present improved Mars Odyssey Neutron Spectrometer (MONS) maps of
near-surface Water-Equivalent Hydrogen (WEH) on Mars that have intriguing
implications for the global distribution of ""excess"" ice, which occurs when the
mass fraction of water ice exceeds the threshold amount needed to saturate the
pore volume in normal soils. We have refined the crossover technique of Feldman
et al. (2011) by using spatial deconvolution and Gaussian weighting to create
the first globally self-consistent map of WEH. At low latitudes, our new maps
indicate that WEH exceeds 15% in several near-equatorial regions, such as
Arabia Terra, which has important implications for the types of hydrated
minerals present at low latitudes. At high latitudes, we demonstrate that the
disparate MONS and Phoenix Robotic Arm (RA) observations of near surface WEH
can be reconciled by a three-layer model incorporating dry soil over fully
saturated pore ice over pure excess ice: such a three-layer model can also
potentially explain the strong anticorrelation of subsurface ice content and
ice table depth observed at high latitudes. At moderate latitudes, we show that
the distribution of recently formed impact craters is also consistent with our
latest MONS results, as both the shallowest ice-exposing crater and deepest
non-ice-exposing crater at each impact site are in good agreement with our
predictions of near-surface WEH. Overall, we find that our new mapping is
consistent with the widespread presence at mid-to-high Martian latitudes of
recently deposited shallow excess ice reservoirs that are not yet in
equilibrium with the atmosphere.
",0,1,0,0,0,0
17640,On distribution of points with conjugate algebraic integer coordinates close to planar curves,"  Let $\varphi:\mathbb{R}\rightarrow \mathbb{R}$ be a continuously
differentiable function on an interval $J\subset\mathbb{R}$ and let
$\boldsymbol{\alpha}=(\alpha_1,\alpha_2)$ be a point with algebraic conjugate
integer coordinates of degree $\leq n$ and of height $\leq Q$. Denote by
$\tilde{M}^n_\varphi(Q,\gamma, J)$ the set of points $\boldsymbol{\alpha}$ such
that $|\varphi(\alpha_1)-\alpha_2|\leq c_1 Q^{-\gamma}$. In this paper we show
that for a real $0<\gamma<1$ and any sufficiently large $Q$ there exist
positive values $c_2<c_3$, which are independent of $Q$, such that $c_2\cdot
Q^{n-\gamma}<# \tilde{M}^n_\varphi(Q,\gamma, J)< c_3\cdot Q^{n-\gamma}$.
",0,0,1,0,0,0
17641,A Critical Investigation of Deep Reinforcement Learning for Navigation,"  The navigation problem is classically approached in two steps: an exploration
step, where map-information about the environment is gathered; and an
exploitation step, where this information is used to navigate efficiently. Deep
reinforcement learning (DRL) algorithms, alternatively, approach the problem of
navigation in an end-to-end fashion. Inspired by the classical approach, we ask
whether DRL algorithms are able to inherently explore, gather and exploit
map-information over the course of navigation. We build upon Mirowski et al.
[2017] work and introduce a systematic suite of experiments that vary three
parameters: the agent's starting location, the agent's target location, and the
maze structure. We choose evaluation metrics that explicitly measure the
algorithm's ability to gather and exploit map-information. Our experiments show
that when trained and tested on the same maps, the algorithm successfully
gathers and exploits map-information. However, when trained and tested on
different sets of maps, the algorithm fails to transfer the ability to gather
and exploit map-information to unseen maps. Furthermore, we find that when the
goal location is randomized and the map is kept static, the algorithm is able
to gather and exploit map-information but the exploitation is far from optimal.
We open-source our experimental suite in the hopes that it serves as a
framework for the comparison of future algorithms and leads to the discovery of
robust alternatives to classical navigation methods.
",1,0,0,0,0,0
17642,Performance Evaluation of 3D Correspondence Grouping Algorithms,"  This paper presents a thorough evaluation of several widely-used 3D
correspondence grouping algorithms, motived by their significance in vision
tasks relying on correct feature correspondences. A good correspondence
grouping algorithm is desired to retrieve as many as inliers from initial
feature matches, giving a rise in both precision and recall. Towards this rule,
we deploy the experiments on three benchmarks respectively addressing shape
retrieval, 3D object recognition and point cloud registration scenarios. The
variety in application context brings a rich category of nuisances including
noise, varying point densities, clutter, occlusion and partial overlaps. It
also results to different ratios of inliers and correspondence distributions
for comprehensive evaluation. Based on the quantitative outcomes, we give a
summarization of the merits/demerits of the evaluated algorithms from both
performance and efficiency perspectives.
",1,0,0,0,0,0
17643,DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car,"  We present DeepPicar, a low-cost deep neural network based autonomous car
platform. DeepPicar is a small scale replication of a real self-driving car
called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
which takes images from a front-facing camera as input and produces car
steering angles as output. DeepPicar uses the same network architecture---9
layers, 27 million connections and 250K parameters---and can drive itself in
real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
deep learning based real-time control of autonomous vehicles. We also
systematically compare other contemporary embedded computing platforms using
the DeepPicar's CNN-based real-time control workload. We find that all tested
platforms, including the Pi 3, are capable of supporting the CNN-based
real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
However, we find that shared resource contention remains an important issue
that must be considered in applying CNN models on shared memory based embedded
computing platforms; we observe up to 11.6X execution time increase in the CNN
based control loop due to shared resource contention. To protect the CNN
workload, we also evaluate state-of-the-art cache partitioning and memory
bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
ineffective, while memory bandwidth throttling is an effective solution.
",1,0,0,0,0,0
17644,Statistics of turbulence in the energy-containing range of Taylor-Couette compared to canonical wall-bounded flows,"  Considering structure functions of the streamwise velocity component in a
framework akin to the extended self-similarity hypothesis (ESS), de Silva
\textit{et al.} (\textit{J. Fluid Mech.}, vol. 823,2017, pp. 498-510) observed
that remarkably the \textit{large-scale} (energy-containing range) statistics
in canonical wall bounded flows exhibit universal behaviour. In the present
study, we extend this universality, which was seen to encompass also flows at
moderate Reynolds number, to Taylor-Couette flow. In doing so, we find that
also the transversal structure function of the spanwise velocity component
exhibits the same universal behaviour across all flow types considered. We
further demonstrate that these observations are consistent with predictions
developed based on an attached-eddy hypothesis. These considerations also yield
a possible explanation for the efficacy of the ESS framework by showing that it
relaxes the self-similarity assumption for the attached eddy contributions. By
taking the effect of streamwise alignment into account, the attached eddy model
predicts different behaviour for structure functions in the streamwise and in
the spanwise directions and that this effect cancels in the ESS-framework ---
both consistent with the data. Moreover, it is demonstrated here that also the
additive constants, which were previously believed to be flow dependent, are
indeed universal at least in turbulent boundary layers and pipe flow where
high-Reynolds number data are currently available.
",0,1,0,0,0,0
17645,Physics-Based Modeling of TID Induced Global Static Leakage in Different CMOS Circuits,"  Compact modeling of inter-device radiation-induced leakage underneath the
gateless thick STI oxide is presented and validated taking into account CMOS
technology and hardness parameters, dose-rate and annealing effects, and
dependence on electric modes under irradiation. It was shown that proposed
approach can be applied for description of dose dependent static leakage
currents in complex FPGA circuits.
",0,1,0,0,0,0
17646,A multiplier inclusion theorem on product domains,"  In this note it is shown that the class of all multipliers from the
$d$-parameter Hardy space $H^1_{\mathrm{prod}} (\mathbb{T}^d)$ to $L^2
(\mathbb{T}^d)$ is properly contained in the class of all multipliers from $L
\log^{d/2} L (\mathbb{T}^d)$ to $L^2(\mathbb{T}^d)$.
",0,0,1,0,0,0
17647,Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains,"  We consider using a battery storage system simultaneously for peak shaving
and frequency regulation through a joint optimization framework which captures
battery degradation, operational constraints and uncertainties in customer load
and regulation signals. Under this framework, using real data we show the
electricity bill of users can be reduced by up to 15\%. Furthermore, we
demonstrate that the saving from joint optimization is often larger than the
sum of the optimal savings when the battery is used for the two individual
applications. A simple threshold real-time algorithm is proposed and achieves
this super-linear gain. Compared to prior works that focused on using battery
storage systems for single applications, our results suggest that batteries can
achieve much larger economic benefits than previously thought if they jointly
provide multiple services.
",1,0,1,0,0,0
17648,Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks,"  The nervous system encodes continuous information from the environment in the
form of discrete spikes, and then decodes these to produce smooth motor
actions. Understanding how spikes integrate, represent, and process information
to produce behavior is one of the greatest challenges in neuroscience.
Information theory has the potential to help us address this challenge.
Informational analyses of deep and feed-forward artificial neural networks
solving static input-output tasks, have led to the proposal of the
\emph{Information Bottleneck} principle, which states that deeper layers encode
more relevant yet minimal information about the inputs. Such an analyses on
networks that are recurrent, spiking, and perform control tasks is relatively
unexplored. Here, we present results from a Mutual Information analysis of a
recurrent spiking neural network that was evolved to perform the classic
pole-balancing task. Our results show that these networks deviate from the
\emph{Information Bottleneck} principle prescribed for feed-forward networks.
",1,0,0,0,0,0
17649,Physical Properties of Sub-galactic Clumps at 0.5 $\leq z \leq$ 1.5 in the UVUDF,"  We present an investigation of clumpy galaxies in the Hubble Ultra Deep Field
at 0.5 $\leq z \leq$ 1.5 in the rest-frame far-ultraviolet (FUV) using HST WFC3
broadband imaging in F225W, F275W, and F336W. An analysis of 1,404 galaxies
yields 209 galaxies that host 403 kpc-scale clumps. These host galaxies appear
to be typical star-forming galaxies, with an average of 2 clumps per galaxy and
reaching a maximum of 8 clumps. We measure the photometry of the clumps, and
determine the mass, age, and star formation rates (SFR) utilizing the
SED-fitting code FAST. We find that clumps make an average contribution of 19%
to the total rest-frame FUV flux of their host galaxy. Individually, clumps
contribute a median of 5% to the host galaxy SFR and an average of $\sim$4% to
the host galaxy mass, with total clump contributions to the host galaxy stellar
mass ranging widely from less than 1% up to 93%. Clumps in the outskirts of
galaxies are typically younger, with higher star formation rates, than clumps
in the inner regions. The results are consistent with clump migration theories
in which clumps form through violent gravitational instabilities in gas-rich
turbulent disks, eventually migrate toward the center of the galaxies, and
coalesce into the bulge.
",0,1,0,0,0,0
17650,Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference,"  We propose the use of incomplete dot products (IDP) to dynamically adjust the
number of input channels used in each layer of a convolutional neural network
during feedforward inference. IDP adds monotonically non-increasing
coefficients, referred to as a ""profile"", to the channels during training. The
profile orders the contribution of each channel in non-increasing order. At
inference time, the number of channels used can be dynamically adjusted to
trade off accuracy for lowered power consumption and reduced latency by
selecting only a beginning subset of channels. This approach allows for a
single network to dynamically scale over a computation range, as opposed to
training and deploying multiple networks to support different levels of
computation scaling. Additionally, we extend the notion to multiple profiles,
each optimized for some specific range of computation scaling. We present
experiments on the computation and accuracy trade-offs of IDP for popular image
classification models and datasets. We demonstrate that, for MNIST and
CIFAR-10, IDP reduces computation significantly, e.g., by 75%, without
significantly compromising accuracy. We argue that IDP provides a convenient
and effective means for devices to lower computation costs dynamically to
reflect the current computation budget of the system. For example, VGG-16 with
50% IDP (using only the first 50% of channels) achieves 70% in accuracy on the
CIFAR-10 dataset compared to the standard network which achieves only 35%
accuracy when using the reduced channel set.
",1,0,0,1,0,0
17651,Graded components of Local cohomology modules II,"  Let $A$ be a commutative Noetherian ring containing a field $K$ of
characteristic zero and let $R= A[X_1, \ldots, X_m]$. Consider $R$ as standard
graded with $°A=0$ and $°X_i=1$ for all $i$. We present a few results
about the behavior of the graded components of local cohomology modules
$H_I^i(R)$ where $I$ is an arbitrary homogeneous ideal in $R$. We mostly
restrict our attention to the Vanishing, Tameness and Rigidity problems.
",0,0,1,0,0,0
17652,Four-Dimensional Painlevé-Type Equations Associated with Ramified Linear Equations III: Garnier Systems and Fuji-Suzuki Systems,"  This is the last part of a series of three papers entitled ""Four-dimensional
Painlevé-type equations associated with ramified linear equations"". In this
series of papers we aim to construct the complete degeneration scheme of
four-dimensional Painlevé-type equations. In the present paper, we consider
the degeneration of the Garnier system in two variables and the Fuji-Suzuki
system.
",0,0,1,0,0,0
17653,You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction,"  In the cost per click (CPC) pricing model, an advertiser pays an ad network
only when a user clicks on an ad; in turn, the ad network gives a share of that
revenue to the publisher where the ad was impressed. Still, advertisers may be
unsatisfied with ad networks charging them for ""valueless"" clicks, or so-called
accidental clicks. [...] Charging advertisers for such clicks is detrimental in
the long term as the advertiser may decide to run their campaigns on other ad
networks. In addition, machine-learned click models trained to predict which ad
will bring the highest revenue may overestimate an ad click-through rate, and
as a consequence negatively impacting revenue for both the ad network and the
publisher. In this work, we propose a data-driven method to detect accidental
clicks from the perspective of the ad network. We collect observations of time
spent by users on a large set of ad landing pages - i.e., dwell time. We notice
that the majority of per-ad distributions of dwell time fit to a mixture of
distributions, where each component may correspond to a particular type of
clicks, the first one being accidental. We then estimate dwell time thresholds
of accidental clicks from that component. Using our method to identify
accidental clicks, we then propose a technique that smoothly discounts the
advertiser's cost of accidental clicks at billing time. Experiments conducted
on a large dataset of ads served on Yahoo mobile apps confirm that our
thresholds are stable over time, and revenue loss in the short term is
marginal. We also compare the performance of an existing machine-learned click
model trained on all ad clicks with that of the same model trained only on
non-accidental clicks. There, we observe an increase in both ad click-through
rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
using the latter. [...]
",0,0,0,1,0,0
17654,Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis,"  An important task for many if not all the scientific domains is efficient
knowledge integration, testing and codification. It is often solved with model
construction in a controllable computational environment. In spite of that, the
throughput of in-silico simulation-based observations become similarly
intractable for thorough analysis. This is especially the case in molecular
biology, which served as a subject for this study. In this project, we aimed to
test some approaches developed to deal with the curse of dimensionality. Among
these we found dimension reduction techniques especially appealing. They can be
used to identify irrelevant variability and help to understand critical
processes underlying high-dimensional datasets. Additionally, we subjected our
data sets to nonlinear time series analysis, as those are well established
methods for results comparison. To investigate the usefulness of dimension
reduction methods, we decided to base our study on a concrete sample set. The
example was taken from the domain of systems biology concerning dynamic
evolution of sub-cellular signaling. Particularly, the dataset relates to the
yeast pheromone pathway and is studied in-silico with a stochastic model. The
model reconstructs signal propagation stimulated by a mating pheromone. In the
paper, we elaborate on the reason of multidimensional analysis problem in the
context of molecular signaling, and next, we introduce the model of choice,
simulation details and obtained time series dynamics. A description of used
methods followed by a discussion of results and their biological interpretation
finalize the paper.
",0,0,0,1,1,0
17655,Equilibrium points and basins of convergence in the linear restricted four-body problem with angular velocity,"  The planar linear restricted four-body problem is used in order to determine
the Newton-Raphson basins of convergence associated with the equilibrium
points. The parametric variation of the position as well as of the stability of
the libration points is monitored when the values of the mass parameter $b$ as
well as of the angular velocity $\omega$ vary in predefined intervals. The
regions on the configuration $(x,y)$ plane occupied by the basins of attraction
are revealed using the multivariate version of the Newton-Raphson iterative
scheme. The correlations between the attracting domains of the equilibrium
points and the corresponding number of iterations needed for obtaining the
desired accuracy are also illustrated. We perform a thorough and systematic
numerical investigation by demonstrating how the parameters $b$ and $\omega$
influence the shape, the geometry and of course the fractality of the
converging regions. Our numerical outcomes strongly indicate that these two
parameters are indeed two of the most influential factors in this dynamical
system.
",0,1,0,0,0,0
17656,Nonmonotonic dependence of polymer glass mechanical response on chain bending stiffness,"  We investigate the mechanical properties of amorphous polymers by means of
coarse-grained simulations and nonaffine lattice dynamics theory. A small
increase of polymer chain bending stiffness leads first to softening of the
material, while hardening happens only upon further strengthening of the
backbones. This nonmonotonic variation of the storage modulus $G'$ with bending
stiffness is caused by a competition between additional resistance to
deformation offered by stiffer backbones and decreased density of the material
due to a necessary decrease in monomer-monomer coordination. This
counter-intuitive finding suggests that the strength of polymer glasses may in
some circumstances be enhanced by softening the bending of constituent chains.
",0,1,0,0,0,0
17657,Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation,"  Skin cancer is a major public health problem, as is the most common type of
cancer and represents more than half of cancer diagnoses worldwide. Early
detection influences the outcome of the disease and motivates our work. We
investigate the composition of CNN committees and data augmentation for the the
ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards
Melanoma Detection) facing the peculiarities of dealing with such a small,
unbalanced, biological database. For that, we explore committees of
Convolutional Neural Networks trained over the ISBI challenge training dataset
artificially augmented by both classical image processing transforms and image
warping guided by specialist knowledge about the lesion axis and improve the
final classifier invariance to common melanoma variations.
",1,0,0,0,0,0
17658,Arbitrage and Geometry,"  This article introduces the notion of arbitrage for a situation involving a
collection of investments and a payoff matrix describing the return to an
investor of each investment under each of a set of possible scenarios. We
explain the Arbitrage Theorem, discuss its geometric meaning, and show its
equivalence to Farkas' Lemma. We then ask a seemingly innocent question: given
a random payoff matrix, what is the probability of an arbitrage opportunity?
This question leads to some interesting geometry involving hyperplane
arrangements and related topics.
",0,0,1,1,0,0
17659,ADE surfaces and their moduli,"  We define a class of surfaces and surface pairs corresponding to the ADE root
lattices and construct compactifications of their moduli spaces, generalizing
Losev-Manin spaces of curves.
",0,0,1,0,0,0
17660,An Efficient Descriptor Model for Designing Materials for Solar Cells,"  An efficient descriptor model for fast screening of potential materials for
solar cell applications is presented. It works for both excitonic and
non-excitonic solar cells materials, and in addition to the energy gap it
includes the absorption spectrum ($\alpha(E)$) of the material. The charge
transport properties of the explored materials are modeled using the
characteristic diffusion length ($L_{d}$) determined for the respective family
of compounds. The presented model surpasses the widely used Scharber model
developed for bulk-heterojunction solar cells [Scharber \textit{et al.,
Advanced Materials}, 2006, Vol. 18, 789]. Using published experimental data, we
show that the presented model is more accurate in predicting the achievable
efficiencies. Although the focus of this work is on organic photovoltaics
(OPV), for which the original Scharber model was developed, the model presented
here is applicable also to other solar cell technologies. To model both
excitonic and non-excitonic systems, two different sets of parameters are used
to account for the different modes of operation. The analysis of the presented
descriptor model clearly shows the benefit of including $\alpha(E)$ and $L_{d}$
in view of improved screening results.
",0,1,0,0,0,0
17661,Multiplication of a Schubert polynomial by a Stanley symmetric polynomial,"  We prove, combinatorially, that the product of a Schubert polynomial by a
Stanley symmetric polynomial is a truncated Schubert polynomial. Using Monk's
rule, we derive a nonnegative combinatorial formula for the Schubert polynomial
expansion of a truncated Schubert polynomial. Combining these results, we give
a nonnegative combinatorial rule for the product of a Schubert and a Schur
polynomial in the Schubert basis.
",0,0,1,0,0,0
17662,Topological nodal line states and a potential catalyst of hydrogen evolution in the TiSi family,"  Topological nodal line (DNL) semimetals, formed by a closed loop of the
inverted bands in the bulk, result in the nearly flat drumhead-like surface
states with a high electronic density near the Fermi level. The high catalytic
active sites associated with the high electronic densities, the good carrier
mobility, and the proper thermodynamic stabilities with $\Delta
G_{H^*}$$\approx$0 are currently the prerequisites to seek the alternative
candidates to precious platinum for catalyzing electrochemical hydrogen (HER)
production from water. Within this context, it is natural to consider whether
or not the DNLs are a good candidate for the HER because its non-trivial
surface states provide a robust platform to activate possibly chemical
reactions. Here, through first-principles calculations we reported on a new DNL
TiSi-type family with a closed Dirac nodal line consisting of the linear band
crossings in the $k_y$ = 0 plane. The hydrogen adsorption on the (010) and
(110) surfaces yields the $\Delta G_{H^*}$ to be almost zero. The topological
charge carries have been revealed to participate in this HER. The results are
highlighting that TiSi not only is a promising catalyst for the HER but also
paves a new routine to design topological quantum catalyst utilizing the
topological DNL-induced surface bands as active sites, rather than edge sites-,
vacancy-, dopant-, strain-, or heterostructure-created active sites.
",0,1,0,0,0,0
17663,Boosting Variational Inference: an Optimization Perspective,"  Variational inference is a popular technique to approximate a possibly
intractable Bayesian posterior with a more tractable one. Recently, boosting
variational inference has been proposed as a new paradigm to approximate the
posterior by a mixture of densities by greedily adding components to the
mixture. However, as is the case with many other variational inference
algorithms, its theoretical properties have not been studied. In the present
work, we study the convergence properties of this approach from a modern
optimization viewpoint by establishing connections to the classic Frank-Wolfe
algorithm. Our analyses yields novel theoretical insights regarding the
sufficient conditions for convergence, explicit rates, and algorithmic
simplifications. Since a lot of focus in previous works for variational
inference has been on tractability, our work is especially important as a much
needed attempt to bridge the gap between probabilistic models and their
corresponding theoretical properties.
",1,0,0,1,0,0
17664,Nature of carrier injection in metal/2D semiconductor interface and its implications to the limits of contact resistance,"  Monolayers of transition metal dichalcogenides (TMDCs) exhibit excellent
electronic and optical properties. However, the performance of these
two-dimensional (2D) devices are often limited by the large resistance offered
by the metal contact interface. Till date, the carrier injection mechanism from
metal to 2D TMDC layers remains unclear, with widely varying reports of
Schottky barrier height (SBH) and contact resistance (Rc), particularly in the
monolayer limit. In this work, we use a combination of theory and experiments
in Au and Ni contacted monolayer MoS2 device to conclude the following points:
(i) the carriers are injected at the source contact through a cascade of two
potential barriers - the barrier heights being determined by the degree of
interaction between the metal and the TMDC layer; (ii) the conventional
Richardson equation becomes invalid due to the multi-dimensional nature of the
injection barriers, and using Bardeen-Tersoff theory, we derive the appropriate
form of the Richardson equation that describes such composite barrier; (iii) we
propose a novel transfer length method (TLM) based SBH extraction methodology,
to reliably extract SBH by eliminating any confounding effect of temperature
dependent channel resistance variation; (iv) we derive the Landauer limit of
the contact resistance achievable in such devices. A comparison of the limits
with the experimentally achieved contact resistance reveals plenty of room for
technological improvements.
",0,1,0,0,0,0
17665,Characteristics of stratified flows of Newtonian/non-Newtonian shear-thinning fluids,"  Exact solutions for laminar stratified flows of Newtonian/non-Newtonian
shear-thinning fluids in horizontal and inclined channels are presented. An
iterative algorithm is proposed to compute the laminar solution for the general
case of a Carreau non-Newtonian fluid. The exact solution is used to study the
effect of the rheology of the shear-thinning liquid on two-phase flow
characteristics considering both gas/liquid and liquid/liquid systems.
Concurrent and counter-current inclined systems are investigated, including the
mapping of multiple solution boundaries. Aspects relevant to practical
applications are discussed, such as the insitu hold-up, or lubrication effects
achieved by adding a less viscous phase. A characteristic of this family of
systems is that, even if the liquid has a complex rheology (Carreau fluid), the
two-phase stratified flow can behave like the liquid is Newtonian for a wide
range of operational conditions. The capability of the two-fluid model to yield
satisfactory predictions in the presence of shear-thinning liquids is tested,
and an algorithm is proposed to a priori predict if the Newtonian (zero shear
rate viscosity) behaviour arises for a given operational conditions in order to
avoid large errors in the predictions of flow characteristics when the
power-law is considered for modelling the shear-thinning behaviour. Two-fluid
model closures implied by the exact solution and the effect of a turbulent gas
layer are also addressed.
",0,1,0,0,0,0
17666,Stochastic Chebyshev Gradient Descent for Spectral Optimization,"  A large class of machine learning techniques requires the solution of
optimization problems involving spectral functions of parametric matrices, e.g.
log-determinant and nuclear norm. Unfortunately, computing the gradient of a
spectral function is generally of cubic complexity, as such gradient descent
methods are rather expensive for optimizing objectives involving the spectral
function. Thus, one naturally turns to stochastic gradient methods in hope that
they will provide a way to reduce or altogether avoid the computation of full
gradients. However, here a new challenge appears: there is no straightforward
way to compute unbiased stochastic gradients for spectral functions. In this
paper, we develop unbiased stochastic gradients for spectral-sums, an important
subclass of spectral functions. Our unbiased stochastic gradients are based on
combining randomized trace estimators with stochastic truncation of the
Chebyshev expansions. A careful design of the truncation distribution allows us
to offer distributions that are variance-optimal, which is crucial for fast and
stable convergence of stochastic gradient methods. We further leverage our
proposed stochastic gradients to devise stochastic methods for objective
functions involving spectral-sums, and rigorously analyze their convergence
rate. The utility of our methods is demonstrated in numerical experiments.
",0,0,0,1,0,0
17667,Poisson Bracket and Symplectic Structure of Covariant Canonical Formalism of Fields,"  The covariant canonical formalism is a covariant extension of the traditional
canonical formalism of fields. In contrast to the traditional canonical theory,
it has a remarkable feature that canonical equations of gauge theories or
gravity are not only manifestly Lorentz covariant but also gauge covariant or
diffeomorphism covariant. A mathematical peculiarity of the covariant canonical
formalism is that its canonical coordinates are differential forms on a
manifold. In the present paper, we find a natural Poisson bracket of this new
canonical theory, and study symplectic structure behind it. The phase space of
the theory is identified with a ringed space with the structure sheaf of the
graded algebra of ""differentiable"" differential forms on the manifold. The
Poisson and the symplectic structure we found can be even or odd, depending on
the dimension of the manifold. Our Poisson structure is an example of physical
application of Poisson structure defined on the graded algebra of differential
forms.
",0,0,1,0,0,0
17668,Cascading Failures in Interdependent Systems: Impact of Degree Variability and Dependence,"  We study cascading failures in a system comprising interdependent
networks/systems, in which nodes rely on other nodes both in the same system
and in other systems to perform their function. The (inter-)dependence among
nodes is modeled using a dependence graph, where the degree vector of a node
determines the number of other nodes it can potentially cause to fail in each
system through aforementioned dependency. In particular, we examine the impact
of the variability and dependence properties of node degrees on the probability
of cascading failures. We show that larger variability in node degrees hampers
widespread failures in the system, starting with random failures. Similarly,
positive correlations in node degrees make it harder to set off an epidemic of
failures, thereby rendering the system more robust against random failures.
",1,1,0,1,0,0
17669,Critical exponent for geodesic currents,"  For any geodesic current we associated a quasi-metric space. For a subclass
of geodesic currents, called filling, it defines a metric and we study the
critical exponent associated to this space. We show that is is equal to the
exponential growth rate of the intersection function for closed curves.
",0,0,1,0,0,0
17670,Human and Machine Speaker Recognition Based on Short Trivial Events,"  Trivial events are ubiquitous in human to human conversations, e.g., cough,
laugh and sniff. Compared to regular speech, these trivial events are usually
short and unclear, thus generally regarded as not speaker discriminative and so
are largely ignored by present speaker recognition research. However, these
trivial events are highly valuable in some particular circumstances such as
forensic examination, as they are less subjected to intentional change, so can
be used to discover the genuine speaker from disguised speech. In this paper,
we collect a trivial event speech database that involves 75 speakers and 6
types of events, and report preliminary speaker recognition results on this
database, by both human listeners and machines. Particularly, the deep feature
learning technique recently proposed by our group is utilized to analyze and
recognize the trivial events, which leads to acceptable equal error rates
(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
Comparing different types of events, 'hmm' seems more speaker discriminative.
",1,0,0,0,0,0
17671,AdiosStMan: Parallelizing Casacore Table Data System Using Adaptive IO System,"  In this paper, we investigate the Casacore Table Data System (CTDS) used in
the casacore and CASA libraries, and methods to parallelize it. CTDS provides a
storage manager plugin mechanism for third-party devel- opers to design and
implement their own CTDS storage managers. Hav- ing this in mind, we looked
into various storage backend techniques that can possibly enable parallel I/O
for CTDS by implementing new storage managers. After carrying on benchmarks
showing the excellent parallel I/O throughput of the Adaptive IO System
(ADIOS), we implemented an ADIOS based parallel CTDS storage manager. We then
applied the CASA MSTransform frequency split task to verify the ADIOS Storage
Manager. We also ran a series of performance tests to examine the I/O
throughput in a massively parallel scenario.
",1,1,0,0,0,0
17672,Direct Experimental Observation of the Gas Filamentation Effect using a Two-bunch X-ray FEL Beam,"  We report the experimental observation of the filamentation effect in gas
devices designed for X-ray Free-electron Lasers. The measurements were carried
out at the Linac Coherent Light Source on the X-ray Correlation Spectroscopy
(XCS) instrument using a Two-bunch FEL beam at 6.5 keV with 122.5 ns separation
passing through an Argon gas cell. The relative intensities of the two pulses
of the Two-bunch beam were measured, after and before the gas cell, from the
X-ray scattering off thin targets by using fast diodes with sufficient temporal
resolution. It was found that the after-to-before ratio of the intensities of
the second pulse was consistently higher than that of the first pulse,
revealing lower effective attenuation of the gas cell due to the heating and
subsequent gas density reduction in the beam path by the first pulse. This
measurement is important in guiding the design and/or mitigating the adverse
effect in gas devices for high repetition-rate FELs such as the LCLS-II and the
European XFEL or other future high repetition-rate upgrade to existing FEL
facilities
",0,1,0,0,0,0
17673,Coherence measurements of scattered incoherent light for lensless identification of an object's location and size,"  In absence of a lens to form an image, incoherent or partially coherent light
scattering off an obstructive or reflective object forms a broad intensity
distribution in the far field with only feeble spatial features. We show here
that measuring the complex spatial coherence function can help in the
identification of the size and location of a one-dimensional object placed in
the path of a partially coherent light source. The complex coherence function
is measured in the far field through wavefront sampling, which is performed via
dynamically reconfigurable slits implemented on a digital micromirror device
(DMD). The impact of an object -- parameterized by size and location -- that
either intercepts or reflects incoherent light is studied. The experimental
results show that measuring the spatial coherence function as a function of the
separation between two slits located symmetrically around the optical axis can
identify the object transverse location and angle subtended from the detection
plane (the ratio of the object width to the axial distance from the detector).
The measurements are in good agreement with numerical simulations of a forward
model based on Fresnel propagators. The rapid refresh rate of DMDs may enable
real-time operation of such a lensless coherency imaging scheme.
",0,1,0,0,0,0
17674,MVP2P: Layer-Dependency-Aware Live MVC Video Streaming over Peer-to-Peer Networks,"  Multiview video supports observing a scene from different viewpoints. The
Joint Video Team (JVT) developed H.264/MVC to enhance the compression
efficiency for multiview video, however, MVC encoded multiview video (MVC
video) still requires high bitrates for transmission. This paper investigates
live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to
minimize the server bandwidth costs whist ensuring high streaming quality to
peers. MVC employs intra-view and inter-view prediction structures, which leads
to a complicated layer dependency relationship. As the peers' outbound
bandwidth is shared while supplying all the MVC video layers, the bandwidth
allocation to one MVC layer affects the available outbound bandwidth of the
other layers. To optimise the utilisation of the peers' outbound bandwidth for
providing video layers, a maximum flow based model is proposed which considers
the MVC video layer dependency and the layer supplying relationship between
peers. Based on the model, a layer dependency aware live MVC video streaming
method over a BitTorrent-like P2P network is proposed, named MVP2P. The key
components of MVP2P include a chunk scheduling strategy and a peer selection
strategy for receiving peers, and a bandwidth scheduling algorithm for
supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is
compared with existing methods considering the constraints of peer bandwidth,
peer numbers, view switching rates, and peer churns. The test results show that
MVP2P significantly outperforms the existing methods.
",1,0,0,0,0,0
17675,High order finite element simulations for fluid dynamics validated by experimental data from the fda benchmark nozzle model,"  The objective of the present work is to construct a sound mathematical,
numerical and computational framework relevant to blood flow simulations and to
assess it through a careful validation against experimental data. We perform
simulations of a benchmark proposed by the FDA for fluid flow in an idealized
medical device, under different flow regimes. The results are evaluated using
metrics proposed in the literature and the findings are in very good agreement
with the validation experiment.
",0,1,1,0,0,0
17676,Twin Networks: Matching the Future for Sequence Generation,"  We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a ""backward"" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and plays no role during sampling or inference. We hypothesize that
our approach eases modeling of long-term dependencies by implicitly forcing the
forward states to hold information about the longer-term future (as contained
in the backward states). We show empirically that our approach achieves 9%
relative improvement for a speech recognition task, and achieves significant
improvement on a COCO caption generation task.
",1,0,0,1,0,0
17677,Structure and Randomness of Continuous-Time Discrete-Event Processes,"  Loosely speaking, the Shannon entropy rate is used to gauge a stochastic
process' intrinsic randomness; the statistical complexity gives the cost of
predicting the process. We calculate, for the first time, the entropy rate and
statistical complexity of stochastic processes generated by finite unifilar
hidden semi-Markov models---memoryful, state-dependent versions of renewal
processes. Calculating these quantities requires introducing novel mathematical
objects ({\epsilon}-machines of hidden semi-Markov processes) and new
information-theoretic methods to stochastic processes.
",0,1,1,1,0,0
17678,In situ high resolution real-time quantum efficiency imaging for photocathodes,"  Aspects of the preparation process and performance degradation are two major
problems of photocathodes. The lack of a means for dynamic quantum efficiency
measurements results in the inability to observe the inhomogeneity of the
cathode surface by fine structural analysis and in real time.Here we present a
simple and scalable technique for in situ real-time quantum efficiency
diagnosis. An incoherent light source provides uniform illumination on the
cathode surface, and solenoid magnets are used as lens for focusing and imaging
the emitted electron beam on a downstream scintillator screen, which converts
the quantum efficiency information into fluorescence intensity distribution.
The microscopic discontinuity and the dynamic changes of the quantum efficiency
of a gallium arsenide photocathode are observed at a resolution of a few
microns. An unexpected uneven decrease of the quantum efficiency is also
recorded. The work demonstrates a new observation method for photoemission
materials research.
",0,1,0,0,0,0
17679,Competitive division of a mixed manna,"  A mixed manna contains goods (that everyone likes), bads (that everyone
dislikes), as well as items that are goods to some agents, but bads or satiated
to others.
If all items are goods and utility functions are homothetic, concave (and
monotone), the Competitive Equilibrium with Equal Incomes maximizes the Nash
product of utilities: hence it is welfarist (determined utility-wise by the
feasible set of profiles), single-valued and easy to compute.
We generalize the Gale-Eisenberg Theorem to a mixed manna. The Competitive
division is still welfarist and related to the product of utilities or
disutilities. If the zero utility profile (before any manna) is Pareto
dominated, the competitive profile is unique and still maximizes the product of
utilities. If the zero profile is unfeasible, the competitive profiles are the
critical points of the product of disutilities on the efficiency frontier, and
multiplicity is pervasive. In particular the task of dividing a mixed manna is
either good news for everyone, or bad news for everyone.
We refine our results in the practically important case of linear
preferences, where the axiomatic comparison between the division of goods and
that of bads is especially sharp. When we divide goods and the manna improves,
everyone weakly benefits under the competitive rule; but no reasonable rule to
divide bads can be similarly Resource Monotonic. Also, the much larger set of
Non Envious and Efficient divisions of bads can be disconnected so that it will
admit no continuous selection.
",1,0,1,0,0,0
17680,Understanding Black-box Predictions via Influence Functions,"  How can we explain the predictions of a black-box model? In this paper, we
use influence functions -- a classic technique from robust statistics -- to
trace a model's prediction through the learning algorithm and back to its
training data, thereby identifying training points most responsible for a given
prediction. To scale up influence functions to modern machine learning
settings, we develop a simple, efficient implementation that requires only
oracle access to gradients and Hessian-vector products. We show that even on
non-convex and non-differentiable models where the theory breaks down,
approximations to influence functions can still provide valuable information.
On linear models and convolutional neural networks, we demonstrate that
influence functions are useful for multiple purposes: understanding model
behavior, debugging models, detecting dataset errors, and even creating
visually-indistinguishable training-set attacks.
",1,0,0,1,0,0
17681,Linear and nonlinear photonic Jackiw-Rebbi states in waveguide arrays,"  We study analytically and numerically the optical analogue of the
Jackiw-Rebbi states in quantum field theory. These solutions exist at the
interface of two binary waveguide arrays which are described by two Dirac
equations with opposite sign masses. We show that these special states are
topologically robust not only in the linear regime, but also in nonlinear
regimes (with both focusing and de-focusing nonlinearity). We also reveal that
one can generate the Jackiw-Rebbi states starting from Dirac solitons.
",0,1,0,0,0,0
17682,"A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations","  Progress in science has advanced the development of human society across
history, with dramatic revolutions shaped by information theory, genetic
cloning, and artificial intelligence, among the many scientific achievements
produced in the 20th century. However, the way that science advances itself is
much less well-understood. In this work, we study the evolution of scientific
development over the past century by presenting an anatomy of 89 million
digitalized papers published between 1900 and 2015. We find that science has
benefited from the shift from individual work to collaborative effort, with
over 90% of the world-leading innovations generated by collaborations in this
century, nearly four times higher than they were in the 1900s. We discover that
rather than the frequent myopic- and self-referencing that was common in the
early 20th century, modern scientists instead tend to look for literature
further back and farther around. Finally, we also observe the globalization of
scientific development from 1900 to 2015, including 25-fold and 7-fold
increases in international collaborations and citations, respectively, as well
as a dramatic decline in the dominant accumulation of citations by the US, the
UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are
meant to serve as a starter for exploring the visionary ways in which science
has developed throughout the past century, generating insight into and an
impact upon the current scientific innovations and funding policies.
",1,1,0,0,0,0
17683,Interval-type theorems concerning means,"  Each family $\mathcal{M}$ of means has a natural, partial order (point-wise
order), that is $M \le N$ iff $M(x) \le N(x)$ for all admissible $x$.
In this setting we can introduce the notion of interval-type set (a subset
$\mathcal{I} \subset \mathcal{M}$ such that whenever $M \le P \le N$ for some
$M,\,N \in \mathcal{I}$ and $P \in \mathcal{M}$ then $P \in \mathcal{I}$). For
example, in the case of power means there exists a natural isomorphism between
interval-type sets and intervals contained in real numbers. Nevertheless there
appear a number of interesting objects for a families which cannot be linearly
ordered.
In the present paper we consider this property for Gini means and Hardy
means. Moreover some results concerning $L^\infty$ metric among (abstract)
means will be obtained.
",0,0,1,0,0,0
17684,Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning,"  We propose a fully distributed actor-critic algorithm approximated by deep
neural networks, named \textit{Diff-DAC}, with application to single-task and
to average multitask reinforcement learning (MRL). Each agent has access to
data from its local task only, but it aims to learn a policy that performs well
on average for the whole set of tasks. During the learning process, agents
communicate their value-policy parameters to their neighbors, diffusing the
information across the network, so that they converge to a common policy, with
no need for a central node. The method is scalable, since the computational and
communication costs per agent grow with its number of neighbors. We derive
Diff-DAC's from duality theory and provide novel insights into the standard
actor-critic framework, showing that it is actually an instance of the dual
ascent method that approximates the solution of a linear program. Experiments
suggest that Diff-DAC can outperform the single previous distributed MRL
approach (i.e., Dist-MTLPS) and even the centralized architecture.
",1,0,0,1,0,0
17685,On locally compact semitopological $0$-bisimple inverse $ω$-semigroups,"  We describe the structure of Hausdorff locally compact semitopological
$0$-bisimple inverse $\omega$-semigroups with compact maximal subgroups. In
particular, we show that a Hausdorff locally compact semitopological
$0$-bisimple inverse $\omega$-semigroup with a compact maximal subgroup is
either compact or topologically isomorphic to the topological sum of its
$\mathscr{H}$-classes. We describe the structure of Hausdorff locally compact
semitopological $0$-bisimple inverse $\omega$-semigroups with a monothetic
maximal subgroups. In particular we prove the dichotomy for $T_1$ locally
compact semitopological Reilly semigroup
$\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ with adjoined zero and
with a non-annihilating homomorphism $\theta\colon \mathbb{Z}_{+}\to
\mathbb{Z}_{+}$: $\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ is
either compact or discrete. At the end we discuss on the remainder under the
closure of the discrete Reilly semigroup $\textbf{B}(\mathbb{Z}_{+},\theta)^0$
in a semitopological semigroup.
",0,0,1,0,0,0
17686,Theory of Disorder-Induced Half-Integer Thermal Hall Conductance,"  Electrons that are confined to a single Landau level in a two dimensional
electron gas realize the effects of strong electron-electron repulsion in its
purest form. The kinetic energy of individual electrons is completely quenched
and all physical properties are dictated solely by many-body effects. A
remarkable consequence is the emergence of new quasiparticles with fractional
charge and exotic quantum statistics of which the most exciting ones are
non-Abelian quasiparticles. A non-integer quantized thermal Hall conductance
$\kappa_{xy}$ (in units of temperature times the universal constant $\pi^2
k_B^2 /3 h$; $h$ is the Planck constant and $k_B$ the Boltzmann constant)
necessitates the existence of such quasiparticles. It has been predicted, and
verified numerically, that such states are realized in the clean half-filled
first Landau level of electrons with Coulomb repulsion, with $\kappa_{xy}$
being either $3/2$ or $7/2$. Excitingly, a recent experiment has indeed
observed a half-integer value, which was measured, however, to be
$\kappa_{xy}=5/2$. We resolve this contradiction within a picture where smooth
disorder results in the formation of mesoscopic puddles with locally
$\kappa_{xy}=3/2$ or $7/2$. Interactions between these puddles generate a
coherent macroscopic state, which is reflected in an extended plateau with
quantized $\kappa_{xy}=5/2$. The topological properties of quasiparticles at
large distances are determined by the macroscopic phase, and not by the
microscopic puddle where they reside. In principle, the same mechanism might
also allow non-Abelian quasiparticles to emerge from a system comprised of
microscopic Abelian puddles.
",0,1,0,0,0,0
17687,On Security Research Towards Future Mobile Network Generations,"  Over the last decades, numerous security and privacy issues in all three
active mobile network generations have been revealed that threaten users as
well as network providers. In view of the newest generation (5G) currently
under development, we now have the unique opportunity to identify research
directions for the next generation based on existing security and privacy
issues as well as already proposed defenses. This paper aims to unify security
knowledge on mobile phone networks into a comprehensive overview and to derive
pressing open research questions. To achieve this systematically, we develop a
methodology that categorizes known attacks by their aim, proposed defenses,
underlying causes, and root causes. Further, we assess the impact and the
efficacy of each attack and defense. We then apply this methodology to existing
literature on attacks and defenses in all three network generations. By doing
so, we identify ten causes and four root causes of attacks. Mapping the attacks
to proposed defenses and suggestions for the 5G specification enables us to
uncover open research questions and challenges for the development of
next-generation mobile networks. The problems of unsecured pre-authentication
traffic and jamming attacks exist across all three mobile generations. They
should be addressed in the future, in particular, to wipe out the class of
downgrade attacks and, thereby, strengthen the users' privacy. Further advances
are needed in the areas of inter-operator protocols as well as secure baseband
implementations. Additionally, mitigations against denial-of-service attacks by
smart protocol design represent an open research question.
",1,0,0,0,0,0
17688,Channel Simulation in Quantum Metrology,"  In this review we discuss how channel simulation can be used to simplify the
most general protocols of quantum parameter estimation, where unlimited
entanglement and adaptive joint operations may be employed. Whenever the
unknown parameter encoded in a quantum channel is completely transferred in an
environmental program state simulating the channel, the optimal adaptive
estimation cannot beat the standard quantum limit. In this setting, we
elucidate the crucial role of quantum teleportation as a primitive operation
which allows one to completely reduce adaptive protocols over suitable
teleportation-covariant channels and derive matching upper and lower bounds for
parameter estimation. For these channels, we may express the quantum Cramér
Rao bound directly in terms of their Choi matrices. Our review considers both
discrete- and continuous-variable systems, also presenting some new results for
bosonic Gaussian channels using an alternative sub-optimal simulation. It is an
open problem to design simulations for quantum channels that achieve the
Heisenberg limit.
",0,1,0,0,0,0
17689,Ranking Causal Influence of Financial Markets via Directed Information Graphs,"  A non-parametric method for ranking stock indices according to their mutual
causal influences is presented. Under the assumption that indices reflect the
underlying economy of a country, such a ranking indicates which countries exert
the most economic influence in an examined subset of the global economy. The
proposed method represents the indices as nodes in a directed graph, where the
edges' weights are estimates of the pair-wise causal influences, quantified
using the directed information functional. This method facilitates using a
relatively small number of samples from each index. The indices are then ranked
according to their net-flow in the estimated graph (sum of the incoming weights
subtracted from the sum of outgoing weights). Daily and minute-by-minute data
from nine indices (three from Asia, three from Europe and three from the US)
were analyzed. The analysis of daily data indicates that the US indices are the
most influential, which is consistent with intuition that the indices
representing larger economies usually exert more influence. Yet, it is also
shown that an index representing a small economy can strongly influence an
index representing a large economy if the smaller economy is indicative of a
larger phenomenon. Finally, it is shown that while inter-region interactions
can be captured using daily data, intra-region interactions require more
frequent samples.
",0,0,0,0,0,1
17690,Shear banding in metallic glasses described by alignments of Eshelby quadrupoles,"  Plastic deformation of metallic glasses performed well below the glass
transition temperature leads to the formation of shear bands as a result of
shear localization. It is believed that shear banding originates from
individual stress concentrators having quadrupolar symmetry. To elucidate the
underlying mechanisms of shear band formation, microstructural investigations
were carried out on sheared zones using transmission electron microscopy. Here
we show evidence of a characteristic signature present in shear bands
manifested in the form of sinusoidal density variations. We present an
analytical solution for the observed post-deformation state derived from
continuum mechanics using an alignment of quadrupolar stress field
perturbations for the plastic events. Since we observe qualitatively similar
features for three different types of metallic glasses that span the entire
range of characteristic properties of metallic glasses, we conclude that the
reported deformation behavior is generic for all metallic glasses, and thus has
far-reaching consequences for the deformation behavior of amorphous solids in
general.
",0,1,0,0,0,0
17691,Detection of low dimensionality and data denoising via set estimation techniques,"  This work is closely related to the theories of set estimation and manifold
estimation.
Our object of interest is a, possibly lower-dimensional, compact set $S
\subset {\mathbb R}^d$.
The general aim is to identify (via stochastic procedures) some qualitative
or quantitative features of $S$, of geometric or topological character. The
available information is just a random sample of points drawn on $S$.
The term ""to identify"" means here to achieve a correct answer almost surely
(a.s.) when the sample size tends to infinity. More specifically the paper aims
at giving some partial answers to the following questions: is $S$ full
dimensional? Is $S$ ""close to a lower dimensional set"" $\mathcal{M}$? If so,
can we estimate $\mathcal{M}$ or some functionals of $\mathcal{M}$ (in
particular, the Minkowski content of $\mathcal{M}$)? As an important auxiliary
tool in the answers of these questions, a denoising procedure is proposed in
order to partially remove the noise in the original data. The theoretical
results are complemented with some simulations and graphical illustrations.
",0,0,1,1,0,0
17692,Deep Networks with Shape Priors for Nucleus Detection,"  Detection of cell nuclei in microscopic images is a challenging research
topic, because of limitations in cellular image quality and diversity of
nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between
multiple cell nuclei. This has been a topic of enduring interest with promising
recent success shown by deep learning methods. These methods train for example
convolutional neural networks (CNNs) with a training set of input images and
known, labeled nuclei locations. Many of these methods are supplemented by
spatial or morphological processing. We develop a new approach that we call
Shape Priors with Convolutional Neural Networks (SP-CNN) to perform
significantly enhanced nuclei detection. A set of canonical shapes is prepared
with the help of a domain expert. Subsequently, we present a new network
structure that can incorporate `expected behavior' of nucleus shapes via two
components: {\em learnable} layers that perform the nucleus detection and a
{\em fixed} processing part that guides the learning with prior information.
Analytically, we formulate a new regularization term that is targeted at
penalizing false positives while simultaneously encouraging detection inside
cell nucleus boundary. Experimental results on a challenging dataset reveal
that SP-CNN is competitive with or outperforms several state-of-the-art
methods.
",0,0,0,1,0,0
17693,"A sheaf-theoretic model for SL(2,C) Floer homology","  Given a Heegaard splitting of a three-manifold Y, we consider the SL(2,C)
character variety of the Heegaard surface, and two complex Lagrangians
associated to the handlebodies. We focus on the smooth open subset
corresponding to irreducible representations. On that subset, the intersection
of the Lagrangians is an oriented d-critical locus in the sense of Joyce. Bussi
associates to such an intersection a perverse sheaf of vanishing cycles. We
prove that in our setting, the perverse sheaf is an invariant of Y, i.e., it is
independent of the Heegaard splitting. The hypercohomology of this sheaf can be
viewed as a model for (the dual of) SL(2,C) instanton Floer homology. We also
present a framed version of this construction, which takes into account
reducible representations. We give explicit computations for lens spaces and
Brieskorn spheres, and discuss the connection to the Kapustin-Witten equations
and Khovanov homology.
",0,0,1,0,0,0
17694,Discriminative models for multi-instance problems with tree-structure,"  Modeling network traffic is gaining importance in order to counter modern
threats of ever increasing sophistication. It is though surprisingly difficult
and costly to construct reliable classifiers on top of telemetry data due to
the variety and complexity of signals that no human can manage to interpret in
full. Obtaining training data with sufficiently large and variable body of
labels can thus be seen as prohibitive problem. The goal of this work is to
detect infected computers by observing their HTTP(S) traffic collected from
network sensors, which are typically proxy servers or network firewalls, while
relying on only minimal human input in model training phase. We propose a
discriminative model that makes decisions based on all computer's traffic
observed during predefined time window (5 minutes in our case). The model is
trained on collected traffic samples over equally sized time window per large
number of computers, where the only labels needed are human verdicts about the
computer as a whole (presumed infected vs. presumed clean). As part of training
the model itself recognizes discriminative patterns in traffic targeted to
individual servers and constructs the final high-level classifier on top of
them. We show the classifier to perform with very high precision, while the
learned traffic patterns can be interpreted as Indicators of Compromise. In the
following we implement the discriminative model as a neural network with
special structure reflecting two stacked multi-instance problems. The main
advantages of the proposed configuration include not only improved accuracy and
ability to learn from gross labels, but also automatic learning of server types
(together with their detectors) which are typically visited by infected
computers.
",1,0,0,0,0,0
17695,Functional advantages offered by many-body coherences in biochemical systems,"  Quantum coherence phenomena driven by electronic-vibrational (vibronic)
interactions, are being reported in many pulse (e.g. laser) driven chemical and
biophysical systems. But what systems-level advantage(s) do such many-body
coherences offer to future technologies? We address this question for pulsed
systems of general size N, akin to the LHCII aggregates found in green plants.
We show that external pulses generate vibronic states containing particular
multipartite entanglements, and that such collective vibronic states increase
the excitonic transfer efficiency. The strength of these many-body coherences
and their robustness to decoherence, increase with aggregate size N and do not
require strong electronic-vibrational coupling. The implications for energy and
information transport are discussed.
",0,1,0,0,0,0
17696,Privacy Mining from IoT-based Smart Homes,"  Recently, a wide range of smart devices are deployed in a variety of
environments to improve the quality of human life. One of the important
IoT-based applications is smart homes for healthcare, especially for elders.
IoT-based smart homes enable elders' health to be properly monitored and taken
care of. However, elders' privacy might be disclosed from smart homes due to
non-fully protected network communication or other reasons. To demonstrate how
serious this issue is, we introduce in this paper a Privacy Mining Approach
(PMA) to mine privacy from smart homes by conducting a series of deductions and
analyses on sensor datasets generated by smart homes. The experimental results
demonstrate that PMA is able to deduce a global sensor topology for a smart
home and disclose elders' privacy in terms of their house layouts.
",0,0,0,1,0,0
17697,Idempotent ordered semigroup,"  An element e of an ordered semigroup $(S,\cdot,\leq)$ is called an ordered
idempotent if $e\leq e^2$. We call an ordered semigroup $S$ idempotent ordered
semigroup if every element of $S$ is an ordered idempotent. Every idempotent
semigroup is a complete semilattice of rectangular idempotent semigroups and in
this way we arrive to many other important classes of idempotent ordered
semigroups.
",0,0,1,0,0,0
17698,Hybrid bounds for twists of $GL(3)$ $L$-functions,"  Let $\pi$ be a Hecke-Maass cusp form for $SL(3,\mathbb{Z})$ and
$\chi=\chi_1\chi_2$ a Dirichlet character with $\chi_i$ primitive modulo $M_i$.
Suppose that $M_1$, $M_2$ are primes such that
$\max\{(M|t|)^{1/3+2\delta/3},M^{2/5}|t|^{-9/20},
M^{1/2+2\delta}|t|^{-3/4+2\delta}\}(M|t|)^{\varepsilon}<M_1< \min\{
(M|t|)^{2/5},(M|t|)^{1/2-8\delta}\}(M|t|)^{-\varepsilon}$ for any
$\varepsilon>0$, where $M=M_1M_2$, $|t|\geq 1$ and $0<\delta< 1/52$. Then we
have $$ L\left(\frac{1}{2}+it,\pi\otimes \chi\right)\ll_{\pi,\varepsilon}
(M|t|)^{3/4-\delta+\varepsilon}. $$
",0,0,1,0,0,0
17699,Modeling stochastic skew of FX options using SLV models with stochastic spot/vol correlation and correlated jumps,"  It is known that the implied volatility skew of FX options demonstrates a
stochastic behavior which is called stochastic skew. In this paper we create
stochastic skew by assuming the spot/instantaneous variance correlation to be
stochastic. Accordingly, we consider a class of SLV models with stochastic
correlation where all drivers - the spot, instantaneous variance and their
correlation are modeled by Levy processes. We assume all diffusion components
to be fully correlated as well as all jump components. A new fully implicit
splitting finite-difference scheme is proposed for solving forward PIDE which
is used when calibrating the model to market prices of the FX options with
different strikes and maturities. The scheme is unconditionally stable, of
second order of approximation in time and space, and achieves a linear
complexity in each spatial direction. The results of simulation obtained by
using this model demonstrate capacity of the presented approach in modeling
stochastic skew.
",0,0,1,0,0,0
17700,Computational Study of Amplitude-to-Phase Conversion in a Modified Uni-Traveling Carrier (MUTC) Photodetector,"  We calculate the amplitude-to-phase (AM-to-PM) noise conversion in a modified
unitraveling carrier (MUTC) photodetector. We obtained two nulls as measured in
the experiments, and we explain their origin. The nulls appear due to the
transit time variation when the average photocurrent varies, and the transit
time variation is due to the change of electron velocity when the average
photocurrent varies. We also show that the AM-to-PM conversion coefficient
depends only on the pulse energy and is independent of the pulse duration when
the duration is less than 500 fs. When the pulse duration is larger than 500
fs, the nulls of the AM-to-PM conversion coefficient shift to larger average
photocurrents. This shift occurs because the increase in that pulse duration
leads to a decrease in the peak photocurrent. The AM-to-PM noise conversion
coefficient changes as the repetition rate varies. However, the repetition rate
does not change the AM-to-PM conversion coefficient as a function of input
optical pulse energy. The repetition rate changes the average photocurrent. We
propose a design that would in theory improve the performance of the device.
",0,1,0,0,0,0
17701,Direct frequency-comb spectroscopy of $6S_{1/2}$-$8S_{1/2}$ transitions of atomic cesium,"  Direct frequency-comb spectroscopy is used to probe the absolute frequencies
of $6S_{1/2}$-$8S_{1/2}$ two-photon transitions of atomic cesium in hot vapor
environment. By utilizing the coherent control method of temporally splitting
the laser spectrum above and below the two-photon resonance frequency,
Doppler-free absorption is built in two spatially distinct locations and imaged
for high-precision spectroscopy. Theoretical analysis finds that these
transition lines are measured with uncertainty below $5\times10^{-10}$, mainly
contributed from laser-induced AC Stark shift.
",0,1,0,0,0,0
17702,Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources,"  A variety of energy resources has been identified as being flexible in their
electric energy consumption or generation. This energetic flexibility can be
used for various purposes such as minimizing energy procurement costs or
providing ancillary services to power grids. To fully leverage the flexibility
available from distributed small-scale resources, their flexibility must be
quantified and aggregated.
This paper introduces a generic and scalable approach for flexible energy
systems to quantitatively describe and price their flexibility based on
zonotopic sets. The description proposed allows aggregators to efficiently pool
the flexibility of large numbers of systems and to make control and market
decisions on the aggregate level. In addition, an algorithm is presented that
distributes aggregate-level control decisions among the individual systems of
the pool in an economically fair and computationally efficient way. Finally, it
is shown how the zonotopic description of flexibility enables an efficient
computation of aggregate regulation power bid-curves.
",1,0,0,0,0,0
17703,Mapping Walls of Indoor Environment using RGB-D Sensor,"  Inferring walls configuration of indoor environment could help robot
""understand"" the environment better. This allows the robot to execute a task
that involves inter-room navigation, such as picking an object in the kitchen.
In this paper, we present a method to inferring walls configuration from a
moving RGB-D sensor. Our goal is to combine a simple wall configuration model
and fast wall detection method in order to get a system that works online, is
real-time, and does not need a Manhattan World assumption. We tested our
preliminary work, i.e. wall detection and measurement from moving RGB-D sensor,
with MIT Stata Center Dataset. The performance of our method is reported in
terms of accuracy and speed of execution.
",1,0,0,0,0,0
17704,The Effects of Memory Replay in Reinforcement Learning,"  Experience replay is a key technique behind many recent advances in deep
reinforcement learning. Allowing the agent to learn from earlier memories can
speed up learning and break undesirable temporal correlations. Despite its
wide-spread application, very little is understood about the properties of
experience replay. How does the amount of memory kept affect learning dynamics?
Does it help to prioritize certain experiences? In this paper, we address these
questions by formulating a dynamical systems ODE model of Q-learning with
experience replay. We derive analytic solutions of the ODE for a simple
setting. We show that even in this very simple setting, the amount of memory
kept can substantially affect the agent's performance. Too much or too little
memory both slow down learning. Moreover, we characterize regimes where
prioritized replay harms the agent's learning. We show that our analytic
solutions have excellent agreement with experiments. Finally, we propose a
simple algorithm for adaptively changing the memory buffer size which achieves
consistently good empirical performance.
",1,0,0,1,0,0
17705,Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning,"  We investigate the problem of dynamic portfolio optimization in
continuous-time, finite-horizon setting for a portfolio of two stocks and one
risk-free asset. The stocks follow the Cointelation model. The proposed
optimization methods are twofold. In what we call an Stochastic Differential
Equation approach, we compute the optimal weights using mean-variance criterion
and power utility maximization. We show that dynamically switching between
these two optimal strategies by introducing a triggering function can further
improve the portfolio returns. We contrast this with the machine learning
clustering methodology inspired by the band-wise Gaussian mixture model. The
first benefit of the machine learning over the Stochastic Differential Equation
approach is that we were able to achieve the same results though a simpler
channel. The second advantage is a flexibility to regime change.
",0,0,0,0,0,1
17706,High-mass Starless Clumps in the inner Galactic Plane: the Sample and Dust Properties,"  We report a sample of 463 high-mass starless clump (HMSC) candidates within
$-60°<l<60°$ and $-1°<b<1°$. This sample has been singled out from
10861 ATLASGAL clumps. All of these sources are not associated with any known
star-forming activities collected in SIMBAD and young stellar objects
identified using color-based criteria. We also make sure that the HMSC
candidates have neither point sources at 24 and 70 \micron~nor strong extended
emission at 24 $\mu$m. Most of the identified HMSCs are infrared ($\le24$
$\mu$m) dark and some are even dark at 70 $\mu$m. Their distribution shows
crowding in Galactic spiral arms and toward the Galactic center and some
well-known star-forming complexes. Many HMSCs are associated with large-scale
filaments. Some basic parameters were attained from column density and dust
temperature maps constructed via fitting far-infrared and submillimeter
continuum data to modified blackbodies. The HMSC candidates have sizes, masses,
and densities similar to clumps associated with Class II methanol masers and
HII regions, suggesting they will evolve into star-forming clumps. More than
90% of the HMSC candidates have densities above some proposed thresholds for
forming high-mass stars. With dust temperatures and luminosity-to-mass ratios
significantly lower than that for star-forming sources, the HMSC candidates are
externally heated and genuinely at very early stages of high-mass star
formation. Twenty sources with equivalent radius $r_\mathrm{eq}<0.15$ pc and
mass surface density $\Sigma>0.08$ g cm$^{-2}$ could be possible high-mass
starless cores. Further investigations toward these HMSCs would undoubtedly
shed light on comprehensively understanding the birth of high-mass stars.
",0,1,0,0,0,0
17707,Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics,"  Artificial neural networks that learn to perform Principal Component Analysis
(PCA) and related tasks using strictly local learning rules have been
previously derived based on the principle of similarity matching: similar pairs
of inputs should map to similar pairs of outputs. However, the operation of
these networks (and of similar networks) requires a fixed-point iteration to
determine the output corresponding to a given input, which means that dynamics
must operate on a faster time scale than the variation of the input. Further,
during these fast dynamics such networks typically ""disable"" learning, updating
synaptic weights only once the fixed-point iteration has been resolved. Here,
we derive a network for PCA-based dimensionality reduction that avoids this
fast fixed-point iteration. The key novelty of our approach is a modification
of the similarity matching objective to encourage near-diagonality of a
synaptic weight matrix. We then approximately invert this matrix using a Taylor
series approximation, replacing the previous fast iterations. In the offline
setting, our algorithm corresponds to a dynamical system, the stability of
which we rigorously analyze. In the online setting (i.e., with stochastic
gradients), we map our algorithm to a familiar neural network architecture and
give numerical results showing that our method converges at a competitive rate.
The computational complexity per iteration of our online algorithm is linear in
the total degrees of freedom, which is in some sense optimal.
",0,0,0,1,1,0
17708,Exploring to learn visual saliency: The RL-IAC approach,"  The problem of object localization and recognition on autonomous mobile
robots is still an active topic. In this context, we tackle the problem of
learning a model of visual saliency directly on a robot. This model, learned
and improved on-the-fly during the robot's exploration provides an efficient
tool for localizing relevant objects within their environment. The proposed
approach includes two intertwined components. On the one hand, we describe a
method for learning and incrementally updating a model of visual saliency from
a depth-based object detector. This model of saliency can also be exploited to
produce bounding box proposals around objects of interest. On the other hand,
we investigate an autonomous exploration technique to efficiently learn such a
saliency model. The proposed exploration, called Reinforcement
Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
exploration so that samples selected by the robot are likely to improve the
current model of saliency. We then demonstrate that such a saliency model
learned directly on a robot outperforms several state-of-the-art saliency
techniques, and that RL-IAC can drastically decrease the required time for
learning a reliable saliency model.
",1,0,0,0,0,0
17709,Fast and Robust Shortest Paths on Manifolds Learned from Data,"  We propose a fast, simple and robust algorithm for computing shortest paths
and distances on Riemannian manifolds learned from data. This amounts to
solving a system of ordinary differential equations (ODEs) subject to boundary
conditions. Here standard solvers perform poorly because they require
well-behaved Jacobians of the ODE, and usually, manifolds learned from data
imply unstable and ill-conditioned Jacobians. Instead, we propose a fixed-point
iteration scheme for solving the ODE that avoids Jacobians. This enhances the
stability of the solver, while reduces the computational cost. In experiments
involving both Riemannian metric learning and deep generative models we
demonstrate significant improvements in speed and stability over both
general-purpose state-of-the-art solvers as well as over specialized solvers.
",1,0,0,1,0,0
17710,Typed Graph Networks,"  Recently, the deep learning community has given growing attention to neural
architectures engineered to learn problems in relational domains. Convolutional
Neural Networks employ parameter sharing over the image domain, tying the
weights of neural connections on a grid topology and thus enforcing the
learning of a number of convolutional kernels. By instantiating trainable
neural modules and assembling them in varied configurations (apart from grids),
one can enforce parameter sharing over graphs, yielding models which can
effectively be fed with relational data. In this context, vertices in a graph
can be projected into a hyperdimensional real space and iteratively refined
over many message-passing iterations in an end-to-end differentiable
architecture. Architectures of this family have been referred to with several
definitions in the literature, such as Graph Neural Networks, Message-passing
Neural Networks, Relational Networks and Graph Networks. In this paper, we
revisit the original Graph Neural Network model and show that it generalises
many of the recent models, which in turn benefit from the insight of thinking
about vertex \textbf{types}. To illustrate the generality of the original
model, we present a Graph Neural Network formalisation, which partitions the
vertices of a graph into a number of types. Each type represents an entity in
the ontology of the problem one wants to learn. This allows - for instance -
one to assign embeddings to edges, hyperedges, and any number of global
attributes of the graph. As a companion to this paper we provide a
Python/Tensorflow library to facilitate the development of such architectures,
with which we instantiate the formalisation to reproduce a number of models
proposed in the current literature.
",1,0,0,1,0,0
17711,The effect of different in-chain impurities on the magnetic properties of the spin chain compound SrCuO$_2$ probed by NMR,"  The S=1/2 Heisenberg spin chain compound SrCuO2 doped with different amounts
of nickel (Ni), palladium (Pd), zinc (Zn) and cobalt (Co) has been studied by
means of Cu nuclear magnetic resonance (NMR). Replacing only a few of the S=1/2
Cu ions with Ni, Pd, Zn or Co has a major impact on the magnetic properties of
the spin chain system. In the case of Ni, Pd and Zn an unusual line broadening
in the low temperature NMR spectra reveals the existence of an impurity-induced
local alternating magnetization (LAM), while exponentially decaying
spin-lattice relaxation rates $T_1^{-1}$ towards low temperatures indicate the
opening of spin gaps. A distribution of gap magnitudes is proven by a stretched
spin-lattice relaxation and a variation of $T_1^{-1}$ within the broad
resonance lines. These observations depend strongly on the impurity
concentration and therefore can be understood using the model of finite
segments of the spin 1/2 antiferromagnetic Heisenberg chain, i.e. pure chain
segmentation due to S = 0 impurities. This is surprising for Ni as it was
previously assumed to be a magnetic impurity with S = 1 which is screened by
the neighboring copper spins. In order to confirm the S = 0 state of the Ni, we
performed x-ray absorption spectroscopy (XAS) and compared the measurements to
simulated XAS spectra based on multiplet ligand-field theory. Furthermore, Zn
doping leads to much smaller effects on both the NMR spectra and the
spin-lattice relaxation rates, indicating that Zn avoids occupying Cu sites.
For magnetic Co impurities, $T_1^{-1}$ does not obey the gap like decrease, and
the low-temperature spectra get very broad. This could be related to the
increase of the Neel temperature which was observed by recent muSR and
susceptibility measurements, and is most likely an effect of the impurity spin
$S\neq0$.
",0,1,0,0,0,0
17712,L-groups and the Langlands program for covering groups: a historical introduction,"  In this joint introduction to an Asterisque volume, we give a short
discussion of the historical developments in the study of nonlinear covering
groups, touching on their structure theory, representation theory and the
theory of automorphic forms. This serves as a historical motivation and sets
the scene for the papers in the volume. Our discussion is necessarily
subjective and will undoubtedly leave out the contributions of many authors, to
whom we apologize in earnest.
",0,0,1,0,0,0
17713,Uncertainty Reduction for Stochastic Processes on Complex Networks,"  Many real-world systems are characterized by stochastic dynamical rules where
a complex network of interactions among individual elements probabilistically
determines their state. Even with full knowledge of the network structure and
of the stochastic rules, the ability to predict system configurations is
generally characterized by a large uncertainty. Selecting a fraction of the
nodes and observing their state may help to reduce the uncertainty about the
unobserved nodes. However, choosing these points of observation in an optimal
way is a highly nontrivial task, depending on the nature of the stochastic
process and on the structure of the underlying interaction pattern. In this
paper, we introduce a computationally efficient algorithm to determine
quasioptimal solutions to the problem. The method leverages network sparsity to
reduce computational complexity from exponential to almost quadratic, thus
allowing the straightforward application of the method to mid-to-large-size
systems. Although the method is exact only for equilibrium stochastic processes
defined on trees, it turns out to be effective also for out-of-equilibrium
processes on sparse loopy networks.
",1,1,0,0,0,0
17714,Light propagation in Extreme Conditions - The role of optically clear tissues and scattering layers in optical biomedical imaging,"  The field of biomedical imaging has undergone a rapid growth in recent years,
mostly due to the implementation of ad-hoc designed experimental setups,
theoretical support methods and numerical reconstructions. Especially for
biological samples, the high number of scattering events occurring during the
photon propagation process limit the penetration depth and the possibility to
outperform direct imaging in thicker and not transparent samples. In this
thesis, we will examine theoretically and experimentally the scattering process
from two opposite points of view, focusing also on the continuous stimulus
offered by the will to tackle some specific challenges in the emerging optical
imaging science. Firstly, we will discuss the light propagation in diffusive
biological tissues considering the particular case of the presence of optically
transparent regions enclosed in a highly scattering environment. The correct
inclusion of this information, can ultimately lead to higher resolution
reconstruction, especially in neuroimaging. On the other hand, we will examine
the extreme case of the three-dimensional imaging of a totally hidden sample,
in which the phase has been scrambled by a random scattering layer. By making
use of appropriate numerical methods, we will prove how it is possible to
outperform such hidden reconstruction in a very efficient way, opening the path
toward the unexplored field of three-dimensional hidden imaging. Finally, we
will present how, the properties noticed while addressing these problems,
leaded us to the development of a novel alignment-free three-dimensional
tomographic technique that we refer to as Phase-Retrieved Tomography.
Ultimately, we used this technique for the study of the fluorescence
distribution in a three-dimensional spherical tumor model, the cancer cell
spheroid, one of the most important biological model for the study of such
disease.
",0,1,0,0,0,0
17715,Design discussion on the ISDA Common Domain Model,"  A new initiative from the International Swaps and Derivatives Association
(ISDA) aims to establish a ""Common Domain Model"" (ISDA CDM): a new standard for
data and process representation across the full range of derivatives
instruments. Design of the ISDA CDM is at an early stage and the draft
definition contains considerable complexity. This paper contributes by offering
insight, analysis and discussion relating to key topics in the design space
such as data lineage, timestamps, consistency, operations, events, state and
state transitions.
",1,0,0,0,0,0
17716,Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models,"  Hierarchical models are utilized in a wide variety of problems which are
characterized by task hierarchies, where predictions on smaller subtasks are
useful for trying to predict a final task. Typically, neural networks are first
trained for the subtasks, and the predictions of these networks are
subsequently used as additional features when training a model and doing
inference for a final task. In this work, we focus on improving learning for
such hierarchical models and demonstrate our method on the task of speaker
trait prediction. Speaker trait prediction aims to computationally identify
which personality traits a speaker might be perceived to have, and has been of
great interest to both the Artificial Intelligence and Social Science
communities. Persuasiveness prediction in particular has been of interest, as
persuasive speakers have a large amount of influence on our thoughts, opinions
and beliefs. In this work, we examine how leveraging the relationship between
related speaker traits in a hierarchical structure can help improve our ability
to predict how persuasive a speaker is. We present a novel algorithm that
allows us to backpropagate through this hierarchy. This hierarchical model
achieves a 25% relative error reduction in classification accuracy over current
state-of-the art methods on the publicly available POM dataset.
",1,0,0,0,0,0
17717,Atomistic simulations of dislocation/precipitation interactions in Mg-Al alloys and implications for precipitation hardening,"  Atomistic simulations were carried out to analyze the interaction between $<
a>$ basal dislocations and precipitates in Mg-Al alloys and the associated
strengthening mechanisms.
",0,1,0,0,0,0
17718,Recovering Dense Tissue Multispectral Signal from in vivo RGB Images,"  Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
clinical applications, such as 1) narrow band imaging for vascular
visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
clinical decision making [1]; 3) tissue classification and identification of
pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
be generally divided into two types: spatial scanning and spectral scanning.
However, the trade-off between spatial/spectral resolution, the acquisition
time, and the hardware complexity hampers implementation in real-world
applications, especially intra-operatively. Acquiring high resolution images in
real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
side effect caused by breathing, heartbeat, and other sources of motion.
Therefore, we developed an algorithm to recover a pixel-level MSI stack using
only the captured snapshot RGB images from a normal camera. We refer to this
technique as ""super-spectral-resolution"". The proposed method enables recovery
of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
second (FPS) on a GPU. Multispectral data captured from porcine bowel and
sheep/rabbit uteri in vivo has been used for training, and the algorithm has
been validated using unseen in vivo animal experiments.
",1,0,0,0,0,0
17719,Atomically thin gallium layers from solid-melt exfoliation,"  Among the large number of promising two-dimensional (2D) atomic layer
crystals, true metallic layers are rare. Through combined theoretical and
experimental approaches, we report on the stability and successful exfoliation
of atomically thin gallenene sheets, having two distinct atomic arrangements
along crystallographic twin directions of the parent alpha-gallium. Utilizing
the weak interface between solid and molten phases of gallium, a solid-melt
interface exfoliation technique is developed to extract these layers. Phonon
dispersion calculations show that gallenene can be stabilized with bulk gallium
lattice parameters. The electronic band structure of gallenene shows a
combination of partially filled Dirac cone and the non-linear dispersive band
near Fermi level suggesting that gallenene should behave as a metallic layer.
Furthermore it is observed that strong interaction of gallenene with other 2D
semiconductors induces semiconducting to metallic phase transitions in the
latter paving the way for using gallenene as interesting metallic contacts in
2D devices.
",0,1,0,0,0,0
17720,Novel Universality Classes in Ferroelectric Liquid Crystals,"  Starting from a Langevin formulation of a thermally perturbed nonlinear
elastic model of the ferroelectric smectic-C$^*$ (SmC${*}$) liquid crystals in
the presence of an electric field, this article characterizes the hitherto
unexplored dynamical phase transition from a thermo-electrically forced
ferroelectric SmC${}^{*}$ phase to a chiral nematic liquid crystalline phase
and vice versa. The theoretical analysis is based on a combination of dynamic
renormalization (DRG) and numerical simulation of the emergent model. While the
DRG architecture predicts a generic transition to the Kardar-Parisi-Zhang (KPZ)
universality class at dynamic equilibrium, in agreement with recent
experiments, the numerical simulations of the model show simultaneous existence
of two phases, one a ""subdiffusive"" (SD) phase characterized by a dynamical
exponent value of 1, and the other a KPZ phase, characterized by a dynamical
exponent value of 1.5. The SD phase flows over to the KPZ phase with increased
external forcing, offering a new universality paradigm, hitherto unexplored in
the context of ferroelectric liquid crystals.
",0,1,0,0,0,0
17721,Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies,"  We present a feature functional theory - binding predictor (FFT-BP) for the
protein-ligand binding affinity prediction. The underpinning assumptions of
FFT-BP are as follows: i) representability: there exists a microscopic feature
vector that can uniquely characterize and distinguish one protein-ligand
complex from another; ii) feature-function relationship: the macroscopic
features, including binding free energy, of a complex is a functional of
microscopic feature vectors; and iii) similarity: molecules with similar
microscopic features have similar macroscopic features, such as binding
affinity. Physical models, such as implicit solvent models and quantum theory,
are utilized to extract microscopic features, while machine learning algorithms
are employed to rank the similarity among protein-ligand complexes. A large
variety of numerical validations and tests confirms the accuracy and robustness
of the proposed FFT-BP model. The root mean square errors (RMSEs) of FFT-BP
blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core
set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99,
2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation
coefficients are 0.75, 0.80, and 0.78, respectively.
",1,1,0,0,0,0
17722,Combining and Steganography of 3D Face Textures,"  One of the serious issues in communication between people is hiding
information from others, and the best way for this, is deceiving them. Since
nowadays face images are mostly used in three dimensional format, in this paper
we are going to steganography 3D face images, detecting which by curious people
will be impossible. As in detecting face only its texture is important, we
separate texture from shape matrices, for eliminating half of the extra
information, steganography is done only for face texture, and for
reconstructing 3D face, we can use any other shape. Moreover, we will indicate
that, by using two textures, how two 3D faces can be combined. For a complete
description of the process, first, 2D faces are used as an input for building
3D faces, and then 3D textures are hidden within other images.
",1,0,0,0,0,0
17723,Degree of sequentiality of weighted automata,"  Weighted automata (WA) are an important formalism to describe quantitative
properties. Obtaining equivalent deterministic machines is a longstanding
research problem. In this paper we consider WA with a set semantics, meaning
that the semantics is given by the set of weights of accepting runs. We focus
on multi-sequential WA that are defined as finite unions of sequential WA. The
problem we address is to minimize the size of this union. We call this minimum
the degree of sequentiality of (the relation realized by) the WA. For a given
positive integer k, we provide multiple characterizations of relations realized
by a union of k sequential WA over an infinitary finitely generated group: a
Lipschitz-like machine independent property, a pattern on the automaton (a new
twinning property) and a subclass of cost register automata. When possible, we
effectively translate a WA into an equivalent union of k sequential WA. We also
provide a decision procedure for our twinning property for commutative
computable groups thus allowing to compute the degree of sequentiality. Last,
we show that these results also hold for word transducers and that the
associated decision problem is Pspace-complete.
",1,0,0,0,0,0
17724,On the orders of the non-Frattini elements of a finite group,"  Let $G$ be a finite group and let $p_1,\dots,p_n$ be distinct primes. If $G$
contains an element of order $p_1\cdots p_n,$ then there is an element in $G$
which is not contained in the Frattini subgroup of $G$ and whose order is
divisible by $p_1\cdots p_n.$
",0,0,1,0,0,0
17725,Varieties of general type with small volumes,"  Generalize Kobayashi's example for the Noether inequality in dimension three,
we provide examples of n-folds of general type with small volumes.
",0,0,1,0,0,0
17726,Translations in the exponential Orlicz space with Gaussian weight,"  We study the continuity of space translations on non-parametric exponential
families based on the exponential Orlicz space with Gaussian reference density.
",0,0,1,1,0,0
17727,Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime,"  In this study, we propose shrinkage methods based on {\it generalized ridge
regression} (GRR) estimation which is suitable for both multicollinearity and
high dimensional problems with small number of samples (large $p$, small $n$).
Also, it is obtained theoretical properties of the proposed estimators for
Low/High Dimensional cases. Furthermore, the performance of the listed
estimators is demonstrated by both simulation studies and real-data analysis,
and compare its performance with existing penalty methods. We show that the
proposed methods compare well to competing regularization techniques.
",0,0,1,1,0,0
17728,A path integral approach to Bayesian inference in Markov processes,"  We formulate Bayesian updates in Markov processes by means of path integral
techniques and derive the imaginary-time Schrödinger equation with
likelihood to direct the inference incorporated as a potential for the
posterior probability distribution
",0,0,1,1,0,0
17729,Strong-coupling charge density wave in a one-dimensional topological metal,"  Scanning tunnelling microscopy and low energy electron diffraction show a
dimerization-like reconstruction in the one-dimensional atomic chains on
Bi(114) at low temperatures. While one-dimensional systems are generally
unstable against such a distortion, its observation is not expected for this
particular surface, since there are several factors that should prevent it: One
is the particular spin texture of the Fermi surface, which resembles a
one-dimensional topological state, and spin protection should hence prevent the
formation of the reconstruction. The second is the very short nesting vector $2
k_F$, which is inconsistent with the observed lattice distortion. A
nesting-driven mechanism of the reconstruction is indeed excluded by the
absence of any changes in the electronic structure near the Fermi surface, as
observed by angle-resolved photoemission spectroscopy. However, distinct
changes in the electronic structure at higher binding energies are found to
accompany the structural phase transition. This, as well as the observed short
correlation length of the pairing distortion, suggest that the transition is of
the strong coupling type and driven by phonon entropy rather than electronic
entropy.
",0,1,0,0,0,0
17730,$\mathfrak A$-principal Hopf hypersurfaces in complex quadrics,"  A real hypersurface in the complex quadric $Q^m=SO_{m+2}/SO_mSO_2$ is said to
be $\mathfrak A$-principal if its unit normal vector field is singular of type
$\mathfrak A$-principal everywhere. In this paper, we show that a $\mathfrak
A$-principal Hopf hypersurface in $Q^m$, $m\geq3$ is an open part of a tube
around a totally geodesic $Q^{m+1}$ in $Q^m$. We also show that such real
hypersurfaces are the only contact real hypersurfaces in $Q^m$. %, this answers
affirmatively a question posted by Berndt (cf. \cite{berndt1})}. The
classification for pseudo-Einstein real hypersurfaces in $Q^m$, $m\geq3$, is
also obtained.
",0,0,1,0,0,0
17731,On the stochastic phase stability of Ti2AlC-Cr2AlC,"  The quest towards expansion of the MAX design space has been accelerated with
the recent discovery of several solid solution and ordered phases involving at
least two MAX end members. Going beyond the nominal MAX compounds enables not
only fine tuning of existing properties but also entirely new functionality.
This search, however, has been mostly done through painstaking experiments as
knowledge of the phase stability of the relevant systems is rather scarce. In
this work, we report the first attempt to evaluate the finite-temperature
pseudo-binary phase diagram of the Ti2AlC-Cr2AlC via first-principles-guided
Bayesian CALPHAD framework that accounts for uncertainties not only in ab
initio calculations and thermodynamic models but also in synthesis conditions
in reported experiments. The phase stability analyses are shown to have good
agreement with previous experiments. The work points towards a promising way of
investigating phase stability in other MAX Phase systems providing the
knowledge necessary to elucidate possible synthesis routes for MAX systems with
unprecedented properties.
",0,1,0,0,0,0
17732,Note on Attacking Object Detectors with Adversarial Stickers,"  Deep learning has proven to be a powerful tool for computer vision and has
seen widespread adoption for numerous tasks. However, deep learning algorithms
are known to be vulnerable to adversarial examples. These adversarial inputs
are created such that, when provided to a deep learning algorithm, they are
very likely to be mislabeled. This can be problematic when deep learning is
used to assist in safety critical decisions. Recent research has shown that
classifiers can be attacked by physical adversarial examples under various
physical conditions. Given the fact that state-of-the-art objection detection
algorithms are harder to be fooled by the same set of adversarial examples,
here we show that these detectors can also be attacked by physical adversarial
examples. In this note, we briefly show both static and dynamic test results.
We design an algorithm that produces physical adversarial inputs, which can
fool the YOLO object detector and can also attack Faster-RCNN with relatively
high success rate based on transferability. Furthermore, our algorithm can
compress the size of the adversarial inputs to stickers that, when attached to
the targeted object, result in the detector either mislabeling or not detecting
the object a high percentage of the time. This note provides a small set of
results. Our upcoming paper will contain a thorough evaluation on other object
detectors, and will present the algorithm.
",1,0,0,0,0,0
17733,All or Nothing Caching Games with Bounded Queries,"  We determine the value of some search games where our goal is to find all of
some hidden treasures using queries of bounded size. The answer to a query is
either empty, in which case we lose, or a location, which contains a treasure.
We prove that if we need to find $d$ treasures at $n$ possible locations with
queries of size at most $k$, then our chance of winning is $\frac{k^d}{\binom
nd}$ if each treasure is at a different location and
$\frac{k^d}{\binom{n+d-1}d}$ if each location might hide several treasures for
large enough $n$. Our work builds on some results by Csóka who has studied a
continuous version of this problem, known as Alpern's Caching Game; we also
prove that the value of Alpern's Caching Game is $\frac{k^d}{\binom{n+d-1}d}$
for integer $k$ and large enough $n$.
",1,0,1,0,0,0
17734,"HATS-43b, HATS-44b, HATS-45b, and HATS-46b: Four Short Period Transiting Giant Planets in the Neptune-Jupiter Mass Range","  We report the discovery of four short period extrasolar planets transiting
moderately bright stars from photometric measurements of the HATSouth network
coupled to additional spectroscopic and photometric follow-up observations.
While the planet masses range from 0.26 to 0.90 M$_J$, the radii are all
approximately a Jupiter radii, resulting in a wide range of bulk densities. The
orbital period of the planets range from 2.7d to 4.7d, with HATS-43b having an
orbit that appears to be marginally non-circular (e= 0.173$\pm$0.089). HATS-44
is notable for a high metallicity ([Fe/H]= 0.320$\pm$0.071). The host stars
spectral types range from late F to early K, and all of them are moderately
bright (13.3<V<14.4), allowing the execution of future detailed follow-up
observations. HATS-43b and HATS-46b, with expected transmission signals of 2350
ppm and 1500 ppm, respectively, are particularly well suited targets for
atmospheric characterisation via transmission spectroscopy.
",0,1,0,0,0,0
17735,Well-balanced mesh-based and meshless schemes for the shallow-water equations,"  We formulate a general criterion for the exact preservation of the ""lake at
rest"" solution in general mesh-based and meshless numerical schemes for the
strong form of the shallow-water equations with bottom topography. The main
idea is a careful mimetic design for the spatial derivative operators in the
momentum flux equation that is paired with a compatible averaging rule for the
water column height arising in the bottom topography source term. We prove
consistency of the mimetic difference operators analytically and demonstrate
the well-balanced property numerically using finite difference and RBF-FD
schemes in the one- and two-dimensional cases.
",0,1,1,0,0,0
17736,An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk,"  The use of CVA to cover credit risk is widely spread, but has its
limitations. Namely, dealers face the problem of the illiquidity of instruments
used for hedging it, hence forced to warehouse credit risk. As a result,
dealers tend to offer a limited OTC derivatives market to highly risky
counterparties. Consequently, those highly risky entities rarely have access to
hedging services precisely when they need them most. In this paper we propose a
method to overcome this limitation. We propose to extend the CVA risk-neutral
framework to compute an initial margin (IM) specific to each counterparty,
which depends on the credit quality of the entity at stake, transforming the
effective credit rating of a given netting set to AAA, regardless of the credit
rating of the counterparty. By transforming CVA requirement into IM ones, as
proposed in this paper, an institution could rely on the existing mechanisms
for posting and calling of IM, hence ensuring the operational viability of this
new form of managing warehoused risk. The main difference with the currently
standard framework is the creation of a Specific Initial Margin, that depends
in the credit rating of the counterparty and the characteristics of the netting
set in question. In this paper we propose a methodology for such transformation
in a sound manner, and hence this method overcomes some of the limitations of
the CVA framework.
",0,0,0,0,0,1
17737,Ensemble Pruning based on Objection Maximization with a General Distributed Framework,"  Ensemble pruning, selecting a subset of individual learners from an original
ensemble, alleviates the deficiencies of ensemble learning on the cost of time
and space. Accuracy and diversity serve as two crucial factors while they
usually conflict with each other. To balance both of them, we formalize the
ensemble pruning problem as an objection maximization problem based on
information entropy. Then we propose an ensemble pruning method including a
centralized version and a distributed version, in which the latter is to speed
up the former's execution. At last, we extract a general distributed framework
for ensemble pruning, which can be widely suitable for most of existing
ensemble pruning methods and achieve less time consuming without much accuracy
decline. Experimental results validate the efficiency of our framework and
methods, particularly with regard to a remarkable improvement of the execution
speed, accompanied by gratifying accuracy performance.
",0,0,0,1,0,0
17738,Psychophysical laws as reflection of mental space properties,"  The paper is devoted to the relationship between psychophysics and physics of
mind. The basic trends in psychophysics development are briefly discussed with
special attention focused on Teghtsoonian's hypotheses. These hypotheses pose
the concept of the universality of inner psychophysics and enable to speak
about psychological space as an individual object with its own properties.
Turning to the two-component description of human behavior (I. Lubashevsky,
Physics of the Human Mind, Springer, 2017) the notion of mental space is
formulated and human perception of external stimuli is treated as the emergence
of the corresponding images in the mental space. On one hand, these images are
caused by external stimuli and their magnitude bears the information about the
intensity of the corresponding stimuli. On the other hand, the individual
structure of such images as well as their subsistence after emergence is
determined only by the properties of mental space on its own. Finally, the
mental operations of image comparison and their scaling are defined in a way
allowing for the bounded capacity of human cognition. As demonstrated, the
developed theory of stimulus perception is able to explain the basic
regularities of psychophysics, e.g., (i) the regression and range effects
leading to the overestimation of weak stimuli and the underestimation of strong
stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (\textit{iii})
the sequential (memory) effects. As the final result, a solution to the
Fechner-Stevens dilemma is proposed. This solution posits that Fechner's
logarithmic law is not a consequences of Weber's law but stems from the
interplay of uncertainty in evaluating stimulus intensities and the multi-step
scaling required to overcome the stimulus incommensurability.
",0,0,0,0,1,0
17739,Shimura curves in the Prym locus,"  We study Shimura curves of PEL type in $\mathsf{A}_g$ generically contained
in the Prym locus. We study both the unramified Prym locus, obtained using
étale double covers, and the ramified Prym locus, corresponding to double
covers ramified at two points. In both cases we consider the family of all
double covers compatible with a fixed group action on the base curve. We
restrict to the case where the family is 1-dimensional and the quotient of the
base curve by the group is $\mathbb{P}^1$. We give a simple criterion for the
image of these families under the Prym map to be a Shimura curve. Using
computer algebra we check all the examples gotten in this way up to genus 28.
We obtain 43 Shimura curves generically contained in the unramified Prym locus
and 9 families generically contained in the ramified Prym locus. Most of these
curves are not generically contained in the Jacobian locus.
",0,0,1,0,0,0
17740,Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies,"  Data-driven anomaly detection methods suffer from the drawback of detecting
all instances that are statistically rare, irrespective of whether the detected
instances have real-world significance or not. In this paper, we are interested
in the problem of specifically detecting anomalous instances that are known to
have high real-world utility, while ignoring the low-utility statistically
anomalous instances. To this end, we propose a novel method called Latent
Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution.
This method uses the EM algorithm to simultaneously incorporate the Geometric
Entropy Minimization principle for identifying statistical anomalies, and the
Maximum Entropy Discrimination principle to incorporate utility labels, in
order to detect high-utility anomalies. We apply our method in both simulated
and real datasets to demonstrate that it has superior performance over existing
alternatives that independently pre-process with unsupervised anomaly detection
algorithms before classifying.
",1,0,0,1,0,0
17741,View-Invariant Recognition of Action Style Self-Dissimilarity,"  Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different subjects regardless of viewing direction and camera
parameters. We investigate two frameworks using these invariant style
dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
indicate remarkably good discriminant characteristics for the proposed
invariant measures for gender recognition from video data.
",1,0,0,0,0,0
17742,Conducting Highly Principled Data Science: A Statistician's Job and Joy,"  Highly Principled Data Science insists on methodologies that are: (1)
scientifically justified, (2) statistically principled, and (3) computationally
efficient. An astrostatistics collaboration, together with some reminiscences,
illustrates the increased roles statisticians can and should play to ensure
this trio, and to advance the science of data along the way.
",0,0,0,1,0,0
17743,Regularizing nonlinear Schroedinger equations through partial off-axis variations,"  We study a class of focusing nonlinear Schroedinger-type equations derived
recently by Dumas, Lannes and Szeftel within the mathematical description of
high intensity laser beams [7]. These equations incorporate the possibility of
a (partial) off-axis variation of the group velocity of such laser beams
through a second order partial differential operator acting in some, but not
necessarily all, spatial directions. We study the well-posedness theory for
such models and obtain a regularizing effect, even in the case of only partial
off-axis dependence. This provides an answer to an open problem posed in [7].
",0,0,1,0,0,0
17744,On h-Lexicalized Restarting Automata,"  Following some previous studies on restarting automata, we introduce a
refined model - the h-lexicalized restarting automaton (h-RLWW). We argue that
this model is useful for expressing lexicalized syntax in computational
linguistics. We compare the input languages, which are the languages
traditionally considered in automata theory, to the so-called basic and
h-proper languages, which are (implicitly) used by categorial grammars, the
original tool for the description of lexicalized syntax. The basic and h-proper
languages allow us to stress several nice properties of h-lexicalized
restarting automata, and they are suitable for modeling the analysis by
reduction and, subsequently, for the development of categories of a lexicalized
syntax. Based on the fact that a two-way deterministic monotone restarting
automaton can be transformed into an equivalent deterministic monotone
RL-automaton in (Marcus) contextual form, we obtain a transformation from
monotone RLWW-automata that recognize the class CFL of context-free languages
as their input languages to deterministic monotone h-RLWW-automata that
recognize CFL through their h-proper languages. Through this transformation we
obtain automata with the complete correctness preserving property and an
infinite hierarchy within CFL, based on the size of the read/write window.
Additionally, we consider h-RLWW-automata that are allowed to perform multiple
rewrite steps per cycle, and we establish another infinite hierarchy above CFL
that is based on the number of rewrite steps that may be executed within a
cycle. The corresponding separation results and their proofs illustrate the
transparency of h-RLWW-automata that work with the (complete or cyclic)
correctness preserving property
",1,0,0,0,0,0
17745,Bayesian Nonparametric Inference for M/G/1 Queueing Systems,"  In this work, nonparametric statistical inference is provided for the
continuous-time M/G/1 queueing model from a Bayesian point of view. The
inference is based on observations of the inter-arrival and service times.
Beside other characteristics of the system, particular interest is in the
waiting time distribution which is not accessible in closed form. Thus, we use
an indirect statistical approach by exploiting the Pollaczek-Khinchine
transform formula for the Laplace transform of the waiting time distribution.
Due to this, an estimator is defined and its frequentist validation in terms of
posterior consistency and posterior normality is studied. It will turn out that
we can hereby make inference for the observables separately and compose the
results subsequently by suitable techniques.
",0,0,1,1,0,0
17746,Symbol Invariant of Partition and the Construction,"  The symbol is used to describe the Springer correspondence for the classical
groups. We propose equivalent definitions of symbols for rigid partitions in
the $B_n$, $C_n$, and $D_n$ theories uniformly. Analysing the new definition of
symbol in detail, we give rules to construct symbol of a partition, which are
easy to remember and to operate on. We introduce formal operations of a
partition, which reduce the difficulties in the proof of the construction
rules. According these rules, we give a closed formula of symbols for different
theories uniformly. As applications, previous results can be illustrated more
clearly by the construction rules of symbol.
",0,0,1,0,0,0
17747,Fairness in representation: quantifying stereotyping as a representational harm,"  While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.
",1,0,0,1,0,0
17748,Probing the possibility of hotspots on the central neutron star in HESS J1731-347,"  The X-ray spectra of the neutron stars located in the centers of supernova
remnants Cas A and HESS J1731-347 are well fit with carbon atmosphere models.
These fits yield plausible neutron star sizes for the known or estimated
distances to these supernova remnants. The evidence in favor of the presence of
a pure carbon envelope at the neutron star surface is rather indirect and is
based on the assumption that the emission is generated uniformly by the entire
stellar surface. Although this assumption is supported by the absence of
pulsations, the observational upper limit on the pulsed fraction is not very
stringent. In an attempt to quantify this evidence, we investigate the
possibility that the observed spectrum of the neutron star in HESS J1731-347 is
a combination of the spectra produced in a hydrogen atmosphere of the hotspots
and of the cooler remaining part of the neutron star surface. The lack of
pulsations in this case has to be explained either by a sufficiently small
angle between the neutron star spin axis and the line of sight, or by a
sufficiently small angular distance between the hotspots and the neutron star
rotation poles. As the observed flux from a non-uniformly emitting neutron star
depends on the angular distribution of the radiation emerging from the
atmosphere, we have computed two new grids of pure carbon and pure hydrogen
atmosphere model spectra accounting for Compton scattering. Using new hydrogen
models, we have evaluated the probability of a geometry that leads to a pulsed
fraction below the observed upper limit to be about 8.2 %. Such a geometry thus
seems to be rather improbable but cannot be excluded at this stage.
",0,1,0,0,0,0
17749,Some Elementary Partition Inequalities and Their Implications,"  We prove various inequalities between the number of partitions with the bound
on the largest part and some restrictions on occurrences of parts. We explore
many interesting consequences of these partition inequalities. In particular,
we show that for $L\geq 1$, the number of partitions with $l-s \leq L$ and
$s=1$ is greater than the number of partitions with $l-s\leq L$ and $s>1$. Here
$l$ and $s$ are the largest part and the smallest part of the partition,
respectively.
",0,0,1,0,0,0
17750,The terrestrial late veneer from core disruption of a lunar-sized impactor,"  Overabundances in highly siderophile elements (HSEs) of Earth's mantle can be
explained by conveyance from a singular, immense (3000 km in a diameter) ""Late
Veneer"" impactor of chondritic composition, subsequent to lunar formation and
terrestrial core-closure. Such rocky objects of approximately lunar mass (about
0.01 M_E) ought to be differentiated, such that nearly all of their HSE payload
is sequestered into iron cores. Here, we analyze the mechanical and chemical
fate of the core of such a Late Veneer impactor, and trace how its HSEs are
suspended - and thus pollute - the mantle. For the statistically most-likely
oblique collision (about 45degree), the impactor's core elongates and
thereafter disintegrates into a metallic hail of small particles (about 10 m).
Some strike the orbiting Moon as sesquinary impactors, but most re-accrete to
Earth as secondaries with further fragmentation. We show that a single oblique
impactor provides an adequate amount of HSEs to the primordial terrestrial
silicate reservoirs via oxidation of (<m-sized) metal particles with a hydrous,
pre-impact, early Hadean Earth.
",0,1,0,0,0,0
17751,A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image,"  We consider a Bayesian model for inversion of observed amplitude variation
with offset (AVO) data into lithology/fluid classes, and study in particular
how the choice of prior distribution for the lithology/fluid classes influences
the inversion results. Two distinct prior distributions are considered, a
simple manually specified Markov random field prior with a first order
neighborhood and a Markov mesh model with a much larger neighborhood estimated
from a training image. They are chosen to model both horisontal connectivity
and vertical thickness distribution of the lithology/fluid classes, and are
compared on an offshore clastic oil reservoir in the North Sea. We combine both
priors with the same linearised Gaussian likelihood function based on a
convolved linearised Zoeppritz relation and estimate properties of the
resulting two posterior distributions by simulating from these distributions
with the Metropolis-Hastings algorithm.
The influence of the prior on the marginal posterior probabilities for the
lithology/fluid classes is clearly observable, but modest. The importance of
the prior on the connectivity properties in the posterior realisations,
however, is much stronger. The larger neighborhood of the Markov mesh prior
enables it to identify and model connectivity and curvature much better than
what can be done by the first order neighborhood Markov random field prior. As
a result, we conclude that the posterior realisations based on the Markov mesh
prior appear with much higher lateral connectivity, which is geologically
plausible.
",0,0,0,1,0,0
17752,Correct Brillouin zone and electronic structure of BiPd,"  A promising route to the realization of Majorana fermions is in
non-centrosymmetric superconductors, in which spin-orbit-coupling lifts the
spin degeneracy of both bulk and surface bands. A detailed assessment of the
electronic structure is critical to evaluate their suitability for this through
establishing the topological properties of the electronic structure. This
requires correct identification of the time-reversal-invariant momenta. One
such material is BiPd, a recently rediscovered non-centrosymmetric
superconductor which can be grown in large, high-quality single crystals and
has been studied by several groups using angular resolved photoemission to
establish its surface electronic structure. Many of the published electronic
structure studies on this material are based on a reciprocal unit cell which is
not the actual Brillouin zone of the material. We show here the consequences of
this for the electronic structures and show how the inferred topological nature
of the material is affected.
",0,1,0,0,0,0
17753,Even faster sorting of (not only) integers,"  In this paper we introduce RADULS2, the fastest parallel sorter based on
radix algorithm. It is optimized to process huge amounts of data making use of
modern multicore CPUs. The main novelties include: extremely optimized
algorithm for handling tiny arrays (up to about a hundred of records) that
could appear even billions times as subproblems to handle and improved
processing of larger subarrays with better use of non-temporal memory stores.
",1,0,0,0,0,0
17754,Evidential Deep Learning to Quantify Classification Uncertainty,"  Deterministic neural nets have been shown to learn effective predictors on a
wide range of machine learning problems. However, as the standard approach is
to train the network to minimize a prediction loss, the resultant model remains
ignorant to its prediction confidence. Orthogonally to Bayesian neural nets
that indirectly infer prediction uncertainty through weight uncertainties, we
propose explicit modeling of the same using the theory of subjective logic. By
placing a Dirichlet distribution on the class probabilities, we treat
predictions of a neural net as subjective opinions and learn the function that
collects the evidence leading to these opinions by a deterministic neural net
from data. The resultant predictor for a multi-class classification problem is
another Dirichlet distribution whose parameters are set by the continuous
output of a neural net. We provide a preliminary analysis on how the
peculiarities of our new loss function drive improved uncertainty estimation.
We observe that our method achieves unprecedented success on detection of
out-of-distribution queries and endurance against adversarial perturbations.
",0,0,0,1,0,0
17755,Neutron interference in the Earth's gravitational field,"  This work relates to the famous experiments, performed in 1975 and 1979 by
Werner et al., measuring neutron interference and neutron Sagnac effects in the
earth's gravitational field. Employing the method of Stodolsky in its weak
field approximation, explicit expressions are derived for the two phase shifts,
which turn out to be in agreement with the experiments and with the previously
obtained expressions derived from semi-classical arguments: these expressions
are simply modified by relativistic correction factors.
",0,1,0,0,0,0
17756,Solve For Shortest Paths Problem Within Logarithm Runtime,"  The Shortest Paths Problem (SPP) is no longer unresolved. Just for a large
scalar of instance on this problem, even we cannot know if an algorithm
achieves the computing. Those cutting-edge methods are still in the low
performance. If we go to a strategy the best-first-search to deal with
computing, it is awkward that the technical barrier from another field: the
database, which with the capable of Online Oriented. In this paper, we will
introduce such a synthesis to solve for SPP which comprises various modules
therein including such database leads to finish the task in a logarithm
runtime.
Through experiments taken on three typical instances on mega-scalar data for
transaction in a common laptop, we show off a totally robust, tractable and
practical applicability for other projects.
",1,0,0,0,0,0
17757,A short proof of the error term in Simpson's rule,"  In this paper we present a short and elementary proof for the error in
Simpson's rule.
",0,0,1,0,0,0
17758,Neutrino mass and dark energy constraints from redshift-space distortions,"  Cosmology in the near future promises a measurement of the sum of neutrino
masses, a fundamental Standard Model parameter, as well as
substantially-improved constraints on the dark energy. We use the shape of the
BOSS redshift-space galaxy power spectrum, in combination with CMB and
supernova data, to constrain the neutrino masses and the dark energy. Essential
to this calculation are several recent advances in non-linear cosmological
perturbation theory, including FFT methods, redshift space distortions, and
scale-dependent growth. Our 95% confidence upper bound of 200 meV on the sum of
masses degrades substantially to 770 meV when the dark energy equation of state
and its first derivative are also allowed to vary, representing a significant
challenge to current constraints. We also study the impact of additional galaxy
bias parameters, finding that a velocity bias or a more complicated
scale-dependent density bias shift the preferred neutrino mass values 20%-30%
lower while minimally impacting the other cosmological parameters.
",0,1,0,0,0,0
17759,Dielectrophoretic assembly of liquid-phase-exfoliated TiS3 nanoribbons for photodetecting applications,"  Liquid-phase-exfoliation is a technique capable of producing large quantities
of two-dimensional material in suspension. Despite many efforts in the
optimization of the exfoliation process itself not much has been done towards
the integration of liquid-phase-exfoliated materials in working solid-state
devices. In this article, we use dielectrophoresis to direct the assembly of
liquid-phase-exfoliated TiS3 nanoribbons between two gold electrodes to produce
photodetectors working in the visible. Through electrical and optical
measurements we characterize the responsivity of the device and we find values
as large as 3.8 mA/W, which improve of more than one order of magnitude on the
state-of-the-art for devices based on liquid-phase-exfoliated two-dimensional
materials assembled by drop-casting or ink-jet methods.
",0,1,0,0,0,0
17760,Motion optimization and parameter identification for a human and lower-back exoskeleton model,"  Designing an exoskeleton to reduce the risk of low-back injury during lifting
is challenging. Computational models of the human-robot system coupled with
predictive movement simulations can help to simplify this design process. Here,
we present a study that models the interaction between a human model actuated
by muscles and a lower-back exoskeleton. We provide a computational framework
for identifying the spring parameters of the exoskeleton using an optimal
control approach and forward-dynamics simulations. This is applied to generate
dynamically consistent bending and lifting movements in the sagittal plane. Our
computations are able to predict motions and forces of the human and
exoskeleton that are within the torque limits of a subject. The identified
exoskeleton could also yield a considerable reduction of the peak lower-back
torques as well as the cumulative lower-back load during the movements. This
work is relevant to the research communities working on human-robot
interaction, and can be used as a basis for a better human-centered design
process.
",1,0,0,0,0,0
17761,A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images,"  Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
use a publicly available dataset to assess and compare their methods. Different
systems have been designed for the detection of pulmonary embolism (PE), but
none of them have used any public datasets. All papers have used their own
private dataset. In order to fill this gap, we have collected 5160 slices of
computed tomography angiography (CTA) images acquired from 20 patients, and
after labeling the image by experts in this field, we provided a reliable
dataset which is now publicly available. In some situation, PE detection can be
difficult, for example when it occurs in the peripheral branches or when
patients have pulmonary diseases (such as parenchymal disease). Therefore, the
efficiency of CAD systems highly depends on the dataset. In the given dataset,
66% of PE are located in peripheral branches, and different pulmonary diseases
are also included.
",1,0,0,0,0,0
17762,"Comment on Ben-Amotz and Honig, ""Average entropy dissipation in irreversible mesoscopic processes,"" Phys. Rev. Lett. 96, 020602 (2006)","  We point out that most of the classical thermodynamics results in the paper
have been known in the literature, see Kestin and Woods, for quite some time
and are not new, contrary to what the authors imply. As shown by Kestin, these
results are valid for quasistatic irreversible processes only and not for
arbitrary irreversible processes as suggested in the paper. Thus, the
application to the Jarzynski process is limited.
",0,1,0,0,0,0
17763,High-transmissivity Silicon Visible-wavelength Metasurface Designs based on Truncated-cone Nanoantennae,"  High-transmissivity all-dielectric metasurfaces have recently attracted
attention towards the realization of ultra-compact optical devices and systems.
Silicon based metasurfaces, in particular, are highly promising considering the
possibility of monolithic integration with VLSI circuits. Realization of
silicon based metasurfaces operational in the visible wavelengths remains a
challenge. A numerical study of silicon metasurfaces based on stepped truncated
cone shaped nanoantenna elements is presented. Metasurfaces based on the
stepped conical geometry can be designed for operation in the 700nm to 800nm
wavelength window and achieve full cycle phase response (0 to pi with an
improved transmittance in comparison with previously reported cylindrical
geometry [1]. A systematic parameter study of the influence of various
geometrical parameters on the achievable amplitude and phase coverage is
reported.
",0,1,0,0,0,0
17764,Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data,"  We proposed a probabilistic approach to joint modeling of participants'
reliability and humans' regularity in crowdsourced affective studies.
Reliability measures how likely a subject will respond to a question seriously;
and regularity measures how often a human will agree with other
seriously-entered responses coming from a targeted population.
Crowdsourcing-based studies or experiments, which rely on human self-reported
affect, pose additional challenges as compared with typical crowdsourcing
studies that attempt to acquire concrete non-affective labels of objects. The
reliability of participants has been massively pursued for typical
non-affective crowdsourcing studies, whereas the regularity of humans in an
affective experiment in its own right has not been thoroughly considered. It
has been often observed that different individuals exhibit different feelings
on the same test question, which does not have a sole correct response in the
first place. High reliability of responses from one individual thus cannot
conclusively result in high consensus across individuals. Instead, globally
testing consensus of a population is of interest to investigators. Built upon
the agreement multigraph among tasks and workers, our probabilistic model
differentiates subject regularity from population reliability. We demonstrate
the method's effectiveness for in-depth robust analysis of large-scale
crowdsourced affective data, including emotion and aesthetic assessments
collected by presenting visual stimuli to human subjects.
",1,0,0,1,0,0
17765,Quantum key distribution protocol with pseudorandom bases,"  Quantum key distribution (QKD) offers a way for establishing
information-theoretically secure communications. An important part of QKD
technology is a high-quality random number generator (RNG) for quantum states
preparation and for post-processing procedures. In the present work, we
consider a novel class of prepare-and-measure QKD protocols, utilizing
additional pseudorandomness in the preparation of quantum states. We study one
of such protocols and analyze its security against the intercept-resend attack.
We demonstrate that, for single-photon sources, the considered protocol gives
better secret key rates than the BB84 and the asymmetric BB84 protocol.
However, the protocol strongly requires single-photon sources.
",1,0,0,0,0,0
17766,Deep clustering of longitudinal data,"  Deep neural networks are a family of computational models that have led to a
dramatical improvement of the state of the art in several domains such as
image, voice or text analysis. These methods provide a framework to model
complex, non-linear interactions in large datasets, and are naturally suited to
the analysis of hierarchical data such as, for instance, longitudinal data with
the use of recurrent neural networks. In the other hand, cohort studies have
become a tool of importance in the research field of epidemiology. In such
studies, variables are measured repeatedly over time, to allow the practitioner
to study their temporal evolution as trajectories, and, as such, as
longitudinal data. This paper investigates the application of the advanced
modelling techniques provided by the deep learning framework in the analysis of
the longitudinal data provided by cohort studies. Methods: A method for
visualizing and clustering longitudinal dataset is proposed, and compared to
other widely used approaches to the problem on both real and simulated
datasets. Results: The proposed method is shown to be coherent with the
preexisting procedures on simple tasks, and to outperform them on more complex
tasks such as the partitioning of longitudinal datasets into non-spherical
clusters. Conclusion: Deep artificial neural networks can be used to visualize
longitudinal data in a low dimensional manifold that is much simpler to
interpret than traditional longitudinal plots are. Consequently, practitioners
should start considering the use of deep artificial neural networks for the
analysis of their longitudinal data in studies to come.
",0,0,0,1,0,0
17767,Economic Implications of Blockchain Platforms,"  In an economy with asymmetric information, the smart contract in the
blockchain protocol mitigates uncertainty. Since, as a new trading platform,
the blockchain triggers segmentation of market and differentiation of agents in
both the sell and buy sides of the market, it recomposes the asymmetric
information and generates spreads in asset price and quality between itself and
a traditional platform. We show that marginal innovation and sophistication of
the smart contract have non-monotonic effects on the trading value in the
blockchain platform, its fundamental value, the price of cryptocurrency, and
consumers' welfare. Moreover, a blockchain manager who controls the level of
the innovation of the smart contract has an incentive to keep it lower than the
first best when the underlying information asymmetry is not severe, leading to
welfare loss for consumers.
",0,0,0,0,0,1
17768,Proof Theory and Ordered Groups,"  Ordering theorems, characterizing when partial orders of a group extend to
total orders, are used to generate hypersequent calculi for varieties of
lattice-ordered groups (l-groups). These calculi are then used to provide new
proofs of theorems arising in the theory of ordered groups. More precisely: an
analytic calculus for abelian l-groups is generated using an ordering theorem
for abelian groups; a calculus is generated for l-groups and new decidability
proofs are obtained for the equational theory of this variety and extending
finite subsets of free groups to right orders; and a calculus for representable
l-groups is generated and a new proof is obtained that free groups are
orderable.
",0,0,1,0,0,0
17769,DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features,"  As the foundation of driverless vehicle and intelligent robots, Simultaneous
Localization and Mapping(SLAM) has attracted much attention these days.
However, non-geometric modules of traditional SLAM algorithms are limited by
data association tasks and have become a bottleneck preventing the development
of SLAM. To deal with such problems, many researchers seek to Deep Learning for
help. But most of these studies are limited to virtual datasets or specific
environments, and even sacrifice efficiency for accuracy. Thus, they are not
practical enough.
We propose DF-SLAM system that uses deep local feature descriptors obtained
by the neural network as a substitute for traditional hand-made features.
Experimental results demonstrate its improvements in efficiency and stability.
DF-SLAM outperforms popular traditional SLAM systems in various scenes,
including challenging scenes with intense illumination changes. Its versatility
and mobility fit well into the need for exploring new environments. Since we
adopt a shallow network to extract local descriptors and remain others the same
as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
",1,0,0,0,0,0
17770,A-NICE-MC: Adversarial Training for MCMC,"  Existing Markov Chain Monte Carlo (MCMC) methods are either based on
general-purpose and domain-agnostic schemes which can lead to slow convergence,
or hand-crafting of problem-specific proposals by an expert. We propose
A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
produce samples with desired properties. First, we propose an efficient
likelihood-free adversarial training method to train a Markov chain and mimic a
given data distribution. Then, we leverage flexible volume preserving flows to
obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
train efficient Markov chains to sample from a prescribed posterior
distribution by iteratively improving the quality of both the model and the
samples. A-NICE-MC provides the first framework to automatically design
efficient domain-specific MCMC proposals. Empirical results demonstrate that
A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
deep neural networks, and is able to significantly outperform competing methods
such as Hamiltonian Monte Carlo.
",1,0,0,1,0,0
17771,"Starobinsky-like Inflation, Supercosmology and Neutrino Masses in No-Scale Flipped SU(5)","  We embed a flipped ${\rm SU}(5) \times {\rm U}(1)$ GUT model in a no-scale
supergravity framework, and discuss its predictions for cosmic microwave
background observables, which are similar to those of the Starobinsky model of
inflation. Measurements of the tilt in the spectrum of scalar perturbations in
the cosmic microwave background, $n_s$, constrain significantly the model
parameters. We also discuss the model's predictions for neutrino masses, and
pay particular attention to the behaviours of scalar fields during and after
inflation, reheating and the GUT phase transition. We argue in favor of strong
reheating in order to avoid excessive entropy production which could dilute the
generated baryon asymmetry.
",0,1,0,0,0,0
17772,"'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies","  The interactive computation paradigm is reviewed and a particular example is
extended to form the stochastic analog of a computational process via a
transcription of a minimal Turing Machine into an equivalent asynchronous
Cellular Automaton with an exponential waiting times distribution of effective
transitions. Furthermore, a special toolbox for analytic derivation of
recursive relations of important statistical and other quantities is introduced
in the form of an Inductive Combinatorial Hierarchy.
",1,1,0,0,0,0
17773,Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem,"  IIn recent years, there has been a growing interest in applying data
assimilation (DA) methods, originally designed for state estimation, to the
model selection problem. In this setting, Carrassi et al. (2017) introduced the
contextual formulation of model evidence (CME) and showed that CME can be
efficiently computed using a hierarchy of ensemble-based DA procedures.
Although Carrassi et al. (2017) analyzed the DA methods most commonly used for
operational atmospheric and oceanic prediction worldwide, they did not study
these methods in conjunction with localization to a specific domain. Yet any
application of ensemble DA methods to realistic geophysical models requires the
implementation of some form of localization. The present study extends the
theory for estimating CME to ensemble DA methods with domain localization. The
domain-localized CME (DL-CME) developed herein is tested for model selection
with two models: (i) the Lorenz 40-variable mid-latitude atmospheric dynamics
model (L95); and (ii) the simplified global atmospheric SPEEDY model. The CME
is compared to the root-mean-square-error (RMSE) as a metric for model
selection. The experiments show that CME improves systematically over the RMSE,
and that this skill improvement is further enhanced by applying localization in
the estimate of the CME, using the DL-CME. The potential use and range of
applications of the CME and DL-CME as a model selection metric are also
discussed.
",0,0,0,1,0,0
17774,An efficient spectral-Galerkin approximation and error analysis for Maxwell transmission eigenvalue problems in spherical geometries,"  We propose and analyze an efficient spectral-Galerkin approximation for the
Maxwell transmission eigenvalue problem in spherical geometry. Using a vector
spherical harmonic expansion, we reduce the problem to a sequence of equivalent
one-dimensional TE and TM modes that can be solved individually in parallel.
For the TE mode, we derive associated generalized eigenvalue problems and
corresponding pole conditions. Then we introduce weighted Sobolev spaces based
on the pole condition and prove error estimates for the generalized eigenvalue
problem. The TM mode is a coupled system with four unknown functions, which is
challenging for numerical calculation. To handle it, we design an effective
algorithm using Legendre-type vector basis functions. Finally, we provide some
numerical experiments to validate our theoretical results and demonstrate the
efficiency of the algorithms.
",0,0,1,0,0,0
17775,Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables,"  Mendelian randomization uses genetic variants to make causal inferences about
the effect of a risk factor on an outcome. With fine-mapped genetic data, there
may be hundreds of genetic variants in a single gene region any of which could
be used to assess this causal relationship. However, using too many genetic
variants in the analysis can lead to spurious estimates and inflated Type 1
error rates. But if only a few genetic variants are used, then the majority of
the data is ignored and estimates are highly sensitive to the particular choice
of variants. We propose an approach based on summarized data only (genetic
association and correlation estimates) that uses principal components analysis
to form instruments. This approach has desirable theoretical properties: it
takes the totality of data into account and does not suffer from numerical
instabilities. It also has good properties in simulation studies: it is not
particularly sensitive to varying the genetic variants included in the analysis
or the genetic correlation matrix, and it does not have greatly inflated Type 1
error rates. Overall, the method gives estimates that are not so precise as
those from variable selection approaches (such as using a conditional analysis
or pruning approach to select variants), but are more robust to seemingly
arbitrary choices in the variable selection step. Methods are illustrated by an
example using genetic associations with testosterone for 320 genetic variants
to assess the effect of sex hormone-related pathways on coronary artery disease
risk, in which variable selection approaches give inconsistent inferences.
",0,0,0,1,0,0
17776,Building competitive direct acoustics-to-word models for English conversational speech recognition,"  Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
received increasing attention compared to conventional sub-word based automatic
speech recognition models using phones, characters, or context-dependent hidden
Markov model states. This is because A2W models recognize words from speech
without any decoder, pronunciation lexicon, or externally-trained language
model, making training and decoding with such models simple. Prior work has
shown that A2W models require orders of magnitude more training data in order
to perform comparably to conventional models. Our work also showed this
accuracy gap when using the English Switchboard-Fisher data set. This paper
describes a recipe to train an A2W model that closes this gap and is at-par
with state-of-the-art sub-word based models. We achieve a word error rate of
8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder
or language model. We find that model initialization, training data order, and
regularization have the most impact on the A2W model performance. Next, we
present a joint word-character A2W model that learns to first spell the word
and then recognize it. This model provides a rich output to the user instead of
simple word hypotheses, making it especially useful in the case of words unseen
or rarely-seen during training.
",1,0,0,1,0,0
17777,Convolutional Dictionary Learning: Acceleration and Convergence,"  Convolutional dictionary learning (CDL or sparsifying CDL) has many
applications in image processing and computer vision. There has been growing
interest in developing efficient algorithms for CDL, mostly relying on the
augmented Lagrangian (AL) method or the variant alternating direction method of
multipliers (ADMM). When their parameters are properly tuned, AL methods have
shown fast convergence in CDL. However, the parameter tuning process is not
trivial due to its data dependence and, in practice, the convergence of AL
methods depends on the AL parameters for nonconvex CDL problems. To moderate
these problems, this paper proposes a new practically feasible and convergent
Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
BPG-M-based CDL is investigated with different block updating schemes and
majorization matrix designs, and further accelerated by incorporating some
momentum coefficient formulas and restarting techniques. All of the methods
investigated incorporate a boundary artifacts removal (or, more generally,
sampling) operator in the learning model. Numerical experiments show that,
without needing any parameter tuning process, the proposed BPG-M approach
converges more stably to desirable solutions of lower objective values than the
existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
Compared to the ADMM approaches, the BPG-M method using a multi-block updating
scheme is particularly useful in single-threaded CDL algorithm handling large
datasets, due to its lower memory requirement and no polynomial computational
complexity. Image denoising experiments show that, for relatively strong
additive white Gaussian noise, the filters learned by BPG-M-based CDL
outperform those trained by the ADMM approach.
",1,0,1,0,0,0
17778,The late-time light curve of the type Ia supernova SN 2011fe,"  We present late-time optical $R$-band imaging data from the Palomar Transient
Factory (PTF) for the nearby type Ia supernova SN 2011fe. The stacked PTF light
curve provides densely sampled coverage down to $R\simeq22$ mag over 200 to 620
days past explosion. Combining with literature data, we estimate the
pseudo-bolometric light curve for this event from 200 to 1600 days after
explosion, and constrain the likely near-infrared contribution. This light
curve shows a smooth decline consistent with radioactive decay, except over
~450 to ~600 days where the light curve appears to decrease faster than
expected based on the radioactive isotopes presumed to be present, before
flattening at around 600 days. We model the 200-1600d pseudo-bolometric light
curve with the luminosity generated by the radioactive decay chains of
$^{56}$Ni, $^{57}$Ni and $^{55}$Co, and find it is not consistent with models
that have full positron trapping and no infrared catastrophe (IRC); some
additional energy escape other than optical/near-IR photons is required.
However, the light curve is consistent with models that allow for positron
escape (reaching 75% by day 500) and/or an IRC (with 85% of the flux emerging
in non-optical wavelengths by day 600). The presence of the $^{57}$Ni decay
chain is robustly detected, but the $^{55}$Co decay chain is not formally
required, with an upper mass limit estimated at 0.014 M$_{\odot}$. The
measurement of the $^{57}$Ni/$^{56}$Ni mass ratio is subject to significant
systematic uncertainties, but all of our fits require a high ratio >0.031 (>1.3
in solar abundances).
",0,1,0,0,0,0
17779,Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors,"  In this article, we consider Markov chain Monte Carlo(MCMC) algorithms for
exploring the intractable posterior density associated with Bayesian probit
linear mixed models under improper priors on the regression coefficients and
variance components. In particular, we construct the two-block Gibbs sampler
using the data augmentation (DA) techniques. Furthermore, we prove geometric
ergodicity of the Gibbs sampler, which is the foundation for building central
limit theorems for MCMC based estimators and subsequent inferences. The
conditions for geometric convergence are similar to those guaranteeing
posterior propriety. We also provide conditions for posterior propriety when
the design matrices take commonly observed forms. In general, the Haar
parameter expansion for DA (PX- DA) algorithm is an improvement of the DA
algorithm and it has been shown that it is theoretically at least as good as
the DA algorithm. Here we construct a Haar PX-DA algorithm, which has
essentially the same computational cost as the two-block Gibbs sampler.
",0,0,1,1,0,0
17780,The complexity of the Multiple Pattern Matching Problem for random strings,"  We generalise a multiple string pattern matching algorithm, recently proposed
by Fredriksson and Grabowski [J. Discr. Alg. 7, 2009], to deal with arbitrary
dictionaries on an alphabet of size $s$. If $r_m$ is the number of words of
length $m$ in the dictionary, and $\phi(r) = \max_m \ln(s\, m\, r_m)/m$, the
complexity rate for the string characters to be read by this algorithm is at
most $\kappa_{{}_\textrm{UB}}\, \phi(r)$ for some constant
$\kappa_{{}_\textrm{UB}}$. On the other side, we generalise the classical lower
bound of Yao [SIAM J. Comput. 8, 1979], for the problem with a single pattern,
to deal with arbitrary dictionaries, and determine it to be at least
$\kappa_{{}_\textrm{LB}}\, \phi(r)$. This proves the optimality of the
algorithm, improving and correcting previous claims.
",1,0,0,0,0,0
17781,Functoriality and uniformity in Hrushovski's groupoid-cover correspondence,"  The correspondence between definable connected groupoids in a theory $T$ and
internal generalised imaginary sorts of $T$, established by Hrushovski in
[""Groupoids, imaginaries and internal covers,"" Turkish Journal of Mathematics,
2012], is here extended in two ways: First, it is shown that the correspondence
is in fact an equivalence of categories, with respect to appropriate notions of
morphism. Secondly, the equivalence of categories is shown to vary uniformly in
definable families, with respect to an appropriate relativisation of these
categories. Some elaboration on Hrushovki's original constructions are also
included.
",0,0,1,0,0,0
17782,Simple Conditions for Metastability of Continuous Markov Chains,"  A family $\{Q_{\beta}\}_{\beta \geq 0}$ of Markov chains is said to exhibit
$\textit{metastable mixing}$ with $\textit{modes}$
$S_{\beta}^{(1)},\ldots,S_{\beta}^{(k)}$ if its spectral gap (or some other
mixing property) is very close to the worst conductance
$\min(\Phi_{\beta}(S_{\beta}^{(1)}), \ldots, \Phi_{\beta}(S_{\beta}^{(k)}))$ of
its modes. We give simple sufficient conditions for a family of Markov chains
to exhibit metastability in this sense, and verify that these conditions hold
for a prototypical Metropolis-Hastings chain targeting a mixture distribution.
Our work differs from existing work on metastability in that, for the class of
examples we are interested in, it gives an asymptotically exact formula for the
spectral gap (rather than a bound that can be very far from sharp) while at the
same time giving technical conditions that are easier to verify for many
statistical examples. Our bounds from this paper are used in a companion paper
to compare the mixing times of the Hamiltonian Monte Carlo algorithm and a
random walk algorithm for multimodal target distributions.
",0,0,0,1,0,0
17783,Deterministic preparation of highly non-classical macroscopic quantum states,"  We present a scheme to deterministically prepare non-classical quantum states
of a massive mirror including highly non-Gaussian states exhibiting sizeable
negativity of the Wigner function. This is achieved by exploiting the
non-linear light-matter interaction in an optomechanical cavity by driving the
system with optimally designed frequency patterns. Our scheme reveals to be
resilient against mechanical and optical damping, as well as mechanical thermal
noise and imperfections in the driving scheme. Our proposal thus opens a
promising route for table-top experiments to explore and exploit macroscopic
quantum phenomena.
",0,1,0,0,0,0
17784,On Local Optimizers of Acquisition Functions in Bayesian Optimization,"  Bayesian optimization is a sample-efficient method for finding a global
optimum of an expensive-to-evaluate black-box function. A global solution is
found by accumulating a pair of query point and corresponding function value,
repeating these two procedures: (i) learning a surrogate model for the
objective function using the data observed so far; (ii) the maximization of an
acquisition function to determine where next to query the objective function.
Convergence guarantees are only valid when the global optimizer of the
acquisition function is found and selected as the next query point. In
practice, however, local optimizers of acquisition functions are also used,
since searching the exact optimizer of the acquisition function is often a
non-trivial or time-consuming task. In this paper we present an analysis on the
behavior of local optimizers of acquisition functions, in terms of
instantaneous regrets over global optimizers. We also present the performance
analysis when multi-started local optimizers are used to find the maximum of
the acquisition function. Numerical experiments confirm the validity of our
theoretical analysis.
",1,0,0,1,0,0
17785,One Password: An Encryption Scheme for Hiding Users' Register Information,"  In recent years, the attack which leverages register information (e.g.
accounts and passwords) leaked from 3rd party applications to try other
applications is popular and serious. We call this attack ""database collision"".
Traditionally, people have to keep dozens of accounts and passwords for
different applications to prevent this attack. In this paper, we propose a
novel encryption scheme for hiding users' register information and preventing
this attack. Specifically, we first hash the register information using
existing safe hash function. Then the hash string is hidden, instead a
coefficient vector is stored for verification. Coefficient vectors of the same
register information are generated randomly for different applications. Hence,
the original information is hardly cracked by dictionary based attack or
database collision in practice. Using our encryption scheme, each user only
needs to keep one password for dozens of applications.
",1,0,0,0,0,0
17786,The RoPES project with HARPS and HARPS-N. I. A system of super-Earths orbiting the moderately active K-dwarf HD 176986,"  We report the discovery of a system of two super-Earths orbiting the
moderately active K-dwarf HD 176986. This work is part of the RoPES RV program
of G- and K-type stars, which combines radial velocities (RVs) from the HARPS
and HARPS-N spectrographs to search for short-period terrestrial planets. HD
176986 b and c are super-Earth planets with masses of 5.74 and 9.18
M$_{\oplus}$, orbital periods of 6.49 and 16.82 days, and distances of 0.063
and 0.119 AU in orbits that are consistent with circular. The host star is a
K2.5 dwarf, and despite its modest level of chromospheric activity (log(R'hk) =
- 4.90 +- 0.04), it shows a complex activity pattern. Along with the discovery
of the planets, we study the magnetic cycle and rotation of the star. HD 176986
proves to be suitable for testing the available RV analysis technique and
further our understanding of stellar activity.
",0,1,0,0,0,0
17787,On the possibility of developing quasi-cw high-power high-pressure laser on 4p-4s transition of ArI with electron beam - optical pumping: quenching of 4s (3P2) lower laser level,"  A new electron beam-optical procedure is proposed for quasi-cw pumping of
high-pressure large-volume He-Ar laser on 4p[1/2]1 - 4s[3/2]2 argon atom
transition at the wavelength of 912.5 nm. It consists of creation and
maintenance of a necessary density of 4s[3/2]2 metastable state in the gain
medium by a fast electron beam and subsequent optically pumping of the upper
laser level via the classical three-level scheme using a laser diode.
Absorption probing is used to study collisional quenching of Ar* metastable in
electron-beam-excited high-pressure He-Ar mixtures with a low content of argon.
The rate constants for plasma-chemical reactions Ar*+He+Ar-Ar2*+He (3.6 +-
0.4)x10-33 cm6/s, Ar+2He-HeAr*+He (4.4 +- 0.9)x10-36 cm6/s and
Ar*+He-Products+He (2.4 +- 0.3)x10-15 cm3/s are for the first time measured.
",0,1,0,0,0,0
17788,An Adaptive Characteristic-wise Reconstruction WENOZ scheme for Gas Dynamic Euler Equations,"  Due to its excellent shock-capturing capability and high resolution, the WENO
scheme family has been widely used in varieties of compressive flow simulation.
However, for problems containing strong shocks and contact discontinuities,
such as the Lax shock tube problem, the WENO scheme still produces numerical
oscillations. To avoid such numerical oscillations, the characteristic-wise
construction method should be applied. Compared to component-wise
reconstruction, characteristic-wise reconstruction leads to much more
computational cost and thus is not suite for large scale simulation such as
direct numeric simulation of turbulence. In this paper, an adaptive
characteristic-wise reconstruction WENO scheme, i.e. the AdaWENO scheme, is
proposed to improve the computational efficiency of the characteristic-wise
reconstruction method. The new scheme performs characteristic-wise
reconstruction near discontinuities while switching to component-wise
reconstruction for smooth regions. Meanwhile, a new calculation strategy for
the WENO smoothness indicators is implemented to reduce over-all computational
cost. Several one dimensional and two dimensional numerical tests are performed
to validate and evaluate the AdaWENO scheme. Numerical results show that
AdaWENO maintains essentially non-oscillatory flow field near discontinuities
as the characteristic-wise reconstruction method. Besieds, compared to the
component-wise reconstruction, AdaWENO is about 40\% faster which indicates its
excellent efficiency.
",0,1,0,0,0,0
17789,A level set-based structural optimization code using FEniCS,"  This paper presents an educational code written using FEniCS, based on the
level set method, to perform compliance minimization in structural
optimization. We use the concept of distributed shape derivative to compute a
descent direction for the compliance, which is defined as a shape functional.
The use of the distributed shape derivative is facilitated by FEniCS, which
allows to handle complicated partial differential equations with a simple
implementation. The code is written for compliance minimization in the
framework of linearized elasticity, and can be easily adapted to tackle other
functionals and partial differential equations. We also provide an extension of
the code for compliant mechanisms. We start by explaining how to compute shape
derivatives, and discuss the differences between the distributed and boundary
expressions of the shape derivative. Then we describe the implementation in
details, and show the application of this code to some classical benchmarks of
topology optimization. The code is available at
this http URL, and the main file is also given in
the appendix.
",0,0,1,0,0,0
17790,Transaction Support over Redis: An Overview,"  This document outlines the approach to supporting cross-node transactions
over a Redis cluster.
",1,0,0,0,0,0
17791,Subgraphs and motifs in a dynamic airline network,"  How does the small-scale topological structure of an airline network behave
as the network evolves? To address this question, we study the dynamic and
spatial properties of small undirected subgraphs using 15 years of data on
Southwest Airlines' domestic route service. We find that this real-world
network has much in common with random graphs, and describe a possible
power-law scaling between subgraph counts and the number of edges in the
network, that appears to be quite robust to changes in network density and
size. We use analytic formulae to identify statistically over- and
under-represented subgraphs, known as motifs and anti-motifs, and discover the
existence of substantial topology transitions. We propose a simple
subgraph-based node ranking measure, that is not always highly correlated with
standard node centrality, and can identify important nodes relative to specific
topologies, and investigate the spatial ""distribution"" of the triangle subgraph
using graphical tools. Our results have implications for the way in which
subgraphs can be used to analyze real-world networks.
",1,0,0,0,0,0
17792,On the Glitch Phenomenon,"  The Principle of the Glitch states that for any device which makes a discrete
decision based upon a continuous range of possible inputs, there are inputs for
which it will take arbitrarily long to reach a decision. The appropriate
mathematical setting for studying this principle is described. This involves
defining the concept of continuity for mappings on sets of functions. It can
then be shown that the glitch principle follows from the continuous behavior of
the device.
",1,0,1,0,0,0
17793,Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods,"  In this article we perform an asymptotic analysis of Bayesian parallel
density estimators which are based on logspline density estimation. The
parallel estimator we introduce is in the spirit of a kernel density estimator
introduced in recent studies. We provide a numerical procedure that produces
the density estimator itself in place of the sampling algorithm. We then derive
an error bound for the mean integrated squared error for the full data
posterior density estimator. We also investigate the parameters that arise from
logspline density estimation and the numerical approximation procedure. Our
investigation identifies specific choices of parameters for logspline density
estimation that result in the error bound scaling appropriately in relation to
these choices.
",0,0,1,1,0,0
17794,Tunable Weyl and Dirac states in the nonsymmorphic compound $\rm\mathbf{CeSbTe}$,"  Recent interest in topological semimetals has lead to the proposal of many
new topological phases that can be realized in real materials. Next to Dirac
and Weyl systems, these include more exotic phases based on manifold band
degeneracies in the bulk electronic structure. The exotic states in topological
semimetals are usually protected by some sort of crystal symmetry and the
introduction of magnetic order can influence these states by breaking time
reversal symmetry. Here we show that we can realize a rich variety of different
topological semimetal states in a single material, $\rm CeSbTe$. This compound
can exhibit different types of magnetic order that can be accessed easily by
applying a small field. It allows, therefore, for tuning the electronic
structure and can drive it through a manifold of topologically distinct phases,
such as the first nonsymmorphic magnetic topological material with an
eight-fold band crossing at a high symmetry point. Our experimental results are
backed by a full magnetic group theory analysis and ab initio calculations.
This discovery introduces a realistic and promising platform for studying the
interplay of magnetism and topology.
",0,1,0,0,0,0
17795,Personalised Query Suggestion for Intranet Search with Temporal User Profiling,"  Recent research has shown the usefulness of using collective user interaction
data (e.g., query logs) to recommend query modification suggestions for
Intranet search. However, most of the query suggestion approaches for Intranet
search follow an ""one size fits all"" strategy, whereby different users who
submit an identical query would get the same query suggestion list. This is
problematic, as even with the same query, different users may have different
topics of interest, which may change over time in response to the user's
interaction with the system. We address the problem by proposing a personalised
query suggestion framework for Intranet search. For each search session, we
construct two temporal user profiles: a click user profile using the user's
clicked documents and a query user profile using the user's submitted queries.
We then use the two profiles to re-rank the non-personalised query suggestion
list returned by a state-of-the-art query suggestion method for Intranet
search. Experimental results on a large-scale query logs collection show that
our personalised framework significantly improves the quality of suggested
queries.
",1,0,0,0,0,0
17796,Navigability of Random Geometric Graphs in the Universe and Other Spacetimes,"  Random geometric graphs in hyperbolic spaces explain many common structural
and dynamical properties of real networks, yet they fail to predict the correct
values of the exponents of power-law degree distributions observed in real
networks. In that respect, random geometric graphs in asymptotically de Sitter
spacetimes, such as the Lorentzian spacetime of our accelerating universe, are
more attractive as their predictions are more consistent with observations in
real networks. Yet another important property of hyperbolic graphs is their
navigability, and it remains unclear if de Sitter graphs are as navigable as
hyperbolic ones. Here we study the navigability of random geometric graphs in
three Lorentzian manifolds corresponding to universes filled only with dark
energy (de Sitter spacetime), only with matter, and with a mixture of dark
energy and matter as in our universe. We find that these graphs are navigable
only in the manifolds with dark energy. This result implies that, in terms of
navigability, random geometric graphs in asymptotically de Sitter spacetimes
are as good as random hyperbolic graphs. It also establishes a connection
between the presence of dark energy and navigability of the discretized causal
structure of spacetime, which provides a basis for a different approach to the
dark energy problem in cosmology.
",1,1,0,0,0,0
17797,Learning Geometric Concepts with Nasty Noise,"  We study the efficient learnability of geometric concept classes -
specifically, low-degree polynomial threshold functions (PTFs) and
intersections of halfspaces - when a fraction of the data is adversarially
corrupted. We give the first polynomial-time PAC learning algorithms for these
concept classes with dimension-independent error guarantees in the presence of
nasty noise under the Gaussian distribution. In the nasty noise model, an
omniscient adversary can arbitrarily corrupt a small fraction of both the
unlabeled data points and their labels. This model generalizes well-studied
noise models, including the malicious noise model and the agnostic (adversarial
label noise) model. Prior to our work, the only concept class for which
efficient malicious learning algorithms were known was the class of
origin-centered halfspaces.
Specifically, our robust learning algorithm for low-degree PTFs succeeds
under a number of tame distributions -- including the Gaussian distribution
and, more generally, any log-concave distribution with (approximately) known
low-degree moments. For LTFs under the Gaussian distribution, we give a
polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$
is the noise rate. At the core of our PAC learning results is an efficient
algorithm to approximate the low-degree Chow-parameters of any bounded function
in the presence of nasty noise. To achieve this, we employ an iterative
spectral method for outlier detection and removal, inspired by recent work in
robust unsupervised learning. Our aforementioned algorithm succeeds for a range
of distributions satisfying mild concentration bounds and moment assumptions.
The correctness of our robust learning algorithm for intersections of
halfspaces makes essential use of a novel robust inverse independence lemma
that may be of broader interest.
",1,0,0,0,0,0
17798,The relationship between the number of editorial board members and the scientific output of universities in the chemistry field,"  Editorial board members, who are considered the gatekeepers of scientific
journals, play an important role in academia, and may directly or indirectly
affect the scientific output of a university. In this article, we used the
quantile regression method among a sample of 1,387 university in chemistry to
characterize the correlation between the number of editorial board members and
the scientific output of their universities. Furthermore, we used time-series
data and the Granger causality test to explore the causal relationship between
the number of editorial board members and the number of articles of some top
universities. Our results suggest that the number of editorial board members is
positively and significantly related to the scientific output (as measured by
the number of articles, total number of citations, citations per paper, and h
index) of their universities. However, the Granger causality test results
suggest that the causal relationship between the number of editorial board
members and the number of articles of some top universities is not obvious.
Combining these findings with the results of qualitative interviews with
editorial board members, we discuss the causal relationship between the number
of editorial board members and the scientific output of their universities.
",1,0,0,1,0,0
17799,A Non-standard Standard Model,"  This paper examines the Standard Model under the strong-electroweak gauge
group $SU_S(3)\times U_{EW}(2)$ subject to the condition $u_{EW}(2)\not\cong
su_I(2)\oplus u_Y(1)$. Physically, the condition ensures that all electroweak
gauge bosons interact with each other prior to symmetry breaking --- as one
might expect from $U(2)$ invariance. This represents a crucial shift in the
notion of physical gauge bosons: Unlike the Standard Model which posits a
change of Lie algebra basis induced by spontaneous symmetry breaking, here the
basis is unaltered and $A,\,Z^0,\,W^\pm$ represent (modulo $U_{EW}(2)$ gauge
transformations) the physical bosons both \emph{before} and after spontaneous
symmetry breaking.
Our choice of $u_{EW}(2)$ basis requires some modification of the matter
field sector of the Standard Model. Careful attention to the product group
structure calls for strong-electroweak degrees of freedom in the
$(\mathbf{3},\mathbf{2})$ and the $(\mathbf{3},\overline{\mathbf{2}})$ of
$SU_S(3)\times U_{EW}(2)$ that possess integer electric charge just like
leptons. These degrees of freedom play the role of quarks, and they lead to a
modified Lagrangian that nevertheless reproduces transition rates and cross
sections equivalent to the Standard Model.
The close resemblance between quark and lepton electroweak doublets in this
picture suggests a mechanism for a phase transition between quarks and leptons
that stems from the product structure of the gauge group. Our hypothesis is
that the strong and electroweak bosons see each other as a source of
decoherence. In effect, leptons get identified with the $SU_S(3)$-trace of
quark representations. This mechanism allows for possible extensions of the
Standard Model that don't require large inclusive multiplets of matter fields.
As an example, we propose and investigate a model that turns out to have some
promising cosmological implications.
",0,1,0,0,0,0
17800,Robust functional regression model for marginal mean and subject-specific inferences,"  We introduce flexible robust functional regression models, using various
heavy-tailed processes, including a Student $t$-process. We propose efficient
algorithms in estimating parameters for the marginal mean inferences and in
predicting conditional means as well interpolation and extrapolation for the
subject-specific inferences. We develop bootstrap prediction intervals for
conditional mean curves. Numerical studies show that the proposed model
provides robust analysis against data contamination or distribution
misspecification, and the proposed prediction intervals maintain the nominal
confidence levels. A real data application is presented as an illustrative
example.
",0,0,0,1,0,0
17801,Linear theory for single and double flap wavemakers,"  In this paper, we are concerned with deterministic wave generation in a
hydrodynamic laboratory. A linear wavemaker theory is developed based on the
fully dispersive water wave equations. The governing field equation is the
Laplace equation for potential flow with several boundary conditions: the
dynamic and kinematic boundary condition at the free surface, the lateral
boundary condition at the wavemaker and the bottom boundary condition. In this
work, we consider both single-flap and double-flap wavemakers. The velocity
potential and surface wave elevation are derived, and the relation between the
propagating wave height and wavemaker stroke is formulated. This formulation is
then used to find how to operate the wavemaker in an efficient way to generate
the desired propagating waves with minimal disturbances near the wavemaker.
",0,1,0,0,0,0
17802,Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images,"  Dual energy CT (DECT) enhances tissue characterization because it can produce
images of basis materials such as soft-tissue and bone. DECT is of great
interest in applications to medical imaging, security inspection and
nondestructive testing. Theoretically, two materials with different linear
attenuation coefficients can be accurately reconstructed using DECT technique.
However, the ability to reconstruct three or more basis materials is clinically
and industrially important. Under the assumption that there are at most three
materials in each pixel, there are a few methods that estimate multiple
material images from DECT measurements by enforcing sum-to-one and a box
constraint ([0 1]) derived from both the volume and mass conservation
assumption. The recently proposed image-domain multi-material decomposition
(MMD) method introduces edge-preserving regularization for each material image
which neglects the relations among material images, and enforced the assumption
that there are at most three materials in each pixel using a time-consuming
loop over all possible material-triplet in each iteration of optimizing its
cost function. We propose a new image-domain MMD method for DECT that considers
the prior information that different material images have common edges and
encourages sparsity of material composition in each pixel using regularization.
",0,1,0,0,0,0
17803,A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model,"  In wind farms, wake interaction leads to losses in power capture and
accelerated structural degradation when compared to freestanding turbines. One
method to reduce wake losses is by misaligning the rotor with the incoming flow
using its yaw actuator, thereby laterally deflecting the wake away from
downstream turbines. However, this demands an accurate and computationally
tractable model of the wind farm dynamics. This problem calls for a closed-loop
solution. This tutorial paper fills the scientific gap by demonstrating the
full closed-loop controller synthesis cycle using a steady-state surrogate
model. Furthermore, a novel, computationally efficient and modular
communication interface is presented that enables researchers to
straight-forwardly test their control algorithms in large-eddy simulations.
High-fidelity simulations of a 9-turbine farm show a power production increase
of up to 11% using the proposed closed-loop controller compared to traditional,
greedy wind farm operation.
",1,0,0,0,0,0
17804,UCB Exploration via Q-Ensembles,"  We show how an ensemble of $Q^*$-functions can be leveraged for more
effective exploration in deep reinforcement learning. We build on well
established algorithms from the bandit setting, and adapt them to the
$Q$-learning setting. We propose an exploration strategy based on
upper-confidence bounds (UCB). Our experiments show significant gains on the
Atari benchmark.
",1,0,0,1,0,0
17805,Towards a Deep Improviser: a prototype deep learning post-tonal free music generator,"  Two modest-sized symbolic corpora of post-tonal and post-metric keyboard
music have been constructed, one algorithmic, the other improvised. Deep
learning models of each have been trained and largely optimised. Our purpose is
to obtain a model with sufficient generalisation capacity that in response to a
small quantity of separate fresh input seed material, it can generate outputs
that are distinctive, rather than recreative of the learned corpora or the seed
material. This objective has been first assessed statistically, and as judged
by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has
been generated using the approach, and informal judgements place it roughly on
a par with algorithmic and composed music in related forms. Future work will
aim to enhance the model such that it can be evaluated in relation to
expression, meaning and utility in real-time performance.
",1,0,0,0,0,0
17806,Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data,"  Existing urban boundaries are usually defined by government agencies for
administrative, economic, and political purposes. Defining urban boundaries
that consider socio-economic relationships and citizen commute patterns is
important for many aspects of urban and regional planning. In this paper, we
describe a method to delineate urban boundaries based upon human interactions
with physical space inferred from social media. Specifically, we depicted the
urban boundaries of Great Britain using a mobility network of Twitter user
spatial interactions, which was inferred from over 69 million geo-located
tweets. We define the non-administrative anthropographic boundaries in a
hierarchical fashion based on different physical movement ranges of users
derived from the collective mobility patterns of Twitter users in Great
Britain. The results of strongly connected urban regions in the form of
communities in the network space yield geographically cohesive, non-overlapping
urban areas, which provide a clear delineation of the non-administrative
anthropographic urban boundaries of Great Britain. The method was applied to
both national (Great Britain) and municipal scales (the London metropolis).
While our results corresponded well with the administrative boundaries, many
unexpected and interesting boundaries were identified. Importantly, as the
depicted urban boundaries exhibited a strong instance of spatial proximity, we
employed a gravity model to understand the distance decay effects in shaping
the delineated urban boundaries. The model explains how geographical distances
found in the mobility patterns affect the interaction intensity among different
non-administrative anthropographic urban areas, which provides new insights
into human spatial interactions with urban space.
",1,1,0,0,0,0
17807,Avoiding a Tragedy of the Commons in the Peer Review Process,"  Peer review is the foundation of scientific publication, and the task of
reviewing has long been seen as a cornerstone of professional service. However,
the massive growth in the field of machine learning has put this community
benefit under stress, threatening both the sustainability of an effective
review process and the overall progress of the field. In this position paper,
we argue that a tragedy of the commons outcome may be avoided by emphasizing
the professional aspects of this service. In particular, we propose a rubric to
hold reviewers to an objective standard for review quality. In turn, we also
propose that reviewers be given appropriate incentive. As one possible such
incentive, we explore the idea of financial compensation on a per-review basis.
We suggest reasonable funding models and thoughts on long term effects.
",1,0,0,0,0,0
17808,Partially Recursive Acceptance Rejection,"  Generating random variates from high-dimensional distributions is often done
approximately using Markov chain Monte Carlo. In certain cases, perfect
simulation algorithms exist that allow one to draw exactly from the stationary
distribution, but most require $O(n \ln(n))$ time, where $n$ measures the size
of the input. In this work a new protocol for creating perfect simulation
algorithms that runs in $O(n)$ time for a wider range of parameters on several
models (such as Strauss, Ising, and random cluster) than was known previously.
This work represents an extension of the popping algorithms due to Wilson.
",1,0,1,0,0,0
17809,Reifenberg Flatness and Oscillation of the Unit Normal Vector,"  We show (under mild topological assumptions) that small oscillation of the
unit normal vector implies Reifenberg flatness. We then apply this observation
to the study of chord-arc domains and to a quantitative version of a two-phase
free boundary problem for harmonic measure previously studied by Kenig-Toro.
",0,0,1,0,0,0
17810,Stability and optimality of distributed secondary frequency control schemes in power networks,"  We present a systematic method for designing distributed generation and
demand control schemes for secondary frequency regulation in power networks
such that stability and an economically optimal power allocation can be
guaranteed. A dissipativity condition is imposed on net power supply variables
to provide stability guarantees. Furthermore, economic optimality is achieved
by explicit decentralized steady state conditions on the generation and
controllable demand. We discuss how various classes of dynamics used in recent
studies fit within our framework and give examples of higher order generation
and controllable demand dynamics that can be included within our analysis. In
case of linear dynamics, we discuss how the proposed dissipativity condition
can be efficiently verified using an appropriate linear matrix inequality.
Moreover, it is shown how the addition of a suitable observer layer can relax
the requirement for demand measurements in the employed controller. The
efficiency and practicality of the proposed results are demonstrated with a
simulation on the Northeast Power Coordinating Council (NPCC) 140-bus system.
",1,0,1,0,0,0
17811,A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism,"  Terrorism has become one of the most tedious problems to deal with and a
prominent threat to mankind. To enhance counter-terrorism, several research
works are developing efficient and precise systems, data mining is not an
exception. Immense data is floating in our lives, though the scarce
availability of authentic terrorist attack data in the public domain makes it
complicated to fight terrorism. This manuscript focuses on data mining
classification techniques and discusses the role of United Nations in
counter-terrorism. It analyzes the performance of classifiers such as Lazy
Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for
observing the trends for terrorist attacks around the world. The database for
experiment purpose is created from different public and open access sources for
years 1970-2015 comprising of 156,772 reported attacks causing massive losses
of lives and property. This work enumerates the losses occurred, trends in
attack frequency and places more prone to it, by considering the attack
responsibilities taken as evaluation class.
",1,0,0,1,0,0
17812,OVI 6830Å Imaging Polarimetry of Symbiotic Stars,"  I present here the first results from an ongoing pilot project with the 1.6 m
telescope at the OPD, Brasil, aimed at the detection of the OVI $\lambda$6830
line via linear polarization in symbiotic stars. The main goal is to
demonstrate that OVI imaging polarimetry is an efficient technique for
discovering new symbiotic stars. The OVI $\lambda$6830 line is found in 5 out
of 9 known symbiotic stars, in which the OVI line has already been
spectroscopically confirmed, with at least 3-$\sigma$ detection. Three new
symbiotic star candidates have also been found.
",0,1,0,0,0,0
17813,MMGAN: Manifold Matching Generative Adversarial Network,"  It is well-known that GANs are difficult to train, and several different
techniques have been proposed in order to stabilize their training. In this
paper, we propose a novel training method called manifold-matching, and a new
GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
representing the vector representations of real and fake images. If these two
manifolds match, it means that real and fake images are statistically
identical. To assist the manifold-matching task, we also use i) kernel tricks
to find better manifold structures, ii) moving-averaged manifolds across
mini-batches, and iii) a regularizer based on correlation matrix to suppress
mode collapse.
We conduct in-depth experiments with three image datasets and compare with
several state-of-the-art GAN models. 32.4% of images generated by the proposed
MMGAN are recognized as fake images during our user study (16% enhancement
compared to other state-of-the-art model). MMGAN achieved an unsupervised
inception score of 7.8 for CIFAR-10.
",1,0,0,0,0,0
17814,Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts,"  Acute respiratory infections have epidemic and pandemic potential and thus
are being studied worldwide, albeit in many different contexts and study
formats. Predicting infection from symptom data is critical, though using
symptom data from varied studies in aggregate is challenging because the data
is collected in different ways. Accordingly, different symptom profiles could
be more predictive in certain studies, or even symptoms of the same name could
have different meanings in different contexts. We assess state-of-the-art
transfer learning methods for improving prediction of infection from symptom
data in multiple types of health care data ranging from clinical, to home-visit
as well as crowdsourced studies. We show interesting characteristics regarding
six different study types and their feature domains. Further, we demonstrate
that it is possible to use data collected from one study to predict infection
in another, at close to or better than using a single dataset for prediction on
itself. We also investigate in which conditions specific transfer learning and
domain adaptation methods may perform better on symptom data. This work has the
potential for broad applicability as we show how it is possible to transfer
learning from one public health study design to another, and data collected
from one study may be used for prediction of labels for another, even collected
through different study designs, populations and contexts.
",0,0,0,1,1,0
17815,Constrained empirical Bayes priors on regression coefficients,"  Under model uncertainty, empirical Bayes (EB) procedures can have undesirable
properties such as extreme estimates of inclusion probabilities (Scott &
Berger, 2010) or inconsistency under the null model (Liang et al., 2008). To
avoid these issues, we define empirical Bayes priors with constraints that
ensure that the estimates of the hyperparameters are at least as ""vague"" as
those of proper default priors. In our examples, we observe that constrained EB
procedures are better behaved than their unconstrained counterparts and that
the Bayesian Information Criterion (BIC) is similar to an intuitively appealing
constrained EB procedure.
",0,0,1,1,0,0
17816,A finite field analogue for Appell series F_3,"  In this paper we introduce a finite field analogue for the Appell series F_3
and give some reduction formulae and certain generating functions for this
function over finite fields.
",0,0,1,0,0,0
17817,Two-way Two-tape Automata,"  In this article we consider two-way two-tape (alternating) automata accepting
pairs of words and we study some closure properties of this model. Our main
result is that such alternating automata are not closed under complementation
for non-unary alphabets. This improves a similar result of Kari and Moore for
picture languages. We also show that these deterministic, non-deterministic and
alternating automata are not closed under composition.
",1,0,0,0,0,0
17818,Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing,"  360$^{\circ}$ video requires human viewers to actively control ""where"" to
look while watching the video. Although it provides a more immersive experience
of the visual content, it also introduces additional burden for viewers;
awkward interfaces to navigate the video lead to suboptimal viewing
experiences. Virtual cinematography is an appealing direction to remedy these
problems, but conventional methods are limited to virtual environments or rely
on hand-crafted heuristics. We propose a new algorithm for virtual
cinematography that automatically controls a virtual camera within a
360$^{\circ}$ video. Compared to the state of the art, our algorithm allows
more general camera control, avoids redundant outputs, and extracts its output
videos substantially more efficiently. Experimental results on over 7 hours of
real ""in the wild"" video show that our generalized camera control is crucial
for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is
essential for making the generalized control computationally tractable.
",1,0,0,0,0,0
17819,Zero-Shot Learning by Generating Pseudo Feature Representations,"  Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
classes without any training instances. In this paper we present a simple but
high-performance ZSL approach by generating pseudo feature representations
(GPFR). Given the dataset of seen classes and side information of unseen
classes (e.g. attributes), we synthesize feature-level pseudo representations
for novel concepts, which allows us access to the formulation of unseen class
predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to
acquire understandings about attributes, then construct a cognitive repository
of attributes filtered by confidence margins, and finally generate pseudo
feature representations using a probability based sampling strategy to
facilitate subsequent training process of class predictor. We demonstrate the
effectiveness in ZSL settings and the extensibility in supervised recognition
scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For
several popular ZSL benchmark datasets, our approach also shows compelling
results on zero-shot recognition task, especially leading to tremendous
improvement to state-of-the-art mAP on zero-shot retrieval task.
",1,0,0,0,0,0
17820,Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection,"  Speaker change detection (SCD) is an important task in dialog modeling. Our
paper addresses the problem of text-based SCD, which differs from existing
audio-based studies and is useful in various scenarios, for example, processing
dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
and enhancing audio SCD with textual information. We formulate text-based SCD
as a matching problem of utterances before and after a certain decision point;
we propose a hierarchical recurrent neural network (RNN) with static
sentence-level attention. Experimental results show that neural networks
consistently achieve better performance than feature-based approaches, and that
our attention-based model significantly outperforms non-attention neural
networks.
",1,0,0,0,0,0
17821,Bivariate Discrete Generalized Exponential Distribution,"  In this paper we develop a bivariate discrete generalized exponential
distribution, whose marginals are discrete generalized exponential distribution
as proposed by Nekoukhou, Alamatsaz and Bidram (""Discrete generalized
exponential distribution of a second type"", Statistics, 47, 876 - 887, 2013).
It is observed that the proposed bivariate distribution is a very flexible
distribution and the bivariate geometric distribution can be obtained as a
special case of this distribution. The proposed distribution can be seen as a
natural discrete analogue of the bivariate generalized exponential distribution
proposed by Kundu and Gupta (""Bivariate generalized exponential distribution"",
Journal of Multivariate Analysis, 100, 581 - 593, 2009). We study different
properties of this distribution and explore its dependence structures. We
propose a new EM algorithm to compute the maximum likelihood estimators of the
unknown parameters which can be implemented very efficiently, and discuss some
inferential issues also. The analysis of one data set has been performed to
show the effectiveness of the proposed model. Finally we propose some open
problems and conclude the paper.
",0,0,0,1,0,0
17822,"Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings","  Over 150,000 new people in the United States are diagnosed with colorectal
cancer each year. Nearly a third die from it (American Cancer Society). The
only approved noninvasive diagnosis tools currently involve fecal blood count
tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five
minutes and are available over the counter for as low as \$15. They are highly
specific, yet not nearly as sensitive, yielding a high percentage (25%) of
false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too
generalized, meaning that a positive result could mean much more than just
colorectal cancer, and could just as easily mean hemorrhoids, anal fissure,
proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer,
rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis
from radiation therapy, and others. Stool DNA tests, the modern benchmark for
CRC screening, have a much higher sensitivity and specificity, but also cost
\$600, take two weeks to process, and are not for high-risk individuals or
people with a history of polyps. To yield a cheap and effective CRC screening
alternative, a unique ensemble-based classification algorithm is put in place
that considers the FIT result, BMI, smoking history, and diabetic status of
patients. This method is tested under ten-fold cross validation to have a .95
AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once
clinically validated, this test promises to be cheaper, faster, and potentially
more accurate when compared to a stool DNA test.
",1,0,0,1,0,0
17823,More or Less? Predict the Social Influence of Malicious URLs on Social Media,"  Users of Online Social Networks (OSNs) interact with each other more than
ever. In the context of a public discussion group, people receive, read, and
write comments in response to articles and postings. In the absence of access
control mechanisms, OSNs are a great environment for attackers to influence
others, from spreading phishing URLs, to posting fake news. Moreover, OSN user
behavior can be predicted by social science concepts which include conformity
and the bandwagon effect. In this paper, we show how social recommendation
systems affect the occurrence of malicious URLs on Facebook. We exploit
temporal features to build a prediction framework, having greater than 75%
accuracy, to predict whether the following group users' behavior will increase
or not. Included in this work, we demarcate classes of URLs, including those
malicious URLs classified as creating critical damage, as well as those of a
lesser nature which only inflict light damage such as aggressive commercial
advertisements and spam content. It is our hope that the data and analyses in
this paper provide a better understanding of OSN user reactions to different
categories of malicious URLs, thereby providing a way to mitigate the influence
of these malicious URL attacks.
",1,0,0,0,0,0
17824,Path-Following through Control Funnel Functions,"  We present an approach to path following using so-called control funnel
functions. Synthesizing controllers to ""robustly"" follow a reference trajectory
is a fundamental problem for autonomous vehicles. Robustness, in this context,
requires our controllers to handle a specified amount of deviation from the
desired trajectory. Our approach considers a timing law that describes how fast
to move along a given reference trajectory and a control feedback law for
reducing deviations from the reference. We synthesize both feedback laws using
""control funnel functions"" that jointly encode the control law as well as its
correctness argument over a mathematical model of the vehicle dynamics. We
adapt a previously described demonstration-based learning algorithm to
synthesize a control funnel function as well as the associated feedback law. We
implement this law on top of a 1/8th scale autonomous vehicle called the
Parkour car. We compare the performance of our path following approach against
a trajectory tracking approach by specifying trajectories of varying lengths
and curvatures. Our experiments demonstrate the improved robustness obtained
from the use of control funnel functions.
",1,0,0,0,0,0
17825,Tunable Anomalous Andreev Reflection and Triplet Pairings in Spin Orbit Coupled Graphene,"  We theoretically study scattering process and superconducting triplet
correlations in a graphene junction comprised of ferromagnet-RSO-superconductor
in which RSO stands for a region with Rashba spin orbit interaction. Our
results reveal spin-polarized subgap transport through the system due to an
anomalous equal-spin Andreev reflection in addition to conventional back
scatterings. We calculate equal- and opposite-spin pair correlations near the
F-RSO interface and demonstrate direct link of the anomalous Andreev reflection
and equal-spin pairings arised due to the proximity effect in the presence of
RSO interaction. Moreover, we show that the amplitude of anomalous Andreev
reflection, and thus the triplet pairings, are experimentally controllable when
incorporating the influences of both tunable strain and Fermi level in the
nonsuperconducting region. Our findings can be confirmed by a conductance
spectroscopy experiment and provide better insights into the proximity-induced
RSO coupling in graphene layers reported in recent experiments.
",0,1,0,0,0,0
17826,Conditional Model Selection in Mixed-Effects Models with cAIC4,"  Model selection in mixed models based on the conditional distribution is
appropriate for many practical applications and has been a focus of recent
statistical research. In this paper we introduce the R-package cAIC4 that
allows for the computation of the conditional Akaike Information Criterion
(cAIC). Computation of the conditional AIC needs to take into account the
uncertainty of the random effects variance and is therefore not
straightforward. We introduce a fast and stable implementation for the
calculation of the cAIC for linear mixed models estimated with lme4 and
additive mixed models estimated with gamm4 . Furthermore, cAIC4 offers a
stepwise function that allows for a fully automated stepwise selection scheme
for mixed models based on the conditional AIC. Examples of many possible
applications are presented to illustrate the practical impact and easy handling
of the package.
",0,0,0,1,0,0
17827,Inequalities for the lowest magnetic Neumann eigenvalue,"  We study the ground state energy of the Neumann magnetic Laplacian on planar
domains. For a constant magnetic field we consider the question whether, under
an assumption of fixed area, the disc maximizes this eigenvalue. More
generally, we discuss old and new bounds obtained on this problem.
",0,0,1,0,0,0
17828,Automated optimization of large quantum circuits with continuous parameters,"  We develop and implement automated methods for optimizing quantum circuits of
the size and type expected in quantum computations that outperform classical
computers. We show how to handle continuous gate parameters and report a
collection of fast algorithms capable of optimizing large-scale quantum
circuits. For the suite of benchmarks considered, we obtain substantial
reductions in gate counts. In particular, we provide better optimization in
significantly less time than previous approaches, while making minimal
structural changes so as to preserve the basic layout of the underlying quantum
algorithms. Our results help bridge the gap between the computations that can
be run on existing hardware and those that are expected to outperform classical
computers.
",1,0,0,0,0,0
17829,Testing the simplifying assumption in high-dimensional vine copulas,"  Testing the simplifying assumption in high-dimensional vine copulas is a
difficult task because tests must be based on estimated observations and amount
to checking constraints on high-dimensional distributions. So far,
corresponding tests have been limited to single conditional copulas with a
low-dimensional set of conditioning variables. We propose a novel testing
procedure that is computationally feasible for high-dimensional data sets and
that exhibits a power that decreases only slightly with the dimension. By
discretizing the support of the conditioning variables and incorporating a
penalty in the test statistic, we mitigate the curse of dimensions by looking
for the possibly strongest deviation from the simplifying assumption. The use
of a decision tree renders the test computationally feasible for large
dimensions. We derive the asymptotic distribution of the test and analyze its
finite sample performance in an extensive simulation study. The utility of the
test is demonstrated by its application to 10 data sets with up to 49
dimensions.
",0,0,0,1,0,0
17830,TSP With Locational Uncertainty: The Adversarial Model,"  In this paper we study a natural special case of the Traveling Salesman
Problem (TSP) with point-locational-uncertainty which we will call the {\em
adversarial TSP} problem (ATSP). Given a metric space $(X, d)$ and a set of
subsets $R = \{R_1, R_2, ... , R_n\} : R_i \subseteq X$, the goal is to devise
an ordering of the regions, $\sigma_R$, that the tour will visit such that when
a single point is chosen from each region, the induced tour over those points
in the ordering prescribed by $\sigma_R$ is as short as possible. Unlike the
classical locational-uncertainty-TSP problem, which focuses on minimizing the
expected length of such a tour when the point within each region is chosen
according to some probability distribution, here, we focus on the {\em
adversarial model} in which once the choice of $\sigma_R$ is announced, an
adversary selects a point from each region in order to make the resulting tour
as long as possible. In other words, we consider an offline problem in which
the goal is to determine an ordering of the regions $R$ that is optimal with
respect to the ""worst"" point possible within each region being chosen by an
adversary, who knows the chosen ordering. We give a $3$-approximation when $R$
is a set of arbitrary regions/sets of points in a metric space. We show how
geometry leads to improved constant factor approximations when regions are
parallel line segments of the same lengths, and a polynomial-time approximation
scheme (PTAS) for the important special case in which $R$ is a set of disjoint
unit disks in the plane.
",1,0,0,0,0,0
17831,Exact solutions to three-dimensional generalized nonlinear Schrodinger equations with varying potential and nonlinearities,"  It is shown that using the similarity transformations, a set of
three-dimensional p-q nonlinear Schrodinger (NLS) equations with inhomogeneous
coefficients can be reduced to one-dimensional stationary NLS equation with
constant or varying coefficients, thus allowing for obtaining exact localized
and periodic wave solutions. In the suggested reduction the original
coordinates in the (1+3)-space are mapped into a set of one-parametric
coordinate surfaces, whose parameter plays the role of the coordinate of the
one-dimensional equation. We describe the algorithm of finding solutions and
concentrate on power (linear and nonlinear) potentials presenting a number of
case examples. Generalizations of the method are also discussed.
",0,1,1,0,0,0
17832,A Study of Reinforcement Learning for Neural Machine Translation,"  Recent studies have shown that reinforcement learning (RL) is an effective
approach for improving the performance of neural machine translation (NMT)
system. However, due to its instability, successfully RL training is
challenging, especially in real-world systems where deep models and large
datasets are leveraged. In this paper, taking several large-scale translation
tasks as testbeds, we conduct a systematic study on how to train better NMT
models using reinforcement learning. We provide a comprehensive comparison of
several important factors (e.g., baseline reward, reward shaping) in RL
training. Furthermore, to fill in the gap that it remains unclear whether RL is
still beneficial when monolingual data is used, we propose a new method to
leverage RL to further boost the performance of NMT systems trained with
source/target monolingual data. By integrating all our findings, we obtain
competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17
Chinese-English translation tasks, especially setting a state-of-the-art
performance on WMT17 Chinese-English translation task.
",0,0,0,1,0,0
17833,Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification,"  Artificial neural network (ANN) is a very useful tool in solving learning
problems. Boosting the performances of ANN can be mainly concluded from two
aspects: optimizing the architecture of ANN and normalizing the raw data for
ANN. In this paper, a novel method which improves the effects of ANN by
preprocessing the raw data is proposed. It totally leverages the fact that
different features should play different roles. The raw data set is firstly
preprocessed by principle component analysis (PCA), and then its principle
components are weighted by their corresponding eigenvalues. Several aspects of
analysis are carried out to analyze its theory and the applicable occasions.
Three classification problems are launched by an active learning algorithm to
verify the proposed method. From the empirical results, conclusion comes to the
fact that the proposed method can significantly improve the performance of ANN.
",1,0,0,1,0,0
17834,Maximum likelihood estimation of determinantal point processes,"  Determinantal point processes (DPPs) have wide-ranging applications in
machine learning, where they are used to enforce the notion of diversity in
subset selection problems. Many estimators have been proposed, but surprisingly
the basic properties of the maximum likelihood estimator (MLE) have received
little attention. The difficulty is that it is a non-concave maximization
problem, and such functions are notoriously difficult to understand in high
dimensions, despite their importance in modern machine learning. Here we study
both the local and global geometry of the expected log-likelihood function. We
prove several rates of convergence for the MLE and give a complete
characterization of the case where these are parametric. We also exhibit a
potential curse of dimensionality where the asymptotic variance of the MLE
scales exponentially with the dimension of the problem. Moreover, we exhibit an
exponential number of saddle points, and give evidence that these may be the
only critical points.
",0,0,1,1,0,0
17835,Orthogonal Machine Learning: Power and Limitations,"  Double machine learning provides $\sqrt{n}$-consistent estimates of
parameters of interest even when high-dimensional or nonparametric nuisance
parameters are estimated at an $n^{-1/4}$ rate. The key is to employ
Neyman-orthogonal moment equations which are first-order insensitive to
perturbations in the nuisance parameters. We show that the $n^{-1/4}$
requirement can be improved to $n^{-1/(2k+2)}$ by employing a $k$-th order
notion of orthogonality that grants robustness to more complex or
higher-dimensional nuisance parameters. In the partially linear regression
setting popular in causal inference, we show that we can construct second-order
orthogonal moments if and only if the treatment residual is not normally
distributed. Our proof relies on Stein's lemma and may be of independent
interest. We conclude by demonstrating the robustness benefits of an explicit
doubly-orthogonal estimation procedure for treatment effect.
",1,0,1,1,0,0
17836,Numerical investigation of supersonic shock-wave/boundary-layer interaction in transitional and turbulent regime,"  We perform direct numerical simulations of shock-wave/boundary-layer
interactions (SBLI) at Mach number M = 1.7 to investigate the influence of the
state of the incoming boundary layer on the interaction properties. We
reproduce and extend the flow conditions of the experiments performed by
Giepman et al., in which a spatially evolving laminar boundary layer over a
flat plate is initially tripped by an array of distributed roughness elements
and impinged further downstream by an oblique shock wave. Four SBLI cases are
considered, based on two different shock impingement locations along the
streamwise direction, corresponding to transitional and turbulent interactions,
and two different shock strengths, corresponding to flow deflection angles 3
degreees and 6 degrees. We find that, for all flow cases, shock induced
separation is not observed, the boundary layer remains attached for the 3
degrees case and close to incipient separation for the 6 degrees case,
independent of the state of the incoming boundary layer. The findings of this
work suggest that a transitional interaction might be the optimal solution for
practical SBLI applications, as it removes the large separation bubble typical
of laminar interactions and reduces the extent of the high-friction region
associated with an incoming turbulent boundary layer.
",0,1,0,0,0,0
17837,Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations,"  Latent factor models for recommender systems represent users and items as low
dimensional vectors. Privacy risks of such systems have previously been studied
mostly in the context of recovery of personal information in the form of usage
records from the training data. However, the user representations themselves
may be used together with external data to recover private user information
such as gender and age. In this paper we show that user vectors calculated by a
common recommender system can be exploited in this way. We propose the
privacy-adversarial framework to eliminate such leakage of private information,
and study the trade-off between recommender performance and leakage both
theoretically and empirically using a benchmark dataset. An advantage of the
proposed method is that it also helps guarantee fairness of results, since all
implicit knowledge of a set of attributes is scrubbed from the representations
used by the model, and thus can't enter into the decision making. We discuss
further applications of this method towards the generation of deeper and more
insightful recommendations.
",0,0,0,1,0,0
17838,Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage,"  We establish large sample approximations for an arbitray number of bilinear
forms of the sample variance-covariance matrix of a high-dimensional vector
time series using $ \ell_1$-bounded and small $\ell_2$-bounded weighting
vectors. Estimation of the asymptotic covariance structure is also discussed.
The results hold true without any constraint on the dimension, the number of
forms and the sample size or their ratios. Concrete and potential applications
are widespread and cover high-dimensional data science problems such as tests
for large numbers of covariances, sparse portfolio optimization and projections
onto sparse principal components or more general spanning sets as frequently
considered, e.g. in classification and dictionary learning. As two specific
applications of our results, we study in greater detail the asymptotics of the
trace functional and shrinkage estimation of covariance matrices. In shrinkage
estimation, it turns out that the asymptotics differs for weighting vectors
bounded away from orthogonaliy and nearly orthogonal ones in the sense that
their inner product converges to 0.
",0,0,1,1,0,0
17839,Bayesian Paragraph Vectors,"  Word2vec (Mikolov et al., 2013) has proven to be successful in natural
language processing by capturing the semantic relationships between different
words. Built on top of single-word embeddings, paragraph vectors (Le and
Mikolov, 2014) find fixed-length representations for pieces of text with
arbitrary lengths, such as documents, paragraphs, and sentences. In this work,
we propose a novel interpretation for neural-network-based paragraph vectors by
developing an unsupervised generative model whose maximum likelihood solution
corresponds to traditional paragraph vectors. This probabilistic formulation
allows us to go beyond point estimates of parameters and to perform Bayesian
posterior inference. We find that the entropy of paragraph vectors decreases
with the length of documents, and that information about posterior uncertainty
improves performance in supervised learning tasks such as sentiment analysis
and paraphrase detection.
",1,0,0,1,0,0
17840,Subset Synchronization in Monotonic Automata,"  We study extremal and algorithmic questions of subset and careful
synchronization in monotonic automata. We show that several synchronization
problems that are hard in general automata can be solved in polynomial time in
monotonic automata, even without knowing a linear order of the states preserved
by the transitions. We provide asymptotically tight bounds on the maximum
length of a shortest word synchronizing a subset of states in a monotonic
automaton and a shortest word carefully synchronizing a partial monotonic
automaton. We provide a complexity framework for dealing with problems for
monotonic weakly acyclic automata over a three-letter alphabet, and use it to
prove NP-completeness and inapproximability of problems such as {\sc Finite
Automata Intersection} and the problem of computing the rank of a subset of
states in this class. We also show that checking whether a monotonic partial
automaton over a four-letter alphabet is carefully synchronizing is NP-hard.
Finally, we give a simple necessary and sufficient condition when a strongly
connected digraph with a selected subset of vertices can be transformed into a
deterministic automaton where the corresponding subset of states is
synchronizing.
",1,0,0,0,0,0
17841,Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification,"  The selection of West Java governor is one event that seizes the attention of
the public is no exception to social media users. Public opinion on a
prospective regional leader can help predict electability and tendency of
voters. Data that can be used by the opinion mining process can be obtained
from Twitter. Because the data is very varied form and very unstructured, it
must be managed and uninformed using data pre-processing techniques into
semi-structured data. This semi-structured information is followed by a
classification stage to categorize the opinion into negative or positive
opinions. The research methodology uses a literature study where the research
will examine previous research on a similar topic. The purpose of this study is
to find the right architecture to develop it into the application of twitter
opinion mining to know public sentiments toward the election of the governor of
west java. The result of this research is that Twitter opinion mining is part
of text mining where opinions in Twitter if they want to be classified, must go
through the preprocessing text stage first. The preprocessing step required
from twitter data is cleansing, case folding, POS Tagging and stemming. The
resulting text mining architecture is an architecture that can be used for text
mining research with different topics.
",1,0,0,0,0,0
17842,Ray: A Distributed Framework for Emerging AI Applications,"  The next generation of AI applications will continuously interact with the
environment and learn from these interactions. These applications impose new
and demanding systems requirements, both in terms of performance and
flexibility. In this paper, we consider these requirements and present Ray---a
distributed system to address them. Ray implements a unified interface that can
express both task-parallel and actor-based computations, supported by a single
dynamic execution engine. To meet the performance requirements, Ray employs a
distributed scheduler and a distributed and fault-tolerant store to manage the
system's control state. In our experiments, we demonstrate scaling beyond 1.8
million tasks per second and better performance than existing specialized
systems for several challenging reinforcement learning applications.
",1,0,0,1,0,0
17843,A blockchain-based Decentralized System for proper handling of temporary Employment contracts,"  Temporary work is an employment situation useful and suitable in all
occasions in which business needs to adjust more easily and quickly to workload
fluctuations or maintain staffing flexibility. Temporary workers play therefore
an important role in many companies, but this kind of activity is subject to a
special form of legal protections and many aspects and risks must be taken into
account both employers and employees. In this work we propose a
blockchain-based system that aims to ensure respect for the rights for all
actors involved in a temporary employment, in order to provide employees with
the fair and legal remuneration (including taxes) of work performances and a
protection in the case employer becomes insolvent. At the same time, our system
wants to assist the employer in processing contracts with a fully automated and
fast procedure. To resolve these problems we propose the D-ES (Decentralized
Employment System). We first model the employment relationship as a state
system. Then we describe the enabling technology that makes us able to realize
the D-ES. In facts, we propose the implementation of a DLT (Decentralized
Ledger Technology) based system, consisting in a blockchain system and of a
web-based environment. Thanks the decentralized application platforms that
makes us able to develop smart contracts, we define a discrete event control
system that works inside the blockchain. In addition, we discuss the temporary
work in agriculture as a interesting case of study.
",1,0,0,0,0,0
17844,Valley polarized relaxation and upconversion luminescence from Tamm-Plasmon Trion-Polaritons with a MoSe2 monolayer,"  Transition metal dichalcogenides represent an ideal testbed to study
excitonic effects, spin-related phenomena and fundamental light-matter coupling
in nanoscopic condensed matter systems. In particular, the valley degree of
freedom, which is unique to such direct band gap monolayers with broken
inversion symmetry, adds fundamental interest in these materials. Here, we
implement a Tamm-plasmon structure with an embedded MoSe2 monolayer and study
the formation of polaritonic quasi-particles. Strong coupling conditions
between the Tamm-mode and the trion resonance of MoSe2 are established,
yielding bright luminescence from the polaritonic ground state under
non-resonant optical excitation. We demonstrate, that tailoring the
electrodynamic environment of the monolayer results in a significantly
increased valley polarization. This enhancement can be related to change in
recombination dynamics shown in time-resolved photoluminescence measurements.
We furthermore observe strong upconversion luminescence from resonantly excited
polariton states in the lower polariton branch. This upconverted polariton
luminescence is shown to preserve the valley polarization of the
trion-polariton, which paves the way towards combining spin-valley physics and
exciton scattering experiments.
",0,1,0,0,0,0
17845,Machine learning based localization and classification with atomic magnetometers,"  We demonstrate identification of position, material, orientation and shape of
objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic
induction imaging supported by machine learning. Machine learning maximizes the
information extracted from the images created by the magnetometer,
demonstrating the use of hidden data. Localization 2.6 times better than the
spatial resolution of the imaging system and successful classification up to
97$\%$ are obtained. This circumvents the need of solving the inverse problem,
and demonstrates the extension of machine learning to diffusive systems such as
low-frequency electrodynamics in media. Automated collection of task-relevant
information from quantum-based electromagnetic imaging will have a relevant
impact from biomedicine to security.
",0,1,0,0,0,0
17846,On the difficulty of finding spines,"  We prove that the set of symplectic lattices in the Siegel space
$\mathfrak{h}_g$ whose systoles generate a subspace of dimension at least 3 in
$\mathbb{R}^{2g}$ does not contain any $\mathrm{Sp}(2g,\mathbb{Z})$-equivariant
deformation retract of $\mathfrak{h}_g$.
",0,0,1,0,0,0
17847,Distributed Decoding of Convolutional Network Error Correction Codes,"  A Viterbi-like decoding algorithm is proposed in this paper for generalized
convolutional network error correction coding. Different from classical Viterbi
algorithm, our decoding algorithm is based on minimum error weight rather than
the shortest Hamming distance between received and sent sequences. Network
errors may disperse or neutralize due to network transmission and convolutional
network coding. Therefore, classical decoding algorithm cannot be employed any
more. Source decoding was proposed by multiplying the inverse of network
transmission matrix, where the inverse is hard to compute. Starting from the
Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to
the minimum error weight under our model. Inspired by Viterbi algorithm, we
propose a Viterbi-like decoding algorithm based on minimum error weight of
combined error vectors, which can be carried out directly at sink nodes and can
correct any network errors within the capability of convolutional network error
correction codes (CNECC). Under certain situations, the proposed algorithm can
realize the distributed decoding of CNECC.
",1,0,0,0,0,0
17848,Temperature induced phase transition from cycloidal to collinear antiferromagnetism in multiferroic Bi$_{0.9}$Sm$_{0.1}$FeO$_3$ driven by $f$-$d$ induced magnetic anisotropy,"  In multiferroic BiFeO$_3$ a cycloidal antiferromagnetic structure is coupled
to a large electric polarization at room temperature, giving rise to
magnetoelectric functionality that may be exploited in novel multiferroic-based
devices. In this paper, we demonstrate that by substituting samarium for 10% of
the bismuth ions the periodicity of the room temperature cycloid is increased,
and by cooling below $\sim15$ K the magnetic structure tends towards a simple
G-type antiferromagnet, which is fully established at 1.5 K. We show that this
transition results from $f-d$ exchange coupling, which induces a local
anisotropy on the iron magnetic moments that destroys the cycloidal order - a
result of general significance regarding the stability of non-collinear
magnetic structures in the presence of multiple magnetic sublattices.
",0,1,0,0,0,0
17849,POSEYDON - Converting the DAFNE Collider into a double Positron Facility: a High Duty-Cycle pulse stretcher and a storage ring,"  This project proposes to reuse the DAFNE accelerator complex for producing a
high intensity (up to 10^10), high-quality beam of high-energy (up to 500 MeV)
positrons for HEP experiments, mainly - but not only - motivated by light dark
particles searches. Such a facility would provide a unique source of
ultra-relativistic, narrow-band and low-emittance positrons, with a high duty
factor, without employing a cold technology, that would be an ideal facility
for exploring the existence of light dark matter particles, produced in
positron-on-target annihilations into a photon+missing mass, and using the
bump-hunt technique. The PADME experiment, that will use the extracted beam
from the DAFNE BTF, is indeed limited by the low duty-factor (10^-5=200 ns/20
ms). The idea is to use a variant of the third of integer resonant extraction,
with the aim of getting a <10^-6 m rad emittance and, at the same time,
tailoring the scheme to the peculiar optics of the DAFNE machine. In
alternative, the possibility of kicking the positrons by means of channelling
effects in crystals can be evaluated. This would not only increase the
extraction efficiency but also improve the beam quality, thanks to the high
collimation of channelled particles.
",0,1,0,0,0,0
17850,Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations,"  Understanding planetary interiors is directly linked to our ability of
simulating exotic quantum mechanical systems such as hydrogen (H) and
hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equations
of State (EOSs) tables based on Density Functional Theory (DFT), are commonly
used by planetary scientists, although this method allows only for a
qualitative description of the phase diagram, due to an incomplete treatment of
electronic interactions. Here we report Quantum Monte Carlo (QMC) molecular
dynamics simulations of pure H and H-He mixture. We calculate the first QMC EOS
at 6000 K for an H-He mixture of a proto-solar composition, and show the
crucial influence of He on the H metallization pressure. Our results can be
used to calibrate other EOS calculations and are very timely given the accurate
determination of Jupiter's gravitational field from the NASA Juno mission and
the effort to determine its structure.
",0,1,0,0,0,0
17851,Identifying exogenous and endogenous activity in social media,"  The occurrence of new events in a system is typically driven by external
causes and by previous events taking place inside the system. This is a general
statement, applying to a range of situations including, more recently, to the
activity of users in Online social networks (OSNs). Here we develop a method
for extracting from a series of posting times the relative contributions of
exogenous, e.g. news media, and endogenous, e.g. information cascade. The
method is based on the fitting of a generalized linear model (GLM) equipped
with a self-excitation mechanism. We test the method with synthetic data
generated by a nonlinear Hawkes process, and apply it to a real time series of
tweets with a given hashtag. In the empirical dataset, the estimated
contributions of exogenous and endogenous volumes are close to the amounts of
original tweets and retweets respectively. We conclude by discussing the
possible applications of the method, for instance in online marketing.
",1,0,0,0,0,0
17852,Water flow in Carbon and Silicon Carbide nanotubes,"  In this work the conduction of ion-water solution through two discrete
bundles of armchair carbon and silicon carbide nanotubes, as useful membranes
for water desalination, is studied. In order that studies on different types of
nanotubes be comparable, the chiral vectors of C and Si-C nanotubes are
selected as (7,7) and (5,5), respectively, so that a similar volume of fluid is
investigated flowing through two similar dimension membranes. Different
hydrostatic pressures are applied and the flow rates of water and ions are
calculated through molecular dynamics simulations. Consequently, according to
conductance of water per each nanotube, per nanosecond, it is perceived that at
lower pressures (below 150 MPa) the Si-C nanotubes seem to be more applicable,
while higher hydrostatic pressures make carbon nanotube membranes more suitable
for water desalination.
",0,1,0,0,0,0
17853,Multidimensional extremal dependence coefficients,"  Extreme values modeling has attracting the attention of researchers in
diverse areas such as the environment, engineering, or finance. Multivariate
extreme value distributions are particularly suitable to model the tails of
multidimensional phenomena. The analysis of the dependence among multivariate
maxima is useful to evaluate risk. Here we present new multivariate extreme
value models, as well as, coefficients to assess multivariate extremal
dependence.
",0,0,1,1,0,0
17854,A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas,"  Systems subject to uncertain inputs produce uncertain responses. Uncertainty
quantification (UQ) deals with the estimation of statistics of the system
response, given a computational model of the system and a probabilistic model
of its inputs. In engineering applications it is common to assume that the
inputs are mutually independent or coupled by a Gaussian or elliptical
dependence structure (copula). In this paper we overcome such limitations by
modelling the dependence structure of multivariate inputs as vine copulas. Vine
copulas are models of multivariate dependence built from simpler pair-copulas.
The vine representation is flexible enough to capture complex dependencies.
This paper formalises the framework needed to build vine copula models of
multivariate inputs and to combine them with virtually any UQ method. The
framework allows for a fully automated, data-driven inference of the
probabilistic input model on available input data. The procedure is exemplified
on two finite element models of truss structures, both subject to inputs with
non-Gaussian dependence structures. For each case, we analyse the moments of
the model response (using polynomial chaos expansions), and perform a
structural reliability analysis to calculate the probability of failure of the
system (using the first order reliability method and importance sampling).
Reference solutions are obtained by Monte Carlo simulation. The results show
that, while the Gaussian assumption yields biased statistics, the vine copula
representation achieves significantly more precise estimates, even when its
structure needs to be fully inferred from a limited amount of observations.
",0,0,0,1,0,0
17855,Pachinko Prediction: A Bayesian method for event prediction from social media data,"  The combination of large open data sources with machine learning approaches
presents a potentially powerful way to predict events such as protest or social
unrest. However, accounting for uncertainty in such models, particularly when
using diverse, unstructured datasets such as social media, is essential to
guarantee the appropriate use of such methods. Here we develop a Bayesian
method for predicting social unrest events in Australia using social media
data. This method uses machine learning methods to classify individual postings
to social media as being relevant, and an empirical Bayesian approach to
calculate posterior event probabilities. We use the method to predict events in
Australian cities over a period in 2017/18.
",1,0,0,0,0,0
17856,A Digital Hardware Fast Algorithm and FPGA-based Prototype for a Novel 16-point Approximate DCT for Image Compression Applications,"  The discrete cosine transform (DCT) is the key step in many image and video
coding standards. The 8-point DCT is an important special case, possessing
several low-complexity approximations widely investigated. However, 16-point
DCT transform has energy compaction advantages. In this sense, this paper
presents a new 16-point DCT approximation with null multiplicative complexity.
The proposed transform matrix is orthogonal and contains only zeros and ones.
The proposed transform outperforms the well-know Walsh-Hadamard transform and
the current state-of-the-art 16-point approximation. A fast algorithm for the
proposed transform is also introduced. This fast algorithm is experimentally
validated using hardware implementations that are physically realized and
verified on a 40 nm CMOS Xilinx Virtex-6 XC6VLX240T FPGA chip for a maximum
clock rate of 342 MHz. Rapid prototypes on FPGA for 8-bit input word size shows
significant improvement in compressed image quality by up to 1-2 dB at the cost
of only eight adders compared to the state-of-art 16-point DCT approximation
algorithm in the literature [S. Bouguezel, M. O. Ahmad, and M. N. S. Swamy. A
novel transform for image compression. In {\em Proceedings of the 53rd IEEE
International Midwest Symposium on Circuits and Systems (MWSCAS)}, 2010].
",1,0,0,1,0,0
17857,Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice,"  It is well known that the initialization of weights in deep neural networks
can have a dramatic impact on learning speed. For example, ensuring the mean
squared singular value of a network's input-output Jacobian is $O(1)$ is
essential for avoiding the exponential vanishing or explosion of gradients. The
stronger condition that all singular values of the Jacobian concentrate near
$1$ is a property known as dynamical isometry. For deep linear networks,
dynamical isometry can be achieved through orthogonal weight initialization and
has been shown to dramatically speed up learning; however, it has remained
unclear how to extend these results to the nonlinear setting. We address this
question by employing powerful tools from free probability theory to compute
analytically the entire singular value distribution of a deep network's
input-output Jacobian. We explore the dependence of the singular value
distribution on the depth of the network, the weight initialization, and the
choice of nonlinearity. Intriguingly, we find that ReLU networks are incapable
of dynamical isometry. On the other hand, sigmoidal networks can achieve
isometry, but only with orthogonal weight initialization. Moreover, we
demonstrate empirically that deep nonlinear networks achieving dynamical
isometry learn orders of magnitude faster than networks that do not. Indeed, we
show that properly-initialized deep sigmoidal networks consistently outperform
deep ReLU networks. Overall, our analysis reveals that controlling the entire
distribution of Jacobian singular values is an important design consideration
in deep learning.
",1,0,0,1,0,0
17858,Singular perturbation for abstract elliptic equations and application,"  Boundary value problem for complete second order elliptic equation is
considered in Banach space. The equation and boundary conditions involve a
small and spectral parameter. The uniform L_{p}-regularity properties with
respect to space variable and parameters are established. Here, the explicit
formula for the solution is given and behavior of solution is derived when the
small parameter approaches zero. It used to obtain singular perturbation result
for abstract elliptic equation
",0,0,1,0,0,0
17859,Pruning and Nonparametric Multiple Change Point Detection,"  Change point analysis is a statistical tool to identify homogeneity within
time series data. We propose a pruning approach for approximate nonparametric
estimation of multiple change points. This general purpose change point
detection procedure `cp3o' applies a pruning routine within a dynamic program
to greatly reduce the search space and computational costs. Existing
goodness-of-fit change point objectives can immediately be utilized within the
framework. We further propose novel change point algorithms by applying cp3o to
two popular nonparametric goodness of fit measures: `e-cp3o' uses E-statistics,
and `ks-cp3o' uses Kolmogorov-Smirnov statistics. Simulation studies highlight
the performance of these algorithms in comparison with parametric and other
nonparametric change point methods. Finally, we illustrate these approaches
with climatological and financial applications.
",0,0,0,1,0,0
17860,Context encoding enables machine learning-based quantitative photoacoustics,"  Real-time monitoring of functional tissue parameters, such as local blood
oxygenation, based on optical imaging could provide groundbreaking advances in
the diagnosis and interventional therapy of various diseases. While
photoacoustic (PA) imaging is a novel modality with great potential to measure
optical absorption deep inside tissue, quantification of the measurements
remains a major challenge. In this paper, we introduce the first machine
learning based approach to quantitative PA imaging (qPAI), which relies on
learning the fluence in a voxel to deduce the corresponding optical absorption.
The method encodes relevant information of the measured signal and the
characteristics of the imaging system in voxel-based feature vectors, which
allow the generation of thousands of training samples from a single simulated
PA image. Comprehensive in silico experiments suggest that context encoding
(CE)-qPAI enables highly accurate and robust quantification of the local
fluence and thereby the optical absorption from PA images.
",1,1,0,0,0,0
17861,Simulating the interaction between a falling super-quadric object and a soap film,"  The interaction that occurs between a light solid object and a horizontal
soap film of a bamboo foam contained in a cylindrical tube is simulated in 3D.
We vary the shape of the falling object from a sphere to a cube by changing a
single shape parameter as well as varying the initial orientation and position
of the object. We investigate in detail how the soap film deforms in all these
cases, and determine the network and pressure forces that a foam exerts on a
falling object, due to surface tension and bubble pressure respectively. We
show that a cubic particle in a particular orientation experiences the largest
drag force, and that this orientation is also the most likely outcome of
dropping a cube from an arbitrary orientation through a bamboo foam.
",0,1,0,0,0,0
17862,Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach,"  Compared with relational database (RDB), graph database (GDB) is a more
intuitive expression of the real world. Each node in the GDB is a both storage
and logic unit. Since it is connected to its neighboring nodes through edges,
and its neighboring information could be easily obtained in one-step graph
traversal. It is able to conduct local computation independently and all nodes
can do their local work in parallel. Then the whole system can be maximally
analyzed and assessed in parallel to largely improve the computation
performance without sacrificing the precision of final results. This paper
firstly introduces graph database, power system graph modeling and potential
graph computing applications in power systems. Two iterative methods based on
graph database and PageRank are presented and their convergence are discussed.
Vertex contraction is proposed to improve the performance by eliminating
zero-impedance branch. A combination of the two iterative methods is proposed
to make use of their advantages. Testing results based on a provincial 1425-bus
system demonstrate that the proposed comprehensive approach is a good candidate
for power flow analysis.
",1,0,0,0,0,0
17863,A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm,"  In this paper, we present an algorithm for the sparse signal recovery problem
that incorporates damped Gaussian generalized approximate message passing
(GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning
(SBL). In particular, GGAMP is used to implement the E-step in SBL in place of
matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with
appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to
arbitrary measurement matrix $\boldsymbol{A}$ than the standard damped GAMP
algorithm while being much lower complexity than the standard SBL algorithm. We
then extend the approach from the single measurement vector (SMV) case to the
temporally correlated multiple measurement vector (MMV) case, leading to the
GGAMP-TSBL algorithm. We verify the robustness and computational advantages of
the proposed algorithms through numerical experiments.
",1,0,0,1,0,0
17864,Sub-Nanometer Channels Embedded in Two-Dimensional Materials,"  Two-dimensional (2D) materials are among the most promising candidates for
next-generation electronics due to their atomic thinness, allowing for flexible
transparent electronics and ultimate length scaling. Thus far, atomically-thin
p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have
been demonstrated. While 2D materials achieve the thinnest possible devices,
precise nanoscale control over the lateral dimensions is also necessary. Here,
we report the direct synthesis of sub-nanometer-wide 1D MoS2 channels embedded
within WSe2 monolayers, using a dislocation-catalyzed approach. The 1D channels
have edges free of misfit dislocations and dangling bonds, forming a coherent
interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D
superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics
simulations, we have identified other combinations of 2D materials where 1D
channels can also be formed. The electronic band structure of these 1D channels
offer the promise of carrier confinement in a direct-gap material and charge
separation needed to access the ultimate length scales necessary for future
electronic applications.
",0,1,0,0,0,0
17865,Emotion in Reinforcement Learning Agents and Robots: A Survey,"  This article provides the first survey of computational models of emotion in
reinforcement learning (RL) agents. The survey focuses on agent/robot emotions,
and mostly ignores human user emotions. Emotions are recognized as functional
in decision-making by influencing motivation and action selection. Therefore,
computational emotion models are usually grounded in the agent's decision
making architecture, of which RL is an important subclass. Studying emotions in
RL-based agents is useful for three research fields. For machine learning (ML)
researchers, emotion models may improve learning efficiency. For the
interactive ML and human-robot interaction (HRI) community, emotions can
communicate state and enhance user investment. Lastly, it allows affective
modelling (AM) researchers to investigate their emotion theories in a
successful AI agent class. This survey provides background on emotion theory
and RL. It systematically addresses 1) from what underlying dimensions (e.g.,
homeostasis, appraisal) emotions can be derived and how these can be modelled
in RL-agents, 2) what types of emotions have been derived from these
dimensions, and 3) how these emotions may either influence the learning
efficiency of the agent or be useful as social signals. We also systematically
compare evaluation criteria, and draw connections to important RL sub-domains
like (intrinsic) motivation and model-based RL. In short, this survey provides
both a practical overview for engineers wanting to implement emotions in their
RL agents, and identifies challenges and directions for future emotion-RL
research.
",1,0,0,1,0,0
17866,Tensor Networks in a Nutshell,"  Tensor network methods are taking a central role in modern quantum physics
and beyond. They can provide an efficient approximation to certain classes of
quantum states, and the associated graphical language makes it easy to describe
and pictorially reason about quantum circuits, channels, protocols, open
systems and more. Our goal is to explain tensor networks and some associated
methods as quickly and as painlessly as possible. Beginning with the key
definitions, the graphical tensor network language is presented through
examples. We then provide an introduction to matrix product states. We conclude
the tutorial with tensor contractions evaluating combinatorial counting
problems. The first one counts the number of solutions for Boolean formulae,
whereas the second is Penrose's tensor contraction algorithm, returning the
number of $3$-edge-colorings of $3$-regular planar graphs.
",0,1,0,0,0,0
17867,Cheryl's Birthday,"  We present four logic puzzles and after that their solutions. Joseph Yeo
designed 'Cheryl's Birthday'. Mike Hartley came up with a novel solution for
'One Hundred Prisoners and a Light Bulb'. Jonathan Welton designed 'A Blind
Guess' and 'Abby's Birthday'. Hans van Ditmarsch and Barteld Kooi authored the
puzzlebook 'One Hundred Prisoners and a Light Bulb' that contains other
knowledge puzzles, and that can also be found on the webpage
this http URL dedicated to the book.
",1,0,0,0,0,0
17868,Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow,"  Exploration bonus derived from the novelty of the states in an environment
has become a popular approach to motivate exploration for deep reinforcement
learning agents in the past few years. Recent methods such as curiosity-driven
exploration usually estimate the novelty of new observations by the prediction
errors of their system dynamics models. Due to the capacity limitation of the
models and difficulty of performing next-frame prediction, however, these
methods typically fail to balance between exploration and exploitation in
high-dimensional observation tasks, resulting in the agents forgetting the
visited paths and exploring those states repeatedly. Such inefficient
exploration behavior causes significant performance drops, especially in large
environments with sparse reward signals. In this paper, we propose to introduce
the concept of optical flow estimation from the field of computer vision to
deal with the above issue. We propose to employ optical flow estimation errors
to examine the novelty of new observations, such that agents are able to
memorize and understand the visited states in a more comprehensive fashion. We
compare our method against the previous approaches in a number of experimental
experiments. Our results indicate that the proposed method appears to deliver
superior and long-lasting performance than the previous methods. We further
provide a set of comprehensive ablative analysis of the proposed method, and
investigate the impact of optical flow estimation on the learning curves of the
DRL agents.
",1,0,0,0,0,0
17869,Lenient Multi-Agent Deep Reinforcement Learning,"  Much of the success of single agent deep reinforcement learning (DRL) in
recent years can be attributed to the use of experience replay memories (ERM),
which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling
stored state transitions. However, care is required when using ERMs for
multi-agent deep reinforcement learning (MA-DRL), as stored transitions can
become outdated because agents update their policies in parallel [11]. In this
work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to
decaying temperature values that control the amount of leniency applied towards
negative policy updates that are sampled from the ERM. This introduces optimism
in the value-function update, and has been shown to facilitate cooperation in
tabular fully-cooperative multi-agent reinforcement learning problems. We
evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN
(HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN,
that uses average reward learning near terminal states. Evaluations take place
in extended variations of the Coordinated Multi-Agent Object Transportation
Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic
rewards. We find that LDQN agents are more likely to converge to the optimal
policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN
agents.
",1,0,0,0,0,0
17870,A New Framework for Synthetic Aperture Sonar Micronavigation,"  Synthetic aperture imaging systems achieve constant azimuth resolution by
coherently summating the observations acquired along the aperture path. At this
aim, their locations have to be known with subwavelength accuracy. In
underwater Synthetic Aperture Sonar (SAS), the nature of propagation and
navigation in water makes the retrieval of this information challenging.
Inertial sensors have to be employed in combination with signal processing
techniques, which are usually referred to as micronavigation. In this paper we
propose a novel micronavigation approach based on the minimization of an error
function between two contiguous pings having some mutual information. This
error is obtained by comparing the vector space intersections between the pings
orthogonal projectors. The effectiveness and generality of the proposed
approach is demonstrated by means of simulations and by means of an experiment
performed in a controlled environment.
",1,0,0,0,0,0
17871,Emotionalism within People-Oriented Software Design,"  In designing most software applications, much effort is placed upon the
functional goals, which make a software system useful. However, the failure to
consider emotional goals, which make a software system pleasurable to use, can
result in disappointment and system rejection even if utilitarian goals are
well implemented. Although several studies have emphasized the importance of
people's emotional goals in developing software, there is little advice on how
to address these goals in the software system development process. This paper
proposes a theoretically-sound and practical method by combining the theories
and techniques of software engineering, requirements engineering, and decision
making. The outcome of this study is the Emotional Goal Systematic Analysis
Technique (EG-SAT), which facilitates the process of finding software system
capabilities to address emotional goals in software design. EG-SAT is easy to
learn and easy to use technique that helps analysts to gain insights into how
to address people's emotional goals. To demonstrate the method in use, a
two-part evaluation is conducted. First, EG-SAT is used to analyze the
emotional goals of potential users of a mobile learning application that
provides information about low carbon living for tradespeople and professionals
in the building industry in Australia. The results of using EG-SAT in this case
study are compared with a professionally-developed baseline. Second, we ran a
semi-controlled experiment in which 12 participants were asked to apply EG-SAT
and another technique on part of our case study. The outcomes show that EG-SAT
helped participants to both analyse emotional goals and gain valuable insights
about the functional and non-functional goals for addressing people's emotional
goals.
",1,0,0,0,0,0
17872,Quantifying the Estimation Error of Principal Components,"  Principal component analysis is an important pattern recognition and
dimensionality reduction tool in many applications. Principal components are
computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$
that approximates a population covariance $\Sigma$, and these eigenvectors are
often used to extract structural information about the variables (or
attributes) of the studied population. Since PCA is based on the
eigendecomposition of the proxy covariance $\widehat{\Sigma}$ rather than the
ground-truth $\Sigma$, it is important to understand the approximation error in
each individual eigenvector as a function of the number of available samples.
The recent results of Kolchinskii and Lounici yield such bounds. In the present
paper we sharpen these bounds and show that eigenvectors can often be
reconstructed to a required accuracy from a sample of strictly smaller size
order.
",0,0,1,1,0,0
17873,On Convex Programming Relaxations for the Permanent,"  In recent years, several convex programming relaxations have been proposed to
estimate the permanent of a non-negative matrix, notably in the works of
Gurvits and Samorodnitsky. However, the origins of these relaxations and their
relationships to each other have remained somewhat mysterious. We present a
conceptual framework, implicit in the belief propagation literature, to
systematically arrive at these convex programming relaxations for estimating
the permanent -- as approximations to an exponential-sized max-entropy convex
program for computing the permanent. Further, using standard convex programming
techniques such as duality, we establish equivalence of these aforementioned
relaxations to those based on capacity-like quantities studied by Gurvits and
Anari et al.
",1,0,1,0,0,0
17874,Many cubic surfaces contain rational points,"  Building on recent work of Bhargava--Elkies--Schnidman and Kriz--Li, we
produce infinitely many smooth cubic surfaces defined over the field of
rational numbers that contain rational points.
",0,0,1,0,0,0
17875,Representation learning of drug and disease terms for drug repositioning,"  Drug repositioning (DR) refers to identification of novel indications for the
approved drugs. The requirement of huge investment of time as well as money and
risk of failure in clinical trials have led to surge in interest in drug
repositioning. DR exploits two major aspects associated with drugs and
diseases: existence of similarity among drugs and among diseases due to their
shared involved genes or pathways or common biological effects. Existing
methods of identifying drug-disease association majorly rely on the information
available in the structured databases only. On the other hand, abundant
information available in form of free texts in biomedical research articles are
not being fully exploited. Word-embedding or obtaining vector representation of
words from a large corpora of free texts using neural network methods have been
shown to give significant performance for several natural language processing
tasks. In this work we propose a novel way of representation learning to obtain
features of drugs and diseases by combining complementary information available
in unstructured texts and structured datasets. Next we use matrix completion
approach on these feature vectors to learn projection matrix between drug and
disease vector spaces. The proposed method has shown competitive performance
with state-of-the-art methods. Further, the case studies on Alzheimer's and
Hypertension diseases have shown that the predicted associations are matching
with the existing knowledge.
",1,0,0,0,0,0
17876,Analysis of a remarkable singularity in a nonlinear DDE,"  In this work we investigate the dynamics of the nonlinear DDE
(delay-differential equation)
x''(t)+x(t-T)+x(t)^3=0
where T is the delay. For T=0 this system is conservative and exhibits no
limit cycles. For T>0, no matter how small, an infinite number of limit cycles
exist, their amplitudes going to infinity in the limit as T approaches zero.
We investigate this situation in three ways: 1) Harmonic Balance, 2)
Melnikov's integral, and 3) Adding damping to regularize the singularity.
",0,1,1,0,0,0
17877,Characterization of Lipschitz functions in terms of variable exponent Lebesgue spaces,"  Our aim is to characterize the Lipschitz functions by variable exponent
Lebesgue spaces. We give some characterizations of the boundedness of the
maximal or nonlinear commutators of the Hardy-Littlewood maximal function and
sharp maximal function in variable exponent Lebesgue spaces when the symbols
$b$ belong to the Lipschitz spaces, by which some new characterizations of
Lipschitz spaces and nonnegative Lipschitz functions are obtained. Some
equivalent relations between the Lipschitz norm and the variable exponent
Lebesgue norm are also given.
",0,0,1,0,0,0
17878,Superconductivity Induced by Interfacial Coupling to Magnons,"  We consider a thin normal metal sandwiched between two ferromagnetic
insulators. At the interfaces, the exchange coupling causes electrons within
the metal to interact with magnons in the insulators. This electron-magnon
interaction induces electron-electron interactions, which, in turn, can result
in p-wave superconductivity. In the weak-coupling limit, we solve the gap
equation numerically and estimate the critical temperature. In YIG-Au-YIG
trilayers, superconductivity sets in at temperatures somewhere in the interval
between 1 and 10 K. EuO-Au-EuO trilayers require a lower temperature, in the
range from 0.01 to 1 K.
",0,1,0,0,0,0
17879,Privacy Assessment of De-identified Opal Data: A report for Transport for NSW,"  We consider the privacy implications of public release of a de-identified
dataset of Opal card transactions. The data was recently published at
this https URL. It
consists of tap-on and tap-off counts for NSW's four modes of public transport,
collected over two separate week-long periods. The data has been further
treated to improve privacy by removing small counts, aggregating some stops and
routes, and perturbing the counts. This is a summary of our findings.
",1,0,0,0,0,0
17880,On the Importance of Correlations in Rational Choice: A Case for Non-Nashian Game Theory,"  The Nash equilibrium paradigm, and Rational Choice Theory in general, rely on
agents acting independently from each other. This note shows how this
assumption is crucial in the definition of Rational Choice Theory. It explains
how a consistent Alternate Rational Choice Theory, as suggested by Jean-Pierre
Dupuy, can be built on the exact opposite assumption, and how it provides a
viable account for alternate, actually observed behavior of rational agents
that is based on correlations between their decisions.
The end goal of this note is three-fold: (i) to motivate that the Perfect
Prediction Equilibrium, implementing Dupuy's notion of projected time and
previously called ""projected equilibrium"", is a reasonable approach in certain
real situations and a meaningful complement to the Nash paradigm, (ii) to
summarize common misconceptions about this equilibrium, and (iii) to give a
concise motivation for future research on non-Nashian game theory.
",1,0,0,0,0,0
17881,Reliable estimation of prediction uncertainty for physico-chemical property models,"  The predictions of parameteric property models and their uncertainties are
sensitive to systematic errors such as inconsistent reference data, parametric
model assumptions, or inadequate computational methods. Here, we discuss the
calibration of property models in the light of bootstrapping, a sampling method
akin to Bayesian inference that can be employed for identifying systematic
errors and for reliable estimation of the prediction uncertainty. We apply
bootstrapping to assess a linear property model linking the 57Fe Moessbauer
isomer shift to the contact electron density at the iron nucleus for a diverse
set of 44 molecular iron compounds. The contact electron density is calculated
with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91,
PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error
diagnostics and reliable, locally resolved uncertainties for isomer-shift
predictions. Pure and hybrid density functionals yield average prediction
uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter
being close to the average experimental uncertainty of 0.02 mm/s. Furthermore,
we show that both model parameters and prediction uncertainty depend
significantly on the composition and number of reference data points.
Accordingly, we suggest that rankings of density functionals based on
performance measures (e.g., the coefficient of correlation, r2, or the
root-mean-square error, RMSE) should not be inferred from a single data set.
This study presents the first statistically rigorous calibration analysis for
theoretical Moessbauer spectroscopy, which is of general applicability for
physico-chemical property models and not restricted to isomer-shift
predictions. We provide the statistically meaningful reference data set MIS39
and a new calibration of the isomer shift based on the PBE0 functional.
",0,1,0,0,0,0
17882,Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques,"  Split manufacturing is a promising technique to defend against fab-based
malicious activities such as IP piracy, overbuilding, and insertion of hardware
Trojans. However, a network flow-based proximity attack, proposed by Wang et
al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing
is highly vulnerable. Here in this work, we present two practical layout
techniques towards secure split manufacturing: (i) gate-level graph coloring
and (ii) clustering of same-type gates. Our approach shows promising results
against the advanced proximity attack, lowering its success rate by 5.27x,
3.19x, and 1.73x on average compared to the unprotected layouts when splitting
at metal layers M1, M2, and M3, respectively. Also, it largely outperforms
previous defense efforts; we observe on average 8x higher resilience when
compared to representative prior art. At the same time, extensive simulations
on ISCAS'85 and MCNC benchmarks reveal that our techniques incur an acceptable
layout overhead. Apart from this empirical study, we provide---for the first
time---a theoretical framework for quantifying the layout-level resilience
against any proximity-induced information leakage. Towards this end, we
leverage the notion of mutual information and provide extensive results to
validate our model.
",1,0,0,0,0,0
17883,Unsupervised robotic sorting: Towards autonomous decision making robots,"  Autonomous sorting is a crucial task in industrial robotics which can be very
challenging depending on the expected amount of automation. Usually, to decide
where to sort an object, the system needs to solve either an instance retrieval
(known object) or a supervised classification (predefined set of classes)
problem. In this paper, we introduce a new decision making module, where the
robotic system chooses how to sort the objects in an unsupervised way. We call
this problem Unsupervised Robotic Sorting (URS) and propose an implementation
on an industrial robotic system, using deep CNN feature extraction and standard
clustering algorithms. We carry out extensive experiments on various standard
datasets to demonstrate the efficiency of the proposed image clustering
pipeline. To evaluate the robustness of our URS implementation, we also
introduce a complex real world dataset containing images of objects under
various background and lighting conditions. This dataset is used to fine tune
the design choices (CNN and clustering algorithm) for URS. Finally, we propose
a method combining our pipeline with ensemble clustering to use multiple images
of each object. This redundancy of information about the objects is shown to
increase the clustering results.
",1,0,0,0,0,0
17884,Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis,"  The Whittle likelihood is widely used for Bayesian nonparametric estimation
of the spectral density of stationary time series. However, the loss of
efficiency for non-Gaussian time series can be substantial. On the other hand,
parametric methods are more powerful if the model is well-specified, but may
fail entirely otherwise. Therefore, we suggest a nonparametric correction of a
parametric likelihood taking advantage of the efficiency of parametric models
while mitigating sensitivities through a nonparametric amendment. Using a
Bernstein-Dirichlet prior for the nonparametric spectral correction, we show
posterior consistency and illustrate the performance of our procedure in a
simulation study and with LIGO gravitational wave data.
",0,0,0,1,0,0
17885,A consistent approach to unstructured mesh generation for geophysical models,"  Geophysical model domains typically contain irregular, complex fractal-like
boundaries and physical processes that act over a wide range of scales.
Constructing geographically constrained boundary-conforming spatial
discretizations of these domains with flexible use of anisotropically, fully
unstructured meshes is a challenge. The problem contains a wide range of scales
and a relatively large, heterogeneous constraint parameter space. Approaches
are commonly ad hoc, model or application specific and insufficiently
described. Development of new spatial domains is frequently time-consuming,
hard to repeat, error prone and difficult to ensure consistent due to the
significant human input required. As a consequence, it is difficult to
reproduce simulations, ensure a provenance in model data handling and
initialization, and a challenge to conduct model intercomparisons rigorously.
Moreover, for flexible unstructured meshes, there is additionally a greater
potential for inconsistencies in model initialization and forcing parameters.
This paper introduces a consistent approach to unstructured mesh generation for
geophysical models, that is automated, quick-to-draft and repeat, and provides
a rigorous and robust approach that is consistent to the source data
throughout. The approach is enabling further new research in complex
multi-scale domains, difficult or not possible to achieve with existing
methods. Examples being actively pursued in a range of geophysical modeling
efforts are presented alongside the approach, together with the implementation
library Shingle and a selection of its verification test cases.
",1,1,0,0,0,0
17886,Metric Map Merging using RFID Tags & Topological Information,"  A map merging component is crucial for the proper functionality of a
multi-robot system performing exploration, since it provides the means to
integrate and distribute the most important information carried by the agents:
the explored-covered space and its exact (depending on the SLAM accuracy)
morphology. Map merging is a prerequisite for an intelligent multi-robot team
aiming to deploy a smart exploration technique. In the current work, a metric
map merging approach based on environmental information is proposed, in
conjunction with spatially scattered RFID tags localization. This approach is
divided into the following parts: the maps approximate rotation calculation via
the obstacles poses and localized RFID tags, the translation employing the best
localized common RFID tag and finally the transformation refinement using an
ICP algorithm.
",1,0,0,0,0,0
17887,Learning Local Feature Aggregation Functions with Backpropagation,"  This paper introduces a family of local feature aggregation functions and a
novel method to estimate their parameters, such that they generate optimal
representations for classification (or any task that can be expressed as a cost
function minimization problem). To achieve that, we compose the local feature
aggregation function with the classifier cost function and we backpropagate the
gradient of this cost function in order to update the local feature aggregation
function parameters. Experiments on synthetic datasets indicate that our method
discovers parameters that model the class-relevant information in addition to
the local feature space. Further experiments on a variety of motion and visual
descriptors, both on image and video datasets, show that our method outperforms
other state-of-the-art local feature aggregation functions, such as Bag of
Words, Fisher Vectors and VLAD, by a large margin.
",1,0,0,1,0,0
17888,Sub-harmonic Injection Locking in Metronomes,"  In this paper, we demonstrate sub-harmonic injection locking (SHIL) in
mechanical metronomes. To do so, we first formulate metronome's physical
compact model, focusing on its nonlinear terms for friction and the escapement
mechanism. Then we analyze metronomes using phase-macromodel-based techniques
and show that the phase of their oscillation is in fact very immune to periodic
perturbation at twice its natural frequency, making SHIL difficult. Guided by
the phase-macromodel-based analysis, we are able to modify the escapement
mechanism of metronomes such that SHIL can happen more easily. Then we verify
the occurrence of SHIL in experiments. To our knowledge, this is the first
demonstration of SHIL in metronomes; As such, it provides many valuable
insights into the modelling, simulation, analysis and design of nonlinear
oscillators. The demonstration is also suitable to use for teaching the subject
of injection locking and SHIL.
",0,1,0,0,0,0
17889,Spin Seebeck effect in a polar antiferromagnet $α$-Cu$_{2}$V$_{2}$O$_{7}$,"  We have studied the longitudinal spin Seebeck effect in a polar
antiferromagnet $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ in contact with a Pt film.
Below the antiferromagnetic transition temperature of
$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$, spin Seebeck voltages whose magnetic field
dependence is similar to that reported in antiferromagnetic MnF$_{2}$$\mid$Pt
bilayers are observed. Though a small weak-ferromagnetic moment appears owing
to the Dzyaloshinskii-Moriya interaction in $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$,
the magnetic field dependence of spin Seebeck voltages is found to be
irrelevant to the weak ferromagnetic moments. The dependences of the spin
Seebeck voltages on magnetic fields and temperature are analyzed by a magnon
spin current theory. The numerical calculation of spin Seebeck voltages using
magnetic parameters of $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ determined by previous
neutron scattering studies reveals that the magnetic-field and temperature
dependences of the spin Seebeck voltages for
$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$$\mid$Pt are governed by the changes in magnon
lifetimes with magnetic fields and temperature.
",0,1,0,0,0,0
17890,Mackey algebras which are Gorenstein,"  We complete the picture available in the literature by showing that the
integral Mackey algebra is Gorenstein if and only if the group order is
square-free, in which case it must have Gorenstein dimension one. We illustrate
this result by looking in details at the examples of the cyclic group of order
four and the Klein four group.
",0,0,1,0,0,0
17891,Efficient acquisition rules for model-based approximate Bayesian computation,"  Approximate Bayesian computation (ABC) is a method for Bayesian inference
when the likelihood is unavailable but simulating from the model is possible.
However, many ABC algorithms require a large number of simulations, which can
be costly. To reduce the computational cost, Bayesian optimisation (BO) and
surrogate models such as Gaussian processes have been proposed. Bayesian
optimisation enables one to intelligently decide where to evaluate the model
next but common BO strategies are not designed for the goal of estimating the
posterior distribution. Our paper addresses this gap in the literature. We
propose to compute the uncertainty in the ABC posterior density, which is due
to a lack of simulations to estimate this quantity accurately, and define a
loss function that measures this uncertainty. We then propose to select the
next evaluation location to minimise the expected loss. Experiments show that
the proposed method often produces the most accurate approximations as compared
to common BO strategies.
",0,0,0,1,0,0
17892,Active Learning for Regression Using Greedy Sampling,"  Regression problems are pervasive in real-world applications. Generally a
substantial amount of labeled samples are needed to build a regression model
with good generalization ability. However, many times it is relatively easy to
collect a large number of unlabeled samples, but time-consuming or expensive to
label them. Active learning for regression (ALR) is a methodology to reduce the
number of labeled samples, by selecting the most beneficial ones to label,
instead of random selection. This paper proposes two new ALR approaches based
on greedy sampling (GS). The first approach (GSy) selects new samples to
increase the diversity in the output space, and the second (iGS) selects new
samples to increase the diversity in both input and output spaces. Extensive
experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15
subjects on EEG-based driver drowsiness estimation, verified their
effectiveness and robustness.
",0,0,0,1,0,0
17893,Centroid estimation based on symmetric KL divergence for Multinomial text classification problem,"  We define a new method to estimate centroid for text classification based on
the symmetric KL-divergence between the distribution of words in training
documents and their class centroids. Experiments on several standard data sets
indicate that the new method achieves substantial improvements over the
traditional classifiers.
",0,0,0,1,0,0
17894,Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering,"  Calculation of near-neighbor interactions among high dimensional, irregularly
distributed data points is a fundamental task to many graph-based or
kernel-based machine learning algorithms and applications. Such calculations,
involving large, sparse interaction matrices, expose the limitation of
conventional data-and-computation reordering techniques for improving space and
time locality on modern computer memory hierarchies. We introduce a novel
method for obtaining a matrix permutation that renders a desirable sparsity
profile. The method is distinguished by the guiding principle to obtain a
profile that is block-sparse with dense blocks. Our profile model and measure
capture the essential properties affecting space and time locality, and permit
variation in sparsity profile without imposing a restriction to a fixed
pattern. The second distinction lies in an efficient algorithm for obtaining a
desirable profile, via exploring and exploiting multi-scale cluster structure
hidden in but intrinsic to the data. The algorithm accomplishes its task with
key components for lower-dimensional embedding with data-specific principal
feature axes, hierarchical data clustering, multi-level matrix compression
storage, and multi-level interaction computations. We provide experimental
results from case studies with two important data analysis algorithms. The
resulting performance is remarkably comparable to the BLAS performance for the
best-case interaction governed by a regularly banded matrix with the same
sparsity.
",1,0,0,0,0,0
17895,Evolution and Recent Developments of the Gaseous Photon Detectors Technologies,"  The evolution and the present status of the gaseous photon detectors
technologies are reviewed. The most recent developments in several branches of
the field are described, in particular the installation and commissioning of
the first large area MPGD-based detectors of single photons on COMPASS RICH-1.
Investigation of novel detector architectures, different materials and various
applications are reported, and the quest for visible light gaseous photon
detectors is discussed. The progress on the use of gaseous photon detector
related techniques in the field of cryogenic applications and gaseous or liquid
scintillation imaging are presented.
",0,1,0,0,0,0
17896,Superconducting Qubit-Resonator-Atom Hybrid System,"  We propose a hybrid quantum system, where an $LC$ resonator inductively
interacts with a flux qubit and is capacitively coupled to a Rydberg atom.
Varying the external magnetic flux bias controls the flux-qubit flipping and
the flux qubit-resonator interface. The atomic spectrum is tuned via an
electrostatic field, manipulating the qubit-state transition of atom and the
atom-resonator coupling. Different types of entanglement of superconducting,
photonic, and atomic qubits can be prepared via simply tuning the flux bias and
electrostatic field, leading to the implementation of three-qubit Toffoli logic
gate.
",0,1,0,0,0,0
17897,Heroes and Zeroes: Predicting the Impact of New Video Games on Twitch.tv,"  Video games and the playing thereof have been a fixture of American culture
since their introduction in the arcades of the 1980s. However, it was not until
the recent proliferation of broadband connections robust and fast enough to
handle live video streaming that players of video games have transitioned from
a content consumer role to a content producer role. Simultaneously, the rise of
social media has revealed how interpersonal connections drive user engagement
and interest. In this work, we discuss the recent proliferation of video game
streaming, particularly on Twitch.tv, analyze trends and patterns in video game
viewing, and develop predictive models for determining if a new game will have
substantial impact on the streaming ecosystem.
",1,0,0,0,0,0
17898,Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study,"  Learning the latent representation of data in unsupervised fashion is a very
interesting process that provides relevant features for enhancing the
performance of a classifier. For speech emotion recognition tasks, generating
effective features is crucial. Currently, handcrafted features are mostly used
for speech emotion recognition, however, features learned automatically using
deep learning have shown strong success in many problems, especially in image
processing. In particular, deep generative models such as Variational
Autoencoders (VAEs) have gained enormous success for generating features for
natural images. Inspired by this, we propose VAEs for deriving the latent
representation of speech signals and use this representation to classify
emotions. To the best of our knowledge, we are the first to propose VAEs for
speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate
that features learned by VAEs can produce state-of-the-art results for speech
emotion classification.
",1,0,0,1,0,0
17899,Insense: Incoherent Sensor Selection for Sparse Signals,"  Sensor selection refers to the problem of intelligently selecting a small
subset of a collection of available sensors to reduce the sensing cost while
preserving signal acquisition performance. The majority of sensor selection
algorithms find the subset of sensors that best recovers an arbitrary signal
from a number of linear measurements that is larger than the dimension of the
signal. In this paper, we develop a new sensor selection algorithm for sparse
(or near sparse) signals that finds a subset of sensors that best recovers such
signals from a number of measurements that is much smaller than the dimension
of the signal. Existing sensor selection algorithms cannot be applied in such
situations. Our proposed Incoherent Sensor Selection (Insense) algorithm
minimizes a coherence-based cost function that is adapted from recent results
in sparse recovery theory. Using six datasets, including two real-world
datasets on microbial diagnostics and structural health monitoring, we
demonstrate the superior performance of Insense for sparse-signal sensor
selection.
",1,0,0,0,0,0
17900,Discrete Distribution for a Wiener Process Range and its Properties,"  We introduce the discrete distribution of a Wiener process range. Rather than
finding some basic distributional properties including hazard rate function,
moments, Stress-strength parameter and order statistics of this distribution,
this work studies some basic properties of the truncated version of this
distribution. The effectiveness of this distribution is established using a
data set.
",0,0,1,1,0,0
17901,Coherent control of flexural vibrations in dual-nanoweb fibers using phase-modulated two-frequency light,"  Coherent control of the resonant response in spatially extended
optomechanical structures is complicated by the fact that the optical drive is
affected by the back-action from the generated phonons. Here we report a new
approach to coherent control based on stimulated Raman-like scattering, in
which the optical pressure can remain unaffected by the induced vibrations even
in the regime of strong optomechanical interactions. We demonstrate
experimentally coherent control of flexural vibrations simultaneously along the
whole length of a dual-nanoweb fiber, by imprinting steps in the relative phase
between the components of a two-frequency pump signal,the beat frequency being
chosen to match a flexural resonance. Furthermore, sequential switching of the
relative phase at time intervals shorter than the lifetime of the vibrations
reduces their amplitude to a constant value that is fully adjustable by tuning
the phase-modulation depth and switching rate. The results may trigger new
developments in silicon photonics, since such coherent control uniquely
decouples the amplitude of optomechanical oscillations from power-dependent
thermal effects and nonlinear optical loss.
",0,1,0,0,0,0
17902,Static and Dynamic Magnetic Properties of FeMn/Pt Multilayers,"  Recently we have demonstrated the presence of spin-orbit toque in FeMn/Pt
multilayers which, in combination with the anisotropy field, is able to rotate
its magnetization consecutively from 0o to 360o without any external field.
Here, we report on an investigation of static and dynamic magnetic properties
of FeMn/Pt multilayers using combined techniques of magnetometry, ferromagnetic
resonance, inverse spin Hall effect and spin Hall magnetoresistance
measurements. The FeMn/Pt multilayer was found to exhibit ferromagnetic
properties, and its temperature dependence of saturation magnetization can be
fitted well using a phenomenological model by including a finite distribution
in Curie temperature due to subtle thickness variations across the multilayer
samples. The non-uniformity in static magnetic properties is also manifested in
the ferromagnetic resonance spectra, which typically exhibit a broad resonance
peak. A damping parameter of around 0.106 is derived from the frequency
dependence of ferromagnetic resonance linewidth, which is comparable to the
reported values for other types of Pt-based multilayers. Clear inverse spin
Hall signals and spin Hall magnetoresistance have been observed in all samples
below the Curie temperature, which corroborate the strong spin-orbit torque
effect observed previously.
",0,1,0,0,0,0
17903,Ultraproducts of crossed product von Neumann algebras,"  We study a relationship between the ultraproduct of a crossed product von
Neumann algebra and the crossed product of an ultraproduct von Neumann algebra.
As an application, the continuous core of an ultraproduct von Neumann algebra
is described.
",0,0,1,0,0,0
17904,Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing,"  A fundamental issue for statistical classification models in a streaming
environment is that the joint distribution between predictor and response
variables changes over time (a phenomenon also known as concept drifts), such
that their classification performance deteriorates dramatically. In this paper,
we first present a hierarchical hypothesis testing (HHT) framework that can
detect and also adapt to various concept drift types (e.g., recurrent or
irregular, gradual or abrupt), even in the presence of imbalanced data labels.
A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR),
is implemented under the HHT framework thereafter. By substituting a
widely-acknowledged retraining scheme with an adaptive training strategy, we
further demonstrate that the concept drift adaptation capability of HLFR can be
significantly boosted. The theoretical analysis on the Type-I and Type-II
errors of HLFR is also performed. Experiments on both simulated and real-world
datasets illustrate that our methods outperform state-of-the-art methods in
terms of detection precision, detection delay as well as the adaptability
across different concept drift types.
",1,0,0,1,0,0
17905,Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values,"  The problem of machine learning with missing values is common in many areas.
A simple approach is to first construct a dataset without missing values simply
by discarding instances with missing entries or by imputing a fixed value for
each missing entry, and then train a prediction model with the new dataset. A
drawback of this naive approach is that the uncertainty in the missing entries
is not properly incorporated in the prediction. In order to evaluate prediction
uncertainty, the multiple imputation (MI) approach has been studied, but the
performance of MI is sensitive to the choice of the probabilistic model of the
true values in the missing entries, and the computational cost of MI is high
because multiple models must be trained. In this paper, we propose an
alternative approach called the Interval-based Prediction Uncertainty Bounding
(IPUB) method. The IPUB method represents the uncertainties due to missing
entries as intervals, and efficiently computes the lower and upper bounds of
the prediction results when all possible training sets constructed by imputing
arbitrary values in the intervals are considered. The IPUB method can be
applied to a wide class of convex learning algorithms including penalized
least-squares regression, support vector machine (SVM), and logistic
regression. We demonstrate the advantages of the IPUB method by comparing it
with an existing method in numerical experiment with benchmark datasets.
",0,0,0,1,0,0
17906,Methods for Interpreting and Understanding Deep Neural Networks,"  This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.
",1,0,0,1,0,0
17907,Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents,"  In this digital era, one thing that still holds the convention is a printed
archive. Printed documents find their use in many critical domains such as
contract papers, legal tenders and proof of identity documents. As more
advanced printing, scanning and image editing techniques are becoming
available, forgeries on these legal tenders pose a serious threat. Ability to
easily and reliably identify source printer of a printed document can help a
lot in reducing this menace. During printing procedure, printer hardware
introduces certain distortions in printed characters' locations and shapes
which are invisible to naked eyes. These distortions are referred as geometric
distortions, their profile (or signature) is generally unique for each printer
and can be used for printer classification purpose. This paper proposes a set
of features for characterizing text-line-level geometric distortions, referred
as geometric distortion signatures and presents a novel system to use them for
identification of the origin of a printed document. Detailed experiments
performed on a set of thirteen printers demonstrate that the proposed system
achieves state of the art performance and gives much higher accuracy under
small training size constraint. For four training and six test pages of three
different fonts, the proposed method gives 99\% classification accuracy.
",1,0,0,0,0,0
17908,The duration of load effect in lumber as stochastic degradation,"  This paper proposes a gamma process for modelling the damage that accumulates
over time in the lumber used in structural engineering applications when stress
is applied. The model separates the stochastic processes representing features
internal to the piece of lumber on the one hand, from those representing
external forces due to applied dead and live loads. The model applies those
external forces through a time-varying population level function designed for
time-varying loads. The application of this type of model, which is standard in
reliability analysis, is novel in this context, which has been dominated by
accumulated damage models (ADMs) over more than half a century. The proposed
model is compared with one of the traditional ADMs. Our statistical results
based on a Bayesian analysis of experimental data highlight the limitations of
using accelerated testing data to assess long-term reliability, as seen in the
wide posterior intervals. This suggests the need for more comprehensive testing
in future applications, or to encode appropriate expert knowledge in the priors
used for Bayesian analysis.
",0,0,0,1,0,0
17909,Partial Bridging of Vaccine Efficacy to New Populations,"  Suppose one has data from one or more completed vaccine efficacy trials and
wishes to estimate the efficacy in a new setting. Often logistical or ethical
considerations make running another efficacy trial impossible. Fortunately, if
there is a biomarker that is the primary modifier of efficacy, then the
biomarker-conditional efficacy may be identical in the completed trials and the
new setting, or at least informative enough to meaningfully bound this
quantity. Given a sample of this biomarker from the new population, we might
hope we can bridge the results of the completed trials to estimate the vaccine
efficacy in this new population. Unfortunately, even knowing the true
conditional efficacy in the new population fails to identify the marginal
efficacy due to the unknown conditional unvaccinated risk. We define a curve
that partially identifies (lower bounds) the marginal efficacy in the new
population as a function of the population's marginal unvaccinated risk, under
the assumption that one can identify bounds on the conditional unvaccinated
risk in the new population. Interpreting the curve only requires identifying
plausible regions of the marginal unvaccinated risk in the new population. We
present a nonparametric estimator of this curve and develop valid lower
confidence bounds that concentrate at a parametric rate. We use vaccine
terminology throughout, but the results apply to general binary interventions
and bounded outcomes.
",0,0,0,1,0,0
17910,An induced map between rationalized classifying spaces for fibrations,"  Let $B{ aut}_1X$ be the Dold-Lashof classifying space of orientable
fibrations with fiber $X$. For a rationally weakly trivial map $f:X\to Y$, our
strictly induced map $a_f: (Baut_1X)_0\to (Baut_1Y)_0$ induces a natural map
from a $X_0$-fibration to a $Y_0$-fibration. It is given by a map between the
differential graded Lie algebras of derivations of Sullivan models. We note
some conditions that the map $a_f$ admits a section and note some relations
with the Halperin conjecture. Furthermore we give the obstruction class for a
lifting of a classifying map $h: B\to (Baut_1Y)_0$ and apply it for liftings of
$G$-actions on $Y$ for a compact connected Lie group $G$ as the case of $B=BG$
and evaluating of rational toral ranks as $r_0(Y)\leq r_0(X)$.
",0,0,1,0,0,0
17911,The Statistical Recurrent Unit,"  Sophisticated gated recurrent neural network architectures like LSTMs and
GRUs have been shown to be highly effective in a myriad of applications. We
develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
learn long term dependencies in data by only keeping moving averages of
statistics. The SRU's architecture is simple, un-gated, and contains a
comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
sophisticated LSTM and GRU alternatives, often outperforming one or both in
various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
unbiased manner by optimizing respective architectures' hyperparameters in a
Bayesian optimization scheme for both synthetic and real-world tasks.
",1,0,0,1,0,0
17912,Many-body localization in the droplet spectrum of the random XXZ quantum spin chain,"  We study many-body localization properties of the disordered XXZ spin chain
in the Ising phase. Disorder is introduced via a random magnetic field in the
$z$-direction. We prove a strong form of dynamical exponential clustering for
eigenstates in the droplet spectrum: For any pair of local observables
separated by a distance $\ell$, the sum of the associated correlators over
these states decays exponentially in $\ell$, in expectation. This exponential
clustering persists under the time evolution in the droplet spectrum. Our
result applies to the large disorder regime as well as to the strong Ising
phase at fixed disorder, with bounds independent of the support of the
observables.
",0,0,1,0,0,0
17913,FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices,"  Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by $48\%$ to
$78\%$ and energy consumption by $37\%$ to $69\%$ compared with the
state-of-the-art compression algorithms.
",1,0,0,0,0,0
17914,Robust MPC for tracking of nonholonomic robots with additive disturbances,"  In this paper, two robust model predictive control (MPC) schemes are proposed
for tracking control of nonholonomic systems with bounded disturbances:
tube-MPC and nominal robust MPC (NRMPC). In tube-MPC, the control signal
consists of a control action and a nonlinear feedback law based on the
deviation of the actual states from the states of a nominal system. It renders
the actual trajectory within a tube centered along the optimal trajectory of
the nominal system. Recursive feasibility and input-to-state stability are
established and the constraints are ensured by tightening the input domain and
the terminal region. While in NRMPC, an optimal control sequence is obtained by
solving an optimization problem based on the current state, and the first
portion of this sequence is applied to the real system in an open-loop manner
during each sampling period. The state of nominal system model is updated by
the actual state at each step, which provides additional a feedback. By
introducing a robust state constraint and tightening the terminal region,
recursive feasibility and input-to-state stability are guaranteed. Simulation
results demonstrate the effectiveness of both strategies proposed.
",1,0,0,0,0,0
17915,Investigation on the use of Hidden-Markov Models in automatic transcription of music,"  Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
and have been widely used in two main tasks of Automatic Music Transcription
(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
estimation, and sequential post-processing, i.e. correcting note segmentation
using training data. In this paper, we employ the multi-pitch estimation method
called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
by integrating different HMM-based modules in this framework. For note
segmentation, we use two different twostate on/o? HMMs, including a
higher-order one for duration modeling. For sequential post-processing, we
focused on a musicological modeling of polyphonic harmonic transitions, using a
first- and second-order HMMs whose states are defined through candidate note
mixtures. These different PLCA plus HMM systems have been evaluated
comparatively on two different instrument repertoires, namely the piano (using
the MAPS database) and the marovany zither. Our results show that the use of
HMMs could bring noticeable improvements to transcription results, depending on
the instrument repertoire.
",1,0,0,1,0,0
17916,Network archaeology: phase transition in the recoverability of network history,"  Network growth processes can be understood as generative models of the
structure and history of complex networks. This point of view naturally leads
to the problem of network archaeology: Reconstructing all the past states of a
network from its structure---a difficult permutation inference problem. In this
paper, we introduce a Bayesian formulation of network archaeology, with a
generalization of preferential attachment as our generative mechanism. We
develop a sequential importance sampling algorithm to evaluate the posterior
averages of this model, as well as an efficient heuristic that uncovers the
history of a network in linear time. We use these methods to identify and
characterize a phase transition in the quality of the reconstructed history,
when they are applied to artificial networks generated by the model itself.
Despite the existence of a no-recovery phase, we find that non-trivial
inference is possible in a large portion of the parameter space as well as on
empirical data.
",0,0,0,1,0,0
17917,"Big Data, Data Science, and Civil Rights","  Advances in data analytics bring with them civil rights implications.
Data-driven and algorithmic decision making increasingly determine how
businesses target advertisements to consumers, how police departments monitor
individuals or groups, how banks decide who gets a loan and who does not, how
employers hire, how colleges and universities make admissions and financial aid
decisions, and much more. As data-driven decisions increasingly affect every
corner of our lives, there is an urgent need to ensure they do not become
instruments of discrimination, barriers to equality, threats to social justice,
and sources of unfairness. In this paper, we argue for a concrete research
agenda aimed at addressing these concerns, comprising five areas of emphasis:
(i) Determining if models and modeling procedures exhibit objectionable bias;
(ii) Building awareness of fairness into machine learning methods; (iii)
Improving the transparency and control of data- and model-driven decision
making; (iv) Looking beyond the algorithm(s) for sources of bias and
unfairness-in the myriad human decisions made during the problem formulation
and modeling process; and (v) Supporting the cross-disciplinary scholarship
necessary to do all of that well.
",1,0,0,0,0,0
17918,Global well-posedness for 2-D Boussinesq system with the temperature-dependent viscosity and supercritical dissipation,"  The present paper is dedicated to the global well-posedness issue for the
Boussinesq system with the temperature-dependent viscosity in $\mathbb{R}^2.$
We aim at extending the work by Abidi and Zhang ( Adv. Math. 2017 (305)
1202--1249 ) to a supercritical dissipation for temperature.
",0,0,1,0,0,0
17919,"CO~($J = 1-0$) Observations of a Filamentary Molecular Cloud in the Galactic Region Centered at $l = 150\arcdeg, b = 3.5\arcdeg$","  We present large-field (4.25~$\times$~3.75 deg$^2$) mapping observations
toward the Galactic region centered at $l = 150\arcdeg, b = 3.5\arcdeg$ in the
$J = 1-0$ emission line of CO isotopologues ($^{12}$CO, $^{13}$CO, and
C$^{18}$O), using the 13.7 m millimeter-wavelength telescope of the Purple
Mountain Observatory. Based on the $^{13}$CO observations, we reveal a
filamentary cloud in the Local Arm at a velocity range of $-$0.5 to
6.5~km~s$^{-1}$. This molecular cloud contains 1 main filament and 11
sub-filaments, showing the so-called ""ridge-nest"" structure. The main filament
and three sub-filaments are also detected in the C$^{18}$O line. The velocity
structures of most identified filaments display continuous distribution with
slight velocity gradients. The measured median excitation temperature, line
width, length, width, and linear mass of the filaments are $\sim$9.28~K,
0.85~km~s$^{-1}$, 7.30~pc, 0.79~pc, and 17.92~$M_\sun$~pc$^{-1}$, respectively,
assuming a distance of 400~pc. We find that the four filaments detected in the
C$^{18}$O line are thermally supercritical, and two of them are in the
virialized state, and thus tend to be gravitationally bound. We identify in
total 146 $^{13}$CO clumps in the cloud, about 77$\%$ of the clumps are
distributed along the filaments. About 56$\%$ of the virialized clumps are
found to be associated with the supercritical filaments. Three young stellar
object (YSO) candidates are also identified in the supercritical filaments,
based on the complementary infrared (IR) data. These results indicate that the
supercritical filaments, especially the virialized filaments, may contain
star-forming activities.
",0,1,0,0,0,0
17920,Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition,"  Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to the unseen data conditions. However, unsupervised adaptation is often
challenging, as one must generate some hypothesis given a model and then use
that hypothesis to bootstrap the model to the unseen data conditions.
Unfortunately, reliability of such hypotheses is often poor, given the mismatch
between the training and testing datasets. In such cases, a model hypothesis
confidence measure enables performing data selection for the model adaptation.
Underlying this approach is the fact that for unseen data conditions, data
variability is introduced to the model, which the model propagates to its
output decision, impacting decision reliability. In a fully connected network,
this data variability is propagated as distortions from one layer to the next.
This work aims to estimate the propagation of such distortion in the form of
network activation entropy, which is measured over a short- time running window
on the activation from each neuron of a given hidden layer, and these
measurements are then used to compute summary entropy. This work demonstrates
that such an entropy measure can help to select data for unsupervised model
adaptation, resulting in performance gains in speech recognition tasks. Results
from standard benchmark speech recognition tasks show that the proposed
approach can alleviate the performance degradation experienced under unseen
data conditions by iteratively adapting the model to the unseen datas acoustic
condition.
",1,0,0,1,0,0
17921,Asymmetric Preheating,"  We study the generation of the matter-antimatter asymmetry during bosonic
preheating, focusing on the sources of the asymmetry. If the asymmetry appears
in the multiplication factor of the resonant particle production, the
matter-antimatter ratio will grow during preheating. On the other hand, if the
asymmetry does not grow during preheating, one has to find out another reason.
We consider several scenarios for the asymmetric preheating to distinguish the
sources of the asymmetry. We also discuss a new baryogenesis scenario, in which
the asymmetry is generated without introducing neither loop corrections nor
rotation of a field.
",0,1,0,0,0,0
17922,A multi-device dataset for urban acoustic scene classification,"  This paper introduces the acoustic scene classification task of DCASE 2018
Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task,
and evaluates the performance of a baseline system in the task. As in previous
years of the challenge, the task is defined for classification of short audio
samples into one of predefined acoustic scene classes, using a supervised,
closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes
2018 dataset consists of ten different acoustic scenes and was recorded in six
large European cities, therefore it has a higher acoustic variability than the
previous datasets used for this task, and in addition to high-quality binaural
recordings, it also includes data recorded with mobile devices. We also present
the baseline system consisting of a convolutional neural network and its
performance in the subtasks using the recommended cross-validation setup.
",1,0,0,0,0,0
17923,Asymptotically preserving particle-in-cell methods for inhomogenous strongly magnetized plasmas,"  We propose a class of Particle-In-Cell (PIC) methods for the Vlasov-Poisson
system with a strong and inhomogeneous external magnetic field with fixed
direction, where we focus on the motion of particles in the plane orthogonal to
the magnetic field (so-called poloidal directions). In this regime, the time
step can be subject to stability constraints related to the smallness of Larmor
radius and plasma frequency. To avoid this limitation, our approach is based on
first and higher-order semi-implicit numerical schemes already validated on
dissipative systems [3] and for homogeneous magnetic fields [10]. Thus, when
the magnitude of the external magnetic field becomes large, this method
provides a consistent PIC discretization of the guiding-center system taking
into account variations of the magnetic field. We carry out some theoretical
proofs and perform several numerical experiments that establish a solid
validation of the method and its underlying concepts.
",0,0,1,0,0,0
17924,Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates,"  Methodological research rarely generates a broad interest, yet our work on
the validity of cluster inference methods for functional magnetic resonance
imaging (fMRI) created intense discussion on both the minutia of our approach
and its implications for the discipline. In the present work, we take on
various critiques of our work and further explore the limitations of our
original work. We address issues about the particular event-related designs we
used, considering multiple event types and randomisation of events between
subjects. We consider the lack of validity found with one-sample permutation
(sign flipping) tests, investigating a number of approaches to improve the
false positive control of this widely used procedure. We found that the
combination of a two-sided test and cleaning the data using ICA FIX resulted in
nominal false positive rates for all datasets, meaning that data cleaning is
not only important for resting state fMRI, but also for task fMRI. Finally, we
discuss the implications of our work on the fMRI literature as a whole,
estimating that at least 10% of the fMRI studies have used the most problematic
cluster inference method (P = 0.01 cluster defining threshold), and how
individual studies can be interpreted in light of our findings. These
additional results underscore our original conclusions, on the importance of
data sharing and thorough evaluation of statistical methods on realistic null
data.
",0,0,0,1,0,0
17925,An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning,"  Deep learning is an established framework for learning hierarchical data
representations. While compute power is in abundance, one of the main
challenges in applying this framework to robotic grasping has been obtaining
the amount of data needed to learn these representations, and structuring the
data to the task at hand. Among contemporary approaches in the literature, we
highlight key properties that have encouraged the use of deep learning
techniques, and in this paper, detail our experience in developing a simulator
for collecting cylindrical precision grasps of a multi-fingered dexterous
robotic hand.
",1,0,0,1,0,0
17926,A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry,"  Demographic studies suggest that changes in the retinal vasculature geometry,
especially in vessel width, are associated with the incidence or progression of
eye-related or systemic diseases. To date, the main information source for
width estimation from fundus images has been the intensity profile between
vessel edges. However, there are many factors affecting the intensity profile:
pathologies, the central light reflex and local illumination levels, to name a
few. In this study, we introduce three information sources for width
estimation. These are the probability profiles of vessel interior, centreline
and edge locations generated by a deep network. The probability profiles
provide direct access to vessel geometry and are used in the likelihood
calculation for a Bayesian method, particle filtering. We also introduce a
geometric model which can handle non-ideal conditions of the probability
profiles. Our experiments conducted on the REVIEW dataset yielded consistent
estimates of vessel width, even in cases when one of the vessel edges is
difficult to identify. Moreover, our results suggest that the method is better
than human observers at locating edges of low contrast vessels.
",1,0,0,0,0,0
17927,Quantum Harmonic Analysis of the Density Matrix: Basics,"  In this Review we will study rigorously the notion of mixed states and their
density matrices. We mostly give complete proofs. We will also discuss the
quantum-mechanical consequences of possible variations of Planck's constant h.
This Review has been written having in mind two readerships: mathematical
physicists and quantum physicists. The mathematical rigor is maximal, but the
language and notation we use throughout should be familiar to physicists.
",0,0,1,0,0,0
17928,The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila,"  Background: The chromatin remodelers of the SWI/SNF family are critical
transcriptional regulators. Recognition of lysine acetylation through a
bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
this function is attributed to SNF2/Brg1.
Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
two proteins with potential chromatin interacting domains and four proteins
without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
localize exclusively to the transcriptionally active macronucleus (MAC) during
growth and development, consistent with a role in transcription. While
Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
to be a subunit of several additional protein complexes, including putative
SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
histones correlated with active transcription. Consistent with our AP-MS and
histone array data suggesting a role in regulation of gene expression, ChIP-Seq
analysis of Ibd1 indicated that it primarily binds near promoters and within
gene bodies of highly expressed genes during growth.
Conclusions: Our results suggest that through recognizing specific histones
marks, Ibd1 targets active chromatin regions of highly expressed genes in
Tetrahymena where it subsequently might coordinate the recruitment of several
chromatin remodeling complexes to regulate the transcriptional landscape of
vegetatively growing Tetrahymena cells.
",0,0,0,0,1,0
17929,A Bayesian Perspective on Generalization and Stochastic Gradient Descent,"  We consider two questions at the heart of machine learning; how can we
predict if a minimum will generalize to the test set, and why does stochastic
gradient descent find minima that generalize well? Our work responds to Zhang
et al. (2016), who showed deep neural networks can easily memorize randomly
labeled training data, despite generalizing well on real labels of the same
inputs. We show that the same phenomenon occurs in small linear models. These
observations are explained by the Bayesian evidence, which penalizes sharp
minima but is invariant to model parameterization. We also demonstrate that,
when one holds the learning rate fixed, there is an optimum batch size which
maximizes the test set accuracy. We propose that the noise introduced by small
mini-batches drives the parameters towards minima whose evidence is large.
Interpreting stochastic gradient descent as a stochastic differential equation,
we identify the ""noise scale"" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon
N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$
the batch size. Consequently the optimum batch size is proportional to both the
learning rate and the size of the training set, $B_{opt} \propto \epsilon N$.
We verify these predictions empirically.
",1,0,0,1,0,0
17930,Higher Derivative Field Theories: Degeneracy Conditions and Classes,"  We provide a full analysis of ghost free higher derivative field theories
with coupled degrees of freedom. Assuming the absence of gauge symmetries, we
derive the degeneracy conditions in order to evade the Ostrogradsky ghosts, and
analyze which (non)trivial classes of solutions this allows for. It is shown
explicitly how Lorentz invariance avoids the propagation of ""half"" degrees of
freedom. Moreover, for a large class of theories, we construct the field
redefinitions and/or (extended) contact transformations that put the theory in
a manifestly first order form. Finally, we identify which class of theories
cannot be brought to first order form by such transformations.
",0,1,0,0,0,0
17931,3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds,"  We introduce a novel approach for predicting the progression of adolescent
idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
images. Recent progress in machine learning have allowed to improve
classification and prognosis rates, but lack a probabilistic framework to
measure uncertainty in the data. We propose a discriminative probabilistic
manifold embedding where locally linear mappings transform data points from
high-dimensional space to corresponding low-dimensional coordinates. A
discriminant adjacency matrix is constructed to maximize the separation between
progressive and non-progressive groups of patients diagnosed with scoliosis,
while minimizing the distance in latent variables belonging to the same class.
To predict the evolution of deformation, a baseline reconstruction is projected
onto the manifold, from which a spatiotemporal regression model is built from
parallel transport curves inferred from neighboring exemplars. Rate of
progression is modulated from the spine flexibility and curve magnitude of the
3D spine deformation. The method was tested on 745 reconstructions from 133
subjects using longitudinal 3D reconstructions of the spine, with results
demonstrating the discriminatory framework can identify between progressive and
non-progressive of scoliotic patients with a classification rate of 81% and
prediction differences of 2.1$^{o}$ in main curve angulation, outperforming
other manifold learning methods. Our method achieved a higher prediction
accuracy and improved the modeling of spatiotemporal morphological changes in
highly deformed spines compared to other learning methods.
",1,0,0,1,0,0
17932,The Galaxy's Veil of Excited Hydrogen,"  Many of the baryons in our Galaxy probably lie outside the well known disk
and bulge components. Despite a wealth of evidence for the presence of some gas
in galactic halos, including absorption line systems in the spectra of quasars,
high velocity neutral hydrogen clouds in our Galaxy halo, line emitting ionised
hydrogen originating from galactic winds in nearby starburst galaxies, and the
X-ray coronas surrounding the most massive galaxies, accounting for the gas in
the halo of any galaxy has been observationally challenging primarily because
of its low density in the expansive halo. The most sensitive measurements come
from detecting absorption by the intervening gas in the spectra of distant
objects such as quasars or distant halo stars, but these have typically been
limited to a few lines of sight to sufficiently bright objects. Massive
spectroscopic surveys of millions of objects provide an alternative approach to
the problem. Here, we present the first evidence for a widely distributed,
neutral, excited hydrogen component of the Galaxy's halo. It is observed as the
slight, (0.779 $\pm$ 0.006)\%, absorption of flux near the rest wavelength of
H$\alpha$ in the combined spectra of hundreds of thousands of galaxy spectra
and is ubiquitous in high latitude lines of sight. This observation provides an
avenue to tracing, both spatially and kinematically, the majority of the gas in
the halo of our Galaxy.
",0,1,0,0,0,0
17933,Photonic-chip supercontinuum with tailored spectra for precision frequency metrology,"  Supercontinuum generation using chip-integrated photonic waveguides is a
powerful approach for spectrally broadening pulsed laser sources with very low
pulse energies and compact form factors. When pumped with a mode-locked laser
frequency comb, these waveguides can coherently expand the comb spectrum to
more than an octave in bandwidth to enable self-referenced stabilization.
However, for applications in frequency metrology and precision spectroscopy, it
is desirable to not only support self-referencing, but also to generate
low-noise combs with customizable broadband spectra. In this work, we
demonstrate dispersion-engineered waveguides based on silicon nitride that are
designed to meet these goals and enable precision optical metrology experiments
across large wavelength spans. We perform a clock comparison measurement and
report a clock-limited relative frequency instability of $3.8\times10^{-15}$ at
$\tau = 2$ seconds between a 1550 nm cavity-stabilized reference laser and
NIST's calcium atomic clock laser at 657 nm using a two-octave
waveguide-supercontinuum comb.
",0,1,0,0,0,0
17934,Endogenizing Epistemic Actions,"  Through a series of examples, we illustrate some important drawbacks that the
action logic framework suffers from in its ability to represent the dynamics of
information updates. We argue that these problems stem from the fact that the
action model, a central construct designed to encode agents' uncertainty about
actions, is itself effectively common knowledge amongst the agents. In response
to these difficulties, we motivate and propose an alternative semantics that
avoids them by (roughly speaking) endogenizing the action model. We discuss the
relationship to action logic, and provide a sound and complete axiomatization.
",1,0,0,0,0,0
17935,The Meaning of Memory Safety,"  We give a rigorous characterization of what it means for a programming
language to be memory safe, capturing the intuition that memory safety supports
local reasoning about state. We formalize this principle in two ways. First, we
show how a small memory-safe language validates a noninterference property: a
program can neither affect nor be affected by unreachable parts of the state.
Second, we extend separation logic, a proof system for heap-manipulating
programs, with a memory-safe variant of its frame rule. The new rule is
stronger because it applies even when parts of the program are buggy or
malicious, but also weaker because it demands a stricter form of separation
between parts of the program state. We also consider a number of pragmatically
motivated variations on memory safety and the reasoning principles they
support. As an application of our characterization, we evaluate the security of
a previously proposed dynamic monitor for memory safety of heap-allocated data.
",1,0,0,0,0,0
17936,The Flexible Group Spatial Keyword Query,"  We present a new class of service for location based social networks, called
the Flexible Group Spatial Keyword Query, which enables a group of users to
collectively find a point of interest (POI) that optimizes an aggregate cost
function combining both spatial distances and keyword similarities. In
addition, our query service allows users to consider the tradeoffs between
obtaining a sub-optimal solution for the entire group and obtaining an
optimimized solution but only for a subgroup.
We propose algorithms to process three variants of the query: (i) the group
nearest neighbor with keywords query, which finds a POI that optimizes the
aggregate cost function for the whole group of size n, (ii) the subgroup
nearest neighbor with keywords query, which finds the optimal subgroup and a
POI that optimizes the aggregate cost function for a given subgroup size m (m
<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
which finds optimal subgroups and corresponding POIs for each of the subgroup
sizes in the range [m, n]. We design query processing algorithms based on
branch-and-bound and best-first paradigms. Finally, we provide theoretical
bounds and conduct extensive experiments with two real datasets which verify
the effectiveness and efficiency of the proposed algorithms.
",1,0,0,0,0,0
17937,Undersampled windowed exponentials and their applications,"  We characterize the completeness and frame/basis property of a union of
under-sampled windowed exponentials of the form $$ {\mathcal F}(g): =\{e^{2\pi
i n x}: n\ge 0\}\cup \{g(x)e^{2\pi i nx}: n<0\} $$ for $L^2[-1/2,1/2]$ by the
spectra of the Toeplitz operators with symbol $g$. Using this characterization,
we classify all real-valued functions $g$ such that ${\mathcal F}(g)$ is
complete or forms a frame/basis. Conversely, we use the classical
Kadec-1/4-theorem in non-harmonic Fourier series to determine all $\xi$ such
that the Toeplitz operators with symbol $e^{2\pi i \xi x}$ is injective or
invertible. These results demonstrate an elegant interaction between frame
theory of windowed exponentials and Toeplitz operators. Finally, as an
application, we use our results to answer some open questions in dynamical
sampling, phase retrieval and derivative samplings on $\ell^2({\mathbb Z})$ and
Paley-Wiener spaces of bandlimited functions.
",0,0,1,0,0,0
17938,Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs,"  Deep learning models are often successfully trained using gradient descent,
despite the worst case hardness of the underlying non-convex optimization
problem. The key question is then under what conditions can one prove that
optimization will succeed. Here we provide a strong result of this kind. We
consider a neural net with one hidden layer and a convolutional structure with
no overlap and a ReLU activation function. For this architecture we show that
learning is NP-complete in the general case, but that when the input
distribution is Gaussian, gradient descent converges to the global optimum in
polynomial time. To the best of our knowledge, this is the first global
optimality guarantee of gradient descent on a convolutional neural network with
ReLU activations.
",0,0,1,1,0,0
17939,Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine,"  Computer based recognition and detection of abnormalities in ECG signals is
proposed. For this purpose, the Support Vector Machines (SVM) are combined with
the advantages of Hermite transform representation. SVM represent a special
type of classification techniques commonly used in medical applications.
Automatic classification of ECG could make the work of cardiologic departments
faster and more efficient. It would also reduce the number of false diagnosis
and, as a result, save lives. The working principle of the SVM is based on
translating the data into a high dimensional feature space and separating it
using a linear classificator. In order to provide an optimal representation for
SVM application, the Hermite transform domain is used. This domain is proved to
be suitable because of the similarity of the QRS complex with Hermite basis
functions. The maximal signal information is obtained using a small set of
features that are used for detection of irregular QRS complexes. The aim of the
paper is to show that these features can be employed for automatic ECG signal
analysis.
",1,0,0,0,0,0
17940,On the Lipschitz equivalence of self-affine sets,"  Let $A$ be an expanding $d\times d$ matrix with integer entries and
${\mathcal D}\subset {\mathbb Z}^d$ be a finite digit set. Then the pair $(A,
{\mathcal D})$ defines a unique integral self-affine set $K=A^{-1}(K+{\mathcal
D})$. In this paper, by replacing the Euclidean norm with a pseudo-norm $w$ in
terms of $A$, we construct a hyperbolic graph on $(A, {\mathcal D})$ and show
that $K$ can be identified with the hyperbolic boundary. Moreover, if $(A,
{\mathcal D})$ safisfies the open set condition, we also prove that two totally
disconnected integral self-affine sets are Lipschitz equivalent if an only if
they have the same $w$-Hausdorff dimension, that is, their digit sets have
equal cardinality. We extends some well-known results in the self-similar sets
to the self-affine sets.
",0,0,1,0,0,0
17941,The Gross-Pitaevskii equations of a static and spherically symmetric condensate of gravitons,"  In this paper we consider the Dvali and Gómez assumption that the end state
of a gravitational collapse is a Bose-Einstein condensate of gravitons. We then
construct the two Gross-Pitaevskii equations for a static and spherically
symmetric configuration of the condensate. These two equations correspond to
the constrained minimisation of the gravitational Hamiltonian with respect to
the redshift and the Newtonian potential, per given number of gravitons. We
find that the effective geometry of the condensate is the one of a gravastar (a
DeSitter star) with a sub-Planckian cosmological constant, for masses larger
than the Planck scale. Thus, a condensate corresponding to a semiclassical
black hole, is always quantum and weakly coupled. Finally, we obtain that the
boundary of our gravastar, although it is not the location of a horizon,
corresponds to the Schwarzschild radius.
",0,1,0,0,0,0
17942,Stability and instability in saddle point dynamics - Part I,"  We consider the problem of convergence to a saddle point of a concave-convex
function via gradient dynamics. Since first introduced by Arrow, Hurwicz and
Uzawa in [1] such dynamics have been extensively used in diverse areas, there
are, however, features that render their analysis non trivial. These include
the lack of convergence guarantees when the function considered is not strictly
concave-convex and also the non-smoothness of subgradient dynamics. Our aim in
this two part paper is to provide an explicit characterization to the
asymptotic behaviour of general gradient and subgradient dynamics applied to a
general concave-convex function. We show that despite the nonlinearity and
non-smoothness of these dynamics their $\omega$-limit set is comprised of
trajectories that solve only explicit linear ODEs that are characterized within
the paper.
More precisely, in Part I an exact characterization is provided to the
asymptotic behaviour of unconstrained gradient dynamics. We also show that when
convergence to a saddle point is not guaranteed then the system behaviour can
be problematic, with arbitrarily small noise leading to an unbounded variance.
In Part II we consider a general class of subgradient dynamics that restrict
trajectories in an arbitrary convex domain, and show that their limiting
trajectories are solutions of subgradient dynamics on only affine subspaces.
The latter is a smooth class of dynamics with an asymptotic behaviour exactly
characterized in Part I, as solutions to explicit linear ODEs. These results
are used to formulate corresponding convergence criteria and are demonstrated
with several examples and applications presented in Part II.
",1,0,1,0,0,0
17943,Continual Prediction of Notification Attendance with Classical and Deep Network Approaches,"  We investigate to what extent mobile use patterns can predict -- at the
moment it is posted -- whether a notification will be clicked within the next
10 minutes. We use a data set containing the detailed mobile phone usage logs
of 279 users, who over the course of 5 weeks received 446,268 notifications
from a variety of apps. Besides using classical gradient-boosted trees, we
demonstrate how to make continual predictions using a recurrent neural network
(RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users,
with a possible operation point of 50% sensitivity and 80% specificity
considering all notification types (an increase of 40% with respect to a
probabilistic baseline). These results enable automatic, intelligent handling
of mobile phone notifications without the need for user feedback or
personalization. Furthermore, they showcase how forego feature-extraction by
using RNNs for continual predictions directly on mobile usage logs. To the best
of our knowledge, this is the first work that leverages mobile sensor data for
continual, context-aware predictions of interruptibility using deep neural
networks.
",1,0,0,0,0,0
17944,"Extensions of Operators, Liftings of Monads and Distributive Laws","  In a previous study, the algebraic formulation of the First Fundamental
Theorem of Calculus (FFTC) is shown to allow extensions of differential and
Rota-Baxter operators on the one hand, and to give rise to liftings of monads
and comonads, and mixed distributive laws on the other. Generalizing the FFTC,
we consider in this paper a class of constraints between a differential
operator and a Rota-Baxter operator. For a given constraint, we show that the
existences of extensions of differential and Rota-Baxter operators, of liftings
of monads and comonads, and of mixed distributive laws are equivalent. We
further give a classification of the constraints satisfying these equivalent
conditions.
",0,0,1,0,0,0
17945,Cascaded Incremental Nonlinear Dynamic Inversion Control for MAV Disturbance Rejection,"  Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near
obstacles by their ability to withstand wind gusts. Currently widespread
position control methods such as Proportional Integral Derivative control do
not perform well under the influence of gusts. Incremental Nonlinear Dynamic
Inversion (INDI) is a sensor-based control technique that can control nonlinear
systems subject to disturbances. It was developed for the attitude control of
manned aircraft or MAVs. In this paper we generalize this method to the outer
loop control of MAVs under severe gust loads. Significant improvements over a
traditional Proportional Integral Derivative (PID) controller are demonstrated
in an experiment where the quadrotor flies in and out of a windtunnel exhaust
at 10 m/s. The control method does not rely on frequent position updates, as is
demonstrated in an outside experiment using a standard GPS module. Finally, we
investigate the effect of using a linearization to calculate thrust vector
increments, compared to a nonlinear calculation. The method requires little
modeling and is computationally efficient.
",1,0,0,0,0,0
17946,"Numerical Evaluation of Elliptic Functions, Elliptic Integrals and Modular Forms","  We describe algorithms to compute elliptic functions and their relatives
(Jacobi theta functions, modular forms, elliptic integrals, and the
arithmetic-geometric mean) numerically to arbitrary precision with rigorous
error bounds for arbitrary complex variables. Implementations in ball
arithmetic are available in the open source Arb library. We discuss the
algorithms from a concrete implementation point of view, with focus on
performance at tens to thousands of digits of precision.
",1,0,0,0,0,0
17947,In situ Electric Field Skyrmion Creation in Magnetoelectric Cu$_2$OSeO$_3$,"  Magnetic skyrmions are localized nanometric spin textures with quantized
winding numbers as the topological invariant. Rapidly increasing attention has
been paid to the investigations of skyrmions since their experimental discovery
in 2009, due both to the fundamental properties and the promising potential in
spintronics based applications. However, controlled creation of skyrmions
remains a pivotal challenge towards technological applications. Here, we report
that skyrmions can be created locally by electric field in the magnetoelectric
helimagnet Cu$\mathsf{_2}$OSeO$\mathsf{_3}$. Using Lorentz transmission
electron microscopy, we successfully write skyrmions in situ from a helical
spin background. Our discovery is highly coveted since it implies that
skyrmionics can be integrated into contemporary field effect transistor based
electronic technology, where very low energy dissipation can be achieved, and
hence realizes a large step forward to its practical applications.
",0,1,0,0,0,0
17948,An online sequence-to-sequence model for noisy speech recognition,"  Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminative models called Sequence-to-Sequence models, that
can almost match the accuracy of state of the art generative models. While
these models are easy to train as they can be trained end-to-end in a single
step, they have a practical limitation that they can only be used for offline
recognition. This is because the models require that the entirety of the input
sequence be available at the beginning of inference, an assumption that is not
valid for instantaneous speech recognition. To address this problem, online
sequence-to-sequence models were recently introduced. These models are able to
start producing outputs as data arrives, and the model feels confident enough
to output partial transcripts. These models, like sequence-to-sequence are
causal - the output produced by the model until any time, $t$, affects the
features that are computed subsequently. This makes the model inherently more
powerful than generative models that are unable to change features that are
computed from the data. This paper highlights two main contributions - an
improvement to online sequence-to-sequence model training, and its application
to noisy settings with mixed speech from two speakers.
",1,0,0,1,0,0
17949,Existence of infinite Viterbi path for pairwise Markov models,"  For hidden Markov models one of the most popular estimates of the hidden
chain is the Viterbi path -- the path maximising the posterior probability. We
consider a more general setting, called the pairwise Markov model, where the
joint process consisting of finite-state hidden regime and observation process
is assumed to be a Markov chain. We prove that under some conditions it is
possible to extend the Viterbi path to infinity for almost every observation
sequence which in turn enables to define an infinite Viterbi decoding of the
observation process, called the Viterbi process. This is done by constructing a
block of observations, called a barrier, which ensures that the Viterbi path
goes trough a given state whenever this block occurs in the observation
sequence.
",0,0,1,1,0,0
17950,Coding for Segmented Edit Channels,"  This paper considers insertion and deletion channels with the additional
assumption that the channel input sequence is implicitly divided into segments
such that at most one edit can occur within a segment. No segment markers are
available in the received sequence. We propose code constructions for the
segmented deletion, segmented insertion, and segmented insertion-deletion
channels based on subsets of Varshamov-Tenengolts codes chosen with
pre-determined prefixes and/or suffixes. The proposed codes, constructed for
any finite alphabet, are zero-error and can be decoded segment-by-segment. We
also derive an upper bound on the rate of any zero-error code for the segmented
edit channel, in terms of the segment length. This upper bound shows that the
rate scaling of the proposed codes as the segment length increases is the same
as that of the maximal code.
",1,0,0,0,0,0
17951,Charge compensation at the interface between the polar NaCl(111) surface and a NaCl aqueous solution,"  Periodic supercell models of electric double layers formed at the interface
between a charged surface and an electrolyte are subject to serious finite size
errors and require certain adjustments in the treatment of the long-range
electrostatic interactions. In a previous publication (C. Zhang, M. Sprik,
Phys. Rev. B 94, 245309 (2016)) we have shown how this can be achieved using
finite field methods. The test system was the familiar simple point charge
model of a NaCl aqueous solution confined between two oppositely charged walls.
Here this method is extended to the interface between the (111) polar surface
of a NaCl crystal and a high concentration NaCl aqueous solution. The crystal
is kept completely rigid and the compensating charge screening the polarization
can only be provided by the electrolyte. We verify that the excess electrolyte
ionic charge at the interface conforms to the Tasker 1/2 rule for compensating
charge in the theory of polar rocksalt (111) surfaces. The interface can be
viewed as an electric double layer with a net charge. We define a generalized
Helmholtz capacitance $C_\text{H}$ which can be computed by varying the applied
electric field. We find $C_\text{H} = 8.23 \, \mu \mathrm{Fcm}^{-2}$, which
should be compared to the $4.23 \, \mu \mathrm{Fcm}^{-2}$ for the (100)
non-polar surface of the same NaCl crystal. This is rationalized by the
observation that compensating ions shed their first solvation shell adsorbing
as contact ions pairs on the polar surface.
",0,1,0,0,0,0
17952,CrowdTone: Crowd-powered tone feedback and improvement system for emails,"  In this paper, we present CrowdTone, a system designed to help people set the
appropriate tone in their email communication. CrowdTone utilizes the context
and content of an email message to identify and set the appropriate tone
through a consensus-building process executed by crowd workers. We evaluated
CrowdTone with 22 participants, who provided a total of 29 emails that they had
received in the past, and ran them through CrowdTone. Participants and
professional writers assessed the quality of improvements finding a substantial
increase in the percentage of emails deemed ""appropriate"" or ""very appropriate""
- from 25% to more than 90% by recipients, and from 45% to 90% by professional
writers. Additionally, the recipients' feedback indicated that more than 90% of
the CrowdTone processed emails showed improvement.
",1,0,0,0,0,0
17953,A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines,"  Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field approximation of widely-connected systems with weak
interactions coming from spin-glass theory. While the TAP approach has been
extensively studied for fully-visible binary spin systems, our construction is
generalized to latent-variable models, as well as to arbitrarily distributed
real-valued spin systems with bounded support. In our numerical experiments, we
demonstrate the effective deterministic training of our proposed models and are
able to show interesting features of unsupervised learning which could not be
directly observed with sampling. Additionally, we demonstrate how to utilize
our TAP-based framework for leveraging trained RBMs as joint priors in
denoising problems.
",1,1,0,1,0,0
17954,Integrability conditions for Compound Random Measures,"  Compound random measures (CoRM's) are a flexible and tractable framework for
vectors of completely random measure. In this paper, we provide conditions to
guarantee the existence of a CoRM. Furthermore, we prove some interesting
properties of CoRM's when exponential scores and regularly varying Lévy
intensities are considered.
",0,0,1,1,0,0
17955,Tunable low energy Ps beam for the anti-hydrogen free fall and for testing gravity with a Mach-Zehnder interferometer,"  The test of gravitational force on antimatter in the field of the matter
gravitational field, produced by earth, can be done by a free fall experiment
which involves only General Relativity, and with a Mach-Zehnder interferometer
which involves Quantum Mechanics. This article presents a new method to produce
a tunable low energy (Ps ) beam suitable for trapping the (Hbar + ) ion in a
free fall experiment, and suitable for a gravity Mach-Zehnder interferometer
with (Ps). The low energy (Ps) beam is tunable in the [10 eV, 100 eV] range.
",0,1,0,0,0,0
17956,An analysis of incorporating an external language model into a sequence-to-sequence model,"  Attention-based sequence-to-sequence models for automatic speech recognition
jointly train an acoustic model, language model, and alignment mechanism. Thus,
the language model component is only trained on transcribed audio-text pairs.
This leads to the use of shallow fusion with an external language model at
inference time. Shallow fusion refers to log-linear interpolation with a
separately trained language model at each step of the beam search. In this
work, we investigate the behavior of shallow fusion across a range of
conditions: different types of language models, different decoding units, and
different tasks. On Google Voice Search, we demonstrate that the use of shallow
fusion with a neural LM with wordpieces yields a 9.1% relative word error rate
reduction (WERR) over our competitive attention-based sequence-to-sequence
model, obviating the need for second-pass rescoring.
",1,0,0,0,0,0
17957,Multi-Generator Generative Adversarial Nets,"  We propose a new approach to train the Generative Adversarial Nets (GANs)
with a mixture of generators to overcome the mode collapsing problem. The main
intuition is to employ multiple generators, instead of using a single one as in
the original GAN. The idea is simple, yet proven to be extremely effective at
covering diverse data modes, easily overcoming the mode collapse and delivering
state-of-the-art results. A minimax formulation is able to establish among a
classifier, a discriminator, and a set of generators in a similar spirit with
GAN. Generators create samples that are intended to come from the same
distribution as the training data, whilst the discriminator determines whether
samples are true data or generated by generators, and the classifier specifies
which generator a sample comes from. The distinguishing feature is that
internal samples are created from multiple generators, and then one of them
will be randomly selected as final output similar to the mechanism of a
probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
divergence (JSD) between the mixture of generators' distributions and the
empirical data distribution is minimal, whilst the JSD among generators'
distributions is maximal, hence effectively avoiding the mode collapse. By
utilizing parameter sharing, our proposed model adds minimal computational cost
to the standard GAN, and thus can also efficiently scale to large-scale
datasets. We conduct extensive experiments on synthetic 2D data and natural
image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
performance of our MGAN in achieving state-of-the-art Inception scores over
latest baselines, generating diverse and appealing recognizable objects at
different resolutions, and specializing in capturing different types of objects
by generators.
",1,0,0,1,0,0
17958,Neural Style Transfer: A Review,"  The seminal work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNNs) in creating artistic imagery by separating and
recombining image content and style. This process of using CNNs to render a
content image in different styles is referred to as Neural Style Transfer
(NST). Since then, NST has become a trending topic both in academic literature
and industrial applications. It is receiving increasing attention and a variety
of approaches are proposed to either improve or extend the original NST
algorithm. In this paper, we aim to provide a comprehensive overview of the
current progress towards NST. We first propose a taxonomy of current algorithms
in the field of NST. Then, we present several evaluation methods and compare
different NST algorithms both qualitatively and quantitatively. The review
concludes with a discussion of various applications of NST and open problems
for future research. A list of papers discussed in this review, corresponding
codes, pre-trained models and more comparison results are publicly available at
this https URL.
",1,0,0,1,0,0
17959,Distribution of water in the G327.3-0.6 massive star-forming region,"  We aim at characterizing the large-scale distribution of H2O in G327.3-0.6, a
massive star-forming region made of individual objects in different
evolutionary phases. We investigate variations of H2O abundance as function of
evolution. We present Herschel continuum maps at 89 and 179 $\mu$m of the whole
region and an APEX map at 350 {\mu}m of the IRDC. New spectral HIFI maps toward
the IRDC region covering low-energy H2O lines at 987 and 1113 GHz are also
presented and combined with HIFI pointed observations of the G327 hot core. We
infer the physical properties of the gas through optical depth analysis and
radiative transfer modeling. The continuum emission at 89 and 179 {\mu}m
follows the thermal continuum emission at longer wavelengths, with a peak at
the position of the hot core, a secondary peak in the Hii region, and an
arch-like layer of hot gas west of the Hii region. The same morphology is
observed in the 1113 GHz line, in absorption toward all dust condensations.
Optical depths of ~80 and 15 are estimated and correspond to column densities
of 10^15 and 2 10^14 cm-2, for the hot core and IRDC position. These values
indicate an H2O to H2 ratio of 3 10^-8 toward the hot core; the abundance of
H2O does not change along the IRDC with values of some 10^-8. Infall (over ~
20"") is detected toward the hot core position with a rate of 1-1.3 10^-2 M_sun
/yr, high enough to overcome the radiation pressure due to the stellar
luminosity. The source structure of the hot core region is complex, with a cold
outer gas envelope in expansion, situated between the outflow and the observer,
extending over 0.32 pc. The outflow is seen face-on and centered away from the
hot core. The distribution of H2O along the IRDC is roughly constant with an
abundance peak in the more evolved object. These water abundances are in
agreement with previous studies in other massive objects and chemical models.
",0,1,0,0,0,0
17960,Straightening rule for an $m'$-truncated polynomial ring,"  We consider a certain quotient of a polynomial ring categorified by both the
isomorphic Green rings of the symmetric groups and Schur algebras generated by
the signed Young permutation modules and mixed powers respectively. They have
bases parametrised by pairs of partitions whose second partitions are multiples
of the odd prime $p$ the characteristic of the underlying field. We provide an
explicit formula rewriting a signed Young permutation module (respectively,
mixed power) in terms of signed Young permutation modules (respectively, mixed
powers) labelled by those pairs of partitions. As a result, for each partition
$\lambda$, we discovered the number of compositions $\delta$ such that $\delta$
can be rearranged to $\lambda$ and whose partial sums of $\delta$ are not
divisible by $p$.
",0,0,1,0,0,0
17961,Effective Blog Pages Extractor for Better UGC Accessing,"  Blog is becoming an increasingly popular media for information publishing.
Besides the main content, most of blog pages nowadays also contain noisy
information such as advertisements etc. Removing these unrelated elements can
improves user experience, but also can better adapt the content to various
devices such as mobile phones. Though template-based extractors are highly
accurate, they may incur expensive cost in that a large number of template need
to be developed and they will fail once the template is updated. To address
these issues, we present a novel template-independent content extractor for
blog pages. First, we convert a blog page into a DOM-Tree, where all elements
including the title and body blocks in a page correspond to subtrees. Then we
construct subtree candidate set for the title and the body blocks respectively,
and extract both spatial and content features for elements contained in the
subtree. SVM classifiers for the title and the body blocks are trained using
these features. Finally, the classifiers are used to extract the main content
from blog pages. We test our extractor on 2,250 blog pages crawled from nine
blog sites with obviously different styles and templates. Experimental results
verify the effectiveness of our extractor.
",1,0,0,0,0,0
17962,The Principle of Similitude in Biology: From Allometry to the Formulation of Dimensionally Homogenous `Laws',"  Meaningful laws of nature must be independent of the units employed to
measure the variables. The principle of similitude (Rayleigh 1915) or
dimensional homogeneity, states that only commensurable quantities (ones having
the same dimension) may be compared, therefore, meaningful laws of nature must
be homogeneous equations in their various units of measurement, a result which
was formalized in the $\rm \Pi$ theorem (Vaschy 1892; Buckingham 1914).
However, most relations in allometry do not satisfy this basic requirement,
including the `3/4 Law' (Kleiber 1932) that relates the basal metabolic rate
and body mass, which it is sometimes claimed to be the most fundamental
biological rate (Brown et al. 2004) and the closest to a law in life sciences
(West \& Brown 2004). Using the $\rm \Pi$ theorem, here we show that it is
possible to construct a unique homogeneous equation for the metabolic rates, in
agreement with data in the literature. We find that the variations in the
dependence of the metabolic rates on body mass are secondary, coming from
variations in the allometric dependence of the heart frequencies. This includes
not only different classes of animals (mammals, birds, invertebrates) but also
different exercise conditions (basal and maximal). Our results demonstrate that
most of the differences found in the allometric exponents (White et al. 2007)
are due to compare incommensurable quantities and that our dimensionally
homogenous formula, unify these differences into a single formulation. We
discuss the ecological implications of this new formulation in the context of
the Malthusian's, Fenchel's and the total energy consumed in a lifespan
relations.
",0,1,0,0,0,0
17963,New insight into the dynamics of rhodopsin photoisomerization from one-dimensional quantum-classical modeling,"  Characterization of the primary events involved in the $cis-trans$
photoisomerization of the rhodopsin retinal chromophore was approximated by a
minimum one-dimensional quantum-classical model. The developed mathematical
model is identical to that obtained using conventional quantum-classical
approaches, and multiparametric quantum-chemical or molecular dynamics (MD)
computations were not required. The quantum subsystem of the model includes
three electronic states for rhodopsin: (i) the ground state, (ii) the excited
state, and (iii) the primary photoproduct in the ground state. The resultant
model is in perfect agreement with experimental data in terms of the quantum
yield, the time required to reach the conical intersection and to complete the
quantum evolution, the range of the characteristic low frequencies active
within the primary events of the $11-cis$ retinal isomerization, and the
coherent character of the photoreaction. An effective redistribution of excess
energy between the vibration modes of rhodopsin was revealed by analysis of the
dissipation process. The results confirm the validity of the minimal model,
despite its one-dimensional character. The fundamental nature of the
photoreaction was therefore demonstrated using a minimum mathematical model for
the first time.
",0,1,0,0,0,0
17964,High-order schemes for the Euler equations in cylindrical/spherical coordinates,"  We consider implementations of high-order finite difference Weighted
Essentially Non-Oscillatory (WENO) schemes for the Euler equations in
cylindrical and spherical coordinate systems with radial dependence only. The
main concern of this work lies in ensuring both high-order accuracy and
conservation. Three different spatial discretizations are assessed: one that is
shown to be high-order accurate but not conservative, one conservative but not
high-order accurate, and a new approach that is both high-order accurate and
conservative. For cylindrical and spherical coordinates, we present convergence
results for the advection equation and the Euler equations with an acoustics
problem; we then use the Sod shock tube and the Sedov point-blast problems in
cylindrical coordinates to verify our analysis and implementations.
",0,1,1,0,0,0
17965,Suppressing correlations in massively parallel simulations of lattice models,"  For lattice Monte Carlo simulations parallelization is crucial to make
studies of large systems and long simulation time feasible, while sequential
simulations remain the gold-standard for correlation-free dynamics. Here,
various domain decomposition schemes are compared, concluding with one which
delivers virtually correlation-free simulations on GPU Extensive simulations of
the octahedron model for $2+1$ dimensional Karda--Parisi--Zhang surface growth,
which is very sensitive to correlation in the site-selection dynamics, were
performed to show self-consistency of the parallel runs and agreement with the
sequential algorithm. We present a GPU implementation providing a speedup of
about $30\times$ over a parallel CPU implementation on a single socket and at
least $180\times$ with respect to the sequential reference.
",0,1,0,0,0,0
17966,New bounds on the strength of some restrictions of Hindman's Theorem,"  We prove upper and lower bounds on the effective content and logical strength
for a variety of natural restrictions of Hindman's Finite Sums Theorem. For
example, we show that Hindman's Theorem for sums of length at most 2 and 4
colors implies $\mathsf{ACA}_0$. An emerging {\em leitmotiv} is that the known
lower bounds for Hindman's Theorem and for its restriction to sums of at most 2
elements are already valid for a number of restricted versions which have
simple proofs and better computability- and proof-theoretic upper bounds than
the known upper bound for the full version of the theorem. We highlight the
role of a sparsity-like condition on the solution set, which we call apartness.
",0,0,1,0,0,0
17967,"3D Face Morphable Models ""In-the-Wild""","  3D Morphable Models (3DMMs) are powerful statistical models of 3D facial
shape and texture, and among the state-of-the-art methods for reconstructing
facial shape from single images. With the advent of new 3D sensors, many 3D
facial datasets have been collected containing both neutral as well as
expressive faces. However, all datasets are captured under controlled
conditions. Thus, even though powerful 3D facial shape models can be learnt
from such data, it is difficult to build statistical texture models that are
sufficient to reconstruct faces captured in unconstrained conditions
(""in-the-wild""). In this paper, we propose the first, to the best of our
knowledge, ""in-the-wild"" 3DMM by combining a powerful statistical model of
facial shape, which describes both identity and expression, with an
""in-the-wild"" texture model. We show that the employment of such an
""in-the-wild"" texture model greatly simplifies the fitting procedure, because
there is no need to optimize with regards to the illumination parameters.
Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
images. Finally, we have captured the first 3D facial database with relatively
unconstrained conditions and report quantitative evaluations with
state-of-the-art performance. Complementary qualitative reconstruction results
are demonstrated on standard ""in-the-wild"" facial databases. An open source
implementation of our technique is released as part of the Menpo Project.
",1,0,0,0,0,0
17968,Image Segmentation to Distinguish Between Overlapping Human Chromosomes,"  In medicine, visualizing chromosomes is important for medical diagnostics,
drug development, and biomedical research. Unfortunately, chromosomes often
overlap and it is necessary to identify and distinguish between the overlapping
chromosomes. A segmentation solution that is fast and automated will enable
scaling of cost effective medicine and biomedical research. We apply neural
network-based image segmentation to the problem of distinguishing between
partially overlapping DNA chromosomes. A convolutional neural network is
customized for this problem. The results achieved intersection over union (IOU)
scores of 94.7% for the overlapping region and 88-94% on the non-overlapping
chromosome regions.
",1,0,0,1,0,0
17969,Implementing Large-Scale Agile Frameworks: Challenges and Recommendations,"  Based on 13 agile transformation cases over 15 years, this article identifies
nine challenges associated with implementing SAFe, Scrum-at-Scale, Spotify,
LeSS, Nexus, and other mixed or customised large-scale agile frameworks. These
challenges should be considered by organizations aspiring to pursue a
large-scale agile strategy. This article also provides recommendations for
practitioners and agile researchers.
",1,0,0,0,0,0
17970,On The Limiting Distributions of the Total Height On Families of Trees,"  A symbolic-computational algorithm, fully implemented in Maple, is described,
that computes explicit expressions for generating functions that enable the
efficient computations of the expectation, variance, and higher moments, of the
random variable `sum of distances to the root', defined on any given family of
rooted ordered trees (defined by degree restrictions). Taking limits, we
confirm, via elementary methods, the fact, due to David Aldous, and expanded by
Svante Janson and others, that the limiting (scaled) distributions are all the
same, and coincide with the limiting distribution of the same random variable,
when it is defined on labeled rooted trees.
",0,0,1,0,0,0
17971,HESS J1826$-$130: A Very Hard $γ$-Ray Spectrum Source in the Galactic Plane,"  HESS J1826$-$130 is an unidentified hard spectrum source discovered by
H.E.S.S. along the Galactic plane, the spectral index being $\Gamma$ = 1.6 with
an exponential cut-off at about 12 TeV. While the source does not have a clear
counterpart at longer wavelengths, the very hard spectrum emission at TeV
energies implies that electrons or protons accelerated up to several hundreds
of TeV are responsible for the emission. In the hadronic case, the VHE emission
can be produced by runaway cosmic-rays colliding with the dense molecular
clouds spatially coincident with the H.E.S.S. source.
",0,1,0,0,0,0
17972,Laser opacity in underdense preplasma of solid targets due to quantum electrodynamics effects,"  We investigate how next-generation laser pulses at 10 PW $-$ 200 PW interact
with a solid target in the presence of a relativistically underdense preplasma
produced by amplified spontaneous emission (ASE). Laser hole boring and
relativistic transparency are strongly restrained due to the generation of
electron-positron pairs and $\gamma$-ray photons via quantum electrodynamics
(QED) processes. A pair plasma with a density above the initial preplasma
density is formed, counteracting the electron-free channel produced by the hole
boring. This pair-dominated plasma can block the laser transport and trigger an
avalanche-like QED cascade, efficiently transfering the laser energy to
photons. This renders a 1-$\rm\mu m$-scalelength, underdense preplasma
completely opaque to laser pulses at this power level. The QED-induced opacity
therefore sets much higher contrast requirements for such pulse in solid-target
experiments than expected by classical plasma physics. Our simulations show for
example, that proton acceleration from the rear of a solid with a preplasma
would be strongly impaired.
",0,1,0,0,0,0
17973,Consequentialist conditional cooperation in social dilemmas with imperfect information,"  Social dilemmas, where mutual cooperation can lead to high payoffs but
participants face incentives to cheat, are ubiquitous in multi-agent
interaction. We wish to construct agents that cooperate with pure cooperators,
avoid exploitation by pure defectors, and incentivize cooperation from the
rest. However, often the actions taken by a partner are (partially) unobserved
or the consequences of individual actions are hard to predict. We show that in
a large class of games good strategies can be constructed by conditioning one's
behavior solely on outcomes (ie. one's past rewards). We call this
consequentialist conditional cooperation. We show how to construct such
strategies using deep reinforcement learning techniques and demonstrate, both
analytically and experimentally, that they are effective in social dilemmas
beyond simple matrix games. We also show the limitations of relying purely on
consequences and discuss the need for understanding both the consequences of
and the intentions behind an action.
",1,0,0,0,0,0
17974,"Casimir free energy of dielectric films: Classical limit, low-temperature behavior and control","  The Casimir free energy of dielectric films, both free-standing in vacuum and
deposited on metallic or dielectric plates, is investigated. It is shown that
the values of the free energy depend considerably on whether the calculation
approach used neglects or takes into account the dc conductivity of film
material. We demonstrate that there are the material-dependent and universal
classical limits in the former and latter cases, respectively. The analytic
behavior of the Casimir free energy and entropy for a free-standing dielectric
film at low temperature in found. According to our results, the Casimir entropy
goes to zero when the temperature vanishes if the calculation approach with
neglected dc conductivity of a film is employed. If the dc conductivity is
taken into account, the Casimir entropy takes the positive value at zero
temperature, depending on the parameters of a film, i.e., the Nernst heat
theorem is violated. By considering the Casimir free energy of silica and
sapphire films deposited on a Au plate in the framework of two calculation
approaches, we argue that physically correct values are obtained by
disregarding the role of dc conductivity. A comparison with the well known
results for the configuration of two parallel plates is made. Finally, we
compute the Casimir free energy of silica, sapphire and Ge films deposited on
high-resistivity Si plates of different thicknesses and demonstrate that it can
be positive, negative and equal to zero. Possible applications of the obtained
results to thin films used in microelectronics are discussed.
",0,1,0,0,0,0
17975,Discriminant chronicles mining: Application to care pathways analytics,"  Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well
defined populations. As medico-administrative databases cover a large part of
the population, they have become very interesting to carry PE studies. Such
databases provide longitudinal care pathways in real condition containing
timestamped care events, especially drug deliveries. Temporal pattern mining
becomes a strategic choice to gain valuable insights about drug uses. In this
paper we propose DCM, a new discriminant temporal pattern mining algorithm. It
extracts chronicle patterns that occur more in a studied population than in a
control population. We present results on the identification of possible
associations between hospitalizations for seizure and anti-epileptic drug
switches in care pathway of epileptic patients.
",1,0,0,0,0,0
17976,Adversarial Symmetric Variational Autoencoder,"  A new form of variational autoencoder (VAE) is developed, in which the joint
distribution of data and codes is considered in two (symmetric) forms: ($i$)
from observed data fed through the encoder to yield codes, and ($ii$) from
latent codes drawn from a simple prior and propagated through the decoder to
manifest data. Lower bounds are learned for marginal log-likelihood fits
observed data and latent codes. When learning with the variational bound, one
seeks to minimize the symmetric Kullback-Leibler divergence of joint density
functions from ($i$) and ($ii$), while simultaneously seeking to maximize the
two marginal log-likelihoods. To facilitate learning, a new form of adversarial
training is developed. An extensive set of experiments is performed, in which
we demonstrate state-of-the-art data reconstruction and generation on several
image benchmark datasets.
",1,0,0,0,0,0
17977,The singular locus of hypersurface sections containing a closed subscheme over finite fields,"  We prove that there exist hypersurfaces that contain a given closed subscheme
$Z$ of the projective space over a finite field and intersect a given smooth
scheme $X$ off of $Z$ smoothly, if the intersection $V = Z \cap X$ is smooth.
Furthermore, we can give a bound on the dimension of the singular locus of the
hypersurface section and prescribe finitely many local conditions on the
hypersurface. This is an analogue of a Bertini theorem of Bloch over finite
fields and is proved using Poonen's closed point sieve. We also show a similar
theorem for the case where $V$ is not smooth.
",0,0,1,0,0,0
17978,A Statistical Perspective on Inverse and Inverse Regression Problems,"  Inverse problems, where in broad sense the task is to learn from the noisy
response about some unknown function, usually represented as the argument of
some known functional form, has received wide attention in the general
scientific disciplines. How- ever, in mainstream statistics such inverse
problem paradigm does not seem to be as popular. In this article we provide a
brief overview of such problems from a statistical, particularly Bayesian,
perspective.
We also compare and contrast the above class of problems with the perhaps
more statistically familiar inverse regression problems, arguing that this
class of problems contains the traditional class of inverse problems. In course
of our review we point out that the statistical literature is very scarce with
respect to both the inverse paradigms, and substantial research work is still
necessary to develop the fields.
",0,0,1,1,0,0
17979,Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling,"  This work presents a model reduction approach for problems with coherent
structures that propagate over time such as convection-dominated flows and
wave-type phenomena. Traditional model reduction methods have difficulties with
these transport-dominated problems because propagating coherent structures
typically introduce high-dimensional features that require high-dimensional
approximation spaces. The approach proposed in this work exploits the locality
in space and time of propagating coherent structures to derive efficient
reduced models. First, full-model solutions are approximated locally in time
via local reduced spaces that are adapted with basis updates during time
stepping. The basis updates are derived from querying the full model at a few
selected spatial coordinates. Second, the locality in space of the coherent
structures is exploited via an adaptive sampling scheme that selects at which
components to query the full model for computing the basis updates. Our
analysis shows that, in probability, the more local the coherent structure is
in space, the fewer full-model samples are required to adapt the reduced basis
with the proposed adaptive sampling scheme. Numerical results on benchmark
examples with interacting wave-type structures and time-varying transport
speeds and on a model combustor of a single-element rocket engine demonstrate
the wide applicability of our approach and the significant runtime speedups
compared to full models and traditional reduced models.
",1,0,0,0,0,0
17980,Weighted parallel SGD for distributed unbalanced-workload training system,"  Stochastic gradient descent (SGD) is a popular stochastic optimization method
in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel
SGD, often require all nodes to have the same performance or to consume equal
quantities of data. However, these requirements are difficult to satisfy when
the parallel SGD algorithms run in a heterogeneous computing environment;
low-performance nodes will exert a negative influence on the final result. In
this paper, we propose an algorithm called weighted parallel SGD (WP-SGD).
WP-SGD combines weighted model parameters from different nodes in the system to
produce the final output. WP-SGD makes use of the reduction in standard
deviation to compensate for the loss from the inconsistency in performance of
nodes in the cluster, which means that WP-SGD does not require that all nodes
consume equal quantities of data. We also analyze the theoretical feasibility
of running two other parallel SGD algorithms combined with WP-SGD in a
heterogeneous environment. The experimental results show that WP-SGD
significantly outperforms the traditional parallel SGD algorithms on
distributed training systems with an unbalanced workload.
",1,0,0,1,0,0
17981,Neural Lander: Stable Drone Landing Control using Learned Dynamics,"  Precise trajectory control near ground is difficult for multi-rotor drones,
due to the complex ground effects caused by interactions between multi-rotor
airflow and the environment. Conventional control methods often fail to
properly account for these complex effects and fall short in accomplishing
smooth landing. In this paper, we present a novel deep-learning-based robust
nonlinear controller (Neural-Lander) that improves control performance of a
quadrotor during landing. Our approach blends together a nominal dynamics model
coupled with a Deep Neural Network (DNN) that learns the high-order
interactions. We employ a novel application of spectral normalization to
constrain the DNN to have bounded Lipschitz behavior. Leveraging this Lipschitz
property, we design a nonlinear feedback linearization controller using the
learned model and prove system stability with disturbance rejection. To the
best of our knowledge, this is the first DNN-based nonlinear feedback
controller with stability guarantees that can utilize arbitrarily large neural
nets. Experimental results demonstrate that the proposed controller
significantly outperforms a baseline linear proportional-derivative (PD)
controller in both 1D and 3D landing cases. In particular, we show that
compared to the PD controller, Neural-Lander can decrease error in z direction
from 0.13m to zero, and mitigate average x and y drifts by 90% and 34%
respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from
0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes
well to new test inputs outside the training domain.
",1,0,0,0,0,0
17982,Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach,"  This communication presents a longitudinal model-free control approach for
computing the wheel torque command to be applied on a vehicle. This setting
enables us to overcome the problem of unknown vehicle parameters for generating
a suitable control law. An important parameter in this control setting is made
time-varying for ensuring finite-time stability. Several convincing computer
simulations are displayed and discussed. Overshoots become therefore smaller.
The driving comfort is increased and the robustness to time-delays is improved.
",1,0,1,0,0,0
17983,"Review of Geraint F. Lewis and Luke A. Barnes, A Fortunate Universe: Life in a Finely Tuned Cosmos","  This new book by cosmologists Geraint F. Lewis and Luke A. Barnes is another
entry in the long list of cosmology-centered physics books intended for a large
audience. While many such books aim at advancing a novel scientific theory, A
Fortunate Universe has no such scientific pretense. Its goals are to assert
that the universe is fine-tuned for life, to defend that this fact can
reasonably motivate further scientific inquiry as to why it is so, and to show
that the multiverse and intelligent design hypotheses are reasonable proposals
to explain this fine-tuning. This book's potential contribution, therefore,
lies in how convincingly and efficiently it can make that case.
",0,1,0,0,0,0
17984,Introducing SPAIN (SParse Audion INpainter),"  A novel sparsity-based algorithm for audio inpainting is proposed by
translating the SPADE algorithm by Kitić et. al.---the state-of-the-art for
audio declipping---into the task of audio inpainting. SPAIN (SParse Audio
INpainter) comes in synthesis and analysis variants. Experiments show that both
A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms and
that A-SPAIN performs on a par with the state-of-the-art method based on linear
prediction.
",1,0,0,0,0,0
17985,Palindromic Decompositions with Gaps and Errors,"  Identifying palindromes in sequences has been an interesting line of research
in combinatorics on words and also in computational biology, after the
discovery of the relation of palindromes in the DNA sequence with the HIV
virus. Efficient algorithms for the factorization of sequences into palindromes
and maximal palindromes have been devised in recent years. We extend these
studies by allowing gaps in decompositions and errors in palindromes, and also
imposing a lower bound to the length of acceptable palindromes.
We first present an algorithm for obtaining a palindromic decomposition of a
string of length n with the minimal total gap length in time O(n log n * g) and
space O(n g), where g is the number of allowed gaps in the decomposition. We
then consider a decomposition of the string in maximal \delta-palindromes (i.e.
palindromes with \delta errors under the edit or Hamming distance) and g
allowed gaps. We present an algorithm to obtain such a decomposition with the
minimal total gap length in time O(n (g + \delta)) and space O(n g).
",1,0,0,0,0,0
17986,End-of-Use Core Triage in Extreme Scenarios Based on a Threshold Approach,"  Remanufacturing is a significant factor in securing sustainability through a
circular economy. Sorting plays a significant role in remanufacturing
pre-processing inspections. Its significance can increase when remanufacturing
facilities encounter extreme situations, such as abnormally huge core arrivals.
Our main objective in this work is switching from less efficient to a more
efficient model and to characterize extreme behavior of core arrival in
remanufacturing and applying the developed model to triage cores. Central
tendency core flow models are not sufficient to handle extreme situations,
however, complementary Extreme Value (EV) approaches have shown to improve
model efficiency. Extreme core flows to remanufacturing facilities are rare but
still likely and can adversely affect remanufacturing business operations. In
this investigation, extreme end-of-use core flow is modelled by a threshold
approach using the Generalized Pareto Distribution (GPD). It is shown that GPD
has better performance than its maxima-block GEV counterpart from practical and
data efficiency perspectives. The model is validated by a synthesized big
dataset, tested by sophisticated statistical Anderson Darling (AD) test, and is
applied to a case of extreme flow to a valve shop in order to predict
probability of over-capacity arrivals that is critical in remanufacturing
business management. Finally, the GPD model combined with triage strategies is
used to initiate investigations into the efficacy of different triage methods
in remanufacturing operations.
",0,0,0,1,0,0
17987,A temperature-dependent implicit-solvent model of polyethylene glycol in aqueous solution,"  A temperature (T)-dependent coarse-grained (CG) Hamiltonian of polyethylene
glycol/oxide (PEG/PEO) in aqueous solution is reported to be used in
implicit-solvent material models in a wide temperature (i.e., solvent quality)
range. The T-dependent nonbonded CG interactions are derived from a combined
""bottom-up"" and ""top-down"" approach. The pair potentials calculated from
atomistic replica-exchange molecular dynamics simulations in combination with
the iterative Boltzmann inversion are post-refined by benchmarking to
experimental data of the radius of gyration. For better handling and a fully
continuous transferability in T-space, the pair potentials are conveniently
truncated and mapped to an analytic formula with three structural parameters
expressed as explicit continuous functions of T. It is then demonstrated that
this model without further adjustments successfully reproduces other
experimentally known key thermodynamic properties of semi-dilute PEG solutions
such as the full equation of state (i.e., T-dependent osmotic pressure) for
various chain lengths as well as their cloud point (or collapse) temperature.
",0,1,0,0,0,0
17988,Directional Statistics and Filtering Using libDirectional,"  In this paper, we present libDirectional, a MATLAB library for directional
statistics and directional estimation. It supports a variety of commonly used
distributions on the unit circle, such as the von Mises, wrapped normal, and
wrapped Cauchy distributions. Furthermore, various distributions on
higher-dimensional manifolds such as the unit hypersphere and the hypertorus
are available. Based on these distributions, several recursive filtering
algorithms in libDirectional allow estimation on these manifolds. The
functionality is implemented in a clear, well-documented, and object-oriented
structure that is both easy to use and easy to extend.
",1,0,0,1,0,0
17989,A solution of the dark energy and its coincidence problem based on local antigravity sources without fine-tuning or new scales,"  A novel idea is proposed for a natural solution of the dark energy and its
cosmic coincidence problem. The existence of local antigravity sources,
associated with astrophysical matter configurations distributed throughout the
universe, can lead to a recent cosmic acceleration effect. Various physical
theories can be compatible with this idea, but here, in order to test our
proposal, we focus on quantum originated spherically symmetric metrics matched
with the cosmological evolution through the simplest Swiss cheese model. In the
context of asymptotically safe gravity, we have explained the observed amount
of dark energy using Newton's constant, the galaxy or cluster length scales,
and dimensionless order one parameters predicted by the theory, without
fine-tuning or extra unproven energy scales. The interior modified
Schwarzschild-de Sitter metric allows us to approximately interpret this result
as that the standard cosmological constant is a composite quantity made of the
above parameters, instead of a fundamental one.
",0,1,0,0,0,0
17990,Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples,"  Self-paced learning and hard example mining re-weight training instances to
improve learning accuracy. This paper presents two improved alternatives based
on lightweight estimates of sample uncertainty in stochastic gradient descent
(SGD): the variance in predicted probability of the correct class across
iterations of mini-batch SGD, and the proximity of the correct class
probability to the decision threshold. Extensive experimental results on six
datasets show that our methods reliably improve accuracy in various network
architectures, including additional gains on top of other popular training
techniques, such as residual learning, momentum, ADAM, batch normalization,
dropout, and distillation.
",0,0,0,1,0,0
17991,"Optical signature of Weyl electronic structures in tantalum pnictides Ta$Pn$ ($Pn=$ P, As)","  To investigate the electronic structure of Weyl semimetals Ta$Pn$ ($Pn=$P,
As), optical conductivity [$\sigma(\omega)$] spectra are measured over a wide
range of photon energies and temperatures, and these measured values are
compared with band calculations. Two significant structures can be observed: a
bending structure at $\hbar\omega\sim$85 meV in TaAs, and peaks at
$\hbar\omega\sim$ 50 meV (TaP) and $\sim$30 meV (TaAs). The bending structure
can be explained by the interband transition between saddle points connecting a
set of $W_2$ Weyl points. The temperature dependence of the peak intensity can
be fitted by assuming the interband transition between saddle points connecting
a set of $W_1$ Weyl points. Owing to the different temperature dependence of
the Drude weight in both materials, it is found that the Weyl points of TaAs
are located near the Fermi level, whereas those of TaP are further away.
",0,1,0,0,0,0
17992,"A study of cyber security in hospitality industry- threats and countermeasures: case study in Reno, Nevada","  The purpose of this study is to analyze cyber security and security practices
of electronic information and network system, network threats, and techniques
to prevent the cyber attacks in hotels. Helping the information technology
directors and chief information officers (CIO) is the aim of this study to
advance policy for security of electronic information in hotels and suggesting
some techniques and tools to secure the computer networks. This research is
completely qualitative while the case study and interviews have done in 5
random hotels in Reno, Nevada, United States of America. The interview has done
with 50 hotel guests, 10 front desk employees, 3 IT manager and 2 assistant of
General manager. The results show that hotels' cyber security is very low and
hotels are very vulnerable in this regard and at the end, the implications and
contribution of the study is mentioned.
",1,0,0,0,0,0
17993,Weak Fraisse categories,"  We develop the theory of weak Fraisse categories, where the crucial concept
is the weak amalgamation property, discovered relatively recently in model
theory. We show that, in a suitable framework, every weak Fraisse category has
its unique limit, a special object in a bigger category, characterized by
certain variant of injectivity. This significantly extends the known theory of
Fraisse limits.
",0,0,1,0,0,0
17994,Multiple Stakeholders in Music Recommender Systems,"  Music recommendation services collectively spin billions of songs for
millions of listeners on a daily basis. Users can typically listen to a variety
of songs tailored to their personal tastes and preferences. Music is not the
only type of content encountered in these services, however. Advertisements are
generally interspersed throughout the music stream to generate revenue for the
business. Additional content may include artist messaging, ticketing, sports,
news and weather. In this paper, we discuss issues that arise when multiple
content providers are stakeholders in the recommendation process. These
stakeholders each have their own objectives and must work in concert to sustain
a healthy music recommendation service.
",1,0,0,0,0,0
17995,Large time behavior of solution to nonlinear Dirac equation in $1+1$ dimensions,"  This paper studies the large time behavior of solution for a class of
nonlinear massless Dirac equations in $R^{1+1}$. It is shown that the solution
will tend to travelling wave solution when time tends to infinity.
",0,0,1,0,0,0
17996,Compositions of Functions and Permutations Specified by Minimal Reaction Systems,"  This paper studies mathematical properties of reaction systems that was
introduced by Enrenfeucht and Rozenberg as computational models inspired by
biochemical reaction in the living cells. In particular, we continue the study
on the generative power of functions specified by minimal reaction systems
under composition initiated by Salomaa. Allowing degenerate reaction systems,
functions specified by minimal reaction systems over a quarternary alphabet
that are permutations generate the alternating group on the power set of the
background set.
",1,0,0,0,0,0
17997,The center problem for the Lotka reactions with generalized mass-action kinetics,"  Chemical reaction networks with generalized mass-action kinetics lead to
power-law dynamical systems. As a simple example, we consider the Lotka
reactions and the resulting planar ODE. We characterize the parameters
(positive coefficients and real exponents) for which the unique positive
equilibrium is a center.
",0,0,1,0,0,0
17998,Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure,"  In this paper, we study the missing sample recovery problem using methods
based on sparse approximation. In this regard, we investigate the algorithms
used for solving the inverse problem associated with the restoration of missed
samples of image signal. This problem is also known as inpainting in the
context of image processing and for this purpose, we suggest an iterative
sparse recovery algorithm based on constrained $l_1$-norm minimization with a
new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
is a simplified version of the Structural SIMilarity (SSIM) index, which is
convex and error-sensitive. The optimization problem incorporating this
criterion, is then solved via Alternating Direction Method of Multipliers
(ADMM). Simulation results show the efficiency of the proposed method for
missing sample recovery of 1D patch vectors and inpainting of 2D image signals.
",1,0,0,1,0,0
17999,Skin cancer reorganization and classification with deep neural network,"  As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization strategy is critical for melanoma therapy.
However, well-trained dermatologists dominant the diagnostic accuracy. In order
to solve this problem, many effort focus on developing automatic image analysis
systems. Here we report a novel strategy based on deep learning technique, and
achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
we build a segmentation neural network (skin_segnn), which achieved very high
lesion boundary detection accuracy; 2) We build another very deep neural
network based on Google inception v3 network (skin_recnn) and its well-trained
weight. The novel designed transfer learning based deep neural network
skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
",1,0,0,0,0,0
18000,Rescaled extrapolation for vector-valued functions,"  We extend Rubio de Francia's extrapolation theorem for functions valued in
UMD Banach function spaces, leading to short proofs of some new and known
results. In particular we prove Littlewood-Paley-Rubio de Francia-type
estimates and boundedness of variational Carleson operators for Banach function
spaces with UMD concavifications.
",0,0,1,0,0,0
18001,Separation of the charge density wave and superconducting states by an intermediate semimetal phase in pressurized TaTe2,"  In layered transition metal dichalcogenides (LTMDCs) that display both charge
density waves (CDWs) and superconductivity, the superconducting state generally
emerges directly on suppression of the CDW state. Here, however, we report a
different observation for pressurized TaTe2, a non-superconducting CDW-bearing
LTMDC at ambient pressure. We find that a superconducting state does not occur
in TaTe2 after the full suppression of its CDW state, which we observe at about
3 GPa, but, rather, a non-superconducting semimetal state is observed. At a
higher pressure, ~21 GPa, where both the semimetal state and the corresponding
positive magnetoresistance effect are destroyed, superconductivity finally
emerges and remains present up to ~50 GPa, the high pressure limit of our
measurements. Our pressure-temperature phase diagram for TaTe2 demonstrates
that the CDW and the superconducting phases in TaTe2 do not directly transform
one to the other, but rather are separated by a semimetal state, - the first
experimental case where the CDW and superconducting states are separated by an
intermediate phase in LTMDC systems.
",0,1,0,0,0,0
18002,Zero distribution for Angelesco Hermite--Padé polynomials,"  We consider the problem of zero distribution of the first kind
Hermite--Padé polynomials associated with a vector function $\vec f = (f_1,
\dots, f_s)$ whose components $f_k$ are functions with a finite number of
branch points in plane. We assume that branch sets of component functions are
well enough separated (which constitute the Angelesco case). Under this
condition we prove a theorem on limit zero distribution for such polynomials.
The limit measures are defined in terms of a known vector equilibrium problem.
Proof of the theorem is based on the methods developed by H.~Stahl,
A.~A.~Gonchar and the author. These methods obtained some further
generalization in the paper in application to systems of polynomials defined by
systems of complex orthogonality relations.
Together with the characterization of the limit zero distributions of
Hermite--Padé polynomials by a vector equilibrium problem we consider an
alternative characterization using a Riemann surface $\mathcal R(\vec f)$
associated with $\vec f$. In this terms we present a more general (without
Angelesco condition) conjecture on the zero distribution of Hermite--Padé
polynomials.
Bibliography: 72 items.
",0,0,1,0,0,0
18003,Atomic-scale identification of novel planar defect phases in heteroepitaxial YBa$_2$Cu$_3$O$_{7-δ}$ thin films,"  We have discovered two novel types of planar defects that appear in
heteroepitaxial YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO123) thin films, grown by
pulsed-laser deposition (PLD) either with or without a
La$_{2/3}$Ca$_{1/3}$MnO$_3$ (LCMO) overlayer, using the combination of
high-angle annular dark-field scanning transmission electron microscopy
(HAADF-STEM) imaging and electron energy loss spectroscopy (EELS) mapping for
unambiguous identification. These planar lattice defects are based on the
intergrowth of either a BaO plane between two CuO chains or multiple Y-O layers
between two CuO$_2$ planes, resulting in non-stoichiometric layer sequences
that could directly impact the high-$T_c$ superconductivity.
",0,1,0,0,0,0
18004,Self-consistent assessment of Englert-Schwinger model on atomic properties,"  Our manuscript investigates a self-consistent solution of the statistical
atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES
model) and benchmarks it against atomic Kohn-Sham and two orbital-free models
of the Thomas-Fermi-Dirac (TFD)-$\lambda$vW family. Results show that the ES
model generally offers the same accuracy as the well-known TFD-$\frac{1}{5}$vW
model; however, the ES model corrects the failure in Pauli potential
near-nucleus region. We also point to the inability of describing low-$Z$ atoms
as the foremost concern in improving the present model.
",0,1,0,0,0,0
18005,Target volatility option pricing in lognormal fractional SABR model,"  We examine in this article the pricing of target volatility options in the
lognormal fractional SABR model. A decomposition formula by Ito's calculus
yields a theoretical replicating strategy for the target volatility option,
assuming the accessibilities of all variance swaps and swaptions. The same
formula also suggests an approximation formula for the price of target
volatility option in small time by the technique of freezing the coefficient.
Alternatively, we also derive closed formed expressions for a small volatility
of volatility expansion of the price of target volatility option. Numerical
experiments show accuracy of the approximations in a reasonably wide range of
parameters.
",0,0,0,0,0,1
18006,Robust Blind Deconvolution via Mirror Descent,"  We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in mission critical applications. Further, many blind deconvolution methods
based on deep architectures internally make use of or optimize the basic
formulation, so a clearer understanding of how this sub-module behaves, when it
can be solved, and what noise injection it can tolerate is a first order
requirement. We derive new insights into the theoretical underpinnings of blind
deconvolution. The algorithm that emerges has nice convergence guarantees and
is provably robust in a sense we formalize in the paper. Interestingly, these
technical results play out very well in practice, where on standard datasets
our algorithm yields results competitive with or superior to the state of the
art. Keywords: blind deconvolution, robust continuous optimization
",1,0,0,1,0,0
18007,One-Dimensional Symmetry Protected Topological Phases and their Transitions,"  We present a unified perspective on symmetry protected topological (SPT)
phases in one dimension and address the open question of what characterizes
their phase transitions. In the first part of this work we use symmetry as a
guide to map various well-known fermionic and spin SPTs to a Kitaev chain with
coupling of range $\alpha \in \mathbb Z$. This unified picture uncovers new
properties of old models --such as how the cluster state is the fixed point
limit of the Affleck-Kennedy-Lieb-Tasaki state in disguise-- and elucidates the
connection between fermionic and bosonic phases --with the Hubbard chain
interpolating between four Kitaev chains and a spin chain in the Haldane phase.
In the second part, we study the topological phase transitions between these
models in the presence of interactions. This leads us to conjecture that the
critical point between any SPT with $d$-dimensional edge modes and the trivial
phase has a central charge $c \geq \log_2 d$. We analytically verify this for
many known transitions. This agrees with the intuitive notion that the phase
transition is described by a delocalized edge mode, and that the central charge
of a conformal field theory is a measure of the gapless degrees of freedom.
",0,1,0,0,0,0
18008,"Proceedings Eighth International Symposium on Games, Automata, Logics and Formal Verification","  This volume contains the proceedings of the Eighth International Symposium on
Games, Automata, Logic and Formal Verification (GandALF 2017). The symposium
took place in Roma, Italy, from the 20th to the 22nd of September 2017. The
GandALF symposium was established by a group of Italian computer scientists
interested in mathematical logic, automata theory, game theory, and their
applications to the specification, design, and verification of complex systems.
Its aim is to provide a forum where people from different areas, and possibly
with different backgrounds, can fruitfully interact. GandALF has a truly
international spirit, as witnessed by the composition of the program and
steering committee and by the country distribution of the submitted papers.
",1,0,0,0,0,0
18009,Valence Bonds in Random Quantum Magnets: Theory and Application to YbMgGaO4,"  We analyze the effect of quenched disorder on spin-1/2 quantum magnets in
which magnetic frustration promotes the formation of local singlets. Our
results include a theory for 2d valence-bond solids subject to weak bond
randomness, as well as extensions to stronger disorder regimes where we make
connections with quantum spin liquids. We find, on various lattices, that the
destruction of a valence-bond solid phase by weak quenched disorder leads
inevitably to the nucleation of topological defects carrying spin-1/2 moments.
This renormalizes the lattice into a strongly random spin network with
interesting low-energy excitations. Similarly when short-ranged valence bonds
would be pinned by stronger disorder, we find that this putative glass is
unstable to defects that carry spin-1/2 magnetic moments, and whose residual
interactions decide the ultimate low energy fate. Motivated by these results we
conjecture Lieb-Schultz-Mattis-like restrictions on ground states for
disordered magnets with spin-1/2 per statistical unit cell. These conjectures
are supported by an argument for 1d spin chains. We apply insights from this
study to the phenomenology of YbMgGaO$_4$, a recently discovered triangular
lattice spin-1/2 insulator which was proposed to be a quantum spin liquid. We
instead explore a description based on the present theory. Experimental
signatures, including unusual specific heat, thermal conductivity, and
dynamical structure factor, and their behavior in a magnetic field, are
predicted from the theory, and compare favorably with existing measurements on
YbMgGaO$_4$ and related materials.
",0,1,0,0,0,0
18010,TuckER: Tensor Factorization for Knowledge Graph Completion,"  Knowledge graphs are structured representations of real world facts. However,
they typically contain only a small subset of all possible facts. Link
prediction is a task of inferring missing facts based on existing ones. We
propose TuckER, a relatively simple but powerful linear model based on Tucker
decomposition of the binary tensor representation of knowledge graph triples.
TuckER outperforms all previous state-of-the-art models across standard link
prediction datasets. We prove that TuckER is a fully expressive model, deriving
the bound on its entity and relation embedding dimensionality for full
expressiveness which is several orders of magnitude smaller than the bound of
previous state-of-the-art models ComplEx and SimplE. We further show that
several previously introduced linear models can be viewed as special cases of
TuckER.
",1,0,0,1,0,0
18011,Taming Non-stationary Bandits: A Bayesian Approach,"  We consider the multi armed bandit problem in non-stationary environments.
Based on the Bayesian method, we propose a variant of Thompson Sampling which
can be used in both rested and restless bandit scenarios. Applying discounting
to the parameters of prior distribution, we describe a way to systematically
reduce the effect of past observations. Further, we derive the exact expression
for the probability of picking sub-optimal arms. By increasing the exploitative
value of Bayes' samples, we also provide an optimistic version of the
algorithm. Extensive empirical analysis is conducted under various scenarios to
validate the utility of proposed algorithms. A comparison study with various
state-of-the-arm algorithms is also included.
",1,0,0,1,0,0
18012,Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization,"  We propose a new randomized coordinate descent method for a convex
optimization template with broad applications. Our analysis relies on a novel
combination of four ideas applied to the primal-dual gap function: smoothing,
acceleration, homotopy, and coordinate descent with non-uniform sampling. As a
result, our method features the first convergence rate guarantees among the
coordinate descent methods, that are the best-known under a variety of common
structure assumptions on the template. We provide numerical evidence to support
the theoretical results with a comparison to state-of-the-art algorithms.
",0,0,0,1,0,0
18013,Guarantees for Greedy Maximization of Non-submodular Functions with Applications,"  We investigate the performance of the standard Greedy algorithm for
cardinality constrained maximization of non-submodular nondecreasing set
functions. While there are strong theoretical guarantees on the performance of
Greedy for maximizing submodular functions, there are few guarantees for
non-submodular ones. However, Greedy enjoys strong empirical performance for
many important non-submodular functions, e.g., the Bayesian A-optimality
objective in experimental design. We prove theoretical guarantees supporting
the empirical performance. Our guarantees are characterized by a combination of
the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In
particular, we prove that Greedy enjoys a tight approximation guarantee of
$\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained
maximization. In addition, we bound the submodularity ratio and curvature for
several important real-world objectives, including the Bayesian A-optimality
objective, the determinantal function of a square submatrix and certain linear
programs with combinatorial constraints. We experimentally validate our
theoretical findings for both synthetic and real-world applications.
",1,0,1,0,0,0
18014,Affine Metrics and Associated Algebroid Structures: Application to General Relativity,"  In this paper, algebroid bundle associated to affine metrics provide an
structure for unification of gravity and electromagnetism and, geometrization
of matter.
",0,0,1,0,0,0
18015,Controllability of temporal networks: An analysis using higher-order networks,"  The control of complex networks is a significant challenge, especially when
the network topology of the system to be controlled is dynamic. Addressing this
challenge, here we introduce a novel approach which allows exploring the
controllability of temporal networks. Studying six empirical data sets, we
particularly show that order correlations in the sequence of interactions can
both increase or decrease the time needed to achieve full controllability.
Counter-intuitively, we find that this effect can be opposite than the effect
of order correlations on other dynamical processes. Specifically, we show that
order correlations that speed up a diffusion process in a given system can slow
down the control of the same system, and vice-versa. Building on the
higher-order graphical modeling framework introduced in recent works, we
further demonstrate that spectral properties of higher-order network topologies
can be used to analytically explain this phenomenon.
",1,1,0,0,0,0
18016,Mapping momentum-dependent electron-phonon coupling and non-equilibrium phonon dynamics with ultrafast electron diffuse scattering,"  Despite their fundamental role in determining material properties, detailed
momentum-dependent information on the strength of electron-phonon and
phonon-phonon coupling (EPC and PPC, respectively) across the entire Brillouin
zone (BZ) has proved difficult to obtain. Here we demonstrate that ultrafast
electron diffuse scattering (UEDS) directly provides such information. By
exploiting symmetry-based selection rules and time-resolution, scattering from
different phonon branches can be distinguished even without energy resolution.
Using graphite as a model system, we show that UEDS patterns map the relative
EPC and PPC strength through their profound sensitivity to photoinduced changes
in phonon populations. We measure strong EPC to the $K$-point transverse
optical phonon of $A_1'$ symmetry ($K-A_1'$) and along the entire longitudinal
optical branch between $\Gamma-K$, not only to the $\Gamma-E_{2g}$ phonon as
previously emphasized. We also determine that the subsequent phonon relaxation
pathway involves three stages; decay via several identifiable channels to
transverse acoustic (TA) and longitudinal acoustic (LA) phonons (1-2 ps),
intraband thermalization of the non-equilibrium TA/LA phonon populations (30-40
ps) and interband relaxation of the LA/TA modes (115 ps). Combining UEDS with
ultrafast angle-resolved photoelectron spectroscopy will yield a complete
picture of the dynamics within and between electron and phonon subsystems,
helping to unravel complex phases in which the intertwined nature of these
systems have a strong influence on emergent properties.
",0,1,0,0,0,0
18017,Meridional Circulation Dynamics in a Cyclic Convective Dynamo,"  Surface observations indicate that the speed of the solar meridional
circulation in the photosphere varies in anti-phase with the solar cycle. The
current explanation for the source of this variation is that inflows into
active regions alter the global surface pattern of the meridional circulation.
When these localized inflows are integrated over a full hemisphere, they
contribute to the slow down of the axisymmetric poleward horizontal component.
The behavior of this large scale flow deep inside the convection zone remains
largely unknown. Present helioseismic techniques are not sensitive enough to
capture the dynamics of this weak large scale flow. Moreover, the large time of
integration needed to map the meridional circulation inside the convection
zone, also masks some of the possible dynamics on shorter timescales. In this
work we examine the dynamics of the meridional circulation that emerges from a
3D MHD global simulation of the solar convection zone. Our aim is to assess and
quantify the behavior of meridional circulation deep inside the convection
zone, where the cyclic large-scale magnetic field can reach considerable
strength. Our analyses indicate that the meridional circulation morphology and
amplitude are both highly influenced by the magnetic field, via the impact of
magnetic torques on the global angular momentum distribution. A dynamic feature
induced by these magnetic torques is the development of a prominent upward flow
at mid latitudes in the lower convection zone that occurs near the equatorward
edge of the toroidal bands and that peaks during cycle maximum. Globally, the
dynamo-generated large-scale magnetic field drives variations in the meridional
flow, in stark contrast to the conventional kinematic flux transport view of
the magnetic field being advected passively by the flow.
",0,1,0,0,0,0
18018,Bayesian analysis of 210Pb dating,"  In many studies of environmental change of the past few centuries, 210Pb
dating is used to obtain chronologies for sedimentary sequences. One of the
most commonly used approaches to estimate the ages of depths in a sequence is
to assume a constant rate of supply (CRS) or influx of `unsupported' 210Pb from
the atmosphere, together with a constant or varying amount of `supported'
210Pb. Current 210Pb dating models do not use a proper statistical framework
and thus provide poor estimates of errors. Here we develop a new model for
210Pb dating, where both ages and values of supported and unsupported 210Pb
form part of the parameters. We apply our model to a case study from Canada as
well as to some simulated examples. Our model can extend beyond the current CRS
approach, deal with asymmetric errors and mix 210Pb with other types of dating,
thus obtaining more robust, realistic and statistically better defined
estimates.
",0,0,0,1,0,0
18019,Analytic continuation with Padé decomposition,"  The ill-posed analytic continuation problem for Green's functions or
self-energies can be done using the Padé rational polynomial approximation.
However, to extract accurate results from this approximation, high precision
input data of the Matsubara Green's function are needed. The calculation of the
Matsubara Green's function generally involves a Matsubara frequency summation
which cannot be evaluated analytically. Numerical summation is requisite but it
converges slowly with the increase of the Matsubara frequency. Here we show
that this slow convergence problem can be significantly improved by utilizing
the Padé decomposition approach to replace the Matsubara frequency summation
by a Padé frequency summation, and high precision input data can be obtained
to successfully perform the Padé analytic continuation.
",0,1,0,0,0,0
18020,Imperative Functional Programs that Explain their Work,"  Program slicing provides explanations that illustrate how program outputs
were produced from inputs. We build on an approach introduced in prior work by
Perera et al., where dynamic slicing was defined for pure higher-order
functional programs as a Galois connection between lattices of partial inputs
and partial outputs. We extend this approach to imperative functional programs
that combine higher-order programming with references and exceptions. We
present proofs of correctness and optimality of our approach and a
proof-of-concept implementation and experimental evaluation.
",1,0,0,0,0,0
18021,Synthetic dimensions in ultracold molecules: quantum strings and membranes,"  Synthetic dimensions alter one of the most fundamental properties in nature,
the dimension of space. They allow, for example, a real three-dimensional
system to act as effectively four-dimensional. Driven by such possibilities,
synthetic dimensions have been engineered in ongoing experiments with ultracold
matter. We show that rotational states of ultracold molecules can be used as
synthetic dimensions extending to many - potentially hundreds of - synthetic
lattice sites. Microwaves coupling rotational states drive fully controllable
synthetic inter-site tunnelings, enabling, for example, topological band
structures. Interactions leads to even richer behavior: when molecules are
frozen in a real space lattice with uniform synthetic tunnelings, dipole
interactions cause the molecules to aggregate to a narrow strip in the
synthetic direction beyond a critical interaction strength, resulting in a
quantum string or a membrane, with an emergent condensate that lives on this
string or membrane. All these phases can be detected using measurements of
rotational state populations.
",0,1,0,0,0,0
18022,Self-Organization and The Origins of Life: The Managed-Metabolism Hypothesis,"  The managed-metabolism hypothesis suggests that a cooperation barrier must be
overcome if self-producing chemical organizations are to transition from
non-life to life. This barrier prevents un-managed, self-organizing,
autocatalytic networks of molecular species from individuating into complex,
cooperative organizations. The barrier arises because molecular species that
could otherwise make significant cooperative contributions to the success of an
organization will often not be supported within the organization, and because
side reactions and other free-riding processes will undermine cooperation. As a
result, the barrier seriously limits the possibility space that can be explored
by un-managed organizations, impeding individuation, complex functionality and
the transition to life. The barrier can be overcome comprehensively by
appropriate management which implements a system of evolvable constraints. The
constraints support beneficial co-operators and suppress free riders. In this
way management can manipulate the chemical processes of an autocatalytic
organization, producing novel processes that serve the interests of the
organization as a whole and that could not arise and persist spontaneously in
an un-managed chemical organization. Management self-organizes because it is
able to capture some of the benefits that are produced when its management of
an autocatalytic organization promotes beneficial cooperation. Selection
therefore favours the emergence of managers that take over and manage chemical
organizations so as to overcome the cooperation barrier. The managed-metabolism
hypothesis shows that if management is to overcome the cooperation barrier
comprehensively, its interventions must be digitally coded. In this way, the
hypothesis accounts for the two-tiered structure of all living cells in which a
digitally-coded genetic apparatus manages an analogically-informed metabolism.
",0,1,0,0,0,0
18023,"Pile-up Reduction, Bayesian Decomposition and Applications of Silicon Drift Detectors at LCLS","  Silicon drift detectors (SDDs) revolutionized spectroscopy in fields as
diverse as geology and dentistry. For a subset of experiments at ultra-fast,
x-ray free-electron lasers (FELs), SDDs can make substantial contributions.
Often the unknown spectrum is interesting, carrying science data, or the
background measurement is useful to identify unexpected signals. Many
measurements involve only several discrete photon energies known a priori. We
designed a pulse function (a combination of gradual step and exponential decay
function) and demonstrated that for individual pulses the signal amplitude,
peaking time, and pulse amplitude are interrelated and the signal amplitude and
peaking time are obtained for each pulse by fitting. Avoiding pulse shaping
reduced peaking times to tens of nanoseconds, resulting in reduced pulse
pile-up and allowing decomposition of remaining pulse pile-up at photon
separation times down to 100~ns while yielding time-of-arrival information with
precision of 10~nanoseconds. At pulsed sources or high photon rates, photon
pile-up still occurs. We showed that the area of one photon peaks is not
suitable for estimating high photon rates while pile-up spectrum fitting is
relatively simple and preferable to pile-up spectrum deconvolution. We
developed a photon pile-up model for constant intensity sources, extended it to
variable intensity sources (typical for FELs) and used it to fit a complex
pile-up spectrum, demonstrating its accuracy. Based on the pile-up model, we
developed a Bayesian pile-up decomposition method that allows decomposing
pile-up of single events with up to 6 photons from 6 monochromatic lines with
99% accuracy. The usefulness of SDDs will continue into the x-ray FEL era of
science. Their successors, the ePixS hybrid pixel detectors, already offer
hundreds of pixels, each with similar performance to an SDD, in a compact,
robust and affordable package.
",0,1,0,0,0,0
18024,Online Learning to Rank in Stochastic Click Models,"  Online learning to rank is a core problem in information retrieval and
machine learning. Many provably efficient algorithms have been recently
proposed for this problem in specific click models. The click model is a model
of how the user interacts with a list of documents. Though these results are
significant, their impact on practice is limited, because all proposed
algorithms are designed for specific click models and lack convergence
guarantees in other models. In this work, we propose BatchRank, the first
online learning to rank algorithm for a broad class of click models. The class
encompasses two most fundamental click models, the cascade and position-based
models. We derive a gap-dependent upper bound on the $T$-step regret of
BatchRank and evaluate it on a range of web search queries. We observe that
BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an
existing algorithm for the cascade model.
",1,0,0,1,0,0
18025,Transforming Coroutining Logic Programs into Equivalent CHR Programs,"  We extend a technique called Compiling Control. The technique transforms
coroutining logic programs into logic programs that, when executed under the
standard left-to-right selection rule (and not using any delay features) have
the same computational behavior as the coroutining program. In recent work, we
revised Compiling Control and reformulated it as an instance of Abstract
Conjunctive Partial Deduction. This work was mostly focused on the program
analysis performed in Compiling Control. In the current paper, we focus on the
synthesis of the transformed program. Instead of synthesizing a new logic
program, we synthesize a CHR(Prolog) program which mimics the coroutining
program. The synthesis to CHR yields programs containing only simplification
rules, which are particularly amenable to certain static analysis techniques.
The programs are also more concise and readable and can be ported to CHR
implementations embedded in other languages than Prolog.
",1,0,0,0,0,0
18026,Gap and rings carved by vortices in protoplanetary dust,"  Large-scale vortices in protoplanetary disks are thought to form and survive
for long periods of time. Hence, they can significantly change the global disk
evolution and particularly the distribution of the solid particles embedded in
the gas, possibly explaining asymmetries and dust concentrations recently
observed at sub-millimeter and millimeter wavelengths. We investigate the
spatial distribution of dust grains using a simple model of protoplanetary disk
hosted by a giant gaseous vortex. We explore the dependence of the results on
grain size and deduce possible consequences and predictions for observations of
the dust thermal emission at sub-millimeter and millimeter wavelengths. Global
2D simulations with a bi-fluid code are used to follow the evolution of a
single population of solid particles aerodynamically coupled to the gas.
Possible observational signatures of the dust thermal emission are obtained
using simulators of ALMA and ngVLA observations. We find that a giant vortex
not only captures dust grains with Stokes number St < 1 but can also affect the
distribution of larger grains (with St '~' 1) carving a gap associated to a
ring composed of incompletely trapped particles. The results are presented for
different particle size and associated to their possible signatures in disk
observations. Gap clearing in the dust spatial distribution could be due to the
interaction with a giant gaseous vortex and their associated spiral waves,
without the gravitational assistance of a planet. Hence, strong dust
concentrations at short sub-mm wavelengths associated with a gap and an
irregular ring at longer mm and cm wavelengths could indicate the presence of
an unseen gaseous vortex.
",0,1,0,0,0,0
18027,Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning,"  This paper introduces a new nonlinear dictionary learning method for
histograms in the probability simplex. The method leverages optimal transport
theory, in the sense that our aim is to reconstruct histograms using so-called
displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary
atoms; such atoms are themselves synthetic histograms in the probability
simplex. Our method simultaneously estimates such atoms, and, for each
datapoint, the vector of weights that can optimally reconstruct it as an
optimal transport barycenter of such atoms. Our method is computationally
tractable thanks to the addition of an entropic regularization to the usual
optimal transportation problem, leading to an approximation scheme that is
efficient, parallel and simple to differentiate. Both atoms and weights are
learned using a gradient-based descent method. Gradients are obtained by
automatic differentiation of the generalized Sinkhorn iterations that yield
barycenters with entropic smoothing. Because of its formulation relying on
Wasserstein barycenters instead of the usual matrix product between dictionary
and codes, our method allows for nonlinear relationships between atoms and the
reconstruction of input data. We illustrate its application in several
different image processing settings.
",1,0,0,1,0,0
18028,A Generalization of Smillie's Theorem on Strongly Cooperative Tridiagonal Systems,"  Smillie (1984) proved an interesting result on the stability of nonlinear,
time-invariant, strongly cooperative, and tridiagonal dynamical systems. This
result has found many applications in models from various fields including
biology, ecology, and chemistry. Smith (1991) has extended Smillie's result and
proved entrainment in the case where the vector field is time-varying and
periodic. We use the theory of linear totally nonnegative differential systems
developed by Schwarz (1970) to give a generalization of these two results. This
is based on weakening the requirement for strong cooperativity to
cooperativity, and adding an additional observability-type condition.
",1,0,0,0,0,0
18029,Geospatial Semantics,"  Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.
",1,0,0,0,0,0
18030,Stability of the sum of two solitary waves for (gDNLS) in the energy space,"  In this paper, we continue the study in \cite{MiaoTX:DNLS:Stab}. We use the
perturbation argument, modulational analysis and the energy argument in
\cite{MartelMT:Stab:gKdV, MartelMT:Stab:NLS} to show the stability of the sum
of two solitary waves with weak interactions for the generalized derivative
Schrödinger equation (gDNLS) in the energy space. Here (gDNLS) hasn't the
Galilean transformation invariance, the pseudo-conformal invariance and the
gauge transformation invariance, and the case $\sigma>1$ we considered
corresponds to the $L^2$-supercritical case.
",0,0,1,0,0,0
18031,A BERT Baseline for the Natural Questions,"  This technical note describes a new baseline for the Natural Questions. Our
model is based on BERT and reduces the gap between the model F1 scores reported
in the original dataset paper and the human upper bound by 30% and 50% relative
for the long and short answer tasks respectively. This baseline has been
submitted to the official NQ leaderboard at
ai.google.com/research/NaturalQuestions and we plan to opensource the code for
it in the near future.
",1,0,0,0,0,0
18032,Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks,"  Even as we advance the frontiers of physics knowledge, our understanding of
how this knowledge evolves remains at the descriptive levels of Popper and
Kuhn. Using the APS publications data sets, we ask in this letter how new
knowledge is built upon old knowledge. We do so by constructing year-to-year
bibliographic coupling networks, and identify in them validated communities
that represent different research fields. We then visualize their evolutionary
relationships in the form of alluvial diagrams, and show how they remain intact
through APS journal splits. Quantitatively, we see that most fields undergo
weak Popperian mixing, and it is rare for a field to remain isolated/undergo
strong mixing. The sizes of fields obey a simple linear growth with
recombination. We can also reliably predict the merging between two fields, but
not for the considerably more complex splitting. Finally, we report a case
study of two fields that underwent repeated merging and splitting around 1995,
and how these Kuhnian events are correlated with breakthroughs on BEC, quantum
teleportation, and slow light. This impact showed up quantitatively in the
citations of the BEC field as a larger proportion of references from during and
shortly after these events.
",1,1,0,0,0,0
18033,On a lower bound for the energy functional on a family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$,"  We study the energy functional on the set of Lagrangian tori in
$\mathbb{C}P^2$ . We prove that the value of the energy functional on a certain
family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$ is strictly
larger than energy of the Clifford torus.
",0,0,1,0,0,0
18034,Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks,"  Viewing the trajectory of a patient as a dynamical system, a recurrent neural
network was developed to learn the course of patient encounters in the
Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
extracted from Electronic Medical Records (EMR) of about 12000 patients who
were admitted to the PICU over a period of more than 10 years were leveraged.
The RNN model ingests a sequence of measurements which include physiologic
observations, laboratory results, administered drugs and interventions, and
generates temporally dynamic predictions for in-ICU mortality at user-specified
times. The RNN's ICU mortality predictions offer significant improvements over
those from two clinically-used scores and static machine learning algorithms.
",1,0,1,1,0,0
18035,Global Strichartz estimates for the Schrödinger equation with non zero boundary conditions and applications,"  We consider the Schrödinger equation on a half space in any dimension with
a class of nonhomogeneous boundary conditions including Dirichlet, Neuman and
the so-called transparent boundary conditions. Building upon recent local in
time Strichartz estimates (for Dirichlet boundary conditions), we obtain global
Strichartz estimates for initial data in $H^s,\ 0\leq s\leq 2$ and boundary
data in a natural space $\mathcal{H}^s$. For $s\geq 1/2$, the issue of
compatibility conditions requires a thorough analysis of the $\mathcal{H}^s$
space. As an application we solve nonlinear Schrödinger equations and
construct global asymptotically linear solutions for small data. A discussion
is included on the appropriate notion of scattering in this framework, and the
optimality of the $\mathcal{H}^s$ space.
",0,0,1,0,0,0
18036,Structure of $^{20}$Ne states in the resonance $^{16}$O+$α$ elastic scattering,"  Background
The nuclear structure of the cluster bands in $^{20}$Ne presents a challenge
for different theoretical approaches. It is especially difficult to explain the
broad 0$^+$, 2$^+$ states at 9 MeV excitation energy. Simultaneously, it is
important to obtain more reliable experimental data for these levels in order
to quantitatively assess the theoretical framework.
Purpose
To obtain new data on $^{20}$Ne $\alpha$ cluster structure. Method Thick
target inverse kinematics technique was used to study the $^{16}$O+$\alpha$
resonance elastic scattering and the data were analyzed using an \textit{R}
matrix approach. The $^{20}$Ne spectrum, the cluster and nucleon spectroscopic
factors were calculated using cluster-nucleon configuration interaction model
(CNCIM).
Results
We determined the parameters of the broad resonances in
\textsuperscript{20}Ne: 0$^+$ level at 8.77 $\pm$ 0.150 MeV with a width of 750
(+500/-220) keV; 2$^+$ level at 8.75 $\pm$ 0.100 MeV with the width of 695
$\pm$ 120 keV; the width of 9.48 MeV level of 65 $\pm$ 20 keV and showed that
9.19 MeV, 2$^+$ level (if exists) should have width $\leq$ 10 keV. The detailed
comparison of the theoretical CNCIM predictions with the experimental data on
cluster states was made.
Conclusions
Our experimental results by the TTIK method generally confirm the adopted
data on $\alpha$ cluster levels in $^{20}$Ne. The CNCIM gives a good
description of the $^{20}$Ne positive parity states up to an excitation energy
of $\sim$ 7 MeV, predicting reasonably well the excitation energy of the states
and their cluster and single particle properties. At higher excitations, the
qualitative disagreement with the experimentally observed structure is evident,
especially for broad resonances.
",0,1,0,0,0,0
18037,Terminal-Pairability in Complete Bipartite Graphs,"  We investigate the terminal-pairibility problem in the case when the base
graph is a complete bipartite graph, and the demand graph is also bipartite
with the same color classes. We improve the lower bound on maximum value of
$\Delta(D)$ which still guarantees that the demand graph $D$ is
terminal-pairable in this setting. We also prove a sharp theorem on the maximum
number of edges such a demand graph can have.
",0,0,1,0,0,0
18038,Rate-optimal Meta Learning of Classification Error,"  Meta learning of optimal classifier error rates allows an experimenter to
empirically estimate the intrinsic ability of any estimator to discriminate
between two populations, circumventing the difficult problem of estimating the
optimal Bayes classifier. To this end we propose a weighted nearest neighbor
(WNN) graph estimator for a tight bound on the Bayes classification error; the
Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators
[berisha2016], the proposed estimator is non-parametric and does not require
density estimation. However, unlike previous approaches the proposed estimator
is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero
at the fastest possible rate of $O(1/M+1/N)$ where $M,N$ are the sample sizes
of the respective populations. We illustrate the proposed WNN meta estimator
for several simulated and real data sets.
",0,0,0,1,0,0
18039,"Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures","  Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.
",1,0,0,0,0,0
18040,Geometric features of Vessiot--Guldberg Lie algebras of conformal and Killing vector fields on $\mathbb{R}^2$,"  This paper locally classifies finite-dimensional Lie algebras of conformal
and Killing vector fields on $\mathbb{R}^2$ relative to an arbitrary
pseudo-Riemannian metric. Several results about their geometric properties are
detailed, e.g. their invariant distributions and induced symplectic structures.
Findings are illustrated with two examples of physical nature: the
Milne--Pinney equation and the projective Schrödinger equation on the Riemann
sphere.
",0,1,0,0,0,0
18041,Fermion inter-particle potentials in 5D and a dimensional restriction prescription to 4D,"  This work sets out to compute and discuss effects of spin, velocity and
dimensionality on inter-particle potentials systematically derived from gauge
field-theoretic models. We investigate the interaction of fermionic particles
by the exchange of a vector field in a parity-preserving description in
five-dimensional $(5D)$ space-time. A particular dimensional reduction
prescription is adopted $-$ reduction by dimensional restriction $-$ and
special effects, like a pseudo-spin dependence, show up in four dimensions
$(4D)$. What we refer to as pseudo-spin shall be duly explained. The main idea
we try to convey is that the calculation of the potentials in $5D$ and the
consequent reduction to $4D$ exhibits new effects that are not present if the
potential is calculated in $4D$ after the action has been reduced.
",0,1,0,0,0,0
18042,Characterizations of idempotent discrete uninorms,"  In this paper we provide an axiomatic characterization of the idempotent
discrete uninorms by means of three conditions only: conservativeness,
symmetry, and nondecreasing monotonicity. We also provide an alternative
characterization involving the bisymmetry property. Finally, we provide a
graphical characterization of these operations in terms of their contour plots,
and we mention a few open questions for further research.
",1,0,1,0,0,0
18043,Constraints on the sum of neutrino masses using cosmological data including the latest extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample,"  We investigate the constraints on the sum of neutrino masses ($\Sigma m_\nu$)
using the most recent cosmological data, which combines the distance
measurement from baryonic acoustic oscillation in the extended Baryon
Oscillation Spectroscopic Survey DR14 quasar sample with the power spectra of
temperature and polarization anisotropies in the cosmic microwave background
from the Planck 2015 data release. We also use other low-redshift observations
including the baryonic acoustic oscillation at relatively low redshifts, the
supernovae of type Ia and the local measurement of Hubble constant. In the
standard cosmological constant $\Lambda$ cold dark matter plus massive neutrino
model, we obtain the $95\%$ \acl{CL} upper limit to be $\Sigma
m_\nu<0.129~\mathrm{eV}$ for the degenerate mass hierarchy, $\Sigma
m_{\nu}<0.159~\mathrm{eV}$ for the normal mass hierarchy, and $\Sigma
m_{\nu}<0.189~\mathrm{eV}$ for the inverted mass hierarchy. Based on Bayesian
evidence, we find that the degenerate hierarchy is positively supported, and
the current data combination can not distinguish normal and inverted
hierarchies. Assuming the degenerate mass hierarchy, we extend our study to
non-standard cosmological models including the generic dark energy, the spatial
curvature, and the extra relativistic degrees of freedom, respectively, but
find these models not favored by the data.
",0,1,0,0,0,0
18044,The effect of the choice of neural network depth and breadth on the size of its hypothesis space,"  We show that the number of unique function mappings in a neural network
hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
the number of neurons in the hidden layer $l$.
",0,0,0,1,0,0
18045,Fairwashing: the risk of rationalization,"  Black-box explanation is the problem of explaining how a machine learning
model -- whose internal logic is hidden to the auditor and generally complex --
produces its outcomes. Current approaches for solving this problem include
model explanation, outcome explanation as well as model inspection. While these
techniques can be beneficial by providing interpretability, they can be used in
a negative manner to perform fairwashing, which we define as promoting the
perception that a machine learning model respects some ethical values while it
might not be the case. In particular, we demonstrate that it is possible to
systematically rationalize decisions taken by an unfair black-box model using
the model explanation as well as the outcome explanation approaches with a
given fairness metric. Our solution, LaundryML, is based on a regularized rule
list enumeration algorithm whose objective is to search for fair rule lists
approximating an unfair black-box model. We empirically evaluate our
rationalization technique on black-box models trained on real-world datasets
and show that one can obtain rule lists with high fidelity to the black-box
model while being considerably less unfair at the same time.
",1,0,0,1,0,0
18046,Volume Dependence of N-Body Bound States,"  We derive the finite-volume correction to the binding energy of an N-particle
quantum bound state in a cubic periodic volume. Our results are applicable to
bound states with arbitrary composition and total angular momentum, and in any
number of spatial dimensions. The only assumptions are that the interactions
have finite range. The finite-volume correction is a sum of contributions from
all possible breakup channels. In the case where the separation is into two
bound clusters, our result gives the leading volume dependence up to
exponentially small corrections. If the separation is into three or more
clusters, there is a power-law factor that is beyond the scope of this work,
however our result again determines the leading exponential dependence. We also
present two independent methods that use finite-volume data to determine
asymptotic normalization coefficients. The coefficients are useful to determine
low-energy capture reactions into weakly bound states relevant for nuclear
astrophysics. Using the techniques introduced here, one can even extract the
infinite-volume energy limit using data from a single-volume calculation. The
derived relations are tested using several exactly solvable systems and
numerical examples. We anticipate immediate applications to lattice
calculations of hadronic, nuclear, and cold atomic systems.
",0,1,1,0,0,0
18047,Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees,"  Additive models, such as produced by gradient boosting, and full interaction
models, such as classification and regression trees (CART), are widely used
algorithms that have been investigated largely in isolation. We show that these
models exist along a spectrum, revealing never-before-known connections between
these two approaches. This paper introduces a novel technique called
tree-structured boosting for creating a single decision tree, and shows that
this method can produce models equivalent to CART or gradient boosted stumps at
the extremes by varying a single parameter. Although tree-structured boosting
is designed primarily to provide both the model interpretability and predictive
performance needed for high-stake applications like medicine, it also can
produce decision trees represented by hybrid models between CART and boosted
stumps that can outperform either of these approaches.
",1,0,0,1,0,0
18048,Magnetic field influenced electron-impurity scattering and magnetotransport,"  We formulate a quasiclassical theory ($\omega_c\tau \lesssim 1$ with
$\omega_c$ as the cyclotron frequency and $\tau$ as the relaxation time) to
study the influence of magnetic field on electron-impurity scattering process
in the two-dimensional electron gas. We introduce a general recipe based on an
abstraction of the detailed impurity scattering process to define the
scattering parameter such as the incoming and outgoing momentum and coordinate
jump. In this picture, we can conveniently describe the skew scattering and
coordinate jump, which will eventually modify the Boltzmann equation. We find
an anomalous Hall resistivity different from the conventional Boltzmann-Drude
result and a negative magnetoresistivity parabolic in magnetic field. The
origin of these results has been analyzed. The relevance between our theory and
recent simulation and experimental works is also discussed. Our theory
dominates in dilute impurity system where the correlation effect is negligible.
",0,1,0,0,0,0
18049,Evidence for depletion of heavy silicon isotopes at comet 67P/Churyumov-Gerasimenko,"  Context. The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis
(ROSINA) was designed to measure the composition of the gas in the coma of
comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency's
Rosetta mission. In addition to the volatiles, ROSINA measured refractories
sputtered off the comet by the interaction of solar wind protons with the
surface of the comet.
Aims. The origin of different solar system materials is still heavily
debated. Isotopic ratios can be used to distinguish between different
reservoirs and investigate processes occurring during the formation of the
solar system.
Methods. ROSINA consisted of two mass spectrometers and a pressure sensor. In
the ROSINA Double Focusing Mass Spectrometer (DFMS), the neutral gas of
cometary origin was ionized and then deflected in an electric and a magnetic
field that separated the ions based on their mass-to-charge ratio. The DFMS had
a high mass resolution, dynamic range, and sensitivity that allowed detection
of rare species and the known major volatiles.
Results. We measured the relative abundance of all three stable silicon
isotopes with the ROSINA instrument on board the Rosetta spacecraft.
Furthermore, we measured $^{13}$C/$^{12}$C in C$_2$H$_4$, C$_2$H$_5$, and CO.
The DFMS in situ measurements indicate that the average silicon isotopic
composition shows depletion in the heavy isotopes $^{29}$Si and $^{30}$Si with
respect to $^{28}$Si and solar abundances, while $^{13}$C to $^{12}$C is
analytically indistinguishable from bulk planetary and meteorite compositions.
Although the origin of the deficiency of the heavy silicon isotopes cannot be
explained unambiguously, we discuss mechanisms that could have contributed to
the measured depletion of the isotopes $^{29}$Si and $^{30}$Si.
",0,1,0,0,0,0
18050,"Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots","  Walking quadruped robots face challenges in positioning their feet and
lifting their legs during gait cycles over uneven terrain. The robot Laika is
under development as a quadruped with a flexible, actuated spine designed to
assist with foot movement and balance during these gaits. This paper presents
the first set of hardware designs for the spine of Laika, a physical prototype
of those designs, and tests in both hardware and simulations that show the
prototype's capabilities. Laika's spine is a tensegrity structure, used for its
advantages with weight and force distribution, and represents the first working
prototype of a tensegrity spine for a quadruped robot. The spine bends by
adjusting the lengths of the cables that separate its vertebrae, and twists
using an actuated rotating vertebra at its center. The current prototype of
Laika has stiff legs attached to the spine, and is used as a test setup for
evaluation of the spine itself. This work shows the advantages of Laika's spine
by demonstrating the spine lifting each of the robot's four feet, both as a
form of balancing and as a precursor for a walking gait. These foot motions,
using specific combinations of bending and rotation movements of the spine, are
measured in both simulation and hardware experiments. Hardware data are used to
calibrate the simulations, such that the simulations can be used for control of
balancing or gait cycles in the future. Future work will attach actuated legs
to Laika's spine, and examine balancing and gait cycles when combined with leg
movements.
",1,0,0,0,0,0
18051,The landscape of NeuroImage-ing research,"  As the field of neuroimaging grows, it can be difficult for scientists within
the field to gain and maintain a detailed understanding of its ever-changing
landscape. While collaboration and citation networks highlight important
contributions within the field, the roles of and relations among specific areas
of study can remain quite opaque. Here, we apply techniques from network
science to map the landscape of neuroimaging research documented in the journal
NeuroImage over the past decade. We create a network in which nodes represent
research topics, and edges give the degree to which these topics tend to be
covered in tandem. The network displays small-world architecture, with
communities characterized by common imaging modalities and medical
applications, and with bridges that integrate these distinct subfields. Using
node-level analysis, we quantify the structural roles of individual topics
within the neuroimaging landscape, and find high levels of clustering within
the structural MRI subfield as well as increasing participation among topics
related to psychiatry. The overall prevalence of a topic is unrelated to the
prevalence of its neighbors, but the degree to which a topic becomes more or
less popular over time is strongly related to changes in the prevalence of its
neighbors. Broadly, this work presents a cohesive model for understanding the
landscape of neuroimaging research across the field, in broad subfields, and
within specific topic areas.
",1,0,0,1,0,0
18052,To tune or not to tune the number of trees in random forest?,"  The number of trees T in the random forest (RF) algorithm for supervised
learning has to be set by the user. It is controversial whether T should simply
be set to the largest computationally manageable value or whether a smaller T
may in some cases be better. While the principle underlying bagging is that
""more trees are better"", in practice the classification error rate sometimes
reaches a minimum before increasing again for increasing number of trees. The
goal of this paper is four-fold: (i) providing theoretical results showing that
the expected error rate may be a non-monotonous function of the number of trees
and explaining under which circumstances this happens; (ii) providing
theoretical results showing that such non-monotonous patterns cannot be
observed for other performance measures such as the Brier score and the
logarithmic loss (for classification) and the mean squared error (for
regression); (iii) illustrating the extent of the problem through an
application to a large number (n = 306) of datasets from the public database
OpenML; (iv) finally arguing in favor of setting it to a computationally
feasible large number, depending on convergence properties of the desired
performance measure.
",1,0,0,1,0,0
18053,SPH Modeling of Short-crested Waves,"  This study investigates short-crested wave breaking over a planar beach by
using the mesh-free Smoothed Particle Hydrodynamics model, GPUSPH. The
short-crested waves are created by generating intersecting wave trains in a
numerical wave basin. We examine the influence of beach slope, incident wave
height, and incident wave angle on the generated short-crested waves.
Short-crested wave breaking over a steeper beach generates stronger rip
currents, and larger circulation cells in front of the beach. Intersecting wave
trains with a larger incident wave height drive a more complicated
short-crested wave field including isolated breakers and wave amplitude
diffraction. Nearshore circulation induced by short-crested wave breaking is
greatly influenced by the incident wave angle (or the rip current spacing).
There is no secondary circulation cell between the nodal line and the antinodal
line if the rip current spacing is narrow. However, there are multiple
secondary circulation cells observed when the rip current spacing is relatively
large.
",0,1,0,0,0,0
18054,Tunneling Field-Effect Junctions with WS$_2$ barrier,"  Transition metal dichalcogenides (TMDCs), with their two-dimensional
structures and sizable bandgaps, are good candidates for barrier materials in
tunneling field-effect transistor (TFET) formed from atomic precision vertical
stacks of graphene and insulating crystals of a few atomic layers in thickness.
We report first-principles study of the electronic properties of the
Graphene/WS$_2$/Graphene sandwich structure revealing strong interface effects
on dielectric properties and predicting a high ON/OFF ratio with an appropriate
WS$_2$ thickness and a suitable range of the gate voltage. Both the band
spin-orbit coupling splitting and the dielectric constant of the WS$_2$ layer
depend on its thickness when in contact with the graphene electrodes,
indicating strong influence from graphene across the interfaces. The dielectric
constant is significantly reduced from the bulk WS$_2$ value. The effective
barrier height varies with WS$_2$ thickness and can be tuned by a gate voltage.
These results are critical for future nanoelectronic device designs.
",0,1,0,0,0,0
18055,Topological Larkin-Ovchinnikov phase and Majorana zero mode chain in bilayer superconducting topological insulator films,"  We theoretically study bilayer superconducting topological insulator film, in
which superconductivity exists for both top and bottom surface states. We show
that an in-plane magnetic field can drive the system into Larkin-Ovchinnikov
(LO) phase, where electrons are paired with finite momenta. The LO phase is
topologically non-trivial and characterized by a Z 2 topological invariant,
leading to a Majorana zero mode chain along the edge perpendicular to in-plane
magnetic fields.
",0,1,0,0,0,0
18056,Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems,"  In this paper, we consider a three-node cooperative wireless powered
communication system consisting of a multi-antenna hybrid access point (H-AP)
and a single-antenna relay and a single-antenna user. The energy constrained
relay and user first harvest energy in the downlink and then the relay assists
the user using the harvested power for information transmission in the uplink.
The optimal energy beamforming vector and the time split between harvest and
cooperation are investigated. To reduce the computational complexity,
suboptimal designs are also studied, where closed-form expressions are derived
for the energy beamforming vector and the time split. For comparison purposes,
we also present a detailed performance analysis in terms of the achievable
outage probability and the average throughput of an intuitive energy
beamforming scheme, where the H-AP directs all the energy towards the user. The
findings of the paper suggest that implementing multiple antennas at the H-AP
can significantly improve the system performance, and the closed-form
suboptimal energy beamforming vector and time split yields near optimal
performance. Also, for the intuitive beamforming scheme, a diversity order of
(N+1)/2 can be achieved, where N is the number of antennas at the H-AP.
",1,0,0,0,0,0
18057,Asai cube L-functions and the local Langlands conjecture,"  Let $F$ be a non-archimedean locally compact field. We study a class of
Langlands-Shahidi pairs $({\bf H},{\bf L})$, consisting of a quasi-split
connected reductive group $\bf H$ over $F$ and a Levi subgroup $\bf L$ which is
closely related to a product of restriction of scalars of ${\rm GL}_1$'s or
${\rm GL}_2$'s. We prove the compatibility of the resulting local factors with
the Langlands correspondence. In particular, let $E$ be a cubic separable
extension of $F$. We consider a simply connected quasi-split semisimple group
$\bf H$ over $F$ of type $D_4$, with triality corresponding to $E$, and let
$\bf L$ be its Levi subgroup with derived group ${\rm Res}_{E/F} {\rm SL}_2$.
In this way we obtain Asai cube local factors attached to irreducible smooth
representations of ${\rm GL}_2(E)$; we prove that they are Weil-Deligne factors
obtained via the local Langlands correspondence for ${\rm GL}_2(E)$ and tensor
induction from $E$ to $F$. A consequence is that Asai cube $\gamma$- and
$\varepsilon$-factors become stable under twists by highly ramified characters.
",0,0,1,0,0,0
18058,What's in a game? A theory of game models,"  Game semantics is a rich and successful class of denotational models for
programming languages. Most game models feature a rather intuitive setup, yet
surprisingly difficult proofs of such basic results as associativity of
composition of strategies. We set out to unify these models into a basic
abstract framework for game semantics, game settings. Our main contribution is
the generic construction, for any game setting, of a category of games and
strategies. Furthermore, we extend the framework to deal with innocence, and
prove that innocent strategies form a subcategory. We finally show that our
constructions cover many concrete cases, mainly among the early models and the
very recent sheaf-based ones.
",1,0,0,0,0,0
18059,Proposal for the Detection of Magnetic Monopoles in Spin Ice via Nanoscale Magnetometry,"  We present a proposal for applying nanoscale magnetometry to the search for
magnetic monopoles in the spin ice materials holmium and dysprosium titanate.
Employing Monte Carlo simulations of the dipolar spin ice model, we find that
when cooled to below $1.5\,$K these materials exhibit a sufficiently low
monopole density to enable the direct observation of magnetic fields from
individual monopoles. At these temperatures we demonstrate that noise
spectroscopy can capture the intrinsic fluctuations associated with monopole
dynamics, allowing one to isolate the qualitative effects associated with both
the Coulomb interaction between monopoles and the topological constraints
implied by Dirac strings. We describe in detail three different nanoscale
magnetometry platforms (muon spin rotation, nitrogen vacancy defects, and
nanoSQUID arrays) that can be used to detect monopoles in these experiments,
and analyze the advantages of each.
",0,1,0,0,0,0
18060,Rule-Based Spanish Morphological Analyzer Built From Spell Checking Lexicon,"  Preprocessing tools for automated text analysis have become more widely
available in major languages, but non-English tools are often still limited in
their functionality. When working with Spanish-language text, researchers can
easily find tools for tokenization and stemming, but may not have the means to
extract more complex word features like verb tense or mood. Yet Spanish is a
morphologically rich language in which such features are often identifiable
from word form. Conjugation rules are consistent, but many special verbs and
nouns take on different rules. While building a complete dictionary of known
words and their morphological rules would be labor intensive, resources to do
so already exist, in spell checkers designed to generate valid forms of known
words. This paper introduces a set of tools for Spanish-language morphological
analysis, built using the COES spell checking tools, to label person, mood,
tense, gender and number, derive a word's root noun or verb infinitive, and
convert verbs to their nominal form.
",1,0,0,0,0,0
18061,Learning Edge Representations via Low-Rank Asymmetric Projections,"  We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine learning tasks.
Unlike previous work, we (1) explicitly model an edge as a function of node
embeddings, and we (2) propose a novel objective, the ""graph likelihood"", which
contrasts information from sampled random walks with non-existent edges.
Individually, both of these contributions improve the learned representations,
especially when there are memory constraints on the total size of the
embeddings. When combined, our contributions enable us to significantly improve
the state-of-the-art by learning more concise representations that better
preserve the graph structure.
We evaluate our method on a variety of link-prediction task including social
networks, collaboration networks, and protein interactions, showing that our
proposed method learn representations with error reductions of up to 76% and
55%, on directed and undirected graphs. In addition, we show that the
representations learned by our method are quite space efficient, producing
embeddings which have higher structure-preserving accuracy but are 10 times
smaller.
",1,0,0,1,0,0
18062,"Inference, Prediction, and Control of Networked Epidemics","  We develop a feedback control method for networked epidemic spreading
processes. In contrast to most prior works which consider mean field, open-loop
control schemes, the present work develops a novel framework for feedback
control of epidemic processes which leverages incomplete observations of the
stochastic epidemic process in order to control the exact dynamics of the
epidemic outbreak. We develop an observation model for the epidemic process,
and demonstrate that if the set of observed nodes is sufficiently well
structured, then the random variables which denote the process' infections are
conditionally independent given the observations. We then leverage the attained
conditional independence property to construct tractable mechanisms for the
inference and prediction of the process state, avoiding the need to use mean
field approximations or combinatorial representations. We conclude by
formulating a one-step lookahead controller for the discrete-time
Susceptible-Infected-Susceptible (SIS) epidemic process which leverages the
developed Bayesian inference and prediction mechanisms, and causes the epidemic
to die out at a chosen rate.
",0,0,1,0,0,0
18063,The Weisfeiler-Leman algorithm and the diameter of Schreier graphs,"  We prove that the number of iterations taken by the Weisfeiler-Leman
algorithm for configurations coming from Schreier graphs is closely linked to
the diameter of the graphs themselves: an upper bound is found for general
Schreier graphs, and a lower bound holds for particular cases, such as for
Schreier graphs with $G=\mbox{SL}_{n}({\mathbb F}_{q})$ ($q>2$) acting on
$k$-tuples of vectors in ${\mathbb F}_{q}^{n}$; moreover, an exact expression
is found in the case of Cayley graphs.
",0,0,1,0,0,0
18064,Correlations between primes in short intervals on curves over finite fields,"  We prove an analogue of the Hardy-Littlewood conjecture on the asymptotic
distribution of prime constellations in the setting of short intervals in
function fields of smooth projective curves over finite fields.
",0,0,1,0,0,0
18065,Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions,"  Besides enabling an enhanced mobile broadband, next generation of mobile
networks (5G) are envisioned for the support of massive connectivity of
heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
number of use-cases including smart cities, environment monitoring, smart
vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
capabilities and need cloud services. Hence, connecting these devices through
5G systems requires huge spectrum resources in addition to handling the massive
connectivity and improved security. This article discusses the challenges
facing the support of IoTs through 5G systems. The focus is devoted to
discussing physical layer limitations in terms of spectrum resources and radio
access channel connectivity. We show how sparsity can be exploited for
addressing these challenges especially in terms of enabling wideband spectrum
management and handling the connectivity by exploiting device-to-device
communications and edge-cloud. Moreover, we identify major open problems and
research directions that need to be explored towards enabling the support of
massive heterogeneous IoTs through 5G systems.
",1,0,0,0,0,0
18066,Existence and convexity of local solutions to degenerate hessian equations,"  In this work, we prove the existence of local convex solution to the
degenerate Hessian equation
",0,0,1,0,0,0
18067,Superexponential estimates and weighted lower bounds for the square function,"  We prove the following superexponential distribution inequality: for any
integrable $g$ on $[0,1)^{d}$ with zero average, and any $\lambda>0$ \[ |\{ x
\in [0,1)^{d} \; :\; g \geq\lambda \}| \leq e^{-
\lambda^{2}/(2^{d}\|S(g)\|_{\infty}^{2})}, \] where $S(g)$ denotes the
classical dyadic square function in $[0,1)^{d}$. The estimate is sharp when
dimension $d$ tends to infinity in the sense that the constant $2^{d}$ in the
denominator cannot be replaced by $C2^{d}$ with $0<C<1$ independent of $d$ when
$d \to \infty$.
For $d=1$ this is a classical result of Chang--Wilson--Wolff [4]; however, in
the case $d>1$ they work with a special square function $S_\infty$, and their
result does not imply the estimates for the classical square function.
Using good $\lambda$ inequalities technique we then obtain unweighted and
weighted $L^p$ lower bounds for $S$; to get the corresponding good $\lambda$
inequalities we need to modify the classical construction.
We also show how to obtain our superexponential distribution inequality
(although with worse constants) from the weighted $L^2$ lower bounds for $S$,
obtained in [5].
",0,0,1,0,0,0
18068,Reframing the S\&P500 Network of Stocks along the \nth{21} Century,"  Since the beginning of the new millennium, stock markets went through every
state from long-time troughs, trade suspensions to all-time highs. The
literature on asset pricing hence assumes random processes to be underlying the
movement of stock returns. Observed procyclicality and time-varying correlation
of stock returns tried to give the apparently random behavior some sort of
structure. However, common misperceptions about the co-movement of asset prices
in the years preceding the \emph{Great Recession} and the \emph{Global
Commodity Crisis}, is said to have even fueled the crisis' economic impact.
Here we show how a varying macroeconomic environment influences stocks'
clustering into communities. From a sample of 296 stocks of the S\&P 500 index,
distinct periods in between 2004 and 2011 are used to develop networks of
stocks. The Minimal Spanning Tree analysis of those time-varying networks of
stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market
to clustered community structures in both periods, helping to restore the stock
market's ceased order of the pre-crises era. However, a comparison of the
emergent clusters with the \textit{General Industry Classification Standard}
conveys the impression that industry sectors do not play a major role in that
order.
",0,0,0,0,0,1
18069,Point-contact spectroscopy of superconducting energy gap in $\rm DyNi_2B_2C$,"  The superconducting energy gap in $\rm DyNi_2B_2C$ has been investigated
using a point-contact technique based on the Andreev reflection from a normal
(N)-superconductor (S) boundary, where N is Ag. The observed differential
resistance $dV/dI$ is well described by the Blonder-Tinkham-Klapwijk (BTK)
theory based on the BSC density of states with zero broadening parameter.
Typically, the intensity of the gap structure amounts to several percentage of
the normal state resistance, which is an order of magnitude less than predicted
by the theory. For $\rm DyNi_2B_2C$ with $T_c<T_N$ (the Neel temperature), we
found gap values satisfying the ratio of $2\Delta_0/k_BT_c=3.63\pm 0.05$
similar to other superconducting nickel-borocarbides, both nonmagnetic and
magnetic with $T_c\geq T_N$. The superconducting gap nonlinearity is
superimposed on the antiferromagnetic structure in $dV/dI(V)$ which is
suppressed at the magnetic field of the order of 3T applied nominally in the
$ab$-plane and temperature $\geq 11~K$. The observed superconducting properties
depend on the exact composition and structure at the surface of the crystal.
",0,1,0,0,0,0
18070,Parameterized Complexity of Safe Set,"  In this paper we study the problem of finding a small safe set $S$ in a graph
$G$, i.e. a non-empty set of vertices such that no connected component of
$G[S]$ is adjacent to a larger component in $G - S$. We enhance our
understanding of the problem from the viewpoint of parameterized complexity by
showing that (1) the problem is W[2]-hard when parameterized by the pathwidth
$pw$ and cannot be solved in time $n^{o(pw)}$ unless the ETH is false, (2) it
admits no polynomial kernel parameterized by the vertex cover number $vc$
unless $\mathrm{PH} = \Sigma^{\mathrm{p}}_{3}$, but (3) it is fixed-parameter
tractable (FPT) when parameterized by the neighborhood diversity $nd$, and (4)
it can be solved in time $n^{f(cw)}$ for some double exponential function $f$
where $cw$ is the clique-width. We also present (5) a faster FPT algorithm when
parameterized by solution size.
",1,0,0,0,0,0
18071,A Matrix Variate Skew-t Distribution,"  Although there is ample work in the literature dealing with skewness in the
multivariate setting, there is a relative paucity of work in the matrix variate
paradigm. Such work is, for example, useful for modelling three-way data. A
matrix variate skew-t distribution is derived based on a mean-variance matrix
normal mixture. An expectation-conditional maximization algorithm is developed
for parameter estimation. Simulated data are used for illustration.
",0,0,1,1,0,0
18072,Status Updates Through Multicast Networks,"  Using age of information as the freshness metric, we examine a multicast
network in which real-time status updates are generated by the source and sent
to a group of $n$ interested receivers. We show that in order to keep the
information freshness at each receiver, the source should terminate the
transmission of the current update and start sending a new update packet as
soon as it receives the acknowledgements back from any $k$ out of $n$ nodes. As
the source stopping threshold $k$ increases, a node is more likely to get the
latest generated update, but the age of the most recent update is more likely
to become outdated. We derive the age minimized stopping threshold $k$ that
balances the likelihood of getting the latest update and the freshness of the
latest update for shifted exponential link delay. Through numerical evaluations
for different stopping strategies, we find that waiting for the
acknowledgements from the earliest $k$ out of $n$ nodes leads to lower average
age than waiting for a pre-selected group of $k$ nodes. We also observe that a
properly chosen threshold $k$ can prevent information staleness for increasing
number of nodes $n$ in the multicast network.
",1,0,0,0,0,0
18073,Extension of Convolutional Neural Network with General Image Processing Kernels,"  We applied pre-defined kernels also known as filters or masks developed for
image processing to convolution neural network. Instead of letting neural
networks find its own kernels, we used 41 different general-purpose kernels of
blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
the first layer of the convolution neural networks. This architecture, thus
named as general filter convolutional neural network (GFNN), can reduce
training time by 30% with a better accuracy compared to the regular
convolutional neural network (CNN). GFNN also can be trained to achieve 90%
accuracy with only 500 samples. Furthermore, even though these kernels are not
specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
nor any other special algorithms.
",1,0,0,1,0,0
18074,Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network,"  This paper proposes a neural network architecture and training scheme to
learn the start and end time of sound events (strong labels) in an audio
recording given just the list of sound events existing in the audio without
time information (weak labels). We achieve this by using a stacked
convolutional and recurrent neural network with two prediction layers in
sequence one for the strong followed by the weak label. The network is trained
using frame-wise log mel-band energy as the input audio feature, and weak
labels provided in the dataset as labels for the weak label prediction layer.
Strong labels are generated by replicating the weak labels as many number of
times as the frames in the input audio feature, and used for strong label layer
during training. We propose to control what the network learns from the weak
and strong labels by different weighting for the loss computed in the two
prediction layers. The proposed method is evaluated on a publicly available
dataset of 155 hours with 17 sound event classes. The method achieves the best
error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
the unseen test split.
",1,0,0,0,0,0
18075,Categoricity and Universal Classes,"  Let $(\mathcal{K} ,\subseteq )$ be a universal class with
$LS(\mathcal{K})=\lambda$ categorical in regular $\kappa >\lambda^+$ with
arbitrarily large models, and let $\mathcal{K}^*$ be the class of all
$\mathcal{A}\in\mathcal{K}_{>\lambda}$ for which there is $\mathcal{B} \in
\mathcal{K}_{\ge\kappa}$ such that $\mathcal{A}\subseteq\mathcal{B}$. We prove
that $\mathcal{K}^*$ is categorical in every $\xi >\lambda^+$,
$\mathcal{K}_{\ge\beth_{(2^{\lambda^+})^+}} \subseteq \mathcal{K}^{*}$, and the
models of $\mathcal{K}^*_{>\lambda^+}$ are essentially vector spaces (or
trivial i.e. disintegrated).
",0,0,1,0,0,0
18076,"Water, High-Altitude Condensates, and Possible Methane Depletion in the Atmosphere of the Warm Super-Neptune WASP-107b","  The super-Neptune exoplanet WASP-107b is an exciting target for atmosphere
characterization. It has an unusually large atmospheric scale height and a
small, bright host star, raising the possibility of precise constraints on its
current nature and formation history. We report the first atmospheric study of
WASP-107b, a Hubble Space Telescope measurement of its near-infrared
transmission spectrum. We determined the planet's composition with two
techniques: atmospheric retrieval based on the transmission spectrum and
interior structure modeling based on the observed mass and radius. The interior
structure models set a $3\,\sigma$ upper limit on the atmospheric metallicity
of $30\times$ solar. The transmission spectrum shows strong evidence for water
absorption ($6.5\,\sigma$ confidence), and the retrieved water abundance is
consistent with expectations for a solar abundance pattern. The inferred
carbon-to-oxygen ratio is subsolar at $2.7\,\sigma$ confidence, which we
attribute to possible methane depletion in the atmosphere. The spectral
features are smaller than predicted for a cloud-free composition, crossing less
than one scale height. A thick condensate layer at high altitudes (0.1 - 3
mbar) is needed to match the observations. We find that physically motivated
cloud models with moderate sedimentation efficiency ($f_\mathrm{sed} = 0.3$) or
hazes with a particle size of 0.3 $\mu$m reproduce the observed spectral
feature amplitude. Taken together, these findings serve as an illustration of
the diversity and complexity of exoplanet atmospheres. The community can look
forward to more such results with the high precision and wide spectral coverage
afforded by future observing facilities.
",0,1,0,0,0,0
18077,Partial Order on the set of Boolean Regulatory Functions,"  Logical models have been successfully used to describe regulatory and
signaling networks without requiring quantitative data. However, existing data
is insufficient to adequately define a unique model, rendering the
parametrization of a given model a difficult task.
Here, we focus on the characterization of the set of Boolean functions
compatible with a given regulatory structure, i.e. the set of all monotone
nondegenerate Boolean functions. We then propose an original set of rules to
locally explore the direct neighboring functions of any function in this set,
without explicitly generating the whole set. Also, we provide relationships
between the regulatory functions and their corresponding dynamics.
Finally, we illustrate the usefulness of this approach by revisiting
Probabilistic Boolean Networks with the model of T helper cell differentiation
from Mendoza & Xenarios.
",1,0,0,0,1,0
18078,Scalable solvers for complex electromagnetics problems,"  In this work, we present scalable balancing domain decomposition by
constraints methods for linear systems arising from arbitrary order edge finite
element discretizations of multi-material and heterogeneous 3D problems. In
order to enforce the continuity across subdomains of the method, we use a
partition of the interface objects (edges and faces) into sub-objects
determined by the variation of the physical coefficients of the problem. For
multi-material problems, a constant coefficient condition is enough to define
this sub-partition of the objects. For arbitrarily heterogeneous problems, a
relaxed version of the method is defined, where we only require that the
maximal contrast of the physical coefficient in each object is smaller than a
predefined threshold. Besides, the addition of perturbation terms to the
preconditioner is empirically shown to be effective in order to deal with the
case where the two coefficients of the model problem jump simultaneously across
the interface. The new method, in contrast to existing approaches for problems
in curl-conforming spaces, preserves the simplicity of the original
preconditioner, i.e., no spectral information is required, whilst providing
robustness with regard to coefficient jumps and heterogeneous materials. A
detailed set of numerical experiments, which includes the application of the
preconditioner to 3D realistic cases, shows excellent weak scalability
properties of the implementation of the proposed algorithms.
",1,0,0,0,0,0
18079,A flux-splitting method for hyperbolic-equation system of magnetized electron fluids in quasi-neutral plasmas,"  A flux-splitting method is proposed for the hyperbolic-equation system (HES)
of magnetized electron fluids in quasi-neutral plasmas. The numerical fluxes
are split into four categories, which are computed by using an upwind method
which incorporates a flux-vector splitting (FVS) and advection upstream
splitting method (AUSM). The method is applied to a test calculation condition
of uniformly distributed and angled magnetic lines of force. All of the
pseudo-time advancement terms converge monotonically and the conservation laws
are strictly satisfied in the steady state. The calculation results are
compared with those computed by using the elliptic-parabolic-equation system
(EPES) approach using a magnetic-field-aligned mesh (MFAM). Both qualitative
and quantitative comparisons yield good agreements of results, indicating that
the HES approach with the flux-splitting method attains a high computational
accuracy.
",0,1,0,0,0,0
18080,TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation,"  We address unsupervised optical flow estimation for ego-centric motion. We
argue that optical flow can be cast as a geometrical warping between two
successive video frames and devise a deep architecture to estimate such
transformation in two stages. First, a dense pixel-level flow is computed with
a geometric prior imposing strong spatial constraints. Such prior is typical of
driving scenes, where the point of view is coherent with the vehicle motion. We
show how such global transformation can be approximated with an homography and
how spatial transformer layers can be employed to compute the flow field
implied by such transformation. The second stage then refines the prediction
feeding a second deeper network. A final reconstruction loss compares the
warping of frame X(t) with the subsequent frame X(t+1) and guides both
estimates. The model, which we named TransFlow, performs favorably compared to
other unsupervised algorithms, and shows better generalization compared to
supervised methods with a 3x reduction in error on unseen data.
",1,0,0,0,0,0
18081,A General Pipeline for 3D Detection of Vehicles,"  Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D detection networks. To identify the 3D box, an effective model
fitting algorithm is developed based on generalised car models and score maps.
A two-stage convolutional neural network (CNN) is proposed to refine the
detected 3D box. This pipeline is tested on the KITTI dataset using two
different 2D detection networks. The 3D detection results based on these two
networks are similar, demonstrating the flexibility of the proposed pipeline.
The results rank second among the 3D detection algorithms, indicating its
competencies in 3D detection.
",0,0,0,1,0,0
18082,Search for CII Emission on Cosmological Scales at Redshift Z~2.6,"  We present a search for CII emission over cosmological scales at
high-redshifts. The CII line is a prime candidate to be a tracer of star
formation over large-scale structure since it is one of the brightest emission
lines from galaxies. Redshifted CII emission appears in the submillimeter
regime, meaning it could potentially be present in the higher frequency
intensity data from the Planck satellite used to measure the cosmic infrared
background (CIB). We search for CII emission over redshifts z=2-3.2 in the
Planck 545 GHz intensity map by cross-correlating the 3 highest frequency
Planck maps with spectroscopic quasars and CMASS galaxies from the Sloan
Digital Sky Survey III (SDSS-III), which we then use to jointly fit for CII
intensity, CIB parameters, and thermal Sunyaev-Zeldovich (SZ) emission. We
report a measurement of an anomalous emission
$\mathrm{I_\nu}=6.6^{+5.0}_{-4.8}\times10^4$ Jy/sr at 95% confidence, which
could be explained by CII emission, favoring collisional excitation models of
CII emission that tend to be more optimistic than models based on CII
luminosity scaling relations from local measurements; however, a comparison of
Bayesian information criteria reveal that this model and the CIB & SZ only
model are equally plausible. Thus, more sensitive measurements will be needed
to confirm the existence of large-scale CII emission at high redshifts.
Finally, we forecast that intensity maps from Planck cross-correlated with
quasars from the Dark Energy Spectroscopic Instrument (DESI) would increase our
sensitivity to CII emission by a factor of 5, while the proposed Primordial
Inflation Explorer (PIXIE) could increase the sensitivity further.
",0,1,0,0,0,0
18083,Ricci flow and diffeomorphism groups of 3-manifolds,"  We complete the proof of the Generalized Smale Conjecture, apart from the
case of $RP^3$, and give a new proof of Gabai's theorem for hyperbolic
3-manifolds. We use an approach based on Ricci flow through singularities,
which applies uniformly to spherical space forms other than $S^3$ and $RP^3$
and hyperbolic manifolds, to prove that the moduli space of metrics of constant
sectional curvature is contractible. As a corollary, for such a 3-manifold $X$,
the inclusion $\text{Isom} (X,g)\to \text{Diff}(X)$ is a homotopy equivalence
for any Riemannian metric $g$ of constant sectional curvature.
",0,0,1,0,0,0
18084,RLlib: Abstractions for Distributed Reinforcement Learning,"  Reinforcement learning (RL) algorithms involve the deep nesting of highly
irregular computation patterns, each of which typically exhibits opportunities
for distributed computation. We argue for distributing RL components in a
composable way by adapting algorithms for top-down hierarchical control,
thereby encapsulating parallelism and resource requirements within
short-running compute tasks. We demonstrate the benefits of this principle
through RLlib: a library that provides scalable software primitives for RL.
These primitives enable a broad range of algorithms to be implemented with high
performance, scalability, and substantial code reuse. RLlib is available at
this https URL.
",1,0,0,0,0,0
18085,A Multiscale-Analysis of Stochastic Bistable Reaction-Diffusion Equations,"  A multiscale analysis of 1D stochastic bistable reaction-diffusion equations
with additive noise is carried out w.r.t. travelling waves within the
variational approach to stochastic partial differential equations. It is shown
with explicit error estimates on appropriate function spaces that up to lower
order w.r.t. the noise amplitude, the solution can be decomposed into the
orthogonal sum of a travelling wave moving with random speed and into Gaussian
fluctuations. A stochastic differential equation describing the speed of the
travelling wave and a linear stochastic partial differential equation
describing the fluctuations are derived in terms of the coefficients. Our
results extend corresponding results obtained for stochastic neural field
equations to the present class of stochastic dynamics.
",0,0,1,0,0,0
18086,MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis,"  Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.
",1,0,0,1,0,0
18087,SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability,"  We propose a new technique, Singular Vector Canonical Correlation Analysis
(SVCCA), a tool for quickly comparing two representations in a way that is both
invariant to affine transform (allowing comparison between different layers and
networks) and fast to compute (allowing more comparisons to be calculated than
with previous methods). We deploy this tool to measure the intrinsic
dimensionality of layers, showing in some cases needless over-parameterization;
to probe learning dynamics throughout training, finding that networks converge
to final representations from the bottom up; to show where class-specific
information in networks is formed; and to suggest new training regimes that
simultaneously save computation and overfit less. Code:
this https URL
",1,0,0,1,0,0
18088,Discrete Choice and Rational Inattention: a General Equivalence Result,"  This paper establishes a general equivalence between discrete choice and
rational inattention models. Matejka and McKay (2015, AER) showed that when
information costs are modelled using the Shannon entropy function, the
resulting choice probabilities in the rational inattention model take the
multinomial logit form. By exploiting convex-analytic properties of the
discrete choice model, we show that when information costs are modelled using a
class of generalized entropy functions, the choice probabilities in any
rational inattention model are observationally equivalent to some additive
random utility discrete choice model and vice versa. Thus any additive random
utility model can be given an interpretation in terms of boundedly rational
behavior. This includes empirically relevant specifications such as the probit
and nested logit models.
",0,0,0,1,0,0
18089,A Hardy inequality for ultraspherical expansions with an application to the sphere,"  We prove a Hardy inequality for ultraspherical expansions by using a proper
ground state representation. From this result we deduce some uncertainty
principles for this kind of expansions. Our result also implies a Hardy
inequality on spheres with a potential having a double singularity.
",0,0,1,0,0,0
18090,Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors,"  Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
Recent work has proposed the use of a horseshoe prior over node pre-activations
of a Bayesian neural network, which effectively turns off nodes that do not
help explain the data. In this work, we propose several modeling and inference
advances that consistently improve the compactness of the model learned while
maintaining predictive performance, especially in smaller-sample settings
including reinforcement learning.
",0,0,0,1,0,0
18091,Efficient Reinforcement Learning via Initial Pure Exploration,"  In several realistic situations, an interactive learning agent can practice
and refine its strategy before going on to be evaluated. For instance, consider
a student preparing for a series of tests. She would typically take a few
practice tests to know which areas she needs to improve upon. Based of the
scores she obtains in these practice tests, she would formulate a strategy for
maximizing her scores in the actual tests. We treat this scenario in the
context of an agent exploring a fixed-horizon episodic Markov Decision Process
(MDP), where the agent can practice on the MDP for some number of episodes (not
necessarily known in advance) before starting to incur regret for its actions.
During practice, the agent's goal must be to maximize the probability of
following an optimal policy. This is akin to the problem of Pure Exploration
(PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose
a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE),
which is similar to its bandit counterpart. We show that the Bayesian simple
regret converges at an optimal exponential rate when using PSPE.
When the agent starts being evaluated, its goal would be to minimize the
cumulative regret incurred. This is akin to the problem of Reinforcement
Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning
algorithm (PSRL) initialized with the posteriors of the practice phase. We
hypothesize that this PSPE + PSRL combination is an optimal strategy for
minimizing regret in RL problems with an initial practice phase. We show
empirical results which prove that having a lower simple regret at the end of
the practice phase results in having lower cumulative regret during evaluation.
",1,0,0,1,0,0
18092,Stochastic Evolution of Augmented Born--Infeld Equations,"  This paper compares the results of applying a recently developed method of
stochastic uncertainty quantification designed for fluid dynamics to the
Born-Infeld model of nonlinear electromagnetism. The similarities in the
results are striking. Namely, the introduction of Stratonovich cylindrical
noise into each of their Hamiltonian formulations introduces stochastic Lie
transport into their dynamics in the same form for both theories. Moreover, the
resulting stochastic partial differential equations (SPDE) retain their
unperturbed form, except for an additional term representing induced Lie
transport by the set of divergence-free vector fields associated with the
spatial correlations of the cylindrical noise. The explanation for this
remarkable similarity lies in the method of construction of the Hamiltonian for
the Stratonovich stochastic contribution to the motion in both cases; which is
done via pairing spatial correlation eigenvectors for cylindrical noise with
the momentum map for the deterministic motion. This momentum map is responsible
for the well-known analogy between hydrodynamics and electromagnetism. The
momentum map for the Maxwell and Born-Infeld theories of electromagnetism
treated here is the 1-form density known as the Poynting vector. Two Appendices
treat the Hamiltonian structures underlying these results.
",0,1,1,0,0,0
18093,T-matrix evaluation of acoustic radiation forces on nonspherical objects in Bessel beams,"  Acoustical radiation force (ARF) induced by a single Bessel beam with
arbitrary order and location on a nonspherical shape is studied with the
emphasis on the physical mechanism and parameter conditions of negative
(pulling) forces. Numerical experiments are conducted to verify the T-matrix
method (TMM) for axial ARFs. This study may guide the experimental set-up to
find negative axial ARF quickly and effectively based on the predicted
parameters with TMM, and could be extended for lateral forces. The present work
could help to design acoustic tweezers numerical toolbox, which provides an
alternate to the optic tweezers.
",0,1,0,0,0,0
18094,On the commutative center of Moufang loops,"  We construct two infinite series of Moufang loops of exponent $3$ whose
commutative center (i.e. the set of elements that commute with all elements of
the loop) is not a normal subloop. In particular, we obtain examples of such
loops of orders $3^8$ and $3^{11}$ one of which can be defined as the Moufang
triplication of the free Burnside group $B(3,3)$.
",0,0,1,0,0,0
18095,The Kontsevich tetrahedral flow in 2D: a toy model,"  In the paper ""Formality conjecture"" (1996) Kontsevich designed a universal
flow $\dot{\mathcal{P}}=\mathcal{Q}_{a:b}(\mathcal{P})=a\Gamma_{1}+b\Gamma_{2}$
on the spaces of Poisson structures $\mathcal{P}$ on all affine manifolds of
dimension $n \geqslant 2$. We prove a claim from $\textit{loc. cit.}$ stating
that if $n=2$, the flow $\mathcal{Q}_{1:0}=\Gamma_{1}(\mathcal{P})$ is
Poisson-cohomology trivial: $\Gamma_{1}(\mathcal{P})$ is the Schouten bracket
of $\mathcal{P}$ with $\mathcal{X}$, for some vector field $\mathcal{X}$; we
examine the structure of the space of solutions $\mathcal{X}$. Both the
construction of differential polynomials $\Gamma_{1}(\mathcal{P})$ and
$\Gamma_{2}(\mathcal{P})$ and the technique to study them remain valid in
higher dimensions $n \geqslant 3$, but neither the trivializing vector field
$\mathcal{X}$ nor the setting $b:=0$ survive at $n\geqslant 3$, where the
balance is $a:b=1:6$.
",0,0,1,0,0,0
18096,Twin Learning for Similarity and Clustering: A Unified Kernel Approach,"  Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise and outliers. Thus the learned similarity matrix is often not
suitable, let alone optimal, for the subsequent clustering. In addition,
nonlinear similarity often exists in many real world data which, however, has
not been effectively considered by most existing methods. To tackle these two
challenges, we propose a model to simultaneously learn cluster indicator matrix
and similarity information in kernel spaces in a principled way. We show
theoretical relationships to kernel k-means, k-means, and spectral clustering
methods. Then, to address the practical issue of how to select the most
suitable kernel for a particular clustering task, we further extend our model
with a multiple kernel learning ability. With this joint model, we can
automatically accomplish three subtasks of finding the best cluster indicator
matrix, the most accurate similarity relations and the optimal combination of
multiple kernels. By leveraging the interactions between these three subtasks
in a joint framework, each subtask can be iteratively boosted by using the
results of the others towards an overall optimal solution. Extensive
experiments are performed to demonstrate the effectiveness of our method.
",1,0,0,1,0,0
18097,Big Data Analysis Using Shrinkage Strategies,"  In this paper, we apply shrinkage strategies to estimate regression
coefficients efficiently for the high-dimensional multiple regression model,
where the number of samples is smaller than the number of predictors. We assume
in the sparse linear model some of the predictors have very weak influence on
the response of interest. We propose to shrink estimators more than usual.
Specifically, we use integrated estimation strategies in sub and full models
and shrink the integrated estimators by incorporating a bounded measurable
function of some weights. The exhibited double shrunken estimators improve the
prediction performance of sub models significantly selected from existing
Lasso-type variable selection methods. Monte Carlo simulation studies as well
as real examples of eye data and Riboavin data confirm the superior performance
of the estimators in the high-dimensional regression model.
",0,0,0,1,0,0
18098,Interpreter fr topologists,"  Let M be a transitive model of set theory. There is a canonical
interpretation functor between the category of regular Hausdorff, continuous
open images of Cech-complete spaces of M and the same category in V, preserving
many concepts of topology, functional analysis, and dynamics. The functor can
be further canonically extended to the category of Borel subspaces. This
greatly simplifies and extends similar results of Fremlin.
",0,0,1,0,0,0
18099,Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks,"  We propose a parallel-data-free voice-conversion (VC) method that can learn a
mapping from source to target speech without relying on parallel data. The
proposed method is general purpose, high quality, and parallel-data free and
works without any extra data, modules, or alignment procedure. It also avoids
over-smoothing, which occurs in many conventional statistical model-based VC
methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial
network (CycleGAN) with gated convolutional neural networks (CNNs) and an
identity-mapping loss. A CycleGAN learns forward and inverse mappings
simultaneously using adversarial and cycle-consistency losses. This makes it
possible to find an optimal pseudo pair from unpaired data. Furthermore, the
adversarial loss contributes to reducing over-smoothing of the converted
feature sequence. We configure a CycleGAN with gated CNNs and train it with an
identity-mapping loss. This allows the mapping function to capture sequential
and hierarchical structures while preserving linguistic information. We
evaluated our method on a parallel-data-free VC task. An objective evaluation
showed that the converted feature sequence was near natural in terms of global
variance and modulation spectra. A subjective evaluation showed that the
quality of the converted speech was comparable to that obtained with a Gaussian
mixture model-based method under advantageous conditions with parallel and
twice the amount of data.
",1,0,0,1,0,0
18100,Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA,"  Least-squares models such as linear regression and Linear Discriminant
Analysis (LDA) are amongst the most popular statistical learning techniques.
However, since their computation time increases cubically with the number of
features, they are inefficient in high-dimensional neuroimaging datasets.
Fortunately, for k-fold cross-validation, an analytical approach has been
developed that yields the exact cross-validated predictions in least-squares
models without explicitly training the model. Its computation time grows with
the number of test samples. Here, this approach is systematically investigated
in the context of cross-validation and permutation testing. LDA is used
exemplarily but results hold for all other least-squares methods. Furthermore,
a non-trivial extension to multi-class LDA is formally derived. The analytical
approach is evaluated using complexity calculations, simulations, and
permutation testing of an EEG/MEG dataset. Depending on the ratio between
features and samples, the analytical approach is up to 10,000x faster than the
standard approach (retraining the model on each training set). This allows for
a fast cross-validation of least-squares models and multi-class LDA in
high-dimensional data, with obvious applications in multi-dimensional datasets,
Representational Similarity Analysis, and permutation testing.
",0,0,0,1,0,0
18101,"A new, large-scale map of interstellar reddening derived from HI emission","  We present a new map of interstellar reddening, covering the 39\% of the sky
with low {\rm HI} column densities ($N_{\rm HI} < 4\times10^{20}\,\rm cm^{-2}$
or $E(B-V)\approx 45\rm\, mmag$) at $16\overset{'}{.}1$ resolution, based on
all-sky observations of Galactic HI emission by the HI4PI Survey. In this low
column density regime, we derive a characteristic value of $N_{\rm HI}/E(B-V) =
8.8\times10^{21}\, \rm\, cm^{2}\, mag^{-1}$ for gas with $|v_{\rm LSR}| <
90\,\rm km\, s^{-1}$ and find no significant reddening associated with gas at
higher velocities. We compare our HI-based reddening map with the Schlegel,
Finkbeiner, and Davis (1998, SFD) reddening map and find them consistent to
within a scatter of $\simeq 5\,\rm mmag$. Further, the differences between our
map and the SFD map are in excellent agreement with the low resolution
($4\overset{\circ}{.}5$) corrections to the SFD map derived by Peek and Graves
(2010) based on observed reddening toward passive galaxies. We therefore argue
that our HI-based map provides the most accurate interstellar reddening
estimates in the low column density regime to date. Our reddening map is made
publicly available (this http URL).
",0,1,0,0,0,0
18102,Seasonal modulation of seismicity: the competing/collaborative effect of the snow and ice load on the lithosphere,"  Seasonal patterns associated with stress modulation, as evidenced by
earthquake occurrence, have been detected in regions characterized by present
day mountain building and glacial retreat in the Northern Hemisphere. In the
Himalaya and the Alps, seismicity is peaking in spring and summer; opposite
behaviour is observed in the Apennines. This diametrical behaviour, confirmed
by recent strong earthquakes, well correlates with the dominant tectonic
regime: peak in spring and summer in shortening areas, peak in fall and winter
in extensional areas. The analysis of the seasonal effect is extended to
several shortening (e.g. Zagros and Caucasus) and extensional regions, and
counter-examples from regions where no seasonal modulation is expected (e.g.
Tropical Atlantic Ridge). This study generalizes to different seismotectonic
settings the early observations made about short-term (seasonal) and long-term
(secular) modulation of seismicity and confirms, with some statistical
significance, that snow and ice thaw may cause crustal deformations that
modulate the occurrence of major earthquakes.
",0,1,0,0,0,0
18103,Existence and nonexistence of positive solutions to some fully nonlinear equation in one dimension,"  In this paper, we consider the existence (and nonexistence) of solutions to
\[
-\mathcal{M}_{\lambda,\Lambda}^\pm (u'') + V(x) u = f(u) \quad {\rm in} \
\mathbf{R}
\] where $\mathcal{M}_{\lambda,\Lambda}^+$ and
$\mathcal{M}_{\lambda,\Lambda}^-$ denote the Pucci operators with $0< \lambda
\leq \Lambda < \infty$, $V(x)$ is a bounded function, $f(s)$ is a continuous
function and its typical example is a power-type nonlinearity $f(s)
=|s|^{p-1}s$ $(p>1)$. In particular, we are interested in positive solutions
which decay at infinity, and the existence (and nonexistence) of such solutions
is proved.
",0,0,1,0,0,0
18104,Shiba Bound States across the mobility edge in doped InAs nanowires,"  We present a study of Andreev Quantum Dots (QDots) fabricated with
small-diameter (30 nm) Si-doped InAs nanowires where the Fermi level can be
tuned across a mobility edge separating localized states from delocalized
states. The transition to the insulating phase is identified by a drop in the
amplitude and width of the excited levels and is found to have remarkable
consequences on the spectrum of superconducting SubGap Resonances (SGRs). While
at deeply localized levels, only quasiparticles co-tunneling is observed, for
slightly delocalized levels, Shiba bound states form and a parity changing
quantum phase transition is identified by a crossing of the bound states at
zero energy. Finally, in the metallic regime, single Andreev resonances are
observed.
",0,1,0,0,0,0
18105,The Helsinki Neural Machine Translation System,"  We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
is applied in the news translation task at WMT 2017, where it ranked first in
both the human and automatic evaluations for English--Finnish. We discuss the
success of English--Finnish translations and the overall advantage of NMT over
a strong SMT baseline. We also discuss our submissions for English--Latvian,
English--Chinese and Chinese--English.
",1,0,0,0,0,0
18106,Spatial distribution of nuclei in progressive nucleation: modeling and application,"  Phase transformations ruled by non-simultaneous nucleation and growth do not
lead to random distribution of nuclei. Since nucleation is only allowed in the
untransformed portion of space, positions of nuclei are correlated. In this
article an analytical approach is presented for computing pair-correlation
function of nuclei in progressive nucleation. This quantity is further employed
for characterizing the spatial distribution of nuclei through the nearest
neighbor distribution function. The modeling is developed for nucleation in 2D
space with power growth law and it is applied to describe electrochemical
nucleation where correlation effects are significant. Comparison with both
computer simulations and experimental data lends support to the model which
gives insights into the transition from Poissonian to correlated nearest
neighbor probability density.
",0,1,0,0,0,0
18107,Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements,"  This work considers resilient, cooperative state estimation in unreliable
multi-agent networks. A network of agents aims to collaboratively estimate the
value of an unknown vector parameter, while an {\em unknown} subset of agents
suffer Byzantine faults. Faulty agents malfunction arbitrarily and may send out
{\em highly unstructured} messages to other agents in the network. As opposed
to fault-free networks, reaching agreement in the presence of Byzantine faults
is far from trivial. In this paper, we propose a computationally-efficient
algorithm that is provably robust to Byzantine faults. At each iteration of the
algorithm, a good agent (1) performs a gradient descent update based on noisy
local measurements, (2) exchanges its update with other agents in its
neighborhood, and (3) robustly aggregates the received messages using
coordinate-wise trimmed means. Under mild technical assumptions, we establish
that good agents learn the true parameter asymptotically in almost sure sense.
We further complement our analysis by proving (high probability) {\em
finite-time} convergence rate, encapsulating network characteristics.
",1,0,0,0,0,0
18108,Constraining Polarized Foregrounds for EOR Experiments II: Polarization Leakage Simulations in the Avoidance Scheme,"  A critical challenge in the observation of the redshifted 21-cm line is its
separation from bright Galactic and extragalactic foregrounds. In particular,
the instrumental leakage of polarized foregrounds, which undergo significant
Faraday rotation as they propagate through the interstellar medium, may
harmfully contaminate the 21-cm power spectrum. We develop a formalism to
describe the leakage due to instrumental widefield effects in visibility-based
power spectra measured with redundant arrays, extending the delay-spectrum
approach presented in Parsons et al. (2012). We construct polarized sky models
and propagate them through the instrument model to simulate realistic full-sky
observations with the Precision Array to Probe the Epoch of Reionization. We
find that the leakage due to a population of polarized point sources is
expected to be higher than diffuse Galactic polarization at any $k$ mode for a
30~m reference baseline. For the same reference baseline, a foreground-free
window at $k > 0.3 \, h$~Mpc$^{-1}$ can be defined in terms of leakage from
diffuse Galactic polarization even under the most pessimistic assumptions. If
measurements of polarized foreground power spectra or a model of polarized
foregrounds are given, our method is able to predict the polarization leakage
in actual 21-cm observations, potentially enabling its statistical subtraction
from the measured 21-cm power spectrum.
",0,1,0,0,0,0
18109,Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts,"  For autonomous robots in dynamic environments mixed with human, it is vital
to detect impending collision quickly and robustly. The biological visual
systems evolved over millions of years may provide us efficient solutions for
collision detection in complex environments. In the cockpit of locusts, two
Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
which respond to looming objects rigorously with high firing rates. Compared to
LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
dark moving objects against bright background in depth while not responding to
light objects embedded in dark background - a similar situation which ground
vehicles and robots are facing with. However, little work has been done on
modeling LGMD2, let alone its potential in robotics and other vision-based
applications. In this article, we propose a novel way of modeling LGMD2 neuron,
with biased ON and OFF pathways splitting visual streams into parallel channels
encoding brightness increments and decrements separately to fulfill its
selectivity. Moreover, we apply a biophysical mechanism of spike frequency
adaptation to shape the looming selectivity in such a collision-detecting
neuron model. The proposed visual neural network has been tested with
systematic experiments, challenged against synthetic and real physical stimuli,
as well as image streams from the sensor of a miniature robot. The results
demonstrated this framework is able to detect looming dark objects embedded in
bright backgrounds selectively, which make it ideal for ground mobile
platforms. The robotic experiments also showed its robustness in collision
detection - it performed well for near range navigation in an arena with many
obstacles. Its enhanced collision selectivity to dark approaching objects
versus receding and translating ones has also been verified via systematic
experiments.
",1,0,0,0,1,0
18110,Multifractal invariant measures in expanding piecewise linear coupled maps,"  We analyze invariant measures of two coupled piecewise linear and everywhere
expanding maps on the synchronization manifold. We observe that though the
individual maps have simple and smooth functions as their stationary densities,
they become multifractal as soon as two of them are coupled nonlinearly even
with a small coupling. For some maps, the multifractal spectrum seems to be
robust with the coupling or map parameters and for some other maps, there is a
substantial variation. The origin of the multifractal spectrum here is
intriguing as it does not seem to conform to the existing theory of
multifractal functions.
",0,1,0,0,0,0
18111,Virtually free finite-normal-subgroup-free groups are strongly verbally closed,"  Any virtually free group $H$ containing no non-trivial finite normal subgroup
(e.g., the infinite dihedral group) is a retract of any finitely generated
group containing $H$ as a verbally closed subgroup.
",0,0,1,0,0,0
18112,Stochastic Geometry-based Comparison of Secrecy Enhancement Techniques in D2D Networks,"  This letter presents a performance comparison of two popular secrecy
enhancement techniques in wireless networks: (i) creating guard zones by
restricting transmissions of legitimate transmitters whenever any eavesdropper
is detected in their vicinity, and (ii) adding artificial noise to the
confidential messages to make it difficult for the eavesdroppers to decode
them. Focusing on a noise-limited regime, we use tools from stochastic geometry
to derive the secrecy outage probability at the eavesdroppers as well as the
coverage probability at the legitimate users for both these techniques. Using
these results, we derive a threshold on the density of the eavesdroppers below
which no secrecy enhancing technique is required to ensure a target secrecy
outage probability. For eavesdropper densities above this threshold, we
concretely characterize the regimes in which each technique outperforms the
other. Our results demonstrate that guard zone technique is better when the
distances between the transmitters and their legitimate receivers are higher
than a certain threshold.
",1,0,1,0,0,0
18113,Linear convergence of SDCA in statistical estimation,"  In this paper, we consider stochastic dual coordinate (SDCA) {\em without}
strongly convex assumption or convex assumption. We show that SDCA converges
linearly under mild conditions termed restricted strong convexity. This covers
a wide array of popular statistical models including Lasso, group Lasso, and
logistic regression with $\ell_1$ regularization, corrected Lasso and linear
regression with SCAD regularizer. This significantly improves previous
convergence results on SDCA for problems that are not strongly convex. As a by
product, we derive a dual free form of SDCA that can handle general
regularization term, which is of interest by itself.
",1,0,0,1,0,0
18114,Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium,"  Regularized inversion methods for image reconstruction are used widely due to
their tractability and ability to combine complex physical sensor models with
useful regularity criteria. Such methods motivated the recently developed
Plug-and-Play prior method, which provides a framework to use advanced
denoising algorithms as regularizers in inversion. However, the need to
formulate regularized inversion as the solution to an optimization problem
limits the possible regularity conditions and physical sensor models.
In this paper, we introduce Consensus Equilibrium (CE), which generalizes
regularized inversion to include a much wider variety of both forward
components and prior components without the need for either to be expressed
with a cost function. CE is based on the solution of a set of equilibrium
equations that balance data fit and regularity. In this framework, the problem
of MAP estimation in regularized inversion is replaced by the problem of
solving these equilibrium equations, which can be approached in multiple ways.
The key contribution of CE is to provide a novel framework for fusing
multiple heterogeneous models of physical sensors or models learned from data.
We describe the derivation of the CE equations and prove that the solution of
the CE equations generalizes the standard MAP estimate under appropriate
circumstances.
We also discuss algorithms for solving the CE equations, including ADMM with
a novel form of preconditioning and Newton's method. We give examples to
illustrate consensus equilibrium and the convergence properties of these
algorithms and demonstrate this method on some toy problems and on a denoising
example in which we use an array of convolutional neural network denoisers,
none of which is tuned to match the noise level in a noisy image but which in
consensus can achieve a better result than any of them individually.
",1,0,1,0,0,0
18115,Using angular pair upweighting to improve 3D clustering measurements,"  Three dimensional galaxy clustering measurements provide a wealth of
cosmological information. However, obtaining spectra of galaxies is expensive,
and surveys often only measure redshifts for a subsample of a target galaxy
population. Provided that the spectroscopic data is representative, we argue
that angular pair upweighting should be used in these situations to improve the
3D clustering measurements. We present a toy model showing mathematically how
such a weighting can improve measurements, and provide a practical example of
its application using mocks created for the Baryon Oscillation Spectroscopic
Survey (BOSS). Our analysis of mocks suggests that, if an angular clustering
measurement is available over twice the area covered spectroscopically,
weighting gives a $\sim$10-20% reduction of the variance of the monopole
correlation function on the BAO scale.
",0,1,0,0,0,0
18116,Maximal entries of elements in certain matrix monoids,"  Let $L_u=\begin{bmatrix}1 & 0\\u & 1\end{bmatrix}$ and $R_v=\begin{bmatrix}1
& v\\0 & 1\end{bmatrix}$ be matrices in $SL_2(\mathbb Z)$ with $u, v\geq 1$.
Since the monoid generated by $L_u$ and $R_v$ is free, we can associate a depth
to each element based on its product representation. In the cases where $u=v=2$
and $u=v=3$, Bromberg, Shpilrain, and Vdovina determined the depth $n$ matrices
containing the maximal entry for each $n\geq 1$. By using ideas from our
previous work on $(u,v)$-Calkin-Wilf trees, we extend their results for any $u,
v\geq 1$ and in the process we recover the Fibonacci and some Lucas sequences.
As a consequence we obtain bounds which guarantee collision resistance on a
family of hashing functions based on $L_u$ and $R_v$.
",0,0,1,0,0,0
18117,Generative Modeling of Multimodal Multi-Human Behavior,"  This work presents a methodology for modeling and predicting human behavior
in settings with N humans interacting in highly multimodal scenarios (i.e.
where there are many possible highly-distinct futures). A motivating example
includes robots interacting with humans in crowded environments, such as
self-driving cars operating alongside human-driven vehicles or human-robot
collaborative bin packing in a warehouse. Our approach to model human behavior
in such uncertain environments is to model humans in the scene as nodes in a
graphical model, with edges encoding relationships between them. For each
human, we learn a multimodal probability distribution over future actions from
a dataset of multi-human interactions. Learning such distributions is made
possible by recent advances in the theory of conditional variational
autoencoders and deep learning approximations of probabilistic graphical
models. Specifically, we learn action distributions conditioned on interaction
history, neighboring human behavior, and candidate future agent behavior in
order to take into account response dynamics. We demonstrate the performance of
such a modeling approach in modeling basketball player trajectories, a highly
multimodal, multi-human scenario which serves as a proxy for many robotic
applications.
",1,0,0,0,0,0
18118,Deep Sets,"  We study the problem of designing models for machine learning tasks defined
on \emph{sets}. In contrast to traditional approach of operating on fixed
dimensional vectors, we consider objective functions defined on sets that are
invariant to permutations. Such problems are widespread, ranging from
estimation of population statistics \cite{poczos13aistats}, to anomaly
detection in piezometer data of embankment dams \cite{Jung15Exploration}, to
cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem
characterizes the permutation invariant functions and provides a family of
functions to which any permutation invariant objective function must belong.
This family of functions has a special structure which enables us to design a
deep network architecture that can operate on sets and which can be deployed on
a variety of scenarios including both unsupervised and supervised learning
tasks. We also derive the necessary and sufficient conditions for permutation
equivariance in deep models. We demonstrate the applicability of our method on
population statistic estimation, point cloud classification, set expansion, and
outlier detection.
",1,0,0,1,0,0
18119,Assortment Optimization under a Single Transition Model,"  In this paper, we consider a Markov chain choice model with single
transition. In this model, customers arrive at each product with a certain
probability. If the arrived product is unavailable, then the seller can
recommend a subset of available products to the customer and the customer will
purchase one of the recommended products or choose not to purchase with certain
transition probabilities. The distinguishing features of the model are that the
seller can control which products to recommend depending on the arrived product
and that each customer either purchases a product or leaves the market after
one transition.
We study the assortment optimization problem under this model. Particularly,
we show that this problem is generally NP-Hard even if each product could only
transit to at most two products. Despite the complexity of the problem, we
provide polynomial time algorithms for several special cases, such as when the
transition probabilities are homogeneous with respect to the starting point, or
when each product can only transit to one other product. We also provide a
tight performance bound for revenue-ordered assortments. In addition, we
propose a compact mixed integer program formulation that can solve this problem
of large size. Through extensive numerical experiments, we show that the
proposed algorithms can solve the problem efficiently and the obtained
assortments could significantly improve the revenue of the seller than under
the Markov chain choice model.
",1,0,1,0,0,0
18120,Deconstructing Type III,"  SAS introduced Type III methods to address difficulties in dummy-variable
models for effects of multiple factors and covariates. Type III methods are
widely used in practice; they are the default method in many statistical
computing packages. Type III sums of squares (SSs) are defined by an algorithm,
and an explicit mathematical formulation does not seem to exist. For that
reason, their properties have not been rigorously proven. Some that are widely
believed to be true are not always true. An explicit formulation is derived in
this paper. It is used as a basis to prove fundamental properties of Type III
estimable functions and SSs. It is shown that, in any given setting, Type III
effects include all estimable ANOVA effects, and that if all of an ANOVA effect
is estimable then the Type III SS tests it exactly. The setting for these
results is general, comprising linear models for the mean vector of a response
that include arbitrary sets of effects of factors and covariates.
",0,0,0,1,0,0
18121,The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time,"  Parents and teachers often express concern about the extensive use of social
media by youngsters. Some of them see emoticons, undecipherable initialisms and
loose grammar typical for social media as evidence of language degradation. In
this paper, we use a simple measure of text complexity to investigate how the
complexity of public posts on a popular social networking site changes over
time. We analyze a unique dataset that contains texts posted by 942, 336 users
from a large European city across nine years. We show that the chosen
complexity measure is correlated with the academic performance of users: users
from high-performing schools produce more complex texts than users from
low-performing schools. We also find that complexity of posts increases with
age. Finally, we demonstrate that overall language complexity of posts on the
social networking site is constantly increasing. We call this phenomenon the
digital Flynn effect. Our results may suggest that the worries about language
degradation are not warranted.
",1,0,0,0,0,0
18122,Epistemic Modeling with Justifications,"  Existing logical models do not fairly represent epistemic situations with
fallible justifications, e.g., Russell's Prime Minister example, though such
scenarios have long been at the center of epistemic studies. We introduce
justification epistemic models, JEM, which can handle such scenarios. JEM makes
justifications prime objects and draws a distinction between accepted and
knowledge-producing justifications; belief and knowledge become derived
notions. Furthermore, Kripke models can be viewed as special cases of JEMs with
additional assumptions of evidence insensitivity and common knowledge of the
model. We argue that JEM can be applied to a range of epistemic scenarios in
CS, AI, Game Theory, etc.
",1,0,0,0,0,0
18123,Emergent universal critical behavior of the 2D $N$-color Ashkin-Teller model in the presence of correlated disorder,"  We study the critical behavior of the 2D $N$-color Ashkin-Teller model in the
presence of random bond disorder whose correlations decays with the distance
$r$ as a power-law $r^{-a}$. We consider the case when the spins of different
colors sitting at the same site are coupled by the same bond and map this
problem onto the 2D system of $N/2$ flavors of interacting Dirac fermions in
the presence of correlated disorder. Using renormalization group we show that
for $N=2$, a ""weakly universal"" scaling behavior at the continuous transition
becomes universal with new critical exponents. For $N>2$, the first-order phase
transition is rounded by the correlated disorder and turns into a continuous
one.
",0,1,0,0,0,0
18124,Transport signatures of topological superconductivity in a proximity-coupled nanowire,"  We study the conductance of a junction between the normal and superconducting
segments of a nanowire, both of which are subjected to spin-orbit coupling and
an external magnetic field. We directly compare the transport properties of the
nanowire assuming two different models for the superconducting segment: one
where we put superconductivity by hand into the wire, and one where
superconductivity is induced through a tunneling junction with a bulk s-wave
superconductor. While these two models are equivalent at low energies and at
weak coupling between the nanowire and the superconductor, we show that there
are several interesting qualitative differences away from these two limits. In
particular, the tunneling model introduces an additional conductance peak at
the energy corresponding to the bulk gap of the parent superconductor. By
employing a combination of analytical methods at zero temperature and numerical
methods at finite temperature, we show that the tunneling model of the
proximity effect reproduces many more of the qualitative features that are seen
experimentally in such a nanowire system.
",0,1,0,0,0,0
18125,An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation,"  Training of neural machine translation (NMT) models usually uses mini-batches
for efficiency purposes. During the mini-batched training process, it is
necessary to pad shorter sentences in a mini-batch to be equal in length to the
longest sentence therein for efficient computation. Previous work has noted
that sorting the corpus based on the sentence length before making mini-batches
reduces the amount of padding and increases the processing speed. However,
despite the fact that mini-batch creation is an essential step in NMT training,
widely used NMT toolkits implement disparate strategies for doing so, which
have not been empirically validated or compared. This work investigates
mini-batch creation strategies with experiments over two different datasets.
Our results suggest that the choice of a mini-batch creation strategy has a
large effect on NMT training and some length-based sorting strategies do not
always work well compared with simple shuffling.
",1,0,0,0,0,0
18126,Network analyses of 4D genome datasets automate detection of community-scale gene structure and plasticity,"  Chromosome conformation capture and Hi-C technologies provide gene-gene
proximity datasets of stationary cells, revealing chromosome territories,
topologically associating domains, and chromosome topology. Imaging of tagged
DNA sequences in live cells through the lac operator reporter system provides
dynamic datasets of chromosomal loci. Chromosome modeling explores the
mechanisms underlying 3D genome structure and dynamics. Here, we automate 4D
genome dataset analysis with network-based tools as an alternative to gene-gene
proximity statistics and visual structure determination. Temporal network
models and community detection algorithms are applied to 4D modeling of G1 in
budding yeast with transient crosslinking of $5 kb$ domains in the nucleolus,
analyzing datasets from four decades of transient binding timescales. Network
tools detect and track transient gene communities (clusters) within the
nucleolus, their size, number, persistence time, and frequency of gene
exchanges. An optimal, weak binding affinity is revealed that maximizes
community-scale plasticity whereby large communities persist, frequently
exchanging genes.
",0,0,0,0,1,0
18127,Predicate Pairing for Program Verification,"  It is well-known that the verification of partial correctness properties of
imperative programs can be reduced to the satisfiability problem for
constrained Horn clauses (CHCs). However, state-of-the-art solvers for CHCs
(CHC solvers) based on predicate abstraction are sometimes unable to verify
satisfiability because they look for models that are definable in a given class
A of constraints, called A-definable models. We introduce a transformation
technique, called Predicate Pairing (PP), which is able, in many interesting
cases, to transform a set of clauses into an equisatisfiable set whose
satisfiability can be proved by finding an A-definable model, and hence can be
effectively verified by CHC solvers. We prove that, under very general
conditions on A, the unfold/fold transformation rules preserve the existence of
an A-definable model, i.e., if the original clauses have an A-definable model,
then the transformed clauses have an A-definable model. The converse does not
hold in general, and we provide suitable conditions under which the transformed
clauses have an A-definable model iff the original ones have an A-definable
model. Then, we present the PP strategy which guides the application of the
transformation rules with the objective of deriving a set of clauses whose
satisfiability can be proved by looking for A-definable models. PP introduces a
new predicate defined by the conjunction of two predicates together with some
constraints. We show through some examples that an A-definable model may exist
for the new predicate even if it does not exist for its defining atomic
conjuncts. We also present some case studies showing that PP plays a crucial
role in the verification of relational properties of programs (e.g., program
equivalence and non-interference). Finally, we perform an experimental
evaluation to assess the effectiveness of PP in increasing the power of CHC
solving.
",1,0,0,0,0,0
18128,Entanglement entropy and computational complexity of the Anderson impurity model out of equilibrium I: quench dynamics,"  We study the growth of entanglement entropy in density matrix renormalization
group calculations of the real-time quench dynamics of the Anderson impurity
model. We find that with appropriate choice of basis, the entropy growth is
logarithmic in both the interacting and noninteracting single-impurity models.
The logarithmic entropy growth is understood from a noninteracting chain model
as a critical behavior separating regimes of linear growth and saturation of
entropy, corresponding respectively to an overlapping and gapped energy spectra
of the set of bath states. We find that with an appropriate choices of basis
(energy-ordered bath orbitals), logarithmic entropy growth is the generic
behavior of quenched impurity models. A noninteracting calculation of a
double-impurity Anderson model supports the conclusion in the multi-impurity
case. The logarithmic growth of entanglement entropy enables studies of quench
dynamics to very long times.
",0,1,0,0,0,0
18129,Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting,"  In this work, we show that saturating output activation functions, such as
the softmax, impede learning on a number of standard classification tasks.
Moreover, we present results showing that the utility of softmax does not stem
from the normalization, as some have speculated. In fact, the normalization
makes things worse. Rather, the advantage is in the exponentiation of error
gradients. This exponential gradient boosting is shown to speed up convergence
and improve generalization. To this end, we demonstrate faster convergence and
better performance on diverse classification tasks: image classification using
CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the
latter case, using the state-of-the-art neural network architecture, the model
converged 33% faster with our method (roughly two days of training less) than
with the standard softmax activation, and with a slightly better performance to
boot.
",1,0,0,0,0,0
18130,A model theoretic Rieffel's theorem of quantum 2-torus,"  We defined a notion of quantum 2-torus $T_\theta$ in ""Masanori Itai and Boris
Zilber, Notes on a model theory of quantum 2-torus $T_q^2$ for generic $q$,
arXiv:1503.06045v1 [mathLO]"" and studied its model theoretic property. In this
note we associate quantum 2-tori $T_\theta$ with the structure over ${\mathbb
C}_\theta = ({\mathbb C}, +, \cdot, y = x^\theta),$ where $\theta \in {\mathbb
R} \setminus {\mathbb Q}$, and introduce the notion of geometric isomorphisms
between such quantum 2-tori.
We show that this notion is closely connected with the fundamental notion of
Morita equivalence of non-commutative geometry. Namely, we prove that the
quantum 2-tori $T_{\theta_1}$ and $T_{\theta_2}$ are Morita equivalent if and
only if $\theta_2 = {\displaystyle \frac{a \theta_1 + b}{c \theta_1 + d}}$ for
some $ \left( \begin{array}{cc} a & b \\ c & d \end{array} \right)
\in {\rm GL}_2({\mathbb Z})$ with $|ad - bc| = 1$. This is our version of
Rieffel's Theorem in ""M. A. Rieffel and A. Schwarz, Morita equivalence of
multidimensional noncummutative tori, Internat. J. Math. 10, 2 (1999) 289-299""
which characterises Morita equivalence of quantum tori in the same terms.
The result in essence confirms that the representation $T_\theta$ in terms of
model-theoretic geometry \cite{IZ} is adequate to its original definition in
terms of non-commutative geometry.
",0,0,1,0,0,0
18131,Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets,"  We release two artificial datasets, Simulated Flying Shapes and Simulated
Planar Manipulator that allow to test the learning ability of video processing
systems. In particular, the dataset is meant as a tool which allows to easily
assess the sanity of deep neural network models that aim to encode, reconstruct
or predict video frame sequences. The datasets each consist of 90000 videos.
The Simulated Flying Shapes dataset comprises scenes showing two objects of
equal shape (rectangle, triangle and circle) and size in which one object
approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF
planar manipulator that executes a pick-and-place task in which it has to place
a size-varying circle on a squared platform. Different from other widely used
datasets such as moving MNIST [1], [2], the two presented datasets involve
goal-oriented tasks (e.g. the manipulator grasping an object and placing it on
a platform), rather than showing random movements. This makes our datasets more
suitable for testing prediction capabilities and the learning of sophisticated
motions by a machine learning model. This technical document aims at providing
an introduction into the usage of both datasets.
",1,0,0,0,0,0
18132,Couplings and quantitative contraction rates for Langevin dynamics,"  We introduce a new probabilistic approach to quantify convergence to
equilibrium for (kinetic) Langevin processes. In contrast to previous analytic
approaches that focus on the associated kinetic Fokker-Planck equation, our
approach is based on a specific combination of reflection and synchronous
coupling of two solutions of the Langevin equation. It yields contractions in a
particular Wasserstein distance, and it provides rather precise bounds for
convergence to equilibrium at the borderline between the overdamped and the
underdamped regime. In particular, we are able to recover kinetic behavior in
terms of explicit lower bounds for the contraction rate. For example, for a
rescaled double-well potential with local minima at distance $a$, we obtain a
lower bound for the contraction rate of order $\Omega (a^{-1})$ provided the
friction coefficient is of order $\Theta (a^{-1})$.
",0,0,1,0,0,0
18133,Stacked transfer learning for tropical cyclone intensity prediction,"  Tropical cyclone wind-intensity prediction is a challenging task considering
drastic changes climate patterns over the last few decades. In order to develop
robust prediction models, one needs to consider different characteristics of
cyclones in terms of spatial and temporal characteristics. Transfer learning
incorporates knowledge from a related source dataset to compliment a target
datasets especially in cases where there is lack or data. Stacking is a form of
ensemble learning focused for improving generalization that has been recently
used for transfer learning problems which is referred to as transfer stacking.
In this paper, we employ transfer stacking as a means of studying the effects
of cyclones whereby we evaluate if cyclones in different geographic locations
can be helpful for improving generalization performs. Moreover, we use
conventional neural networks for evaluating the effects of duration on cyclones
in prediction performance. Therefore, we develop an effective strategy that
evaluates the relationships between different types of cyclones through
transfer learning and conventional learning methods via neural networks.
",1,0,0,1,0,0
18134,Generalized Gray codes with prescribed ends of small dimensions,"  Given pairwise distinct vertices $\{\alpha_i , \beta_i\}^k_{i=1}$ of the
$n$-dimensional hypercube $Q_n$ such that the distance of $\alpha_i$ and
$\beta_i$ is odd, are there paths $P_i$ between $\alpha_i$ and $\beta_i$ such
that $\{V (P_i)\}^k_{i=1}$ partitions $V(Q_n)$? A positive solution for every
$n\ge1$ and $k=1$ is known as a Gray code of dimension $n$. In this paper we
settle this problem for small values of $n$.
",1,0,0,0,0,0
18135,Elementary-base cirquent calculus I: Parallel and choice connectives,"  Cirquent calculus is a proof system manipulating circuit-style constructs
rather than formulas. Using it, this article constructs a sound and complete
axiomatization CL16 of the propositional fragment of computability logic (the
game-semantically conceived logic of computational problems - see
this http URL ) whose logical vocabulary consists
of negation and parallel and choice connectives, and whose atoms represent
elementary, i.e. moveless, games.
",1,0,1,0,0,0
18136,"Targeted and Imaging-guided In Vivo Photodynamic Therapy of Tumors Using Dual-functional, Aggregation-induced Emission Nanoparticles","  Dual-functional nanoparticles, with the property of aggregation-induced
emission and the capability of reactive oxygen species, were used to achieve
passive/active targeting of tumor. Good contrast in in vivo imaging and obvious
therapeutic efficiency were realized with a low dose of AIE nanoparticles as
well as a low power density of light, resulting in negligible side effects.
",0,1,0,0,0,0
18137,TOSC: an algorithm for the tomography of spotted transit chords,"  Photometric observations of planetary transits may show localized bumps,
called transit anomalies, due to the possible crossing of photospheric
starspots. The aim of this work is to analyze the transit anomalies and derive
the temperature profile inside the transit belt along the transit direction. We
develop the algorithm TOSC, a tomographic inverse-approach tool which, by means
of simple algebra, reconstructs the flux distribution along the transit belt.
We test TOSC against some simulated scenarios. We find that TOSC provides
robust results for light curves with photometric accuracies better than 1~mmag,
returning the spot-photosphere temperature contrast with an accuracy better
than 100~K. TOSC is also robust against the presence of unocculted spots,
provided that the apparent planetary radius given by the fit of the transit
light curve is used in place of the true radius. The analysis of real data with
TOSC returns results consistent with previous studies.
",0,1,0,0,0,0
18138,Bayesian uncertainty quantification for epidemic spread on networks,"  While there exist a number of mathematical approaches to modeling the spread
of disease on a network, analyzing such systems in the presence of uncertainty
introduces significant complexity. In scenarios where system parameters must be
inferred from limited observations, general approaches to uncertainty
quantification can generate approximate distributions of the unknown
parameters, but these methods often become computationally expensive if the
underlying disease model is complex. In this paper, we apply the recent
massively parallelizable Bayesian uncertainty quantification framework $\Pi4U$
to a model of a disease spreading on a network of communities, showing that the
method can accurately and tractably recover system parameters and select
optimal models in this setting.
",0,0,0,1,0,0
18139,Switching divergences for spectral learning in blind speech dereverberation,"  When recorded in an enclosed room, a sound signal will most certainly get
affected by reverberation. This not only undermines audio quality, but also
poses a problem for many human-machine interaction technologies that use speech
as their input. In this work, a new blind, two-stage dereverberation approach
based in a generalized \beta-divergence as a fidelity term over a non-negative
representation is proposed. The first stage consists of learning the spectral
structure of the signal solely from the observed spectrogram, while the second
stage is devoted to model reverberation. Both steps are taken by minimizing a
cost function in which the aim is put either in constructing a dictionary or a
good representation by changing the divergence involved. In addition, an
approach for finding an optimal fidelity parameter for dictionary learning is
proposed. An algorithm for implementing the proposed method is described and
tested against state-of-the-art methods. Results show improvements for both
artificial reverberation and real recordings.
",1,0,0,0,0,0
18140,The correlation between the sizes of globular cluster systems and their host dark matter haloes,"  The sizes of entire systems of globular clusters (GCs) depend on the
formation and destruction histories of the GCs themselves, but also on the
assembly, merger and accretion history of the dark matter (DM) haloes that they
inhabit. Recent work has shown a linear relation between total mass of globular
clusters in the globular cluster system and the mass of its host dark matter
halo, calibrated from weak lensing. Here we extend this to GC system sizes, by
studying the radial density profiles of GCs around galaxies in nearby galaxy
groups. We find that radial density profiles of the GC systems are well fit
with a de Vaucouleurs profile. Combining our results with those from the
literature, we find tight relationship ($\sim 0.2$ dex scatter) between the
effective radius of the GC system and the virial radius (or mass) of its host
DM halo. The steep non-linear dependence of this relationship ($R_{e, GCS}
\propto R_{200}^{2.5 - 3}$) is currently not well understood, but is an
important clue regarding the assembly history of DM haloes and of the GC
systems that they host.
",0,1,0,0,0,0
18141,Variation of field enhancement factor near the emitter tip,"  The field enhancement factor at the emitter tip and its variation in a close
neighbourhood determines the emitter current in a Fowler-Nordheim like
formulation. For an axially symmetric emitter with a smooth tip, it is shown
that the variation can be accounted by a $\cos{\tilde{\theta}}$ factor in
appropriately defined normalized co-ordinates. This is shown analytically for a
hemi-ellipsoidal emitter and confirmed numerically for other emitter shapes
with locally quadratic tips.
",0,1,0,0,0,0
18142,Benchmark Environments for Multitask Learning in Continuous Domains,"  As demand drives systems to generalize to various domains and problems, the
study of multitask, transfer and lifelong learning has become an increasingly
important pursuit. In discrete domains, performance on the Atari game suite has
emerged as the de facto benchmark for assessing multitask learning. However, in
continuous domains there is a lack of agreement on standard multitask
evaluation environments which makes it difficult to compare different
approaches fairly. In this work, we describe a benchmark set of tasks that we
have developed in an extendable framework based on OpenAI Gym. We run a simple
baseline using Trust Region Policy Optimization and release the framework
publicly to be expanded and used for the systematic comparison of multitask,
transfer, and lifelong learning in continuous domains.
",1,0,0,0,0,0
18143,A Multiple Linear Regression Approach For Estimating the Market Value of Football Players in Forward Position,"  In this paper, market values of the football players in the forward positions
are estimated using multiple linear regression by including the physical and
performance factors in 2017-2018 season. Players from 4 major leagues of Europe
are examined, and by applying the test for homoscedasticity, a reasonable
regression model within 0.10 significance level is built, and the most and the
least affecting factors are explained in detail.
",0,0,0,1,0,0
18144,Application of the Huang-Hilbert transform and natural time to the analysis of Seismic Electric Signal activities,"  The Huang-Hilbert transform is applied to Seismic Electric Signal (SES)
activities in order to decompose them into a number of Intrinsic Mode Functions
(IMFs) and study which of these functions better represent the SES. The results
are compared to those obtained from the analysis in a new time domain termed
natural time after having subtracted the magnetotelluric background from the
original signal. It is shown that the instantaneous amplitudes of the IMFs can
be used for the distinction of SES from artificial noises when combined with
the natural time analysis.
",0,1,0,0,0,0
18145,The Hurwitz Subgroups of $E_6(2)$,"  We prove that the exceptional group $E_6(2)$ is not a Hurwitz group. In the
course of proving this, we complete the classification up to conjugacy of all
Hurwitz subgroups of $E_6(2)$, in particular, those isomorphic to $L_2(8)$ and
$L_3(2)$.
",0,0,1,0,0,0
18146,Three IQs of AI Systems and their Testing Methods,"  The rapid development of artificial intelligence has brought the artificial
intelligence threat theory as well as the problem about how to evaluate the
intelligence level of intelligent products. Both need to find a quantitative
method to evaluate the intelligence level of intelligence systems, including
human intelligence. Based on the standard intelligence system and the extended
Von Neumann architecture, this paper proposes General IQ, Service IQ and Value
IQ evaluation methods for intelligence systems, depending on different
evaluation purposes. Among them, the General IQ of intelligence systems is to
answer the question of whether the artificial intelligence can surpass the
human intelligence, which is reflected in putting the intelligence systems on
an equal status and conducting the unified evaluation. The Service IQ and Value
IQ of intelligence systems are used to answer the question of how the
intelligent products can better serve the human, reflecting the intelligence
and required cost of each intelligence system as a product in the process of
serving human.
",1,0,0,0,0,0
18147,Polynomial functors in manifold calculus,"  Let M be a smooth manifold, and let O(M) be the poset of open subsets of M.
Manifold calculus, due to Goodwillie and Weiss, is a calculus of functors
suitable for studying contravariant functors (cofunctors) F: O(M)--> Top from
O(M) to the category of spaces. Weiss showed that polynomial cofunctors of
degree <= k are determined by their values on O_k(M), where O_k(M) is the full
subposet of O(M) whose objects are open subsets diffeomorphic to the disjoint
union of at most k balls. Afterwards Pryor showed that one can replace O_k(M)
by more general subposets and still recover the same notion of polynomial
cofunctor. In this paper, we generalize these results to cofunctors from O(M)
to any simplicial model category C. If conf(k, M) stands for the unordered
configuration space of k points in M, we also show that the category of
homogeneous cofunctors O(M) --> C of degree k is weakly equivalent to the
category of linear cofunctors O(conf(k, M)) --> C provided that C has a zero
object. Using a completely different approach, we also show that if C is a
general model category and F: O_k(M) --> C is an isotopy cofunctor, then the
homotopy right Kan extension of F along the inclusion O_k(M) --> O(M) is also
an isotopy cofunctor.
",0,0,1,0,0,0
18148,Tikhonov Regularization for Long Short-Term Memory Networks,"  It is a well-known fact that adding noise to the input data often improves
network performance. While the dropout technique may be a cause of memory loss,
when it is applied to recurrent connections, Tikhonov regularization, which can
be regarded as the training with additive noise, avoids this issue naturally,
though it implies regularizer derivation for different architectures. In case
of feedforward neural networks this is straightforward, while for networks with
recurrent connections and complicated layers it leads to some difficulties. In
this paper, a Tikhonov regularizer is derived for Long-Short Term Memory (LSTM)
networks. Although it is independent of time for simplicity, it considers
interaction between weights of the LSTM unit, which in theory makes it possible
to regularize the unit with complicated dependences by using only one parameter
that measures the input data perturbation. The regularizer that is proposed in
this paper has three parameters: one to control the regularization process, and
other two to maintain computation stability while the network is being trained.
The theory developed in this paper can be applied to get such regularizers for
different recurrent neural networks with Hadamard products and Lipschitz
continuous functions.
",1,0,0,1,0,0
18149,On the shape operator of relatively parallel hypersurfaces in the $n$-dimensional relative differential geometry,"  We deal with hypersurfaces in the framework of the $n$-dimensional relative
differential geometry. We consider a hypersurface $\varPhi$ of
$\mathbb{R}^{n+1}$ with position vector field $\mathbf{x}$, which is relatively
normalized by a relative normalization $\mathbf{y}$. Then $\mathbf{y}$ is also
a relative normalization of every member of the one-parameter family
$\mathcal{F}$ of hypersurfaces $\varPhi_\mu$ with position vector field
$$\mathbf{x}_\mu = \mathbf{x} + \mu \, \mathbf{y},$$ where $\mu$ is a real
constant. We call every hypersurface $\varPhi_\mu \in \mathcal{F}$ relatively
parallel to $\varPhi$ at the ""relative distance"" $\mu$. In this paper we study
(a) the shape (or Weingarten) operator,
(b) the relative principal curvatures,
(c) the relative mean curvature functions and
(d) the affine normalization
of a relatively parallel hypersurface $\left( \varPhi_\mu,\mathbf{y}\right)$
to $\left(\varPhi,\mathbf{y}\right)$.
",0,0,1,0,0,0
18150,Soft Pneumatic Gelatin Actuator for Edible Robotics,"  We present a fully edible pneumatic actuator based on gelatin-glycerol
composite. The actuator is monolithic, fabricated via a molding process, and
measures 90 mm in length, 20 mm in width, and 17 mm in thickness. Thanks to the
composite mechanical characteristics similar to those of silicone elastomers,
the actuator exhibits a bending angle of 170.3 ° and a blocked force of
0.34 N at the applied pressure of 25 kPa. These values are comparable to
elastomer based pneumatic actuators. As a validation example, two actuators are
integrated to form a gripper capable of handling various objects, highlighting
the high performance and applicability of the edible actuator. These edible
actuators, combined with other recent edible materials and electronics, could
lay the foundation for a new type of edible robots.
",1,0,0,0,0,0
18151,Deep learning for extracting protein-protein interactions from biomedical literature,"  State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on ""difficult"" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences.
",1,0,0,0,0,0
18152,Hierarchical VampPrior Variational Fair Auto-Encoder,"  Decision making is a process that is extremely prone to different biases. In
this paper we consider learning fair representations that aim at removing
nuisance (sensitive) information from the decision process. For this purpose,
we propose to use deep generative modeling and adapt a hierarchical Variational
Auto-Encoder to learn these fair representations. Moreover, we utilize the
mutual information as a useful regularizer for enforcing fairness of a
representation. In experiments on two benchmark datasets and two scenarios
where the sensitive variables are fully and partially observable, we show that
the proposed approach either outperforms or performs on par with the current
best model.
",0,0,0,1,0,0
18153,MHD Models of Gamma-ray Emission in WR 11,"  Recent reports claiming tentative association of the massive star binary
system gamma^2 Velorum (WR 11) with a high-energy gamma-ray source observed by
Fermi-LAT contrast the so-far exclusive role of Eta Carinae as the hitherto
only detected gamma-ray emitter in the source class of particle-accelerating
colliding-wind binary systems. We aim to shed light on this claim of
association by providing dedicated model predictions for the nonthermal photon
emission spectrum of WR 11. We use three-dimensional magneto-hydrodynamic
modeling to trace the structure and conditions of the wind-collision region of
WR 11 throughout its 78.5 day orbit, including the important effect of
radiative braking in the stellar winds. A transport equation is then solved in
the wind-collision region to determine the population of relativistic electrons
and protons which are subsequently used to compute nonthermal photon emission
components. We find that - if WR 11 be indeed confirmed as the responsible
object for the observed gamma-ray emission - its radiation will unavoidably be
of hadronic origin owing to the strong radiation fields in the binary system
which inhibit the acceleration of electrons to energies suffciently high for
observable inverse Compton radiation. Different conditions in wind-collision
region near the apastron and periastron configuration lead to significant
variability on orbital time scales. The bulk of the hadronic gamma-ray emission
originates at a 400 solar radii wide region at the apex.
",0,1,0,0,0,0
18154,Caulking the Leakage Effect in MEEG Source Connectivity Analysis,"  Simplistic estimation of neural connectivity in MEEG sensor space is
impossible due to volume conduction. The only viable alternative is to carry
out connectivity estimation in source space. Among the neuroscience community
this is claimed to be impossible or misleading due to Leakage: linear mixing of
the reconstructed sources. To address this problematic we propose a novel
solution method that caulks the Leakage in MEEG source activity and
connectivity estimates: BC-VARETA. It is based on a joint estimation of source
activity and connectivity in the frequency domain representation of MEEG time
series. To achieve this, we go beyond current methods that assume a fixed
gaussian graphical model for source connectivity. In contrast we estimate this
graphical model in a Bayesian framework by placing priors on it, which allows
for highly optimized computations of the connectivity, via a new procedure
based on the local quadratic approximation under quite general prior models. A
further contribution of this paper is the rigorous definition of leakage via
the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic
distances over the cortical manifold. Both measures are extended for the first
time to quantify Connectivity Leakage by defining them on the cartesian product
of cortical manifolds. Using these measures, we show that BC-VARETA outperforms
most state of the art inverse solvers by several orders of magnitude.
",0,0,0,0,1,0
18155,New ADS Functionality for the Curator,"  In this paper we provide an update concerning the operations of the NASA
Astrophysics Data System (ADS), its services and user interface, and the
content currently indexed in its database. As the primary information system
used by researchers in Astronomy, the ADS aims to provide a comprehensive index
of all scholarly resources appearing in the literature. With the current effort
in our community to support data and software citations, we discuss what steps
the ADS is taking to provide the needed infrastructure in collaboration with
publishers and data providers. A new API provides access to the ADS search
interface, metrics, and libraries allowing users to programmatically automate
discovery and curation tasks. The new ADS interface supports a greater
integration of content and services with a variety of partners, including ORCID
claiming, indexing of SIMBAD objects, and article graphics from a variety of
publishers. Finally, we highlight how librarians can facilitate the ingest of
gray literature that they curate into our system.
",1,1,0,0,0,0
18156,Survey of reasoning using Neural networks,"  Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small memory contexts, but as context becomes larger than a threshold, it is
difficult to use them. The Solution is to use large external memory. Still, it
poses many challenges like, how to train neural networks for discrete memory
representation, how to describe long term dependencies in sequential data etc.
Most prominent neural architectures for such tasks are Memory networks:
inference components combined with long term memory and Neural Turing Machines:
neural networks using external memory resources. Also, additional techniques
like attention mechanism, end to end gradient descent on discrete memory
representation are needed to support these solutions. Preliminary results of
above neural architectures on simple algorithms (sorting, copying) and Question
Answering (based on story, dialogs) application are comparable with the state
of the art. In this paper, I explain these architectures (in general), the
additional techniques used and the results of their application.
",1,0,0,0,0,0
18157,Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturing,"  The pharmaceutical industry has witnessed exponential growth in transforming
operations towards continuous manufacturing to effectively achieve increased
profitability, reduced waste, and extended product range. Model Predictive
Control (MPC) can be applied for enabling this vision, in providing superior
regulation of critical quality attributes. For MPC, obtaining a workable model
is of fundamental importance, especially in the presence of complex reaction
kinetics and process dynamics. Whilst physics-based models are desirable, it is
not always practical to obtain one effective and fit-for-purpose model.
Instead, within industry, data-driven system-identification approaches have
been found to be useful and widely deployed in MPC solutions. In this work, we
demonstrated the applicability of Recurrent Neural Networks (RNNs) for MPC
applications in continuous pharmaceutical manufacturing. We have shown that
RNNs are especially well-suited for modeling dynamical systems due to their
mathematical structure and satisfactory closed-loop control performance can be
yielded for MPC in continuous pharmaceutical manufacturing.
",1,0,0,0,0,0
18158,"A unitary ""quantization commutes with reduction"" map for the adjoint action of a compact Lie group","  Let $K$ be a simply connected compact Lie group and $T^{\ast}(K)$ its
cotangent bundle. We consider the problem of ""quantization commutes with
reduction"" for the adjoint action of $K$ on $T^{\ast}(K).$ We quantize both
$T^{\ast}(K)$ and the reduced phase space using geometric quantization with
half-forms. We then construct a geometrically natural map from the space of
invariant elements in the quantization of $T^{\ast}(K)$ to the quantization of
the reduced phase space. We show that this map is a constant multiple of a
unitary map.
",0,0,1,0,0,0
18159,Electron Paramagnetic Resonance Spectroscopy of Er$^{3+}$:Y$_2$SiO$_5$ Using Josephson Bifurcation Amplifier: Observation of Hyperfine and Quadrupole Structures,"  We performed magnetic field and frequency tunable electron paramagnetic
resonance spectroscopy of an Er$^{3+}$ doped Y$_2$SiO$_5$ crystal by observing
the change in flux induced on a direct current-superconducting quantum
interference device (dc-SQUID) loop of a tunable Josephson bifurcation
amplifer. The observed spectra show multiple transitions which agree well with
the simulated energy levels, taking into account the hyperfine and quadrupole
interactions of $^{167}$Er. The sensing volume is about 0.15 pl, and our
inferred measurement sensitivity (limited by external flux noise) is
approximately $1.5\times10^4$ electron spins for a 1 s measurement. The
sensitivity value is two orders of magnitude better than similar schemes using
dc-SQUID switching readout.
",0,1,0,0,0,0
18160,Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length,"  In this paper, a new Smartphone sensor based algorithm is proposed to detect
accurate distance estimation. The algorithm consists of two phases, the first
phase is for detecting the peaks from the Smartphone accelerometer sensor. The
other one is for detecting the step length which varies from step to step. The
proposed algorithm is tested and implemented in real environment and it showed
promising results. Unlike the conventional approaches, the error of the
proposed algorithm is fixed and is not affected by the long distance.
Keywords distance estimation, peaks, step length, accelerometer.
",1,0,0,0,0,0
18161,Digging Into Self-Supervised Monocular Depth Estimation,"  Depth-sensing is important for both navigation and scene understanding.
However, procuring RGB images with corresponding depth data for training deep
models is challenging; large-scale, varied, datasets with ground truth training
data are scarce. Consequently, several recent methods have proposed treating
the training of monocular color-to-depth estimation networks as an image
reconstruction problem, thus forgoing the need for ground truth depth.
There are multiple concepts and design decisions for these networks that seem
sensible, but give mixed or surprising results when tested. For example,
binocular stereo as the source of self-supervision seems cumbersome and hard to
scale, yet results are less blurry compared to training with monocular videos.
Such decisions also interplay with questions about architectures, loss
functions, image scales, and motion handling. In this paper, we propose a
simple yet effective model, with several general architectural and loss
innovations, that surpasses all other self-supervised depth estimation
approaches on KITTI.
",0,0,0,1,0,0
18162,The Causality/Repair Connection in Databases: Causality-Programs,"  In this work, answer-set programs that specify repairs of databases are used
as a basis for solving computational and reasoning problems about causes for
query answers from databases.
",1,0,0,0,0,0
18163,Two-temperature logistic regression based on the Tsallis divergence,"  We develop a variant of multiclass logistic regression that achieves three
properties: i) We minimize a non-convex surrogate loss which makes the method
robust to outliers, ii) our method allows transitioning between non-convex and
convex losses by the choice of the parameters, iii) the surrogate loss is Bayes
consistent, even in the non-convex case. The algorithm has one weight vector
per class and the surrogate loss is a function of the linear activations (one
per class). The surrogate loss of an example with linear activation vector
$\mathbf{a}$ and class $c$ has the form $-\log_{t_1} \exp_{t_2} (a_c -
G_{t_2}(\mathbf{a}))$ where the two temperatures $t_1$ and $t_2$ ""temper"" the
$\log$ and $\exp$, respectively, and $G_{t_2}$ is a generalization of the
log-partition function. We motivate this loss using the Tsallis divergence. As
the temperature of the logarithm becomes smaller than the temperature of the
exponential, the surrogate loss becomes ""more quasi-convex"". Various tunings of
the temperatures recover previous methods and tuning the degree of
non-convexity is crucial in the experiments. The choice $t_1<1$ and $t_2>1$
performs best experimentally. We explain this by showing that $t_1 < 1$ caps
the surrogate loss and $t_2 >1$ makes the predictive distribution have a heavy
tail.
",1,0,0,1,0,0
18164,Bootstrapped synthetic likelihood,"  Approximate Bayesian computation (ABC) and synthetic likelihood (SL)
techniques have enabled the use of Bayesian inference for models that may be
simulated, but for which the likelihood cannot be evaluated pointwise at values
of an unknown parameter $\theta$. The main idea in ABC and SL is to, for
different values of $\theta$ (usually chosen using a Monte Carlo algorithm),
build estimates of the likelihood based on simulations from the model
conditional on $\theta$. The quality of these estimates determines the
efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to
improve an estimated likelihood at $\theta$ is to simulate more times from the
model conditional on $\theta$, which is infeasible in cases where the simulator
is computationally expensive. In this paper we describe how to use
bootstrapping as a means for improving SL estimates whilst using fewer
simulations from the model, and also investigate its use in ABC. Further, we
investigate the use of the bag of little bootstraps as a means for applying
this approach to large datasets, yielding Monte Carlo algorithms that
accurately approximate posterior distributions whilst only simulating
subsamples of the full data. Examples of the approach applied to i.i.d.,
temporal and spatial data are given.
",1,0,0,1,0,0
18165,The Weinstein conjecture for iterated planar contact structures,"  In this paper, we introduce the notions of an iterated planar Lefschetz
fibration and an iterated planar open book decomposition and prove the
Weinstein conjecture for contact manifolds supporting an open book that has
iterated planar pages. For $n\geq 1$, we show that a $(2n+1)$-dimensional
contact manifold $M$ supporting an iterated planar open book decomposition
satisfies the Weinstein conjecture.
",0,0,1,0,0,0
18166,Privacy-Preserving Deep Learning via Weight Transmission,"  This paper considers the scenario that multiple data owners wish to apply a
machine learning method over the combined dataset of all owners to obtain the
best possible learning output but do not want to share the local datasets owing
to privacy concerns. We design systems for the scenario that the stochastic
gradient descent (SGD) algorithm is used as the machine learning method because
SGD (or its variants) is at the heart of recent deep learning techniques over
neural networks. Our systems differ from existing systems in the following
features: {\bf (1)} any activation function can be used, meaning that no
privacy-preserving-friendly approximation is required; {\bf (2)} gradients
computed by SGD are not shared but the weight parameters are shared instead;
and {\bf (3)} robustness against colluding parties even in the extreme case
that only one honest party exists. We prove that our systems, while
privacy-preserving, achieve the same learning accuracy as SGD and hence retain
the merit of deep learning with respect to accuracy. Finally, we conduct
several experiments using benchmark datasets, and show that our systems
outperform previous system in terms of learning accuracies.
",0,0,0,1,0,0
18167,CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience,"  Peer code review locates common coding rule violations and simple logical
errors in the early phases of software development, and thus reduces overall
cost. However, in GitHub, identifying an appropriate code reviewer for a pull
request is a non-trivial task given that reliable information for reviewer
identification is often not readily available. In this paper, we propose a code
reviewer recommendation technique that considers not only the relevant
cross-project work history (e.g., external library experience) but also the
experience of a developer in certain specialized technologies associated with a
pull request for determining her expertise as a potential code reviewer. We
first motivate our technique using an exploratory study with 10 commercial
projects and 10 associated libraries external to those projects. Experiments
using 17,115 pull requests from 10 commercial projects and six open source
projects show that our technique provides 85%--92% recommendation accuracy,
about 86% precision and 79%--81% recall in code reviewer recommendation, which
are highly promising. Comparison with the state-of-the-art technique also
validates the empirical findings and the superiority of our recommendation
technique.
",1,0,0,0,0,0
18168,Charged Perfect Fluid Distribution for Cosmological Universe Interacting With Massive Scalar Field in Brans-Dicke Theory,"  Considering a spherically-symmetric non-static cosmological flat model of
Robertson-Walker universe we have investigated the problem of perfect fluid
distribution interacting with the gravitational field in presence of massive
scalar field and electromagnetic field in B-D theory. Exact solutions have been
obtained by using a general approach of solving the partial differential
equations and it has been observed that the electromagnetic field cannot
survive for the cosmological flat model due to the influence caused by the
presence of massive scalar field.
",0,1,0,0,0,0
18169,Finite homogeneous geometries,"  This paper reproduces the text of a part of the Author's DPhil thesis. It
gives a proof of the classification of non-trivial, finite homogeneous
geometries of sufficiently high dimension which does not depend on the
classification of the finite simple groups.
",0,0,1,0,0,0
18170,Weakly-Private Information Retrieval,"  Private information retrieval (PIR) protocols make it possible to retrieve a
file from a database without disclosing any information about the identity of
the file being retrieved. These protocols have been rigorously explored from an
information-theoretic perspective in recent years. While existing protocols
strictly impose that no information is leaked on the file's identity, this work
initiates the study of the tradeoffs that can be achieved by relaxing the
requirement of perfect privacy. In case the user is willing to leak some
information on the identity of the retrieved file, we study how the PIR rate,
as well as the upload cost and access complexity, can be improved. For the
particular case of replicated servers, we propose two weakly-private
information retrieval schemes based on two recent PIR protocols and a family of
schemes based on partitioning. Lastly, we compare the performance of the
proposed schemes.
",1,0,0,0,0,0
18171,Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget,"  Iterative algorithms, like gradient descent, are common tools for solving a
variety of problems, such as model fitting. For this reason, there is interest
in creating differentially private versions of them. However, their conversion
to differentially private algorithms is often naive. For instance, a fixed
number of iterations are chosen, the privacy budget is split evenly among them,
and at each iteration, parameters are updated with a noisy gradient. In this
paper, we show that gradient-based algorithms can be improved by a more careful
allocation of privacy budget per iteration. Intuitively, at the beginning of
the optimization, gradients are expected to be large, so that they do not need
to be measured as accurately. However, as the parameters approach their optimal
values, the gradients decrease and hence need to be measured more accurately.
We add a basic line-search capability that helps the algorithm decide when more
accurate gradient measurements are necessary. Our gradient descent algorithm
works with the recently introduced zCDP version of differential privacy. It
outperforms prior algorithms for model fitting and is competitive with the
state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly
weaker definition than zCDP.
",0,0,0,1,0,0
18172,"""The universal meaning of the quantum of action"", by Jun Ishiwara","  Commented translation of the paper ""Universelle Bedeutung des
Wirkungsquantums"", published by Jun Ishiwara in German in the Proceedings of
Tokyo Mathematico-Physical Society 8 106-116 (1915). In his work, Ishiwara,
tenured at Sendai University, Japan, proposed - simultaneously with Arnold
Sommerfeld, William Wilson and Niels Bohr in Europe - the pase-space-integral
quantization, a rule that would be incorporated into the old-quantum-mechanics
formalism.
",0,1,0,0,0,0
18173,Barrier to recombination of oppositely charged large polarons,"  Electronic charge carriers in ionic materials can self-trap to form large
polarons. Interference between the ionic displacements associated with
oppositely charged large polarons increases as they approach one another.
Initially this interference produces an attractive potential that fosters their
merger. However, for small enough separations this interference generates a
repulsive interaction between oppositely charged large polarons. In suitable
circumstances this repulsion can overwhelm their direct Coulomb attraction.
Then the resulting net repulsion between oppositely charged large polarons
constitutes a potential barrier which impedes their recombination.
",0,1,0,0,0,0
18174,Metric Reduction and Generalized Holomorphic Structures,"  In this paper, metric reduction in generalized geometry is investigated. We
show how the Bismut connections on the quotient manifold are obtained from
those on the original manifold. The result facilitates the analysis of
generalized K$\ddot{a}$hler reduction, which motivates the concept of metric
generalized principal bundles and our approach to construct a family of
generalized holomorphic line bundles over $\mathbb{C}P^2$ equipped with some
non-trivial generalized K$\ddot{a}$hler structures.
",0,0,1,0,0,0
18175,Learning of Optimal Forecast Aggregation in Partial Evidence Environments,"  We consider the forecast aggregation problem in repeated settings, where the
forecasts are done on a binary event. At each period multiple experts provide
forecasts about an event. The goal of the aggregator is to aggregate those
forecasts into a subjective accurate forecast. We assume that experts are
Bayesian; namely they share a common prior, each expert is exposed to some
evidence, and each expert applies Bayes rule to deduce his forecast. The
aggregator is ignorant with respect to the information structure (i.e.,
distribution over evidence) according to which experts make their prediction.
The aggregator observes the experts' forecasts only. At the end of each period
the actual state is realized. We focus on the question whether the aggregator
can learn to aggregate optimally the forecasts of the experts, where the
optimal aggregation is the Bayesian aggregation that takes into account all the
information (evidence) in the system.
We consider the class of partial evidence information structures, where each
expert is exposed to a different subset of conditionally independent signals.
Our main results are positive; We show that optimal aggregation can be learned
in polynomial time in a quite wide range of instances of the partial evidence
environments. We provide a tight characterization of the instances where
learning is possible and impossible.
",0,0,0,1,0,0
18176,Distributed Unknown-Input-Observers for Cyber Attack Detection and Isolation in Formation Flying UAVs,"  In this paper, cyber attack detection and isolation is studied on a network
of UAVs in a formation flying setup. As the UAVs communicate to reach consensus
on their states while making the formation, the communication network among the
UAVs makes them vulnerable to a potential attack from malicious adversaries.
Two types of attacks pertinent to a network of UAVs have been considered: a
node attack on the UAVs and a deception attack on the communication between the
UAVs. UAVs formation control presented using a consensus algorithm to reach a
pre-specified formation. A node and a communication path deception cyber
attacks on the UAV's network are considered with their respective models in the
formation setup. For these cyber attacks detection, a bank of Unknown Input
Observer (UIO) based distributed fault detection scheme proposed to detect and
identify the compromised UAV in the formation. A rule based on the residuals
generated using the bank of UIOs are used to detect attacks and identify the
compromised UAV in the formation. Further, an algorithm developed to remove the
faulty UAV from the network once an attack detected and the compromised UAV
isolated while maintaining the formation flight with a missing UAV node.
",1,0,0,0,0,0
18177,Exponential Decay of the lengths of Spectral Gaps for Extended Harper's Model with Liouvillean Frequency,"  In this paper, we study the non-self dual extended Harper's model with
Liouvillean frequency. By establishing quantitative reducibility results
together with the averaging method, we prove that the lengths of spectral gaps
decay exponentially.
",0,0,1,0,0,0
18178,Topological Landau-Zener Bloch Oscillations in Photonic Floquet Lieb Lattices,"  The Lieb Lattice exhibits intriguing properties that are of general interest
in both the fundamental physics and practical applications. Here, we
investigate the topological Landau-Zener Bloch oscillation in a photonic
Floquet Lieb lattice, where the dimerized helical waveguides is constructed to
realize the synthetic spin-orbital interaction through the Floquet mechanism,
rendering us to study the impacts of topological transition from trivial gaps
to non-trivial ones. The compact localized states of flat bands supported by
the local symmetry of Lieb lattice will be associated with other bands by
topological invariants, Chern number, and involved into Landau-Zener transition
during Bloch oscillation. Importantly, the non-trivial geometrical phases after
topological transitions will be taken into account for constructive and
destructive interferences of wave functions. The numerical calculations of
continuum photonic medium demonstrate reasonable agreements with theoretical
tight-binding model. Our results provide an ongoing effort to realize designed
quantum materials with tailored properties.
",0,1,0,0,0,0
18179,Assessing the impact of bulk and shear viscosities on large scale structure formation,"  It is analyzed the effects of both bulk and shear viscosities on the
perturbations, relevant for structure formation in late time cosmology. It is
shown that shear viscosity can be as effective as the bulk viscosity on
suppressing the growth of perturbations and delaying the nonlinear regime. A
statistical analysis of the shear and bulk viscous effects is performed and
some constraints on these viscous effects are given.
",0,1,0,0,0,0
18180,Correlations in eigenfunctions of quantum chaotic systems with sparse Hamiltonian matrices,"  In most realistic models for quantum chaotic systems, the Hamiltonian
matrices in unperturbed bases have a sparse structure. We study correlations in
eigenfunctions of such systems and derive explicit expressions for some of the
correlation functions with respect to energy. The analytical results are tested
in several models by numerical simulations. An application is given for a
relation between transition probabilities.
",0,1,0,0,0,0
18181,Fast Trajectory Optimization for Legged Robots using Vertex-based ZMP Constraints,"  This paper combines the fast Zero-Moment-Point (ZMP) approaches that work
well in practice with the broader range of capabilities of a Trajectory
Optimization formulation, by optimizing over body motion, footholds and Center
of Pressure simultaneously. We introduce a vertex-based representation of the
support-area constraint, which can treat arbitrarily oriented point-, line-,
and area-contacts uniformly. This generalization allows us to create motions
such quadrupedal walking, trotting, bounding, pacing, combinations and
transitions between these, limping, bipedal walking and push-recovery all with
the same approach. This formulation constitutes a minimal representation of the
physical laws (unilateral contact forces) and kinematic restrictions (range of
motion) in legged locomotion, which allows us to generate various motion in
less than a second. We demonstrate the feasibility of the generated motions on
a real quadruped robot.
",1,0,1,0,0,0
18182,Electric Field Properties inside Central Gap of Dipole Micro/Nano Antennas Operating at 30 THz,"  This work investigates the influence of geometric variations in dipole
micro/nano antennas, regarding their implications on the characteristics of the
electric field inside the gap space of antenna monopoles. The gap is the
interface for a metal-Insulator-Metal (MIM) rectifier diode and it needs to be
carefully optimized, in order to allow better electric current generation by
tunneling current mechanisms. The arrangement (antenna + diode or rectenna) was
designed to operate around 30 Terahertz (THz).
",0,1,0,0,0,0
18183,Towards Understanding the Invertibility of Convolutional Neural Networks,"  Several recent works have empirically observed that Convolutional Neural Nets
(CNNs) are (approximately) invertible. To understand this approximate
invertibility phenomenon and how to leverage it more effectively, we focus on a
theoretical explanation and develop a mathematical model of sparse signal
recovery that is consistent with CNNs with random weights. We give an exact
connection to a particular model of model-based compressive sensing (and its
recovery algorithms) and random-weight CNNs. We show empirically that several
learned networks are consistent with our mathematical analysis and then
demonstrate that with such a simple theoretical framework, we can obtain
reasonable re- construction results on real images. We also discuss gaps
between our model assumptions and the CNN trained for classification in
practical scenarios.
",1,0,0,1,0,0
18184,An effective algorithm for hyperparameter optimization of neural networks,"  A major challenge in designing neural network (NN) systems is to determine
the best structure and parameters for the network given the data for the
machine learning problem at hand. Examples of parameters are the number of
layers and nodes, the learning rates, and the dropout rates. Typically, these
parameters are chosen based on heuristic rules and manually fine-tuned, which
may be very time-consuming, because evaluating the performance of a single
parametrization of the NN may require several hours. This paper addresses the
problem of choosing appropriate parameters for the NN by formulating it as a
box-constrained mathematical optimization problem, and applying a
derivative-free optimization tool that automatically and effectively searches
the parameter space. The optimization tool employs a radial basis function
model of the objective function (the prediction accuracy of the NN) to
accelerate the discovery of configurations yielding high accuracy. Candidate
configurations explored by the algorithm are trained to a small number of
epochs, and only the most promising candidates receive full training. The
performance of the proposed methodology is assessed on benchmark sets and in
the context of predicting drug-drug interactions, showing promising results.
The optimization tool used in this paper is open-source.
",1,0,0,0,0,0
18185,On estimation in varying coefficient models for sparse and irregularly sampled functional data,"  In this paper, we study a smoothness regularization method for a varying
coefficient model based on sparse and irregularly sampled functional data which
is contaminated with some measurement errors. We estimate the one-dimensional
covariance and cross-covariance functions of the underlying stochastic
processes based on a reproducing kernel Hilbert space approach. We then obtain
least squares estimates of the coefficient functions. Simulation studies
demonstrate that the proposed method has good performance. We illustrate our
method by an analysis of longitudinal primary biliary liver cirrhosis data.
",0,0,0,1,0,0
18186,Large-degree asymptotics of rational Painleve-IV functions associated to generalized Hermite polynomials,"  The Painleve-IV equation has three families of rational solutions generated
by the generalized Hermite polynomials. Each family is indexed by two positive
integers m and n. These functions have applications to nonlinear wave
equations, random matrices, fluid dynamics, and quantum mechanics. Numerical
studies suggest the zeros and poles form a deformed n by m rectangular grid.
Properly scaled, the zeros and poles appear to densely fill certain curvilinear
rectangles as m and n tend to infinity with r=m/n fixed. Generalizing a method
of Bertola and Bothner used to study rational Painleve-II functions, we express
the generalized Hermite rational Painleve-IV functions in terms of certain
orthogonal polynomials on the unit circle. Using the Deift-Zhou nonlinear
steepest-descent method, we asymptotically analyze the associated
Riemann-Hilbert problem in the limit as n tends to infinity with m=r*n for r
fixed. We obtain an explicit characterization of the boundary curve and
determine the leading-order asymptotic expansion of the functions in the
pole-free region.
",0,1,1,0,0,0
18187,Lattice Boltzmann study of chemically-driven self-propelled droplets,"  We numerically study the behavior of self-propelled liquid droplets whose
motion is triggered by a Marangoni-like flow. This latter is generated by
variations of surfactant concentration which affect the droplet surface tension
promoting its motion. In the present paper a model for droplets with a third
amphiphilic component is adopted. The dynamics is described by Navier-Stokes
and convection-diffusion equations, solved by lattice Boltzmann method coupled
with finite-difference schemes. We focus on two cases. First the study of
self-propulsion of an isolated droplet is carried on and, then, the interaction
of two self-propelled droplets is investigated. In both cases, when the
surfactant migrates towards the interface, a quadrupolar vortex of the velocity
field forms inside the droplet and causes the motion. A weaker dipolar field
emerges instead when the surfactant is mainly diluted in the bulk. The dynamics
of two interacting droplets is more complex and strongly depends on their
reciprocal distance. If, in a head-on collision, droplets are close enough, the
velocity field initially attracts them until a motionless steady state is
achieved. If the droplets are vertically shifted, the hydrodynamic field leads
to an initial reciprocal attraction followed by a scattering along opposite
directions. This hydrodynamic interaction acts on a separation of some droplet
radii otherwise it becomes negligible and droplets motion is only driven by
Marangoni effect. Finally, if one of the droplets is passive, this latter is
generally advected by the fluid flow generated by the active one.
",0,1,0,0,0,0
18188,RDMAvisor: Toward Deploying Scalable and Simple RDMA as a Service in Datacenters,"  RDMA is increasingly adopted by cloud computing platforms to provide low CPU
overhead, low latency, high throughput network services. On the other hand,
however, it is still challenging for developers to realize fast deployment of
RDMA-aware applications in the datacenter, since the performance is highly
related to many lowlevel details of RDMA operations. To address this problem,
we present a simple and scalable RDMA as Service (RaaS) to mitigate the impact
of RDMA operational details. RaaS provides careful message buffer management to
improve CPU/memory utilization and improve the scalability of RDMA operations.
These optimized designs lead to simple and flexible programming model for
common and knowledgeable users. We have implemented a prototype of RaaS, named
RDMAvisor, and evaluated its performance on a cluster with a large number of
connections. Our experiment results demonstrate that RDMAvisor achieves high
throughput for thousand of connections and maintains low CPU and memory
overhead through adaptive RDMA transport selection.
",1,0,0,0,0,0
18189,Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics,"  We recently introduced a method to approximate functions of Hermitian Matrix
Product Operators or Tensor Trains that are of the form $\mathsf{Tr} f(A)$.
Functions of this type occur in several applications, most notably in quantum
physics. In this work we aim at extending the theoretical understanding of our
method by showing several properties of our algorithm that can be used to
detect and correct errors in its results. Most importantly, we show that there
exists a more computationally efficient version of our algorithm for certain
inputs. To illustrate the usefulness of our finding, we prove that several
classes of spin Hamiltonians in quantum physics fall into this input category.
We finally support our findings with numerical results obtained for an example
from quantum physics.
",1,1,0,0,0,0
18190,Explicit polynomial sequences with maximal spaces of partial derivatives and a question of K. Mulmuley,"  We answer a question of K. Mulmuley: In [Efremenko-Landsberg-Schenck-Weyman]
it was shown that the method of shifted partial derivatives cannot be used to
separate the padded permanent from the determinant. Mulmuley asked if this
""no-go"" result could be extended to a model without padding. We prove this is
indeed the case using the iterated matrix multiplication polynomial. We also
provide several examples of polynomials with maximal space of partial
derivatives, including the complete symmetric polynomials. We apply Koszul
flattenings to these polynomials to have the first explicit sequence of
polynomials with symmetric border rank lower bounds higher than the bounds
attainable via partial derivatives.
",1,0,1,0,0,0
18191,Affine processes under parameter uncertainty,"  We develop a one-dimensional notion of affine processes under parameter
uncertainty, which we call non-linear affine processes. This is done as
follows: given a set of parameters for the process, we construct a
corresponding non-linear expectation on the path space of continuous processes.
By a general dynamic programming principle we link this non-linear expectation
to a variational form of the Kolmogorov equation, where the generator of a
single affine process is replaced by the supremum over all corresponding
generators of affine processes with parameters in the parameter set. This
non-linear affine process yields a tractable model for Knightian uncertainty,
especially for modelling interest rates under ambiguity.
We then develop an appropriate Ito-formula, the respective term-structure
equations and study the non-linear versions of the Vasicek and the
Cox-Ingersoll-Ross (CIR) model. Thereafter we introduce the non-linear
Vasicek-CIR model. This model is particularly suitable for modelling interest
rates when one does not want to restrict the state space a priori and hence the
approach solves this modelling issue arising with negative interest rates.
",0,0,0,0,0,1
18192,Plane graphs without 4- and 5-cycles and without ext-triangular 7-cycles are 3-colorable,"  Listed as No. 53 among the one hundred famous unsolved problems in [J. A.
Bondy, U. S. R. Murty, Graph Theory, Springer, Berlin, 2008] is Steinberg's
conjecture, which states that every planar graph without 4- and 5-cycles is
3-colorable. In this paper, we show that plane graphs without 4- and 5-cycles
are 3-colorable if they have no ext-triangular 7-cycles. This implies that (1)
planar graphs without 4-, 5-, 7-cycles are 3-colorable, and (2) planar graphs
without 4-, 5-, 8-cycles are 3-colorable, which cover a number of known results
in the literature motivated by Steinberg's conjecture.
",0,0,1,0,0,0
18193,Temporal Logic Task Planning and Intermittent Connectivity Control of Mobile Robot Networks,"  In this paper, we develop a distributed intermittent communication and task
planning framework for mobile robot teams. The goal of the robots is to
accomplish complex tasks, captured by local Linear Temporal Logic formulas, and
share the collected information with all other robots and possibly also with a
user. Specifically, we consider situations where the robot communication
capabilities are not sufficient to form reliable and connected networks while
the robots move to accomplish their tasks. In this case, intermittent
communication protocols are necessary that allow the robots to temporarily
disconnect from the network in order to accomplish their tasks free of
communication constraints. We assume that the robots can only communicate with
each other when they meet at common locations in space. Our distributed control
framework jointly determines local plans that allow all robots fulfill their
assigned temporal tasks, sequences of communication events that guarantee
information exchange infinitely often, and optimal communication locations that
minimize a desired distance metric. Simulation results verify the efficacy of
the proposed controllers.
",1,0,0,0,0,0
18194,Second-oder analysis in second-oder cone programming,"  The paper conducts a second-order variational analysis for an important class
of nonpolyhedral conic programs generated by the so-called
second-order/Lorentz/ice-cream cone $Q$. From one hand, we prove that the
indicator function of $Q$ is always twice epi-differentiable and apply this
result to characterizing the uniqueness of Lagrange multipliers at stationary
points together with an error bound estimate in the general second-order cone
setting involving ${\cal C}^2$-smooth data. On the other hand, we precisely
calculate the graphical derivative of the normal cone mapping to $Q$ under the
weakest metric subregularity constraint qualification and then give an
application of the latter result to a complete characterization of isolated
calmness for perturbed variational systems associated with second-order cone
programs. The obtained results seem to be the first in the literature in these
directions for nonpolyhedral problems without imposing any nondegeneracy
assumptions.
",0,0,1,0,0,0
18195,Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study,"  Interpreting the performance of deep learning models beyond test set accuracy
is challenging. Characteristics of individual data points are often not
considered during evaluation, and each data point is treated equally. We
examine the impact of a test set question's difficulty to determine if there is
a relationship between difficulty and performance. We model difficulty using
well-studied psychometric methods on human response patterns. Experiments on
Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
likelihood of answering a question correctly is impacted by the question's
difficulty. As DNNs are trained with more data, easy examples are learned more
quickly than hard examples.
",1,0,0,0,0,0
18196,The homotopy theory of coalgebras over simplicial comonads,"  We apply the Acyclicity Theorem of Hess, Kerdziorek, Riehl, and Shipley
(recently corrected by Garner, Kedziorek, and Riehl) to establishing the
existence of model category structure on categories of coalgebras over comonads
arising from simplicial adjunctions, under mild conditions on the adjunction
and the associated comonad. We study three concrete examples of such
adjunctions where the left adjoint is comonadic and show that in each case the
component of the derived counit of the comparison adjunction at any fibrant
object is an isomorphism, while the component of the derived unit at any
1-connected object is a weak equivalence. To prove this last result, we explain
how to construct explicit fibrant replacements for 1-connected coalgebras in
the image of the canonical comparison functor from the Postnikov decompositions
of their underlying simplicial sets. We also show in one case that the derived
unit is precisely the Bousfield-Kan completion map.
",0,0,1,0,0,0
18197,Spatio-temporal analysis of regional unemployment rates: A comparison of model based approaches,"  This study aims to analyze the methodologies that can be used to estimate the
total number of unemployed, as well as the unemployment rates for 28 regions of
Portugal, designated as NUTS III regions, using model based approaches as
compared to the direct estimation methods currently employed by INE (National
Statistical Institute of Portugal). Model based methods, often known as small
area estimation methods (Rao, 2003), ""borrow strength"" from neighbouring
regions and in doing so, aim to compensate for the small sample sizes often
observed in these areas. Consequently, it is generally accepted that model
based methods tend to produce estimates which have lesser variation. Other
benefit in employing model based methods is the possibility of including
auxiliary information in the form of variables of interest and latent random
structures. This study focuses on the application of Bayesian hierarchical
models to the Portuguese Labor Force Survey data from the 1st quarter of 2011
to the 4th quarter of 2013. Three different data modeling strategies are
considered and compared: Modeling of the total unemployed through Poisson,
Binomial and Negative Binomial models; modeling of rates using a Beta model;
and modeling of the three states of the labor market (employed, unemployed and
inactive) by a Multinomial model. The implementation of these models is based
on the \textit{Integrated Nested Laplace Approximation} (INLA) approach, except
for the Multinomial model which is implemented based on the method of Monte
Carlo Markov Chain (MCMC). Finally, a comparison of the performance of these
models, as well as the comparison of the results with those obtained by direct
estimation methods at NUTS III level are given.
",0,0,0,1,0,0
18198,Accelerating solutions of one-dimensional unsteady PDEs with GPU-based swept time-space decomposition,"  The expedient design of precision components in aerospace and other high-tech
industries requires simulations of physical phenomena often described by
partial differential equations (PDEs) without exact solutions. Modern design
problems require simulations with a level of resolution difficult to achieve in
reasonable amounts of time---even in effectively parallelized solvers. Though
the scale of the problem relative to available computing power is the greatest
impediment to accelerating these applications, significant performance gains
can be achieved through careful attention to the details of memory
communication and access. The swept time-space decomposition rule reduces
communication between sub-domains by exhausting the domain of influence before
communicating boundary values. Here we present a GPU implementation of the
swept rule, which modifies the algorithm for improved performance on this
processing architecture by prioritizing use of private (shared) memory,
avoiding interblock communication, and overwriting unnecessary values. It shows
significant improvement in the execution time of finite-difference solvers for
one-dimensional unsteady PDEs, producing speedups of 2--9$\times$ for a range
of problem sizes, respectively, compared with simple GPU versions and
7--300$\times$ compared with parallel CPU versions. However, for a more
sophisticated one-dimensional system of equations discretized with a
second-order finite-volume scheme, the swept rule performs 1.2--1.9$\times$
worse than a standard implementation for all problem sizes.
",1,1,0,0,0,0
18199,Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks,"  This article presents the use of Answer Set Programming (ASP) to mine
sequential patterns. ASP is a high-level declarative logic programming paradigm
for high level encoding combinatorial and optimization problem solving as well
as knowledge representation and reasoning. Thus, ASP is a good candidate for
implementing pattern mining with background knowledge, which has been a data
mining issue for a long time. We propose encodings of the classical sequential
pattern mining tasks within two representations of embeddings (fill-gaps vs
skip-gaps) and for various kinds of patterns: frequent, constrained and
condensed. We compare the computational performance of these encodings with
each other to get a good insight into the efficiency of ASP encodings. The
results show that the fill-gaps strategy is better on real problems due to
lower memory consumption. Finally, compared to a constraint programming
approach (CPSM), another declarative programming paradigm, our proposal showed
comparable performance.
",1,0,0,1,0,0
18200,Fukaya categories in Koszul duality theory,"  In this paper, we define $A_{\infty}$-Koszul duals for directed
$A_{\infty}$-categories in terms of twists in their $A_{\infty}$-derived
categories. Then, we compute a concrete formula of $A_{\infty}$-Koszul duals
for path algebras with directed $A_n$-type Gabriel quivers. To compute an
$A_\infty$-Koszul dual of such an algebra $A$, we construct a directed
subcategory of a Fukaya category which are $A_\infty$-derived equivalent to the
category of $A$-modules and compute Dehn twists as twists. The formula unveils
all the ext groups of simple modules of the parh algebras and their higher
composition structures.
",0,0,1,0,0,0
18201,BSDEs and SDEs with time-advanced and -delayed coefficients,"  This paper introduces a class of backward stochastic differential equations
(BSDEs), whose coefficients not only depend on the value of its solutions of
the present but also the past and the future. For a sufficiently small time
delay or a sufficiently small Lipschitz constant, the existence and uniqueness
of such BSDEs is obtained. As an adjoint process, a class of stochastic
differential equations (SDEs) is introduced, whose coefficients also depend on
the present, the past and the future of its solutions. The existence and
uniqueness of such SDEs is proved for a sufficiently small time advance or a
sufficiently small Lipschitz constant. A duality between such BSDEs and SDEs is
established.
",0,0,1,0,0,0
18202,Characterizing information importance and the effect on the spread in various graph topologies,"  In this paper we present a thorough analysis of the nature of news in
different mediums across the ages, introducing a unique mathematical model to
fit the characteristics of information spread. This model enhances the
information diffusion model to account for conflicting information and the
topical distribution of news in terms of popularity for a given era. We
translate this information to a separate graphical node model to determine the
spread of a news item given a certain category and relevance factor. The two
models are used as a base for a simulation of information dissemination for
varying graph topoligies. The simulation is stress-tested and compared against
real-world data to prove its relevancy. We are then able to use these
simulations to deduce some conclusive statements about the optimization of
information spread.
",1,1,0,0,0,0
18203,Quantum criticality at the superconductor to insulator transition revealed by specific heat measurements,"  The superconductor-insulator transition (SIT) is considered an excellent
example of a quantum phase transition which is driven by quantum fluctuations
at zero temperature. The quantum critical point is characterized by a diverging
correlation length and a vanishing energy scale. Low energy fluctuations near
quantum criticality may be experimentally detected by specific heat, $c_{\rm
p}$, measurements. Here, we use a unique highly sensitive experiment to measure
$c_{\rm p}$ of two-dimensional granular Pb films through the SIT. The specific
heat shows the usual jump at the mean field superconducting transition
temperature $T_{\rm c}^{\rm {mf}}$ marking the onset of Cooper pairs formation.
As the film thickness is tuned toward the SIT, $T_{\rm c}^{\rm {mf}}$ is
relatively unchanged, while the magnitude of the jump and low temperature
specific heat increase significantly. This behaviour is taken as the
thermodynamic fingerprint of quantum criticality in the vicinity of a quantum
phase transition.
",0,1,0,0,0,0
18204,Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference,"  We introduce a novel formulation of motion planning, for continuous-time
trajectories, as probabilistic inference. We first show how smooth
continuous-time trajectories can be represented by a small number of states
using sparse Gaussian process (GP) models. We next develop an efficient
gradient-based optimization algorithm that exploits this sparsity and GP
interpolation. We call this algorithm the Gaussian Process Motion Planner
(GPMP). We then detail how motion planning problems can be formulated as
probabilistic inference on a factor graph. This forms the basis for GPMP2, a
very efficient algorithm that combines GP representations of trajectories with
fast, structure-exploiting inference via numerical optimization. Finally, we
extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan
when conditions change. We benchmark our algorithms against several
sampling-based and trajectory optimization-based motion planning algorithms on
planning problems in multiple environments. Our evaluation reveals that GPMP2
is several times faster than previous algorithms while retaining robustness. We
also benchmark iGPMP2 on replanning problems, and show that it can find
successful solutions in a fraction of the time required by GPMP2 to replan from
scratch.
",1,0,0,0,0,0
18205,Polynomially and Infinitesimally Injective Modules,"  The injective polynomial modules for a general linear group $G$ of degree $n$
are labelled by the partitions with at most $n$ parts. Working over an
algebraically closed field of characteristic $p$, we consider the question of
which partitions correspond to polynomially injective modules that are also
injective as modules for the restricted enveloping algebra of the Lie algebra
of $G$. The question is related to the ""index of divisibility"" of a polynomial
module in general, and an explicit answer is given for $n=2$.
",0,0,1,0,0,0
18206,Nonstandard Methods in Ramsey Theory and Combinatorial Number Theory,"  The goal of this present manuscript is to introduce the reader to the
nonstandard method and to provide an overview of its most prominent
applications in Ramsey theory and combinatorial number theory.
",0,0,1,0,0,0
18207,When is Network Lasso Accurate?,"  The ""least absolute shrinkage and selection operator"" (Lasso) method has been
adapted recently for networkstructured datasets. In particular, this network
Lasso method allows to learn graph signals from a small number of noisy signal
samples by using the total variation of a graph signal for regularization.
While efficient and scalable implementations of the network Lasso are
available, only little is known about the conditions on the underlying network
structure which ensure network Lasso to be accurate. By leveraging concepts of
compressed sensing, we address this gap and derive precise conditions on the
underlying network topology and sampling set which guarantee the network Lasso
for a particular loss function to deliver an accurate estimate of the entire
underlying graph signal. We also quantify the error incurred by network Lasso
in terms of two constants which reflect the connectivity of the sampled nodes.
",0,0,0,1,0,0
18208,T-ROME: A Simple and Energy Efficient Tree Routing Protocol for Low-Power Wake-up Receivers,"  Wireless sensor networks are deployed in many monitoring applications but
still suffer from short lifetimes originating from limited energy sources and
storages. Due to their low-power consumption and their on-demand communication
ability, wake-up receivers represent an energy efficient and simple enhancement
to wireless sensor nodes and wireless sensor network protocols. In this
context, wake-up receivers have the ability to increase the network lifetime.
In this article, we present T-ROME, a simple and energy efficient cross-layer
routing protocol for wireless sensor nodes containing wake-up receivers. The
protocol makes use of the different transmission ranges of wake-up and main
radios in order to save energy by skipping nodes during data transfer. With
respect to energy consumption and latency, T-ROME outperforms existing
protocols in many scenarios. Here, we describe and analyze the cross layer
multi-hop protocol by means of a Markov chain model that we verify using a
laboratory test setup.
",1,0,0,0,0,0
18209,Classifying the Correctness of Generated White-Box Tests: An Exploratory Study,"  White-box test generator tools rely only on the code under test to select
test inputs, and capture the implementation's output as assertions. If there is
a fault in the implementation, it could get encoded in the generated tests.
Tool evaluations usually measure fault-detection capability using the number of
such fault-encoding tests. However, these faults are only detected, if the
developer can recognize that the encoded behavior is faulty. We designed an
exploratory study to investigate how developers perform in classifying
generated white-box test as faulty or correct. We carried out the study in a
laboratory setting with 54 graduate students. The tests were generated for two
open-source projects with the help of the IntelliTest tool. The performance of
the participants were analyzed using binary classification metrics and by
coding their observed activities. The results showed that participants
incorrectly classified a large number of both fault-encoding and correct tests
(with median misclassification rate 33% and 25% respectively). Thus the real
fault-detection capability of test generators could be much lower than
typically reported, and we suggest to take this human factor into account when
evaluating generated white-box tests.
",1,0,0,0,0,0
18210,Numerical Simulations of Saturn's B Ring: Granular Friction as a Mediator between Self-Gravity and Viscous Overstability,"  We study the B ring's complex optical depth structure. The source of this
structure may be the complex dynamics of the Keplerian shear and the
self-gravity of the ring particles. The outcome of these dynamic effects
depends sensitively on the collisional and physical properties of the
particles. Two mechanisms can emerge that dominate the macroscopic physical
structure of the ring: self-gravity wakes and viscous overstability. Here we
study the interplay between these two mechanisms by using our recently
developed particle collision method that allows us to better model the
inter-particle contact physics. We find that for a constant ring surface
density and particle internal density, particles with rough surfaces tend to
produce axisymmetric ring features associated with the viscous overstability,
while particles with smoother surfaces produce self-gravity wakes.
",0,1,0,0,0,0
18211,Shock-darkening in ordinary chondrites: determination of the pressure-temperature conditions by shock physics mesoscale modeling,"  We determined the shock-darkening pressure range in ordinary chondrites using
the iSALE shock physics code. We simulated planar shock waves on a mesoscale in
a sample layer at different nominal pressures. Iron and troilite grains were
resolved in a porous olivine matrix in the sample layer. We used equations of
state (Tillotson EoS and ANEOS) and basic strength and thermal properties to
describe the material phases. We used Lagrangian tracers to record peak shock
pressures in each material unit. The post-shock temperatures (and the fractions
of tracers experiencing temperatures above the melting point) for each material
were estimated after the passage of the shock wave and after reflections of the
shock at grain boundaries in the heterogeneous materials. The results showed
that shock-darkening, associated with troilite melt and the onset of olivine
melt, happened between 40 and 50 GPa - with 52 GPa being the pressure at which
all tracers in the troilite material reach the melting point. We demonstrate
the difficulties of shock heating in iron and also the importance of porosity.
Material impedances, grain shapes and the porosity models available in the
iSALE code are discussed. We also discussed possible not-shock-related triggers
for iron melt.
",0,1,0,0,0,0
18212,On the Global Fluctuations of Block Gaussian Matrices,"  In this paper we study the global fluctuations of block Gaussian matrices
within the framework of second-order free probability theory. In order to
compute the second-order Cauchy transform of these matrices, we introduce a
matricial second-order conditional expectation and compute the matricial
second-order Cauchy transform of a certain type of non-commutative random
variables. As a by-product, using the linearization technique, we obtain the
second-order Cauchy transform of non-commutative rational functions evaluated
on selfadjoint Gaussian matrices.
",0,0,1,0,0,0
18213,Carnot Efficiency of Publication,"  This paper analyzes publication efficiency in terms of Hirsch-index or
h-index and total citations, with an analogy to the Carnot efficiency used in
thermodynamics. Such publication efficiency, with typical value of 30%, can be
utilized to normalize the research output judgment, favoring quality outputs in
reduced quantity, which is currently lacking in many discipline.
",1,0,0,0,0,0
18214,EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras,"  Event-based cameras have shown great promise in a variety of situations where
frame based cameras suffer, such as high speed motions and high dynamic range
scenes. However, developing algorithms for event measurements requires a new
class of hand crafted algorithms. Deep learning has shown great success in
providing model free solutions to many problems in the vision community, but
existing networks have been developed with frame based images in mind, and
there does not exist the wealth of labeled data for events as there does for
images for supervised training. To these points, we present EV-FlowNet, a novel
self-supervised deep learning pipeline for optical flow estimation for event
based cameras. In particular, we introduce an image based representation of a
given event stream, which is fed into a self-supervised neural network as the
sole input. The corresponding grayscale images captured from the same camera at
the same time as the events are then used as a supervisory signal to provide a
loss function at training time, given the estimated flow from the network. We
show that the resulting network is able to accurately predict optical flow from
events only in a variety of different scenes, with performance competitive to
image based networks. This method not only allows for accurate estimation of
dense optical flow, but also provides a framework for the transfer of other
self-supervised methods to the event-based domain.
",1,0,0,0,0,0
18215,Deep Active Learning for Named Entity Recognition,"  Deep learning has yielded state-of-the-art performance on many natural
language processing tasks including named entity recognition (NER). However,
this typically requires large amounts of labeled data. In this work, we
demonstrate that the amount of labeled training data can be drastically reduced
when deep learning is combined with active learning. While active learning is
sample-efficient, it can be computationally expensive since it requires
iterative retraining. To speed this up, we introduce a lightweight architecture
for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and
word encoders and a long short term memory (LSTM) tag decoder. The model
achieves nearly state-of-the-art performance on standard datasets for the task
while being computationally much more efficient than best performing models. We
carry out incremental active learning, during the training process, and are
able to nearly match state-of-the-art performance with just 25\% of the
original training data.
",1,0,0,0,0,0
18216,Bipedal Walking with Corrective Actions in the Tilt Phase Space,"  Many methods exist for a bipedal robot to keep its balance while walking. In
addition to step size and timing, other strategies are possible that influence
the stability of the robot without interfering with the target direction and
speed of locomotion. This paper introduces a multifaceted feedback controller
that uses numerous different feedback mechanisms, collectively termed
corrective actions, to stabilise a core keypoint-based gait. The feedback
controller is experimentally effective, yet free of any physical model of the
robot, very computationally inexpensive, and requires only a single 6-axis IMU
sensor. Due to these low requirements, the approach is deemed to be highly
portable between robots, and was specifically also designed to target lower
cost robots that have suboptimal sensing, actuation and computational
resources. The IMU data is used to estimate the yaw-independent tilt
orientation of the robot, expressed in the so-called tilt phase space, and is
the source of all feedback provided by the controller. Experimental validation
is performed in simulation as well as on real robot hardware.
",1,0,0,0,0,0
18217,TimeNet: Pre-trained deep recurrent neural network for time series classification,"  Inspired by the tremendous success of deep Convolutional Neural Networks as
generic feature extractors for images, we propose TimeNet: a deep recurrent
neural network (RNN) trained on diverse time series in an unsupervised manner
using sequence to sequence (seq2seq) models to extract features from time
series. Rather than relying on data from the problem domain, TimeNet attempts
to generalize time series representation across domains by ingesting time
series from several domains simultaneously. Once trained, TimeNet can be used
as a generic off-the-shelf feature extractor for time series. The
representations or embeddings given by a pre-trained TimeNet are found to be
useful for time series classification (TSC). For several publicly available
datasets from UCR TSC Archive and an industrial telematics sensor data from
vehicles, we observe that a classifier learned over the TimeNet embeddings
yields significantly better performance compared to (i) a classifier learned
over the embeddings given by a domain-specific RNN, as well as (ii) a nearest
neighbor classifier based on Dynamic Time Warping.
",1,0,0,0,0,0
18218,Differentially Private Federated Learning: A Client Level Perspective,"  Federated learning is a recent advance in privacy protection. In this
context, a trusted curator aggregates parameters optimized in decentralized
fashion by multiple clients. The resulting model is then distributed back to
all clients, ultimately converging to a joint representative model without
explicitly having to share the data. However, the protocol is vulnerable to
differential attacks, which could originate from any party contributing during
federated optimization. In such an attack, a client's contribution during
training and information about their data set is revealed through analyzing the
distributed model. We tackle this problem and propose an algorithm for client
sided differential privacy preserving federated optimization. The aim is to
hide clients' contributions during training, balancing the trade-off between
privacy loss and model performance. Empirical studies suggest that given a
sufficiently large number of participating clients, our proposed procedure can
maintain client-level differential privacy at only a minor cost in model
performance.
",1,0,0,1,0,0
18219,"Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available","  Okapi is a new causally consistent geo-replicated key- value store. Okapi
leverages two key design choices to achieve high performance. First, it relies
on hybrid logical/physical clocks to achieve low latency even in the presence
of clock skew. Second, Okapi achieves higher resource efficiency and better
availability, at the expense of a slight increase in update visibility latency.
To this end, Okapi implements a new stabilization protocol that uses a
combination of vector and scalar clocks and makes a remote update visible when
its delivery has been acknowledged by every data center. We evaluate Okapi with
different workloads on Amazon AWS, using three geographically distributed
regions and 96 nodes. We compare Okapi with two recent approaches to causal
consistency, Cure and GentleRain. We show that Okapi delivers up to two orders
of magnitude better performance than GentleRain and that Okapi achieves up to
3.5x lower latency and a 60% reduction of the meta-data overhead with respect
to Cure.
",1,0,0,0,0,0
18220,Emergence of epithelial cell density waves,"  Epithelial cell monolayers exhibit traveling mechanical waves. We rationalize
this observation thanks to a hydrodynamic description of the monolayer as a
compressible, active and polar material. We show that propagating waves of the
cell density, polarity, velocity and stress fields may be due to a Hopf
bifurcation occurring above threshold values of active coupling coefficients.
",0,1,0,0,0,0
18221,Complex Analysis of Real Functions IV: Non-Integrable Real Functions,"  In the context of the complex-analytic structure within the unit disk
centered at the origin of the complex plane, that was presented in a previous
paper, we show that a certain class of non-integrable real functions can be
represented within that same structure. In previous papers it was shown that
essentially all integrable real functions, as well as all singular Schwartz
distributions, can be represented within that same complex-analytic structure.
The large class of non-integrable real functions which we analyze here can
therefore be represented side by side with those other real objects, thus
allowing all these objects to be treated in a unified way.
",0,0,1,0,0,0
18222,Convolution Aware Initialization,"  Initialization of parameters in deep neural networks has been shown to have a
big impact on the performance of the networks (Mishkin & Matas, 2015). The
initialization scheme devised by He et al, allowed convolution activations to
carry a constrained mean which allowed deep networks to be trained effectively
(He et al., 2015a). Orthogonal initializations and more generally orthogonal
matrices in standard recurrent networks have been proved to eradicate the
vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of
current initialization schemes do not take fully into account the intrinsic
structure of the convolution operator. Using the duality of the Fourier
transform and the convolution operator, Convolution Aware Initialization builds
orthogonal filters in the Fourier space, and using the inverse Fourier
transform represents them in the standard space. With Convolution Aware
Initialization we noticed not only higher accuracy and lower loss, but faster
convergence. We achieve new state of the art on the CIFAR10 dataset, and
achieve close to state of the art on various other tasks.
",1,0,0,1,0,0
18223,A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation,"  We consider a multitask estimation problem where nodes in a network are
divided into several connected clusters, with each cluster performing a
least-mean-squares estimation of a different random parameter vector. Inspired
by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
strategy whose mean stability can be ensured whenever individual nodes are
stable in the mean, regardless of the inter-cluster cooperation weights. In
addition, the proposed strategy is able to achieve an asymptotically unbiased
estimation, when the parameters have same mean. We also develop an
inter-cluster cooperation weights selection scheme that allows each node in the
network to locally optimize its inter-cluster cooperation weights. Numerical
results demonstrate that our approach leads to a lower average steady-state
network mean-square deviation, compared with using weights selected by various
other commonly adopted methods in the literature.
",1,0,0,0,0,0
18224,High Resolution Observations of the Massive Protostar in IRAS18566+0408,"  We report 3 mm continuum, CH3CN(5-4) and 13CS(2-1) line observations with
CARMA, in conjunction with 6 and 1.3 cm continuum VLA data, and 12 and 25
micron broadband data from the Subaru Telescope toward the massive proto-star
IRAS18566+0408. The VLA data resolve the ionized jet into 4 components aligned
in the E-W direction. Radio components A, C, and D have flat cm SEDs indicative
of optically thin emission from ionized gas, and component B has a spectral
index alpha = 1.0, and a decreasing size with frequency proportional to
frequency to the -0.5 power. Emission from the CARMA 3 mm continuum, and from
the 13CS(2-1), and CH3CN(5-4) spectral lines is compact (i.e. < 6700 AU), and
peaks near the position of VLA cm source, component B. Analysis of these lines
indicates hot, and dense molecular gas, typical for HMCs. Our Subaru telescope
observations detect a single compact source, coincident with radio component B,
demonstrating that most of the energy in IRAS18566+0408 originates from a
region of size < 2400 AU. We also present UKIRT near-infrared archival data for
IRAS18566+0408 which show extended K-band emission along the jet direction. We
detect an E-W velocity shift of about 10 km/sec over the HMC in the CH3CN lines
possibly tracing the interface of the ionized jet with the surrounding core
gas. Our data demonstrate the presence of an ionized jet at the base of the
molecular outflow, and support the hypothesis that massive protostars with
O-type luminosity form with a mechanism similar to lower mass stars.
",0,1,0,0,0,0
18225,MEDL and MEDLA: Methods for Assessment of Scaling by Medians of Log-Squared Nondecimated Wavelet Coefficients,"  High-frequency measurements and images acquired from various sources in the
real world often possess a degree of self-similarity and inherent regular
scaling. When data look like a noise, the scaling exponent may be the only
informative feature that summarizes such data. Methods for the assessment of
self-similarity by estimating Hurst exponent often involve analysis of rate of
decay in a spectrum defined in various multiresolution domains. When this
spectrum is calculated using discrete non-decimated wavelet transforms, due to
increased autocorrelation in wavelet coefficients, the estimators of $H$ show
increased bias compared to the estimators that use traditional orthogonal
transforms. At the same time, non-decimated transforms have a number of
advantages when employed for calculation of wavelet spectra and estimation of
Hurst exponents: the variance of the estimator is smaller, input signals and
images could be of arbitrary size, and due to the shift-invariance, the local
scaling can be assessed as well. We propose two methods based on robust
estimation and resampling that alleviate the effect of increased
autocorrelation while maintaining all advantages of non-decimated wavelet
transforms. The proposed methods extend the approaches in existing literature
where the logarithmic transformation and pairing of wavelet coefficients are
used for lowering the bias. In a simulation study we use fractional Brownian
motions with a range of theoretical Hurst exponents. For such signals for which
""true"" $H$ is known, we demonstrate bias reduction and overall reduction of the
mean-squared error by the two proposed estimators. For fractional Brownian
motions, both proposed methods yield estimators of $H$ that are asymptotically
normal and unbiased.
",0,0,0,1,0,0
18226,A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications,"  In the k-partition problem (k-PP), one is given an edge-weighted undirected
graph, and one must partition the node set into at most k subsets, in order to
minimise (or maximise) the total weight of the edges that have their end-nodes
in the same cluster. Various hierarchical variants of this problem have been
studied in the context of data mining. We consider a 'two-level' variant that
arises in mobile wireless communications. We show that an exact algorithm based
on intelligent preprocessing, cutting planes and symmetry-breaking is capable
of solving small- and medium-size instances to proven optimality, and providing
strong lower bounds for larger instances.
",1,0,1,0,0,0
18227,Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA,"  The present study reports interesting findings in regard to emotional arousal
based activities while listening to two Hindustani classical ragas of contrast
emotion. EEG data was taken on 5 naive listeners while they listened to two
ragas Bahar and Mia ki Malhar which are conventionally known to portray
contrast emotions. The EEG data were analyzed with the help of two robust non
linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation
Analysis (DFA). A comparative study of the Hurst Exponents obtained from the
two methods have been shown which shows that DFA provides more rigorous results
compared to AFA when it comes to the scaling analysis of biosignal data. The
results and implications have been discussed in detail.
",0,1,0,0,0,0
18228,The light pollution as a surrogate for urban population of the US cities,"  We show that the definition of the city boundaries can have a dramatic
influence on the scaling behavior of the night-time light (NTL) as a function
of population (POP) in the US. Precisely, our results show that the arbitrary
geopolitical definition based on the Metropolitan/Consolidated Metropolitan
Statistical Areas (MSA/CMSA) leads to a sublinear power-law growth of NTL with
POP. On the other hand, when cities are defined according to a more natural
agglomeration criteria, namely, the City Clustering Algorithm (CCA), an
isometric relation emerges between NTL and population. This discrepancy is
compatible with results from previous works showing that the scaling behaviors
of various urban indicators with population can be substantially different for
distinct definitions of city boundaries. Moreover, considering the CCA
definition as more adequate than the MSA/CMSA one because the former does not
violate the expected extensivity between land population and area of their
generated clusters, we conclude that, without loss of generality, the CCA
measures of light pollution and population could be interchangeably utilized in
future studies.
",0,1,0,0,0,0
18229,"Analytic calculation of radio emission from parameterized extensive air showers, a tool to extract shower parameters","  The radio intensity and polarization footprint of a cosmic-ray induced
extensive air shower is determined by the time-dependent structure of the
current distribution residing in the plasma cloud at the shower front. In turn,
the time dependence of the integrated charge-current distribution in the plasma
cloud, the longitudinal shower structure, is determined by interesting physics
which one would like to extract such as the location and multiplicity of the
primary cosmic-ray collision or the values of electric fields in the atmosphere
during thunderstorms. To extract the structure of a shower from its footprint
requires solving a complicated inverse problem. For this purpose we have
developed a code that semi-analytically calculates the radio footprint of an
extensive air shower given an arbitrary longitudinal structure. This code can
be used in a optimization procedure to extract the optimal longitudinal shower
structure given a radio footprint. On the basis of air-shower universality we
propose a simple parametrization of the structure of the plasma cloud. This
parametrization is based on the results of Monte-Carlo shower simulations.
Deriving the parametrization also teaches which aspects of the plasma cloud are
important for understanding the features seen in the radio-emission footprint.
The calculated radio footprints are compared with microscopic CoREAS
simulations.
",0,1,0,0,0,0
18230,Akhmediev Breathers and Peregrine Solitary Waves in a Quadratic Medium,"  We investigate the formation of optical localized nonlinear structures,
evolving upon a non-zero background plane wave, in a dispersive quadratic
medium. We show the existence of quadratic Akhmediev breathers and Peregrine
solitary waves, in the regime of cascading second-harmonic generation. This
finding opens a novel path for the excitation of extreme rogue waves and for
the description of modulation instability in quadratic nonlinear optics.
",0,1,0,0,0,0
18231,Novel approaches to spectral properties of correlated electron materials: From generalized Kohn-Sham theory to screened exchange dynamical mean field theory,"  The most intriguing properties of emergent materials are typically
consequences of highly correlated quantum states of their electronic degrees of
freedom. Describing those materials from first principles remains a challenge
for modern condensed matter theory. Here, we review, apply and discuss novel
approaches to spectral properties of correlated electron materials, assessing
current day predictive capabilities of electronic structure calculations. In
particular, we focus on the recent Screened Exchange Dynamical Mean-Field
Theory scheme and its relation to generalized Kohn-Sham theory. These concepts
are illustrated on the transition metal pnictide BaCo$_2$As$_2$ and elemental
zinc and cadmium.
",0,1,0,0,0,0
18232,Flux-Rope Twist in Eruptive Flares and CMEs: due to Zipper and Main-Phase Reconnection,"  The nature of three-dimensional reconnection when a twisted flux tube erupts
during an eruptive flare or coronal mass ejection is considered. The
reconnection has two phases: first of all, 3D ""zipper reconnection"" propagates
along the initial coronal arcade, parallel to the polarity inversion line
(PIL), then subsequent quasi-2D ""main phase reconnection"" in the low corona
around a flux rope during its eruption produces coronal loops and chromospheric
ribbons that propagate away from the PIL in a direction normal to it.
One scenario starts with a sheared arcade: the zipper reconnection creates a
twisted flux rope of roughly one turn ($2\pi$ radians of twist), and then main
phase reconnection builds up the bulk of the erupting flux rope with a
relatively uniform twist of a few turns. A second scenario starts with a
pre-existing flux rope under the arcade. Here the zipper phase can create a
core with many turns that depend on the ratio of the magnetic fluxes in the
newly formed flare ribbons and the new flux rope. Main phase reconnection then
adds a layer of roughly uniform twist to the twisted central core. Both phases
and scenarios are modeled in a simple way that assumes the initial magnetic
flux is fragmented along the PIL. The model uses conservation of magnetic
helicity and flux, together with equipartition of magnetic helicity, to deduce
the twist of the erupting flux rope in terms the geometry of the initial
configuration.
Interplanetary observations show some flux ropes have a fairly uniform twist,
which could be produced when the zipper phase and any pre-existing flux rope
possess small or moderate twist (up to one or two turns). Other interplanetary
flux ropes have highly twisted cores (up to five turns), which could be
produced when there is a pre-existing flux rope and an active zipper phase that
creates substantial extra twist.
",0,1,0,0,0,0
18233,On the classification of Kantor pairs and structurable algebras in characteristic 5,"  We observe that any finite-dimensional central simple 5-graded Lie algebra
over over a field k of characteristic not 2,3 is necessarily classical, i.e. a
twisted form of a Chevalley Lie algebra. Consequently, the classification of
central simple structurable algebras and Kantor pairs over fields of
characteristic 5 derives from the classification of simple algebraic groups.
Using the classification of nilpotent conjugacy classes, we list all 5-gradings
on Lie algebras of simple algebraic groups that give rise to simple
structurable algebras over algebraically closed fields.
",0,0,1,0,0,0
18234,Cross-lingual Distillation for Text Classification,"  Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.
",1,0,0,0,0,0
18235,Characterization of Majorana-Ising phase transition in a helical liquid system,"  We map an interacting helical liquid system, coupled to an external magnetic
field and s-wave superconductor, to an XYZ spin system, and it undergoes
Majorana-Ising transition by tuning of parameters. In the Majorana state,
lowest excitation gap decays exponentially with system size, and the system has
degenerate ground state in the thermodynamic limit. On the contrary, the gap
opens in the Ising phase even in the thermodynamic limit. We also study other
criteria to characterize the transition, such as edge spin correlation with its
neighbor $C(r=1)$, local susceptibility $\chi_i$, superconducting order
parameter of edge spin $P(r=1)$, and longitudinal structure factor $S(k)$. The
ground state degeneracy and three other criteria lead to the same critical
value of parameters for Majorana-Ising phase transition in the thermodynamic
limit. We study, for the first time, the entanglement spectrum of the reduced
density matrix of the helical liquid system. The system shows finite Schmidt
gap and non-degeneracy of the entanglement spectrum in the Ising limit. The
Schmidt gap closes in the Majorana state, and all the eigenvalues are either
doubly or multiply degenerate.
",0,1,0,0,0,0
18236,Monotonicity and symmetry of nonnegative solutions to $ -Δu=f(u) $ in half-planes and strips,"  We consider nonnegative solutions to $-\Delta u=f(u)$ in half-planes and
strips, under zero Dirichlet boundary condition. Exploiting a
rotating$\&$sliding line technique, we prove symmetry and monotonicity
properties of the solutions, under very general assumptions on the nonlinearity
$f$. In fact we provide a unified approach that works in all the cases
$f(0)<0$, $f(0)= 0$ or $f(0)> 0$. Furthermore we make the effort to deal with
nonlinearities $f$ that may be not locally-Lipschitz continuous. We also
provide explicite examples showing the sharpness of our assumptions on the
nonlinear function $f$.
",0,0,1,0,0,0
18237,Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting,"  Rapidly growing product lines and services require a finer-granularity
forecast that considers geographic locales. However the open question remains,
how to assess the quality of a spatio-temporal forecast? In this manuscript we
introduce a metric to evaluate spatio-temporal forecasts. This metric is based
on an Opti- mal Transport (OT) problem. The metric we propose is a constrained
OT objec- tive function using the Gini impurity function as a regularizer. We
demonstrate through computer experiments both the qualitative and the
quantitative charac- teristics of the Gini regularized OT problem. Moreover, we
show that the Gini regularized OT problem converges to the classical OT
problem, when the Gini regularized problem is considered as a function of
{\lambda}, the regularization parame-ter. The convergence to the classical OT
solution is faster than the state-of-the-art Entropic-regularized OT[Cuturi,
2013] and results in a numerically more stable algorithm.
",1,0,0,1,0,0
18238,Indicators of Conformal Field Theory: entanglement entropy and multiple-point correlators,"  The entanglement entropy (EE) of quantum systems is often used as a test of
low-energy descriptions by conformal field theory (CFT). Here we point out that
this is not a reliable indicator, as the EE often shows the same behavior even
when a CFT description is not correct (as long as the system is asymptotically
scale-invariant). We use constraints on the scaling dimension given by the CFT
with SU(2) symmetry to provide alternative tests with two- and four-point
correlation functions, showing examples for quantum spin models in 1+1
dimensions. In the case of a critical amplitude-product state expressed in the
valence-bond basis (where the amplitudes decay as a power law of the bond
length and the wave function is the product of all bond amplitudes), we show
that even though the EE exhibits the expected CFT behavior, there is no CFT
description of this state. We provide numerical tests of the behavior predicted
by CFT for the correlation functions in the critical transverse-field Ising
chain and the $J$-$Q$ spin chain, where the conformal structure is well
understood. That behavior is not reproduced in the amplitude-product state.
",0,1,0,0,0,0
18239,Classical Entanglement Structure in the Wavefunction of Inflationary Fluctuations,"  We argue that the preferred classical variables that emerge from a pure
quantum state are determined by its entanglement structure in the form of
redundant records: information shared between many subsystems. Focusing on the
early universe, we ask how classical metric perturbations emerge from vacuum
fluctuations in an inflationary background. We show that the squeezing of the
quantum state for super-horizon modes, along with minimal gravitational
interactions, leads to decoherence and to an exponential number of records of
metric fluctuations on very large scales, $\lambda/\lambda_{\rm
Hubble}>\Delta_\zeta^{-2/3}$, where $\Delta_\zeta\lesssim 10^{-5}$ is the
amplitude of scalar metric fluctuations. This determines a preferred
decomposition of the inflationary wavefunction into orthogonal ""branches""
corresponding to classical metric perturbations, which defines an inflationary
entropy production rate and accounts for the emergence of stochastic,
inhomogeneous spacetime geometry.
",0,1,0,0,0,0
18240,Pulsejet engine dynamics in vertical motion using momentum conservation,"  The momentum conservation law is applied to analyse the dynamics of pulsejet
engine in vertical motion in a uniform gravitational field in the absence of
friction. The model predicts existence of a terminal speed given frequency of
the short pulses. The conditions that the engine does not return to the
starting position are identified. The number of short periodic pulses after
which the engine returns to the starting position is found to be independent of
the exhaust velocity and gravitational field intensity for certain frequency of
the pulses. The pulsejet engine and turbojet engine aircraft models of dynamics
are compared. Also the octopus dynamics is modelled. The paper is addressed to
intermediate undergraduate students of classical mechanics and aerospace
engineering.
",0,1,0,0,0,0
18241,Mitigating Blackout Risk via Maintenance : Inference from Simulation Data,"  Whereas maintenance has been recognized as an important and effective means
for risk management in power systems, it turns out to be intractable if
cascading blackout risk is considered due to the extremely high computational
complexity. In this paper, based on the inference from the blackout simulation
data, we propose a methodology to efficiently identify the most influential
component(s) for mitigating cascading blackout risk in a large power system. To
this end, we first establish an analytic relationship between maintenance
strategies and blackout risk estimation by inferring from the data of cascading
outage simulations. Then we formulate the component maintenance decision-making
problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility
of blackout risk estimation, leading to an adaptive method to determine the
least required number of simulations, which servers as a crucial parameter of
the optimization model. Finally, we devise two heuristic algorithms to find
approximate optimal solutions to the model with very high efficiency. Numerical
experiments well manifest the efficacy and high efficiency of our methodology.
",0,0,0,1,0,0
18242,Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows,"  In the 1950's Hopf gave examples of non-round convex 2-spheres in Euclidean
3-space with rotational symmetry that satisfy a linear relationship between
their principal curvatures. In this paper we investigate conditions under which
evolving a smooth rotationally symmetric sphere by a linear combination of its
radii of curvature yields a Hopf sphere. When the coefficients of the flow have
certain integer values, the fate of an initial sphere is entirely determined by
the local geometry of its isolated umbilic points. A surprising variety of
behaviours is uncovered: convergence to round spheres and non-round Hopf
spheres, as well as divergence to infinity.
The critical quantity is the rate of vanishing of the astigmatism - the
difference of the radii of curvature - at the isolated umbilic points. It is
proven that the size of this quantity versus the coefficient in the flow
function determines the fate of the evolution.
The geometric setting for the equation is Radius of Curvature space, viewed
as a pair of hyperbolic/AdS half-planes joined along their boundary, the
umbilic horizon. A rotationally symmetric sphere determines a parameterized
curve in this plane with end-points on the umbilic horizon. The slope of the
curve at the umbilic horizon is linked by the Codazzi-Mainardi equations to the
rate of vanishing of astigmatism, and for generic initial conditions can be
used to determine the outcome of the flow.
The slope can jump during the flow, and a number of examples are given:
instant jumps of the initial slope, as well as umbilic circles that contract to
points in finite time and 'pop' the slope. Finally, we present soliton-like
solutions: curves that evolve under linear flows by mutual hyperbolic/AdS
isometries (dilation and translation) of Radius of Curvature space. A
forthcoming paper will apply these geometric ideas to non-linear curvature
flows.
",0,0,1,0,0,0
18243,On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems,"  We revisit the linear programming approach to deterministic, continuous time,
infinite horizon discounted optimal control problems. In the first part, we
relax the original problem to an infinite-dimensional linear program over a
measure space and prove equivalence of the two formulations under mild
assumptions, significantly weaker than those found in the literature until now.
The proof is based on duality theory and mollification techniques for
constructing approximate smooth subsolutions to the associated
Hamilton-Jacobi-Bellman equation. In the second part, we assume polynomial data
and use Lasserre's hierarchy of primal-dual moment-sum-of-squares semidefinite
relaxations to approximate the value function and design an approximate optimal
feedback controller. We conclude with an illustrative example.
",1,0,1,0,0,0
18244,Granular permittivity representation in extremely near-field light-matter interactions processes,"  Light-matter interaction processes are significantly affected by surrounding
electromagnetic environment. Dielectric materials are usually introduced into
an interaction picture via their classical properties, e.g. permittivity,
appearing in constitutive relations. While this approach was proven to be
applicable in many occasions, it might face limitations when an emitter is
situated very close to a material boundary. In this case nonlocal extend of a
quantum wave function of an emitter becomes comparable with a distance to a
boundary and a lattice constant of a material. Here a semi-classical model,
taking into account material's granularity, is developed. In particular,
spontaneous emission process in the vicinity of flat boundaries is considered.
The material boundary is divided into a pair areas - far zone is modeled as a
continuous phase, while the near zone next to a nonlocal emitter is represented
with a discrete array of polarizable particles. This array resembles optical
properties of the continuous phase under the standard homogenization procedure.
Local field effects were shown to lead orders of magnitude corrections to
spontaneous emission rates in the case of sub-nanometer emitter-surface
separation distances. The developed mesoscopic model enables addressing few
aspects of local field corrections in quite complex scenarios, where quantum ab
initio techniques yet face challenges owing to involved computational
complexity. The developed method could be utilized for designs of quantum
sources and networks, enhanced with structured electromagnetic environment.
",0,1,0,0,0,0
18245,Application of Superhalogens in the Design of Organic Superconductors,"  Bechgaard salts, (TMTSF)2X (TMTSF = tetramethyl tetraselenafulvalene and X =
complex anion), form the most popular series of organic superconductors. In
these salts, TMTSF molecules act as super-electron donor and X as acceptor. We
computationally examine the electronic structure and properties of X in
commonly used (TMTSF)2X (X = NO3, BF4, ClO4, PF6) superconductors and notice
that they belong to the class of superhalogens due to their higher vertical
detachment energy than halogen anions. This prompted us to choose other
superhalogens such as X = BO2, BH4, B2F7, AuF6 and study their (TMTSF)2X
complexes. Our findings suggest that these complexes behave more or less
similar to those of established (TMTSF)2X superconductors, particularly for X =
BO2 and B2F7. We, therefore, believe that the concept of superhalogen can be
successfully applied in the design of novel organic superconductors.
",0,1,0,0,0,0
18246,Inhomogeneous hard-core bosonic mixture with checkerboard supersolid phase: Quantum and thermal phase diagram,"  We introduce an inhomogeneous bosonic mixture composed of two kinds of
hard-core and semi-hard-core bosons with different nilpotency conditions and
demonstrate that in contrast with the standard hard-core Bose-Hubbard model,
our bosonic mixture with nearest- and next-nearest-neighbor interactions on a
square lattice develops the checkerboard supersolid phase characterized by the
simultaneous superfluid and checkerboard solid orders. Our bosonic mixture is
created from a two-orbital Bose-Hubbard model including two kinds of bosons: a
single-orbital boson and a two-orbital boson. By mapping the bosonic mixture to
an anisotropic inhomogeneous spin model in the presence of a magnetic field, we
study the ground-state phase diagram of the model by means of cluster mean
field theory and linear spin-wave theory and show that various phases such as
solid, superfluid, supersolid, and Mott insulator appear in the phase diagram
of the mixture. Competition between the interactions and magnetic field causes
the mixture to undergo different kinds of first- and second-order phase
transitions. By studying the behavior of the spin-wave excitations, we find the
reasons of all first- and second-order phase transitions. We also obtain the
temperature phase diagram of the system using cluster mean field theory. We
show that the checkerboard supersolid phase persists at finite temperature
comparable with the interaction energies of bosons.
",0,1,0,0,0,0
18247,Width-$k$ Generalizations of Classical Permutation Statistics,"  We introduce new natural generalizations of the classical descent and
inversion statistics for permutations, called width-$k$ descents and width-$k$
inversions. These variations induce generalizations of the excedance and major
statistics, providing a framework in which the most well-known
equidistributivity results for classical statistics are paralleled. We explore
additional relationships among the statistics providing specific formulas in
certain special cases. Moreover, we explore the behavior of these width-$k$
statistics in the context of pattern avoidance.
",0,0,1,0,0,0
18248,Klout Topics for Modeling Interests and Expertise of Users Across Social Networks,"  This paper presents Klout Topics, a lightweight ontology to describe social
media users' topics of interest and expertise. Klout Topics is designed to: be
human-readable and consumer-friendly; cover multiple domains of knowledge in
depth; and promote data extensibility via knowledge base entities. We discuss
why this ontology is well-suited for text labeling and interest modeling
applications, and how it compares to available alternatives. We show its
coverage against common social media interest sets, and examples of how it is
used to model the interests of over 780M social media users on Klout.com.
Finally, we open the ontology for external use.
",1,0,0,0,0,0
18249,Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic,"  Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
in a gene, genetic pathway, or network, can provide novel insight into complex
diseases, above that which can be gleaned from studying SNPs individually.
Common challenges in set-based genetic association testing include weak effect
sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
single-SNP effects often ranging from extremely sparse to moderately sparse in
number. Motivated by these challenges, we propose the Generalized Berk-Jones
(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
provides advantages over the Generalized Higher Criticism (GHC) test when
signals in a SNP-set are moderately sparse. We also provide an analytic p-value
calculation procedure for SNP-sets of any finite size. Using this p-value
calculation, we illustrate that the rejection region for GBJ can be described
as a compromise of those for BJ and GHC. We develop an omnibus statistic as
well, and we show that this omnibus test is robust to the degree of signal
sparsity. An additional advantage of our method is the ability to conduct
inference using individual SNP summary statistics from a genome-wide
association study. We evaluate the finite sample performance of the GBJ though
simulation studies and application to gene-level association analysis of breast
cancer risk.
",0,0,0,1,0,0
18250,Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix,"  The theme of this paper is three-phase distribution system modeling suitable
for the Z-Bus load-flow. Detailed models of wye and delta constant-power,
constant-current, and constant-impedance loads are presented. Models of
transmission lines, voltage regulators, and transformers that build the bus
admittance matrix (Y-Bus) are laid out. The Z-Bus load-flow is then reviewed
and the singularity of the Y-Bus in case of certain transformer connections is
rigorously discussed. Based on realistic assumptions and conventional
modifications, the invertibility of the Y-Bus is proved. Last but not least,
the MATLAB scripts that construct the detailed component models for the IEEE
37-bus, IEEE 123-bus, and 8500-node feeders as well as the European 906-bus
low-voltage feeder are provided.
",1,0,1,0,0,0
18251,$c$-vectors of 2-Calabi--Yau categories and Borel subalgebras of ${\mathfrak{sl}}_{\infty}$,"  We develop a general framework for $c$-vectors of 2-Calabi--Yau categories,
which deals with cluster tilting subcategories that are not reachable from each
other and contain infinitely many indecomposable objects. It does not rely on
iterative sequences of mutations.
We prove a categorical (infinite-rank) generalization of the
Nakanishi--Zelevinsky duality for $c$-vectors and establish two formulae for
the effective computation of $c$-vectors -- one in terms of indices and the
other in terms of dimension vectors for cluster tilted algebras.
In this framework, we construct a correspondence between the $c$-vectors of
the cluster categories ${\mathscr{C}}(A_{\infty})$ of type $A_{\infty}$ due to
Igusa--Todorov and the roots of the Borel subalgebras of
${\mathfrak{sl}}_{\infty}$. Contrary to the finite dimensional case, the Borel
subalgebras of ${\mathfrak{sl}}_{\infty}$ are not conjugate to each other. On
the categorical side, the cluster tilting subcategories of
${\mathscr{C}}(A_{\infty})$ exhibit different homological properties. The
correspondence builds a bridge between the two classes of objects.
",0,0,1,0,0,0
18252,Electrostatic interaction between non-identical charged particles at an electrolyte interface,"  In this thesis we study the lateral electrostatic interaction between a pair
of non-identical, moderately charged colloidal particles trapped at an
electrolyte interface in the limit of short inter-particle separations. Using a
simplified model system we solve the problem analytically within the framework
of linearised Poisson-Boltzmann theory and classical density functional theory.
In the first step, we calculate the electrostatic potential inside the system
exactly as well as within the widely used superposition approximation. Then
these results are used to calculate the surface and line interaction energy
densities between the particles. Contrary to the case of identical particles,
depending upon the parameters of the system, we obtain that both the surface
and the line interaction can vary non-monotonically with varying separation
between the particles and the superposition approximation fails to predict the
correct qualitative behaviours in most cases. Additionally, the superposition
approximation is unable to predict the energy contributions quantitatively even
at large distances. We also provide expression for the constant (independent of
the inter-particle separation) interaction parameters, i.e., the surface
tension, the line tension and the interfacial tension. Our results are expected
to be of use for modelling particle-interaction at fluid interfaces and, in
particular, for emulsion stabilization using oppositely charged particles.
",0,1,0,0,0,0
18253,The weighted connection and sectional curvature for manifolds with density,"  In this paper we study sectional curvature bounds for Riemannian manifolds
with density from the perspective of a weighted torsion free connection
introduced recently by the last two authors. We develop two new tools for
studying weighted sectional curvature bounds: a new weighted Rauch comparison
theorem and a modified notion of convexity for distance functions. As
applications we prove generalizations of theorems of Preissman and Byers for
negative curvature, the (homeomorphic) quarter-pinched sphere theorem, and
Cheeger's finiteness theorem. We also improve results of the first two authors
for spaces of positive weighted sectional curvature and symmetry.
",0,0,1,0,0,0
18254,Control and Limit Enforcements for VSC Multi-Terminal HVDC in Newton Power Flow,"  This paper proposes a novel method to automatically enforce controls and
limits for Voltage Source Converter (VSC) based multi-terminal HVDC in the
Newton power flow iteration process. A general VSC MT-HVDC model with primary
PQ or PV control and secondary voltage control is formulated. Both the
dependent and independent variables are included in the propose formulation so
that the algebraic variables of the VSC MT-HVDC are adjusted simultaneously.
The proposed method also maintains the number of equations and the dimension of
the Jacobian matrix unchanged so that, when a limit is reached and a control is
released, the Jacobian needs no re-factorization. Simulations on the IEEE
14-bus and Polish 9241-bus systems are performed to demonstrate the
effectiveness of the method.
",1,0,0,0,0,0
18255,A.Ya. Khintchine's Work in Probability Theory,"  The paper is devoted to the contribution in the Probability Theory of the
well-known Soviet mathematician Alexander Yakovlevich Khintchine (1894-1959).
Several of his results are described, in particular those fundamental results
on the infinitely divisible distributions. Attention is paid also to his
interaction with Paul Levy. The content of the paper is related to our joint
book The Legacy of A.Ya. Khintchine's Work in Probability Theory, published in
2010 by Cambridge Scientific Publishers.
",0,0,1,0,0,0
18256,Dynamic Complexity under Definable Changes,"  This paper studies dynamic complexity under definable change operations in
the DynFO framework by Patnaik and Immerman. It is shown that for changes
definable by parameter-free first-order formulas, all (uniform) $AC^1$ queries
can be maintained by first-order dynamic programs. Furthermore, many
maintenance results for single-tuple changes are extended to more powerful
change operations: (1) The reachability query for undirected graphs is
first-order maintainable under single tuple changes and first-order defined
insertions, likewise the reachability query for directed acyclic graphs under
quantifier-free insertions. (2) Context-free languages are first-order
maintainable under $\Sigma_1$-defined changes. These results are complemented
by several inexpressibility results, for example, that the reachability query
cannot be maintained by quantifier-free programs under definable,
quantifier-free deletions.
",1,0,0,0,0,0
18257,The Planar Sandwich and Other 1D Planar Heat Flow Test Problems in ExactPack,"  This report documents the implementation of several related 1D heat flow
problems in the verification package ExactPack. In particular, the planar
sandwich class defined by Dawes et al., as well as the classes
PlanarSandwichHot, PlanarSandwichHalf, and other generalizations of the planar
sandwich problem, are defined and documented here. A rather general treatment
of 1D heat flow is presented, whose main results have been implemented in the
class Rod1D. All planar sandwich classes are derived from the parent class
Rod1D.
",0,1,0,0,0,0
18258,Difficulties of Timestamping Archived Web Pages,"  We show that state-of-the-art services for creating trusted timestamps in
blockchain-based networks do not adequately allow for timestamping of web
pages. They accept data by value (e.g., images and text), but not by reference
(e.g., URIs of web pages). Also, we discuss difficulties in repeatedly
generating the same cryptographic hash value of an archived web page. We then
introduce several requirements to be fulfilled in order to produce repeatable
hash values for archived web pages.
",1,0,0,0,0,0
18259,Cops and robber on grids and tori,"  This paper is a contribution to the classical cops and robber problem on a
graph, directed to two-dimensional grids and toroidal grids. These studies are
generally aimed at determining the minimum number of cops needed to capture the
robber and proposing algorithms for the capture. We apply some new concepts to
propose a new solution to the problem on grids that was already solved under a
different approach, and apply these concepts to give efficient algorithms for
the capture on toroidal grids. As for grids, we show that two cops suffice even
in a semi-torus (i.e. a grid with toroidal closure in one dimension) and three
cops are necessary and sufficient in a torus. Then we treat the problem in
function of any number k of cops, giving efficient algorithms for grids and
tori and computing lower and upper bounds on the capture time. Conversely we
determine the minimum value of k needed for any given capture time and study a
possible speed-up phenomenon.
",1,0,0,0,0,0
18260,Measuring Gender Inequalities of German Professions on Wikipedia,"  Wikipedia is a community-created online encyclopedia; arguably, it is the
most popular and largest knowledge resource on the Internet. Thus, reliability
and neutrality are of high importance for Wikipedia. Previous research [3]
reveals gender bias in Google search results for many professions and
occupations. Also, Wikipedia was criticized for existing gender bias in
biographies [4] and gender gap in the editor community [5, 6]. Thus, one could
expect that gender bias related to professions and occupations may be present
in Wikipedia. The term gender bias is used here in the sense of conscious or
unconscious favoritism towards one gender over another [47] with respect to
professions and occupations. The objective of this work is to identify and
assess gender bias. To this end, the German Wikipedia articles about
professions and occupations were analyzed on three dimensions: redirections,
images, and people mentioned in the articles. This work provides evidence for
systematic overrepresentation of men in all three dimensions; female bias is
only present for a few professions.
",1,0,0,0,0,0
18261,Contact Adaption during Epidemics: A Multilayer Network Formulation Approach,"  People change their physical contacts as a preventive response to infectious
disease propagations. Yet, only a few mathematical models consider the coupled
dynamics of the disease propagation and the contact adaptation process. This
paper presents a model where each agent has a default contact neighborhood set,
and switches to a different contact set once she becomes alert about infection
among her default contacts. Since each agent can adopt either of two possible
neighborhood sets, the overall contact network switches among 2^N possible
configurations. Notably, a two-layer network representation can fully model the
underlying adaptive, state-dependent contact network. Contact adaptation
influences the size of the disease prevalence and the epidemic threshold---a
characteristic measure of a contact network robustness against epidemics---in a
nonlinear fashion. Particularly, the epidemic threshold for the presented
adaptive contact network belongs to the solution of a nonlinear
Perron-Frobenius (NPF) problem, which does not depend on the contact adaptation
rate monotonically. Furthermore, the network adaptation model predicts a
counter-intuitive scenario where adaptively changing contacts may adversely
lead to lower network robustness against epidemic spreading if the contact
adaptation is not fast enough. An original result for a class of NPF problems
facilitate the analytical developments in this paper.
",1,0,0,0,0,0
18262,Estimation in the convolution structure density model. Part I: oracle inequalities,"  We study the problem of nonparametric estimation under $\bL_p$-loss, $p\in
[1,\infty)$, in the framework of the convolution structure density model on
$\bR^d$. This observation scheme is a generalization of two classical
statistical models, namely density estimation under direct and indirect
observations. In Part I the original pointwise selection rule from a family of
""kernel-type"" estimators is proposed. For the selected estimator, we prove an
$\bL_p$-norm oracle inequality and several of its consequences. In Part II the
problem of adaptive minimax estimation under $\bL_p$--loss over the scale of
anisotropic Nikol'skii classes is addressed. We fully characterize the behavior
of the minimax risk for different relationships between regularity parameters
and norm indexes in the definitions of the functional class and of the risk. We
prove that the selection rule proposed in Part I leads to the construction of
an optimally or nearly optimally (up to logarithmic factor) adaptive estimator.
",0,0,1,1,0,0
18263,Candidate exoplanet host HD131399A: a nascent Am star,"  Direct imaging suggests that there is a Jovian exoplanet around the primary
A-star in the triple-star system HD131399. We investigate a high-quality
spectrum of the primary component HD131399A obtained with FEROS on the ESO/MPG
2.2m telescope, aiming to characterise the star's atmospheric and fundamental
parameters, and to determine elemental abundances at high precision and
accuracy. The aim is to constrain the chemical composition of the birth cloud
of the system and therefore the bulk composition of the putative planet. A
hybrid non-local thermal equilibrium (non-LTE) model atmosphere technique is
adopted for the quantitative spectral analysis. Comparison with the most recent
stellar evolution models yields the fundamental parameters. The atmospheric and
fundamental stellar parameters of HD131399A are constrained to Teff=9200+-100
K, log g=4.37+-0.10, M=1.95+0.08-0.06 Msun, R=1.51+0.13-0.10 Rsun, and log
L/Lsun=1.17+-0.07, locating the star on the zero-age main sequence. Non-LTE
effects on the derived metal abundances are often smaller than 0.1dex, but can
reach up to ~0.8dex for individual lines. The observed lighter elements up to
calcium are overall consistent with present-day cosmic abundances, with a C/O
ratio of 0.45$\pm$0.07 by number, while the heavier elements show mild
overabundances. We conclude that the birth cloud of the system had a standard
chemical composition, but we witness the onset of the Am phenomenon in the
slowly rotating star. We furthermore show that non-LTE analyses have the
potential to solve the remaining discrepancies between observed abundances and
predictions by diffusion models for Am stars. Moreover, the present case allows
mass loss, not turbulent mixing, to be identified as the main transport process
competing with diffusion in very young Am stars.
",0,1,0,0,0,0
18264,Toward Quantum Combinatorial Games,"  In this paper, we propose a Quantum variation of combinatorial games,
generalizing the Quantum Tic-Tac-Toe proposed by Allan Goff. A combinatorial
game is a two-player game with no chance and no hidden information, such as Go
or Chess. In this paper, we consider the possibility of playing superpositions
of moves in such games. We propose different rulesets depending on when
superposed moves should be played, and prove that all these rulesets may lead
similar games to different outcomes. We then consider Quantum variations of the
game of Nim. We conclude with some discussion on the relative interest of the
different rulesets.
",1,0,0,0,0,0
18265,Markov $L_2$ inequality with the Gegenbauer weight,"  For the Gegenbauer weight function $w_{\lambda}(t)=(1-t^2)^{\lambda-1/2}$,
$\lambda>-1/2$, we denote by $\Vert\cdot\Vert_{w_{\lambda}}$ the associated
$L_2$-norm, $$ \Vert
f\Vert_{w_{\lambda}}:=\Big(\int_{-1}^{1}w_{\lambda}(t)f^2(t)\,dt\Big)^{1/2}. $$
We study the Markov inequality $$ \Vert p^{\prime}\Vert_{w_{\lambda}}\leq
c_{n}(\lambda)\,\Vert p\Vert_{w_{\lambda}},\qquad p\in \mathcal{P}_n, $$ where
$\mathcal{P}_n$ is the class of algebraic polynomials of degree not exceeding
$n$. Upper and lower bounds for the best Markov constant $c_{n}(\lambda)$ are
obtained, which are valid for all $n\in \mathbb{N}$ and $\lambda>-\frac{1}{2}$.
",0,0,1,0,0,0
18266,A Note on Some Approximation Kernels on the Sphere,"  We produce precise estimates for the Kogbetliantz kernel for the
approximation of functions on the sphere. Furthermore, we propose and study a
new approximation kernel, which has slightly better properties.
",0,0,1,0,0,0
18267,Machine Learning as Statistical Data Assimilation,"  We identify a strong equivalence between neural network based machine
learning (ML) methods and the formulation of statistical data assimilation
(DA), known to be a problem in statistical physics. DA, as used widely in
physical and biological sciences, systematically transfers information in
observations to a model of the processes producing the observations. The
correspondence is that layer label in the ML setting is the analog of time in
the data assimilation setting. Utilizing aspects of this equivalence we discuss
how to establish the global minimum of the cost functions in the ML context,
using a variational annealing method from DA. This provides a design method for
optimal networks for ML applications and may serve as the basis for
understanding the success of ""deep learning"". Results from an ML example are
presented.
When the layer label is taken to be continuous, the Euler-Lagrange equation
for the ML optimization problem is an ordinary differential equation, and we
see that the problem being solved is a two point boundary value problem. The
use of continuous layers is denoted ""deepest learning"". The Hamiltonian version
provides a direct rationale for back propagation as a solution method for the
canonical momentum; however, it suggests other solution methods are to be
preferred.
",1,0,0,1,0,0
18268,Nonparametric estimation of the kernel function of symmetric stable moving average random functions,"  We use the empirical normalized (smoothed) periodogram of a $S\alpha S$
moving average random function to estimate its kernel function from high
frequency observation data. The weak consistency of the estimator is shown. A
simulation study of the performance of the estimates rounds up the paper.
",0,0,1,1,0,0
18269,Fermions in Two Dimensions: Scattering and Many-Body Properties,"  Ultracold atomic Fermi gases in two-dimensions (2D) are an increasingly
popular topic of research. The interaction strength between spin-up and
spin-down particles in two-component Fermi gases can be tuned in experiments,
allowing for a strongly interacting regime where the gas properties are yet to
be fully understood. We have probed this regime for 2D Fermi gases by
performing T=0 ab initio diffusion Monte Carlo calculations. The many-body
dynamics are largely dependent on the two-body interactions, therefore we start
with an in-depth look at scattering theory in 2D. We show the partial-wave
expansion and its relation to the scattering length and effective range. Then
we discuss our numerical methods for determining these scattering parameters.
We close out this discussion by illustrating the details of bound states in 2D.
Transitioning to the many-body system, we use variationally optimized wave
functions to calculate ground-state properties of the gas over a range of
interaction strengths. We show results for the energy per particle and
parametrize an equation of state. We then proceed to determine the chemical
potential for the strongly interacting gas.
",0,1,0,0,0,0
18270,Moving Horizon Estimation for ARMAX process with t-Distribution Noise,"  In this paper, instead of the usual Gaussian noise assumption,
$t$-distribution noise is assumed. A Maximum Likelihood Estimator using the
most recent N measurements is proposed for the Auto-Regressive-Moving-Average
with eXogenous input (ARMAX) process with this assumption. The proposed
estimator is robust to outliers because the `thick tail' of the t-distribution
reduces the effect of large errors in the likelihood function. Instead of
solving the resulting nonlinear estimator numerically, the Influence Function
is used to formulate a computationally efficient recursive solution, which
reduces to the traditional Moving Horizon Estimator when the noise is Gaussian.
The formula for the variance of the estimate is derived. This formula shows
explicitly how the variance of the estimate is affected by the number of
measurements and noise variance. The simulation results show that the proposed
estimator has smaller variance and is more robust to outliers than the Moving
Window Least-Squares Estimator. For the same accuracy, the proposed estimator
is an order of magnitude faster than the particle filter.
",1,0,0,0,0,0
18271,Chimera states in brain networks: empirical neural vs. modular fractal connectivity,"  Complex spatiotemporal patterns, called chimera states, consist of coexisting
coherent and incoherent domains and can be observed in networks of coupled
oscillators. The interplay of synchrony and asynchrony in complex brain
networks is an important aspect in studies of both brain function and disease.
We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex
networks motivated by its potential application to epileptology and epilepsy
surgery. We compare two topologies: an empirical structural neural connectivity
derived from diffusion-weighted magnetic resonance imaging and a mathematically
constructed network with modular fractal connectivity. We analyse the
properties of chimeras and partially synchronized states, and obtain regions of
their stability in the parameter planes. Furthermore, we qualitatively simulate
the dynamics of epileptic seizures and study the influence of the removal of
nodes on the network synchronizability, which can be useful for applications to
epileptic surgery.
",0,1,0,0,0,0
18272,Bayesian surrogate learning in dynamic simulator-based regression problems,"  The estimation of unknown values of parameters (or hidden variables, control
variables) that characterise a physical system often relies on the comparison
of measured data with synthetic data produced by some numerical simulator of
the system as the parameter values are varied. This process often encounters
two major difficulties: the generation of synthetic data for each considered
set of parameter values can be computationally expensive if the system model is
complicated; and the exploration of the parameter space can be inefficient
and/or incomplete, a typical example being when the exploration becomes trapped
in a local optimum of the objection function that characterises the mismatch
between the measured and synthetic data. A method to address both these issues
is presented, whereby: a surrogate model (or proxy), which emulates the
computationally expensive system simulator, is constructed using deep recurrent
networks (DRN); and a nested sampling (NS) algorithm is employed to perform
efficient and robust exploration of the parameter space. The analysis is
performed in a Bayesian context, in which the samples characterise the full
joint posterior distribution of the parameters, from which parameter estimates
and uncertainties are easily derived. The proposed approach is compared with
conventional methods in some numerical examples, for which the results
demonstrate that one can accelerate the parameter estimation process by at
least an order of magnitude.
",1,0,0,1,0,0
18273,Stationary solutions for the ellipsoidal BGK model in a slab,"  We address the boundary value problem for the ellipsoidal BGK model of the
Boltzmann equation posed in a bounded interval. The existence of a unique mild
solution is established under the assumption that the inflow boundary data does
not concentrate too much around the zero velocity, and the gas is sufficiently
rarefied.
",0,0,1,0,0,0
18274,Raman signatures of monoclinic distortion in (Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ complex perovskites,"  Octahedral tilting is most common distortion process observed in
centrosymmetric perovskite compounds (ABO$_{3}$). Indeed, crucial physical
properties of this oxide stem from the tilts of BO$_{6}$ rigid octahedra. In
microwave ceramics with perovskite-type structure, there is a close relation
between the temperature coefficient of resonant frequency and tilt system of
the perovskite structure. However, in many cases, limited access facilities are
needed to assign correctly the space group, including neutron scattering and
transmission electron microscopy. Here, we combine the Raman scattering and
group-theory calculations to probe the structural distortion in the perovskite
(Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ solid solution, which exhibits a
structural phase transition at $x$ $\geq$ 0.7, from D$_{3d}^{3}$ trigonal to
C$_{2h}^{3}$ monoclinic cell. Both phases are related by an octahedral tilting
distortion ($a^{0}b^{-}b^{-}$ in Glazer notation). Low temperature Raman
spectra corroborate the group-theoretical predictions for
Sr$_{3}$CaNb$_{2}$O$_{9}$ compound, since 36 modes detected at 25 K agree well
with those 42 (25A$_{g}$ $\oplus$ 17B$_{g}$) predicted ones.
",0,1,0,0,0,0
18275,On Quadratic Penalties in Elastic Weight Consolidation,"  Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel
algorithm designed to safeguard against catastrophic forgetting in neural
networks. EWC can be seen as an approximation to Laplace propagation (Eskin et
al, 2004), and this view is consistent with the motivation given by Kirkpatrick
et al (2017). In this note, I present an extended derivation that covers the
case when there are more than two tasks. I show that the quadratic penalties in
EWC are inconsistent with this derivation and might lead to double-counting
data from earlier tasks.
",1,0,0,1,0,0
18276,The design of the ILD forward region,"  Following the decision to reduce the L* from 4.4 m to 4.1 m, the BeamCal had
to be moved closer to the interaction point. Results of a study of how this
affects the beamstrahlung and backward scattering backgrounds show that the
e+e- pair background depositions from beamstrahlung at the BeamCal rises by
20%. The background from backscattered electrons and positrons in the inner
pixel layers rises almost by a factor two, so does the number of photons in the
tracker.
",0,1,0,0,0,0
18277,Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors,"  Transition metal oxide memristors, or resistive random-access memory (RRAM)
switches, are under intense development for storage-class memory because of
their favorable operating power, endurance, speed, and density. Their
commercial deployment critically depends on predictive compact models based on
understanding nanoscale physico-chemical forces, which remains elusive and
controversial owing to the difficulties in directly observing atomic motions
during resistive switching, Here, using scanning transmission synchrotron x-ray
spectromicroscopy to study in-situ switching of hafnium oxide memristors, we
directly observed the formation of a localized oxygen-deficiency-derived
conductive channel surrounded by a low-conductivity ring of excess oxygen.
Subsequent thermal annealing homogenized the segregated oxygen, resetting the
cells towards their as-grown resistance state. We show that the formation and
dissolution of the conduction channel are successfully modeled by radial
thermophoresis and Fick diffusion of oxygen atoms driven by Joule heating. This
confirmation and quantification of two opposing nanoscale radial forces that
affect bipolar memristor switching are important components for any future
physics-based compact model for the electronic switching of these devices.
",0,1,0,0,0,0
18278,Improving Massive MIMO Belief Propagation Detector with Deep Neural Network,"  In this paper, deep neural network (DNN) is utilized to improve the belief
propagation (BP) detection for massive multiple-input multiple-output (MIMO)
systems. A neural network architecture suitable for detection task is firstly
introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
based on two modified BP detectors, damped BP and max-sum BP. The correction
factors in these algorithms are optimized through deep learning techniques,
aiming at improved detection performance. Numerical results are presented to
demonstrate the performance of the DNN detectors in comparison with various BP
modifications. The neural network is trained once and can be used for multiple
online detections. The results show that, compared to other state-of-the-art
detectors, the DNN detectors can achieve lower bit error rate (BER) with
improved robustness against various antenna configurations and channel
conditions at the same level of complexity.
",0,0,0,1,0,0
18279,Trend Analysis on the Metadata of Program Comprehension Papers,"  As program comprehension is a vast research area, it is necessary to get an
overview of its rising and falling trends. We performed an n-gram frequency
analysis on titles, abstracts and keywords of 1885 articles about program
comprehension from the years 2000-2014. According to this analysis, the most
rising trends are feature location and open source systems, the most falling
ones are program slicing and legacy systems.
",1,0,0,0,0,0
18280,Combinatorial views on persistent characters in phylogenetics,"  The so-called binary perfect phylogeny with persistent characters has
recently been thoroughly studied in computational biology as it is less
restrictive than the well known binary perfect phylogeny. Here, we focus on the
notion of (binary) persistent characters, i.e. characters that can be realized
on a phylogenetic tree by at most one $0 \rightarrow 1$ transition followed by
at most one $1 \rightarrow 0$ transition in the tree, and analyze these
characters under different aspects. First, we illustrate the connection between
persistent characters and Maximum Parsimony, where we characterize persistent
characters in terms of the first phase of the famous Fitch algorithm.
Afterwards we focus on the number of persistent characters for a given
phylogenetic tree. We show that this number solely depends on the balance of
the tree. To be precise, we develop a formula for counting the number of
persistent characters for a given phylogenetic tree based on an index of tree
balance, namely the Sackin index. Lastly, we consider the question of how many
(carefully chosen) binary characters together with their persistence status are
needed to uniquely determine a phylogenetic tree and provide an upper bound for
the number of characters needed.
",0,0,0,0,1,0
18281,Distribution of residuals in the nonparametric IV model with application to separability testing,"  We develop a uniform asymptotic expansion for the empirical distribution
function of residuals in the nonparametric IV regression. Such expansion opens
a door for construction of a broad range of residual-based specification tests
in nonparametric IV models. Building on obtained result, we develop a test for
the separability of unobservables in econometric models with endogeneity. The
test is based on verifying the independence condition between residuals of the
NPIV estimator and the instrument and can distinguish between the non-separable
and the separable specification under endogeneity.
",0,0,1,1,0,0
18282,Scene Graph Generation by Iterative Message Passing,"  Understanding a visual scene goes beyond recognizing individual objects in
isolation. Relationships between objects also constitute rich semantic
information about the scene. In this work, we explicitly model the objects and
their relationships using scene graphs, a visually-grounded graphical structure
of an image. We propose a novel end-to-end model that generates such structured
scene representation from an input image. The model solves the scene graph
inference problem using standard RNNs and learns to iteratively improves its
predictions via message passing. Our joint inference model can take advantage
of contextual cues to make better predictions on objects and their
relationships. The experiments show that our model significantly outperforms
previous methods for generating scene graphs using Visual Genome dataset and
inferring support relations with NYU Depth v2 dataset.
",1,0,0,0,0,0
18283,On the Apparent Power Law in CDM Halo Pseudo Phase Space Density Profiles,"  We investigate the apparent power-law scaling of the pseudo phase space
density (PPSD) in CDM halos. We study fluid collapse, using the close analogy
between the gas entropy and the PPSD in the fluid approximation. Our
hydrodynamic calculations allow for a precise evaluation of logarithmic
derivatives. For scale-free initial conditions, entropy is a power law in
Lagrangian (mass) coordinates, but not in Eulerian (radial) coordinates. The
deviation from a radial power law arises from incomplete hydrostatic
equilibrium (HSE), linked to bulk inflow and mass accretion, and the
convergence to the asymptotic central power-law slope is very slow. For more
realistic collapse, entropy is not a power law with either radius or mass due
to deviations from HSE and scale-dependent initial conditions. Instead, it is a
slowly rolling power law that appears approximately linear on a log-log plot.
Our fluid calculations recover PPSD power-law slopes and residual amplitudes
similar to N-body simulations, indicating that deviations from a power law are
not numerical artefacts. In addition, we find that realistic collapse is not
self-similar: scale lengths such as the shock radius and the turnaround radius
are not power-law functions of time. We therefore argue that the apparent
power-law PPSD cannot be used to make detailed dynamical inferences or
extrapolate halo profiles inward, and that it does not indicate any hidden
integrals of motion. We also suggest that the apparent agreement between the
PPSD and the asymptotic Bertschinger slope is purely coincidental.
",0,1,0,0,0,0
18284,Heavy tailed approximate identities and $σ$-stable Markov kernels,"  The aim of this paper is to present some results relating the properties of
stability, concentration and approximation to the identity of convolution
through not necessarily mollification type families of heavy tailed Markov
kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$
mollification of $L^{\sigma(t)}$ selected from the family
$\mathcal{L}=\{L^{\sigma}:
\widehat{L^{\sigma}}(\xi)=e^{-|\xi|^\sigma},0<\sigma<2\}$, by a given function
$\sigma$ with values in the interval $(0,2)$. We show that a basic Harnack type
inequality, introduced by C.~Calderón in the convolution case, becomes at
once natural to the setting and useful to connect the concepts of stability,
concentration and approximation of the identity. Some of the general results
are extended to spaces of homogeneous type since most of the concepts involved
in the theory are given in terms of metric and measure.
",0,0,1,0,0,0
18285,Spreading of correlations in the Falicov-Kimball model,"  We study dynamical properties of the one- and two-dimensional Falicov-Kimball
model using lattice Monte Carlo simulations. In particular, we calculate the
spreading of charge correlations in the equilibrium model and after an
interaction quench. The results show a reduction of the light-cone velocity
with interaction strength at low temperature, while the phase velocity
increases. At higher temperature, the initial spreading is determined by the
Fermi velocity of the noninteracting system and the maximum range of the
correlations decreases with increasing interaction strength. Charge order
correlations in the disorder potential enhance the range of the correlations.
We also use the numerically exact lattice Monte Carlo results to benchmark the
accuracy of equilibrium and nonequilibrium dynamical cluster approximation
calculations. It is shown that the bias introduced by the mapping to a
periodized cluster is substantial, and that from a numerical point of view, it
is more efficient to simulate the lattice model directly.
",0,1,0,0,0,0
18286,Direct Estimation of Information Divergence Using Nearest Neighbor Ratios,"  We propose a direct estimation method for Rényi and f-divergence measures
based on a new graph theoretical interpretation. Suppose that we are given two
sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where
$\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN)
graph of $Y$ in the joint data set $(X,Y)$, we show that the average powered
ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN
points is proportional to Rényi divergence of $X$ and $Y$ densities. A
similar method can also be used to estimate f-divergence measures. We derive
bias and variance rates, and show that for the class of $\gamma$-Hölder
smooth functions, the estimator achieves the MSE rate of
$O(N^{-2\gamma/(\gamma+d)})$. Furthermore, by using a weighted ensemble
estimation technique, for density functions with continuous and bounded
derivatives of up to the order $d$, and some extra conditions at the support
set boundary, we derive an ensemble estimator that achieves the parametric MSE
rate of $O(1/N)$. Our estimators are more computationally tractable than other
competing estimators, which makes them appealing in many practical
applications.
",1,0,0,1,0,0
18287,Efficiently Learning Mixtures of Mallows Models,"  Mixtures of Mallows models are a popular generative model for ranking data
coming from a heterogeneous population. They have a variety of applications
including social choice, recommendation systems and natural language
processing. Here we give the first polynomial time algorithm for provably
learning the parameters of a mixture of Mallows models with any constant number
of components. Prior to our work, only the two component case had been settled.
Our analysis revolves around a determinantal identity of Zagier which was
proven in the context of mathematical physics, which we use to show polynomial
identifiability and ultimately to construct test functions to peel off one
component at a time.
To complement our upper bounds, we show information-theoretic lower bounds on
the sample complexity as well as lower bounds against restricted families of
algorithms that make only local queries. Together, these results demonstrate
various impediments to improving the dependence on the number of components.
They also motivate the study of learning mixtures of Mallows models from the
perspective of beyond worst-case analysis. In this direction, we show that when
the scaling parameters of the Mallows models have separation, there are much
faster learning algorithms.
",0,0,0,1,0,0
18288,Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices,"  Optimal scheduling of hydrogen production in dynamic pricing power market can
maximize the profit of hydrogen producer; however, it highly depends on the
accurate forecast of hydrogen consumption. In this paper, we propose a deep
leaning based forecasting approach for predicting hydrogen consumption of fuel
cell vehicles in future taxi industry. The cost of hydrogen production is
minimized by utilizing the proposed forecasting tool to reduce the hydrogen
produced during high cost on-peak hours and guide hydrogen producer to store
sufficient hydrogen during low cost off-peak hours.
",0,0,0,1,0,0
18289,Determination of the thermopower of microscale samples with an AC method,"  A modified AC method based on micro-fabricated heater and resistive
thermometers has been applied to measure the thermopower of microscale samples.
A sinusoidal current with frequency {\omega} is passed to the heater to
generate an oscillatory temperature difference across the sample at a frequency
2{\omega}, which simultaneously induces an AC thermoelectric voltage, also at
the frequency 2{\omega}. A key step of the method is to extract amplitude and
phase of the oscillatory temperature difference by probing the AC temperature
variation at each individual thermometer. The sign of the thermopower is
determined by examining the phase difference between the oscillatory
temperature difference and the AC thermoelectric voltage. The technique has
been compared with the popular DC method by testing both n-type and p-type thin
film samples. Both methods yielded consistent results, which verified the
reliability of the newly proposed AC method.
",0,1,0,0,0,0
18290,Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program,"  This project explores public opinion on the Supplemental Nutrition Assistance
Program (SNAP) in news and social media outlets, and tracks elected
representatives' voting records on issues relating to SNAP and food insecurity.
We used machine learning, sentiment analysis, and text mining to analyze
national and state level coverage of SNAP in order to gauge perceptions of the
program over time across these outlets. Results indicate that the majority of
news coverage has negative sentiment, more partisan news outlets have more
extreme sentiment, and that clustering of negative reporting on SNAP occurs in
the Midwest. Our final results and tools will be displayed in an on-line
application that the ACFB Advocacy team can use to inform their communication
to relevant stakeholders.
",1,0,0,0,0,0
18291,"Hasse diagrams of non-isomorphic posets with $n$ elements, $2\leq n \leq 7,$ and the number of posets with $10$ elements, without the aid of any computer program","  Let $P(n)$ be the set of all posets with $n$ elements and $NIP(n)$ the set of
non-isomorphic posets with $n$ elements. Let $P^{(j)}(n)$, $1\leq j\leq 2^n,$
be the number of all posets with $n$ elements possessing exactly $j$
antichains. We have determined the numbers $P^{(j)}(7),$ $1\leq j\leq 128$, and
using a result of M. Erné \cite{EM4}, we compute $|P(10)|$ without the aid of
any computer program. We include the Hasse diagrams of all the non-isomorphic
posets of $P(7)$.
",0,0,1,0,0,0
18292,Variable screening with multiple studies,"  Advancement in technology has generated abundant high-dimensional data that
allows integration of multiple relevant studies. Due to their huge
computational advantage, variable screening methods based on marginal
correlation have become promising alternatives to the popular regularization
methods for variable selection. However, all these screening methods are
limited to single study so far. In this paper, we consider a general framework
for variable screening with multiple related studies, and further propose a
novel two-step screening procedure using a self-normalized estimator for
high-dimensional regression analysis in this framework. Compared to the
one-step procedure and rank-based sure independence screening (SIS) procedure,
our procedure greatly reduces false negative errors while keeping a low false
positive rate. Theoretically, we show that our procedure possesses the sure
screening property with weaker assumptions on signal strengths and allows the
number of features to grow at an exponential rate of the sample size. In
addition, we relax the commonly used normality assumption and allow
sub-Gaussian distributions. Simulations and a real transcriptomic application
illustrate the advantage of our method as compared to the rank-based SIS
method.
",0,0,1,1,0,0
18293,Gaussian-Constrained training for speaker verification,"  Neural models, in particular the d-vector and x-vector architectures, have
produced state-of-the-art performance on many speaker verification tasks.
However, two potential problems of these neural models deserve more
investigation. Firstly, both models suffer from `information leak', which means
that some parameters participating in model training will be discarded during
inference, i.e, the layers that are used as the classifier. Secondly, both
models do not regulate the distribution of the derived speaker vectors. This
`unconstrained distribution' may degrade the performance of the subsequent
scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
training approach that (1) discards the parametric classifier, and (2) enforces
the distribution of the derived speaker vectors to be Gaussian. Our experiments
on the VoxCeleb and SITW databases demonstrated that this new training approach
produced more representative and regular speaker embeddings, leading to
consistent performance improvement.
",1,0,0,0,0,0
18294,Structural Change in (Economic) Time Series,"  Methods for detecting structural changes, or change points, in time series
data are widely used in many fields of science and engineering. This chapter
sketches some basic methods for the analysis of structural changes in time
series data. The exposition is confined to retrospective methods for univariate
time series. Several recent methods for dating structural changes are compared
using a time series of oil prices spanning more than 60 years. The methods
broadly agree for the first part of the series up to the mid-1980s, for which
changes are associated with major historical events, but provide somewhat
different solutions thereafter, reflecting a gradual increase in oil prices
that is not well described by a step function. As a further illustration, 1990s
data on the volatility of the Hang Seng stock market index are reanalyzed.
",0,1,0,1,0,0
18295,Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Using SMT Solvers,"  Statecharts are frequently used as a modeling formalism in the design of
state-based systems. Formal verification techniques are also often applied to
prove certain properties about the behavior of the system. One of the most
efficient techniques for formal verification is Counterexample-Guided
Abstraction Refinement (CEGAR), which reduces the complexity of systems by
automatically building and refining abstractions. In our paper we present a
novel adaptation of the CEGAR approach to hierarchical statechart models. First
we introduce an encoding of the statechart to logical formulas that preserves
information about the state hierarchy. Based on this encoding we propose
abstraction and refinement techniques that utilize the hierarchical structure
of statecharts and also handle variables in the model. The encoding allows us
to use SMT solvers for the systematic exploration and verification of the
abstract model, including also bounded model checking. We demonstrate the
applicability and efficiency of our abstraction techniques with measurements on
an industry-motivated example.
",1,0,0,0,0,0
18296,Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities,"  Recently the dynamics of signed networks, where the ties among the agents can
be both positive (attractive) or negative (repulsive) have attracted
substantial attention of the research community. Examples of such networks are
models of opinion dynamics over signed graphs, recently introduced by Altafini
(2012,2013) and extended to discrete-time case by Meng et al. (2014). It has
been shown that under mild connectivity assumptions these protocols provide the
convergence of opinions in absolute value, whereas their signs may differ. This
""modulus consensus"" may correspond to the polarization of the opinions (or
bipartite consensus, including the usual consensus as a special case), or their
convergence to zero. In this paper, we demonstrate that the phenomenon of
modulus consensus in the discrete-time Altafini model is a manifestation of a
more general and profound fact, regarding the solutions of a special recurrent
inequality. Although such a recurrent inequality does not provide the
uniqueness of a solution, it can be shown that, under some natural assumptions,
each of its bounded solutions has a limit and, moreover, converges to
consensus. A similar property has previously been established for special
continuous-time differential inequalities (Proskurnikov, Cao, 2016). Besides
analysis of signed networks, we link the consensus properties of recurrent
inequalities to the convergence analysis of distributed optimization algorithms
and the problems of Schur stability of substochastic matrices.
",1,1,1,0,0,0
18297,Mobility Transition at Grain Boundaries in Two-Step Sintered 8 mol% Yttria Stabilized Zirconia,"  Stagnation of grain growth is often attributed to impurity segregation.
Yttria-stabilized cubic zirconia does not evidence any segregation-induced
slowdown, as its grain growth obeys the parabolic law when the grain size
increases by more than one order of magnitude. However, lowering the
temperature below 1300 oC triggers an abrupt slowdown, constraining the average
grains to grow by less than 0.5 ${\mu}$m in 1000 h despite a relatively large
driving force imparted in the fine grains of ~0.5 ${\mu}$m. Yet isolated
pockets of abnormally large grains, along with pockets of abnormally small
grains, emerge in the same latter sample. Such microstructure bifurcation has
never been observed before, and can only be explained by an inhomogeneous
distribution of immobile four-grain junctions. The implications of these
findings for two-step sintering are discussed.
",0,1,0,0,0,0
18298,"Sufficient conditions for the forcing theorem, and turning proper classes into sets","  We present three natural combinatorial properties for class forcing notions,
which imply the forcing theorem to hold. We then show that all known sufficent
conditions for the forcing theorem (except for the forcing theorem itself),
including the three properties presented in this paper, imply yet another
regularity property for class forcing notions, namely that proper classes of
the ground model cannot become sets in a generic extension, that is they do not
have set-sized names in the ground model. We then show that over certain models
of Gödel-Bernays set theory without the power set axiom, there is a notion of
class forcing which turns a proper class into a set, however does not satisfy
the forcing theorem. Moreover, we show that the property of not turning proper
classes into sets can be used to characterize pretameness over such models of
Gödel-Bernays set theory.
",0,0,1,0,0,0
18299,Cauchy problem for effectively hyperbolic operators with triple characteristics,"  We study the Cauchy problem for effectively hyperbolic operators $P$ with
principal symbol $p(t, x,\tau,\xi)$ having triple characteristics on $t = 0$.
Under a condition (E) we show that such operators are strongly hyperbolic, that
is the Cauchy problem is well posed for $p(t, x,D_t, D_x) + Q(t, x, D_t, D_x)$
with arbitrary lower order term $Q$. The proof is based on energy estimates
with weight $t^{-N}$ for a first order pseudo-differential system, where $N$
depends on lower order terms. For our analysis we construct a non-negative
definite symmetrizer $S(t)$ and we prove a version of Fefferman-Phong type
inequality for ${\rm Re}\, (S(t)U, U)_{L^2({\mathbb R}^n)}$ with a lower bound
$-C t^{-1}\|\langle D \rangle^{-1}U\|_{L^2(\mathbb R^n)}$.
",0,0,1,0,0,0
18300,\textit{Ab Initio} results for the Static Structure Factor of the Warm Dense Electron Gas,"  The uniform electron gas at finite temperature is of high current interest
for warm dense matter research. The complicated interplay of quantum degeneracy
and Coulomb coupling effects is fully contained in the pair distribution
function or, equivalently, the static strucutre factor. By combining exact
quantum Monte Carlo results for large wave vectors with the long-range behavior
from the Singwi-Tosi-Land-Sjölander approximation, we are able to obtain
highly accurate data for the static structure factor over the entire $k$-range.
This allows us to gauge the accuracy of previous approximations and discuss
their respective shortcomings. Further, our new data will serve as valuable
input for the computation of other quantities.
",0,1,0,0,0,0
18301,Delooping the functor calculus tower,"  We study a connection between mapping spaces of bimodules and of
infinitesimal bimodules over an operad. As main application and motivation of
our work, we produce an explicit delooping of the manifold calculus tower
associated to the space of smooth maps $D^{m}\rightarrow D^{n}$ pf discs,
$n\geq m$, avoiding any given multisingularity and coinciding with the standard
inclusion near $\partial D^{m}$. In particular, we give a new proof of the
delooping of the space of disc embeddings in terms of little discs operads maps
with the advantage that it can be applied to more general mapping spaces.
",0,0,1,0,0,0
18302,Area Law Violations and Quantum Phase Transitions in Modified Motzkin Walk Spin Chains,"  Area law violations for entanglement entropy in the form of a square root has
recently been studied for one-dimensional frustration-free quantum systems
based on the Motzkin walks and their variations. Here we consider a Motzkin
walk with a different Hilbert space on each step of the walk spanned by
elements of a {\it Symmetric Inverse Semigroup} with the direction of each step
governed by its algebraic structure. This change alters the number of paths
allowed in the Motzkin walk and introduces a ground state degeneracy sensitive
to boundary perturbations. We study the frustration-free spin chains based on
three symmetric inverse semigroups, $\cS^3_1$, $\cS^3_2$ and $\cS^2_1$. The
system based on $\cS^3_1$ and $\cS^3_2$ provide examples of quantum phase
transitions in one dimensions with the former exhibiting a transition between
the area law and a logarithmic violation of the area law and the latter
providing an example of transition from logarithmic scaling to a square root
scaling in the system size, mimicking a colored $\cS^3_1$ system. The system
with $\cS^2_1$ is much simpler and produces states that continue to obey the
area law.
",0,1,0,0,0,0
18303,Diffusion and confusion of chaotic iteration based hash functions,"  To guarantee the integrity and security of data transmitted through the
Internet, hash functions are fundamental tools. But recent researches have
shown that security flaws exist in the most widely used hash functions. So a
new way to improve their security performance is urgently demanded. In this
article, we propose new hash functions based on chaotic iterations, which have
chaotic properties as defined by Devaney. The corresponding diffusion and
confusion analyzes are provided and a comparative study between the proposed
hash functions is carried out, to make their use more applicable in any
security context.
",1,1,0,0,0,0
18304,L1188: a promising candidate of cloud-cloud collision triggering the formation of the low- and intermediate-mass stars,"  We present a new large-scale (4 square degrees) simultaneous $^{12}$CO,
$^{13}$CO, and C$^{18}$O ($J$=1$-$0) mapping of L1188 with the PMO 13.7-m
telescope. Our observations have revealed that L1188 consists of two nearly
orthogonal filamentary molecular clouds at two clearly separated velocities.
Toward the intersection showing large velocity spreads, we find several
bridging features connecting the two clouds in velocity, and an open arc
structure which exhibits high excitation temperatures, enhanced $^{12}$CO and
$^{13}$CO emission, and broad $^{12}$CO line wings. This agrees with the
scenario that the two clouds are colliding with each other. The distribution of
young stellar object (YSO) candidates implies an enhancement of star formation
in the intersection of the two clouds. We suggest that a cloud-cloud collision
happened in L1188 about 1~Myr ago, possibly triggering the formation of low-
and intermediate-mass YSOs in the intersection.
",0,1,0,0,0,0
18305,Asymmetric Matrix-Valued Covariances for Multivariate Random Fields on Spheres,"  Matrix-valued covariance functions are crucial to geostatistical modeling of
multivariate spatial data. The classical assumption of symmetry of a
multivariate covariance function is overlay restrictive and has been considered
as unrealistic for most of real data applications. Despite of that, the
literature on asymmetric covariance functions has been very sparse. In
particular, there is some work related to asymmetric covariances on Euclidean
spaces, depending on the Euclidean distance. However, for data collected over
large portions of planet Earth, the most natural spatial domain is a sphere,
with the corresponding geodesic distance being the natural metric. In this
work, we propose a strategy based on spatial rotations to generate asymmetric
covariances for multivariate random fields on the $d$-dimensional unit sphere.
We illustrate through simulations as well as real data analysis that our
proposal allows to achieve improvements in the predictive performance in
comparison to the symmetric counterpart.
",0,0,1,1,0,0
18306,A dynamic stochastic blockmodel for interaction lengths,"  We propose a new dynamic stochastic blockmodel that focuses on the analysis
of interaction lengths in networks. The model does not rely on a discretization
of the time dimension and may be used to analyze networks that evolve
continuously over time. The framework relies on a clustering structure on the
nodes, whereby two nodes belonging to the same latent group tend to create
interactions and non-interactions of similar lengths. We introduce a fast
variational expectation-maximization algorithm to perform inference, and adapt
a widely used clustering criterion to perform model choice. Finally, we test
our methodology on artificial data, and propose a demonstration on a dataset
concerning face-to-face interactions between students in a high-school.
",0,0,0,1,0,0
18307,Do we agree on user interface aesthetics of Android apps?,"  Context: Visual aesthetics is increasingly seen as an essential factor in
perceived usability, interaction, and overall appraisal of user interfaces
especially with respect to mobile applications. Yet, a question that remains is
how to assess and to which extend users agree on visual aesthetics. Objective:
This paper analyzes the inter-rater agreement on visual aesthetics of user
interfaces of Android apps as a basis for guidelines and evaluation models.
Method: We systematically collected ratings on the visual aesthetics of 100
user interfaces of Android apps from 10 participants and analyzed the frequency
distribution, reliability and influencing design aspects. Results: In general,
user interfaces of Android apps are perceived more ugly than beautiful. Yet,
raters only moderately agree on the visual aesthetics. Disagreements seem to be
related to subtle differences with respect to layout, shapes, colors,
typography, and background images. Conclusion: Visual aesthetics is a key
factor for the success of apps. However, the considerable disagreement of
raters on the perceived visual aesthetics indicates the need for a better
understanding of this software quality with respect to mobile apps.
",1,0,0,0,0,0
18308,A Solution for Large-scale Multi-object Tracking,"  A large-scale multi-object tracker based on the generalised labeled
multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking
a very large, unknown and time-varying number of objects simultaneously, in the
presence of a high number of false alarms, as well as misdetections and
measurement origin uncertainty due to closely spaced objects. The algorithm is
demonstrated on a simulated large-scale tracking scenario, where the peak
number objects appearing simultaneously exceeds one million. To evaluate the
performance of the proposed tracker, we also introduce a new method of applying
the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for
its evaluation in large-scale scenarios.
",0,0,0,1,0,0
18309,Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting,"  The coefficient of determination, known as $R^2$, is commonly used as a
goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat
controversial when fitting nonlinear models, although it may be generalised on
a case-by-case basis to deal with specific models such as the logistic model.
Assume we are fitting a parametric distribution to a data set using, say, the
maximum likelihood estimation method. A general approach to measure the
goodness-of-fit of the fitted parameters, which we advocate herein, is to use a
nonparametric measure for model comparison between the raw data and the fitted
model. In particular, for this purpose we put forward the {\em Survival
Jensen-Shannon divergence} ($SJS$) and its empirical counterpart (${\cal
E}SJS$) as a metric which is bounded, and is a natural generalisation of the
Jensen-Shannon divergence. We demonstrate, via a straightforward procedure
making use of the ${\cal E}SJS$, that it can be used as part of maximum
likelihood estimation or curve fitting as a measure of goodness-of-fit,
including the construction of a confidence interval for the fitted parametric
distribution. Furthermore, we show the validity of the proposed method with
simulated data, and three empirical data sets of interest to researchers in
sociophysics and econophysics.
",0,0,0,0,0,1
18310,Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements,"  A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of
a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several
time instants. Agent B measures the signals' direction of arrival with respect
to Agent B's inertial navigation frame. Semidefinite programming and the
Orthogonal Procrustes algorithm are employed, and accuracy is improved through
maximum likelihood estimation. The method is validated using flight data and
simulations. A three-agent extension is explored.
",1,0,0,0,0,0
18311,Optical Surface Properties and their RF Limitations of European XFEL Cavities,"  The inner surface of superconducting cavities plays a crucial role to achieve
highest accelerating fields and low losses. The industrial fabrication of
cavities for the European X-Ray Free Electron Laser (XFEL) and the
International Linear Collider (ILC) HiGrade Research Project allowed for an
investigation of this interplay. For the serial inspection of the inner
surface, the optical inspection robot OBACHT was constructed and to analyze the
large amount of data, represented in the images of the inner surface, an image
processing and analysis code was developed and new variables to describe the
cavity surface were obtained. This quantitative analysis identified vendor
specific surface properties which allow to perform a quality control and
assurance during the production. In addition, a strong negative correlation of
$\rho= -0.93$ with a significance of $6\,\sigma$ of the integrated grain
boundary area $\sum{\mathrm{A}}$ versus the maximal achievable accelerating
field $\mathrm{E_{acc,max}}$ has been found.
",0,1,0,0,0,0
18312,A comment on `An improved macroscale model for gas slip flow in porous media',"  In a recent paper by Lasseux, Valdés-Parada and Porter (J.~Fluid~Mech.
\textbf{805} (2016) 118-146), it is found that the apparent gas permeability of
the porous media is a nonlinear function of the Knudsen number. However, this
result is highly questionable, because the adopted Navier-Stokes equations and
the first-order velocity-slip boundary condition are first-order (in terms of
the Knudsen number) approximations of the Boltzmann equation and the kinetic
boundary condition for rarefied gas flows. Our numerical simulations based on
the Bhatnagar-Gross-Krook kinetic equation and regularized 20-moment equations
prove that the Navier-Stokes equations with the first-order velocity-slip
boundary condition are only accurate at a very small Knudsen number limit,
where the apparent gas permeability is a linear function of the Knudsen number.
",0,1,0,0,0,0
18313,AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training,"  Highly distributed training of Deep Neural Networks (DNNs) on future compute
platforms (offering 100 of TeraOps/s of computational capacity) is expected to
be severely communication constrained. To overcome this limitation, new
gradient compression techniques are needed that are computationally friendly,
applicable to a wide variety of layers seen in Deep Neural Networks and
adaptable to variations in network architectures as well as their
hyper-parameters. In this paper we introduce a novel technique - the Adaptive
Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized
selection of gradient residues and automatically tunes the compression rate
depending on local activity. We show excellent results on a wide spectrum of
state of the art Deep Learning models in multiple domains (vision, speech,
language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers
(SGD with momentum, Adam) and network parameters (number of learners,
minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate
end-to-end compression rates of ~200X for fully-connected and recurrent layers,
and ~40X for convolutional layers, without any noticeable degradation in model
accuracies.
",1,0,0,1,0,0
18314,Halo-independent determination of the unmodulated WIMP signal in DAMA: the isotropic case,"  We present a halo-independent determination of the unmodulated signal
corresponding to the DAMA modulation if interpreted as due to dark matter
weakly interacting massive particles (WIMPs). First we show how a modulated
signal gives information on the WIMP velocity distribution function in the
Galactic rest frame, from which the unmodulated signal descends. Then we
perform a mathematically-sound profile likelihood analysis in which we profile
the likelihood over a continuum of nuisance parameters (namely, the WIMP
velocity distribution). As a first application of the method, which is very
general and valid for any class of velocity distributions, we restrict the
analysis to velocity distributions that are isotropic in the Galactic frame. In
this way we obtain halo-independent maximum-likelihood estimates and confidence
intervals for the DAMA unmodulated signal. We find that the estimated
unmodulated signal is in line with expectations for a WIMP-induced modulation
and is compatible with the DAMA background+signal rate. Specifically, for the
isotropic case we find that the modulated amplitude ranges between a few
percent and about 25% of the unmodulated amplitude, depending on the WIMP mass.
",0,1,0,0,0,0
18315,Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?,"  Deep reinforcement learning has achieved many recent successes, but our
understanding of its strengths and limitations is hampered by the lack of rich
environments in which we can fully characterize optimal behavior, and
correspondingly diagnose individual actions against such a characterization.
Here we consider a family of combinatorial games, arising from work of Erdos,
Selfridge, and Spencer, and we propose their use as environments for evaluating
and comparing different approaches to reinforcement learning. These games have
a number of appealing features: they are challenging for current learning
approaches, but they form (i) a low-dimensional, simply parametrized
environment where (ii) there is a linear closed form solution for optimal
behavior from any state, and (iii) the difficulty of the game can be tuned by
changing environment parameters in an interpretable way. We use these
Erdos-Selfridge-Spencer games not only to compare different algorithms, but
test for generalization, make comparisons to supervised learning, analyse
multiagent play, and even develop a self play algorithm. Code can be found at:
this https URL
",1,0,0,1,0,0
18316,Memristor equations: incomplete physics and undefined passivity/activity,"  In his seminal paper, Chua presented a fundamental physical claim by
introducing the memristor, ""The missing circuit element"". The memristor
equations were originally supposed to represent a passive circuit element
because, with active circuitry, arbitrary elements can be realized without
limitations. Therefore, if the memristor equations do not guarantee that the
circuit element can be realized by a passive system, the fundamental physics
claim about the memristor as ""missing circuit element"" loses all its weight.
The question of passivity/activity belongs to physics thus we incorporate
thermodynamics into the study of this problem. We show that the memristor
equations are physically incomplete regarding the problem of
passivity/activity. As a consequence, the claim that the present memristor
functions describe a passive device lead to unphysical results, such as
violating the Second Law of thermodynamics, in infinitely large number of
cases. The seminal memristor equations cannot introduce a new physical circuit
element without making the model more physical such as providing the
Fluctuation Dissipation Theory of memristors.
",1,0,0,0,0,0
18317,Astrophysical uncertainties on the local dark matter distribution and direct detection experiments,"  The differential event rate in Weakly Interacting Massive Particle (WIMP)
direct detection experiments depends on the local dark matter density and
velocity distribution. Accurate modelling of the local dark matter distribution
is therefore required to obtain reliable constraints on the WIMP particle
physics properties. Data analyses typically use a simple Standard Halo Model
which might not be a good approximation to the real Milky Way (MW) halo. We
review observational determinations of the local dark matter density, circular
speed and escape speed and also studies of the local dark matter distribution
in simulated MW-like galaxies. We discuss the effects of the uncertainties in
these quantities on the energy spectrum and its time and direction dependence.
Finally we conclude with an overview of various methods for handling these
astrophysical uncertainties.
",0,1,0,0,0,0
18318,On Reliability-Aware Server Consolidation in Cloud Datacenters,"  In the past few years, datacenter (DC) energy consumption has become an
important issue in technology world. Server consolidation using virtualization
and virtual machine (VM) live migration allows cloud DCs to improve resource
utilization and hence energy efficiency. In order to save energy, consolidation
techniques try to turn off the idle servers, while because of workload
fluctuations, these offline servers should be turned on to support the
increased resource demands. These repeated on-off cycles could affect the
hardware reliability and wear-and-tear of servers and as a result, increase the
maintenance and replacement costs. In this paper we propose a holistic
mathematical model for reliability-aware server consolidation with the
objective of minimizing total DC costs including energy and reliability costs.
In fact, we try to minimize the number of active PMs and racks, in a
reliability-aware manner. We formulate the problem as a Mixed Integer Linear
Programming (MILP) model which is in form of NP-complete. Finally, we evaluate
the performance of our approach in different scenarios using extensive
numerical MATLAB simulations.
",1,0,0,0,0,0
18319,A novel quantum dynamical approach in electron microscopy combining wave-packet propagation with Bohmian trajectories,"  The numerical analysis of the diffraction features rendered by transmission
electron microscopy (TEM) typically relies either on classical approximations
(Monte Carlo simulations) or quantum paraxial tomography (the multislice method
and any of its variants). Although numerically advan- tageous (relatively
simple implementations and low computational costs), they involve important
approximations and thus their range of applicability is limited. To overcome
such limitations, an alternative, more general approach is proposed, based on
an optimal combination of wave-packet propagation with the on-the-fly
computation of associated Bohmian trajectories. For the sake of clarity, but
without loss of generality, the approach is used to analyze the diffraction of
an electron beam by a thin aluminum slab as a function of three different
incidence (work) conditions which are of interest in electron microscopy: the
probe width, the tilting angle, and the beam energy. Specifically, it is shown
that, because there is a dependence on particular thresholds of the beam
energy, this approach provides a clear description of the diffraction process
at any energy, revealing at the same time any diversion of the beam inside the
material towards directions that cannot be accounted for by other conventional
methods, which is of much interest when dealing with relatively low energies
and/or relatively large tilting angles.
",0,1,0,0,0,0
18320,Convex equipartitions of colored point sets,"  We show that any $d$-colored set of points in general position in
$\mathbb{R}^d$ can be partitioned into $n$ subsets with disjoint convex hulls
such that the set of points and all color classes are partitioned as evenly as
possible. This extends results by Holmsen, Kynčl & Valculescu (2017) and
establishes a special case of their general conjecture. Our proof utilizes a
result obtained independently by Soberón and by Karasev in 2010, on
simultaneous equipartitions of $d$ continuous measures in $\mathbb{R}^d$ by $n$
convex regions. This gives a convex partition of $\mathbb{R}^d$ with the
desired properties, except that points may lie on the boundaries of the
regions. In order to resolve the ambiguous assignment of these points, we set
up a network flow problem. The equipartition of the continuous measures gives a
fractional flow. The existence of an integer flow then yields the desired
partition of the point set.
",0,0,1,0,0,0
18321,Logic Programming Petri Nets,"  With the purpose of modeling, specifying and reasoning in an integrated
fashion with procedural and declarative aspects (both commonly present in cases
or scenarios), the paper introduces Logic Programming Petri Nets (LPPN), an
extension to the Petri Net notation providing an interface to logic programming
constructs. Two semantics are presented. First, a hybrid operational semantics
that separates the process component, treated with Petri nets, from the
constraint/terminological component, treated with Answer Set Programming (ASP).
Second, a denotational semantics maps the notation to ASP fully, via Event
Calculus. These two alternative specifications enable a preliminary evaluation
in terms of reasoning efficiency.
",1,0,0,0,0,0
18322,Unique Continuation through Hyperplane for Higher Order Parabolic and Shrödinger Equations,"  We consider higher order parabolic operator $\partial_t+(-\Delta_x)^m$ and
higher order Schrödinger operator $i^{-1}\partial_t+(-\Delta_x)^m$ in
$X=\{(t,x)\in\mathbb{R}^{1+n};~|t|<A,|x_n|<B\}$ where $m$ is any positive
integer. Under certain lower order and regularity assumptions, we prove that if
the solution for linear problem vanishes when $x_n>0$, then the solution
vanishes in $X$. Such results are given globally, and we also prove some
related local results.
",0,0,1,0,0,0
18323,Fashion Conversation Data on Instagram,"  The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.
",1,0,0,1,0,0
18324,Viscous flow in a soft valve,"  Fluid-structure interactions are ubiquitous in nature and technology.
However, the systems are often so complex that numerical simulations or ad hoc
assumptions must be used to gain insight into the details of the complex
interactions between the fluid and solid mechanics. In this paper, we present
experiments and theory on viscous flow in a simple bioinspired soft valve which
illustrate essential features of interactions between hydrodynamic and elastic
forces at low Reynolds numbers. The setup comprises a sphere connected to a
spring located inside a tapering cylindrical channel. The spring is aligned
with the central axis of the channel and a pressure drop is applied across the
sphere, thus forcing the liquid through the narrow gap between the sphere and
the channel walls. The sphere's equilibrium position is determined by a balance
between spring and hydrodynamic forces. Since the gap thickness changes with
the sphere's position, the system has a pressure-dependent hydraulic
resistance. This leads to a non-linear relation between applied pressure and
flow rate: flow initially increases with pressure, but decreases when the
pressure exceeds a certain critical value as the gap closes. To rationalize
these observations, we propose a mathematical model that reduced the complexity
of the flow to a two-dimensional lubrication approximation. A closed-form
expression for the pressure-drop/flow rate is obtained which reveals that the
flow rate $Q$ depends on the pressure drop $\Delta p$, sphere radius $a$, gap
thickness $h_0$, and viscosity $\eta$ as $Q\sim \eta^{-1}
a^{1/2}h_0^{5/2}\left(\Delta p_c-\Delta p\right)^{5/2}\Delta p$, where the
critical pressure $\Delta p_c$ scales with the spring constant $k$ and sphere
radius $a$ as $\Delta p_c\sim k a^{-2}$. These predictions compared favorably
to the results of our experiments with no free parameters.
",0,1,0,0,0,0
18325,Superheavy Thermal Dark Matter and Primordial Asymmetries,"  The early universe could feature multiple reheating events, leading to jumps
in the visible sector entropy density that dilute both particle asymmetries and
the number density of frozen-out states. In fact, late time entropy jumps are
usually required in models of Affleck-Dine baryogenesis, which typically
produces an initial particle-antiparticle asymmetry that is much too large. An
important consequence of late time dilution, is that a smaller dark matter
annihilation cross section is needed to obtain the observed dark matter relic
density. For cosmologies with high scale baryogenesis, followed by
radiation-dominated dark matter freeze-out, we show that the perturbative
unitarity mass bound on thermal relic dark matter is relaxed to $10^{10}$ GeV.
We proceed to study superheavy asymmetric dark matter models, made possible by
a sizable entropy injection after dark matter freeze-out, and identify how the
Affleck-Dine mechanism would generate the baryon and dark asymmetries.
",0,1,0,0,0,0
18326,Time-dependent population imaging for solid high harmonic generation,"  We propose an intuitive method, called time-dependent population imaging
(TDPI), to map the dynamical processes of high harmonic generation (HHG) in
solids by solving the time-dependent Schrödinger equation (TDSE). It is
shown that the real-time dynamical characteristics of HHG in solids, such as
the instantaneous photon energies of emitted harmonics, can be read directly
from the energy-resolved population oscillations of electrons in the TDPIs.
Meanwhile, the short and long trajectories of solid HHG are illustrated clearly
from TDPI. By using the TDPI, we also investigate the effects of
carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate
the HHG dynamics driven by two-color fields. Our results show that the TDPI
provides a powerful tool to study the ultrafast dynamics in strong fields for
various laser-solid configurations and gain an insight into HHG processes in
solids.
",0,1,0,0,0,0
18327,Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics,"  Predicting epidemic dynamics is of great value in understanding and
controlling diffusion processes, such as infectious disease spread and
information propagation. This task is intractable, especially when surveillance
resources are very limited. To address the challenge, we study the problem of
active surveillance, i.e., how to identify a small portion of system components
as sentinels to effect monitoring, such that the epidemic dynamics of an entire
system can be readily predicted from the partial data collected by such
sentinels. We propose a novel measure, the gamma value, to identify the
sentinels by modeling a sentinel network with row sparsity structure. We design
a flexible group sparse Bayesian learning algorithm to mine the sentinel
network suitable for handling both linear and non-linear dynamical systems by
using the expectation maximization method and variational approximation. The
efficacy of the proposed algorithm is theoretically analyzed and empirically
validated using both synthetic and real-world data.
",1,0,0,1,0,0
18328,FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models,"  Limitations in processing capabilities and memory of today's computers make
spiking neuron-based (human) whole-brain simulations inevitably characterized
by a compromise between bio-plausibility and computational cost. It translates
into brain models composed of a reduced number of neurons and a simplified
neuron's mathematical model. Taking advantage of the sparse character of
brain-like computation, eventdriven technique allows us to carry out efficient
simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky
Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven
compatible and exhibits some realistic neuronal features, opening new horizons
in whole-brain modelling. In this paper we present FNS, a LIFL-based exact
event-driven spiking neural network framework implemented in Java and oriented
to wholebrain simulations. FNS combines spiking/synaptic whole-brain modelling
with the event-driven approach, allowing us to define heterogeneous modules and
multi-scale connectivity with delayed connections and plastic synapses,
providing fast simulations at the same time. A novel parallelization strategy
is also implemented in order to further speed up simulations. This paper
presents mathematical models, software implementation and simulation routines
on which FNS is based. Finally, a reduced brain network model (1400 neurons and
45000 synapses) is synthesized on the basis of real brain structural data, and
the resulting model activity is compared with associated brain functional
(source-space MEG) data. The conducted test shows a good matching between the
activity of model and that of the emulated subject, in outstanding simulation
times (about 20s for simulating 4s of activity with a normal PC). Dedicated
sections of stimuli editing and output synthesis allow the neuroscientist to
introduce and extract brain-like signals, respectively...
",0,0,0,0,1,0
18329,"Synchronization, phase slips and coherent structures in area-preserving maps","  The problem of synchronization of coupled Hamiltonian systems exhibits
interesting features due to the non-uniform or mixed nature (regular and
chaotic) of the phase space. We study these features by investigating the
synchronization of unidirectionally coupled area-preserving maps coupled by the
Pecora-Carroll method. We find that coupled standard maps show complete
synchronization for values of the nonlinearity parameter at which regular
structures are still present in phase space. The distribution of
synchronization times has a power law tail indicating long synchronization
times for at least some of the synchronizing trajectories. With the
introduction of coherent structures using parameter perturbation in the system,
this distribution crosses over to exponential behavior, indicating shorter
synchronization times, and the number of initial conditions which synchronize
increases significantly, indicating an enhancement in the basin of
synchronization. On the other hand, coupled blinking vortex maps display both
phase synchronization and phase slips, depending on the location of the initial
conditions. We discuss the implication of our results.
",0,1,0,0,0,0
18330,Design of Real-time Semantic Segmentation Decoder for Automated Driving,"  Semantic segmentation remains a computationally intensive algorithm for
embedded deployment even with the rapid growth of computation power. Thus
efficient network design is a critical aspect especially for applications like
automated driving which requires real-time performance. Recently, there has
been a lot of research on designing efficient encoders that are mostly task
agnostic. Unlike image classification and bounding box object detection tasks,
decoders are computationally expensive as well for semantic segmentation task.
In this work, we focus on efficient design of the segmentation decoder and
assume that an efficient encoder is already designed to provide shared features
for a multi-task learning system. We design a novel efficient non-bottleneck
layer and a family of decoders which fit into a small run-time budget using
VGG10 as efficient encoder. We demonstrate in our dataset that experimentation
with various design choices led to an improvement of 10\% from a baseline
performance.
",1,0,0,1,0,0
18331,"Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health","  Motivated by applications of mixed longitudinal studies, where a group of
subjects entering the study at different ages (cross-sectional) are followed
for successive years (longitudinal), we consider nonparametric covariance
estimation with samples of noisy and partially-observed functional
trajectories. To ensure model identifiability and estimation consistency, we
introduce and carefully discuss the reduced rank and neighboring incoherence
condition. The proposed algorithm is based on a sequential-aggregation scheme,
which is non-iterative, with only basic matrix operations and closed-form
solutions in each step. The good performance of the proposed method is
supported by both theory and numerical experiments. We also apply the proposed
procedure to a midlife women's working memory study based on the data from the
Study of Women's Health Across the Nation (SWAN).
",0,0,1,1,0,0
18332,Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data,"  The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent science, we
proposed the use of deep convolutional neural networks for the detection and
characterization of gravitational wave signals in real-time. This method, Deep
Filtering, was initially demonstrated using simulated LIGO noise. In this
article, we present the extension of Deep Filtering using real data from the
first observing run of LIGO, for both detection and parameter estimation of
gravitational waves from binary black hole mergers with continuous data streams
from multiple LIGO detectors. We show for the first time that machine learning
can detect and estimate the true parameters of a real GW event observed by
LIGO. Our comparisons show that Deep Filtering is far more computationally
efficient than matched-filtering, while retaining similar sensitivity and lower
errors, allowing real-time processing of weak time-series signals in
non-stationary non-Gaussian noise, with minimal resources, and also enables the
detection of new classes of gravitational wave sources that may go unnoticed
with existing detection algorithms. This approach is uniquely suited to enable
coincident detection campaigns of gravitational waves and their multimessenger
counterparts in real-time.
",1,1,0,0,0,0
18333,Note on local coisotropic Floer homology and leafwise fixed points,"  I outline a construction of a local Floer homology for a coisotropic
submanifold of a symplectic manifold and explain how it can be used to show
that leafwise fixed points of Hamiltonian diffeomorphisms exist.
",0,0,1,0,0,0
18334,Geometric Surface-Based Tracking Control of a Quadrotor UAV under Actuator Constraints,"  This paper presents contributions on nonlinear tracking control systems for a
quadrotor unmanned micro aerial vehicle. New controllers are proposed based on
nonlinear surfaces composed by tracking errors that evolve directly on the
nonlinear configuration manifold thus inherently including in the control
design the nonlinear characteristics of the SE(3) configuration space. In
particular geometric surface-based controllers are developed, and through
rigorous stability proofs they are shown to have desirable closed loop
properties that are almost global. A region of attraction, independent of the
position error, is produced and its effects are analyzed. A strategy allowing
the quadrotor to achieve precise attitude tracking while simultaneously
following a desired position command and complying to actuator constraints in a
computationally inexpensive manner is derived. This important contribution
differentiates this work from existing Geometric Nonlinear Control System
solutions (GNCSs) since the commanded thrusts can be realized by the majority
of quadrotors produced by the industry. The new features of the proposed GNCSs
are illustrated by numerical simulations of aggressive maneuvers and a
comparison with a GNCSs from the bibliography.
",1,0,0,0,0,0
18335,Estimating the rate of defects under imperfect sampling inspection - a new approach,"  We consider the problem of estimating the the rate of defects (mean number of
defects per item), given counts of defects detected by two independent
imperfect inspectors on a sample of items. In contrast with the well-known
method of Capture-Recapture, here we {\it{do not}} have information regarding
the number of defects jointly detected by {\it{both}} inspectors. We solve this
problem by constructing two types of estimators - a simple moment-type
estimator, and a more complicated maximum-likelihood estimator. The performance
of these estimators is studied analytically and by means of simulations. It is
shown that the maximum-likelihood estimator is superior to the moment-type
estimator. A systematic comparison with the Capture-Recapture method is also
made.
",0,0,0,1,0,0
18336,Measuring heavy-tailedness of distributions,"  Different questions related with analysis of extreme values and outliers
arise frequently in practice. To exclude extremal observations and outliers is
not a good decision because they contain important information about the
observed distribution. The difficulties with their usage are usually related to
the estimation of the tail index in case it exists. There are many measures for
the center of the distribution, e.g. mean, mode, median. There are many
measures of the variance, asymmetry, and kurtosis, but there is no easy
characteristic for heavy-tailedness of the observed distribution. Here we
propose such a measure, give some examples and explore some of its properties.
This allows us to introduce a classification of the distributions, with respect
to their heavy-tailedness. The idea is to help and navigate practitioners for
accurate and easier work in the field of probability distributions.
Using the properties of the defined characteristics some distribution
sensitive extremal index estimators are proposed and their properties are
partially investigated.
",0,0,0,1,0,0
18337,Inductive $k$-independent graphs and $c$-colorable subgraphs in scheduling: A review,"  Inductive $k$-independent graphs generalize chordal graphs and have recently
been advocated in the context of interference-avoiding wireless communication
scheduling. The NP-hard problem of finding maximum-weight induced $c$-colorable
subgraphs, which is a generalization of finding maximum independent sets,
naturally occurs when selecting $c$ sets of pairwise non-conflicting jobs
(modeled as graph vertices). We investigate the parameterized complexity of
this problem on inductive $k$-independent graphs. We show that the Independent
Set problem is W[1]-hard even on 2-simplicial 3-minoes---a subclass of
inductive 2-independent graphs. In contrast, we prove that the more general
Maximum $c$-Colorable Subgraph problem is fixed-parameter tractable on
edge-wise unions of cluster and chordal graphs, which are 2-simplicial. In both
cases, the parameter is the solution size. Aside from this, we survey other
graph classes between inductive 1-inductive and inductive 2-inductive graphs
with applications in scheduling.
",1,0,0,0,0,0
18338,The word and order problems for self-similar and automata groups,"  We prove that the word problem is undecidable in functionally recursive
groups, and that the order problem is undecidable in automata groups, even
under the assumption that they are contracting.
",1,0,1,0,0,0
18339,Machine Teaching: A New Paradigm for Building Machine Learning Systems,"  The current processes for building machine learning systems require
practitioners with deep knowledge of machine learning. This significantly
limits the number of machine learning systems that can be created and has led
to a mismatch between the demand for machine learning systems and the ability
for organizations to build them. We believe that in order to meet this growing
demand for machine learning systems we must significantly increase the number
of individuals that can teach machines. We postulate that we can achieve this
goal by making the process of teaching machines easy, fast and above all,
universally accessible.
While machine learning focuses on creating new algorithms and improving the
accuracy of ""learners"", the machine teaching discipline focuses on the efficacy
of the ""teachers"". Machine teaching as a discipline is a paradigm shift that
follows and extends principles of software engineering and programming
languages. We put a strong emphasis on the teacher and the teacher's
interaction with data, as well as crucial components such as techniques and
design principles of interaction and visualization.
In this paper, we present our position regarding the discipline of machine
teaching and articulate fundamental machine teaching principles. We also
describe how, by decoupling knowledge about machine learning algorithms from
the process of teaching, we can accelerate innovation and empower millions of
new uses for machine learning models.
",1,0,0,1,0,0
18340,Totally geodesic submanifolds of Teichmuller space,"  We show that any totally geodesic submanifold of Teichmuller space of
dimension greater than one covers a totally geodesic subvariety, and only
finitely many totally geodesic subvarieties of dimension greater than one exist
in each moduli space.
",0,0,1,0,0,0
18341,Towards a splitting of the $K(2)$-local string bordism spectrum,"  We show that $K(2)$-locally, the smash product of the string bordism spectrum
and the spectrum $T_2$ splits into copies of Morava $E$-theories. Here, $T_2$
is related to the Thom spectrum of the canonical bundle over $\Omega SU(4)$.
",0,0,1,0,0,0
18342,Osmotic and diffusio-osmotic flow generation at high solute concentration. I. Mechanical approaches,"  In this paper, we explore various forms of osmotic transport in the regime of
high solute concentration. We consider both the osmosis across membranes and
diffusio-osmosis at solid interfaces, driven by solute concentration gradients.
We follow a mechanical point of view of osmotic transport, which allows us to
gain much insight into the local mechanical balance underlying osmosis. We
demonstrate in particular how the general expression of the osmotic pressure
for mixtures, as obtained classically from the thermodynamic framework, emerges
from the mechanical balance controlling non-equilibrium transport under solute
gradients. Expressions for the rejection coefficient of osmosis and the
diffusio-osmotic mobilities are accordingly obtained. These results generalize
existing ones in the dilute solute regime to mixtures with arbitrary
concentrations.
",0,1,0,0,0,0
18343,Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis,"  Small $p$-values are often required to be accurately estimated in large scale
genomic studies for the adjustment of multiple hypothesis tests and the ranking
of genomic features based on their statistical significance. For those
complicated test statistics whose cumulative distribution functions are
analytically intractable, existing methods usually do not work well with small
$p$-values due to lack of accuracy or computational restrictions. We propose a
general approach for accurately and efficiently calculating small $p$-values
for a broad range of complicated test statistics based on the principle of the
cross-entropy method and Markov chain Monte Carlo sampling techniques. We
evaluate the performance of the proposed algorithm through simulations and
demonstrate its application to three real examples in genomic studies. The
results show that our approach can accurately evaluate small to extremely small
$p$-values (e.g. $10^{-6}$ to $10^{-100}$). The proposed algorithm is helpful
to the improvement of existing test procedures and the development of new test
procedures in genomic studies.
",0,0,0,1,0,0
18344,Does the Testing Level affect the Prevalence of Coincidental Correctness?,"  Researchers have previously shown that Coincidental Correctness (CC) is
prevalent; however, the benchmarks they used are considered inadequate
nowadays. They have also recognized the negative impact of CC on the
effectiveness of fault localization and testing. The aim of this paper is to
study Coincidental Correctness, using more realistic code, mainly from the
perspective of unit testing. This stems from the fact that the practice of unit
testing has grown tremendously in recent years due to the wide adoption of
software development processes, such as Test-Driven Development. We quantified
the presence of CC in unit testing using the Defects4J benchmark. This entailed
manually injecting two code checkers for each of the 395 defects in Defects4J:
1) a weak checker that detects weak CC tests by monitoring whether the defect
was reached; and 2) a strong checker that detects strong CC tests by monitoring
whether the defect was reached and the program has transitioned into an
infectious state. We also conducted preliminary experiments (using Defects4J,
NanoXML and JTidy) to assess the pervasiveness of CC at the unit testing level
in comparison to that at the integration and system levels. Our study showed
that unit testing is not immune to CC, as it exhibited 7.2x more strong CC
tests than failing tests and 8.3x more weak CC tests than failing tests.
However, our preliminary results suggested that it might be less prone to CC
than integration testing and system testing.
",1,0,0,0,0,0
18345,How spread changes affect the order book: Comparing the price responses of order deletions and placements to trades,"  We observe the effects of the three different events that cause spread
changes in the order book, namely trades, deletions and placement of limit
orders. By looking at the frequencies of the relative amounts of price changing
events, we discover that deletions of orders open the bid-ask spread of a stock
more often than trades do. We see that once the amount of spread changes due to
deletions exceeds the amount of the ones due to trades, other observables in
the order book change as well. We then look at how these spread changing events
affect the prices of stocks, by means of the price response. We not only see
that the self-response of stocks is positive for both spread changing trades
and deletions and negative for order placements, but also cross-response to
other stocks and therefore the market as a whole. In addition, the
self-response function of spread-changing trades is similar to that of all
trades. This leads to the conclusion that spread changing deletions and order
placements have a similar effect on the order book and stock prices over time
as trades.
",0,0,0,0,0,1
18346,High-Dimensional Dependency Structure Learning for Physical Processes,"  In this paper, we consider the use of structure learning methods for
probabilistic graphical models to identify statistical dependencies in
high-dimensional physical processes. Such processes are often synthetically
characterized using PDEs (partial differential equations) and are observed in a
variety of natural phenomena, including geoscience data capturing atmospheric
and hydrological phenomena. Classical structure learning approaches such as the
PC algorithm and variants are challenging to apply due to their high
computational and sample requirements. Modern approaches, often based on sparse
regression and variants, do come with finite sample guarantees, but are usually
highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$
for sparsity inducing constraint or regularization. In this paper, we present
ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning,
which estimates an edge specific parameter $\lambda_{ij}$ in the first step,
and uses these parameters to learn the structure in the second step. Both steps
of our algorithm use (inexact) ADMM to solve suitable linear programs, and all
iterations can be done in closed form in an efficient block parallel manner. We
compare ACLIME-ADMM with baselines on both synthetic data simulated by partial
differential equations (PDEs) that model advection-diffusion processes, and
real data (50 years) of daily global geopotential heights to study information
flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and
competitive, usually better than the baselines especially on difficult
problems. On real data, ACLIME-ADMM recovers the underlying structure of global
atmospheric circulation, including switches in wind directions at the equator
and tropics entirely from the data.
",1,0,0,1,0,0
18347,Lax orthogonal factorisations in monad-quantale-enriched categories,"  We show that, for a quantale $V$ and a $\mathsf{Set}$-monad $\mathbb{T}$
laxly extended to $V$-$\mathsf{Rel}$, the presheaf monad on the category of
$(\mathbb{T},V)$-categories is simple, giving rise to a lax orthogonal
factorisation system (lofs) whose corresponding weak factorisation system has
embeddings as left part. In addition, we present presheaf submonads and study
the LOFSs they define. This provides a method of constructing weak
factorisation systems on some well-known examples of topological categories
over $\mathsf{Set}$.
",0,0,1,0,0,0
18348,Radiation reaction for spinning bodies in effective field theory I: Spin-orbit effects,"  We compute the leading Post-Newtonian (PN) contributions at linear order in
the spin to the radiation-reaction acceleration and spin evolution for binary
systems, which enter at fourth PN order. The calculation is carried out, from
first principles, using the effective field theory framework for spinning
compact objects, in both the Newton-Wigner and covariant spin supplementary
conditions. A non-trivial consistency check is performed on our results by
showing that the energy loss induced by the resulting radiation-reaction force
is equivalent to the total emitted power in the far zone, up to so-called
""Schott terms."" We also find that, at this order, the radiation reaction has no
net effect on the evolution of the spins. The spin-spin contributions to
radiation reaction are reported in a companion paper.
",0,1,0,0,0,0
18349,On the geometric notion of connection and its expression in tangent categories,"  Tangent categories provide an axiomatic approach to key structural aspects of
differential geometry that exist not only in the classical category of smooth
manifolds but also in algebraic geometry, homological algebra, computer
science, and combinatorics. Generalizing the notion of (linear) connection on a
smooth vector bundle, Cockett and Cruttwell have defined a notion of connection
on a differential bundle in an arbitrary tangent category. Herein, we establish
equivalent formulations of this notion of connection that reduce the amount of
specified structure required. Further, one of our equivalent formulations
substantially reduces the number of axioms imposed, and others provide useful
abstract conceptualizations of connections. In particular, we show that a
connection on a differential bundle E over M is equivalently given by a single
morphism K that induces a suitable decomposition of TE as a biproduct. We also
show that a connection is equivalently given by a vertical connection K for
which a certain associated diagram is a limit diagram.
",0,0,1,0,0,0
18350,Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula,"  An objective Bayesian approach to estimate the number of degrees of freedom
$(\nu)$ for the multivariate $t$ distribution and for the $t$-copula, when the
parameter is considered discrete, is proposed. Inference on this parameter has
been problematic for the multivariate $t$ and, for the absence of any method,
for the $t$-copula. An objective criterion based on loss functions which allows
to overcome the issue of defining objective probabilities directly is employed.
The support of the prior for $\nu$ is truncated, which derives from the
property of both the multivariate $t$ and the $t$-copula of convergence to
normality for a sufficiently large number of degrees of freedom. The
performance of the priors is tested on simulated scenarios. The R codes and the
replication material are available as a supplementary material of the
electronic version of the paper and on real data: daily logarithmic returns of
IBM and of the Center for Research in Security Prices Database.
",0,0,0,1,0,0
18351,PROBE: Predictive Robust Estimation for Visual-Inertial Navigation,"  Navigation in unknown, chaotic environments continues to present a
significant challenge for the robotics community. Lighting changes,
self-similar textures, motion blur, and moving objects are all considerable
stumbling blocks for state-of-the-art vision-based navigation algorithms. In
this paper we present a novel technique for improving localization accuracy
within a visual-inertial navigation system (VINS). We make use of training data
to learn a model for the quality of visual features with respect to
localization error in a given environment. This model maps each visual
observation from a predefined prediction space of visual-inertial predictors
onto a scalar weight, which is then used to scale the observation covariance
matrix. In this way, our model can adjust the influence of each observation
according to its quality. We discuss our choice of predictors and report
substantial reductions in localization error on 4 km of data from the KITTI
dataset, as well as on experimental datasets consisting of 700 m of indoor and
outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.
",1,0,0,0,0,0
18352,Ethical Artificial Intelligence - An Open Question,"  Artificial Intelligence (AI) is an effective science which employs strong
enough approaches, methods, and techniques to solve unsolvable real world based
problems. Because of its unstoppable rise towards the future, there are also
some discussions about its ethics and safety. Shaping an AI friendly
environment for people and a people friendly environment for AI can be a
possible answer for finding a shared context of values for both humans and
robots. In this context, objective of this paper is to address the ethical
issues of AI and explore the moral dilemmas that arise from ethical algorithms,
from pre set or acquired values. In addition, the paper will also focus on the
subject of AI safety. As general, the paper will briefly analyze the concerns
and potential solutions to solving the ethical issues presented and increase
readers awareness on AI safety as another related research interest.
",1,0,0,0,0,0
18353,Self-Attentive Model for Headline Generation,"  Headline generation is a special type of text summarization task. While the
amount of available training data for this task is almost unlimited, it still
remains challenging, as learning to generate headlines for news articles
implies that the model has strong reasoning about natural language. To overcome
this issue, we applied recent Universal Transformer architecture paired with
byte-pair encoding technique and achieved new state-of-the-art results on the
New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2
F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score
36.81 and ROUGE-2 F1-score 22.15 on it.
",1,0,0,0,0,0
18354,Localized Recombining Plasma in G166.0+4.3: A Supernova Remnant with an Unusual Morphology,"  We observed the Galactic mixed-morphology supernova remnant G166.0+4.3 with
Suzaku. The X-ray spectrum in the western part of the remnant is well
represented by a one-component ionizing plasma model. The spectrum in the
northeastern region can be explained by two components. One is the Fe-rich
component with the electron temperature $kT_e = 0.87_{-0.03}^{+0.02}$ keV. The
other is the recombining plasma component of lighter elements with $kT_e =
0.46\pm0.03$ keV, the initial temperature $kT_{init} = 3$ keV (fixed) and the
ionization parameter $n_et = (6.1_{-0.4}^{+0.5}) \times 10^{11} \rm cm^{-3} s$.
As the formation process of the recombining plasma, two scenarios, the
rarefaction and thermal conduction, are considered. The former would not be
favored since we found the recombining plasma only in the northeastern region
whereas the latter would explain the origin of the RP. In the latter scenario,
an RP is anticipated in a part of the remnant where blast waves are in contact
with cool dense gas. The emission measure suggests higher ambient gas density
in the northeastern region. The morphology of the radio shell and a GeV
gamma-ray emission also suggest a molecular cloud in the region.
",0,1,0,0,0,0
18355,Visualizing Design Erosion: How Big Balls of Mud are Made,"  Software systems are not static, they have to undergo frequent changes to
stay fit for purpose, and in the process of doing so, their complexity
increases. It has been observed that this process often leads to the erosion of
the systems design and architecture and with it, the decline of many desirable
quality attributes, such as maintainability. This process can be captured in
terms of antipatterns-atomic violations of widely accepted design principles.
We present a visualisation that exposes the design of evolving Java programs,
highlighting instances of selected antipatterns including their emergence and
cancerous growth. This visualisation assists software engineers and architects
in assessing, tracing and therefore combating design erosion. We evaluated the
effectiveness of the visualisation in four case studies with ten participants.
",1,0,0,0,0,0
18356,Hierarchical Representations for Efficient Architecture Search,"  We explore efficient neural architecture search methods and show that a
simple yet powerful evolutionary algorithm can discover new architectures with
excellent performance. Our approach combines a novel hierarchical genetic
representation scheme that imitates the modularized design pattern commonly
adopted by human experts, and an expressive search space that supports complex
topologies. Our algorithm efficiently discovers architectures that outperform a
large number of manually designed models for image classification, obtaining
top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which
is competitive with the best existing neural architecture search approaches. We
also present results using random search, achieving 0.3% less top-1 accuracy on
CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36
hours down to 1 hour.
",1,0,0,1,0,0
18357,A return to eddy viscosity model for epistemic UQ in RANS closures,"  For the purpose of Uncertainty Quantification (UQ) of Reynolds-Averaged
Navier-Stokes closures, we introduce a framework in which perturbations in the
eigenvalues of the anisotropy tensor are made in order to bound a
Quantity-of-Interest based on limiting states of turbulence. To make the
perturbations representative of local flow features, we introduce two
additional transport equations for linear combinations of these aforementioned
eigenvalues. The location, magnitude and direction of the eigenvalue
perturbations are now governed by the model transport equations. The general
behavior of our discrepancy model is determined by two coefficients, resulting
in a low-dimensional UQ problem. We will furthermore show that the behavior of
the model is intuitive and rooted in the physical interpretation of
misalignment between the mean strain and Reynolds stresses.
",0,1,0,0,0,0
18358,A Neural Network model with Bidirectional Whitening,"  We present here a new model and algorithm which performs an efficient Natural
gradient descent for Multilayer Perceptrons. Natural gradient descent was
originally proposed from a point of view of information geometry, and it
performs the steepest descent updates on manifolds in a Riemannian space. In
particular, we extend an approach taken by the ""Whitened neural networks""
model. We make the whitening process not only in feed-forward direction as in
the original model, but also in the back-propagation phase. Its efficacy is
shown by an application of this ""Bidirectional whitened neural networks"" model
to a handwritten character recognition data (MNIST data).
",0,0,0,1,0,0
18359,Regularity gradient estimates for weak solutions of singular quasi-linear parabolic equations,"  This paper studies the Sobolev regularity estimates of weak solutions of a
class of singular quasi-linear elliptic problems of the form $u_t -
\mbox{div}[\mathbb{A}(x,t,u,\nabla u)]= \mbox{div}[{\mathbf F}]$ with
homogeneous Dirichlet boundary conditions over bounded spatial domains. Our
main focus is on the case that the vector coefficients $\mathbb{A}$ are
discontinuous and singular in $(x,t)$-variables, and dependent on the solution
$u$. Global and interior weighted $W^{1,p}(\Omega, \omega)$-regularity
estimates are established for weak solutions of these equations, where $\omega$
is a weight function in some Muckenhoupt class of weights. The results obtained
are even new for linear equations, and for $\omega =1$, because of the
singularity of the coefficients in $(x,t)$-variables
",0,0,1,0,0,0
18360,Learning to Avoid Errors in GANs by Manipulating Input Spaces,"  Despite recent advances, large scale visual artifacts are still a common
occurrence in images generated by GANs. Previous work has focused on improving
the generator's capability to accurately imitate the data distribution
$p_{data}$. In this paper, we instead explore methods that enable GANs to
actively avoid errors by manipulating the input space. The core idea is to
apply small changes to each noise vector in order to shift them away from areas
in the input space that tend to result in errors. We derive three different
architectures from that idea. The main one of these consists of a simple
residual module that leads to significantly less visual artifacts, while only
slightly decreasing diversity. The module is trivial to add to existing GANs
and costs almost zero computation and memory.
",1,0,0,1,0,0
18361,Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search,"  This work proposes a process for efficiently searching over combinations of
individual object 6D pose hypotheses in cluttered scenes, especially in cases
involving occlusions and objects resting on each other. The initial set of
candidate object poses is generated from state-of-the-art object detection and
global point cloud registration techniques. The best-scored pose per object by
using these techniques may not be accurate due to overlaps and occlusions.
Nevertheless, experimental indications provided in this work show that object
poses with lower ranks may be closer to the real poses than ones with high
ranks according to registration techniques. This motivates a global
optimization process for improving these poses by taking into account
scene-level physical interactions between objects. It also implies that the
Cartesian product of candidate poses for interacting objects must be searched
so as to identify the best scene-level hypothesis. To perform the search
efficiently, the candidate poses for each object are clustered so as to reduce
their number but still keep a sufficient diversity. Then, searching over the
combinations of candidate object poses is performed through a Monte Carlo Tree
Search (MCTS) process that uses the similarity between the observed depth image
of the scene and a rendering of the scene given the hypothesized pose as a
score that guides the search procedure. MCTS handles in a principled way the
tradeoff between fine-tuning the most promising poses and exploring new ones,
by using the Upper Confidence Bound (UCB) technique. Experimental results
indicate that this process is able to quickly identify in cluttered scenes
physically-consistent object poses that are significantly closer to ground
truth compared to poses found by point cloud registration methods.
",1,0,0,0,0,0
18362,On the benefits of output sparsity for multi-label classification,"  The multi-label classification framework, where each observation can be
associated with a set of labels, has generated a tremendous amount of attention
over recent years. The modern multi-label problems are typically large-scale in
terms of number of observations, features and labels, and the amount of labels
can even be comparable with the amount of observations. In this context,
different remedies have been proposed to overcome the curse of dimensionality.
In this work, we aim at exploiting the output sparsity by introducing a new
loss, called the sparse weighted Hamming loss. This proposed loss can be seen
as a weighted version of classical ones, where active and inactive labels are
weighted separately. Leveraging the influence of sparsity in the loss function,
we provide improved generalization bounds for the empirical risk minimizer, a
suitable property for large-scale problems. For this new loss, we derive rates
of convergence linear in the underlying output-sparsity rather than linear in
the number of labels. In practice, minimizing the associated risk can be
performed efficiently by using convex surrogates and modern convex optimization
algorithms. We provide experiments on various real-world datasets demonstrating
the pertinence of our approach when compared to non-weighted techniques.
",1,0,1,1,0,0
18363,"On wrapping number, adequacy and the crossing number of satellite knots","  In this work we establish the tightest lower bound up-to-date for the minimal
crossing number of a satellite knot based on the minimal crossing number of the
companion used to build the satellite. If $M$ is the wrapping number of the
pattern knot, we essentially show that $c(Sat(P,C))>\frac{M^2}{2}c(C)$. The
existence of this bound will be proven when the companion knot is adequate, and
it will be further tuned in the case of the companion being alternating.
",0,0,1,0,0,0
18364,On the Applicability of Delicious for Temporal Search on Web Archives,"  Web archives are large longitudinal collections that store webpages from the
past, which might be missing on the current live Web. Consequently, temporal
search over such collections is essential for finding prominent missing
webpages and tasks like historical analysis. However, this has been challenging
due to the lack of popularity information and proper ground truth to evaluate
temporal retrieval models. In this paper we investigate the applicability of
external longitudinal resources to identify important and popular websites in
the past and analyze the social bookmarking service Delicious for this purpose.
The timestamped bookmarks on Delicious provide explicit cues about popular
time periods in the past along with relevant descriptors. These are valuable to
identify important documents in the past for a given temporal query. Focusing
purely on recall, we analyzed more than 12,000 queries and find that using
Delicious yields average recall values from 46% up to 100%, when limiting
ourselves to the best represented queries in the considered dataset. This
constitutes an attractive and low-overhead approach for quick access into Web
archives by not dealing with the actual contents.
",1,0,0,0,0,0
18365,The Geometry and Topology of Data and Information for Analytics of Processes and Behaviours: Building on Bourdieu and Addressing New Societal Challenges,"  We begin by summarizing the relevance and importance of inductive analytics
based on the geometry and topology of data and information. Contemporary issues
are then discussed. These include how sampling data for representativity is
increasingly to be questioned. While we can always avail of analytics from a
""bag of tools and techniques"", in the application of machine learning and
predictive analytics, nonetheless we present the case for Bourdieu and
Benzécri-based science of data, as follows. This is to construct bridges
between data sources and position-taking, and decision-making. There is summary
presentation of a few case studies, illustrating and exemplifying application
domains.
",1,0,0,0,0,0
18366,"Conformal Twists, Yang-Baxter $σ$-models & Holographic Noncommutativity","  Expanding upon earlier results [arXiv:1702.02861], we present a compendium of
$\sigma$-models associated with integrable deformations of AdS$_5$ generated by
solutions to homogenous classical Yang-Baxter equation. Each example we study
from four viewpoints: conformal (Drinfeld) twists, closed string gravity
backgrounds, open string parameters and proposed dual noncommutative (NC) gauge
theory. Irrespective of whether the deformed background is a solution to
supergravity or generalized supergravity, we show that the open string metric
associated with each gravity background is undeformed AdS$_5$ with constant
open string coupling and the NC structure $\Theta$ is directly related to the
conformal twist. One novel feature is that $\Theta$ exhibits ""holographic
noncommutativity"": while it may exhibit non-trivial dependence on the
holographic direction, its value everywhere in the bulk is uniquely determined
by its value at the boundary, thus facilitating introduction of a dual NC gauge
theory. We show that the divergence of the NC structure $\Theta$ is directly
related to the unimodularity of the twist. We discuss the implementation of an
outer automorphism of the conformal algebra as a coordinate transformation in
the AdS bulk and discuss its implications for Yang-Baxter $\sigma$-models and
self-T-duality based on fermionic T-duality. Finally, we comment on
implications of our results for the integrability of associated open strings
and planar integrability of dual NC gauge theories.
",0,1,0,0,0,0
18367,AWEsome: An open-source test platform for airborne wind energy systems,"  In this paper we present AWEsome (Airborne Wind Energy Standardized
Open-source Model Environment), a test platform for airborne wind energy
systems that consists of low-cost hardware and is entirely based on open-source
software. It can hence be used without the need of large financial investments,
in particular by research groups and startups to acquire first experiences in
their flight operations, to test novel control strategies or technical designs,
or for usage in public relations. Our system consists of a modified
off-the-shelf model aircraft that is controlled by the pixhawk autopilot
hardware and the ardupilot software for fixed wing aircraft. The aircraft is
attached to the ground by a tether. We have implemented new flight modes for
the autonomous tethered flight of the aircraft along periodic patterns. We
present the principal functionality of our algorithms. We report on first
successful tests of these modes in real flights.
",1,0,0,0,0,0
18368,A one-dimensional mathematical model of collecting lymphatics coupled with an electro-fluid-mechanical contraction model and valve dynamics,"  We propose a one-dimensional model for collecting lymphatics coupled with a
novel Electro-Fluid-Mechanical Contraction (EFMC) model for dynamical
contractions, based on a modified FitzHugh-Nagumo model for action potentials.
The one-dimensional model for a compliant lymphatic vessel is a set of
hyperbolic Partial Differential Equations (PDEs). The EFMC model combines the
electrical activity of lymphangions (action potentials) with fluid-mechanical
feedback (stretch of the lymphatic wall and wall shear stress) and the
mechanical variation of the lymphatic wall properties (contractions). The EFMC
model is governed by four Ordinary Differential Equations (ODEs) and
phenomenologically relies on: (1) environmental calcium influx, (2)
stretch-activated calcium influx, and (3) contraction inhibitions induced by
wall shear stresses. We carried out a complete mathematical analysis of the
stability of the stationary state of the EFMC model. Overall, the EFMC model
allows imitating the influence of pressure and wall shear stress on the
frequency of contractions observed experimentally. Lymphatic valves are
modelled using a well-established lumped-parameter model which allows
simulating stenotic and regurgitant valves. We analysed several lymphodynamical
indexes of a single lymphangion for a wide range of upstream and downstream
pressure combinations. Stenotic and regurgitant valves were modelled, and their
effects are here quantified. Results for stenotic valves showed in the
downstream lymphangion that for low frequencies of contractions the Calculated
Pump Flow (CPF) index remained almost unaltered, while for high frequencies the
CPF dramatically decreased depending on the severity of the stenosis (up to 93%
for a severe stenosis). Results for incompetent valves showed that the net flow
during a lymphatic cycle tends to zero as the degree of incompetence increases.
",0,1,1,0,0,0
18369,$A_{n}$-type surface singularity and nondisplaceable Lagrangian tori,"  We prove the existence of a one-parameter family of nondisplaceable
Lagrangian tori near a linear chain of Lagrangian 2-spheres in a symplectic
4-manifold. When the symplectic structure is rational we prove that the
deformed Floer cohomology groups of these tori are nontrivial. The proof uses
the idea of toric degeneration to analyze the full potential functions with
bulk deformations of these tori.
",0,0,1,0,0,0
18370,Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction,"  Haptic feedback is essential to acquire immersive experience when interacting
in virtual or augmented reality. Although the existing promising magnetic
levitation (maglev) haptic system has advantages of none mechanical friction,
its performance is limited by its navigation method, which mainly results from
the challenge that it is difficult to obtain high precision, high frame rate
and good stability with lightweight design at the same. In this study, we
propose to perform the visual-inertial fusion navigation based on
sequence-to-sequence learning for the maglev haptic interaction. Cascade LSTM
based-increment learning method is first presented to progressively learn the
increments of the target variables. Then, two cascade LSTM networks are
separately trained for accomplishing the visual-inertial fusion navigation in a
loosely-coupled mode. Additionally, we set up a maglev haptic platform as the
system testbed. Experimental results show that the proposed cascade LSTM
based-increment learning method can achieve high-precision prediction, and our
cascade LSTM based visual-inertial fusion navigation method can reach 200Hz
while maintaining high-precision (the mean absolute error of the position and
orientation is respectively less than 1mm and 0.02°)navigation for the
maglev haptic interaction application.
",1,0,0,0,0,0
18371,Local Feature Descriptor Learning with Adaptive Siamese Network,"  Although the recent progress in the deep neural network has led to the
development of learnable local feature descriptors, there is no explicit answer
for estimation of the necessary size of a neural network. Specifically, the
local feature is represented in a low dimensional space, so the neural network
should have more compact structure. The small networks required for local
feature descriptor learning may be sensitive to initial conditions and learning
parameters and more likely to become trapped in local minima. In order to
address the above problem, we introduce an adaptive pruning Siamese
Architecture based on neuron activation to learn local feature descriptors,
making the network more computationally efficient with an improved recognition
rate over more complex networks. Our experiments demonstrate that our learned
local feature descriptors outperform the state-of-art methods in patch
matching.
",1,0,0,1,0,0
18372,Unbounded cache model for online language modeling with open vocabulary,"  Recently, continuous cache models were proposed as extensions to recurrent
neural network language models, to adapt their predictions to local changes in
the data distribution. These models only capture the local context, of up to a
few thousands tokens. In this paper, we propose an extension of continuous
cache models, which can scale to larger contexts. In particular, we use a large
scale non-parametric memory component that stores all the hidden activations
seen in the past. We leverage recent advances in approximate nearest neighbor
search and quantization algorithms to store millions of representations while
searching them efficiently. We conduct extensive experiments showing that our
approach significantly improves the perplexity of pre-trained language models
on new distributions, and can scale efficiently to much larger contexts than
previously proposed local cache models.
",1,0,0,0,0,0
18373,Finite-Range Coulomb Gas Models of Banded Random Matrices and Quantum Kicked Rotors,"  Dyson demonstrated an equivalence between infinite-range Coulomb gas models
and classical random matrix ensembles for study of eigenvalue statistics. We
introduce finite-range Coulomb gas (FRCG) models via a Brownian matrix process,
and study them analytically and by Monte-Carlo simulations. These models yield
new universality classes, and provide a theoretical framework for study of
banded random matrices (BRM) and quantum kicked rotors (QKR). We demonstrate
that, for a BRM of bandwidth b and a QKR of chaos parameter {\alpha}, the
appropriate FRCG model has the effective range d = (b^2)/N = ({\alpha}^2)/N,
for large N matrix dimensionality. As d increases, there is a transition from
Poisson to classical random matrix statistics.
",0,1,0,0,0,0
18374,Dual Loomis-Whitney inequalities via information theory,"  We establish lower bounds on the volume and the surface area of a geometric
body using the size of its slices along different directions. In the first part
of the paper, we derive volume bounds for convex bodies using generalized
subadditivity properties of entropy combined with entropy bounds for
log-concave random variables. In the second part, we investigate a new notion
of Fisher information which we call the $L_1$-Fisher information, and show that
certain superadditivity properties of the $L_1$-Fisher information lead to
lower bounds for the surface areas of polyconvex sets in terms of its slices.
",1,0,0,0,0,0
18375,Cubic lead perovskite PbMoO3 with anomalous metallic behavior,"  A previously unreported Pb-based perovskite PbMoO$_3$ is obtained by
high-pressure and high-temperature synthesis. This material crystallizes in the
$Pm\bar{3}m$ cubic structure at room temperature, making it distinct from
typical Pb-based perovskite oxides with a structural distortion. PbMoO$_3$
exhibits a metallic behavior down to 0.1 K with an unusual $T$-sub linear
dependence of the electrical resistivity. Moreover, a large specific heat is
observed at low temperatures accompanied by a peak in $C_P/T^3$ around 10 K, in
marked contrast to the isostructural metallic system SrMoO$_3$. These transport
and thermal properties for PbMoO$_3$, taking into account anomalously large Pb
atomic displacements detected through diffraction experiments, are attributed
to a low-energy vibrational mode, associated with incoherent off-centering of
lone pair Pb$^{2+}$ cations. We discuss the unusual behavior of the electrical
resistivity in terms of a polaron-like conduction, mediated by the strong
coupling between conduction electrons and optical phonons of the local
low-energy vibrational mode.
",0,1,0,0,0,0
18376,On Long Memory Origins and Forecast Horizons,"  Most long memory forecasting studies assume that the memory is generated by
the fractional difference operator. We argue that the most cited theoretical
arguments for the presence of long memory do not imply the fractional
difference operator, and assess the performance of the autoregressive
fractionally integrated moving average $(ARFIMA)$ model when forecasting series
with long memory generated by nonfractional processes. We find that high-order
autoregressive $(AR)$ models produce similar or superior forecast performance
than $ARFIMA$ models at short horizons. Nonetheless, as the forecast horizon
increases, the $ARFIMA$ models tend to dominate in forecast performance. Hence,
$ARFIMA$ models are well suited for forecasts of long memory processes
regardless of the long memory generating mechanism, particularly for medium and
long forecast horizons. Additionally, we analyse the forecasting performance of
the heterogeneous autoregressive ($HAR$) model which imposes restrictions on
high-order $AR$ models. We find that the structure imposed by the $HAR$ model
produces better long horizon forecasts than $AR$ models of the same order, at
the price of inferior short horizon forecasts in some cases. Our results have
implications for, among others, Climate Econometrics and Financial Econometrics
models dealing with long memory series at different forecast horizons. We show
in an example that while a short memory autoregressive moving average $(ARMA)$
model gives the best performance when forecasting the Realized Variance of the
S\&P 500 up to a month ahead, the $ARFIMA$ model gives the best performance for
longer forecast horizons.
",0,0,1,0,0,0
18377,Detecting Disguised Plagiarism,"  Source code plagiarism detection is a problem that has been addressed several
times before; and several tools have been developed for that purpose. In this
research project we investigated a set of possible disguises that can be
mechanically applied to plagiarized source code to defeat plagiarism detection
tools. We propose a preprocessor to be used with existing plagiarism detection
tools to ""normalize"" source code before checking it, thus making such disguises
ineffective.
",1,0,0,0,0,0
18378,Stellar population synthesis based modelling of the Milky Way using asteroseismology of dwarfs and subgiants from Kepler,"  Early attempts to apply asteroseismology to study the Galaxy have already
shown unexpected discrepancies for the mass distribution of stars between the
Galactic models and the data; a result that is still unexplained. Here, we
revisit the analysis of the asteroseismic sample of dwarf and subgiant stars
observed by Kepler and investigate in detail the possible causes for the
reported discrepancy. We investigate two models of the Milky Way based on
stellar population synthesis, Galaxia and TRILEGAL. In agreement with previous
results, we find that TRILEGAL predicts more massive stars compared to Galaxia,
and that TRILEGAL predicts too many blue stars compared to 2MASS observations.
Both models fail to match the distribution of the stellar sample in $(\log
g,T_{\rm eff})$ space, pointing to inaccuracies in the models and/or the
assumed selection function. When corrected for this mismatch in $(\log g,T_{\rm
eff})$ space, the mass distribution calculated by Galaxia is broader and the
mean is shifted toward lower masses compared to that of the observed stars.
This behaviour is similar to what has been reported for the Kepler red giant
sample. The shift between the mass distributions is equivalent to a change of
2\% in $\nu_{\rm max}$, which is within the current uncertainty in the
$\nu_{\rm max}$ scaling relation. Applying corrections to the $\Delta \nu$
scaling relation predicted by the stellar models makes the observed mass
distribution significantly narrower, but there is no change to the mean.
",0,1,0,0,0,0
18379,EmbNum: Semantic labeling for numerical values with deep metric learning,"  Semantic labeling for numerical values is a task of assigning semantic labels
to unknown numerical attributes. The semantic labels could be numerical
properties in ontologies, instances in knowledge bases, or labeled data that
are manually annotated by domain experts. In this paper, we refer to semantic
labeling as a retrieval setting where the label of an unknown attribute is
assigned by the label of the most relevant attribute in labeled data. One of
the greatest challenges is that an unknown attribute rarely has the same set of
values with the similar one in the labeled data. To overcome the issue,
statistical interpretation of value distribution is taken into account.
However, the existing studies assume a specific form of distribution. It is not
appropriate in particular to apply open data where there is no knowledge of
data in advance. To address these problems, we propose a neural numerical
embedding model (EmbNum) to learn useful representation vectors for numerical
attributes without prior assumptions on the distribution of data. Then, the
""semantic similarities"" between the attributes are measured on these
representation vectors by the Euclidean distance. Our empirical experiments on
City Data and Open Data show that EmbNum significantly outperforms
state-of-the-art methods for the task of numerical attribute semantic labeling
regarding effectiveness and efficiency.
",0,0,0,1,0,0
18380,A Network Epidemic Model for Online Community Commissioning Data,"  A statistical model assuming a preferential attachment network, which is
generated by adding nodes sequentially according to a few simple rules, usually
describes real-life networks better than a model assuming, for example, a
Bernoulli random graph, in which any two nodes have the same probability of
being connected, does. Therefore, to study the propogation of ""infection""
across a social network, we propose a network epidemic model by combining a
stochastic epidemic model and a preferential attachment model. A simulation
study based on the subsequent Markov Chain Monte Carlo algorithm reveals an
identifiability issue with the model parameters. Finally, the network epidemic
model is applied to a set of online commissioning data.
",1,0,0,1,0,0
18381,Descent and Galois theory for Hopf categories,"  Descent theory for linear categories is developed. Given a linear category as
an extension of a diagonal category, we introduce descent data, and the
category of descent data is isomorphic to the category of representations of
the diagonal category, if some flatness assumptions are satisfied. Then
Hopf-Galois descent theory for linear Hopf categories, the Hopf algebra version
of a linear category, is developed. This leads to the notion of Hopf-Galois
category extension. We have a dual theory, where actions by dual linear Hopf
categories on linear categories are considered. Hopf-Galois category extensions
over groupoid algebras correspond to strongly graded linear categories.
",0,0,1,0,0,0
18382,On winning strategies for Banach-Mazur games,"  We give topological and game theoretic definitions and theorems nec- essary
for defining a Banach-Mazur game, and apply these definitions to formalize the
game. We then state and prove two theorems which give necessary conditions for
existence of winning strategies for players in a Banach-Mazur game.
",0,0,1,0,0,0
18383,Dynamical instability of the electric transport in strongly fluctuating superconductors,"  Theory of the influence of the thermal fluctuations on the electric transport
beyond linear response in superconductors is developed within the framework of
the time dependent Ginzburg - Landau approach. The I - V curve is calculated
using the dynamical self - consistent gaussian approximation. Under certain
conditions it exhibits a reentrant behaviour acquiring an S - shape form. The
unstable region below a critical temperature $T^{\ast }$ is determined for
arbitrary dimensionality ($D=1,2,3$) of the thermal fluctuations. The results
are applied to analyse the transport data on nanowires and several classes of
2D superconductors: metallic thin films, layered and atomically thick novel
materials.
",0,1,0,0,0,0
18384,Controlling Multimode Optomechanical Interactions via Interference,"  We demonstrate optomechanical interference in a multimode system, in which an
optical mode couples to two mechanical modes. A phase-dependent
excitation-coupling approach is developed, which enables the observation of
constructive and destructive optomechanical interferences. The destructive
interference prevents the coupling of the mechanical system to the optical
mode, suppressing optically-induced mechanical damping. These studies establish
optomechanical interference as an essential tool for controlling the
interactions between light and mechanical oscillators.
",0,1,0,0,0,0
18385,Online Adaptive Principal Component Analysis and Its extensions,"  We propose algorithms for online principal component analysis (PCA) and
variance minimization for adaptive settings. Previous literature has focused on
upper bounding the static adversarial regret, whose comparator is the optimal
fixed action in hindsight. However, static regret is not an appropriate metric
when the underlying environment is changing. Instead, we adopt the adaptive
regret metric from the previous literature and propose online adaptive
algorithms for PCA and variance minimization, that have sub-linear adaptive
regret guarantees. We demonstrate both theoretically and experimentally that
the proposed algorithms can adapt to the changing environments.
",1,0,0,1,0,0
18386,Diffusion MRI measurements in challenging head and brain regions via cross-term spatiotemporally encoding,"  Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging
approach delivering single-scan 2D NMR images with unprecedented resilience to
field inhomogeneities. The method relies on performing a pre-acquisition
encoding and a subsequent image read out while using the disturbing frequency
inhomogeneities as part of the image formation processes, rather than as
artifacts to be overwhelmed by the application of external gradients. This
study introduces the use of this new single-shot MRI technique as a
diffusion-monitoring tool, for accessing regions that have hitherto been
unapproachable by diffusion-weighted imaging (DWI) methods. In order to achieve
this, xSPEN MRIs intrinsic diffusion weighting effects are formulated using a
customized, spatially-localized b-matrix analysis; with this, we devise a novel
diffusion-weighting scheme that both exploits and overcomes xSPENs strong
intrinsic weighting effects. The ability to provide reliable and robust
diffusion maps in challenging head and brain regions, including the eyes and
the optic nerves, is thus demonstrated in humans at 3T; new avenues for imaging
other body regions are also briefly discussed.
",0,1,0,0,0,0
18387,Game-Theoretic Capital Asset Pricing in Continuous Time,"  We derive formulas for the performance of capital assets in continuous time
from an efficient market hypothesis, with no stochastic assumptions and no
assumptions about the beliefs or preferences of investors. Our efficient market
hypothesis says that a speculator with limited means cannot beat a particular
index by a substantial factor. Our results include a formula that resembles the
classical CAPM formula for the expected simple return of a security or
portfolio.
This version of the article was essentially written in December 2001 but
remains a working paper.
",0,0,0,0,0,1
18388,Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation,"  Apart from solving complicated problems that require a certain level of
intelligence, fine-tuned deep neural networks can also create fast algorithms
for slow, numerical tasks. In this paper, we introduce an improved version of
[1]'s work, a fast, deep-learning framework capable of generating the full
workspace of serial-link manipulators. The architecture consists of two neural
networks: an estimation net that approximates the manipulator Jacobian, and a
confidence net that measures the confidence of the approximation. We also
introduce M3 (Manipulability Maps of Manipulators), a MATLAB robotics library
based on [2](RTB), the datasets generated by which are used by this work.
Results have shown that not only are the neural networks significantly faster
than numerical inverse kinematics, it also offers superior accuracy when
compared to other machine learning alternatives. Implementations of the
algorithm (based on Keras[3]), including benchmark evaluation script, are
available at this https URL . The M3
Library APIs and datasets are also available at
this https URL .
",1,0,0,0,0,0
18389,Linear regression without correspondence,"  This article considers algorithmic and statistical aspects of linear
regression when the correspondence between the covariates and the responses is
unknown. First, a fully polynomial-time approximation scheme is given for the
natural least squares optimization problem in any constant dimension. Next, in
an average-case and noise-free setting where the responses exactly correspond
to a linear function of i.i.d. draws from a standard multivariate normal
distribution, an efficient algorithm based on lattice basis reduction is shown
to exactly recover the unknown linear function in arbitrary dimension. Finally,
lower bounds on the signal-to-noise ratio are established for approximate
recovery of the unknown linear function by any estimator.
",1,0,1,1,0,0
18390,Toward sensitive document release with privacy guarantees,"  Privacy has become a serious concern for modern Information Societies. The
sensitive nature of much of the data that are daily exchanged or released to
untrusted parties requires that responsible organizations undertake appropriate
privacy protection measures. Nowadays, much of these data are texts (e.g.,
emails, messages posted in social media, healthcare outcomes, etc.) that,
because of their unstructured and semantic nature, constitute a challenge for
automatic data protection methods. In fact, textual documents are usually
protected manually, in a process known as document redaction or sanitization.
To do so, human experts identify sensitive terms (i.e., terms that may reveal
identities and/or confidential information) and protect them accordingly (e.g.,
via removal or, preferably, generalization). To relieve experts from this
burdensome task, in a previous work we introduced the theoretical basis of
C-sanitization, an inherently semantic privacy model that provides the basis to
the development of automatic document redaction/sanitization algorithms and
offers clear and a priori privacy guarantees on data protection; even though
its potential benefits C-sanitization still presents some limitations when
applied to practice (mainly regarding flexibility, efficiency and accuracy). In
this paper, we propose a new more flexible model, named (C, g(C))-sanitization,
which enables an intuitive configuration of the trade-off between the desired
level of protection (i.e., controlled information disclosure) and the
preservation of the utility of the protected data (i.e., amount of semantics to
be preserved). Moreover, we also present a set of technical solutions and
algorithms that provide an efficient and scalable implementation of the model
and improve its practical accuracy, as we also illustrate through empirical
experiments.
",1,0,0,0,0,0
18391,Remarks on the Birch-Swinnerton-Dyer conjecture,"  We give a brief description of the Birch-Swinnerton-Dyer conjecture and
present related conjectures. We describe the relation between the nilpotent
orbits of SL(2,R) and CM points.
",0,0,1,0,0,0
18392,Trajectory Optimization for Cooperative Dual-band UAV Swarms,"  Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse
wireless communication fields. They can act as high-altitude flying relays to
support communications between ground nodes due to their ability to provide
line-of-sight links. With the flourishing Internet of Things, several types of
new applications are emerging. In this paper, we focus on bandwidth hungry and
delay-tolerant applications where multiple pairs of transceivers require the
support of UAVs to complete their transmissions. To do so, the UAVs have the
possibility to employ two different bands namely the typical microwave and the
high-rate millimeter wave bands. In this paper, we develop a generic framework
to assign UAVs to supported transceivers and optimize their trajectories such
that a weighted function of the total service time is minimized. Taking into
account both the communication time needed to relay the message and the flying
time of the UAVs, a mixed non-linear programming problem aiming at finding the
stops at which the UAVs hover to forward the data to the receivers is
formulated. An iterative approach is then developed to solve the problem.
First, a mixed linear programming problem is optimally solved to determine the
path of each available UAV. Then, a hierarchical iterative search is executed
to enhance the UAV stops' locations and reduce the service time. The behavior
of the UAVs and the benefits of the proposed framework are showcased for
selected scenarios.
",1,0,0,0,0,0
18393,A monad for full ground reference cells,"  We present a denotational account of dynamic allocation of potentially cyclic
memory cells using a monad on a functor category. We identify the collection of
heaps as an object in a different functor category equipped with a monad for
adding hiding/encapsulation capabilities to the heaps. We derive a monad for
full ground references supporting effect masking by applying a state monad
transformer to the encapsulation monad. To evaluate the monad, we present a
denotational semantics for a call-by-value calculus with full ground
references, and validate associated code transformations.
",1,0,1,0,0,0
18394,Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work,"  Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot.
",1,0,0,0,0,0
18395,Existence of either a periodic collisional orbit or infinitely many consecutive collision orbits in the planar circular restricted three-body problem,"  In the restricted three-body problem, consecutive collision orbits are those
orbits which start and end at collisions with one of the primaries. Interests
for such orbits arise not only from mathematics but also from various
engineering problems. In this article, using Floer homology, we show that there
are either a periodic collisional orbit, or infinitely many consecutive
collision orbits in the planar circular restricted three-body problem on each
bounded component of the energy hypersurface for Jacobi energy below the first
critical value.
",0,0,1,0,0,0
18396,Finding AND-OR Hierarchies in Workflow Nets,"  This paper presents the notion of AND-OR reduction, which reduces a WF net to
a smaller net by iteratively contracting certain well-formed subnets into
single nodes until no more such contractions are possible. This reduction can
reveal the hierarchical structure of a WF net, and since it preserves certain
semantical properties such as soundness, it can help with analysing and
understanding why a WF net is sound or not. The reduction can also be used to
verify if a WF net is an AND-OR net. This class of WF nets was introduced in
earlier work, and arguably describes nets that follow good hierarchical design
principles. It is shown that the AND-OR reduction is confluent up to
isomorphism, which means that despite the inherent non-determinism that comes
from the choice of subnets that are contracted, the final result of the
reduction is always the same up to the choice of the identity of the nodes.
Based on this result, a polynomial-time algorithm is presented that computes
this unique result of the AND-OR reduction. Finally, it is shown how this
algorithm can be used to verify if a WF net is an AND-OR net.
",1,0,0,0,0,0
18397,Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture,"  The present work shows the application of transfer learning for a pre-trained
deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
complete the training task and archive an overall average accuracy of $94.7\%$.
The DNN presents a $20\%$ score of misclassification for an external test
dataset. The accuracy of the proposed methodology is equivalent to ones using
HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of
being independent on special equipment to classify wheat kernel for FHB
symptoms.
",0,0,0,1,0,0
18398,About simple variational splines from the Hamiltonian viewpoint,"  In this paper, we study simple splines on a Riemannian manifold $Q$ from the
point of view of the Pontryagin maximum principle (PMP) in optimal control
theory. The control problem consists in finding smooth curves matching two
given tangent vectors with the control being the curve's acceleration, while
minimizing a given cost functional. We focus on cubic splines (quadratic cost
function) and on time-minimal splines (constant cost function) under bounded
acceleration. We present a general strategy to solve for the optimal
hamiltonian within the PMP framework based on splitting the variables by means
of a linear connection. We write down the corresponding hamiltonian equations
in intrinsic form and study the corresponding hamiltonian dynamics in the case
$Q$ is the $2$-sphere. We also elaborate on possible applications, including
landmark cometrics in computational anatomy.
",0,0,1,0,0,0
18399,Adaptive Grasp Control through Multi-Modal Interactions for Assistive Prosthetic Devices,"  The hand is one of the most complex and important parts of the human body.
The dexterity provided by its multiple degrees of freedom enables us to perform
many of the tasks of daily living which involve grasping and manipulating
objects of interest. Contemporary prosthetic devices for people with
transradial amputations or wrist disarticulation vary in complexity, from
passive prosthetics to complex devices that are body or electrically driven.
One of the important challenges in developing smart prosthetic hands is to
create devices which are able to mimic all activities that a person might
perform and address the needs of a wide variety of users. The approach explored
here is to develop algorithms that permit a device to adapt its behavior to the
preferences of the operator through interactions with the wearer. This device
uses multiple sensing modalities including muscle activity from a myoelectric
armband, visual information from an on-board camera, tactile input through a
touchscreen interface, and speech input from an embedded microphone. Presented
within this paper are the design, software and controls of a platform used to
evaluate this architecture as well as results from experiments deigned to
quantify the performance.
",1,0,0,0,0,0
18400,Generalization of Effective Conductance Centrality for Egonetworks,"  We study the popular centrality measure known as effective conductance or in
some circles as information centrality. This is an important notion of
centrality for undirected networks, with many applications, e.g., for random
walks, electrical resistor networks, epidemic spreading, etc. In this paper, we
first reinterpret this measure in terms of modulus (energy) of families of
walks on the network. This modulus centrality measure coincides with the
effective conductance measure on simple undirected networks, and extends it to
much more general situations, e.g., directed networks as well. Secondly, we
study a variation of this modulus approach in the egocentric network paradigm.
Egonetworks are networks formed around a focal node (ego) with a specific order
of neighborhoods. We propose efficient analytical and approximate methods for
computing these measures on both undirected and directed networks. Finally, we
describe a simple method inspired by the modulus point-of-view, called shell
degree, which proved to be a useful tool for network science.
",1,1,0,0,0,0
18401,Three-dimensional oscillatory magnetic reconnection,"  Here we detail the dynamic evolution of localised reconnection regions about
three-dimensional (3D) magnetic null points by using numerical simulation. We
demonstrate for the first time that reconnection triggered by the localised
collapse of a 3D null point due to an external MHD wave involves a
self-generated oscillation, whereby the current sheet and outflow jets undergo
a reconnection reversal process during which back-pressure formation at the jet
heads acts to prise open the collapsed field before overshooting the
equilibrium into an opposite-polarity configuration. The discovery that
reconnection at fully 3D nulls can proceed naturally in a time-dependent and
periodic fashion is suggestive that oscillatory reconnection mechanisms may
play a role in explaining periodicity in astrophysical phenomena associated
with magnetic reconnection, such as the observed quasi-periodicity of solar and
stellar flare emission. Furthermore, we find a consequence of oscillatory
reconnection is the generation of a plethora of freely-propagating MHD waves
which escape the vicinity of the reconnection region
",0,1,0,0,0,0
18402,Limits of Risk Predictability in a Cascading Alternating Renewal Process Model,"  Most risk analysis models systematically underestimate the probability and
impact of catastrophic events (e.g., economic crises, natural disasters, and
terrorism) by not taking into account interconnectivity and interdependence of
risks. To address this weakness, we propose the Cascading Alternating Renewal
Process (CARP) to forecast interconnected global risks. However, assessments of
the model's prediction precision are limited by lack of sufficient ground truth
data. Here, we establish prediction precision as a function of input data size
by using alternative long ground truth data generated by simulations of the
CARP model with known parameters. We illustrate the approach on a model of
fires in artificial cities assembled from basic city blocks with diverse
housing. The results confirm that parameter recovery variance exhibits power
law decay as a function of the length of available ground truth data. Using
CARP, we also demonstrate estimation using a disparate dataset that also has
dependencies: real-world prediction precision for the global risk model based
on the World Economic Forum Global Risk Report. We conclude that the CARP model
is an efficient method for predicting catastrophic cascading events with
potential applications to emerging local and global interconnected risks.
",1,1,0,0,0,0
18403,Self-contracted curves have finite length,"  A curve $\theta$: $I\to E$ in a metric space $E$ equipped with the distance
$d$, where $I\subset \R$ is a (possibly unbounded) interval, is called
self-contracted, if for any triple of instances of time $\{t_i\}_{i=1}^3\subset
I$ with $t_1\leq t_2\leq t_3$ one has $d(\theta(t_3),\theta(t_2))\leq
d(\theta(t_3),\theta(t_1))$. We prove that if $E$ is a finite-dimensional
normed space with an arbitrary norm, the trace of $\theta$ is bounded, then
$\theta$ has finite length, i.e. is rectifiable, thus answering positively the
question raised in~\cite{Lemenant16sc-rectif}.
",0,0,1,0,0,0
18404,Unsupervised robust nonparametric learning of hidden community properties,"  We consider learning of fundamental properties of communities in large noisy
networks, in the prototypical situation where the nodes or users are split into
two classes according to a binary property, e.g., according to their opinions
or preferences on a topic. For learning these properties, we propose a
nonparametric, unsupervised, and scalable graph scan procedure that is, in
addition, robust against a class of powerful adversaries. In our setup, one of
the communities can fall under the influence of a knowledgeable adversarial
leader, who knows the full network structure, has unlimited computational
resources and can completely foresee our planned actions on the network. We
prove strong consistency of our results in this setup with minimal assumptions.
In particular, the learning procedure estimates the baseline activity of normal
users asymptotically correctly with probability 1; the only assumption being
the existence of a single implicit community of asymptotically negligible
logarithmic size. We provide experiments on real and synthetic data to
illustrate the performance of our method, including examples with adversaries.
",1,0,0,1,0,0
18405,OPEB: Open Physical Environment Benchmark for Artificial Intelligence,"  Artificial Intelligence methods to solve continuous- control tasks have made
significant progress in recent years. However, these algorithms have important
limitations and still need significant improvement to be used in industry and
real- world applications. This means that this area is still in an active
research phase. To involve a large number of research groups, standard
benchmarks are needed to evaluate and compare proposed algorithms. In this
paper, we propose a physical environment benchmark framework to facilitate
collaborative research in this area by enabling different research groups to
integrate their designed benchmarks in a unified cloud-based repository and
also share their actual implemented benchmarks via the cloud. We demonstrate
the proposed framework using an actual implementation of the classical
mountain-car example and present the results obtained using a Reinforcement
Learning algorithm.
",1,0,0,0,0,0
18406,The First Measurement of the $2^{3}S_{1} \rightarrow 3^{3}P - 2^{3}P$ Tune-Out Wavelength in He*,"  The workhorse of atomic physics: quantum electrodynamics is one of the best
tested theories in physics. However recent discrepancies have shed doubt on its
accuracy for complex atomic systems. To facilitate the development of the
theory further we aim to measure transition dipole matrix elements of
metastable helium (He*) (the ideal 3 body test-bed) to the highest accuracy
thus far. We have undertaken a measurement of the `tune-out wavelength' which
occurs when the contributions to the dynamic polarizability from all atomic
transitions sum to zero; thus illuminating an atom with this wavelength of
light then produces no net energy shift. This provides a strict constraint on
the transition dipole matrix elements without the complication and inaccuracy
of other methods.
Using a novel atom-laser based technique we have made the first measurement
of the the tune-out wavelength in metastable helium between the
$3^{3}P_{1,2,3}$ and $2^{3}P_{1,2,3}$ states at 413.07(2)nm which compares well
with the predicted value\cite{Mitroy2013} of 413.02(9). We have additionally
developed many of the methods necessary to improve this measurement to the
100fm level of accuracy where it will form the most accurate determination of
transition rate information ever made in He* and provide a stringent test for
atomic QED simulations. We believe this measurement to be one of the most
sensitive ever made of an optical dipole potential, able to detect changes in
potentials of $\sim200pK$ and is widely applicable to other species and areas
of atom optics.
",0,1,0,0,0,0
18407,WheelCon: A wheel control-based gaming platform for studying human sensorimotor control,"  Feedback control theory has been extensively implemented to theoretically
model human sensorimotor control. However, experimental platforms capable of
manipulating important components of multiple feedback loops lack development.
This paper describes the WheelCon, which is an open source platform aimed at
resolving such insufficiencies. WheelCon enables safely simulation of the
canonical sensorimotor task such as riding a mountain bike down a steep,
twisting, bumpy trail etc., with provided only a computer, standard display,
and an inexpensive gaming steering wheel with a force feedback motor. The
platform provides flexibility, as will be demonstrated in the demos provided,
so that researchers may manipulate the disturbances, delay, and quantization
(data rate) in the layered feedback loops, including a high-level advanced plan
layer and a low-level delayed reflex layer. In this paper, we illustrate
WheelCon's graphical user interface (GUI), the input and output of existing
demos, and how to design new games. In addition, we present the basic feedback
model, and we show the testing results from our demo games which align well
with prediction from the model. In short, the platform is featured as cheap,
simple to use, and flexible to program for effective sensorimotor neuroscience
research and control engineering education.
",0,0,0,0,1,0
18408,Depression and Self-Harm Risk Assessment in Online Forums,"  Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
(""RSDD"") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.
",1,0,0,0,0,0
18409,Quantum dilogarithm identities for the square product of A-type Dynkin quivers,"  The famous pentagon identity for quantum dilogarithms has a generalization
for every Dynkin quiver, due to Reineke. A more advanced generalization is
associated with a pair of alternating Dynkin quivers, due to Keller. The
description and proof of Keller's identities involves cluster algebras and
cluster categories, and the statement of the identity is implicit. In this
paper we describe Keller's identities explicitly, and prove them by a dimension
counting argument. Namely, we consider quiver representations
$\boldsymbol{\mathrm{Rep}}_\gamma$ together with a superpotential function
$W_\gamma$, and calculate the Betti numbers of the equivariant $W_\gamma$ rapid
decay cohomology algebra of $\boldsymbol{\mathrm{Rep}}_\gamma$ in two different
ways corresponding to two natural stratifications of
$\boldsymbol{\mathrm{Rep}}_\gamma$. This approach is suggested by Kontsevich
and Soibelman in relation with the Cohomological Hall Algebra of quivers, and
the associated Donaldson-Thomas invariants.
",0,0,1,0,0,0
18410,A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems,"  Traditional Linear Genetic Programming (LGP) algorithms are based only on the
selection mechanism to guide the search. Genetic operators combine or mutate
random portions of the individuals, without knowing if the result will lead to
a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
methods were proposed to overcome this issue through a probability model that
captures the structure of the fit individuals and use it to sample new
individuals. This work proposes the use of LGP with a Stochastic Context-Free
Grammar (SCFG), that has a probability distribution that is updated according
to selected individuals. We proposed a method for adapting the grammar into the
linear representation of LGP. Tests performed with the proposed probabilistic
method, and with two hybrid approaches, on several symbolic regression
benchmark problems show that the results are statistically better than the
obtained by the traditional LGP.
",1,0,0,1,0,0
18411,"Trace-free ${\rm SL}(2,\mathbb{C})$-representations of arborescent links","  Given a link $L\subset S^3$, a representation $\pi_1(S^3-L)\to{\rm
SL}(2,\mathbb{C})$ is {\it trace-free} if it sends each meridian to an element
with trace zero. We present a method for completely determining trace-free
${\rm SL}(2,\mathbb{C})$-representations for arborescent links. Concrete
computations are done for a class of 3-bridge arborescent links.
",0,0,1,0,0,0
18412,Algorithms for Weighted Sums of Squares Decomposition of Non-negative Univariate Polynomials,"  It is well-known that every non-negative univariate real polynomial can be
written as the sum of two polynomial squares with real coefficients. When one
allows a weighted sum of finitely many squares instead of a sum of two squares,
then one can choose all coefficients in the representation to lie in the field
generated by the coefficients of the polynomial.
In this article, we describe, analyze and compare both from the theoretical
and practical points of view, two algorithms computing such a weighted sums of
squares decomposition for univariate polynomials with rational coefficients.
The first algorithm, due to the third author relies on real root isolation,
quadratic approximations of positive polynomials and square-free decomposition
but its complexity was not analyzed. We provide bit complexity estimates, both
on runtime and output size of this algorithm. They are exponential in the
degree of the input univariate polynomial and linear in the maximum bitsize of
its complexity. This analysis is obtained using quantifier elimination and root
isolation bounds.
The second algorithm, due to Chevillard, Harrison, Joldes and Lauter, relies
on complex root isolation and square-free decomposition and has been introduced
for certifying positiveness of polynomials in the context of computer
arithmetics. Again, its complexity was not analyzed. We provide bit complexity
estimates, both on runtime and output size of this algorithm, which are
polynomial in the degree of the input polynomial and linear in the maximum
bitsize of its complexity. This analysis is obtained using Vieta's formula and
root isolation bounds.
Finally, we report on our implementations of both algorithms. While the
second algorithm is, as expected from the complexity result, more efficient on
most of examples, we exhibit families of non-negative polynomials for which the
first algorithm is better.
",1,0,0,0,0,0
18413,The Calkin algebra is $\aleph_1$-universal,"  We discuss the existence of (injectively) universal C*-algebras and prove
that all C*-algebras of density character $\aleph_1$ embed into the Calkin
algebra, $Q(H)$. Together with other results, this shows that each of the
following assertions is relatively consistent with ZFC: (i) $Q(H)$ is a
$2^{\aleph_0}$-universal C*-algebra. (ii) There exists a
$2^{\aleph_0}$-universal C*-algebra, but $Q(H)$ is not
$2^{\aleph_0}$-universal. (iii) A $2^{\aleph_0}$-universal C*-algebra does not
exist. We also prove that it is relatively consistent with ZFC that (iv) there
is no $\aleph_1$-universal nuclear C*-algebra, and that (v) there is no
$\aleph_1$-universal simple nuclear C*-algebra.
",0,0,1,0,0,0
18414,Learning Structured Semantic Embeddings for Visual Recognition,"  Numerous embedding models have been recently explored to incorporate semantic
knowledge into visual recognition. Existing methods typically focus on
minimizing the distance between the corresponding images and texts in the
embedding space but do not explicitly optimize the underlying structure. Our
key observation is that modeling the pairwise image-image relationship improves
the discrimination ability of the embedding model. In this paper, we propose
the structured discriminative and difference constraints to learn
visual-semantic embeddings. First, we exploit the discriminative constraints to
capture the intra- and inter-class relationships of image embeddings. The
discriminative constraints encourage separability for image instances of
different classes. Second, we align the difference vector between a pair of
image embeddings with that of the corresponding word embeddings. The difference
constraints help regularize image embeddings to preserve the semantic
relationships among word embeddings. Extensive evaluations demonstrate the
effectiveness of the proposed structured embeddings for single-label
classification, multi-label classification, and zero-shot recognition.
",1,0,0,0,0,0
18415,Estimators for a Class of Bivariate Measures of Concordance for Copulas,"  In the present paper we propose and study estimators for a wide class of
bivariate measures of concordance for copulas. These measures of concordance
are generated by a copula and generalize Spearman's rho and Gini's gamma. In
the case of Spearman's rho and Gini's gamma the estimators turn out to be the
usual sample versions of these measures of concordance.
",0,0,1,1,0,0
18416,Variational treatment of electron-polyatomic molecule scattering calculations using adaptive overset grids,"  The Complex Kohn variational method for electron-polyatomic molecule
scattering is formulated using an overset grid representation of the scattering
wave function. The overset grid consists of a central grid and multiple dense,
atom-centered subgrids that allow the simultaneous spherical expansions of the
wave function about multiple centers. Scattering boundary conditions are
enforced by using a basis formed by the repeated application of the free
particle Green's function and potential, $\hat{G}^+_0\hat{V}$ on the overset
grid in a ""Born-Arnoldi"" solution of the working equations. The theory is shown
to be equivalent to a specific Padé approximant to the $T$-matrix, and has
rapid convergence properties, both in the number of numerical basis functions
employed and the number of partial waves employed in the spherical expansions.
The method is demonstrated in calculations on methane and CF$_4$ in the
static-exchange approximation, and compared in detail with calculations
performed with the numerical Schwinger variational approach based on single
center expansions. An efficient procedure for operating with the free-particle
Green's function and exchange operators (to which no approximation is made) is
also described.
",0,1,0,0,0,0
18417,Geometry of quantum dynamics in infinite dimension,"  We develop a geometric approach to quantum mechanics based on the concept of
the Tulczyjew triple. Our approach is genuinely infinite-dimensional and
including a Lagrangian formalism in which self-adjoint (Schroedinger) operators
are obtained as Lagrangian submanifolds associated with the Lagrangian. As a
byproduct we obtain also results concerning coadjoint orbits of the unitary
group in infinite dimension, embedding of the Hilbert projective space of pure
states in the unitary group, and an approach to self-adjoint extensions of
symmetric relations.
",0,0,1,0,0,0
18418,Canonical tilting relative generators,"  Given a relatively projective birational morphism $f\colon X\to Y$ of smooth
algebraic spaces with dimension of fibers bounded by 1, we construct tilting
relative (over $Y$) generators $T_{X,f}$ and $S_{X,f}$ in $\mathcal{D}^b(X)$.
We develop a piece of general theory of strict admissible lattice filtrations
in triangulated categories and show that $\mathcal{D}^b(X)$ has such a
filtration $\mathcal{L}$ where the lattice is the set of all birational
decompositions $f \colon X \xrightarrow{g} Z \xrightarrow{h} Y$ with smooth
$Z$. The $t$-structures related to $T_{X,f}$ and $S_{X,f}$ are proved to be
glued via filtrations left and right dual to $\mathcal{L}$. We realise all such
$Z$ as the fine moduli spaces of simple quotients of $\mathcal{O}_X$ in the
heart of the $t$-structure for which $S_{X,g}$ is a relative projective
generator over $Y$. This implements the program of interpreting relevant smooth
contractions of $X$ in terms of a suitable system of $t$-structures on
$\mathcal{D}^b(X)$.
",0,0,1,0,0,0
18419,Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models,"  Current theories hold that brain function is highly related to long-range
physical connections through axonal bundles, namely extrinsic connectiv-ity.
However, obtaining a groupwise cortical parcellation based on extrinsic
connectivity remains challenging. Current parcellation methods are
compu-tationally expensive; need tuning of several parameters or rely on ad-hoc
constraints. Furthermore, none of these methods present a model for the
cortical extrinsic connectivity of the cortex. To tackle these problems, we
propose a parsimonious model for the extrinsic connectivity and an efficient
parceling technique based on clustering of tractograms. Our technique allows
the creation of single subject and groupwise parcellations of the whole cortex.
The parcellations obtained with our technique are in agreement with structural
and functional parcellations in the literature. In particular, the motor and
sensory cortex are subdivided in agreement with the human ho-munculus of
Penfield. We illustrate this by comparing our resulting parcels with the motor
strip mapping included in the Human Connectome Project data.
",0,0,0,1,0,0
18420,Modeling Biological Problems in Computer Science: A Case Study in Genome Assembly,"  As computer scientists working in bioinformatics/computational biology, we
often face the challenge of coming up with an algorithm to answer a biological
question. This occurs in many areas, such as variant calling, alignment, and
assembly. In this tutorial, we use the example of the genome assembly problem
to demonstrate how to go from a question in the biological realm to a solution
in the computer science realm. We show the modeling process step-by-step,
including all the intermediate failed attempts.
",1,0,0,0,0,0
18421,MEC: Memory-efficient Convolution for Deep Neural Network,"  Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or Winograd-based algorithm. However, all these indirect methods
have high memory-overhead, which creates performance degradation and offers a
poor trade-off between performance and memory consumption. In this work, we
propose a memory-efficient convolution or MEC with compact lowering, which
reduces memory-overhead substantially and accelerates convolution process. MEC
lowers the input matrix in a simple yet efficient/compact way (i.e., much less
memory-overhead), and then executes multiple small matrix multiplications in
parallel to get convolution completed. Additionally, the reduced memory
footprint improves memory sub-system efficiency, improving performance. Our
experimental results show that MEC reduces memory consumption significantly
with good speedup on both mobile and server platforms, compared with other
indirect convolution algorithms.
",1,0,0,0,0,0
18422,On age of 6070 Rheinland and 54827 (2001 NQ8) asteroid pair,"  In this paper we present results of our studying of famous very young pair of
asteroids 6070 Rheinland and 54827 (2001 NQ8). We have done numeric integration
of orbits of pair with only planet perturbations and include Ceres and Vesta
effect. We have confirmed results of previous studying, obtained with different
integrators. And we confirm significant effect of Ceres and Vesta perturbation
on dynamic of this pair. We find that effect of other massive asteroids is
insignificant. According our results, more probable age of 6070 Rheinland and
54827 (2001 NQ8) pair is 16.2 kyrs. Our value of age is very close to most
recent age determination by Vokrouhlicky et al [12], obtained with different
method. After the compare our results, we can conclude, that non-gravitational
forces are small and large number of clones is not necessary in studying of
this pair. As an additional way of studying of close orbits dynamics, we
calculate relative velocity in pair during numeric integration. Normal
component of velocity show a very good convergence at epoch of closest
encounter in pair.
",0,1,0,0,0,0
18423,First-Order Reversal Curves of the Magnetostructural Phase Transition in FeTe,"  We apply the first-order reversal curve (FORC) method, borrowed from studies
of ferromagnetic materials, to the magneto-structural phase transition of FeTe.
FORC measurements reveal two features in the hysteretic phase transition, even
in samples where traditional temperature measurements display only a single
transition. For Fe1.13Te, the influence of magnetic field suggests that the
main feature is primarily structural while a smaller, slightly
higher-temperature transition is magnetic in origin. By contrast Fe1.03Te has a
single transition which shows a uniform response to magnetic field, indicating
a stronger coupling of the magnetic and structural phase transitions. We also
introduce uniaxial stress, which spreads the distribution width without
changing the underlying energy barrier of the transformation. The work shows
how FORC can help disentangle the roles of the magnetic and structural phase
transitions in FeTe.
",0,1,0,0,0,0
18424,Data Analytics on Online Labor Markets: Opportunities and Challenges,"  The data-driven economy has led to a significant shortage of data scientists.
To address this shortage, this study explores the prospects of outsourcing data
analysis tasks to freelancers available on online labor markets (OLMs) by
identifying the essential factors for this endeavor. Specifically, we explore
the skills required from freelancers, collect information about the skills
present on major OLMs, and identify the main hurdles for out-/crowd-sourcing
data analysis. Adopting a sequential mixed-method approach, we interviewed 20
data scientists and subsequently surveyed 80 respondents from OLMs. Besides
confirming the need for expected skills such as technical/mathematical
capabilities, it also identifies less known ones such as domain understanding,
an eye for aesthetic data visualization, good communication skills, and a
natural understanding of the possibilities/limitations of data analysis in
general. Finally, it elucidates obstacles for crowdsourcing like the
communication overhead, knowledge gaps, quality assurance, and data
confidentiality, which need to be mitigated.
",1,0,0,0,0,0
18425,Analyzing the Robustness of Nearest Neighbors to Adversarial Examples,"  Motivated by safety-critical applications, test-time attacks on classifiers
via adversarial examples has recently received a great deal of attention.
However, there is a general lack of understanding on why adversarial examples
arise; whether they originate due to inherent properties of data or due to lack
of training samples remains ill-understood. In this work, we introduce a
theoretical framework analogous to bias-variance theory for understanding these
effects.
We use our framework to analyze the robustness of a canonical non-parametric
classifier - the k-nearest neighbors. Our analysis shows that its robustness
properties depend critically on the value of k - the classifier may be
inherently non-robust for small k, but its robustness approaches that of the
Bayes Optimal classifier for fast-growing k. We propose a novel modified
1-nearest neighbor classifier, and guarantee its robustness in the large sample
limit. Our experiments suggest that this classifier may have good robustness
properties even for reasonable data set sizes.
",1,0,0,1,0,0
18426,Non Uniform On Chip Power Delivery Network Synthesis Methodology,"  In this paper, we proposed a non-uniform power delivery network (PDN)
synthesis methodology. It first constructs initial PDN using uniform approach.
Then preliminary power integrity analysis is performed to derive IR-safe
candidate window. Congestion map is obtained based global route congestion
estimation. A self-adaptive non-uniform PDN synthesis is then performed to
globally and locally optimize PDN over selected regions. The PDN synthesis is
congestion-driven and IR- guarded. Experimental results show significant timing
important in trade-off small PDN length reduction with no EM/IR impact. We
further explored potential power savings using our non-uniform PDN synthesis
methodology.
",1,0,0,0,0,0
18427,Translating ceRNA susceptibilities into correlation functions,"  Competition to bind microRNAs induces an effective positive crosstalk between
their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While
such an effect is known to play a significant role in specific conditions,
estimating its strength from data and, experimentally, in physiological
conditions appears to be far from simple. Here we show that the susceptibility
of ceRNAs to different types of perturbations affecting their competitors (and
hence their tendency to crosstalk) can be encoded in quantities as intuitive
and as simple to measure as correlation functions. We confirm this scenario by
extensive numerical simulations and validate it by re-analyzing PTEN's
crosstalk pattern from TCGA breast cancer dataset. These results clarify the
links between different quantities used to estimate the intensity of ceRNA
crosstalk and provide new keys to analyze transcriptional datasets and
effectively probe ceRNA networks in silico.
",0,1,0,0,0,0
18428,Impact of energetic particle orbits on long range frequency chirping of BGK modes,"  Long range frequency chirping of Bernstein-Greene-Kruskal modes, whose
existence is determined by the fast particles, is investigated in cases where
these particles do not move freely and their motion is bounded to restricted
orbits. An equilibrium oscillating potential, which creates different orbit
topologies of energetic particles, is included into the bump-on-tail
instability problem of a plasma wave. With respect to fast particles dynamics,
the extended model captures the range of particles motion (trapped/passing)
with energy and thus represents a more realistic 1D picture of the long range
sweeping events observed for weakly damped modes, e.g. global Alfven
eigenmodes, in tokamaks. The Poisson equation is solved numerically along with
bounce averaging the Vlasov equation in the adiabatic regime. We demonstrate
that the shape and the saturation amplitude of the nonlinear mode structure
depends not only on the amount of deviation from the initial eigenfrequency but
also on the initial energy of the resonant electrons in the equilibrium
potential. Similarly, the results reveal that the resonant electrons following
different equilibrium orbits in the electrostatic potential lead to different
rates of frequency evolution. As compared to the previous model [Breizman B.N.
2010 Nucl. Fusion 50 084014], it is shown that the frequency sweeps with lower
rates. The additional physics included in the model enables a more complete 1D
description of the range of phenomena observed in experiments.
",0,1,0,0,0,0
18429,An Executable Specification of Typing Rules for Extensible Records based on Row Polymorphism,"  Type inference is an application domain that is a natural fit for logic
programming (LP). LP systems natively support unification, which serves as a
basic building block of typical type inference algorithms. In particular,
polymorphic type inference in the Hindley--Milner type system (HM) can be
succinctly specified and executed in Prolog. In our previous work, we have
demonstrated that more advanced features of parametric polymorphism beyond HM,
such as type-constructor polymorphism and kind polymorphism, can be similarly
specified in Prolog. Here, we demonstrate a specification for records, which is
one of the most widely supported compound data structures in real-world
programming languages, and discuss the advantages and limitations of Prolog as
a specification language for type systems. Record types are specified as
order-irrelevant collections of named fields mapped to their corresponding
types. In addition, an open-ended collection is used to support row
polymorphism for record types to be extensible.
",1,0,0,0,0,0
18430,Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets,"  We introduce a multi-factor stochastic volatility model for commodities that
incorporates seasonality and the Samuelson effect. Conditions on the seasonal
term under which the corresponding volatility factor is well-defined are given,
and five different specifications of the seasonality pattern are proposed. We
calculate the joint characteristic function of two futures prices for different
maturities in the risk-neutral measure. The model is then presented under the
physical measure, and its state-space representation is derived, in order to
estimate the parameters with the Kalman filter for time series of corn, cotton,
soybean, sugar and wheat futures from 2007 to 2017. The seasonal model
significantly outperforms the nested non-seasonal model in all five markets,
and we show which seasonality patterns are particularly well-suited in each
case. We also confirm the importance of correctly modelling the Samuelson
effect in order to account for futures with different maturities. Our results
are clearly confirmed in a robustness check carried out with an alternative
dataset of constant maturity futures for the same agricultural markets.
",0,0,0,0,0,1
18431,A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging,"  Single-Photon Avalanche Diodes (SPAD) are affordable photodetectors, capable
to collect extremely fast low-energy events, due to their single-photon
sensibility. This makes them very suitable for time-of-flight-based range
imaging systems, allowing to reduce costs and power requirements, without
sacrifizing much temporal resolution. In this work we describe a computational
model to simulate the behaviour of SPAD sensors, aiming to provide a realistic
camera model for time-resolved light transport simulation, with applications on
prototyping new reconstructions techniques based on SPAD time-of-flight data.
Our model accounts for the major effects of the sensor on the incoming signal.
We compare our model against real-world measurements, and apply it to a variety
of scenarios, including complex multiply-scattered light transport.
",1,1,0,0,0,0
18432,On Certain Tilting Modules for SL2,"  We give a complete picture of when the tensor product of an induced module
and a Weyl module is a tilting module for the algebraic group $SL_2$ over an
algebraically closed field of characteristic $p$. Whilst the result is
recursive by nature, we give an explicit statement in terms of the $p$-adic
expansions of the highest weight of each module.
",0,0,1,0,0,0
18433,First-order Methods Almost Always Avoid Saddle Points,"  We establish that first-order methods avoid saddle points for almost all
initializations. Our results apply to a wide variety of first-order methods,
including gradient descent, block coordinate descent, mirror descent and
variants thereof. The connecting thread is that such algorithms can be studied
from a dynamical systems perspective in which appropriate instantiations of the
Stable Manifold Theorem allow for a global stability analysis. Thus, neither
access to second-order derivative information nor randomness beyond
initialization is necessary to provably avoid saddle points.
",1,0,0,1,0,0
18434,Faster Algorithms for Computing Maximal 2-Connected Subgraphs in Sparse Directed Graphs,"  Connectivity related concepts are of fundamental interest in graph theory.
The area has received extensive attention over four decades, but many problems
remain unsolved, especially for directed graphs. A directed graph is
2-edge-connected (resp., 2-vertex-connected) if the removal of any edge (resp.,
vertex) leaves the graph strongly connected. In this paper we present improved
algorithms for computing the maximal 2-edge- and 2-vertex-connected subgraphs
of a given directed graph. These problems were first studied more than 35 years
ago, with $\widetilde{O}(mn)$ time algorithms for graphs with m edges and n
vertices being known since the late 1980s. In contrast, the same problems for
undirected graphs are known to be solvable in linear time. Henzinger et al.
[ICALP 2015] recently introduced $O(n^2)$ time algorithms for the directed
case, thus improving the running times for dense graphs. Our new algorithms run
in time $O(m^{3/2})$, which further improves the running times for sparse
graphs.
The notion of 2-connectivity naturally generalizes to k-connectivity for
$k>2$. For constant values of k, we extend one of our algorithms to compute the
maximal k-edge-connected in time $O(m^{3/2} \log{n})$, improving again for
sparse graphs the best known algorithm by Henzinger et al. [ICALP 2015] that
runs in $O(n^2 \log n)$ time.
",1,0,0,0,0,0
18435,Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting,"  Generative adversarial networks (GANs) are an exciting alternative to
algorithms for solving density estimation problems---using data to assess how
likely samples are to be drawn from the same distribution. Instead of
explicitly computing these probabilities, GANs learn a generator that can match
the given probabilistic source. This paper looks particularly at this matching
capability in the context of problems with one-dimensional outputs. We identify
a class of function decompositions with properties that make them well suited
to the critic role in a leading approach to GANs known as Wasserstein GANs. We
show that Taylor and Fourier series decompositions belong to our class, provide
examples of these critics outperforming standard GAN approaches, and suggest
how they can be scaled to higher dimensional problems in the future.
",1,0,0,1,0,0
18436,Fingering instabilities and pattern formation in a two-component dipolar Bose-Einstein condensate,"  We study fingering instabilities and pattern formation at the interface of an
oppositely polarized two-component Bose-Einstein condensate with strong
dipole-dipole interactions in three dimensions. It is shown that the rotational
symmetry is spontaneously broken by fingering instability when the
dipole-dipole interactions are strengthened. Frog-shaped and mushroom-shaped
patterns emerge during the dynamics due to the dipolar interactions. We also
demonstrate the spontaneous density modulation and domain growth of a
two-component dipolar BEC in the dynamics. Bogoliubov analyses in the
two-dimensional approximation are performed, and the characteristic lengths of
the domains are estimated analytically. Patterns resembling those in magnetic
classical fluids are modulated when the number ratio of atoms, the trap ratio
of the external potential, or tilted polarization with respect to the z
direction is varied.
",0,1,0,0,0,0
18437,Fast Information-theoretic Bayesian Optimisation,"  Information-theoretic Bayesian optimisation techniques have demonstrated
state-of-the-art performance in tackling important global optimisation
problems. However, current information-theoretic approaches require many
approximations in implementation, introduce often-prohibitive computational
overhead and limit the choice of kernels available to model the objective. We
develop a fast information-theoretic Bayesian Optimisation method, FITBO, that
avoids the need for sampling the global minimiser, thus significantly reducing
computational overhead. Moreover, in comparison with existing approaches, our
method faces fewer constraints on kernel choice and enjoys the merits of
dealing with the output space. We demonstrate empirically that FITBO inherits
the performance associated with information-theoretic Bayesian optimisation,
while being even faster than simpler Bayesian optimisation approaches, such as
Expected Improvement.
",0,0,0,1,0,0
18438,ExoMol molecular line lists XX: a comprehensive line list for H$_3^+$,"  H$_3^+$ is a ubiquitous and important astronomical species whose spectrum has
been observed in the interstellar medium, planets and tentatively in the
remnants of supernova SN1897a. Its role as a cooler is important for gas giant
planets and exoplanets, and possibly the early Universe. All this makes the
spectral properties, cooling function and partition function of H$_3^+$ key
parameters for astronomical models and analysis. A new high-accuracy, very
extensive line list for H$_3^+$ called MiZATeP was computed as part of the
ExoMol project alongside a temperature-dependent cooling function and partition
function as well as lifetimes for %individual excited states. These data are
made available in electronic form as supplementary data to this article and at
this http URL
",0,1,0,0,0,0
18439,Robust and Sparse Regression in GLM by Stochastic Optimization,"  The generalized linear model (GLM) plays a key role in regression analyses.
In high-dimensional data, the sparse GLM has been used but it is not robust
against outliers. Recently, the robust methods have been proposed for the
specific example of the sparse GLM. Among them, we focus on the robust and
sparse linear regression based on the $\gamma$-divergence. The estimator of the
$\gamma$-divergence has strong robustness under heavy contamination. In this
paper, we extend the robust and sparse linear regression based on the
$\gamma$-divergence to the robust and sparse GLM based on the
$\gamma$-divergence with a stochastic optimization approach in order to obtain
the estimate. We adopt the randomized stochastic projected gradient descent as
a stochastic optimization approach and extend the established convergence
property to the classical first-order necessary condition. By virtue of the
stochastic optimization approach, we can efficiently estimate parameters for
very large problems. Particularly, we show the linear regression, logistic
regression and Poisson regression with $L_1$ regularization in detail as
specific examples of robust and sparse GLM. In numerical experiments and real
data analysis, the proposed method outperformed comparative methods.
",0,0,0,1,0,0
18440,A Hint-Based Technique for System Level Model-Based Test Case Prioritization,"  Test Case Prioritization (TCP) techniques aim at proposing new test case
execution orders to favor the achievement of certain testing goal, such as
fault detection. Current TCP research focus mainly on code-based regression
testing; however in the Model-Based Testing (MBT) context, we still need more
investigation. General TCP techniques do not use historical information, since
this information is often unavailable. Therefore, techniques use different
sources of information to guide prioritization. We propose a novel technique
that guides its operation using provided hints, the Hint-Based Adaptive Random
Prioritization - HARP. Hints are indications or suggestions provided by
developers about error-prone functionalities. As hints may be hard to collect
automatically, we also propose an approach of collecting them. To validate our
findings, we performed an experiment measuring the effect of introducing hints
to HARP. It shows that hints can improve HARP's performance comparing to its
baseline. Then, we investigated the ability of developers/managers to provide
good hints and used them in a case study. This analysis showed that developers
and managers are able to provide useful hints, which improves HARP's fault
detection comparing to its baseline. Nonetheless, the provided hints should be
consensual among the development team members.
",1,0,0,0,0,0
18441,Control of birhythmicity: A self-feedback approach,"  Birhythmicity occurs in many natural and artificial systems. In this paper we
propose a self-feedback scheme to control birhythmicity. To establish the
efficacy and generality of the proposed control scheme, we apply it on three
birhythmic oscillators from diverse fields of natural science, namely, an
energy harvesting system, the p53-Mdm2 network for protein genesis (the OAK
model) and a glycolysis model (modified Decroly-Goldbeter model). Using the
harmonic decomposition technique and energy balance method we derive the
analytical conditions for the control of birhythmicity. A detailed numerical
bifurcation analysis in the parameter space establishes that the control scheme
is capable of eliminating birhythmicity and it can also induce transitions
between different forms of bistability. As the proposed control scheme is quite
general, it can be applied for control of several real systems, particularly in
biochemical and engineering systems.
",0,1,0,0,0,0
18442,Forecasting the magnitude and onset of El Nino based on climate network,"  El Nino is probably the most influential climate phenomenon on interannual
time scales. It affects the global climate system and is associated with
natural disasters and serious consequences in many aspects of human life.
However, the forecasting of the onset and in particular the magnitude of El
Nino are still not accurate, at least more than half a year in advance. Here,
we introduce a new forecasting index based on network links representing the
similarity of low frequency temporal temperature anomaly variations between
different sites in the El Nino 3.4 region. We find that significant upward
trends and peaks in this index forecast with high accuracy both the onset and
magnitude of El Nino approximately 1 year ahead. The forecasting procedure we
developed improves in particular the prediction of the magnitude of El Nino and
is validated based on several, up to more than a century long, datasets.
",0,1,0,0,0,0
18443,Weighted $1\times1$ cut-and-project sets in bounded distance to a lattice,"  Recent results of Grepstad and Lev are used to show that weighted
cut-and-project sets with one-dimensional physical space and one-dimensional
internal space are bounded distance equivalent to some lattice if the weight
function $h$ is continuous on the internal space, and if $h$ is either
piecewise linear, or twice differentiable with bounded curvature.
",0,0,1,0,0,0
18444,On Ordinal Invariants in Well Quasi Orders and Finite Antichain Orders,"  We investigate the ordinal invariants height, length, and width of well quasi
orders (WQO), with particular emphasis on width, an invariant of interest for
the larger class of orders with finite antichain condition (FAC). We show that
the width in the class of FAC orders is completely determined by the width in
the class of WQOs, in the sense that if we know how to calculate the width of
any WQO then we have a procedure to calculate the width of any given FAC order.
We show how the width of WQO orders obtained via some classical constructions
can sometimes be computed in a compositional way. In particular, this allows
proving that every ordinal can be obtained as the width of some WQO poset. One
of the difficult questions is to give a complete formula for the width of
Cartesian products of WQOs. Even the width of the product of two ordinals is
only known through a complex recursive formula. Although we have not given a
complete answer to this question we have advanced the state of knowledge by
considering some more complex special cases and in particular by calculating
the width of certain products containing three factors. In the course of
writing the paper we have discovered that some of the relevant literature was
written on cross-purposes and some of the notions re-discovered several times.
Therefore we also use the occasion to give a unified presentation of the known
results.
",0,0,1,0,0,0
18445,Indices in XML Databases,"  With XML becoming a standard for business information representation and
exchange, stor-ing, indexing, and querying XML documents have rapidly become
major issues in database research. In this context, query processing and
optimization are primordial, native-XML data-bases not being mature yet. Data
structures such as indices, which help enhance performances substantially, are
extensively researched, especially since XML data bear numerous specifici-ties
with respect to relational data. In this paper, we survey state-of-the-art XML
indices and discuss the main issues, tradeoffs and future trends in XML
indexing. We also present an in-dex that we specifically designed for the
particular architecture of XML data warehouses.
",1,0,0,0,0,0
18446,Kernel theorems for modulation spaces,"  We deal with kernel theorems for modulation spaces. We completely
characterize the continuity of a linear operator on the modulation spaces $M^p$
for every $1\leq p\leq\infty$, by the membership of its kernel to (mixed)
modulation spaces. Whereas Feichtinger's kernel theorem (which we recapture as
a special case) is the modulation space counterpart of Schwartz' kernel theorem
for temperate distributions, our results do not have a couterpart in
distribution theory. This reveals the superiority, in some respects, of the
modulation space formalism upon distribution theory, as already emphasized in
Feichtinger's manifesto for a post-modern harmonic analysis, tailored to the
needs of mathematical signal processing. The proof uses in an essential way a
discretization of the problem by means of Gabor frames. We also show the
equivalence of the operator norm and the modulation space norm of the
corresponding kernel. For operators acting on $M^{p,q}$ a similar
characterization is not expected, but sufficient conditions for boundedness can
be sated in the same spirit.
",0,0,1,0,0,0
18447,On the Performance of Network Parallel Training in Artificial Neural Networks,"  Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an increasing need to obtain real-time results from such models, it has
become pivotal to use parallelization as a mechanism for speeding up network
training and deployment. In this work we propose an implementation of Network
Parallel Training through Cannon's Algorithm for matrix multiplication. We show
that increasing the number of processes speeds up training until the point
where process communication costs become prohibitive; this point varies by
network complexity. We also show through empirical efficiency calculations that
the speedup obtained is superlinear.
",1,0,0,1,0,0
18448,Fair Forests: Regularized Tree Induction to Minimize Model Bias,"  The potential lack of fairness in the outputs of machine learning algorithms
has recently gained attention both within the research community as well as in
society more broadly. Surprisingly, there is no prior work developing
tree-induction algorithms for building fair decision trees or fair random
forests. These methods have widespread popularity as they are one of the few to
be simultaneously interpretable, non-linear, and easy-to-use. In this paper we
develop, to our knowledge, the first technique for the induction of fair
decision trees. We show that our ""Fair Forest"" retains the benefits of the
tree-based approach, while providing both greater accuracy and fairness than
other alternatives, for both ""group fairness"" and ""individual fairness.'"" We
also introduce new measures for fairness which are able to handle multinomial
and continues attributes as well as regression problems, as opposed to binary
attributes and labels only. Finally, we demonstrate a new, more robust
evaluation procedure for algorithms that considers the dataset in its entirety
rather than only a specific protected attribute.
",1,0,0,1,0,0
18449,Discretization of SU(2) and the Orthogonal Group Using Icosahedral Symmetries and the Golden Numbers,"  The vertices of the four dimensional $120$-cell form a non-crystallographic
root system whose corresponding symmetry group is the Coxeter group $H_{4}$.
There are two special coordinate representations of this root system in which
they and their corresponding Coxeter groups involve only rational numbers and
the golden ratio $\tau$. The two are related by the conjugation $\tau
\mapsto\tau' = -1/\tau$. This paper investigates what happens when the two root
systems are combined and the group generated by both versions of $H_{4}$ is
allowed to operate on them. The result is a new, but infinite, `root system'
$\Sigma$ which itself turns out to have a natural structure of the unitary
group $SU(2,\mathcal R)$ over the ring $\mathcal R = \mathbb
Z[\frac{1}{2},\tau]$ (called here golden numbers). Acting upon it is the
naturally associated infinite reflection group $H^{\infty}$, which we prove is
of index $2$ in the orthogonal group $O(4,\mathcal R)$. The paper makes
extensive use of the quaternions over $\mathcal R$ and leads to highly
structured discretized filtration of $SU(2)$. We use this to offer a simple and
effective way to approximate any element of $SU(2)$ to any degree of accuracy
required using the repeated actions of just five fixed reflections, a process
that may find application in computational methods in quantum mechanics.
",0,0,1,0,0,0
18450,Raman spectra of crystalline secondary amides,"  The study of single-crystal Raman spectra of a series of crystalline
secondary amides (acetanilide, methacetin, phenacetine, orthorhombic and
monoclinic polymorphs of paracetamol) as well as simple amides formanilide and
benzanilide in the temperature range 5-300 K was carried out. The series of
compounds with the same molecular fragment, i.e. acetamide group, can serve as
a model system to study the interrelation between the latter and the properties
of the intermolecular ""peptide-type"" NH...O=C hydrogen bonds. For all the
""acetamide family"" of compounds, similar changes in the Raman spectra were
observed on cooling the samples: an appearance of new Amide I(-) and Amide I(+)
bands that are red and blue shifted respectively from the conventional Amide I
band by around of 5-10 inverse centimeters. An appropriated changes in the same
temperature range were observed for the N-H out-of-plane bending (Amide V) and
N-H stretching vibrations of the N-H...O=C hydrogen bond. All the spectral
changes on cooling the samples can be supposed to result from delocalization of
the Amide I and N-H modes and appearance of dynamical splitting at low
temperature.
",0,1,0,0,0,0
18451,Open-World Visual Recognition Using Knowledge Graphs,"  In a real-world setting, visual recognition systems can be brought to make
predictions for images belonging to previously unknown class labels. In order
to make semantically meaningful predictions for such inputs, we propose a
two-step approach that utilizes information from knowledge graphs. First, a
knowledge-graph representation is learned to embed a large set of entities into
a semantic space. Second, an image representation is learned to embed images
into the same space. Under this setup, we are able to predict structured
properties in the form of relationship triples for any open-world image. This
is true even when a set of labels has been omitted from the training protocols
of both the knowledge graph and image embeddings. Furthermore, we append this
learning framework with appropriate smoothness constraints and show how prior
knowledge can be incorporated into the model. Both these improvements combined
increase performance for visual recognition by a factor of six compared to our
baseline. Finally, we propose a new, extended dataset which we use for
experiments.
",1,0,0,1,0,0
18452,Mass-Imbalanced Ionic Hubbard Chain,"  A repulsive Hubbard model with both spin-asymmetric hopping (${t_\uparrow\neq
t_\downarrow}$) and a staggered potential (of strength $\Delta$) is studied in
one dimension. The model is a compound of the mass-imbalanced (${t_\uparrow\neq
t_\downarrow}$, ${\Delta=0}$) and ionic (${t_\uparrow = t_\downarrow}$,
${\Delta>0}$) Hubbard models, and may be realized by cold atoms in engineered
optical lattices. We use mostly mean-field theory to determine the phases and
phase transitions in the ground state for a half-filled band (one particle per
site). We find that a period-two modulation of the particle (or charge) density
and an alternating spin density coexist for arbitrary Hubbard interaction
strength, ${U\geqslant 0}$. The amplitude of the charge modulation is largest
at ${U=0}$, decreases with increasing $U$ and tends to zero for
${U\rightarrow\infty}$. The amplitude for spin alternation increases with $U$
and tends to saturation for ${U\rightarrow\infty}$. Charge order dominates
below a critical value $U_c$, whereas magnetic order dominates above. The
mean-field Hamiltonian has two gap parameters, $\Delta_\uparrow$ and
$\Delta_\downarrow$, which have to be determined self-consistently. For
${U<U_c}$ both parameters are positive, for ${U>U_c}$ they have different
signs, and for ${U=U_c}$ one gap parameter jumps from a positive to a negative
value. The weakly first-order phase transition at $U_c$ can be interpreted in
terms of an avoided criticality (or metallicity). The system is reluctant to
restore a symmetry that has been broken explicitly.
",0,1,0,0,0,0
18453,Optical properties of Xe color centers in diamond,"  Optical properties of color centers in diamond have been the subject of
intense research due to their promising applications in quantum photonics. In
this work we study the optical properties of Xe related color centers implanted
into nitrogen rich (type IIA) and an ultrapure, electronic grade diamond. The
Xe defect has two zero phonon lines at ~ 794 and 811 nm, which can be
effectively excited using both green and red excitation, however, its emission
in the nitrogen rich diamond is brighter. Near resonant excitation is performed
at cryogenic temperatures and luminescence is probed under strong magnetic
field. Our results are important towards the understanding of the Xe related
defect and other near infrared color centers in diamond.
",0,1,0,0,0,0
18454,"Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir","  Julian Besag was an outstanding statistical scientist, distinguished for his
pioneering work on the statistical theory and analysis of spatial processes,
especially conditional lattice systems. His work has been seminal in
statistical developments over the last several decades ranging from image
analysis to Markov chain Monte Carlo methods. He clarified the role of
auto-logistic and auto-normal models as instances of Markov random fields and
paved the way for their use in diverse applications. Later work included
investigations into the efficacy of nearest neighbour models to accommodate
spatial dependence in the analysis of data from agricultural field trials,
image restoration from noisy data, and texture generation using lattice models.
",0,0,0,1,0,0
18455,Wake fields in a rectangular dielectric-lined accelerating structure with transversal isotropic loading,"  Dielectric lined waveguides are under extensive study as accelerating
structures that can be excited by electron beams. Rectangular dielectric
structures are used both in proof of principle experiments for new accelerating
schemes and for studying the electronic properties of the structure loading
material. Analysis of Cherenkov radiation generated by high current
relativistic electron bunch passing through a rectangular waveguide with
transversal isotropic dielectric loading has been carried out. Some of the
materials used for the waveguide loading of accelerating structures (sapphire,
ceramic films) possess significant anisotropic properties. In turn, it can
influence excitation parameters of the wakefields generated by an electron
beam. General solutions for the fields generated by a relativistic electron
beam propagating in a rectangular dielectric waveguide have been derived using
the orthogonal eigenmode decomposition method for the transverse operators of
the Helmholtz equation. The analytical expression for the combined Cherenkov
and Coulomb fields in terms of a superposition of LSM and LSE-modes of
rectangular waveguide with transversal isotropic dielectric loading has been
obtained. Numerical modelling of the longitudinal and transverse (deflecting)
wakefields has been carried out as well. It is shown that the dielectric
anisotropy causes frequency shift in comparison to the dielectric-lined
waveguide with the isotropic dielectric loading.
",0,1,0,0,0,0
18456,Hyperfine Structure of the $B^3Π_1$ State and Predictions of Optical Cycling Behavior in the $X\rightarrow B$ transition of TlF,"  The rotational and hyperfine spectrum of the $X^1\Sigma^+ \rightarrow
B^3\Pi_1$ transition in TlF molecules was measured using laser-induced
fluorescence from both a thermal and a cryogenic molecular beam. Rotational and
hyperfine constants for the $B$ state are obtained. The large magnetic
hyperfine interaction of the Tl nuclear spin leads to significant mixing of the
lowest $B$ state rotational levels. Updated, more precise measurements of the
$B\rightarrow X$ vibrational branching fractions are also presented. The
combined rovibrational branching fractions allow for the prediction of the
number of photons that can be scattered in a given TlF optical cycling scheme.
",0,1,0,0,0,0
18457,Prevalence of DNSSEC for hospital websites in Illinois,"  The domain name system translates human friendly web addresses to a computer
readable internet protocol address. This basic infrastructure is insecure and
can be manipulated. Deployment of technology to secure the DNS system has been
slow, reaching about 20% of all web sites based in the USA. Little is known
about the efforts hospitals and health systems make to secure the domain name
system for their websites. To investigate the prevalence of implementing Domain
Name System Security Extensions (DNSSEC), we analyzed the websites of the 210
public hospitals in the state of Illinois, USA. Only one Illinois hospital
website was found to have implemented DNSSEC by December, 2017.
",1,0,0,0,0,0
18458,Mathematical analysis of plasmonic resonance for 2-D photonic crystal,"  In this article, we study the plasmonic resonance of infinite photonic
crystal mounted by the double negative nanoparticles in two dimensions. The
corresponding physical model is described by the Helmholz equation with so
called Bloch wave condition in a periodic domain. By using the quasi-periodic
layer potential techniques and the spectral theorem of quasi-periodic
Neumann-Poincar{é} operator, the quasi-static expansion of the near field in
the presence of nanoparticles is derived. Furthermore, when the magnetic
permeability of nanoparticles satisfies the Drude model, we give the conditions
under which the plasmonic resonance occurs, and the rate of blow up of near
field energy with respect to nanoparticle's bulk electron relaxation rate and
filling factor are also obtained. It indicates that one can appropriately
control the bulk electron relaxation rate or filling factor of nanoparticle in
photonic crystal structure such that the near field energy attains its maximum,
and enhancing the efficiency of energy utilization.
",0,0,1,0,0,0
18459,Thermo-Optical Chaos and Direct Soliton Generation in Microresonators,"  We investigate, numerically and experimentally, the effect of thermo-optical
(TO) chaos on direct soliton generation (DSG) in microresonators. When the pump
laser is scanned from blue to red and then stopped at a fixed wavelength, we
find that the solitons generated sometimes remain (survive) and sometimes
annihilate subsequent to the end of the scan. We refer to the possibility of
these different outcomes arising under identical laser scan conditions as
coexistence of soliton annihilation and survival. Numerical simulations that
include the thermal dynamics show that the coexistence of soliton annihilation
and survival is explained by TO chaos accompanying comb generation. The random
fluctuations of the cavity resonance occurring under TO chaos are also found to
trigger spontaneous soliton generation after the laser scan ends. The
coexistence of soliton annihilation and survival as well as spontaneous soliton
generation are observed experimentally in a silicon-nitride microresonator. The
elucidation of the role of TO chaos provides important insights into the
soliton generation dynamics in microresonators, which may eventually facilitate
straightforward soliton generation in fully-integrated microresonators.
",0,1,0,0,0,0
18460,Rigidity of closed metric measure spaces with nonnegative curvature,"  We show that one-dimensional circle is the only case for closed smooth metric
measure spaces with nonnegative Bakry-Émery Ricci curvature whose spectrum
of the weighted Laplacian has an optimal positive upper bound. This result
extends the work of Hang-Wang in the manifold case (Int. Math. Res. Not. 18
(2007), Art. ID rnm064, 9pp).
",0,0,1,0,0,0
18461,Combining Agile with Traditional V Model for Enhancement of Maturity in Software Development,"  In the field of software engineering there are many new archetypes are
introducing day to day Improve the efficiency and effectiveness of software
development. Due to dynamic environment organizations are frequently exchanging
their software constraint to meet their objectives. The propose research is a
new approach by integrating the traditional V model and agile methodology to
combining the strength of these models while minimizing their individual
weakness.The fluctuating requirements of emerging a carried software system and
accumulative cost of operational software are imposing researchers and experts
to determine innovative and superior means for emerging software application at
slight business or at enterprise level are viewing for. Agile methodology has
its own benefits but there are deficiency several of the features of
traditional software development methodologies that are essential for success.
Thats why an embedded approach will be the right answer for software industry
rather than a pure agile approach. This research shows how agile embedded
traditional can play a vital role in development of software. A survey
conducted to find the impact of this approach in industry. Both qualitative and
quantitative analysis performed.
",1,0,0,0,0,0
18462,Histograms of Gaussian normal distribution for feature matching in clutter scenes,"  3D feature descriptor provide information between corresponding models and
scenes. 3D objection recognition in cluttered scenes, however, remains a
largely unsolved problem. Practical applications impose several challenges
which are not fully addressed by existing methods. Especially in cluttered
scenes there are many feature mismatches between scenes and models. We
therefore propose Histograms of Gaussian Normal Distribution (HGND) for
extracting salient features on a local reference frame (LRF) that enables us to
solve this problem. We propose a LRF on each local surface patches using the
scatter matrix's eigenvectors. Then the HGND information of each salient point
is calculated on the LRF, for which we use both the mesh and point data of the
depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50
cluttered scenes of the UWA Dataset are made to evaluate the robustness and
descriptiveness of our HGND. Experiments carried out by us demonstrate that
HGND obtains a more reliable matching rate than state-of-the-art approaches in
cluttered situations.
",1,0,0,0,0,0
18463,The temporalized Massey's method,"  We propose and throughly investigate a temporalized version of the popular
Massey's technique for rating actors in sport competitions. The method can be
described as a dynamic temporal process in which team ratings are updated at
every match according to their performance during the match and the strength of
the opponent team. Using the Italian soccer dataset, we empirically show that
the method has a good foresight prediction accuracy.
",1,1,0,0,0,0
18464,Introduction to Nonnegative Matrix Factorization,"  In this paper, we introduce and provide a short overview of nonnegative
matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
complexity, algorithms, and its link with extended formulations of polyhedra.
In order to put NMF into perspective, the more general problem class of
constrained low-rank matrix approximation problems is first briefly introduced.
",1,0,1,1,0,0
18465,Randomized Dynamic Mode Decomposition,"  This paper presents a randomized algorithm for computing the near-optimal
low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging
techniques to compute low-rank matrix approximations at a fraction of the cost
of deterministic algorithms, easing the computational challenges arising in the
area of `big data'. The idea is to derive a small matrix from the
high-dimensional data, which is then used to efficiently compute the dynamic
modes and eigenvalues. The algorithm is presented in a modular probabilistic
framework, and the approximation quality can be controlled via oversampling and
power iterations. The effectiveness of the resulting randomized DMD (rDMD)
algorithm is demonstrated on several benchmark examples of increasing
complexity, providing an accurate and efficient approach to extract
spatiotemporal coherent structures from big data in a framework that scales
with the intrinsic rank of the data, rather than the ambient measurement
dimension.
",1,0,1,0,0,0
18466,On the convergence of a fully discrete scheme of LES type to physically relevant solutions of the incompressible Navier-Stokes,"  Obtaining reliable numerical simulations of turbulent fluids is a challenging
problem in computational fluid mechanics. The Large Eddy Simulations (LES)
models are efficient tools to approximate turbulent fluids and an important
step in the validation of these models is the ability to reproduce relevant
properties of the flow. In this paper we consider a fully discrete
approximation of the Navier-Stokes-Voigt model by an implicit Euler algorithm
(with respect to the time variable) and a Fourier-Galerkin method (in the space
variables). We prove the convergence to weak solutions of the incompressible
Navier-Stokes equations satisfying the natural local entropy condition, hence
selecting the so-called physically relevant solutions
",0,0,1,0,0,0
18467,Hardy-Sobolev-Maz'ya inequalities for higher order derivatives on half spaces,"  By using, among other things, the Fourier analysis techniques on hyperbolic
and symmetric spaces, we establish the Hardy-Sobolev-Maz'ya inequalities for
higher order derivatives on half spaces. The proof relies on a
Hardy-Littlewood-Sobolev inequality on hyperbolic spaces which is of its
independent interest. We also give an alternative proof of Benguria, Frank and
Loss' work concerning the sharp constant in the Hardy-Sobolev-Maz'ya inequality
in the three dimensional upper half space. Finally, we show the sharp constant
in the Hardy-Sobolev-Maz'ya inequality for bi-Laplacian in the upper half space
of dimension five coincides with the Sobolev constant.
",0,0,1,0,0,0
18468,Smooth invariants of focus-focus singularities and obstructions to product decomposition,"  We study focus-focus singularities (also known as nodal singularities, or
pinched tori) of Lagrangian fibrations on symplectic $4$-manifolds. We show
that, in contrast to elliptic and hyperbolic singularities, there exist
homeomorphic focus-focus singularities which are not diffeomorphic.
Furthermore, we obtain an algebraic description of the moduli space of
focus-focus singularities up to smooth equivalence, and show that for double
pinched tori this space is one-dimensional. Finally, we apply our construction
to disprove Zung's conjecture which says that any non-degenerate singularity
can be smoothly decomposed into an almost direct product of standard
singularities.
",0,1,1,0,0,0
18469,A note about Euler's inequality and automated reasoning with dynamic geometry,"  Using implicit loci in GeoGebra Euler's $R\geq 2r$ inequality can be
investigated in a novel way. Some unavoidable side effects of the implicit
locus computation introduce unexpected algebraic curves. By using a mixture of
symbolic and numerical methods a possible approach is sketched up to
investigate the situation.
",1,0,1,0,0,0
18470,The MUSE view of He 2-10: no AGN ionization but a sparkling starburst,"  We study the physical and dynamical properties of the ionized gas in the
prototypical HII galaxy Henize 2-10 using MUSE integral field spectroscopy. The
large scale dynamics is dominated by extended outflowing bubbles, probably the
results of massive gas ejection from the central star forming regions. We
derive a mass outflow rate dMout/dt~0.30 Msun/yr, corresponding to mass loading
factor eta~0.4, in range with similar measurements in local LIRGs. Such a
massive outflow has a total kinetic energy that is sustainable by the stellar
winds and Supernova Remnants expected in the galaxy. We use classical emission
line diagnostic to study the dust extinction, electron density and ionization
conditions all across the galaxy, confirming the extreme nature of the highly
star forming knots in the core of the galaxy, which show high density and high
ionization parameter. We measure the gas phase metallicity in the galaxy taking
into account the strong variation of the ionization parameter, finding that the
external parts of the galaxy have abundances as low as 12 + log(O/H)~8.3, while
the central star forming knots are highly enriched with super solar
metallicity. We find no sign of AGN ionization in the galaxy, despite the
recent claim of the presence of a super massive active Black Hole in the core
of He~2-10. We therefore reanalyze the X-ray data that were used to propose the
presence of the AGN, but we conclude that the observed X-ray emission can be
better explained with sources of a different nature, such as a Supernova
Remnant.
",0,1,0,0,0,0
18471,Modeling Oral Multispecies Biofilm Recovery After Antibacterial Treatment,"  Recovery of multispecies oral biofilms is investigated following treatment by
chlorhexidine gluconate (CHX), iodine-potassium iodide (IPI) and Sodium
hypochlorite (NaOCl) both experimentally and theoretically. Experimentally,
biofilms taken from two donors were exposed to the three antibacterial
solutions (irrigants) for 10 minutes, respectively. We observe that (a) live
bacterial cell ratios decline for a week after the exposure and the trend
reverses beyond a week; after fifteen weeks, live bacterial cell ratios in
biofilms fully return to their pretreatment levels; (b) NaOCl is shown as the
strongest antibacterial agent for the oral biofilms; (c) multispecies oral
biofilms from different donors showed no difference in their susceptibility to
all the bacterial solutions. Guided by the experiment, a mathematical model for
biofilm dynamics is developed, accounting for multiple bacterial phenotypes,
quorum sensing, and growth factor proteins, to describe the nonlinear time
evolutionary behavior of the biofilms. The model captures time evolutionary
dynamics of biofilms before and after antibacterial treatment very well. It
reveals the crucial role played by quorum sensing molecules and growth factors
in biofilm recovery and verifies that the source of biofilms has a minimal to
their recovery. The model is also applied to describe the state of biofilms of
various ages treated by CHX, IPI and NaOCl, taken from different donors. Good
agreement with experimental data predicted by the model is obtained as well,
confirming its applicability to modeling biofilm dynamics in general.
",0,0,0,0,1,0
18472,On constant multi-commodity flow-cut gaps for directed minor-free graphs,"  The multi-commodity flow-cut gap is a fundamental parameter that affects the
performance of several divide \& conquer algorithms, and has been extensively
studied for various classes of undirected graphs. It has been shown by Linial,
London and Rabinovich \cite{linial1994geometry} and by Aumann and Rabani
\cite{aumann1998log} that for general $n$-vertex graphs it is bounded by
$O(\log n)$ and the Gupta-Newman-Rabinovich-Sinclair conjecture
\cite{gupta2004cuts} asserts that it is $O(1)$ for any family of graphs that
excludes some fixed minor.
The flow-cut gap is poorly understood for the case of directed graphs. We
show that for uniform demands it is $O(1)$ on directed series-parallel graphs,
and on directed graphs of bounded pathwidth. These are the first constant upper
bounds of this type for some non-trivial family of directed graphs. We also
obtain $O(1)$ upper bounds for the general multi-commodity flow-cut gap on
directed trees and cycles. These bounds are obtained via new embeddings and
Lipschitz quasipartitions for quasimetric spaces, which generalize analogous
results form the metric case, and could be of independent interest. Finally, we
discuss limitations of methods that were developed for undirected graphs, such
as random partitions, and random embeddings.
",1,0,0,0,0,0
18473,Molecular Gas during the Post-Starburst Phase: Low Gas Fractions in Green Valley Seyfert Post-Starburst Galaxies,"  Post-starbursts (PSBs) are candidate for rapidly transitioning from
star-bursting to quiescent galaxies. We study the molecular gas evolution of
PSBs at z ~ 0.03 - 0.2. We undertook new CO (2-1) observations of 22 Seyfert
PSBs candidates using the ARO Submillimeter Telescope. This sample complements
previous samples of PSBs by including green valley PSBs with Seyfert-like
emission, allowing us to analyze for the first time the molecular gas
properties of 116 PSBs with a variety of AGN properties. The distribution of
molecular gas to stellar mass fractions in PSBs is significantly different than
normal star-forming galaxies in the COLD GASS survey. The combined samples of
PSBs with Seyfert-like emission line ratios have a gas fraction distribution
which is even more significantly different and is broader (~ 0.03-0.3). Most of
them have lower gas fractions than normal star-forming galaxies. We find a
highly significant correlation between the WISE 12 micron to 4.6 micron flux
ratios and molecular gas fractions in both PSBs and normal galaxies. We detect
molecular gas in 27% of our Seyfert PSBs. Taking into account the upper limits,
the mean and the dispersion of the distribution of the gas fraction in our
Seyfert PSB sample are much smaller (mean = 0.025, std dev. = 0.018) than
previous samples of Seyfert PSBs or PSBs in general (mean ~ 0.1 - 0.2, std dev.
~ 0.1 - 0.2).
",0,1,0,0,0,0
18474,Bit-Reversible Version of Milne's Fourth-Order Time-Reversible Integrator for Molecular Dynamics,"  We point out that two of Milne's fourth-order integrators are well-suited to
bit-reversible simulations. The fourth-order method improves on the accuracy of
Levesque and Verlet's algorithm and simplifies the definition of the velocity
$v$ and energy $e = (q^2 + v^2)/2$ . ( We use this one-dimensional oscillator
problem as an illustration throughout this paper ). Milne's integrator is
particularly useful for the analysis of Lyapunov ( exponential ) instability in
dynamical systems, including manybody molecular dynamics. We include the
details necessary to the implementation of Milne's Algorithms.
",0,1,0,0,0,0
18475,Solvable Hydrodynamics of Quantum Integrable Systems,"  The conventional theory of hydrodynamics describes the evolution in time of
chaotic many-particle systems from local to global equilibrium. In a quantum
integrable system, local equilibrium is characterized by a local generalized
Gibbs ensemble or equivalently a local distribution of pseudo-momenta. We study
time evolution from local equilibria in such models by solving a certain
kinetic equation, the ""Bethe-Boltzmann"" equation satisfied by the local
pseudo-momentum density. Explicit comparison with density matrix
renormalization group time evolution of a thermal expansion in the XXZ model
shows that hydrodynamical predictions from smooth initial conditions can be
remarkably accurate, even for small system sizes. Solutions are also obtained
in the Lieb-Liniger model for free expansion into vacuum and collisions between
clouds of particles, which model experiments on ultracold one-dimensional Bose
gases.
",0,1,0,0,0,0
18476,Compressed sensing and optimal denoising of monotone signals,"  We consider the problems of compressed sensing and optimal denoising for
signals $\mathbf{x_0}\in\mathbb{R}^N$ that are monotone, i.e.,
$\mathbf{x_0}(i+1) \geq \mathbf{x_0}(i)$, and sparsely varying, i.e.,
$\mathbf{x_0}(i+1) > \mathbf{x_0}(i)$ only for a small number $k$ of indices
$i$. We approach the compressed sensing problem by minimizing the total
variation norm restricted to the class of monotone signals subject to equality
constraints obtained from a number of measurements $A\mathbf{x_0}$. For random
Gaussian sensing matrices $A\in\mathbb{R}^{m\times N}$ we derive a closed form
expression for the number of measurements $m$ required for successful
reconstruction with high probability. We show that the probability undergoes a
phase transition as $m$ varies, and depends not only on the number of change
points, but also on their location. For denoising we regularize with the same
norm and derive a formula for the optimal regularizer weight that depends only
mildly on $\mathbf{x_0}$. We obtain our results using the statistical dimension
tool.
",1,0,1,1,0,0
18477,On minimax nonparametric estimation of signal in Gaussian noise,"  For the problem of nonparametric estimation of signal in Gaussian noise we
point out the strong asymptotically minimax estimators on maxisets for linear
estimators (see \cite{ker93,rio}). It turns out that the order of rates of
convergence of Pinsker estimator on this maxisets is worse than the order of
rates of convergence for the class of linear estimators considered on this
maxisets. We show that balls in Sobolev spaces are maxisets for Pinsker
estimators.
",0,0,1,1,0,0
18478,Implicit Bias of Gradient Descent on Linear Convolutional Networks,"  We show that gradient descent on full-width linear convolutional networks of
depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
penalty in the frequency domain. This is in contrast to linearly fully
connected networks, where gradient descent converges to the hard margin linear
support vector machine solution, regardless of depth.
",0,0,0,1,0,0
18479,Oxidation of clofibric acid in aqueous solution using a non-thermal plasma discharge or gamma radiation,"  In this work, we study degradation of clofibric acid (CFA) in aqueous
solution using either ionizing radiation from a $^{60}$Co source or a
non-thermal plasma produced by discharges in the air above the solution. The
results obtained with the two technologies are compared in terms of
effectiveness of CFA degradation and its by-products. In both cases the CFA
degradation follows a quasi-exponential decay in time well modelled by a
kinetic scheme which considers the competition between CFA and all reaction
intermediates for the reactive species generated in solution as well as the
amount of the end product formed. A new degradation law is deduced to explain
the results. Although the end-product CO$_2$ was detected and the CFA
conversion found to be very high under the studied conditions, HPLC analysis
reveals several degradation intermediates still bearing the aromatic ring with
the chlorine substituent. The extent of mineralization is rather limited. The
energy yield is found to be higher in the gamma radiation experiments.
",0,1,0,0,0,0
18480,Quenching current by flux-flow instability in iron-chalcogenides thin films,"  The stability against quench is one of the main issue to be pursued in a
superconducting material which should be able to perform at very high levels of
current densities. Here we focus on the connection between the critical current
$I_c$ and the quenching current $I^*$ associated to the so-called flux-flow
instability phenomenon, which sets in as an abrupt transition from the flux
flow state to the normal state. To this purpose, we analyze several
current-voltage characteristics of three types of iron-based thin films,
acquired at different temperature and applied magnetic field values. For these
samples, we discuss the impact of a possible coexistence of intrinsic
electronic mechanisms and extrinsic thermal effects on the quenching current
dependence upon the applied magnetic field. The differences between the
quenching current and the critical current are reported also in the case of
predominant intrinsic mechanisms. Carrying out a comparison with
high-temperature cuprate superconductors, we suggest which material can be the
best trade-off between maximum operating temperature, higher upper critical
field and stability under high current bias.
",0,1,0,0,0,0
18481,New cardinality estimation algorithms for HyperLogLog sketches,"  This paper presents new methods to estimate the cardinalities of data sets
recorded by HyperLogLog sketches. A theoretically motivated extension to the
original estimator is presented that eliminates the bias for small and large
cardinalities. Based on the maximum likelihood principle a second unbiased
method is derived together with a robust and efficient numerical algorithm to
calculate the estimate. The maximum likelihood approach can also be applied to
more than a single HyperLogLog sketch. In particular, it is shown that it gives
more precise cardinality estimates for union, intersection, or relative
complements of two sets that are both represented by HyperLogLog sketches
compared to the conventional technique using the inclusion-exclusion principle.
All the new methods are demonstrated and verified by extensive simulations.
",1,0,0,0,0,0
18482,The EUSO@TurLab Project,"  The TurLab facility is a laboratory, equipped with a 5 m diameter and 1 m
depth rotating tank, located in the Physics Department of the University of
Turin. The tank has been built mainly to study problems where system rotation
plays a key role in the fluid behaviour such as in atmospheric and oceanic
flows at different scales. The tank can be filled with different fluids of
variable density, which enables studies in layered conditions such as sea
waves. The tank can be also used to simulate the terrestrial surface with the
optical characteristics of different environments such as snow, grass, ocean,
land with soil, stones etc., fogs and clouds. As it is located in an extremely
dark place, the light intensity can be controlled artificially. Such
capabilities of the TurLab facility are applied to perform experiments related
to the observation of Extreme Energy Cosmic Rays (EECRs) from space using the
fluorescence technique, as in the case of the JEM-EUSO mission, where the
diffuse night brightness and artificial light sources can vary significantly in
time and space inside the Field of View (FoV) of the telescope. Here we will
report the currently ongoing activity at the TurLab facility in the framework
of the JEM-EUSO mission (EUSO@TurLab).
",0,1,0,0,0,0
18483,A finite element method framework for modeling rotating machines with superconducting windings,"  Electrical machines employing superconductors are attractive solutions in a
variety of application domains. Numerical models are powerful and necessary
tools to optimize their design and predict their performance. The
electromagnetic modeling of superconductors by finite-element method (FEM) is
usually based on a power-law resistivity for their electrical behavior. The
implementation of such constitutive law in conventional models of electrical
machines is quite problematic: the magnetic vector potential directly gives the
electric field and requires using a power-law depending on it. This power-law
is a non-bounded function that can generate enormous uneven values in low
electric field regions that can destroy the reliability of solutions. The
method proposed here consists in separating the model of an electrical machine
in two parts, where the magnetic field is calculated with the most appropriate
formulation: the H-formulation in the part containing the superconductors and
the A-formulation in the part containing conventional conductors (and possibly
permanent magnets). The main goal of this work is to determine and to correctly
apply the continuity conditions on the boundary separating the two regions.
Depending on the location of such boundary -- in the fixed or rotating part of
the machine -- the conditions that one needs to apply are different. In
addition, the application of those conditions requires the use of Lagrange
multipliers satisfying the field transforms of the electromagnetic quantities
in the two reference systems, the fixed and the rotating one. In this article,
several exemplary cases for the possible configurations are presented. In order
to emphasize and capture the essential point of this modeling strategy, the
discussed examples are rather simple. Nevertheless, they constitute a solid
starting point for modeling more complex and realistic devices.
",0,1,0,0,0,0
18484,Triangular Decomposition of Matrices in a Domain,"  Deterministic recursive algorithms for the computation of matrix triangular
decompositions with permutations like LU and Bruhat decomposition are presented
for the case of commutative domains. This decomposition can be considered as a
generalization of LU and Bruhat decompositions, because they both may be easily
obtained from this triangular decomposition. Algorithms have the same
complexity as the algorithm of matrix multiplication.
",1,0,0,0,0,0
18485,Practical Bayesian Optimization for Transportation Simulators,"  We provide a method to solve optimization problem when objective function is
a complex stochastic simulator of an urban transportation system. To reach this
goal, a Bayesian optimization framework is introduced. We show how the choice
of prior and inference algorithm effect the outcome of our optimization
procedure. We develop dimensionality reduction techniques that allow for our
optimization techniques to be applicable for real-life problems. We develop a
distributed, Gaussian Process Bayesian regression and active learning models
that allow parallel execution of our algorithms and enable usage of high
performance computing. We present a fully Bayesian approach that is more sample
efficient and reduces computational budget. Our framework is supported by
theoretical analysis and an empirical study. We demonstrate our framework on
the problem of calibrating a multi-modal transportation network of city of
Bloomington, Illinois. Finally, we discuss directions for further research.
",0,0,0,1,0,0
18486,The Ising distribution as a latent variable model,"  It is shown that the Ising distribution can be treated as a latent variable
model, where a set of N real-valued, correlated random variables are drawn and
used to generate N binary spins independently. This allows to approximate the
Ising distribution by a simpler model where the latent variables follow a
multivariate normal distribution, the so-called Cox distribution. The
approximation is formally related to an advanced mean field technique known as
adaptive TAP, and its domain of validity is similar. When valid, it allows a
principled replacement of the Ising distribution by a distribution much easier
to sample and manipulate.
",0,0,0,1,1,0
18487,Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm,"  Multi-agent systems (MAS) is able to characterize the behavior of individual
agent and the interaction between agents. Thus, it motivates us to leverage the
distributed constraint optimization problem (DCOP), a framework of modeling
MAS, to solve the user association problem in heterogeneous networks (HetNets).
Two issues we have to consider when we take DCOP into the application of HetNet
including: (i) How to set up an effective model by DCOP taking account of the
negtive impact of the increment of users on the modeling process (ii) Which
kind of algorithms is more suitable to balance the time consumption and the
quality of soltuion. Aiming to overcome these issues, we firstly come up with
an ECAV-$\eta$ (Each Connection As Variable) model in which a parameter $\eta$
with an adequate assignment ($\eta=3$ in this paper) is able to control the
scale of the model. After that, a Markov chain (MC) based algorithm is proposed
on the basis of log-sum-exp function. Experimental results show that the
solution obtained by DCOP framework is better than the one obtained by the
Max-SINR algorithm. Comparing with the Lagrange dual decomposition based method
(LDD), the solution performance has been improved since there is no need to
transform original problem into a satisfied one. In addition, it is also
apparent that the DCOP based method has better robustness than LDD when the
number of users increases but the available resource at base stations are
limited.
",1,0,0,0,0,0
18488,Linear growth of streaming instability in pressure bumps,"  Streaming instability is a powerful mechanism which concentrates dust grains
in pro- toplanetary discs, eventually up to the stage where they collapse
gravitationally and form planetesimals. Previous studies inferred that it
should be ineffective in viscous discs, too efficient in inviscid discs, and
may not operate in local pressure maxima where solids accumulate. From a linear
analysis of stability, we show that streaming instability behaves differently
inside local pressure maxima. Under the action of the strong differential
advection imposed by the bump, a novel unstable mode develops and grows even
when gas viscosity is large. Hence, pressure bumps are found to be the only
places where streaming instability occurs in viscous discs. This offers a
promising way to conciliate models of planet formation with recent observations
of young discs.
",0,1,0,0,0,0
18489,"On the Gevrey regularity for Sums of Squares of vector fields, study of some models","  The micro-local Gevrey regularity of a class of ""sums of squares"" with real
analytic coefficients is studied in detail. Some partial regularity result is
also given.
",0,0,1,0,0,0
18490,Structure theorems for star-commuting power partial isometries,"  We give a new formulation and proof of a theorem of Halmos and Wallen on the
structure of power partial isometries on Hilbert space. We then use this
theorem to give a structure theorem for a finite set of partial isometries
which star-commute: each operator commutes with the others and with their
adjoints.
",0,0,1,0,0,0
18491,On free Gelfand--Dorfman--Novikov superalgebras and a PBW type theorem,"  We construct a linear basis of a free GDN superalgebra over a field of
characteristic $\neq 2$.
As applications, we prove a PBW theorem, that is, any GDN superalgebra can be
embedded into its universal enveloping commutative associative differential
superalgebra. An Engel theorem under some assumptions is given.
",0,0,1,0,0,0
18492,An inexact iterative Bregman method for optimal control problems,"  In this article we investigate an inexact iterative regularization method
based on generalized Bregman distances of an optimal control problem with
control constraints. We show robustness and convergence of the inexact Bregman
method under a regularity assumption, which is a combination of a source
condition and a regularity assumption on the active sets. We also take the
discretization error into account. Numerical results are presented to
demonstrate the algorithm.
",0,0,1,0,0,0
18493,Mixture Models in Astronomy,"  Mixture models combine multiple components into a single probability density
function. They are a natural statistical model for many situations in
astronomy, such as surveys containing multiple types of objects, cluster
analysis in various data spaces, and complicated distribution functions. This
chapter in the CRC Handbook of Mixture Analysis is concerned with astronomical
applications of mixture models for cluster analysis, classification, and
semi-parametric density estimation. We present several classification examples
from the literature, including identification of a new class, analysis of
contaminants, and overlapping populations. In most cases, mixtures of normal
(Gaussian) distributions are used, but it is sometimes necessary to use
different distribution functions derived from astrophysical experience. We also
address the use of mixture models for the analysis of spatial distributions of
objects, like galaxies in redshift surveys or young stars in star-forming
regions. In the case of galaxy clustering, mixture models may not be the
optimal choice for understanding the homogeneous and isotropic structure of
voids and filaments. However, we show that mixture models, using astrophysical
models for star clusters, may provide a natural solution to the problem of
subdividing a young stellar population into subclusters. Finally, we explore
how mixture models can be used for mathematically advanced modeling of data
with heteroscedastic uncertainties or missing values, providing two example
algorithms, the measurement error regression model of Kelly (2007) and the
Extreme Deconvolution model of Bovy et al. (2011). The challenges presented by
astronomical science, aided by the public availability of catalogs from major
surveys and missions, are a rich area for collaboration between statisticians
and astronomers.
",0,1,0,1,0,0
18494,Safe Medicine Recommendation via Medical Knowledge Graph Embedding,"  Most of the existing medicine recommendation systems that are mainly based on
electronic medical records (EMRs) are significantly assisting doctors to make
better clinical decisions benefiting both patients and caregivers. Even though
the growth of EMRs is at a lighting fast speed in the era of big data, content
limitations in EMRs restrain the existed recommendation systems to reflect
relevant medical facts, such as drug-drug interactions. Many medical knowledge
graphs that contain drug-related information, such as DrugBank, may give hope
for the recommendation systems. However, the direct use of these knowledge
graphs in the systems suffers from robustness caused by the incompleteness of
the graphs. To address these challenges, we stand on recent advances in graph
embedding learning techniques and propose a novel framework, called Safe
Medicine Recommendation (SMR), in this paper. Specifically, SMR first
constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and
medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly
embeds diseases, medicines, patients, and their corresponding relations into a
shared lower dimensional space. Finally, SMR uses the embeddings to decompose
the medicine recommendation into a link prediction process while considering
the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR
is the first to learn embeddings of a patient-disease-medicine graph for
medicine recommendation in the world. Extensive experiments on real datasets
are conducted to evaluate the effectiveness of proposed framework.
",1,0,0,0,0,0
18495,Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression,"  Deep neural networks (DNNs) have achieved great success in solving a variety
of machine learning (ML) problems, especially in the domain of image
recognition. However, recent research showed that DNNs can be highly vulnerable
to adversarially generated instances, which look seemingly normal to human
observers, but completely confuse DNNs. These adversarial samples are crafted
by adding small perturbations to normal, benign images. Such perturbations,
while imperceptible to the human eye, are picked up by DNNs and cause them to
misclassify the manipulated instances with high confidence. In this work, we
explore and demonstrate how systematic JPEG compression can work as an
effective pre-processing step in the classification pipeline to counter
adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient
Sign Method, DeepFool). An important component of JPEG compression is its
ability to remove high frequency signal components, inside square blocks of an
image. Such an operation is equivalent to selective blurring of the image,
helping remove additive perturbations. Further, we propose an ensemble-based
technique that can be constructed quickly from a given well-performing DNN, and
empirically show how such an ensemble that leverages JPEG compression can
protect a model from multiple types of adversarial attacks, without requiring
knowledge about the model.
",1,0,0,0,0,0
18496,Numerical Simulation of Bloch Equations for Dynamic Magnetic Resonance Imaging,"  Magnetic Resonance Imaging (MRI) is a widely applied non-invasive imaging
modality based on non-ionizing radiation which gives excellent images and soft
tissue contrast of living tissues. We consider the modified Bloch problem as a
model of MRI for flowing spins in an incompressible flow field. After
establishing the well-posedness of the corresponding evolution problem, we
analyze its spatial semidiscretization using discontinuous Galerkin methods.
The high frequency time evolution requires a proper explicit and adaptive
temporal discretization. The applicability of the approach is shown for basic
examples.
",1,0,1,0,0,0
18497,End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks,"  Speech enhancement model is used to map a noisy speech to a clean speech. In
the training stage, an objective function is often adopted to optimize the
model parameters. However, in most studies, there is an inconsistency between
the model optimization criterion and the evaluation criterion on the enhanced
speech. For example, in measuring speech intelligibility, most of the
evaluation metric is based on a short-time objective intelligibility (STOI)
measure, while the frame based minimum mean square error (MMSE) between
estimated and clean speech is widely used in optimizing the model. Due to the
inconsistency, there is no guarantee that the trained model can provide optimal
performance in applications. In this study, we propose an end-to-end
utterance-based speech enhancement framework using fully convolutional neural
networks (FCN) to reduce the gap between the model optimization and evaluation
criterion. Because of the utterance-based optimization, temporal correlation
information of long speech segments, or even at the entire utterance level, can
be considered when perception-based objective functions are used for the direct
optimization. As an example, we implement the proposed FCN enhancement
framework to optimize the STOI measure. Experimental results show that the STOI
of test speech is better than conventional MMSE-optimized speech due to the
consistency between the training and evaluation target. Moreover, by
integrating the STOI in model optimization, the intelligibility of human
subjects and automatic speech recognition (ASR) system on the enhanced speech
is also substantially improved compared to those generated by the MMSE
criterion.
",1,0,0,1,0,0
18498,The connectivity of graphs of graphs with self-loops and a given degree sequence,"  `Double edge swaps' transform one graph into another while preserving the
graph's degree sequence, and have thus been used in a number of popular Markov
chain Monte Carlo (MCMC) sampling techniques. However, while double edge-swaps
can transform, for any fixed degree sequence, any two graphs inside the classes
of simple graphs, multigraphs, and pseudographs, this is not true for graphs
which allow self-loops but not multiedges (loopy graphs). Indeed, we exactly
characterize the degree sequences where double edge swaps cannot reach every
valid loopy graph and develop an efficient algorithm to determine such degree
sequences. The same classification scheme to characterize degree sequences can
be used to prove that, for all degree sequences, loopy graphs are connected by
a combination of double and triple edge swaps. Thus, we contribute the first
MCMC sampler that uniformly samples loopy graphs with any given sequence.
",1,1,1,0,0,0
18499,The role of the background in past and future X-ray missions,"  Background has played an important role in X-ray missions, limiting the
exploitation of science data in several and sometimes unexpected ways. In this
presentation I review past X-ray missions focusing on some important lessons we
can learn from them. I then go on discussing prospects for overcoming
background related limitations in future ones.
",0,1,0,0,0,0
18500,Securing Manufacturing Intelligence for the Industrial Internet of Things,"  Widespread interest in the emerging area of predictive analytics is driving
industries such as manufacturing to explore new approaches to the collection
and management of data provided from Industrial Internet of Things (IIoT)
devices. Often, analytics processing for Business Intelligence (BI) is an
intensive task, and it also presents both an opportunity for competitive
advantage as well as a security vulnerability in terms of the potential for
losing Intellectual Property (IP). This article explores two approaches to
securing BI in the manufacturing domain. Simulation results indicate that a
Unified Threat Management (UTM) model is simpler to maintain and has less
potential vulnerabilities than a distributed security model. Conversely, a
distributed model of security out-performs the UTM model and offers more scope
for the use of existing hardware resources. In conclusion, a hybrid security
model is proposed where security controls are segregated into a multi-cloud
architecture.
",1,0,0,0,0,0
18501,Boson-vortex duality in compressible spin-orbit coupled BECs,"  Using a (1+2)-dimensional boson-vortex duality between non-linear
electrodynamics and a two-component compressible Bose-Einstein condensate (BEC)
with spin-orbit (SO) coupling, we obtain generalised versions of the
hydrodynamic continuity and Euler equations where the phase defect and
non-defect degrees of freedom enter separately. We obtain the generalised
Magnus force on vortices under SO coupling, and associate the linear
confinement of vortices due to SO coupling with instanton fluctuations of the
dual theory.
",0,1,0,0,0,0
18502,Communicating Correlated Sources Over an Interference Channel,"  A new coding technique, based on \textit{fixed block-length} codes, is
proposed for the problem of communicating a pair of correlated sources over a
$2-$user interference channel. Its performance is analyzed to derive a new set
of sufficient conditions. The latter is proven to be strictly less binding than
the current known best, which is due to Liu and Chen [Dec, 2011]. Our findings
are inspired by Dueck's example [March, 1981].
",1,0,0,0,0,0
18503,Simulating polaron biophysics with Rydberg atoms,"  Transport of excitations along proteins can be formulated in a quantum
physics context, based on the periodicity and vibrational modes of the
structures. Exact solutions are very challenging to obtain on classical
computers, however, approximate solutions based on the Davydov ansatz have
demonstrated the possibility of stabilized solitonic excitations along the
protein. We propose an alternative study based on a chain of ultracold atoms.
We investigate the experimental parameters to control such a quantum simulator
based on dressed Rydberg atoms. We show that there is a feasible range of
parameters where a quantum simulator can directly mimic the Davydov equations
and their solutions. Such a quantum simulator opens up new directions for the
study of transport phenomena in a biophysical context.
",0,1,0,0,0,0
18504,"Solubility limit of methyl red and methylene blue in microemulsions and liquid crystals of water, sds and pentanol systems","  Solubility of dyes in amphiphilic association structures of water, SDS and
penthanol system (i.e. in the phases of microemulsions and liquid crystals) was
attracted much interest due to its wide industrial and technological
applications. This research was focused on understanding the solubility
limitation of methyl red and methylene blue in microemulsion and liquid crystal
phases. Experimental results showed that the highest solubility of methyl red
was in LLC, followed by w/o microemulsion and o/w microemulsion, respectively,
whereas the highest solubility of methylene blue was in w/o microemulsion,
followed by o/w microemulsion and LLC, respectively. Hence, a chemical dynamics
strongly played an important role in the solubility limitation of methyl red
and methylene blue in microemulsions and liquid crystal phases.
",0,1,0,0,0,0
18505,Towards Visual Ego-motion Learning in Robots,"  Many model-based Visual Odometry (VO) algorithms have been proposed in the
past decade, often restricted to the type of camera optics, or the underlying
motion manifold observed. We envision robots to be able to learn and perform
these tasks, in a minimally supervised setting, as they gain more experience.
To this end, we propose a fully trainable solution to visual ego-motion
estimation for varied camera optics. We propose a visual ego-motion learning
architecture that maps observed optical flow vectors to an ego-motion density
estimate via a Mixture Density Network (MDN). By modeling the architecture as a
Conditional Variational Autoencoder (C-VAE), our model is able to provide
introspective reasoning and prediction for ego-motion induced scene-flow.
Additionally, our proposed model is especially amenable to bootstrapped
ego-motion learning in robots where the supervision in ego-motion estimation
for a particular camera sensor can be obtained from standard navigation-based
sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
experiments, we show the utility of our proposed approach in enabling the
concept of self-supervised learning for visual ego-motion estimation in
autonomous robots.
",1,0,0,0,0,0
18506,Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks,"  Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).
",1,0,0,0,0,0
18507,Key Management and Learning based Two Level Data Security for Metering Infrastructure of Smart Grid,"  In the smart grid, smart meters, and numerous control and monitoring
applications employ bidirectional wireless communication, where security is a
critical issue. In key management based encryption method for the smart grid,
the Trusted Third Party (TTP), and links between the smart meter and the third
party are assumed to be fully trusted and reliable. However, in wired/wireless
medium, a man-in-middle may want to interfere, monitor and control the network,
thus exposing its vulnerability. Acknowledging this, in this paper, we propose
a novel two level encryption method based on two partially trusted simple
servers (constitutes the TTP) which implement this method without increasing
packet overhead. One server is responsible for data encryption between the
meter and control center/central database, and the other server manages the
random sequence of data transmission. Numerical calculation shows that the
number of iterations required to decode a message is large which is quite
impractical. Furthermore, we introduce One-class support vector machine
(machine learning) algorithm for node-to-node authentication utilizing the
location information and the data transmission history (node identity, packet
size and frequency of transmission). This secures data communication privacy
without increasing the complexity of the conventional key management scheme.
",1,0,0,0,0,0
18508,A numerical study of the homogeneous elliptic equation with fractional order boundary conditions,"  We consider the homogeneous equation ${\mathcal A} u=0$, where ${\mathcal A}$
is a symmetric and coercive elliptic operator in $H^1(\Omega)$ with $\Omega$
bounded domain in ${\mathbb R}^d$. The boundary conditions involve fractional
power $\alpha$, $ 0 < \alpha <1$, of the Steklov spectral operator arising in
Dirichlet to Neumann map. For such problems we discuss two different numerical
methods: (1) a computational algorithm based on an approximation of the
integral representation of the fractional power of the operator and (2)
numerical technique involving an auxiliary Cauchy problem for an
ultra-parabolic equation and its subsequent approximation by a time stepping
technique. For both methods we present numerical experiment for a model
two-dimensional problem that demonstrate the accuracy, efficiency, and
stability of the algorithms.
",0,0,1,0,0,0
18509,Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles,"  Crowded environments modify the diffusion of macromolecules, generally
slowing their movement and inducing transient anomalous subdiffusion. The
presence of obstacles also modifies the kinetics and equilibrium behavior of
tracers. While previous theoretical studies of particle diffusion have
typically assumed either impenetrable obstacles or binding interactions that
immobilize the particle, in many cellular contexts bound particles remain
mobile. Examples include membrane proteins or lipids with some entry and
diffusion within lipid domains and proteins that can enter into membraneless
organelles or compartments such as the nucleolus. Using a lattice model, we
studied the diffusive movement of tracer particles which bind to soft
obstacles, allowing tracers and obstacles to occupy the same lattice site. For
sticky obstacles, bound tracer particles are immobile, while for slippery
obstacles, bound tracers can hop without penalty to adjacent obstacles. In both
models, binding significantly alters tracer motion. The type and degree of
motion while bound is a key determinant of the tracer mobility: slippery
obstacles can allow nearly unhindered diffusion, even at high obstacle filling
fraction. To mimic compartmentalization in a cell, we examined how obstacle
size and a range of bound diffusion coefficients affect tracer dynamics. The
behavior of the model is similar in two and three spatial dimensions. Our work
has implications for protein movement and interactions within cells.
",0,1,0,0,0,0
18510,Thermodynamics of a Quantum Ising system coupled to a spin bath: Zero Temperature Results,"  We study the effect of coupling a spin bath environment to a system which, at
low energies, can be modeled as a quantum Ising system. A field theoretic
formalism incorporating both thermal and quantum fluctuations is developed to
derive results for the thermodynamic properties and response functions, both
for a toy model and for the $LiHoF_4$ system, in which spin-8 electronic spins
couple to a spin-$7/2$ nuclear spin bath: the phase transition then occurs in a
system of electronuclear degrees of freedom, coupled by long-range dipolar
interactions. The quantum Ising phase transition still exists, and one
hybridized mode of the Ising and bath spins always goes soft at the transition.
",0,1,0,0,0,0
18511,Observation of a Lamb band gap in a polymer waveguide with periodic cross-like cavities,"  The quest for large and low frequency band gaps is one of the principal
objectives pursued in a number of engineering applications, ranging from noise
absorption to vibration control, to seismic wave abatement. For this purpose, a
plethora of complex architectures (including multi-phase materials) and
multi-physics approaches have been proposed in the past, often involving
difficulties in their practical realization.
To address this issue, in this work we propose an easy-to-manufacture design
able to open large, low frequency complete Lamb band gaps exploiting a suitable
arrangement of masses and stiffnesses produced by cavities in a monolithic
material. The performance of the designed structure is evaluated by numerical
simulations and confirmed by Scanning Laser Doppler Vibrometer (SLDV)
measurements on an isotropic polyvinyl chloride plate in which a square ring
region of cross-like cavities is fabricated. The full wave field reconstruction
clearly confirms the ability of even a limited number of unit cell rows of the
proposed design to efficiently attenuate Lamb waves. In addition, numerical
simulations show that the structure allows to shift of the central frequency of
the BG through geometrical modifications. The design may be of interest for
applications in which large BGs at low frequencies are required.
",0,1,0,0,0,0
18512,Search for nucleon decays with EXO-200,"  A search for instability of nucleons bound in $^{136}$Xe nuclei is reported
with 223 kg$\cdot$yr exposure of $^{136}$Xe in the EXO-200 experiment. Lifetime
limits of 3.3$\times 10^{23}$ and 1.9$\times 10^{23}$ yrs are established for
nucleon decay to $^{133}$Sb and $^{133}$Te, respectively. These are the most
stringent to date, exceeding the prior decay limits by a factor of 9 and 7,
respectively.
",0,1,0,0,0,0
18513,The multiplicity of massive stars: a 2016 view,"  Massive stars like company. Here, we provide a brief overview of progresses
made over the last 5 years by a number of medium and large surveys. These
results provide new insights on the observed and intrinsic multiplicity
properties of main sequence massive stars and on the initial conditions for
their future evolution. They also bring new interesting constraints on the
outcome of the massive star formation process.
",0,1,0,0,0,0
18514,Principal Eigenvalue of Mixed Problem for the Fractional Laplacian: Moving the Boundary Conditions,"  We analyze the behavior of the eigenvalues of the following non local mixed
problem $\left\{ \begin{array}{rcll} (-\Delta)^{s} u &=& \lambda_1(D) \ u
&\inn\Omega,\\ u&=&0&\inn D,\\ \mathcal{N}_{s}u&=&0&\inn N. \end{array}\right $
Our goal is to construct different sequences of problems by modifying the
configuration of the sets $D$ and $N$, and to provide sufficient and necessary
conditions on the size and the location of these sets in order to obtain
sequences of eigenvalues that in the limit recover the eigenvalues of the
Dirichlet or Neumann problem. We will see that the non locality plays a crucial
role here, since the sets $D$ and $N$ can have infinite measure, a phenomenon
that does not appear in the local case (see for example \cite{D,D2,CP}).
",0,0,1,0,0,0
18515,Two-dimensional Bose and Fermi gases beyond weak coupling,"  Using a formalism based on the two-body S-matrix we study two-dimensional
Bose and Fermi gases with both attractive and repulsive interactions.
Approximate analytic expressions, valid at weak coupling and beyond, are
developed and applied to the Berezinskii-Kosterlitz-Thouless (BKT) transition.
We successfully recover the correct logarithmic functional form of the critical
chemical potential and density for the Bose gas. For fermions, the BKT critical
temperature is calculated in BCS and BEC regimes through consideration of Tan's
contact.
",0,1,0,0,0,0
18516,Anisotropy of magnetic interactions and symmetry of the order parameter in unconventional superconductor Sr$_{2}$RuO$_{4}$,"  Sr$_2$RuO$_4$ is the best candidate for spin-triplet superconductivity, an
unusual and elusive superconducting state of fundamental importance. In the
last three decades Sr$_2$RuO$_4$ has been very carefully studied and despite
its apparent simplicity when compared with strongly correlated high-$T_{c}$
cuprates, for which the pairing symmetry is understood, there is no scenario
that can explain all the major experimental observations, a conundrum that has
generated tremendous interest. Here we present a density-functional based
analysis of magnetic interactions in Sr$_{2}$RuO$_{4}$ and discuss the role of
magnetic anisotropy in its unconventional superconductivity. Our goal is
twofold. First, we access the possibility of the superconducting order
parameter rotation in an external magnetic field of 200 Oe, and conclude that
the spin-orbit interaction in this material is several orders of magnitude too
strong to be consistent with this hypothesis. Thus, the observed invariance of
the Knight shift across $T_{c}$ has no plausible explanation, and casts doubt
on using the Knight shift as an ultimate litmus paper for the pairing symmetry.
Second, we propose a quantitative double-exchange-like model for combining
itinerant fermions with an anisotropic Heisenberg magnetic Hamiltonian. This
model is complementary to the Hubbard-model-based calculations published so
far, and forms an alternative framework for exploring superconducting symmetry
in Sr$_{2}$RuO$_{4}.$ As an example, we use this model to analyze the
degeneracy between various $p-$triplet states in the simplest mean-field
approximation, and show that it splits into a single and two doublets with the
ground state defined by the competition between the ""Ising"" and ""compass""
anisotropic terms.
",0,1,0,0,0,0
18517,Loss of Regularity of Solutions of the Lighthill Problem for Shock Diffraction for Potential Flow,"  We are concerned with the regularity of solutions of the Lighthill problem
for shock diffraction by a convex corned wedge, which can be formulated as a
free boundary problem. In this paper, we prove that there is no regular
solution that is subsonic up to the wedge corner for potential flow. This
indicates that, if the solution is subsonic at the wedge corner, at least a
characteristic discontinuity (vortex sheet or entropy wave) is expected to be
generated, which is consistent with the experimental and computational results.
In order to achieve the non-existence result, a weak maximum principle for the
solution is established, and several other mathematical techniques are
developed. The methods and techniques developed here are also useful to the
other problems with similar difficulties.
",0,0,1,0,0,0
18518,"Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory","  The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product as a multispectral (MS) image corrected for geometric, atmospheric,
adjacency and topographic effects, stacked with its scene classification map
(SCM), whose legend includes quality layers such as cloud and cloud-shadow. No
ESA EO Level 2 product has ever been systematically generated at the ground
segment. To contribute toward filling an information gap from EO big data to
the ESA EO Level 2 product, an original Stage 4 validation (Val) of the
Satellite Image Automatic Mapper (SIAM) lightweight computer program was
conducted by independent means on an annual Web-Enabled Landsat Data (WELD)
image composite time-series of the conterminous U.S. The core of SIAM is a one
pass prior knowledge based decision tree for MS reflectance space
hyperpolyhedralization into static color names presented in literature in
recent years. For the sake of readability this paper is split into two. The
present Part 1 Theory provides the multidisciplinary background of a priori
color naming in cognitive science, from linguistics to computer vision. To cope
with dictionaries of MS color names and land cover class names that do not
coincide and must be harmonized, an original hybrid guideline is proposed to
identify a categorical variable pair relationship. An original quantitative
measure of categorical variable pair association is also proposed. The
subsequent Part 2 Validation discusses Stage 4 Val results collected by an
original protocol for wall-to-wall thematic map quality assessment without
sampling where the test and reference map legends can differ. Conclusions are
that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4
class taxonomy of the FAO Land Cover Classification System at the Dichotomous
Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.
",1,0,0,0,0,0
18519,Performance Analysis of MEC Approach for Haplotype Assembly,"  The Minimum Error Correction (MEC) approach is used as a metric for
reconstruction of haplotypes from NGS reads. In this paper, we show that the
MEC may encounter with imprecise reconstructed haplotypes for some NGS devices.
Specifically, using mathematical derivations, we evaluate this approach for the
SOLiD, Illumina, 454, Ion, Pacific BioSciences, Oxford Nanopore, and 10X
Genomics devices. Our results reveal that the MEC yields inexact haplotypes for
the Illumina MiniSeq, 454 GS Junior+, Ion PGM 314, and Oxford Nanopore MK 1
MinION.
",0,0,0,0,1,0
18520,Opinion Recommendation using Neural Memory Model,"  We present opinion recommendation, a novel task of jointly predicting a
custom review with a rating score that a certain user would give to a certain
product or service, given existing reviews and rating scores to the product or
service by other users, and the reviews that the user has given to other
products and services. A characteristic of opinion recommendation is the
reliance of multiple data sources for multi-task joint learning, which is the
strength of neural models. We use a single neural network to model users and
products, capturing their correlation and generating customised product
representations using a deep memory network, from which customised ratings and
reviews are constructed jointly. Results show that our opinion recommendation
system gives ratings that are closer to real user ratings on Yelp.com data
compared with Yelp's own ratings, and our methods give better results compared
to several pipelines baselines using state-of-the-art sentiment rating and
summarization systems.
",1,0,0,0,0,0
18521,Distributed Optimization of Multi-Beam Directional Communication Networks,"  We formulate an optimization problem for maximizing the data rate of a common
message transmitted from nodes within an airborne network broadcast to a
central station receiver while maintaining a set of intra-network rate demands.
Assuming that the network has full-duplex links with multi-beam directional
capability, we obtain a convex multi-commodity flow problem and use a
distributed augmented Lagrangian algorithm to solve for the optimal flows
associated with each beam in the network. For each augmented Lagrangian
iteration, we propose a scaled gradient projection method to minimize the local
Lagrangian function that incorporates the local topology of each node in the
network. Simulation results show fast convergence of the algorithm in
comparison to simple distributed primal dual methods and highlight performance
gains over standard minimum distance-based routing.
",0,0,1,0,0,0
18522,Mean conservation for density estimation via diffusion using the finite element method,"  We propose boundary conditions for the diffusion equation that maintain the
initial mean and the total mass of a discrete data sample in the density
estimation process. A complete study of this framework with numerical
experiments using the finite element method is presented for the one
dimensional diffusion equation, some possible applications of this results are
presented as well. We also comment on a similar methodology for the
two-dimensional diffusion equation for future applications in two-dimensional
domains.
",0,0,1,0,0,0
18523,Asymptotic properties of maximum likelihood estimator for the growth rate of a stable CIR process based on continuous time observations,"  We consider a stable Cox--Ingersoll--Ross process driven by a standard Wiener
process and a spectrally positive strictly stable Lévy process, and we study
asymptotic properties of the maximum likelihood estimator (MLE) for its growth
rate based on continuous time observations. We distinguish three cases:
subcritical, critical and supercritical. In all cases we prove strong
consistency of the MLE in question, in the subcritical case asymptotic
normality, and in the supercritical case asymptotic mixed normality are shown
as well. In the critical case the description of the asymptotic behavior of the
MLE in question remains open.
",0,0,1,1,0,0
18524,Indoor Office Wideband Penetration Loss Measurements at 73 GHz,"  This paper presents millimeter wave (mmWave) penetration loss measurements
and analysis at 73 GHz using a wideband sliding correlator channel sounder in
an indoor office environment. Penetration loss was measured using a carefully
controlled measurement setup for many common indoor building materials such as
glass doors, glass windows, closet doors, steel doors, and whiteboard writing
walls. Measurements were conducted using narrowbeam transmitter (TX) and
receiver (RX) horn antennas that were boresight-aligned with a test material
between the antennas. Overall, 21 different locations were measured for 6
different materials such that the same type of material was tested in at least
two locations in order to characterize the effect of penetration loss for
materials with similar composition. As shown here, attenuation through common
materials ranged between 0.8 dB/cm and 9.9 dB/cm for co-polarized antennas,
while cross-polarized antennas exhibited similar attenuation for most
materials, but up to 23.4 dB/cm of attenuation for others. The penetration loss
results presented here are useful for site-specific planning tools that will
model indoor mmWave networks, without the need for expensive measurement
campaigns.
",1,0,0,0,0,0
18525,The SPEDE spectrometer,"  The electron spectrometer, SPEDE, has been developed and will be employed in
conjunction with the Miniball spectrometer at the HIE-ISOLDE facility, CERN.
SPEDE allows for direct measurement of internal conversion electrons emitted
in-flight, without employing magnetic fields to transport or momentum filter
the electrons. Together with the Miniball spectrometer, it enables simultaneous
observation of {\gamma} rays and conversion electrons in Coulomb-excitation
experiments using radioactive ion beams.
",0,1,0,0,0,0
18526,Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations,"  A control design approach is developed for a general class of uncertain
strict-feedback-like nonlinear systems with dynamic uncertain input
nonlinearities with time delays. The system structure considered in this paper
includes a nominal uncertain strict-feedback-like subsystem, the input signal
to which is generated by an uncertain nonlinear input unmodeled dynamics that
is driven by the entire system state (including unmeasured state variables) and
is also allowed to depend on time delayed versions of the system state variable
and control input signals. The system also includes additive uncertain
nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
input nonlinearities with time-varying uncertain time delays. The proposed
control design approach provides a globally stabilizing delay-independent
robust adaptive output-feedback dynamic controller based on a dual dynamic
high-gain scaling based structure.
",1,0,1,0,0,0
18527,Multifractal Analysis of Pulsar Timing Residuals: Assessment of Gravitational Wave Detection,"  We introduce a pipeline including multifractal detrended cross-correlation
analysis (MF-DXA) modified by either singular value decomposition or the
adaptive method to examine the statistical properties of the pulsar timing
residual ($PTR$) induced by a gravitational wave (GW) signal. We propose a new
algorithm, the so-called irregular-MF-DXA, to deal with irregular data
sampling. Inspired by the quadrupolar nature of the spatial cross-correlation
function of a gravitational wave background, a new cross-correlation function,
$\bar{\sigma}_{\times}$, derived from irregular-MF-DXA will be introduced. We
show that, this measure reveals the quadrupolar signature in the $PTRs$ induced
by stochastic GWB. We propose four strategies based on the $y$-intercept of
fluctuation functions, the generalized Hurst exponent, and the width of the
singularity spectrum to determine the dimensionless amplitude and power-law
exponent of the characteristic strain spectrum as
$\mathcal{H}_c(f)\sim\mathcal{A}_{yr}(f/f_{yr})^{\zeta}$ for stochastic GWB.
Using the value of Hurst exponent, one can clarify the type of GWs. We apply
our pipeline to explore 20 millisecond pulsars observed by Parkes Pulsar Timing
Array. The computed scaling exponents confirm that all data are classified into
a nonstationary class implying the universality feature. The value of the Hurst
exponent is in the range $H\in [0.56,0.87]$. The $q$-dependency of the
generalized Hurst exponent demonstrates that the observed $PTRs$ have
multifractal behavior, and the source of this multifractality is mainly
attributed to the correlation of data which is another universality of the
observed datasets. Multifractal analysis of available $PTRs$ datasets reveals
an upper bound on the dimensionless amplitude of the GWB, $\mathcal{A}_{yr}<
2.0\times 10^{-15}$.
",0,1,0,0,0,0
18528,Recovery of Bennu's Orientation for the OSIRIS-REx Mission: Implications for the Spin State Accuracy and Geolocation Errors,"  The goal of the OSIRIS-REx mission is to return a sample of asteroid material
from Near-Earth Asteroid (101955) Bennu. The role of the navigation and flight
dynamics team is critical for the spacecraft to execute a precisely planned
sampling maneuver over a specifically-selected landing site. In particular, the
orientation of Bennu needs to be recovered with good accuracy during orbital
operations to contribute as small an error as possible to the landing error
budget. Although Bennu is well characterized from Earth-based radar
observations, its orientation dynamics are not sufficiently known to exclude
the presence of a small wobble. To better understand this contingency and
evaluate how well the orientation can be recovered in the presence of a large
1$^{\circ}$ wobble, we conduct a comprehensive simulation with the NASA GSFC
GEODYN orbit determination and geodetic parameter estimation software. We
describe the dynamic orientation modeling implemented in GEODYN in support of
OSIRIS-REx operations, and show how both altimetry and imagery data can be used
as either undifferenced (landmark, direct altimetry) or differenced (image
crossover, altimetry crossover) measurements. We find that these two different
types of data contribute differently to the recovery of instrument pointing or
planetary orientation. When upweighted, the absolute measurements help reduce
the geolocation errors, despite poorer astrometric (inertial) performance. We
find that with no wobble present, all the geolocation requirements are met.
While the presence of a large wobble is detrimental, the recovery is still
reliable thanks to the combined use of altimetry and imagery data.
",0,1,0,0,0,0
18529,Energy Scale of Lorentz Violation in Rainbow Gravity,"  We modify the standard relativistic dispersion relation in a way which breaks
Lorentz symmetry - the effect is predicted in a high-energy regime of some
modern theories of quantum gravity. We show that it is possible to realise this
scenario within the framework of Rainbow Gravity which introduces two new
energy-dependent functions $f_1(E)$ and $f_2(E)$ into the dispersion relation.
Additionally, we assume that the gravitational constant $G$ and the
cosmological constant $\Lambda$ also depend on energy $E$ and introduce the
scaling function $h(E)$ in order to express this dependence. For cosmological
applications we specify the functions $f_1$ and $f_2$ in order to fit massless
particles which allows us to derive modified cosmological equations. Finally,
by using Hubble+SNIa+BAO(BOSS+Lyman $\alpha$)+CMB data, we constrain the energy
scale $E_{LV}$ to be at least of the order of $10^{16}$ GeV at $1\sigma$ which
is the GUT scale or even higher $10^{17}$ GeV at $3\sigma$. Our claim is that
this energy can be interpreted as the decoupling scale of massless particles
from spacetime Lorentz violating effects.
",0,1,0,0,0,0
18530,How to construct wavelets on local fields of positive characteristic,"  We present an algorithm for construction step wavelets on local fields of
positive characteristic.
",0,0,1,0,0,0
18531,Fates of the dense cores formed by fragmentation of filaments: do they fragment again or not?,"  Fragmentation of filaments into dense cores is thought to be an important
step in forming stars. The bar-mode instability of spherically collapsing cores
found in previous linear analysis invokes a possibility of re-fragmentation of
the cores due to their ellipsoidal (prolate or oblate) deformation. To
investigate this possibility, here we perform three-dimensional
self-gravitational hydrodynamics simulations that follow all the way from
filament fragmentation to subsequent core collapse. We assume the gas is
polytropic with index \gamma, which determines the stability of the bar-mode.
For the case that the fragmentation of isolated hydrostatic filaments is
triggered by the most unstable fragmentation mode, we find the bar mode grows
as collapse proceeds if \gamma < 1.1, in agreement with the linear analysis.
However, it takes more than ten orders-of-magnitude increase in the central
density for the distortion to become non-linear. In addition to this fiducial
case, we also study non-fiducial ones such as the fragmentation is triggered by
a fragmentation mode with a longer wavelength and it occurs during radial
collapse of filaments and find the distortion rapidly grows. In most of
astrophysical applications, the effective polytropic index of collapsing gas
exceeds 1.1 before ten orders-of-magnitude increase in the central density.
Thus, supposing the fiducial case of filament fragmentation, re-fragmentation
of dense cores would not be likely and their final mass would be determined
when the filaments fragment.
",0,1,0,0,0,0
18532,Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures,"  What transpires from recent research is that temperatures and radiative
forcing seem to be characterized by a linear trend with two changes in the rate
of growth. The first occurs in the early 60s and indicates a very large
increase in the rate of growth of both temperature and radiative forcing
series. This was termed as the ""onset of sustained global warming"". The second
is related to the more recent so-called hiatus period, which suggests that
temperatures and total radiative forcing have increased less rapidly since the
mid-90s compared to the larger rate of increase from 1960 to 1990. There are
two issues that remain unresolved. The first is whether the breaks in the slope
of the trend functions of temperatures and radiative forcing are common. This
is important because common breaks coupled with the basic science of climate
change would strongly suggest a causal effect from anthropogenic factors to
temperatures. The second issue relates to establishing formally via a proper
testing procedure that takes into account the noise in the series, whether
there was indeed a `hiatus period' for temperatures since the mid 90s. This is
important because such a test would counter the widely held view that the
hiatus is the product of natural internal variability. Our paper provides tests
related to both issues. The results show that the breaks in temperatures and
radiative forcing are common and that the hiatus is characterized by a
significant decrease in their rate of growth. The statistical results are of
independent interest and applicable more generally.
",0,0,0,1,0,0
18533,"How to Stop Consensus Algorithms, locally?","  This paper studies problems on locally stopping distributed consensus
algorithms over networks where each node updates its state by interacting with
its neighbors and decides by itself whether certain level of agreement has been
achieved among nodes. Since an individual node is unable to access the states
of those beyond its neighbors, this problem becomes challenging. In this work,
we first define the stopping problem for generic distributed algorithms. Then,
a distributed algorithm is explicitly provided for each node to stop consensus
updating by exploring the relationship between the so-called local and global
consensus. Finally, we show both in theory and simulation that its
effectiveness depends both on the network size and the structure.
",1,0,0,0,0,0
18534,Seoul National University Camera II (SNUCAM-II): The New SED Camera for the Lee Sang Gak Telescope (LSGT),"  We present the characteristics and the performance of the new CCD camera
system, SNUCAM-II (Seoul National University CAMera system II) that was
installed on the Lee Sang Gak Telescope (LSGT) at the Siding Spring Observatory
in 2016. SNUCAM-II consists of a deep depletion chip covering a wide wavelength
from 0.3 {\mu}m to 1.1 {\mu}m with high sensitivity (QE at > 80% over 0.4 to
0.9 {\mu}m). It is equipped with the SDSS ugriz filters and 13 medium band
width (50 nm) filters, enabling us to study spectral energy distributions
(SEDs) of diverse objects from extragalactic sources to solar system objects.
On LSGT, SNUCAM-II offers 15.7 {\times} 15.7 arcmin field-of-view (FOV) at a
pixel scale of 0.92 arcsec and a limiting magnitude of g = 19.91 AB mag and
z=18.20 AB mag at 5{\sigma} with 180 sec exposure time for point source
detection.
",0,1,0,0,0,0
18535,A Bridge Between Hyperparameter Optimization and Larning-to-learn,"  We consider a class of a nested optimization problems involving inner and
outer objectives. We observe that by taking into explicit account the
optimization dynamics for the inner objective it is possible to derive a
general framework that unifies gradient-based hyperparameter optimization and
meta-learning (or learning-to-learn). Depending on the specific setting, the
variables of the outer objective take either the meaning of hyperparameters in
a supervised learning problem or parameters of a meta-learner. We show that
some recently proposed methods in the latter setting can be instantiated in our
framework and tackled with the same gradient-based algorithms. Finally, we
discuss possible design patterns for learning-to-learn and present encouraging
preliminary experiments for few-shot learning.
",1,0,0,1,0,0
18536,Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks,"  Designing a new drug is a lengthy and expensive process. As the space of
potential molecules is very large (10^23-10^60), a common technique during drug
discovery is to start from a molecule which already has some of the desired
properties. An interdisciplinary team of scientists generates hypothesis about
the required changes to the prototype. In this work, we develop an algorithmic
unsupervised-approach that automatically generates potential drug molecules
given a prototype drug. We show that the molecules generated by the system are
valid molecules and significantly different from the prototype drug. Out of the
compounds generated by the system, we identified 35 FDA-approved drugs. As an
example, our system generated Isoniazid - one of the main drugs for
Tuberculosis. The system is currently being deployed for use in collaboration
with pharmaceutical companies to further analyze the additional generated
molecules.
",0,0,0,1,0,0
18537,Convex and non-convex regularization methods for spatial point processes intensity estimation,"  This paper deals with feature selection procedures for spatial point
processes intensity estimation. We consider regularized versions of estimating
equations based on Campbell theorem derived from two classical functions:
Poisson likelihood and logistic regression likelihood. We provide general
conditions on the spatial point processes and on penalty functions which ensure
consistency, sparsity and asymptotic normality. We discuss the numerical
implementation and assess finite sample properties in a simulation study.
Finally, an application to tropical forestry datasets illustrates the use of
the proposed methods.
",0,0,1,1,0,0
18538,Glass Transition in Supercooled Liquids with Medium Range Crystalline Order,"  The origins of rapid dynamical slow down in glass forming liquids in the
growth of static length scales, possibly associated with identifiable
structural ordering, is a much debated issue. Growth of medium range
crystalline order (MRCO) has been observed in various model systems to be
associated with glassy behaviour. Such observations raise the question about
the eventual state reached by a glass former, if allowed to relax for
sufficiently long times. Is a slowly growing crystalline order responsible for
slow dynamics? Are the molecular mechanisms for glass transition in liquids
with and without MRCO the same? If yes, glass formers with MRCO provide a
paradigm for understanding glassy behaviour generically. If not, systems with
MRCO form a new class of glass forming materials whose molecular mechanism for
slow dynamics may be easier to understand in terms of growing crystalline
order, and should be approached in that manner, even while they will not
provide generic insights. In this study we perform extensive molecular dynamics
simulations of a number of glass forming liquids in two dimensions and show
that the static and dynamic properties of glasses with MRCO are different from
other glass forming liquids with no predominant local order. We also resolve an
important issue regarding the so-called Point-to-set method for determining
static length scales, and demonstrate it to be a robust, order agnostic, method
for determining static correlation lengths in glass formers.
",0,1,0,0,0,0
18539,Natural and Artificial Spectral Edges in Exoplanets,"  Technological civilizations may rely upon large-scale photovoltaic arrays to
harness energy from their host star. Photovoltaic materials, such as silicon,
possess distinctive spectral features, including an ""artificial edge"" that is
characteristically shifted in wavelength shortwards of the ""red edge"" of
vegetation. Future observations of reflected light from exoplanets would be
able to detect both natural and artificial edges photometrically, if a
significant fraction of the planet's surface is covered by vegetation or
photovoltaic arrays respectively. The stellar energy thus tapped can be
utilized for terraforming activities by transferring heat and light from the
day side to the night side on tidally locked exoplanets, thereby producing
detectable artifacts.
",0,1,0,0,0,0
18540,The damage inflicted by a computer virus: A new estimation method,"  This paper addressed the issue of estimating the damage caused by a computer
virus. First, an individual-level delayed SIR model capturing the spreading
process of a digital virus is derived. Second, the damage inflicted by the
virus is modeled as the sum of the economic losses and the cost for developing
the antivirus. Next, the impact of different factors, including the delay and
the network structure, on the damage is explored by means of computer
simulations. Thereby some measures of reducing the damage of a virus are
recommended. To our knowledge, this is the first time the antivirus-developing
cost is taken into account when estimating the damage of a virus.
",1,1,0,0,0,0
18541,Deep Fault Analysis and Subset Selection in Solar Power Grids,"  Non-availability of reliable and sustainable electric power is a major
problem in the developing world. Renewable energy sources like solar are not
very lucrative in the current stage due to various uncertainties like weather,
storage, land use among others. There also exists various other issues like
mis-commitment of power, absence of intelligent fault analysis, congestion,
etc. In this paper, we propose a novel deep learning-based system for
predicting faults and selecting power generators optimally so as to reduce
costs and ensure higher reliability in solar power systems. The results are
highly encouraging and they suggest that the approaches proposed in this paper
have the potential to be applied successfully in the developing world.
",1,0,0,1,0,0
18542,Hirzebruch L-polynomials and multiple zeta values,"  We express the coefficients of the Hirzebruch L-polynomials in terms of
certain alternating multiple zeta values. In particular, we show that every
monomial in the Pontryagin classes appears with a non-zero coefficient, with
the expected sign. Similar results hold for the polynomials associated to the
A-hat genus.
",0,0,1,0,0,0
18543,On Nontrivial Zeros of Riemann Zeta Function,"  Let {\Xi} be a function relating to the Riemann zeta function with . In this
paper, we construct a function containing and {\Xi} , and prove that satisfies
a nonadjoint boundary value problem to a nonsingular differential equation if
is any nontrivial zero of {\Xi} . Inspecting properties of and using known
results of nontrivial zeros of , we derive that nontrivial zeros of all have
real part equal to , which concludes that Riemann Hypothesis is true.
",0,0,1,0,0,0
18544,The reliability of a nutritional meta-analysis study,"  Background: Many researchers have studied the relationship between diet and
health. There are papers showing an association between the consumption of
sugar-sweetened beverages and Type 2 diabetes. Many meta-analyses use
individual studies that do not adjust for multiple testing or multiple modeling
and thus provide biased estimates of effect. Hence the claims reported in a
meta-analysis paper may be unreliable if the primary papers do not ensure
unbiased estimates of effect. Objective: Determine the statistical reliability
of 10 papers and indirectly the reliability of the meta-analysis study. Method:
Ten primary papers used in a meta-analysis paper and counted the numbers of
outcomes, predictors, and covariates. We estimated the size of the potential
analysis search space available to the authors of these papers; i.e. the number
of comparisons and models available. Since we noticed that there were
differences between predictors and covariates cited in the abstract and in the
text, we applied this formula to information found in the abstracts, Space A,
as well as the text, Space T, of each primary paper. Results: The median and
range of the number of comparisons possible across the primary papers are 6.5
and (2-12,288) for abstracts, and 196,608 and (3,072-117,117,952) the texts.
Note that the median of 6.5 for Space A is misleading as each primary study has
60-165 foods not mentioned in the abstract. Conclusion: Given that testing is
at the 0.05 level and the number of comparisons is very large, nominal
statistical significance is very weak support for a claim. The claims in these
papers are not statistically supported and hence are unreliable. Thus, the
claims of the meta-analysis paper lack evidentiary confirmation.
",0,0,0,1,0,0
18545,Composite Fermions on a Torus,"  We achieve an explicit construction of the lowest Landau level (LLL)
projected wave functions for composite fermions in the periodic (torus)
geometry. To this end, we first demonstrate how the vortex attachment of the
composite fermion (CF) theory can be accomplished in the torus geometry to
produce the ""unprojected"" wave functions satisfying the correct
(quasi-)periodic boundary conditions. We then consider two methods for
projecting these wave functions into the LLL. The direct projection produces
valid wave functions but can be implemented only for very small systems. The
more powerful and more useful projection method of Jain and Kamilla fails in
the torus geometry because it does not preserve the periodic boundary
conditions and thus takes us out of the original Hilbert space. We have
succeeded in constructing a modified projection method that is consistent with
both the periodic boundary conditions and the general structure of the CF
theory. This method is valid for a large class of states of composite fermions,
called ""proper states,"" which includes the incompressible ground states at
electron filling factors $\nu=\frac{n}{2pn+ 1}$, their charged and neutral
excitations, and also the quasidegenerate ground states at arbitrary filling
factors of the form $\nu=\frac{\nu^*}{2p\nu^*+ 1}$, where $n$ and $p$ are
integers and $\nu^*$ is the CF filling factor. Comparison with exact results
known for small systems for the ground and excited states at filling factors
$\nu=1/3$, 2/5 and 3/7 demonstrates our LLL-projected wave functions to be
extremely accurate representations of the actual Coulomb eigenstates. Our
construction enables the study of large systems of composite fermions on the
torus, thereby opening the possibility of investigating numerous interesting
questions and phenomena.
",0,1,0,0,0,0
18546,ROCKER: A Refinement Operator for Key Discovery,"  The Linked Data principles provide a decentral approach for publishing
structured data in the RDF format on the Web. In contrast to structured data
published in relational databases where a key is often provided explicitly,
finding a set of properties that allows identifying a resource uniquely is a
non-trivial task. Still, finding keys is of central importance for manifold
applications such as resource deduplication, link discovery, logical data
compression and data integration. In this paper, we address this research gap
by specifying a refinement operator, dubbed ROCKER, which we prove to be
finite, proper and non-redundant. We combine the theoretical characteristics of
this operator with two monotonicities of keys to obtain a time-efficient
approach for detecting keys, i.e., sets of properties that describe resources
uniquely. We then utilize a hash index to compute the discriminability score
efficiently. Therewith, we ensure that our approach can scale to very large
knowledge bases. Results show that ROCKER yields more accurate results, has a
comparable runtime, and consumes less memory w.r.t. existing state-of-the-art
techniques.
",1,0,0,0,0,0
18547,Double-slit Fraunhofer pattern as the signature of the Josephson effect between Berezinskii superconductors through the ferromagnetic vortex,"  I apply the recently developed formalism of generalized quasiclassical theory
to show that using hybrid superconducting systems with non-collinear strong
ferromagnets one can realize the Josephson junction between Berezinskii-type
superconductors. The reported calculation reproduces main features observed in
the recent experiment, namely the the slightly asymmetric double-slit
Fraunhofer interference pattern of the Josephson current through the
ferromagnetic vortex. The double-slit structure results from the spatially
inhomogeneous Berezinskii state with the amplitude controlled by the local
angle between magnetic moments in two ferromagnetic layers. The critical
current asymmetry by the sign of magnetic field can signal the presence of
spontaneous supercurrents generated by the non-coplanar magnetic texture near
the core of the ferromagnetic vortex core. I demonstrate that ferromagnetic
vortex can induce spontaneous vorticity in the odd-frequency order parameter
manifesting the possibility of the emergent magnetic field to create
topological defects.
",0,1,0,0,0,0
18548,A class of differential quadratic algebras and their symmetries,"  We study a multi-parametric family of quadratic algebras in four generators,
which includes coordinate algebras of noncommutative four-planes and, as
quotient algebras, noncommutative three spheres. Particular subfamilies
comprise Sklyanin algebras and Connes--Dubois-Violette planes. We determine
quantum groups of symmetries for the general algebras and construct
finite-dimensional covariant differential calculi.
",0,0,1,0,0,0
18549,Introduction to compact and discrete quantum groups,"  These are notes from introductory lectures at the graduate school
""Topological Quantum Groups"" in Będlewo (June 28--July 11, 2015). The notes
present the passage from Hopf algebras to compact quantum groups and sketch the
notion of discrete quantum groups viewed as duals of compact quantum groups.
",0,0,1,0,0,0
18550,An optimization approach to adaptive multi-dimensional capital management,"  Firms should keep capital to offer sufficient protection against the risks
they are facing. In the insurance context methods have been developed to
determine the minimum capital level required, but less so in the context of
firms with multiple business lines including allocation. The individual capital
reserve of each line can be represented by means of classical models, such as
the conventional Cramér-Lundberg model, but the challenge lies in soundly
modelling the correlations between the business lines. We propose a simple yet
versatile approach that allows for dependence by introducing a common
environmental factor. We present a novel Bayesian approach to calibrate the
latent environmental state distribution based on observations concerning the
claim processes. The calibration approach is adjusted for an environmental
factor that changes over time. The convergence of the calibration procedure
towards the true environmental state is deduced. We then point out how to
determine the optimal initial capital of the different business lines under
specific constraints on the ruin probability of subsets of business lines. Upon
combining the above findings, we have developed an easy-to-implement approach
to capital risk management in a multi-dimensional insurance risk model.
",0,0,0,0,0,1
18551,Low Energy Phonons in $Bi_2Sr_2CaCu_2O_{8+δ}$ and their Possible Interaction with Electrons Measured by Inelastic Neutron Scattering,"  Angle-resolved photoemission (ARPES) experiments on copper oxide
superconductors revealed enigmatic kinks in electronic dispersions near 10 meV
presumably due to phonons or impuritites. We used inelastic neutron scattering
to measure phonon branches below 15 meV in a large single crystal sample of
optimally-doped $Bi_2Sr_2CaCu_2O_{8+\delta}$ (BSCCO). The high quality dataset
covered several Brilloiun zones with different final energies. In addition to
acoustic branches, optic branches disperse from 4 meV and 7 meV zone center
energies. The 4 meV branch interacts with acoustic phonons at small
wavevectors, which destroys the LA character of the acoustic branch beyond
~0.15 reciprocal lattice units. We propose a mechanism that explains the low
energy electronic dispersion features based on this observation.
",0,1,0,0,0,0
18552,Spectral properties of complex Airy operator on the semi-axis,"  We prove the theorem on the completeness of the root functions of the
Schroedinger operator $L=-d^2/dx^2+p(x)$ on the semi-axis $\mathbb R_+$ with a
complex--valued potential $p(x)$. It is assumed that the potential $p = q \pm
ir$ is such that the real functions $q$ and $r$ are subject the conditions $$
q(x) \geqslant c r(x), \quad r(x) \geqslant c_0+ c_1 x^\alpha, \quad \alpha >0,
$$ where the constants $c, \ c_0\in \mathbb R$, $c_1>0$ and $\arg(\pm i+c) <
2\alpha\pi/(2+\alpha)$. For the case of the Airy operator $L_c=-d^2/dx^2+cx$,
$c=const$, this theorem imply the completeness of the system of the
eigenfunctions of this operator if $|\arg c|<2\pi/3$. Using another technique
based on the asymptotic behavior of the Airy functions we prove that the
completeness theorem for the operator $L_c$ remains valid, provided that $|\arg
c|<5\pi/6$.
",0,0,1,0,0,0
18553,Webs and $q$-Howe dualities in types $\mathbf{B}\mathbf{C}\mathbf{D}$,"  We define web categories describing intertwiners for the orthogonal and
symplectic Lie algebras, and, in the quantized setup, for certain orthogonal
and symplectic coideal subalgebras. They generalize the Brauer category, and
allow us to prove quantum versions of some classical type
$\mathbf{B}\mathbf{C}\mathbf{D}$ Howe dualities.
",0,0,1,0,0,0
18554,Structural Controllability of Linear Time-invariant Systems,"  One version of the concept of structural controllability defined for
single-input systems by Lin and subsequently generalized to multi-input systems
by others, states that a parameterized matrix pair $(A, B)$ whose nonzero
entries are distinct parameters, is structurally controllable if values can be
assigned to the parameters which cause the resulting matrix pair to be
controllable. In this paper the concept of structural controllability is
broadened to allow for the possibility that a parameter may appear in more than
one location in the pair $(A, B)$. Subject to a certain condition on the
parameterization called the ""binary assumption"", an explicit graph-theoretic
characterization of such matrix pairs is derived.
",1,0,0,0,0,0
18555,Structure of martingale transports in finite dimensions,"  We study the structure of martingale transports in finite dimensions. We
consider the family $\mathcal{M}(\mu,\nu) $ of martingale measures on
$\mathbb{R}^N \times \mathbb{R}^N$ with given marginals $\mu,\nu$, and
construct a family of relatively open convex sets $\{C_x:x\in \mathbb{R}^N \}$,
which forms a partition of $\mathbb{R}^N$, and such that any martingale
transport in $\mathcal{M}(\mu,\nu) $ sends mass from $x$ to within
$\overline{C_x}$, $\mu(dx)$--a.e. Our results extend the analogous
one-dimensional results of M. Beiglböck and N. Juillet (2016) and M.
Beiglböck, M. Nutz, and N. Touzi (2015). We conjecture that the decomposition
is canonical and minimal in the sense that it allows to characterise the
martingale polar sets, i.e. the sets which have zero mass under all measures in
$\mathcal{M}(\mu,\nu)$, and offers the martingale analogue of the
characterisation of transport polar sets proved in M. Beiglböck, M.
Goldstern, G. Maresch, and W. Schachermayer (2009).
",0,0,1,0,0,0
18556,Simplifying branched covering surface-knots by chart moves involving black vertices,"  A branched covering surface-knot is a surface-knot in the form of a branched
covering over an oriented surface-knot $F$, where we include the case when the
covering has no branch points. A branched covering surface-knot is presented by
a graph called a chart on a surface diagram of $F$. We can simplify a branched
covering surface-knot by an addition of 1-handles with chart loops to a form
such that its chart is the union of free edges and 1-handles with chart loops.
We investigate properties of such simplifications for the case when branched
covering surface-knots have a non-zero number of branch points, using chart
moves involving black vertices.
",0,0,1,0,0,0
18557,$K$-surfaces with free boundaries,"  A well-known question in classical differential geometry and geometric
analysis asks for a description of possible boundaries of $K$-surfaces, which
are smooth, compact hypersurfaces in $\mathbb{R}^d$ having constant Gauss
curvature equal to $K \geq 0$. This question generated a considerable amount of
remarkable results in the last few decades. Motivated by these developments
here we study the question of determining a $K$-surface when only part of its
boundary is fixed, and in addition the surface hits a given manifold at some
fixed angle. While this general setting is out of reach for us at the present,
we settle a model case of the problem, which in its analytic formulation
reduces to a Bernoulli type free boundary problem for the Monge-Ampère
equation. We study both the cases of 0-curvature and of positive curvature. The
formulation of the free boundary condition and its regularity are the most
delicate and challenging questions addressed in this work. In this regard we
introduce a notion of a Blaschke extension of a solution which might be of
independent interest.
The problem we study can also be interpreted as the Alt-Caffarelli problem
for the Monge-Ampère equation. Moreover, it also relates to the problem of
isometric embedding of a positive metric on the annulus with partially
prescribed boundary and optimal transport with free mass.
",0,0,1,0,0,0
18558,The graphs of join-semilattices and the shape of congruence lattices of particle lattices,"  We attach to each $\langle 0, \vee \rangle$-semilattice a graph
$\boldsymbol{G}_{\boldsymbol{S}}$ whose vertices are join-irreducible elements
of $\boldsymbol{S}$ and whose edges correspond to the reflexive dependency
relation. We study properties of the graph $\boldsymbol{G}_{\boldsymbol{S}}$
both when $\boldsymbol{S}$ is a join-semilattice and when it is a lattice. We
call a $\langle 0, \vee \rangle$-semilattice $\boldsymbol{S}$ particle provided
that the set of its join-irreducible elements join-generates $\boldsymbol{S}$
and it satisfies DCC. We prove that the congruence lattice of a particle
lattice is anti-isomorphic to the lattice of hereditary subsets of the
corresponding graph that are closed in a certain zero-dimensional topology.
Thus we extend the result known for principally chain finite lattices.
",0,0,1,0,0,0
18559,Optimal Quasi-Gray Codes: The Alphabet Matters,"  A quasi-Gray code of dimension $n$ and length $\ell$ over an alphabet
$\Sigma$ is a sequence of distinct words $w_1,w_2,\dots,w_\ell$ from $\Sigma^n$
such that any two consecutive words differ in at most $c$ coordinates, for some
fixed constant $c>0$. In this paper we are interested in the read and write
complexity of quasi-Gray codes in the bit-probe model, where we measure the
number of symbols read and written in order to transform any word $w_i$ into
its successor $w_{i+1}$.
We present construction of quasi-Gray codes of dimension $n$ and length $3^n$
over the ternary alphabet $\{0,1,2\}$ with worst-case read complexity $O(\log
n)$ and write complexity $2$. This generalizes to arbitrary odd-size alphabets.
For the binary alphabet, we present quasi-Gray codes of dimension $n$ and
length at least $2^n - 20n$ with worst-case read complexity $6+\log n$ and
write complexity $2$. This complements a recent result by Raskin [Raskin '17]
who shows that any quasi-Gray code over binary alphabet of length $2^n$ has
read complexity $\Omega(n)$.
Our results significantly improve on previously known constructions and for
the odd-size alphabets we break the $\Omega(n)$ worst-case barrier for
space-optimal (non-redundant) quasi-Gray codes with constant number of writes.
We obtain our results via a novel application of algebraic tools together with
the principles of catalytic computation [Buhrman et al. '14, Ben-Or and Cleve
'92, Barrington '89, Coppersmith and Grossman '75].
",1,0,0,0,0,0
18560,Revisiting the pre-main-sequence evolution of stars I. Importance of accretion efficiency and deuterium abundance,"  Recent theoretical work has shown that the pre-main-sequence (PMS) evolution
of stars is much more complex than previously envisioned. Instead of the
traditional steady, one-dimensional solution, accretion may be episodic and not
necessarily symmetrical, thereby affecting the energy deposited inside the star
and its interior structure. Given this new framework, we want to understand
what controls the evolution of accreting stars. We use the MESA stellar
evolution code with various sets of conditions. In particular, we account for
the (unknown) efficiency of accretion in burying gravitational energy into the
protostar through a parameter, $\xi$, and we vary the amount of deuterium
present. We confirm the findings of previous works that the evolution changes
significantly with the amount of energy that is lost during accretion. We find
that deuterium burning also regulates the PMS evolution. In the low-entropy
accretion scenario, the evolutionary tracks in the H-R diagram are
significantly different from the classical tracks and are sensitive to the
deuterium content. A comparison of theoretical evolutionary tracks and
observations allows us to exclude some cold accretion models ($\xi\sim 0$) with
low deuterium abundances. We confirm that the luminosity spread seen in
clusters can be explained by models with a somewhat inefficient injection of
accretion heat. The resulting evolutionary tracks then become sensitive to the
accretion heat efficiency, initial core entropy, and deuterium content. In this
context, we predict that clusters with a higher D/H ratio should have less
scatter in luminosity than clusters with a smaller D/H. Future work on this
issue should include radiation-hydrodynamic simulations to determine the
efficiency of accretion heating and further observations to investigate the
deuterium content in star-forming regions. (abbrev.)
",0,1,0,0,0,0
18561,Surface-assisted carrier excitation in plasmonic nanostructure,"  We present a quantum-mechanical model for surface-assisted carrier excitation
by optical fields in plasmonic nanostructures of arbitrary shape. We derive an
explicit expression, in terms of local fields inside the metal structure, for
surface absorbed power and surface scattering rate that determine the
enhancement of carrier excitation efficiency near the metal-dielectric
interface. We show that surface scattering is highly sensitive to the local
field polarization, and can be incorporated into metal dielectric function
along with phonon and impurity scattering. We also show that the obtained
surface scattering rate describes surface-assisted plasmon decay (Landau
damping) in nanostructures larger than the nonlocality scale. Our model can be
used for calculations of plasmon-assisted hot carrier generation rates in
photovoltaics and photochemistry applications.
",0,1,0,0,0,0
18562,Existence of global weak solutions to the kinetic Hookean dumbbell model for incompressible dilute polymeric fluids,"  We explore the existence of global weak solutions to the Hookean dumbbell
model, a system of nonlinear partial differential equations that arises from
the kinetic theory of dilute polymers, involving the unsteady incompressible
Navier--Stokes equations in a bounded domain in two or three space dimensions,
coupled to a Fokker--Planck-type parabolic equation. We prove the existence of
large-data global weak solutions in the case of two space dimensions.
Indirectly, our proof also rigorously demonstrates that, in two space
dimensions at least, the Oldroyd-B model is the macroscopic closure of the
Hookean dumbbell model. In three space dimensions, we prove the existence of
large-data global weak subsolutions to the model, which are weak solutions with
a defect measure, where the defect measure appearing in the Navier--Stokes
momentum equation is the divergence of a symmetric positive semidefinite
matrix-valued Radon measure.
",0,0,1,0,0,0
18563,Tight Bounds for Online Coloring of Basic Graph Classes,"  We resolve a number of long-standing open problems in online graph coloring.
More specifically, we develop tight lower bounds on the performance of online
algorithms for fundamental graph classes. An important contribution is that our
bounds also hold for randomized online algorithms, for which hardly any results
were known. Technically, we construct lower bounds for chordal graphs. The
constructions then allow us to derive results on the performance of randomized
online algorithms for the following further graph classes: trees, planar,
bipartite, inductive, bounded-treewidth and disk graphs. It shows that the best
competitive ratio of both deterministic and randomized online algorithms is
$\Theta(\log n)$, where $n$ is the number of vertices of a graph. Furthermore,
we prove that this guarantee cannot be improved if an online algorithm has a
lookahead of size $O(n/\log n)$ or access to a reordering buffer of size
$n^{1-\epsilon}$, for any $0<\epsilon\leq 1$. A consequence of our results is
that, for all of the above mentioned graph classes except bipartite graphs, the
natural $\textit{First Fit}$ coloring algorithm achieves an optimal
performance, up to constant factors, among deterministic and randomized online
algorithms.
",1,0,0,0,0,0
18564,Comparison of PCA with ICA from data distribution perspective,"  We performed an empirical comparison of ICA and PCA algorithms by applying
them on two simulated noisy time series with varying distribution parameters
and level of noise. In general, ICA shows better results than PCA because it
takes into account higher moments of data distribution. On the other hand, PCA
remains quite sensitive to the level of correlations among signals.
",0,0,0,1,0,0
18565,The Special Polarization Characteristic Features of a Three-Dimensional Terahertz Photonic Crystal with a Silicon Inverse Diamond Structure,"  The band structure of a Si inverse diamond structure whose lattice point
shape was vacant regular octahedrons was calculated using plane wave expansion
method and a complete photonic band gap was theoretically confirmed at around
0.4 THz. It is said that three-dimensional photonic crystals have no
polarization anisotropy in photonic band gap (stop gap, stop band) of high
symmetry points in normal incidence. However, it was experimentally confirmed
that the polarization orientation of a reflected light was different from that
of a incident light, {I(X,Y)}, where (X,Y) is the coordinate system fixed in
the photonic crystal. It was studied on a plane (001) at around X point's
photonic band gap (0.36 - 0.44 THz) for incident light direction [001]
($\Gamma$-X direction) by rotating a sample in the plane (001), relatively. The
polarization orientation of the reflected light was parallel to that of the
incident light for the incident polarization orientation I(1,1), I(1,-1). In
contrast, the former was perpendicular to the latter for the incident
polarization orientation I(1,0), I(0,-1) in the vicinity of 0.38 THz. As far as
the photonic crystal in this work is concerned, method of resolution and
synthesis of the incident polarization vector isn't apparently able to apply to
the analysis of experimental results.
",0,1,0,0,0,0
18566,Toward Finding Latent Cities with Non-Negative Matrix Factorization,"  In the last decade, digital footprints have been used to cluster population
activity into functional areas of cities.
However, a key aspect has been overlooked: we experience our cities not only
by performing activities at specific destinations, but also by moving from one
place to another.
In this paper, we propose to analyze and cluster the city based on how people
move through it. Particularly, we introduce Mobilicities, automatically
generated travel patterns inferred from mobile phone network data using NMF, a
matrix factorization model.
We evaluate our method in a large city and we find that mobilicities reveal
latent but at the same time interpretable mobility structures of the city. Our
results provide evidence on how clustering and visualization of aggregated
phone logs could be used in planning systems to interactively analyze city
structure and population activity.
",1,0,0,0,0,0
18567,Time-Sensitive Networking for robotics,"  We argue that Time-Sensitive Networking (TSN) will become the de facto
standard for real-time communications in robotics. We present a review and
classification of the different communication standards which are relevant for
the field and introduce the typical problems with traditional switched Ethernet
networks. We discuss some of the TSN features relevant for deterministic
communications and evaluate experimentally one of the shaping mechanisms in an
exemplary robotic scenario. In particular, and based on our results, we claim
that many of the existing real-time industrial solutions will slowly be
replaced by TSN. And that this will lead towards a unified landscape of
physically interoperable robot and robot components.
",1,0,0,0,0,0
18568,Active Orthogonal Matching Pursuit for Sparse Subspace Clustering,"  Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering
high-dimensional data points lying in a union of low-dimensional subspaces.
However, while $\ell_1$ optimization-based SSC algorithms suffer from high
computational complexity, other variants of SSC, such as Orthogonal Matching
Pursuit-based SSC (OMP-SSC), lose clustering accuracy in pursuit of improving
time efficiency. In this letter, we propose a novel Active OMP-SSC, which
improves clustering accuracy of OMP-SSC by adaptively updating data points and
randomly dropping data points in the OMP process, while still enjoying the low
computational complexity of greedy pursuit algorithms. We provide heuristic
analysis of our approach, and explain how these two active steps achieve a
better tradeoff between connectivity and separation. Numerical results on both
synthetic data and real-world data validate our analyses and show the
advantages of the proposed active algorithm.
",1,0,0,1,0,0
18569,Experimental and theoretical study of AC losses in variable asymmetrical magnetic environments,"  Measurements of AC losses in a HTS-tape placed in between of two bulk
magnetic shields of high permeability were performed by applying calorimetric
techniques for various asymmetrical shielding arrangements. The experiment was
supported by analytical calculations and finite-element simulations of the
field and current distributions, based on the Bean model of the critical state.
The simulated current and field profiles perfectly reproduce the analytic
solutions known for certain shielding geometries. The evaluation of the
consequent AC losses exhibits good agreement with measurements for the central
position of the tape between the magnets but increasing discrepancy when the
tape is approaching the shields. This can be explained by the increasing
contribution of the eddy currents and magnetic hysteresis losses in the
conducting shields.
",0,1,0,0,0,0
18570,Robustness of semiparametric efficiency in nearly-true models for two-phase samples,"  We examine the performance of efficient and AIPW estimators under two-phase
sampling when the complete-data model is nearly correctly specified, in the
sense that the misspecification is not reliably detectable from the data by any
possible diagnostic or test. Asymptotic results for these nearly true models
are obtained by representing them as sequences of misspecified models that are
mutually contiguous with a correctly specified model. We find that for the
least-favourable direction of model misspecification the bias in the efficient
estimator induced can be comparable to the extra variability in the AIPW
estimator, so that the mean squared error of the efficient estimator is no
longer lower. This can happen when the most-powerful test for the model
misspecification still has modest power. We verify that the theoretical results
agree with simulation in three examples: a simple informative-sampling model
for a Normal mean, logistic regression in the classical case-control design,
and linear regression in a two-phase design.
",0,0,1,1,0,0
18571,A* CCG Parsing with a Supertag and Dependency Factored Model,"  We propose a new A* CCG parsing model in which the probability of a tree is
decomposed into factors of CCG categories and its syntactic dependencies both
defined on bi-directional LSTMs. Our factored model allows the precomputation
of all probabilities and runs very efficiently, while modeling sentence
structures explicitly via dependencies. Our model achieves the state-of-the-art
results on English and Japanese CCG parsing.
",1,0,0,0,0,0
18572,Numerical studies of Thompson's group F and related groups,"  We have developed polynomial-time algorithms to generate terms of the
cogrowth series for groups $\mathbb{Z}\wr \mathbb{Z},$ the lamplighter group,
$(\mathbb{Z}\wr \mathbb{Z})\wr \mathbb{Z}$ and the Navas-Brin group $B.$ We
have also given an improved algorithm for the coefficients of Thompson's group
$F,$ giving 32 terms of the cogrowth series. We develop numerical techniques to
extract the asymptotics of these various cogrowth series. We present improved
rigorous lower bounds on the growth-rate of the cogrowth series for Thompson's
group $F$ using the method from \cite{HHR15} applied to our extended series. We
also generalise their method by showing that it applies to loops on any locally
finite graph. Unfortunately, lower bounds less than 16 do not help in
determining amenability.
Again for Thompson's group $F$ we prove that, if the group is amenable, there
cannot be a sub-dominant stretched exponential term in the
asymptotics\footnote{ }. Yet the numerical data provides compelling evidence
for the presence of such a term. This observation suggests a potential path to
a proof of non-amenability: If the universality class of the cogrowth sequence
can be determined rigorously, it will likely prove non-amenability.
We estimate the asymptotics of the cogrowth coefficients of $F$ to be $$ c_n
\sim c \cdot \mu^n \cdot \kappa^{n^\sigma \log^\delta{n}} \cdot n^g,$$ where
$\mu \approx 15,$ $\kappa \approx 1/e,$ $\sigma \approx 1/2,$ $\delta \approx
1/2,$ and $g \approx -1.$ The growth constant $\mu$ must be 16 for amenability.
These two approaches, plus a third based on extrapolating lower bounds, support
the conjecture \cite{ERvR15, HHR15} that the group is not amenable.
",0,0,1,0,0,0
18573,The modularity of action and perception revisited using control theory and active inference,"  The assumption that action and perception can be investigated independently
is entrenched in theories, models and experimental approaches across the brain
and mind sciences. In cognitive science, this has been a central point of
contention between computationalist and 4Es (enactive, embodied, extended and
embedded) theories of cognition, with the former embracing the ""classical
sandwich"", modular, architecture of the mind and the latter actively denying
this separation can be made. In this work we suggest that the modular
independence of action and perception strongly resonates with the separation
principle of control theory and furthermore that this principle provides formal
criteria within which to evaluate the implications of the modularity of action
and perception. We will also see that real-time feedback with the environment,
often considered necessary for the definition of 4Es ideas, is not however a
sufficient condition to avoid the ""classical sandwich"". Finally, we argue that
an emerging framework in the cognitive and brain sciences, active inference,
extends ideas derived from control theory to the study of biological systems
while disposing of the separation principle, describing non-modular models of
behaviour strongly aligned with 4Es theories of cognition.
",0,0,0,0,1,0
18574,Clique-based Method for Social Network Clustering,"  In this article, we develop a clique-based method for social network
clustering. We introduce a new index to evaluate the quality of clustering
results, and propose an efficient algorithm based on recursive bipartition to
maximize an objective function of the proposed index. The optimization problem
is NP-hard, so we approximate the semi-optimal solution via an implicitly
restarted Lanczos method. One of the advantages of our algorithm is that the
proposed index of each community in the clustering result is guaranteed to be
higher than some predetermined threshold, $p$, which is completely controlled
by users. We also account for the situation that $p$ is unknown. A statistical
procedure of controlling both under-clustering and over-clustering errors
simultaneously is carried out to select localized threshold for each
subnetwork, such that the community detection accuracy is optimized.
Accordingly, we propose a localized clustering algorithm based on binary tree
structure. Finally, we exploit the stochastic blockmodels to conduct simulation
studies and demonstrate the accuracy and efficiency of our algorithms, both
numerically and graphically.
",1,0,0,1,0,0
18575,Fermionic Matrix Product States and One-Dimensional Short-Range Entangled Phases with Anti-Unitary Symmetries,"  We extend the formalism of Matrix Product States (MPS) to describe
one-dimensional gapped systems of fermions with both unitary and anti-unitary
symmetries. Additionally, systems with orientation-reversing spatial symmetries
are considered. The short-ranged entangled phases of such systems are
classified by three invariants, which characterize the projective action of the
symmetry on edge states. We give interpretations of these invariants as
properties of states on the closed chain. The relationship between fermionic
MPS systems at an RG fixed point and equivariant algebras is exploited to
derive a group law for the stacking of fermionic phases. The result generalizes
known classifications to symmetry groups that are non-trivial extensions of
fermion parity and time-reversal.
",0,1,0,0,0,0
18576,How tracer particles sample the complexity of turbulence,"  On their roller coaster ride through turbulence, tracer particles sample the
fluctuations of the underlying fields in space and time. Quantitatively
relating particle and field statistics remains a fundamental challenge in a
large variety of turbulent flows. We quantify how tracer particles sample
turbulence by expressing their temporal velocity fluctuations in terms of an
effective probabilistic sampling of spatial velocity field fluctuations. To
corroborate our theory, we investigate an extensive suite of direct numerical
simulations of hydrodynamic turbulence covering a Taylor-scale Reynolds number
range from 150 to 430. Our approach allows the assessment of particle
statistics from the knowledge of flow field statistics only, therefore opening
avenues to a new generation of models for transport in complex flows.
",0,1,0,0,0,0
18577,Rare Nash Equilibria and the Price of Anarchy in Large Static Games,"  We study a static game played by a finite number of agents, in which agents
are assigned independent and identically distributed random types and each
agent minimizes its objective function by choosing from a set of admissible
actions that depends on its type. The game is anonymous in the sense that the
objective function of each agent depends on the actions of other agents only
through the empirical distribution of their type-action pairs. We study the
asymptotic behavior of Nash equilibria, as the number of agents tends to
infinity, first by deriving laws of large numbers characterizes almost sure
limit points of Nash equilibria in terms of so-called Cournot-Nash equilibria
of an associated nonatomic game. Our main results are large deviation
principles that characterize the probability of rare Nash equilibria and
associated conditional limit theorems describing the behavior of equilibria
conditioned on a rare event. The results cover situations when neither the
finite-player game nor the associated nonatomic game has a unique equilibrium.
In addition, we study the asymptotic behavior of the price of anarchy,
complementing existing worst-case bounds with new probabilistic bounds in the
context of congestion games, which are used to model traffic routing in
networks.
",1,0,1,0,0,0
18578,"Componentwise different tail solutions for bivariate stochastic recurrence equations -- with application to GARCH(1,1) processes --","  We study bivariate stochastic recurrence equations (SREs) motivated by
applications to GARCH(1,1) processes. If coefficient matrices of SREs have
strictly positive entries, then the Kesten result applies and it gives
solutions with regularly varying tails. Moreover, the tail indices are the same
for all coordinates. However, for applications, this framework is too
restrictive. We study SREs when coefficients are triangular matrices and prove
that the coordinates of the solution may exhibit regularly varying tails with
different indices. We also specify each tail index together with its constant.
The results are used to characterize regular variations of bivariate stationary
GARCH(1,1) processes.
",0,0,1,1,0,0
18579,Partial constraint singularities in elastic rods,"  We present a unified classical treatment of partially constrained elastic
rods. Partial constraints often entail singularities in both shapes and
reactions. Our approach encompasses both sleeve and adhesion problems, and
provides simple and unambiguous derivations of counterintuitive results in the
literature. Relationships between reaction forces and moments, geometry, and
adhesion energies follow from the balance of energy during quasistatic motion.
We also relate our approach to the balance of material momentum and the concept
of a driving traction. The theory is generalizable and can be applied to a wide
array of contact, adhesion, gripping, and locomotion problems.
",0,1,0,0,0,0
18580,Personalization in Goal-Oriented Dialog,"  The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of rules. However, incorporating personalization into such systems
is a largely unexplored topic as there are no existing corpora to facilitate
such work. In this paper, we present a new dataset of goal-oriented dialogs
which are influenced by speaker profiles attached to them. We analyze the
shortcomings of an existing end-to-end dialog system based on Memory Networks
and propose modifications to the architecture which enable personalization. We
also investigate personalization in dialog as a multi-task learning problem,
and show that a single model which shares features among various profiles
outperforms separate models for each profile.
",1,0,0,0,0,0
18581,Maximum Margin Principal Components,"  Principal Component Analysis (PCA) is a very successful dimensionality
reduction technique, widely used in predictive modeling. A key factor in its
widespread use in this domain is the fact that the projection of a dataset onto
its first $K$ principal components minimizes the sum of squared errors between
the original data and the projected data over all possible rank $K$
projections. Thus, PCA provides optimal low-rank representations of data for
least-squares linear regression under standard modeling assumptions. On the
other hand, when the loss function for a prediction problem is not the
least-squares error, PCA is typically a heuristic choice of dimensionality
reduction -- in particular for classification problems under the zero-one loss.
In this paper we target classification problems by proposing a straightforward
alternative to PCA that aims to minimize the difference in margin distribution
between the original and the projected data. Extensive experiments show that
our simple approach typically outperforms PCA on any particular dataset, in
terms of classification error, though this difference is not always
statistically significant, and despite being a filter method is frequently
competitive with Partial Least Squares (PLS) and Lasso on a wide range of
datasets.
",1,0,0,1,0,0
18582,A New self-propelled magnetic bearing with helical windings,"  In this work a design is proposed for an active, permanent magnet based,
self-propelled magnetic bearing i.e. levitating motor having the following
features : (a) simple winding structure, (b) high load supporting capacity, (c)
no eccentricity sensors, (d) stable confinement in all translational
dimensions, (e) stable confinement in all rotational dimensions and (f) high
efficiency. This design uses an architecture consisting of a helically wound
three-phase stator, and a rotor with the magnets also arranged in a helical
manner. Active control is used to excite the rotor at a torque angle lying in
the second quadrant. This torque angle is independent of the rotor's position
inside the stator cavity hence the control algorithm is similar to that of a
conventional permanent magnet synchronous motor. It is motivated through a
physical argument that the bearing rotor develops a lift force proportional to
the output torque and that it remains stably confined in space. These
assertions are then proved rigorously through a calculation of the magnetic
fields, forces and torques. The stiffness matrix of the system is presented and
a discussion of stable and unstable operating regions is given.
",0,1,0,0,0,0
18583,A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection,"  How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it really possible to see all the occlusions in a dataset?
We argue that like categories, occlusions and object deformations also follow a
long-tail. Some occlusions and deformations are so rare that they hardly
happen; yet we want to learn a model invariant to such occurrences. In this
paper, we propose an alternative solution. We propose to learn an adversarial
network that generates examples with occlusions and deformations. The goal of
the adversary is to generate examples that are difficult for the object
detector to classify. In our framework both the original detector and adversary
are learned in a joint manner. Our experimental results indicate a 2.3% mAP
boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
compared to the Fast-RCNN pipeline. We also release the code for this paper.
",1,0,0,0,0,0
18584,Non-thermalization in trapped atomic ion spin chains,"  Linear arrays of trapped and laser cooled atomic ions are a versatile
platform for studying emergent phenomena in strongly-interacting many-body
systems. Effective spins are encoded in long-lived electronic levels of each
ion and made to interact through laser mediated optical dipole forces. The
advantages of experiments with cold trapped ions, including high spatiotemporal
resolution, decoupling from the external environment, and control over the
system Hamiltonian, are used to measure quantum effects not always accessible
in natural condensed matter samples. In this review we highlight recent work
using trapped ions to explore a variety of non-ergodic phenomena in long-range
interacting spin-models which are heralded by memory of out-of-equilibrium
initial conditions. We observe long-lived memory in static magnetizations for
quenched many-body localization and prethermalization, while memory is
preserved in the periodic oscillations of a driven discrete time crystal state.
",0,1,0,0,0,0
18585,Generalized Uniformity Testing,"  In this work, we revisit the problem of uniformity testing of discrete
probability distributions. A fundamental problem in distribution testing,
testing uniformity over a known domain has been addressed over a significant
line of works, and is by now fully understood.
The complexity of deciding whether an unknown distribution is uniform over
its unknown (and arbitrary) support, however, is much less clear. Yet, this
task arises as soon as no prior knowledge on the domain is available, or
whenever the samples originate from an unknown and unstructured universe. In
this work, we introduce and study this generalized uniformity testing question,
and establish nearly tight upper and lower bound showing that -- quite
surprisingly -- its sample complexity significantly differs from the
known-domain case. Moreover, our algorithm is intrinsically adaptive, in
contrast to the overwhelming majority of known distribution testing algorithms.
",1,0,1,1,0,0
18586,"Refraction in exoplanet atmospheres: Photometric signatures, implications for transmission spectroscopy, and search in Kepler data","  Refraction deflects photons that pass through atmospheres, which affects
transit light curves. Refraction thus provides an avenue to probe physical
properties of exoplanet atmospheres and to constrain the presence of clouds and
hazes. In addition, an effective surface can be imposed by refraction, thereby
limiting the pressure levels probed by transmission spectroscopy. The main
objective of the paper is to model the effects of refraction on photometric
light curves for realistic planets and to explore the dependencies on
atmospheric physical parameters. We also explore under which circumstances
transmission spectra are significantly affected by refraction. Finally, we
search for refraction signatures in photometric residuals in Kepler data. We
use the model of Hui & Seager (2002) to compute deflection angles and
refraction transit light curves, allowing us to explore the parameter space of
atmospheric properties. The observational search is performed by stacking large
samples of transit light curves from Kepler. We find that out-of-transit
refraction shoulders are the most easily observable features, which can reach
peak amplitudes of ~10 parts per million (ppm) for planets around Sun-like
stars. More typical amplitudes are a few ppm or less for Jovians and at the
sub-ppm level for super-Earths. Interestingly, the signal-to-noise ratio of any
refraction residuals for planets orbiting Sun-like hosts are expected to be
similar for planets orbiting red dwarfs. We also find that the maximum depth
probed by transmission spectroscopy is not limited by refraction for weakly
lensing planets, but that the incidence of refraction can vary significantly
for strongly lensing planets. We find no signs of refraction features in the
stacked Kepler light curves, which is in agreement with our model predictions.
",0,1,0,0,0,0
18587,Balancing Efficiency and Coverage in Human-Robot Dialogue Collection,"  We describe a multi-phased Wizard-of-Oz approach to collecting human-robot
dialogue in a collaborative search and navigation task. The data is being used
to train an initial automated robot dialogue system to support collaborative
exploration tasks. In the first phase, a wizard freely typed robot utterances
to human participants. For the second phase, this data was used to design a GUI
that includes buttons for the most common communications, and templates for
communications with varying parameters. Comparison of the data gathered in
these phases show that the GUI enabled a faster pace of dialogue while still
maintaining high coverage of suitable responses, enabling more efficient
targeted data collection, and improvements in natural language understanding
using GUI-collected data. As a promising first step towards interactive
learning, this work shows that our approach enables the collection of useful
training data for navigation-based HRI tasks.
",1,0,0,0,0,0
18588,Reinforcement Learning Based Argument Component Detection,"  Argument component detection (ACD) is an important sub-task in argumentation
mining. ACD aims at detecting and classifying different argument components in
natural language texts. Historical annotations (HAs) are important features the
human annotators consider when they manually perform the ACD task. However, HAs
are largely ignored by existing automatic ACD techniques. Reinforcement
learning (RL) has proven to be an effective method for using HAs in some
natural language processing tasks. In this work, we propose a RL-based ACD
technique, and evaluate its performance on two well-annotated corpora. Results
suggest that, in terms of classification accuracy, HAs-augmented RL outperforms
plain RL by at most 17.85%, and outperforms the state-of-the-art supervised
learning algorithm by at most 11.94%.
",1,0,0,0,0,0
18589,VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera,"  We present VUNet, a novel view(VU) synthesis method for mobile robots in
dynamic environments, and its application to the estimation of future
traversability. Our method predicts future images for given virtual robot
velocity commands using only RGB images at previous and current time steps. The
future images result from applying two types of image changes to the previous
and current images: 1) changes caused by different camera pose, and 2) changes
due to the motion of the dynamic obstacles. We learn to predict these two types
of changes disjointly using two novel network architectures, SNet and DNet. We
combine SNet and DNet to synthesize future images that we pass to our
previously presented method GONet to estimate the traversable areas around the
robot. Our quantitative and qualitative evaluation indicate that our approach
for view synthesis predicts accurate future images in both static and dynamic
environments. We also show that these virtual images can be used to estimate
future traversability correctly. We apply our view synthesis-based
traversability estimation method to two applications for assisted
teleoperation.
",1,0,0,0,0,0
18590,Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?,"  We introduce a new class of priors for Bayesian hypothesis testing, which we
name ""cake priors"". These priors circumvent Bartlett's paradox (also called the
Jeffreys-Lindley paradox); the problem associated with the use of diffuse
priors leading to nonsensical statistical inferences. Cake priors allow the use
of diffuse priors (having one's cake) while achieving theoretically justified
inferences (eating it too). We demonstrate this methodology for Bayesian
hypotheses tests for scenarios under which the one and two sample t-tests, and
linear models are typically derived. The resulting Bayesian test statistic
takes the form of a penalized likelihood ratio test statistic. By considering
the sampling distribution under the null and alternative hypotheses we show for
independent identically distributed regular parametric models that Bayesian
hypothesis tests using cake priors are Chernoff-consistent, i.e., achieve zero
type I and II errors asymptotically. Lindley's paradox is also discussed. We
argue that a true Lindley's paradox will only occur with small probability for
large sample sizes.
",0,0,1,1,0,0
18591,On a Novel Speech Representation Using Multitapered Modified Group Delay Function,"  In this paper, a novel multitaper modified group delay function-based
representation for speech signals is proposed. With a set of phoneme-based
experiments, it is shown that the proposed method performs better that an
existing multitaper magnitude (MT-MAG) estimation technique, in terms of
variance and MSE, both in spectral- and cepstral-domains. In particular, the
performance of MT-MOGDF is found to be the best with the Thomson tapers.
Additionally, the utility of the MT-MOGDF technique is highlighted in a speaker
recognition experimental setup, where an improvement of around $20\%$ compared
to the next-best technique is obtained. Moreover, the computational
requirements of the proposed technique is comparable to that of MT-MAG. The
proposed feature can be used in for many speech-related applications; in
particular, it is best suited among those that require information of speaker
and speech.
",1,0,0,0,0,0
18592,Persistent Flows and Non-Reciprocal Interactions in Deterministic Networks,"  This paper studies deterministic consensus networks with discrete-time
dynamics under persistent flows and non-reciprocal agent interactions. An arc
describing the interaction strength between two agents is said to be persistent
if its weight function has an infinite $l_1$ norm. We discuss two balance
conditions on the interactions between agents which generalize the arc-balance
and cut-balance conditions in the literature respectively. The proposed
conditions require that such a balance should be satisfied over each time
window of a fixed length instead of at each time instant. We prove that in both
cases global consensus is reached if and only if the persistent graph, which
consists of all the persistent arcs, contains a directed spanning tree. The
convergence rates of the system to consensus are also provided in terms of the
interactions between agents having taken place. The results are obtained under
a weak condition without assuming the existence of a positive lower bound of
all the nonzero weights of arcs and are compared with the existing results.
Illustrative examples are provided to show the critical importance of the
nontrivial lower boundedness of the self-confidence of the agents.
",1,0,0,0,0,0
18593,Linking Fluid and Kinetic Scales in Solar Wind Turbulence,"  We investigate possible links between the large-scale and small-scale
features of solar wind fluctuations across the frequency break separating fluid
and kinetic regimes. The aim is to correlate the magnetic field fluctuations
polarization at dissipative scales with the particular state of turbulence
within the inertial range of fluctuations. We found clear correlations between
each type of polarization within the kinetic regime and fluid parameters within
the inertial range. Moreover, for the first time in literature, we showed that
left-handed and right-handed polarized fluctuations occupy different areas of
the plasma instabilities-temperature anisotropy plot, as expected for
Alfv$\acute{\textrm{e}}$n Ion Cyclotron and Kinetic Alfv$\acute{\textrm{e}}$n
waves, respectively.
",0,1,0,0,0,0
18594,Thresholds For Detecting An Anomalous Path From Noisy Environments,"  We consider the ""searching for a trail in a maze"" composite hypothesis
testing problem, in which one attempts to detect an anomalous directed path in
a lattice 2D box of side n based on observations on the nodes of the box. Under
the signal hypothesis, one observes independent Gaussian variables of unit
variance at all nodes, with zero, mean off the anomalous path and mean \mu_n on
it. Under the null hypothesis, one observes i.i.d. standard Gaussians on all
nodes. Arias-Castro et al. (2008) showed that if the unknown directed path
under the signal hypothesis has known the initial location, then detection is
possible (in the minimax sense) if \mu_n >> 1/\sqrt log n, while it is not
possible if \mu_n << 1/ log n\sqrt log log n. In this paper, we show that this
result continues to hold even when the initial location of the unknown path is
not known. As is the case with Arias-Castro et al. (2008), the upper bound here
also applies when the path is undirected. The improvement is achieved by
replacing the linear detection statistic used in Arias-Castro et al. (2008)
with a polynomial statistic, which is obtained by employing a multi-scale
analysis on a quadratic statistic to bootstrap its performance. Our analysis is
motivated by ideas developed in the context of the analysis of random polymers
in Lacoin (2010).
",0,0,1,1,0,0
18595,On the topology of real Bott manifolds,"  The main aim of this article is to give a necessary and sufficient condition
for a real Bott manifold to admit a spin structure and further give a
combinatorial characterization for the spin structure in terms of the
associated acyclic digraph.
",0,0,1,0,0,0
18596,Relative Error Tensor Low Rank Approximation,"  We consider relative error low rank approximation of $tensors$ with respect
to the Frobenius norm: given an order-$q$ tensor $A \in
\mathbb{R}^{\prod_{i=1}^q n_i}$, output a rank-$k$ tensor $B$ for which
$\|A-B\|_F^2 \leq (1+\epsilon)$OPT, where OPT $= \inf_{\textrm{rank-}k~A'}
\|A-A'\|_F^2$. Despite the success on obtaining relative error low rank
approximations for matrices, no such results were known for tensors. One
structural issue is that there may be no rank-$k$ tensor $A_k$ achieving the
above infinum. Another, computational issue, is that an efficient relative
error low rank approximation algorithm for tensors would allow one to compute
the rank of a tensor, which is NP-hard. We bypass these issues via (1)
bicriteria and (2) parameterized complexity solutions:
(1) We give an algorithm which outputs a rank $k' = O((k/\epsilon)^{q-1})$
tensor $B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT in $nnz(A) + n \cdot
\textrm{poly}(k/\epsilon)$ time in the real RAM model. Here $nnz(A)$ is the
number of non-zero entries in $A$.
(2) We give an algorithm for any $\delta >0$ which outputs a rank $k$ tensor
$B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT and runs in $ ( nnz(A) + n
\cdot \textrm{poly}(k/\epsilon) + \exp(k^2/\epsilon) ) \cdot n^\delta$ time in
the unit cost RAM model.
For outputting a rank-$k$ tensor, or even a bicriteria solution with
rank-$Ck$ for a certain constant $C > 1$, we show a $2^{\Omega(k^{1-o(1)})}$
time lower bound under the Exponential Time Hypothesis.
Our results give the first relative error low rank approximations for tensors
for a large number of robust error measures for which nothing was known, as
well as column row and tube subset selection. We also obtain new results for
matrices, such as $nnz(A)$-time CUR decompositions, improving previous
$nnz(A)\log n$-time algorithms, which may be of independent interest.
",1,0,0,0,0,0
18597,Bayesian Boolean Matrix Factorisation,"  Boolean matrix factorisation aims to decompose a binary data matrix into an
approximate Boolean product of two low rank, binary matrices: one containing
meaningful patterns, the other quantifying how the observations can be
expressed as a combination of these patterns. We introduce the OrMachine, a
probabilistic generative model for Boolean matrix factorisation and derive a
Metropolised Gibbs sampler that facilitates efficient parallel posterior
inference. On real world and simulated data, our method outperforms all
currently existing approaches for Boolean matrix factorisation and completion.
This is the first method to provide full posterior inference for Boolean Matrix
factorisation which is relevant in applications, e.g. for controlling false
positive rates in collaborative filtering and, crucially, improves the
interpretability of the inferred patterns. The proposed algorithm scales to
large datasets as we demonstrate by analysing single cell gene expression data
in 1.3 million mouse brain cells across 11 thousand genes on commodity
hardware.
",1,0,0,1,0,0
18598,Global existence and convergence of $Q$-curvature flow on manifolds of even dimension,"  Using a negative gradient flow approach, we generalize and unify some
existence theorems for the problem of prescribing $Q$-curvature first by Baird,
Fardoun, and Regbaoui (Calc. Var. 27 75-104) for $4$-manifolds with a possible
sign-changing curvature candidate then by Brendle (Ann. Math. 158 323-343) for
$n$-manifolds with even $n$ with positive curvature candidate to the case of
$n$-manifolds of all even dimension with sign-changing curvature candidates.
Making use of the \L ojasiewicz--Simon inequality, we also address the rate of
the convergence.
",0,0,1,0,0,0
18599,Group Embeddings with Algorithmic Properties,"  We show that every countable group H with solvable word problem (=computable
group) can be subnormally embedded into a 2-generated group G which also has
solvable word problem. Moreover, the membership problem for H < G is also
solvable. We also give estimates of time and space complexity of the word
problem in G and of the membership problem for H < G.
",0,0,1,0,0,0
18600,General purpose graphics-processing-unit implementation of cosmological domain wall network evolution,"  Topological defects unavoidably form at symmetry breaking phase transitions
in the early Universe. To probe the parameter space of theoretical models and
set tighter experimental constraints (exploiting the recent advances in
astrophysical observations), one requires more and more demanding simulations,
and therefore more hardware resources and computation time. Improving the speed
and efficiency of existing codes is essential. Here we present a General
Purpose Graphics Processing Unit implementation of the canonical
Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall
networks. This is ported to the Open Computing Language standard, and as a
consequence significant speed-ups are achieved both in 2D and 3D simulations.
",0,1,0,0,0,0
18601,A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures,"  We formulate, and present a numerical method for solving, an inverse problem
for inferring parameters of a deterministic model from stochastic observational
data (quantities of interest). The solution, given as a probability measure, is
derived using a Bayesian updating approach for measurable maps that finds a
posterior probability measure, that when propagated through the deterministic
model produces a push-forward measure that exactly matches the observed
probability measure on the data. Our approach for finding such posterior
measures, which we call consistent Bayesian inference, is simple and only
requires the computation of the push-forward probability measure induced by the
combination of a prior probability measure and the deterministic model. We
establish existence and uniqueness of observation-consistent posteriors and
present stability and error analysis. We also discuss the relationships between
consistent Bayesian inference, classical/statistical Bayesian inference, and a
recently developed measure-theoretic approach for inference. Finally,
analytical and numerical results are presented to highlight certain properties
of the consistent Bayesian approach and the differences between this approach
and the two aforementioned alternatives for inference.
",0,0,1,1,0,0
18602,Coherent State Mapping Ring-Polymer Molecular Dynamics for Non-Adiabatic quantum propagations,"  We introduce the coherent state mapping ring-polymer molecular dynamics
(CS-RPMD), a new method that accurately describes electronic non-adiabatic
dynamics with explicit nuclear quantization. This new approach is derived by
using coherent state mapping representation for the electronic degrees of
freedom (DOF) and the ring-polymer path-integral representation for the nuclear
DOF. CS-RPMD Hamiltonian does not contain any inter-bead coupling term in the
state-dependent potential, which is a key feature that ensures correct
electronic Rabi oscillations. Hamilton's equation of motion is used to sample
initial configurations and propagate the trajectories, preserving the
distribution with classical symplectic evolution. In the special one-bead limit
for mapping variables, CS-RPMD preserves the detailed balance. Numerical tests
of this method with a two-state model system show a very good agreement with
exact quantum results over a broad range of electronic couplings.
",0,1,0,0,0,0
18603,Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem,"  Stochastic gradient descent in continuous time (SGDCT) provides a
computationally efficient method for the statistical learning of
continuous-time models, which are widely used in science, engineering, and
finance. The SGDCT algorithm follows a (noisy) descent direction along a
continuous stream of data. The parameter updates occur in continuous time and
satisfy a stochastic differential equation. This paper analyzes the asymptotic
convergence rate of the SGDCT algorithm by proving a central limit theorem
(CLT) for strongly convex objective functions and, under slightly stronger
conditions, for non-convex objective functions as well. An L$^p$ convergence
rate is also proven for the algorithm in the strongly convex case. The
mathematical analysis lies at the intersection of stochastic analysis and
statistical learning.
",0,0,1,1,0,0
18604,KMS states on $C^*$-algebras associated to a family of $*$-commuting local homeomorphisms,"  We consider a family of $*$-commuting local homeomorphisms on a compact
space, and build a compactly aligned product system of Hilbert bimodules (in
the sense of Fowler). This product system has a Nica-Toeplitz algebra and a
Cuntz-Pimsner algebra. Both algebras carry a gauge action of a
higher-dimensional torus, and there are many possible dynamics obtained by
composing with different embeddings of the real line in this torus. We study
the KMS states of these dynamics. For large inverse temperatures, we describe
the simplex of KMS states on the Nica-Toeplitz algebra. To study KMS states for
smaller inverse temperature, we consider a preferred dynamics for which there
is a single critical inverse temperature. We find a KMS state on the
Nica-Toeplitz algebra at this critical inverse temperature which factors
through the Cuntz-Pimsner algebra. We then illustrate our results by
considering backward shifts on the infinite-path spaces of a class of
$k$-graphs.
",0,0,1,0,0,0
18605,Multi-Labelled Value Networks for Computer Go,"  This paper proposes a new approach to a novel value network architecture for
the game Go, called a multi-labelled (ML) value network. In the ML value
network, different values (win rates) are trained simultaneously for different
settings of komi, a compensation given to balance the initiative of playing
first. The ML value network has three advantages, (a) it outputs values for
different komi, (b) it supports dynamic komi, and (c) it lowers the mean
squared error (MSE). This paper also proposes a new dynamic komi method to
improve game-playing strength. This paper also performs experiments to
demonstrate the merits of the architecture. First, the MSE of the ML value
network is generally lower than the value network alone. Second, the program
based on the ML value network wins by a rate of 67.6% against the program based
on the value network alone. Third, the program with the proposed dynamic komi
method significantly improves the playing strength over the baseline that does
not use dynamic komi, especially for handicap games. To our knowledge, up to
date, no handicap games have been played openly by programs using value
networks. This paper provides these programs with a useful approach to playing
handicap games.
",1,0,0,0,0,0
18606,Lifting high-dimensional nonlinear models with Gaussian regressors,"  We study the problem of recovering a structured signal $\mathbf{x}_0$ from
high-dimensional data $\mathbf{y}_i=f(\mathbf{a}_i^T\mathbf{x}_0)$ for some
nonlinear (and potentially unknown) link function $f$, when the regressors
$\mathbf{a}_i$ are iid Gaussian. Brillinger (1982) showed that ordinary
least-squares estimates $\mathbf{x}_0$ up to a constant of proportionality
$\mu_\ell$, which depends on $f$. Recently, Plan & Vershynin (2015) extended
this result to the high-dimensional setting deriving sharp error bounds for the
generalized Lasso. Unfortunately, both least-squares and the Lasso fail to
recover $\mathbf{x}_0$ when $\mu_\ell=0$. For example, this includes all even
link functions. We resolve this issue by proposing and analyzing an alternative
convex recovery method. In a nutshell, our method treats such link functions as
if they were linear in a lifted space of higher-dimension. Interestingly, our
error analysis captures the effect of both the nonlinearity and the problem's
geometry in a few simple summary parameters.
",0,0,0,1,0,0
18607,Functional Conceptual Substratum as a New Cognitive Mechanism for Mathematical Creation,"  We describe a new cognitive ability, i.e., functional conceptual substratum,
used implicitly in the generation of several mathematical proofs and
definitions. Furthermore, we present an initial (first-order) formalization of
this mechanism together with its relation to classic notions like primitive
positive definability and Diophantiveness. Additionally, we analyze the
semantic variability of functional conceptual substratum when small syntactic
modifications are done. Finally, we describe mathematically natural inference
rules for definitions inspired by functional conceptual substratum and show
that they are sound and complete w.r.t. standard calculi.
",0,0,1,0,0,0
18608,Applying Gromov's Amenable Localization to Geodesic Flows,"  Let $M$ be a compact connected smooth Riemannian $n$-manifold with boundary.
We combine Gromov's amenable localization technique with the Poincaré
duality to study the traversally generic geodesic flows on $SM$, the space of
the spherical tangent bundle. Such flows generate stratifications of $SM$,
governed by rich universal combinatorics. The stratification reflects the ways
in which the geodesic flow trajectories interact with the boundary $\d(SM)$.
Specifically, we get lower estimates of the numbers of connected components of
these flow-generated strata of any given codimension $k$. These universal
bounds are expressed in terms of the normed homology $H_k(M; \R)$ and $H_k(DM;
\R)$, where $DM = M\cup_{\d M} M$ denotes the double of $M$. The norms here are
the Gromov simplicial semi-norms in homology. The more complex the metric on
$M$ is, the more numerous the strata of $SM$ and $S(DM)$ are. So one may regard
our estimates as analogues of the Morse inequalities for the geodesics on
manifolds with boundary.
It turns out that some close relatives of the normed homology spaces form
obstructions to the existence of globally $k$-convex traversally generic
metrics on $M$.
",0,0,1,0,0,0
18609,Modeling the Ellsberg Paradox by Argument Strength,"  We present a formal measure of argument strength, which combines the ideas
that conclusions of strong arguments are (i) highly probable and (ii) their
uncertainty is relatively precise. Likewise, arguments are weak when their
conclusion probability is low or when it is highly imprecise. We show how the
proposed measure provides a new model of the Ellsberg paradox. Moreover, we
further substantiate the psychological plausibility of our approach by an
experiment (N = 60). The data show that the proposed measure predicts human
inferences in the original Ellsberg task and in corresponding argument strength
tasks. Finally, we report qualitative data taken from structured interviews on
folk psychological conceptions on what argument strength means.
",1,0,1,0,0,0
18610,"Correction to the paper ""Some remarks on Davie's uniqueness theorem""","  The property 4 in Proposition 2.3 from the paper ""Some remarks on Davie's
uniqueness theorem"" is replaced with a weaker assertion which is sufficient for
the proof of the main results. Technical details and improvements are given.
",0,0,1,0,0,0
18611,An Estimation and Analysis Framework for the Rasch Model,"  The Rasch model is widely used for item response analysis in applications
ranging from recommender systems to psychology, education, and finance. While a
number of estimators have been proposed for the Rasch model over the last
decades, the available analytical performance guarantees are mostly asymptotic.
This paper provides a framework that relies on a novel linear minimum
mean-squared error (L-MMSE) estimator which enables an exact, nonasymptotic,
and closed-form analysis of the parameter estimation error under the Rasch
model. The proposed framework provides guidelines on the number of items and
responses required to attain low estimation errors in tests or surveys. We
furthermore demonstrate its efficacy on a number of real-world collaborative
filtering datasets, which reveals that the proposed L-MMSE estimator performs
on par with state-of-the-art nonlinear estimators in terms of predictive
performance.
",0,0,0,1,0,0
18612,Do planets remember how they formed?,"  One of the most directly observable features of a transiting multi-planet
system is their size-ordering when ranked in orbital separation. Kepler has
revealed a rich diversity of outcomes, from perfectly ordered systems, like
Kepler-80, to ostensibly disordered systems, like Kepler-20. Under the
hypothesis that systems are born via preferred formation pathways, one might
reasonably expect non-random size-orderings reflecting these processes.
However, subsequent dynamical evolution, often chaotic and turbulent in nature,
may erode this information and so here we ask - do systems remember how they
formed? To address this, we devise a model to define the entropy of a planetary
system's size-ordering, by first comparing differences between neighboring
planets and then extending to accommodate differences across the chain. We
derive closed-form solutions for many of the micro state occupancies and
provide public code with look-up tables to compute entropy for up to ten-planet
systems. All three proposed entropy definitions exhibit the expected property
that their credible interval increases with respect to a proxy for time. We
find that the observed Kepler multis display a highly significant deficit in
entropy compared to a randomly generated population. Incorporating a filter for
systems deemed likely to be dynamically packed, we show that this result is
robust against the possibility of missing planets too. Put together, our work
establishes that Kepler systems do indeed remember something of their younger
years and highlights the value of information theory for exoplanetary science.
",0,1,0,0,0,0
18613,Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone,"  We investigate the atmospheric dynamics of terrestrial planets in synchronous
rotation within the habitable zone of low-mass stars using the Community
Atmosphere Model (CAM). The surface temperature contrast between day and night
hemispheres decreases with an increase in incident stellar flux, which is
opposite the trend seen on gas giants. We define three dynamical regimes in
terms of the equatorial Rossby deformation radius and the Rhines length. The
slow rotation regime has a mean zonal circulation that spans from day to night
side, with both the Rossby deformation radius and the Rhines length exceeding
planetary radius, which occurs for planets around stars with effective
temperatures of 3300 K to 4500 K (rotation period > 20 days). Rapid rotators
have a mean zonal circulation that partially spans a hemisphere and with banded
cloud formation beneath the substellar point, with the Rossby deformation
radius is less than planetary radius, which occurs for planets orbiting stars
with effective temperatures of less than 3000 K (rotation period < 5 days). In
between is the Rhines rotation regime, which retains a thermally-direct
circulation from day to night side but also features midlatitude
turbulence-driven zonal jets. Rhines rotators occur for planets around stars in
the range of 3000 K to 3300 K (rotation period ~ 5 to 20 days), where the
Rhines length is greater than planetary radius but the Rossby deformation
radius is less than planetary radius. The dynamical state can be
observationally inferred from comparing the morphology of the thermal emission
phase curves of synchronously rotating planets.
",0,1,0,0,0,0
18614,Versatile Large-Area Custom-Feature van der Waals Epitaxy of Topological Insulators,"  As the focus of applied research in topological insulators (TI) evolves, the
need to synthesize large-area TI films for practical device applications takes
center stage. However, constructing scalable and adaptable processes for
high-quality TI compounds remains a challenge. To this end, a versatile van der
Waals epitaxy (vdWE) process for custom-feature Bismuth Telluro-Sulfide TI
growth and fabrication is presented, achieved through selective-area
fluorination and modification of surface free-energy on mica. The TI features
grow epitaxially in large single-crystal trigonal domains, exhibiting armchair
or zigzag crystalline edges highly oriented with the underlying mica lattice
and only two preferred domain orientations mirrored at $180^\circ$. As-grown
feature thickness dependence on lateral dimensions and denuded zones at
boundaries are observed, as explained by a semi-empirical two-species surface
migration model with robust estimates of growth parameters and elucidating the
role of selective-area surface modification. Topological surface states
contribute up to 60% of device conductance at room-temperature, indicating
excellent electronic quality. High-yield microfabrication and the adaptable
vdWE growth mechanism with readily alterable precursor and substrate
combinations, lend the process versatility to realize crystalline TI synthesis
in arbitrary shapes and arrays suitable for facile integration with processes
ranging from rapid prototyping to scalable manufacturing.
",0,1,0,0,0,0
18615,Sharp off-diagonal weighted norm estimates for the Bergman projection,"  We prove that for $1<p\le q<\infty$, $qp\geq {p'}^2$ or $p'q'\geq q^2$,
$\frac{1}{p}+\frac{1}{p'}=\frac{1}{q}+\frac{1}{q'}=1$, $$\|\omega
P_\alpha(f)\|_{L^p(\mathcal{H},y^{\alpha+(2+\alpha)(\frac{q}{p}-1)}dxdy)}\le
C_{p,q,\alpha}[\omega]_{B_{p,q,\alpha}}^{(\frac{1}{p'}+\frac{1}{q})\max\{1,\frac{p'}{q}\}}\|\omega
f\|_{L^p(\mathcal{H},y^{\alpha}dxdy)}$$ where $P_\alpha$ is the weighted
Bergman projection of the upper-half plane $\mathcal{H}$, and
$$[\omega]_{B_{p,q,\alpha}}:=\sup_{I\subset
\mathbb{R}}\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{q}dV_\alpha\right)\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{-p'}dV_\alpha\right)^{\frac{q}{p'}},$$
with $Q_I=\{z=x+iy\in \mathbb{C}: x\in I, 0<y<|I|\}$.
",0,0,1,0,0,0
18616,An Automated Scalable Framework for Distributing Radio Astronomy Processing Across Clusters and Clouds,"  The Low Frequency Array (LOFAR) radio telescope is an international aperture
synthesis radio telescope used to study the Universe at low frequencies. One of
the goals of the LOFAR telescope is to conduct deep wide-field surveys. Here we
will discuss a framework for the processing of the LOFAR Two Meter Sky Survey
(LoTSS). This survey will produce close to 50 PB of data within five years.
These data rates require processing at locations with high-speed access to the
archived data. To complete the LoTSS project, the processing software needs to
be made portable and moved to clusters with a high bandwidth connection to the
data archive. This work presents a framework that makes the LOFAR software
portable, and is used to scale out LOFAR data reduction. Previous work was
successful in preprocessing LOFAR data on a cluster of isolated nodes. This
framework builds upon it and and is currently operational. It is designed to be
portable, scalable, automated and general. This paper describes its design and
high level operation and the initial results processing LoTSS data.
",0,1,0,0,0,0
18617,Learning multiple visual domains with residual adapters,"  There is a growing interest in learning data representations that work well
for many different types of problems and data. In this paper, we look in
particular at the task of learning a single visual representation that can be
successfully utilized in the analysis of very different types of images, from
dog breeds to stop signs and digits. Inspired by recent work on learning
networks that predict the parameters of another, we develop a tunable deep
network architecture that, by means of adapter residual modules, can be steered
on the fly to diverse visual domains. Our method achieves a high degree of
parameter sharing while maintaining or even improving the accuracy of
domain-specific representations. We also introduce the Visual Decathlon
Challenge, a benchmark that evaluates the ability of representations to capture
simultaneously ten very different visual domains and measures their ability to
recognize well uniformly.
",1,0,0,1,0,0
18618,Cosmic quantum optical probing of quantum gravity through a gravitational lensLens,"  We consider the nonunitary quantum dynamics of neutral massless scalar
particles used to model photons around a massive gravitational lens. The
gravitational interaction between the lensing mass and asymptotically free
particles is described by their second-quantized scattering wavefunctions.
Remarkably, the zero-point spacetime fluctuations can induce significant
decoherence of the scattered states with spontaneous emission of gravitons,
thereby reducing the particles' coherence as well as energy. This new effect
suggests that, when photon polarizations are negligible, such quantum gravity
phenomena could lead to measurable anomalous redshift of recently studied
astrophysical lasers through a gravitational lens in the range of black holes
and galaxy clusters.
",0,1,0,0,0,0
18619,Combinatorial Miller-Hagberg Algorithm for Randomization of Dense Networks,"  We propose a slightly revised Miller-Hagberg (MH) algorithm that efficiently
generates a random network from a given expected degree sequence. The revision
was to replace the approximated edge probability between a pair of nodes with a
combinatorically calculated edge probability that better captures the
likelihood of edge presence especially where edges are dense. The computational
complexity of this combinatorial MH algorithm is still in the same order as the
original one. We evaluated the proposed algorithm through several numerical
experiments. The results demonstrated that the proposed algorithm was
particularly good at accurately representing high-degree nodes in dense,
heterogeneous networks. This algorithm may be a useful alternative of other
more established network randomization methods, given that the data are
increasingly becoming larger and denser in today's network science research.
",1,0,0,0,0,0
18620,Topological Insulators in Random Lattices,"  Our understanding of topological insulators is based on an underlying
crystalline lattice where the local electronic degrees of freedom at different
sites hybridize with each other in ways that produce nontrivial band topology,
and the search for material systems to realize such phases have been strongly
influenced by this. Here we theoretically demonstrate topological insulators in
systems with a random distribution of sites in space, i. e., a random lattice.
This is achieved by constructing hopping models on random lattices whose ground
states possess nontrivial topological nature (characterized e. g., by Bott
indices) that manifests as quantized conductances in systems with a boundary.
By tuning parameters such as the density of sites (for a given range of fermion
hopping), we can achieve transitions from trivial to topological phases. We
discuss interesting features of these transitions. In two spatial dimensions,
we show this for all five symmetry classes (A, AII, D, DIII and C) that are
known to host nontrivial topology in crystalline systems. We expect similar
physics to be realizable in any dimension and provide an explicit example of a
$Z_2$ topological insulator on a random lattice in three spatial dimensions.
Our study not only provides a deeper understanding of the topological phases of
non-interacting fermions, but also suggests new directions in the pursuit of
the laboratory realization of topological quantum matter.
",0,1,0,0,0,0
18621,DiGrad: Multi-Task Reinforcement Learning with Shared Actions,"  Most reinforcement learning algorithms are inefficient for learning multiple
tasks in complex robotic systems, where different tasks share a set of actions.
In such environments a compound policy may be learnt with shared neural network
parameters, which performs multiple tasks concurrently. However such compound
policy may get biased towards a task or the gradients from different tasks
negate each other, making the learning unstable and sometimes less data
efficient. In this paper, we propose a new approach for simultaneous training
of multiple tasks sharing a set of common actions in continuous action spaces,
which we call as DiGrad (Differential Policy Gradient). The proposed framework
is based on differential policy gradients and can accommodate multi-task
learning in a single actor-critic network. We also propose a simple heuristic
in the differential policy gradient update to further improve the learning. The
proposed architecture was tested on 8 link planar manipulator and 27 degrees of
freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2
end effectors respectively. We show that our approach supports efficient
multi-task learning in complex robotic systems, outperforming related methods
in continuous action spaces.
",1,0,0,1,0,0
18622,Narratives of Quantum Theory in the Age of Quantum Technologies,"  Quantum technologies can be presented to the public with or without
introducing a strange trait of quantum theory responsible for their
non-classical efficiency. Traditionally the message was centered on the
superposition principle, while entanglement and properties such as
contextuality have been gaining ground recently. A less theoretical approach is
focused on simple protocols that enable technological applications. It results
in a pragmatic narrative built with the help of the resource paradigm and
principle-based reconstructions. I discuss the advantages and weaknesses of
these methods. To illustrate the importance of new metaphors beyond the
Schrödinger cat, I briefly describe a non-mathematical narrative about
entanglement that conveys an idea of some of its unusual properties. If quantum
technologists are to succeed in building trust in their work, they ought to
provoke an aesthetic perception in the public commensurable with the
mathematical beauty of quantum theory experienced by the physicist. The power
of the narrative method lies in its capacity to do so.
",0,1,0,0,0,0
18623,Is Information in the Brain Represented in Continuous or Discrete Form?,"  The question of continuous-versus-discrete information representation in the
brain is a fundamental yet unresolved physiological question. Historically,
most analyses assume a continuous representation without considering the
alternative possibility of a discrete representation. Our work explores the
plausibility of both representations, and answers the question from a
communications engineering perspective. Drawing on the well-established
Shannon's communications theory, we posit that information in the brain is
represented in a discrete form. Using a computer simulation, we show that
information cannot be communicated reliably between neurons using a continuous
representation, due to the presence of noise; neural information has to be in a
discrete form. In addition, we designed 3 (human) behavioral experiments on
probability estimation and analyzed the data using a novel discrete (quantized)
model of probability. Under a discrete model of probability, two distinct
probabilities (say, 0.57 and 0.58) are treated indifferently. We found that
data from all participants were better fit to discrete models than continuous
ones. Furthermore, we re-analyzed the data from a published (human) behavioral
study on intertemporal choice using a novel discrete (quantized) model of
intertemporal choice. Under such a model, two distinct time delays (say, 16
days and 17 days) are treated indifferently. We found corroborating results,
showing that data from all participants were better fit to discrete models than
continuous ones. In summary, all results reported here support our discrete
hypothesis of information representation in the brain, which signifies a major
demarcation from the current understanding of the brain's physiology.
",0,0,0,0,1,0
18624,Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians,"  Background-Foreground classification is a fundamental well-studied problem in
computer vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
Gaussian Mixture Model (GMM) into an adaptive cascade of classifiers. This way
we achieve a good improvement in speed without compensating for accuracy. In
the training phase, we learn multiple KDEs for different durations to be used
as strong prior distribution and detect probable oscillating pixels which
usually results in misclassifications. We propose a confidence measure for the
classifier based on temporal consistency and the prior distribution. The
confidence measure thus derived is used to adapt the learning rate and the
thresholds of the model, to improve accuracy. The confidence measure is also
employed to perform temporal and spatial sampling in a principled way. We
demonstrate a speed-up factor of 5x to 10x and 17 percent average improvement
in accuracy over several standard videos.
",1,0,0,1,0,0
18625,Multilevel nested simulation for efficient risk estimation,"  We investigate the problem of computing a nested expectation of the form
$\mathbb{P}[\mathbb{E}[X|Y]
\!\geq\!0]\!=\!\mathbb{E}[\textrm{H}(\mathbb{E}[X|Y])]$ where $\textrm{H}$ is
the Heaviside function. This nested expectation appears, for example, when
estimating the probability of a large loss from a financial portfolio. We
present a method that combines the idea of using Multilevel Monte Carlo (MLMC)
for nested expectations with the idea of adaptively selecting the number of
samples in the approximation of the inner expectation, as proposed by (Broadie
et al., 2011). We propose and analyse an algorithm that adaptively selects the
number of inner samples on each MLMC level and prove that the resulting MLMC
method with adaptive sampling has an $\mathcal{O}\left(
\varepsilon^{-2}|\log\varepsilon|^2 \right)$ complexity to achieve a root
mean-squared error $\varepsilon$. The theoretical analysis is verified by
numerical experiments on a simple model problem. We also present a stochastic
root-finding algorithm that, combined with our adaptive methods, can be used to
compute other risk measures such as Value-at-Risk (VaR) and Conditional
Value-at-Risk (CVaR), with the latter being achieved with
$\mathcal{O}\left(\varepsilon^{-2}\right)$ complexity.
",0,0,0,0,0,1
18626,Construction of a relativistic Ornstein-Uhlenbeck process,"  Based on a version of Dudley's Wiener process on the mass shell in the
momentum Minkowski space of a massive point particle, a model of a relativistic
Ornstein--Uhlenbeck process is constructed by addition of a specific drift
term. The invariant distribution of this momentum process as well as other
associated processes are computed.
",0,0,1,0,0,0
18627,Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments,"  Path planning for autonomous vehicles in arbitrary environments requires a
guarantee of safety, but this can be impractical to ensure in real-time when
the vehicle is described with a high-fidelity model. To address this problem,
this paper develops a method to perform trajectory design by considering a
low-fidelity model that accounts for model mismatch. The presented method
begins by computing a conservative Forward Reachable Set (FRS) of a
high-fidelity model's trajectories produced when tracking trajectories of a
low-fidelity model over a finite time horizon. At runtime, the vehicle
intersects this FRS with obstacles in the environment to eliminate trajectories
that can lead to a collision, then selects an optimal plan from the remaining
safe set. By bounding the time for this set intersection and subsequent path
selection, this paper proves a lower bound for the FRS time horizon and sensing
horizon to guarantee safety. This method is demonstrated in simulation using a
kinematic Dubin's car as the low-fidelity model and a dynamic unicycle as the
high-fidelity model.
",1,0,0,0,0,0
18628,"A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark","  Training deep networks is expensive and time-consuming with the training
period increasing with data size and growth in model parameters. In this paper,
we provide a framework for distributed training of deep networks over a cluster
of CPUs in Apache Spark. The framework implements both Data Parallelism and
Model Parallelism making it suitable to use for deep networks which require
huge training data and model parameters which are too big to fit into the
memory of a single machine. It can be scaled easily over a cluster of cheap
commodity hardware to attain significant speedup and obtain better results
making it quite economical as compared to farm of GPUs and supercomputers. We
have proposed a new algorithm for training of deep networks for the case when
the network is partitioned across the machines (Model Parallelism) along with
detailed cost analysis and proof of convergence of the same. We have developed
implementations for Fully-Connected Feedforward Networks, Convolutional Neural
Networks, Recurrent Neural Networks and Long Short-Term Memory architectures.
We present the results of extensive simulations demonstrating the speedup and
accuracy obtained by our framework for different sizes of the data and model
parameters with variation in the number of worker cores/partitions; thereby
showing that our proposed framework can achieve significant speedup (upto 11X
for CNN) and is also quite scalable.
",1,0,0,1,0,0
18629,"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","  The electroencephalogram (EEG) provides a non-invasive, minimally
restrictive, and relatively low cost measure of mesoscale brain dynamics with
high temporal resolution. Although signals recorded in parallel by multiple,
near-adjacent EEG scalp electrode channels are highly-correlated and combine
signals from many different sources, biological and non-biological, independent
component analysis (ICA) has been shown to isolate the various source generator
processes underlying those recordings. Independent components (IC) found by ICA
decomposition can be manually inspected, selected, and interpreted, but doing
so requires both time and practice as ICs have no particular order or intrinsic
interpretations and therefore require further study of their properties.
Alternatively, sufficiently-accurate automated IC classifiers can be used to
classify ICs into broad source categories, speeding the analysis of EEG studies
with many subjects and enabling the use of ICA decomposition in near-real-time
applications. While many such classifiers have been proposed recently, this
work presents the ICLabel project comprised of (1) an IC dataset containing
spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
recordings, (2) a website for collecting crowdsourced IC labels and educating
EEG researchers and practitioners about IC interpretation, and (3) the
automated ICLabel classifier. The classifier improves upon existing methods in
two ways: by improving the accuracy of the computed label estimates and by
enhancing its computational efficiency. The ICLabel classifier outperforms or
performs comparably to the previous best publicly available method for all
measured IC categories while computing those labels ten times faster than that
classifier as shown in a rigorous comparison against all other publicly
available EEG IC classifiers.
",1,0,0,1,0,0
18630,Inference on a New Class of Sample Average Treatment Effects,"  We derive new variance formulas for inference on a general class of estimands
of causal average treatment effects in a Randomized Control Trial (RCT). We
generalize Robins (1988) and show that when the estimand of interest is the
Sample Average Treatment Effect of the Treated (SATT or SATC for controls), a
consistent variance estimator exists. Although these estimands are equal to the
Sample Average Treatment Effect (SATE) in expectation, potentially large
differences in both accuracy and coverage can occur by the change of estimand,
even asymptotically. Inference on the SATE, even using a conservative
confidence interval, provides incorrect coverage of the SATT or SATC. We derive
the variance and limiting distribution of a new and general class of
estimands---any mixing between SATT and SATC---for which the SATE is a specific
case. We demonstrate the applicability of the new theoretical results using
Monte-Carlo simulations and an empirical application with hundreds of online
experiments with an average sample size of approximately one hundred million
observations per experiment. An R package, estCI, that implements all the
proposed estimation procedures is available.
",0,0,1,1,0,0
18631,A split step Fourier/discontinuous Galerkin scheme for the Kadomtsev--Petviashvili equation,"  In this paper we propose a method to solve the Kadomtsev--Petviashvili
equation based on splitting the linear part of the equation from the nonlinear
part. The linear part is treated using FFTs, while the nonlinear part is
approximated using a semi-Lagrangian discontinuous Galerkin approach of
arbitrary order.
We demonstrate the efficiency and accuracy of the numerical method by
providing a range of numerical simulations. In particular, we find that our
approach can outperform the numerical methods considered in the literature by
up to a factor of five. Although we focus on the Kadomtsev--Petviashvili
equation in this paper, the proposed numerical scheme can be extended to a
range of related models as well.
",0,1,1,0,0,0
18632,Dense Transformer Networks,"  The key idea of current deep learning methods for dense prediction is to
apply a model on a regular patch centered on each pixel to make pixel-wise
predictions. These methods are limited in the sense that the patches are
determined by network architecture instead of learned from data. In this work,
we propose the dense transformer networks, which can learn the shapes and sizes
of patches from data. The dense transformer networks employ an encoder-decoder
architecture, and a pair of dense transformer modules are inserted into each of
the encoder and decoder paths. The novelty of this work is that we provide
technical solutions for learning the shapes and sizes of patches from data and
efficiently restoring the spatial correspondence required for dense prediction.
The proposed dense transformer modules are differentiable, thus the entire
network can be trained. We apply the proposed networks on natural and
biological image segmentation tasks and show superior performance is achieved
in comparison to baseline methods.
",1,0,0,1,0,0
18633,Tetragonal CH3NH3PbI3 Is Ferroelectric,"  Halide perovskite (HaP) semiconductors are revolutionizing photovoltaic (PV)
solar energy conversion by showing remarkable performance of solar cells made
with esp. tetragonal methylammonium lead tri-iodide (MAPbI3). In particular,
the low voltage loss of these cells implies a remarkably low recombination rate
of photogenerated carriers. It was suggested that low recombination can be due
to spatial separation of electrons and holes, a possibility if MAPbI3 is a
semiconducting ferroelectric, which, however, requires clear experimental
evidence. As a first step we show that, in operando, MAPbI3 (unlike MAPbBr3) is
pyroelectric, which implies it can be ferroelectric. The next step, proving it
is (not) ferroelectric, is challenging, because of the material s relatively
high electrical conductance (a consequence of an optical band gap suitable for
PV conversion!) and low stability under high applied bias voltage. This
excludes normal measurements of a ferroelectric hysteresis loop to prove
ferroelctricity s hallmark for switchable polarization. By adopting an approach
suitable for electrically leaky materials as MAPbI3, we show here ferroelectric
hysteresis from well-characterized single crystals at low temperature (still
within the tetragonal phase, which is the room temperature stable phase). Using
chemical etching, we also image polar domains, the structural fingerprint for
ferroelectricity, periodically stacked along the polar axis of the crystal,
which, as predicted by theory, scale with the overall crystal size. We also
succeeded in detecting clear second-harmonic generation, direct evidence for
the material s non-centrosymmetry. We note that the material s ferroelectric
nature, can, but not obviously need to be important in a PV cell, operating
around room temperature.
",0,1,0,0,0,0
18634,A Simple and Efficient MapReduce Algorithm for Data Cube Materialization,"  Data cube materialization is a classical database operator introduced in Gray
et al.~(Data Mining and Knowledge Discovery, Vol.~1), which is critical for
many analysis tasks. Nandi et al.~(Transactions on Knowledge and Data
Engineering, Vol.~6) first studied cube materialization for large scale
datasets using the MapReduce framework, and proposed a sophisticated
modification of a simple broadcast algorithm to handle a dataset with a 216GB
cube size within 25 minutes with 2k machines in 2012. We take a different
approach, and propose a simple MapReduce algorithm which (1) minimizes the
total number of copy-add operations, (2) leverages locality of computation, and
(3) balances work evenly across machines. As a result, the algorithm shows
excellent performance, and materialized a real dataset with a cube size of
35.0G tuples and 1.75T bytes in 54 minutes, with 0.4k machines in 2014.
",1,0,0,0,0,0
18635,"Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations","  The success of deep convolutional architectures is often attributed in part
to their ability to learn multiscale and invariant representations of natural
signals. However, a precise study of these properties and how they affect
learning guarantees is still missing. In this paper, we consider deep
convolutional representations of signals; we study their invariance to
translations and to more general groups of transformations, their stability to
the action of diffeomorphisms, and their ability to preserve signal
information. This analysis is carried by introducing a multilayer kernel based
on convolutional kernel networks and by studying the geometry induced by the
kernel mapping. We then characterize the corresponding reproducing kernel
Hilbert space (RKHS), showing that it contains a large class of convolutional
neural networks with homogeneous activation functions. This analysis allows us
to separate data representation from learning, and to provide a canonical
measure of model complexity, the RKHS norm, which controls both stability and
generalization of any learned model. In addition to models in the constructed
RKHS, our stability analysis also applies to convolutional networks with
generic activations such as rectified linear units, and we discuss its
relationship with recent generalization bounds based on spectral norms.
",1,0,0,1,0,0
18636,Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition,"  In this paper, we aim at the completion problem of high order tensor data
with missing entries. The existing tensor factorization and completion methods
suffer from the curse of dimensionality when the order of tensor N>>3. To
overcome this problem, we propose an efficient algorithm called TT-WOPT
(Tensor-train Weighted OPTimization) to find the latent core tensors of tensor
data and recover the missing entries. Tensor-train decomposition, which has the
powerful representation ability with linear scalability to tensor order, is
employed in our algorithm. The experimental results on synthetic data and
natural image completion demonstrate that our method significantly outperforms
the other related methods. Especially when the missing rate of data is very
high, e.g., 85% to 99%, our algorithm can achieve much better performance than
other state-of-the-art algorithms.
",1,0,0,0,0,0
18637,On the inner products of some Deligne--Lusztig type representations,"  In this paper we introduce a family of Deligne--Lusztig type varieties
attached to connected reductive groups over quotients of discrete valuation
rings, naturally generalising the higher Deligne--Lusztig varieties and some
constructions related to the algebraisation problem raised by Lusztig. We
establish the inner product formula between the representations associated to
these varieties and the higher Deligne--Lusztig representations.
",0,0,1,0,0,0
18638,An invariant for embedded Fano manifolds covered by linear spaces,"  For an embedded Fano manifold $X$, we introduce a new invariant $S_X$ related
to the dimension of covering linear spaces. The aim of this paper is to
classify Fano manifolds $X$ which have large $S_X$.
",0,0,1,0,0,0
18639,Delivery Latency Trade-Offs of Heterogeneous Contents in Fog Radio Access Networks,"  A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables
content delivery via the caching of popular content at edge nodes (ENs) and
cloud processing. The existing information-theoretic analyses of F-RAN systems,
and special cases thereof, make the assumption that all requests should be
guaranteed the same delivery latency, which results in identical latency for
all files in the content library. In practice, however, contents may have
heterogeneous timeliness requirements depending on the applications that
operate on them. Given per-EN cache capacity constraint, there exists a
fundamental trade-off among the delivery latencies of different users'
requests, since contents that are allocated more cache space generally enjoy
lower delivery latencies. For the case with two ENs and two users, the optimal
latency trade-off is characterized in the high-SNR regime in terms of the
Normalized Delivery Time (NDT) metric. The main results are illustrated by
numerical examples.
",1,0,0,0,0,0
18640,Leaking Uninitialized Secure Enclave Memory via Structure Padding (Extended Abstract),"  Intel software guard extensions (SGX) aims to provide an isolated execution
environment, known as an enclave, for a user-level process to maximize its
confidentiality and integrity. In this paper, we study how uninitialized data
inside a secure enclave can be leaked via structure padding. We found that,
during ECALL and OCALL, proxy functions that are automatically generated by the
Intel SGX Software Development Kit (SDK) fully copy structure variables from an
enclave to the normal memory to return the result of an ECALL function and to
pass input parameters to an OCALL function. If the structure variables contain
padding bytes, uninitialized enclave memory, which might contain confidential
data like a private key, can be copied to the normal memory through the padding
bytes. We also consider potential countermeasures against these security
threats.
",1,0,0,0,0,0
18641,Asymptotic and numerical analysis of a stochastic PDE model of volume transmission,"  Volume transmission is an important neural communication pathway in which
neurons in one brain region influence the neurotransmitter concentration in the
extracellular space of a distant brain region. In this paper, we apply
asymptotic analysis to a stochastic partial differential equation model of
volume transmission to calculate the neurotransmitter concentration in the
extracellular space. Our model involves the diffusion equation in a
three-dimensional domain with interior holes that randomly switch between being
either sources or sinks. These holes model nerve varicosities that alternate
between releasing and absorbing neurotransmitter, according to when they fire
action potentials. In the case that the holes are small, we compute
analytically the first two nonzero terms in an asymptotic expansion of the
average neurotransmitter concentration. The first term shows that the
concentration is spatially constant to leading order and that this constant is
independent of many details in the problem. Specifically, this constant first
term is independent of the number and location of nerve varicosities, neural
firing correlations, and the size and geometry of the extracellular space. The
second term shows how these factors affect the concentration at second order.
Interestingly, the second term is also spatially constant under some mild
assumptions. We verify our asymptotic results by high-order numerical
simulation using radial basis function-generated finite differences.
",0,0,0,0,1,0
18642,Mixtures of Hidden Truncation Hyperbolic Factor Analyzers,"  The mixture of factor analyzers model was first introduced over 20 years ago
and, in the meantime, has been extended to several non-Gaussian analogues. In
general, these analogues account for situations with heavy tailed and/or skewed
clusters. An approach is introduced that unifies many of these approaches into
one very general model: the mixture of hidden truncation hyperbolic factor
analyzers (MHTHFA) model. In the process of doing this, a hidden truncation
hyperbolic factor analysis model is also introduced. The MHTHFA model is
illustrated for clustering as well as semi-supervised classification using two
real datasets.
",0,0,0,1,0,0
18643,"Representations on Partially Holomorphic Cohomology Spaces, Revisited","  This is a semi--expository update and rewrite of my 1974 AMS AMS Memoir
describing Plancherel formulae and partial Dolbeault cohomology realizations
for standard tempered representations for general real reductive Lie groups.
Even after so many years, much of that Memoir is up to date, but of course
there have been a number of refinements, advances and new developments, most of
which have applied to smaller classes of real reductive Lie groups. Here we
rewrite that AMS Memoir in in view of these advances and indicate the ties with
some of the more recent (or at least less classical) approaches to geometric
realization of unitary representations.
",0,0,1,0,0,0
18644,Underground tests of quantum mechanics. Whispers in the cosmic silence?,"  By performing X-rays measurements in the ""cosmic silence"" of the underground
laboratory of Gran Sasso, LNGS-INFN, we test a basic principle of quantum
mechanics: the Pauli Exclusion Principle (PEP), for electrons. We present the
achieved results of the VIP experiment and the ongoing VIP2 measurement aiming
to gain two orders of magnitude improvement in testing PEP. We also use a
similar experimental technique to search for radiation (X and gamma) predicted
by continuous spontaneous localization models, which aim to solve the
""measurement problem"".
",0,1,0,0,0,0
18645,Implications for Post-Processing Nucleosynthesis of Core-Collapse Supernova Models with Lagrangian Particles,"  We investigate core-collapse supernova (CCSN) nucleosynthesis with
self-consistent, axisymmetric (2D) simulations performed using the
radiation-hydrodynamics code Chimera. Computational costs have traditionally
constrained the evolution of the nuclear composition within multidimensional
CCSN models to, at best, a 14-species $\alpha$-network capable of tracking only
$(\alpha,\gamma)$ reactions from $^{4}$He to $^{60}$Zn. Such a simplified
network limits the ability to accurately evolve detailed composition and
neutronization or calculate the nuclear energy generation rate. Lagrangian
tracer particles are commonly used to extend the nuclear network evolution by
incorporating more realistic networks in post-processing nucleosynthesis
calculations. However, limitations such as poor spatial resolution of the
tracer particles, inconsistent thermodynamic evolution, including misestimation
of expansion timescales, and uncertain determination of the multidimensional
mass-cut at the end of the simulation impose uncertainties inherent to this
approach. We present a detailed analysis of the impact of such uncertainties
for four self-consistent axisymmetric CCSN models initiated from stellar
metallicity, non-rotating progenitors of 12 $M_\odot$, 15 $M_\odot$, 20
$M_\odot$, and 25 $M_\odot$ and evolved with the smaller $\alpha$-network to
more than 1 s after the launch of an explosion.
",0,1,0,0,0,0
18646,High Performance Parallel Image Reconstruction for New Vacuum Solar Telescope,"  Many technologies have been developed to help improve spatial resolution of
observational images for ground-based solar telescopes, such as adaptive optics
(AO) systems and post-processing reconstruction. As any AO system correction is
only partial, it is indispensable to use post-processing reconstruction
techniques. In the New Vacuum Solar Telescope (NVST), speckle masking method is
used to achieve the diffraction limited resolution of the telescope. Although
the method is very promising, the computation is quite intensive, and the
amount of data is tremendous, requiring several months to reconstruct
observational data of one day on a high-end computer. To accelerate image
reconstruction, we parallelize the program package on a high performance
cluster. We describe parallel implementation details for several reconstruction
procedures. The code is written in C language using Message Passing Interface
(MPI) and optimized for parallel processing in a multi-processor environment.
We show the excellent performance of parallel implementation, and the whole
data processing speed is about 71 times faster than before. Finally, we analyze
the scalability of the code to find possible bottlenecks, and propose several
ways to further improve the parallel performance. We conclude that the
presented program is capable of executing in real-time reconstruction
applications at NVST.
",0,1,0,0,0,0
18647,Approximation of Bandwidth for the Interactive Operation in Video on Demand System,"  An interactive session of video-on-demand (VOD) streaming procedure deserves
smooth data transportation for the viewer, irrespective of their geographic
location. To access the required video, bandwidth management during the video
objects transportation at any interactive session is a mandatory prerequisite.
It has been observed in the domain likes movie on demand, electronic
encyclopedia, interactive games, and educational resources. The required data
is imported from the distributed storage servers through the high speed
backbone network. This paper presents the viewer driven session based
multi-user model with respect to the overlay mesh network. In virtue of
reality, the direct implication of this work elaborately shows the required
bandwidth is a causal part in the video on demand system. The analytic model of
session based single viewer bandwidth requirement model presents the bandwidth
requirement for any interactive session like, pause, move slow, rewind, skip
some number of frames, or move fast with some constant number of frames. This
work presents the bandwidth requirement model for any interactive session that
brings the trade-off in data-transportation and storage costs for different
system resources and also for the various system configurations.
",1,0,0,0,0,0
18648,Twisted Recurrence via Polynomial Walks,"  In this paper we show how polynomial walks can be used to establish a twisted
recurrence for sets of positive density in $\mathbb{Z}^d$. In particular, we
prove that if $\Gamma \leq \operatorname{GL}_d(\mathbb{Z})$ is finitely
generated by unipotents and acts irreducibly on $\mathbb{R}^d$, then for any
set $B \subset \mathbb{Z}^d$ of positive density, there exists $k \geq 1$ such
that for any $v \in k \mathbb{Z}^d$ one can find $\gamma \in \Gamma$ with
$\gamma v \in B - B$. Our method does not require the linearity of the action,
and we prove a twisted recurrence for semigroups of maps from $\mathbb{Z}^d$ to
$\mathbb{Z}^d$ satisfying some irreducibility and polynomial assumptions. As
one of the consequences, we prove a non-linear analog of Bogolubov's theorem --
for any set $B \subset \mathbb{Z}^2$ of positive density, and $p(n) \in
\mathbb{Z}[n]$, with $p(0) = 0$ and $\operatorname{deg}(p) \geq 2$, there
exists $k \geq 1$ such that $k \mathbb{Z} \subset \{ x - p(y) \, | \, (x,y) \in
B-B \}$. Unlike the previous works on twisted recurrence that used recent
results of Benoist-Quint and Bourgain-Furman-Lindenstrauss-Mozes on
equidistribution of random walks on automorphism groups of tori, our method
relies on the classical Weyl equidistribution for polynomial orbits on tori.
",0,0,1,0,0,0
18649,Well-posedness and scattering for the Boltzmann equations: Soft potential with cut-off,"  We prove the global existence of the unique mild solution for the Cauchy
problem of the cut-off Boltzmann equation for soft potential model $\gamma=2-N$
with initial data small in $L^N_{x,v}$ where $N=2,3$ is the dimension. The
proof relies on the existing inhomogeneous Strichartz estimates for the kinetic
equation by Ovcharov and convolution-like estimates for the gain term of the
Boltzmann collision operator by Alonso, Carneiro and Gamba. The global dynamics
of the solution is also characterized by showing that the small global solution
scatters with respect to the kinetic transport operator in $L^N_{x,v}$. Also
the connection between function spaces and cut-off soft potential model
$-N<\gamma<2-N$ is characterized in the local well-posedness result for the
Cauchy problem with large initial data.
",0,0,1,0,0,0
18650,Wikipedia in academia as a teaching tool: from averse to proactive faculty profiles,"  This study concerned the active use of Wikipedia as a teaching tool in the
classroom in higher education, trying to identify different usage profiles and
their characterization. A questionnaire survey was administrated to all
full-time and part-time teachers at the Universitat Oberta de Catalunya and the
Universitat Pompeu Fabra, both in Barcelona, Spain. The questionnaire was
designed using the Technology Acceptance Model as a reference, including items
about teachers web 2.0 profile, Wikipedia usage, expertise, perceived
usefulness, easiness of use, visibility and quality, as well as Wikipedia
status among colleagues and incentives to use it more actively. Clustering and
statistical analysis were carried out using the k-medoids algorithm and
differences between clusters were assessed by means of contingency tables and
generalized linear models (logit). The respondents were classified in four
clusters, from less to more likely to adopt and use Wikipedia in the classroom,
namely averse (25.4%), reluctant (17.9%), open (29.5%) and proactive (27.2%).
Proactive faculty are mostly men teaching part-time in STEM fields, mainly
engineering, while averse faculty are mostly women teaching full-time in
non-STEM fields. Nevertheless, questionnaire items related to visibility,
quality, image, usefulness and expertise determine the main differences between
clusters, rather than age, gender or domain. Clusters involving a positive view
of Wikipedia and at least some frequency of use clearly outnumber those with a
strictly negative stance. This goes against the common view that faculty
members are mostly sceptical about Wikipedia. Environmental factors such as
academic culture and colleagues opinion are more important than faculty
personal characteristics, especially with respect to what they think about
Wikipedia quality.
",1,0,0,0,0,0
18651,A trans-disciplinary review of deep learning research for water resources scientists,"  Deep learning (DL), a new-generation of artificial neural network research,
has transformed industries, daily lives and various scientific disciplines in
recent years. DL represents significant progress in the ability of neural
networks to automatically engineer problem-relevant features and capture highly
complex data distributions. I argue that DL can help address several major new
and old challenges facing research in water sciences such as
inter-disciplinarity, data discoverability, hydrologic scaling, equifinality,
and needs for parameter regionalization. This review paper is intended to
provide water resources scientists and hydrologists in particular with a simple
technical overview, trans-disciplinary progress update, and a source of
inspiration about the relevance of DL to water. The review reveals that various
physical and geoscientific disciplines have utilized DL to address data
challenges, improve efficiency, and gain scientific insights. DL is especially
suited for information extraction from image-like data and sequential data.
Techniques and experiences presented in other disciplines are of high relevance
to water research. Meanwhile, less noticed is that DL may also serve as a
scientific exploratory tool. A new area termed 'AI neuroscience,' where
scientists interpret the decision process of deep networks and derive insights,
has been born. This budding sub-discipline has demonstrated methods including
correlation-based analysis, inversion of network-extracted features,
reduced-order approximations by interpretable models, and attribution of
network decisions to inputs. Moreover, DL can also use data to condition
neurons that mimic problem-specific fundamental organizing units, thus
revealing emergent behaviors of these units. Vast opportunities exist for DL to
propel advances in water sciences.
",1,0,0,1,0,0
18652,A new NS3 Implementation of CCNx 1.0 Protocol,"  The ccns3Sim project is an open source implementation of the CCNx 1.0
protocols for the NS3 simulator. We describe the implementation and several
important features including modularity and process delay simulation. The
ccns3Sim implementation is a fresh NS3-specific implementation. Like NS3
itself, it uses C++98 standard, NS3 code style, NS3 smart pointers, NS3 xUnit,
and integrates with the NS3 documentation and manual. A user or developer does
not need to learn two systems. If one knows NS3, one should be able to get
started with the CCNx code right away. A developer can easily use their own
implementation of the layer 3 protocol, layer 4 protocol, forwarder, routing
protocol, Pending Interest Table (PIT) or Forwarding Information Base (FIB) or
Content Store (CS). A user may configure or specify a new implementation for
any of these features at runtime in the simulation script. In this paper, we
describe the software architecture and give examples of using the simulator. We
evaluate the implementation with several example experiments on ICN caching.
",1,0,0,0,0,0
18653,MultiRefactor: Automated Refactoring To Improve Software Quality,"  In this paper, a new approach is proposed for automated software maintenance.
The tool is able to perform 26 different refactorings. It also contains a large
selection of metrics to measure the impact of the refactorings on the software
and six different search based optimization algorithms to improve the software.
This tool contains both mono-objective and multi-objective search techniques
for software improvement and is fully automated. The paper describes the
various capabilities of the tool, the unique aspects of it, and also presents
some research results from experimentation. The individual metrics are tested
across five different codebases to deduce the most effective metrics for
general quality improvement. It is found that the metrics that relate to more
specific elements of the code are more useful for driving change in the search.
The mono-objective genetic algorithm is also tested against the multi-objective
algorithm to see how comparable the results gained are with three separate
objectives. When comparing the best solutions of each individual objective the
multi-objective approach generates suitable improvements in quality in less
time, allowing for rapid maintenance cycles.
",1,0,0,0,0,0
18654,Learning Postural Synergies for Categorical Grasping through Shape Space Registration,"  Every time a person encounters an object with a given degree of familiarity,
he/she immediately knows how to grasp it. Adaptation of the movement of the
hand according to the object geometry happens effortlessly because of the
accumulated knowledge of previous experiences grasping similar objects. In this
paper, we present a novel method for inferring grasp configurations based on
the object shape. Grasping knowledge is gathered in a synergy space of the
robotic hand built by following a human grasping taxonomy. The synergy space is
constructed through human demonstrations employing a exoskeleton that provides
force feedback, which provides the advantage of evaluating the quality of the
grasp. The shape descriptor is obtained by means of a categorical non-rigid
registration that encodes typical intra-class variations. This approach is
especially suitable for on-line scenarios where only a portion of the object's
surface is observable. This method is demonstrated through simulation and real
robot experiments by grasping objects never seen before by the robot.
",1,0,0,0,0,0
18655,Perturbations of self-adjoint operators in semifinite von Neumann algebras: Kato-Rosenblum theorem,"  In the paper, we prove an analogue of the Kato-Rosenblum theorem in a
semifinite von Neumann algebra. Let $\mathcal{M}$ be a countably decomposable,
properly infinite, semifinite von Neumann algebra acting on a Hilbert space
$\mathcal{H}$ and let $\tau$ be a faithful normal semifinite tracial weight of
$\mathcal M$. Suppose that $H$ and $H_1$ are self-adjoint operators affiliated
with $\mathcal{M}$. We show that if $H-H_1$ is in $\mathcal{M}\cap
L^{1}\left(\mathcal{M},\tau\right)$, then the ${norm}$ absolutely continuous
parts of $H$ and $H_1$ are unitarily equivalent. This implies that the real
part of a non-normal hyponormal operator in $\mathcal M$ is not a perturbation
by $\mathcal{M}\cap L^{1}\left(\mathcal{M},\tau\right)$ of a diagonal operator.
Meanwhile, for $n\ge 2$ and $1\leq p<n$, by modifying Voiculescu's invariant we
give examples of commuting $n$-tuples of self-adjoint operators in
$\mathcal{M}$ that are not arbitrarily small perturbations of commuting
diagonal operators modulo $\mathcal{M}\cap L^{p}\left(\mathcal{M},\tau\right)$.
",0,0,1,0,0,0
18656,Dust radiative transfer modelling of the infrared ring around the magnetar SGR 1900$+$14,"  A peculiar infrared ring-like structure was discovered by {\em Spitzer}
around the strongly magnetised neutron star SGR 1900$+$14. This infrared
structure was suggested to be due to a dust-free cavity, produced by the SGR
Giant Flare occurred in 1998, and kept illuminated by surrounding stars. Using
a 3D dust radiative transfer code, we aimed at reproducing the emission
morphology and the integrated emission flux of this structure assuming
different spatial distributions and densities for the dust, and different
positions for the illuminating stars. We found that a dust-free ellipsoidal
cavity can reproduce the shape, flux, and spectrum of the ring-like infrared
emission, provided that the illuminating stars are inside the cavity and that
the interstellar medium has high gas density ($n_H\sim$1000 cm$^{-3}$). We
further constrain the emitting region to have a sharp inner boundary and to be
significantly extended in the radial direction, possibly even just a cavity in
a smooth molecular cloud. We discuss possible scenarios for the formation of
the dustless cavity and the particular geometry that allows it to be IR-bright.
",0,1,0,0,0,0
18657,Application of transfer matrix and transfer function analysis to grating-type dielectric laser accelerators: ponderomotive focusing of electrons,"  The question of suitability of transfer matrix description of electrons
traversing grating-type dielectric laser acceleration (DLA) structures is
addressed. It is shown that although matrix considerations lead to interesting
insights, the basic transfer properties of DLA cells cannot be described by a
matrix. A more general notion of a transfer function is shown to be a simple
and useful tool for formulating problems of particle dynamics in DLA. As an
example, a focusing structure is proposed which works simultaneously for all
electron phases.
",0,1,0,0,0,0
18658,Discretization-free Knowledge Gradient Methods for Bayesian Optimization,"  This paper studies Bayesian ranking and selection (R&S) problems with
correlated prior beliefs and continuous domains, i.e. Bayesian optimization
(BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely
studied for discrete R&S problems, which sample the one-step Bayes-optimal
point. When used over continuous domains, previous work on the knowledge
gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely
on a discretized finite approximation. However, the discretization introduces
error and scales poorly as the dimension of domain grows. In this paper, we
develop a fast discretization-free knowledge gradient method for Bayesian
optimization. Our method is not restricted to the fully sequential setting, but
useful in all settings where knowledge gradient can be used over continuous
domains. We show how our method can be generalized to handle (i) batch of
points suggestion (parallel knowledge gradient); (ii) the setting where
derivative information is available in the optimization process
(derivative-enabled knowledge gradient). In numerical experiments, we
demonstrate that the discretization-free knowledge gradient method finds global
optima significantly faster than previous Bayesian optimization algorithms on
both synthetic test functions and real-world applications, especially when
function evaluations are noisy; and derivative-enabled knowledge gradient can
further improve the performances, even outperforming the gradient-based
optimizer such as BFGS when derivative information is available.
",1,0,1,1,0,0
18659,Measuring the Robustness of Graph Properties,"  In this paper, we propose a perturbation framework to measure the robustness
of graph properties. Although there are already perturbation methods proposed
to tackle this problem, they are limited by the fact that the strength of the
perturbation cannot be well controlled. We firstly provide a perturbation
framework on graphs by introducing weights on the nodes, of which the magnitude
of perturbation can be easily controlled through the variance of the weights.
Meanwhile, the topology of the graphs are also preserved to avoid
uncontrollable strength in the perturbation. We then extend the measure of
robustness in the robust statistics literature to the graph properties.
",1,0,0,1,0,0
18660,"Better Software Analytics via ""DUO"": Data Mining Algorithms Using/Used-by Optimizers","  This paper claims that a new field of empirical software engineering research
and practice is emerging: data mining using/used-by optimizers for empirical
studies, or DUO. For example, data miners can generate the models that are
explored by optimizers.Also, optimizers can advise how to best adjust the
control parameters of a data miner. This combined approach acts like an agent
leaning over the shoulder of an analyst that advises ""ask this question next""
or ""ignore that problem, it is not relevant to your goals"". Further, those
agents can help us build ""better"" predictive models, where ""better"" can be
either greater predictive accuracy, or faster modeling time (which, in turn,
enables the exploration of a wider range of options). We also caution that the
era of papers that just use data miners is coming to an end. Results obtained
from an unoptimized data miner can be quickly refuted, just by applying an
optimizer to produce a different (and better performing) model. Our conclusion,
hence, is that for software analytics it is possible, useful and necessary to
combine data mining and optimization using DUO.
",1,0,0,0,0,0
18661,Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks,"  We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm.
",1,0,0,0,0,0
18662,DRYVR:Data-driven verification and compositional reasoning for automotive systems,"  We present the DRYVR framework for verifying hybrid control systems that are
described by a combination of a black-box simulator for trajectories and a
white-box transition graph specifying mode switches. The framework includes (a)
a probabilistic algorithm for learning sensitivity of the continuous
trajectories from simulation data, (b) a bounded reachability analysis
algorithm that uses the learned sensitivity, and (c) reasoning techniques based
on simulation relations and sequential composition, that enable verification of
complex systems under long switching sequences, from the reachability analysis
of a simpler system under shorter sequences. We demonstrate the utility of the
framework by verifying a suite of automotive benchmarks that include powertrain
control, automatic transmission, and several autonomous and ADAS features like
automatic emergency braking, lane-merge, and auto-passing controllers.
",1,0,0,0,0,0
18663,Deep Reinforcement Learning for Programming Language Correction,"  Novice programmers often struggle with the formal syntax of programming
languages. To assist them, we design a novel programming language correction
framework amenable to reinforcement learning. The framework allows an agent to
mimic human actions for text navigation and editing. We demonstrate that the
agent can be trained through self-exploration directly from the raw input, that
is, program text itself, without any knowledge of the formal syntax of the
programming language. We leverage expert demonstrations for one tenth of the
training data to accelerate training. The proposed technique is evaluated on
6975 erroneous C programs with typographic errors, written by students during
an introductory programming course. Our technique fixes 14% more programs and
29% more compiler error messages relative to those fixed by a state-of-the-art
tool, DeepFix, which uses a fully supervised neural machine translation
approach.
",1,0,0,0,0,0
18664,Dispersionless and multicomponent BKP hierarchies with quantum torus symmetries,"  In this article, we will construct the additional perturbative quantum torus
symmetry of the dispersionless BKP hierarchy basing on the $W_{\infty}$
infinite dimensional Lie symmetry. These results show that the complete quantum
torus symmetry is broken from the BKP hierarchy to its dispersionless
hierarchy. Further a series of additional flows of the multicomponent BKP
hierarchy will be defined and these flows constitute an $N$-folds direct
product of the positive half of the quantum torus symmetries.
",0,1,1,0,0,0
18665,A Sampling Framework for Solving Physics-driven Inverse Source Problems,"  Partial differential equations are central to describing many physical
phenomena. In many applications these phenomena are observed through a sensor
network, with the aim of inferring their underlying properties. Leveraging from
certain results in sampling and approximation theory, we present a new
framework for solving a class of inverse source problems for physical fields
governed by linear partial differential equations. Specifically, we demonstrate
that the unknown field sources can be recovered from a sequence of, so called,
generalised measurements by using multidimensional frequency estimation
techniques. Next we show that---for physics-driven fields---this sequence of
generalised measurements can be estimated by computing a linear weighted-sum of
the sensor measurements; whereby the exact weights (of the sums) correspond to
those that reproduce multidimensional exponentials, when used to linearly
combine translates of a particular prototype function related to the Green's
function of our underlying field. Explicit formulae are then derived for the
sequence of weights, that map sensor samples to the exact sequence of
generalised measurements when the Green's function satisfies the generalised
Strang-Fix condition. Otherwise, the same mapping yields a close approximation
of the generalised measurements. Based on this new framework we develop
practical, noise robust, sensor network strategies for solving the inverse
source problem, and then present numerical simulation results to verify their
performance.
",1,0,1,0,0,0
18666,An Application of $h$-principle to Manifold Calculus,"  Manifold calculus is a form of functor calculus that analyzes contravariant
functors from some categories of manifolds to topological spaces by providing
analytic approximations to them. In this paper we apply the theory of
h-principle to construct several examples of analytic functors in this sense.
We prove that the analytic approximation of the Lagrangian embeddings functor
$\mathrm{emb}_{\mathrm{Lag}}(-,N)$ is the totally real embeddings functor
$\mathrm{emb}_{\mathrm{TR}}(-,N)$. Under certain conditions we provide a
geometric construction for the homotopy fiber of $ \mathrm{emb}(M,N)
\rightarrow \mathrm{imm}(M,N)$. This construction also provides an example of a
functor which is itself empty when evaluated on most manifolds but it's
analytic approximation is almost always non-empty.
",0,0,1,0,0,0
18667,Grasping Unknown Objects in Clutter by Superquadric Representation,"  In this paper, a quick and efficient method is presented for grasping unknown
objects in clutter. The grasping method relies on real-time superquadric (SQ)
representation of partial view objects and incomplete object modelling, well
suited for unknown symmetric objects in cluttered scenarios which is followed
by optimized antipodal grasping. The incomplete object models are processed
through a mirroring algorithm that assumes symmetry to first create an
approximate complete model and then fit for SQ representation. The grasping
algorithm is designed for maximum force balance and stability, taking advantage
of the quick retrieval of dimension and surface curvature information from the
SQ parameters. The pose of the SQs with respect to the direction of gravity is
calculated and used together with the parameters of the SQs and specification
of the gripper, to select the best direction of approach and contact points.
The SQ fitting method has been tested on custom datasets containing objects in
isolation as well as in clutter. The grasping algorithm is evaluated on a PR2
and real time results are presented. Initial results indicate that though the
method is based on simplistic shape information, it outperforms other learning
based grasping algorithms that also work in clutter in terms of time-efficiency
and accuracy.
",1,0,0,0,0,0
18668,Learning to Play with Intrinsically-Motivated Self-Aware Agents,"  Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to mathematically formalize these abilities using a
neural network that implements curiosity-driven intrinsic motivation. Using a
simple but ecologically naturalistic simulated environment in which an agent
can move and interact with objects it sees, we propose a ""world-model"" network
that learns to predict the dynamic consequences of the agent's actions.
Simultaneously, we train a separate explicit ""self-model"" that allows the agent
to track the error map of its own world-model, and then uses the self-model to
adversarially challenge the developing world-model. We demonstrate that this
policy causes the agent to explore novel and informative interactions with its
environment, leading to the generation of a spectrum of complex behaviors,
including ego-motion prediction, object attention, and object gathering.
Moreover, the world-model that the agent learns supports improved performance
on object dynamics prediction, detection, localization and recognition tasks.
Taken together, our results are initial steps toward creating flexible
autonomous agents that self-supervise in complex novel physical environments.
",0,0,0,1,0,0
18669,$\ell_1$-minimization method for link flow correction,"  A computational method, based on $\ell_1$-minimization, is proposed for the
problem of link flow correction, when the available traffic flow data on many
links in a road network are inconsistent with respect to the flow conservation
law. Without extra information, the problem is generally ill-posed when a large
portion of the link sensors are unhealthy. It is possible, however, to correct
the corrupted link flows \textit{accurately} with the proposed method under a
recoverability condition if there are only a few bad sensors which are located
at certain links. We analytically identify the links that are robust to
miscounts and relate them to the geometric structure of the traffic network by
introducing the recoverability concept and an algorithm for computing it. The
recoverability condition for corrupted links is simply the associated
recoverability being greater than 1. In a more realistic setting, besides the
unhealthy link sensors, small measurement noises may be present at the other
sensors. Under the same recoverability condition, our method guarantees to give
an estimated traffic flow fairly close to the ground-truth data and leads to a
bound for the correction error. Both synthetic and real-world examples are
provided to demonstrate the effectiveness of the proposed method.
",1,1,0,0,0,0
18670,Current Flow Group Closeness Centrality for Complex Networks,"  Current flow closeness centrality (CFCC) has a better discriminating ability
than the ordinary closeness centrality based on shortest paths. In this paper,
we extend this notion to a group of vertices in a weighted graph, and then
study the problem of finding a subset $S$ of $k$ vertices to maximize its CFCC
$C(S)$, both theoretically and experimentally. We show that the problem is
NP-hard, but propose two greedy algorithms for minimizing the reciprocal of
$C(S)$ with provable guarantees using the monotoncity and supermodularity. The
first is a deterministic algorithm with an approximation factor
$(1-\frac{k}{k-1}\cdot\frac{1}{e})$ and cubic running time; while the second is
a randomized algorithm with a
$(1-\frac{k}{k-1}\cdot\frac{1}{e}-\epsilon)$-approximation and nearly-linear
running time for any $\epsilon > 0$. Extensive experiments on model and real
networks demonstrate that our algorithms are effective and efficient, with the
second algorithm being scalable to massive networks with more than a million
vertices.
",1,0,0,0,0,0
18671,Visualization of Constraint Handling Rules: Semantics and Applications,"  The work in the paper presents an animation extension ($CHR^{vis}$) to
Constraint Handling Rules (CHR). Visualizations have always helped programmers
understand data and debug programs. A picture is worth a thousand words. It can
help identify where a problem is or show how something works. It can even
illustrate a relation that was not clear otherwise. $CHR^{vis}$ aims at
embedding animation and visualization features into CHR programs. It thus
enables users, while executing programs, to have such executions animated. The
paper aims at providing the operational semantics for $CHR^{vis}$. The
correctness of $CHR^{vis}$ programs is also discussed. Some applications of the
new extension are also introduced.
",1,0,0,0,0,0
18672,Simplified Energy Landscape for Modularity Using Total Variation,"  Networks capture pairwise interactions between entities and are frequently
used in applications such as social networks, food networks, and protein
interaction networks, to name a few. Communities, cohesive groups of nodes,
often form in these applications, and identifying them gives insight into the
overall organization of the network. One common quality function used to
identify community structure is modularity. In Hu et al. [SIAM J. App. Math.,
73(6), 2013], it was shown that modularity optimization is equivalent to
minimizing a particular nonconvex total variation (TV) based functional over a
discrete domain. They solve this problem, assuming the number of communities is
known, using a Merriman, Bence, Osher (MBO) scheme.
We show that modularity optimization is equivalent to minimizing a convex
TV-based functional over a discrete domain, again, assuming the number of
communities is known. Furthermore, we show that modularity has no convex
relaxation satisfying certain natural conditions. We therefore, find a
manageable non-convex approximation using a Ginzburg Landau functional, which
provably converges to the correct energy in the limit of a certain parameter.
We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et
al. and which is 7 times faster at solving the associated diffusion equation
due to the fact that the underlying discretization is unconditionally stable.
Our numerical tests include a hyperspectral video whose associated graph has
2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper
of Hu et al.
",0,0,1,1,0,0
18673,"A Hard Look at the Neutron Stars and Accretion Disks in 4U 1636-53, GX 17+2, and 4U 1705-44 with $\emph{NuSTAR}$","  We present $\emph{NuSTAR}$ observations of neutron star (NS) low-mass X-ray
binaries: 4U 1636-53, GX 17+2, and 4U 1705-44. We observed 4U 1636-53 in the
hard state, with an Eddington fraction, $F_{\mathrm{Edd}}$, of 0.01; GX 17+2
and 4U 1705-44 were in the soft state with fractions of 0.57 and 0.10,
respectively. Each spectrum shows evidence for a relativistically broadened Fe
K$_{\alpha}$ line. Through accretion disk reflection modeling, we constrain the
radius of the inner disk in 4U 1636-53 to be $R_{in}=1.03\pm0.03$ ISCO
(innermost stable circular orbit) assuming a dimensionless spin parameter
$a_{*}=cJ/GM^{2}=0.0$, and $R_{in}=1.08\pm0.06$ ISCO for $a_{*}=0.3$ (errors
quoted at 1 $\sigma$). This value proves to be model independent. For
$a_{*}=0.3$ and $M=1.4\ M_{\odot}$, for example, $1.08\pm0.06$ ISCO translates
to a physical radius of $R=10.8\pm0.6$ km, and the neutron star would have to
be smaller than this radius (other outcomes are possible for allowed spin
parameters and masses). For GX 17+2, $R_{in}=1.00-1.04$ ISCO for $a_{*}=0.0$
and $R_{in}=1.03-1.30$ ISCO for $a_{*}=0.3$. For $a_{*}=0.3$ and $M=1.4\
M_{\odot}$, $R_{in}=1.03-1.30$ ISCO translates to $R=10.3-13.0$ km. The inner
accretion disk in 4U 1705-44 may be truncated just above the stellar surface,
perhaps by a boundary layer or magnetosphere; reflection models give a radius
of 1.46-1.64 ISCO for $a_{*}=0.0$ and 1.69-1.93 ISCO for $a_{*}=0.3$. We
discuss the implications that our results may have on the equation of state of
ultradense, cold matter and our understanding of the innermost accretion flow
onto neutron stars with low surface magnetic fields, and systematic errors
related to the reflection models and spacetime metric around less idealized
neutron stars.
",0,1,0,0,0,0
18674,Approximating Weighted Duo-Preservation in Comparative Genomics,"  Motivated by comparative genomics, Chen et al. [9] introduced the Maximum
Duo-preservation String Mapping (MDSM) problem in which we are given two
strings $s_1$ and $s_2$ from the same alphabet and the goal is to find a
mapping $\pi$ between them so as to maximize the number of duos preserved. A
duo is any two consecutive characters in a string and it is preserved in the
mapping if its two consecutive characters in $s_1$ are mapped to same two
consecutive characters in $s_2$. The MDSM problem is known to be NP-hard and
there are approximation algorithms for this problem [3, 5, 13], but all of them
consider only the ""unweighted"" version of the problem in the sense that a duo
from $s_1$ is preserved by mapping to any same duo in $s_2$ regardless of their
positions in the respective strings. However, it is well-desired in comparative
genomics to find mappings that consider preserving duos that are ""closer"" to
each other under some distance measure [19]. In this paper, we introduce a
generalized version of the problem, called the Maximum-Weight Duo-preservation
String Mapping (MWDSM) problem that captures both duos-preservation and
duos-distance measures in the sense that mapping a duo from $s_1$ to each
preserved duo in $s_2$ has a weight, indicating the ""closeness"" of the two
duos. The objective of the MWDSM problem is to find a mapping so as to maximize
the total weight of preserved duos. In this paper, we give a polynomial-time
6-approximation algorithm for this problem.
",1,0,0,0,0,0
18675,On the virtual singular braid monoid,"  We study the algebraic structures of the virtual singular braid monoid,
$VSB_n$, and the virtual singular pure braid monoid, $VSP_n$. The monoid
$VSB_n$ is the splittable extension of $VSP_n$ by the symmetric group $S_n$. We
also construct a representation of $VSB_n$.
",0,0,1,0,0,0
18676,Discrete Games in Endogenous Networks: Equilibria and Policy,"  In games of friendship links and behaviors, I propose $k$-player Nash
stability---a family of equilibria, indexed by a measure of robustness given by
the number of permitted link changes, which is (ordinally and cardinally)
ranked in a probabilistic sense. Application of the proposed framework to
adolescents' tobacco smoking and friendship decisions suggests that: (a.)
friendship networks respond to increases of tobacco prices and this response
amplifies the intended policy effect on smoking, (b.) racially desegregating
high-schools, via stimulating the social interactions of students with
different intrinsic propensity to smoke, decreases the overall smoking
prevalence, (c.) adolescents are averse to sharing friends so that there is a
rivalry for friendships, (d.) when data on individuals' friendship network is
not available, the importance of price centered policy tools is underestimated.
",1,1,0,0,0,0
18677,Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies,"  Proxies for regulatory reforms based on categorical variables are
increasingly used in empirical evaluation models. We surveyed 63 studies that
rely on such indices to analyze the effects of entry liberalization,
privatization, unbundling, and independent regulation of the electricity,
natural gas, and telecommunications sectors. We highlight methodological issues
related to the use of these proxies. Next, taking stock of the literature, we
provide practical advice for the design of the empirical strategy and discuss
the selection of control and instrumental variables to attenuate endogeneity
problems undermining identification of the effects of regulatory reforms.
",0,0,0,0,0,1
18678,Theory of ground states for classical Heisenberg spin systems II,"  We apply the theory of ground states for classical, finite, Heisenberg spin
systems previously published to a couple of spin systems that can be considered
as finite models $K_{12},\,K_{15}$ and $K_{18}$ of the AF Kagome lattice. The
model $K_{12}$ is isomorphic to the cuboctahedron. In particular, we find
three-dimensional ground states that cannot be viewed as resulting from the
well-known independent rotation of subsets of spin vectors. For a couple of
ground states with translational symmetry we calculate the corresponding wave
numbers. Finally we study the model $K_{12w}$ without boundary conditions which
exhibits new phenomena as, e.~g., two-dimensional families of three-dimensional
ground states.
",0,1,0,0,0,0
18679,Relative Property (T) for Nilpotent Subgroups,"  We show that relative Property (T) for the abelianization of a nilpotent
normal subgroup implies relative Property (T) for the subgroup itself. This and
other results are a consequence of a theorem of independent interest, which
states that if $H$ is a closed subgroup of a locally compact group $G$, and $A$
is a closed subgroup of the center of $H$, such that $A$ is normal in $G$, and
$(G/A, H/A)$ has relative Property (T), then $(G, H^{(1)})$ has relative
Property (T), where $H^{(1)}$ is the closure of the commutator subgroup of $H$.
In fact, the assumption that $A$ is in the center of $H$ can be replaced with
the weaker assumption that $A$ is abelian and every $H$-invariant finite
measure on the unitary dual of $A$ is supported on the set of fixed points.
",0,0,1,0,0,0
18680,Discriminant analysis in small and large dimensions,"  We study the distributional properties of the linear discriminant function
under the assumption of normality by comparing two groups with the same
covariance matrix but different mean vectors. A stochastic representation for
the discriminant function coefficients is derived which is then used to obtain
their asymptotic distribution under the high-dimensional asymptotic regime. We
investigate the performance of the classification analysis based on the
discriminant function in both small and large dimensions. A stochastic
representation is established which allows to compute the error rate in an
efficient way. We further compare the calculated error rate with the optimal
one obtained under the assumption that the covariance matrix and the two mean
vectors are known. Finally, we present an analytical expression of the error
rate calculated in the high-dimensional asymptotic regime. The finite-sample
properties of the derived theoretical results are assessed via an extensive
Monte Carlo study.
",0,0,1,1,0,0
18681,Sparse Bayesian Inference for Dense Semantic Mapping,"  Despite impressive advances in simultaneous localization and mapping, dense
robotic mapping remains challenging due to its inherent nature of being a
high-dimensional inference problem. In this paper, we propose a dense semantic
robotic mapping technique that exploits sparse Bayesian models, in particular,
the relevance vector machine, for high-dimensional sequential inference. The
technique is based on the principle of automatic relevance determination and
produces sparse models that use a small subset of the original dense training
set as the dominant basis. The resulting map posterior is continuous, and
queries can be made efficiently at any resolution. Moreover, the technique has
probabilistic outputs per semantic class through Bayesian inference. We
evaluate the proposed relevance vector semantic map using publicly available
benchmark datasets, NYU Depth V2 and KITTI; and the results show promising
improvements over the state-of-the-art techniques.
",1,0,0,0,0,0
18682,Singularities and Semistable Degenerations for Symplectic Topology,"  We overview our recent work defining and studying normal crossings varieties
and subvarieties in symplectic topology. This work answers a question of Gromov
on the feasibility of introducing singular (sub)varieties into symplectic
topology in the case of normal crossings singularities. It also provides a
necessary and sufficient condition for smoothing normal crossings symplectic
varieties. In addition, we explain some connections with other areas of
mathematics and discuss a few directions for further research.
",0,0,1,0,0,0
18683,One-step Local M-estimator for Integrated Jump-Diffusion Models,"  In this paper, robust nonparametric estimators, instead of local linear
estimators, are adapted for infinitesimal coefficients associated with
integrated jump-diffusion models to avoid the impact of outliers on accuracy.
Furthermore, consider the complexity of iteration of the solution for local
M-estimator, we propose the one-step local M-estimators to release the
computation burden. Under appropriate regularity conditions, we prove that
one-step local M-estimators and the fully iterative M-estimators have the same
performance in consistency and asymptotic normality. Through simulation, our
method present advantages in bias reduction, robustness and reducing
computation cost. In addition, the estimators are illustrated empirically
through stock index under different sampling frequency.
",0,0,1,1,0,0
18684,"Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations","  Numerical simulations of Einstein's field equations provide unique insights
into the physics of compact objects moving at relativistic speeds, and which
are driven by strong gravitational interactions. Numerical relativity has
played a key role to firmly establish gravitational wave astrophysics as a new
field of research, and it is now paving the way to establish whether
gravitational wave radiation emitted from compact binary mergers is accompanied
by electromagnetic and astro-particle counterparts. As numerical relativity
continues to blend in with routine gravitational wave data analyses to validate
the discovery of gravitational wave events, it is essential to develop open
source tools to streamline these studies. Motivated by our own experience as
users and developers of the open source, community software, the Einstein
Toolkit, we present an open source, Python package that is ideally suited to
monitor and post-process the data products of numerical relativity simulations,
and compute the gravitational wave strain at future null infinity in high
performance environments. We showcase the application of this new package to
post-process a large numerical relativity catalog and extract higher-order
waveform modes from numerical relativity simulations of eccentric binary black
hole mergers and neutron star mergers. This new software fills a critical void
in the arsenal of tools provided by the Einstein Toolkit Consortium to the
numerical relativity community.
",1,1,0,0,0,0
18685,Uniform confidence bands for nonparametric errors-in-variables regression,"  This paper develops a method to construct uniform confidence bands for a
nonparametric regression function where a predictor variable is subject to a
measurement error. We allow for the distribution of the measurement error to be
unknown, but assume that there is an independent sample from the measurement
error distribution. The sample from the measurement error distribution need not
be independent from the sample on response and predictor variables. The
availability of a sample from the measurement error distribution is satisfied
if, for example, either 1) validation data or 2) repeated measurements (panel
data) on the latent predictor variable with measurement errors, one of which is
symmetrically distributed, are available. The proposed confidence band builds
on the deconvolution kernel estimation and a novel application of the
multiplier (or wild) bootstrap method. We establish asymptotic validity of the
proposed confidence band under ordinary smooth measurement error densities,
showing that the proposed confidence band contains the true regression function
with probability approaching the nominal coverage probability. To the best of
our knowledge, this is the first paper to derive asymptotically valid uniform
confidence bands for nonparametric errors-in-variables regression. We also
propose a novel data-driven method to choose a bandwidth, and conduct
simulation studies to verify the finite sample performance of the proposed
confidence band. Applying our method to a combination of two empirical data
sets, we draw confidence bands for nonparametric regressions of medical costs
on the body mass index (BMI), accounting for measurement errors in BMI.
Finally, we discuss extensions of our results to specification testing, cases
with additional error-free regressors, and confidence bands for conditional
distribution functions.
",0,0,1,1,0,0
18686,Unconventional superconductivity in the BiS$_2$-based layered superconductor NdO$_{0.71}$F$_{0.29}$BiS$_2$,"  We investigate the superconducting-gap anisotropy in one of the recently
discovered BiS$_2$-based superconductors, NdO$_{0.71}$F$_{0.29}$BiS$_2$ ($T_c$
$\sim$ 5 K), using laser-based angle-resolved photoemission spectroscopy.
Whereas the previously discovered high-$T_c$ superconductors such as copper
oxides and iron-based superconductors, which are believed to have
unconventional superconducting mechanisms, have $3d$ electrons in their
conduction bands, the conduction band of BiS$_2$-based superconductors mainly
consists of Bi 6$p$ electrons, and hence the conventional superconducting
mechanism might be expected. Contrary to this expectation, we observe a
strongly anisotropic superconducting gap. This result strongly suggests that
the pairing mechanism for NdO$_{0.71}$F$_{0.29}$BiS$_2$ is unconventional one
and we attribute the observed anisotropy to competitive or cooperative multiple
paring interactions.
",0,1,0,0,0,0
18687,Low noise sensitivity analysis of Lq-minimization in oversampled systems,"  The class of Lq-regularized least squares (LQLS) are considered for
estimating a p-dimensional vector \b{eta} from its n noisy linear observations
y = X\b{eta}+w. The performance of these schemes are studied under the
high-dimensional asymptotic setting in which p grows linearly with n. In this
asymptotic setting, phase transition diagrams (PT) are often used for comparing
the performance of different estimators. Although phase transition analysis is
shown to provide useful information for compressed sensing, the fact that it
ignores the measurement noise not only limits its applicability in many
application areas, but also may lead to misunderstandings. For instance,
consider a linear regression problem in which n > p and the signal is not
exactly sparse. If the measurement noise is ignored in such systems,
regularization techniques, such as LQLS, seem to be irrelevant since even the
ordinary least squares (OLS) returns the exact solution. However, it is
well-known that if n is not much larger than p then the regularization
techniques improve the performance of OLS. In response to this limitation of PT
analysis, we consider the low-noise sensitivity analysis. We show that this
analysis framework (i) reveals the advantage of LQLS over OLS, (ii) captures
the difference between different LQLS estimators even when n > p, and (iii)
provides a fair comparison among different estimators in high signal-to-noise
ratios. As an application of this framework, we will show that under mild
conditions LASSO outperforms other LQLS even when the signal is dense. Finally,
by a simple transformation we connect our low-noise sensitivity framework to
the classical asymptotic regime in which n/p goes to infinity and characterize
how and when regularization techniques offer improvements over ordinary least
squares, and which regularizer gives the most improvement when the sample size
is large.
",0,0,1,1,0,0
18688,Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks with Channel Uncertainty,"  In this paper, we firstly exploit the inter-user interference (IUI) and
inter-cell interference (ICI) as useful references to develop a robust
transceiver design based on interference alignment for a downlink multi-user
multi-cell multiple-input multiple-output (MIMO) interference network under
channel estimation error. At transmitters, we propose a two-tier transmit
beamforming strategy, we first achieve the inner beamforming direction and
allocated power by minimizing the interference leakage as well as maximizing
the system energy efficiency, respectively. Then, for the outer beamformer
design, we develop an efficient conjugate gradient Grassmann manifold subspace
tracking algorithm to minimize the distances between the subspace spanned by
interference and the interference subspace in the time varying channel. At
receivers, we propose a practical interference alignment based on fast and
robust fast data projection method (FDPM) subspace tracking algorithm, to
achieve the receive beamformer under channel uncertainty. Numerical results
show that our proposed robust transceiver design achieves better performance
compared with some existing methods in terms of the sum rate and the energy
efficiency.
",1,0,0,0,0,0
18689,Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks,"  Inspired by the recent work of Carleo and Troyer[1], we apply machine
learning methods to quantum mechanics in this article. The radial basis
function network in a discrete basis is used as the variational wavefunction
for the ground state of a quantum system. Variational Monte Carlo(VMC)
calculations are carried out for some simple Hamiltonians. The results are in
good agreements with theoretical values. The smallest eigenvalue of a Hermitian
matrix can also be acquired using VMC calculations. Our results demonstrate
that machine learning techniques are capable of solving quantum mechanical
problems.
",0,1,0,0,0,0
18690,Scale-free networks are rare,"  A central claim in modern network science is that real-world networks are
typically ""scale free,"" meaning that the fraction of nodes with degree $k$
follows a power law, decaying like $k^{-\alpha}$, often with $2 < \alpha < 3$.
However, empirical evidence for this belief derives from a relatively small
number of real-world networks. We test the universality of scale-free structure
by applying state-of-the-art statistical tools to a large corpus of nearly 1000
network data sets drawn from social, biological, technological, and
informational sources. We fit the power-law model to each degree distribution,
test its statistical plausibility, and compare it via a likelihood ratio test
to alternative, non-scale-free models, e.g., the log-normal. Across domains, we
find that scale-free networks are rare, with only 4% exhibiting the
strongest-possible evidence of scale-free structure and 52% exhibiting the
weakest-possible evidence. Furthermore, evidence of scale-free structure is not
uniformly distributed across sources: social networks are at best weakly scale
free, while a handful of technological and biological networks can be called
strongly scale free. These results undermine the universality of scale-free
networks and reveal that real-world networks exhibit a rich structural
diversity that will likely require new ideas and mechanisms to explain.
",1,0,0,1,1,0
18691,"Enlargeability, foliations, and positive scalar curvature","  We extend the deep and important results of Lichnerowicz, Connes, and
Gromov-Lawson which relate geometry and characteristic numbers to the existence
and non-existence of metrics of positive scalar curvature (PSC). In particular,
we show: that a spin foliation with Hausdorff homotopy groupoid of an
enlargeable manifold admits no PSC metric; that any metric of PSC on such a
foliation is bounded by a multiple of the reciprocal of the foliation K-area of
the ambient manifold; and that Connes' vanishing theorem for characteristic
numbers of PSC foliations extends to a vanishing theorem for Haefliger
cohomology classes.
",0,0,1,0,0,0
18692,A simple mathematical model for unemployment: a case study in Portugal with optimal control,"  We propose a simple mathematical model for unemployment. Despite its
simpleness, we claim that the model is more realistic and useful than recent
models available in the literature. A case study with real data from Portugal
supports our claim. An optimal control problem is formulated and solved, which
provides some non-trivial and interesting conclusions.
",0,0,0,0,0,1
18693,Interpreting Deep Neural Networks Through Variable Importance,"  While the success of deep neural networks (DNNs) is well-established across a
variety of domains, our ability to explain and interpret these methods is
limited. Unlike previously proposed local methods which try to explain
particular classification decisions, we focus on global interpretability and
ask a universally applicable question: given a trained model, which features
are the most important? In the context of neural networks, a feature is rarely
important on its own, so our strategy is specifically designed to leverage
partial covariance structures and incorporate variable dependence into feature
ranking. Our methodological contributions in this paper are two-fold. First, we
propose an effect size analogue for DNNs that is appropriate for applications
with highly collinear predictors (ubiquitous in computer vision). Second, we
extend the recently proposed ""RelATive cEntrality"" (RATE) measure (Crawford et
al., 2019) to the Bayesian deep learning setting. RATE applies an information
theoretic criterion to the posterior distribution of effect sizes to assess
feature significance. We apply our framework to three broad application areas:
computer vision, natural language processing, and social science.
",1,0,0,1,0,0
18694,Conformality of $1/N$ corrections in SYK-like models,"  The Sachdev-Ye--Kitaev is a quantum mechanical model of $N$ Majorana fermions
which displays a number of appealing features -- solvability in the strong
coupling regime, near-conformal invariance and maximal chaos -- which make it a
suitable model for black holes in the context of the AdS/CFT holography. In
this paper we show for the colored SYK model and several of its tensor model
cousins that the next-to-leading order in the $N$ expansion preserves the
conformal invariance of the $2$-point function in the strong coupling regime,
up to the contribution of the Goldstone bosons leading to the spontaneous
breaking of the symmetry and which are already seen in the leading order
$4$-point function. We also comment on the composite field approach for
computing correlation functions in colored tensor models.
",0,1,0,0,0,0
18695,Segal-type models of higher categories,"  Higher category theory is an exceedingly active area of research, whose rapid
growth has been driven by its penetration into a diverse range of scientific
fields. Its influence extends through key mathematical disciplines, notably
homotopy theory, algebraic geometry and algebra, mathematical physics, to
encompass important applications in logic, computer science and beyond. Higher
categories provide a unifying language whose greatest strength lies in its
ability to bridge between diverse areas and uncover novel applications.
In this foundational work we introduce a new approach to higher categories.
It builds upon the theory of iterated internal categories, one of the simplest
possible higher categorical structures available, by adopting a novel and
remarkably simple ""weak globularity"" postulate and demonstrating that the
resulting model provides a fully general theory of weak n-categories. The
latter are among the most complex of the higher structures, and are crucial for
applications. We show that this new model of ""weakly globular n-fold
categories"" is suitably equivalent to the well studied model of weak
n-categories due to Tamsamani and Simpson.
",0,0,1,0,0,0
18696,Low-Mass Dark Matter Search with CDMSlite,"  The SuperCDMS experiment is designed to directly detect weakly interacting
massive particles (WIMPs) that may constitute the dark matter in our Galaxy.
During its operation at the Soudan Underground Laboratory, germanium detectors
were run in the CDMSlite mode to gather data sets with sensitivity specifically
for WIMPs with masses ${<}$10 GeV/$c^2$. In this mode, a higher detector-bias
voltage is applied to amplify the phonon signals produced by drifting charges.
This paper presents studies of the experimental noise and its effect on the
achievable energy threshold, which is demonstrated to be as low as 56
eV$_{\text{ee}}$ (electron equivalent energy). The detector-biasing
configuration is described in detail, with analysis corrections for voltage
variations to the level of a few percent. Detailed studies of the
electric-field geometry, and the resulting successful development of a fiducial
parameter, eliminate poorly measured events, yielding an energy resolution
ranging from ${\sim}$9 eV$_{\text{ee}}$ at 0 keV to 101 eV$_{\text{ee}}$ at
${\sim}$10 eV$_{\text{ee}}$. New results are derived for astrophysical
uncertainties relevant to the WIMP-search limits, specifically examining how
they are affected by variations in the most probable WIMP velocity and the
Galactic escape velocity. These variations become more important for WIMP
masses below 10 GeV/$c^2$. Finally, new limits on spin-dependent low-mass
WIMP-nucleon interactions are derived, with new parameter space excluded for
WIMP masses $\lesssim$3 GeV/$c^2$
",0,1,0,0,0,0
18697,Gravity with free initial conditions: a solution to the cosmological constant problem testable by CMB B-mode polarization,"  In standard general relativity the universe cannot be started with arbitrary
initial conditions, because four of the ten components of the Einstein's field
equations (EFE) are constraints on initial conditions. In the previous work it
was proposed to extend the gravity theory to allow free initial conditions,
with a motivation to solve the cosmological constant problem. This was done by
setting four constraints on metric variations in the action principle, which is
reasonable because the gravity's physical degrees of freedom are at most six.
However, there are two problems about this theory; the three constraints in
addition to the unimodular condition were introduced without clear physical
meanings, and the flat Minkowski spacetime is unstable against perturbations.
Here a new set of gravitational field equations is derived by replacing the
three constraints with new ones requiring that geodesic paths remain geodesic
against metric variations. The instability problem is then naturally solved.
Implications for the cosmological constant $\Lambda$ are unchanged; the theory
converges into EFE with nonzero $\Lambda$ by inflation, but $\Lambda$ varies on
scales much larger than the present Hubble horizon. Then galaxies are formed
only in small $\Lambda$ regions, and the cosmological constant problem is
solved by the anthropic argument. Because of the increased degrees of freedom
in metric dynamics, the theory predicts new non-oscillatory modes of metric
anisotropy generated by quantum fluctuation during inflation, and CMB B-mode
polarization would be observed differently from the standard predictions by
general relativity.
",0,1,0,0,0,0
18698,Gradual Learning of Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many
sequence-to-sequence modeling tasks. However, RNNs are difficult to train and
tend to suffer from overfitting. Motivated by the Data Processing Inequality
(DPI), we formulate the multi-layered network as a Markov chain, introducing a
training method that comprises training the network gradually and using
layer-wise gradient clipping. We found that applying our methods, combined with
previously introduced regularization and optimization methods, resulted in
improvements in state-of-the-art architectures operating in language modeling
tasks.
",1,0,0,1,0,0
18699,Borel subsets of the real line and continuous reducibility,"  We study classes of Borel subsets of the real line $\mathbb{R}$ such as
levels of the Borel hierarchy and the class of sets that are reducible to the
set $\mathbb{Q}$ of rationals, endowed with the Wadge quasi-order of
reducibility with respect to continuous functions on $\mathbb{R}$. Notably, we
explore several structural properties of Borel subsets of $\mathbb{R}$ that
diverge from those of Polish spaces with dimension zero. Our first main result
is on the existence of embeddings of several posets into the restriction of
this quasi-order to any Borel class that is strictly above the classes of open
and closed sets, for instance the linear order $\omega_1$, its reverse
$\omega_1^\star$ and the poset $\mathcal{P}(\omega)/\mathsf{fin}$ of inclusion
modulo finite error. As a consequence of its proof, it is shown that there are
no complete sets for these classes. We further extend the previous theorem to
targets that are reducible to $\mathbb{Q}$. These non-structure results
motivate the study of further restrictions of the Wadge quasi-order. In our
second main theorem, we introduce a combinatorial property that is shown to
characterize those $F_\sigma$ sets that are reducible to $\mathbb{Q}$. This is
applied to construct a minimal set below $\mathbb{Q}$ and prove its uniqueness
up to Wadge equivalence. We finally prove several results concerning gaps and
cardinal characteristics of the Wadge quasi-order and thereby answer questions
of Brendle and Geschke.
",0,0,1,0,0,0
18700,Pairing from dynamically screened Coulomb repulsion in bismuth,"  Recently, Prakash et. al. have discovered bulk superconductivity in single
crystals of bismuth, which is a semi metal with extremely low carrier density.
At such low density, we argue that conventional electron-phonon coupling is too
weak to be responsible for the binding of electrons into Cooper pairs. We study
a dynamically screened Coulomb interaction with effective attraction generated
on the scale of the collective plasma modes. We model the electronic states in
bismuth to include three Dirac pockets with high velocity and one hole pocket
with a significantly smaller velocity. We find a weak coupling instability,
which is greatly enhanced by the presence of the hole pocket. Therefore, we
argue that bismuth is the first material to exhibit superconductivity driven by
retardation effects of Coulomb repulsion alone. By using realistic parameters
for bismuth we find that the acoustic plasma mode does not play the central
role in pairing. We also discuss a matrix element effect, resulting from the
Dirac nature of the conduction band, which may affect $T_c$ in the $s$-wave
channel without breaking time-reversal symmetry.
",0,1,0,0,0,0
18701,Calabi-Yau metrics on canonical bundles of complex flag manifolds,"  In the present paper we provide a description of complete Calabi-Yau metrics
on the canonical bundle of generalized complex flag manifolds. By means of Lie
theory we give an explicit description of complete Ricci-flat Kähler metrics
obtained through the Calabi ansatz technique. We use this approach to provide
several explicit examples of noncompact complete Calabi-Yau manifolds, these
examples include canonical bundles of non-toric flag manifolds (e.g. Grassmann
manifolds and full flag manifolds).
",0,0,1,0,0,0
18702,The ratio of normalizing constants for Bayesian graphical Gaussian model selection,"  Many graphical Gaussian selection methods in a Bayesian framework use the
G-Wishart as the conjugate prior on the precision matrix. The Bayes factor to
compare a model governed by a graph G and a model governed by the neighboring
graph G-e, derived from G by deleting an edge e, is a function of the ratios of
prior and posterior normalizing constants of the G-Wishart for G and G-e.
While more recent methods avoid the computation of the posterior ratio,
computing the ratio of prior normalizing constants, (2) below, has remained a
computational stumbling block. In this paper, we propose an explicit analytic
approximation to (2) which is equal to the ratio of two Gamma functions
evaluated at (delta+d)/2 and (delta+d+1)/2 respectively, where delta is the
shape parameter of the G-Wishart and d is the number of paths of length two
between the endpoints of e. This approximation allows us to avoid Monte Carlo
methods, is computationally inexpensive and is scalable to high-dimensional
problems. We show that the ratio of the approximation to the true value is
always between zero and one and so, one cannot incur wild errors.
In the particular case where the paths between the endpoints of e are
disjoint, we show that the approximation is very good. When the paths between
these two endpoints are not disjoint we give a sufficient condition for the
approximation to be good. Numerical results show that the ratio of the
approximation to the true value of the prior ratio is always between .55 and 1
and very often close to 1. We compare the results obtained with a model search
using our approximation and a search using the double Metropolis-Hastings
algorithm to compute the prior ratio. The results are extremely close.
",0,0,1,1,0,0
18703,Angiogenic Factors produced by Hypoxic Cells are a leading driver of Anastomoses in Sprouting Angiogenesis---a computational study,"  Angiogenesis - the growth of new blood vessels from a pre-existing
vasculature - is key in both physiological processes and on several
pathological scenarios such as cancer progression or diabetic retinopathy. For
the new vascular networks to be functional, it is required that the growing
sprouts merge either with an existing functional mature vessel or with another
growing sprout. This process is called anastomosis. We present a systematic 2D
and 3D computational study of vessel growth in a tissue to address the
capability of angiogenic factor gradients to drive anastomosis formation. We
consider that these growth factors are produced only by tissue cells in
hypoxia, i.e. until nearby vessels merge and become capable of carrying blood
and irrigating their vicinity. We demonstrate that this increased production of
angiogenic factors by hypoxic cells is able to promote vessel anastomoses
events in both 2D and 3D. The simulations also verify that the morphology of
these networks has an increased resilience toward variations in the endothelial
cell's proliferation and chemotactic response. The distribution of tissue
cell`s and the concentration of the growth factors they produce are the major
factors in determining the final morphology of the network.
",0,0,0,0,1,0
18704,Henri Bénard: Thermal convection and vortex shedding,"  We present in this article the work of Henri Bénard (1874-1939), French
physicist who began the systematic experimental study of two hydrodynamic
systems: the thermal convection of fluids heated from below (the
Rayleigh-Bénard convection and the Bénard-Marangoni convection) and the
periodical vortex shedding behind a bluff body in a flow (the
Bénard-Kármán vortex street). Across his scientific biography, we review
the interplay between experiments and theory in these two major subjects of
fluid mechanics.
",0,1,0,0,0,0
18705,Surjective H-Colouring: New Hardness Results,"  A homomorphism from a graph G to a graph H is a vertex mapping f from the
vertex set of G to the vertex set of H such that there is an edge between
vertices f(u) and f(v) of H whenever there is an edge between vertices u and v
of G. The H-Colouring problem is to decide whether or not a graph G allows a
homomorphism to a fixed graph H. We continue a study on a variant of this
problem, namely the Surjective H-Colouring problem, which imposes the
homomorphism to be vertex-surjective. We build upon previous results and show
that this problem is NP-complete for every connected graph H that has exactly
two vertices with a self-loop as long as these two vertices are not adjacent.
As a result, we can classify the computational complexity of Surjective
H-Colouring for every graph H on at most four vertices.
",1,0,1,0,0,0
18706,Subband adaptive filter trained by differential evolution for channel estimation,"  The normalized subband adaptive filter (NSAF) is widely accepted as a
preeminent adaptive filtering algorithm because of its efficiency under the
colored excitation. However, the convergence rate of NSAF is slow. To address
this drawback, in this paper, a variant of the NSAF, called the differential
evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE
strategy. It is worth noticing that there are several papers concerning
designing DE strategies for adaptive filter. But their signal models are still
the single adaptive filter model rather than the fullband adaptive filter model
considered in this paper. Thus, the problem considered in our work is quite
different from those. The proposed DE-NSAF algorithm is based on real-valued
manipulations and has fast convergence rate for searching the global solution
of optimized weight vector. Moreover, a design step of new algorithm is given
in detail. Simulation results demonstrate the improved performance of the
proposed DE-NSAF algorithm in terms of the convergence rate.
",1,0,0,0,0,0
18707,Distributionally Robust Games: f-Divergence and Learning,"  In this paper we introduce the novel framework of distributionally robust
games. These are multi-player games where each player models the state of
nature using a worst-case distribution, also called adversarial distribution.
Thus each player's payoff depends on the other players' decisions and on the
decision of a virtual player (nature) who selects an adversarial distribution
of scenarios. This paper provides three main contributions. Firstly, the
distributionally robust game is formulated using the statistical notions of
$f$-divergence between two distributions, here represented by the adversarial
distribution, and the exact distribution. Secondly, the complexity of the
problem is significantly reduced by means of triality theory. Thirdly,
stochastic Bregman learning algorithms are proposed to speedup the computation
of robust equilibria. Finally, the theoretical findings are illustrated in a
convex setting and its limitations are tested with a non-convex non-concave
function.
",1,0,1,0,0,0
18708,Modeling and Reasoning About Wireless Networks: A Graph-based Calculus Approach,"  We propose a graph-based process calculus for modeling and reasoning about
wireless networks with local broadcasts. Graphs are used at syntactical level
to describe the topological structures of networks. This calculus is equipped
with a reduction semantics and a labelled transition semantics. The former is
used to define weak barbed congruence. The latter is used to define a
parameterized weak bisimulation emphasizing locations and local broadcasts. We
prove that weak bisimilarity implies weak barbed congruence. The potential
applications are illustrated by some examples and two case studies.
",1,0,0,0,0,0
18709,"Weyl's law on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will prove the Weyl's law for the asymptotic formula of
Dirichlet eigenvalues on metric measure spaces with generalized Ricci curvature
bounded from below.
",0,0,1,0,0,0
18710,The area of the Mandelbrot set and Zagier's conjecture,"  We prove Zagier's conjecture regarding the 2-adic valuation of the
coefficients $\{b_m\}$ that appear in Ewing and Schober's series formula for
the area of the Mandelbrot set in the case where $m\equiv 2 \mod 4$.
",0,0,1,0,0,0
18711,Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization,"  We develop an approximate formula for evaluating a cross-validation estimator
of predictive likelihood for multinomial logistic regression regularized by an
$\ell_1$-norm. This allows us to avoid repeated optimizations required for
literally conducting cross-validation; hence, the computational time can be
significantly reduced. The formula is derived through a perturbative approach
employing the largeness of the data size and the model dimensionality. An
extension to the elastic net regularization is also addressed. The usefulness
of the approximate formula is demonstrated on simulated data and the ISOLET
dataset from the UCI machine learning repository.
",0,1,0,1,0,0
18712,Realizing an optimization approach inspired from Piagets theory on cognitive development,"  The objective of this paper is to introduce an artificial intelligence based
optimization approach, which is inspired from Piagets theory on cognitive
development. The approach has been designed according to essential processes
that an individual may experience while learning something new or improving his
/ her knowledge. These processes are associated with the Piagets ideas on an
individuals cognitive development. The approach expressed in this paper is a
simple algorithm employing swarm intelligence oriented tasks in order to
overcome single-objective optimization problems. For evaluating effectiveness
of this early version of the algorithm, test operations have been done via some
benchmark functions. The obtained results show that the approach / algorithm
can be an alternative to the literature in terms of single-objective
optimization. The authors have suggested the name: Cognitive Development
Optimization Algorithm (CoDOA) for the related intelligent optimization
approach.
",1,0,1,0,0,0
18713,Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions,"  In conventional chemisorption model, the d-band center theory (augmented
sometimes with the upper edge of d-band for imporved accuarcy) plays a central
role in predicting adsorption energies and catalytic activity as a function of
d-band center of the solid surfaces, but it requires density functional
calculations that can be quite costly for large scale screening purposes of
materials. In this work, we propose to use the d-band width of the muffin-tin
orbital theory (to account for local coordination environment) plus
electronegativity (to account for adsorbate renormalization) as a simple set of
alternative descriptors for chemisorption, which do not demand the ab initio
calculations. This pair of descriptors are then combined with machine learning
methods, namely, artificial neural network (ANN) and kernel ridge regression
(KRR), to allow large scale materials screenings. We show, for a toy set of 263
alloy systems, that the CO adsorption energy can be predicted with a remarkably
small mean absolute deviation error of 0.05 eV, a significantly improved result
as compared to 0.13 eV obtained with descriptors including costly d-band center
calculations in literature. We achieved this high accuracy by utilizing an
active learning algorithm, without which the accuracy was 0.18 eV otherwise. As
a practical application of this machine, we identified Cu3Y@Cu as a highly
active and cost-effective electrochemical CO2 reduction catalyst to produce CO
with the overpotential 0.37 V lower than Au catalyst.
",0,1,0,1,0,0
18714,Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility,"  Extracting significant places or places of interest (POIs) using individuals'
spatio-temporal data is of fundamental importance for human mobility analysis.
Classical clustering methods have been used in prior work for detecting POIs,
but without considering temporal constraints. Usually, the involved parameters
for clustering are difficult to determine, e.g., the optimal cluster number in
hierarchical clustering. Currently, researchers either choose heuristic values
or use spatial distance-based optimization to determine an appropriate
parameter set. We argue that existing research does not optimally address
temporal information and thus leaves much room for improvement. Considering
temporal constraints in human mobility, we introduce an effective clustering
approach - namely POI clustering with temporal constraints (PC-TC) - to extract
POIs from spatio-temporal data of human mobility. Following human mobility
nature in modern society, our approach aims to extract both global POIs (e.g.,
workplace or university) and local POIs (e.g., library, lab, and canteen).
Based on two publicly available datasets including 193 individuals, our
evaluation results show that PC-TC has much potential for next place prediction
in terms of granularity (i.e., the number of extracted POIs) and
predictability.
",0,0,0,1,0,0
18715,The maximum number of zeros of $r(z) - \overline{z}$ revisited,"  Generalizing several previous results in the literature on rational harmonic
functions, we derive bounds on the maximum number of zeros of functions $f(z) =
\frac{p(z)}{q(z)} - \overline{z}$, which depend on both $\mathrm{deg}(p)$ and
$\mathrm{deg}(q)$. Furthermore, we prove that any function that attains one of
these upper bounds is regular.
",0,0,1,0,0,0
18716,High Luminosity Large Hadron Collider HL-LHC,"  HL-LHC federates the efforts and R&D of a large international community
towards the ambitious HL- LHC objectives and contributes to establishing the
European Research Area (ERA) as a focal point of global research cooperation
and a leader in frontier knowledge and technologies. HL-LHC relies on strong
participation from various partners, in particular from leading US and Japanese
laboratories. This participation will be required for the execution of the
construction phase as a global project. In particular, the US LHC Accelerator
R&D Program (LARP) has developed some of the key technologies for the HL-LHC,
such as the large-aperture niobium-tin ($Nb_{3}Sn) quadrupoles and the crab
cavities. The proposed governance model is tailored accordingly and should pave
the way for the organization of the construction phase.
",0,1,0,0,0,0
18717,Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies,"  Various approaches have been proposed to learn visuo-motor policies for
real-world robotic applications. One solution is first learning in simulation
then transferring to the real world. In the transfer, most existing approaches
need real-world images with labels. However, the labelling process is often
expensive or even impractical in many robotic applications. In this paper, we
propose an adversarial discriminative sim-to-real transfer approach to reduce
the cost of labelling real data. The effectiveness of the approach is
demonstrated with modular networks in a table-top object reaching task where a
7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter
through visual observations. The adversarial transfer approach reduced the
labelled real data requirement by 50%. Policies can be transferred to real
environments with only 93 labelled and 186 unlabelled real images. The
transferred visuo-motor policies are robust to novel (not seen in training)
objects in clutter and even a moving target, achieving a 97.8% success rate and
1.8 cm control accuracy.
",1,0,0,0,0,0
18718,Algebraic Bethe ansatz for the trigonometric sl(2) Gaudin model with triangular boundary,"  In the derivation of the generating function of the Gaudin Hamiltonians with
boundary terms, we follow the same approach used previously in the rational
case, which in turn was based on Sklyanin's method in the periodic case. Our
derivation is centered on the quasi-classical expansion of the linear
combination of the transfer matrix of the XXZ Heisenberg spin chain and the
central element, the so-called Sklyanin determinant. The corresponding Gaudin
Hamiltonians with boundary terms are obtained as the residues of the generating
function. By defining the appropriate Bethe vectors which yield strikingly
simple off-shell action of the generating function, we fully implement the
algebraic Bethe ansatz, obtaining the spectrum of the generating function and
the corresponding Bethe equations.
",0,1,0,0,0,0
18719,Marked Temporal Dynamics Modeling based on Recurrent Neural Network,"  We are now witnessing the increasing availability of event stream data, i.e.,
a sequence of events with each event typically being denoted by the time it
occurs and its mark information (e.g., event type). A fundamental problem is to
model and predict such kind of marked temporal dynamics, i.e., when the next
event will take place and what its mark will be. Existing methods either
predict only the mark or the time of the next event, or predict both of them,
yet separately. Indeed, in marked temporal dynamics, the time and the mark of
the next event are highly dependent on each other, requiring a method that
could simultaneously predict both of them. To tackle this problem, in this
paper, we propose to model marked temporal dynamics by using a mark-specific
intensity function to explicitly capture the dependency between the mark and
the time of the next event. Extensive experiments on two datasets demonstrate
that the proposed method outperforms state-of-the-art methods at predicting
marked temporal dynamics.
",1,0,0,0,0,0
18720,A Bayesian framework for distributed estimation of arrival rates in asynchronous networks,"  In this paper we consider a network of agents monitoring a spatially
distributed arrival process. Each node measures the number of arrivals seen at
its monitoring point in a given time-interval with the objective of estimating
the unknown local arrival rate. We propose an asynchronous distributed approach
based on a Bayesian model with unknown hyperparameter, where each node computes
the minimum mean square error (MMSE) estimator of its local arrival rate in a
distributed way. As a result, the estimation at each node ""optimally"" fuses the
information from the whole network through a distributed optimization
algorithm. Moreover, we propose an ad-hoc distributed estimator, based on a
consensus algorithm for time-varying and directed graphs, which exhibits
reduced complexity and exponential convergence. We analyze the performance of
the proposed distributed estimators, showing that they: (i) are reliable even
in presence of limited local data, and (ii) improve the estimation accuracy
compared to the purely decentralized setup. Finally, we provide a statistical
characterization of the proposed estimators. In particular, for the ad-hoc
estimator, we show that as the number of nodes goes to infinity its mean square
error converges to the optimal one. Numerical Monte Carlo simulations confirm
the theoretical characterization and highlight the appealing performances of
the estimators.
",1,0,1,0,0,0
18721,Rigidity of branching microstructures in shape memory alloys,"  We analyze generic sequences for which the geometrically linear energy
\[E_\eta(u,\chi):= \eta^{-\frac{2}{3}}\int_{B_{0}(1)} \left| e(u)-
\sum_{i=1}^3 \chi_ie_i\right|^2 d x+\eta^\frac{1}{3} \sum_{i=1}^3
|D\chi_i|(B_{0}(1))\] remains bounded in the limit $\eta \to 0$. Here $ e(u)
:=1/2(Du + Du^T)$ is the (linearized) strain of the displacement $u$, the
strains $e_i$ correspond to the martensite strains of a shape memory alloy
undergoing cubic-to-tetragonal transformations and $\chi_i:B_{0}(1) \to
\{0,1\}$ is the partition into phases. In this regime it is known that in
addition to simple laminates also branched structures are possible, which if
austenite was present would enable the alloy to form habit planes.
In an ansatz-free manner we prove that the alignment of macroscopic
interfaces between martensite twins is as predicted by well-known rank-one
conditions. Our proof proceeds via the non-convex, non-discrete-valued
differential inclusion \[e(u) \in \bigcup_{1\leq i\neq j\leq 3}
\operatorname{conv} \{e_i,e_j\}\] satisfied by the weak limits of bounded
energy sequences and of which we classify all solutions. In particular, there
exist no convex integration solutions of the inclusion with complicated
geometric structures.
",0,0,1,0,0,0
18722,"Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository","  Machine learning qualifies computers to assimilate with data, without being
solely programmed [1, 2]. Machine learning can be classified as supervised and
unsupervised learning. In supervised learning, computers learn an objective
that portrays an input to an output hinged on training input-output pairs [3].
Most efficient and widely used supervised learning algorithms are K-Nearest
Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
paper is to implement these elegant learning algorithms on eleven different
datasets from the UCI machine learning repository to observe the variation of
accuracies for each of the algorithms on all datasets. Analyzing the accuracy
of the algorithms will give us a brief idea about the relationship of the
machine learning algorithms and the data dimensionality. All the algorithms are
developed in Matlab. Upon such accuracy observation, the comparison can be
built among KNN, SVM, LMNN, and ENN regarding their performances on each
dataset.
",0,0,0,1,0,0
18723,Network Classification in Temporal Networks Using Motifs,"  Network classification has a variety of applications, such as detecting
communities within networks and finding similarities between those representing
different aspects of the real world. However, most existing work in this area
focus on examining static undirected networks without considering directed
edges or temporality. In this paper, we propose a new methodology that utilizes
feature representation for network classification based on the temporal motif
distribution of the network and a null model for comparing against random
graphs. Experimental results show that our method improves accuracy by up
$10\%$ compared to the state-of-the-art embedding method in network
classification, for tasks such as classifying network type, identifying
communities in email exchange network, and identifying users given their
app-switching behaviors.
",1,0,0,0,0,0
18724,The strength of Ramsey's theorem for pairs and arbitrarily many colors,"  In this paper, we show that $\mathrm{RT}^{2}+\mathsf{WKL}_0$ is a
$\Pi^{1}_{1}$-conservative extension of $\mathrm{B}\Sigma^0_3$.
",0,0,1,0,0,0
18725,Joint Power Allocation and Beamforming for Energy-Efficient Two-Way Multi-Relay Communications,"  This paper considers the joint design of user power allocation and relay
beamforming in relaying communications, in which multiple pairs of
single-antenna users exchange information with each other via multiple-antenna
relays in two time slots. All users transmit their signals to the relays in the
first time slot while the relays broadcast the beamformed signals to all users
in the second time slot. The aim is to maximize the system's energy efficiency
(EE) subject to quality-of-service (QoS) constraints in terms of exchange
throughput requirements. The QoS constraints are nonconvex with many nonlinear
cross-terms, so finding a feasible point is already computationally
challenging. The sum throughput appears in the numerator while the total
consumption power appears in the denominator of the EE objective function. The
former is a nonconcave function and the latter is a nonconvex function, making
fractional programming useless for EE optimization. Nevertheless, efficient
iterations of low complexity to obtain its optimized solutions are developed.
The performances of the multiple-user and multiple-relay networks under various
scenarios are evaluated to show the merit of the paper development.
",1,0,0,0,0,0
18726,Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen,"  Armed conflict has led to an unprecedented number of internally displaced
persons (IDPs) - individuals who are forced out of their homes but remain
within their country. IDPs often urgently require shelter, food, and
healthcare, yet prediction of when large fluxes of IDPs will cross into an area
remains a major challenge for aid delivery organizations. Accurate forecasting
of IDP migration would empower humanitarian aid groups to more effectively
allocate resources during conflicts. We show that monthly flow of IDPs from
province to province in both Syria and Yemen can be accurately forecasted one
month in advance, using publicly available data. We model monthly IDP flow
using data on food price, fuel price, wage, geospatial, and news data. We find
that machine learning approaches can more accurately forecast migration trends
than baseline persistence models. Our findings thus potentially enable
proactive aid allocation for IDPs in anticipation of forecasted arrivals.
",0,0,0,1,0,0
18727,Entropy? Honest!,"  Here we deconstruct, and then in a reasoned way reconstruct, the concept of
""entropy of a system,"" paying particular attention to where the randomness may
be coming from. We start with the core concept of entropy as a COUNT associated
with a DESCRIPTION; this count (traditionally expressed in logarithmic form for
a number of good reasons) is in essence the number of possibilities---specific
instances or ""scenarios,"" that MATCH that description. Very natural (and
virtually inescapable) generalizations of the idea of description are the
probability distribution and of its quantum mechanical counterpart, the density
operator.
We track the process of dynamically updating entropy as a system evolves.
Three factors may cause entropy to change: (1) the system's INTERNAL DYNAMICS;
(2) unsolicited EXTERNAL INFLUENCES on it; and (3) the approximations one has
to make when one tries to predict the system's future state. The latter task is
usually hampered by hard-to-quantify aspects of the original description,
limited data storage and processing resource, and possibly algorithmic
inadequacy. Factors 2 and 3 introduce randomness into one's predictions and
accordingly degrade them. When forecasting, as long as the entropy bookkeping
is conducted in an HONEST fashion, this degradation will ALWAYS lead to an
entropy increase.
To clarify the above point we introduce the notion of HONEST ENTROPY, which
coalesces much of what is of course already done, often tacitly, in responsible
entropy-bookkeping practice. This notion, we believe, will help to fill an
expressivity gap in scientific discourse. With its help we shall prove that ANY
dynamical system---not just our physical universe---strictly obeys Clausius's
original formulation of the second law of thermodynamics IF AND ONLY IF it is
invertible. Thus this law is a TAUTOLOGICAL PROPERTY of invertible systems!
",0,1,0,0,0,0
18728,Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors,"  We study the problem of generating adversarial examples in a black-box
setting in which only loss-oracle access to a model is available. We introduce
a framework that conceptually unifies much of the existing work on black-box
attacks, and we demonstrate that the current state-of-the-art methods are
optimal in a natural sense. Despite this optimality, we show how to improve
black-box attacks by bringing a new element into the problem: gradient priors.
We give a bandit optimization-based algorithm that allows us to seamlessly
integrate any such priors, and we explicitly identify and incorporate two
examples. The resulting methods use two to four times fewer queries and fail
two to five times less often than the current state-of-the-art.
",0,0,0,1,0,0
18729,A Dynamic Model of Central Counterparty Risk,"  We introduce a dynamic model of the default waterfall of derivatives CCPs and
propose a risk sensitive method for sizing the initial margin (IM), and the
default fund (DF) and its allocation among clearing members. Using a Markovian
structure model of joint credit migrations, our evaluation of DF takes into
account the joint credit quality of clearing members as they evolve over time.
Another important aspect of the proposed methodology is the use of the time
consistent dynamic risk measures for computation of IM and DF. We carry out a
comprehensive numerical study, where, in particular, we analyze the advantages
of the proposed methodology and its comparison with the currently prevailing
methods used in industry.
",0,0,0,0,0,1
18730,Half-Duplex Base Station with Adaptive Scheduling of the in-Band Uplink-Receptions and Downlink-Transmissions,"  In this paper, we propose a novel reception/transmission scheme for
half-duplex base stations (BSs). In particular, we propose a half-duplex BS
that employes in-band uplink-receptions from user 1 and downlink-transmissions
to user 2, which occur in different time slots. Furthermore, we propose optimal
adaptive scheduling of the in-band uplink-receptions and downlink-transmissions
of the BS such that the uplink-downlink rate/throughput region is maximized and
the outage probabilities of the uplink and downlink channels are minimized.
Practically, this results in selecting whether in a given time slot the BS
should receive from user 1 or transmit to user 2 based on the qualities of the
in-band uplink-reception and downlink-transmission channels. Compared to the
performance achieved with a conventional full-duplex division (FDD) base
station, two main gains can be highlighted: 1) Increased uplink-downlink
rate/throughput region; 2) Doubling of the diversity gain of both the uplink
and downlink channels.
",1,0,0,0,0,0
18731,HAT-P-26b: A Neptune-Mass Exoplanet with a Well Constrained Heavy Element Abundance,"  A correlation between giant-planet mass and atmospheric heavy elemental
abundance was first noted in the past century from observations of planets in
our own Solar System, and has served as a cornerstone of planet formation
theory. Using data from the Hubble and Spitzer Space Telescopes from 0.5 to 5
microns, we conducted a detailed atmospheric study of the transiting
Neptune-mass exoplanet HAT-P-26b. We detected prominent H2O absorption bands
with a maximum base-to-peak amplitude of 525ppm in the transmission spectrum.
Using the water abundance as a proxy for metallicity, we measured HAT-P-26b's
atmospheric heavy element content [4.8 (-4.0 +21.5) times solar]. This likely
indicates that HAT-P-26b's atmosphere is primordial and obtained its gaseous
envelope late in its disk lifetime, with little contamination from metal-rich
planetesimals.
",0,1,0,0,0,0
18732,Robust Localization Using Range Measurements with Unknown and Bounded Errors,"  Cooperative geolocation has attracted significant research interests in
recent years. A large number of localization algorithms rely on the
availability of statistical knowledge of measurement errors, which is often
difficult to obtain in practice. Compared with the statistical knowledge of
measurement errors, it can often be easier to obtain the measurement error
bound. This work investigates a localization problem assuming unknown
measurement error distribution except for a bound on the error. We first
formulate this localization problem as an optimization problem to minimize the
worst-case estimation error, which is shown to be a non-convex optimization
problem. Then, relaxation is applied to transform it into a convex one.
Furthermore, we propose a distributed algorithm to solve the problem, which
will converge in a few iterations. Simulation results show that the proposed
algorithms are more robust to large measurement errors than existing algorithms
in the literature. Geometrical analysis providing additional insights is also
provided.
",0,0,0,1,0,0
18733,Intuitionistic Non-Normal Modal Logics: A general framework,"  We define a family of intuitionistic non-normal modal logics; they can bee
seen as intuitionistic counterparts of classical ones. We first consider
monomodal logics, which contain only one between Necessity and Possibility. We
then consider the more important case of bimodal logics, which contain both
modal operators. In this case we define several interactions between Necessity
and Possibility of increasing strength, although weaker than duality. For all
logics we provide both a Hilbert axiomatisation and a cut-free sequent
calculus, on its basis we also prove their decidability. We then give a
semantic characterisation of our logics in terms of neighbourhood models. Our
semantic framework captures modularly not only our systems but also already
known intuitionistic non-normal modal logics such as Constructive K (CK) and
the propositional fragment of Wijesekera's Constructive Concurrent Dynamic
Logic.
",1,0,0,0,0,0
18734,Externalities in Socially-Based Resource Sharing Network,"  This paper investigates the impact of link formation between a pair of agents
on resource availability of other agents in a social cloud network, which is a
special case of socially-based resource sharing systems. Specifically, we study
the correlation between externalities, network size, and network density.
We first conjecture and experimentally support that if an agent experiences
positive externalities, then its closeness (harmonic centrality measure) should
increase. Next, we show the following for ring networks: in less populated
networks no agent experiences positive externalities; in more populated
networks a set of agents experience positive externalities, and larger the
distance between agents forming a link, more the number of beneficiaries; and
the number of beneficiaries is always less than the number of
non-beneficiaries. Finally, we show that network density is inversely
proportional to positive externalities, and further, it plays a crucial role in
determining the kind of externalities.
",1,0,0,0,0,0
18735,Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits,"  Consider the problem: given data pair $(\mathbf{x}, \mathbf{y})$ drawn from a
population with $f_*(x) = \mathbf{E}[\mathbf{y} | \mathbf{x} = x]$, specify a
neural network and run gradient flow on the weights over time until reaching
any stationarity. How does $f_t$, the function computed by the neural network
at time $t$, relate to $f_*$, in terms of approximation and representation?
What are the provable benefits of the adaptive representation by neural
networks compared to the pre-specified fixed basis representation in the
classical nonparametric literature? We answer the above questions via a dynamic
reproducing kernel Hilbert space (RKHS) approach indexed by the training
process of neural networks. We show that when reaching any local stationarity,
gradient flow learns an adaptive RKHS representation, and performs the global
least squares projection onto the adaptive RKHS, simultaneously. In addition,
we prove that as the RKHS is data-adaptive and task-specific, the residual for
$f_*$ lies in a subspace that is smaller than the orthogonal complement of the
RKHS, formalizing the representation and approximation benefits of neural
networks.
",1,0,1,1,0,0
18736,On utility maximization without passing by the dual problem,"  We treat utility maximization from terminal wealth for an agent with utility
function $U:\mathbb{R}\to\mathbb{R}$ who dynamically invests in a
continuous-time financial market and receives a possibly unbounded random
endowment. We prove the existence of an optimal investment without introducing
the associated dual problem. We rely on a recent result of Orlicz space theory,
due to Delbaen and Owari which leads to a simple and transparent proof.
Our results apply to non-smooth utilities and even strict concavity can be
relaxed. We can handle certain random endowments with non-hedgeable risks,
complementing earlier papers. Constraints on the terminal wealth can also be
incorporated.
As examples, we treat frictionless markets with finitely many assets and
large financial markets.
",0,0,1,0,0,0
18737,Stochastic Generative Hashing,"  Learning-based binary hashing has become a powerful paradigm for fast search
and retrieval in massive databases. However, due to the requirement of discrete
outputs for the hash functions, learning such functions is known to be very
challenging. In addition, the objective functions adopted by existing hashing
techniques are mostly chosen heuristically. In this paper, we propose a novel
generative approach to learn hash functions through Minimum Description Length
principle such that the learned hash codes maximally compress the dataset and
can also be used to regenerate the inputs. We also develop an efficient
learning algorithm based on the stochastic distributional gradient, which
avoids the notorious difficulty caused by binary output constraints, to jointly
optimize the parameters of the hash function and the associated generative
model. Extensive experiments on a variety of large-scale datasets show that the
proposed method achieves better retrieval results than the existing
state-of-the-art methods.
",1,0,0,1,0,0
18738,Nonlinear learning and learning advantages in evolutionary games,"  The idea of incompetence as a learning or adaptation function was introduced
in the context of evolutionary games as a fixed parameter. However, live
organisms usually perform different nonlinear adaptation functions such as a
power law or exponential fitness growth. Here, we examine how the functional
form of the learning process may affect the social competition between
different behavioral types. Further, we extend our results for the evolutionary
games where fluctuations in the environment affect the behavioral adaptation of
competing species and demonstrate importance of the starting level of
incompetence for survival. Hence, we define a new concept of learning
advantages that becomes crucial when environments are constantly changing and
requiring rapid adaptation from species. This may lead to the evolutionarily
weak phase when even evolutionary stable populations become vulnerable to
invasions.
",0,0,0,0,1,0
18739,Information-theoretic Limits for Community Detection in Network Models,"  We analyze the information-theoretic limits for the recovery of node labels
in several network models. This includes the Stochastic Block Model, the
Exponential Random Graph Model, the Latent Space Model, the Directed
Preferential Attachment Model, and the Directed Small-world Model. For the
Stochastic Block Model, the non-recoverability condition depends on the
probabilities of having edges inside a community, and between different
communities. For the Latent Space Model, the non-recoverability condition
depends on the dimension of the latent space, and how far and spread are the
communities in the latent space. For the Directed Preferential Attachment Model
and the Directed Small-world Model, the non-recoverability condition depends on
the ratio between homophily and neighborhood size. We also consider dynamic
versions of the Stochastic Block Model and the Latent Space Model.
",1,0,0,1,0,0
18740,Proofs of life: molecular-biology reasoning simulates cell behaviors from first principles,"  We axiomatize the molecular-biology reasoning style, verify compliance of the
standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced
technologies to predict phenotypes and life cycles from genotypes. The key is
to note that `reductionist discipline' entails constructive reasoning, i.e.,
that any argument for a compound property is constructed from more basic
arguments. Proof theory makes explicit the inner structure of the axiomatized
reasoning style and allows the permissible dynamics to be presented as a mode
of computation that can be executed and analyzed. Constructivity and
executability guarantee simulation when working over domain-specific languages.
Here, we exhibit phenotype properties for genotype reasons: a molecular-biology
argument is an open-system concurrent computation that results in compartment
changes and is performed among processes of physiology change as determined
from the molecular programming of given DNA. Life cycles are the possible
sequentializations of the processes. A main implication of our construction is
that technical correctness provides a complementary perspective on science that
is as fundamental there as it is for pure mathematics, provided mature
reductionism exists.
",0,0,0,0,1,0
18741,Beyond recursion operators,"  We briefly recall the history of the Nijenhuis torsion of (1,1)-tensors on
manifolds and of the lesser-known Haantjes torsion. We then show how the
Haantjes manifolds of Magri and the symplectic-Haantjes structures of Tempesta
and Tondo generalize the classical approach to integrable systems in the
bi-hamiltonian and symplectic-Nijenhuis formalisms, the sequence of powers of
the recursion operator being replaced by a family of commuting Haantjes
operators.
",0,0,1,0,0,0
18742,On Classical Integrability of the Hydrodynamics of Quantum Integrable Systems,"  Recently, a hydrodynamic description of local equilibrium dynamics in quantum
integrable systems was discovered. In the diffusionless limit, this is
equivalent to a certain ""Bethe-Boltzmann"" kinetic equation, which has the form
of an integro-differential conservation law in $(1+1)$D. The purpose of the
present work is to investigate the sense in which the Bethe-Boltzmann equation
defines an ""integrable kinetic equation"". To this end, we study a class of $N$
dimensional systems of evolution equations that arise naturally as
finite-dimensional approximations to the Bethe-Boltzmann equation. We obtain
non-local Poisson brackets and Hamiltonian densities for these equations and
derive an infinite family of first integrals, parameterized by $N$ functional
degrees of freedom. We find that the conserved charges arising from quantum
integrability map to Casimir invariants of the hydrodynamic bracket and their
group velocities map to Hamiltonian flows. Some results from the
finite-dimensional setting extend to the underlying integro-differential
equation, providing evidence for its integrability in the hydrodynamic sense.
",0,1,0,0,0,0
18743,Transition to turbulence when the Tollmien-Schlichting and bypass routes coexist,"  Plane Poiseuille flow, the pressure driven flow between parallel plates,
shows a route to turbulence connected with a linear instability to
Tollmien-Schlichting (TS) waves, and another one, the bypass transition, that
is triggered with finite amplitude perturbation. We use direct numerical
simulations to explore the arrangement of the different routes to turbulence
among the set of initial conditions. For plates that are a distance $2H$ apart
and in a domain of width $2\pi H$ and length $2\pi H$ the subcritical
instability to TS waves sets in at $Re_{c}=5815$ that extends down to
$Re_{TS}\approx4884$. The bypass route becomes available above $Re_E=459$ with
the appearance of three-dimensional finite-amplitude traveling waves. The
bypass transition covers a large set of finite amplitude perturbations. Below
$Re_c$, TS appear for a tiny set of initial conditions that grows with
increasing Reynolds number. Above $Re_c$ the previously stable region becomes
unstable via TS waves, but a sharp transition to the bypass route can still be
identified. Both routes lead to the same turbulent in the final stage of the
transition, but on different time scales. Similar phenomena can be expected in
other flows where two or more routes to turbulence compete.
",0,1,0,0,0,0
18744,Interactive Exploration and Discovery of Scientific Publications with PubVis,"  With an exponentially growing number of scientific papers published each
year, advanced tools for exploring and discovering publications of interest are
becoming indispensable. To empower users beyond a simple keyword search
provided e.g. by Google Scholar, we present the novel web application PubVis.
Powered by a variety of machine learning techniques, it combines essential
features to help researchers find the content most relevant to them. An
interactive visualization of a large collection of scientific publications
provides an overview of the field and encourages the user to explore articles
beyond a narrow research focus. This is augmented by personalized content based
article recommendations as well as an advanced full text search to discover
relevant references. The open sourced implementation of the app can be easily
set up and run locally on a desktop computer to provide access to content
tailored to the specific needs of individual users. Additionally, a PubVis demo
with access to a collection of 10,000 papers can be tested online.
",1,0,0,0,0,0
18745,Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements,"  In many phase II trials in solid tumours, patients are assessed using
endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST)
scale. Often, analyses are based on the response rate. This is the proportion
of patients who have an observed tumour shrinkage above a pre-defined level and
no new tumour lesions. The augmented binary method has been proposed to improve
the precision of the estimator of the response rate. The method involves
modelling the tumour shrinkage to avoid dichotomising it. However, in many
trials the best observed response is used as the primary outcome. In such
trials, patients are followed until progression, and their best observed RECIST
outcome is used as the primary endpoint. In this paper, we propose a method
that extends the augmented binary method so that it can be used when the
outcome is best observed response. We show through simulated data and data from
a real phase II cancer trial that this method improves power in both single-arm
and randomised trials. The average gain in power compared to the traditional
analysis is equivalent to approximately a 35% increase in sample size. A
modified version of the method is proposed to reduce the computational effort
required. We show this modified method maintains much of the efficiency
advantages.
",0,0,0,1,0,0
18746,Evans-Selberg potential on planar domains,"  We provide explicit formulas of Evans kernels, Evans-Selberg potentials and
fundamental metrics on potential-theoretically parabolic planar domains.
",0,0,1,0,0,0
18747,Bridging the Gap Between Computational Photography and Visual Recognition,"  What is the current state-of-the-art for image restoration and enhancement
applied to degraded images acquired under less than ideal circumstances? Can
the application of such algorithms as a pre-processing step to improve image
interpretability for manual analysis or automatic visual recognition to
classify scene content? While there have been important advances in the area of
computational photography to restore or enhance the visual quality of an image,
the capabilities of such techniques have not always translated in a useful way
to visual recognition tasks. Consequently, there is a pressing need for the
development of algorithms that are designed for the joint problem of improving
visual appearance and recognition, which will be an enabling factor for the
deployment of visual recognition tools in many real-world scenarios. To address
this, we introduce the UG^2 dataset as a large-scale benchmark composed of
video imagery captured under challenging conditions, and two enhancement tasks
designed to test algorithmic impact on visual quality and automatic object
recognition. Furthermore, we propose a set of metrics to evaluate the joint
improvement of such tasks as well as individual algorithmic advances, including
a novel psychophysics-based evaluation regime for human assessment and a
realistic set of quantitative measures for object recognition performance. We
introduce six new algorithms for image restoration or enhancement, which were
created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
2018. Under the proposed evaluation regime, we present an in-depth analysis of
these algorithms and a host of deep learning-based and classic baseline
approaches. From the observed results, it is evident that we are in the early
days of building a bridge between computational photography and visual
recognition, leaving many opportunities for innovation in this area.
",1,0,0,0,0,0
18748,On some conjectures of Samuels and Feige,"  Let $\mu_1 \ge \dotsc \ge \mu_n > 0$ and $\mu_1 + \dotsm + \mu_n = 1$. Let
$X_1, \dotsc, X_n$ be independent non-negative random variables with $EX_1 =
\dotsc = EX_n = 1$, and let $Z = \sum_{i=1}^n \mu_i X_i$. Let $M = \max_{1 \le
i \le n} \mu_i = \mu_1$, and let $\delta > 0$ and $T = 1 + \delta$. Both
Samuels and Feige formulated conjectures bounding the probability $P(Z < T)$
from above. We prove that Samuels' conjecture implies a conjecture of Feige.
",0,0,1,0,0,0
18749,Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States,"  Making the right decision in traffic is a challenging task that is highly
dependent on individual preferences as well as the surrounding environment.
Therefore it is hard to model solely based on expert knowledge. In this work we
use Deep Reinforcement Learning to learn maneuver decisions based on a compact
semantic state representation. This ensures a consistent model of the
environment across scenarios as well as a behavior adaptation function,
enabling on-line changes of desired behaviors without re-training. The input
for the neural network is a simulated object list similar to that of Radar or
Lidar sensors, superimposed by a relational semantic scene description. The
state as well as the reward are extended by a behavior adaptation function and
a parameterization respectively. With little expert knowledge and a set of
mid-level actions, it can be seen that the agent is capable to adhere to
traffic rules and learns to drive safely in a variety of situations.
",1,0,0,1,0,0
18750,Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows,"  We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but
simulating data from the model is possible. SNL trains an autoregressive flow
on simulated data in order to learn a model of the likelihood in the region of
high posterior density. A sequential training procedure guides simulations and
reduces simulation cost by orders of magnitude. We show that SNL is more
robust, more accurate and requires less tuning than related neural-based
methods, and we discuss diagnostics for assessing calibration, convergence and
goodness-of-fit.
",0,0,0,1,0,0
18751,Creativity: Generating Diverse Questions using Variational Autoencoders,"  Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as ""creativity"". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.
",1,0,0,0,0,0
18752,Cooling dynamics of a single trapped ion via elastic collisions with small-mass atoms,"  We demonstrated sympathetic cooling of a single ion in a buffer gas of
ultracold atoms with small mass. Efficient collisional cooling was realized by
suppressing collision-induced heating. We attempt to explain the experimental
results with a simple rate equation model and provide a quantitative discussion
of the cooling efficiency per collision. The knowledge we obtained in this work
is an important ingredient for advancing the technique of sympathetic cooling
of ions with neutral atoms.
",0,1,0,0,0,0
18753,Modulational instability in the full-dispersion Camassa-Holm equation,"  We determine the stability and instability of a sufficiently small and
periodic traveling wave to long wavelength perturbations, for a nonlinear
dispersive equation which extends a Camassa-Holm equation to include all the
dispersion of water waves and the Whitham equation to include nonlinearities of
medium amplitude waves. In the absence of the effects of surface tension, the
result qualitatively agrees with the Benjamin-Feir instability of a Stokes
wave. In the presence of the effects of surface tension, it qualitatively
agrees with those from formal asymptotic expansions of the physical problem and
it improves upon that for the Whitham equation, correctly predicting the limit
of strong surface tension. We discuss the modulational stability and
instability in the Camassa-Holm equation and related models.
",0,1,1,0,0,0
18754,Evaluating and Modelling Hanabi-Playing Agents,"  Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addition to an Information Set Monte Carlo Tree Search
(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
predictor version that uses a model of the agents with which it is paired. We
observe a significant improvement in game-playing strength from this agent in
comparison to IS-MCTS, resulting from its consideration of what the other
agents in a game would do. In addition, we create a flawed rule-based agent to
highlight the predictor's capabilities with such an agent.
",1,0,0,0,0,0
18755,Rank-related dimension bounds for subspaces of bilinear forms over finite fields,"  Let q be a power of a prime and let V be a vector space of finite dimension n
over the field of order q. Let Bil(V) denote the set of all bilinear forms
defined on V x V, let Symm(V) denote the subspace of Bil(V) consisting of
symmetric bilinear forms, and Alt(V) denote the subspace of alternating
bilinear forms. Let M denote a subspace of any of the spaces Bil(V), Symm(V),
or Alt(V). In this paper we investigate hypotheses on the rank of the non-zero
elements of M which lead to reasonable bounds for dim M. Typically, we look at
the case where exactly two or three non-zero ranks occur, one of which is
usually n. In the case that M achieves the maximal dimension predicted by the
dimension bound, we try to enumerate the number of forms of a given rank in M
and describe geometric properties of the radicals of the degenerate elements of
M.
",0,0,1,0,0,0
18756,Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks,"  Bayesian Networks have been widely used in the last decades in many fields,
to describe statistical dependencies among random variables. In general,
learning the structure of such models is a problem with considerable
theoretical interest that still poses many challenges. On the one hand, this is
a well-known NP-complete problem, which is practically hardened by the huge
search space of possible solutions. On the other hand, the phenomenon of
I-equivalence, i.e., different graphical structures underpinning the same set
of statistical dependencies, may lead to multimodal fitness landscapes further
hindering maximum likelihood approaches to solve the task. Despite all these
difficulties, greedy search methods based on a likelihood score coupled with a
regularization term to account for model complexity, have been shown to be
surprisingly effective in practice. In this paper, we consider the formulation
of the task of learning the structure of Bayesian Networks as an optimization
problem based on a likelihood score. Nevertheless, our approach do not adjust
this score by means of any of the complexity terms proposed in the literature;
instead, it accounts directly for the complexity of the discovered solutions by
exploiting a multi-objective optimization procedure. To this extent, we adopt
NSGA-II and define the first objective function to be the likelihood of a
solution and the second to be the number of selected arcs. We thoroughly
analyze the behavior of our method on a wide set of simulated data, and we
discuss the performance considering the goodness of the inferred solutions both
in terms of their objective functions and with respect to the retrieved
structure. Our results show that NSGA-II can converge to solutions
characterized by better likelihood and less arcs than classic approaches,
although paradoxically frequently characterized by a lower similarity to the
target network.
",0,0,0,1,0,0
18757,Simultaneous Localization and Layout Model Selection in Manhattan Worlds,"  In this paper, we will demonstrate how Manhattan structure can be exploited
to transform the Simultaneous Localization and Mapping (SLAM) problem, which is
typically solved by a nonlinear optimization over feature positions, into a
model selection problem solved by a convex optimization over higher order
layout structures, namely walls, floors, and ceilings. Furthermore, we show how
our novel formulation leads to an optimization procedure that automatically
performs data association and loop closure and which ultimately produces the
simplest model of the environment that is consistent with the available
measurements. We verify our method on real world data sets collected with
various sensing modalities.
",1,0,0,0,0,0
18758,Robust method for finding sparse solutions to linear inverse problems using an L2 regularization,"  We analyzed the performance of a biologically inspired algorithm called the
Corrected Projections Algorithm (CPA) when a sparseness constraint is required
to unambiguously reconstruct an observed signal using atoms from an
overcomplete dictionary. By changing the geometry of the estimation problem,
CPA gives an analytical expression for a binary variable that indicates the
presence or absence of a dictionary atom using an L2 regularizer. The
regularized solution can be implemented using an efficient real-time
Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it
very robust to noise, and CPA outperforms other methods in identifying known
atoms in the presence of strong novel atoms in the signal.
",1,0,0,1,0,0
18759,Humanoid Robot-Application and Influence,"  Application of humanoid robots has been common in the field of healthcare and
education. It has been recurrently used to improve social behavior and mollify
distress level among children with autism, cancer and cerebral palsy. This
article discusses the same from a human factors perspective. It shows how
people of different age and gender have a different opinion towards the
application and acceptance of humanoid robots. Additionally, this article
highlights the influence of cerebral condition and social interaction on a user
behavior and attitude towards humanoid robots. Our study performed a literature
review and found that (a) children and elderly individuals prefer humanoid
robots due to inactive social interaction, (b) The deterministic behavior of
humanoid robots can be acknowledged to improve social behavior of autistic
children, (c) Trust on humanoid robots is highly driven by its application and
a user age, gender, and social life.
",1,0,0,0,0,0
18760,Onsager's Conjecture for the Incompressible Euler Equations in Bounded Domains,"  The goal of this note is to show that, also in a bounded domain $\Omega
\subset \mathbb{R}^n$, with $\partial \Omega\in C^2$, any weak solution,
$(u(x,t),p(x,t))$, of the Euler equations of ideal incompressible fluid in
$\Omega\times (0,T) \subset \mathbb{R}^n\times\mathbb{R}_t$, with the
impermeability boundary condition: $u\cdot \vec n =0$ on
$\partial\Omega\times(0,T)$, is of constant energy on the interval $(0,T)$
provided the velocity field $u \in L^3((0,T);
C^{0,\alpha}(\overline{\Omega}))$, with $\alpha>\frac13\,.$
",0,1,1,0,0,0
18761,Fuzzy logic based approaches for gene regulatory network inference,"  The rapid advancement in high-throughput techniques has fueled the generation
of large volume of biological data rapidly with low cost. Some of these
techniques are microarray and next generation sequencing which provides genome
level insight of living cells. As a result, the size of most of the biological
databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These
biological data are analyzed using computational techniques for knowledge
discovery - which is one of the objectives of bioinformatics research. Gene
regulatory network (GRN) is a gene-gene interaction network which plays pivotal
role in understanding gene regulation process and disease studies. From the
last couple of decades, the researchers are interested in developing
computational algorithms for GRN inference (GRNI) using high-throughput
experimental data. Several computational approaches have been applied for
inferring GRN from gene expression data including statistical techniques
(correlation coefficient), information theory (mutual information), regression
based approaches, probabilistic approaches (Bayesian networks, naive byes),
artificial neural networks, and fuzzy logic. The fuzzy logic, along with its
hybridization with other intelligent approach, is well studied in GRNI due to
its several advantages. In this paper, we present a consolidated review on
fuzzy logic and its hybrid approaches for GRNI developed during last two
decades.
",0,0,0,0,1,0
18762,Instrumentation for nuclear magnetic resonance in zero and ultralow magnetic field,"  We review instrumentation for nuclear magnetic resonance (NMR) in zero and
ultra-low magnetic field (ZULF, below 0.1 $\mu$T) where detection is based on a
low-cost, non-cryogenic, spin-exchange relaxation free (SERF) $^{87}$Rb atomic
magnetometer. The typical sensitivity is 20-30 fT/Hz$^{1/2}$ for signal
frequencies below 1 kHz and NMR linewidths range from Hz all the way down to
tens of mHz. These features enable precision measurements of chemically
informative nuclear spin-spin couplings as well as nuclear spin precession in
ultra-low magnetic fields.
",0,1,0,0,0,0
18763,Countable dense homogeneity and the Cantor set,"  It is shown that CH implies the existence of a compact Hausdorff space that
is countable dense homogeneous, crowded and does not contain topological copies
of the Cantor set. This contrasts with a previous result by the author which
says that for any crowded Hausdorff space $X$ of countable $\pi$-weight, if
${}^\omega{X}$ is countable dense homogeneous, then $X$ must contain a
topological copy of the Cantor set.
",0,0,1,0,0,0
18764,Dimension Estimation Using Random Connection Models,"  Information about intrinsic dimension is crucial to perform dimensionality
reduction, compress information, design efficient algorithms, and do
statistical adaptation. In this paper we propose an estimator for the intrinsic
dimension of a data set. The estimator is based on binary neighbourhood
information about the observations in the form of two adjacency matrices, and
does not require any explicit distance information. The underlying graph is
modelled according to a subset of a specific random connection model, sometimes
referred to as the Poisson blob model. Computationally the estimator scales
like n log n, and we specify its asymptotic distribution and rate of
convergence. A simulation study on both real and simulated data shows that our
approach compares favourably with some competing methods from the literature,
including approaches that rely on distance information.
",0,0,1,1,0,0
18765,Optimal projection of observations in a Bayesian setting,"  Optimal dimensionality reduction methods are proposed for the Bayesian
inference of a Gaussian linear model with additive noise in presence of
overabundant data. Three different optimal projections of the observations are
proposed based on information theory: the projection that minimizes the
Kullback-Leibler divergence between the posterior distributions of the original
and the projected models, the one that minimizes the expected Kullback-Leibler
divergence between the same distributions, and the one that maximizes the
mutual information between the parameter of interest and the projected
observations. The first two optimization problems are formulated as the
determination of an optimal subspace and therefore the solution is computed
using Riemannian optimization algorithms on the Grassmann manifold. Regarding
the maximization of the mutual information, it is shown that there exists an
optimal subspace that minimizes the entropy of the posterior distribution of
the reduced model; a basis of the subspace can be computed as the solution to a
generalized eigenvalue problem; an a priori error estimate on the mutual
information is available for this particular solution; and that the
dimensionality of the subspace to exactly conserve the mutual information
between the input and the output of the models is less than the number of
parameters to be inferred. Numerical applications to linear and nonlinear
models are used to assess the efficiency of the proposed approaches, and to
highlight their advantages compared to standard approaches based on the
principal component analysis of the observations.
",0,0,1,1,0,0
18766,Notes on the Multiplicative Ergodic Theorem,"  The Oseledets Multiplicative Ergodic theorem is a basic result with numerous
applications throughout dynamical systems. These notes provide an introduction
to this theorem, as well as subsequent generalizations. They are based on
lectures at summer schools in Brazil, France, and Russia.
",0,0,1,0,0,0
18767,Targeted Learning with Daily EHR Data,"  Electronic health records (EHR) data provide a cost and time-effective
opportunity to conduct cohort studies of the effects of multiple time-point
interventions in the diverse patient population found in real-world clinical
settings. Because the computational cost of analyzing EHR data at daily (or
more granular) scale can be quite high, a pragmatic approach has been to
partition the follow-up into coarser intervals of pre-specified length. Current
guidelines suggest employing a 'small' interval, but the feasibility and
practical impact of this recommendation has not been evaluated and no formal
methodology to inform this choice has been developed. We start filling these
gaps by leveraging large-scale EHR data from a diabetes study to develop and
illustrate a fast and scalable targeted learning approach that allows to follow
the current recommendation and study its practical impact on inference. More
specifically, we map daily EHR data into four analytic datasets using 90, 30,
15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
treatment rules with each dataset, and compare the resulting inferences. To
overcome the computational challenges presented by the size of these data, we
propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
latest advances in scalable data-adaptive machine-learning software, xgboost
and h2o, for estimation of the TMLE nuisance parameters.
",0,0,0,1,0,0
18768,Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques,"  A framework for the generation of bridge-specific fragility utilizing the
capabilities of machine learning and stripe-based approach is presented in this
paper. The proposed methodology using random forests helps to generate or
update fragility curves for a new set of input parameters with less
computational effort and expensive re-simulation. The methodology does not
place any assumptions on the demand model of various components and helps to
identify the relative importance of each uncertain variable in their seismic
demand model. The methodology is demonstrated through the case studies of
multi-span concrete bridges in California. Geometric, material and structural
uncertainties are accounted for in the generation of bridge models and
fragility curves. It is also noted that the traditional lognormality assumption
on the demand model leads to unrealistic fragility estimates. Fragility results
obtained the proposed methodology curves can be deployed in risk assessment
platform such as HAZUS for regional loss estimation.
",0,0,0,1,0,0
18769,Accountability of AI Under the Law: The Role of Explanation,"  The ubiquity of systems using artificial intelligence or ""AI"" has brought
increasing attention to how those systems should be regulated. The choice of
how to regulate AI systems will require care. AI systems have the potential to
synthesize large amounts of data, allowing for greater levels of
personalization and precision than ever before---applications range from
clinical decision support to autonomous driving and predictive policing. That
said, there exist legitimate concerns about the intentional and unintentional
negative consequences of AI systems. There are many ways to hold AI systems
accountable. In this work, we focus on one: explanation. Questions about a
legal right to explanation from AI systems was recently debated in the EU
General Data Protection Regulation, and thus thinking carefully about when and
how explanation from AI systems might improve accountability is timely. In this
work, we review contexts in which explanation is currently required under the
law, and then list the technical considerations that must be considered if we
desired AI systems that could provide kinds of explanations that are currently
required of humans.
",1,0,0,1,0,0
18770,Frequency-oriented sub-sampling by photonic Fourier transform and I/Q demodulation,"  Sub-sampling can acquire directly a passband within a broad radio frequency
(RF) range, avoiding down-conversion and low-phase-noise tunable local
oscillation (LO). However, sub-sampling suffers from band folding and
self-image interference. In this paper we propose a frequency-oriented
sub-sampling to solve the two problems. With ultrashort optical pulse and a
pair of chromatic dispersions, the broadband RF signal is firstly short-time
Fourier-transformed to a spectrum-spread pulse. Then a time slot, corresponding
to the target spectrum slice, is coherently optical-sampled with
in-phase/quadrature (I/Q) demodulation. We demonstrate the novel bandpass
sampling by a numerical example, which shows the desired uneven intensity
response, i.e. pre-filtering. We show in theory that appropriate time-stretch
capacity from dispersion can result in pre-filtering bandwidth less than
sampling rate. Image rejection due to I/Q sampling is also analyzed. A
proof-of-concept experiment, which is based on a time-lens sampling source and
chirped fiber Bragg gratings (CFBGs), shows the center-frequency-tunable
pre-filtered sub-sampling with bandwidth of 6 GHz around, as well as imaging
rejection larger than 26 dB. Our technique may benefit future broadband RF
receivers for frequency-agile Radar or channelization.
",0,1,0,0,0,0
18771,A moment map picture of relative balanced metrics on extremal Kähler manifolds,"  We give a moment map interpretation of some relatively balanced metrics. As
an application, we extend a result of S. K. Donaldson on constant scalar
curvature Kähler metrics to the case of extremal metrics. Namely, we show
that a given extremal metric is the limit of some specific relatively balanced
metrics. As a corollary, we recover uniqueness and splitting results for
extremal metrics in the polarized case.
",0,0,1,0,0,0
18772,Traffic models with adversarial vehicle behaviour,"  We examine the impact of adversarial actions on vehicles in traffic. Current
advances in assisted/autonomous driving technologies are supposed to reduce the
number of casualties, but this seems to be desired despite the recently proved
insecurity of in-vehicle communication buses or components. Fortunately to some
extent, while compromised cars have become a reality, the numerous attacks
reported so far on in-vehicle electronics are exclusively concerned with
impairments of a single target. In this work we put adversarial behavior under
a more complex scenario where driving decisions deluded by corrupted
electronics can affect more than one vehicle. Particularly, we focus our
attention on chain collisions involving multiple vehicles that can be amplified
by simple adversarial interventions, e.g., delaying taillights or falsifying
speedometer readings. We provide metrics for assessing adversarial impact and
consider safety margins against adversarial actions. Moreover, we discuss
intelligent adversarial behaviour by which the creation of rogue platoons is
possible and speed manipulations become stealthy to human drivers. We emphasize
that our work does not try to show the mere fact that imprudent speeds and
headways lead to chain-collisions, but points out that an adversary may favour
such scenarios (eventually keeping his actions stealthy for human drivers) and
further asks for quantifying the impact of adversarial activity or whether
existing traffic regulations are prepared for such situations.
",1,0,0,0,0,0
18773,Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments,"  This paper presents two novel control methodologies for the cooperative
manipulation of an object by N robotic agents. Firstly, we design an adaptive
control protocol which employs quaternion feedback for the object orientation
to avoid potential representation singularities. Secondly, we propose a control
protocol that guarantees predefined transient and steady-state performance for
the object trajectory. Both methodologies are decentralized, since the agents
calculate their own signals without communicating with each other, as well as
robust to external disturbances and model uncertainties. Moreover, we consider
that the grasping points are rigid, and avoid the need for force/torque
measurements. Load distribution is also included via a grasp matrix
pseudo-inverse to account for potential differences in the agents' power
capabilities. Finally, simulation and experimental results with two robotic
arms verify the theoretical findings.
",1,0,0,0,0,0
18774,Conditionally conjugate mean-field variational Bayes for logistic models,"  Variational Bayes (VB) is a common strategy for approximate Bayesian
inference, but simple methods are only available for specific classes of models
including, in particular, representations having conditionally conjugate
constructions within an exponential family. Models with logit components are an
apparently notable exception to this class, due to the absence of conjugacy
between the logistic likelihood and the Gaussian priors for the coefficients in
the linear predictor. To facilitate approximate inference within this widely
used class of models, Jaakkola and Jordan (2000) proposed a simple variational
approach which relies on a family of tangent quadratic lower bounds of logistic
log-likelihoods, thus restoring conjugacy between these approximate bounds and
the Gaussian priors. This strategy is still implemented successfully, but less
attempts have been made to formally understand the reasons underlying its
excellent performance. To cover this key gap, we provide a formal connection
between the above bound and a recent Pólya-gamma data augmentation for
logistic regression. Such a result places the computational methods associated
with the aforementioned bounds within the framework of variational inference
for conditionally conjugate exponential family models, thereby allowing recent
advances for this class to be inherited also by the methods relying on Jaakkola
and Jordan (2000).
",0,0,1,1,0,0
18775,A $\frac{3}{2}$-Approximation Algorithm for Tree Augmentation via Chvátal-Gomory Cuts,"  The weighted tree augmentation problem (WTAP) is a fundamental network design
problem. We are given an undirected tree $G = (V,E)$, an additional set of
edges $L$ called links and a cost vector $c \in \mathbb{R}^L_{\geq 1}$. The
goal is to choose a minimum cost subset $S \subseteq L$ such that $G = (V, E
\cup S)$ is $2$-edge-connected. In the unweighted case, that is, when we have
$c_\ell = 1$ for all $\ell \in L$, the problem is called the tree augmentation
problem (TAP).
Both problems are known to be APX-hard, and the best known approximation
factors are $2$ for WTAP by (Frederickson and JáJá, '81) and $\tfrac{3}{2}$
for TAP due to (Kortsarz and Nutov, TALG '16). In the case where all link costs
are bounded by a constant $M$, (Adjiashvili, SODA '17) recently gave a $\approx
1.96418+\varepsilon$-approximation algorithm for WTAP under this assumption.
This is the first approximation with a better guarantee than $2$ that does not
require restrictions on the structure of the tree or the links.
In this paper, we improve Adjiashvili's approximation to a
$\frac{3}{2}+\varepsilon$-approximation for WTAP under the bounded cost
assumption. We achieve this by introducing a strong LP that combines
$\{0,\frac{1}{2}\}$-Chvátal-Gomory cuts for the standard LP for the problem
with bundle constraints from Adjiashvili. We show that our LP can be solved
efficiently and that it is exact for some instances that arise at the core of
Adjiashvili's approach. This results in the improved guarantee of
$\frac{3}{2}+\varepsilon$. For TAP, this is the best known LP-based result, and
matches the bound of $\frac{3}{2}+\varepsilon$ achieved by the best SDP-based
algorithm due to (Cheriyan and Gao, arXiv '15).
",1,0,0,0,0,0
18776,On Identifying Disaster-Related Tweets: Matching-based or Learning-based?,"  Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach.
",1,0,0,0,0,0
18777,Self-Stabilizing Disconnected Components Detection and Rooted Shortest-Path Tree Maintenance in Polynomial Steps,"  We deal with the problem of maintaining a shortest-path tree rooted at some
process r in a network that may be disconnected after topological changes. The
goal is then to maintain a shortest-path tree rooted at r in its connected
component, V\_r, and make all processes of other components detecting that r is
not part of their connected component. We propose, in the composite atomicity
model, a silent self-stabilizing algorithm for this problem working in
semi-anonymous networks, where edges have strictly positive weights. This
algorithm does not require any a priori knowledge about global parameters of
the network. We prove its correctness assuming the distributed unfair daemon,
the most general daemon. Its stabilization time in rounds is at most 3nmax+D,
where nmax is the maximum number of non-root processes in a connected component
and D is the hop-diameter of V\_r. Furthermore, if we additionally assume that
edge weights are positive integers, then it stabilizes in a polynomial number
of steps: namely, we exhibit a bound in O(maxi nmax^3 n), where maxi is the
maximum weight of an edge and n is the number of processes.
",1,0,0,0,0,0
18778,Optimizing wearable assistive devices with neuromuscular models and optimal control,"  The coupling of human movement dynamics with the function and design of
wearable assistive devices is vital to better understand the interaction
between the two. Advanced neuromuscular models and optimal control formulations
provide the possibility to study and improve this interaction. In addition,
optimal control can also be used to generate predictive simulations that
generate novel movements for the human model under varying optimization
criterion.
",1,0,0,0,0,0
18779,Preferential placement for community structure formation,"  Various models have been recently proposed to reflect and predict different
properties of complex networks. However, the community structure, which is one
of the most important properties, is not well studied and modeled. In this
paper, we suggest a principle called ""preferential placement"", which allows to
model a realistic clustering structure. We provide an extensive empirical
analysis of the obtained structure as well as some theoretical results.
",1,1,0,0,0,0
18780,Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization,"  In this paper, we consider the problem of sequentially optimizing a black-box
function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
process bandit optimization. We provide algorithm-independent lower bounds on
the simple regret, measuring the suboptimality of a single point reported after
$T$ rounds, and on the cumulative regret, measuring the sum of regrets over the
$T$ chosen points. For the isotropic squared-exponential kernel in $d$
dimensions, we find that an average simple regret of $\epsilon$ requires $T =
\Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the
average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}}
\big)$, thus matching existing upper bounds up to the replacement of $d/2$ by
$2d+O(1)$ in both cases. For the Matérn-$\nu$ kernel, we give analogous
bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and
$\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting
gaps to the existing upper bounds.
",1,0,0,1,0,0
18781,Hirota bilinear equations for Painlevé transcendents,"  We present some observations on the tau-function for the fourth Painlevé
equation. By considering a Hirota bilinear equation of order four for this
tau-function, we describe the general form of the Taylor expansion around an
arbitrary movable zero. The corresponding Taylor series for the tau-functions
of the first and second Painlevé equations, as well as that for the
Weierstrass sigma function, arise naturally as special cases, by setting
certain parameters to zero.
",0,1,1,0,0,0
18782,Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data,"  Representational Similarity Analysis (RSA) aims to explore similarities
between neural activities of different stimuli. Classical RSA techniques employ
the inverse of the covariance matrix to explore a linear model between the
neural activities and task events. However, calculating the inverse of a
large-scale covariance matrix is time-consuming and can reduce the stability
and robustness of the final analysis. Notably, it becomes severe when the
number of samples is too large. For facing this shortcoming, this paper
proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
proposed method is not restricted to a linear model. In fact, there is a
growing interest in finding more effective ways of using multi-subject and
whole-brain fMRI data. Searchlight technique can extend RSA from the localized
brain regions to the whole-brain regions with smaller memory footprint in each
process. Based on Searchlight, we propose a new method called Spatiotemporal
Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
the whole-brain data. Further, our approach can handle some computational
challenges while dealing with large-scale, multi-subject fMRI data.
Experimental studies on multi-subject datasets confirm that both proposed
approaches achieve superior performance to other state-of-the-art RSA
algorithms.
",0,0,0,1,1,0
18783,Diffeomorphic random sampling using optimal information transport,"  In this article we explore an algorithm for diffeomorphic random sampling of
nonuniform probability distributions on Riemannian manifolds. The algorithm is
based on optimal information transport (OIT)---an analogue of optimal mass
transport (OMT). Our framework uses the deep geometric connections between the
Fisher-Rao metric on the space of probability densities and the right-invariant
information metric on the group of diffeomorphisms. The resulting sampling
algorithm is a promising alternative to OMT, in particular as our formulation
is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to
Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when
a large number of samples from a low dimensional nonuniform distribution is
needed.
",0,0,1,1,0,0
18784,On approximations by trigonometric polynomials of classes of functions defined by moduli of smoothness,"  In this paper, we give a characterization of Nikol'ski\u{\i}-Besov type
classes of functions, given by integral representations of moduli of
smoothness, in terms of series over the moduli of smoothness. Also, necessary
and sufficient conditions in terms of monotone or lacunary Fourier coefficients
for a function to belong to a such a class are given. In order to prove our
results, we make use of certain recent reverse Copson- and Leindler-type
inequalities.
",0,0,1,0,0,0
18785,Topologically protected Dirac plasmons in graphene,"  Topological optical states exhibit unique immunity to defects and the ability
to propagate without losses rendering them ideal for photonic applications.A
powerful class of such states is based on time-reversal symmetry breaking of
the optical response.However, existing proposals either involve sophisticated
and bulky structural designs or can only operate in the microwave regime. Here,
we propose and provide a theoretical proof-of-principle demonstration for
highly confined topologically protected optical states to be realized at
infrared frequencies in a simple 2D material structure-a periodically patterned
graphene monolayer-subject to a magnetic field below 1 tesla. In our graphene
honeycomb superlattice structures plasmons exhibit substantial nonreciprocal
behavior at the superlattice junctions, leading to the emergence of
topologically protected edge states and localized bulk modes enabled by the
strong magneto-optical response of this material, which leads to
time-reversal-symmetry breaking already at moderate static magnetic fields. The
proposed approach is simple and robust for realizing topologically nontrivial
2D optical states, not only in graphene, but also in other 2D atomic layers,
and could pave the way for realizing fast, nanoscale, defect-immune devices for
integrated photonics applications.
",0,1,0,0,0,0
18786,The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates,"  In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
for multi-armed bandits with covariates. We focus on a setting where the
covariates are supported on a metric space of low intrinsic dimension, such as
a manifold embedded within a high dimensional ambient feature space. The
algorithm is conceptually simple and straightforward to implement. The
k-Nearest Neighbour UCB algorithm does not require prior knowledge of the
either the intrinsic dimension of the marginal distribution or the time
horizon. We prove a regret bound for the k-Nearest Neighbour UCB algorithm
which is minimax optimal up to logarithmic factors. In particular, the
algorithm automatically takes advantage of both low intrinsic dimensionality of
the marginal distribution over the covariates and low noise in the data,
expressed as a margin condition. In addition, focusing on the case of bounded
rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB
algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting
of multi-armed bandits with covariates. Finally, we present empirical results
which demonstrate the ability of both the k-Nearest Neighbour UCB and k-Nearest
Neighbour KL-UCB to take advantage of situations where the data is supported on
an unknown sub-manifold of a high-dimensional feature space.
",0,0,0,1,0,0
18787,Proportional Mean Residual Life Model with Censored Survival Data under Case-cohort Design,"  Proportional mean residual life model is studied for analysing survival data
from the case-cohort design. To simultaneously estimate the regression
parameters and the baseline mean residual life function, weighted estimating
equations based on an inverse selection probability are proposed. The resulting
regression coefficients estimates are shown to be consistent and asymptotic
normal with easily estimated variance-covariance. Simulation studies show that
the proposed estimators perform very well. An application to a real dataset
from the South Welsh nickel refiners study is also given to illustrate the
methodology.
",0,0,1,1,0,0
18788,Shape differentiation of a steady-state reaction-diffusion problem arising in Chemical Engineering: the case of non-smooth kinetic with dead core,"  In this paper we consider an extension of the results in shape
differentiation of semilinear equations with smooth nonlinearity presented in
J.I. Díaz and D. Gómez-Castro: An Application of Shape Differentiation to
the Effectiveness of a Steady State Reaction-Diffusion Problem Arising in
Chemical Engineering. Electron. J. Differ. Equations in 2015 to the case in
which the nonlinearities might be less smooth. Namely we will show that Gateaux
shape derivatives exists when the nonlinearity is only Lipschitz continuous,
and we will give a definition of the derivative when the nonlinearity has a
blow up. In this direction, we will study the case of root-type nonlinearities.
",0,0,1,0,0,0
18789,"Analytic evaluation of Coulomb integrals for one, two and three-electron distance operators, $R_{C1}^{-n}R_{D1}^{-m}$, $R_{C1}^{-n}r_{12}^{-m}$ and $r_{12}^{-n}r_{13}^{-m}$ with $n, m=0,1,2$","  The state of the art for integral evaluation is that analytical solutions to
integrals are far more useful than numerical solutions. We evaluate certain
integrals analytically that are necessary in some approaches in quantum
chemistry. In the title, where R stands for nucleus-electron and r for
electron-electron distances, the $(n,m)=(0,0)$ case is trivial, the
$(n,m)=(1,0)$ and (0,1) cases are well known, fundamental milestone in
integration and widely used in computation chemistry, as well as based on
Laplace transformation with integrand exp(-$a^2t^2$). The rest of the cases are
new and need the other Laplace transformation with integrand exp(-$a^2t$) also,
as well as the necessity of a two dimensional version of Boys function comes up
in case. These analytic expressions (up to Gaussian function integrand) are
useful for manipulation with higher moments of inter-electronic distances, for
example in correlation calculations.
",0,1,0,0,0,0
18790,Herschel-PACS photometry of faint stars,"  Our aims are to determine flux densities and their photometric accuracy for a
set of seventeen stars that range in flux from intermediately bright (<2.5 Jy)
to faint (>5 mJy) in the far-infrared (FIR). We also aim to derive
signal-to-noise dependence with flux and time, and compare the results with
predictions from the Herschel exposure-time calculation tool. The PACS faint
star sample has allowed a comprehensive sensitivity assessment of the PACS
photometer. Accurate photometry allows us to establish a set of five FIR
primary standard candidates, namely alpha Ari, epsilon Lep, omega,Cap, HD41047
and 42Dra, which are 2 -- 20 times fainter than the faintest PACS fiducial
standard (gamma Dra) with absolute accuracy of <6%. For three of these primary
standard candidates, essential stellar parameters are known, meaning that a
dedicated flux model code may be run.
",0,1,0,0,0,0
18791,Role of zero synapses in unsupervised feature learning,"  Synapses in real neural circuits can take discrete values, including zero
(silent or potential) synapses. The computational role of zero synapses in
unsupervised feature learning of unlabeled noisy data is still unclear, thus it
is important to understand how the sparseness of synaptic activity is shaped
during learning and its relationship with receptive field formation. Here, we
formulate this kind of sparse feature learning by a statistical mechanics
approach. We find that learning decreases the fraction of zero synapses, and
when the fraction decreases rapidly around a critical data size, an
intrinsically structured receptive field starts to develop. Further increasing
the data size refines the receptive field, while a very small fraction of zero
synapses remain to act as contour detectors. This phenomenon is discovered not
only in learning a handwritten digits dataset, but also in learning retinal
neural activity measured in a natural-movie-stimuli experiment.
",1,1,0,0,0,0
18792,On the Pervasiveness of Difference-Convexity in Optimization and Statistics,"  With the increasing interest in applying the methodology of
difference-of-convex (dc) optimization to diverse problems in engineering and
statistics, this paper establishes the dc property of many well-known functions
not previously known to be of this class. Motivated by a quadratic programming
based recourse function in two-stage stochastic programming, we show that the
(optimal) value function of a copositive (thus not necessarily convex)
quadratic program is dc on the domain of finiteness of the program when the
matrix in the objective function's quadratic term and the constraint matrix are
fixed. The proof of this result is based on a dc decomposition of a piecewise
LC1 function (i.e., functions with Lipschitz gradients). Armed with these new
results and known properties of dc functions existed in the literature, we show
that many composite statistical functions in risk analysis, including the
value-at-risk (VaR), conditional value-at-risk (CVaR), expectation-based,
VaR-based, and CVaR-based random deviation functions are all dc. Adding the
known class of dc surrogate sparsity functions that are employed as
approximations of the l_0 function in statistical learning, our work
significantly expands the family of dc functions and positions them for
fruitful applications.
",0,0,1,0,0,0
18793,Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage,"  The penetration of distributed renewable energy (DRE) greatly raises the risk
of distribution network operation such as peak shaving and voltage stability.
Battery energy storage (BES) has been widely accepted as the most potential
application to cope with the challenge of high penetration of DRE. To cope with
the uncertainties and variability of DRE, a stochastic day-ahead dynamic
optimal power flow (DOPF) and its algorithm are proposed. The overall economy
is achieved by fully considering the DRE, BES, electricity purchasing and
active power losses. The rainflow algorithm-based cycle counting method of BES
is incorporated in the DOPF model to capture the cell degradation, greatly
extending the expected BES lifetime and achieving a better economy. DRE
scenarios are generated to consider the uncertainties and correlations based on
the Copula theory. To solve the DOPF model, we propose a Lagrange
relaxation-based algorithm, which has a significantly reduced complexity with
respect to the existing techniques. For this reason, the proposed algorithm
enables much more scenarios incorporated in the DOPF model and better captures
the DRE uncertainties and correlations. Finally, numerical studies for the
day-ahead DOPF in the IEEE 123-node test feeder are presented to demonstrate
the merits of the proposed method. Results show that the actual BES life
expectancy of the proposed model has increased to 4.89 times compared with the
traditional ones. The problems caused by DRE are greatly alleviated by fully
capturing the uncertainties and correlations with the proposed method.
",0,0,1,0,0,0
18794,Achievable Rate Region of the Zero-Forcing Precoder in a 2 X 2 MU-MISO Broadcast VLC Channel with Per-LED Peak Power Constraint and Dimming Control,"  In this paper, we consider the 2 X 2 multi-user multiple-input-single-output
(MU-MISO) broadcast visible light communication (VLC) channel with two light
emitting diodes (LEDs) at the transmitter and a single photo diode (PD) at each
of the two users. We propose an achievable rate region of the Zero-Forcing (ZF)
precoder in this 2 X 2 MU-MISO VLC channel under a per-LED peak and average
power constraint, where the average optical power emitted from each LED is
fixed for constant lighting, but is controllable (referred to as dimming
control in IEEE 802.15.7 standard on VLC). We analytically characterize the
proposed rate region boundary and show that it is Pareto-optimal. Further
analysis reveals that the largest rate region is achieved when the fixed
per-LED average optical power is half of the allowed per-LED peak optical
power. We also propose a novel transceiver architecture where the channel
encoder and dimming control are separated which greatly simplifies the
complexity of the transceiver. A case study of an indoor VLC channel with the
proposed transceiver reveals that the achievable information rates are
sensitive to the placement of the LEDs and the PDs. An interesting observation
is that for a given placement of LEDs in a 5 m X 5 m X 3 m room, even with a
substantial displacement of the users from their optimum placement, reduction
in the achievable rates is not significant. This observation could therefore be
used to define ""coverage zones"" within a room where the reduction in the
information rates to the two users is within an acceptable tolerance limit.
",1,0,1,0,0,0
18795,Autonomous Electric Race Car Design,"  Autonomous driving and electric vehicles are nowadays very active research
and development areas. In this paper we present the conversion of a standard
Kyburz eRod into an autonomous vehicle that can be operated in challenging
environments such as Swiss mountain passes. The overall hardware and software
architectures are described in detail with a special emphasis on the sensor
requirements for autonomous vehicles operating in partially structured
environments. Furthermore, the design process itself and the finalized system
architecture are presented. The work shows state of the art results in
localization and controls for self-driving high-performance electric vehicles.
Test results of the overall system are presented, which show the importance of
generalizable state estimation algorithms to handle a plethora of conditions.
",1,0,0,0,0,0
18796,Temporal Stable Community in Time-Varying Networks,"  Identifying community structure of a complex network provides insight to the
interdependence between the network topology and emergent collective behaviors
of networks, while detecting such invariant communities in a time-varying
network is more challenging. In this paper, we define the temporal stable
community and newly propose the concept of dynamic modularity to evaluate the
stable community structures in time-varying networks, which is robust against
small changes as verified by several empirical time-varying network datasets.
Besides, using the volatility features of temporal stable communities in
functional brain networks, we successfully differentiate the ADHD (Attention
Deficit Hyperactivity Disorder) patients and healthy controls efficiently.
",0,0,0,0,1,0
18797,The shape of a rapidly rotating polytrope with index unity,"  We show that the solutions obtained in the paper `An exact solution for
arbitrarily rotating gaseous polytropes with index unity' by Kong, Zhang, and
Schubert represent only approximate solutions of the free-boundary
Euler-Poisson system of equations describing uniformly rotating,
self-gravitating polytropes with index unity. We discuss the quality of such
solutions as approximations to the rigidly rotating equilibrium polytropic
configurations.
",0,1,0,0,0,0
18798,Measurement of the Planck constant at the National Institute of Standards and Technology from 2015 to 2017,"  Researchers at the National Institute of Standards and Technology(NIST) have
measured the value of the Planck constant to be $h =6.626\,069\,934(89)\times
10^{-34}\,$J$\,$s (relative standard uncertainty $13\times 10^{-9}$). The
result is based on over 10$\,$000 weighings of masses with nominal values
ranging from 0.5$\,$kg to 2$\,$kg with the Kibble balance NIST-4. The
uncertainty has been reduced by more than twofold relative to a previous
determination because of three factors: (1) a much larger data set than
previously available, allowing a more realistic, and smaller, Type A
evaluation; (2) a more comprehensive measurement of the back action of the
weighing current on the magnet by weighing masses up to 2$\,$kg, decreasing the
uncertainty associated with magnet non-linearity; (3) a rigorous investigation
of the dependence of the geometric factor on the coil velocity reducing the
uncertainty assigned to time-dependent leakage of current in the coil.
",0,1,0,0,0,0
18799,Event Stream-Based Process Discovery using Abstract Representations,"  The aim of process discovery, originating from the area of process mining, is
to discover a process model based on business process execution data. A
majority of process discovery techniques relies on an event log as an input. An
event log is a static source of historical data capturing the execution of a
business process. In this paper we focus on process discovery relying on online
streams of business process execution events. Learning process models from
event streams poses both challenges and opportunities, i.e. we need to handle
unlimited amounts of data using finite memory and, preferably, constant time.
We propose a generic architecture that allows for adopting several classes of
existing process discovery techniques in context of event streams. Moreover, we
provide several instantiations of the architecture, accompanied by
implementations in the process mining tool-kit ProM (this http URL).
Using these instantiations, we evaluate several dimensions of stream-based
process discovery. The evaluation shows that the proposed architecture allows
us to lift process discovery to the streaming domain.
",1,0,0,1,0,0
18800,"On $C$-bases, partition pairs and filtrations for induced or restricted Specht modules","  We obtain alternative explicit Specht filtrations for the induced and the
restricted Specht modules in the Hecke algebra of the symmetric group (defined
over the ring $A=\mathbb Z[q^{1/2},q^{-1/2}]$ where $q$ is an indeterminate)
using $C$-bases for these modules. Moreover, we provide a link between a
certain $C$-basis for the induced Specht module and the notion of pairs of
partitions.
",0,0,1,0,0,0
18801,Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment,"  Network embeddings, which learn low-dimensional representations for each
vertex in a large-scale network, have received considerable attention in recent
years. For a wide range of applications, vertices in a network are typically
accompanied by rich textual information such as user profiles, paper abstracts,
etc. We propose to incorporate semantic features into network embeddings by
matching important words between text sequences for all pairs of vertices. We
introduce a word-by-word alignment framework that measures the compatibility of
embeddings between word pairs, and then adaptively accumulates these alignment
features with a simple yet effective aggregation function. In experiments, we
evaluate the proposed framework on three real-world benchmarks for downstream
tasks, including link prediction and multi-label vertex classification. Results
demonstrate that our model outperforms state-of-the-art network embedding
methods by a large margin.
",1,0,0,0,0,0
18802,Spin Precession Experiments for Light Axionic Dark Matter,"  Axion-like particles are promising candidates to make up the dark matter of
the universe, but it is challenging to design experiments that can detect them
over their entire allowed mass range. Dark matter in general, and in particular
axion-like particles and hidden photons, can be as light as roughly $10^{-22}
\;\rm{eV}$ ($\sim 10^{-8} \;\rm{Hz}$), with astrophysical anomalies providing
motivation for the lightest masses (""fuzzy dark matter""). We propose
experimental techniques for direct detection of axion-like dark matter in the
mass range from roughly $10^{-13} \;\rm{eV}$ ($\sim 10^2 \;\rm{Hz}$) down to
the lowest possible masses. In this range, these axion-like particles act as a
time-oscillating magnetic field coupling only to spin, inducing effects such as
a time-oscillating torque and periodic variations in the spin-precession
frequency with the frequency and direction set by fundamental physics. We show
how these signals can be measured using existing experimental technology,
including torsion pendulums, atomic magnetometers, and atom interferometry.
These experiments demonstrate a strong discovery capability, with future
iterations of these experiments capable of pushing several orders of magnitude
past current astrophysical bounds.
",0,1,0,0,0,0
18803,Persistence barcodes and Laplace eigenfunctions on surfaces,"  We obtain restrictions on the persistence barcodes of Laplace-Beltrami
eigenfunctions and their linear combinations on compact surfaces with
Riemannian metrics. Some applications to uniform approximation by linear
combinations of Laplace eigenfunctions are also discussed.
",0,0,1,0,0,0
18804,DFT study of ionic liquids adsorption on circumcoronene shaped graphene,"  Carbon materials have a range of properties such as high electrical
conductivity, high specific surface area, and mechanical flexibility are
relevant for electrochemical applications. Carbon materials are utilised in
energy conversion-and-storage devices along with electrolytes of complementary
properties. In this work, we study the interaction of highly concentrated
electrolytes (ionic liquids) at a model carbon surface (circumcoronene) using
density functional theory methods. Our results indicate the decisive role of
the dispersion interactions that noticeably strengthen the circumcoronene-ion
interaction. Also, we focus on the adsorption of halide anions as the
electrolytes containing these ions are promising for practical use in
supercapacitors and solar cells.
",0,1,0,0,0,0
18805,On the overestimation of the largest eigenvalue of a covariance matrix,"  In this paper, we use a new approach to prove that the largest eigenvalue of
the sample covariance matrix of a normally distributed vector is bigger than
the true largest eigenvalue with probability 1 when the dimension is infinite.
We prove a similar result for the smallest eigenvalue.
",0,0,1,1,0,0
18806,Thermodynamic Mechanism of Life and Aging,"  Life is a complex biological phenomenon represented by numerous chemical,
physical and biological processes performed by a biothermodynamic
system/cell/organism. Both living organisms and inanimate objects are subject
to aging, a biological and physicochemical process characterized by changes in
biological and thermodynamic state. Thus, the same physical laws govern
processes in both animate and inanimate matter. All life processes lead to
change of an organism's state. The change of biological and thermodynamic state
of an organism in time underlies all of three kinds of aging (chronological,
biological and thermodynamic). Life and aging of an organism both start at the
moment of fertilization and continue through entire lifespan. Fertilization
represents formation of a new organism. The new organism represents a new
thermodynamic system. From the very beginning, it changes its state by changing
thermodynamic parameters. The change of thermodynamic parameters is observed as
aging and can be related to change in entropy. Entropy is thus the parameter
that is related to all others and describes aging in the best manner. In the
beginning, entropy change appears as a consequence of accumulation of matter
(growth). Later, decomposition and configurational changes dominate, as a
consequence of various chemical reactions (free radical, decomposition,
fragmentation, accumulation of lipofuscin-like substances...).
",0,0,0,0,1,0
18807,Flat families of point schemes for connected graded algebras,"  We study truncated point schemes of connected graded algebras as families
over the parameter space of varying relations for the algebras, proving that
the families are flat over the open dense locus where the point schemes achieve
the expected (i.e. minimal) dimension.
When the truncated point scheme is zero-dimensional we obtain its number of
points counted with multiplicity via a Chow ring computation. This latter
application in particular confirms a conjecture of Brazfield to the effect that
a generic two-generator, two-relator 4-dimensional Artin-Schelter regular
algebra has seventeen truncated point modules of length six.
",0,0,1,0,0,0
18808,Multi-task Learning in the Computerized Diagnosis of Breast Cancer on DCE-MRIs,"  Hand-crafted features extracted from dynamic contrast-enhanced magnetic
resonance images (DCE-MRIs) have shown strong predictive abilities in
characterization of breast lesions. However, heterogeneity across medical image
datasets hinders the generalizability of these features. One of the sources of
the heterogeneity is the variation of MR scanner magnet strength, which has a
strong influence on image quality, leading to variations in the extracted image
features. Thus, statistical decision algorithms need to account for such data
heterogeneity. Despite the variations, we hypothesize that there exist
underlying relationships between the features extracted from the datasets
acquired with different magnet strength MR scanners. We compared the use of a
multi-task learning (MTL) method that incorporates those relationships during
the classifier training to support vector machines run on a merged dataset that
includes cases with various MRI strength images. As a result, higher predictive
power is achieved with the MTL method.
",0,1,0,0,0,0
18809,Real-time Distracted Driver Posture Classification,"  In this paper, we present a new dataset for ""distracted driver"" posture
estimation. In addition, we propose a novel system that achieves 95.98% driving
posture estimation classification accuracy. The system consists of a
genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
that a weighted ensemble of classifiers using a genetic algorithm yields in
better classification confidence. We also study the effect of different visual
elements (i.e. hands and face) in distraction detection and classification by
means of face and hand localizations. Finally, we present a thinned version of
our ensemble that could achieve a 94.29% classification accuracy and operate in
a realtime environment.
",1,0,0,0,0,0
18810,Time Series Cube Data Model,"  The purpose of this document is to create a data model and its serialization
for expressing generic time series data. Already existing IVOA data models are
reused as much as possible. The model is also made as generic as possible to be
open to new extensions but at the same time closed for modifications. This
enables maintaining interoperability throughout different versions of the data
model. We define the necessary building blocks for metadata discovery,
serialization of time series data and understanding it by clients. We present
several categories of time series science cases with examples of
implementation. We also take into account the most pressing topics for time
series providers like tracking original images for every individual point of a
light curve or time-derived axes like frequency for gravitational wave
analysis. The main motivation for the creation of a new model is to provide a
unified time series data publishing standard - not only for light curves but
also more generic time series data, e.g., radial velocity curves, power
spectra, hardness ratio, provenance linkage, etc. The flexibility is the most
crucial part of our model - we are not dependent on any physical domain or
frame models. While images or spectra are already stable and standardized
products, the time series related domains are still not completely evolved and
new ones will likely emerge in near future. That is why we need to keep models
like Time Series Cube DM independent of any underlying physical models. In our
opinion, this is the only correct and sustainable way for future development of
IVOA standards.
",1,1,0,0,0,0
18811,Markov modeling of peptide folding in the presence of protein crowders,"  We use Markov state models (MSMs) to analyze the dynamics of a
$\beta$-hairpin-forming peptide in Monte Carlo (MC) simulations with
interacting protein crowders, for two different types of crowder proteins
[bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the
temperature used, the peptide can be folded or unfolded and bound or unbound to
crowder molecules. Four or five major free-energy minima can be identified. To
estimate the dominant MC relaxation times of the peptide, we build MSMs using a
range of different time resolutions or lag times. We show that stable
relaxation-time estimates can be obtained from the MSM eigenfunctions through
fits to autocorrelation data. The eigenfunctions remain sufficiently accurate
to permit stable relaxation-time estimation down to small lag times, at which
point simple estimates based on the corresponding eigenvalues have large
systematic uncertainties. The presence of the crowders have a stabilizing
effect on the peptide, especially with BPTI crowders, which can be attributed
to a reduced unfolding rate $k_\text{u}$, while the folding rate $k_\text{f}$
is left largely unchanged.
",0,0,0,0,1,0
18812,Ultra-fast magnetization manipulation using single femtosecond light and hot-electrons pulse,"  Current induced magnetization manipulation is a key issue for spintronic
application. Therefore, deterministic switching of the magnetization at the
picoseconds timescale with a single electronic pulse represents a major step
towards the future developments of ultrafast spintronic. Here, we have studied
the ultrafast magnetization dynamics in engineered Gdx[FeCo]1-x based structure
to compare the effect of femtosecond laser and hot-electrons pulses. We
demonstrate that a single femtosecond hot-electrons pulse allows a
deterministic magnetization reversal in either Gd-rich and FeCo-rich alloys
similarly to a femtosecond laser pulse. In addition, we show that the limiting
factor of such manipulation for perpendicular magnetized films arises from the
multi-domain formation due to dipolar interaction. By performing time resolved
measurements under various field, we demonstrate that the same magnetization
dynamics is observed for both light and hot-electrons excitation and that the
full magnetization reversal take place within 5 ps. The energy efficiency of
the ultra-fast current induced magnetization manipulation is optimized thanks
to the ballistic transport of hot-electrons before reaching the GdFeCo magnetic
layer.
",0,1,0,0,0,0
18813,Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network,"  Orientation effects on the resistivity of copper grain boundaries are studied
systematically with two different atomistic tight binding methods. A
methodology is developed to model the resistivity of grain boundaries using the
Embedded Atom Model, tight binding methods and non-equilibrum Green's functions
(NEGF). The methodology is validated against first principles calculations for
small, ultra-thin body grain boundaries (<5nm) with 6.4% deviation in the
resistivity. A statistical ensemble of 600 large, random structures with grains
is studied. For structures with three grains, it is found that the distribution
of resistivities is close to normal. Finally, a compact model for grain
boundary resistivity is constructed based on a neural network.
",0,1,0,0,0,0
18814,Some remarkable infinite product identities involving Fibonacci and Lucas numbers,"  By applying the classic telescoping summation formula and its variants to
identities involving inverse hyperbolic tangent functions having inverse powers
of the golden ratio as arguments and employing subtle properties of the
Fibonacci and Lucas numbers, we derive interesting general infinite product
identities involving these numbers.
",0,0,1,0,0,0
18815,Limit Theorems in Mallows Distance for Processes with Gibssian Dependence,"  In this paper, we explore the connection between convergence in distribution
and Mallows distance in the context of positively associated random variables.
Our results extend some known invariance principles for sequences with FKG
property. Applications for processes with Gibbssian dependence structures are
included.
",0,0,1,0,0,0
18816,Localized-endemic state transition in the susceptible-infected-susceptible model on networks,"  It is a longstanding debate concerning the absence of threshold for the
susceptible-infected-susceptible spreading model on networks with localized
state. The key to resolve this controversy is the dynamical interaction
pattern, which has not been uncovered. Here we show that the interaction
driving the localized-endemic state transition is not the global interaction
between a node and all the other nodes on the network, but exists at the level
of super node composed of highly connected node and its neighbors. The internal
interactions within a super node induce localized state with limited lifetime,
while the interactions between neighboring super nodes via a path of two hops
enable them to avoid trapping in the absorbing state, marking the onset of
endemic state. The hybrid interactions render highly connected nodes
exponentially increasing infection density, which truly account for the null
threshold. These results are crucial for correctly understanding diverse
recurrent contagion phenomena
",0,1,0,0,0,0
18817,Analytic approximation of solutions of parabolic partial differential equations with variable coefficients,"  A complete family of solutions for the one-dimensional reaction-diffusion
equation \[ u_{xx}(x,t)-q(x)u(x,t) = u_t(x,t) \] with a coefficient $q$
depending on $x$ is constructed. The solutions represent the images of the heat
polynomials under the action of a transmutation operator. Their use allows one
to obtain an explicit solution of the noncharacteristic Cauchy problem for the
considered equation with sufficiently regular Cauchy data as well as to solve
numerically initial boundary value problems. In the paper the Dirichlet
boundary conditions are considered however the proposed method can be easily
extended onto other standard boundary conditions. The proposed numerical method
is shown to reveal good accuracy.
",0,0,1,0,0,0
18818,Final-State Constrained Optimal Control via a Projection Operator Approach,"  In this paper we develop a numerical method to solve nonlinear optimal
control problems with final-state constraints. Specifically, we extend the
PRojection Operator based Netwon's method for Trajectory Optimization (PRONTO),
which was proposed by Hauser for unconstrained optimal control problems. While
in the standard method final-state constraints can be only approximately
handled by means of a terminal penalty, in this work we propose a methodology
to meet the constraints exactly. Moreover, our method guarantees recursive
feasibility of the final-state constraint. This is an appealing property
especially in realtime applications in which one would like to be able to stop
the computation even if the desired tolerance has not been reached, but still
satisfy the constraints. Following the same conceptual idea of PRONTO, the
proposed strategy is based on two main steps which (differently from the
standard scheme) preserve the feasibility of the final-state constraints: (i)
solve a quadratic approximation of the nonlinear problem to find a descent
direction, and (ii) get a (feasible) trajectory by means of a feedback law
(which turns out to be a nonlinear projection operator). To find the (feasible)
descent direction we take advantage of final-state constrained Linear Quadratic
optimal control methods, while the second step is performed by suitably
designing a constrained version of the trajectory tracking projection operator.
The effectiveness of the proposed strategy is tested on the optimal state
transfer of an inverted pendulum.
",1,0,0,0,0,0
18819,Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach,"  In the pursuit of real-time motion planning, a commonly adopted practice is
to compute a trajectory by running a planning algorithm on a simplified,
low-dimensional dynamical model, and then employ a feedback tracking controller
that tracks such a trajectory by accounting for the full, high-dimensional
system dynamics. While this strategy of planning with model mismatch generally
yields fast computation times, there are no guarantees of dynamic feasibility,
which hampers application to safety-critical systems. Building upon recent work
that addressed this problem through the lens of Hamilton-Jacobi (HJ)
reachability, we devise an algorithmic framework whereby one computes, offline,
for a pair of ""planner"" (i.e., low-dimensional) and ""tracking"" (i.e.,
high-dimensional) models, a feedback tracking controller and associated
tracking bound. This bound is then used as a safety margin when generating
motion plans via the low-dimensional model. Specifically, we harness the
computational tool of sum-of-squares (SOS) programming to design a bilinear
optimization algorithm for the computation of the feedback tracking controller
and associated tracking bound. The algorithm is demonstrated via numerical
experiments, with an emphasis on investigating the trade-off between the
increased computational scalability afforded by SOS and its intrinsic
conservativeness. Collectively, our results enable scaling the appealing
strategy of planning with model mismatch to systems that are beyond the reach
of HJ analysis, while maintaining safety guarantees.
",1,0,0,0,0,0
18820,Robust Gaussian Stochastic Process Emulation,"  We consider estimation of the parameters of a Gaussian Stochastic Process
(GaSP), in the context of emulation (approximation) of computer models for
which the outcomes are real-valued scalars. The main focus is on estimation of
the GaSP parameters through various generalized maximum likelihood methods,
mostly involving finding posterior modes; this is because full Bayesian
analysis in computer model emulation is typically prohibitively expensive. The
posterior modes that are studied arise from objective priors, such as the
reference prior. These priors have been studied in the literature for the
situation of an isotropic covariance function or under the assumption of
separability in the design of inputs for model runs used in the GaSP
construction. In this paper, we consider more general designs (e.g., a Latin
Hypercube Design) with a class of commonly used anisotropic correlation
functions, which can be written as a product of isotropic correlation
functions, each having an unknown range parameter and a fixed roughness
parameter. We discuss properties of the objective priors and marginal
likelihoods for the parameters of the GaSP and establish the posterior
propriety of the GaSP parameters, but our main focus is to demonstrate that
certain parameterizations result in more robust estimation of the GaSP
parameters than others, and that some parameterizations that are in common use
should clearly be avoided. These results are applicable to many frequently used
covariance functions, e.g., power exponential, Mat{é}rn, rational quadratic
and spherical covariance. We also generalize the results to the GaSP model with
a nugget parameter. Both theoretical and numerical evidence is presented
concerning the performance of the studied procedures.
",0,0,1,1,0,0
18821,Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST,"  Nine transiting Earth-sized planets have recently been discovered around
nearby late M dwarfs, including the TRAPPIST-1 planets and two planets
discovered by the MEarth survey, GJ 1132b and LHS 1140b. These planets are the
smallest known planets that may have atmospheres amenable to detection with
JWST. We present model thermal emission and transmission spectra for each
planet, varying composition and surface pressure of the atmosphere. We base
elemental compositions on those of Earth, Titan, and Venus and calculate the
molecular compositions assuming chemical equilibrium, which can strongly depend
on temperature. Both thermal emission and transmission spectra are sensitive to
the atmospheric composition; thermal emission spectra are sensitive to surface
pressure and temperature. We predict the observability of each planet's
atmosphere with JWST. GJ 1132b and TRAPPIST-1b are excellent targets for
emission spectroscopy with JWST/MIRI, requiring fewer than 10 eclipse
observations. Emission photometry for TRAPPIST-1c requires 5-15 eclipses; LHS
1140b and TRAPPIST-1d, TRAPPIST-1e, and TRAPPIST-1f, which could possibly have
surface liquid water, may be accessible with photometry. Seven of the nine
planets are strong candidates for transmission spectroscopy measurements with
JWST, though the number of transits required depends strongly on the planets'
actual masses. Using the measured masses, fewer than 20 transits are required
for a 5 sigma detection of spectral features for GJ 1132b and six of the
TRAPPIST-1 planets. Dedicated campaigns to measure the atmospheres of these
nine planets will allow us, for the first time, to probe formation and
evolution processes of terrestrial planetary atmospheres beyond our solar
system.
",0,1,0,0,0,0
18822,Front Propagation for Nonlocal KPP Reaction-Diffusion Equations in Periodic Media,"  We study front propagation phenomena for a large class of nonlocal KPP-type
reaction-diffusion equations in oscillatory environments, which model various
forms of population growth with periodic dependence. The nonlocal diffusion is
an anisotropic integro-differential operator of order $\alpha \in (0,2)$.
",0,0,1,0,0,0
18823,Distributed Functional Observers for LTI Systems,"  We study the problem of designing distributed functional observers for LTI
systems. Specifically, we consider a setting consisting of a state vector that
evolves over time according to a dynamical process. A set of nodes distributed
over a communication network wish to collaboratively estimate certain functions
of the state. We first show that classical existence conditions for the design
of centralized functional observers do not directly translate to the
distributed setting, due to the coupling that exists between the dynamics of
the functions of interest and the diverse measurements at the various nodes.
Accordingly, we design transformations that reveal such couplings and identify
portions of the corresponding dynamics that are locally detectable at each
sensor node. We provide sufficient conditions on the network, along with state
estimate update and exchange rules for each node, that guarantee asymptotic
reconstruction of the functions at each sensor node.
",0,0,1,0,0,0
18824,Wavelet eigenvalue regression for $n$-variate operator fractional Brownian motion,"  In this contribution, we extend the methodology proposed in Abry and Didier
(2017) to obtain the first joint estimator of the real parts of the Hurst
eigenvalues of $n$-variate OFBM. The procedure consists of a wavelet regression
on the log-eigenvalues of the sample wavelet spectrum. The estimator is shown
to be consistent for any time reversible OFBM and, under stronger assumptions,
also asymptotically normal starting from either continuous or discrete time
measurements. Simulation studies establish the finite sample effectiveness of
the methodology and illustrate its benefits compared to univariate-like
(entrywise) analysis. As an application, we revisit the well-known self-similar
character of Internet traffic by applying the proposed methodology to 4-variate
time series of modern, high quality Internet traffic data. The analysis reveals
the presence of a rich multivariate self-similarity structure.
",0,0,1,1,0,0
18825,Deep Neural Network for Analysis of DNA Methylation Data,"  Many researches demonstrated that the DNA methylation, which occurs in the
context of a CpG, has strong correlation with diseases, including cancer. There
is a strong interest in analyzing the DNA methylation data to find how to
distinguish different subtypes of the tumor. However, the conventional
statistical methods are not suitable for analyzing the highly dimensional DNA
methylation data with bounded support. In order to explicitly capture the
properties of the data, we design a deep neural network, which composes of
several stacked binary restricted Boltzmann machines, to learn the low
dimensional deep features of the DNA methylation data. Experiments show these
features perform best in breast cancer DNA methylation data cluster analysis,
comparing with some state-of-the-art methods.
",0,0,0,1,1,0
18826,One look at the rating of scientific publications and corresponding toy-model,"  A toy-model of publications and citations processes is proposed. The model
shows that the role of randomness in the processes is essential and cannot be
ignored. Some other aspects of scientific publications rating are discussed.
",1,0,0,1,0,0
18827,"Alignment, Orientation, and Coulomb Explosion of Difluoroiodobenzene Studied with the Pixel Imaging Mass Spectrometry (PImMS) Camera","  Laser-induced adiabatic alignment and mixed-field orientation of
2,6-difluoroiodobenzene (C6H3F2I) molecules are probed by Coulomb explosion
imaging following either near-infrared strong-field ionization or
extreme-ultraviolet multi-photon inner-shell ionization using free-electron
laser pulses. The resulting photoelectrons and fragment ions are captured by a
double-sided velocity map imaging spectrometer and projected onto two
position-sensitive detectors. The ion side of the spectrometer is equipped with
the Pixel Imaging Mass Spectrometry (PImMS) camera, a time-stamping pixelated
detector that can record the hit positions and arrival times of up to four ions
per pixel per acquisition cycle. Thus, the time-of-flight trace and ion
momentum distributions for all fragments can be recorded simultaneously. We
show that we can obtain a high degree of one- and three-dimensional alignment
and mixed- field orientation, and compare the Coulomb explosion process induced
at both wavelengths.
",0,1,0,0,0,0
18828,Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics,"  We developed and used a collection of statistical methods (unsupervised
machine learning) to extract relevant information from a Twitter supplied data
set consisting of alleged Russian trolls who (allegedly) attempted to influence
the 2016 US Presidential election. These unsupervised statistical methods allow
fast identification of (i) emergent language communities within the troll
population, (ii) the transnational scope of the operation and (iii) operational
characteristics of trolls that can be used for future identification. Using
natural language processing, manifold learning and Fourier analysis, we
identify an operation that includes not only the 2016 US election, but also the
French National and both local and national German elections. We show the
resulting troll population is composed of users with common, but clearly
customized, behavioral characteristics.
",1,0,0,0,0,0
18829,"Distributed, scalable and gossip-free consensus optimization with application to data analysis","  Distributed algorithms for solving additive or consensus optimization
problems commonly rely on first-order or proximal splitting methods. These
algorithms generally come with restrictive assumptions and at best enjoy a
linear convergence rate. Hence, they can require many iterations or
communications among agents to converge. In many cases, however, we do not seek
a highly accurate solution for consensus problems. Based on this we propose a
controlled relaxation of the coupling in the problem which allows us to compute
an approximate solution, where the accuracy of the approximation can be
controlled by the level of relaxation. The relaxed problem can be efficiently
solved in a distributed way using a combination of primal-dual interior-point
methods (PDIPMs) and message-passing. This algorithm purely relies on
second-order methods and thus requires far fewer iterations and communications
to converge. This is illustrated in numerical experiments, showing its superior
performance compared to existing methods.
",0,0,1,0,0,0
18830,Consistency of Maximum Likelihood for Continuous-Space Network Models,"  Network analysis needs tools to infer distributions over graphs of arbitrary
size from a single graph. Assuming the distribution is generated by a
continuous latent space model which obeys certain natural symmetry and
smoothness properties, we establish three levels of consistency for
non-parametric maximum likelihood inference as the number of nodes grows: (i)
the estimated locations of all nodes converge in probability on their true
locations; (ii) the distribution over locations in the latent space converges
on the true distribution; and (iii) the distribution over graphs of arbitrary
size converges.
",0,0,1,1,0,0
18831,Consistency Results for Stationary Autoregressive Processes with Constrained Coefficients,"  We consider stationary autoregressive processes with coefficients restricted
to an ellipsoid, which includes autoregressive processes with absolutely
summable coefficients. We provide consistency results under different norms for
the estimation of such processes using constrained and penalized estimators. As
an application we show some weak form of universal consistency. Simulations
show that directly including the constraint in the estimation can lead to more
robust results.
",0,0,0,1,0,0
18832,Higher order mobile coverage control with application to localization,"  Most current results on coverage control using mobile sensors require that
one partitioned cell is associated with precisely one sensor. In this paper, we
consider a class of coverage control problems involving higher order Voronoi
partitions, motivated by applications where more than one sensor is required to
monitor and cover one cell. Such applications are frequent in scenarios
requiring the sensors to localize targets. We introduce a framework depending
on a coverage performance function incorporating higher order Voronoi cells and
then design a gradient-based controller which allows the multi-sensor system to
achieve a local equilibrium in a distributed manner. The convergence properties
are studied and related to Lloyd algorithm. We study also the extension to
coverage of a discrete set of points. In addition, we provide a number of real
world scenarios where our framework can be applied. Simulation results are also
provided to show the controller performance.
",1,0,0,0,0,0
18833,Borg's Periodicity Theorems for first order self-adjoint systems with complex potentials,"  A self-adjoint first order system with Hermitian $\pi$-periodic potential
$Q(z)$, integrable on compact sets, is considered. It is shown that all zeros
of $\Delta + 2e^{-i\int_0^\pi \Im q dt}$ are double zeros if and only if this
self-adjoint system is unitarily equivalent to one in which $Q(z)$ is
$\frac{\pi}{2}$-periodic. Furthermore, the zeros of $\Delta - 2e^{-i\int_0^\pi
\Im q dt}$ are all double zeros if and only if the associated self-adjoint
system is unitarily equivalent to one in which $Q(z) = \sigma_2 Q(z) \sigma_2$.
Here $\Delta$ denotes the discriminant of the system and $\sigma_0$, $\sigma_2$
are Pauli matrices. Finally, it is shown that all instability intervals vanish
if and only if $Q = r\sigma_0 + q\sigma_2$, for some real valued $\pi$-periodic
functions $r$ and $q$ integrable on compact sets.
",0,0,1,0,0,0
18834,On the Ubiquity of Information Inconsistency for Conjugate Priors,"  Informally, ""Information Inconsistency"" is the property that has been
observed in many Bayesian hypothesis testing and model selection procedures
whereby the Bayesian conclusion does not become definitive when the data seems
to become definitive. An example is that, when performing a t-test using
standard conjugate priors, the Bayes factor of the alternative hypothesis to
the null hypothesis remains bounded as the t statistic grows to infinity. This
paper shows that information inconsistency is ubiquitous in Bayesian hypothesis
testing under conjugate priors. Yet the title does not fully describe the
paper, since we also show that theoretically recommended priors, including
scale mixtures of conjugate priors and adaptive priors, are information
consistent. Hence the paper is simply a forceful warning that use of conjugate
priors in testing and model selection is highly problematical, and should be
replaced by the information consistent alternatives.
",0,0,1,1,0,0
18835,Competition evolution of Rayleigh-Taylor bubbles,"  Material mixing induced by a Rayleigh-Taylor instability occurs ubiquitously
in either nature or engineering when a light fluid pushes against a heavy
fluid, accompanying with the formation and evolution of chaotic bubbles. Its
general evolution involves two mechanisms: bubble-merge and bubble-competition.
The former obeys a universa1 evolution law and has been well-studied, while the
latter depends on many factors and has not been well-recognized. In this paper,
we establish a theory for the latter to clarify and quantify the longstanding
open question: the dependence of bubbles evolution on the dominant factors of
arbitrary density ratio, broadband initial perturbations and various material
properties (e.g., viscosity, miscibility, surface tensor). Evolution of the
most important characteristic quantities, i.e., the diameter of dominant bubble
$D$ and the height of bubble zone $h$, is derived: (i) the $D$ expands
self-similarly with steady aspect ratio $\beta \equiv D/h \thickapprox (1{\rm{
+ }}A)/4$, depending only on dimensionless density ratio $A$, and (ii) the $h$
grows quadratically with constant growth coefficient $\alpha \equiv h/(Ag{t^2})
\thickapprox [2\phi/{\ln}(2{\eta _{\rm{0}}})]^2$, depending on both
dimensionless initial perturbation amplitude ${\eta _{\rm{0}}}$ and
material-property-associated linear growth rate ratio
$\phi\equiv\Gamma_{actual}/\Gamma_{ideal}\leqslant1$. The theory successfully
explains the continued puzzle about the widely varying $\alpha\in (0.02,0.12)$
in experiments and simulations, conducted at all value of $A \in (0,1)$ and
widely varying value of ${\eta _{\rm{0}}} \in [{10^{ - 7}},{10^{ - 2}}]$ with
different materials. The good agreement between theory and experiments implies
that majority of actual mixing depends on initial perturbations and material
properties, to which more attention should be paid in either natural or
engineering problems.
",0,1,0,0,0,0
18836,Nonlinear oblique projections,"  We construct nonlinear oblique projections along subalgebras of nilpotent Lie
algebras in terms of the Baker-Campbell-Hausdorff multiplication. We prove that
these nonlinear projections are real analytic on every Schubert cell of the
Grassmann manifold whose points are the subalgebras of the nilpotent Lie
algebra under consideration.
",0,0,1,0,0,0
18837,Granger Mediation Analysis of Multiple Time Series with an Application to fMRI,"  It becomes increasingly popular to perform mediation analysis for complex
data from sophisticated experimental studies. In this paper, we present Granger
Mediation Analysis (GMA), a new framework for causal mediation analysis of
multiple time series. This framework is motivated by a functional magnetic
resonance imaging (fMRI) experiment where we are interested in estimating the
mediation effects between a randomized stimulus time series and brain activity
time series from two brain regions. The stable unit treatment assumption for
causal mediation analysis is thus unrealistic for this type of time series
data. To address this challenge, our framework integrates two types of models:
causal mediation analysis across the variables and vector autoregressive models
across the temporal observations. We further extend this framework to handle
multilevel data to address individual variability and correlated errors between
the mediator and the outcome variables. These models not only provide valid
causal mediation for time series data but also model the causal dynamics across
time. We show that the modeling parameters in our models are identifiable, and
we develop computationally efficient methods to maximize the likelihood-based
optimization criteria. Simulation studies show that our method reduces the
estimation bias and improve statistical power, compared to existing approaches.
On a real fMRI data set, our approach not only infers the causal effects of
brain pathways but accurately captures the feedback effect of the outcome
region on the mediator region.
",0,0,0,1,0,0
18838,Parameterization of Sequence of MFCCs for DNN-based voice disorder detection,"  In this article a DNN-based system for detection of three common voice
disorders (vocal nodules, polyps and cysts; laryngeal neoplasm; unilateral
vocal paralysis) is presented. The input to the algorithm is (at least 3-second
long) audio recording of sustained vowel sound /a:/. The algorithm was
developed as part of the ""2018 FEMH Voice Data Challenge"" organized by Far
Eastern Memorial Hospital and obtained score value (defined in the challenge
specification) of 77.44. This was the second best result before final
submission. Final challenge results are not yet known during writing of this
document. The document also reports changes that were made for the final
submission which improved the score value in cross-validation by 0.6% points.
",1,0,0,0,0,0
18839,Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems,"  A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware.
",1,0,0,0,0,0
18840,Structural Connectome Validation Using Pairwise Classification,"  In this work, we study the extent to which structural connectomes and
topological derivative measures are unique to individual changes within human
brains. To do so, we classify structural connectome pairs from two large
longitudinal datasets as either belonging to the same individual or not. Our
data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
(PPMI). We achieve 0.99 area under the ROC curve score for features which
represent either weights or network structure of the connectomes (node degrees,
PageRank and local efficiency). Our approach may be useful for eliminating
noisy features as a preprocessing step in brain aging studies and early
diagnosis classification problems.
",1,0,0,0,0,0
18841,Deep Robust Kalman Filter,"  A Robust Markov Decision Process (RMDP) is a sequential decision making model
that accounts for uncertainty in the parameters of dynamic systems. This
uncertainty introduces difficulties in learning an optimal policy, especially
for environments with large state spaces. We propose two algorithms, RTD-DQN
and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation
schemes such as deep neural networks. The RTD-DQN algorithm incorporates the
robust Bellman temporal difference error into a robust loss function, yielding
robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian
method, based on the Extended Kalman Filter (EKF), that accounts for both the
uncertainty in the weights of the approximated value function and the
uncertainty in the transition probabilities, improving the robustness of the
agent. We provide theoretical results for our approach and test the proposed
algorithms on a continuous state domain.
",1,0,0,1,0,0
18842,Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach,"  The Fisher information matrix (FIM) is a fundamental quantity to represent
the characteristics of a stochastic model, including deep neural networks
(DNNs). The present study reveals novel statistics of FIM that are universal
among a wide class of DNNs. To this end, we use random weights and large width
limits, which enables us to utilize mean field theories. We investigate the
asymptotic statistics of the FIM's eigenvalues and reveal that most of them are
close to zero while the maximum takes a huge value. This implies that the
eigenvalue distribution has a long tail. Because the landscape of the parameter
space is defined by the FIM, it is locally flat in most dimensions, but
strongly distorted in others. We also demonstrate the potential usage of the
derived statistics through two exercises. First, small eigenvalues that induce
flatness can be connected to a norm-based capacity measure of generalization
ability. Second, the maximum eigenvalue that induces the distortion enables us
to quantitatively estimate an appropriately sized learning rate for gradient
methods to converge.
",0,0,0,1,0,0
18843,An extension problem and trace Hardy inequality for the sublaplacian on $H$-type groups,"  In this paper we study the extension problem for the sublaplacian on a
$H$-type group and use the solutions to prove trace Hardy and Hardy
inequalities for fractional powers of the sublaplacian.
",0,0,1,0,0,0
18844,Centered Isotonic Regression: Point and Interval Estimation for Dose-Response Studies,"  Univariate isotonic regression (IR) has been used for nonparametric
estimation in dose-response and dose-finding studies. One undesirable property
of IR is the prevalence of piecewise-constant stretches in its estimates,
whereas the dose-response function is usually assumed to be strictly
increasing. We propose a simple modification to IR, called centered isotonic
regression (CIR). CIR's estimates are strictly increasing in the interior of
the dose range. In the absence of monotonicity violations, CIR and IR both
return the original observations. Numerical examination indicates that for
sample sizes typical of dose-response studies and with realistic dose-response
curves, CIR provides a substantial reduction in estimation error compared with
IR when monotonicity violations occur. We also develop analytical interval
estimates for IR and CIR, with good coverage behavior. An R package implements
these point and interval estimates.
",0,0,0,1,0,0
18845,Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection,"  Measuring domain relevance of data and identifying or selecting well-fit
domain data for machine translation (MT) is a well-studied topic, but denoising
is not yet. Denoising is concerned with a different type of data quality and
tries to reduce the negative impact of data noise on MT training, in
particular, neural MT (NMT) training. This paper generalizes methods for
measuring and selecting data for domain MT and applies them to denoising NMT
training. The proposed approach uses trusted data and a denoising curriculum
realized by online data selection. Intrinsic and extrinsic evaluations of the
approach show its significant effectiveness for NMT to train on data with
severe noise.
",0,0,0,1,0,0
18846,Discovering Signals from Web Sources to Predict Cyber Attacks,"  Cyber attacks are growing in frequency and severity. Over the past year alone
we have witnessed massive data breaches that stole personal information of
millions of people and wide-scale ransomware attacks that paralyzed critical
infrastructure of several countries. Combating the rising cyber threat calls
for a multi-pronged strategy, which includes predicting when these attacks will
occur. The intuition driving our approach is this: during the planning and
preparation stages, hackers leave digital traces of their activities on both
the surface web and dark web in the form of discussions on platforms like
hacker forums, social media, blogs and the like. These data provide predictive
signals that allow anticipating cyber attacks. In this paper, we describe
machine learning techniques based on deep neural networks and autoregressive
time series models that leverage external signals from publicly available Web
sources to forecast cyber attacks. Performance of our framework across ground
truth data over real-world forecasting tasks shows that our methods yield a
significant lift or increase of F1 for the top signals on predicted cyber
attacks. Our results suggest that, when deployed, our system will be able to
provide an effective line of defense against various types of targeted cyber
attacks.
",1,0,0,1,0,0
18847,Higher-degree Smoothness of Perturbations I,"  In this paper and its sequels, we give an unified treatment of the
higher-degree smoothness of admissible perturbations and related results used
in the global perturbation method for GW and Floer theories.
",0,0,1,0,0,0
18848,Space Telescope and Optical Reverberation Mapping Project. V. Optical Spectroscopic Campaign and Emission-Line Analysis for NGC 5548,"  We present the results of an optical spectroscopic monitoring program
targeting NGC 5548 as part of a larger multi-wavelength reverberation mapping
campaign. The campaign spanned six months and achieved an almost daily cadence
with observations from five ground-based telescopes. The H$\beta$ and He II
$\lambda$4686 broad emission-line light curves lag that of the 5100 $\AA$
optical continuum by $4.17^{+0.36}_{-0.36}$ days and $0.79^{+0.35}_{-0.34}$
days, respectively. The H$\beta$ lag relative to the 1158 $\AA$ ultraviolet
continuum light curve measured by the Hubble Space Telescope is roughly
$\sim$50% longer than that measured against the optical continuum, and the lag
difference is consistent with the observed lag between the optical and
ultraviolet continua. This suggests that the characteristic radius of the
broad-line region is $\sim$50% larger than the value inferred from optical data
alone. We also measured velocity-resolved emission-line lags for H$\beta$ and
found a complex velocity-lag structure with shorter lags in the line wings,
indicative of a broad-line region dominated by Keplerian motion. The responses
of both the H$\beta$ and He II $\lambda$4686 emission lines to the driving
continuum changed significantly halfway through the campaign, a phenomenon also
observed for C IV, Ly $\alpha$, He II(+O III]), and Si IV(+O IV]) during the
same monitoring period. Finally, given the optical luminosity of NGC 5548
during our campaign, the measured H$\beta$ lag is a factor of five shorter than
the expected value implied by the $R_\mathrm{BLR} - L_\mathrm{AGN}$ relation
based on the past behavior of NGC 5548.
",0,1,0,0,0,0
18849,An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of $\mathcal{O}(ε^{-3/2})$ for Nonconvex Optimization,"  An algorithm for solving smooth nonconvex optimization problems is proposed
that, in the worst-case, takes $\mathcal{O}(\epsilon^{-3/2})$ iterations to
drive the norm of the gradient of the objective function below a prescribed
positive real number $\epsilon$ and can take $\mathcal{O}(\epsilon^{-3})$
iterations to drive the leftmost eigenvalue of the Hessian of the objective
above $-\epsilon$. The proposed algorithm is a general framework that covers a
wide range of techniques including quadratically and cubically regularized
Newton methods, such as the Adaptive Regularisation using Cubics (ARC) method
and the recently proposed Trust-Region Algorithm with Contractions and
Expansions (TRACE). The generality of our method is achieved through the
introduction of generic conditions that each trial step is required to satisfy,
which in particular allow for inexact regularized Newton steps to be used.
These conditions center around a new subproblem that can be approximately
solved to obtain trial steps that satisfy the conditions. A new instance of the
framework, distinct from ARC and TRACE, is described that may be viewed as a
hybrid between quadratically and cubically regularized Newton methods.
Numerical results demonstrate that our hybrid algorithm outperforms a cublicly
regularized Newton method.
",0,0,1,0,0,0
18850,An inverse problem for Maxwell's equations with Lipschitz parameters,"  We consider an inverse boundary value problem for Maxwell's equations, which
aims to recover the electromagnetic material properties of a body from
measurements on the boundary. We show that a Lipschitz continuous conductivity,
electric permittivity, and magnetic permeability are uniquely determined by
knowledge of all tangential electric and magnetic fields on the boundary of the
body at a fixed frequency.
",0,0,1,0,0,0
18851,Context Aware Robot Navigation using Interactively Built Semantic Maps,"  We discuss the process of building semantic maps, how to interactively label
entities in them, and how to use them to enable context-aware navigation
behaviors in human environments. We utilize planar surfaces, such as walls and
tables, and static objects, such as door signs, as features for our semantic
mapping approach. Users can interactively annotate these features by having the
robot follow him/her, entering the label through a mobile app, and performing a
pointing gesture toward the landmark of interest. Our gesture based approach
can reliably estimate which object is being pointed at and detect ambiguous
gestures with probabilistic modeling. Our person following method attempts to
maximize future utility by a search for future actions assuming constant
velocity model for the human. We describe a method to extract metric goals from
a semantic map landmark and to plan a human aware path that takes into account
the personal spaces of people. Finally, we demonstrate context-awareness for
person following in two scenarios: interactive labeling and door passing. We
believe that future navigation approaches and service robotics applications can
be made more effective by further exploiting the structure of human
environments.
",1,0,0,0,0,0
18852,Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data,"  Multimodal sensory data resembles the form of information perceived by humans
for learning, and are easy to obtain in large quantities. Compared to unimodal
data, synchronization of concepts between modalities in such data provides
supervision for disentangling the underlying explanatory factors of each
modality. Previous work leveraging multimodal data has mainly focused on
retaining only the modality-invariant factors while discarding the rest. In
this paper, we present a partitioned variational autoencoder (PVAE) and several
training objectives to learn disentangled representations, which encode not
only the shared factors, but also modality-dependent ones, into separate latent
variables. Specifically, PVAE integrates a variational inference framework and
a multimodal generative model that partitions the explanatory factors and
conditions only on the relevant subset of them for generation. We evaluate our
model on two parallel speech/image datasets, and demonstrate its ability to
learn disentangled representations by qualitatively exploring within-modality
and cross-modality conditional generation with semantics and styles specified
by examples. For quantitative analysis, we evaluate the classification accuracy
of automatically discovered semantic units. Our PVAE can achieve over 99%
accuracy on both modalities.
",0,0,0,1,0,0
18853,Blind Gain and Phase Calibration via Sparse Spectral Methods,"  Blind gain and phase calibration (BGPC) is a bilinear inverse problem
involving the determination of unknown gains and phases of the sensing system,
and the unknown signal, jointly. BGPC arises in numerous applications, e.g.,
blind albedo estimation in inverse rendering, synthetic aperture radar
autofocus, and sensor array auto-calibration. In some cases, sparse structure
in the unknown signal alleviates the ill-posedness of BGPC. Recently there has
been renewed interest in solutions to BGPC with careful analysis of error
bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem,
and propose to solve it via power iteration, or in the sparsity or joint
sparsity case, via truncated power iteration. Under certain assumptions, the
unknown gains, phases, and the unknown signal can be recovered simultaneously.
Numerical experiments show that power iteration algorithms work not only in the
regime predicted by our main results, but also in regimes where theoretical
analysis is limited. We also show that our power iteration algorithms for BGPC
compare favorably with competing algorithms in adversarial conditions, e.g.,
with noisy measurement or with a bad initial estimate.
",1,0,0,0,0,0
18854,"Reconciling cooperation, biodiversity and stability in complex ecological communities","  Empirical observations show that ecological communities can have a huge
number of coexisting species, also with few or limited number of resources.
These ecosystems are characterized by multiple type of interactions, in
particular displaying cooperative behaviors. However, standard modeling of
population dynamics based on Lotka-Volterra type of equations predicts that
ecosystem stability should decrease as the number of species in the community
increases and that cooperative systems are less stable than communities with
only competitive and/or exploitative interactions. Here we propose a stochastic
model of population dynamics, which includes exploitative interactions as well
as cooperative interactions induced by cross-feeding. The model is exactly
solved and we obtain results for relevant macro-ecological patterns, such as
species abundance distributions and correlation functions. In the large system
size limit, any number of species can coexist for a very general class of
interaction networks and stability increases as the number of species grows.
For pure mutualistic/commensalistic interactions we determine the topological
properties of the network that guarantee species coexistence. We also show that
the stationary state is globally stable and that inferring species interactions
through species abundance correlation analysis may be misleading. Our
theoretical approach thus show that appropriate models of cooperation naturally
leads to a solution of the long-standing question about complexity-stability
paradox and on how highly biodiverse communities can coexist.
",0,0,0,0,1,0
18855,The Young L Dwarf 2MASS J11193254-1137466 is a Planetary-Mass Binary,"  We have discovered that the extremely red, low-gravity L7 dwarf 2MASS
J11193254-1137466 is a 0.14"" (3.6 AU) binary using Keck laser guide star
adaptive optics imaging. 2MASS J11193254-1137466 has previously been identified
as a likely member of the TW Hydrae Association (TWA). Using our updated
photometric distance and proper motion, a kinematic analysis based on the
BANYAN II model gives an 82% probability of TWA membership. At TWA's 10$\pm$3
Myr age and using hot-start evolutionary models, 2MASS J11193254-1137466AB is a
pair of $3.7^{+1.2}_{-0.9}$ $M_{\rm Jup}$ brown dwarfs, making it the
lowest-mass binary discovered to date. We estimate an orbital period of
$90^{+80}_{-50}$ years. One component is marginally brighter in $K$ band but
fainter in $J$ band, making this a probable flux-reversal binary, the first
discovered with such a young age. We also imaged the spectrally similar TWA L7
dwarf WISEA J114724.10-204021.3 with Keck and found no sign of binarity. Our
evolutionary model-derived $T_{\rm eff}$ estimate for WISEA J114724.10-204021.3
is $\approx$230 K higher than for 2MASS J11193254-1137466AB, at odds with their
spectral similarity. This discrepancy suggests that WISEA J114724.10-204021.3
may actually be a tight binary with masses and temperatures very similar to
2MASS J11193254-1137466AB, or further supporting the idea that near-infrared
spectra of young ultracool dwarfs are shaped by factors other than temperature
and gravity. 2MASS J11193254-1137466AB will be an essential benchmark for
testing evolutionary and atmospheric models in the young planetary-mass regime.
",0,1,0,0,0,0
18856,Diversity of Abundance Patterns of Light Neutron-capture Elements in Very-metal-poor Stars,"  We determine the abundances of neutron-capture elements from Sr to Eu for
five very-metal-poor stars (-3<[Fe/H]<-2) in the Milky Way halo to reveal the
origin of light neutron-capture elements. Previous spectroscopic studies have
shown evidence of at least two components in the r-process; one referred to as
the ""main r-process"" and the other as the ""weak r-process,"" which is mainly
responsible for producing heavy and light neutron-capture elements,
respectively. Observational studies of metal-poor stars suggest that there is a
universal pattern in the main r-process, similar to the abundance pattern of
the r-process component of solar-system material. Still, it is uncertain
whether the abundance pattern of the weak r-process shows universality or
diversity, due to the sparseness of measured light neutron-capture elements. We
have detected the key elements, Mo, Ru, and Pd, in five target stars to give an
answer to this question. The abundance patterns of light neutron-capture
elements from Sr to Pd suggest a diversity in the weak r-process. In
particular, scatter in the abundance ratio between Ru and Pd is significant
when the abundance patterns are normalized at Zr. Our results are compared with
the elemental abundances predicted by nucleosynthesis models of supernovae with
parameters such as electron fraction or proto-neutron-star mass, to investigate
sources of such diversity in the abundance patterns of light neutron-capture
elements. This paper presents that the variation in the abundances of observed
stars can be explained with a small range of parameters, which can serve as
constraints on future modeling of supernova models.
",0,1,0,0,0,0
18857,Hypergames and Cyber-Physical Security for Control Systems,"  The identification of the Stuxnet worm in 2010 provided a highly publicized
example of a cyber attack used to damage an industrial control system
physically. This raised public awareness about the possibility of similar
attacks against other industrial targets -- including critical infrastructure.
In this paper, we use hypergames to analyze how adversarial perturbations can
be used to manipulate a system using optimal control. Hypergames form an
extension of game theory that enables us to model strategic interactions where
the players may have significantly different perceptions of the game(s) they
are playing. Past work with hypergames has been limited to relatively simple
interactions consisting of a small set of discrete choices for each player, but
here, we apply hypergames to larger systems with continuous variables. We find
that manipulating constraints can be a more effective attacker strategy than
directly manipulating objective function parameters. Moreover, the attacker
need not change the underlying system to carry out a successful attack -- it
may be sufficient to deceive the defender controlling the system. It is
possible to scale our approach up to even larger systems, but the ability to do
so will depend on the characteristics of the system in question, and we
identify several characteristics that will make those systems amenable to
hypergame analysis.
",1,0,0,0,0,0
18858,Measuring the academic reputation through citation networks via PageRank,"  The objective assessment of the prestige of an academic institution is a
difficult and hotly debated task. In the last few years, different types of
University Rankings have been proposed to quantify the excellence of different
research institutions in the world. Albeit met with criticism in some cases,
the relevance of university rankings is being increasingly acknowledged:
indeed, rankings are having a major impact on the design of research policies,
both at the institutional and governmental level. Yet, the debate on what
rankings are {\em exactly} measuring is enduring. Here, we address the issue by
measuring a quantitive and reliable proxy of the academic reputation of a given
institution and by evaluating its correlation with different university
rankings. Specifically, we study citation patterns among universities in five
different Web of Science Subject Categories and use the \pr~algorithm on the
five resulting citation networks. The rationale behind our work is that
scientific citations are driven by the reputation of the reference so that the
PageRank algorithm is expected to yield a rank which reflects the reputation of
an academic institution in a specific field. Our results allow to quantifying
the prestige of a set of institutions in a certain research field based only on
hard bibliometric data. Given the volume of the data analysed, our findings are
statistically robust and less prone to bias, at odds with ad--hoc surveys often
employed by ranking bodies in order to attain similar results. Because our
findings are found to correlate extremely well with the ARWU Subject rankings,
the approach we propose in our paper may open the door to new, Academic Ranking
methodologies that go beyond current methods by reconciling the qualitative
evaluation of Academic Prestige with its quantitative measurements via
publication impact.
",1,0,0,0,0,0
18859,A Model Order Reduction Algorithm for Estimating the Absorption Spectrum,"  The ab initio description of the spectral interior of the absorption spectrum
poses both a theoretical and computational challenge for modern electronic
structure theory. Due to the often spectrally dense character of this domain in
the quantum propagator's eigenspectrum for medium-to-large sized systems,
traditional approaches based on the partial diagonalization of the propagator
often encounter oscillatory and stagnating convergence. Electronic structure
methods which solve the molecular response problem through the solution of
spectrally shifted linear systems, such as the complex polarization propagator,
offer an alternative approach which is agnostic to the underlying spectral
density or domain location. This generality comes at a seemingly high
computational cost associated with solving a large linear system for each
spectral shift in some discretization of the spectral domain of interest. We
present a novel, adaptive solution based on model order reduction techniques
via interpolation. Model order reduction reduces the computational complexity
of mathematical models and is ubiquitous in the simulation of dynamical
systems. The efficiency and effectiveness of the proposed algorithm in the ab
initio prediction of X-Ray absorption spectra is demonstrated using a test set
of challenging water clusters which are spectrally dense in the neighborhood of
the oxygen K-edge. Based on a single, user defined tolerance we automatically
determine the order of the reduced models and approximate the absorption
spectrum up to the given tolerance. We also illustrate that the automatically
determined model order increases logarithmically with the problem dimension,
compared to a linear increase of the number of eigenvalues within the energy
window. Furthermore, we observed that the computational cost of the proposed
algorithm only scales quadratically with respect to the problem dimension.
",1,1,0,0,0,0
18860,"Uniform rank gradient, cost and local-global convergence","  We analyze the rank gradient of finitely generated groups with respect to
sequences of subgroups of finite index that do not necessarily form a chain, by
connecting it to the cost of p.m.p. actions. We generalize several results that
were only known for chains before. The connection is made by the notion of
local-global convergence.
In particular, we show that for a finitely generated group $\Gamma$ with
fixed price $c$, every Farber sequence has rank gradient $c-1$. By adapting
Lackenby's trichotomy theorem to this setting, we also show that in a finitely
presented amenable group, every sequence of subgroups with index tending to
infinity has vanishing rank gradient.
",0,0,1,0,0,0
18861,Optimal design of a model energy conversion device,"  Fuel cells, batteries, thermochemical and other energy conversion devices
involve the transport of a number of (electro-)chemical species through
distinct materials so that they can meet and react at specified multi-material
interfaces. Therefore, morphology or arrangement of these different materials
can be critical in the performance of an energy conversion device. In this
paper, we study a model problem motivated by a solar-driven thermochemical
conversion device that splits water into hydrogen and oxygen. We formulate the
problem as a system of coupled multi-material reaction-diffusion equations
where each species diffuses selectively through a given material and where the
reaction occurs at multi-material interfaces. We express the problem of optimal
design of the material arrangement as a saddle point problem and obtain an
effective functional which shows that regions with very fine phase mixtures of
the material arise naturally. To explore this further, we introduce a
phase-field formulation of the optimal design problem, and numerically study
selected examples.
",0,1,1,0,0,0
18862,Mining Communication Data in a Music Community: A Preliminary Analysis,"  Comments play an important role within online creative communities because
they make it possible to foster the production and improvement of authors'
artifacts. We investigate how comment-based communication help shape members'
behavior within online creative communities. In this paper, we report the
results of a preliminary study aimed at mining the communication network of a
music community for collaborative songwriting, where users collaborate online
by first uploading new songs and then by adding new tracks and providing
feedback in forms of comments.
",1,0,0,0,0,0
18863,Multidimensional VlasovPoisson Simulations with High-order Monotonicity- and Positivity-preserving Schemes,"  We develop new numerical schemes for Vlasov--Poisson equations with
high-order accuracy. Our methods are based on a spatially
monotonicity-preserving (MP) scheme and are modified suitably so that
positivity of the distribution function is also preserved. We adopt an
efficient semi-Lagrangian time integration scheme that is more accurate and
computationally less expensive than the three-stage TVD Runge-Kutta
integration. We apply our spatially fifth- and seventh-order schemes to a suite
of simulations of collisionless self-gravitating systems and electrostatic
plasma simulations, including linear and nonlinear Landau damping in one
dimension and Vlasov--Poisson simulations in a six-dimensional phase space. The
high-order schemes achieve a significantly improved accuracy in comparison with
the third-order positive-flux-conserved scheme adopted in our previous study.
With the semi-Lagrangian time integration, the computational cost of our
high-order schemes does not significantly increase, but remains roughly the
same as that of the third-order scheme. Vlasov--Poisson simulations on $128^3
\times 128^3$ mesh grids have been successfully performed on a massively
parallel computer.
",0,1,0,0,0,0
18864,Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy,"  Markov Chain Monte Carlo based Bayesian data analysis has now become the
method of choice for analyzing and interpreting data in almost all disciplines
of science. In astronomy, over the last decade, we have also seen a steady
increase in the number of papers that employ Monte Carlo based Bayesian
analysis. New, efficient Monte Carlo based methods are continuously being
developed and explored. In this review, we first explain the basics of Bayesian
theory and discuss how to set up data analysis problems within this framework.
Next, we provide an overview of various Monte Carlo based methods for
performing Bayesian data analysis. Finally, we discuss advanced ideas that
enable us to tackle complex problems and thus hold great promise for the
future. We also distribute downloadable computer software (available at
this https URL ) that implements some of the algorithms and
examples discussed here.
",0,1,0,1,0,0
18865,Stable splitting of mapping spaces via nonabelian Poincaré duality,"  We use nonabelian Poincaré duality to recover the stable splitting of
compactly supported mapping spaces, $\rm{Map_c}$$(M,\Sigma^nX)$, where $M$ is a
parallelizable $n$-manifold. Our method for deriving this splitting is new, and
naturally extends to give a more general stable splitting of the space of
compactly supported sections of a certain bundle on $M$ with fibers
$\Sigma^nX$, twisted by the tangent bundle of $M$. This generalization
incorporates possible $O(n)$-actions on $X$ as well as accommodating
non-parallelizable manifolds.
",0,0,1,0,0,0
18866,Static Gesture Recognition using Leap Motion,"  In this report, an automated bartender system was developed for making orders
in a bar using hand gestures. The gesture recognition of the system was
developed using Machine Learning techniques, where the model was trained to
classify gestures using collected data. The final model used in the system
reached an average accuracy of 95%. The system raised ethical concerns both in
terms of user interaction and having such a system in a real world scenario,
but it could initially work as a complement to a real bartender.
",1,0,0,1,0,0
18867,B-spline-like bases for $C^2$ cubics on the Powell-Sabin 12-split,"  For spaces of constant, linear, and quadratic splines of maximal smoothness
on the Powell-Sabin 12-split of a triangle, the so-called S-bases were recently
introduced. These are simplex spline bases with B-spline-like properties on the
12-split of a single triangle, which are tied together across triangles in a
Bézier-like manner.
In this paper we give a formal definition of an S-basis in terms of certain
basic properties. We proceed to investigate the existence of S-bases for the
aforementioned spaces and additionally the cubic case, resulting in an
exhaustive list. From their nature as simplex splines, we derive simple
differentiation and recurrence formulas to other S-bases. We establish a
Marsden identity that gives rise to various quasi-interpolants and domain
points forming an intuitive control net, in terms of which conditions for
$C^0$-, $C^1$-, and $C^2$-smoothness are derived.
",1,0,0,0,0,0
18868,Synthesizing Correlations with Computational Likelihood Approach: Vitamin C Data,"  It is known that the primary source of dietary vitamin C is fruit and
vegetables and the plasma level of vitamin C has been considered a good
surrogate biomarker of vitamin C intake by fruit and vegetable consumption. To
combine the information about association between vitamin C intake and the
plasma level of vitamin C, numerical approximation methods for likelihood
function of correlation coefficient are studied. The least squares approach is
used to estimate a log-likelihood function by a function from a space of
B-splines having desirable mathematical properties. The likelihood interval
from the Highest Likelihood Regions (HLR) is used for further inference. This
approach can be easily extended to the realm of meta-analysis involving sample
correlations from different studies by use of an approximated combined
likelihood function. The sample correlations between vitamin C intake and serum
level of vitamin C from many studies are used to illustrate application of this
approach.
",0,0,0,1,0,0
18869,Axion detection via Topological Casimir Effect,"  We propose a new table-top experimental configuration for the direct
detection of dark matter axions with mass in the $(10^{-6} \rm eV - 10^{-2} \rm
eV)$ range using non-perturbative effects in a system with non-trivial spatial
topology. Different from most experimental setups found in literature on direct
dark matter axion detection, which relies on $\dot{\theta}$ or
$\vec{\nabla}\theta$, we found that our system is in principle sensitive to a
static $\theta\geq 10^{-14}$ and can also be used to set limit on the
fundamental constant $\theta_{\rm QED}$ which becomes the fundamental
observable parameter of the Maxwell system if some conditions are met.
Connection with Witten effect when the induced electric charge $e'$ is
proportional to $\theta$ and the magnetic monopole becomes the dyon with
non-vanishing $e'=-e \frac{\theta}{2\pi}$ is also discussed.
",0,1,0,0,0,0
18870,Novel Feature-Based Clustering of Micro-Panel Data (CluMP),"  Micro-panel data are collected and analysed in many research and industry
areas. Cluster analysis of micro-panel data is an unsupervised learning
exploratory method identifying subgroup clusters in a data set which include
homogeneous objects in terms of the development dynamics of monitored
variables. The supply of clustering methods tailored to micro-panel data is
limited. The present paper focuses on a feature-based clustering method,
introducing a novel two-step characteristic-based approach designed for this
type of data. The proposed CluMP method aims to identify clusters that are at
least as internally homogeneous and externally heterogeneous as those obtained
by alternative methods already implemented in the statistical system R. We
compare the clustering performance of the devised algorithm with two extant
methods using simulated micro-panel data sets. Our approach has yielded similar
or better outcomes than the other methods, the advantage of the proposed
algorithm being time efficiency which makes it applicable for large data sets.
",0,0,0,1,0,0
18871,Ab initio effective Hamiltonians for cuprate superconductors,"  Ab initio low-energy effective Hamiltonians of two typical high-temperature
copper-oxide superconductors, whose mother compounds are La$_2$CuO$_4$ and
HgBa$_2$CuO$_4$, are derived by utilizing the multi-scale ab initio scheme for
correlated electrons (MACE). The effective Hamiltonians obtained in the present
study serve as platforms of future studies to accurately solve the low-energy
effective Hamiltonians beyond the density functional theory. It allows further
study on the superconducting mechanism from the first principles and
quantitative basis without adjustable parameters not only for the available
cuprates but also for future design of higher Tc in general. More concretely,
we derive effective Hamiltonians for three variations, 1)one-band Hamiltonian
for the antibonding orbital generated from strongly hybridized Cu
$3d_{x^2-y^2}$ and O $2p_\sigma$ orbitals 2)two-band Hamiltonian constructed
from the antibonding orbital and Cu $3d_{3z^2-r^2}$ orbital hybridized mainly
with the apex oxygen $p_z$ orbital 3)three-band Hamiltonian consisting mainly
of Cu $3d_{x^2-y^2}$ orbitals and two O $2p_\sigma$ orbitals. Differences
between the Hamiltonians for La$_2$CuO$_4$ and HgBa$_2$CuO$_4$, which have
relatively low and high critical temperatures, respectively, at optimally doped
compounds, are elucidated. The main differences are summarized as i) the oxygen
$2p_\sigma$ orbitals are farther(~3.7eV) below from the Cu $d_{x^2-y^2}$
orbital for the La compound than the Hg compound(~2.4eV) in the three-band
Hamiltonian. This causes a substantial difference in the character of the
$d_{x^2-y^2}-2p_\sigma$ antibonding band at the Fermi level and makes the
effective onsite Coulomb interaction U larger for the La compound than the Hg
compound for the two- and one-band Hamiltonians. ii)The ratio of the
second-neighbor to the nearest transfer t'/t is also substantially different
(~0.26) for the Hg and ~0.15 for the La compound in the one-band Hamiltonian.
",0,1,0,0,0,0
18872,Longitudinal data analysis using matrix completion,"  In clinical practice and biomedical research, measurements are often
collected sparsely and irregularly in time while the data acquisition is
expensive and inconvenient. Examples include measurements of spine bone mineral
density, cancer growth through mammography or biopsy, a progression of defect
of vision, or assessment of gait in patients with neurological disorders. Since
the data collection is often costly and inconvenient, estimation of progression
from sparse observations is of great interest for practitioners.
From the statistical standpoint, such data is often analyzed in the context
of a mixed-effect model where time is treated as both random and fixed effect.
Alternatively, researchers analyze Gaussian processes or functional data where
observations are assumed to be drawn from a certain distribution of processes.
These models are flexible but rely on probabilistic assumptions and require
very careful implementation.
In this study, we propose an alternative elementary framework for analyzing
longitudinal data, relying on matrix completion. Our method yields point
estimates of progression curves by iterative application of the SVD. Our
framework covers multivariate longitudinal data, regression and can be easily
extended to other settings.
We apply our methods to understand trends of progression of motor impairment
in children with Cerebral Palsy. Our model approximates individual progression
curves and explains 30% of the variability. Low-rank representation of
progression trends enables discovering that subtypes of Cerebral Palsy exhibit
different progression trends.
",0,0,0,1,0,0
18873,Emission-line Diagnostics of Nearby HII Regions Including Supernova Hosts,"  We present a new model of the optical nebular emission from HII regions by
combin- ing the results of the Binary Population and Spectral Synthesis (bpass)
code with the photoion- ization code cloudy (Ferland et al. 1998). We explore a
variety of emission-line diagnostics of these star-forming HII regions and
examine the effects of metallicity and interacting binary evo- lution on the
nebula emission-line production. We compare the line emission properties of HII
regions with model stellar populations, and provide new constraints on their
stellar populations and supernova progenitors. We find that models including
massive binary stars can successfully match all the observational constraints
and provide reasonable age and mass estimation of the HII regions and supernova
progenitors.
",0,1,0,0,0,0
18874,On monomial linearisation and supercharacters of pattern subgroups,"  Column closed pattern subgroups $U$ of the finite upper unitriangular groups
$U_n(q)$ are defined as sets of matrices in $U_n(q)$ having zeros in a
prescribed set of columns besides the diagonal ones. We explain Jedlitschky's
construction of monomial linearisation and apply this to $C U$ yielding a
generalisation of Yan's coadjoint cluster representations. Then we give a
complete classification of the resulting supercharacters, by describing the
resulting orbits and determining the Hom-spaces between orbit modules.
",0,0,1,0,0,0
18875,The Structural Fate of Individual Multicomponent Metal-Oxide Nanoparticles in Polymer Nanoreactors,"  Multicomponent nanoparticles can be synthesized with either homogeneous or
phase-segregated architectures depending on the synthesis conditions and
elements incorporated. To understand the parameters that determine their
structural fate, multicomponent metal-oxide nanoparticles consisting of
combinations of Co, Ni, and Cu were synthesized via scanning probe block
copolymer lithography and characterized using correlated electron microscopy.
These studies revealed that the miscibility, ratio of the metallic components,
and the synthesis temperature determine the crystal structure and architecture
of the nanoparticles. A Co-Ni-O system forms a rock salt structure largely due
to the miscibility of CoO and NiO, while Cu-Ni-O, which has large miscibility
gaps, forms either homogeneous oxides, heterojunctions, or alloys depending on
the annealing temperature and composition. Moreover, a higher ordered
structure, Co-Ni-Cu-O, was found to follow the behavior of lower ordered
systems.
",0,1,0,0,0,0
18876,On Compression of Unsupervised Neural Nets by Pruning Weak Connections,"  Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep
Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised
weight initialization and density estimation. In this paper,we demonstrate that
the parameters of these neural nets can be dramatically reduced without
affecting their performance. We describe a method to reduce the parameters
required by RBM which is the basic building block for deep architectures.
Further we propose an unsupervised sparse deep architectures selection
algorithm to form sparse deep neural networks.Experimental results show that
there is virtually no loss in either generative or discriminative performance.
",1,0,0,0,0,0
18877,A matrix generalization of a theorem of Fine,"  In 1947 Nathan Fine gave a beautiful product for the number of binomial
coefficients $\binom{n}{m}$, for $m$ in the range $0 \leq m \leq n$, that are
not divisible by $p$. We give a matrix product that generalizes Fine's formula,
simultaneously counting binomial coefficients with $p$-adic valuation $\alpha$
for each $\alpha \geq 0$. For each $n$ this information is naturally encoded in
a polynomial generating function, and the sequence of these polynomials is
$p$-regular in the sense of Allouche and Shallit. We also give a further
generalization to multinomial coefficients.
",1,0,1,0,0,0
18878,Training Multi-Task Adversarial Network For Extracting Noise-Robust Speaker Embedding,"  Under noisy environments, to achieve the robust performance of speaker
recognition is still a challenging task. Motivated by the promising performance
of multi-task training in a variety of image processing tasks, we explore the
potential of multi-task adversarial training for learning a noise-robust
speaker embedding. In this paper we present a novel framework which consists of
three components: an encoder that extracts noise-robust speaker embedding; a
classifier that classifies the speakers; a discriminator that discriminates the
noise type of the speaker embedding. Besides, we propose a training strategy
using the training accuracy as an indicator to stabilize the multi-class
adversarial optimization process. We conduct our experiments on the English and
Mandarin corpus and the experimental results demonstrate that our proposed
multi-task adversarial training method could greatly outperform the other
methods without adversarial training in noisy environments. Furthermore,
experiments indicate that our method is also able to improve the speaker
verification performance the clean condition.
",1,0,0,0,0,0
18879,Playing Games with Bounded Entropy,"  In this paper, we consider zero-sum repeated games in which the maximizer is
restricted to strategies requiring no more than a limited amount of randomness.
Particularly, we analyze the maxmin payoff of the maximizer in two models: the
first model forces the maximizer to randomize her action in each stage just by
conditioning her decision to outcomes of a given sequence of random source,
whereas, in the second model, the maximizer is a team of players who are free
to privately randomize their corresponding actions but do not have access to
any explicit source of shared randomness needed for cooperation. The works of
Gossner and Vieille, and Gossner and Tomala adopted the method of types to
establish their results; however, we utilize the idea of random hashing which
is the core of randomness extractors in the information theory literature. In
addition, we adopt the well-studied tool of simulation of a source from another
source. By utilizing these tools, we are able to simplify the prior results and
generalize them as well. We characterize the maxmin payoff of the maximizer in
the repeated games under study. Particularly, the maxmin payoff of the first
model is fully described by the function $J(h)$ which is the maximum payoff
that the maximizer can secure in a one-shot game by choosing mixed strategies
of entropy at most $h$. In the second part of the paper, we study the
computational aspects of $J(h)$. We offer three explicit lower bounds on the
entropy-payoff trade-off curve. To do this, we provide and utilize new results
for the set of distributions that guarantee a certain payoff for Alice. In
particular, we study how this set of distributions shrinks as we increase the
security level. While the use of total variation distance is common in game
theory, our derivation indicates the suitability of utilizing the
Renyi-divergence of order two.
",1,0,0,0,0,0
18880,Exact Hausdorff and packing measures for random self-similar code-trees with necks,"  Random code-trees with necks were introduced recently to generalise the
notion of $V$-variable and random homogeneous sets. While it is known that the
Hausdorff and packing dimensions coincide irrespective of overlaps, their exact
Hausdorff and packing measure has so far been largely ignored. In this article
we consider the general question of an appropriate gauge function for positive
and finite Hausdorff and packing measure. We first survey the current state of
knowledge and establish some bounds on these gauge functions. We then show that
self-similar code-trees do not admit a gauge functions that simultaneously give
positive and finite Hausdorff measure almost surely. This surprising result is
in stark contrast to the random recursive model and sheds some light on the
question of whether $V$-variable sets interpolate between random homogeneous
and random recursive sets. We conclude by discussing implications of our
results.
",0,0,1,0,0,0
18881,On Modules over a G-set,"  Let R be a commutative ring with unity, M a module over R and let S be a
G-set for a finite group G. We define a set MS to be the set of elements
expressed as the formal finite sum of the form similar to the elements of group
ring RG. The set MS is a module over the group ring RG under the addition and
the scalar multiplication similar to the RG-module MG. With this notion, we not
only generalize but also unify the theories of both of the group algebra and
the group module, and we also establish some significant properties of MS. In
particular, we describe a method for decomposing a given RG-module MS as a
direct sum of RG-submodules. Furthermore, we prove the semisimplicity problem
of MS with regard to the properties of M, S and G.
",0,0,1,0,0,0
18882,Polynomial-time algorithms for the Longest Induced Path and Induced Disjoint Paths problems on graphs of bounded mim-width,"  We give the first polynomial-time algorithms on graphs of bounded maximum
induced matching width (mim-width) for problems that are not locally checkable.
In particular, we give $n^{\mathcal{O}(w)}$-time algorithms on graphs of
mim-width at most $w$, when given a decomposition, for the following problems:
Longest Induced Path, Induced Disjoint Paths and $H$-Induced Topological Minor
for fixed $H$. Our results imply that the following graph classes have
polynomial-time algorithms for these three problems: Interval and Bi-Interval
graphs, Circular Arc, Permutation and Circular Permutation graphs, Convex
graphs, $k$-Trapezoid, Circular $k$-Trapezoid, $k$-Polygon, Dilworth-$k$ and
Co-$k$-Degenerate graphs for fixed $k$.
",1,0,0,0,0,0
18883,Bug or Not? Bug Report Classification Using N-Gram IDF,"  Previous studies have found that a significant number of bug reports are
misclassified between bugs and non-bugs, and that manually classifying bug
reports is a time-consuming task. To address this problem, we propose a bug
reports classification model with N-gram IDF, a theoretical extension of
Inverse Document Frequency (IDF) for handling words and phrases of any length.
N-gram IDF enables us to extract key terms of any length from texts, these key
terms can be used as the features to classify bug reports. We build
classification models with logistic regression and random forest using features
from N-gram IDF and topic modeling, which is widely used in various software
engineering tasks. With a publicly available dataset, our results show that our
N-gram IDF-based models have a superior performance than the topic-based models
on all of the evaluated cases. Our models show promising results and have a
potential to be extended to other software engineering tasks.
",1,0,0,0,0,0
18884,Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection,"  Cell nuclei detection is a challenging research topic because of limitations
in cellular image quality and diversity of nuclear morphology, i.e. varying
nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been
a topic of enduring interest with promising recent success shown by deep
learning methods. These methods train Convolutional Neural Networks (CNNs) with
a training set of input images and known, labeled nuclei locations. Many such
methods are supplemented by spatial or morphological processing. Using a set of
canonical cell nuclei shapes, prepared with the help of a domain expert, we
develop a new approach that we call Shape Priors with Convolutional Neural
Networks (SP-CNN). We further extend the network to introduce a shape prior
(SP) layer and then allowing it to become trainable (i.e. optimizable). We call
this network tunable SP-CNN (TSP-CNN). In summary, we present new network
structures that can incorporate 'expected behavior' of nucleus shapes via two
components: learnable layers that perform the nucleus detection and a fixed
processing part that guides the learning with prior information. Analytically,
we formulate two new regularization terms that are targeted at: 1) learning the
shapes, 2) reducing false positives while simultaneously encouraging detection
inside the cell nucleus boundary. Experimental results on two challenging
datasets reveal that the proposed SP-CNN and TSP-CNN can outperform
state-of-the-art alternatives.
",1,0,0,1,0,0
18885,Anisotropic hydrodynamic turbulence in accretion disks,"  Recently, the vertical shear instability (VSI) has become an attractive
purely hydrodynamic candidate for the anomalous angular momentum transport
required for weakly ionized accretion disks. In direct three-dimensional
numerical simulations of VSI turbulence in disks, a meridional circulation
pattern was observed that is opposite to the usual viscous flow behavior. Here,
we investigate whether this feature can possibly be explained by an anisotropy
of the VSI turbulence. Using three-dimensional hydrodynamical simulations, we
calculate the turbulent Reynolds stresses relevant for angular momentum
transport for a representative section of a disk.
We find that the vertical stress is significantly stronger than the radial
stress. Using our results in viscous disk simulations with different viscosity
coefficients for the radial and vertical direction, we find good agreement with
the VSI turbulence for the stresses and meridional flow; this provides
additional evidence for the anisotropy. The results are important with respect
to the transport of small embedded particles in disks.
",0,1,0,0,0,0
18886,Optimal Algorithms for Distributed Optimization,"  In this paper, we study the optimal convergence rate for distributed convex
optimization problems in networks. We model the communication restrictions
imposed by the network as a set of affine constraints and provide optimal
complexity bounds for four different setups, namely: the function $F(\xb)
\triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either
strongly convex or smooth or just convex. Our results show that Nesterov's
accelerated gradient descent on the dual problem can be executed in a
distributed manner and obtains the same optimal rates as in the centralized
version of the problem (up to constant or logarithmic factors) with an
additional cost related to the spectral gap of the interaction matrix. Finally,
we discuss some extensions to the proposed setup such as proximal friendly
functions, time-varying graphs, improvement of the condition numbers.
",1,0,0,1,0,0
18887,Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks,"  Brain-Computer Interface (BCI) uses brain signals in order to provide a new
method for communication between human and outside world. Feature extraction,
selection and classification are among the main matters of concerns in signal
processing stage of BCI. In this article, we present our findings about the
most effective features and classifiers in some brain tasks. Six different
groups of classical features and twelve classifiers have been examined in nine
datasets of brain signal. The results indicate that energy of brain signals in
{\alpha} and \b{eta} frequency bands, together with some statistical parameters
are more effective, comparing to the other types of extracted features. In
addition, Bayesian classifier with Gaussian distribution assumption and also
Support Vector Machine (SVM) show to classify different BCI datasets more
accurately than the other classifiers. We believe that the results can give an
insight about a strategy for blind classification of brain signals in
brain-computer interface.
",1,0,0,1,0,0
18888,On exceptional compact homogeneous geometries of type C3,"  We provide a uniform framework to study the exceptional homogeneous compact
geometries of type C3. This framework is then used to show that these are
simply connected, answering a question by Kramer and Lytchak, and to calculate
the full automorphism groups.
",0,0,1,0,0,0
18889,Intensity estimation of transaction arrivals on the intraday electricity market,"  In the following paper we present a simple intensity estimation method of
transaction arrivals on the intraday electricity market. Assuming the
interarrival times distribution, we utilize a maximum likelihood estimation.
The method's performance is briefly tested using German Intraday Continuous
data. Despite the simplicity of the method, the results are encouraging. The
supplementary materials containing the R-codes and the data are attached to
this paper.
",0,0,0,1,0,1
18890,Capacitive Mechanism of Oxygen Functional Groups on Carbon Surface in Supercapacitors,"  Oxygen functional groups are one of the most important subjects in the study
of electrochemical properties of carbon materials which can change the
wettability, conductivity and pore size distributions of carbon materials, and
can occur redox reactions. In the electrode materials of carbon-based
supercapacitors, the oxygen functional groups have widely been used to improve
the capacitive performance. In this paper, we not only analyzed the reasons for
the increase of the capacity that promoted by oxygen functional groups in the
charge-discharge cycling tests, but also analyzed the mechanism how the
pseudocapacitance was provided by the oxygen functional groups in the
acid/alkaline aqueous electrolyte. Moreover, we also discussed the effect of
the oxygen functional groups in electrochemical impedance spectroscopy.
",0,1,0,0,0,0
18891,On conditional least squares estimation for affine diffusions based on continuous time observations,"  We study asymptotic properties of conditional least squares estimators for
the drift parameters of two-factor affine diffusions based on continuous time
observations. We distinguish three cases: subcritical, critical and
supercritical. For all the drift parameters, in the subcritical and
supercritical cases, asymptotic normality and asymptotic mixed normality is
proved, while in the critical case, non-standard asymptotic behavior is
described.
",0,0,1,1,0,0
18892,Discretization error estimates for penalty formulations of a linearized Canham-Helfrich type energy,"  This paper is concerned with minimization of a fourth-order linearized
Canham-Helfrich energy subject to Dirichlet boundary conditions on curves
inside the domain. Such problems arise in the modeling of the mechanical
interaction of biomembranes with embedded particles. There, the curve
conditions result from the imposed particle--membrane coupling. We prove
almost-$H^{\frac{5}{2}}$ regularity of the solution and then consider two
possible penalty formulations. For the combination of these penalty
formulations with a Bogner-Fox-Schmit finite element discretization we prove
discretization error estimates which are optimal in view of the solution's
reduced regularity. The error estimates are based on a general estimate for
linear penalty problems in Hilbert spaces. Finally, we illustrate the
theoretical results by numerical computations. An important feature of the
presented discretization is that it does not require to resolve the particle
boundary. This is crucial in order to avoid re-meshing if the presented problem
arises as subproblem in a model where particles are allowed to move or rotate.
",0,0,1,0,0,0
18893,On the Humphreys conjecture on support varieties of tilting modules,"  Let $G$ be a simply-connected semisimple algebraic group over an
algebraically closed field of characteristic $p$, assumed to be larger than the
Coxeter number. The ""support variety"" of a $G$-module $M$ is a certain closed
subvariety of the nilpotent cone of $G$, defined in terms of cohomology for the
first Frobenius kernel $G_1$. In the 1990s, Humphreys proposed a conjectural
description of the support varieties of tilting modules; this conjecture has
been proved for $G = \mathrm{SL}_n$ in earlier work of the second author.
In this paper, we show that for any $G$, the support variety of a tilting
module always contains the variety predicted by Humphreys, and that they
coincide (i.e., the Humphreys conjecture is true) when $p$ is sufficiently
large. We also prove variants of these statements involving ""relative support
varieties.""
",0,0,1,0,0,0
18894,Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks,"  Machine learning models have been widely used in security applications such
as intrusion detection, spam filtering, and virus or malware detection.
However, it is well-known that adversaries are always trying to adapt their
attacks to evade detection. For example, an email spammer may guess what
features spam detection models use and modify or remove those features to avoid
detection. There has been some work on making machine learning models more
robust to such attacks. However, one simple but promising approach called {\em
randomization} is underexplored. This paper proposes a novel
randomization-based approach to improve robustness of machine learning models
against evasion attacks. The proposed approach incorporates randomization into
both model training time and model application time (meaning when the model is
used to detect attacks). We also apply this approach to random forest, an
existing ML method which already has some degree of randomness. Experiments on
intrusion detection and spam filtering data show that our approach further
improves robustness of random-forest method. We also discuss how this approach
can be applied to other ML models.
",0,0,0,1,0,0
18895,Further extension of the generalized Hurwitz-Lerch Zeta function of two variables,"  The main aim of this paper is to give a new generalization of Hurwitz-Lerch
Zeta function of two variables.Also, we investigate several interesting
properties such as integral representations, summation formula and a connection
with generalized hypergeometric function. To strengthen the main results we
also consider many important special cases.
",0,0,1,0,0,0
18896,A Physarum-inspired model for the probit-based stochastic user equilibrium problem,"  Stochastic user equilibrium is an important issue in the traffic assignment
problems, tradition models for the stochastic user equilibrium problem are
designed as mathematical programming problems. In this article, a
Physarum-inspired model for the probit-based stochastic user equilibrium
problem is proposed. There are two main contributions of our work. On the one
hand, the origin Physarum model is modified to find the shortest path in
traffic direction networks with the properties of two-way traffic
characteristic. On the other hand, the modified Physarum-inspired model could
get the equilibrium flows when traveller's perceived transportation cost
complies with normal distribution. The proposed method is constituted with a
two-step procedure. First, the modified Physarum model is applied to get the
auxiliary flows. Second, the auxiliary flows are averaged to obtain the
equilibrium flows. Numerical examples are conducted to illustrate the
performance of the proposed method, which is compared with the Method of
Successive Average method.
",1,0,0,0,0,0
18897,Going Higher in First-Order Quantifier Alternation Hierarchies on Words,"  We investigate quantifier alternation hierarchies in first-order logic on
finite words. Levels in these hierarchies are defined by counting the number of
quantifier alternations in formulas. We prove that one can decide membership of
a regular language in the levels $\mathcal{B}{\Sigma}_2$ (finite boolean
combinations of formulas having only one alternation) and ${\Sigma}_3$
(formulas having only two alternations and beginning with an existential
block). Our proofs work by considering a deeper problem, called separation,
which, once solved for lower levels, allows us to solve membership for higher
levels.
",1,0,0,0,0,0
18898,Invariance of Ideal Limit Points,"  Let $\mathcal{I}$ be an analytic P-ideal [respectively, a summable ideal] on
the positive integers and let $(x_n)$ be a sequence taking values in a metric
space $X$. First, it is shown that the set of ideal limit points of $(x_n)$ is
an $F_\sigma$-set [resp., a closet set]. Let us assume that $X$ is also
separable and the ideal $\mathcal{I}$ satisfies certain additional assumptions,
which however includes several well-known examples, e.g., the collection of
sets with zero asymptotic density, sets with zero logarithmic density, and some
summable ideals. Then, it is shown that the set of ideal limit points of
$(x_n)$ is equal to the set of ideal limit points of almost all its
subsequences.
",0,0,1,0,0,0
18899,"Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching","  The unprecedented demand for large amount of data has catalyzed the trend of
combining human insights with machine learning techniques, which facilitate the
use of crowdsourcing to enlist label information both effectively and
efficiently. The classic work on crowdsourcing mainly focuses on the label
inference problem under the categorization setting. However, inferring the true
label requires sophisticated aggregation models that usually can only perform
well under certain assumptions. Meanwhile, no matter how complicated the
aggregation model is, the true model that generated the crowd labels remains
unknown. Therefore, the label inference problem can never infer the ground
truth perfectly. Based on the fact that the crowdsourcing labels are abundant
and utilizing aggregation will lose such kind of rich annotation information
(e.g., which worker provided which labels), we believe that it is critical to
take the diverse labeling abilities of the crowdsourcing workers as well as
their correlations into consideration. To address the above challenge, we
propose to tackle three research problems, namely inference, learning, and
teaching.
",0,0,0,1,0,0
18900,Learning With Errors and Extrapolated Dihedral Cosets,"  The hardness of the learning with errors (LWE) problem is one of the most
fruitful resources of modern cryptography. In particular, it is one of the most
prominent candidates for secure post-quantum cryptography. Understanding its
quantum complexity is therefore an important goal. We show that under quantum
polynomial time reductions, LWE is equivalent to a relaxed version of the
dihedral coset problem (DCP), which we call extrapolated DCP (eDCP). The extent
of extrapolation varies with the LWE noise rate. By considering different
extents of extrapolation, our result generalizes Regev's famous proof that if
DCP is in BQP (quantum poly-time) then so is LWE (FOCS'02). We also discuss a
connection between eDCP and Childs and Van Dam's algorithm for generalized
hidden shift problems (SODA'07). Our result implies that a BQP solution for LWE
might not require the full power of solving DCP, but rather only a solution for
its relaxed version, eDCP, which could be easier.
",1,0,0,0,0,0
18901,Fermi bubbles: high latitude X-ray supersonic shell,"  The nature of the bipolar, $\gamma$-ray Fermi bubbles (FB) is still unclear,
in part because their faint, high-latitude X-ray counterpart has until now
eluded a clear detection. We stack ROSAT data at varying distances from the FB
edges, thus boosting the signal and identifying an expanding shell behind the
southwest, southeast, and northwest edges, albeit not in the dusty northeast
sector near Loop I. A Primakoff-like model for the underlying flow is invoked
to show that the signals are consistent with halo gas heated by a strong,
forward shock to $\sim$keV temperatures. Assuming ion--electron thermal
equilibrium then implies a $\sim10^{56}$ erg event near the Galactic centre
$\sim7$ Myr ago. However, the reported high absorption-line velocities suggest
a preferential shock-heating of ions, and thus more energetic ($\sim 10^{57}$
erg), younger ($\lesssim 3$ Myr) FBs.
",0,1,0,0,0,0
18902,Passivity-Based Generalization of Primal-Dual Dynamics for Non-Strictly Convex Cost Functions,"  In this paper, we revisit primal-dual dynamics for convex optimization and
present a generalization of the dynamics based on the concept of passivity. It
is then proved that supplying a stable zero to one of the integrators in the
dynamics allows one to eliminate the assumption of strict convexity on the cost
function based on the passivity paradigm together with the invariance principle
for Caratheodory systems. We then show that the present algorithm is also a
generalization of existing augmented Lagrangian-based primal-dual dynamics, and
discuss the benefit of the present generalization in terms of noise reduction
and convergence speed.
",1,0,0,0,0,0
18903,"An integral formula for the powered sum of the independent, identically and normally distributed random variables","  The distribution of the sum of r-th power of standard normal random variables
is a generalization of the chi-squared distribution. In this paper, we
represent the probability density function of the random variable by an
one-dimensional absolutely convergent integral with the characteristic
function. Our integral formula is expected to be applied for evaluation of the
density function. Our integral formula is based on the inversion formula, and
we utilize a summation method. We also discuss on our formula in the view point
of hyperfunctions.
",0,0,1,0,0,0
18904,A Dynamic Boosted Ensemble Learning Method Based on Random Forest,"  We propose a dynamic boosted ensemble learning method based on random forest
(DBRF), a novel ensemble algorithm that incorporates the notion of hard example
mining into Random Forest (RF) and thus combines the high accuracy of Boosting
algorithm with the strong generalization of Bagging algorithm. Specifically, we
propose to measure the quality of each leaf node of every decision tree in the
random forest to determine hard examples. By iteratively training and then
removing easy examples from training data, we evolve the random forest to focus
on hard examples dynamically so as to learn decision boundaries better. Data
can be cascaded through these random forests learned in each iteration in
sequence to generate predictions, thus making RF deep. We also propose to use
evolution mechanism and smart iteration mechanism to improve the performance of
the model. DBRF outperforms RF on three UCI datasets and achieved
state-of-the-art results compared to other deep models. Moreover, we show that
DBRF is also a new way of sampling and can be very useful when learning from
imbalanced data.
",0,0,0,1,0,0
18905,"Inter-Operator Resource Management for Millimeter Wave, Multi-Hop Backhaul Networks","  In this paper, a novel framework is proposed for optimizing the operation and
performance of a large-scale, multi-hop millimeter wave (mmW) backhaul within a
wireless small cell network (SCN) that encompasses multiple mobile network
operators (MNOs). The proposed framework enables the small base stations (SBSs)
to jointly decide on forming the multi-hop, mmW links over backhaul
infrastructure that belongs to multiple, independent MNOs, while properly
allocating resources across those links. In this regard, the problem is
addressed using a novel framework based on matching theory that is composed to
two, highly inter-related stages: a multi-hop network formation stage and a
resource management stage. One unique feature of this framework is that it
jointly accounts for both wireless channel characteristics and economic factors
during both network formation and resource management. The multi-hop network
formation stage is formulated as a one-to-many matching game which is solved
using a novel algorithm, that builds on the so-called deferred acceptance
algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW
backhaul network. Then, a one-to-many matching game is formulated to enable
proper resource allocation across the formed multi-hop network. This game is
then shown to exhibit peer effects and, as such, a novel algorithm is developed
to find a stable and optimal resource management solution that can properly
cope with these peer effects. Simulation results show that the proposed
framework yields substantial gains, in terms of the average sum rate, reaching
up to 27% and 54%, respectively, compared to a non-cooperative scheme in which
inter-operator sharing is not allowed and a random allocation approach. The
results also show that our framework provides insights on how to manage pricing
and the cost of the cooperative mmW backhaul network for the MNOs.
",1,0,0,0,0,0
18906,An Online Learning Approach to Generative Adversarial Networks,"  We consider the problem of training generative models with a Generative
Adversarial Network (GAN). Although GANs can accurately model complex
distributions, they are known to be difficult to train due to instabilities
caused by a difficult minimax optimization problem. In this paper, we view the
problem of training GANs as finding a mixed strategy in a zero-sum game.
Building on ideas from online learning we propose a novel training method named
Chekhov GAN 1 . On the theory side, we show that our method provably converges
to an equilibrium for semi-shallow GAN architectures, i.e. architectures where
the discriminator is a one layer network and the generator is arbitrary. On the
practical side, we develop an efficient heuristic guided by our theoretical
results, which we apply to commonly used deep GAN architectures. On several
real world tasks our approach exhibits improved stability and performance
compared to standard GAN training.
",1,0,0,1,0,0
18907,Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data,"  Sound event detection (SED) is typically posed as a supervised learning
problem requiring training data with strong temporal labels of sound events.
However, the production of datasets with strong labels normally requires
unaffordable labor cost. It limits the practical application of supervised SED
methods. The recent advances in SED approaches focuses on detecting sound
events by taking advantages of weakly labeled or unlabeled training data. In
this paper, we propose a joint framework to solve the SED task using
large-scale unlabeled in-domain data. In particular, a state-of-the-art general
audio tagging model is first employed to predict weak labels for unlabeled
data. On the other hand, a weakly supervised architecture based on the
convolutional recurrent neural network (CRNN) is developed to solve the strong
annotations of sound events with the aid of the unlabeled data with predicted
labels. It is found that the SED performance generally increases as more
unlabeled data is added into the training. To address the noisy label problem
of unlabeled data, an ensemble strategy is applied to increase the system
robustness. The proposed system is evaluated on the SED dataset of DCASE 2018
challenge. It reaches a F1-score of 21.0%, resulting in an improvement of 10%
over the baseline system.
",1,0,0,0,0,0
18908,Revealing the Coulomb interaction strength in a cuprate superconductor,"  We study optimally doped
Bi$_{2}$Sr$_{2}$Ca$_{0.92}$Y$_{0.08}$Cu$_{2}$O$_{8+\delta}$ (Bi2212) using
angle-resolved two-photon photoemission spectroscopy. Three spectral features
are resolved near 1.5, 2.7, and 3.6 eV above the Fermi level. By tuning the
photon energy, we determine that the 2.7-eV feature arises predominantly from
unoccupied states. The 1.5- and 3.6-eV features reflect unoccupied states whose
spectral intensities are strongly modulated by the corresponding occupied
states. These unoccupied states are consistent with the prediction from a
cluster perturbation theory based on the single-band Hubbard model. Through
this comparison, a Coulomb interaction strength U of 2.7 eV is extracted. Our
study complements equilibrium photoemission spectroscopy and provides a direct
spectroscopic measurement of the unoccupied states in cuprates. The determined
Coulomb U indicates that the charge-transfer gap of optimally doped Bi2212 is
1.1 eV.
",0,1,0,0,0,0
18909,Ordering dynamics of self-propelled particles in an inhomogeneous medium,"  Ordering dynamics of self-propelled particles in an inhomogeneous medium in
two-dimensions is studied. We write coarse-grained hydrodynamic equations of
motion for coarse-grained density and velocity fields in the presence of an
external random disorder field, which is quenched in time. The strength of
inhomogeneity is tuned from zero disorder (clean system) to large disorder. In
the clean system, the velocity field grows algebraically as $L_{\rm V} \sim
t^{0.5}$. The density field does not show clean power-law growth; however, it
follows $L_{\rm \rho} \sim t^{0.8}$ approximately. In the inhomogeneous system,
we find a disorder dependent growth. For both the density and the velocity,
growth slow down with increasing strength of disorder. The velocity shows a
disorder dependent power-law growth $L_{\rm V}(t,\Delta) \sim t^{1/\bar z_{\rm
V}(\Delta)}$ for intermediate times. At late times, there is a crossover to
logarithmic growth $L_{\rm V}(t,\Delta) \sim (\ln t)^{1/\varphi}$, where
$\varphi$ is a disorder independent exponent. Two-point correlation functions
for the velocity shows dynamical scaling, but the density does not.
",0,1,0,0,0,0
18910,Inflationary preheating dynamics with ultracold atoms,"  We discuss the amplification of loop corrections in quantum many-body systems
through dynamical instabilities. As an example, we investigate both
analytically and numerically a two-component ultracold atom system in one
spatial dimension. The model features a tachyonic instability, which
incorporates characteristic aspects of the mechanisms for particle production
in early-universe inflaton models. We establish a direct correspondence between
measureable macroscopic growth rates for occupation numbers of the ultracold
Bose gas and the underlying microscopic processes in terms of Feynman loop
diagrams. We analyze several existing ultracold atom setups featuring dynamical
instabilities and propose optimized protocols for their experimental
realization. We demonstrate that relevant dynamical processes can be enhanced
using a seeding procedure for unstable modes and clarify the role of initial
quantum fluctuations and the generation of a non-linear secondary stage for the
amplification of modes.
",0,1,0,0,0,0
18911,Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning,"  Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
",1,0,0,0,0,0
18912,Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection,"  Motivation: Epigenetic heterogeneity within a tumour can play an important
role in tumour evolution and the emergence of resistance to treatment. It is
increasingly recognised that the study of DNA methylation (DNAm) patterns along
the genome -- so-called `epialleles' -- offers greater insight into epigenetic
dynamics than conventional analyses which examine DNAm marks individually.
Results: We have developed a Bayesian model to infer which epialleles are
present in multiple regions of the same tumour. We apply our method to reduced
representation bisulfite sequencing (RRBS) data from multiple regions of one
lung cancer tumour and a matched normal sample. The model borrows information
from all tumour regions to leverage greater statistical power. The total number
of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all
automatically inferred from the data. Uncertainty as to which epiallele an
observed sequencing read originated from is explicitly incorporated by
marginalising over the appropriate posterior densities. The degree to which
tumour samples are contaminated with normal tissue can be estimated and
corrected for. By tracing the distribution of epialleles throughout the tumour
we can infer the phylogenetic history of the tumour, identify epialleles that
differ between normal and cancer tissue, and define a measure of global
epigenetic disorder.
",0,0,1,1,0,0
18913,A bootstrap test to detect prominent Granger-causalities across frequencies,"  Granger-causality in the frequency domain is an emerging tool to analyze the
causal relationship between two time series. We propose a bootstrap test on
unconditional and conditional Granger-causality spectra, as well as on their
difference, to catch particularly prominent causality cycles in relative terms.
In particular, we consider a stochastic process derived applying independently
the stationary bootstrap to the original series. Our null hypothesis is that
each causality or causality difference is equal to the median across
frequencies computed on that process. In this way, we are able to disambiguate
causalities which depart significantly from the median one obtained ignoring
the causality structure. Our test shows power one as the process tends to
non-stationarity, thus being more conservative than parametric alternatives. As
an example, we infer about the relationship between money stock and GDP in the
Euro Area via our approach, considering inflation, unemployment and interest
rates as conditioning variables. We point out that during the period 1999-2017
the money stock aggregate M1 had a significant impact on economic output at all
frequencies, while the opposite relationship is significant only at high
frequencies.
",0,0,0,1,0,1
18914,Optimal segregation of proteins: phase transitions and symmetry breaking,"  Asymmetric segregation of key proteins at cell division -- be it a beneficial
or deleterious protein -- is ubiquitous in unicellular organisms and often
considered as an evolved trait to increase fitness in a stressed environment.
Here, we provide a general framework to describe the evolutionary origin of
this asymmetric segregation. We compute the population fitness as a function of
the protein segregation asymmetry $a$, and show that the value of $a$ which
optimizes the population growth manifests a phase transition between symmetric
and asymmetric partitioning phases. Surprisingly, the nature of phase
transition is different for the case of beneficial proteins as opposed to
proteins which decrease the single-cell growth rate. Our study elucidates the
optimization problem faced by evolution in the context of protein segregation,
and motivates further investigation of asymmetric protein segregation in
biological systems.
",0,0,0,0,1,0
18915,Optimal make-take fees for market making regulation,"  We consider an exchange who wishes to set suitable make-take fees to attract
liquidity on its platform. Using a principal-agent approach, we are able to
describe in quasi-explicit form the optimal contract to propose to a market
maker. This contract depends essentially on the market maker inventory
trajectory and on the volatility of the asset. We also provide the optimal
quotes that should be displayed by the market maker. The simplicity of our
formulas allows us to analyze in details the effects of optimal contracting
with an exchange, compared to a situation without contract. We show in
particular that it leads to higher quality liquidity and lower trading costs
for investors.
",0,0,0,0,0,1
18916,Spontaneous symmetry breaking due to the trade-off between attractive and repulsive couplings,"  Spontaneous symmetry breaking (SSB) is an important phenomenon observed in
various fields including physics and biology. In this connection, we here show
that the trade-off between attractive and repulsive couplings can induce
spontaneous symmetry breaking in a homogeneous system of coupled oscillators.
With a simple model of a system of two coupled Stuart-Landau oscillators, we
demonstrate how the tendency of attractive coupling in inducing in-phase
synchronized (IPS) oscillations and the tendency of repulsive coupling in
inducing out-of-phase synchronized (OPS) oscillations compete with each other
and give rise to symmetry breaking oscillatory (SBO) states and interesting
multistabilities. Further, we provide explicit expressions for synchronized and
anti-synchronized oscillatory states as well as the so called oscillation death
(OD) state and study their stability. If the Hopf bifurcation parameter
(${\lambda}$) is greater than the natural frequency ($\omega$) of the system,
the attractive coupling favours the emergence of an anti-symmetric OD state via
a Hopf bifurcation whereas the repulsive coupling favours the emergence of a
similar state through a saddle-node bifurcation. We show that an increase in
the repulsive coupling not only destabilizes the IPS state but also facilitates
the re-entrance of the IPS state.
",0,1,0,0,0,0
18917,Evolutionary Image Composition Using Feature Covariance Matrices,"  Evolutionary algorithms have recently been used to create a wide range of
artistic work. In this paper, we propose a new approach for the composition of
new images from existing ones, that retain some salient features of the
original images. We introduce evolutionary algorithms that create new images
based on a fitness function that incorporates feature covariance matrices
associated with different parts of the images. This approach is very flexible
in that it can work with a wide range of features and enables targeting
specific regions in the images. For the creation of the new images, we propose
a population-based evolutionary algorithm with mutation and crossover operators
based on random walks. Our experimental results reveal a spectrum of
aesthetically pleasing images that can be obtained with the aid of our
evolutionary process.
",1,0,0,0,0,0
18918,Community Detection with Colored Edges,"  In this paper, we prove a sharp limit on the community detection problem with
colored edges. We assume two equal-sized communities and there are $m$
different types of edges. If two vertices are in the same community, the
distribution of edges follows $p_i=\alpha_i\log{n}/n$ for $1\leq i \leq m$,
otherwise the distribution of edges is $q_i=\beta_i\log{n}/n$ for $1\leq i \leq
m$, where $\alpha_i$ and $\beta_i$ are positive constants and $n$ is the total
number of vertices. Under these assumptions, a fundamental limit on community
detection is characterized using the Hellinger distance between the two
distributions. If $\sum_{i=1}^{m} {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2 >2$,
then the community detection via maximum likelihood (ML) estimator is possible
with high probability. If $\sum_{i=1}^m {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2
< 2$, the probability that the ML estimator fails to detect the communities
does not go to zero.
",1,1,0,0,0,0
18919,"Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling","  Nonlocal neural networks have been proposed and shown to be effective in
several computer vision tasks, where the nonlocal operations can directly
capture long-range dependencies in the feature space. In this paper, we study
the nature of diffusion and damping effect of nonlocal networks by doing
spectrum analysis on the weight matrices of the well-trained networks, and then
propose a new formulation of the nonlocal block. The new block not only learns
the nonlocal interactions but also has stable dynamics, thus allowing deeper
nonlocal structures. Moreover, we interpret our formulation from the general
nonlocal modeling perspective, where we make connections between the proposed
nonlocal network and other nonlocal models, such as nonlocal diffusion process
and Markov jump process.
",0,0,0,1,0,0
18920,Proof of FLT by Algebra Identities and Linear Algebra,"  The main aim of the present paper is to represent an exact and simple proof
for FLT by using properties of the algebra identities and linear algebra.
",0,0,1,0,0,0
18921,An Observational Diagnostic for Distinguishing Between Clouds and Haze in Hot Exoplanet Atmospheres,"  The nature of aerosols in hot exoplanet atmospheres is one of the primary
vexing questions facing the exoplanet field. The complex chemistry, multiple
formation pathways, and lack of easily identifiable spectral features
associated with aerosols make it especially challenging to constrain their key
properties. We propose a transmission spectroscopy technique to identify the
primary aerosol formation mechanism for the most highly irradiated hot Jupiters
(HIHJs). The technique is based on the expectation that the two key types of
aerosols -- photochemically generated hazes and equilibrium condensate clouds
-- are expected to form and persist in different regions of a highly irradiated
planet's atmosphere. Haze can only be produced on the permanent daysides of
tidally-locked hot Jupiters, and will be carried downwind by atmospheric
dynamics to the evening terminator (seen as the trailing limb during transit).
Clouds can only form in cooler regions on the night side and morning terminator
of HIHJs (seen as the leading limb during transit). Because opposite limbs are
expected to be impacted by different types of aerosols, ingress and egress
spectra, which primarily probe opposing sides of the planet, will reveal the
dominant aerosol formation mechanism. We show that the benchmark HIHJ,
WASP-121b, has a transmission spectrum consistent with partial aerosol coverage
and that ingress-egress spectroscopy would constrain the location and formation
mechanism of those aerosols. In general, using this diagnostic we find that
observations with JWST and potentially with HST should be able to distinguish
between clouds and haze for currently known HIHJs.
",0,1,0,0,0,0
18922,$\mathcal{P}$-schemes and Deterministic Polynomial Factoring over Finite Fields,"  We introduce a family of mathematical objects called $\mathcal{P}$-schemes,
where $\mathcal{P}$ is a poset of subgroups of a finite group $G$. A
$\mathcal{P}$-scheme is a collection of partitions of the right coset spaces
$H\backslash G$, indexed by $H\in\mathcal{P}$, that satisfies a list of axioms.
These objects generalize the classical notion of association schemes as well as
the notion of $m$-schemes (Ivanyos et al. 2009).
Based on $\mathcal{P}$-schemes, we develop a unifying framework for the
problem of deterministic factoring of univariate polynomials over finite fields
under the generalized Riemann hypothesis (GRH).
",1,0,1,0,0,0
18923,Similarity Preserving Representation Learning for Time Series Analysis,"  A considerable amount of machine learning algorithms take instance-feature
matrices as their inputs. As such, they cannot directly analyze time series
data due to its temporal nature, usually unequal lengths, and complex
properties. This is a great pity since many of these algorithms are effective,
robust, efficient, and easy to use. In this paper, we bridge this gap by
proposing an efficient representation learning framework that is able to
convert a set of time series with equal or unequal lengths to a matrix format.
In particular, we guarantee that the pairwise similarities between time series
are well preserved after the transformation. The learned feature representation
is particularly suitable to the class of learning problems that are sensitive
to data similarities. Given a set of $n$ time series, we first construct an
$n\times n$ partially observed similarity matrix by randomly sampling $O(n \log
n)$ pairs of time series and computing their pairwise similarities. We then
propose an extremely efficient algorithm that solves a highly non-convex and
NP-hard problem to learn new features based on the partially observed
similarity matrix. We use the learned features to conduct experiments on both
data classification and clustering tasks. Our extensive experimental results
demonstrate that the proposed framework is both effective and efficient.
",1,0,0,0,0,0
18924,Modeling Social Organizations as Communication Networks,"  We identify the ""organization"" of a human social group as the communication
network(s) within that group. We then introduce three theoretical approaches to
analyzing what determines the structures of human organizations. All three
approaches adopt a group-selection perspective, so that the group's network
structure is (approximately) optimal, given the information-processing
limitations of agents within the social group, and the exogenous welfare
function of the overall group. In the first approach we use a new sub-field of
telecommunications theory called network coding, and focus on a welfare
function that involves the ability of the organization to convey information
among the agents. In the second approach we focus on a scenario where agents
within the organization must allocate their future communication resources when
the state of the future environment is uncertain. We show how this formulation
can be solved with a linear program. In the third approach, we introduce an
information synthesis problem in which agents within an organization receive
information from various sources and must decide how to transform such
information and transmit the results to other agents in the organization. We
propose leveraging the computational power of neural networks to solve such
problems. These three approaches formalize and synthesize work in fields
including anthropology, archeology, economics and psychology that deal with
organization structure, theory of the firm, span of control and cognitive
limits on communication.
",1,1,0,0,0,0
18925,The Ricci flow on solvmanifolds of real type,"  We show that for any solvable Lie group of real type, any homogeneous Ricci
flow solution converges in Cheeger-Gromov topology to a unique non-flat
solvsoliton, which is independent of the initial left-invariant metric. As an
application, we obtain results on the isometry groups of non-flat solvsoliton
metrics and Einstein solvmanifolds.
",0,0,1,0,0,0
18926,Model-based clustering of multi-tissue gene expression data,"  Recently, it has become feasible to generate large-scale, multi-tissue gene
expression data, where expression profiles are obtained from multiple tissues
or organs sampled from dozens to hundreds of individuals. When traditional
clustering methods are applied to this type of data, important information is
lost, because they either require all tissues to be analyzed independently,
ignoring dependencies and similarities between tissues, or to merge tissues in
a single, monolithic dataset, ignoring individual characteristics of tissues.
We developed a Bayesian model-based multi-tissue clustering algorithm, revamp,
which can incorporate prior information on physiological tissue similarity, and
which results in a set of clusters, each consisting of a core set of genes
conserved across tissues as well as differential sets of genes specific to one
or more subsets of tissues. Using data from seven vascular and metabolic
tissues from over 100 individuals in the STockholm Atherosclerosis Gene
Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by
revamp are more enriched for tissue-dependent protein-protein interactions
compared to alternative approaches. We further demonstrate that revamp results
in easily interpretable multi-tissue gene expression associations to key
coronary artery disease processes and clinical phenotypes in the STAGE
individuals. Revamp is implemented in the Lemon-Tree software, available at
this https URL
",0,0,0,0,1,0
18927,Fastest Convergence for Q-learning,"  The Zap Q-learning algorithm introduced in this paper is an improvement of
Watkins' original algorithm and recent competitors in several respects. It is a
matrix-gain algorithm designed so that its asymptotic variance is optimal.
Moreover, an ODE analysis suggests that the transient behavior is a close match
to a deterministic Newton-Raphson implementation. This is made possible by a
two time-scale update equation for the matrix gain sequence.
The analysis suggests that the approach will lead to stable and efficient
computation even for non-ideal parameterized settings. Numerical experiments
confirm the quick convergence, even in such non-ideal cases.
A secondary goal of this paper is tutorial. The first half of the paper
contains a survey on reinforcement learning algorithms, with a focus on minimum
variance algorithms.
",1,0,1,0,0,0
18928,New Bounds on the Field Size for Maximally Recoverable Codes Instantiating Grid-like Topologies,"  In recent years, the rapidly increasing amounts of data created and processed
through the internet resulted in distributed storage systems employing erasure
coding based schemes. Aiming to balance the tradeoff between data recovery for
correlated failures and efficient encoding and decoding, distributed storage
systems employing maximally recoverable codes came up. Unifying a number of
topologies considered both in theory and practice, Gopalan \cite{Gopalan2017}
initiated the study of maximally recoverable codes for grid-like topologies.
In this paper, we focus on the maximally recoverable codes that instantiate
grid-like topologies $T_{m\times n}(1,b,0)$. To characterize the property of
codes for these topologies, we introduce the notion of \emph{pseudo-parity
check matrix}. Then, using the hypergraph independent set approach, we
establish the first polynomial upper bound on the field size needed for
achieving the maximal recoverability in topologies $T_{m\times n}(1,b,0)$, when
$n$ is large enough. And we further improve this general upper bound for
topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$. By relating the
problem to generalized \emph{Sidon sets} in $\mathbb{F}_q$, we also obtain
non-trivial lower bounds on the field size for maximally recoverable codes that
instantiate topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$.
",1,0,0,0,0,0
18929,Two-Step Disentanglement for Financial Data,"  In this work, we address the problem of disentanglement of factors that
generate a given data into those that are correlated with the labeling and
those that are not. Our solution is simpler than previous solutions and employs
adversarial training in a straightforward manner. We demonstrate the new method
on visual datasets as well as on financial data. In order to evaluate the
latter, we developed a hypothetical trading strategy whose performance is
affected by the performance of the disentanglement, namely, it trades better
when the factors are better separated.
",1,0,0,1,0,0
18930,Optimal control of a Vlasov-Poisson plasma by an external magnetic field - Analysis of a tracking type optimal control problem,"  In the paper ""Optimal control of a Vlasov-Poisson plasma by an external
magnetic field - The basics for variational calculus"" [arXiv:1708.02464] we
have already introduced a set of admissible magnetic fields and we have proved
that each of those fields induces a unique strong solution of the
Vlasov-Poisson system. We have also established that the field-state operator
that maps any admissible field onto its corresponding solution is continuous
and weakly compact. In this paper we will show that this operator is also
Fréchet differentiable and we will continue to analyze the optimal control
problem that was introduced in [arXiv:1708.02464]. More precisely, we will
establish necessary and sufficient conditions for local optimality and we will
show that an optimal solution is unique under certain conditions.
",0,0,1,0,0,0
18931,Towards an algebraic natural proofs barrier via polynomial identity testing,"  We observe that a certain kind of algebraic proof - which covers essentially
all known algebraic circuit lower bounds to date - cannot be used to prove
lower bounds against VP if and only if what we call succinct hitting sets exist
for VP. This is analogous to the Razborov-Rudich natural proofs barrier in
Boolean circuit complexity, in that we rule out a large class of lower bound
techniques under a derandomization assumption. We also discuss connections
between this algebraic natural proofs barrier, geometric complexity theory, and
(algebraic) proof complexity.
",1,0,1,0,0,0
18932,In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference,"  In recent years, a great deal of interest has focused on conducting inference
on the parameters in a linear model in the high-dimensional setting. In this
paper, we consider a simple and very naïve two-step procedure for this
task, in which we (i) fit a lasso model in order to obtain a subset of the
variables; and (ii) fit a least squares model on the lasso-selected set.
Conventional statistical wisdom tells us that we cannot make use of the
standard statistical inference tools for the resulting least squares model
(such as confidence intervals and $p$-values), since we peeked at the data
twice: once in running the lasso, and again in fitting the least squares model.
However, in this paper, we show that under a certain set of assumptions, with
high probability, the set of variables selected by the lasso is deterministic.
Consequently, the naïve two-step approach can yield confidence intervals
that have asymptotically correct coverage, as well as p-values with proper
Type-I error control. Furthermore, this two-step approach unifies two existing
camps of work on high-dimensional inference: one camp has focused on inference
based on a sub-model selected by the lasso, and the other has focused on
inference using a debiased version of the lasso estimator.
",0,0,1,1,0,0
18933,Asymptotic Analysis of Plausible Tree Hash Modes for SHA-3,"  Discussions about the choice of a tree hash mode of operation for a
standardization have recently been undertaken. It appears that a single tree
mode cannot address adequately all possible uses and specifications of a
system. In this paper, we review the tree modes which have been proposed, we
discuss their problems and propose remedies. We make the reasonable assumption
that communicating systems have different specifications and that software
applications are of different types (securing stored content or live-streamed
content). Finally, we propose new modes of operation that address the resource
usage problem for the three most representative categories of devices and we
analyse their asymptotic behavior.
",1,0,0,0,0,0
18934,Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling,"  In this paper we present a neurally plausible model of robot reaching
inspired by human infant reaching that is based on embodied artificial
intelligence, which emphasizes the importance of the sensory-motor interaction
of an agent and the world. This model encompasses both learning sensory-motor
correlations through motor babbling and also arm motion planning using
spreading activation. This model is organized in three layers of neural maps
with parallel structures representing the same sensory-motor space. The motor
babbling period shapes the structure of the three neural maps as well as the
connections within and between them. We describe an implementation of this
model and an investigation of this implementation using a simple reaching task
on a humanoid robot. The robot has learned successfully to plan reaching
motions from a test set with high accuracy and smoothness.
",1,0,0,0,0,0
18935,When Will AI Exceed Human Performance? Evidence from AI Experts,"  Advances in artificial intelligence (AI) will transform modern life by
reshaping transportation, health, science, finance, and the military. To adapt
public policy, we need to better anticipate these advances. Here we report the
results from a large survey of machine learning researchers on their beliefs
about progress in AI. Researchers predict AI will outperform humans in many
activities in the next ten years, such as translating languages (by 2024),
writing high-school essays (by 2026), driving a truck (by 2027), working in
retail (by 2031), writing a bestselling book (by 2049), and working as a
surgeon (by 2053). Researchers believe there is a 50% chance of AI
outperforming humans in all tasks in 45 years and of automating all human jobs
in 120 years, with Asian respondents expecting these dates much sooner than
North Americans. These results will inform discussion amongst researchers and
policymakers about anticipating and managing trends in AI.
",1,0,0,0,0,0
18936,E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) are a key technology for emerging
applications such as automatic speech recognition, machine translation or image
description. Long Short Term Memory (LSTM) networks are the most successful RNN
implementation, as they can learn long term dependencies to achieve high
accuracy. Unfortunately, the recurrent nature of LSTM networks significantly
constrains the amount of parallelism and, hence, multicore CPUs and many-core
GPUs exhibit poor efficiency for RNN inference. In this paper, we present
E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM
computation. The main goal of E-PUR is to support large recurrent neural
networks for low-power mobile devices. E-PUR provides an efficient hardware
implementation of LSTM networks that is flexible to support diverse
applications. One of its main novelties is a technique that we call Maximizing
Weight Locality (MWL), which improves the temporal locality of the memory
accesses for fetching the synaptic weights, reducing the memory requirements by
a large extent. Our experimental results show that E-PUR achieves real-time
performance for different LSTM networks, while reducing energy consumption by
orders of magnitude with respect to general-purpose processors and GPUs, and it
requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA
Tegra X1, E-PUR provides an average energy reduction of 92x.
",1,0,0,0,0,0
18937,Increasing Geminid meteor shower activity,"  Mathematical modelling has shown that activity of the Geminid meteor shower
should rise with time, and that was confirmed by analysis of visual
observations 1985--2016. We do not expect any outburst activity of the Geminid
shower in 2017, even though the asteroid (3200) Phaethon has close approach to
Earth in December of 2017. A small probability to observe dust ejected at
perihelia 2009--2016 still exists.
",0,1,0,0,0,0
18938,The Bennett-Orlicz norm,"  Lederer and van de Geer (2013) introduced a new Orlicz norm, the
Bernstein-Orlicz norm, which is connected to Bernstein type inequalities. Here
we introduce another Orlicz norm, the Bennett-Orlicz norm, which is connected
to Bennett type inequalities. The new Bennett-Orlicz norm yields inequalities
for expectations of maxima which are potentially somewhat tighter than those
resulting from the Bernstein-Orlicz norm when they are both applicable. We
discuss cross connections between these norms, exponential inequalities of the
Bernstein, Bennett, and Prokhorov types, and make comparisons with results of
Talagrand (1989, 1994), and Boucheron, Lugosi, and Massart (2013).
",0,0,1,1,0,0
18939,Temporal Justification Logic,"  Justification logics are modal-like logics with the additional capability of
recording the reason, or justification, for modalities in syntactic structures,
called justification terms. Justification logics can be seen as explicit
counterparts to modal logics. The behavior and interaction of agents in
distributed system is often modeled using logics of knowledge and time. In this
paper, we sketch some preliminary ideas on how the modal knowledge part of such
logics of knowledge and time could be replaced with an appropriate
justification logic.
",1,0,0,0,0,0
18940,Simultaneous Multiparty Communication Complexity of Composed Functions,"  In the Number On the Forehead (NOF) multiparty communication model, $k$
players want to evaluate a function $F : X_1 \times\cdots\times X_k\rightarrow
Y$ on some input $(x_1,\dots,x_k)$ by broadcasting bits according to a
predetermined protocol. The input is distributed in such a way that each player
$i$ sees all of it except $x_i$. In the simultaneous setting, the players
cannot speak to each other but instead send information to a referee. The
referee does not know the players' input, and cannot give any information back.
At the end, the referee must be able to recover $F(x_1,\dots,x_k)$ from what
she obtained.
A central open question, called the $\log n$ barrier, is to find a function
which is hard to compute for $polylog(n)$ or more players (where the $x_i$'s
have size $poly(n)$) in the simultaneous NOF model. This has important
applications in circuit complexity, as it could help to separate $ACC^0$ from
other complexity classes. One of the candidates belongs to the family of
composed functions. The input to these functions is represented by a $k\times
(t\cdot n)$ boolean matrix $M$, whose row $i$ is the input $x_i$ and $t$ is a
block-width parameter. A symmetric composed function acting on $M$ is specified
by two symmetric $n$- and $kt$-variate functions $f$ and $g$, that output
$f\circ g(M)=f(g(B_1),\dots,g(B_n))$ where $B_j$ is the $j$-th block of width
$t$ of $M$. As the majority function $MAJ$ is conjectured to be outside of
$ACC^0$, Babai et. al. suggested to study $MAJ\circ MAJ_t$, with $t$ large
enough.
So far, it was only known that $t=1$ is not enough for $MAJ\circ MAJ_t$ to
break the $\log n$ barrier in the simultaneous deterministic NOF model. In this
paper, we extend this result to any constant block-width $t>1$, by giving a
protocol of cost $2^{O(2^t)}\log^{2^{t+1}}(n)$ for any symmetric composed
function when there are $2^{\Omega(2^t)}\log n$ players.
",1,0,0,0,0,0
18941,Online Learning for Changing Environments using Coin Betting,"  A key challenge in online learning is that classical algorithms can be slow
to adapt to changing environments. Recent studies have proposed ""meta""
algorithms that convert any online learning algorithm to one that is adaptive
to changing environments, where the adaptivity is analyzed in a quantity called
the strongly-adaptive regret. This paper describes a new meta algorithm that
has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$
better than other algorithms with the same time complexity, where $T$ is the
time horizon. We also extend our algorithm to achieve a first-order (i.e.,
dependent on the observed losses) strongly-adaptive regret bound for the first
time, to our knowledge. At its heart is a new parameter-free algorithm for the
learning with expert advice (LEA) problem in which experts sometimes do not
output advice for consecutive time steps (i.e., \emph{sleeping} experts). This
algorithm is derived by a reduction from optimal algorithms for the so-called
coin betting problem. Empirical results show that our algorithm outperforms
state-of-the-art methods in both learning with expert advice and metric
learning scenarios.
",1,0,0,1,0,0
18942,On the global sup-norm of GL(3) cusp forms,"  Let $\phi$ be a spherical Hecke-Maass cusp form on the non-compact space
$\mathrm{PGL}_3(\mathbb{Z})\backslash\mathrm{PGL}_3(\mathbb{R})$. We establish
various pointwise upper bounds for $\phi$ in terms of its Laplace eigenvalue
$\lambda_\phi$. These imply, for $\phi$ arithmetically normalized and tempered
at the archimedean place, the bound $\|\phi\|_\infty\ll_\epsilon
\lambda_{\phi}^{39/40+\epsilon}$ for the global sup-norm (without restriction
to a compact subset). On the way, we derive a new uniform upper bound for the
$\mathrm{GL}_3$ Jacquet-Whittaker function.
",0,0,1,0,0,0
18943,Four revolutions in physics and the second quantum revolution -- a unification of force and matter by quantum information,"  Newton's mechanical revolution unifies the motion of planets in the sky and
falling of apple on earth. Maxwell's electromagnetic revolution unifies
electricity, magnetism, and light. Einstein's relativity revolution unifies
space with time, and gravity with space-time distortion. The quantum revolution
unifies particle with waves, and energy with frequency. Each of those
revolution changes our world view. In this article, we will describe a
revolution that is happening now: the second quantum revolution which unifies
matter/space with information. In other words, the new world view suggests that
elementary particles (the bosonic force particles and fermionic matter
particles) all originated from quantum information (qubits): they are
collective excitations of an entangled qubit ocean that corresponds to our
space. The beautiful geometric Yang-Mills gauge theory and the strange Fermi
statistics of matter particles now have a common algebraic quantum
informational origin.
",0,1,0,0,0,0
18944,Concept Drift Learning with Alternating Learners,"  Data-driven predictive analytics are in use today across a number of
industrial applications, but further integration is hindered by the requirement
of similarity among model training and test data distributions. This paper
addresses the need of learning from possibly nonstationary data streams, or
under concept drift, a commonly seen phenomenon in practical applications. A
simple dual-learner ensemble strategy, alternating learners framework, is
proposed. A long-memory model learns stable concepts from a long relevant time
window, while a short-memory model learns transient concepts from a small
recent window. The difference in prediction performance of these two models is
monitored and induces an alternating policy to select, update and reset the two
models. The method features an online updating mechanism to maintain the
ensemble accuracy, and a concept-dependent trigger to focus on relevant data.
Through empirical studies the method demonstrates effective tracking and
prediction when the steaming data carry abrupt and/or gradual changes.
",1,0,0,1,0,0
18945,A new proof of the competitive exclusion principle in the chemostat,"  We give an new proof of the well-known competitive exclusion principle in the
chemostat model with $n$ species competing for a single resource, for any set
of increasing growth functions. The proof is constructed by induction on the
number of the species, after being ordered. It uses elementary analysis and
comparisons of solutions of ordinary differential equations.
",0,0,1,0,0,0
18946,From Random Differential Equations to Structural Causal Models: the stochastic case,"  Random Differential Equations provide a natural extension of Ordinary
Differential Equations to the stochastic setting. We show how, and under which
conditions, every equilibrium state of a Random Differential Equation (RDE) can
be described by a Structural Causal Model (SCM), while pertaining the causal
semantics. This provides an SCM that captures the stochastic and causal
behavior of the RDE, which can model both cycles and confounders. This enables
the study of the equilibrium states of the RDE by applying the theory and
statistical tools available for SCMs, for example, marginalizations and Markov
properties, as we illustrate by means of an example. Our work thus provides a
direct connection between two fields that so far have been developing in
isolation.
",0,0,0,1,0,0
18947,Ties That Bind - Characterizing Classes by Attributes and Social Ties,"  Given a set of attributed subgraphs known to be from different classes, how
can we discover their differences? There are many cases where collections of
subgraphs may be contrasted against each other. For example, they may be
assigned ground truth labels (spam/not-spam), or it may be desired to directly
compare the biological networks of different species or compound networks of
different chemicals.
In this work we introduce the problem of characterizing the differences
between attributed subgraphs that belong to different classes. We define this
characterization problem as one of partitioning the attributes into as many
groups as the number of classes, while maximizing the total attributed quality
score of all the given subgraphs.
We show that our attribute-to-class assignment problem is NP-hard and an
optimal $(1 - 1/e)$-approximation algorithm exists. We also propose two
different faster heuristics that are linear-time in the number of attributes
and subgraphs. Unlike previous work where only attributes were taken into
account for characterization, here we exploit both attributes and social ties
(i.e. graph structure).
Through extensive experiments, we compare our proposed algorithms, show
findings that agree with human intuition on datasets from Amazon co-purchases,
Congressional bill sponsorships, and DBLP co-authorships. We also show that our
approach of characterizing subgraphs is better suited for sense-making than
discriminating classification approaches.
",1,1,0,0,0,0
18948,Probing dark matter with star clusters: a dark matter core in the ultra-faint dwarf Eridanus II,"  We present a new technique to probe the central dark matter (DM) density
profile of galaxies that harnesses both the survival and observed properties of
star clusters. As a first application, we apply our method to the `ultra-faint'
dwarf Eridanus II (Eri II) that has a lone star cluster ~45 pc from its centre.
Using a grid of collisional $N$-body simulations, incorporating the effects of
stellar evolution, external tides and dynamical friction, we show that a DM
core for Eri II naturally reproduces the size and the projected position of its
star cluster. By contrast, a dense cusped galaxy requires the cluster to lie
implausibly far from the centre of Eri II (>1 kpc), with a high inclination
orbit that must be observed at a particular orbital phase. Our results,
therefore, favour a dark matter core. This implies that either a cold DM cusp
was `heated up' at the centre of Eri II by bursty star formation, or we are
seeing an evidence for physics beyond cold DM.
",0,1,0,0,0,0
18949,Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s),"  We propose the ambiguity problem for the foreground object segmentation task
and motivate the importance of estimating and accounting for this ambiguity
when designing vision systems. Specifically, we distinguish between images
which lead multiple annotators to segment different foreground objects
(ambiguous) versus minor inter-annotator differences of the same object. Taking
images from eight widely used datasets, we crowdsource labeling the images as
""ambiguous"" or ""not ambiguous"" to segment in order to construct a new dataset
we call STATIC. Using STATIC, we develop a system that automatically predicts
which images are ambiguous. Experiments demonstrate the advantage of our
prediction system over existing saliency-based methods on images from vision
benchmarks and images taken by blind people who are trying to recognize objects
in their environment. Finally, we introduce a crowdsourcing system to achieve
cost savings for collecting the diversity of all valid ""ground truth""
foreground object segmentations by collecting extra segmentations only when
ambiguity is expected. Experiments show our system eliminates up to 47% of
human effort compared to existing crowdsourcing methods with no loss in
capturing the diversity of ground truths.
",1,0,0,0,0,0
18950,On Some Generalized Polyhedral Convex Constructions,"  Generalized polyhedral convex sets, generalized polyhedral convex functions
on locally convex Hausdorff topological vector spaces, and the related
constructions such as sum of sets, sum of functions, directional derivative,
infimal convolution, normal cone, conjugate function, subdifferential, are
studied thoroughly in this paper. Among other things, we show how a generalized
polyhedral convex set can be characterized via the finiteness of the number of
its faces. In addition, it is proved that the infimal convolution of a
generalized polyhedral convex function and a polyhedral convex function is a
polyhedral convex function. The obtained results can be applied to scalar
optimization problems described by generalized polyhedral convex sets and
generalized polyhedral convex functions.
",0,0,1,0,0,0
18951,mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion,"  Proteins are commonly used by biochemical industry for numerous processes.
Refining these proteins' properties via mutations causes stability effects as
well. Accurate computational method to predict how mutations affect protein
stability are necessary to facilitate efficient protein design. However,
accuracy of predictive models is ultimately constrained by the limited
availability of experimental data. We have developed mGPfusion, a novel
Gaussian process (GP) method for predicting protein's stability changes upon
single and multiple mutations. This method complements the limited experimental
data with large amounts of molecular simulation data. We introduce a Bayesian
data fusion model that re-calibrates the experimental and in silico data
sources and then learns a predictive GP model from the combined data. Our
protein-specific model requires experimental data only regarding the protein of
interest and performs well even with few experimental measurements. The
mGPfusion models proteins by contact maps and infers the stability effects
caused by mutations with a mixture of graph kernels. Our results show that
mGPfusion outperforms state-of-the-art methods in predicting protein stability
on a dataset of 15 different proteins and that incorporating molecular
simulation data improves the model learning and prediction accuracy.
",0,0,0,1,1,0
18952,How Do Software Startups Pivot? Empirical Results from a Multiple Case Study,"  In order to handle intense time pressure and survive in dynamic market,
software startups have to make crucial decisions constantly on whether to
change directions or stay on chosen courses, or in the terms of Lean Startup,
to pivot or to persevere. The existing research and knowledge on software
startup pivots are very limited. In this study, we focused on understanding the
pivoting processes of software startups, and identified the triggering factors
and pivot types. To achieve this, we employed a multiple case study approach,
and analyzed the data obtained from four software startups. The initial
findings show that different software startups make different types of pivots
related to business and technology during their product development life cycle.
The pivots are triggered by various factors including negative customer
feedback.
",1,0,0,0,0,0
18953,Some criteria for Wind Riemannian completeness and existence of Cauchy hypersurfaces,"  Recently, a link between Lorentzian and Finslerian Geometries has been
carried out, leading to the notion of wind Riemannian structure (WRS), a
generalization of Finslerian Randers metrics. Here, we further develop this
notion and its applications to spacetimes, by introducing some
characterizations and criteria for the completeness of WRS's.
As an application, we consider a general class of spacetimes admitting a time
function $t$ generated by the flow of a complete Killing vector field
(generalized standard stationary spacetimes or, more precisely, SSTK ones) and
derive simple criteria ensuring that its slices $t=$ constant are Cauchy.
Moreover, a brief summary on the Finsler/Lorentz link for readers with some
acquaintance in Lorentzian Geometry, plus some simple examples in Mathematical
Relativity, are provided.
",0,0,1,0,0,0
18954,A generalization of Schönemann's theorem via a graph theoretic method,"  Recently, Grynkiewicz et al. [{\it Israel J. Math.} {\bf 193} (2013),
359--398], using tools from additive combinatorics and group theory, proved
necessary and sufficient conditions under which the linear congruence
$a_1x_1+\cdots +a_kx_k\equiv b \pmod{n}$, where $a_1,\ldots,a_k,b,n$ ($n\geq
1$) are arbitrary integers, has a solution $\langle x_1,\ldots,x_k \rangle \in
\Z_{n}^k$ with all $x_i$ distinct modulo $n$. So, it would be an interesting
problem to give an explicit formula for the number of such solutions. Quite
surprisingly, this problem was first considered, in a special case, by
Schönemann almost two centuries ago(!) but his result seems to have been
forgotten. Schönemann [{\it J. Reine Angew. Math.} {\bf 1839} (1839),
231--243] proved an explicit formula for the number of such solutions when
$b=0$, $n=p$ a prime, and $\sum_{i=1}^k a_i \equiv 0 \pmod{p}$ but $\sum_{i \in
I} a_i \not\equiv 0 \pmod{p}$ for all $I\varsubsetneq \lbrace 1, \ldots,
k\rbrace$. In this paper, we generalize Schönemann's theorem using a result
on the number of solutions of linear congruences due to D. N. Lehmer and also a
result on graph enumeration recently obtained by Ardila et al. [{\it Int. Math.
Res. Not.} {\bf 2015} (2015), 3830--3877]. This seems to be a rather uncommon
method in the area; besides, our proof technique or its modifications may be
useful for dealing with other cases of this problem (or even the general case)
or other relevant problems.
",1,0,0,0,0,0
18955,Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning,"  In order for robots to perform mission-critical tasks, it is essential that
they are able to quickly adapt to changes in their environment as well as to
injuries and or other bodily changes. Deep reinforcement learning has been
shown to be successful in training robot control policies for operation in
complex environments. However, existing methods typically employ only a single
policy. This can limit the adaptability since a large environmental
modification might require a completely different behavior compared to the
learning environment. To solve this problem, we propose Map-based Multi-Policy
Reinforcement Learning (MMPRL), which aims to search and store multiple
policies that encode different behavioral features while maximizing the
expected reward in advance of the environment change. Thanks to these policies,
which are stored into a multi-dimensional discrete map according to its
behavioral feature, adaptation can be performed within reasonable time without
retraining the robot. An appropriate pre-trained policy from the map can be
recalled using Bayesian optimization. Our experiments show that MMPRL enables
robots to quickly adapt to large changes without requiring any prior knowledge
on the type of injuries that could occur. A highlight of the learned behaviors
can be found here: this https URL .
",1,0,0,0,0,0
18956,Testing convexity of a discrete distribution,"  Based on the convex least-squares estimator, we propose two different
procedures for testing convexity of a probability mass function supported on N
with an unknown finite support. The procedures are shown to be asymptotically
calibrated.
",0,0,1,1,0,0
18957,Small and Strong Formulations for Unions of Convex Sets from the Cayley Embedding,"  There is often a significant trade-off between formulation strength and size
in mixed integer programming (MIP). When modeling convex disjunctive
constraints (e.g. unions of convex sets), adding auxiliary continuous variables
can sometimes help resolve this trade-off. However, standard formulations that
use such auxiliary continuous variables can have a worse-than-expected
computational effectiveness, which is often attributed precisely to these
auxiliary continuous variables. For this reason, there has been considerable
interest in constructing strong formulations that do not use continuous
auxiliary variables. We introduce a technique to construct formulations without
these detrimental continuous auxiliary variables. To develop this technique we
introduce a natural non-polyhedral generalization of the Cayley embedding of a
family of polytopes and show it inherits many geometric properties of the
original embedding. We then show how the associated formulation technique can
be used to construct small and strong formulation for a wide range of
disjunctive constraints. In particular, we show it can recover and generalize
all known strong formulations without continuous auxiliary variables.
",0,0,1,0,0,0
18958,Safe Robotic Grasping: Minimum Impact-Force Grasp Selection,"  This paper addresses the problem of selecting from a choice of possible
grasps, so that impact forces will be minimised if a collision occurs while the
robot is moving the grasped object along a post-grasp trajectory. Such
considerations are important for safety in human-robot interaction, where even
a certified ""human-safe"" (e.g. compliant) arm may become hazardous once it
grasps and begins moving an object, which may have significant mass, sharp
edges or other dangers. Additionally, minimising collision forces is critical
to preserving the longevity of robots which operate in uncertain and hazardous
environments, e.g. robots deployed for nuclear decommissioning, where removing
a damaged robot from a contaminated zone for repairs may be extremely difficult
and costly. Also, unwanted collisions between a robot and critical
infrastructure (e.g. pipework) in such high-consequence environments can be
disastrous. In this paper, we investigate how the safety of the post-grasp
motion can be considered during the pre-grasp approach phase, so that the
selected grasp is optimal in terms applying minimum impact forces if a
collision occurs during a desired post-grasp manipulation. We build on the
methods of augmented robot-object dynamics models and ""effective mass"" and
propose a method for combining these concepts with modern grasp and trajectory
planners, to enable the robot to achieve a grasp which maximises the safety of
the post-grasp trajectory, by minimising potential collision forces. We
demonstrate the effectiveness of our approach through several experiments with
both simulated and real robots.
",1,0,0,0,0,0
18959,Nonparametric Kernel Density Estimation for Univariate Curent Status Data,"  We derive estimators of the density of the event times of current status
data. The estimators are derived for the situations where the distribution of
the observation times is known and where this distribution is unknown. The
density estimators are constructed from kernel estimators of the density of
transformed current status data, which have a distribution similar to uniform
deconvolution data. Expansions of the expectation and variance as well as
asymptotic normality are derived. A reference density based bandwidth selection
method is proposed. A simulated example is presented.
",0,0,1,1,0,0
18960,Non-Kähler Mirror Symmetry of the Iwasawa Manifold,"  We propose a new approach to the Mirror Symmetry Conjecture in a form
suitable to possibly non-Kähler compact complex manifolds whose canonical
bundle is trivial. We apply our methods by proving that the Iwasawa manifold
$X$, a well-known non-Kähler compact complex manifold of dimension $3$, is
its own mirror dual to the extent that its Gauduchon cone, replacing the
classical Kähler cone that is empty in this case, corresponds to what we call
the local universal family of essential deformations of $X$. These are obtained
by removing from the Kuranishi family the two ""superfluous"" dimensions of
complex parallelisable deformations that have a similar geometry to that of the
Iwasawa manifold. The remaining four dimensions are shown to have a clear
geometric meaning including in terms of the degeneration at $E_2$ of the
Frölicher spectral sequence. On the local moduli space of ""essential"" complex
structures, we obtain a canonical Hodge decomposition of weight $3$ and a
variation of Hodge structures, construct coordinates and Yukawa couplings while
implicitly proving a local Torelli theorem. On the metric side of the mirror,
we construct a variation of Hodge structures parametrised by a subset of the
complexified Gauduchon cone of the Iwasawa manifold using the sGG property of
all the small deformations of this manifold proved in earlier joint work of the
author with L. Ugarte. Finally, we define a mirror map linking the two
variations of Hodge structures and we highlight its properties.
",0,0,1,0,0,0
18961,Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias,"  Empirical researchers often trim observations with small denominator A when
they estimate moments of the form E[B/A]. Large trimming is a common practice
to mitigate variance, but it incurs large trimming bias. This paper provides a
novel method of correcting large trimming bias. If a researcher is willing to
assume that the joint distribution between A and B is smooth, then a large
trimming bias may be estimated well. With the bias correction, we also develop
a valid and robust inference result for E[B/A].
",0,0,0,1,0,0
18962,FPGA-based real-time 105-channel data acquisition platform for imaging system,"  In this paper, a real-time 105-channel data acquisition platform based on
FPGA for imaging will be implemented for mm-wave imaging systems. PC platform
is also realized for imaging results monitoring purpose. Mm-wave imaging
expands our vision by letting us see things under poor visibility conditions.
With this extended vision ability, a wide range of military imaging missions
would benefit, such as surveillance, precision targeting, navigation, and
rescue. Based on the previously designed imager modules, this project would go
on finishing the PCB design (both schematic and layout) of the following signal
processing systems consisting of Programmable Gain Amplifier(PGA) (4 PGA for
each ADC) and 16-channel Analog to Digital Converter (ADC) (7 ADC in total).
Then the system verification would be performed on the Artix-7 35T Arty FPGA
with the developing of proper controlling code to configure the ADC and realize
the communication between the FPGA and the PC (through both UART and Ethernet).
For the verification part, a simple test on a breadboard with a simple analog
input (generated from a resistor divider) would first be performed. After the
PCB design is finished, the whole system would be tested again with a precise
reference and analog input.
",1,0,0,0,0,0
18963,Zn-induced in-gap electronic states in La214 probed by uniform magnetic susceptibility: relevance to the suppression of superconducting Tc,"  Substitution of isovalent non-magnetic defects, such as Zn, in CuO2 plane
strongly modifies the magnetic properties of strongly electron correlated hole
doped cuprate superconductors. The reason for enhanced uniform magnetic
susceptibility, \c{hi}, in Zn substituted cuprates is debatable. So far, the
observed magnetic behavior has been analyzed mainly in terms of two somewhat
contrasting scenarios, (a) that due to independent localized moments appearing
in the vicinity of Zn arising because of the strong electronic/magnetic
correlations present in the host compound and (b) that due to transfer of
quasiparticle spectral weight and creation of weakly localized low energy
electronic states associated with each Zn atom in place of an in-plane Cu. If
the second scenario is correct, one should expect a direct correspondence
between Zn induced suppression of superconducting transition temperature, Tc,
and the extent of the enhanced magnetic susceptibility at low temperature. In
this case, the low-T enhancement of \c{hi} would be due to weakly localized
quasiparticle states at low energy and these electronic states will be
precluded from taking part in Cooper pairing. We explore this second
possibility by analyzing the \c{hi}(T) data for La2-xSrxCu1-yZnyO4 with
different hole contents, p (= x), and Zn concentrations (y) in this paper.
Results of our analysis support this scenario.
",0,1,0,0,0,0
18964,Predicate Specialization for Definitional Higher-order Logic Programs,"  Higher-order logic programming is an interesting extension of traditional
logic programming that allows predicates to appear as arguments and variables
to be used where predicates typically occur. Higher-order characteristics are
indeed desirable but on the other hand they are also usually more expensive to
support. In this paper we propose a program specialization technique based on
partial evaluation that can be applied to a modest but useful class of
higher-order logic programs and can transform them into first-order programs
without introducing additional data structures. The resulting first-order
programs can be executed by conventional logic programming interpreters and
benefit from other optimizations that might be available. We provide an
implementation and experimental results that suggest the efficiency of the
transformation.
",1,0,0,0,0,0
18965,Metastability and bifurcation in superconducting nanorings,"  We describe an approach, based on direct numerical solution of the Usadel
equation, to finding stationary points of the free energy of superconducting
nanorings. We consider both uniform (equilibrium) solutions and the critical
droplets that mediate activated transitions between them. For the uniform
solutions, we compute the critical current as a function of the temperature,
thus obtaining a correction factor to Bardeen's 1962 interpolation formula. For
the droplets, we present a metastability chart that shows the activation energy
as a function of the temperature and current. A comparison of the activation
energy for a ring to experimental results for a wire connected to
superconducting leads reveals a discrepancy at large currents. We discuss
possible reasons for it. We also discuss the nature of the bifurcation point at
which the droplet merges with the uniform solution.
",0,1,0,0,0,0
18966,Origin of life in a digital microcosm,"  While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.
",1,1,0,0,0,0
18967,The equivalence of two tax processes,"  We introduce two models of taxation, the latent and natural tax processes,
which have both been used to represent loss-carry-forward taxation on the
capital of an insurance company. In the natural tax process, the tax rate is a
function of the current level of capital, whereas in the latent tax process,
the tax rate is a function of the capital that would have resulted if no tax
had been paid. Whereas up to now these two types of tax processes have been
treated separately, we show that, in fact, they are essentially equivalent.
This allows a unified treatment, translating results from one model to the
other. Significantly, we solve the question of existence and uniqueness for the
natural tax process, which is defined via an integral equation. Our results
clarify the existing literature on processes with tax.
",0,0,0,0,0,1
18968,Space-Time Geostatistical Models with both Linear and Seasonal Structures in the Temporal Components,"  We provide a novel approach to model space-time random fields where the
temporal argument is decomposed into two parts. The former captures the linear
argument, which is related, for instance, to the annual evolution of the field.
The latter is instead a circular variable describing, for instance, monthly
observations. The basic intuition behind this construction is to consider a
random field defined over space (a compact set of the $d$-dimensional Euclidean
space) across time, which is considered as the product space $\mathbb{R} \times
\mathbb{S}^1$, with $\mathbb{S}^1$ being the unit circle. Under such framework,
we derive new parametric families of covariance functions. In particular, we
focus on two classes of parametric families. The former being parenthetical to
the Gneiting class of covariance functions. The latter is instead obtained by
proposing a new Lagrangian framework for the space-time domain considered in
the manuscript. Our findings are illustrated through a real dataset of surface
air temperatures. We show that the incorporation of both temporal variables can
produce significant improvements in the predictive performances of the model.
We also discuss the extension of this approach for fields defined spatially on
a sphere, which allows to model space-time phenomena over large portions of
planet Earth.
",0,0,1,1,0,0
18969,Stability and Robust Regulation of Passive Linear Systems,"  We study the stability of coupled impedance passive regular linear systems
under power-preserving interconnections. We present new conditions for strong,
exponential, and non-uniform stability of the closed-loop system. We apply the
stability results to the construction of passive error feedback controllers for
robust output tracking and disturbance rejection for strongly stabilizable
passive systems. In the case of nonsmooth reference and disturbance signals we
present conditions for non-uniform rational and logarithmic rates of
convergence of the output. The results are illustrated with examples on
designing controllers for linear wave and heat equations, and on studying the
stability of a system of coupled partial differential equations.
",0,0,1,0,0,0
18970,Bayes Minimax Competitors of Preliminary Test Estimators in k Sample Problems,"  In this paper, we consider the estimation of a mean vector of a multivariate
normal population where the mean vector is suspected to be nearly equal to mean
vectors of $k-1$ other populations. As an alternative to the preliminary test
estimator based on the test statistic for testing hypothesis of equal means, we
derive empirical and hierarchical Bayes estimators which shrink the sample mean
vector toward a pooled mean estimator given under the hypothesis. The
minimaxity of those Bayesian estimators are shown, and their performances are
investigated by simulation.
",0,0,1,1,0,0
18971,Non-orthogonal Multiple Access for High-reliable and Low-latency V2X Communications,"  In this paper, we consider a dense vehicular communication network where each
vehicle broadcasts its safety information to its neighborhood in each
transmission period. Such applications require low latency and high
reliability, and thus, we propose a non-orthogonal multiple access scheme to
reduce the latency and to improve the packet reception probability. In the
proposed scheme, the BS performs the semi-persistent scheduling to optimize the
time scheduling and allocate frequency resources in a non-orthogonal manner
while the vehicles autonomously perform distributed power control. We formulate
the centralized scheduling and resource allocation problem as equivalent to a
multi-dimensional stable roommate matching problem, in which the users and
time/frequency resources are considered as disjoint sets of players to be
matched with each other. We then develop a novel rotation matching algorithm,
which converges to a q-exchange stable matching after a limited number of
iterations. Simulation results show that the proposed scheme outperforms the
traditional orthogonal multiple access scheme in terms of the latency and
reliability.
",1,0,0,0,0,0
18972,Cohomology monoids of monoids with coefficients in semimodules II,"  We relate the old and new cohomology monoids of an arbitrary monoid $M$ with
coefficients in semimodules over $M$, introduced in the author's previous
papers, to monoid and group extensions. More precisely, the old and new second
cohomology monoids describe Schreier extensions of semimodules by monoids, and
the new third cohomology monoid is related to a certain group extension
problem.
",0,0,1,0,0,0
18973,Lower Bounds for Higher-Order Convex Optimization,"  State-of-the-art methods in convex and non-convex optimization employ
higher-order derivative information, either implicitly or explicitly. We
explore the limitations of higher-order optimization and prove that even for
convex optimization, a polynomial dependence on the approximation guarantee and
higher-order smoothness parameters is necessary. As a special case, we show
Nesterov's accelerated cubic regularization method to be nearly tight.
",1,0,0,1,0,0
18974,Superconductivity in La1-xCexOBiSSe: carrier doping by mixed valence of Ce ions,"  We report the effects of Ce substitution on structural, electronic, and
magnetic properties of layered bismuth-chalcogenide La1-xCexOBiSSe (x = 0-0.9),
which are newly obtained in this study. Metallic conductivity was observed for
x > 0.1 because of electron carriers induced by mixed valence of Ce ions, as
revealed by bond valence sum calculation and magnetization measurements. Zero
resistivity and clear diamagnetic susceptibility were obtained for x = 0.2-0.6,
indicating the emergence of bulk superconductivity in these compounds.
Dome-shaped superconductivity phase diagram with the highest transition
temperature (Tc) of 3.1 K, which is slightly lower than that of F-doped
LaOBiSSe (Tc = 3.7 K), was established. The present study clearly shows that
the mixed valence of Ce ions can be utilized as an alternative approach for
electron-doping in layered bismuth-chalcogenides to induce superconductivity.
",0,1,0,0,0,0
18975,Gaussian Processes Over Graphs,"  We propose Gaussian processes for signals over graphs (GPG) using the apriori
knowledge that the target vectors lie over a graph. We incorporate this
information using a graph- Laplacian based regularization which enforces the
target vectors to have a specific profile in terms of graph Fourier transform
coeffcients, for example lowpass or bandpass graph signals. We discuss how the
regularization affects the mean and the variance in the prediction output. In
particular, we prove that the predictive variance of the GPG is strictly
smaller than the conventional Gaussian process (GP) for any non-trivial graph.
We validate our concepts by application to various real-world graph signals.
Our experiments show that the performance of the GPG is superior to GP for
small training data sizes and under noisy training.
",0,0,0,1,0,0
18976,Differences of Type I error rates for ANOVA and Multilevel-Linear-Models using SAS and SPSS for repeated measures designs,"  To derive recommendations on how to analyze longitudinal data, we examined
Type I error rates of Multilevel Linear Models (MLM) and repeated measures
Analysis of Variance (rANOVA) using SAS and SPSS.We performed a simulation with
the following specifications: To explore the effects of high numbers of
measurement occasions and small sample sizes on Type I error, measurement
occasions of m = 9 and 12 were investigated as well as sample sizes of n = 15,
20, 25 and 30. Effects of non-sphericity in the population on Type I error were
also inspected: 5,000 random samples were drawn from two populations containing
neither a within-subject nor a between-group effect. They were analyzed
including the most common options to correct rANOVA and MLM-results: The
Huynh-Feldt-correction for rANOVA (rANOVA-HF) and the Kenward-Roger-correction
for MLM (MLM-KR), which could help to correct progressive bias of MLM with an
unstructured covariance matrix (MLM-UN). Moreover, uncorrected rANOVA and MLM
assuming a compound symmetry covariance structure (MLM-CS) were also taken into
account. The results showed a progressive bias for MLM-UN for small samples
which was stronger in SPSS than in SAS. Moreover, an appropriate bias
correction for Type I error via rANOVA-HF and an insufficient correction by
MLM-UN-KR for n < 30 were found. These findings suggest MLM-CS or rANOVA if
sphericity holds and a correction of a violation via rANOVA-HF. If an analysis
requires MLM, SPSS yields more accurate Type I error rates for MLM-CS and SAS
yields more accurate Type I error rates for MLM-UN.
",0,0,0,1,0,0
18977,Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes,"  We consider alternate formulations of recently proposed hierarchical Nearest
Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved
convergence, faster computing time, and more robust and reproducible Bayesian
inference. Algorithms are defined that improve CPU memory management and
exploit existing high-performance numerical linear algebra libraries.
Computational and inferential benefits are assessed for alternate NNGP
specifications using simulated datasets and remotely sensed light detection and
ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit
(TIU) in a remote portion of Interior Alaska. The resulting data product is the
first statistically robust map of forest canopy for the TIU.
",0,0,0,1,0,0
18978,Variegation and space weathering on asteroid 21 Lutetia,"  During the flyby in 2010, the OSIRIS camera on-board Rosetta acquired
hundreds of high-resolution images of asteroid Lutetia's surface through a
range of narrow-band filters. While Lutetia appears very bland in the visible
wavelength range, Magrin et al. (2012) tentatively identified UV color
variations in the Baetica cluster, a group of relatively young craters close to
the north pole. As Lutetia remains a poorly understood asteroid, such color
variations may provide clues to the nature of its surface. We take the color
analysis one step further. First we orthorectify the images using a shape model
and improved camera pointing, then apply a variety of techniques (photometric
correction, principal component analysis) to the resulting color cubes. We
characterize variegation in the Baetica crater cluster at high spatial
resolution, identifying crater rays and small, fresh impact craters. We argue
that at least some of the color variation is due to space weathering, which
makes Lutetia's regolith redder and brighter.
",0,1,0,0,0,0
18979,Rokhlin dimension for compact quantum group actions,"  We show that, for a given compact or discrete quantum group $G$, the class of
actions of $G$ on C*-algebras is first-order axiomatizable in the logic for
metric structures. As an application, we extend the notion of Rokhlin property
for $G$-C*-algebra, introduced by Barlak, Szabó, and Voigt in the case when
$G$ is second countable and coexact, to an arbitrary compact quantum group $G$.
All the the preservations and rigidity results for Rokhlin actions of second
countable coexact compact quantum groups obtained by Barlak, Szabó, and
Voigt are shown to hold in this general context. As a further application, we
extend the notion of equivariant order zero dimension for equivariant
*-homomorphisms, introduced in the classical setting by the first and third
authors, to actions of compact quantum groups. This allows us to define the
Rokhlin dimension of an action of a compact quantum group on a C*-algebra,
recovering the Rokhlin property as Rokhlin dimension zero. We conclude by
establishing a preservation result for finite nuclear dimension and finite
decomposition rank when passing to fixed point algebras and crossed products by
compact quantum group actions with finite Rokhlin dimension.
",0,0,1,0,0,0
18980,Parametric Identification Using Weighted Null-Space Fitting,"  In identification of dynamical systems, the prediction error method using a
quadratic cost function provides asymptotically efficient estimates under
Gaussian noise and additional mild assumptions, but in general it requires
solving a non-convex optimization problem. An alternative class of methods uses
a non-parametric model as intermediate step to obtain the model of interest.
Weighted null-space fitting (WNSF) belongs to this class. It is a weighted
least-squares method consisting of three steps. In the first step, a high-order
ARX model is estimated. In a second least-squares step, this high-order
estimate is reduced to a parametric estimate. In the third step, weighted least
squares is used to reduce the variance of the estimates. The method is flexible
in parametrization and suitable for both open- and closed-loop data. In this
paper, we show that WNSF provides estimates with the same asymptotic properties
as PEM with a quadratic cost function when the model orders are chosen
according to the true system. Also, simulation studies indicate that WNSF may
be competitive with state-of-the-art methods.
",1,0,0,0,0,0
18981,Learning Large-Scale Bayesian Networks with the sparsebn Package,"  Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.
",1,0,0,1,0,0
18982,An Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits,"  We consider restless multi-armed bandit (RMAB) with a finite horizon and
multiple pulls per period. Leveraging the Lagrangian relaxation, we approximate
the problem with a collection of single arm problems. We then propose an
index-based policy that uses optimal solutions of the single arm problems to
index individual arms, and offer a proof that it is asymptotically optimal as
the number of arms tends to infinity. We also use simulation to show that this
index-based policy performs better than the state-of-art heuristics in various
problem settings.
",0,0,1,0,0,0
18983,Effective holographic theory of charge density waves,"  We use Gauge/Gravity duality to write down an effective low energy
holographic theory of charge density waves. We consider a simple gravity model
which breaks translations spontaneously in the dual field theory in a
homogeneous manner, capturing the low energy dynamics of phonons coupled to
conserved currents. We first focus on the leading two-derivative action, which
leads to excited states with non-zero strain. We show that including subleading
quartic derivative terms leads to dynamical instabilities of AdS$_2$
translation invariant states and to stable phases breaking translations
spontaneously. We compute analytically the real part of the electric
conductivity. The model allows to construct Lifshitz-like hyperscaling
violating quantum critical ground states breaking translations spontaneously.
At these critical points, the real part of the dc conductivity can be metallic
or insulating.
",0,1,0,0,0,0
18984,Interactive Certificates for Polynomial Matrices with Sub-Linear Communication,"  We develop and analyze new protocols to verify the correctness of various
computations on matrices over F[x], where F is a field. The properties we
verify concern an F[x]-module and therefore cannot simply rely on
previously-developed linear algebra certificates which work only for vector
spaces. Our protocols are interactive certificates, often randomized, and
featuring a constant number of rounds of communication between the prover and
verifier. We seek to minimize the communication cost so that the amount of data
sent during the protocol is significantly smaller than the size of the result
being verified, which can be useful when combining protocols or in some
multi-party settings. The main tools we use are reductions to existing linear
algebra certificates and a new protocol to verify that a given vector is in the
F[x]-linear span of a given matrix.
",1,0,0,0,0,0
18985,Cost Functions for Robot Motion Style,"  We focus on autonomously generating robot motion for day to day physical
tasks that is expressive of a certain style or emotion. Because we seek
generalization across task instances and task types, we propose to capture
style via cost functions that the robot can use to augment its nominal task
cost and task constraints in a trajectory optimization process. We compare two
approaches to representing such cost functions: a weighted linear combination
of hand-designed features, and a neural network parameterization operating on
raw trajectory input. For each cost type, we learn weights for each style from
user feedback. We contrast these approaches to a nominal motion across
different tasks and for different styles in a user study, and find that they
both perform on par with each other, and significantly outperform the baseline.
Each approach has its advantages: featurized costs require learning fewer
parameters and can perform better on some styles, but neural network
representations do not require expert knowledge to design features and could
even learn more complex, nuanced costs than an expert can easily design.
",1,0,0,0,0,0
18986,Revealing the basins of convergence in the planar equilateral restricted four-body problem,"  The planar equilateral restricted four-body problem where two of the
primaries have equal masses is used in order to determine the Newton-Raphson
basins of convergence associated with the equilibrium points. The parametric
variation of the position of the libration points is monitored when the value
of the mass parameter $m_3$ varies in predefined intervals. The regions on the
configuration $(x,y)$ plane occupied by the basins of attraction are revealed
using the multivariate version of the Newton-Raphson iterative scheme. The
correlations between the attracting domains of the equilibrium points and the
corresponding number of iterations needed for obtaining the desired accuracy
are also illustrated. We perform a thorough and systematic numerical
investigation by demonstrating how the dynamical parameter $m_3$ influences the
shape, the geometry and the degree of fractality of the converging regions. Our
numerical outcomes strongly indicate that the mass parameter is indeed one of
the most influential factors in this dynamical system.
",0,1,0,0,0,0
18987,The Eccentric Kozai-Lidov mechanism for Outer Test Particle,"  The secular approximation of the hierarchical three body systems has been
proven to be very useful in addressing many astrophysical systems, from
planets, stars to black holes. In such a system two objects are on a tight
orbit, and the tertiary is on a much wider orbit. Here we study the dynamics of
a system by taking the tertiary mass to zero and solve the hierarchical three
body system up to the octupole level of approximation. We find a rich dynamics
that the outer orbit undergoes due to gravitational perturbations from the
inner binary. The nominal result of the precession of the nodes is mostly
limited for the lowest order of approximation, however, when the octupole-level
of approximation is introduced the system becomes chaotic, as expected, and the
tertiary oscillates below and above 90deg, similarly to the non-test particle
flip behavior (e.g., Naoz 2016). We provide the Hamiltonian of the system and
investigate the dynamics of the system from the quadrupole to the octupole
level of approximations. We also analyze the chaotic and quasi-periodic orbital
evolution by studying the surfaces of sections. Furthermore, including general
relativity, we show case the long term evolution of individual debris disk
particles under the influence of a far away interior eccentric planet. We show
that this dynamics can naturally result in retrograde objects and a puffy disk
after a long timescale evolution (few Gyr) for initially aligned configuration.
",0,1,0,0,0,0
18988,A more symmetric picture for Kasparov's KK-bifunctor,"  For C*-algebras $A$ and $B$, we generalize the notion of a quasihomomorphism
from $A$ to $B$, due to Cuntz, by considering quasihomomorphisms from some
C*-algebra $C$ to $B$ such that $C$ surjects onto $A$, and the two maps forming
a quasihomomorphism agree on the kernel of this surjection. Under an additional
assumption, the group of homotopy classes of such generalized
quasihomomorphisms coincides with $KK(A,B)$. This makes the definition of
Kasparov's bifunctor slightly more symmetric and gives more flexibility for
constructing elements of $KK$-groups. These generalized quasihomomorphisms can
be viewed as pairs of maps directly from $A$ (instead of various $C$'s), but
these maps need not be $*$-homomorphisms.
",0,0,1,0,0,0
18989,Spot dynamics in a reaction-diffusion model of plant root hair initiation,"  We study pattern formation in a 2-D reaction-diffusion (RD) sub-cellular
model characterizing the effect of a spatial gradient of a plant hormone
distribution on a family of G-proteins associated with root-hair (RH)
initiation in the plant cell Arabidopsis thaliana. The activation of these
G-proteins, known as the Rho of Plants (ROPs), by the plant hormone auxin, is
known to promote certain protuberances on root hair cells, which are crucial
for both anchorage and the uptake of nutrients from the soil. Our mathematical
model for the activation of ROPs by the auxin gradient is an extension of the
model of Payne and Grierson [PLoS ONE, 12(4), (2009)], and consists of a
two-component Schnakenberg-type RD system with spatially heterogeneous
coefficients on a 2-D domain. The nonlinear kinetics in this RD system model
the nonlinear interactions between the active and inactive forms of ROPs. By
using a singular perturbation analysis to study 2-D localized spatial patterns
of active ROPs, it is shown that the spatial variations in the nonlinear
reaction kinetics, due to the auxin gradient, lead to a slow spatial alignment
of the localized regions of active ROPs along the longitudinal midline of the
plant cell. Numerical bifurcation analysis, together with time-dependent
numerical simulations of the RD system are used to illustrate both 2-D
localized patterns in the model, and the spatial alignment of localized
structures.
",0,1,0,0,0,0
18990,Incorporating Covariates into Integrated Factor Analysis of Multi-View Data,"  In modern biomedical research, it is ubiquitous to have multiple data sets
measured on the same set of samples from different views (i.e., multi-view
data). For example, in genetic studies, multiple genomic data sets at different
molecular levels or from different cell types are measured for a common set of
individuals to investigate genetic regulation. Integration and reduction of
multi-view data have the potential to leverage information in different data
sets, and to reduce the magnitude and complexity of data for further
statistical analysis and interpretation. In this paper, we develop a novel
statistical model, called supervised integrated factor analysis (SIFA), for
integrative dimension reduction of multi-view data while incorporating
auxiliary covariates. The model decomposes data into joint and individual
factors, capturing the joint variation across multiple data sets and the
individual variation specific to each set respectively. Moreover, both joint
and individual factors are partially informed by auxiliary covariates via
nonparametric models. We devise a computationally efficient
Expectation-Maximization (EM) algorithm to fit the model under some
identifiability conditions. We apply the method to the Genotype-Tissue
Expression (GTEx) data, and provide new insights into the variation
decomposition of gene expression in multiple tissues. Extensive simulation
studies and an additional application to a pediatric growth study demonstrate
the advantage of the proposed method over competing methods.
",0,0,0,1,0,0
18991,Batch-normalized joint training for DNN-based distant speech recognition,"  Improving distant speech recognition is a crucial step towards flexible
human-machine interfaces. Current technology, however, still exhibits a lack of
robustness, especially when adverse acoustic conditions are met. Despite the
significant progress made in the last years on both speech enhancement and
speech recognition, one potential limitation of state-of-the-art technology
lies in composing modules that are not well matched because they are not
trained jointly. To address this concern, a promising approach consists in
concatenating a speech enhancement and a speech recognition deep neural network
and to jointly update their parameters as if they were within a single bigger
network. Unfortunately, joint training can be difficult because the output
distribution of the speech enhancement system may change substantially during
the optimization procedure. The speech recognition module would have to deal
with an input distribution that is non-stationary and unnormalized. To mitigate
this issue, we propose a joint training approach based on a fully
batch-normalized architecture. Experiments, conducted using different datasets,
tasks and acoustic conditions, revealed that the proposed framework
significantly overtakes other competitive solutions, especially in challenging
environments.
",1,0,0,0,0,0
18992,Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects,"  This paper describes a preliminary study for producing and distributing a
large-scale database of embeddings from the Portuguese Twitter stream. We start
by experimenting with a relatively small sample and focusing on three
challenges: volume of training data, vocabulary size and intrinsic evaluation
metrics. Using a single GPU, we were able to scale up vocabulary size from 2048
words embedded and 500K training examples to 32768 words over 10M training
examples while keeping a stable validation loss and approximately linear trend
on training time per epoch. We also observed that using less than 50\% of the
available training examples for each vocabulary size might result in
overfitting. Results on intrinsic evaluation show promising performance for a
vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics
suffer from over-sensitivity to their corresponding cosine similarity
thresholds, indicating that a wider range of metrics need to be developed to
track progress.
",1,0,0,0,0,0
18993,A temperate exo-Earth around a quiet M dwarf at 3.4 parsecs,"  The combination of high-contrast imaging and high-dispersion spectroscopy,
which has successfully been used to detect the atmosphere of a giant planet, is
one of the most promising potential probes of the atmosphere of Earth-size
worlds. The forthcoming generation of extremely large telescopes (ELTs) may
obtain sufficient contrast with this technique to detect O$_2$ in the
atmosphere of those worlds that orbit low-mass M dwarfs. This is strong
motivation to carry out a census of planets around cool stars for which
habitable zones can be resolved by ELTs, i.e. for M dwarfs within $\sim$5
parsecs. Our HARPS survey has been a major contributor to that sample of nearby
planets. Here we report on our radial velocity observations of Ross 128
(Proxima Virginis, GJ447, HIP 57548), an M4 dwarf just 3.4 parsec away from our
Sun. This source hosts an exo-Earth with a projected mass $m \sin i = 1.35
M_\oplus$ and an orbital period of 9.9 days. Ross 128 b receives $\sim$1.38
times as much flux as Earth from the Sun and its equilibrium ranges in
temperature between 269 K for an Earth-like albedo and 213 K for a Venus-like
albedo. Recent studies place it close to the inner edge of the conventional
habitable zone. An 80-day long light curve from K2 campaign C01 demonstrates
that Ross~128~b does not transit. Together with the All Sky Automated Survey
(ASAS) photometry and spectroscopic activity indices, the K2 photometry shows
that Ross 128 rotates slowly and has weak magnetic activity. In a habitability
context, this makes survival of its atmosphere against erosion more likely.
Ross 128 b is the second closest known exo-Earth, after Proxima Centauri b (1.3
parsec), and the closest temperate planet known around a quiet star. The 15 mas
planet-star angular separation at maximum elongation will be resolved by ELTs
($>$ 3$\lambda/D$) in the optical bands of O$_2$.
",0,1,0,0,0,0
18994,Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning,"  Low-rank structures play important role in recent advances of many problems
in image science and data science. As a natural extension of low-rank
structures for data with nonlinear structures, the concept of the
low-dimensional manifold structure has been considered in many data processing
problems. Inspired by this concept, we consider a manifold based low-rank
regularization as a linear approximation of manifold dimension. This
regularization is less restricted than the global low-rank regularization, and
thus enjoy more flexibility to handle data with nonlinear structures. As
applications, we demonstrate the proposed regularization to classical inverse
problems in image sciences and data sciences including image inpainting, image
super-resolution, X-ray computer tomography (CT) image reconstruction and
semi-supervised learning. We conduct intensive numerical experiments in several
image restoration problems and a semi-supervised learning problem of
classifying handwritten digits using the MINST data. Our numerical tests
demonstrate the effectiveness of the proposed methods and illustrate that the
new regularization methods produce outstanding results by comparing with many
existing methods.
",1,0,1,0,0,0
18995,History-aware Autonomous Exploration in Confined Environments using MAVs,"  Many scenarios require a robot to be able to explore its 3D environment
online without human supervision. This is especially relevant for inspection
tasks and search and rescue missions. To solve this high-dimensional path
planning problem, sampling-based exploration algorithms have proven successful.
However, these do not necessarily scale well to larger environments or spaces
with narrow openings. This paper presents a 3D exploration planner based on the
principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle
(MAV) equipped with a limited field-of-view depth sensor randomly samples its
configuration space to find promising future viewpoints. In order to obtain
high sampling efficiency, our planner maintains and uses a history of visited
places, and locally optimizes the robot's orientation with respect to
unobserved space. We evaluate our method in several simulated scenarios, and
compare it against a state-of-the-art exploration algorithm. The experiments
show substantial improvements in exploration time ($2\times$ faster),
computation time, and path length, and advantages in handling difficult
situations such as escaping dead-ends (up to $20\times$ faster). Finally, we
validate the on-line capability of our algorithm on a computational constrained
real world MAV.
",1,0,0,0,0,0
18996,A factor-model approach for correlation scenarios and correlation stress-testing,"  In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives
portfolio, the so-called `London Whale', partly as a consequence of
de-correlations of non-perfectly correlated positions that were supposed to
hedge each other. Motivated by this case, we devise a factor model for
correlations that allows for scenario-based stress testing of correlations. We
derive a number of analytical results related to a portfolio of homogeneous
assets. Using the concept of Mahalanobis distance, we show how to identify
adverse scenarios of correlation risk. In addition, we demonstrate how
correlation and volatility stress tests can be combined. As an example, we
apply the factor-model approach to the ""London Whale"" portfolio and determine
the value-at-risk impact from correlation changes. Since our findings are
particularly relevant for large portfolios, where even small correlation
changes can have a large impact, a further application would be to stress test
portfolios of central counterparties, which are of systemically relevant size.
",0,0,0,0,0,1
18997,Topology data analysis of critical transitions in financial networks,"  We develop a topology data analysis-based method to detect early signs for
critical transitions in financial data. From the time-series of multiple stock
prices, we build time-dependent correlation networks, which exhibit topological
structures. We compute the persistent homology associated to these structures
in order to track the changes in topology when approaching a critical
transition. As a case study, we investigate a portfolio of stocks during a
period prior to the US financial crisis of 2007-2008, and show the presence of
early signs of the critical transition.
",0,1,1,0,0,0
18998,Construction of exact constants of motion and effective models for many-body localized systems,"  One of the defining features of many-body localization is the presence of
extensively many quasi-local conserved quantities. These constants of motion
constitute a corner-stone to an intuitive understanding of much of the
phenomenology of many-body localized systems arising from effective
Hamiltonians. They may be seen as local magnetization operators smeared out by
a quasi-local unitary. However, accurately identifying such constants of motion
remains a challenging problem. Current numerical constructions often capture
the conserved operators only approximately restricting a conclusive
understanding of many-body localization. In this work, we use methods from the
theory of quantum many-body systems out of equilibrium to establish a new
approach for finding a complete set of exact constants of motion which are in
addition guaranteed to represent Pauli-$z$ operators. By this we are able to
construct and investigate the proposed effective Hamiltonian using exact
diagonalization. Hence, our work provides an important tool expected to further
boost inquiries into the breakdown of transport due to quenched disorder.
",0,1,0,0,0,0
18999,Toric manifolds over cyclohedra,"  We study the action of the dihedral group on the (equivariant) cohomology of
the toric manifolds associated with cycle graphs.
",0,0,1,0,0,0
19000,Use of Genome Information-Based Potentials to Characterize Human Adaptation,"  As a living information and communications system, the genome encodes
patterns in single nucleotide polymorphisms (SNPs) reflecting human adaption
that optimizes population survival in differing environments. This paper
mathematically models environmentally induced adaptive forces that quantify
changes in the distribution of SNP frequencies between populations. We make
direct connections between biophysical methods (e.g. minimizing genomic free
energy) and concepts in population genetics. Our unbiased computer program
scanned a large set of SNPs in the major histocompatibility complex region, and
flagged an altitude dependency on a SNP associated with response to oxygen
deprivation. The statistical power of our double-blind approach is demonstrated
in the flagging of mathematical functional correlations of SNP
information-based potentials in multiple populations with specific
environmental parameters. Furthermore, our approach provides insights for new
discoveries on the biology of common variants. This paper demonstrates the
power of biophysical modeling of population diversity for better understanding
genome-environment interactions in biological phenomenon.
",0,0,0,0,1,0
19001,Communication-Efficient Algorithms for Decentralized and Stochastic Optimization,"  We present a new class of decentralized first-order methods for nonsmooth and
stochastic optimization problems defined over multiagent networks. Considering
that communication is a major bottleneck in decentralized optimization, our
main goal in this paper is to develop algorithmic frameworks which can
significantly reduce the number of inter-node communications. We first propose
a decentralized primal-dual method which can find an $\epsilon$-solution both
in terms of functional optimality gap and feasibility residual in
$O(1/\epsilon)$ inter-node communication rounds when the objective functions
are convex and the local primal subproblems are solved exactly. Our major
contribution is to present a new class of decentralized primal-dual type
algorithms, namely the decentralized communication sliding (DCS) methods, which
can skip the inter-node communications while agents solve the primal
subproblems iteratively through linearizations of their local objective
functions. By employing DCS, agents can still find an $\epsilon$-solution in
$O(1/\epsilon)$ (resp., $O(1/\sqrt{\epsilon})$) communication rounds for
general convex functions (resp., strongly convex functions), while maintaining
the $O(1/\epsilon^2)$ (resp., $O(1/\epsilon)$) bound on the total number of
intra-node subgradient evaluations. We also present a stochastic counterpart
for these algorithms, denoted by SDCS, for solving stochastic optimization
problems whose objective function cannot be evaluated exactly. In comparison
with existing results for decentralized nonsmooth and stochastic optimization,
we can reduce the total number of inter-node communication rounds by orders of
magnitude while still maintaining the optimal complexity bounds on intra-node
stochastic subgradient evaluations. The bounds on the subgradient evaluations
are actually comparable to those required for centralized nonsmooth and
stochastic optimization.
",1,0,1,0,0,0
19002,A Machine Learning Approach to Shipping Box Design,"  Having the right assortment of shipping boxes in the fulfillment warehouse to
pack and ship customer's online orders is an indispensable and integral part of
nowadays eCommerce business, as it will not only help maintain a profitable
business but also create great experiences for customers. However, it is an
extremely challenging operations task to strategically select the best
combination of tens of box sizes from thousands of feasible ones to be
responsible for hundreds of thousands of orders daily placed on millions of
inventory products. In this paper, we present a machine learning approach to
tackle the task by formulating the box design problem prescriptively as a
generalized version of weighted $k$-medoids clustering problem, where the
parameters are estimated through a variety of descriptive analytics. We test
this machine learning approach on fulfillment data collected from Walmart U.S.
eCommerce, and our approach is shown to be capable of improving the box
utilization rate by more than $10\%$.
",0,0,0,1,0,0
19003,Precise but Natural Specification for Robot Tasks,"  We present Flipper, a natural language interface for describing high-level
task specifications for robots that are compiled into robot actions. Flipper
starts with a formal core language for task planning that allows expressing
rich temporal specifications and uses a semantic parser to provide a natural
language interface. Flipper provides immediate visual feedback by executing an
automatically constructed plan of the task in a graphical user interface. This
allows the user to resolve potentially ambiguous interpretations. Flipper
extends itself via naturalization: its users can add definitions for
utterances, from which Flipper induces new rules and adds them to the core
language, gradually growing a more and more natural task specification
language. Flipper improves the naturalization by generalizing the definition
provided by users. Unlike other task-specification systems, Flipper enables
natural language interactions while maintaining the expressive power and formal
precision of a programming language. We show through an initial user study that
natural language interactions and generalization can considerably ease the
description of tasks. Moreover, over time, users employ more and more concepts
outside of the initial core language. Such extensions are available to the
Flipper community, and users can use concepts that others have defined.
",1,0,0,0,0,0
19004,Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions,"  This article presents a rigorous analysis for efficient statistically
accurate algorithms for solving the Fokker-Planck equations associated with
high-dimensional nonlinear turbulent dynamical systems with conditional
Gaussian structures. Despite the conditional Gaussianity, these nonlinear
systems contain many strong non-Gaussian features such as intermittency and
fat-tailed probability density functions (PDFs). The algorithms involve a
hybrid strategy that requires only a small number of samples $L$ to capture
both the transient and the equilibrium non-Gaussian PDFs with high accuracy.
Here, a conditional Gaussian mixture in a high-dimensional subspace via an
extremely efficient parametric method is combined with a judicious Gaussian
kernel density estimation in the remaining low-dimensional subspace. Rigorous
analysis shows that the mean integrated squared error in the recovered PDFs in
the high-dimensional subspace is bounded by the inverse square root of the
determinant of the conditional covariance, where the conditional covariance is
completely determined by the underlying dynamics and is independent of $L$.
This is fundamentally different from a direct application of kernel methods to
solve the full PDF, where $L$ needs to increase exponentially with the
dimension of the system and the bandwidth shrinks. A detailed comparison
between different methods justifies that the efficient statistically accurate
algorithms are able to overcome the curse of dimensionality. It is also shown
with mathematical rigour that these algorithms are robust in long time provided
that the system is controllable and stochastically stable. Particularly,
dynamical systems with energy-conserving quadratic nonlinearity as in many
geophysical and engineering turbulence are proved to have these properties.
",0,0,1,1,0,0
19005,Rapid processing of 85Kr/Kr ratios using Atom Trap Trace Analysis,"  We report a methodology for measuring 85Kr/Kr isotopic abundances using Atom
Trap Trace Analysis (ATTA) that increases sample measurement throughput by over
an order of magnitude to 6 samples per 24 hours. The noble gas isotope 85Kr
(half-life = 10.7 yr) is a useful tracer for young groundwater in the age range
of 5-50 years. ATTA, an efficient and selective laser-based atom counting
method, has recently been applied to 85Kr/Kr isotopic abundance measurements,
requiring 5-10 microliters of krypton gas at STP extracted from 50-100 L of
water. Previously a single such measurement required 48 hours. Our new method
demonstrates that we can measure 85Kr/Kr ratios with 3-5% relative uncertainty
every 4 hours, on average, with the same sample requirements.
",0,1,0,0,0,0
19006,Adapting Everyday Manipulation Skills to Varied Scenarios,"  We address the problem of executing tool-using manipulation skills in
scenarios where the objects to be used may vary. We assume that point clouds of
the tool and target object can be obtained, but no interpretation or further
knowledge about these objects is provided. The system must interpret the point
clouds and decide how to use the tool to complete a manipulation task with a
target object; this means it must adjust motion trajectories appropriately to
complete the task. We tackle three everyday manipulations: scraping material
from a tool into a container, cutting, and scooping from a container. Our
solution encodes these manipulation skills in a generic way, with parameters
that can be filled in at run-time via queries to a robot perception module; the
perception module abstracts the functional parts for the tool and extracts key
parameters that are needed for the task. The approach is evaluated in
simulation and with selected examples on a PR2 robot.
",1,0,0,0,0,0
19007,Effect of Particle Number Conservation on the Berry Phase Resulting from Transport of a Bound Quasiparticle around a Superfluid Vortex,"  Motivated by understanding Majorana zero modes in topological superfluids in
particle-number conserving framework beyond the present framework, we study the
effect of particle number conservation on the Berry phase resulting from
transport of a bound quasiparticle around a superfluid vortex. We find that
particle-number non-conserving calculations based on Bogoliubov-de Gennes (BdG)
equations are unable to capture the correct physics when the quasiparticle is
within the penetration depth of the vortex core where the superfluid velocity
is non-zero. Particle number conservation is crucial for deriving the correct
Berry phase in this context, and the Berry phase takes non-universal values
depending on the system parameters and the external trap imposed to bind the
quasiparticle. Of particular relevance to Majorana physics are the findings
that superfluid condensate affects the part of the Berry phase not accounted
for in the standard BdG framework, and that the superfluid many-body ground
state of odd number of fermions involves superfluid condensate deformation due
to the presence of the bound quasiparticle - an effect which is beyond the
description of the BdG equations.
",0,1,0,0,0,0
19008,A new topological insulator - β-InTe strained in the layer plane,"  We have investigated the band structure of the bulk crystal and the (001)
surface of the \beta-InTe layered crystal subjected to biaxial stretching in
the layer plane. The calculation has been carried out using the full-potential
linearized augmented plane wave method (FP LAPW) implemented in WIEN2k. It has
been shown that at the strain \Deltaa/a=0.06, where a is the lattice parameter
in the layer plane, the band gap in the electronic spectrum collapses. With
further strain increase a band inversion occurs. The inclusion of the
spin-orbit interaction reopens the gap in the electronic spectrum of a bulk
crystal, and our calculations show that the spectrum of the surface states has
the form of a Dirac cone, typical for topological insulators.
",0,1,0,0,0,0
19009,Stock management (Gestão de estoques),"  There is a great need to stock materials for production, but storing
materials comes at a cost. Lack of organization in the inventory can result in
a very high cost for the final product, in addition to generating other
problems in the production chain. In this work we present mathematical and
statistical methods applicable to stock management. The stock analysis using
ABC curves serves to identify which are the priority items, the most expensive
and with the highest turnover (demand), and thus determine, through stock
control models, the purchase lot size and the periodicity that minimize the
total costs of storing these materials. Using the Economic Order Quantity (EOQ)
model and the (Q,R) model, the inventory costs of a company were minimized. The
comparison of the results provided by the models was performed.
",0,0,0,1,0,0
19010,From Principal Subspaces to Principal Components with Linear Autoencoders,"  The autoencoder is an effective unsupervised learning model which is widely
used in deep learning. It is well known that an autoencoder with a single
fully-connected hidden layer, a linear activation function and a squared error
cost function trains weights that span the same subspace as the one spanned by
the principal component loading vectors, but that they are not identical to the
loading vectors. In this paper, we show how to recover the loading vectors from
the autoencoder weights.
",0,0,0,1,0,0
19011,A critical nonlinear elliptic equation with non local regional diffusion,"  In this article we are interested in the nonlocal regional Schrödinger
equation with critical exponent \begin{eqnarray*} &\epsilon^{2\alpha}
(-\Delta)_{\rho}^{\alpha}u + u = \lambda u^q + u^{2_{\alpha}^{*}-1} \mbox{ in }
\mathbb{R}^{N}, \\ & u \in H^{\alpha}(\mathbb{R}^{N}), \end{eqnarray*} where
$\epsilon$ is a small positive parameter, $\alpha \in (0,1)$, $q\in
(1,2_{\alpha}^{*}-1)$, $2_{\alpha}^{*} = \frac{2N}{N-2\alpha}$ is the critical
Sobolev exponent, $\lambda >0$ is a parameter and $(-\Delta)_{\rho}^{\alpha}$
is a variational version of the regional laplacian, whose range of scope is a
ball with radius $\rho(x)>0$. We study the existence of a ground state and we
analyze the behavior of semi-classical solutions as $\varepsilon\to 0$.
",0,0,1,0,0,0
19012,"Don't Decay the Learning Rate, Increase the Batch Size","  It is common practice to decay the learning rate. Here we show one can
usually obtain the same learning curve on both training and test sets by
instead increasing the batch size during training. This procedure is successful
for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum,
and Adam. It reaches equivalent test accuracies after the same number of
training epochs, but with fewer parameter updates, leading to greater
parallelism and shorter training times. We can further reduce the number of
parameter updates by increasing the learning rate $\epsilon$ and scaling the
batch size $B \propto \epsilon$. Finally, one can increase the momentum
coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly
reduce the test accuracy. Crucially, our techniques allow us to repurpose
existing training schedules for large batch training with no hyper-parameter
tuning. We train ResNet-50 on ImageNet to $76.1\%$ validation accuracy in under
30 minutes.
",1,0,0,1,0,0
19013,Collisions of Dark Matter Axion Stars with Astrophysical Sources,"  If QCD axions form a large fraction of the total mass of dark matter, then
axion stars could be very abundant in galaxies. As a result, collisions with
each other, and with other astrophysical bodies, can occur. We calculate the
rate and analyze the consequences of three classes of collisions, those
occurring between a dilute axion star and: another dilute axion star, an
ordinary star, or a neutron star. In all cases we attempt to quantify the most
important astrophysical uncertainties; we also pay particular attention to
scenarios in which collisions lead to collapse of otherwise stable axion stars,
and possible subsequent decay through number changing interactions. Collisions
between two axion stars can occur with a high total rate, but the low relative
velocity required for collapse to occur leads to a very low total rate of
collapses. On the other hand, collisions between an axion star and an ordinary
star have a large rate, $\Gamma_\odot \sim 3000$ collisions/year/galaxy, and
for sufficiently heavy axion stars, it is plausible that most or all such
collisions lead to collapse. We identify in this case a parameter space which
has a stable region and a region in which collision triggers collapse, which
depend on the axion number ($N$) in the axion star, and a ratio of mass to
radius cubed characterizing the ordinary star ($M_s/R_s^3$). Finally, we
revisit the calculation of collision rates between axion stars and neutron
stars, improving on previous estimates by taking cylindrical symmetry of the
neutron star distribution into account. Collapse and subsequent decay through
collision processes, if occurring with a significant rate, can affect dark
matter phenomenology and the axion star mass distribution.
",0,1,0,0,0,0
19014,Practical Algorithms for Best-K Identification in Multi-Armed Bandits,"  In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
stochastic bandit arms with unknown reward distributions. Our goal is to
identify the $K$ arms with the largest means with high confidence, by drawing
samples from the arms adaptively. This problem is motivated by various
practical applications and has attracted considerable attention in the past
decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
problem, which have nearly optimal sample complexity bounds (matching the lower
bound up to logarithmic factors) and outperform the state-of-the-art algorithms
for the Best-$K$-Arm problem (even for $K=1$) in practice.
",1,0,0,1,0,0
19015,Hyperfield Grassmannians,"  In a recent paper Baker and Bowler introduced matroids over hyperfields,
offering a common generalization of matroids, oriented matroids, and linear
subspaces of based vector spaces. This paper introduces the notion of a
topological hyperfield and explores the generalization of Grassmannians and
realization spaces to this context, particularly in relating the (hyper)fields
R and C to hyperfields arising in matroid theory and in tropical geometry.
",0,0,1,0,0,0
19016,Statistical estimation in a randomly structured branching population,"  We consider a binary branching process structured by a stochastic trait that
evolves according to a diffusion process that triggers the branching events, in
the spirit of Kimmel's model of cell division with parasite infection. Based on
the observation of the trait at birth of the first n generations of the
process, we construct nonparametric estimator of the transition of the
associated bifurcating chain and study the parametric estimation of the
branching rate. In the limit, as n tends to infinity, we obtain asymptotic
efficiency in the parametric case and minimax optimality in the nonparametric
case.
",0,0,1,0,0,0
19017,Discovering Eastern European PCs by hacking them. Today,"  Computer science would not be the same without personal computers. In the
West the so called PC revolution started in the late '70s and has its roots in
hobbyists and do-it-yourself clubs. In the following years the diffusion of
home and personal computers has made the discipline closer to many people. A
bit later, to a lesser extent, yet in a similar way, the revolution took place
also in East European countries. Today, the scenario of personal computing has
completely changed, however the computers of the '80s are still objects of
fascination for a number of retrocomputing fans who enjoy using, programming
and hacking the old 8-bits. The paper highlights the continuity between
yesterday's hobbyists and today's retrocomputing enthusiasts, particularly
focusing on East European PCs. Besides the preservation of old hardware and
software, the community is engaged in the development of emulators and cross
compilers. Such tools can be used for historical investigation, for example to
trace the origins of the BASIC interpreters loaded in the ROMs of East European
PCs.
",1,0,0,0,0,0
19018,Neural Rating Regression with Abstractive Tips Generation for Recommendation,"  Recently, some E-commerce sites launch a new interaction box called Tips on
their mobile apps. Users can express their experience and feelings or provide
suggestions using short texts typically several words or one sentence. In
essence, writing some tips and giving a numerical rating are two facets of a
user's product assessment action, expressing the user experience and feelings.
Jointly modeling these two facets is helpful for designing a better
recommendation system. While some existing models integrate text information
such as item specifications or user reviews into user and item latent factors
for improving the rating prediction, no existing works consider tips for
improving recommendation quality. We propose a deep learning based framework
named NRT which can simultaneously predict precise ratings and generate
abstractive tips with good linguistic quality simulating user experience and
feelings. For abstractive tips generation, gated recurrent neural networks are
employed to ""translate"" user and item latent representations into a concise
sentence. Extensive experiments on benchmark datasets from different domains
show that NRT achieves significant improvements over the state-of-the-art
methods. Moreover, the generated tips can vividly predict the user experience
and feelings.
",1,0,0,0,0,0
19019,Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs,"  Calibration of the Advanced LIGO detectors is the quantification of the
detectors' response to gravitational waves. Gravitational waves incident on the
detectors cause phase shifts in the interferometer laser light which are read
out as intensity fluctuations at the detector output. Understanding this
detector response to gravitational waves is crucial to producing accurate and
precise gravitational wave strain data. Estimates of binary black hole and
neutron star parameters and tests of general relativity require well-calibrated
data, as miscalibrations will lead to biased results. We describe the method of
producing calibration uncertainty estimates for both LIGO detectors in the
first and second observing runs.
",0,1,0,0,0,0
19020,Entanglement transitions induced by large deviations,"  The probability of large deviations of the smallest Schmidt eigenvalue for
random pure states of bipartite systems, denoted as $A$ and $B$, is computed
analytically using a Coulomb gas method. It is shown that this probability, for
large $N$, goes as $\exp[-\beta N^2\Phi(\zeta)]$, where the parameter $\beta$
is the Dyson index of the ensemble, $\zeta$ is the large deviation parameter
while the rate function $\Phi(\zeta)$ is calculated exactly. Corresponding
equilibrium Coulomb charge density is derived for its large deviations. Effects
of the large deviations of the extreme (largest and smallest) Schmidt
eigenvalues on the bipartite entanglement are studied using the von Neumann
entropy. Effect of these deviations is also studied on the entanglement between
subsystems $1$ and $2$, obtained by further partitioning the subsystem $A$,
using the properties of the density matrix's partial transpose
$\rho_{12}^\Gamma$. The density of states of $\rho_{12}^\Gamma$ is found to be
close to the Wigner's semicircle law with these large deviations. The
entanglement properties are captured very well by a simple random matrix model
for the partial transpose. The model predicts the entanglement transition
across a critical large deviation parameter $\zeta$. Log negativity is used to
quantify the entanglement between subsystems $1$ and $2$. Analytical formulas
for it are derived using the simple model. Numerical simulations are in
excellent agreement with the analytical results.
",0,0,1,1,0,0
19021,"Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and Large-Scale SSD Array Systems","  Large-scale systems with arrays of solid state disks (SSDs) have become
increasingly common in many computing segments. To make such systems resilient,
we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to
replication because erasure coding can offer a significantly lower storage cost
than replication. To understand the impact of using erasure coding on system
performance and other system aspects such as CPU utilization and network
traffic, we build a storage cluster consisting of approximately one hundred
processor cores with more than fifty high-performance SSDs, and evaluate the
cluster with a popular open-source distributed parallel file system, Ceph. Then
we analyze behaviors of systems adopting erasure coding from the following five
viewpoints, compared with those of systems using replication: (1) storage
system I/O performance; (2) computing and software overheads; (3) I/O
amplification; (4) network traffic among storage nodes; (5) the impact of
physical data layout on performance of RS-coded SSD arrays. For all these
analyses, we examine two representative RS configurations, which are used by
Google and Facebook file systems, and compare them with triple replication that
a typical parallel file system employs as a default fault tolerance mechanism.
Lastly, we collect 54 block-level traces from the cluster and make them
available for other researchers.
",1,0,0,0,0,0
19022,Three Questions on Special Homeomorphisms on Subgroups of $R$ and $R^\infty$,"  We provide justifications for two questions on special maps on subgroups of
the reals. We will show that the questions can be treated from different points
of view. We also discuss two versions of Anderson's Involution Conjecture.
",0,0,1,0,0,0
19023,Nearly Semiparametric Efficient Estimation of Quantile Regression,"  As a competitive alternative to least squares regression, quantile regression
is popular in analyzing heterogenous data. For quantile regression model
specified for one single quantile level $\tau$, major difficulties of
semiparametric efficient estimation are the unavailability of a parametric
efficient score and the conditional density estimation. In this paper, with the
help of the least favorable submodel technique, we first derive the
semiparametric efficient scores for linear quantile regression models that are
assumed for a single quantile level, multiple quantile levels and all the
quantile levels in $(0,1)$ respectively. Our main discovery is a one-step
(nearly) semiparametric efficient estimation for the regression coefficients of
the quantile regression models assumed for multiple quantile levels, which has
several advantages: it could be regarded as an optimal way to pool information
across multiple/other quantiles for efficiency gain; it is computationally
feasible and easy to implement, as the initial estimator is easily available;
due to the nature of quantile regression models under investigation, the
conditional density estimation is straightforward by plugging in an initial
estimator. The resulting estimator is proved to achieve the corresponding
semiparametric efficiency lower bound under regularity conditions. Numerical
studies including simulations and an example of birth weight of children
confirms that the proposed estimator leads to higher efficiency compared with
the Koenker-Bassett quantile regression estimator for all quantiles of
interest.
",0,0,0,1,0,0
19024,Relevance of backtracking paths in epidemic spreading on networks,"  The understanding of epidemics on networks has greatly benefited from the
recent application of message-passing approaches, which allow to derive exact
results for irreversible spreading (i.e. diseases with permanent acquired
immunity) in locally-tree like topologies. This success has suggested the
application of the same approach to reversible epidemics, for which an
individual can contract the epidemic and recover repeatedly. The underlying
assumption is that backtracking paths (i.e. an individual is reinfected by a
neighbor he/she previously infected) do not play a relevant role. In this paper
we show that this is not the case for reversible epidemics, since the neglect
of backtracking paths leads to a formula for the epidemic threshold that is
qualitatively incorrect in the large size limit. Moreover we define a modified
reversible dynamics which explicitly forbids direct backtracking events and
show that this modification completely upsets the phenomenology.
",1,0,0,0,0,0
19025,Hybrid graphene tunneling photoconductor with interface engineering towards fast photoresponse and high responsivity,"  Hybrid graphene photoconductor/phototransistor has achieved giant
photoresponsivity, but its response speed dramatically degrades as the expense
due to the long lifetime of trapped interfacial carriers. In this work, by
intercalating a large-area atomically thin MoS2 film into a hybrid graphene
photoconductor, we have developed a prototype tunneling photoconductor, which
exhibits a record-fast response (rising time ~17 ns) and a high responsivity
(~$3\times10^4$ A/W at 635 nm and 16.8 nW illumination) across the broad
spectral range. We demonstrate that the photo-excited carriers generated in
silicon are transferred into graphene through a tunneling process rather than
carrier drift. The atomically thin MoS2 film not only serves as tunneling layer
but also passivates surface states, which in combination delivers a superior
response speed (~3 order of magnitude improved than a device without MoS2
layer), while the responsivity remains high. This intriguing tunneling
photoconductor integrates both fast response and high responsivity and thus has
significant potential in practical applications of optoelectronic devices.
",0,1,0,0,0,0
19026,Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,"  This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recognize a much greater variety of falls than the existing
datasets that existed before and solved privacy issues using the DVS. Secondly,
we introduce the DVS-TN : optimized deep learning network to detect falls using
DVS. Finally, we implemented a fall detection system which can run on
low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
into account various falls situations. Our approach achieved 95.5% on the
F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
",1,0,0,1,0,0
19027,A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks,"  In this paper, we consider the problem of solving linear algebraic equations
of the form $Ax=b$ among multi agents which seek a solution by using local
information in presence of random communication topologies. The equation is
solved by $m$ agents where each agent only knows a subset of rows of the
partitioned matrix $[A,b]$. We formulate the problem such that this formulation
does not need the distribution of random interconnection graphs. Therefore,
this framework includes asynchronous updates or unreliable communication
protocols without B-connectivity assumption. We apply the random
Krasnoselskii-Mann iterative algorithm which converges almost surely and in
mean square to a solution of the problem for any matrices $A$ and $b$ and any
initial conditions of agents' states. We demonestrate that the limit point to
which the agents' states converge is determined by the unique solution of a
convex optimization problem regardless of the distribution of random
communication graphs. Eventually, we show by two numerical examples that the
rate of convergence of the algorithm cannot be guaranteed.
",1,0,0,0,0,0
19028,Doping-induced quantum cross-over in Er$_2$Ti$_{2-x}$Sn$_x$O$_7$,"  We present the results of the investigation of magnetic properties of the
Er$_2$Ti$_{2-x}$Sn$_x$O$_7$ series. For small doping values the ordering
temperature decreases linearly with $x$ while the moment configuration remains
the same as in the $x = 0$ parent compound. Around $x = 1.7$ doping level we
observe a change in the behavior, where the ordering temperature starts to
increase and new magnetic Bragg peaks appear. For the first time we present
evidence of a long-range order (LRO) in Er$_2$Sn$_2$O$_7$ ($x = 2.0$) below
$T_N = 130$ mK. It is revealed that the moment configuration corresponds to a
Palmer-Chalker type with a value of the magnetic moment significantly
renormalized compared to $x = 0$. We discuss our results in the framework of a
possible quantum phase transition occurring close to $x = 1.7$.
",0,1,0,0,0,0
19029,Stochastic Block Models with Multiple Continuous Attributes,"  The stochastic block model (SBM) is a probabilistic model for community
structure in networks. Typically, only the adjacency matrix is used to perform
SBM parameter inference. In this paper, we consider circumstances in which
nodes have an associated vector of continuous attributes that are also used to
learn the node-to-community assignments and corresponding SBM parameters. While
this assumption is not realistic for every application, our model assumes that
the attributes associated with the nodes in a network's community can be
described by a common multivariate Gaussian model. In this augmented,
attributed SBM, the objective is to simultaneously learn the SBM connectivity
probabilities with the multivariate Gaussian parameters describing each
community. While there are recent examples in the literature that combine
connectivity and attribute information to inform community detection, our model
is the first augmented stochastic block model to handle multiple continuous
attributes. This provides the flexibility in biological data to, for example,
augment connectivity information with continuous measurements from multiple
experimental modalities. Because the lack of labeled network data often makes
community detection results difficult to validate, we highlight the usefulness
of our model for two network prediction tasks: link prediction and
collaborative filtering. As a result of fitting this attributed stochastic
block model, one can predict the attribute vector or connectivity patterns for
a new node in the event of the complementary source of information
(connectivity or attributes, respectively). We also highlight two biological
examples where the attributed stochastic block model provides satisfactory
performance in the link prediction and collaborative filtering tasks.
",1,0,0,1,0,0
19030,Asymptotic Independence of Bivariate Order Statistics,"  It is well known that an extreme order statistic and a central order
statistic (os) as well as an intermediate os and a central os from a sample of
iid univariate random variables get asymptotically independent as the sample
size increases. We extend this result to bivariate random variables, where the
os are taken componentwise. An explicit representation of the conditional
distribution of bivariate os turns out to be a powerful tool.
",0,0,1,1,0,0
19031,Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks,"  Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as ""bring me the metal coffee mug"", and recognizing plastic versus metal is
crucial when using a microwave or oven. However, collecting labeled training
data with a robot is often more difficult than unlabeled data. We present a
semi-supervised learning approach for material recognition that uses generative
adversarial networks (GANs) with haptic features such as force, temperature,
and vibration. Our approach achieves state-of-the-art results and enables a
robot to estimate the material class of household objects with ~90% accuracy
when 92% of the training data are unlabeled. We explore how well this approach
can recognize the material of new objects and we discuss challenges facing
generalization. To motivate learning from unlabeled training data, we also
compare results against several common supervised learning classifiers. In
addition, we have released the dataset used for this work which consists of
time-series haptic measurements from a robot that conducted thousands of
interactions with 72 household objects.
",1,0,0,1,0,0
19032,Level set shape and topology optimization of finite strain bilateral contact problems,"  This paper presents a method for the optimization of multi-component
structures comprised of two and three materials considering large motion
sliding contact and separation along interfaces. The structural geometry is
defined by an explicit level set method, which allows for both shape and
topology changes. The mechanical model assumes finite strains, a nonlinear
elastic material behavior, and a quasi-static response. Identification of
overlapping surface position is handled by a coupled parametric representation
of contact surfaces. A stabilized Lagrange method and an active set strategy
are used to model frictionless contact and separation. The mechanical model is
discretized by the extended finite element method which maintains a clear
definition of geometry. Face-oriented ghost penalization and dynamic relaxation
are implemented to improve the stability of the physical response prediction. A
nonlinear programming scheme is used to solve the optimization problem, which
is regularized by introducing a perimeter penalty into the objective function.
Sensitivities are determined by the adjoint method. The main characteristics of
the proposed method are studied by numerical examples in two dimensions. The
numerical results demonstrate improved design performance when compared to
models optimized with a small strain assumption. Additionally, examples with
load path dependent objectives display non-intuitive designs.
",0,0,1,0,0,0
19033,Thermal distortions of non-Gaussian beams in Fabry-Perot cavities,"  Thermal effects are already important in currently operating interferometric
gravitational wave detectors. Planned upgrades of these detectors involve
increasing optical power to combat quantum shot noise. We consider the
ramifications of this increased power for one particular class of laser
beams--wide, flat-topped, mesa beams. In particular we model a single mesa beam
Fabry-Perot cavity having thermoelastically deformed mirrors. We calculate the
intensity profile of the fundamental cavity eigenmode in the presence of
thermal perturbations, and the associated changes in thermal noise. We also
outline an idealized method of correcting for such effects. At each stage we
contrast our results with those of a comparable Gaussian beam cavity. Although
we focus on mesa beams the techniques described are applicable to any
azimuthally symmetric system.
",0,1,0,0,0,0
19034,Rigidity and trace properties of divergence-measure vector fields,"  We show some rigidity properties of divergence-free vector fields defined on
half-spaces. As an application, we prove the existence of the classical trace
for a bounded, divergence-measure vector field $\xi$ defined on the Euclidean
plane, at almost every point of a locally oriented rectifiable set $S$, under
the assumption that its weak normal trace $[\xi\cdot \nu_S]$ attains a local
maximum for the norm of $\xi$ at the point.
",0,0,1,0,0,0
19035,A note on the Almansi property,"  The first goal of this note is to study the Almansi property on an
m-dimensional model in the sense of Greene and Wu and, more generally, in a
Riemannian geometric setting. In particular, we shall prove that the only model
on which the Almansi property is verified is the Euclidean space R^m. In the
second part of the paper we shall study Almansi's property and biharmonicity
for functions which depend on the distance from a given submanifold. Finally,
in the last section we provide an extension to the semi-Euclidean case R^{p,q}
which includes the proof of the classical Almansi property in R^m as a special
instance.
",0,0,1,0,0,0
19036,The Quantum Complexity of Computing Schatten $p$-norms,"  We consider the quantum complexity of computing Schatten $p$-norms and
related quantities, and find that the problem of estimating these quantities is
closely related to the one clean qubit model of computation. We show that the
problem of approximating $\text{Tr}\, (|A|^p)$ for a log-local $n$-qubit
Hamiltonian $A$ and $p=\text{poly}(n)$, up to a suitable level of accuracy, is
contained in DQC1; and that approximating this quantity up to a somewhat higher
level of accuracy is DQC1-hard. In some cases the level of accuracy achieved by
the quantum algorithm is substantially better than a natural classical
algorithm for the problem. The same problem can be solved for arbitrary sparse
matrices in BQP. One application of the algorithm is the approximate
computation of the energy of a graph.
",1,0,0,0,0,0
19037,Bounds for fidelity of semiclassical Lagrangian states in K{ä}hler quantization,"  We define mixed states associated with submanifolds with probability
densities in quantizable closed K{ä}hler manifolds. Then, we address the
problem of comparing two such states via their fidelity. Firstly, we estimate
the sub-fidelity and super-fidelity of two such states, giving lower and upper
bounds for their fidelity, when the underlying submanifolds are two Lagrangian
submanifolds intersecting transversally at a finite number of points, in the
semiclassical limit. Secondly, we investigate a family of examples on the
sphere, for which we manage to obtain a better upper bound for the fidelity. We
conclude by stating a conjecture regarding the fidelity in the general case.
",0,0,1,0,0,0
19038,Precision measurement of antiproton to proton ratio with the Alpha Magnetic Spectrometer on the International Space Station,"  A precision measurement by AMS of the antiproton-to-proton flux ratio in
primary cosmic rays in the absolute rigidity range from 1 to 450 GV is
presented based on $3.49\times10^5$ antiproton events and $2.42\times10^9$
proton events. Above $\sim60$ GV the antiproton to proton flux ratio is
consistent with being rigidity independent. A decreasing behaviour is expected
for this ratio considering the traditional models for the secondary antiproton
flux.
",0,1,0,0,0,0
19039,Stability criteria for the 2D $α$-Euler equations,"  We derive analogues of the classical Rayleigh, Fjortoft and Arnold stability
and instability theorems in the context of the 2D $\alpha$-Euler equations.
",0,1,0,0,0,0
19040,Superconducting spin valves controlled by spiral re-orientation in B20-family magnets,"  We propose a superconducting spin-triplet valve, which consists of a
superconductor and an itinerant magnetic material, with the magnet showing an
intrinsic non-collinear order characterized by a wave vector that may be
aligned in a few equivalent preferred directions under control of a weak
external magnetic field. Re-orienting the spiral direction allows one to
controllably modify long-range spin-triplet superconducting correlations,
leading to spin-valve switching behavior. Our results indicate that the
spin-valve effect may be noticeable. This bilayer may be used as a magnetic
memory element for cryogenic nanoelectronics. It has the following advantages
in comparison to superconducting spin valves proposed previously: (i) it
contains only one magnetic layer, which may be more easily fabricated and
controlled, (ii) its ground states are separated by a potential barrier, which
solves the ""half-select"" problem of the addressed switch of memory elements.
",0,1,0,0,0,0
19041,Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions,"  Learning has propelled the cutting edge of performance in robotic control to
new heights, allowing robots to operate with high performance in conditions
that were previously unimaginable. The majority of the work, however, assumes
that the unknown parts are static or slowly changing. This limits them to
static or slowly changing environments. However, in the real world, a robot may
experience various unknown conditions. This paper presents a method to extend
an existing single mode GP-based safe learning controller to learn an
increasing number of non-linear models for the robot dynamics. We show that
this approach enables a robot to re-use past experience from a large number of
previously visited operating conditions, and to safely adapt when a new and
distinct operating condition is encountered. This allows the robot to achieve
safety and high performance in an large number of operating conditions that do
not have to be specified ahead of time. Our approach runs independently from
the controller, imposing no additional computation time on the control loop
regardless of the number of previous operating conditions considered. We
demonstrate the effectiveness of our approach in experiment on a 900\,kg ground
robot with both physical and artificial changes to its dynamics. All of our
experiments are conducted using vision for localization.
",1,0,0,0,0,0
19042,Calderón-type inequalities for affine frames,"  We prove sharp upper and lower bounds for generalized Calderón's sums
associated to frames on LCA groups generated by affine actions of cocompact
subgroup translations and general measurable families of automorphisms. The
proof makes use of techniques of analysis on metric spaces, and relies on a
counting estimate of lattice points inside metric balls. We will deduce as
special cases Calderón-type inequalities for families of expanding
automorphisms as well as for LCA-Gabor systems.
",0,0,1,0,0,0
19043,Magnetism in Semiconducting Molybdenum Dichalcogenides,"  Transition metal dichalcogenides (TMDs) are interesting for understanding
fundamental physics of two-dimensional materials (2D) as well as for many
emerging technologies, including spin electronics. Here, we report the
discovery of long-range magnetic order below TM = 40 K and 100 K in bulk
semiconducting TMDs 2H-MoTe2 and 2H-MoSe2, respectively, by means of muon
spin-rotation (muSR), scanning tunneling microscopy (STM), as well as density
functional theory (DFT) calculations. The muon spin rotation measurements show
the presence of a large and homogeneous internal magnetic fields at low
temperatures in both compounds indicative of long-range magnetic order. DFT
calculations show that this magnetism is promoted by the presence of defects in
the crystal. The STM measurements show that the vast majority of defects in
these materials are metal vacancies and chalcogen-metal antisites which are
randomly distributed in the lattice at the sub-percent level. DFT indicates
that the antisite defects are magnetic with a magnetic moment in the range of
0.9-2.8 mu_B. Further, we find that the magnetic order stabilized in 2H-MoTe2
and 2H-MoSe2 is highly sensitive to hydrostatic pressure. These observations
establish 2H-MoTe2 and 2H-MoSe2 as a new class of magnetic semiconductors and
opens a path to studying the interplay of 2D physics and magnetism in these
interesting semiconductors.
",0,1,0,0,0,0
19044,Characteristic Polynomial of Certain Hyperplane Arrangements through Graph Theory,"  We give a formula for computing the characteristic polynomial for certain
hyperplane arrangements in terms of the number of bipartite graphs of given
rank and cardinality.
",0,0,1,0,0,0
19045,Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models,"  The process to certify highly Automated Vehicles has not yet been defined by
any country in the world. Currently, companies test Automated Vehicles on
public roads, which is time-consuming and inefficient. We proposed the
Accelerated Evaluation concept, which uses a modified statistics of the
surrounding vehicles and the Importance Sampling theory to reduce the
evaluation time by several orders of magnitude, while ensuring the evaluation
results are statistically accurate. In this paper, we further improve the
accelerated evaluation concept by using Piecewise Mixture Distribution models,
instead of Single Parametric Distribution models. We developed and applied this
idea to forward collision control system reacting to vehicles making cut-in
lane changes. The behavior of the cut-in vehicles was modeled based on more
than 403,581 lane changes collected by the University of Michigan Safety Pilot
Model Deployment Program. Simulation results confirm that the accuracy and
efficiency of the Piecewise Mixture Distribution method outperformed single
parametric distribution methods in accuracy and efficiency, and accelerated the
evaluation process by almost four orders of magnitude.
",1,0,0,0,0,0
19046,Phase shift's influence of two strong pulsed laser waves on effective interaction of electrons,"  The phase shift's influence of two strong pulsed laser waves on effective
interaction of electrons was studied. Considerable amplification of electrons
repulsion in the certain range of phase shifts and waves intensities is shown.
That leads to electrons scatter on greater distances than without an external
field. The value of the distance can be greater on 2-3 order of magnitude. Also
considerable influence of the phase shift of pulses of waves on the possibility
of effective attraction of electrons is shown.
",0,1,0,0,0,0
19047,Topological Sieving of Rings According to their Rigidity,"  We present a novel mechanism for resolving the mechanical rigidity of
nanoscopic circular polymers that flow in a complex environment. The emergence
of a regime of negative differential mobility induced by topological
interactions between the rings and the substrate is the key mechanism for
selective sieving of circular polymers with distinct flexibilities. A simple
model accurately describes the sieving process observed in molecular dynamics
simulations and yields experimentally verifiable analytical predictions, which
can be used as a reference guide for improving filtration procedures of
circular filaments. The topological sieving mechanism we propose ought to be
relevant also in probing the microscopic details of complex substrates.
",0,0,0,0,1,0
19048,A structural Markov property for decomposable graph laws that allows control of clique intersections,"  We present a new kind of structural Markov property for probabilistic laws on
decomposable graphs, which allows the explicit control of interactions between
cliques, so is capable of encoding some interesting structure. We prove the
equivalence of this property to an exponential family assumption, and discuss
identifiability, modelling, inferential and computational implications.
",0,0,0,1,0,0
19049,Fisher consistency for prior probability shift,"  We introduce Fisher consistency in the sense of unbiasedness as a desirable
property for estimators of class prior probabilities. Lack of Fisher
consistency could be used as a criterion to dismiss estimators that are
unlikely to deliver precise estimates in test datasets under prior probability
and more general dataset shift. The usefulness of this unbiasedness concept is
demonstrated with three examples of classifiers used for quantification:
Adjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted
Classify & Count and EM-algorithm are Fisher consistent. A counter-example
shows that CDE-Iterate is not Fisher consistent and, therefore, cannot be
trusted to deliver reliable estimates of class probabilities.
",1,0,0,1,0,0
19050,Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders,"  The notion of disentangled autoencoders was proposed as an extension to the
variational autoencoder by introducing a disentanglement parameter $\beta$,
controlling the learning pressure put on the possible underlying latent
representations. For certain values of $\beta$ this kind of autoencoders is
capable of encoding independent input generative factors in separate elements
of the code, leading to a more interpretable and predictable model behaviour.
In this paper we quantify the effects of the parameter $\beta$ on the model
performance and disentanglement. After training multiple models with the same
value of $\beta$, we establish the existence of consistent variance in one of
the disentanglement measures, proposed in literature. The negative consequences
of the disentanglement to the autoencoder's discriminative ability are also
asserted while varying the amount of examples available during training.
",1,0,0,1,0,0
19051,An omnibus test for the global null hypothesis,"  Global hypothesis tests are a useful tool in the context of, e.g, clinical
trials, genetic studies or meta analyses, when researchers are not interested
in testing individual hypotheses, but in testing whether none of the hypotheses
is false. There are several possibilities how to test the global null
hypothesis when the individual null hypotheses are independent. If it is
assumed that many of the individual null hypotheses are false, combinations
tests have been recommended to maximise power. If, however, it is assumed that
only one or a few null hypotheses are false, global tests based on individual
test statistics are more powerful (e.g., Bonferroni or Simes test). However,
usually there is no a-priori knowledge on the number of false individual null
hypotheses. We therefore propose an omnibus test based on the combination of
p-values. We show that this test yields an impressive overall performance. The
proposed method is implemented in the R-package omnibus.
",0,0,0,1,0,0
19052,Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification,"  We prove that the ordinary least-squares (OLS) estimator attains nearly
minimax optimal performance for the identification of linear dynamical systems
from a single observed trajectory. Our upper bound relies on a generalization
of Mendelson's small-ball method to dependent data, eschewing the use of
standard mixing-time arguments. Our lower bounds reveal that these upper bounds
match up to logarithmic factors. In particular, we capture the correct
signal-to-noise behavior of the problem, showing that more unstable linear
systems are easier to estimate. This behavior is qualitatively different from
arguments which rely on mixing-time calculations that suggest that unstable
systems are more difficult to estimate. We generalize our technique to provide
bounds for a more general class of linear response time-series.
",0,0,0,1,0,0
19053,Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification,"  Motivated by applications in protein function prediction, we consider a
challenging supervised classification setting in which positive labels are
scarce and there are no explicit negative labels. The learning algorithm must
thus select which unlabeled examples to use as negative training points,
possibly ending up with an unbalanced learning problem. We address these issues
by proposing an algorithm that combines active learning (for selecting negative
examples) with imbalance-aware learning (for mitigating the label imbalance).
In our experiments we observe that these two techniques operate
synergistically, outperforming state-of-the-art methods on standard protein
function prediction benchmarks.
",0,0,0,0,1,0
19054,Superconductivity in quantum wires: A symmetry analysis,"  We study properties of quantim wires with spin-orbit coupling and time
reversal symmetry breaking, in normal and superconducting states. Electronic
band structures are classified according to quasi-one-dimensional magnetic
point groups, or magnetic classes. The latter belong to one of three distinct
types, depending on the way the time reversal operation appears in the group
elements. The superconducting gap functions are constructed using antiunitary
operations and have different symmetry properties depending on the type of the
magnetic point group. We obtain the spectrum of the Andreev boundary modes near
the end of the wire in a model-independent way, using the semiclassical
approach with the boundary conditions described by a phenomenological
scattering matrix. Explicit expressions for the bulk topological invariants
controlling the number of the boundary zero modes are presented in the general
multiband case for two types of the magnetic point groups, corresponding to
DIII and BDI symmetry classes.
",0,1,0,0,0,0
19055,Exponentially convergent data assimilation algorithm for Navier-Stokes equations,"  The paper presents a new state estimation algorithm for a bilinear equation
representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS)
equations on a torus in R2. This state equation is subject to uncertain but
bounded noise in the input (Kolmogorov forcing) and initial conditions, and its
output is incomplete and contains bounded noise. The algorithm designs a
time-dependent gain such that the estimation error converges to zero
exponentially. The sufficient condition for the existence of the gain are
formulated in the form of algebraic Riccati equations. To demonstrate the
results we apply the proposed algorithm to the reconstruction a chaotic fluid
flow from incomplete and noisy data.
",0,1,0,0,0,0
19056,A Multi-Modal Approach to Infer Image Affect,"  The group affect or emotion in an image of people can be inferred by
extracting features about both the people in the picture and the overall makeup
of the scene. The state-of-the-art on this problem investigates a combination
of facial features, scene extraction and even audio tonality. This paper
combines three additional modalities, namely, human pose, text-based tagging
and CNN extracted features / predictions. To the best of our knowledge, this is
the first time all of the modalities were extracted using deep neural networks.
We evaluate the performance of our approach against baselines and identify
insights throughout this paper.
",0,0,0,1,0,0
19057,Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks,"  3D beamforming is a promising approach for interference coordination in
cellular networks which brings significant improvements in comparison with
conventional 2D beamforming techniques. This paper investigates the problem of
joint beamforming design and tilt angle adaptation of the BS antenna array for
maximizing energy efficiency (EE) in downlink of multi-cell multi-user
coordinated cellular networks. An iterative algorithm based on fractional
programming approach is introduced to solve the resulting non-convex
optimization problem. In each iteration, users are clustered based on their
elevation angle. Then, optimization of the tilt angle is carried out through a
reduced complexity greedy search to find the best tilt angle for a given
placement of the users. Numerical results show that the proposed algorithm
achieves higher EE compared to the 2D beamforming techniques.
",1,0,1,0,0,0
19058,Testing Degree Corrections in Stochastic Block Models,"  We study sharp detection thresholds for degree corrections in Stochastic
Block Models in the context of a goodness of fit problem. When degree
corrections are relatively dense, a simple test based on the total number of
edges is asymptotically optimal. For sparse degree corrections in non-dense
graphs, simple degree based Higher Criticism Test (Mukherjee, Mukherjee, Sen
2016) is optimal with sharp constants. In contrast, for dense graphs, the
optimal procedure runs in two stages. It involves running a suitable community
recovery algorithm in stage 1, followed by a Higher Criticism Test based on a
linear combination of within and across (estimated) community degrees in stage
2. The necessity of the two step procedure is demonstrated by the failure of
the ordinary Maximum Degree Test in achieving sharp constants. As necessary
tools we also derive asymptotic distribution of the Maximum Degree in
Stochastic Block Models along with moderate deviation and local central limit
type asymptotics of positive linear combinations of independent Binomial random
variables.
",0,0,1,1,0,0
19059,A Survey on Mobile Edge Computing: The Communication Perspective,"  Driven by the visions of Internet of Things and 5G communications, recent
years have seen a paradigm shift in mobile computing, from the centralized
Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of
MEC is to push mobile computing, network control and storage to the network
edges (e.g., base stations and access points) so as to enable
computation-intensive and latency-critical applications at the resource-limited
mobile devices. MEC promises dramatic reduction in latency and mobile energy
consumption, tackling the key challenges for materializing 5G vision. The
promised gains of MEC have motivated extensive efforts in both academia and
industry on developing the technology. A main thrust of MEC research is to
seamlessly merge the two disciplines of wireless communications and mobile
computing, resulting in a wide-range of new designs ranging from techniques for
computation offloading to network architectures. This paper provides a
comprehensive survey of the state-of-the-art MEC research with a focus on joint
radio-and-computational resource management. We also present a research outlook
consisting of a set of promising directions for MEC research, including MEC
system deployment, cache-enabled MEC, mobility management for MEC, green MEC,
as well as privacy-aware MEC. Advancements in these directions will facilitate
the transformation of MEC from theory to practice. Finally, we introduce recent
standardization efforts on MEC as well as some typical MEC application
scenarios.
",1,0,1,0,0,0
19060,Deep Learning Approximation: Zero-Shot Neural Network Speedup,"  Neural networks offer high-accuracy solutions to a range of problems, but are
costly to run in production systems because of computational and memory
requirements during a forward pass. Given a trained network, we propose a
techique called Deep Learning Approximation to build a faster network in a tiny
fraction of the time required for training by only manipulating the network
structure and coefficients without requiring re-training or access to the
training data. Speedup is achieved by by applying a sequential series of
independent optimizations that reduce the floating-point operations (FLOPs)
required to perform a forward pass. First, lossless optimizations are applied,
followed by lossy approximations using singular value decomposition (SVD) and
low-rank matrix decomposition. The optimal approximation is chosen by weighing
the relative accuracy loss and FLOP reduction according to a single parameter
specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an
end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can
be re-gained by finetuning.
",0,0,0,1,0,0
19061,Frequency truncated discrete-time system norm,"  Multirate digital signal processing and model reduction applications require
computation of the frequency truncated norm of a discrete-time system. This
paper explains how to compute the frequency truncated norm of a discrete-time
system. To this end, a much-generalized problem of integrating a transfer
function of a discrete-time system given in the descriptor form over an
interval of limited frequencies is also discussed along with its computation.
",1,0,0,0,0,0
19062,Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation,"  Recent work has shown that state-of-the-art classifiers are quite brittle, in
the sense that a small adversarial change of an originally with high confidence
correctly classified input leads to a wrong classification again with high
confidence. This raises concerns that such classifiers are vulnerable to
attacks and calls into question their usage in safety-critical systems. We show
in this paper for the first time formal guarantees on the robustness of a
classifier by giving instance-specific lower bounds on the norm of the input
manipulation required to change the classifier decision. Based on this analysis
we propose the Cross-Lipschitz regularization functional. We show that using
this form of regularization in kernel methods resp. neural networks improves
the robustness of the classifier without any loss in prediction performance.
",1,0,0,1,0,0
19063,Complete DFM Model for High-Performance Computing SoCs with Guard Ring and Dummy Fill Effect,"  For nanotechnology, the semiconductor device is scaled down dramatically with
additional strain engineering for device enhancement, the overall device
characteristic is no longer dominated by the device size but also circuit
layout. The higher order layout effects, such as well proximity effect (WPE),
oxide spacing effect (OSE) and poly spacing effect (PSE), play an important
role for the device performance, it is critical to understand Design for
Manufacturability (DFM) impacts with various layout topology toward the overall
circuit performance. Currently, the layout effects (WPE, OSE and PSE) are
validated through digital standard cell and analog differential pair test
structure. However, two analog layout structures: the guard ring and dummy fill
impact are not well studied yet, then, this paper describes the current mirror
test circuit to examine the guard ring and dummy fills DFM impacts using TSMC
28nm HPM process.
",1,0,0,0,0,0
19064,Investigating the potential of social network data for transport demand models,"  Location-based social network data offers the promise of collecting the data
from a large base of users over a longer span of time at negligible cost. While
several studies have applied social network data to activity and mobility
analysis, a comparison with travel diaries and general statistics has been
lacking. In this paper, we analysed geo-referenced Twitter activities from a
large number of users in Singapore and neighbouring countries. By combining
this data, population statistics and travel diaries and applying clustering
techniques, we addressed detection of activity locations, as well as spatial
separation and transitions between these locations. Kernel density estimation
performs best to detect activity locations due to the scattered nature of the
twitter data; more activity locations are detected per user than reported in
the travel survey. The descriptive analysis shows that determining home
locations is more difficult than detecting work locations for most planning
zones. Spatial separations between detected activity locations from Twitter
data - as reported in a travel survey and captured by public transport smart
card data - are mostly similarly distributed, but also show relevant
differences for very short and very long distances. This also holds for the
transitions between zones. Whether the differences between Twitter data and
other data sources stem from differences in the population sub-sample,
clustering methodology, or whether social networks are being used significantly
more at specific locations must be determined by further research. Despite
these shortcomings, location-based social network data offers a promising data
source for insights into activity locations and mobility patterns, especially
for regions where travel survey data is not readily available.
",1,1,0,0,0,0
19065,Generalized Internal Boundaries (GIB),"  Representing large-scale motions and topological changes in the finite volume
(FV) framework, while at the same time preserving the accuracy of the numerical
solution, is difficult. In this paper, we present a robust, highly efficient
method designed to achieve this capability. The proposed approach conceptually
shares many of the characteristics of the cut-cell interface tracking method,
but without the need for complex cell splitting/merging operations. The heart
of the new technique is to align existing mesh facets with the geometry to be
represented. We then modify the matrix contributions from these facets such
that they are represented in an identical fashion to traditional boundary
conditions. The collection of such faces is named a Generalised Internal
Boundary (GIB). In order to introduce motion into the system, we rely on the
classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat
that the non-time-dependent motion of elements instantaneously crossing the
interface is handled separately from the time dependent component. The new
methodology is validated through comparison with: a) a body-fitted grid
simulation of an oscillating two dimensional cylinder and b) experimental
results of a butterfly valve.
",1,1,0,0,0,0
19066,Reducing Certification Granularity to Increase Adaptability of Avionics Software,"  A strong certification process is required to insure the safety of airplanes,
and more specifically the robustness of avionics applications. To implement
this process, the development of avionics software must follow long and costly
procedures. Most of these procedures have to be reexecuted each time the
software is modified. In this paper, we propose a framework to reduce the cost
and time impact of a software modification. With this new approach, the piece
of software likely to change is isolated from the rest of the application, so
it can be certified independently. This helps the system integrator to adapt an
avionics application to the specificities of the target airplane, without the
need for a new certification of the application.
",1,0,0,0,0,0
19067,Evolutionary multiplayer games on graphs with edge diversity,"  Evolutionary game dynamics in structured populations has been extensively
explored in past decades. However, most previous studies assume that payoffs of
individuals are fully determined by the strategic behaviors of interacting
parties and social ties between them only serve as the indicator of the
existence of interactions. This assumption neglects important information
carried by inter-personal social ties such as genetic similarity, geographic
proximity, and social closeness, which may crucially affect the outcome of
interactions. To model these situations, we present a framework of evolutionary
multiplayer games on graphs with edge diversity, where different types of edges
describe diverse social ties. Strategic behaviors together with social ties
determine the resulting payoffs of interactants. Under weak selection, we
provide a general formula to predict the success of one behavior over the
other. We apply this formula to various examples which cannot be dealt with
using previous models, including the division of labor and relationship- or
edge-dependent games. We find that labor division facilitates collective
cooperation by decomposing a many-player game into several games of smaller
sizes. The evolutionary process based on relationship-dependent games can be
approximated by interactions under a transformed and unified game. Our work
stresses the importance of social ties and provides effective methods to reduce
the calculating complexity in analyzing the evolution of realistic systems.
",0,0,0,0,1,0
19068,Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks,"  In this paper, we study the robust consensus problem for a set of
discrete-time linear agents to coordinate over an uncertain communication
network, which is to achieve consensus against the transmission errors and
noises resulted from the information exchange between the agents. We model the
network by means of communication links subject to multiplicative stochastic
uncertainties, which are susceptible to describing packet dropout, random
delay, and fading phenomena. Different communication topologies, such as
undirected graphs and leader-follower graphs, are considered. We derive
sufficient conditions for robust consensus in the mean square sense. This
results unveil intrinsic constraints on consensus attainment imposed by the
network synchronizability, the unstable agent dynamics, and the channel
uncertainty variances. Consensus protocols are designed based on the state
information transmitted over the uncertain channels, by solving a modified
algebraic Riccati equation.
",1,0,0,0,0,0
19069,Fractional Topological Elasticity and Fracton Order,"  We analyze the ""higher rank"" gauge theories, that capture some of the
phenomenology of the Fracton order. It is shown that these theories loose gauge
invariance when arbitrarily weak and smooth curvature is introduced. We propose
a resolution to this problem by introducing a theory invariant under
area-preserving diffeomorphisms, which reduce to the ""higher rank"" gauge
transformations upon linearization around a flat background. The proposed
theory is \emph{geometric} in nature and is interpreted as a theory of
\emph{fractional topological elasticity}. This theory exhibits the Fracton
phenomenology. We explore the conservation laws, topological excitations,
linear response, various kinematical constraints, and canonical structure of
the theory. Finally, we emphasize that the very structure of Riemann-Cartan
geometry, which we use to formulate the theory, encodes the Fracton
phenomenology, suggesting that the Fracton order itself is \emph{geometric} in
nature.
",0,1,0,0,0,0
19070,Perturbing Eisenstein polynomials over local fields,"  Let $K$ be a local field whose residue field has characteristic $p$ and let
$L/K$ be a finite separable totally ramified extension. Let $\pi_L$ be a
uniformizer for $L$ and let $f(X)$ be the minimum polynomial for $\pi_L$ over
$K$. Suppose $\tilde{\pi}_L$ is another uniformizer for $L$ such that
$\tilde{\pi}_L\equiv\pi_L+r\pi_L^{\ell+1} \pmod{\pi_L^{\ell+2}}$ for some
$\ell\ge1$ and $r\in O_K$. Let $\tilde{f}(X)$ be the minimum polynomial for
$\tilde{\pi}_L$ over $K$. In this paper we give congruences for the
coefficients of $\tilde{f}(X)$ in terms of $r$ and the coefficients of $f(X)$.
These congruences improve and extend work of Krasner.
",0,0,1,0,0,0
19071,Galactic Orbits of Globular Clusters in the Region of the Galactic Bulge,"  Galactic orbits have been constructed over long time intervals for ten
globular clusters located near the Galactic center. A model with an axially
symmetric gravitational potential for the Galaxy was initially applied, after
which a non-axially symmetric potential corresponding to the central bar was
added. Variations in the trajectories of all these globular clusters in the XY
plane due to the influence of the bar were detected. These were greatest for
the cluster Terzan 4 in the meridional (RZ) plane. The globular clusters Terzan
1, Terzan 2, Terzan 4, Terzan 9, NGC 6522, and NGC 6558 always remained within
the Galactic bulge, no farther than 4 kpc from the Galactic center.
",0,1,0,0,0,0
19072,Streamlines for Motion Planning in Underwater Currents,"  Motion planning for underwater vehicles must consider the effect of ocean
currents. We present an efficient method to compute reachability and cost
between sample points in sampling-based motion planning that supports
long-range planning over hundreds of kilometres in complicated flows. The idea
is to search a reduced space of control inputs that consists of stream
functions whose level sets, or streamlines, optimally connect two given points.
Such stream functions are generated by superimposing a control input onto the
underlying current flow. A streamline represents the resulting path that a
vehicle would follow as it is carried along by the current given that control
input. We provide rigorous analysis that shows how our method avoids exhaustive
search of the control space, and demonstrate simulated examples in complicated
flows including a traversal along the east coast of Australia, using actual
current predictions, between Sydney and Brisbane.
",1,0,0,0,0,0
19073,"Eddington-Limited Accretion in z~2 WISE-selected Hot, Dust-Obscured Galaxies","  Hot, Dust-Obscured Galaxies, or ""Hot DOGs"", are a rare, dusty, hyperluminous
galaxy population discovered by the WISE mission. Predominantly at redshifts
2-3, they include the most luminous known galaxies in the universe. Their high
luminosities likely come from accretion onto highly obscured super massive
black holes (SMBHs). We have conducted a pilot survey to measure the SMBH
masses of five z~2 Hot DOGs via broad H_alpha emission lines, using
Keck/MOSFIRE and Gemini/FLAMINGOS-2. We detect broad H_alpha emission in all
five Hot DOGs. We find substantial corresponding SMBH masses for these Hot DOGs
(~ 10^{9} M_sun), and their derived Eddington ratios are close to unity. These
z~2 Hot DOGs are the most luminous AGNs at given BH masses, suggesting they are
accreting at the maximum rates for their BHs. A similar property is found for
known z~6 quasars. Our results are consistent with scenarios in which Hot DOGs
represent a transitional, high-accretion phase between obscured and unobscured
quasars. Hot DOGs may mark a special evolutionary stage before the red quasar
and optical quasar phases, and they may be present at other cosmic epochs.
",0,1,0,0,0,0
19074,Boltzmann Transport in Nanostructures as a Friction Effect,"  Surface scattering is the key limiting factor to thermal transport in
dielectric crystals as the length scales are reduced or when temperature is
lowered. To explain this phenomenon, it is commonly assumed that the mean free
paths of heat carriers are bound by the crystal size and that thermal
conductivity is reduced in a manner proportional to such mean free paths. We
show here that these conclusions rely on simplifying assumptions and
approximated transport models. Instead, starting from the linearized Boltzmann
transport equation in the relaxon basis, we show how the problem can be reduced
to a set of decoupled linear differential equations. Then, the heat flow can be
interpreted as a hydrodynamic phenomenon, with the relaxon gas being slowed
down in proximity of a surface by friction effects, similar to the flux of a
viscous fluid in a pipe. As an example, we study a ribbon and a trench of
monolayer molybdenum disulphide, describing the procedure to reconstruct the
temperature and thermal conductivity profile in the sample interior and showing
how to estimate the effect of nanostructuring. The approach is general and
could be extended to other transport carriers, such as electrons, or extended
to materials of higher dimensionality and to different geometries, such as thin
films.
",0,1,0,0,0,0
19075,Agile Software Engineering and Systems Engineering at SKA Scale,"  Systems Engineering (SE) is the set of processes and documentation required
for successfully realising large-scale engineering projects, but the classical
approach is not a good fit for software-intensive projects, especially when the
needs of the different stakeholders are not fully known from the beginning, and
requirement priorities might change. The SKA is the ultimate software-enabled
telescope, with enormous amounts of computing hardware and software required to
perform its data reduction. We give an overview of the system and software
engineering processes in the SKA1 development, and the tension between
classical and agile SE.
",1,1,0,0,0,0
19076,Prime geodesic theorem of Gallagher type,"  We reduce the exponent in the error term of the prime geodesic theorem for
compact Riemann surfaces from $\frac{3}{4}$ to $\frac{7}{10}$ outside a set of
finite logarithmic measure.
",0,0,1,0,0,0
19077,PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks,"  This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
method for reducing the privacy cost of information transmitted through a
network. Distributed sensor networks are often used for automated
classification and detection of abnormal events in high-stakes situations, e.g.
fire in buildings, earthquakes, or crowd disasters. Such networks might
transmit privacy-sensitive information, e.g. GPS location of smartphones, which
might be disclosed if the network is compromised. Privacy concerns might slow
down the adoption of the technology, in particular in the scenario of social
sensing where participation is voluntary, thus solutions are needed which
improve privacy without compromising on the event detection accuracy. PriMaL is
implemented as a machine-learning layer that works on top of an existing event
detection algorithm. Experiments are run in a general simulation framework, for
several network topologies and parameter values. The privacy footprint of
state-of-the-art event detection algorithms is compared within the proposed
framework. Results show that PriMaL is able to reduce the privacy cost of a
distributed event detection algorithm below that of the corresponding
centralized algorithm, within the bounds of some assumptions about the
protocol. Moreover the performance of the distributed algorithm is not
statistically worse than that of the centralized algorithm.
",1,0,0,0,0,0
19078,Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU,"  The online problem of computing the top eigenvector is fundamental to machine
learning. In both adversarial and stochastic settings, previous results (such
as matrix multiplicative weight update, follow the regularized leader, follow
the compressed leader, block power method) either achieve optimal regret but
run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total
regret where $d$ is the matrix dimension.
We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which
achieves optimal regret without sacrificing the running time. Our idea is to
""compress"" the matrix strategy to dimension 3 in the adversarial setting, or
dimension 1 in the stochastic setting. These respectively resolve two open
questions regarding the design of optimal and efficient algorithms for the
online eigenvector problem.
",1,0,1,1,0,0
19079,Solving satisfiability using inclusion-exclusion,"  Using Maple, we implement a SAT solver based on the principle of
inclusion-exclusion and the Bonferroni inequalities. Using randomly generated
input, we investigate the performance of our solver as a function of the number
of variables and number of clauses. We also test it against Maple's built-in
tautology procedure. Finally, we implement the Lovász local lemma with Maple
and discuss its applicability to SAT.
",1,0,0,0,0,0
19080,Isometric immersions into manifolds with metallic structures,"  We consider submanifolds into Riemannian manifold with metallic structures.
We obtain some new results for hypersurfaces in these spaces and we express the
fundamental theorem of submanifolds into products spaces in terms of metallic
structures. Moreover, we define new structures called complex metallic
structures. We show that these structures are linked with complex structures.
Then, we consider submanifolds into Riemannian manifold with such structures
with a focus on invariant submanifolds and hypersurfaces. We also express in
particular the fundamental theorem of submanifolds of complex space form in
terms of complex metallic structures.
",0,0,1,0,0,0
19081,Demonstration of an ac Josephson junction laser,"  Superconducting electronic devices have re-emerged as contenders for both
classical and quantum computing due to their fast operation speeds, low
dissipation and long coherence times. An ultimate demonstration of coherence is
lasing. We use one of the fundamental aspects of superconductivity, the ac
Josephson effect, to demonstrate a laser made from a Josephson junction
strongly coupled to a multi-mode superconducting cavity. A dc voltage bias to
the junction provides a source of microwave photons, while the circuit's
nonlinearity allows for efficient down-conversion of higher order Josephson
frequencies down to the cavity's fundamental mode. The simple fabrication and
operation allows for easy integration with a range of quantum devices, allowing
for efficient on-chip generation of coherent microwave photons at low
temperatures.
",0,1,0,0,0,0
19082,"Integrable systems, symmetries and quantization","  These notes correspond to a mini-course given at the Poisson 2016 conference
in Geneva. Starting from classical integrable systems in the sense of
Liouville, we explore the notion of non-degenerate singularity and expose
recent research in connection with semi-toric systems. The quantum and
semiclassical counterpart will also be presented, in the viewpoint of the
inverse question: from the quantum mechanical spectrum, can you recover the
classical system?
",0,1,1,0,0,0
19083,Detecting Friedel oscillations in ultracold Fermi gases,"  Investigating Friedel oscillations in ultracold gases would complement the
studies performed on solid state samples with scanning-tunneling microscopes.
In atomic quantum gases interactions and external potentials can be tuned
freely and the inherently slower dynamics allow to access non-equilibrium
dynamics following a potential or interaction quench. Here, we examine how
Friedel oscillations can be observed in current ultracold gas experiments under
realistic conditions. To this aim we numerically calculate the amplitude of the
Friedel oscillations which a potential barrier provokes in a 1D Fermi gas and
compare it to the expected atomic and photonic shot noise in a density
measurement. We find that to detect Friedel oscillations the signal from
several thousand one-dimensional systems has to be averaged. However, as up to
100 parallel one-dimensional systems can be prepared in a single run with
present experiments, averaging over about 100 images is sufficient.
",0,1,0,0,0,0
19084,Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems,"  While variational methods have been among the most powerful tools for solving
linear inverse problems in imaging, deep (convolutional) neural networks have
recently taken the lead in many challenging benchmarks. A remaining drawback of
deep learning approaches is their requirement for an expensive retraining
whenever the specific problem, the noise level, noise type, or desired measure
of fidelity changes. On the contrary, variational methods have a plug-and-play
nature as they usually consist of separate data fidelity and regularization
terms.
In this paper we study the possibility of replacing the proximal operator of
the regularization used in many convex energy minimization algorithms by a
denoising neural network. The latter therefore serves as an implicit natural
image prior, while the data term can still be chosen independently. Using a
fixed denoising neural network in exemplary problems of image deconvolution
with different blur kernels and image demosaicking, we obtain state-of-the-art
reconstruction results. These indicate the high generalizability of our
approach and a reduction of the need for problem-specific training.
Additionally, we discuss novel results on the analysis of possible optimization
algorithms to incorporate the network into, as well as the choices of algorithm
parameters and their relation to the noise level the neural network is trained
on.
",1,0,0,0,0,0
19085,Heated-Up Softmax Embedding,"  Metric learning aims at learning a distance which is consistent with the
semantic meaning of the samples. The problem is generally solved by learning an
embedding for each sample such that the embeddings of samples of the same
category are compact while the embeddings of samples of different categories
are spread-out in the feature space. We study the features extracted from the
second last layer of a deep neural network based classifier trained with the
cross entropy loss on top of the softmax layer. We show that training
classifiers with different temperature values of softmax function leads to
features with different levels of compactness. Leveraging these insights, we
propose a ""heating-up"" strategy to train a classifier with increasing
temperatures, leading the corresponding embeddings to achieve state-of-the-art
performance on a variety of metric learning benchmarks.
",0,0,0,1,0,0
19086,Fast Matrix Inversion and Determinant Computation for Polarimetric Synthetic Aperture Radar,"  This paper introduces a fast algorithm for simultaneous inversion and
determinant computation of small sized matrices in the context of fully
Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysis.
The proposed fast algorithm is based on the computation of the adjoint matrix
and the symmetry of the input matrix. The algorithm is implemented in a general
purpose graphical processing unit (GPGPU) and compared to the usual approach
based on Cholesky factorization. The assessment with simulated observations and
data from an actual PolSAR sensor show a speedup factor of about two when
compared to the usual Cholesky factorization. Moreover, the expressions
provided here can be implemented in any platform.
",1,0,0,1,0,0
19087,The imprint of neutrinos on clustering in redshift-space,"  (abridged) We investigate the signatures left by the cosmic neutrino
background on the clustering of matter, CDM+baryons and halos in redshift-space
using a set of more than 1000 N-body and hydrodynamical simulations with
massless and massive neutrinos. We find that the effect neutrinos induce on the
clustering of CDM+baryons in redshift-space on small scales is almost entirely
due to the change in $\sigma_8$. Neutrinos imprint a characteristic signature
in the quadrupole of the matter (CDM+baryons+neutrinos) field on small scales,
that can be used to disentangle the effect of $\sigma_8$ and $M_\nu$. We show
that the effect of neutrinos on the clustering of halos is very different, on
all scales, to the one induced by $\sigma_8$. We find that the effects of
neutrinos of the growth rate of CDM+baryons ranges from $\sim0.3\%$ to $2\%$ on
scales $k\in[0.01, 0.5]~h{\rm Mpc}^{-1}$ for neutrinos with masses $M_\nu
\leqslant 0.15$ eV. We compute the bias between the momentum of halos and the
momentum of CDM+baryon and find it to be 1 on large scales for all models with
massless and massive neutrinos considered. This point towards a velocity bias
between halos and total matter on large scales that it is important to account
for in order to extract unbiased neutrino information from velocity/momentum
surveys such as kSZ observations. We show that baryonic effects can affect the
clustering of matter and CDM+baryons in redshift-space by up to a few percent
down to $k=0.5~h{\rm Mpc}^{-1}$. We find that hydrodynamics and astrophysical
processes, as implemented in our simulations, only distort the relative effect
that neutrinos induce on the anisotropic clustering of matter, CDM+baryons and
halos in redshift-space by less than $1\%$. Thus, the effect of neutrinos in
the fully non-linear regime can be written as a transfer function with very
weak dependence on astrophysics.
",0,1,0,0,0,0
19088,Quantum Privacy-Preserving Data Analytics,"  Data analytics (such as association rule mining and decision tree mining) can
discover useful statistical knowledge from a big data set. But protecting the
privacy of the data provider and the data user in the process of analytics is a
serious issue. Usually, the privacy of both parties cannot be fully protected
simultaneously by a classical algorithm. In this paper, we present a quantum
protocol for data mining that can much better protect privacy than the known
classical algorithms: (1) if both the data provider and the data user are
honest, the data user can know nothing about the database except the
statistical results, and the data provider can get nearly no information about
the results mined by the data user; (2) if the data user is dishonest and tries
to disclose private information of the other, she/he will be detected with a
high probability; (3) if the data provider tries to disclose the privacy of the
data user, she/he cannot get any useful information since the data user hides
his privacy among noises.
",1,0,0,0,0,0
19089,Magnetic Properties of Transition-Metal Adsorbed ot-Phosphorus Monolayer: A First-principles and Monte Carlo Study,"  Using the first-principles and Monte Carlo methods, here we systematically
study magnetic properties of monolayer octagonal-tetragonal phosphorus with 3d
transition-metal (TM) adatoms. Different from the puckered hexagonal black
phosphorus monolayer (phosphorene or $\alpha$-P), the octagonal-tetragonal
phase of 2D phosphorus (named as ot-P or $\epsilon$-P in this article) is
buckled with octagon-tetragon structure. Our calculations show that all TMs,
except the closed-shell Zn atom, are able to strongly bind onto monolayer
$ot$-P with significant binding energies. Local magnetic moments (up to 6
$\mu$B) on adatoms of Sc, Ti, V, Cr, Mn, Fe and Co originate from the exchange
and crystal-field splitting of TM 3d orbitals. The magnetic coupling between
localized magnetic states of adatoms is dependent on adatomic distances and
directions. Lastly, the uniformly magnetic order is investigated to screening
two-dimensional dilute ferromagnets with high Curie temperature for
applications of spintronics. It is found that ot-P with V atoms homogeneously
adsorbed at the centre of octagons with a concentration of 5% has the most
stable ferromagnetic ground state. Its Curie temperature is estimated to be 173
K using the Monte Carlo method.
",0,1,0,0,0,0
19090,A bootstrap for the number of $\mathbb{F}_{q^r}$-rational points on a curve over $\mathbb{F}_q$,"  In this note we present a fast algorithm that finds for any $r$ the number
$N_r$ of $\mathbb{F}_{q^r}$ rational points on a smooth absolutely irreducible
curve $C$ defined over $\mathbb{F}_{q}$ assuming that we know $N_1,\cdots,N_g$,
where $g$ is the genus of $C$. The proof of its validity is given in detail and
its working are illustrated with several examples. In an Appendix we list the
Python function in which we have implemented the algorithm together with other
routines used in the examples.
",0,0,1,0,0,0
19091,Doping anatase TiO2 with group V-b and VI-b transition metal atoms: a hybrid functional first-principles study,"  We investigate the role of transition metal atoms of group V-b (V, Nb, and
Ta) and VI-b (Cr, Mo, and W) as n- or p-type dopants in anatase TiO2 using
thermodynamic principles and density functional theory with the
Heyd-Scuseria-Ernzerhof HSE06 hybrid functional. The HSE06 functional provides
a realistic value for the band gap, which ensures a correct classification of
dopants as shallow or deep donors or acceptors. Defect formation energies and
thermodynamic transition levels are calculated taking into account the
constraints imposed by the stability of TiO2 and the solubility limit of the
impurities. Nb, Ta, W and Mo are identified as shallow donors. Although W
provides two electrons, Nb and Ta show a considerably lower formation energy,
in particular under O-poor conditions. Mo donates in principle one electron,
but under specific conditions can turn into a double donor. V impurities are
deep donors and Cr shows up as an amphoteric defect, thereby acting as an
electron trapping center in n-type TiO2 especially under O-rich conditions. A
comparison with the available experimental data yields excellent agreement.
",0,1,0,0,0,0
19092,Patch-planting spin-glass solution for benchmarking,"  We introduce an algorithm to generate (not solve) spin-glass instances with
planted solutions of arbitrary size and structure. First, a set of small
problem patches with open boundaries is solved either exactly or with a
heuristic, and then the individual patches are stitched together to create a
large problem with a known planted solution. Because in these problems
frustration is typically smaller than in random problems, we first assess the
typical computational complexity of the individual patches using population
annealing Monte Carlo, and introduce an approach that allows one to fine-tune
the typical computational complexity of the patch-planted system. The scaling
of the typical computational complexity of these planted instances with various
numbers of patches and patch sizes is investigated and compared to random
instances.
",0,1,0,0,0,0
19093,Evidence for the formation of comet 67P/Churyumov-Gerasimenko through gravitational collapse of a bound clump of pebbles,"  The processes that led to the formation of the planetary bodies in the Solar
System are still not fully understood. Using the results obtained with the
comprehensive suite of instruments on-board ESA's Rosetta mission, we present
evidence that comet 67P/Churyumov-Gerasimenko likely formed through the gentle
gravitational collapse of a bound clump of mm-sized dust aggregates
(""pebbles""), intermixed with microscopic ice particles. This formation scenario
leads to a cometary make-up that is simultaneously compatible with the global
porosity, homogeneity, tensile strength, thermal inertia, vertical temperature
profiles, sizes and porosities of emitted dust, and the steep increase in
water-vapour production rate with decreasing heliocentric distance, measured by
the instruments on-board the Rosetta spacecraft and the Philae lander. Our
findings suggest that the pebbles observed to be abundant in protoplanetary
discs around young stars provide the building material for comets and other
minor bodies.
",0,1,0,0,0,0
19094,Detection of virial shocks in stacked Fermi-LAT clusters,"  Galaxy clusters are thought to grow by accreting mass through large-scale,
strong, yet elusive, virial shocks. Such a shock is expected to accelerate
relativistic electrons, thus generating a spectrally-flat leptonic virial-ring.
However, until now, only the nearby Coma cluster has shown evidence for a
$\gamma$-ray virial ring. We stack Fermi-LAT data for the 112 most massive,
high latitude, extended clusters, enhancing the ring sensitivity by rescaling
clusters to their virial radii and utilizing the expected flat energy spectrum.
In addition to a central unresolved, hard signal (detected at the $\sim
5.8\sigma$ confidence level), probably dominated by AGN, we identify (at the
$5.8\sigma$ confidence level) a bright, spectrally-flat $\gamma$-ray ring at
the expected virial shock position. The ring signal implies that the shock
deposits $\sim 0.6\%$ (with an interpretation uncertainty factor $\sim2$) of
the thermal energy in relativistic electrons over a Hubble time. This result,
consistent with the Coma signal, validates and calibrates the virial shock
model, and indicates that the cumulative emission from such shocks
significantly contributes to the diffuse extragalactic $\gamma$-ray and
low-frequency radio backgrounds.
",0,1,0,0,0,0
19095,Closed-form formulae of hyperbolic metamaterial made by stacked hole-array layers working at terahertz or microwave radiation,"  A metamaterial made by stacked hole-array layers known as a fishnet
metamaterial behaves as a hyperbolic metamaterial at wavelength much longer
than hole-array period. However, the analytical formulae of effective
parameters of a fishnet metamaterial have not been reported hindering the
design of deep-subwavelength imaging devices using this structure. We report
the new closed-form formulae of effective parameters comprising anisotropic
dispersion relation of a fishnet metamaterial working at terahertz or microwave
frequency. These effective parameters of a fishnet metamaterial are consistent
with those obtained by quasi-full solutions using known effective parameters of
a hole-array layer working at frequency below its spoof plasma frequency with
the superlattice period much smaller than the hole-array period. We also
theoretically demonstrate the deep-subwavelength focusing at {\lambda}/83 using
the composite structure of a slit-array layer and a fishnet metamaterial. It is
found that the focused intensity inside a fishnet metamaterial is several times
larger than that without the fishnet metamaterial, but the transmitted
intensity is still restricted by large-wavevector difference in air and a
fishnet metamaterial. Our effective parameters may aid the next-generation
deep-subwavelength imaging devices working at terahertz or microwave radiation.
",0,1,0,0,0,0
19096,RMPflow: A Computational Graph for Automatic Motion Policy Generation,"  We develop a novel policy synthesis algorithm, RMPflow, based on
geometrically consistent transformations of Riemannian Motion Policies (RMPs).
RMPs are a class of reactive motion policies designed to parameterize
non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task
spaces. Given a set of RMPs designed for individual tasks, RMPflow can
consistently combine these local policies to generate an expressive global
policy, while simultaneously exploiting sparse structure for computational
efficiency. We study the geometric properties of RMPflow and provide sufficient
conditions for stability. Finally, we experimentally demonstrate that
accounting for the geometry of task policies can simplify classically difficult
problems, such as planning through clutter on high-DOF manipulation systems.
",1,0,0,0,0,0
19097,Long-range correlations and fractal dynamics in C. elegans: changes with aging and stress,"  Reduced motor control is one of the most frequent features associated with
aging and disease. Nonlinear and fractal analyses have proved to be useful in
investigating human physiological alterations with age and disease. Similar
findings have not been established for any of the model organisms typically
studied by biologists, though. If the physiology of a simpler model organism
displays the same characteristics, this fact would open a new research window
on the control mechanisms that organisms use to regulate physiological
processes during aging and stress. Here, we use a recently introduced animal
tracking technology to simultaneously follow tens of Caenorhabdits elegans for
several hours and use tools from fractal physiology to quantitatively evaluate
the effects of aging and temperature stress on nematode motility. Similarly to
human physiological signals, scaling analysis reveals long-range correlations
in numerous motility variables, fractal properties in behavioral shifts, and
fluctuation dynamics over a wide range of timescales. These properties change
as a result of a superposition of age and stress-related adaptive mechanisms
that regulate motility.
",0,1,0,1,0,0
19098,A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces,"  Recent work of M.D. Johnston et al. has produced sufficient conditions on the
structure of a chemical reaction network which guarantee that the corresponding
discrete state space system exhibits an extinction event. The conditions
consist of a series of systems of equalities and inequalities on the edges of a
modified reaction network called a domination-expanded reaction network. In
this paper, we present a computational implementation of these conditions
written in Python and apply the program on examples drawn from the biochemical
literature, including a model of polyamine metabolism in mammals and a model of
the pentose phosphate pathway in Trypanosoma brucei. We also run the program on
458 models from the European Bioinformatics Institute's BioModels Database and
report our results.
",0,0,1,0,0,0
19099,Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media,"  A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, ""fat shaming"" and other forms of ""sizeism"" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, researchers from a variety of backgrounds are interested in studying
obesity from all angles. To obtain data, traditionally, a person would have to
accurately self-report their body-mass index (BMI) or would have to see a
doctor to have it measured. In this paper, we show how computer vision can be
used to infer a person's BMI from social media images. We hope that our tool,
which we release, helps to advance the study of social aspects related to body
weight.
",1,0,0,0,0,0
19100,Recursive simplex stars,"  This paper proposes a new method which builds a simplex based approximation
of a $d-1$-dimensional manifold $M$ separating a $d$-dimensional compact set
into two parts, and an efficient algorithm classifying points according to this
approximation. In a first variant, the approximation is made of simplices that
are defined in the cubes of a regular grid covering the compact set, from
boundary points that approximate the intersection between $M$ and the edges of
the cubes. All the simplices defined in a cube share the barycentre of the
boundary points located in the cube and include simplices similarly defined in
cube facets, and so on recursively. In a second variant, the Kuhn triangulation
is used to break the cubes into simplices and the approximation is defined in
these simplices from the boundary points computed on their edges, with the same
principle. Both the approximation in cubes and in simplices define a separating
surface on the whole grid and classifying a point on one side or the other of
this surface requires only a small number (at most $d$) of simple tests. Under
some conditions on the definition of the boundary points and on the reach of
$M$, for both variants the Hausdorff distance between $M$ and its approximation
decreases like $\mathcal{O}(d n_G^{-2})$, where $n_G$ is the number of points
on each axis of the grid. The approximation in cubes requires computing less
boundary points than the approximation in simplices but the latter is always a
manifold and is more accurate for a given value of $n_G$. The paper reports
tests of the method when varying $n_G$ and the dimensionality of the space (up
to 9).
",1,0,0,0,0,0
19101,An Analytic Formula for Numbers of Restricted Partitions from Conformal Field Theory,"  We study the correlators of irregular vertex operators in two-dimensional
conformal field theory (CFT) in order to propose an exact analytic formula for
calculating numbers of partitions, that is:
1) for given $N,k$, finding the total number $\lambda(N|k)$ of length $k$
partitions of $N$: $N=n_1+...+n_k;0<n_1\leq{n_2}...\leq{n_k}$.
2) finding the total number $\lambda(N)=\sum_{k=1}^N\lambda(N|k)$ of
partitions of a natural number $N$
We propose an exact analytic expression for $\lambda(N|k)$ by relating
two-point short-distance correlation functions of irregular vertex operators in
$c=1$ conformal field theory ( the form of the operators is established in this
paper): with the first correlator counting the partitions in the upper
half-plane and the second one obtained from the first correlator by conformal
transformations of the form $f(z)=h(z)e^{-{i\over{z}}}$ where $h(z)$ is regular
and non-vanishing at $z=0$. The final formula for $\lambda(N|k)$ is given in
terms of regularized ($\epsilon$-ordered) finite series in the generalized
higher-derivative Schwarzians and incomplete Bell polynomials of the above
conformal transformation at $z=i\epsilon$ ($\epsilon\rightarrow{0}$)
",0,0,1,0,0,0
19102,Hyperopic Cops and Robbers,"  We introduce a new variant of the game of Cops and Robbers played on graphs,
where the robber is invisible unless outside the neighbor set of a cop. The
hyperopic cop number is the corresponding analogue of the cop number, and we
investigate bounds and other properties of this parameter. We characterize the
cop-win graphs for this variant, along with graphs with the largest possible
hyperopic cop number. We analyze the cases of graphs with diameter 2 or at
least 3, focusing on when the hyperopic cop number is at most one greater than
the cop number. We show that for planar graphs, as with the usual cop number,
the hyperopic cop number is at most 3. The hyperopic cop number is considered
for countable graphs, and it is shown that for connected chains of graphs, the
hyperopic cop density can be any real number in $[0,1/2].$
",1,0,0,0,0,0
19103,New pinching estimates for Inverse curvature flows in space forms,"  We prove new pinching estimate for the inverse curvature flow of strictly
convex hypersurfaces in the space form $N$ of constant sectional curvature
$K_N$ with speed given by $F^{-\alpha}$, where $\alpha\in (0,1]$ for $K_N=0,-1$
and $\alpha=1$ for $K_N=1$, $F$ is a smooth, symmetric homogeneous of degree
one function which is inverse concave and has dual $F_*$ approaching zero on
the boundary of the positive cone $\Gamma_+$. We show that the ratio of the
largest principal curvature to the smallest principal curvature of the flow
hypersurface is controlled by its initial value. This can be used to prove the
smooth convergence of the flow.
",0,0,1,0,0,0
19104,"automan: a simple, Python-based, automation framework for numerical computing","  We present an easy-to-use, Python-based framework that allows a researcher to
automate their computational simulations. In particular the framework
facilitates assembling several long-running computations and producing various
plots from the data produced by these computations. The framework makes it
possible to reproduce every figure made for a publication with a single
command. It also allows one to distribute the computations across a network of
computers. The framework has been used to write research papers in numerical
computing. This paper discusses the design of the framework, and the benefits
of using it. The ideas presented are general and should help researchers
organize their computations for better reproducibility.
",1,0,0,0,0,0
19105,Extensive characterization of a high Reynolds number decelerating boundary layer using advanced optical metrology,"  An experiment conducted in the framework of the EUHIT project and designed to
characterize large scale structures in an adverse pressure gradient boundary
layer flow is presented. Up to 16 sCMOS cameras were used in order to perform
large scale turbulent boundary layer PIV measurements with a large field of
view and appropriate spatial resolution. To access the span-wise / wall-normal
signature of the structures as well, stereoscopic PIV measurements in
span-wise/wall-normal planes were performed at specific stream-wise locations.
To complement these large field of view measurements, long-range micro-PIV,
time resolved near wall velocity profiles and film-based measurements were
performed in order to determine the wall-shear stress and its fluctuations at
some specific locations along the model.
",0,1,0,0,0,0
19106,Q-analogues of the Fibo-Stirling numbers,"  Let $F_n$ denote the $n^{th}$ Fibonacci number relative to the initial
conditions $F_0=0$ and $F_1=1$. Bach, Paudyal, and Remmel introduced Fibonacci
analogues of the Stirling numbers called Fibo-Stirling numbers of the first and
second kind. These numbers serve as the connection coefficients between the
Fibo-falling factorial basis $\{(x)_{\downarrow_{F,n}}:n \geq 0\}$ and the
Fibo-rising factorial basis $\{(x)_{\uparrow_{F,n}}:n \geq 0\}$ which are
defined by $(x)_{\downarrow_{F,0}} = (x)_{\uparrow_{F,0}} = 1$ and for $k \geq
1$, $(x)_{\downarrow_{F,k}} = x(x-F_1) \cdots (x-F_{k-1})$ and
$(x)_{\uparrow_{F,k}} = x(x+F_1) \cdots (x+F_{k-1})$. We gave a general rook
theory model which allowed us to give combinatorial interpretations of the
Fibo-Stirling numbers of the first and second kind.
There are two natural $q$-analogues of the falling and rising Fibo-factorial
basis. That is, let $[x]_q = \frac{q^x-1}{q-1}$. Then we let
$[x]_{\downarrow_{q,F,0}} = \overline{[x]}_{\downarrow_{q,F,0}} =
[x]_{\uparrow_{q,F,0}} = \overline{[x]}_{\uparrow_{q,F,0}}=1$ and, for $k > 0$,
we let $[x]_{\downarrow_{q,F,k}} = [x]_q [x-F_1]_q \cdots [x-F_{k-1}]_q$,
$\overline{[x]}_{\downarrow_{q,F,k}}= [x]_q ([x]_q-[F_1]_q) \cdots
([x]_q-[F_{k-1}]_q)$, $[x]_{\uparrow_{q,F,k}}= [x]_q [x+F_1]_q \cdots
[x+F_{k-1}]_q$, and $\overline{[x]}_{\uparrow_{q,F,k}}= [x]_q ([x]_q+[F_1]_q)
\cdots ([x]_q+[F_{k-1}]_q)$.
In this paper, we show we can modify the rook theory model of Bach, Paudyal,
and Remmel to give combinatorial interpretations for the two different types
$q$-analogues of the Fibo-Stirling numbers which arise as the connection
coefficients between the two different $q$-analogues of the Fibonacci falling
and rising factorial bases. \end{abstract}
",0,0,1,0,0,0
19107,Hybrid Dirac Semimetal in CaAgBi Materials Family,"  Based on their formation mechanisms, Dirac points in three-dimensional
systems can be classified as accidental or essential. The former can be further
distinguished into type-I and type-II, depending on whether the Dirac cone
spectrum is completely tipped over along certain direction. Here, we predict
the coexistence of all three kinds of Dirac points in the low-energy band
structure of CaAgBi-family materials with a stuffed Wurtzite structure. Two
pairs of accidental Dirac points reside on the rotational axis, with one pair
being type-I and the other pair type-II; while another essential Dirac point is
pinned at the high symmetry point on the Brillouin zone boundary. Due to broken
inversion symmetry, the band degeneracy around accidental Dirac points is
completely lifted except along the rotational axis, which may enable the
splitting of chiral carriers at a ballistic p-n junction with a double negative
refraction effect. We clarify their symmetry protections, and find both the
Dirac-cone and Fermi arc topological surface states.
",0,1,0,0,0,0
19108,On the martingale property in the rough Bergomi model,"  We consider a class of fractional stochastic volatility models (including the
so-called rough Bergomi model), where the volatility is a superlinear function
of a fractional Gaussian process. We show that the stock price is a true
martingale if and only if the correlation $\rho$ between the driving Brownian
motions of the stock and the volatility is nonpositive. We also show that for
each $\rho<0$ and $m> \frac{1}{1-\rho^2}$, the $m$-th moment of the stock
price is infinite at each positive time.
",0,0,0,0,0,1
19109,Weighted Surface Algebras,"  A finite-dimensional algebra $A$ over an algebraically closed field $K$ is
called periodic if it is periodic under the action of the syzygy operator in
the category of $A-A-$ bimodules. The periodic algebras are self-injective and
occur naturally in the study of tame blocks of group algebras, actions of
finite groups on spheres, hypersurface singularities of finite Cohen-Macaulay
type, and Jacobian algebras of quivers with potentials. Recently, the tame
periodic algebras of polynomial growth have been classified and it is natural
to attempt to classify all tame periodic algebras. We introduce the weighted
surface algebras of triangulated surfaces with arbitrarily oriented triangles
and describe their basic properties. In particular, we prove that all these
algebras, except the singular tetrahedral algebras, are symmetric tame periodic
algebras of period $4$. Moreover, we describe the socle deformations of the
weighted surface algebras and prove that all these algebras are symmetric tame
periodic algebras of period $4$. The main results of the paper form an
important step towards a classification of all periodic symmetric tame algebras
of non-polynomial growth, and lead to a complete description of all algebras of
generalized quaternion type. Further, the orbit closures of the weighted
surface algebras (and their socle deformations) in the affine varieties of
associative $K$-algebra structures contain wide classes of tame symmetric
algebras related to algebras of dihedral and semidihedral types, which occur in
the study of blocks of group algebras with dihedral and semidihedral defect
groups.
",0,0,1,0,0,0
19110,A Statistical Learning Approach to Modal Regression,"  This paper studies the nonparametric modal regression problem systematically
from a statistical learning view. Originally motivated by pursuing a
theoretical understanding of the maximum correntropy criterion based regression
(MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is
essentially modal regression. We show that nonparametric modal regression
problem can be approached via the classical empirical risk minimization. Some
efforts are then made to develop a framework for analyzing and implementing
modal regression. For instance, the modal regression function is described, the
modal regression risk is defined explicitly and its \textit{Bayes} rule is
characterized; for the sake of computational tractability, the surrogate modal
regression risk, which is termed as the generalization risk in our study, is
introduced. On the theoretical side, the excess modal regression risk, the
excess generalization risk, the function estimation error, and the relations
among the above three quantities are studied rigorously. It turns out that
under mild conditions, function estimation consistency and convergence may be
pursued in modal regression as in vanilla regression protocols, such as mean
regression, median regression, and quantile regression. However, it outperforms
these regression models in terms of robustness as shown in our study from a
re-descending M-estimation view. This coincides with and in return explains the
merits of MCCR on robustness. On the practical side, the implementation issues
of modal regression including the computational algorithm and the tuning
parameters selection are discussed. Numerical assessments on modal regression
are also conducted to verify our findings empirically.
",0,0,1,1,0,0
19111,Teaching a Machine to Read Maps with Deep Reinforcement Learning,"  The ability to use a 2D map to navigate a complex 3D environment is quite
remarkable, and even difficult for many humans. Localization and navigation is
also an important problem in domains such as robotics, and has recently become
a focus of the deep reinforcement learning community. In this paper we teach a
reinforcement learning agent to read a map in order to find the shortest way
out of a random maze it has never seen before. Our system combines several
state-of-the-art methods such as A3C and incorporates novel elements such as a
recurrent localization cell. Our agent learns to localize itself based on 3D
first person images and an approximate orientation angle. The agent generalizes
well to bigger mazes, showing that it learned useful localization and
navigation capabilities.
",1,0,0,1,0,0
19112,The fan beam model for the pulse evolution of PSR J0737-3039B,"  Average radio pulse profile of a pulsar B in a double pulsar system PSR
J0737-3039A/B exhibits an interesting behaviour. During the observation period
between 2003 and 2009, the profile evolves from a single-peaked to a
double-peaked form, following disappearance in 2008 indicating that the
geodetic precession of the pulsar is a possible origin of such behaviour. The
known pulsar beam models can be used to determine the geometry of PSR
J0737-3039B in the context of the precession. We study how the fan-beam
geometry performs in explaining the observed variations of the radio profile
morphology. It is shown that the fan beam can successfully reproduce the
observed evolution of the pulse width, and should be considered as a serious
alternative for the conal-like models.
",0,1,0,0,0,0
19113,Resilience of Core-Periphery Networks in the Case of Rich-Club,"  Core-periphery networks are structures that present a set of central and
densely connected nodes, namely the core, and a set of non-central and sparsely
connected nodes, namely the periphery. The rich-club refers to a set in which
the highest degree nodes show a high density of connections. Thus, a network
that displays a rich-club can be interpreted as a core-periphery network in
which the core is made up by a number of hubs. In this paper, we test the
resilience of networks showing a progressively denser rich-club and we observe
how this structure is able to affect the network measures in terms of both
cohesion and efficiency in information flow. Additionally, we consider the case
in which, instead of making the core denser, we add links to the periphery.
These two procedures of core and periphery thickening delineate a decision
process in the placement of new links and allow us to conduct a scenario
analysis that can be helpful in the comprehension and supervision of complex
networks under the resilience perspective. The advantages of the two
procedures, as well as their implications, are discussed in relation to both
network effciency and node heterogeneity.
",1,0,0,0,0,0
19114,Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams,"  The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the $\mbox{Tornado}$ framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.
",1,0,0,1,0,0
19115,Extremes in Random Graphs Models of Complex Networks,"  Regarding the analysis of Web communication, social and complex networks the
fast finding of most influential nodes in a network graph constitutes an
important research problem. We use two indices of the influence of those nodes,
namely, PageRank and a Max-linear model. We consider the PageRank %both as
%Galton-Watson branching process and as an autoregressive process with a random
number of random coefficients that depend on ranks of incoming nodes and their
out-degrees and assume that the coefficients are independent and distributed
with regularly varying tail and with the same tail index. Then it is proved
that the tail index and the extremal index are the same for both PageRank and
the Max-linear model and the values of these indices are found. The
achievements are based on the study of random sequences of a random length and
the comparison of the distribution of their maxima and linear combinations.
",0,0,1,1,0,0
19116,Estimates of the Reconstruction Error in Partially Redressed Warped Frames Expansions,"  In recent work, redressed warped frames have been introduced for the analysis
and synthesis of audio signals with non-uniform frequency and time resolutions.
In these frames, the allocation of frequency bands or time intervals of the
elements of the representation can be uniquely described by means of a warping
map. Inverse warping applied after time-frequency sampling provides the key to
reduce or eliminate dispersion of the warped frame elements in the conjugate
variable, making it possible, e.g., to construct frequency warped frames with
synchronous time alignment through frequency. The redressing procedure is
however exact only when the analysis and synthesis windows have compact support
in the domain where warping is applied. This implies that frequency warped
frames cannot have compact support in the time domain. This property is
undesirable when online computation is required. Approximations in which the
time support is finite are however possible, which lead to small reconstruction
errors. In this paper we study the approximation error for compactly supported
frequency warped analysis-synthesis elements, providing a few examples and case
studies.
",1,0,0,0,0,0
19117,On the Optimality of Secret Key Agreement via Omniscience,"  For the multiterminal secret key agreement problem under a private source
model, it is known that the maximum key rate, i.e., the secrecy capacity, can
be achieved through communication for omniscience, but the omniscience strategy
can be strictly suboptimal in terms of minimizing the public discussion rate.
While a single-letter characterization is not known for the minimum discussion
rate needed for achieving the secrecy capacity, we derive single-letter lower
and upper bounds that yield some simple conditions for omniscience to be
discussion-rate optimal. These conditions turn out to be enough to deduce the
optimality of omniscience for a large class of sources including the
hypergraphical sources. Through conjectures and examples, we explore other
source models to which our methods do not easily extend.
",1,0,0,0,0,0
19118,Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process,"  Constructing a smart wheelchair on a commercially available powered
wheelchair (PWC) platform avoids a host of seating, mechanical design and
reliability issues but requires methods of predicting and controlling the
motion of a device never intended for robotics. Analog joystick inputs are
subject to black-box transformations which may produce intuitive and adaptable
motion control for human operators, but complicate robotic control approaches;
furthermore, installation of standard axle mounted odometers on a commercial
PWC is difficult. In this work, we present an integrated hardware and software
system for predicting the motion of a commercial PWC platform that does not
require any physical or electronic modification of the chair beyond plugging
into an industry standard auxiliary input port. This system uses an RGB-D
camera and an Arduino interface board to capture motion data, including visual
odometry and joystick signals, via ROS communication. Future motion is
predicted using an autoregressive sparse Gaussian process model. We evaluate
the proposed system on real-world short-term path prediction experiments.
Experimental results demonstrate the system's efficacy when compared to a
baseline neural network model.
",1,0,0,0,0,0
19119,Kernelized Hashcode Representations for Relation Extraction,"  Kernel methods have produced state-of-the-art results for a number of NLP
tasks such as relation extraction, but suffer from poor scalability due to the
high cost of computing kernel similarities between natural language structures.
A recently proposed technique, kernelized locality-sensitive hashing (KLSH),
can significantly reduce the computational cost, but is only applicable to
classifiers operating on kNN graphs. Here we propose to use random subspaces of
KLSH codes for efficiently constructing an explicit representation of NLP
structures suitable for general classification methods. Further, we propose an
approach for optimizing the KLSH model for classification problems by
maximizing an approximation of mutual information between the KLSH codes
(feature vectors) and the class labels. We evaluate the proposed approach on
biomedical relation extraction datasets, and observe significant and robust
improvements in accuracy w.r.t. state-of-the-art classifiers, along with
drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
",1,0,0,1,0,0
19120,Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?,"  When you see a person in a crowd, occluded by other persons, you miss visual
information that can be used to recognize, re-identify or simply classify him
or her. You can imagine its appearance given your experience, nothing more.
Similarly, AI solutions can try to hallucinate missing information with
specific deep learning architectures, suitably trained with people with and
without occlusions. The goal of this work is to generate a complete image of a
person, given an occluded version in input, that should be a) without occlusion
b) similar at pixel level to a completely visible people shape c) capable to
conserve similar visual attributes (e.g. male/female) of the original one. For
the purpose, we propose a new approach by integrating the state-of-the-art of
neural network architectures, namely U-nets and GANs, as well as discriminative
attribute classification nets, with an architecture specifically designed to
de-occlude people shapes. The network is trained to optimize a Loss function
which could take into account the aforementioned objectives. As well we propose
two datasets for testing our solution: the first one, occluded RAP, created
automatically by occluding real shapes of the RAP dataset (which collects also
attributes of the people aspect); the second is a large synthetic dataset, AiC,
generated in computer graphics with data extracted from the GTA video game,
that contains 3D data of occluded objects by construction. Results are
impressive and outperform any other previous proposal. This result could be an
initial step to many further researches to recognize people and their behavior
in an open crowded world.
",1,0,0,0,0,0
19121,"John, the semi-conductor : a tool for comprovisation","  This article presents ""John"", an open-source software designed to help
collective free improvisation. It provides generated screen-scores running on
distributed, reactive web-browsers. The musicians can then concurrently edit
the scores in their own browser. John is used by ONE, a septet playing
improvised electro-acoustic music with digital musical instruments (DMI). One
of the original features of John is that its design takes care of leaving the
musician's attention as free as possible. Firstly, a quick review of the
context of screen-based scores will help situate this research in the history
of contemporary music notation. Then I will trace back how improvisation
sessions led to John's particular ""notational perspective"". A brief description
of the software will precede a discussion about the various aspects guiding its
design.
",1,0,0,0,0,0
19122,Eigenvalue and Eigenfunction for the $PT$-symmetric Potential $V = - (ix)^N$,"  The real energy spectrum from the $PT$-symmetric Hamiltonian $H = p^2 -
(ix)^N$ with $x\in\mathbb{C}$ was examined within one pair of Stokes wedges in
1998 by Bender and Boettcher. For this Hamiltonian we discuss the following
three questions. First, since their paper used a Runge-Kutta method to
integrate along a path at the center of the Stokes wedges to calculate
eigenvalues $E$ with high accuracy, we wonder if the same eigenvalues can be
obtained if integrate along some other paths in different shapes. Second, what
the corresponding eigenfunctions look like? Should the eigenfunctions be
independent from the shapes of path or not? Third, since for large $N$ the
Hamiltonian contains many pairs of Stokes wedges symmetric with respect to the
imaginary axis of $x$, thus multiple families of real energy spectrum can be
obtained. What do they look like? Any relation among them?
",0,0,1,0,0,0
19123,Random walks on activity-driven networks with attractiveness,"  Virtually all real-world networks are dynamical entities. In social networks,
the propensity of nodes to engage in social interactions (activity) and their
chances to be selected by active nodes (attractiveness) are heterogeneously
distributed. Here, we present a time-varying network model where each node and
the dynamical formation of ties are characterised by these two features. We
study how these properties affect random walk processes unfolding on the
network when the time scales describing the process and the network evolution
are comparable. We derive analytical solutions for the stationary state and the
mean first passage time of the process and we study cases informed by empirical
observations of social networks. Our work shows that previously disregarded
properties of real social systems such heterogeneous distributions of activity
and attractiveness as well as the correlations between them, substantially
affect the dynamical process unfolding on the network.
",1,1,0,0,0,0
19124,Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data,"  B cells develop high affinity receptors during the course of affinity
maturation, a cyclic process of mutation and selection. At the end of affinity
maturation, a number of cells sharing the same ancestor (i.e. in the same
""clonal family"") are released from the germinal center, their amino acid
frequency profile reflects the allowed and disallowed substitutions at each
position. These clonal-family-specific frequency profiles, called ""substitution
profiles"", are useful for studying the course of affinity maturation as well as
for antibody engineering purposes. However, most often only a single sequence
is recovered from each clonal family in a sequencing experiment, making it
impossible to construct a clonal-family-specific substitution profile. Given
the public release of many high-quality large B cell receptor datasets, one may
ask whether it is possible to use such data in a prediction model for
clonal-family-specific substitution profiles. In this paper, we present the
method ""Substitution Profiles Using Related Families"" (SPURF), a penalized
tensor regression framework that integrates information from a rich assemblage
of datasets to predict the clonal-family-specific substitution profile for any
single input sequence. Using this framework, we show that substitution profiles
from similar clonal families can be leveraged together with simulated
substitution profiles and germline gene sequence information to improve
prediction. We fit this model on a large public dataset and validate the
robustness of our approach on an external dataset. Furthermore, we provide a
command-line tool in an open-source software package
(this https URL) implementing these ideas and providing easy
prediction using our pre-fit models.
",0,0,0,0,1,0
19125,Energy Efficient Mobile Edge Computing in Dense Cellular Networks,"  Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet
the increasing computation demands from mobile devices, with the dense
deployment of Base Stations (BSs), is foreseen as a key step towards the next
generation mobile networks. However, new challenges arise for designing energy
efficient networks since radio access resources and computing resources of BSs
have to be jointly managed, and yet they are complexly coupled with traffic in
both spatial and temporal domains. In this paper, we address the challenge of
incorporating MEC into dense cellular networks, and propose an efficient online
algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which
makes joint computation offloading and BS sleeping decisions in order to
maximize the quality of service while keeping the energy consumption low. Our
algorithm leverages Lyapunov optimization technique, works online and achieves
a close-to-optimal performance without using future information. Our simulation
results show that our algorithm can effectively reduce energy consumption
without sacrificing the user quality of service.
",1,0,0,0,0,0
19126,Delone dynamical systems and spectral convergence,"  In the realm of Delone sets in locally compact, second countable, Hausdorff
groups, we develop a dynamical systems approach in order to study the
continuity behavior of measured quantities arising from point sets. A special
focus is both on the autocorrelation, as well as on the density of states for
random bounded operators. It is shown that for uniquely ergodic limit systems,
the latter measures behave continuously with respect to the Chabauty-Fell
convergence of hulls. In the special situation of Euclidean spaces, our results
complement recent developments in describing spectra as topological limits: we
show that the measured quantities under consideration can be approximated via
periodic analogs.
",0,0,1,0,0,0
19127,On biconservative surfaces in Euclidean spaces,"  In this paper, we study biconservative surfaces with parallel normalized mean
curvature vector in $\mathbb{E}^4$. We obtain complete local classification in
$\mathbb{E}^4$ for a biconservative PNMCV surface. We also give an example to
show the existence of PNMCV biconservative surfaces in $\mathbb{E}^4$.
",0,0,1,0,0,0
19128,Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE,"  In an effort to increase the versatility of finite element codes, we explore
the possibility of automatically creating the Jacobian matrix necessary for the
gradient-based solution of nonlinear systems of equations. Particularly, we aim
to assess the feasibility of employing the automatic differentiation tool
TAPENADE for this purpose on a large Fortran codebase that is the result of
many years of continuous development. As a starting point we will describe the
special structure of finite element codes and the implications that this code
design carries for an efficient calculation of the Jacobian matrix. We will
also propose a first approach towards improving the efficiency of such a
method. Finally, we will present a functioning method for the automatic
implementation of the Jacobian calculation in a finite element software, but
will also point out important shortcomings that will have to be addressed in
the future.
",1,1,0,0,0,0
19129,Asymptotic analysis for Hamilton-Jacobi equations with large drift term,"  We investigate the asymptotic behavior of solutions of Hamilton-Jacobi
equations with large drift term in an open subset of two-dimensional Euclidean
space. When the drift is given by $\varepsilon^{-1} (H_{x_2}, -H_{x_1})$ of a
Hamiltonian $H$, with $\varepsilon > 0$, we establish the convergence, as
$\varepsilon \to 0+$, of solutions of the Hamilton-Jacobi equations and
identify the limit of the solutions as the solution of systems of ordinary
differential equations on a graph. This result generalizes the previous one
obtained by the author to the case where the Hamiltonian $H$ admits a
degenerate critical point and, as a consequence, the graph may have segments
more than four at a node.
",0,0,1,0,0,0
19130,An Upper Bound of the Minimal Dispersion via Delta Covers,"  For a point set of $n$ elements in the $d$-dimensional unit cube and a class
of test sets we are interested in the largest volume of a test set which does
not contain any point. For all natural numbers $n$, $d$ and under the
assumption of a $delta$-cover with cardinality $\vert \Gamma_\delta \vert$ we
prove that there is a point set, such that the largest volume of such a test
set without any point is bounded by $\frac{\log \vert \Gamma_\delta \vert}{n} +
\delta$. For axis-parallel boxes on the unit cube this leads to a volume of at
most $\frac{4d}{n}\log(\frac{9n}{d})$ and on the torus to $\frac{4d}{n}\log
(2n)$.
",1,0,1,0,0,0
19131,Three-dimensional band structure of LaSb and CeSb:Absence of band inversion,"  We have performed angle-resolved photoemission spectroscopy (ARPES) of LaSb
and CeSb, a candidate of topological insulator. Using soft-x-ray photons, we
have accurately determined the three-dimensional bulk band structure and
revealed that the band inversion at the Brillouin-zone corner - a prerequisite
for realizing topological-insulator phase - is absent in both LaSb and CeSb.
Moreover, unlike the ARPES data obtained with soft-x-ray photons, those with
vacuum ultraviolet (VUV) photons were found to suffer significant $k_z$
broadening. These results suggest that LaSb and CeSb are topologically trivial
semimetals, and unusual Dirac-cone-like states observed with VUV photons are
not of the topological origin.
",0,1,0,0,0,0
19132,Stellar-to-halo mass relation of cluster galaxies,"  In the hierarchical formation model, galaxy clusters grow by accretion of
smaller groups or isolated galaxies. During the infall into the centre of a
cluster, the properties of accreted galaxies change. In particular, both
observations and numerical simulations suggest that its dark matter halo is
stripped by the tidal forces of the host.
We use galaxy-galaxy weak lensing to measure the average mass of dark matter
haloes of satellite galaxies as a function of projected distance to the centre
of the host, for different stellar mass bins. Assuming that the stellar
component of the galaxy is less disrupted by tidal stripping, stellar mass can
be used as a proxy of the infall mass. We study the stellar to halo mass
relation of satellites as a function of the cluster-centric distance to measure
tidal stripping.
We use the shear catalogues of the DES science verification archive, the
CFHTLenS and the CFHT Stripe 82 (CS82) surveys, and we select satellites from
the redMaPPer catalogue of clusters. For galaxies located in the outskirts of
clusters, we find a stellar to halo mass relation in good agreement with the
theoretical expectations from \citet{moster2013} for central galaxies. In the
centre of the cluster, we find that this relation is shifted to smaller halo
mass for a given stellar mass. We interpret this finding as further evidence
for tidal stripping of dark matter haloes in high density environments.
",0,1,0,0,0,0
19133,Undecidability and Finite Automata,"  Using a novel rewriting problem, we show that several natural decision
problems about finite automata are undecidable (i.e., recursively unsolvable).
In contrast, we also prove three related problems are decidable. We apply one
result to prove the undecidability of a related problem about k-automatic sets
of rational numbers.
",1,0,0,0,0,0
19134,A globally stable attractor that is locally unstable everywhere,"  We construct two examples of invariant manifolds that despite being locally
unstable at every point in the transverse direction are globally stable. Using
numerical simulations we show that these invariant manifolds temporarily repel
nearby trajectories but act as global attractors. We formulate an explanation
for such global stability in terms of the `rate of rotation' of the stable and
unstable eigenvectors spanning the normal subspace associated with each point
of the invariant manifold. We discuss the role of this rate of rotation on the
transitions between the stable and unstable regimes.
",0,1,0,0,0,0
19135,On Centers and Central Lines of Triangles in the Elliptic Plane,"  We determine barycentric coordinates of triangle centers in the elliptic
plane. The main focus is put on centers that lie on lines whose euclidean limit
(triangle excess --> 0) is the Euler line or the Brocard line. We also
investigate curves which can serve in elliptic geometry as substitutes for the
euclidean nine-point-circle, the first Lemoine circle or the apollonian
circles.
",0,0,1,0,0,0
19136,Stopping Active Learning based on Predicted Change of F Measure for Text Classification,"  During active learning, an effective stopping method allows users to limit
the number of annotations, which is cost effective. In this paper, a new
stopping method called Predicted Change of F Measure will be introduced that
attempts to provide the users an estimate of how much performance of the model
is changing at each iteration. This stopping method can be applied with any
base learner. This method is useful for reducing the data annotation bottleneck
encountered when building text classification systems.
",1,0,0,1,0,0
19137,Statistical properties of random clique networks,"  In this paper, a random clique network model to mimic the large clustering
coefficient and the modular structure that exist in many real complex networks,
such as social networks, artificial networks, and protein interaction networks,
is introduced by combining the random selection rule of the Erdös and Rényi
(ER) model and the concept of cliques. We find that random clique networks
having a small average degree differ from the ER network in that they have a
large clustering coefficient and a power law clustering spectrum, while
networks having a high average degree have similar properties as the ER model.
In addition, we find that the relation between the clustering coefficient and
the average degree shows a non-monotonic behavior and that the degree
distributions can be fit by multiple Poisson curves; we explain the origin of
such novel behaviors and degree distributions.
",1,1,0,0,0,0
19138,"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","  The paucity of videos in current action classification datasets (UCF-101 and
HMDB-51) has made it difficult to identify good video architectures, as most
methods obtain similar performance on existing small-scale benchmarks. This
paper re-evaluates state-of-the-art architectures in light of the new Kinetics
Human Action Video dataset. Kinetics has two orders of magnitude more data,
with 400 human action classes and over 400 clips per class, and is collected
from realistic, challenging YouTube videos. We provide an analysis on how
current architectures fare on the task of action classification on this dataset
and how much performance improves on the smaller benchmark datasets after
pre-training on Kinetics.
We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on
2D ConvNet inflation: filters and pooling kernels of very deep image
classification ConvNets are expanded into 3D, making it possible to learn
seamless spatio-temporal feature extractors from video while leveraging
successful ImageNet architecture designs and even their parameters. We show
that, after pre-training on Kinetics, I3D models considerably improve upon the
state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%
on UCF-101.
",1,0,0,0,0,0
19139,Selfish Cops and Active Robber: Multi-Player Pursuit Evasion on Graphs,"  We introduce and study the game of ""Selfish Cops and Active Robber"" (SCAR)
which can be seen as an multiplayer variant of the ""classic"" two-player Cops
and Robbers (CR) game. In classic CR all cops are controlled by a single
player, who has no preference over which cop captures the robber. In SCAR, on
the other hand, each of N-1 cops is controlled by a separate player, and a
single robber is controlled by the N-th player; and the capturing cop player
receives a higher reward than the non-capturing ones. Consequently, SCAR is an
N-player pursuit game on graphs, in which each cop player has an increased
motive to be the one who captures the robber. The focus of our study is the
existence and properties of SCAR Nash Equilibria (NE). In particular, we prove
that SCAR always has one NE in deterministic positional strategies and (for N
greater than two) another in deterministic nonpositional strategies.
Furthermore, we study conditions which, at equilibrium, guarantee either
capture or escape of the robber and show that (because of the antagonism
between the ""selfish"" cop players) the robber may, in certain SCAR
configurations, be captured later than he would be in classic CR, or even not
captured at all. Finally we define the selfish cop number of a graph and study
its connection to the classic cop number.
",1,0,0,0,0,0
19140,Singly-Thermostated Ergodicity in Gibbs' Canonical Ensemble and the 2016 Ian Snook Prize Award,"  The 2016 Snook Prize has been awarded to Diego Tapias, Alessandro Bravetti,
and David Sanders for their paper -- Ergodicity of One-Dimensional Systems
Coupled to the Logistic Thermostat. They introduced a relatively stiff
hyperbolic tangent thermostat force and successfully tested its ability to
reproduce Gibbs' canonical distribution for the harmonic oscillator, the
quartic oscillator, and the Mexican Hat potentials. Their work constitutes an
effective response to the 2016 Ian Snook Prize Award goal -- Finding ergodic
algorithms for Gibbs' canonical ensemble using a single thermostat variable. We
confirm their work here and highlight an interesting feature of the Mexican Hat
problem when it is solved with an adaptive integrator.
",0,1,0,0,0,0
19141,Robust Estimation via Robust Gradient Estimation,"  We provide a new computationally-efficient class of estimators for risk
minimization. We show that these estimators are robust for general statistical
models: in the classical Huber epsilon-contamination model and in heavy-tailed
settings. Our workhorse is a novel robust variant of gradient descent, and we
provide conditions under which our gradient descent variant provides accurate
estimators in a general convex risk minimization problem. We provide specific
consequences of our theory for linear regression, logistic regression and for
estimation of the canonical parameters in an exponential family. These results
provide some of the first computationally tractable and provably robust
estimators for these canonical statistical models. Finally, we study the
empirical performance of our proposed methods on synthetic and real datasets,
and find that our methods convincingly outperform a variety of baselines.
",0,0,0,1,0,0
19142,SMT Solving for Vesicle Traffic Systems in Cells,"  In biology, there are several questions that translate to combinatorial
search. For example, vesicle traffic systems that move cargo within eukaryotic
cells have been proposed to exhibit several graph properties such as three
connectivity. These properties are consequences of underlying biophysical
constraints. A natural question for biologists is: what are the possible
networks for various combinations of those properties? In this paper, we
present novel SMT based encodings of the properties over vesicle traffic
systems and a tool that searches for the networks that satisfies the properties
using SMT solvers. In our experiments, we show that our tool can search for
networks of sizes that are considered to be relevant by biologists.
",1,0,0,0,0,0
19143,Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission,"  Multipartite viruses replicate through a puzzling evolutionary strategy.
Their genome is segmented into two or more parts, and encapsidated in separate
particles that appear to propagate independently. Completing the replication
cycle, however, requires the full genome, so that a persistent infection of a
host requires the concurrent presence of several particles. This represents an
apparent evolutionary drawback of multipartitism, while its advantages remain
unclear. A transition from monopartite to multipartite viral forms has been
described in vitro under conditions of high multiplicity of infection,
suggesting that cooperation between defective mutants is a plausible
evolutionary pathway towards multipartitism. However, it is unknown how the
putative advantages that multipartitism might enjoy affect its epidemiology, or
if an explicit advantage is needed to explain its ecological persistence. To
disentangle which mechanisms might contribute to the rise and fixation of
multipartitism, we here investigate the interaction between viral spreading
dynamics and host population structure. We set up a compartmental model of the
spread of a virus in its different forms and explore its epidemiology using
both analytical and numerical techniques. We uncover that the impact of host
contact structure on spreading dynamics entails a rich phenomenology of
ecological relationships that includes cooperation, competition, and
commensality. Furthermore, we find out that multipartitism might rise to
fixation even in the absence of explicit microscopic advantages. Multipartitism
allows the virus to colonize environments that could not be invaded by the
monopartite form, while homogeneous contacts between hosts facilitate its
spread. We conjecture that there might have been an increase in the diversity
and prevalence of multipartite viral forms concomitantly with the expansion of
agricultural practices.
",0,0,0,0,1,0
19144,Correlating Cell Shape and Cellular Stress in Motile Confluent Tissues,"  Collective cell migration is a highly regulated process involved in wound
healing, cancer metastasis and morphogenesis. Mechanical interactions among
cells provide an important regulatory mechanism to coordinate such collective
motion. Using a Self-Propelled Voronoi (SPV) model that links cell mechanics to
cell shape and cell motility, we formulate a generalized mechanical inference
method to obtain the spatio-temporal distribution of cellular stresses from
measured traction forces in motile tissues and show that such traction-based
stresses match those calculated from instantaneous cell shapes. We additionally
use stress information to characterize the rheological properties of the
tissue. We identify a motility-induced swim stress that adds to the interaction
stress to determine the global contractility or extensibility of epithelia. We
further show that the temporal correlation of the interaction shear stress
determines an effective viscosity of the tissue that diverges at the
liquid-solid transition, suggesting the possibility of extracting rheological
information directly from traction data.
",0,1,0,0,0,0
19145,Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks,"  We consider the problem of learning function classes computed by neural
networks with various activations (e.g. ReLU or Sigmoid), a task believed to be
computationally intractable in the worst-case. A major open problem is to
understand the minimal assumptions under which these classes admit provably
efficient algorithms. In this work we show that a natural distributional
assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields
polynomial-time algorithms in the non-realizable setting for expressive classes
of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on
the structure of the network or the labels. Given sufficiently-strong
polynomial eigenvalue decay, we obtain {\em fully}-polynomial time algorithms
in {\em all} the relevant parameters with respect to square-loss. Milder decay
assumptions also lead to improved algorithms. This is the first purely
distributional assumption that leads to polynomial-time algorithms for networks
of ReLUs, even with one hidden layer. Further, unlike prior distributional
assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has
been observed in practice on common data sets.
",1,0,0,0,0,0
19146,A New Steganographic Technique Matching the Secret Message and Cover image Binary Value,"  Steganography involves hiding a secret message or image inside another cover
image. Changes are made in the cover image without affecting visual quality of
the image. In contrast to cryptography, Steganography provides complete secrecy
of the communication. Security of very sensitive data can be enhanced by
combining cryptography and steganography. A new technique that uses the concept
of Steganography to obtain the position values from an image is suggested. This
paper proposes a new method where no change is made to the cover image, only
the pixel position LSB (Least Significant Bit) values that match with the
secret message bit values are noted in a separate position file. At the sending
end the position file along with the cover image is sent. At the receiving end
the position file is opened only with a secret key. The bit positions are taken
from the position file and the LSB values from the positions are combined to
get ASCII values and then form characters of the secret message
",1,0,0,0,0,0
19147,On topological fluid mechanics of non-ideal systems and virtual frozen-in dynamics,"  Euler and Navier-Stokes have variant systems with dynamical invariance of
helicity and thus (weak) topological equivalence, allowing a strong `frozen-in'
(to, or, dually, `Lie-carried' by the \textit{virtual} velocity $V$)
formulation of the vorticity with a flavor of `inverse Helmholtz theorem'. We
remark on the non-ideal (statistical) topological fluid mechanics (TFM) for (1)
the Constantin-Iyer formulation of Navier-Stokes, (2) our own extension of the
Gallavotti-Cohen type dynamical ensembles of modified Navier-Stokes with
energy-helicity constraints and (3) the Galerkin truncated Euler, as the
typical case variants with dynamical time reversibility and helicity
invariance. Ideal TFM is thus bridged with non-ideal flows. An example virtual
(Lie-)carrier of the vorticity in a Galerkin-truncated Euler system is
calculated to demonstrate the issue of determining $V$.
",0,1,0,0,0,0
19148,Topological AdS/CFT,"  We define a holographic dual to the Donaldson-Witten topological twist of
$\mathcal{N}=2$ gauge theories on a Riemannian four-manifold. This is described
by a class of asymptotically locally hyperbolic solutions to $\mathcal{N}=4$
gauged supergravity in five dimensions, with the four-manifold as conformal
boundary. Under AdS/CFT, minus the logarithm of the partition function of the
gauge theory is identified with the holographically renormalized supergravity
action. We show that the latter is independent of the metric on the boundary
four-manifold, as required for a topological theory. Supersymmetric solutions
in the bulk satisfy first order differential equations for a twisted $Sp(1)$
structure, which extends the quaternionic Kahler structure that exists on any
Riemannian four-manifold boundary. We comment on applications and extensions,
including generalizations to other topological twists.
",0,0,1,0,0,0
19149,Using highly uniform and smooth Selenium colloids as low-loss magnetodielectric building blocks of optical metafluids,"  We systematically analyzed magnetodielectric resonances of Se colloids for
the first time to exploit the possibility for use as building blocks of
all-dielectric optical metafluids. By taking synergistic advantages of Se
colloids, including (i) high-refractive-index at optical frequencies, (ii)
unprecedented structural uniformity, and (iii) versatile access to copious
quantities, the Kerker-type directional light scattering resulting from
efficient coupling between strong electric and magnetic resonances were
observed directly from Se colloidal suspension. Thus, the use of Se colloid as
a generic magnetodielectric building block highlights an opportunity for the
fluidic low-loss optical antenna, which can be processed via spin-coating and
painting.
",0,1,0,0,0,0
19150,A Vector Field Method for Radiating Black Hole Spacetimes,"  We develop a commuting vector field method for a general class of radiating
spacetimes. The metrics considered are certain long range perturbations of
Minkowski space including those constructed from global stability problems in
general relativity. Our method provides sharp peeling estimates for solutions
to both linear and nonlinear (null form) scalar fields.
",0,0,1,0,0,0
19151,Experimental Constraint on an Exotic Spin- and Velocity-Dependent Interaction in the Sub-meV Range of Axion Mass with a Spin-Exchange Relaxation-Free Magnetometer,"  We conducted a search for an exotic spin- and velocity-dependent interaction
for polarized electrons with an experimental approach based on a
high-sensitivity spin-exchange relaxation-free (SERF) magnetometer, which
serves as both a source of polarized electrons and a magnetic-field sensor. The
experiment aims to sensitively detect magnetic-fieldlike effects from the
exotic interaction between the polarized electrons in a SERF vapor cell and
unpolarized nucleons of a closely located solid-state mass. We report
experimental results on the interaction with 82 h of data averaging, which sets
an experimental limit on the coupling strength around $10^{-19}$ for the axion
mass $m_a \lesssim 10^{-3}$ eV, within the important axion window.
",0,1,0,0,0,0
19152,Generalization Error in Deep Learning,"  Deep learning models have lately shown great performance in various fields
such as computer vision, speech recognition, speech translation, and natural
language processing. However, alongside their state-of-the-art performance, it
is still generally unclear what is the source of their generalization ability.
Thus, an important question is what makes deep neural networks able to
generalize well from the training set to new data. In this article, we provide
an overview of the existing theory and bounds for the characterization of the
generalization error of deep neural networks, combining both classical and more
recent theoretical and empirical results.
",0,0,0,1,0,0
19153,Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation,"  Although block compressive sensing (BCS) makes it tractable to sense
large-sized images and video, its recovery performance has yet to be
significantly improved because its recovered images or video usually suffer
from blurred edges, loss of details, and high-frequency oscillatory artifacts,
especially at a low subrate. This paper addresses these problems by designing a
modified total variation technique that employs multi-block gradient
processing, a denoised Lagrangian multiplier, and patch-based sparse
representation. In the case of video, the proposed recovery method is able to
exploit both spatial and temporal similarities. Simulation results confirm the
improved performance of the proposed method for compressive sensing of images
and video in terms of both objective and subjective qualities.
",1,0,0,0,0,0
19154,A multi-phase-field method for surface tension-induced elasticity,"  A consistent treatment of the coupling of surface energy and elasticity
within the multi-phase- field framework is presented. The model accurately
reproduces stress distribution in a number of analytically tractable, yet
non-trivial, cases including different types of spherical heterogeneities and a
thin plate suspending in a gas environment. It is then used to study the stress
distribution inside elastic bodies with non-spherical geometries, such as a
solid ellipsoid and a sintered structure. In these latter cases, it is shown
that the interplay between deformation and spatially variable surface curvature
leads to heterogeneous stress distribution across the specimen.
",0,1,0,0,0,0
19155,On the restriction theorem for paraboloid in $\mathbb R^4$,"  We prove that recent breaking by Zahl of the $\frac32$ barrier in Wolff's
estimate on the Kakeya maximal operator in $\mathbb R^4$ leads to improving the
$\frac{14}{5}$ threshold for the restriction problem for the paraboloid in
$\mathbb R^4$. One of the ingredients is a new trilinear estimate. The proofs
are deliberately presented in a nontechnical and concise format, so as to make
the arguments more readable and focus attention on the key tools.
",0,0,1,0,0,0
19156,"Existence of Stein Kernels under a Spectral Gap, and Discrepancy Bound","  We establish existence of Stein kernels for probability measures on
$\mathbb{R}^d$ satisfying a Poincaré inequality, and obtain bounds on the
Stein discrepancy of such measures. Applications to quantitative central limit
theorems are discussed, including a new CLT in Wasserstein distance $W_2$ with
optimal rate and dependence on the dimension. As a byproduct, we obtain a
stability version of an estimate of the Poincaré constant of probability
measures under a second moment constraint. The results extend more generally to
the setting of converse weighted Poincaré inequalities. The proof is based on
simple arguments of calculus of variations.
Further, we establish two general properties enjoyed by the Stein
discrepancy, holding whenever a Stein kernel exists: Stein discrepancy is
strictly decreasing along the CLT, and it controls the skewness of a random
vector.
",1,0,1,0,0,0
19157,High-order asynchrony-tolerant finite difference schemes for partial differential equations,"  Synchronizations of processing elements (PEs) in massively parallel
simulations, which arise due to communication or load imbalances between PEs,
significantly affect the scalability of scientific applications. We have
recently proposed a method based on finite-difference schemes to solve partial
differential equations in an asynchronous fashion -- synchronization between
PEs is relaxed at a mathematical level. While standard schemes can maintain
their stability in the presence of asynchrony, their accuracy is drastically
affected. In this work, we present a general methodology to derive
asynchrony-tolerant (AT) finite difference schemes of arbitrary order of
accuracy, which can maintain their accuracy when synchronizations are relaxed.
We show that there are several choices available in selecting a stencil to
derive these schemes and discuss their effect on numerical and computational
performance. We provide a simple classification of schemes based on the stencil
and derive schemes that are representative of different classes. Their
numerical error is rigorously analyzed within a statistical framework to obtain
the overall accuracy of the solution. Results from numerical experiments are
used to validate the performance of the schemes.
",0,1,0,0,0,0
19158,Hazard Analysis and Risk Assessment for an Automated Unmanned Protective Vehicle,"  For future application of automated vehicles in public traffic, ensuring
functional safety is essential. In this context, a hazard analysis and risk
assessment is an important input for designing functionally vehicle automation
systems. In this contribution, we present a detailed hazard analysis and risk
assessment (HARA) according to the ISO 26262 standard for a specific Level 4
application, namely an unmanned protective vehicle operated without human
supervision for motorway hard shoulder roadworks.
",1,0,0,0,0,0
19159,Detailed experimental and numerical analysis of a cylindrical cup deep drawing: pros and cons of using solid-shell elements,"  The Swift test was originally proposed as a formability test to reproduce the
conditions observed in deep drawing operations. This test consists on forming a
cylindrical cup from a circular blank, using a flat bottom cylindrical punch
and has been extensively studied using both analytical and numerical methods.
This test can also be combined with the Demeri test, which consists in cutting
a ring from the wall of a cylindrical cup, in order to open it afterwards to
measure the springback. This combination allows their use as benchmark test, in
order to improve the knowledge concerning the numerical simulation models,
through the comparison between experimental and numerical results. The focus of
this study is the experimental and numerical analyses of the Swift cup test,
followed by the Demeri test, performed with an AA5754-O alloy at room
temperature. In this context, a detailed analysis of the punch force evolution,
the thickness evolution along the cup wall, the earing profile, the strain
paths and their evolution and the ring opening is performed. The numerical
simulation is performed using the finite element code ABAQUS, with solid and
solid-shell elements, in order to compare the computational efficiency of these
type of elements. The results show that the solid-shell element is more
cost-effective than the solid, presenting global accurate predictions, excepted
for the thinning zones. Both the von Mises and the Hill48 yield criteria
predict the strain distributions in the final cup quite accurately. However,
improved knowledge concerning the stress states is still required, because the
Hill48 criterion showed difficulties in the correct prediction of the
springback, whatever the type of finite element adopted.
",0,1,0,0,0,0
19160,Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters - Implications for cosmography,"  Strong gravitational lensing by galaxy clusters is a fundamental tool to
study dark matter and constrain the geometry of the Universe. Recently, the
Hubble Space Telescope Frontier Fields programme has allowed a significant
improvement of mass and magnification measurements but lensing models still
have a residual root mean square between 0.2 arcsec and few arcsec- onds, not
yet completely understood. Systematic errors have to be better understood and
treated in order to use strong lensing clusters as reliable cosmological
probes. We have analysed two simulated Hubble-Frontier-Fields-like clusters
from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use
several estimators (relative bias on magnification, den- sity profiles,
ellipticity and orientation) to quantify the goodness of our reconstructions by
comparing our multiple models, optimized with the parametric software LENSTOOL
, with the input models. We have quantified the impact of systematic errors
arising, first, from the choice of different density profiles and
configurations and, secondly, from the availability of con- straints
(spectroscopic or photometric redshifts, redshift ranges of the background
sources) in the parametric modelling of strong lensing galaxy clusters and
therefore on the retrieval of cosmological parameters. We find that
substructures in the outskirts have a significant im- pact on the position of
the multiple images, yielding tighter cosmological contours. The need for
wide-field imaging around massive clusters is thus reinforced. We show that
competitive cosmological constraints can be obtained also with complex
multimodal clusters and that photometric redshifts improve the constraints on
cosmological parameters when considering a narrow range of (spectroscopic)
redshifts for the sources.
",0,1,0,0,0,0
19161,A Note on the Spectral Transfer Morphisms for Affine Hecke Algebras,"  E. Opdam introduced the tool of spectral transfer morphism (STM) of affine
Hecke algebras to study the formal degrees of unipotent discrete series
representations. He established a uniqueness property of STM for the affine
Hecke algebras associated of unipotent discrete series representations. Based
on this result, Opdam gave an explanation for Lusztig's arithmetic/geometric
correspondence (in Lusztig's classification of unipotent representations of
$p$-adic adjoint simple groups) in terms of harmonic analysis, and partitioned
the unipotent discrete series representations into $L$-packets based on the
Lusztig-Langlands parameters. The present paper provides some omitted details
for the argument of the uniqueness property of STM. In the last section, we
prove that three finite morphisms of algebraic tori are spectral transfer
morphisms, and hence complete the proof of the uniqueness property.
",0,0,1,0,0,0
19162,Batch-Expansion Training: An Efficient Optimization Framework,"  We propose Batch-Expansion Training (BET), a framework for running a batch
optimizer on a gradually expanding dataset. As opposed to stochastic
approaches, batches do not need to be resampled i.i.d. at every iteration, thus
making BET more resource efficient in a distributed setting, and when
disk-access is constrained. Moreover, BET can be easily paired with most batch
optimizers, does not require any parameter-tuning, and compares favorably to
existing stochastic and batch methods. We show that when the batch size grows
exponentially with the number of outer iterations, BET achieves optimal
$O(1/\epsilon)$ data-access convergence rate for strongly convex objectives.
Experiments in parallel and distributed settings show that BET performs better
than standard batch and stochastic approaches.
",1,0,0,0,0,0
19163,Coordinate Descent with Bandit Sampling,"  Coordinate descent methods usually minimize a cost function by updating a
random decision variable (corresponding to one coordinate) at a time. Ideally,
we would update the decision variable that yields the largest decrease in the
cost function. However, finding this coordinate would require checking all of
them, which would effectively negate the improvement in computational
tractability that coordinate descent is intended to afford. To address this, we
propose a new adaptive method for selecting a coordinate. First, we find a
lower bound on the amount the cost function decreases when a coordinate is
updated. We then use a multi-armed bandit algorithm to learn which coordinates
result in the largest lower bound by interleaving this learning with
conventional coordinate descent updates except that the coordinate is selected
proportionately to the expected decrease. We show that our approach improves
the convergence of coordinate descent methods both theoretically and
experimentally.
",1,0,0,1,0,0
19164,Closing the gap for pseudo-polynomial strip packing,"  The set of 2-dimensional packing problems builds an important class of
optimization problems and Strip Packing together with 2-dimensional Bin Packing
and 2-dimensional Knapsack is one of the most famous of these problems. Given a
set of rectangular axis parallel items and a strip with bounded width and
infinite height the objective is to find a packing of the items into the strip
which minimizes the packing height. We speak of pseudo-polynomial Strip Packing
if we consider algorithms with pseudo-polynomial running time with respect to
the width of the strip.
It is known that there is no pseudo-polynomial algorithm for Strip Packing
with a ratio better than $5/4$ unless $\mathrm{P} = \mathrm{NP}$. The best
algorithm so far has a ratio of $(4/3 + \varepsilon)$. In this paper, we close
this gap between inapproximability result and best known algorithm by
presenting an algorithm with approximation ratio $(5/4 + \varepsilon)$ and thus
categorize the problem accurately. The algorithm uses a structural result which
states that each optimal solution can be transformed such that it has one of a
polynomial number of different forms. The strength of this structural result is
that it applies to other problem settings as well for example to Strip Packing
with rotations (90 degrees) and Contiguous Moldable Task Scheduling. This fact
enabled us to present algorithms with approximation ratio $(5/4 + \varepsilon)$
for these problems as well.
",1,0,0,0,0,0
19165,"Notes on the replica symmetric solution of the classical and quantum SK model, including the matrix of second derivatives and the spin glass susceptibility","  A review of the replica symmetric solution of the classical and quantum,
infinite-range, Sherrington-Kirkpatrick spin glass is presented.
",0,1,0,0,0,0
19166,Self-supervised Knowledge Distillation Using Singular Value Decomposition,"  To solve deep neural network (DNN)'s huge training dataset and its high
computation issue, so-called teacher-student (T-S) DNN which transfers the
knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN
has limited range of use, and the knowledge of T-DNN is insufficiently
transferred to S-DNN. To improve the quality of the transferred knowledge from
T-DNN, we propose a new knowledge distillation using singular value
decomposition (SVD). In addition, we define a knowledge transfer as a
self-supervised task and suggest a way to continuously receive information from
T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of
the T-DNN can be up to 1.1\% better than the T-DNN in terms of classification
accuracy. Also assuming the same computational cost, our S-DNN outperforms the
S-DNN driven by the state-of-the-art distillation with a performance advantage
of 1.79\%. code is available on this https URL\_SVD.
",0,0,0,1,0,0
19167,Can justice be fair when it is blind? How social network structures can promote or prevent the evolution of despotism,"  Hierarchy is an efficient way for a group to organize, but often goes along
with inequality that benefits leaders. To control despotic behaviour, followers
can assess leaders decisions by aggregating their own and their neighbours
experience, and in response challenge despotic leaders. But in hierarchical
social networks, this interactional justice can be limited by (i) the high
influence of a small clique who are treated better, and (ii) the low
connectedness of followers. Here we study how the connectedness of a social
network affects the co-evolution of despotism in leaders and tolerance to
despotism in followers. We simulate the evolution of a population of agents,
where the influence of an agent is its number of social links. Whether a leader
remains in power is controlled by the overall satisfaction of group members, as
determined by their joint assessment of the leaders behaviour. We demonstrate
that centralization of a social network around a highly influential clique
greatly increases the level of despotism. This is because the clique is more
satisfied, and their higher influence spreads their positive opinion of the
leader throughout the network. Finally, our results suggest that increasing the
connectedness of followers limits despotism while maintaining hierarchy.
",0,0,0,0,1,0
19168,GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization,"  Bioinformatics tools have been developed to interpret gene expression data at
the gene set level, and these gene set based analyses improve the biologists'
capability to discover functional relevance of their experiment design. While
elucidating gene set individually, inter gene sets association is rarely taken
into consideration. Deep learning, an emerging machine learning technique in
computational biology, can be used to generate an unbiased combination of gene
set, and to determine the biological relevance and analysis consistency of
these combining gene sets by leveraging large genomic data sets. In this study,
we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
with the incorporation of a priori defined gene sets that retain the crucial
biological features in the latent layer. We introduced the concept of the gene
superset, an unbiased combination of gene sets with weights trained by the
autoencoder, where each node in the latent layer is a superset. Trained with
genomic data from TCGA and evaluated with their accompanying clinical
parameters, we showed gene supersets' ability of discriminating tumor subtypes
and their prognostic capability. We further demonstrated the biological
relevance of the top component gene sets in the significant supersets. Using
autoencoder model and gene superset at its latent layer, we demonstrated that
gene supersets retain sufficient biological information with respect to tumor
subtypes and clinical prognostic significance. Superset also provides high
reproducibility on survival analysis and accurate prediction for cancer
subtypes.
",0,0,0,1,1,0
19169,Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter,"  Given a nonconvex function that is an average of $n$ smooth functions, we
design stochastic first-order methods to find its approximate stationary
points. The convergence of our new methods depends on the smallest (negative)
eigenvalue $-\sigma$ of the Hessian, a parameter that describes how nonconvex
the function is.
Our methods outperform known results for a range of parameter $\sigma$, and
can be used to find approximate local minima. Our result implies an interesting
dichotomy: there exists a threshold $\sigma_0$ so that the currently fastest
methods for $\sigma>\sigma_0$ and for $\sigma<\sigma_0$ have different
behaviors: the former scales with $n^{2/3}$ and the latter scales with
$n^{3/4}$.
",1,0,1,1,0,0
19170,Higher-Order Bounded Model Checking,"  We present a Bounded Model Checking technique for higher-order programs. The
vehicle of our study is a higher-order calculus with general references. Our
technique is a symbolic state syntactical translation based on SMT solvers,
adapted to a setting where the values passed and stored during computation can
be functions of arbitrary order. We prove that our algorithm is sound, and
devise an optimisation based on points-to analysis to improve scalability. We
moreover provide a prototype implementation of the algorithm with experimental
results showcasing its performance.
",1,0,0,0,0,0
19171,Smoothing for the fractional Schrodinger equation on the torus and the real line,"  In this paper we study the cubic fractional nonlinear Schrodinger equation
(NLS) on the torus and on the real line. Combining the normal form and the
restricted norm methods we prove that the nonlinear part of the solution is
smoother than the initial data. Our method applies to both focusing and
defocusing nonlinearities. In the case of full dispersion (NLS) and on the
torus, the gain is a full derivative, while on the real line we get a
derivative smoothing with an $\epsilon$ loss. Our result lowers the regularity
requirement of a recent theorem of Kappeler et al. on the periodic defocusing
cubic NLS, and extends it to the focusing case and to the real line. We also
obtain estimates on the higher order Sobolev norms of the global smooth
solutions in the defocusing case.
",0,0,1,0,0,0
19172,Deep Learning Based Cryptographic Primitive Classification,"  Cryptovirological augmentations present an immediate, incomparable threat.
Over the last decade, the substantial proliferation of crypto-ransomware has
had widespread consequences for consumers and organisations alike. Established
preventive measures perform well, however, the problem has not ceased. Reverse
engineering potentially malicious software is a cumbersome task due to platform
eccentricities and obfuscated transmutation mechanisms, hence requiring
smarter, more efficient detection strategies. The following manuscript presents
a novel approach for the classification of cryptographic primitives in compiled
binary executables using deep learning. The model blueprint, a DCNN, is
fittingly configured to learn from variable-length control flow diagnostics
output from a dynamic trace. To rival the size and variability of contemporary
data compendiums, hence feeding the model cognition, a methodology for the
procedural generation of synthetic cryptographic binaries is defined, utilising
core primitives from OpenSSL with multivariate obfuscation, to draw a vastly
scalable distribution. The library, CryptoKnight, rendered an algorithmic pool
of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are
automatically fed in its core model. Converging at 91% accuracy, CryptoKnight
is successfully able to classify the sample algorithms with minimal loss.
",1,0,0,0,0,0
19173,"The Alexandrov-Fenchel type inequalities, revisited","  Various Alexandrov-Fenchel type inequalities have appeared and played
important roles in convex geometry, matrix theory and complex algebraic
geometry. It has been noticed for some time that they share some striking
analogies and have intimate relationships. The purpose of this article is to
shed new light on this by comparatively investigating them in several aspects.
\emph{The principal result} in this article is a complete solution to the
equality characterization problem of various Alexandrov-Fenchel type
inequalities for intersection numbers of nef and big classes on compact
Kähler manifolds, extending earlier results of Boucksom-Favre-Jonsson,
Fu-Xiao and Xiao-Lehmann. Our proof combines a result of Dinh-Nguyên on
Kähler geometry and an idea in convex geometry tracing back to Shephard. In
addition to this central result, we also give a geometric proof of the complex
version of the Alexandrov-Fenchel type inequality for mixed discriminants and a
determinantal type generalization of various Alexandrov-Fenchel type
inequalities.
",0,0,1,0,0,0
19174,Development of non-modal shear induced instabilities in atmospheric tornadoes,"  In this paper we consider the role of nonmodal instabilities in the dynamics
of atmospheric tornadoes. For this purpose we consider the Euler equation,
continuity equation and the equation of state and linearise them. As an example
we study several different velocity profiles: the so-called Rankine vortex
model; the Burgers-Rott vortex model; Sullivan and modified Sullivan vortex
models. It has been shown that in the two dimensional Rankine vortex model no
instability appears in the inner region of a tornado. On the contrary, outside
this area the physical system undergoes strong exponential instability. We have
found that initially perturbed velocity components lead to amplified sound wave
excitations. The similar results have been shown in Burgers-Rott vortex model
as well. As it was numerically estimated, in this case, the unstable wave
increases its energy by a factor of $400$ only in $\sim 0.5$min. According to
the numerical study, in Sullivan and modified Sullivan models, the instability
does not differ much by the growth. Despite the fact that in the inner area the
exponential instability does not appear in a purely two dimensional case, we
have found that in the modified Sullivan vortex even a small contribution from
vertical velocities can drive unstable nonmodal waves.
",0,1,0,0,0,0
19175,DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling,"  Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.
",1,0,0,0,0,0
19176,Time-Inhomogeneous Branching Processes Conditioned on Non-Extinction,"  In this paper, we consider time-inhomogeneous branching processes and
time-inhomogeneous birth-and-death processes, in which the offspring
distribution and birth and death rates (respectively) vary in time. A classical
result of branching processes states that in the critical regime, a process
conditioned on non-extinction and normalized will converge in distribution to a
standard exponential. In a paper of Jagers, time-inhomogeneous branching
processes are shown to exhibit this convergence as well. In this paper, the
hypotheses of Jagers' result are relaxed, further hypotheses are presented for
convergence in moments, and the result is extended to the continuous-time
analogue of time-inhomogeneous birth-and-death processes. In particular, the
new hypotheses suggest a simple characterization of the critical regime.
",0,0,1,0,0,0
19177,The Dirichlet-to-Neumann operator for quantum graphs,"  For a compact, connected metric graphs with a boundary that consists of $k$
vertices, we prove that an arbitrary symmetric $k\times k$ matrix with real
entries can be realized as the Dirichlet-to-Neumann operator for the Laplacian
plus a constant.
",0,0,1,0,0,0
19178,A study of the dual problem of the one-dimensional L-infinity optimal transport problem with applications,"  The Monge-Kantorovich problem for the infinite Wasserstein distance presents
several peculiarities. Among them the lack of convexity and then of a direct
duality. We study in dimension 1 the dual problem introduced by Barron, Bocea
and Jensen. We construct a couple of Kantorovich potentials which is ""as less
trivial as possible"". More precisely, we build a potential which is non
constant around any point that the plan which is locally optimal moves at
maximal distance. As an application, we show that the set of points which are
displaced to maximal distance by a locally optimal transport plan is minimal.
",0,0,1,0,0,0
19179,Strong coupling Bose polarons in a BEC,"  We use a non-perturbative renormalization group approach to develop a unified
picture of the Bose polaron problem, where a mobile impurity is strongly
interacting with a surrounding Bose-Einstein condensate (BEC). A detailed
theoretical analysis of the phase diagram is presented and the
polaron-to-molecule transition is discussed. For attractive polarons we argue
that a description in terms of an effective Fröhlich Hamiltonian with
renormalized parameters is possible. Its strong coupling regime is realized
close to a Feshbach resonance, where we predict a sharp increase of the
effective mass. Already for weaker interactions, before the polaron mass
diverges, we predict a transition to a regime where states exist below the
polaron energy and the attractive polaron is no longer the ground state. On the
repulsive side of the Feshbach resonance we recover the repulsive polaron,
which has a finite lifetime because it can decay into low-lying molecular
states. We show for the entire range of couplings that the polaron energy has
logarithmic corrections in comparison with predictions by the mean-field
approach. We demonstrate that they are a consequence of the polaronic mass
renormalization which is due to quantum fluctuations of correlated phonons in
the polaron cloud.
",0,1,0,0,0,0
19180,Triangle packing in (sparse) tournaments: approximation and kernelization,"  Given a tournament T and a positive integer k, the C_3-Pakcing-T problem asks
if there exists a least k (vertex-)disjoint directed 3-cycles in T. This is the
dual problem in tournaments of the classical minimal feedback vertex set
problem. Surprisingly C_3-Pakcing-T did not receive a lot of attention in the
literature. We show that it does not admit a PTAS unless P=NP, even if we
restrict the considered instances to sparse tournaments, that is tournaments
with a feedback arc set (FAS) being a matching. Focusing on sparse tournaments
we provide a (1+6/(c-1)) approximation algorithm for sparse tournaments having
a linear representation where all the backward arcs have ""length"" at least c.
Concerning kernelization, we show that C_3-Pakcing-T admits a kernel with O(m)
vertices, where m is the size of a given feedback arc set. In particular, we
derive a O(k) vertices kernel for C_3-Pakcing-T when restricted to sparse
instances. On the negative size, we show that C_3-Pakcing-T does not admit a
kernel of (total bit) size O(k^{2-\epsilon}) unless NP is a subset of coNP /
Poly. The existence of a kernel in O(k) vertices for C_3-Pakcing-T remains an
open question.
",1,0,0,0,0,0
19181,"Asymptotics of Chebyshev Polynomials, II. DCT Subsets of $\mathbb{R}$","  We prove Szegő-Widom asymptotics for the Chebyshev polynomials of a
compact subset of $\mathbb{R}$ which is regular for potential theory and obeys
the Parreau-Widom and DCT conditions.
",0,0,1,0,0,0
19182,Tracking Urban Human Activity from Mobile Phone Calling Patterns,"  Timings of human activities are marked by circadian clocks which in turn are
entrained to different environmental signals. In an urban environment the
presence of artificial lighting and various social cues tend to disrupt the
natural entrainment with the sunlight. However, it is not completely understood
to what extent this is the case. Here we exploit the large-scale data analysis
techniques to study the mobile phone calling activity of people in large cities
to infer the dynamics of urban daily rhythms. From the calling patterns of
about 1,000,000 users spread over different cities but lying inside the same
time-zone, we show that the onset and termination of the calling activity
synchronizes with the east-west progression of the sun. We also find that the
onset and termination of the calling activity of users follows a yearly
dynamics, varying across seasons, and that its timings are entrained to solar
midnight. Furthermore, we show that the average mid-sleep time of people living
in urban areas depends on the age and gender of each cohort as a result of
biological and social factors.
",0,1,0,0,0,0
19183,Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues' formula,"  In computational 3D geometric problems involving rotations, it is often that
people have to convert back and forth between a rotational matrix and a
rotation described by an axis and a corresponding angle. For this purpose,
Rodrigues' rotation formula is a very popular expression to use because of its
simplicity and efficiency. Nevertheless, while converting a rotation matrix to
an axis of rotation and the rotation angle, there exists ambiguity. Further
judgement or even manual interference may be necessary in some situations. An
extension of the Rodrigues' formula helps to find the sine and cosine values of
the rotation angle with respect to a given rotation axis is found and this
simple extension may help to accelerate many applications.
",1,0,0,0,0,0
19184,Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants,"  Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that
provides non-invasive and continuous evaluation of regional tissue oxygenation
in real-time. In recent years, NIRS has show promise as a useful monitoring
technology to help detect relative tissue ischemia that could lead to
significant morbidity and mortality in preterm infants. However, some issues
inherent in NIRS technology use on neonates, such as wide fluctuation in
signals, signal dropout and low limit of detection of the device, pose
challenges that may obscure reliable interpretation of the NIRS measurements
using current methods of analysis. In this paper, we propose new statistical
methods to analyse mesenteric rSO2 (regional oxygenation) produced by NIRS to
evaluate oxygenation in intestinal tissues and investigate oxygenation response
to red blood cell transfusion (RBC) in preterm infants. We present a mean area
under the curve (MAUC) measure and a slope measure to capture the mean rSO2
level and temporal trajectory of rSO2, respectively. Estimation methods are
developed for these measures and nonparametric testing procedures are proposed
to detect RBC-related changes in mesenteric oxygenation in preterm infants.
Through simulation studies, we show that the proposed methods demonstrate
improved accuracy in characterizing the mean level and changing pattern of
mesenteric rSO2 and also increased statistical power in detecting RBC-related
changes, as compared with standard approaches. We apply our methods to a NIRS
study in preterm infants receiving RBC transfusion from Emory Univerity to
evaluate the pre- and post-transfusion mesenteric oxygenation in preterm
infants.
",0,0,0,1,0,0
19185,To understand deep learning we need to understand kernel learning,"  Generalization performance of classifiers in deep learning has recently
become a subject of intense study. Deep models, typically over-parametrized,
tend to fit the training data exactly. Despite this ""overfitting"", they perform
well on test data, a phenomenon not yet fully understood.
The first point of our paper is that strong performance of overfitted
classifiers is not a unique feature of deep learning. Using six real-world and
two synthetic datasets, we establish experimentally that kernel machines
trained to have zero classification or near zero regression error perform very
well on test data, even when the labels are corrupted with a high level of
noise. We proceed to give a lower bound on the norm of zero loss solutions for
smooth kernels, showing that they increase nearly exponentially with data size.
We point out that this is difficult to reconcile with the existing
generalization bounds. Moreover, none of the bounds produce non-trivial results
for interpolating solutions.
Second, we show experimentally that (non-smooth) Laplacian kernels easily fit
random labels, a finding that parallels results for ReLU neural networks. In
contrast, fitting noisy data requires many more epochs for smooth Gaussian
kernels. Similar performance of overfitted Laplacian and Gaussian classifiers
on test, suggests that generalization is tied to the properties of the kernel
function rather than the optimization process.
Certain key phenomena of deep learning are manifested similarly in kernel
methods in the modern ""overfitted"" regime. The combination of the experimental
and theoretical results presented in this paper indicates a need for new
theoretical ideas for understanding properties of classical kernel methods. We
argue that progress on understanding deep learning will be difficult until more
tractable ""shallow"" kernel methods are better understood.
",0,0,0,1,0,0
19186,"Atomic Data Revisions for Transitions Relevant to Observations of Interstellar, Circumgalactic, and Intergalactic Matter","  Measurements of element abundances in galaxies from astrophysical
spectroscopy depend sensitively on the atomic data used. With the goal of
making the latest atomic data accessible to the community, we present a
compilation of selected atomic data for resonant absorption lines at
wavelengths longward of 911.753 {\AA} (the \ion{H}{1} Lyman limit), for key
heavy elements (heavier than atomic number 5) of astrophysical interest. In
particular, we focus on the transitions of those ions that have been observed
in the Milky Way interstellar medium (ISM), the circumgalactic medium (CGM) of
the Milky Way and/or other galaxies, and the intergalactic medium (IGM).
We provide wavelengths, oscillator strengths, associated accuracy grades, and
references to the oscillator strength determinations. We also attempt to
compare and assess the recent oscillator strength determinations. For about
22\% of the lines that have updated oscillator strength values, the differences
between the former values and the updated ones are $\gtrsim$~0.1 dex.
Our compilation will be a useful resource for absorption line studies of the
ISM, as well as studies of the CGM and IGM traced by sight lines to quasars and
gamma-ray bursts. Studies (including those enabled by future generations of
extremely large telescopes) of absorption by galaxies against the light of
background galaxies will also benefit from our compilation.
",0,1,0,0,0,0
19187,Using Nonlinear Normal Modes for Execution of Efficient Cyclic Motions in Soft Robots,"  With the aim of getting closer to the performance of the animal
muscleskeletal system, elastic elements are purposefully introduced in the
mechanical structure of soft robots. Indeed, previous works have extensively
shown that elasticity can endow robots with the ability of performing tasks
with increased efficiency, peak performances, and mechanical robustness.
However, despite the many achievements, a general theory of efficient motions
in soft robots is still lacking. Most of the literature focuses on specific
examples, or imposes a prescribed behavior through dynamic cancellations, thus
defeating the purpose of introducing elasticity in the first place. This paper
aims at making a step towards establishing such a general framework. To this
end, we leverage on the theory of oscillations in nonlinear dynamical systems,
and we take inspiration from state of the art theories about how the human
central nervous system manages the muscleskeletal system. We propose to
generate regular and efficient motions in soft robots by stabilizing
sub-manifolds of the state space on which the system would naturally evolve. We
select these sub-manifolds as the nonlinear continuation of linear eigenspaces,
called nonlinear normal modes. In such a way, efficient oscillatory behaviors
can be excited. We show the effectiveness of the methods in simulations on an
elastic inverted pendulum, and experimentally on a segmented elastic leg.
",1,0,0,0,0,0
19188,Efficient Deep Learning on Multi-Source Private Data,"  Machine learning models benefit from large and diverse datasets. Using such
datasets, however, often requires trusting a centralized data aggregator. For
sensitive applications like healthcare and finance this is undesirable as it
could compromise patient privacy or divulge trade secrets. Recent advances in
secure and privacy-preserving computation, including trusted hardware enclaves
and differential privacy, offer a way for mutually distrusting parties to
efficiently train a machine learning model without revealing the training data.
In this work, we introduce Myelin, a deep learning framework which combines
these privacy-preservation primitives, and use it to establish a baseline level
of performance for fully private machine learning.
",0,0,0,1,0,0
19189,Analytical solution of the integral equation for partial wave Coulomb t-matrices at excited-state energy,"  Starting from the integral representation of the three-dimensional Coulomb
transition matrix elaborated by us formerly with the use of specific symmetry
of the interaction in a four-dimensional Euclidean space introduced by Fock,
the possibility of the analytical solving of the integral equation for the
partial wave transition matrices at the excited bound state energy has been
studied. New analytical expressions for the partial s-, p- and d-wave Coulomb
t-matrices for like-charged particles and the expression for the partial d-wave
t-matrix for unlike-charged particles at the energy of the first excited bound
state have been derived.
",0,1,0,0,0,0
19190,Equilateral $p$-gons in $\mathbb R^d$ and deformed spheres and mod $p$ Fadell-Husseini index,"  We introduce the concept of $r$-equilateral $m$-gons. We prove the existence
of $r$-equilateral $p$-gons in $\mathbb R^d$ if $r<d$ and the existence of
equilateral $p$-gons in the image of continuous injective maps $f:S^d\to
\mathbb R^{d+1}$. Our ideas are based mainly in the paper of Y. Soibelman
\cite{soibelman}, in which the topological Borsuk number of $\mathbb{R}^2$ is
calculated by means of topological methods and the paper of P. Blagojević and
G. Ziegler \cite{blagojevictetrahedra} where Fadell-Husseini index is used for
solving a problem related to the topological Borsuk problem for $\mathbb{R}^3$.
",0,0,1,0,0,0
19191,Jónsson posets,"  According to Kearnes and Oman (2013), an ordered set $P$ is \emph{Jónsson}
if it is infinite and the cardinality of every proper initial segment of $P$ is
strictly less than the cardinaliy of $P$. We examine the structure of Jónsson
posets.
",0,0,1,0,0,0
19192,Hypotheses testing on infinite random graphs,"  Drawing on some recent results that provide the formalism necessary to
definite stationarity for infinite random graphs, this paper initiates the
study of statistical and learning questions pertaining to these objects.
Specifically, a criterion for the existence of a consistent test for complex
hypotheses is presented, generalizing the corresponding results on time series.
As an application, it is shown how one can test that a tree has the Markov
property, or, more generally, to estimate its memory.
",1,0,1,1,0,0
19193,On compact splitting complex submanifolds of quotients of bounded symmetric domains,"  In the current article our primary objects of study are compact complex
submanifolds of quotient manifolds of irreducible bounded symmetric domains by
torsion free discrete lattices of automorphisms. We are interested in the
characterization of the totally geodesic submanifolds among compact splitting
complex submanifolds, i.e. under the assumption that the tangent sequence
splits holomorphically over the submanifold.
",0,0,1,0,0,0
19194,Octupolar Tensors for Liquid Crystals,"  A third-order three-dimensional symmetric traceless tensor, called the
\emph{octupolar} tensor, has been introduced to study tetrahedratic nematic
phases in liquid crystals. The octupolar \emph{potential}, a scalar-valued
function generated on the unit sphere by that tensor, should ideally have four
maxima capturing the most probable molecular orientations (on the vertices of a
tetrahedron), but it was recently found to possess an equally generic variant
with \emph{three} maxima instead of four. It was also shown that the
irreducible admissible region for the octupolar tensor in a three-dimensional
parameter space is bounded by a dome-shaped surface, beneath which is a
\emph{separatrix} surface connecting the two generic octupolar states. The
latter surface, which was obtained through numerical continuation, may be
physically interpreted as marking a possible \emph{intra-octupolar} transition.
In this paper, by using the resultant theory of algebraic geometry and the
E-characteristic polynomial of spectral theory of tensors, we give a
closed-form, algebraic expression for both the dome-shaped surface and the
separatrix surface. This turns the envisaged intra-octupolar transition into a
quantitative, possibly observable prediction. Some other properties of
octupolar tensors are also studied.
",0,0,1,0,0,0
19195,Efficient boundary corrected Strang splitting,"  Strang splitting is a well established tool for the numerical integration of
evolution equations. It allows the application of tailored integrators for
different parts of the vector field. However, it is also prone to order
reduction in the case of non-trivial boundary conditions. This order reduction
can be remedied by correcting the boundary values of the intermediate splitting
step. In this paper, three different approaches for constructing such a
correction in the case of inhomogeneous Dirichlet, Neumann, and mixed boundary
conditions are presented. Numerical examples that illustrate the effectivity
and benefits of these corrections are included.
",0,1,0,0,0,0
19196,SOS-convex Semi-algebraic Programs and its Applications to Robust Optimization: A Tractable Class of Nonsmooth Convex Optimization,"  In this paper, we introduce a new class of nonsmooth convex functions called
SOS-convex semialgebraic functions extending the recently proposed notion of
SOS-convex polynomials. This class of nonsmooth convex functions covers many
common nonsmooth functions arising in the applications such as the Euclidean
norm, the maximum eigenvalue function and the least squares functions with
$\ell_1$-regularization or elastic net regularization used in statistics and
compressed sensing. We show that, under commonly used strict feasibility
conditions, the optimal value and an optimal solution of SOS-convex
semi-algebraic programs can be found by solving a single semi-definite
programming problem (SDP). We achieve the results by using tools from
semi-algebraic geometry, convex-concave minimax theorem and a recently
established Jensen inequality type result for SOS-convex polynomials. As an
application, we outline how the derived results can be applied to show that
robust SOS-convex optimization problems under restricted spectrahedron data
uncertainty enjoy exact SDP relaxations. This extends the existing exact SDP
relaxation result for restricted ellipsoidal data uncertainty and answers the
open questions left in [Optimization Letters 9, 1-18(2015)] on how to recover a
robust solution from the semi-definite programming relaxation in this broader
setting.
",0,0,1,0,0,0
19197,An Uncertainty Principle for Estimates of Floquet Multipliers,"  We derive a Cramér-Rao lower bound for the variance of Floquet multiplier
estimates that have been constructed from stable limit cycles perturbed by
noise. To do so, we consider perturbed periodic orbits in the plane. We use a
periodic autoregressive process to model the intersections of these orbits with
cross sections, then passing to the limit of a continuum of sections to obtain
a bound that depends on the continuous flow restricted to the (nontrivial)
Floquet mode. We compare our bound against the empirical variance of estimates
constructed using several cross sections. The section-based estimates are close
to being optimal. We posit that the utility of our bound persists in higher
dimensions when computed along Floquet modes for real and distinct multipliers.
Our bound elucidates some of the empirical observations noted in the
literature; e.g., (a) it is the number of cycles (as opposed to the frequency
of observations) that drives the variance of estimates to zero, and (b) the
estimator variance has a positive lower bound as the noise amplitude tends to
zero.
",0,0,0,1,0,0
19198,Shielding Google's language toxicity model against adversarial attacks,"  Lack of moderation in online communities enables participants to incur in
personal aggression, harassment or cyberbullying, issues that have been
accentuated by extremist radicalisation in the contemporary post-truth politics
scenario. This kind of hostility is usually expressed by means of toxic
language, profanity or abusive statements. Recently Google has developed a
machine-learning-based toxicity model in an attempt to assess the hostility of
a comment; unfortunately, it has been suggested that said model can be deceived
by adversarial attacks that manipulate the text sequence of the comment. In
this paper we firstly characterise such adversarial attacks as using
obfuscation and polarity transformations. The former deceives by corrupting
toxic trigger content with typographic edits, whereas the latter deceives by
grammatical negation of the toxic content. Then, we propose a two--stage
approach to counter--attack these anomalies, bulding upon a recently proposed
text deobfuscation method and the toxicity scoring model. Lastly, we conducted
an experiment with approximately 24000 distorted comments, showing how in this
way it is feasible to restore toxicity of the adversarial variants, while
incurring roughly on a twofold increase in processing time. Even though novel
adversary challenges would keep coming up derived from the versatile nature of
written language, we anticipate that techniques combining machine learning and
text pattern recognition methods, each one targeting different layers of
linguistic features, would be needed to achieve robust detection of toxic
language, thus fostering aggression--free digital interaction.
",1,0,0,0,0,0
19199,Diagrammatic Monte-Carlo for weak-coupling expansion of non-Abelian lattice field theories: large-N U(N)xU(N) principal chiral model,"  We develop numerical tools for Diagrammatic Monte-Carlo simulations of
non-Abelian lattice field theories in the t'Hooft large-N limit based on the
weak-coupling expansion. First we note that the path integral measure of such
theories contributes a bare mass term in the effective action which is
proportional to the bare coupling constant. This mass term renders the
perturbative expansion infrared-finite and allows to study it directly in the
large-N and infinite-volume limits using the Diagrammatic Monte-Carlo approach.
On the exactly solvable example of a large-N O(N) sigma model in D=2 dimensions
we show that this infrared-finite weak-coupling expansion contains, in addition
to powers of bare coupling, also powers of its logarithm, reminiscent of
re-summed perturbation theory in thermal field theory and resurgent
trans-series without exponential terms. We numerically demonstrate the
convergence of these double series to the manifestly non-perturbative dynamical
mass gap. We then develop a Diagrammatic Monte-Carlo algorithm for sampling
planar diagrams in the large-N matrix field theory, and apply it to study this
infrared-finite weak-coupling expansion for large-N U(N)xU(N) nonlinear sigma
model (principal chiral model) in D=2. We sample up to 12 leading orders of the
weak-coupling expansion, which is the practical limit set by the increasingly
strong sign problem at high orders. Comparing Diagrammatic Monte-Carlo with
conventional Monte-Carlo simulations extrapolated to infinite N, we find a good
agreement for the energy density as well as for the critical temperature of the
""deconfinement"" transition. Finally, we comment on the applicability of our
approach to planar QCD at zero and finite density.
",0,1,0,0,0,0
19200,Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks,"  There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information learned from a large
and accurate teacher network. We propose to use conditional adversarial
networks to learn the loss function to transfer knowledge from teacher to
student. The proposed method is particularly effective for relatively small
student networks. Moreover, experimental results show the effect of network
size when the modern networks are used as student. We empirically study the
trade-off between inference time and classification accuracy, and provide
suggestions on choosing a proper student network.
",1,0,0,0,0,0
19201,Transductive Zero-Shot Learning with Adaptive Structural Embedding,"  Zero-shot learning (ZSL) endows the computer vision system with the
inferential capability to recognize instances of a new category that has never
seen before. Two fundamental challenges in it are visual-semantic embedding and
domain adaptation in cross-modality learning and unseen class prediction steps,
respectively. To address both challenges, this paper presents two corresponding
methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective
Strategy (SPASS), respectively. Specifically, ASTE formulates the
visualsemantic interactions in a latent structural SVM framework to adaptively
adjust the slack variables to embody the different reliableness among training
instances. In this way, the reliable instances are imposed with small
punishments, wheras the less reliable instances are imposed with more severe
punishments. Thus, it ensures a more discriminative embedding. On the other
hand, SPASS offers a framework to alleviate the domain shift problem in ZSL,
which exploits the unseen data in an easy to hard fashion. Particularly, SPASS
borrows the idea from selfpaced learning by iteratively selecting the unseen
instances from reliable to less reliable to gradually adapt the knowledge from
the seen domain to the unseen domain. Subsequently, by combining SPASS and
ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively
reinforce the classification capacity. Extensive experiments on three benchmark
datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and
TASTE. Furthermore, we also propose a fast training (FT) strategy to improve
the efficiency of most of existing ZSL methods. The FT strategy is surprisingly
simple and general enough, which can speed up the training time of most
existing methods by 4~300 times while holding the previous performance.
",1,0,0,0,0,0
19202,A geometric attractor mechanism for self-organization of entorhinal grid modules,"  Grid cells in the medial entorhinal cortex (mEC) respond when an animal
occupies a periodic lattice of ""grid fields"" in the environment. The grids are
organized in modules with spatial periods clustered around discrete values
separated by constant ratios reported in the range 1.3-1.8. We propose a
mechanism for dynamical self-organization in the mEC that can produce this
modular structure. In attractor network models of grid formation, the period of
a single module is set by the length scale of recurrent inhibition between
neurons. We show that grid cells will instead form a hierarchy of discrete
modules if a continuous increase in inhibition distance along the dorso-ventral
axis of the mEC is accompanied by excitatory interactions along this axis.
Moreover, constant scale ratios between successive modules arise through
geometric relationships between triangular grids, whose lattice constants are
separated by $\sqrt{3} \approx 1.7$, $\sqrt{7}/2 \approx 1.3$, or other ratios.
We discuss how the interactions required by our model might be tested
experimentally and realized by circuits in the mEC.
",0,0,0,0,1,0
19203,Attacking Strategies and Temporal Analysis Involving Facebook Discussion Groups,"  Online social network (OSN) discussion groups are exerting significant
effects on political dialogue. In the absence of access control mechanisms, any
user can contribute to any OSN thread. Individuals can exploit this
characteristic to execute targeted attacks, which increases the potential for
subsequent malicious behaviors such as phishing and malware distribution. These
kinds of actions will also disrupt bridges among the media, politicians, and
their constituencies.
For the concern of Security Management, blending malicious cyberattacks with
online social interactions has introduced a brand new challenge. In this paper
we describe our proposal for a novel approach to studying and understanding the
strategies that attackers use to spread malicious URLs across Facebook
discussion groups. We define and analyze problems tied to predicting the
potential for attacks focused on threads created by news media organizations.
We use a mix of macro static features and the micro dynamic evolution of posts
and threads to identify likely targets with greater than 90% accuracy. One of
our secondary goals is to make such predictions within a short (10 minute) time
frame. It is our hope that the data and analyses presented in this paper will
support a better understanding of attacker strategies and footprints, thereby
developing new system management methodologies in handing cyber attacks on
social networks.
",1,0,0,0,0,0
19204,Perception-based energy functions in seam-cutting,"  Image stitching is challenging in consumer-level photography, due to
alignment difficulties in unconstrained shooting environment. Recent studies
show that seam-cutting approaches can effectively relieve artifacts generated
by local misalignment. Normally, seam-cutting is described in terms of energy
minimization, however, few of existing methods consider human perception in
their energy functions, which sometimes causes that a seam with minimum energy
is not most invisible in the overlapping region. In this paper, we propose a
novel perception-based energy function in the seam-cutting framework, which
considers the nonlinearity and the nonuniformity of human perception in energy
minimization. Our perception-based approach adopts a sigmoid metric to
characterize the perception of color discrimination, and a saliency weight to
simulate that human eyes incline to pay more attention to salient objects. In
addition, our seam-cutting composition can be easily implemented into other
stitching pipelines. Experiments show that our method outperforms the
seam-cutting method of the normal energy function, and a user study
demonstrates that our composed results are more consistent with human
perception.
",1,0,0,0,0,0
19205,Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo,"  Probabilistic modeling provides the capability to represent and manipulate
uncertainty in data, models, predictions and decisions. We are concerned with
the problem of learning probabilistic models of dynamical systems from measured
data. Specifically, we consider learning of probabilistic nonlinear state-space
models. There is no closed-form solution available for this problem, implying
that we are forced to use approximations. In this tutorial we will provide a
self-contained introduction to one of the state-of-the-art methods---the
particle Metropolis--Hastings algorithm---which has proven to offer a practical
approximation. This is a Monte Carlo based method, where the particle filter is
used to guide a Markov chain Monte Carlo method through the parameter space.
One of the key merits of the particle Metropolis--Hastings algorithm is that it
is guaranteed to converge to the ""true solution"" under mild assumptions,
despite being based on a particle filter with only a finite number of
particles. We will also provide a motivating numerical example illustrating the
method using a modeling language tailored for sequential Monte Carlo methods.
The intention of modeling languages of this kind is to open up the power of
sophisticated Monte Carlo methods---including particle
Metropolis--Hastings---to a large group of users without requiring them to know
all the underlying mathematical details.
",1,0,0,1,0,0
19206,Deep Projective 3D Semantic Segmentation,"  Semantic segmentation of 3D point clouds is a challenging problem with
numerous real-world applications. While deep learning has revolutionized the
field of image semantic segmentation, its impact on point cloud data has been
limited so far. Recent attempts, based on 3D deep learning approaches
(3D-CNNs), have achieved below-expected results. Such methods require
voxelizations of the underlying point cloud data, leading to decreased spatial
resolution and increased memory consumption. Additionally, 3D-CNNs greatly
suffer from the limited availability of annotated datasets.
In this paper, we propose an alternative framework that avoids the
limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first
project the point cloud onto a set of synthetic 2D-images. These images are
then used as input to a 2D-CNN, designed for semantic segmentation. Finally,
the obtained prediction scores are re-projected to the point cloud to obtain
the segmentation results. We further investigate the impact of multiple
modalities, such as color, depth and surface normals, in a multi-stream network
architecture. Experiments are performed on the recent Semantic3D dataset. Our
approach sets a new state-of-the-art by achieving a relative gain of 7.9 %,
compared to the previous best approach.
",1,0,0,0,0,0
19207,Dynamic Pricing with Finitely Many Unknown Valuations,"  Motivated by posted price auctions where buyers are grouped in an unknown
number of latent types characterized by their private values for the good on
sale, we investigate revenue maximization in stochastic dynamic pricing when
the distribution of buyers' private values is supported on an unknown set of
points in [0,1] of unknown cardinality K. This setting can be viewed as an
instance of a stochastic K-armed bandit problem where the location of the arms
(the K unknown valuations) must be learned as well. In the distribution-free
case, we show that our setting is just as hard as K-armed stochastic bandits:
we prove that no algorithm can achieve a regret significantly better than
$\sqrt{KT}$, (where T is the time horizon) and present an efficient algorithm
matching this lower bound up to logarithmic factors. In the
distribution-dependent case, we show that for all K>2 our setting is strictly
harder than K-armed stochastic bandits by proving that it is impossible to
obtain regret bounds that grow logarithmically in time or slower. On the other
hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is
known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log
T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known
regret bounds for discontinuous demand curves, that are at best of order
$(K^{12}/\gamma^8)\sqrt{T}$. When K=2 in the distribution-dependent case, the
hardness of our setting reduces to that of a stochastic 2-armed bandit: we
prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors)
on the regret can be achieved with no information on the demand curve. Finally,
we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the
buyers' decisions are nonstochastic, and the regret is measured with respect to
the best between two fixed valuations one of which is known to the seller.
",0,0,0,1,0,0
19208,"Haar systems, KMS states on von Neumann algebras and $C^*$-algebras on dynamically defined groupoids and Noncommutative Integration","  We analyse certain Haar systems associated to groupoids obtained by certain
natural equivalence relations of dynamical nature on sets like
$\{1,2,...,d\}^\mathbb{Z}$, $\{1,2,...,d\}^\mathbb{N}$, $S^1\times S^1$, or
$(S^1)^\mathbb{N}$, where $S^1$ is the unitary circle. We also describe
properties of transverse functions, quasi-invariant probabilities and KMS
states for some examples of von Neumann algebras (and also $C^*$-Algebras)
associated to these groupoids. We relate some of these KMS states with Gibbs
states of Thermodynamic Formalism. While presenting new results, we will also
describe in detail several examples and basic results on the above topics. In
other words it is also a survey paper. Some known results on non-commutative
integration are presented, more precisely, the relation of transverse measures,
cocycles and quasi-invariant probabilities.
We describe the results in a language which is more familiar to people in
Dynamical Systems. Our intention is to study Haar systems, quasi-invariant
probabilities and von Neumann algebras as a topic on measure theory
(intersected with ergodic theory) avoiding questions of algebraic nature
(which, of course, are also extremely important).
",0,0,1,0,0,0
19209,Restoring a smooth function from its noisy integrals,"  Numerical (and experimental) data analysis often requires the restoration of
a smooth function from a set of sampled integrals over finite bins. We present
the bin hierarchy method that efficiently computes the maximally smooth
function from the sampled integrals using essentially all the information
contained in the data. We perform extensive tests with different classes of
functions and levels of data quality, including Monte Carlo data suffering from
a severe sign problem and physical data for the Green's function of the
Fröhlich polaron.
",0,1,0,1,0,0
19210,Adversarial Active Learning for Deep Networks: a Margin Based Approach,"  We propose a new active learning strategy designed for deep neural networks.
The goal is to minimize the number of data annotation queried from an oracle
during training. Previous active learning strategies scalable for deep networks
were mostly based on uncertain sample selection. In this work, we focus on
examples lying close to the decision boundary. Based on theoretical works on
margin theory for active learning, we know that such examples may help to
considerably decrease the number of annotations. While measuring the exact
distance to the decision boundaries is intractable, we propose to rely on
adversarial examples. We do not consider anymore them as a threat instead we
exploit the information they provide on the distribution of the input space in
order to approximate the distance to decision boundaries. We demonstrate
empirically that adversarial active queries yield faster convergence of CNNs
trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
",0,0,0,1,0,0
19211,Improving SIEM capabilities through an enhanced probe for encrypted Skype traffic detection,"  Nowadays, the Security Information and Event Management (SIEM) systems take
on great relevance in handling security issues for critical infrastructures as
Internet Service Providers. Basically, a SIEM has two main functions: i) the
collection and the aggregation of log data and security information from
disparate network devices (routers, firewalls, intrusion detection systems, ad
hoc probes and others) and ii) the analysis of the gathered data by
implementing a set of correlation rules aimed at detecting potential suspicious
events as the presence of encrypted real-time traffic. In the present work, the
authors propose an enhanced implementation of a SIEM where a particular focus
is given to the detection of encrypted Skype traffic by using an ad-hoc
developed enhanced probe (ESkyPRO) conveniently governed by the SIEM itself.
Such enhanced probe, able to interact with an agent counterpart deployed into
the SIEM platform, is designed by exploiting some machine learning concepts.
The main purpose of the proposed ad-hoc SIEM is to correlate the information
received by ESkyPRO and other types of data obtained by an Intrusion Detection
System (IDS) probe in order to make the encrypted Skype traffic detection as
accurate as possible.
",1,0,0,0,0,0
19212,Escaping Saddle Points with Adaptive Gradient Methods,"  Adaptive methods such as Adam and RMSProp are widely used in deep learning
but are not well understood. In this paper, we seek a crisp, clean and precise
characterization of their behavior in nonconvex settings. To this end, we first
provide a novel view of adaptive methods as preconditioned SGD, where the
preconditioner is estimated in an online manner. By studying the preconditioner
on its own, we elucidate its purpose: it rescales the stochastic gradient noise
to be isotropic near stationary points, which helps escape saddle points.
Furthermore, we show that adaptive methods can efficiently estimate the
aforementioned preconditioner. By gluing together these two components, we
provide the first (to our knowledge) second-order convergence result for any
adaptive method. The key insight from our analysis is that, compared to SGD,
adaptive methods escape saddle points faster, and can converge faster overall
to second-order stationary points.
",1,0,0,1,0,0
19213,The Effect of Temperature on Cu-K-In-Se Thin Films,"  Films of Cu-K-In-Se were co-evaporated at varied K/(K+Cu) compositions and
substrate temperatures (with constant (K+Cu)/In ~ 0.85). Increased Na
composition on the substrate's surface and decreased growth temperature were
both found to favor Cu1-xKxInSe2 (CKIS) alloy formation, relative to
mixed-phase CuInSe2 + KInSe2 formation. Structures from X-ray diffraction
(XRD), band gaps, resistivities, minority carrier lifetimes and carrier
concentrations from time-resolved photoluminescence were in agreement with
previous reports, where low K/(K+Cu) composition films exhibited properties
promising for photovoltaic (PV) absorbers. Films grown at 400-500 C were then
annealed to 600 C under Se, which caused K loss by evaporation in proportion to
initial K/(K+Cu) composition. Similar to growth temperature, annealing drove
CKIS alloy consumption and CuInSe2 + KInSe2 production, as evidenced by high
temperature XRD. Annealing also decomposed KInSe2 and formed K2In12Se19. At
high temperature the KInSe2 crystal lattice gradually contracted as temperature
and time increased, as well as just time. Evaporative loss of K during
annealing could accompany the generation of vacancies on K lattice sites, and
may explain the KInSe2 lattice contraction. This knowledge of Cu-K-In-Se
material chemistry may be used to predict and control minor phase impurities in
Cu(In,Ga)(Se,S)2 PV absorbers-where impurities below typical detection limits
may have played a role in recent world record PV efficiencies that utilized KF
post-deposition treatments.
",0,1,0,0,0,0
19214,Simulations of the Solar System's Early Dynamical Evolution with a Self-Gravitating Planetesimal Disk,"  Over the course of last decade, the Nice model has dramatically changed our
view of the solar system's formation and early evolution. Within the context of
this model, a transient period of planet-planet scattering is triggered by
gravitational interactions between the giant planets and a massive primordial
planetesimal disk, leading to a successful reproduction of the solar system's
present-day architecture. In typical realizations of the Nice model,
self-gravity of the planetesimal disk is routinely neglected, as it poses a
computational bottleneck to the calculations. Recent analyses have shown,
however, that a self-gravitating disk can exhibit behavior that is dynamically
distinct, and this disparity may have significant implications for the solar
system's evolutionary path. In this work, we explore this discrepancy utilizing
a large suite of Nice odel simulations with and without a self-gravitating
planetesimal disk, taking advantage of the inherently parallel nature of
graphic processing units. Our simulations demonstrate that self-consistent
modeling of particle interactions does not lead to significantly different
final planetary orbits from those obtained within conventional simulations.
Moreover, self-gravitating calculations show similar planetesimal evolution to
non-self-gravitating numerical experiments after dynamical instability is
triggered, suggesting that the orbital clustering observed in the distant
Kuiper belt is unlikely to have a self-gravitational origin.
",0,1,0,0,0,0
19215,On weak Fraisse limits,"  Using the natural action of $S_\infty$ we show that a countable hereditary
class $\cC$ of finitely generated structures has the joint embedding property
(JEP) and the weak amalgamation property (WAP) if and only if there is a
structure $M$ whose isomorphism type is comeager in the space of all countable,
infinitely generated structures with age in $\cC$. In this case, $M$ is the
weak Fraïssé limit of $\cC$.
This applies in particular to countable structures with generic automorphisms
and recovers a result by Kechris and Rosendal [\textit{Proc.\ Lond.\ Math.\
Soc.,\ 2007}].
",0,0,1,0,0,0
19216,Promoting Saving for College Through Data Science,"  The cost of attending college has been steadily rising and in 10 years is
estimated to reach $140,000 for a 4-year public university. Recent surveys
estimate just over half of US families are saving for college. State-operated
529 college savings plans are an effective way for families to plan and save
for future college costs, but only 3% of families currently use them. The
Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to
help its residents save for college. In order to increase the number of
families saving for college, the Treasurer and Civis Analytics used data
science techniques to identify the people most likely to sign up for a college
savings plan. In this paper, we will discuss the use of person matching to join
accountholder data from the Treasurer to the Civis National File, as well as
the use of lookalike modeling to identify new potential signups. In order to
avoid reinforcing existing demographic imbalances in who saves for college, the
lookalike models used were ensured to be racially and economically balanced. We
will also discuss how these new signup targets were then individually served
digital ads to encourage opening college savings accounts.
",1,0,0,0,0,0
19217,Rotation Blurring: Use of Artificial Blurring to Reduce Cybersickness in Virtual Reality First Person Shooters,"  Users of Virtual Reality (VR) systems often experience vection, the
perception of self-motion in the absence of any physical movement. While
vection helps to improve presence in VR, it often leads to a form of motion
sickness called cybersickness. Cybersickness is a major deterrent to large
scale adoption of VR.
Prior work has discovered that changing vection (changing the perceived speed
or moving direction) causes more severe cybersickness than steady vection
(walking at a constant speed or in a constant direction). Based on this idea,
we try to reduce the cybersickness caused by character movements in a First
Person Shooter (FPS) game in VR. We propose Rotation Blurring (RB), uniformly
blurring the screen during rotational movements to reduce cybersickness. We
performed a user study to evaluate the impact of RB in reducing cybersickness.
We found that the blurring technique led to an overall reduction in sickness
levels of the participants and delayed its onset. Participants who experienced
acute levels of cybersickness benefited significantly from this technique.
",1,0,0,0,0,0
19218,Comparative Benchmarking of Causal Discovery Techniques,"  In this paper we present a comprehensive view of prominent causal discovery
algorithms, categorized into two main categories (1) assuming acyclic and no
latent variables, and (2) allowing both cycles and latent variables, along with
experimental results comparing them from three perspectives: (a) structural
accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
inference. For (b) and (c) we train causal Bayesian networks with structures as
predicted by each causal discovery technique to carry out counterfactual or
standard predictive inference. We compare causal algorithms on two pub- licly
available and one simulated datasets having different sample sizes: small,
medium and large. Experiments show that structural accuracy of a technique does
not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
surveyed structure learning algorithms do not perform well in terms of
structural accuracy in case of datasets having large number of variables.
",1,0,0,1,0,0
19219,Sensitivity of Love and quasi-Rayleigh waves to model parameters,"  We examine the sensitivity of the Love and the quasi-Rayleigh waves to model
parameters. Both waves are guided waves that propagate in the same model of an
elastic layer above an elastic halfspace. We study their dispersion curves
without any simplifying assumptions, beyond the standard approach of elasticity
theory in isotropic media. We examine the sensitivity of both waves to
elasticity parameters, frequency and layer thickness, for varying frequency and
different modes. In the case of Love waves, we derive and plot the absolute
value of a dimensionless sensitivity coefficient in terms of partial
derivatives, and perform an analysis to find the optimum frequency for
determining the layer thickness. For a coherency of the background information,
we briefly review the Love-wave dispersion relation and provide details of the
less common derivation of the quasi-Rayleigh relation in an appendix. We
compare that derivation to past results in the literature, finding certain
discrepancies among them.
",0,1,0,0,0,0
19220,The image size of iterated rational maps over finite fields,"  Let $\varphi:\mathbb{F}_q\to\mathbb{F}_q$ be a rational map on a fixed finite
field. We give explicit asymptotic formulas for the size of image sets
$\varphi^n(\mathbb{F}_q)$ as a function of $n$. This is done by using
properties of the Galois groups of iterated maps, whose connection to the
question of the size of image sets is established via Chebotarev's Density
Theorem. We then apply these results to provide explicit bounds on the
proportion of periodic points in $\mathbb{F}_q$ in terms of $q$ for certain
rational maps.
",0,0,1,0,0,0
19221,Noncommutative modular symbols and Eisenstein series,"  We form real-analytic Eisenstein series twisted by Manin's noncommutative
modular symbols. After developing their basic properties, these series are
shown to have meromorphic continuations to the entire complex plane and satisfy
functional equations in some cases. This theory neatly contains and generalizes
earlier work in the literature on the properties of Eisenstein series twisted
by classical modular symbols.
",0,0,1,0,0,0
19222,Some rigidity characterizations on critical metrics for quadratic curvature functionals,"  We study closed $n$-dimensional manifolds of which the metrics are critical
for quadratic curvature functionals involving the Ricci curvature, the scalar
curvature and the Riemannian curvature tensor on the space of Riemannian
metrics with unit volume. Under some additional integral conditions, we
classify such manifolds. Moreover, under some curvature conditions, the result
that a critical metric must be Einstein is proved.
",0,0,1,0,0,0
19223,Profinite completions of Burnside-type quotients of surface groups,"  Using quantum representations of mapping class groups we prove that profinite
completions of Burnside-type surface group quotients are not virtually
prosolvable, in general. Further, we construct infinitely many finite simple
characteristic quotients of surface groups.
",0,0,1,0,0,0
19224,Learning Infinite RBMs with Frank-Wolfe,"  In this work, we propose an infinite restricted Boltzmann machine~(RBM),
whose maximum likelihood estimation~(MLE) corresponds to a constrained convex
optimization. We consider the Frank-Wolfe algorithm to solve the program, which
provides a sparse solution that can be interpreted as inserting a hidden unit
at each iteration, so that the optimization process takes the form of a
sequence of finite models of increasing complexity. As a side benefit, this can
be used to easily and efficiently identify an appropriate number of hidden
units during the optimization. The resulting model can also be used as an
initialization for typical state-of-the-art RBM training algorithms such as
contrastive divergence, leading to models with consistently higher test
likelihood than random initialization.
",1,0,0,1,0,0
19225,New low-mass eclipsing binary systems in Praesepe discovered by K2,"  We present the discovery of four low-mass ($M<0.6$ $M_\odot$) eclipsing
binary (EB) systems in the sub-Gyr old Praesepe open cluster using Kepler/K2
time-series photometry and Keck/HIRES spectroscopy. We present a new Gaussian
process eclipsing binary model, GP-EBOP, as well as a method of simultaneously
determining effective temperatures and distances for EBs. Three of the reported
systems (AD 3814, AD 2615 and AD 1508) are detached and double-lined, and
precise solutions are presented for the first two. We determine masses and
radii to 1-3% precision for AD 3814 and to 5-6% for AD 2615. Together with
effective temperatures determined to $\sim$50 K precision, we test the PARSEC
v1.2 and BHAC15 stellar evolution models. Our EB parameters are more consistent
with the PARSEC models, primarily because the BHAC15 temperature scale is
hotter than our data over the mid M-dwarf mass range probed. Both ADs 3814 and
2615, which have orbital periods of 6.0 and 11.6 days, are circularized but not
synchronized. This suggests that either synchronization proceeds more slowly in
fully convective stars than the theory of equilibrium tides predicts or
magnetic braking is currently playing a more important role than tidal forces
in the spin evolution of these binaries. The fourth system (AD 3116) comprises
a brown dwarf transiting a mid M-dwarf, which is the first such system
discovered in a sub-Gyr open cluster. Finally, these new discoveries increase
the number of characterized EBs in sub-Gyr open clusters by 20% (40%) below
$M<1.5$ $M_{\odot}$ ($M<0.6$ $M_{\odot}$).
",0,1,0,0,0,0
19226,The impact of the halide cage on the electronic properties of fully inorganic caesium lead halide perovskites,"  Perovskite solar cells with record power conversion efficiency are fabricated
by alloying both hybrid and fully inorganic compounds. While the basic
electronic properties of the hybrid perovskites are now well understood, key
electronic parameters for solar cell performance, such as the exciton binding
energy of fully inorganic perovskites, are still unknown. By performing magneto
transmission measurements, we determine with high accuracy the exciton binding
energy and reduced mass of fully inorganic CsPbX$_3$ perovskites (X=I, Br, and
an alloy of these). The well behaved (continuous) evolution of the band gap
with temperature in the range $4-270$\,K suggests that fully inorganic
perovskites do not undergo structural phase transitions like their hybrid
counterparts. The experimentally determined dielectric constants indicate that
at low temperature, when the motion of the organic cation is frozen, the
dielectric screening mechanism is essentially the same both for hybrid and
inorganic perovskites, and is dominated by the relative motion of atoms within
the lead-halide cage.
",0,1,0,0,0,0
19227,Discovery of Giant Radio Galaxies from NVSS: Radio & Infrared Properties,"  Giant radio galaxies (GRGs) are one of the largest astrophysical sources in
the Universe with an overall projected linear size of ~0.7 Mpc or more. Last
six decades of radio astronomy research has led to the detection of thousands
of radio galaxies. But only ~ 300 of them can be classified as GRGs. The
reasons behind their large size and rarity are unknown. We carried out a
systematic search for these radio giants and found a large sample of GRGs. In
this paper, we report the discovery of 25 GRGs from NVSS, in the redshift range
(z) ~ 0.07 to 0.67. Their physical sizes range from ~0.8 Mpc to ~4 Mpc. Eight
of these GRGs have sizes greater than 2Mpc which is a rarity. In this paper,
for the first time, we investigate the mid-IR properties of the optical hosts
of the GRGs and classify them securely into various AGN types using the WISE
mid-IR colours. Using radio and IR data, four of the hosts of GRGs were
observed to be radio loud quasars that extend up to 2 Mpc in radio size. These
GRGs missed detection in earlier searches possibly because of their highly
diffuse nature, low surface brightness and lack of optical data. The new GRGs
are a significant addition to the existing sample that will contribute to
better understanding of the physical properties of radio giants.
",0,1,0,0,0,0
19228,SGD: General Analysis and Improved Rates,"  We propose a general yet simple theorem describing the convergence of SGD
under the arbitrary sampling paradigm. Our theorem describes the convergence of
an infinite array of variants of SGD, each of which is associated with a
specific probability law governing the data selection rule used to form
mini-batches. This is the first time such an analysis is performed, and most of
our variants of SGD were never explicitly considered in the literature before.
Our analysis relies on the recently introduced notion of expected smoothness
and does not rely on a uniform bound on the variance of the stochastic
gradients. By specializing our theorem to different mini-batching strategies,
such as sampling with replacement and independent sampling, we derive exact
expressions for the stepsize as a function of the mini-batch size. With this we
can also determine the mini-batch size that optimizes the total complexity, and
show explicitly that as the variance of the stochastic gradient evaluated at
the minimum grows, so does the optimal mini-batch size. For zero variance, the
optimal mini-batch size is one. Moreover, we prove insightful
stepsize-switching rules which describe when one should switch from a constant
to a decreasing stepsize regime.
",1,0,0,1,0,0
19229,Nonconvex One-bit Single-label Multi-label Learning,"  We study an extreme scenario in multi-label learning where each training
instance is endowed with a single one-bit label out of multiple labels. We
formulate this problem as a non-trivial special case of one-bit rank-one matrix
sensing and develop an efficient non-convex algorithm based on alternating
power iteration. The proposed algorithm is able to recover the underlying
low-rank matrix model with linear convergence. For a rank-$k$ model with $d_1$
features and $d_2$ classes, the proposed algorithm achieves $O(\epsilon)$
recovery error after retrieving $O(k^{1.5}d_1 d_2/\epsilon)$ one-bit labels
within $O(kd)$ memory. Our bound is nearly optimal in the order of
$O(1/\epsilon)$. This significantly improves the state-of-the-art sampling
complexity of one-bit multi-label learning. We perform experiments to verify
our theory and evaluate the performance of the proposed algorithm.
",1,0,0,1,0,0
19230,Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus,"  To explore large-scale population indoor interactions, we analyze 18,715
users' WiFi access logs recorded in a Chinese university campus during 3
months, and define two categories of human interactions, the event interaction
(EI) and the temporal interaction (TI). The EI helps construct a transmission
graph, and the TI helps build an interval graph. The dynamics of EIs show that
their active durations are truncated power-law distributed, which is
independent on the number of involved individuals. The transmission duration
presents a truncated power-law behavior at the daily timescale with weekly
periodicity. Besides, those `leaf' individuals in the aggregated contact
network may participate in the `super-connecting cliques' in the aggregated
transmission graph. Analyzing the dynamics of the interval graph, we find that
the probability distribution of TIs' inter-event duration also displays a
truncated power-law pattern at the daily timescale with weekly periodicity,
while the pairwise individuals with burst interactions are prone to randomly
select their interactive locations, and those individuals with periodic
interactions have preferred interactive locations.
",1,1,0,0,0,0
19231,Robust estimators for generalized linear models with a dispersion parameter,"  Highly robust and efficient estimators for the generalized linear model with
a dispersion parameter are proposed. The estimators are based on three steps.
In the first step the maximum rank correlation estimator is used to
consistently estimate the slopes up to a scale factor. In the second step, the
scale factor, the intercept, and the dispersion parameter are consistently
estimated using a MT-estimator of a simple regression model. The combined
estimator is highly robust but inefficient. Then, randomized quantile residuals
based on the initial estimators are used to detect outliers to be rejected and
to define a set S of observations to be retained. Finally, a conditional
maximum likelihood (CML) estimator given the observations in S is computed. We
show that, under the model, S tends to the complete sample for increasing
sample size. Therefore, the CML tends to the unconditional maximum likelihood
estimator. It is therefore highly efficient, while maintaining the high degree
of robustness of the initial estimator. The case of the negative binomial
regression model is studied in detail.
",0,0,0,1,0,0
19232,Abstracting Event-Driven Systems with Lifestate Rules,"  We present lifestate rules--an approach for abstracting event-driven object
protocols. Developing applications against event-driven software frameworks is
notoriously difficult. One reason why is that to create functioning
applications, developers must know about and understand the complex protocols
that abstract the internal behavior of the framework. Such protocols intertwine
the proper registering of callbacks to receive control from the framework with
appropriate application programming interface (API) calls to delegate back to
it. Lifestate rules unify lifecycle and typestate constraints in one common
specification language. Our primary contribution is a model of event-driven
systems from which lifestate rules can be derived. We then apply specification
mining techniques to learn lifestate specifications for Android framework
types. In the end, our implementation is able to find several rules that
characterize actual behavior of the Android framework.
",1,0,0,0,0,0
19233,Boltzmann Encoded Adversarial Machines,"  Restricted Boltzmann Machines (RBMs) are a class of generative neural network
that are typically trained to maximize a log-likelihood objective function. We
argue that likelihood-based training strategies may fail because the objective
does not sufficiently penalize models that place a high probability in regions
where the training data distribution has low probability. To overcome this
problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is
an RBM trained against an adversary that uses the hidden layer activations of
the RBM to discriminate between the training data and the probability
distribution generated by the model. We present experiments demonstrating that
BEAMs outperform RBMs and GANs on multiple benchmarks.
",0,0,0,1,0,0
19234,"A Non-Gaussian, Nonparametric Structure for Gene-Gene and Gene-Environment Interactions in Case-Control Studies Based on Hierarchies of Dirichlet Processes","  It is becoming increasingly clear that complex interactions among genes and
environmental factors play crucial roles in triggering complex diseases. Thus,
understanding such interactions is vital, which is possible only through
statistical models that adequately account for such intricate, albeit unknown,
dependence structures. Bhattacharya & Bhattacharya (2016b) attempt such
modeling, relating finite mixtures composed of Dirichlet processes that
represent unknown number of genetic sub-populations through a hierarchical
matrix-normal structure that incorporates gene-gene interactions, and possible
mutations, induced by environmental variables. However, the product dependence
structure implied by their matrix-normal model seems to be too simple to be
appropriate for general complex, realistic situations. In this article, we
propose and develop a novel nonparametric Bayesian model for case-control
genotype data using hierarchies of Dirichlet processes that offers a more
realistic and nonparametric dependence structure between the genes, induced by
the environmental variables. In this regard, we propose a novel and highly
parallelisable MCMC algorithm that is rendered quite efficient by the
combination of modern parallel computing technology, effective Gibbs sampling
steps, retrospective sampling and Transformation based Markov Chain Monte Carlo
(TMCMC). We use appropriate Bayesian hypothesis testing procedures to detect
the roles of genes and environment in case-control studies. We apply our ideas
to 5 biologically realistic case-control genotype datasets simulated under
distinct set-ups, and obtain encouraging results in each case. We finally apply
our ideas to a real, myocardial infarction dataset, and obtain interesting
results on gene-gene and gene-environment interaction, while broadly agreeing
with the results reported in the literature.
",0,0,0,1,0,0
19235,An Efficient Bayesian Robust Principal Component Regression,"  Principal component regression is a linear regression model with principal
components as regressors. This type of modelling is particularly useful for
prediction in settings with high-dimensional covariates. Surprisingly, the
existing literature treating of Bayesian approaches is relatively sparse. In
this paper, we aim at filling some gaps through the following practical
contribution: we introduce a Bayesian approach with detailed guidelines for a
straightforward implementation. The approach features two characteristics that
we believe are important. First, it effectively involves the relevant principal
components in the prediction process. This is achieved in two steps. The first
one is model selection; the second one is to average out the predictions
obtained from the selected models according to model averaging mechanisms,
allowing to account for model uncertainty. The model posterior probabilities
are required for model selection and model averaging. For this purpose, we
include a procedure leading to an efficient reversible jump algorithm. The
second characteristic of our approach is whole robustness, meaning that the
impact of outliers on inference gradually vanishes as they approach plus or
minus infinity. The conclusions obtained are consequently consistent with the
majority of observations (the bulk of the data).
",0,0,0,1,0,0
19236,Bayesian estimation from few samples: community detection and related problems,"  We propose an efficient meta-algorithm for Bayesian estimation problems that
is based on low-degree polynomials, semidefinite programming, and tensor
decomposition. The algorithm is inspired by recent lower bound constructions
for sum-of-squares and related to the method of moments. Our focus is on sample
complexity bounds that are as tight as possible (up to additive lower-order
terms) and often achieve statistical thresholds or conjectured computational
thresholds.
Our algorithm recovers the best known bounds for community detection in the
sparse stochastic block model, a widely-studied class of estimation problems
for community detection in graphs. We obtain the first recovery guarantees for
the mixed-membership stochastic block model (Airoldi et el.) in constant
average degree graphs---up to what we conjecture to be the computational
threshold for this model. We show that our algorithm exhibits a sharp
computational threshold for the stochastic block model with multiple
communities beyond the Kesten--Stigum bound---giving evidence that this task
may require exponential time.
The basic strategy of our algorithm is strikingly simple: we compute the
best-possible low-degree approximation for the moments of the posterior
distribution of the parameters and use a robust tensor decomposition algorithm
to recover the parameters from these approximate posterior moments.
",1,0,0,1,0,0
19237,X-ray Transform and Boundary Rigidity for Asymptotically Hyperbolic Manifolds,"  We consider the boundary rigidity problem for asymptotically hyperbolic
manifolds. We show injectivity of the X-ray transform in several cases and
consider the non-linear inverse problem which consists of recovering a metric
from boundary measurements for the geodesic flow.
",0,0,1,0,0,0
19238,Pinning of longitudinal phonons in holographic spontaneous helices,"  We consider the spontaneous breaking of translational symmetry and identify
the associated Goldstone mode -- a longitudinal phonon -- in a holographic
model with Bianchi VII helical symmetry. For the first time in holography, we
observe the pinning of this mode after introducing a source for explicit
breaking compatible with the helical symmetry of our setup. We study the
dispersion relation of the resulting pseudo-Goldstone mode, uncovering how its
speed and mass gap depend on the amplitude of the source and temperature. In
addition, we extract the optical conductivity as a function of frequency, which
reveals a metal-insulator transition as a consequence of the pinning.
",0,1,0,0,0,0
19239,An Extension of the Method of Brackets. Part 1,"  The method of brackets is an efficient method for the evaluation of a large
class of definite integrals on the half-line. It is based on a small collection
of rules, some of which are heuristic. The extension discussed here is based on
the concepts of null and divergent series. These are formal representations of
functions, whose coefficients $a_{n}$ have meromorphic representations for $n
\in \mathbb{C}$, but might vanish or blow up when $n \in \mathbb{N}$. These
ideas are illustrated with the evaluation of a variety of entries from the
classical table of integrals by Gradshteyn and Ryzhik.
",0,0,1,0,0,0
19240,optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim(),"  The R package optimParallel provides a parallel version of the gradient-based
optimization methods of optim(). The main function of the package is
optimParallel(), which has the same usage and output as optim(). Using
optimParallel() can significantly reduce optimization times. We introduce the R
package and illustrate its implementation, which takes advantage of the lexical
scoping mechanism of R.
",0,0,0,1,0,0
19241,The barocaloric effect: A Spin-off of the Discovery of High-Temperature Superconductivity,"  Some key results obtained in joint research projects with Alex Müller are
summarized, concentrating on the invention of the barocaloric effect and its
application for cooling as well as on important findings in the field of
high-temperature superconductivity resulting from neutron scattering
experiments.
",0,1,0,0,0,0
19242,Spontaneous currents in superconducting systems with strong spin-orbit coupling,"  We show that Rashba spin-orbit coupling at the interface between a
superconductor and a ferromagnet should produce a spontaneous current in the
atomic thickness region near the interface. This current is counter-balanced by
the superconducting screening current flowing in the region of the width of the
London penetration depth near the interface. Such current carrying state
creates a magnetic field near the superconductor surface, generates a stray
magnetic field outside the sample edges, changes the slope of the temperature
dependence of the critical field $H_{c3}$ and may generate the spontaneous
Abrikosov vortices near the interface.
",0,1,0,0,0,0
19243,Catching Loosely Synchronized Behavior in Face of Camouflage,"  Fraud has severely detrimental impacts on the business of social networks and
other online applications. A user can become a fake celebrity by purchasing
""zombie followers"" on Twitter. A merchant can boost his reputation through fake
reviews on Amazon. This phenomenon also conspicuously exists on Facebook, Yelp
and TripAdvisor, etc. In all the cases, fraudsters try to manipulate the
platform's ranking mechanism by faking interactions between the fake accounts
they control and the target customers.
",1,0,0,0,0,0
19244,Three-dimensional localized-delocalized Anderson transition in the time domain,"  Systems which can spontaneously reveal periodic evolution are dubbed time
crystals. This is in analogy with space crystals that display periodic behavior
in configuration space. While space crystals are modelled with the help of
space periodic potentials, crystalline phenomena in time can be modelled by
periodically driven systems. Disorder in the periodic driving can lead to
Anderson localization in time: the probability for detecting a system at a
fixed point of configuration space becomes exponentially localized around a
certain moment in time. We here show that a three-dimensional system exposed to
a properly disordered pseudo-periodic driving may display a
localized-delocalized Anderson transition in the time domain, in strong analogy
with the usual three-dimensional Anderson transition in disordered systems.
Such a transition could be experimentally observed with ultra-cold atomic
gases.
",0,1,0,0,0,0
19245,Socio-economic constraints to maximum human lifespan,"  The analysis of the demographic transition of the past century and a half,
using both empirical data and mathematical models, has rendered a wealth of
well-established facts, including the dramatic increases in life expectancy.
Despite these insights, such analyses have also occasionally triggered debates
which spill over many disciplines, from genetics, to biology, or demography.
Perhaps the hottest discussion is happening around the question of maximum
human lifespan, which --besides its fascinating historical and philosophical
interest-- poses urgent pragmatic warnings on a number of issues in public and
private decision-making. In this paper, we add to the controversy some results
which, based on purely statistical grounds, suggest that the maximum human
lifespan is not fixed, or has not reached yet a plateau. Quite the contrary,
analysis on reliable data for over 150 years in more than 20 industrialized
countries point at a sustained increase in the maximum age at death.
Furthermore, were this trend to continue, a limitless lifespan could be
achieved by 2102. Finally, we quantify the dependence of increases in the
maximum lifespan on socio-economic factors. Our analysis indicates that in some
countries the observed rising patterns can only be sustained by progressively
larger increases in GDP, setting the problem of longevity in a context of
diminishing returns.
",0,0,0,1,1,0
19246,SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach,"  Cyber-Physical Systems (CPS) revolutionize various application domains with
integration and interoperability of networking, computing systems, and
mechanical devices. Due to its scale and variety, CPS faces a number of
challenges and opens up a few research questions in terms of management,
fault-tolerance, and scalability. We propose a software-defined approach
inspired by Software-Defined Networking (SDN), to address the challenges for a
wider CPS adoption. We thus design a middleware architecture for the correct
and resilient operation of CPS, to manage and coordinate the interacting
devices centrally in the cyberspace whilst not sacrificing the functionality
and performance benefits inherent to a distributed execution.
",1,0,0,0,0,0
19247,Dual Ore's theorem on distributive intervals of finite groups,"  This paper gives a self-contained group-theoretic proof of a dual version of
a theorem of Ore on distributive intervals of finite groups. We deduce a bridge
between combinatorics and representations in finite group theory.
",0,0,1,0,0,0
19248,A construction of trivial Beltrami coefficients,"  A measurable function $\mu$ on the unit disk $\mathbb{D}$ of the complex
plane with $\|\mu\|_\infty<1$ is sometimes called a Beltrami coefficient. We
say that $\mu$ is trivial if it is the complex dilatation $f_{\bar z}/f_z$ of a
quasiconformal automorphism $f$ of $\mathbb{D}$ satisfying the trivial boundary
condition $f(z)=z,~|z|=1.$ Since it is not easy to solve the Beltrami equation
explicitly, to detect triviality of a given Beltrami coefficient is a hard
problem, in general. In the present article, we offer a sufficient condition
for a Beltrami coefficient to be trivial. Our proof is based on Betker's
theorem on Löwner chains.
",0,0,1,0,0,0
19249,A GNS construction of three-dimensional abelian Dijkgraaf-Witten theories,"  We give a detailed account of the so-called ""universal construction"" that
aims to extend invariants of closed manifolds, possibly with additional
structure, to topological field theories and show that it amounts to a
generalization of the GNS construction. We apply this construction to an
invariant defined in terms of the groupoid cardinality of groupoids of bundles
to recover Dijkgraaf-Witten theories, including the vector spaces obtained as a
linearization of spaces of principal bundles.
",0,0,1,0,0,0
19250,Long-path formation in a deformed microdisk laser,"  An asymmetric resonant cavity can be used to form a path that is much longer
than the cavity size. We demonstrate this capability for a deformed microdisk
equipped with two linear waveguides, by constructing a multiply reflected
periodic orbit that is confined by total internal reflection within the
deformed microdisk and outcoupled by the two linear waveguides. Resonant mode
analysis reveals that the modes corresponding to the periodic orbit are
characterized by high quality factors. From measured spectral and far-field
data, we confirm that the fabricated devices can form a path about 9.3 times
longer than the average diameter of the deformed microdisk.
",0,1,0,0,0,0
19251,Spitzer Observations of Large Amplitude Variables in the LMC and IC 1613,"  The 3.6 and 4.5 micron characteristics of AGB variables in the LMC and IC1613
are discussed. For C-rich Mira variables there is a very clear
period-luminosity-colour relation, where the [3.6]-[4.5] colour is associated
with the amount of circumstellar material and correlated with the pulsation
amplitude. The [4.5] period-luminosity relation for dusty stars is
approximately one mag brighter than for their naked counterparts with
comparable periods.
",0,1,0,0,0,0
19252,Synergistic Team Composition,"  Effective teams are crucial for organisations, especially in environments
that require teams to be constantly created and dismantled, such as software
development, scientific experiments, crowd-sourcing, or the classroom. Key
factors influencing team performance are competences and personality of team
members. Hence, we present a computational model to compose proficient and
congenial teams based on individuals' personalities and their competences to
perform tasks of different nature. With this purpose, we extend Wilde's
post-Jungian method for team composition, which solely employs individuals'
personalities. The aim of this study is to create a model to partition agents
into teams that are balanced in competences, personality and gender. Finally,
we present some preliminary empirical results that we obtained when analysing
student performance. Results show the benefits of a more informed team
composition that exploits individuals' competences besides information about
their personalities.
",1,0,0,0,0,0
19253,The inverse hull of 0-left cancellative semigroups,"  Given a semigroup S with zero, which is left-cancellative in the sense that
st=sr \neq 0 implies that t=r, we construct an inverse semigroup called the
inverse hull of S, denoted H(S). When S admits least common multiples, in a
precise sense defined below, we study the idempotent semilattice of H(S), with
a focus on its spectrum. When S arises as the language semigroup for a subsift
X on a finite alphabet, we discuss the relationship between H(S) and several
C*-algebras associated to X appearing in the literature.
",0,0,1,0,0,0
19254,The Hadamard Determinant Inequality - Extensions to Operators on a Hilbert Space,"  A generalization of classical determinant inequalities like Hadamard's
inequality and Fischer's inequality is studied. For a version of the
inequalities originally proved by Arveson for positive operators in von Neumann
algebras with a tracial state, we give a different proof. We also improve and
generalize to the setting of finite von Neumann algebras, some `Fischer-type'
inequalities by Matic for determinants of perturbed positive-definite matrices.
In the process, a conceptual framework is established for viewing these
inequalities as manifestations of Jensen's inequality in conjunction with the
theory of operator monotone and operator convex functions on $[0,\infty)$. We
place emphasis on documenting necessary and sufficient conditions for equality
to hold.
",0,0,1,0,0,0
19255,Experimental results : Reinforcement Learning of POMDPs using Spectral Methods,"  We propose a new reinforcement learning algorithm for partially observable
Markov decision processes (POMDP) based on spectral decomposition methods.
While spectral methods have been previously employed for consistent learning of
(passive) latent variable models such as hidden Markov models, POMDPs are more
challenging since the learner interacts with the environment and possibly
changes the future observations in the process. We devise a learning algorithm
running through epochs, in each epoch we employ spectral techniques to learn
the POMDP parameters from a trajectory generated by a fixed policy. At the end
of the epoch, an optimization oracle returns the optimal memoryless planning
policy which maximizes the expected reward based on the estimated POMDP model.
We prove an order-optimal regret bound with respect to the optimal memoryless
policy and efficient scaling with respect to the dimensionality of observation
and action spaces.
",1,0,0,1,0,0
19256,Discreteness of silting objects and t-structures in triangulated categories,"  We introduce the notion of ST-pairs of triangulated subcategories, a
prototypical example of which is the pair of the bound homotopy category and
the bound derived category of a finite-dimensional algebra. For an ST-pair
$(\C,\D)$, we construct an order-preserving map from silting objects in $\C$ to
bounded $t$-structures on $\D$ and show that the map is bijective if and only
if $\C$ is silting-discrete if and only if $\D$ is $t$-discrete. Based on a
work of Qiu and Woolf, the above result is applied to show that if $\C$ is
silting-discrete then the stability space of $\D$ is contractible. This is used
to obtain the contractibility of the stability spaces of some Calabi--Yau
triangulated categories associated to Dynkin quivers.
",0,0,1,0,0,0
19257,Sketching the order of events,"  We introduce features for massive data streams. These stream features can be
thought of as ""ordered moments"" and generalize stream sketches from ""moments of
order one"" to ""ordered moments of arbitrary order"". In analogy to classic
moments, they have theoretical guarantees such as universality that are
important for learning algorithms.
",1,0,1,1,0,0
19258,Deep Multi-view Learning to Rank,"  We study the problem of learning to rank from multiple sources. Though
multi-view learning and learning to rank have been studied extensively leading
to a wide range of applications, multi-view learning to rank as a synergy of
both topics has received little attention. The aim of the paper is to propose a
composite ranking method while keeping a close correlation with the individual
rankings simultaneously. We propose a multi-objective solution to ranking by
capturing the information of the feature mapping from both within each view as
well as across views using autoencoder-like networks. Moreover, a novel
end-to-end solution is introduced to enhance the joint ranking with minimum
view-specific ranking loss, so that we can achieve the maximum global view
agreements within a single optimization process. The proposed method is
validated on a wide variety of ranking problems, including university ranking,
multi-view lingual text ranking and image data ranking, providing superior
results.
",0,0,0,1,0,0
19259,One-Shot Visual Imitation Learning via Meta-Learning,"  In order for a robot to be a generalist that can perform a wide range of
jobs, it must be able to acquire a wide variety of skills quickly and
efficiently in complex unstructured environments. High-capacity models such as
deep neural networks can enable a robot to represent complex skills, but
learning each skill from scratch then becomes infeasible. In this work, we
present a meta-imitation learning method that enables a robot to learn how to
learn more efficiently, allowing it to acquire new skills from just a single
demonstration. Unlike prior methods for one-shot imitation, our method can
scale to raw pixel inputs and requires data from significantly fewer prior
tasks for effective learning of new skills. Our experiments on both simulated
and real robot platforms demonstrate the ability to learn new tasks,
end-to-end, from a single visual demonstration.
",1,0,0,0,0,0
19260,Second variation of Selberg zeta functions and curvature asymptotics,"  We give an explicit formula for the second variation of the logarithm of the
Selberg zeta function, $Z(s)$, on Teichmüller space. We then use this formula
to determine the asymptotic behavior as $s \to \infty$ of the second variation.
As a consequence, we determine the signature of the Hessian of $\log Z(s)$ for
sufficiently large $s$. As a further consequence, the asymptotic behavior of
the second variation of $\log Z(s)$ shows that the Ricci curvature of the Hodge
bundle $H^0(\mathcal K^m_t)\mapsto t$ over Teichmüller space agrees with the
Quillen curvature up to a term of exponential decay, $O(s^2 e^{-l_0 s}),$ where
$l_0$ is the length of the shortest closed hyperbolic geodesic.
",0,0,1,0,0,0
19261,Magnetic Fields Threading Black Holes: restrictions from general relativity and implications for astrophysical black holes,"  The idea that black hole spin is instrumental in the generation of powerful
jets in active galactic nuclei and X-ray binaries is arguably the most
contentious claim in black hole astrophysics. Because jets are thought to
originate in the context of electromagnetism, and the modeling of Maxwell
fields in curved spacetime around black holes is challenging, various
approximations are made in numerical simulations that fall under the guise of
'ideal magnetohydrodynamics'. But the simplifications of this framework may
struggle to capture relevant details of real astrophysical environments near
black holes. In this work, we highlight tension between analytic and numerical
results, specifically between the analytically derived conserved Noether
currents for rotating black hole spacetimes and the results of general
relativistic numerical simulations (GRMHD). While we cannot definitively
attribute the issue to any specific approximation used in the numerical
schemes, there seem to be natural candidates, which we explore. GRMHD
notwithstanding, if electromagnetic fields around rotating black holes are
brought to the hole by accretion, we show from first principles that prograde
accreting disks likely experience weaker large-scale black hole-threading
fields, implying weaker jets than in retrograde configurations.
",0,1,0,0,0,0
19262,Distance covariance for stochastic processes,"  The distance covariance of two random vectors is a measure of their
dependence. The empirical distance covariance and correlation can be used as
statistical tools for testing whether two random vectors are independent. We
propose an analogs of the distance covariance for two stochastic processes
defined on some interval. Their empirical analogs can be used to test the
independence of two processes.
",0,0,1,1,0,0
19263,On Nonparametric Regression using Data Depth,"  We investigate nonparametric regression methods based on statistical depth
functions. These nonparametric regression procedures can be used in situations,
where the response is multivariate and the covariate is a random element in a
metric space. This includes regression with functional covariate as a special
case. Our objective is to study different features of the conditional
distribution of the response given the covariate. We construct measures of the
center and the spread of the conditional distribution using depth based
nonparametric regression procedures. We establish the asymptotic consistency of
those measures and develop a test for heteroscedasticity based on the measure
of conditional spread. The usefulness of the methodology is demonstrated in
some real datasets. In one dataset consisting of Italian household expenditure
data for the period 1973 to 1992, we regress the expenditure for different
items on their prices. In another dataset, our responses are the nutritional
contents of different meat samples measured by their protein, fat and moisture
contents, and the functional covariate is the absorbance spectra of the meat
samples.
",0,0,0,1,0,0
19264,A Backward Simulation Method for Stochastic Optimal Control Problems,"  A number of optimal decision problems with uncertainty can be formulated into
a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC)
algorithm is a popular numerical method to approach solutions of such
stochastic control problems as analytical solutions are not tractable in
general. This paper generalizes the LSMC algorithm proposed in Shen and Weng
(2017) to solve a wide class of stochastic optimal control models. Our
algorithm has three pillars: a construction of auxiliary stochastic control
model, an artificial simulation of the post-action value of state process, and
a shape-preserving sieve estimation method which equip the algorithm with a
number of merits including bypassing forward simulation and control
randomization, evading extrapolating the value function, and alleviating
computational burden of the tuning parameter selection. The efficacy of the
algorithm is corroborated by an application to pricing equity-linked insurance
products.
",0,0,0,0,0,1
19265,Damped Posterior Linearization Filter,"  The iterated posterior linearization filter (IPLF) is an algorithm for
Bayesian state estimation that performs the measurement update using iterative
statistical regression. The main result behind IPLF is that the posterior
approximation is more accurate when the statistical regression of measurement
function is done in the posterior instead of the prior as is done in
non-iterative Kalman filter extensions. In IPLF, each iteration in principle
gives a better posterior estimate to obtain a better statistical regression and
more accurate posterior estimate in the next iteration. However, IPLF may
diverge. IPLF's fixed- points are not described as solutions to an optimization
problem, which makes it challenging to improve its convergence properties. In
this letter, we introduce a double-loop version of IPLF, where the inner loop
computes the posterior mean using an optimization algorithm. Simulation results
are presented to show that the proposed algorithm has better convergence than
IPLF and its accuracy is similar to or better than other state-of-the-art
algorithms.
",0,0,1,1,0,0
19266,Status updates through M/G/1/1 queues with HARQ,"  We consider a system where randomly generated updates are to be transmitted
to a monitor, but only a single update can be in the transmission service at a
time. Therefore, the source has to prioritize between the two possible
transmission policies: preempting the current update or discarding the new one.
We consider Poisson arrivals and general service time, and refer to this system
as the M/G/1/1 queue. We start by studying the average status update age and
the optimal update arrival rate for these two schemes under general service
time distribution. We then apply these results on two practical scenarios in
which updates are sent through an erasure channel using (a) an infinite
incremental redundancy (IIR) HARQ system and (b) a fixed redundancy (FR) HARQ
system. We show that in both schemes the best strategy would be not to preempt.
Moreover, we also prove that, from an age point of view, IIR is better than FR.
",1,0,0,0,0,0
19267,A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials,"  Most analyses of randomised trials with incomplete outcomes make untestable
assumptions and should therefore be subjected to sensitivity analyses. However,
methods for sensitivity analyses are not widely used. We propose a mean score
approach for exploring global sensitivity to departures from missing at random
or other assumptions about incomplete outcome data in a randomised trial. We
assume a single outcome analysed under a generalised linear model. One or more
sensitivity parameters, specified by the user, measure the degree of departure
from missing at random in a pattern mixture model. Advantages of our method are
that its sensitivity parameters are relatively easy to interpret and so can be
elicited from subject matter experts; it is fast and non-stochastic; and its
point estimate, standard error and confidence interval agree perfectly with
standard methods when particular values of the sensitivity parameters make
those standard methods appropriate. We illustrate the method using data from a
mental health trial.
",0,0,1,1,0,0
19268,On the union complexity of families of axis-parallel rectangles with a low packing number,"  Let R be a family of n axis-parallel rectangles with packing number p-1,
meaning that among any p of the rectangles, there are two with a non-empty
intersection. We show that the union complexity of R is at most O(n+p^2), and
that the (<=k)-level complexity of R is at most O(kn+k^2p^2). Both upper bounds
are tight.
",1,0,1,0,0,0
19269,Using photo-ionisation models to derive carbon and oxygen gas-phase abundances in the rest UV,"  We present a new method to derive oxygen and carbon abundances using the
ultraviolet (UV) lines emitted by the gas-phase ionised by massive stars. The
method is based on the comparison of the nebular emission-line ratios with
those predicted by a large grid of photo-ionisation models. Given the large
dispersion in the O/H - C/O plane, our method firstly fixes C/O using ratios of
appropriate emission lines and, in a second step, calculates O/H and the
ionisation parameter from carbon lines in the UV. We find abundances totally
consistent with those provided by the direct method when we apply this method
to a sample of objects with an empirical determination of the electron
temperature using optical emission lines. The proposed methodology appears as a
powerful tool for systematic studies of nebular abundances in star-forming
galaxies at high redshift.
",0,1,0,0,0,0
19270,A Probabilistic Framework for Location Inference from Social Media,"  We study the extent to which we can infer users' geographical locations from
social media. Location inference from social media can benefit many
applications, such as disaster management, targeted advertising, and news
content tailoring. In recent years, a number of algorithms have been proposed
for identifying user locations on social media platforms such as Twitter and
Facebook from message contents, friend networks, and interactions between
users. In this paper, we propose a novel probabilistic model based on factor
graphs for location inference that offers several unique advantages for this
task. First, the model generalizes previous methods by incorporating content,
network, and deep features learned from social context. The model is also
flexible enough to support both supervised learning and semi-supervised
learning. Second, we explore several learning algorithms for the proposed
model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which
improves the inference accuracy. Third, we validate the proposed model on three
different genres of data - Twitter, Weibo, and Facebook - and demonstrate that
the proposed model can substantially improve the inference accuracy (+3.3-18.5%
by F1-score) over that of several state-of-the-art methods.
",1,0,0,0,0,0
19271,"The Novel ABALONE Photosensor Technology: 4-Year Long Tests of Vacuum Integrity, Internal Pumping and Afterpulsing","  The ABALONE Photosensor Technology (U.S. Patent 9064678 2015) has the
capability of supplanting the expensive 80 year old Photomultiplier Tube (PMT)
manufacture by providing a modern and cost effective alternative product. An
ABALONE Photosensor comprises only three monolithic glass components, sealed
together by our new thin film adhesive. In 2013, we left one of the early
ABALONE Photosensor prototypes intact for continuous stress testing, and here
we report its long term vacuum integrity. The exceptionally low ion
afterpulsing rate (approximately two orders of magnitude lower than in PMTs)
has been constantly improving. We explain the physical and technological
reasons for this achievement. Due to the cost-effectiveness and the specific
combination of features, including low level of radioactivity, integration into
large-area panels, and robustness, this technology can open new horizons in the
fields of fundamental physics, functional medical imaging, and nuclear
security.
",0,1,0,0,0,0
19272,Multi-robot Dubins Coverage with Autonomous Surface Vehicles,"  In large scale coverage operations, such as marine exploration or aerial
monitoring, single robot approaches are not ideal, as they may take too long to
cover a large area. In such scenarios, multi-robot approaches are preferable.
Furthermore, several real world vehicles are non-holonomic, but can be modeled
using Dubins vehicle kinematics. This paper focuses on environmental monitoring
of aquatic environments using Autonomous Surface Vehicles (ASVs). In
particular, we propose a novel approach for solving the problem of complete
coverage of a known environment by a multi-robot team consisting of Dubins
vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle
coverage are NP-complete problems. As such, we present two heuristics methods
based on a variant of the traveling salesman problem -- k-TSP -- formulation
and clustering algorithms that efficiently solve the problem. The proposed
methods are tested both in simulations to assess their scalability and with a
team of ASVs operating on a lake to ensure their applicability in real world.
",1,0,0,0,0,0
19273,New Fairness Metrics for Recommendation that Embrace Differences,"  We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative filtering methods to make unfair predictions against
minority groups of users. We identify the insufficiency of existing fairness
metrics and propose four new metrics that address different forms of
unfairness. These fairness metrics can be optimized by adding fairness terms to
the learning objective. Experiments on synthetic and real data show that our
new metrics can better measure fairness than the baseline, and that the
fairness objectives effectively help reduce unfairness.
",1,0,0,0,0,0
19274,Graph heat mixture model learning,"  Graph inference methods have recently attracted a great interest from the
scientific community, due to the large value they bring in data interpretation
and analysis. However, most of the available state-of-the-art methods focus on
scenarios where all available data can be explained through the same graph, or
groups corresponding to each graph are known a priori. In this paper, we argue
that this is not always realistic and we introduce a generative model for mixed
signals following a heat diffusion process on multiple graphs. We propose an
expectation-maximisation algorithm that can successfully separate signals into
corresponding groups, and infer multiple graphs that govern their behaviour. We
demonstrate the benefits of our method on both synthetic and real data.
",1,0,0,1,0,0
19275,Technical Report: Reactive Navigation in Partially Known Non-Convex Environments,"  This paper presents a provably correct method for robot navigation in 2D
environments cluttered with familiar but unexpected non-convex, star-shaped
obstacles as well as completely unknown, convex obstacles. We presuppose a
limited range onboard sensor, capable of recognizing, localizing and
(leveraging ideas from constructive solid geometry) generating online from its
catalogue of the familiar, non-convex shapes an implicit representation of each
one. These representations underlie an online change of coordinates to a
completely convex model planning space wherein a previously developed online
construction yields a provably correct reactive controller that is pulled back
to the physically sensed representation to generate the actual robot commands.
We extend the construction to differential drive robots, and suggest the
empirical utility of the proposed control architecture using both formal proofs
and numerical simulations.
",1,0,0,0,0,0
19276,Automatic smoothness detection of the resolvent Krylov subspace method for the approximation of $C_0$-semigroups,"  The resolvent Krylov subspace method builds approximations to operator
functions $f(A)$ times a vector $v$. For the semigroup and related operator
functions, this method is proved to possess the favorable property that the
convergence is automatically faster when the vector $v$ is smoother. The user
of the method does not need to know the presented theory and alterations of the
method are not necessary in order to adapt to the (possibly unknown) smoothness
of $v$. The findings are illustrated by numerical experiments.
",0,0,1,0,0,0
19277,On the observability of Pauli crystals,"  The best known manifestation of the Fermi-Dirac statistics is the Pauli
exclusion principle: no two identical fermions can occupy the same one-particle
state. This principle enforces high order correlations in systems of many
identical fermions and is responsible for a particular geometric arrangement of
trapped particles even when all mutual interactions are absent [1]. These
geometric structures, called Pauli crystals, are predicted for a system of $N$
identical atoms trapped in a harmonic potential. They emerge as the most
frequent configurations in a collection of single-shot pictures of the system.
Here we study how fragile Pauli crystals are when realistic experimental
limitations are taken into account. The influence of the number of single-shots
pictures available to analysis, thermal fluctuations and finite efficiency of
detection are considered. The role of these sources of noise on the possibility
of experimental observation of Pauli crystals is shown and conditions necessary
for the detection of the geometrical arrangements of particles are identified.
",0,1,0,0,0,0
19278,Interpolating Between Choices for the Approximate Intermediate Value Theorem,"  This paper proves the approximate intermediate value theorem, constructively
and from notably weak hypotheses: from pointwise rather than uniform
continuity, without assuming that reals are presented with rational
approximants, and without using countable choice. The theorem is that if a
pointwise continuous function has both a negative and a positive value, then it
has values arbitrarily close to 0. The proof builds on the usual classical
proof by bisection, which repeatedly selects the left or right half of an
interval; the algorithm here selects an interval of half the size in a
continuous way, interpolating between those two possibilities.
",1,0,1,0,0,0
19279,HAZMAT II: Ultraviolet Variability of Low-Mass Stars in the GALEX Archive,"  The ultraviolet (UV) light from a host star influences a planet's atmospheric
photochemistry and will affect interpretations of exoplanetary spectra from
future missions like the James Webb Space Telescope. These effects will be
particularly critical in the study of planetary atmospheres around M dwarfs,
including Earth-sized planets in the habitable zone. Given the higher activity
levels of M dwarfs compared to Sun-like stars, time resolved UV data are needed
for more accurate input conditions for exoplanet atmospheric modeling. The
Galaxy Evolution Explorer (\emph{GALEX}) provides multi-epoch photometric
observations in two UV bands: near-ultraviolet (NUV; 1771 -- 2831 \AA) and
far-ultraviolet (FUV; 1344 -- 1786 \AA). Within 30 pc of Earth, there are 357
and 303 M dwarfs in the NUV and FUV bands, respectively, with multiple\GALEX
observations. Simultaneous NUV and FUV detections exist for 145 stars in
both\GALEX bands. Our analyses of these data show that low-mass stars are
typically more variable in the FUV than the NUV. Median variability increases
with later spectral types in the NUV with no clear trend in the FUV. We find
evidence that flares increase the FUV flux density far more than the NUV flux
density, leading to variable FUV to NUV flux density ratios in the \GALEX\
bandpasses.The ratio of FUV to NUV flux is important for interpreting the
presence of atmospheric molecules in planetary atmospheres such as oxygen and
methane as a high FUV to NUV ratio may cause false-positive biosignature
detections. This ratio of flux density in the\GALEX\ bands spans three orders
of magnitude in our sample, from 0.008 to 4.6, and is 1 to 2 orders of
magnitude higher than for G dwarfs like the Sun. These results characterize the
UV behavior for the largest set of low-mass stars to date.
",0,1,0,0,0,0
19280,Exactly solvable Schrödinger equation with double-well potential for hydrogen bond,"  We construct a double-well potential for which the Schrödinger equation can
be exactly solved via reducing to the confluent Heun's one. Thus the wave
function is expressed via the confluent Heun's function. The latter is
tabulated in {\sl {Maple}} so that the obtained solution is easily treated. The
potential is infinite at the boundaries of the final interval that makes it to
be highly suitable for modeling hydrogen bonds (both ordinary and low-barrier
ones). We exemplify theoretical results by detailed treating the hydrogen bond
in $KHCO_3$ and show their good agreement with literature experimental data.
",0,1,0,0,0,0
19281,The co-evolution of emotional well-being with weak and strong friendship ties,"  Social ties are strongly related to well-being. But what characterizes this
relationship? This study investigates social mechanisms explaining how social
ties affect well-being through social integration and social influence, and how
well-being affects social ties through social selection. We hypothesize that
highly integrated individuals - those with more extensive and dense friendship
networks - report higher emotional well-being than others. Moreover, emotional
well-being should be influenced by the well-being of close friends. Finally,
well-being should affect friendship selection when individuals prefer others
with higher levels of well-being, and others whose well-being is similar to
theirs. We test our hypotheses using longitudinal social network and well-being
data of 117 individuals living in a graduate housing community. The application
of a novel extension of Stochastic Actor-Oriented Models for ordered networks
(ordered SAOMs) allows us to detail and test our hypotheses for weak- and
strong-tied friendship networks simultaneously. Results do not support our
social integration and social influence hypotheses but provide evidence for
selection: individuals with higher emotional well-being tend to have more
strong-tied friends, and there are homophily processes regarding emotional
well-being in strong-tied networks. Our study highlights the two-directional
relationship between social ties and well-being, and demonstrates the
importance of considering different tie strengths for various social processes.
",1,0,0,1,0,0
19282,A magnetic version of the Smilansky-Solomyak model,"  We analyze spectral properties of two mutually related families of magnetic
Schrödinger operators, $H_{\mathrm{Sm}}(A)=(i \nabla +A)^2+\omega^2
y^2+\lambda y \delta(x)$ and $H(A)=(i \nabla +A)^2+\omega^2 y^2+ \lambda y^2
V(x y)$ in $L^2(R^2)$, with the parameters $\omega>0$ and $\lambda<0$, where
$A$ is a vector potential corresponding to a homogeneous magnetic field
perpendicular to the plane and $V$ is a regular nonnegative and compactly
supported potential. We show that the spectral properties of the operators
depend crucially on the one-dimensional Schrödinger operators $L=
-\frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda \delta (x)$ and $L (V)=
- \frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda V(x)$, respectively.
Depending on whether the operators $L$ and $L(V)$ are positive or not, the
spectrum of $H_{\mathrm{Sm}}(A)$ and $H(V)$ exhibits a sharp transition.
",0,0,1,0,0,0
19283,John-Nirenberg Radius and Collapse in Conformal Geometry,"  Given a positive function $u\in W^{1,n}$, we define its John-Nirenberg radius
at point $x$ to be the supreme of the radius such that $\int_{B_t}|\nabla\log
u|^n<\epsilon_0^n$ when $n>2$, and $\int_{B_t}|\nabla u|^2<\epsilon_0^2$ when
$n=2$. We will show that for a collapsing sequence in a fixed conformal class
under some curvature conditions, the radius is bounded below by a positive
constant. As applications, we will study the convergence of a conformal metric
sequence on a $4$-manifold with bounded $\|K\|_{W^{1,2}}$, and prove a
generalized Hélein's Convergence Theorem.
",0,0,1,0,0,0
19284,Deep Prior,"  The recent literature on deep learning offers new tools to learn a rich
probability distribution over high dimensional data such as images or sounds.
In this work we investigate the possibility of learning the prior distribution
over neural network parameters using such tools. Our resulting variational
Bayes algorithm generalizes well to new tasks, even when very few training
examples are provided. Furthermore, this learned prior allows the model to
extrapolate correctly far from a given task's training data on a meta-dataset
of periodic signals.
",1,0,0,1,0,0
19285,Generalized two-dimensional linear discriminant analysis with regularization,"  Recent advances show that two-dimensional linear discriminant analysis
(2DLDA) is a successful matrix based dimensionality reduction method. However,
2DLDA may encounter the singularity issue theoretically and the sensitivity to
outliers. In this paper, a generalized Lp-norm 2DLDA framework with
regularization for an arbitrary $p>0$ is proposed, named G2DLDA. There are
mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary
Lp-norm to measure the between-class and within-class scatter, and hence a
proper $p$ can be selected to achieve the robustness. The other one is that by
introducing an extra regularization term, G2DLDA achieves better generalization
performance, and solves the singularity problem. In addition, G2DLDA can be
solved through a series of convex problems with equality constraint, and it has
closed solution for each single problem. Its convergence can be guaranteed
theoretically when $1\leq p\leq2$. Preliminary experimental results on three
contaminated human face databases show the effectiveness of the proposed
G2DLDA.
",0,0,0,1,0,0
19286,Time-Frequency Audio Features for Speech-Music Classification,"  Distinct striation patterns are observed in the spectrograms of speech and
music. This motivated us to propose three novel time-frequency features for
speech-music classification. These features are extracted in two stages. First,
a preset number of prominent spectral peak locations are identified from the
spectra of each frame. These important peak locations obtained from each frame
are used to form Spectral peak sequences (SPS) for an audio interval. In second
stage, these SPS are treated as time series data of frequency locations. The
proposed features are extracted as periodicity, average frequency and
statistical attributes of these spectral peak sequences. Speech-music
categorization is performed by learning binary classifiers on these features.
We have experimented with Gaussian mixture models, support vector machine and
random forest classifiers. Our proposal is validated on four datasets and
benchmarked against three baseline approaches. Experimental results establish
the validity of our proposal.
",1,0,0,0,0,0
19287,Kinodynamic Planning on Constraint Manifolds,"  This paper presents a motion planner for systems subject to kinematic and
dynamic constraints. The former appear when kinematic loops are present in the
system, such as in parallel manipulators, in robots that cooperate to achieve a
given task, or in situations involving contacts with the environment. The
latter are necessary to obtain realistic trajectories, taking into account the
forces acting on the system. The kinematic constraints make the state space
become an implicitly-defined manifold, which complicates the application of
common motion planning techniques. To address this issue, the planner
constructs an atlas of the state space manifold incrementally, and uses this
atlas both to generate random states and to dynamically simulate the steering
of the system towards such states. The resulting tools are then exploited to
construct a rapidly-exploring random tree (RRT) over the state space. To the
best of our knowledge, this is the first randomized kinodynamic planner for
implicitly-defined state spaces. The test cases presented in this paper
validate the approach in significantly-complex systems.
",1,0,0,0,0,0
19288,Binary Classification from Positive-Confidence Data,"  Can we learn a binary classifier from only positive data, without any
negative data or unlabeled data? We show that if one can equip positive data
with confidence (positive-confidence), one can successfully learn a binary
classifier, which we name positive-confidence (Pconf) classification. Our work
is related to one-class classification which is aimed at ""describing"" the
positive class by clustering-related methods, but one-class classification does
not have the ability to tune hyper-parameters and their aim is not on
""discriminating"" positive and negative classes. For the Pconf classification
problem, we provide a simple empirical risk minimization framework that is
model-independent and optimization-independent. We theoretically establish the
consistency and an estimation error bound, and demonstrate the usefulness of
the proposed method for training deep neural networks through experiments.
",1,0,0,1,0,0
19289,Skewing Methods for Variance-Stabilizing Local Linear Regression Estimation,"  It is well-known that kernel regression estimators do not produce a constant
estimator variance over a domain. To correct this problem, Nishida and Kanazawa
(2015) proposed a variance-stabilizing (VS) local variable bandwidth for Local
Linear (LL) regression estimator. In contrast, Choi and Hall (1998) proposed
the skewing (SK) methods for a univariate LL estimator and constructed a convex
combination of one LL estimator and two SK estimators that are symmetrically
placed on both sides of the LL estimator (the convex combination (CC)
estimator) to eliminate higher-order terms in its asymptotic bias. To obtain a
CC estimator with a constant estimator variance without employing the VS local
variable bandwidth, the weight in the convex combination must be determined
locally to produce a constant estimator variance. In this study, we compare the
performances of two VS methods for a CC estimator and find cases in which the
weighting method can superior to the VS bandwidth method in terms of the degree
of variance stabilization.
",0,0,0,1,0,0
19290,Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization,"  To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body to those that are more distant. Here, we formulate and
study computationally the hypothesis that such patterns can emerge
spontaneously as the result of a family of stochastic optimization processes
(evolution strategies with covariance-matrix adaptation), without an innate
encoding of a maturational schedule. In particular, we present simulated
experiments with an arm where a computational learner progressively acquires
reaching skills through adaptive exploration, and we show that a proximodistal
organization appears spontaneously, which we denote PDFF (ProximoDistal
Freezing and Freeing of degrees of freedom). We also compare this emergent
organization between different arm morphologies -- from human-like to quite
unnatural ones -- to study the effect of different kinematic structures on the
emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
stochastic optimization; modelling; evolution strategies; cross-entropy
methods; policy search; morphology.}
",1,0,0,0,0,0
19291,Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes,"  We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary
and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to
error $\varepsilon$ in total variation distance. This improves both the known
upper bounds and lower bounds for this problem. For mixtures of axis-aligned
Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice,
matching a known lower bound. Moreover, these results hold in the
agnostic-learning/robust-estimation setting as well, where the target
distribution is only approximately a mixture of Gaussians.
The upper bound is shown using a novel technique for distribution learning
based on a notion of `compression.' Any class of distributions that allows such
a compression scheme can also be learned with few samples. Moreover, if a class
of distributions has such a compression scheme, then so do the classes of
products and mixtures of those distributions. The core of our main result is
showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized
compression scheme.
",1,0,1,0,0,0
19292,Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME,"  We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
programs that will explain the behavior of XGBoost trained classifiers. We use
the technique based on the LIME approach to locally select the most important
features contributing to the classification decision. Then, in order to explain
the model's global behavior, we propose the LIME-FOLD algorithm ---a
heuristic-based inductive logic programming (ILP) algorithm capable of learning
non-monotonic logic programs---that we apply to a transformed dataset produced
by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
Our experiments with UCI standard benchmarks suggest a significant improvement
in terms of classification evaluation metrics. Meanwhile, the number of induced
rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
",0,0,0,1,0,0
19293,Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation,"  Manipulation of deformable objects, such as ropes and cloth, is an important
but challenging problem in robotics. We present a learning-based system where a
robot takes as input a sequence of images of a human manipulating a rope from
an initial to goal configuration, and outputs a sequence of actions that can
reproduce the human demonstration, using only monocular images as input. To
perform this task, the robot learns a pixel-level inverse dynamics model of
rope manipulation directly from images in a self-supervised manner, using about
60K interactions with the rope collected autonomously by the robot. The human
demonstration provides a high-level plan of what to do and the low-level
inverse model is used to execute the plan. We show that by combining the high
and low-level plans, the robot can successfully manipulate a rope into a
variety of target shapes using only a sequence of human-provided images for
direction.
",1,0,0,0,0,0
19294,Online learning with graph-structured feedback against adaptive adversaries,"  We derive upper and lower bounds for the policy regret of $T$-round online
learning problems with graph-structured feedback, where the adversary is
nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
$\widetilde O(T^{2/3})$ and $\widetilde O(T^{3/4})$ for strongly-observable and
weakly-observable graphs, respectively, based on analyzing a variant of the
Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in
the case of full-information feedback. We also study the particular loss
structure of an oblivious adversary with switching costs, and show that in such
a setting, non-revealing strongly-observable feedback graphs achieve a lower
bound of $\widetilde\Omega(T^{2/3})$, as well.
",0,0,0,1,0,0
19295,Dynamical characteristics of electromagnetic field under conditions of total reflection,"  The dynamical characteristics of electromagnetic fields include energy,
momentum, angular momentum (spin) and helicity. We analyze their spatial
distributions near the planar interface between two transparent and
non-dispersive media, when the incident monochromatic plane wave with arbitrary
polarization is totally reflected, and an evanescent wave is formed in the
medium with lower optical density. Based on the recent arguments in favor of
the Minkowski definition of the electromagnetic momentum in a material medium
[Phys. Rev. A 83, 013823 (2011); 86, 055802 (2012); Phys. Rev. Lett. 119,
073901 (2017)], we derive the explicit expressions for the dynamical
characteristics in both media, with special attention to their behavior at the
interface. Especially, the ""extraordinary"" spin and momentum components
orthogonal to the plane of incidence are described, and the canonical (spin -
orbital) momentum decomposition is performed that contains no singular terms.
The field energy, helicity, the spin momentum and orbital momentum components
are everywhere regular but experience discontinuities at the interface; the
spin components parallel to the interface appear to be continuous, which
testifies for the consistency of the adopted Minkowski picture. The results
supply a meaningful example of the electromagnetic momentum decomposition, with
separation of spatial and polarization degrees of freedom, in inhomogeneous
media, and can be used in engineering the structured fields designed for
optical sorting, dispatching and micromanipulation.
",0,1,0,0,0,0
19296,Functors induced by Cauchy extension of C*-algebras,"  In this paper we give three functors $\mathfrak{P}$, $[\cdot]_K$ and
$\mathfrak{F}$ on the category of C$^\ast$-algebras. The functor $\mathfrak{P}$
assigns to each C$^\ast$-algebra $\mathcal{A}$ a pre-C$^\ast$-algebra
$\mathfrak{P}(\mathcal{A})$ with completion $[\mathcal{A}]_K$. The functor
$[\cdot]_K$ assigns to each C$^\ast$-algebra $\mathcal{A}$ the Cauchy extension
$[\mathcal{A}]_K$ of $\mathcal{A}$ by a non-unital C$^\ast$-algebra
$\mathfrak{F}(\mathcal{A})$. Some properties of these functors are also given.
In particular, we show that the functors $[\cdot]_K$ and $\mathfrak{F}$ are
exact and the functor $\mathfrak{P}$ is normal exact.
",0,0,1,0,0,0
19297,Hierarchically cocompact classifying spaces for mapping class groups of surfaces,"  We define the notion of a hierarchically cocompact classifying space for a
family of subgroups of a group. Our main application is to show that the
mapping class group $\mbox{Mod}(S)$ of any connected oriented compact surface
$S$, possibly with punctures and boundary components and with negative Euler
characteristic has a hierarchically cocompact model for the family of virtually
cyclic subgroups of dimension at most $\mbox{vcd} \mbox{Mod}(S)+1$. When the
surface is closed, we prove that this bound is optimal. In particular, this
answers a question of Lück for mapping class groups of surfaces.
",0,0,1,0,0,0
19298,A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks,"  An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.
",0,0,0,1,1,0
19299,Coherent single-atom superradiance,"  Quantum effects, prevalent in the microscopic scale, generally elusive in
macroscopic systems due to dissipation and decoherence. Quantum phenomena in
large systems emerge only when particles are strongly correlated as in
superconductors and superfluids. Cooperative interaction of correlated atoms
with electromagnetic fields leads to superradiance, the enhanced quantum
radiation phenomenon, exhibiting novel physics such as quantum Dicke phase and
ultranarrow linewidth for optical clocks. Recent researches to imprint atomic
correlation directly demonstrated controllable collective atom-field
interactions. Here, we report cavity-mediated coherent single-atom
superradiance. Single atoms with predefined correlation traverse a high-Q
cavity one by one, emitting photons cooperatively with the atoms already gone
through the cavity. Such collective behavior of time-separated atoms is
mediated by the long-lived cavity field. As a result, a coherent field is
generated in the steady state, whose intensity varies as the square of the
number of traversing atoms during the cavity decay time, exhibiting more than
ten-fold enhancement from noncollective cases. The correlation among single
atoms is prepared with the aligned atomic phase achieved by nanometer-precision
position control of atoms with a nanohole-array aperture. The present work
deepens our understanding of the collective matter-light interaction and
provides an advanced platform for phase-controlled atom-field interactions.
",0,1,0,0,0,0
19300,Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge,"  This paper presents a deep learning framework that is capable of solving
partially observable locomotion tasks based on our novel interpretation of
Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled
error measure and its variance induced by the partial observability of
environment and subtrajectory sampling, respectively. Three major improvements
are introduced in our RDPG based learning framework: tail-step bootstrap of
interpolated temporal difference, initialisation of hidden state using past
trajectory scanning, and injection of external experiences learned by other
agents. The proposed learning framework was implemented to solve the
Bipedal-Walker challenge in OpenAI's gym simulation environment where only
partial state information is available. Our simulation study shows that the
autonomous behaviors generated by the RDPG agent are highly adaptive to a
variety of obstacles and enables the agent to effectively traverse rugged
terrains for long distance with higher success rate than leading contenders.
",1,0,0,0,0,0
19301,Diversified essential properties in halogenated graphenes,"  The significant halogenation effects on the essential properties of graphene
are investigated by the first-principles method. The geometric structures,
electronic properties, and magnetic configurations are greatly diversified
under the various halogen adsorptions. Fluorination, with the strong
multi-orbital chemical bondings, can create the buckled graphene structure,
while the other halogenations do not change the planar {\sigma} bonding in the
presence of single-orbital hybridization. Electronic structures consist of the
carbon-, adatom- and (carbon, adatom)-dominated energy bands. All halogenated
graphenes belong to hole-doped metals except that fluorinated systems are
middle-gap semiconductors at sufficiently high concentration. Moreover, the
metallic ferromagnetism is revealed in certain adatom distributions. The
unusual hybridization-induced features are clearly evidenced in many van Hove
singularities of the density of states. The structure- and adatom-enriched
essential properties are compared with the measured results, and potential
applications are also discussed.
",0,1,0,0,0,0
19302,Quantifying genuine multipartite correlations and their pattern complexity,"  We propose an information-theoretic framework to quantify multipartite
correlations in classical and quantum systems, answering questions such as:
what is the amount of seven-partite correlations in a given state of ten
particles? We identify measures of genuine multipartite correlations, i.e.
statistical dependencies which cannot be ascribed to bipartite correlations,
satisfying a set of desirable properties. Inspired by ideas developed in
complexity science, we then introduce the concept of weaving to classify states
which display different correlation patterns, but cannot be distinguished by
correlation measures. The weaving of a state is defined as the weighted sum of
correlations of every order. Weaving measures are good descriptors of the
complexity of correlation structures in multipartite systems.
",0,1,0,0,0,0
19303,Pulsating low-mass white dwarfs in the frame of new evolutionary sequences: IV. The secular rate of period change,"  We present a theoretical assessment of the expected temporal rates of change
of periods ($\dot{\Pi}$) for low-mass ($M_{\star}/M_{\sun} \lesssim 0.45$) and
extremely low-mass (ELM, $M_{\star}/M_{\sun} \lesssim 0.18-0.20$) white-dwarf
stars, based on fully evolutionary low-mass He-core white dwarf and pre-white
dwarf models. Our analysis is based on a large set of adiabatic periods of
radial and nonradial pulsation modes computed on a suite of low-mass He-core
white dwarf and pre-white dwarf models with masses ranging from $0.1554$ to
$0.4352 M_{\sun}$. We compute the secular rates of period change of radial
($\ell= 0$) and nonradial ($\ell= 1, 2$) $g$ and $p$ modes for stellar models
representative of ELMV and pre-ELMV stars, as well as for stellar objects that
are evolving just before the occurrence of CNO flashes at the early cooling
branches. We found that the theoretically expected magnitude of $\dot{\Pi}$ of
$g$ modes for pre-ELMVs are by far larger than for ELMVs. In turn, $\dot{\Pi}$
of $g$ modes for models evolving before the occurrence of CNO flashes are
larger than the maximum values of the rates of period change predicted for
pre-ELMV stars. Regarding $p$ and radial modes, we found that the larger
absolute values of $\dot{\Pi}$ correspond to pre-ELMV models. We conclude that
any eventual measurement of a rate of period change for a given pulsating
low-mass pre-white dwarf or white dwarf star could shed light about its
evolutionary status. Also, in view of the systematic difficulties in the
spectroscopic classification of stars of the ELM Survey, an eventual
measurement of $\dot{\Pi}$ could help to confirm that a given pulsating star is
an authentic low-mass white dwarf and not a star from another stellar
population.
",0,1,0,0,0,0
19304,Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions,"  The normalized maximum likelihood (NML) is one of the most important
distribution in coding theory and statistics. NML is the unique solution (if
exists) to the pointwise minimax regret problem. However, NML is not defined
even for simple family of distributions such as the normal distributions. Since
there does not exist any meaningful minimax-regret distribution for such case,
it is pointed out that NML with luckiness (LNML) can be employed as an
alternative to NML. In this paper, we develop the closed form of LNMLs for
multivariate normal distributions.
",0,0,1,1,0,0
19305,Tackling Diversity and Heterogeneity by Vertical Memory Management,"  Existing memory management mechanisms used in commodity computing machines
typically adopt hardware based address interleaving and OS directed random
memory allocation to service generic application requests. These conventional
memory management mechanisms are challenged by contention at multiple memory
levels, a daunting variety of workload behaviors, and an increasingly
complicated memory hierarchy. Our ISCA-41 paper proposes vertical partitioning
to eliminate shared resource contention at multiple levels in the memory
hierarchy. Combined with horizontal memory management policies, our framework
supports a flexible policy space for tackling diverse application needs in
production environment and is suitable for future heterogeneous memory systems.
",1,0,0,0,0,0
19306,From the Icosahedron to E8,"  The regular icosahedron is connected to many exceptional objects in
mathematics. Here we describe two constructions of the $\mathrm{E}_8$ lattice
from the icosahedron. One uses a subring of the quaternions called the
""icosians"", while the other uses du Val's work on the resolution of Kleinian
singularities. Together they link the golden ratio, the quaternions, the
quintic equation, the 600-cell, and the Poincare homology 3-sphere. We leave it
as a challenge to the reader to find the connection between these two
constructions.
",0,0,1,0,0,0
19307,Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation,"  This paper demonstrates the feasibility of implementing Real-Time State
Estimators (RTSEs) for Active Distribution Networks (ADNs) in
Field-Programmable Gate Arrays (FPGAs) by presenting an operational prototype.
The prototype is based on a Linear State Estimator (LSE) that uses
synchrophasor measurements from Phasor Measurement Units (PMUs). The underlying
algorithm is the Sequential Discrete Kalman Filter (SDKF), an equivalent
formulation of the Discrete Kalman Filter (DKF) for the case of uncorrelated
measurement noise. In this regard, this work formally proves the equivalence
the SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA
implementation by means of a computational complexity analysis. The developed
prototype is validated using a case study adapted from the IEEE 34-node
distribution test feeder.
",0,0,0,1,0,0
19308,Game-Theoretic Semantics for ATL+ with Applications to Model Checking,"  We develop game-theoretic semantics (GTS) for the fragment ATL+ of the full
Alternating-time Temporal Logic ATL*, essentially extending a recently
introduced GTS for ATL. We first show that the new game-theoretic semantics is
equivalent to the standard semantics of ATL+ (based on perfect recall
strategies). We then provide an analysis, based on the new semantics, of the
memory and time resources needed for model checking ATL+. Based on that, we
establish that strategies that use only a very limited amount of memory suffice
for ATL+. Furthermore, using the GTS we provide a new algorithm for model
checking of ATL+ and identify a natural hierarchy of tractable fragments of
ATL+ that extend ATL.
",1,0,1,0,0,0
19309,Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification,"  The wide usage of Machine Learning (ML) has lead to research on the attack
vectors and vulnerability of these systems. The defenses in this area are
however still an open problem, and often lead to an arms race. We define a
naive, secure classifier at test time and show that a Gaussian Process (GP) is
an instance of this classifier given two assumptions: one concerns the
distances in the training data, the other rejection at test time. Using these
assumptions, we are able to show that a classifier is either secure, or
generalizes and thus learns. Our analysis also points towards another factor
influencing robustness, the curvature of the classifier. This connection is not
unknown for linear models, but GP offer an ideal framework to study this
relationship for nonlinear classifiers. We evaluate on five security and two
computer vision datasets applying test and training time attacks and membership
inference. We show that we only change which attacks are needed to succeed,
instead of alleviating the threat. Only for membership inference, there is a
setting in which attacks are unsuccessful (<10% increase in accuracy over
random guess). Given these results, we define a classification scheme based on
voting, ParGP. This allows us to decide how many points vote and how large the
agreement on a class has to be. This ensures a classification output only in
cases when there is evidence for a decision, where evidence is parametrized. We
evaluate this scheme and obtain promising results.
",0,0,0,1,0,0
19310,Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis,"  We analyse a linear regression problem with nonconvex regularization called
smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
for Gaussian random data. We propose an approximate message passing (AMP)
algorithm considering nonconvex regularization, namely SCAD-AMP, and
analytically show that the stability condition corresponds to the de
Almeida--Thouless condition in spin glass literature. Through asymptotic
analysis, we show the correspondence between the density evolution of SCAD-AMP
and the replica symmetric solution. Numerical experiments confirm that for a
sufficiently large system size, SCAD-AMP achieves the optimal performance
predicted by the replica method. Through replica analysis, a phase transition
between replica symmetric (RS) and replica symmetry breaking (RSB) region is
found in the parameter space of SCAD. The appearance of the RS region for a
nonconvex penalty is a significant advantage that indicates the region of
smooth landscape of the optimization problem. Furthermore, we analytically show
that the statistical representation performance of the SCAD penalty is better
than that of L1-based methods, and the minimum representation error under RS
assumption is obtained at the edge of the RS/RSB phase. The correspondence
between the convergence of the existing coordinate descent algorithm and RS/RSB
transition is also indicated.
",1,0,0,1,0,0
19311,Concurrence Topology of Some Cancer Genomics Data,"  The topological data analysis method ""concurrence topology"" is applied to
mutation frequencies in 69 genes in glioblastoma data. In dimension 1 some
apparent ""mutual exclusivity"" is found. By simulation of data having
approximately the same second order dependence structure as that found in the
data, it appears that one triple of mutations, PTEN, RB1, TP53, exhibits mutual
exclusivity that depends on special features of the third order dependence and
may reflect global dependence among a larger group of genes. A bootstrap
analysis suggests that this form of mutual exclusivity is not uncommon in the
population from which the data were drawn.
",0,0,0,1,0,0
19312,Quasimomentum of an elementary excitation for a system of point bosons with zero boundary conditions,"  As is known, an elementary excitation of a many-particle system with
boundaries is not characterized by a definite momentum. Here, we obtain the
formula for the quasimomentum of an elementary excitation for a one-dimensional
system of $N$ spinless point bosons with zero boundary conditions (BCs). We
also find that the dispersion law $E(p)$ of the system with zero BCs coincides
with that of a system with periodic BCs. The elementary excitations are defined
within a new approach proposed earlier by the author. This approach is
mathematically equivalent to the traditional approach by Lieb, but differs from
it by a simpler way of enumeration of excited states and leads to a single
dispersion law (instead of two ones in the Lieb's approach).
",0,1,0,0,0,0
19313,Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game,"  In a previous paper [ arXiv:1508.03269 ] we described the techniques we
successfully employed for automatically reconstructing the whole move sequence
of a Go game by means of a set of pictures. Now we describe how it is possible
to reconstruct the move sequence by means of a video stream (which may be
provided by an unattended webcam), possibly in real-time. Although the basic
algorithms remain the same, we will discuss the new problems that arise when
dealing with videos, with special care for the ones that could block a
real-time analysis and require an improvement of our previous techniques or
even a completely brand new approach. Eventually we present a number of
preliminary but positive experimental results supporting the effectiveness of
the software we are developing, built on the ideas here outlined.
",1,0,0,0,0,0
19314,Malware Detection by Eating a Whole EXE,"  In this work we introduce malware detection from raw byte sequences as a
fruitful research area to the larger machine learning community. Building a
neural network for such a problem presents a number of interesting challenges
that have not occurred in tasks such as image processing or NLP. In particular,
we note that detection from raw bytes presents a sequence problem with over two
million time steps and a problem where batch normalization appear to hinder the
learning process. We present our initial work in building a solution to tackle
this problem, which has linear complexity dependence on the sequence length,
and allows for interpretable sub-regions of the binary to be identified. In
doing so we will discuss the many challenges in building a neural network to
process data at this scale, and the methods we used to work around them.
",1,0,0,1,0,0
19315,On the Complexity of Model Checking for Syntactically Maximal Fragments of the Interval Temporal Logic HS with Regular Expressions,"  In this paper, we investigate the model checking (MC) problem for Halpern and
Shoham's interval temporal logic HS. In the last years, interval temporal logic
MC has received an increasing attention as a viable alternative to the
traditional (point-based) temporal logic MC, which can be recovered as a
special case. Most results have been obtained under the homogeneity assumption,
that constrains a proposition letter to hold over an interval if and only if it
holds over each component state. Recently, Lomuscio and Michaliszyn proposed a
way to relax such an assumption by exploiting regular expressions to define the
behaviour of proposition letters over intervals in terms of their component
states. When homogeneity is assumed, the exact complexity of MC is a difficult
open question for full HS and for its two syntactically maximal fragments
AA'BB'E' and AA'EB'E'. In this paper, we provide an asymptotically optimal
bound to the complexity of these two fragments under the more expressive
semantic variant based on regular expressions by showing that their MC problem
is AEXP_pol-complete, where AEXP_pol denotes the complexity class of problems
decided by exponential-time bounded alternating Turing Machines making a
polynomially bounded number of alternations.
",1,0,0,0,0,0
19316,Deforming 3-manifolds of bounded geometry and uniformly positive scalar curvature,"  We prove that the moduli space of complete Riemannian metrics of bounded
geometry and uniformly positive scalar curvature on an orientable 3-manifold is
path-connected. This generalizes the main result of the fourth author [Mar12]
in the compact case. The proof uses Ricci flow with surgery as well as
arguments involving performing infinite connected sums with control on the
geometry.
",0,0,1,0,0,0
19317,Optimal and Myopic Information Acquisition,"  We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and ""simple"" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment.
",1,0,1,1,0,0
19318,Infinitely many minimal classes of graphs of unbounded clique-width,"  The celebrated theorem of Robertson and Seymour states that in the family of
minor-closed graph classes, there is a unique minimal class of graphs of
unbounded tree-width, namely, the class of planar graphs. In the case of
tree-width, the restriction to minor-closed classes is justified by the fact
that the tree-width of a graph is never smaller than the tree-width of any of
its minors. This, however, is not the case with respect to clique-width, as the
clique-width of a graph can be (much) smaller than the clique-width of its
minor. On the other hand, the clique-width of a graph is never smaller than the
clique-width of any of its induced subgraphs, which allows us to be restricted
to hereditary classes (that is, classes closed under taking induced subgraphs),
when we study clique-width. Up to date, only finitely many minimal hereditary
classes of graphs of unbounded clique-width have been discovered in the
literature. In the present paper, we prove that the family of such classes is
infinite. Moreover, we show that the same is true with respect to linear
clique-width.
",1,0,1,0,0,0
19319,A Passivity-Based Approach to Nash Equilibrium Seeking over Networks,"  In this paper we consider the problem of distributed Nash equilibrium (NE)
seeking over networks, a setting in which players have limited local
information. We start from a continuous-time gradient-play dynamics that
converges to an NE under strict monotonicity of the pseudo-gradient and assumes
perfect information, i.e., instantaneous all-to-all player communication. We
consider how to modify this gradient-play dynamics in the case of partial, or
networked information between players. We propose an augmented gradient-play
dynamics with correction in which players communicate locally only with their
neighbours to compute an estimate of the other players' actions. We derive the
new dynamics based on the reformulation as a multi-agent coordination problem
over an undirected graph. We exploit incremental passivity properties and show
that a synchronizing, distributed Laplacian feedback can be designed using
relative estimates of the neighbours. Under a strict monotonicity property of
the pseudo-gradient, we show that the augmented gradient-play dynamics
converges to consensus on the NE of the game. We further discuss two cases that
highlight the tradeoff between properties of the game and the communication
graph.
",0,0,1,0,0,0
19320,Magnetic and dielectric investigations of $γ$ - Fe${_2}$WO${_6}$,"  The magnetic, thermodynamic and dielectric properties of the $\gamma$ -
Fe${_2}$WO${_6}$ system is reported. Crystallizing in the centrosymmetric
$Pbcn$ space group, this particular polymorph exhibits a number of different
magnetic transitions, all of which are seen to exhibit a finite
magneto-dielectric coupling. At the lowest measured temperatures, the magnetic
ground state appears to be glass-like, as evidenced by the waiting time
dependence of the magnetic relaxation. Also reflected in the frequency
dependent dielectric measurements, these signatures possibly arise as a
consequence of the oxygen non-stoichiometry, which promotes an inhomogeneous
magnetic and electronic ground state.
",0,1,0,0,0,0
19321,Linear centralization classifier,"  A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We formulate the classifier as a quadratic program with quadratic
constraints. We then simplify this formulation to a linear program that can be
solved effectively using a linear programming solver (e.g., simplex-dual). We
extend the formulation for LCC to enable the use of kernel functions for
non-linear classification applications. We compare our method with two standard
classification methods (support vector machine and linear discriminant
analysis) and four state-of-the-art classification methods when they are
applied to eight standard classification datasets. Our experimental results
show that LCC is able to classify instances more accurately (based on the area
under the receiver operating characteristic) in comparison to other tested
methods on the chosen datasets. We also report the results for LCC with a
particular kernel to solve for synthetic non-linear classification problems.
",1,0,0,1,0,0
19322,Spatio-temporal Person Retrieval via Natural Language Queries,"  In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing people annotated with bounding boxes for each second and with five
natural language descriptions. To retrieve the tube of the person described by
a given natural language query, we design a model that combines methods for
spatio-temporal human detection and multimodal retrieval. We conduct
comprehensive experiments to compare a variety of tube and text representations
and multimodal retrieval methods, and present a strong baseline in this task as
well as demonstrate the efficacy of our tube representation and multimodal
feature embedding technique. Finally, we demonstrate the versatility of our
model by applying it to two other important tasks.
",1,0,0,0,0,0
19323,Thermo-elasto-plastic simulations of femtosecond laser-induced multiple-cavity in fused silica,"  The formation and the interaction of multiple cavities, induced by tightly
focused femtosecond laser pulses, are studied by using a developed numerical
tool, including the thermo-elasto-plastic material response. Simulations are
performed in fused silica in cases of one, two, and four spots of laser energy
deposition. The relaxation of the heated matter, launching shock waves in the
surrounding cold material, leads to cavity formation and emergence of areas
where cracks may be induced. Results show that the laser-induced structure
shape depends on the energy deposition configuration and demonstrate the
potential of the used numerical tool to obtain the desired designed structure
or technological process.
",0,1,0,0,0,0
19324,Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species,"  Machine-learning potentials (MLPs) for atomistic simulations are a promising
alternative to conventional classical potentials. Current approaches rely on
descriptors of the local atomic environment with dimensions that increase
quadratically with the number of chemical species. In this article, we
demonstrate that such a scaling can be avoided in practice. We show that a
mathematically simple and computationally efficient descriptor with constant
complexity is sufficient to represent transition-metal oxide compositions and
biomolecules containing 11 chemical species with a precision of around 3
meV/atom. This insight removes a perceived bound on the utility of MLPs and
paves the way to investigate the physics of previously inaccessible materials
with more than ten chemical species.
",0,1,0,0,0,0
19325,Randomized Constraints Consensus for Distributed Robust Linear Programming,"  In this paper we consider a network of processors aiming at cooperatively
solving linear programming problems subject to uncertainty. Each node only
knows a common cost function and its local uncertain constraint set. We propose
a randomized, distributed algorithm working under time-varying, asynchronous
and directed communication topology. The algorithm is based on a local
computation and communication paradigm. At each communication round, nodes
perform two updates: (i) a verification in which they check-in a randomized
setup-the robust feasibility (and hence optimality) of the candidate optimal
point, and (ii) an optimization step in which they exchange their candidate
bases (minimal sets of active constraints) with neighbors and locally solve an
optimization problem whose constraint set includes: a sampled constraint
violating the candidate optimal point (if it exists), agent's current basis and
the collection of neighbor's basis. As main result, we show that if a processor
successfully performs the verification step for a sufficient number of
communication rounds, it can stop the algorithm since a consensus has been
reached. The common solution is-with high confidence-feasible (and hence
optimal) for the entire set of uncertainty except a subset having arbitrary
small probability measure. We show the effectiveness of the proposed
distributed algorithm on a multi-core platform in which the nodes communicate
asynchronously.
",0,0,1,0,0,0
19326,Continuum limit of the vibrational properties of amorphous solids,"  The low-frequency vibrational and low-temperature thermal properties of
amorphous solids are markedly different from those of crystalline solids. This
situation is counter-intuitive because any solid material is expected to behave
as a homogeneous elastic body in the continuum limit, in which vibrational
modes are phonons following the Debye law. A number of phenomenological
explanations have been proposed, which assume elastic heterogeneities, soft
localized vibrations, and so on. Recently, the microscopic mean-field theories
have been developed to predict the universal non-Debye scaling law. Considering
these theoretical arguments, it is absolutely necessary to directly observe the
nature of the low-frequency vibrations of amorphous solids and determine the
laws that such vibrations obey. Here, we perform an extremely large-scale
vibrational mode analysis of a model amorphous solid. We find that the scaling
law predicted by the mean-field theory is violated at low frequency, and in the
continuum limit, the vibrational modes converge to a mixture of phonon modes
following the Debye law and soft localized modes following another universal
non-Debye scaling law.
",0,1,0,0,0,0
19327,Existence of smooth solutions of multi-term Caputo-type fractional differential equations,"  This paper deals with the initial value problem for the multi-term fractional
differential equation. The fractional derivative is defined in the Caputo
sense. Firstly the initial value problem is transformed into a equivalent
Volterra-type integral equation under appropriate assumptions. Then new
existence results for smooth solutions are established by using the Schauder
fixed point theorem.
",0,0,1,0,0,0
19328,Classes of elementary function solutions to the CEV model. I,"  The CEV model subsumes some of the previous option pricing models. An
important parameter in the model is the parameter b, the elasticity of
volatility. For b=0, b=-1/2, and b=-1 the CEV model reduces respectively to the
BSM model, the square-root model of Cox and Ross, and the Bachelier model. Both
in the case of the BSM model and in the case of the CEV model it has become
traditional to begin a discussion of option pricing by starting with the
vanilla European calls and puts. In the case of BSM model simpler solutions are
the log and power solutions. These contracts, despite the simplicity of their
mathematical description, are attracting increasing attention as a trading
instrument. Similar simple solutions have not been studied so far in a
systematic fashion for the CEV model. We use Kovacic's algorithm to derive, for
all half-integer values of b, all solutions ""in quadratures"" of the CEV
ordinary differential equation. These solutions give rise, by separation of
variables, to simple solutions to the CEV partial differential equation. In
particular, when b=...,-5/2,-2,-3/2,-1, 1, 3/2, 2, 5/2,..., we obtain four
classes of denumerably infinite elementary function solutions, when b=-1/2 and
b=1/2 we obtain two classes of denumerably infinite elementary function
solutions, whereas, when b=0 we find two elementary function solutions. In the
derived solutions we have also dispensed with the unnecessary assumption made
in the the BSM model asserting that the underlying asset pays no dividends
during the life of the option.
",0,0,0,0,0,1
19329,Congruent families and invariant tensors,"  Classical results of Chentsov and Campbell state that -- up to constant
multiples -- the only $2$-tensor field of a statistical model which is
invariant under congruent Markov morphisms is the Fisher metric and the only
invariant $3$-tensor field is the Amari-Chentsov tensor. We generalize this
result for arbitrary degree $n$, showing that any family of $n$-tensors which
is invariant under congruent Markov morphisms is algebraically generated by the
canonical tensor fields defined in an earlier paper.
",0,0,1,1,0,0
19330,Quality of Information in Mobile Crowdsensing: Survey and Research Challenges,"  Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.
",1,0,0,0,0,0
19331,SimBlock: A Blockchain Network Simulator,"  Blockchain, which is a technology for distributedly managing ledger
information over multiple nodes without a centralized system, has elicited
increasing attention. Performing experiments on actual blockchains are
difficult because a large number of nodes in wide areas are necessary. In this
study, we developed a blockchain network simulator SimBlock for such
experiments. Unlike the existing simulators, SimBlock can easily change
behavior of node, so that it enables to investigate the influence of nodes'
behavior on blockchains. We compared some simulation results with the measured
values in actual blockchains to demonstrate the validity of this simulator.
Furthermore, to show practical usage, we conducted two experiments which
clarify the influence of neighbor node selection algorithms and relay networks
on the block propagation time. The simulator could depict the effects of the
two techniques on block propagation time. The simulator will be publicly
available in a few months.
",1,0,0,0,0,0
19332,Bayesian Fused Lasso regression for dynamic binary networks,"  We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online.
",0,0,0,1,0,0
19333,Indefinite Kernel Logistic Regression,"  Traditionally, kernel learning methods requires positive definitiveness on
the kernel, which is too strict and excludes many sophisticated similarities,
that are indefinite, in multimedia area. To utilize those indefinite kernels,
indefinite learning methods are of great interests. This paper aims at the
extension of the logistic regression from positive semi-definite kernels to
indefinite kernels. The model, called indefinite kernel logistic regression
(IKLR), keeps consistency to the regular KLR in formulation but it essentially
becomes non-convex. Thanks to the positive decomposition of an indefinite
matrix, IKLR can be transformed into a difference of two convex models, which
follows the use of concave-convex procedure. Moreover, we employ an inexact
solving scheme to speed up the sub-problem and develop a concave-inexact-convex
procedure (CCICP) algorithm with theoretical convergence analysis. Systematical
experiments on multi-modal datasets demonstrate the superiority of the proposed
IKLR method over kernel logistic regression with positive definite kernels and
other state-of-the-art indefinite learning based algorithms.
",1,0,0,1,0,0
19334,Microstructure and thickening of dense suspensions under extensional and shear flows,"  Dense suspensions are non-Newtonian fluids which exhibit strong shear
thickening and normal stress differences. Using numerical simulation of
extensional and shear flows, we investigate how rheological properties are
determined by the microstructure which is built under flows and by the
interactions between particles. By imposing extensional and shear flows, we can
assess the degree of flow-type dependence in regimes below and above
thickening. Even when the flow-type dependence is hindered, nondissipative
responses, such as normal stress differences, are present and characterise the
non-Newtonian behaviour of dense suspensions.
",0,1,0,0,0,0
19335,Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots,"  One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.
",1,0,0,1,0,0
19336,Rewriting in Free Hypergraph Categories,"  We study rewriting for equational theories in the context of symmetric
monoidal categories where there is a separable Frobenius monoid on each object.
These categories, also called hypergraph categories, are increasingly relevant:
Frobenius structures recently appeared in cross-disciplinary applications,
including the study of quantum processes, dynamical systems and natural
language processing. In this work we give a combinatorial characterisation of
arrows of a free hypergraph category as cospans of labelled hypergraphs and
establish a precise correspondence between rewriting modulo Frobenius structure
on the one hand and double-pushout rewriting of hypergraphs on the other. This
interpretation allows to use results on hypergraphs to ensure decidability of
confluence for rewriting in a free hypergraph category. Our results generalise
previous approaches where only categories generated by a single object (props)
were considered.
",1,0,0,0,0,0
19337,Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix,"  In this work we addressed the issue of applying a stochastic classifier and a
local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier-specific competence and cross-competence measures, which estimates
error pattern of the underlying classifier. At the fusion phase we employed two
weighting approaches based on information theory. The classifier weights
promote base classifiers which are the most susceptible to the correction based
on the fuzzy confusion matrix. During the experimental study, the proposed
approach was compared against two reference methods. The comparison was made in
terms of six different quality criteria. The conducted experiments reveals that
the proposed approach eliminates one of main drawbacks of the original
FCM-based approach i.e. the original approach is vulnerable to the imbalanced
class/label distribution. What is more, the obtained results shows that the
introduced method achieves satisfying classification quality under all
considered quality criteria. Additionally, the impact of fluctuations of data
set characteristics is reduced.
",1,0,0,1,0,0
19338,Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank,"  Wilcoxon Rank-based tests are distribution-free alternatives to the popular
two-sample and paired t-tests. For independent data, they are available in
several R packages such as stats and coin. For clustered data, in spite of the
recent methodological developments, there did not exist an R package that makes
them available at one place. We present a package clusrank where the latest
developments are implemented and wrapped under a unified user-friendly
interface. With different methods dispatched based on the inputs, this package
offers great flexibility in rank-based tests for various clustered data. Exact
tests based on permutations are also provided for some methods. Details of the
major schools of different methods are briefly reviewed. Usages of the package
clusrank are illustrated with simulated data as well as a real dataset from an
ophthalmological study. The package also enables convenient comparison between
selected methods under settings that have not been studied before and the
results are discussed.
",0,0,0,1,0,0
19339,Latent heterogeneous multilayer community detection,"  We propose a method for simultaneously detecting shared and unshared
communities in heterogeneous multilayer weighted and undirected networks. The
multilayer network is assumed to follow a generative probabilistic model that
takes into account the similarities and dissimilarities between the
communities. We make use of a variational Bayes approach for jointly inferring
the shared and unshared hidden communities from multilayer network
observations. We show the robustness of our approach compared to state-of-the
art algorithms in detecting disparate (shared and private) communities on
synthetic data as well as on real genome-wide fibroblast proliferation dataset.
",1,0,0,1,0,0
19340,Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection,"  In this paper, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size affect the MSU. We discovered a condition that preserves
good quality in the MSU under different combinations of these three factors,
providing a new useful criterion to help drive the process of dimension
reduction.
",1,0,0,1,0,0
19341,Approximation Dynamics,"  We describe the approximation of a continuous dynamical system on a p. l.
manifold or Cantor set by a tractable system. A system is tractable when it has
a finite number of chain components and, with respect to a given full
background measure, almost every point is generic for one of a finite number of
ergodic invariant measures with non-overlapping supports. The approximations
use non-degenerate simplicial dynamical systems for p. l. manifolds and
shift-like dynamical systems for Cantor Sets.
",0,0,1,0,0,0
19342,Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance,"  The Wasserstein distance between two probability measures on a metric space
is a measure of closeness with applications in statistics, probability, and
machine learning. In this work, we consider the fundamental question of how
quickly the empirical measure obtained from $n$ independent samples from $\mu$
approaches $\mu$ in the Wasserstein distance of any order. We prove sharp
asymptotic and finite-sample results for this rate of convergence for general
measures on general compact metric spaces. Our finite-sample results show the
existence of multi-scale behavior, where measures can exhibit radically
different rates of convergence as $n$ grows.
",0,0,1,1,0,0
19343,Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography,"  Several recent works have proposed and implemented cryptography as a means to
preserve privacy and security of patients health data. Nevertheless, the
weakest point of electronic health record (EHR) systems that relied on these
cryptographic schemes is key management. Thus, this paper presents the
development of privacy and security system for cryptography-based-EHR by taking
advantage of the uniqueness of fingerprint and iris characteristic features to
secure cryptographic keys in a bio-cryptography framework. The results of the
system evaluation showed significant improvements in terms of time efficiency
of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
likelihood of imposters gaining successful access to the keys protecting
patients protected health information. This result also justifies the
feasibility of implementing fuzzy key binding scheme in real applications,
especially fuzzy vault which demonstrated a better performance during key
reconstruction.
",1,0,0,0,0,0
19344,A Dynamically Reconfigurable Terahertz Array Antenna for Near-field Imaging Applications,"  A proof of concept for high speed near-field imaging with sub-wavelength
resolution using SLM is presented. An 8 channel THz detector array antenna with
an electrode gap of 100 um and length of 5 mm is fabricated using the
commercially available GaAs semiconductor substrate. Each array antenna can be
excited simultaneously by spatially reconfiguring the optical probe beam and
the THz electric field can be recorded using 8 channel lock-in amplifiers. By
scanning the probe beam along the length of the array antenna, a 2D image can
be obtained with amplitude, phase and frequency information.
",0,1,0,0,0,0
19345,Stationary C*-dynamical systems,"  We introduce the notion of stationary actions in the context of C*-algebras.
We develop the basics of the theory, and provide applications to several
ergodic theoretical and operator algebraic rigidity problems.
",0,0,1,0,0,0
19346,The impact of imbalanced training data on machine learning for author name disambiguation,"  In supervised machine learning for author name disambiguation, negative
training data are often dominantly larger than positive training data. This
paper examines how the ratios of negative to positive training data can affect
the performance of machine learning algorithms to disambiguate author names in
bibliographic records. On multiple labeled datasets, three classifiers -
Logistic Regression, Naïve Bayes, and Random Forest - are trained through
representative features such as coauthor names, and title words extracted from
the same training data but with various positive-negative training data ratios.
Results show that increasing negative training data can improve disambiguation
performance but with a few percent of performance gains and sometimes degrade
it. Logistic Regression and Naïve Bayes learn optimal disambiguation models
even with a base ratio (1:1) of positive and negative training data. Also, the
performance improvement by Random Forest tends to quickly saturate roughly
after 1:10 ~ 1:15. These findings imply that contrary to the common practice
using all training data, name disambiguation algorithms can be trained using
part of negative training data without degrading much disambiguation
performance while increasing computational efficiency. This study calls for
more attention from author name disambiguation scholars to methods for machine
learning from imbalanced data.
",0,0,0,1,0,0
19347,SIGNet: Scalable Embeddings for Signed Networks,"  Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.
",1,0,0,1,0,0
19348,Spread of hate speech in online social media,"  The present online social media platform is afflicted with several issues,
with hate speech being on the predominant forefront. The prevalence of online
hate speech has fueled horrific real-world hate-crime such as the mass-genocide
of Rohingya Muslims, communal violence in Colombo and the recent massacre in
the Pittsburgh synagogue. Consequently, It is imperative to understand the
diffusion of such hateful content in an online setting. We conduct the first
study that analyses the flow and dynamics of posts generated by hateful and
non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M
posts. Our observations confirms that hateful content diffuse farther, wider
and faster and have a greater outreach than those of non-hateful users. A
deeper inspection into the profiles and network of hateful and non-hateful
users reveals that the former are more influential, popular and cohesive. Thus,
our research explores the interesting facets of diffusion dynamics of hateful
users and broadens our understanding of hate speech in the online world.
",1,0,0,0,0,0
19349,GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams,"  Designing adaptive classifiers for an evolving data stream is a challenging
task due to the data size and its dynamically changing nature. Combining
individual classifiers in an online setting, the ensemble approach, is a
well-known solution. It is possible that a subset of classifiers in the
ensemble outperforms others in a time-varying fashion. However, optimum weight
assignment for component classifiers is a problem which is not yet fully
addressed in online evolving environments. We propose a novel data stream
ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble
(GOOWE), which assigns optimum weights to the component classifiers using a
sliding window containing the most recent data instances. We map vote scores of
individual classifiers and true class labels into a spatial environment. Based
on the Euclidean distance between vote scores and ideal-points, and using the
linear least squares (LSQ) solution, we present a novel, dynamic, and online
weighting approach. While LSQ is used for batch mode ensemble classifiers, it
is the first time that we adapt and use it for online environments by providing
a spatial modeling of online ensembles. In order to show the robustness of the
proposed algorithm, we use real-world datasets and synthetic data generators
using the MOA libraries. First, we analyze the impact of our weighting system
on prediction accuracy through two scenarios. Second, we compare GOOWE with 8
state-of-the-art ensemble classifiers in a comprehensive experimental
environment. Our experiments show that GOOWE provides improved reactions to
different types of concept drift compared to our baselines. The statistical
tests indicate a significant improvement in accuracy, with conservative time
and memory requirements.
",1,0,0,0,0,0
19350,Graph Product Multilayer Networks: Spectral Properties and Applications,"  This paper aims to establish theoretical foundations of graph product
multilayer networks (GPMNs), a family of multilayer networks that can be
obtained as a graph product of two or more factor networks. Cartesian, direct
(tensor), and strong product operators are considered, and then generalized. We
first describe mathematical relationships between GPMNs and their factor
networks regarding their degree/strength, adjacency, and Laplacian spectra, and
then show that those relationships can still hold for nonsimple and generalized
GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic
thresholds, modeling propagation in nontrivial space and time, and analyzing
higher-order properties of self-similar networks. Directions of future research
are also discussed.
",1,1,0,0,0,0
19351,Multilayer flows in molecular networks identify biological modules in the human proteome,"  A variety of complex systems exhibit different types of relationships
simultaneously that can be modeled by multiplex networks. A typical problem is
to determine the community structure of such systems that, in general, depend
on one or more parameters to be tuned. In this study we propose one measure,
grounded on information theory, to find the optimal value of the relax rate
characterizing Multiplex Infomap, the generalization of the Infomap algorithm
to the realm of multilayer networks. We evaluate our methodology on synthetic
networks, to show that the most representative community structure can be
reliably identified when the most appropriate relax rate is used. Capitalizing
on these results, we use this measure to identify the most reliable meso-scale
functional organization in the human protein-protein interaction multiplex
network and compare the observed clusters against a collection of independently
annotated gene sets from the Molecular Signatures Database (MSigDB). Our
analysis reveals that modules obtained with the optimal value of the relax rate
are biologically significant and, remarkably, with higher functional content
than the ones obtained from the aggregate representation of the human proteome.
Our framework allows us to characterize the meso-scale structure of those
multilayer systems whose layers are not explicitly interconnected each other --
as in the case of edge-colored models -- the ones describing most biological
networks, from proteomes to connectomes.
",0,0,0,0,1,0
19352,Using Artificial Neural Networks (ANN) to Control Chaos,"  Controlling Chaos could be a big factor in getting great stable amounts of
energy out of small amounts of not necessarily stable resources. By definition,
Chaos is getting huge changes in the system's output due to unpredictable small
changes in initial conditions, and that means we could take advantage of this
fact and select the proper control system to manipulate system's initial
conditions and inputs in general and get a desirable output out of otherwise a
Chaotic system. That was accomplished by first building some known chaotic
circuit (Chua circuit) and the NI's MultiSim was used to simulate the ANN
control system. It was shown that this technique can also be used to stabilize
some hard to stabilize electronic systems.
",1,1,0,0,0,0
19353,Glassy quantum dynamics in translation invariant fracton models,"  We investigate relaxation in the recently discovered ""fracton"" models and
discover that these models naturally host glassy quantum dynamics in the
absence of quenched disorder. We begin with a discussion of ""type I"" fracton
models, in the taxonomy of Vijay, Haah, and Fu. We demonstrate that in these
systems, the mobility of charges is suppressed exponentially in the inverse
temperature. We further demonstrate that when a zero temperature type I fracton
model is placed in contact with a finite temperature heat bath, the approach to
equilibrium is a logarithmic function of time over an exponentially wide window
of time scales. Generalizing to the more complex ""type II"" fracton models, we
find that the charges exhibit subdiffusion upto a relaxation time that diverges
at low temperatures as a super-exponential function of inverse temperature.
This behaviour is reminiscent of ""nearly localized"" disordered systems, but
occurs with a translation invariant three-dimensional Hamiltonian. We also
conjecture that fracton models with conserved charge may support a phase which
is a thermal metal but a charge insulator.
",0,1,0,0,0,0
19354,Saliency Guided Hierarchical Robust Visual Tracking,"  A saliency guided hierarchical visual tracking (SHT) algorithm containing
global and local search phases is proposed in this paper. In global search, a
top-down saliency model is novelly developed to handle abrupt motion and
appearance variation problems. Nineteen feature maps are extracted first and
combined with online learnt weights to produce the final saliency map and
estimated target locations. After the evaluation of integration mechanism, the
optimum candidate patch is passed to the local search. In local search, a
superpixel based HSV histogram matching is performed jointly with an L2-RLS
tracker to take both color distribution and holistic appearance feature of the
object into consideration. Furthermore, a linear refinement search process with
fast iterative solver is implemented to attenuate the possible negative
influence of dominant particles. Both qualitative and quantitative experiments
are conducted on a series of challenging image sequences. The superior
performance of the proposed method over other state-of-the-art algorithms is
demonstrated by comparative study.
",1,0,0,0,0,0
19355,Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images,"  The present study shows that the performance of CNN is not significantly
different from the best classical methods and human doctors for classifying
mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does
not need tumor segmentation or feature calculation, it is more convenient and
more objective than the classical methods. However, CNN does not make use of
the import diagnostic features, which have been proved more discriminative than
the texture features for classifying small-sized lymph nodes. Therefore,
incorporating the diagnostic features into CNN is a promising direction for
future research.
",1,1,0,0,0,0
19356,A Semantics for Probabilistic Control-Flow Graphs,"  This article develops a novel operational semantics for probabilistic
control-flow graphs (pCFGs) of probabilistic imperative programs with random
assignment and ""observe"" (or conditioning) statements. The semantics transforms
probability distributions (on stores) as control moves from one node to another
in pCFGs. We relate this semantics to a standard, expectation-transforming,
denotational semantics of structured probabilistic imperative programs, by
translating structured programs into (unstructured) pCFGs, and proving adequacy
of the translation. This shows that the operational semantics can be used
without loss of information, and is faithful to the ""intended"" semantics and
hence can be used to reason about, for example, the correctness of
transformations (as we do in a companion article).
",1,0,0,0,0,0
19357,Generalized Slow Roll in the Unified Effective Field Theory of Inflation,"  We provide a compact and unified treatment of power spectrum observables for
the effective field theory (EFT) of inflation with the complete set of
operators that lead to second-order equations of motion in metric perturbations
in both space and time derivatives, including Horndeski and GLPV theories. We
relate the EFT operators in ADM form to the four additional free functions of
time in the scalar and tensor equations. Using the generalized slow roll
formalism, we show that each power spectrum can be described by an integral
over a single source that is a function of its respective sound horizon. With
this correspondence, existing model independent constraints on the source
function can be simply reinterpreted in the more general inflationary context.
By expanding these sources around an optimized freeze-out epoch, we also
provide characterizations of these spectra in terms of five slow-roll
hierarchies whose leading order forms are compact and accurate as long as EFT
coefficients vary only on timescales greater than an efold. We also clarify the
relationship between the unitary gauge observables employed in the EFT and the
comoving gauge observables of the post-inflationary universe.
",0,1,0,0,0,0
19358,MgO thickness-induced spin reorientation transition in Co0.9Fe0.1/MgO/Co0.9Fe0.1 structure,"  The magnetic anisotropy (MA) of Mo/Au/Co0.9Fe0.1/Au/MgO(0.7 - 3
nm)/Au/Co0.9Fe0.1/Au heterostructure has been investigated at room temperature
as a function of MgO layer thickness (tMgO). Our studies show that while the MA
of the top layer does not change its character upon variation of tMgO, the
uniaxial out-of-plane MA of the bottom one undergoes a spin reorientation
transition at tMgO of about 0.8 nm, switching to the regime where the
coexistence of in- and out-of-plane magnetization alignments is observed. The
magnitudes of the magnetic anisotropy constants have been determined from
ferromagnetic resonance and dc-magnetometry measurements. The origin of MA
evolution has been attributed to a presence of an interlayer exchange coupling
(IEC) between Co0.9Fe0.1 layers through the thin MgO film.
",0,1,0,0,0,0
19359,Bjerrum Pairs in Ionic Solutions: a Poisson-Boltzmann Approach,"  Ionic solutions are often regarded as fully dissociated ions dispersed in a
polar solvent. While this picture holds for dilute solutions, at higher ionic
concentrations, oppositely charged ions can associate into dimers, referred to
as Bjerrum pairs. We consider the formation of such pairs within the nonlinear
Poisson-Boltzmann framework, and investigate their effects on bulk and
interfacial properties of electrolytes. Our findings show that pairs can reduce
the magnitude of the dielectric decrement of ionic solutions as the ionic
concentration increases. We describe the effect of pairs on the Debye screening
length, and relate our results to recent surface-force experiments.
Furthermore, we show that Bjerrum pairs reduce the ionic concentration in bulk
electrolyte and at the proximity of charged surfaces, while they enhance the
attraction between oppositely charged surfaces.
",0,1,0,0,0,0
19360,Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,"  Network embedding methodologies, which learn a distributed vector
representation for each vertex in a network, have attracted considerable
interest in recent years. Existing works have demonstrated that vertex
representation learned through an embedding method provides superior
performance in many real-world applications, such as node classification, link
prediction, and community detection. However, most of the existing methods for
network embedding only utilize topological information of a vertex, ignoring a
rich set of nodal attributes (such as, user profiles of an online social
network, or textual contents of a citation network), which is abundant in all
real-life networks. A joint network embedding that takes into account both
attributional and relational information entails a complete network information
and could further enrich the learned vector representations. In this work, we
present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
Attributed Network Embedding. For a given network, Neural-Brane extracts latent
feature representation of its vertices using a designed neural network model
that unifies network topological information and nodal attributes; Besides, it
utilizes Bayesian personalized ranking objective, which exploits the proximity
ordering between a similar node-pair and a dissimilar node-pair. We evaluate
the quality of vertex embedding produced by Neural-Brane by solving the node
classification and clustering tasks on four real-world datasets. Experimental
results demonstrate the superiority of our proposed method over the
state-of-the-art existing methods.
",0,0,0,1,0,0
19361,Pairwise $k$-Semi-Stratifiable Bispaces and Topological Ordered Spaces,"  In this paper, we continue to study pairwise ($k$-semi-)stratifiable
bitopological spaces. Some new characterizations of pairwise
$k$-semi-stratifiable bitopological spaces are provided. Relationships between
pairwise stratifiable and pairwise $k$-semi-stratifiable bitopological spaces
are further investigated, and an open question recently posed by Li and Lin in
\cite{LL} is completely solved. We also study the quasi-pseudo-metrizability of
a topological ordered space $(X, \tau, \preccurlyeq)$. It is shown that if $(X,
\tau, \preccurlyeq)$ is a ball transitive topological ordered $C$- and
$I$-space such that $\tau$ is metrizable, then its associated bitopological
space $(X,\tau^{\flat},\tau^{\natural})$ is quasi-pseudo-metrizable. This
result provides a partial affirmative answer to a problem in \cite{KM}.
",0,0,1,0,0,0
19362,Ontology based system to guide internship assignment process,"  Internship assignment is a complicated process for universities since it is
necessary to take into account a multiplicity of variables to establish a
compromise between companies' requirements and student competencies acquired
during the university training. These variables build up a complex relations
map that requires the formulation of an exhaustive and rigorous conceptual
scheme. In this research a domain ontological model is presented as support to
the student's decision making for opportunities of University studies level of
the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
and created using methodological approach offering the possibility of improving
the progressive creation, capture and knowledge articulation. In this paper, we
draw a balance taking the demands of the companies across the capabilities of
the students. This will be done through the establishment of an ontological
model of an educational learners' profile and the internship postings which are
written in a free text and using uncontrolled vocabulary. Furthermore, we
outline the process of semantic matching which improves the quality of query
results.
",1,0,0,0,0,0
19363,Vacancy-driven extended stability of cubic metastable Ta-Al-N and Nb-Al-N phases,"  Quantum mechanical calculations had been previously applied to predict phase
stability in many ternary and multinary nitride systems. While the predictions
were very accurate for the Ti-Al-N system, some discrepancies between theory
and experiment were obtained in the case of other systems. Namely, in the case
of Ta-Al-N, the calculations tend to overestimate the minimum Al content
necessary to obtain a metastable solid solution with a cubic structure. In this
work, we present a comprehensive study of the impact of vacancies on the phase
fields in quasi-binary TaN-AlN and NbN-AlN systems. Our calculations clearly
show that presence of point defects strongly enlarges the cubic phase field in
the TaN-AlN system, while the effect is less pronounced in the NbN-AlN case.
The present phase stability predictions agree better with experimental
observations of physical vapour deposited thin films reported in the literature
than that based on perfect, non-defected structures. This study shows that a
representative structural model is crucial for a meaningful comparison with
experimental data.
",0,1,0,0,0,0
19364,Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation,"  This paper addresses the question: Why do neural dialog systems generate
short and meaningless replies? We conjecture that, in a dialog system, an
utterance may have multiple equally plausible replies, causing the deficiency
of neural networks in the dialog application. We propose a systematic way to
mimic the dialog scenario in a machine translation system, and manage to
reproduce the phenomenon of generating short and less meaningful sentences in
the translation setting, showing evidence of our conjecture.
",1,0,0,0,0,0
19365,Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain,"  In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction.
",1,0,0,0,0,0
19366,Non-Archimedean Replicator Dynamics and Eigen's Paradox,"  We present a new non-Archimedean model of evolutionary dynamics, in which the
genomes are represented by p-adic numbers. In this model the genomes have a
variable length, not necessarily bounded, in contrast with the classical models
where the length is fixed. The time evolution of the concentration of a given
genome is controlled by a p-adic evolution equation. This equation depends on a
fitness function f and on mutation measure Q. By choosing a mutation measure of
Gibbs type, and by using a p-adic version of the Maynard Smith Ansatz, we show
the existence of threshold function M_{c}(f,Q), such that the long term
survival of a genome requires that its length grows faster than M_{c}(f,Q).
This implies that Eigen's paradox does not occur if the complexity of genomes
grows at the right pace. About twenty years ago, Scheuring and Poole, Jeffares,
Penny proposed a hypothesis to explain Eigen's paradox. Our mathematical model
shows that this biological hypothesis is feasible, but it requires p-adic
analysis instead of real analysis. More exactly, the Darwin-Eigen cycle
proposed by Poole et al. takes place if the length of the genomes exceeds
M_{c}(f,Q).
",0,0,0,0,1,0
19367,D-optimal design for multivariate polynomial regression via the Christoffel function and semidefinite relaxations,"  We present a new approach to the design of D-optimal experiments with
multivariate polynomial regressions on compact semi-algebraic design spaces. We
apply the moment-sum-of-squares hierarchy of semidefinite programming problems
to solve numerically and approximately the optimal design problem. The geometry
of the design is recovered with semidefinite programming duality theory and the
Christoffel polynomial.
",0,0,1,1,0,0
19368,A dynamic network model to measure exposure diversification in the Austrian interbank market,"  We propose a statistical model for weighted temporal networks capable of
measuring the level of heterogeneity in a financial system. Our model focuses
on the level of diversification of financial institutions; that is, whether
they are more inclined to distribute their assets equally among partners, or if
they rather concentrate their commitment towards a limited number of
institutions. Crucially, a Markov property is introduced to capture time
dependencies and to make our measures comparable across time. We apply the
model on an original dataset of Austrian interbank exposures. The temporal span
encompasses the onset and development of the financial crisis in 2008 as well
as the beginnings of European sovereign debt crisis in 2011. Our analysis
highlights an overall increasing trend for network homogeneity, whereby core
banks have a tendency to distribute their market exposures more equally across
their partners.
",0,0,0,1,0,1
19369,Universal scaling in the Knight shift anomaly of doped periodic Anderson model,"  We report a Dynamical Cluster Approximation (DCA) investigation of the doped
periodic Anderson model (PAM) to explain the universal scaling in the Knight
shift anomaly predicted by the phenomenological two-fluid model and confirmed
in many heavy-fermion compounds. We calculate the quantitative evolution of the
orbital-dependent magnetic susceptibility and reproduce correctly the two-fluid
prediction in a large range of doping and hybridization. Our results confirm
the presence of a temperature/energy scale $T^{\ast}$ for the universal scaling
and show distinctive behavors of the Knight shift anomaly in response to other
""orders"" at low temperatures. However, comparison with the temperature
evolution of the calculated resistivity and quasiparticle spectral peak
indicates a different characteristic temperature from $T^*$, in contradiction
with the experimental observation in CeCoIn$_5$ and other compounds. This
reveals a missing piece in the current model calculations in explaining the
two-fluid phenomenology.
",0,1,0,0,0,0
19370,Reinforcement Learning Algorithm Selection,"  This paper formalises the problem of online algorithm selection in the
context of Reinforcement Learning. The setup is as follows: given an episodic
task and a finite number of off-policy RL algorithms, a meta-algorithm has to
decide which RL algorithm is in control during the next episode so as to
maximize the expected return. The article presents a novel meta-algorithm,
called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is
to freeze the policy updates at each epoch, and to leave a rebooted stochastic
bandit in charge of the algorithm selection. Under some assumptions, a thorough
theoretical analysis demonstrates its near-optimality considering the
structural sampling budget limitations. ESBAS is first empirically evaluated on
a dialogue task where it is shown to outperform each individual algorithm in
most configurations. ESBAS is then adapted to a true online setting where
algorithms update their policies after each transition, which we call SSBAS.
SSBAS is evaluated on a fruit collection task where it is shown to adapt the
stepsize parameter more efficiently than the classical hyperbolic decay, and on
an Atari game, where it improves the performance by a wide margin.
",1,0,1,1,0,0
19371,"On periodic solutions of nonlinear wave equations, including Einstein equations with a negative cosmological constant","  We construct periodic solutions of nonlinear wave equations using analytic
continuation. The construction applies in particular to Einstein equations,
leading to infinite-dimensional families of time-periodic solutions of the
vacuum, or of the Einstein-Maxwell-dilaton-scalar
fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations, with a negative
cosmological constant.
",0,0,1,0,0,0
19372,"Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories","  Attribute-based recognition models, due to their impressive performance and
their ability to generalize well on novel categories, have been widely adopted
for many computer vision applications. However, usually both the attribute
vocabulary and the class-attribute associations have to be provided manually by
domain experts or large number of annotators. This is very costly and not
necessarily optimal regarding recognition performance, and most importantly, it
limits the applicability of attribute-based models to large scale data sets. To
tackle this problem, we propose an end-to-end unsupervised attribute learning
approach. We utilize online text corpora to automatically discover a salient
and discriminative vocabulary that correlates well with the human concept of
semantic attributes. Moreover, we propose a deep convolutional model to
optimize class-attribute associations with a linguistic prior that accounts for
noise and missing data in text. In a thorough evaluation on ImageNet, we
demonstrate that our model is able to efficiently discover and learn semantic
attributes at a large scale. Furthermore, we demonstrate that our model
outperforms the state-of-the-art in zero-shot learning on three data sets:
ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
attribute-based learning on ImageNet and will share the attributes and
associations for future research.
",1,0,0,0,0,0
19373,Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data,"  In this paper, a scale mixture of Normal distributions model is developed for
classification and clustering of data having outliers and missing values. The
classification method, based on a mixture model, focuses on the introduction of
latent variables that gives us the possibility to handle sensitivity of model
to outliers and to allow a less restrictive modelling of missing data.
Inference is processed through a Variational Bayesian Approximation and a
Bayesian treatment is adopted for model learning, supervised classification and
clustering.
",0,0,0,1,0,0
19374,Energy stable discretization of Allen-Cahn type problems modeling the motion of phase boundaries,"  We study the systematic numerical approximation of a class of Allen-Cahn type
problems modeling the motion of phase interfaces. The common feature of these
models is an underlying gradient flow structure which gives rise to a decay of
an associated energy functional along solution trajectories. We first study the
discretization in space by a conforming Galerkin approximation of a variational
principle which characterizes smooth solutions of the problem. Well-posedness
of the resulting semi-discretization is established and the energy decay along
discrete solution trajectories is proven. A problem adapted implicit
time-stepping scheme is then proposed and we establish its well-posed and decay
of the free energy for the fully discrete scheme. Some details about the
numerical realization by finite elements are discussed, in particular the
iterative solution of the nonlinear problems arising in every time-step. The
theoretical results are illustrated by numerical tests which also provide
further evidence for asymptotic expansions of the interface velocities derived
by Alber et al.
",0,0,1,0,0,0
19375,Hidden space reconstruction inspires link prediction in complex networks,"  As a fundamental challenge in vast disciplines, link prediction aims to
identify potential links in a network based on the incomplete observed
information, which has broad applications ranging from uncovering missing
protein-protein interaction to predicting the evolution of networks. One of the
most influential methods rely on similarity indices characterized by the common
neighbors or its variations. We construct a hidden space mapping a network into
Euclidean space based solely on the connection structures of a network.
Compared with real geographical locations of nodes, our reconstructed locations
are in conformity with those real ones. The distances between nodes in our
hidden space could serve as a novel similarity metric in link prediction. In
addition, we hybrid our hidden space method with other state-of-the-art
similarity methods which substantially outperforms the existing methods on the
prediction accuracy. Hence, our hidden space reconstruction model provides a
fresh perspective to understand the network structure, which in particular
casts a new light on link prediction.
",1,1,0,0,0,0
19376,Maximum principles for the fractional p-Laplacian and symmetry of solutions,"  In this paper, we consider nonlinear equations involving the fractional
p-Laplacian $$ (-\lap)_p^s u(x)) \equiv C_{n,s,p} PV \int_{\mathbb{R}^n}
\frac{|u(x)-u(y)|^{p-2}[u(x)-u(y)]}{|x-z|^{n+ps}} dz= f(x,u).$$
We prove a {\em maximum principle for anti-symmetric functions} and obtain
other key ingredients for carrying on the method of moving planes, such as {\em
a key boundary estimate lemma}. Then we establish radial symmetry and
monotonicity for positive solutions to semilinear equations involving the
fractional p-Laplacian in a unit ball and in the whole space. We believe that
the methods developed here can be applied to a variety of problems involving
nonlinear nonlocal operators.
",0,0,1,0,0,0
19377,On slowly rotating axisymmetric solutions of the Einstein-Euler equations,"  In recent works we have constructed axisymmetric solutions to the
Euler-Poisson equations which give mathematical models of slowly uniformly
rotating gaseous stars. We try to extend this result to the study of solutions
of the Einstein-Euler equations in the framework of the general theory of
relativity. Although many interesting studies have been done about axisymmetric
metric in the general theory of relativity, they are restricted to the region
of the vacuum. Mathematically rigorous existence theorem of the axisymmetric
interior solutions of the stationary metric corresponding to the
energy-momentum tensor of the perfect fluid with non-zero pressure may be not
yet established until now except only one found in the pioneering work by U.
Heilig done in 1993. In this article, along a different approach to that of
Heilig's work, axisymmetric stationary solutions of the Einstein-Euler
equations are constructed near those of the Euler-Poisson equations when the
speed of light is sufficiently large in the considered system of units, or,
when the gravitational field is sufficiently weak.
",0,0,1,0,0,0
19378,Gapped paramagnetic state in a frustrated spin-$\frac{1}{2}$ Heisenberg antiferromagnet on the cross-striped square lattice,"  We implement the coupled cluster method to very high orders of approximation
to study the spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$ Heisenberg model on a
cross-striped square lattice. Every nearest-neighbour pair of sites on the
square lattice has an isotropic antiferromagnetic exchange bond of strength
$J_{1}>0$, while the basic square plaquettes in alternate columns have either
both or neither next-nearest-neighbour (diagonal) pairs of sites connected by
an equivalent frustrating bond of strength $J_{2} \equiv \alpha J_{1} > 0$. By
studying the magnetic order parameter (i.e., the average local on-site
magnetization) in the range $0 \leq \alpha \leq 1$ of the frustration parameter
we find that the quasiclassical antiferromagnetic Néel and (so-called)
double Néel states form the stable ground-state phases in the respective
regions $\alpha < \alpha_{1a}^{c} = 0.46(1)$ and $\alpha > \alpha_{1b}^{c} =
0.615(5)$. The double Néel state has Néel
($\cdots\uparrow\downarrow\uparrow\downarrow\cdots$) ordering along the
(column) direction parallel to the stripes of squares with both or no $J_{2}$
bonds, and spins alternating in a pairwise
($\cdots\uparrow\uparrow\downarrow\downarrow\uparrow\uparrow\downarrow\downarrow\cdots$)
fashion along the perpendicular (row) direction, so that the parallel pairs
occur on squares with both $J_{2}$ bonds present. Further explicit calculations
of both the triplet spin gap and the zero-field uniform transverse magnetic
susceptibility provide compelling evidence that the ground-state phase over all
or most of the intermediate regime $\alpha_{1a}^{c} < \alpha < \alpha_{1b}^{c}$
is a gapped state with no discernible long-range magnetic order.
",0,1,0,0,0,0
19379,Clouds in the atmospheres of extrasolar planets. V. The impact of CO2 ice clouds on the outer boundary of the habitable zone,"  Clouds have a strong impact on the climate of planetary atmospheres. The
potential scattering greenhouse effect of CO2 ice clouds in the atmospheres of
terrestrial extrasolar planets is of particular interest because it might
influence the position and thus the extension of the outer boundary of the
classic habitable zone around main sequence stars. Here, the impact of CO2 ice
clouds on the surface temperatures of terrestrial planets with CO2 dominated
atmospheres, orbiting different types of stars is studied. Additionally, their
corresponding effect on the position of the outer habitable zone boundary is
evaluated. For this study, a radiative-convective atmospheric model is used the
calculate the surface temperatures influenced by CO2 ice particles. The clouds
are included using a parametrised cloud model. The atmospheric model includes a
general discrete ordinate radiative transfer that can describe the anisotropic
scattering by the cloud particles accurately. A net scattering greenhouse
effect caused by CO2 clouds is only obtained in a rather limited parameter
range which also strongly depends on the stellar effective temperature. For
cool M-stars, CO2 clouds only provide about 6 K of additional greenhouse
heating in the best case scenario. On the other hand, the surface temperature
for a planet around an F-type star can be increased by 30 K if carbon dioxide
clouds are present. Accordingly, the extension of the habitable zone due to
clouds is quite small for late-type stars. Higher stellar effective
temperatures, on the other hand, can lead to outer HZ boundaries about 0.5 au
farther out than the corresponding clear-sky values.
",0,1,0,0,0,0
19380,Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks,"  Magnetic resonance imaging (MRI) has been proposed as a complimentary method
to measure bone quality and assess fracture risk. However, manual segmentation
of MR images of bone is time-consuming, limiting the use of MRI measurements in
the clinical practice. The purpose of this paper is to present an automatic
proximal femur segmentation method that is based on deep convolutional neural
networks (CNNs). This study had institutional review board approval and written
informed consent was obtained from all subjects. A dataset of volumetric
structural MR images of the proximal femur from 86 subject were
manually-segmented by an expert. We performed experiments by training two
different CNN architectures with multiple number of initial feature maps and
layers, and tested their segmentation performance against the gold standard of
manual segmentations using four-fold cross-validation. Automatic segmentation
of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
architecture based on 3D convolution exceeding the performance of 2D CNNs. The
high segmentation accuracy provided by CNNs has the potential to help bring the
use of structural MRI measurements of bone quality into clinical practice for
management of osteoporosis.
",1,0,0,1,0,0
19381,Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter,"  In Crowdfunding platforms, people turn their prototype ideas into real
products by raising money from the crowd, or invest in someone else's projects.
In reward-based crowdfunding platforms such as Kickstarter and Indiegogo,
selecting accurate reward delivery duration becomes crucial for creators,
backers, and platform providers to keep the trust between the creators and the
backers, and the trust between the platform providers and users. According to
Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little
is known about on-time and late reward delivery projects, and there is no prior
work to estimate reward delivery duration. To fill the gap, in this paper, we
(i) extract novel features that reveal latent difficulty levels of project
rewards; (ii) build predictive models to identify whether a creator will
deliver all rewards in a project on time or not; and (iii) build a regression
model to estimate accurate reward delivery duration (i.e., how long it will
take to produce and deliver all the rewards). Experimental results show that
our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108
NRMSE at the first 5% of the longest reward delivery duration.
",1,0,0,0,0,0
19382,Bayesian Model-Agnostic Meta-Learning,"  Learning to infer Bayesian posterior from a few-shot dataset is an important
step towards robust meta-learning due to the model uncertainty inherent in the
problem. In this paper, we propose a novel Bayesian model-agnostic
meta-learning method. The proposed method combines scalable gradient-based
meta-learning with nonparametric variational inference in a principled
probabilistic framework. During fast adaptation, the method is capable of
learning complex uncertainty structure beyond a point estimate or a simple
Gaussian approximation. In addition, a robust Bayesian meta-update mechanism
with a new meta-loss prevents overfitting during meta-update. Remaining an
efficient gradient-based meta-learner, the method is also model-agnostic and
simple to implement. Experiment results show the accuracy and robustness of the
proposed method in various tasks: sinusoidal regression, image classification,
active learning, and reinforcement learning.
",0,0,0,1,0,0
19383,Distributed matching scheme and a flexible deterministic matching algorithm for arbitrary systems,"  We discuss the distributed matching scheme in accelerators where control of
transverse beam phase space, oscillation, and transport is accomplished by
flexible distribution of focusing elements beyond dedicated matching sections.
Besides freeing accelerator design from fixed matching sections, such a scheme
has many operational advantages, and enables fluid optics manipulation not
possible in conventional schemes. Combined with an interpolation scheme this
can bring about a new paradigm for efficient, flexible, and robust optics
control. A rigorous and deterministic algorithm is developed for its
realization. The algorithm is a matching tool in its own right with unique
characteristics in robustness and determinism. The beam phase space dynamics is
naturally integrated into the algorithm, instead of being treated as generic
numerical parameters as in traditional schemes. It is applicable to a wider
range of problems, such as trading-off between competing options for desired
machine states.
",0,1,0,0,0,0
19384,Group Metrics for Graph Products of Cyclic Groups,"  We complement the characterization of the graph products of cyclic groups
$G(\Gamma, \mathfrak{p})$ admitting a Polish group topology of [9] with the
following result. Let $G = G(\Gamma, \mathfrak{p})$, then the following are
equivalent: (i) there is a metric on $\Gamma$ which induces a separable
topology in which $E_{\Gamma}$ is closed; (ii) $G(\Gamma, \mathfrak{p})$ is
embeddable into a Polish group; (iii) $G(\Gamma, \mathfrak{p})$ is embeddable
into a non-Archimedean Polish group. We also construct left-invariant separable
group ultrametrics for $G = G(\Gamma, \mathfrak{p})$ and $\Gamma$ a closed
graph on the Baire space, which is of independent interest.
",0,0,1,0,0,0
19385,Robust Online Multi-Task Learning with Correlative and Personalized Structures,"  Multi-Task Learning (MTL) can enhance a classifier's generalization
performance by learning multiple related tasks simultaneously. Conventional MTL
works under the offline or batch setting, and suffers from expensive training
cost and poor scalability. To address such inefficiency issues, online learning
techniques have been applied to solve MTL problems. However, most existing
algorithms of online MTL constrain task relatedness into a presumed structure
via a single weight matrix, which is a strict restriction that does not always
hold in practice. In this paper, we propose a robust online MTL framework that
overcomes this restriction by decomposing the weight matrix into two
components: the first one captures the low-rank common structure among tasks
via a nuclear norm and the second one identifies the personalized patterns of
outlier tasks via a group lasso. Theoretical analysis shows the proposed
algorithm can achieve a sub-linear regret with respect to the best linear model
in hindsight. Even though the above framework achieves good performance, the
nuclear norm that simply adds all nonzero singular values together may not be a
good low-rank approximation. To improve the results, we use a log-determinant
function as a non-convex rank approximation. The gradient scheme is applied to
optimize log-determinant function and can obtain a closed-form solution for
this refined problem. Experimental results on a number of real-world
applications verify the efficacy of our method.
",1,0,0,1,0,0
19386,Cobordism maps on PFH induced by Lefschetz fibration over higher genus base,"  In this note, we discuss the cobordism maps on periodic Floer homology(PFH)
induced by Lefschetz fibration. In the first part of the note, we define the
cobordism maps on PFH induced by Lefschetz fibration via Seiberg Witten theory
and the isomorphism between PFH and Seiberg Witten cohomology. The second part
is to define the cobordism maps induced by Lefschetz fibration provided that
the cobordism satisfies certain conditions. Under certain monotone assumptions,
we show that these two definitions in fact are equivalent.
",0,0,1,0,0,0
19387,A short note on the order of the Zhang-Liu matrices over arbitrary fields,"  We give necessary and sufficient conditions for the Zhang-Liu matrices to be
diagonalizable over arbitrary fields and provide the eigen-decomposition when
it is possible. We use this result to calculate the order of these matrices
over any arbitrary field. This generalizes a result of the second author.
",0,0,1,0,0,0
19388,Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017),"  This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag.
",1,0,0,1,0,0
19389,Ferromagnetic transition in a one-dimensional spin-orbit-coupled metal and its mapping to a critical point in smectic liquid crystals,"  We study the quantum phase transition between a paramagnetic and
ferromagnetic metal in the presence of Rashba spin-orbit coupling in one
dimension. Using bosonization, we analyze the transition by means of
renormalization group, controlled by an $\varepsilon$-expansion around the
upper critical dimension of two. We show that the presence of Rashba spin-orbit
coupling allows for a new nonlinear term in the bosonized action, which
generically leads to a fluctuation driven first-order transition. We further
demonstrate that the Euclidean action of this system maps onto a classical
smectic-A -- C phase transition in a magnetic field in two dimensions. We show
that the smectic transition is second-order and is controlled by a new critical
point.
",0,1,0,0,0,0
19390,Riemannian almost product manifolds generated by a circulant structure,"  A 4-dimensional Riemannian manifold equipped with a circulant structure,
which is an isometry with respect to the metric and its fourth power is the
identity, is considered. The almost product manifold associated with the
considered manifold is studied. The relation between the covariant derivatives
of the almost product structure and the circulant structure is obtained. The
conditions for the covariant derivative of the circulant structure, which imply
that an almost product manifold belongs to each of the basic classes of the
Staikova-Gribachev classification, are given.
",0,0,1,0,0,0
19391,Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis,"  Modern software systems provide many configuration options which
significantly influence their non-functional properties. To understand and
predict the effect of configuration options, several sampling and learning
strategies have been proposed, albeit often with significant cost to cover the
highly dimensional configuration space. Recently, transfer learning has been
applied to reduce the effort of constructing performance models by transferring
knowledge about performance behavior across environments. While this line of
research is promising to learn more accurate models at a lower cost, it is
unclear why and when transfer learning works for performance modeling. To shed
light on when it is beneficial to apply transfer learning, we conducted an
empirical study on four popular software systems, varying software
configurations and environmental conditions, such as hardware, workload, and
software versions, to identify the key knowledge pieces that can be exploited
for transfer learning. Our results show that in small environmental changes
(e.g., homogeneous workload change), by applying a linear transformation to the
performance model, we can understand the performance behavior of the target
environment, while for severe environmental changes (e.g., drastic workload
change) we can transfer only knowledge that makes sampling more efficient,
e.g., by reducing the dimensionality of the configuration space.
",1,0,0,1,0,0
19392,"Critique of Barbosa's ""P != NP Proof""","  We review André Luiz Barbosa's paper ""P != NP Proof,"" in which the classes
P and NP are generalized and claimed to be proven separate. We highlight
inherent ambiguities in Barbosa's definitions, and show that attempts to
resolve this ambiguity lead to flaws in the proof of his main result.
",1,0,0,0,0,0
19393,"Two-Armed Bandit Problem, Data Processing, and Parallel Version of the Mirror Descent Algorithm","  We consider the minimax setup for the two-armed bandit problem as applied to
data processing if there are two alternative processing methods available with
different a priori unknown efficiencies. One should determine the most
effective method and provide its predominant application. To this end we use
the mirror descent algorithm (MDA). It is well-known that corresponding minimax
risk has the order $N^{1/2}$ with $N$ being the number of processed data. We
improve significantly the theoretical estimate of the factor using Monte-Carlo
simulations. Then we propose a parallel version of the MDA which allows
processing of data by packets in a number of stages. The usage of parallel
version of the MDA ensures that total time of data processing depends mostly on
the number of packets but not on the total number of data. It is quite
unexpectedly that the parallel version behaves unlike the ordinary one even if
the number of packets is large. Moreover, the parallel version considerably
improves control performance because it provides significantly smaller value of
the minimax risk. We explain this result by considering another parallel
modification of the MDA which behavior is close to behavior of the ordinary
version. Our estimates are based on invariant descriptions of the algorithms.
All estimates are obtained by Monte-Carlo simulations. It's worth noting that
parallel version performs well only for methods with close efficiencies. If
efficiencies differ significantly then one should use the combined algorithm
which at initial sufficiently short control horizon uses ordinary version and
then switches to the parallel version of the MDA.
",0,0,1,1,0,0
19394,Bearing fault diagnosis under varying working condition based on domain adaptation,"  Traditional intelligent fault diagnosis of rolling bearings work well only
under a common assumption that the labeled training data (source domain) and
unlabeled testing data (target domain) are drawn from the same distribution.
When the distribution changes, most fault diagnosis models need to be rebuilt
from scratch using newly recollected labeled training data. However, it is
expensive or impossible to annotate huge amount of training data to rebuild
such new model. Meanwhile, large amounts of labeled training data have not been
fully utilized yet, which is apparently a waste of resources. As one of the
important research directions of transfer learning, domain adaptation (DA)
typically aims at minimizing the differences between distributions of different
domains in order to minimize the cross-domain prediction error by taking full
advantage of information coming from both source and target domains. In this
paper, we present one of the first studies on unsupervised DA in the field of
fault diagnosis of rolling bearings under varying working conditions and a
novel diagnosis strategy based on unsupervised DA using subspace alignment (SA)
is proposed. After processed by unsupervised DA with SA, the distributions of
training data and testing data become close and the classifier trained on
training data can be used to classify the testing data. Experimental results on
the 60 domain adaptation diagnosis problems under varying working condition in
Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems
under varying working conditions in our new data are given to demonstrate the
effectiveness of the proposed method. The proposed methods can effectively
distinguish not only bearing faults categories but also fault severities.
",1,0,0,0,0,0
19395,"Dialectical Rough Sets, Parthood and Figures of Opposition-1","  In one perspective, the main theme of this research revolves around the
inverse problem in the context of general rough sets that concerns the
existence of rough basis for given approximations in a context. Granular
operator spaces and variants were recently introduced by the present author as
an optimal framework for anti-chain based algebraic semantics of general rough
sets and the inverse problem. In the framework, various sub-types of crisp and
non-crisp objects are identifiable that may be missed in more restrictive
formalism. This is also because in the latter cases concepts of complementation
and negation are taken for granted - while in reality they have a complicated
dialectical basis. This motivates a general approach to dialectical rough sets
building on previous work of the present author and figures of opposition. In
this paper dialectical rough logics are invented from a semantic perspective, a
concept of dialectical predicates is formalised, connection with dialetheias
and glutty negation are established, parthood analyzed and studied from the
viewpoint of classical and dialectical figures of opposition by the present
author. Her methods become more geometrical and encompass parthood as a primary
relation (as opposed to roughly equivalent objects) for algebraic semantics.
",1,0,1,0,0,0
19396,On quasi-hereditary algebras,"  In this paper we introduce an easily verifiable sufficient condition to
determine whether an algebra is quasi-hereditary. In the case of monomial
algebras, we give conditions that are both necessary and sufficient to show
whether an algebra is quasi-hereditary.
",0,0,1,0,0,0
19397,Clustering Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters,"  We study the problems of clustering locally asymptotically self-similar
stochastic processes, when the true number of clusters is priorly known. A new
covariance-based dissimilarity measure is introduced, from which the so-called
approximately asymptotically consistent clustering algorithms are obtained. In
a simulation study, clustering data sampled from multifractional Brownian
motions is performed to illustrate the approximated asymptotic consistency of
the proposed algorithms.
",0,0,0,1,0,0
19398,The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): Spectral Maps of the Asteroid Bennu,"  The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point
spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300
cm-1). Its primary purpose is to map the surface composition of the asteroid
Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission.
The information it returns will help guide the selection of the sample site. It
will also provide global context for the sample and high spatial resolution
spectra that can be related to spatially unresolved terrestrial observations of
asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W
average), and robust instrument with the sensitivity needed to detect a 5%
spectral absorption feature on a very dark surface (3% reflectance) in the
inner solar system (0.89-1.35 AU). It, in combination with the other
instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an
asteroid's surface.
",0,1,0,0,0,0
19399,The effect of the smoothness of fractional type operators over their commutators with Lipschitz symbols on weighted spaces,"  We prove boundedness results for integral operators of fractional type and
their higher order commutators between weighted spaces, including $L^p$-$L^q$,
$L^p$-$BMO$ and $L^p$-Lipschitz estimates. The kernels of such operators
satisfy certain size condition and a Lipschitz type regularity, and the symbol
of the commutator belongs to a Lipschitz class. We also deal with commutators
of fractional type operators with less regular kernels satisfying a
Hörmander's type inequality. As far as we know, these last results are new
even in the unweighted case. Moreover, we give a characterization result
involving symbols of the commutators and continuity results for extreme values
of $p$.
",0,0,1,0,0,0
19400,Fast mean-reversion asymptotics for large portfolios of stochastic volatility models,"  We consider a large portfolio limit where the asset prices evolve according
certain stochastic volatility models with default upon hitting a lower barrier.
When the asset prices and the volatilities are correlated via systemic Brownian
Motions, that limit exist and it is described by a SPDE on the positive
half-space with Dirichlet boundary conditions which has been studied in
\cite{HK17}. We study the convergence of the total mass of a solution to this
stochastic initial-boundary value problem when the mean-reversion coefficients
of the volatilities are multiples of a parameter that tends to infinity. When
the volatilities of the volatilities are multiples of the square root of the
same parameter, the convergence is extremely weak. On the other hand, when the
volatilities of the volatilities are independent of this exploding parameter,
the volatilities converge to their means and we can have much better
approximations. Our aim is to use such approximations to improve the accuracy
of certain risk-management methods in markets where fast volatility
mean-reversion is observed.
",0,0,0,0,0,1
19401,Poincaré profiles of groups and spaces,"  We introduce a spectrum of monotone coarse invariants for metric measure
spaces called Poincaré profiles. The two extremes of this spectrum
determine the growth of the space, and the separation profile as defined by
Benjamini--Schramm--Timár. In this paper we focus on properties of the
Poincaré profiles of groups with polynomial growth, and of hyperbolic
spaces, where we deduce a striking connection between these profiles and
conformal dimension. One application of our results is that there is a
collection of hyperbolic Coxeter groups, indexed by a countable dense subset of
$(1,\infty)$, such that $G_s$ does not coarsely embed into $G_t$ whenever
$s<t$.
",0,0,1,0,0,0
19402,NMR Study of the New Magnetic Superconductor CaK(Fe$0.951Ni0.049)4As4: Microscopic Coexistence of Hedgehog Spin-vortex Crystal and Superconductivity,"  Coexistence of a new-type antiferromagnetic (AFM) state, the so-called
hedgehog spin-vortex crystal (SVC), and superconductivity (SC) is evidenced by
$^{75}$As nuclear magnetic resonance study on single-crystalline
CaK(Fe$_{0.951}$Ni$_{0.049}$)$_4$As$_4$. The hedgehog SVC order is clearly
demonstrated by the direct observation of the internal magnetic induction along
the $c$ axis at the As1 site (close to K) and a zero net internal magnetic
induction at the As2 site (close to Ca) below an AFM ordering temperature
$T_{\rm N}$ $\sim$ 52 K. The nuclear spin-lattice relaxation rate 1/$T_1$ shows
a distinct decrease below $T_{\rm c}$ $\sim$ 10 K, providing also unambiguous
evidence for the microscopic coexistence. Furthermore, based on the analysis of
the 1/$T_1$ data, the hedgehog SVC-type spin correlations are found to be
enhanced below $T$ $\sim$ 150 K in the paramagnetic state. These results
indicate the hedgehog SVC-type spin correlations play an important role for the
appearance of SC in the new magnetic superconductor.
",0,1,0,0,0,0
19403,Light spanners for bounded treewidth graphs imply light spanners for $H$-minor-free graphs,"  Grigni and Hung~\cite{GH12} conjectured that H-minor-free graphs have
$(1+\epsilon)$-spanners that are light, that is, of weight $g(|H|,\epsilon)$
times the weight of the minimum spanning tree for some function $g$. This
conjecture implies the {\em efficient} polynomial-time approximation scheme
(PTAS) of the traveling salesperson problem in $H$-minor free graphs; that is,
a PTAS whose running time is of the form $2^{f(\epsilon)}n^{O(1)}$ for some
function $f$. The state of the art PTAS for TSP in H-minor-free-graphs has
running time $n^{1/\epsilon^c}$. We take a further step toward proving this
conjecture by showing that if the bounded treewidth graphs have light greedy
spanners, then the conjecture is true. We also prove that the greedy spanner of
a bounded pathwidth graph is light and discuss the possibility of extending our
proof to bounded treewidth graphs.
",1,0,0,0,0,0
19404,Modeling Daily Seasonality of Mexico City Ozone using Nonseparable Covariance Models on Circles Cross Time,"  Mexico City tracks ground-level ozone levels to assess compliance with
national ambient air quality standards and to prevent environmental health
emergencies. Ozone levels show distinct daily patterns, within the city, and
over the course of the year. To model these data, we use covariance models over
space, circular time, and linear time. We review existing models and develop
new classes of nonseparable covariance models of this type, models appropriate
for quasi-periodic data collected at many locations. With these covariance
models, we use nearest-neighbor Gaussian processes to predict hourly ozone
levels at unobserved locations in April and May, the peak ozone season, to
infer compliance to Mexican air quality standards and to estimate respiratory
health risk associated with ozone. Predicted compliance with air quality
standards and estimated respiratory health risk vary greatly over space and
time. In some regions, we predict exceedance of national standards for more
than a third of the hours in April and May. On many days, we predict that
nearly all of Mexico City exceeds nationally legislated ozone thresholds at
least once. In peak regions, we estimate respiratory risk for ozone to be 55%
higher on average than the annual average risk and as much at 170% higher on
some days.
",0,0,0,1,0,0
19405,Huygens-Fresnel Picture for Electron-Molecule Elastic Scattering,"  The elastic scattering cross sections for a slow electron by C2 and H2
molecules have been calculated within the framework of the non-overlapping
atomic potential model. For the amplitudes of the multiple electron scattering
by a target the wave function of the molecular continuum is represented as a
combination of a plane wave and two spherical waves generated by the centers of
atomic spheres. This wave function obeys the Huygens-Fresnel principle
according to which the electron wave scattering by a system of two centers is
accompanied by generation of two spherical waves; their interaction creates a
diffraction pattern far from the target. Each of the Huygens waves, in turn, is
a superposition of the partial spherical waves with different orbital angular
momenta l and their projections m. The amplitudes of these partial waves are
defined by the corresponding phases of electron elastic scattering by an
isolated atomic potential. In numerical calculations the s- and p-phase shifts
are taken into account. So the number of interfering electron waves is equal to
eight: two of which are the s-type waves and the remaining six waves are of the
p-type with different m values. The calculation of the scattering amplitudes in
closed form (rather than in the form of S-matrix expansion) is reduced to
solving a system of eight inhomogeneous algebraic equations. The differential
and total cross sections of electron scattering by fixed-in-space molecules and
randomly oriented ones have been calculated as well. We conclude by discussing
the special features of the S-matrix method for the case of arbitrary
non-spherical potentials.
",0,1,0,0,0,0
19406,When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity,"  Predicting fine-grained interests of users with temporal behavior is
important to personalization and information filtering applications. However,
existing interest prediction methods are incapable of capturing the subtle
degreed user interests towards particular items, and the internal time-varying
drifting attention of individuals is not studied yet. Moreover, the prediction
process can also be affected by inter-personal influence, known as behavioral
mutual infectivity. Inspired by point process in modeling temporal point
process, in this paper we present a deep prediction method based on two
recurrent neural networks (RNNs) to jointly model each user's continuous
browsing history and asynchronous event sequences in the context of inter-user
behavioral mutual infectivity. Our model is able to predict the fine-grained
interest from a user regarding a particular item and corresponding timestamps
when an occurrence of event takes place. The proposed approach is more flexible
to capture the dynamic characteristic of event sequences by using the temporal
point process to model event data and timely update its intensity function by
RNNs. Furthermore, to improve the interpretability of the model, the attention
mechanism is introduced to emphasize both intra-personal and inter-personal
behavior influence over time. Experiments on real datasets demonstrate that our
model outperforms the state-of-the-art methods in fine-grained user interest
prediction.
",1,0,0,1,0,0
19407,Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer,"  Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.
",1,0,0,0,0,0
19408,Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data,"  Recently, two new indicators (Equalized Mean-based Normalized Proportion
Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which
are intended for sparse scientometrics data. The indicators compare the
proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher
or institution) with the proportion of mentioned papers in the corresponding
fields and publication years (the expected values). In this study, we propose a
third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator
family. The MHq is based on the MH analysis - an established method in
statistics for the comparison of proportions. We test (using citations and
assessments by peers, i.e. F1000Prime recommendations) if the three indicators
can distinguish between different quality levels as defined on the basis of the
assessments by peers. Thus, we test their convergent validity. We find that the
indicator MHq is able to distinguish between the quality levels in most cases
while MNPC and EMNPC are not. Since the MHq is shown in this study to be a
valid indicator, we apply it to six types of zero-inflated altmetrics data and
test whether different altmetrics sources are related to quality. The results
for the various altmetrics demonstrate that the relationship between altmetrics
(Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as
strong as the relationship between citations and assessments by peers.
Actually, the relationship between citations and peer assessments is about two
to three times stronger than the association between altmetrics and assessments
by peers.
",1,0,0,0,0,0
19409,Gravity Formality,"  We show that Willwacher's cyclic formality theorem can be extended to
preserve natural Gravity operations on cyclic multivector fields and cyclic
multidifferential operators. We express this in terms of a homotopy Gravity
quasi-isomorphism with explicit local formulas. For this, we develop operadic
tools related to mixed complexes and cyclic homology and prove that the operad
$\mathsf M_\circlearrowleft$ of natural operations on cyclic operators is
formal and hence quasi-isomorphic to the Gravity operad.
",0,0,1,0,0,0
19410,On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control,"  Process Control Systems (PCSs) are the operating core of Critical
Infrastructures (CIs). As such, anomaly detection has been an active research
field to ensure CI normal operation. Previous approaches have leveraged network
level data for anomaly detection, or have disregarded the existence of process
disturbances, thus opening the possibility of mislabelling disturbances as
attacks and vice versa. In this paper we present an anomaly detection and
diagnostic system based on Multivariate Statistical Process Control (MSPC),
that aims to distinguish between attacks and disturbances. For this end, we
expand traditional MSPC to monitor process level and controller level data. We
evaluate our approach using the Tennessee-Eastman process. Results show that
our approach can be used to distinguish disturbances from intrusions to a
certain extent and we conclude that the proposed approach can be extended with
other sources of data for improving results.
",1,0,0,0,0,0
19411,Quantum hydrodynamic approximations to the finite temperature trapped Bose gases,"  For the quantum kinetic system modelling the Bose-Einstein Condensate that
accounts for interactions between condensate and excited atoms, we use the
Chapman-Enskog expansion to derive its hydrodynamic approximations, include
both Euler and Navier-Stokes approximations. The hydrodynamic approximations
describe not only the macroscopic behavior of the BEC but also its coupling
with the non-condensates, which agrees with Landau's two fluid theory.
",0,1,0,0,0,0
19412,Game-theoretic dynamic investment model with incomplete information: futures contracts,"  Over the past few years, the futures market has been successfully developing
in the North-West region. Futures markets are one of the most effective and
liquid-visible trading mechanisms. A large number of buyers are forced to
compete with each other and raise their prices. A large number of sellers make
them reduce prices. Thus, the gap between the prices of offers of buyers and
sellers is reduced due to high competition, and this is a good criterion for
the liquidity of the market. This high degree of liquidity contributed to the
fact that futures trading took such an important role in commerce and finance.
A multi-step, non-cooperative n persons game is formalized and studied
",0,0,0,0,0,1
19413,Deep Collaborative Learning for Visual Recognition,"  Deep neural networks are playing an important role in state-of-the-art visual
recognition. To represent high-level visual concepts, modern networks are
equipped with large convolutional layers, which use a large number of filters
and contribute significantly to model complexity. For example, more than half
of the weights of AlexNet are stored in the first fully-connected layer (4,096
filters).
We formulate the function of a convolutional layer as learning a large visual
vocabulary, and propose an alternative way, namely Deep Collaborative Learning
(DCL), to reduce the computational complexity. We replace a convolutional layer
with a two-stage DCL module, in which we first construct a couple of smaller
convolutional layers individually, and then fuse them at each spatial position
to consider feature co-occurrence. In mathematics, DCL can be explained as an
efficient way of learning compositional visual concepts, in which the
vocabulary size increases exponentially while the model complexity only
increases linearly. We evaluate DCL on a wide range of visual recognition
tasks, including a series of multi-digit number classification datasets, and
some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012.
We apply DCL to several state-of-the-art network structures, improving the
recognition accuracy meanwhile reducing the number of parameters (16.82% fewer
in AlexNet).
",1,0,0,0,0,0
19414,Co-location Epidemic Tracking on London Public Transports Using Low Power Mobile Magnetometer,"  The public transports provide an ideal means to enable contagious diseases
transmission. This paper introduces a novel idea to detect co-location of
people in such environment using just the ubiquitous geomagnetic field sensor
on the smart phone. Essentially, given that all passengers must share the same
journey between at least two consecutive stations, we have a long window to
match the user trajectory. Our idea was assessed over a painstakingly survey of
over 150 kilometres of travelling distance, covering different parts of London,
using the overground trains, the underground tubes and the buses.
",1,0,0,0,0,0
19415,"Waring-Goldbach Problem: One Square, Four Cubes and Higher Powers","  Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors,
counted according to multiplicity. In this paper, it is proved that, for
$12\leqslant b\leqslant 35$ and for every sufficiently large odd integer $N$,
the equation \begin{equation*}
N=x^2+p_1^3+p_2^3+p_3^3+p_4^3+p_5^4+p_6^b \end{equation*} is solvable with
$x$ being an almost-prime $\mathcal{P}_{r(b)}$ and the other variables primes,
where $r(b)$ is defined in the Theorem. This result constitutes an improvement
upon that of Lü and Mu.
",0,0,1,0,0,0
19416,Realizability of tropical canonical divisors,"  We use recent results by Bainbridge-Chen-Gendron-Grushevsky-Moeller on
compactifications of strata of abelian differentials to give a comprehensive
solution to the realizability problem for effective tropical canonical divisors
in equicharacteristic zero. Given a pair $(\Gamma, D)$ consisting of a stable
tropical curve $\Gamma$ and a divisor $D$ in the canonical linear system on
$\Gamma$, we give a purely combinatorial condition to decide whether there is a
smooth curve $X$ over a non-Archimedean field whose stable reduction has
$\Gamma$ as its dual tropical curve together with a effective canonical divisor
$K_X$ that specializes to $D$. Along the way, we develop a moduli-theoretic
framework to understand Baker's specialization of divisors from algebraic to
tropical curves as a natural toroidal tropicalization map in the sense of
Abramovich-Caporaso-Payne.
",0,0,1,0,0,0
19417,Causal inference for interfering units with cluster and population level treatment allocation programs,"  Interference arises when an individual's potential outcome depends on the
individual treatment level, but also on the treatment level of others. A common
assumption in the causal inference literature in the presence of interference
is partial interference, implying that the population can be partitioned in
clusters of individuals whose potential outcomes only depend on the treatment
of units within the same cluster. Previous literature has defined average
potential outcomes under counterfactual scenarios where treatments are randomly
allocated to units within a cluster. However, within clusters there may be
units that are more or less likely to receive treatment based on covariates or
neighbors' treatment. We define new estimands that describe average potential
outcomes for realistic counterfactual treatment allocation programs, extending
existing estimands to take into consideration the units' covariates and
dependence between units' treatment assignment. We further propose entirely new
estimands for population-level interventions over the collection of clusters,
which correspond in the motivating setting to regulations at the federal (vs.
cluster or regional) level. We discuss these estimands, propose unbiased
estimators and derive asymptotic results as the number of clusters grows.
Finally, we estimate effects in a comparative effectiveness study of power
plant emission reduction technologies on ambient ozone pollution.
",0,0,1,1,0,0
19418,"A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation","  We present a unifying framework to solve several computer vision problems
with event cameras: motion, depth and optical flow estimation. The main idea of
our framework is to find the point trajectories on the image plane that are
best aligned with the event data by maximizing an objective function: the
contrast of an image of warped events. Our method implicitly handles data
association between the events, and therefore, does not rely on additional
appearance information about the scene. In addition to accurately recovering
the motion parameters of the problem, our framework produces motion-corrected
edge-like images with high dynamic range that can be used for further scene
analysis. The proposed method is not only simple, but more importantly, it is,
to the best of our knowledge, the first method that can be successfully applied
to such a diverse set of important vision tasks with event cameras.
",1,0,0,0,0,0
19419,TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing,"  Machine learning models are notoriously difficult to interpret and debug.
This is particularly true of neural networks. In this work, we introduce
automated software testing techniques for neural networks that are well-suited
to discovering errors which occur only for rare inputs. Specifically, we
develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,
random mutations of inputs to a neural network are guided by a coverage metric
toward the goal of satisfying user-specified constraints. We describe how fast
approximate nearest neighbor algorithms can provide this coverage metric. We
then discuss the application of CGF to the following goals: finding numerical
errors in trained neural networks, generating disagreements between neural
networks and quantized versions of those networks, and surfacing undesirable
behavior in character level language models. Finally, we release an open source
library called TensorFuzz that implements the described techniques.
",0,0,0,1,0,0
19420,Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation,"  Inferring interactions between processes promises deeper insight into
mechanisms underlying network phenomena. Renormalised partial directed
coherence (rPDC) is a frequency-domain representation of the concept of Granger
causality while directed partial correlation (DPC) is an alternative approach
for quantifying Granger causality in the time domain. Both methodologies have
been successfully applied to neurophysiological signals for detecting directed
relationships. This paper introduces their application to climatological time
series. We first discuss the application to ENSO -- Monsoon interaction, and
then apply the methodologies to the more challenging air-sea interaction in the
South Atlantic Convergence Zone (SACZ). While in the first case the results
obtained are fully consistent with present knowledge in climate modeling, in
the second case the results are, as expected, less clear, and to fully
elucidate the SACZ air-sea interaction, further investigations on the
specificity and sensitivity of these methodologies are needed.
",0,1,0,0,0,0
19421,Conditions for the invertibility of dual energy data,"  The Alvarez-Macovski method [Alvarez, R. E and Macovski, A.,
""Energy-selective reconstructions in X-ray computerized tomography"", Phys. Med.
Biol. (1976), 733--44] requires the inversion of the transformation from the
line integrals of the basis set coefficients to measurements with multiple
x-ray spectra. Analytical formulas for invertibility of the transformation from
two measurements to two line integrals are derived. It is found that
non-invertible systems have near zero Jacobian determinants on a nearly
straight line in the line integrals plane. Formulas are derived for the points
where the line crosses the axes, thus determining the line. Additional formulas
are derived for the values of the terms of the Jacobian determinant at the
endpoints of the line of non-invertibility. The formulas are applied to a set
of spectra including one suggested by Levine that is not invertible as well as
similar spectra that are invertible and voltage switched x-ray tube spectra
that are also invertible. An iterative inverse transformation algorithm
exhibits large errors with non-invertible spectra.
",0,1,0,0,0,0
19422,"Examples of lattice-polarized K3 surfaces with automorphic discriminant, and Lorentzian Kac--Moody algebras","  Using our results about Lorentzian Kac--Moody algebras and arithmetic mirror
symmetry, we give six series of examples of lattice-polarized K3 surfaces with
automorphic discriminant.
",0,0,1,0,0,0
19423,Quantum ferrofluid turbulence,"  We study the elementary characteristics of turbulence in a quantum ferrofluid
through the context of a dipolar Bose gas condensing from a highly
non-equilibrium thermal state. Our simulations reveal that the dipolar
interactions drive the emergence of polarized turbulence and density
corrugations. The superfluid vortex lines and density fluctuations adopt a
columnar or stratified configuration, depending on the sign of the dipolar
interactions, with the vortices tending to form in the low density regions to
minimize kinetic energy. When the interactions are dominantly dipolar, the
decay of vortex line length is enhanced, closely following a $t^{-3/2}$
behaviour. This system poses exciting prospects for realizing stratified
quantum turbulence and new levels of generating and controlling turbulence
using magnetic fields.
",0,1,0,0,0,0
19424,Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book,"  An extensive empirical literature documents a generally negative correlation,
named the ""leverage effect,"" between asset returns and changes of volatility.
It is more challenging to establish such a return-volatility relationship for
jumps in high-frequency data. We propose new nonparametric methods to assess
and test for a discontinuous leverage effect --- i.e. a relation between
contemporaneous jumps in prices and volatility. The methods are robust to
market microstructure noise and build on a newly developed price-jump
localization and estimation procedure. Our empirical investigation of six years
of transaction data from 320 NASDAQ firms displays no unconditional negative
correlation between price and volatility cojumps. We show, however, that there
is a strong relation between price-volatility cojumps if one conditions on the
sign of price jumps and whether the price jumps are market-wide or
idiosyncratic. Firms' volatility levels strongly explain the cross-section of
discontinuous leverage while debt-to-equity ratios have no significant
explanatory power.
",0,0,1,1,0,0
19425,Group representations that resist worst-case sampling,"  Motivated by expansion in Cayley graphs, we show that there exist infinitely
many groups $G$ with a nontrivial irreducible unitary representation whose
average over every set of $o(\log\log|G|)$ elements of $G$ has operator norm $1
- o(1)$. This answers a question of Lovett, Moore, and Russell, and strengthens
their negative answer to a question of Wigderson.
The construction is the affine group of $\mathbb{F}_p$ and uses the fact that
for every $A \subset \mathbb{F}_p\setminus\{0\}$, there is a set of size
$\exp(\exp(O(|A|)))$ that is almost invariant under both additive and
multiplicatpive translations by elements of $A$.
",0,0,1,0,0,0
19426,On the nearly smooth complex spaces,"  We introduce a class of normal complex spaces having only mild sin-gularities
(close to quotient singularities) for which we generalize the notion of a
(analytic) fundamental class for an analytic cycle and also the notion of a
relative fundamental class for an analytic family of cycles. We also generalize
to these spaces the geometric intersection theory for analytic cycles with
rational positive coefficients and show that it behaves well with respect to
analytic families of cycles. We prove that this intersection theory has most of
the usual properties of the standard geometric intersection theory on complex
manifolds, but with the exception that the intersection cycle of two cycles
with positive integral coefficients that intersect properly may have rational
coefficients. AMS classification. 32 C 20-32 C 25-32 C 36.
",0,0,1,0,0,0
19427,Semi-supervised learning of hierarchical representations of molecules using neural message passing,"  With the rapid increase of compound databases available in medicinal and
material science, there is a growing need for learning representations of
molecules in a semi-supervised manner. In this paper, we propose an
unsupervised hierarchical feature extraction algorithm for molecules (or more
generally, graph-structured objects with fixed number of types of nodes and
edges), which is applicable to both unsupervised and semi-supervised tasks. Our
method extends recently proposed Paragraph Vector algorithm and incorporates
neural message passing to obtain hierarchical representations of subgraphs. We
applied our method to an unsupervised task and demonstrated that it outperforms
existing proposed methods in several benchmark datasets. We also experimentally
showed that semi-supervised tasks enhanced predictive performance compared with
supervised ones with labeled molecules only.
",1,0,0,1,0,0
19428,Tracy-Widom at each edge of real covariance and MANOVA estimators,"  We study the sample covariance matrix for real-valued data with general
population covariance, as well as MANOVA-type covariance estimators in variance
components models under null hypotheses of global sphericity. In the limit as
matrix dimensions increase proportionally, the asymptotic spectra of such
estimators may have multiple disjoint intervals of support, possibly
intersecting the negative half line. We show that the distribution of the
extremal eigenvalue at each regular edge of the support has a GOE Tracy-Widom
limit. Our proof extends a comparison argument of Ji Oon Lee and Kevin
Schnelli, replacing a continuous Green function flow by a discrete Lindeberg
swapping scheme.
",0,0,1,1,0,0
19429,Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images,"  Lung nodule classification is a class imbalanced problem because nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We therefore propose cascaded convolutional
neural networks to cope with the class imbalanced problem. In the proposed
approach, multi-stage convolutional neural networks that perform as
single-sided classifiers filter out obvious non-nodules. Successively, a
convolutional neural network trained with a balanced data set calculates nodule
probabilities. The proposed method achieved the sensitivity of 92.4\% and 94.5%
at 4 and 8 false positives per scan in Free Receiver Operating Characteristics
(FROC) curve analysis, respectively.
",1,0,0,0,0,0
19430,Monochromatic knots and other unusual electromagnetic disturbances: light localised in 3D,"  We introduce and examine a collection of unusual electromagnetic
disturbances. Each of these is an exact, monochromatic solution of Maxwell's
equations in free space with looped electric and magnetic field lines of finite
extent and a localised appearance in all three spatial dimensions. Included are
the first explicit examples of monochromatic electromagnetic knots. We also
consider the generation of our unusual electromagnetic disturbances in the
laboratory, at both low and high frequencies, and highlight possible directions
for future research, including the use of unusual electromagnetic disturbances
as the basis of a new form of three-dimensional display.
",0,1,0,0,0,0
19431,Zero-cycles of degree one on Skorobogatov's bielliptic surface,"  Skorobogatov constructed a bielliptic surface which is a counterexample to
the Hasse principle not explained by the Brauer-Manin obstruction. We show that
this surface has a $0$-cycle of degree 1, as predicted by a conjecture of
Colliot-Thélène.
",0,0,1,0,0,0
19432,Locally Nameless Permutation Types,"  We define ""Locally Nameless Permutation Types"", which fuse permutation types
as used in Nominal Isabelle with the locally nameless representation. We show
that this combination is particularly useful when formalizing programming
languages where bound names may become free during execution (""extrusion""),
common in process calculi. It inherits the generic definition of permutations
and support, and associated lemmas, from the Nominal approach, and the ability
to stay close to pencil-and-paper proofs from the locally nameless approach. We
explain how to use cofinite quantification in this setting, show why reasoning
about renaming is more important here than in languages without extrusion, and
provide results about infinite support, necessary when reasoning about
countable choice.
",1,0,0,0,0,0
19433,On dimension-free variational inequalities for averaging operators in $\mathbb R^d$,"  We study dimension-free $L^p$ inequalities for $r$-variations of the
Hardy--Littlewood averaging operators defined over symmetric convex bodies in
$\mathbb R^d$.
",0,0,1,0,0,0
19434,One-particle density matrix of trapped one-dimensional impenetrable bosons from conformal invariance,"  The one-particle density matrix of the one-dimensional Tonks-Girardeau gas
with inhomogeneous density profile is calculated, thanks to a recent
observation that relates this system to a two-dimensional conformal field
theory in curved space. The result is asymptotically exact in the limit of
large particle density and small density variation, and holds for arbitrary
trapping potentials. In the particular case of a harmonic trap, we recover a
formula obtained by Forrester et al. [Phys. Rev. A 67, 043607 (2003)] from a
different method.
",0,1,1,0,0,0
19435,Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform,"  We propose a 2D generalization to the $M$-band case of the dual-tree
decomposition structure (initially proposed by N. Kingsbury and further
investigated by I. Selesnick) based on a Hilbert pair of wavelets. We
particularly address (\textit{i}) the construction of the dual basis and
(\textit{ii}) the resulting directional analysis. We also revisit the necessary
pre-processing stage in the $M$-band case. While several reconstructions are
possible because of the redundancy of the representation, we propose a new
optimal signal reconstruction technique, which minimizes potential estimation
errors. The effectiveness of the proposed $M$-band decomposition is
demonstrated via denoising comparisons on several image types (natural,
texture, seismics), with various $M$-band wavelets and thresholding strategies.
Significant improvements in terms of both overall noise reduction and direction
preservation are observed.
",1,1,1,0,0,0
19436,Nonlinear Cauchy-Riemann Equations and Liouville Equation For Conformal Metrics,"  We introduce the Nonlinear Cauchy-Riemann equations as Bäcklund
transformations for several nonlinear and linear partial differential
equations. From these equations we treat in details the Laplace and the
Liouville equations by deriving general solution for the nonlinear Liouville
equation. By Möbius transformation we relate solutions for the Poincare
model of hyperbolic geometry, the Klein model in half-plane and the
pseudo-sphere. Conformal form of the constant curvature metrics in these
geometries, stereographic projections and special solutions are discussed. Then
we introduce the hyperbolic analog of the Riemann sphere, which we call the
Riemann pseudosphere. We identify point at infinity on this pseudosphere and
show that it can be used in complex analysis as an alternative to usual Riemann
sphere to extend the complex plane. Interpretation of symmetric and antipodal
points on both, the Riemann sphere and the Riemann pseudo-sphere, are given. By
Möbius transformation and homogenous coordinates, the most general solution
of Liouville equation as discussed by Crowdy is derived.
",0,1,1,0,0,0
19437,SmartPaste: Learning to Adapt Source Code,"  Deep Neural Networks have been shown to succeed at a range of natural
language tasks such as machine translation and text summarization. While tasks
on source code (ie, formal languages) have been considered recently, most work
in this area does not attempt to capitalize on the unique opportunities offered
by its known syntax and structure. In this work, we introduce SmartPaste, a
first task that requires to use such information. The task is a variant of the
program repair problem that requires to adapt a given (pasted) snippet of code
to surrounding, existing source code. As first solutions, we design a set of
deep neural models that learn to represent the context of each variable
location and variable usage in a data flow-sensitive way. Our evaluation
suggests that our models can learn to solve the SmartPaste task in many cases,
achieving 58.6% accuracy, while learning meaningful representation of variable
usages.
",1,0,0,0,0,0
19438,An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population,"  Participants enrolled into randomized controlled trials (RCTs) often do not
reflect real-world populations. Previous research in how best to translate RCT
results to target populations has focused on weighting RCT data to look like
the target data. Simulation work, however, has suggested that an outcome model
approach may be preferable. Here we describe such an approach using source data
from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan
and nateglinide on cardiovascular outcomes and new-onset diabetes in a
pre-diabetic population. Our target data consisted of people with pre-diabetes
serviced at our institution. We used Random Survival Forests to develop
separate outcome models for each of the 4 treatments, estimating the 5-year
risk difference for progression to diabetes and estimated the treatment effect
in our local patient populations, as well as sub-populations, and the results
compared to the traditional weighting approach. Our models suggested that the
treatment effect for valsartan in our patient population was the same as in the
trial, whereas for nateglinide treatment effect was stronger than observed in
the original trial. Our effect estimates were more efficient than the weighting
approach.
",0,0,0,1,0,0
19439,Influence of parameterized small-scale gravity waves on the migrating diurnal tide in Earth's thermosphere,"  Effects of subgrid-scale gravity waves (GWs) on the diurnal migrating tides
are investigated from the mesosphere to the upper thermosphere for September
equinox conditions, using a general circulation model coupled with the extended
spectral nonlinear GW parameterization of Yiğit et al (2008). Simulations
with GW effects cut-off above the turbopause and included in the entire
thermosphere have been conducted. GWs appreciably impact the mean circulation
and cool the thermosphere down by up to 12-18%. GWs significantly affect the
winds modulated by the diurnal migrating tide, in particular in the
low-latitude mesosphere and lower thermosphere and in the high-latitude
thermosphere. These effects depend on the mutual correlation of the diurnal
phases of the GW forcing and tides: GWs can either enhance or reduce the tidal
amplitude. In the low-latitude MLT, the correlation between the direction of
the deposited GW momentum and the tidal phase is positive due to propagation of
a broad spectrum of GW harmonics through the alternating winds. In the Northern
Hemisphere high-latitude thermosphere, GWs act against the tide due to an
anti-correlation of tidal wind and GW momentum, while in the Southern
high-latitudes they weakly enhance the tidal amplitude via a combination of a
partial correlation of phases and GW-induced changes of the circulation. The
variable nature of GW effects on the thermal tide can be captured in GCMs
provided that a GW parameterization (1) considers a broad spectrum of
harmonics, (2) properly describes their propagation, and (3) correctly accounts
for the physics of wave breaking/saturation.
",0,1,0,0,0,0
19440,A Statistical Model for Ideal Team Selection for A National Cricket Squad,"  Cricket is a game played between two teams which consists of eleven players
each. Nowadays cricket game is becoming more and more popular in Bangladesh and
other South Asian Countries. Before a match people are very enthusiastic about
team squads and ""Which players are playing today?"", ""How well will MR. X
perform today?"" are the million dollar questions before a big match. This
article will propose a method using statistical data analysis for recommending
a national team squad. Recent match scorecards for domestic and international
matches played by a specific team in recent years are used to recommend the
ideal squad. Impact point or rating points of all players in different
conditions are calculated and the best ones from different categories are
chosen to form optimal line-ups. To evaluate the efficiency of impact point
system, it will be tested with real time match data to see how much accuracy it
gives.
",0,0,0,1,0,0
19441,The geometry of some generalized affine Springer fibers,"  We study basic geometric properties of some group analogue of affine Springer
fibers and compare with the classical Lie algebra affine Springer fibers. The
main purpose is to formulate a conjecture that relates the number of
irreducible components of such varieties for a reductive group $G$ to certain
weight multiplicities defined by the Langlands dual group $\hat{G}$. We prove
our conjecture in the case of unramified conjugacy class.
",0,0,1,0,0,0
19442,Modelling of a Permanent Magnet Synchronous Machine Using Isogeometric Analysis,"  Isogeometric analysis (IGA) is used to simulate a permanent magnet
synchronous machine. IGA uses non-uniform rational B-splines to parametrise the
domain and to approximate the solution space, thus allowing for the exact
description of the geometries even on the coarsest level of mesh refinement.
Given the properties of the isogeometric basis functions, this choice
guarantees a higher accuracy than the classical finite element method.
For dealing with the different stator and rotor topologies, the domain is
split into two non-overlapping parts on which Maxwell's equations are solved
independently in the context of a classical Dirichlet-to-Neumann domain
decomposition scheme. The results show good agreement with the ones obtained by
the classical finite element approach.
",1,0,0,0,0,0
19443,Learning and Trust in Auction Markets,"  In this paper, we study behavior of bidders in an experimental launch of a
new advertising auction platform by Zillow, as Zillow switched from negotiated
contracts to using auctions in several geographically isolated markets. A
unique feature of this experiment is that the bidders in this market are real
estate agents that bid on their own behalf, not using third-party
intermediaries. To help bidders, Zillow also provided a recommendation tool
that suggested a bid for each bidder.
Our main focus in this paper is on the decisions of bidders whether or not to
adopt the platform-provided bid recommendation. We observe that a significant
proportion of bidders do not use the recommended bid. Using the bid history of
the agents we infer their value, and compare the agents' regret with their
actual bidding history with results they would have obtained following the
recommendation. We find that for half of the agents not following the
recommendation, the increased effort of experimenting with alternate bids
results in increased regret, i.e., they get decreased net value out of the
system. The proportion of agents not following the recommendation slowly
declines as markets mature, but it remains large in most markets that we
observe. We argue that the main reason for this phenomenon is the lack of trust
in the platform-provided tool.
Our work provides an empirical insight into possible design choices for
auction-based online advertising platforms. While search advertising platforms
(such as Google or Bing) allow bidders to submit bids on their own, many
display advertising platforms (such as Facebook) optimize bids on bidders'
behalf and eliminate the need for bids. Our empirical analysis shows that the
latter approach is preferred for markets where bidders are individuals, who
don't have access to third party tools, and who may question the fairness of
platform-provided suggestions.
",1,0,0,0,0,0
19444,"Like trainer, like bot? Inheritance of bias in algorithmic content moderation","  The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpora of texts manually annotated for offence. While such systems could
help encourage more civil debate, they must navigate inherently normatively
contestable boundaries, and are subject to the idiosyncratic norms of the human
raters who provide the training data. An important objective for platforms
implementing such measures might be to ensure that they are not unduly biased
towards or against particular norms of offence. This paper provides some
exploratory methods by which the normative biases of algorithmic content
moderation systems can be measured, by way of a case study using an existing
dataset of comments labelled for offence. We train classifiers on comments
labelled by different demographic subsets (men and women) to understand how
differences in conceptions of offence between these groups might affect the
performance of the resulting models on various test sets. We conclude by
discussing some of the ethical choices facing the implementers of algorithmic
moderation systems, given various desired levels of diversity of viewpoints
amongst discussion participants.
",1,0,0,0,0,0
19445,Holographic Neural Architectures,"  Representation learning is at the heart of what makes deep learning
effective. In this work, we introduce a new framework for representation
learning that we call ""Holographic Neural Architectures"" (HNAs). In the same
way that an observer can experience the 3D structure of a holographed object by
looking at its hologram from several angles, HNAs derive Holographic
Representations from the training set. These representations can then be
explored by moving along a continuous bounded single dimension. We show that
HNAs can be used to make generative networks, state-of-the-art regression
models and that they are inherently highly resistant to noise. Finally, we
argue that because of their denoising abilities and their capacity to
generalize well from very few examples, models based upon HNAs are particularly
well suited for biological applications where training examples are rare or
noisy.
",0,0,0,1,1,0
19446,An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists,"  Topological Data Analysis (tda) is a recent and fast growing eld providing a
set of new topological and geometric tools to infer relevant features for
possibly complex data. This paper is a brief introduction, through a few
selected topics, to basic fundamental and practical aspects of tda for non
experts. 1 Introduction and motivation Topological Data Analysis (tda) is a
recent eld that emerged from various works in applied (algebraic) topology and
computational geometry during the rst decade of the century. Although one can
trace back geometric approaches for data analysis quite far in the past, tda
really started as a eld with the pioneering works of Edelsbrunner et al. (2002)
and Zomorodian and Carlsson (2005) in persistent homology and was popularized
in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the
idea that topology and geometry provide a powerful approach to infer robust
qualitative, and sometimes quantitative, information about the structure of
data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical,
statistical and algorithmic methods to infer, analyze and exploit the complex
topological and geometric structures underlying data that are often represented
as point clouds in Euclidean or more general metric spaces. During the last few
years, a considerable eort has been made to provide robust and ecient data
structures and algorithms for tda that are now implemented and available and
easy to use through standard libraries such as the Gudhi library (C++ and
Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a).
Although it is still rapidly evolving, tda now provides a set of mature and
ecient tools that can be used in combination or complementary to other data
sciences tools. The tdapipeline. tda has recently known developments in various
directions and application elds. There now exist a large variety of methods
inspired by topological and geometric approaches. Providing a complete overview
of all these existing approaches is beyond the scope of this introductory
survey. However, most of them rely on the following basic and standard pipeline
that will serve as the backbone of this paper: 1. The input is assumed to be a
nite set of points coming with a notion of distance-or similarity between them.
This distance can be induced by the metric in the ambient space (e.g. the
Euclidean metric when the data are embedded in R d) or come as an intrinsic
metric dened by a pairwise distance matrix. The denition of the metric on the
data is usually given as an input or guided by the application. It is however
important to notice that the choice of the metric may be critical to reveal
interesting topological and geometric features of the data.
",1,0,1,1,0,0
19447,Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures,"  The resemblance between the methods used in quantum-many body physics and in
machine learning has drawn considerable attention. In particular, tensor
networks (TNs) and deep learning architectures bear striking similarities to
the extent that TNs can be used for machine learning. Previous results used
one-dimensional TNs in image recognition, showing limited scalability and
flexibilities. In this work, we train two-dimensional hierarchical TNs to solve
image recognition problems, using a training algorithm derived from the
multipartite entanglement renormalization ansatz. This approach introduces
novel mathematical connections among quantum many-body physics, quantum
information theory, and machine learning. While keeping the TN unitary in the
training phase, TN states are defined, which optimally encode classes of images
into quantum many-body states. We study the quantum features of the TN states,
including quantum entanglement and fidelity. We find these quantities could be
novel properties that characterize the image classes, as well as the machine
learning tasks. Our work could contribute to the research on
identifying/modeling quantum artificial intelligences.
",0,1,0,1,0,0
19448,Hierarchical Imitation and Reinforcement Learning,"  We study how to effectively leverage expert feedback to learn sequential
decision-making policies. We focus on problems with sparse rewards and long
time horizons, which typically pose significant challenges in reinforcement
learning. We propose an algorithmic framework, called hierarchical guidance,
that leverages the hierarchical structure of the underlying problem to
integrate different modes of expert interaction. Our framework can incorporate
different combinations of imitation learning (IL) and reinforcement learning
(RL) at different levels, leading to dramatic reductions in both expert effort
and cost of exploration. Using long-horizon benchmarks, including Montezuma's
Revenge, we demonstrate that our approach can learn significantly faster than
hierarchical RL, and be significantly more label-efficient than standard IL. We
also theoretically analyze labeling cost for certain instantiations of our
framework.
",0,0,0,1,0,0
19449,Social learning in a simple task allocation game,"  We investigate the effects of social interactions in task al- location using
Evolutionary Game Theory (EGT). We propose a simple task-allocation game and
study how different learning mechanisms can give rise to specialised and non-
specialised colonies under different ecological conditions. By combining
agent-based simulations and adaptive dynamics we show that social learning can
result in colonies of generalists or specialists, depending on ecological
parameters. Agent-based simulations further show that learning dynamics play a
crucial role in task allocation. In particular, introspective individual
learning readily favours the emergence of specialists, while a process
resembling task recruitment favours the emergence of generalists.
",1,0,0,0,0,0
19450,Guided projections for analysing the structure of high-dimensional data,"  A powerful data transformation method named guided projections is proposed
creating new possibilities to reveal the group structure of high-dimensional
data in the presence of noise variables. Utilising projections onto a space
spanned by a selection of a small number of observations allows measuring the
similarity of other observations to the selection based on orthogonal and score
distances. Observations are iteratively exchanged from the selection creating a
non-random sequence of projections which we call guided projections. In
contrast to conventional projection pursuit methods, which typically identify a
low-dimensional projection revealing some interesting features contained in the
data, guided projections generate a series of projections that serve as a basis
not just for diagnostic plots but to directly investigate the group structure
in data. Based on simulated data we identify the strengths and limitations of
guided projections in comparison to commonly employed data transformation
methods. We further show the relevance of the transformation by applying it to
real-world data sets.
",0,0,0,1,0,0
19451,Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows,"  Cascading failures are a critical vulnerability of complex information or
infrastructure networks. Here we investigate the properties of load-based
cascading failures in real and synthetic spatially-embedded network structures,
and propose mitigation strategies to reduce the severity of damages caused by
such failures. We introduce a stochastic method for optimal heterogeneous
distribution of resources (node capacities) subject to a fixed total cost.
Additionally, we design and compare the performance of networks with N-stable
and (N-1)-stable network-capacity allocations by triggering cascades using
various real-world node-attack and node-failure scenarios. We show that failure
mitigation through increased node protection can be effectively achieved
against single node failures. However, mitigating against multiple node
failures is much more difficult due to the combinatorial increase in possible
failures. We analyze the robustness of the system with increasing protection,
and find that a critical tolerance exists at which the system undergoes a phase
transition, and above which the network almost completely survives an attack.
Moreover, we show that cascade-size distributions measured in this region
exhibit a power-law decay. Finally, we find a strong correlation between
cascade sizes induced by individual nodes and sets of nodes. We also show that
network topology alone is a weak factor in determining the progression of
cascading failures.
",1,1,0,0,0,0
19452,Strong electron-hole symmetric Rashba spin-orbit coupling in graphene/monolayer transition metal dichalcogenide heterostructures,"  Despite its extremely weak intrinsic spin-orbit coupling (SOC), graphene has
been shown to acquire considerable SOC by proximity coupling with exfoliated
transition metal dichalcogenides (TMDs). Here we demonstrate strong induced
Rashba SOC in graphene that is proximity coupled to a monolayer TMD film, MoS2
or WSe2, grown by chemical vapor deposition with drastically different Fermi
level positions. Graphene/TMD heterostructures are fabricated with a
pickup-transfer technique utilizing hexagonal boron nitride, which serves as a
flat template to promote intimate contact and therefore a strong interfacial
interaction between TMD and graphene as evidenced by quenching of the TMD
photoluminescence. We observe strong induced graphene SOC that manifests itself
in a pronounced weak anti-localization (WAL) effect in the graphene
magnetoconductance. The spin relaxation rate extracted from the WAL analysis
varies linearly with the momentum scattering time and is independent of the
carrier type. This indicates a dominantly Dyakonov-Perel spin relaxation
mechanism caused by the induced Rashba SOC. Our analysis yields a Rashba SOC
energy of ~1.5 meV in graphene/WSe2 and ~0.9 meV in graphene/MoS2,
respectively. The nearly electron-hole symmetric nature of the induced Rashba
SOC provides a clue to possible underlying SOC mechanisms.
",0,1,0,0,0,0
19453,Robust Statistics for Image Deconvolution,"  We present a blind multiframe image-deconvolution method based on robust
statistics. The usual shortcomings of iterative optimization of the likelihood
function are alleviated by minimizing the M-scale of the residuals, which
achieves more uniform convergence across the image. We focus on the
deconvolution of astronomical images, which are among the most challenging due
to their huge dynamic ranges and the frequent presence of large noise-dominated
regions in the images. We show that high-quality image reconstruction is
possible even in super-resolution and without the use of traditional
regularization terms. Using a robust \r{ho}-function is straightforward to
implement in a streaming setting and, hence our method is applicable to the
large volumes of astronomy images. The power of our method is demonstrated on
observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly
discuss the feasibility of a pipeline based on Graphical Processing Units for
the next generation of telescope surveys.
",1,1,0,0,0,0
19454,Nash and Wardrop equilibria in aggregative games with coupling constraints,"  We consider the framework of aggregative games, in which the cost function of
each agent depends on his own strategy and on the average population strategy.
As first contribution, we investigate the relations between the concepts of
Nash and Wardrop equilibrium. By exploiting a characterization of the two
equilibria as solutions of variational inequalities, we bound their distance
with a decreasing function of the population size. As second contribution, we
propose two decentralized algorithms that converge to such equilibria and are
capable of coping with constraints coupling the strategies of different agents.
Finally, we study the applications of charging of electric vehicles and of
route choice on a road network.
",1,0,1,0,0,0
19455,Magnetoresistance in the superconducting state at the (111) LaAlO$_3$/SrTiO$_3$ interface,"  Condensed matter systems that simultaneously exhibit superconductivity and
ferromagnetism are rare due the antagonistic relationship between conventional
spin-singlet superconductivity and ferromagnetic order. In materials in which
superconductivity and magnetic order is known to coexist (such as some
heavy-fermion materials), the superconductivity is thought to be of an
unconventional nature. Recently, the conducting gas that lives at the interface
between the perovskite band insulators LaAlO$_3$ (LAO) and SrTiO$_3$ (STO) has
also been shown to host both superconductivity and magnetism. Most previous
research has focused on LAO/STO samples in which the interface is in the (001)
crystal plane. Relatively little work has focused on the (111) crystal
orientation, which has hexagonal symmetry at the interface, and has been
predicted to have potentially interesting topological properties, including
unconventional superconducting pairing states. Here we report measurements of
the magnetoresistance of (111) LAO/STO heterostructures at temperatures at
which they are also superconducting. As with the (001) structures, the
magnetoresistance is hysteretic, indicating the coexistence of magnetism and
superconductivity, but in addition, we find that this magnetoresistance is
anisotropic. Such an anisotropic response is completely unexpected in the
superconducting state, and suggests that (111) LAO/STO heterostructures may
support unconventional superconductivity.
",0,1,0,0,0,0
19456,Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data,"  The paper presents the application of Variational Autoencoders (VAE) for data
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
approaches.
",1,0,0,1,0,0
19457,Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings,"  In this paper, we propose a new algorithm for learning general
latent-variable probabilistic graphical models using the techniques of
predictive state representation, instrumental variable regression, and
reproducing-kernel Hilbert space embeddings of distributions. Under this new
learning framework, we first convert latent-variable graphical models into
corresponding latent-variable junction trees, and then reduce the hard
parameter learning problem into a pipeline of supervised learning problems,
whose results will then be used to perform predictive belief propagation over
the latent junction tree during the actual inference procedure. We then give
proofs of our algorithm's correctness, and demonstrate its good performance in
experiments on one synthetic dataset and two real-world tasks from
computational biology and computer vision - classifying DNA splice junctions
and recognizing human actions in videos.
",1,0,0,1,0,0
19458,Can interacting dark energy solve the $H_0$ tension?,"  The answer is Yes! We indeed find that interacting dark energy can alleviate
the current tension on the value of the Hubble constant $H_0$ between the
Cosmic Microwave Background anisotropies constraints obtained from the Planck
satellite and the recent direct measurements reported by Riess et al. 2016. The
combination of these two datasets points towards an evidence for a non-zero
dark matter-dark energy coupling $\xi$ at more than two standard deviations,
with $\xi=-0.26_{-0.12}^{+0.16}$ at $95\%$ CL. However the $H_0$ tension is
better solved when the equation of state of the interacting dark energy
component is allowed to freely vary, with a phantom-like equation of state
$w=-1.184\pm0.064$ (at $68 \%$ CL), ruling out the pure cosmological constant
case, $w=-1$, again at more than two standard deviations. When Planck data are
combined with external datasets, as BAO, JLA Supernovae Ia luminosity
distances, cosmic shear or lensing data, we find good consistency with the
cosmological constant scenario and no compelling evidence for a dark
matter-dark energy coupling.
",0,1,0,0,0,0
19459,Fast Switching Dual Fabry-Perot Cavity Optical Refractometry - Methodologies for Accurate Assessment of Gas Density,"  Dual Fabry-Perot cavity based optical refractometry (DFPC-OR) has a high
potential for assessments of gas density. However, drifts of the FP cavity
often limit its performance. We show that by the use of two narrow-linewidth
fiber lasers locked to two high finesse cavities and Allan-Werle plots that
drift-free DFPC-OR can be obtained for short measurement times (for which the
drifts of the cavity can be disregarded). Based on this, a novel strategy,
termed fast switching DFPC-OR (FS-DFPC-OR), is presented. A set of novel
methodologies for assessment of both gas density and flow rates (in particular
from small leaks) that are not restricted by the conventional limitations
imposed by the drifts of the cavity are presented. The methodologies deal with
assessments in both open and closed (finite-sized) compartments. They
circumvent the problem with volumetric expansion, i.e. that the gas density in
a measurement cavity is not the same as that in the closed external compartment
that should be assessed, by performing a pair of measurements in rapid
succession; the first one serves the purpose of assessing the density of the
gas that has been transferred into the measurement cavity by the gas
equilibration process, while the 2nd is used to automatically calibrate the
system with respect to the relative volumes of the measurement cavity and the
external compartment. The methodologies for assessments of leak rates comprise
triple cavity evacuation assessments, comprising two measurements performed in
rapid succession, supplemented by a 3rd measurement a certain time thereafter.
A clear explanation of why the technique has such a small temperature
dependence is given. It is concluded that FS-DFPC-OR constitutes a novel
strategy that can be used for precise and accurate assessment of gas number
density and gas flows under a variety of conditions, in particular
non-temperature stabilized ones.
",0,1,0,0,0,0
19460,A Large Self-Annotated Corpus for Sarcasm,"  We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
sarcasm research and for training and evaluating systems for sarcasm detection.
The corpus has 1.3 million sarcastic statements -- 10 times more than any
previous dataset -- and many times more instances of non-sarcastic statements,
allowing for learning in both balanced and unbalanced label regimes. Each
statement is furthermore self-annotated -- sarcasm is labeled by the author,
not an independent annotator -- and provided with user, topic, and conversation
context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
detection, and evaluate baseline methods.
",1,0,0,0,0,0
19461,Aerosol properties in the atmospheres of extrasolar giant planets,"  We use a model of aerosol microphysics to investigate the impact of
high-altitude photochemical aerosols on the transmission spectra and
atmospheric properties of close-in exoplanets, such as HD209458b and HD189733b.
The results depend strongly on the temperature profiles in the middle and upper
atmosphere that are poorly understood. Nevertheless, our model of HD189733b,
based on the most recently inferred temperature profiles, produces an aerosol
distribution that matches the observed transmission spectrum. We argue that the
hotter temperature of HD209458b inhibits the production of high-altitude
aerosols and leads to the appearance of a more clear atmosphere than on
HD189733b. The aerosol distribution also depends on the particle composition,
the photochemical production, and the atmospheric mixing. Due to degeneracies
among these inputs, current data cannot constrain the aerosol properties in
detail. Instead, our work highlights the role of different factors in
controlling the aerosol distribution that will prove useful in understanding
different observations, including those from future missions. For the
atmospheric mixing efficiency suggested by general circulation models (GCMs) we
find that aerosol particles are small ($\sim$nm) and probably spherical. We
further conclude that composition based on complex hydrocarbons (soots) is the
most likely candidate to survive the high temperatures in hot Jupiter
atmospheres. Such particles would have a significant impact on the energy
balance of HD189733b's atmosphere and should be incorporated in future studies
of atmospheric structure. We also evaluate the contribution of external sources
in the photochemical aerosol formation and find that their spectral signature
is not consistent with observations.
",0,1,0,0,0,0
19462,Influence of random opinion change in complex networks,"  Opinion formation in the population has attracted extensive research
interest. Various models have been introduced and studied, including the ones
with individuals' free will allowing them to change their opinions. Such
models, however, have not taken into account the fact that individuals with
different opinions may have different levels of loyalty, and consequently,
different probabilities of changing their opinions. In this work, we study on
how the non-uniform distribution of the opinion changing probability may affect
the final state of opinion distribution. By simulating a few different cases
with different symmetric and asymmetric non-uniform patterns of opinion
changing probabilities, we demonstrate the significant effects that the
different loyalty levels of different opinions have on the final state of the
opinion distribution.
",1,1,0,0,0,0
19463,We Are Not Your Real Parents: Telling Causal from Confounded using MDL,"  Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of
finding out whether $X$ jointly causes $Y$ or whether they are all confounded
by an unobserved latent variable $Z$. To do so, we take an
information-theoretic approach based on Kolmogorov complexity. In a nutshell,
we follow the postulate that first encoding the true cause, and then the
effects given that cause, results in a shorter description than any other
encoding of the observed variables.
The ideal score is not computable, and hence we have to approximate it. We
propose to do so using the Minimum Description Length (MDL) principle. We
compare the MDL scores under the models where $X$ causes $Y$ and where there
exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores
are consistent. To find potential confounders we propose using latent factor
modeling, in particular, probabilistic PCA (PPCA).
Empirical evaluation on both synthetic and real-world data shows that our
method, CoCa, performs very well -- even when the true generating process of
the data is far from the assumptions made by the models we use. Moreover, it is
robust as its accuracy goes hand in hand with its confidence.
",1,0,0,1,0,0
19464,Evolution of eccentricity and inclination of hot protoplanets embedded in radiative discs,"  We study the evolution of the eccentricity and inclination of protoplanetary
embryos and low-mass protoplanets (from a fraction of an Earth mass to a few
Earth masses) embedded in a protoplanetary disc, by means of three dimensional
hydrodynamics calculations with radiative transfer in the diffusion limit. When
the protoplanets radiate in the surrounding disc the energy released by the
accretion of solids, their eccentricity and inclination experience a growth
toward values which depend on the luminosity to mass ratio of the planet, which
are comparable to the disc's aspect ratio and which are reached over timescales
of a few thousand years. This growth is triggered by the appearance of a hot,
under-dense region in the vicinity of the planet. The growth rate of the
eccentricity is typically three times larger than that of the inclination. In
long term calculations, we find that the excitation of eccentricity and the
excitation of inclination are not independent. In the particular case in which
a planet has initially a very small eccentricity and inclination, the
eccentricity largely overruns the inclination. When the eccentricity reaches
its asymptotic value, the growth of inclination is quenched, yielding an
eccentric orbit with a very low inclination. As a side result, we find that the
eccentricity and inclination of non-luminous planets are damped more vigorously
in radiative discs than in isothermal discs.
",0,1,0,0,0,0
19465,Definitions in mathematics,"  We discuss various forms of definitions in mathematics and describe rules
governing them.
",0,0,1,0,0,0
19466,Excellence in prime characteristic,"  Fix any field $K$ of characteristic $p$ such that $[K:K^p]$ is finite. We
discuss excellence for Noetherian domains whose fraction field is $K$, showing
for example, that $R$ is excellent if and only if the Frobenius map is finite
on $R$. Furthermore, we show $R$ is excellent if and only if it admits some
non-zero $p^{-e}$-linear map for $R$ or equivalently, that $R$ is a solid
$R$-algebra under Frobenius. In particular, this means that Frobenius split
Noetherian domains that are generically $F$-finite are always excellent. We
also show that non-excellent rings are abundant and easy to construct in prime
characteristic, even within the world of regular local rings of dimension one
in function fields. This paper is mostly expository in nature.
",0,0,1,0,0,0
19467,Asymptotically optimal private estimation under mean square loss,"  We consider the minimax estimation problem of a discrete distribution with
support size $k$ under locally differential privacy constraints. A
privatization scheme is applied to each raw sample independently, and we need
to estimate the distribution of the raw samples from the privatized samples. A
positive number $\epsilon$ measures the privacy level of a privatization
scheme.
In our previous work (arXiv:1702.00610), we proposed a family of new
privatization schemes and the corresponding estimator. We also proved that our
scheme and estimator are order optimal in the regime $e^{\epsilon} \ll k$ under
both $\ell_2^2$ and $\ell_1$ loss. In other words, for a large number of
samples the worst-case estimation loss of our scheme was shown to differ from
the optimal value by at most a constant factor. In this paper, we eliminate
this gap by showing asymptotic optimality of the proposed scheme and estimator
under the $\ell_2^2$ (mean square) loss. More precisely, we show that for any
$k$ and $\epsilon,$ the ratio between the worst-case estimation loss of our
scheme and the optimal value approaches $1$ as the number of samples tends to
infinity.
",1,0,1,1,0,0
19468,Coulomb repulsion of holes and competition between d_{x^2-y^2}-wave and s-wave parings in cuprate superconductors,"  The effect of the Coulomb repulsion of holes on the Cooper instability in an
ensemble of spin-polaron quasiparticles has been analyzed, taking into account
the peculiarities of the crystallographic structure of the CuO$_2$ plane, which
are associated with the presence of two oxygen ions and one copper ion in the
unit cell, as well as the strong spin-fermion coupling. The investigation of
the possibility of implementation superconducting phases with d-wave and s-wave
pairing of the order parameter symmetry has shown that in the entire doping
region only the d-wave pairing satisfies the self-consistency equations, while
there is no solution for the s-wave pairing. This result completely corresponds
to the experimental data on cuprate HTSC. It has been demonstrated analytically
that the intersite Coulomb interaction does not affect the superconducting
d-wave pairing, because its Fourier transform $V_q$ does not appear in the
kernel of the corresponding integral equation.
",0,1,0,0,0,0
19469,Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games,"  Multiplayer Online Battle Arena (MOBA) games have received increasing
worldwide popularity recently. In such games, players compete in teams against
each other by controlling selected game avatars, each of which is designed with
different strengths and weaknesses. Intuitively, putting together game avatars
that complement each other (synergy) and suppress those of opponents
(opposition) would result in a stronger team. In-depth understanding of synergy
and opposition relationships among game avatars benefits player in making
decisions in game avatar drafting and gaining better prediction of match
events. However, due to intricate design and complex interactions between game
avatars, thorough understanding of their relationships is not a trivial task.
In this paper, we propose a latent variable model, namely Game Avatar
Embedding (GAE), to learn avatars' numerical representations which encode
synergy and opposition relationships between pairs of avatars. The merits of
our model are twofold: (1) the captured synergy and opposition relationships
are sensible to experienced human players' perception; (2) the learned
numerical representations of game avatars allow many important downstream
tasks, such as similar avatar search, match outcome prediction, and avatar pick
recommender. To our best knowledge, no previous model is able to simultaneously
support both features. Our quantitative and qualitative evaluations on real
match data from three commercial MOBA games illustrate the benefits of our
model.
",1,0,0,0,0,0
19470,Design and Development of Effective Transmission Mechanisms on a Tendon Driven Hand Orthosis for Stroke Patients,"  Tendon-driven hand orthoses have advantages over exoskeletons with respect to
wearability and safety because of their low-profile design and ability to fit a
range of patients without requiring custom joint alignment. However, no
existing study on a wearable tendon-driven hand orthosis for stroke patients
presents evidence that such devices can overcome spasticity given repeated use
and fatigue, or discusses transmission efficiency. In this study, we propose
two designs that provide effective force transmission by increasing moment arms
around finger joints. We evaluate the designs with geometric models and
experiment using a 3D-printed artificial finger to find force and joint angle
characteristics of the suggested structures. We also perform clinical tests
with stroke patients to demonstrate the feasibility of the designs. The testing
supports the hypothesis that the proposed designs efficiently elicit extension
of the digits in patients with spasticity as compared to existing baselines.
",1,0,0,0,0,0
19471,"eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys","  For years security machine learning research has promised to obviate the need
for signature based detection by automatically learning to detect indicators of
attack. Unfortunately, this vision hasn't come to fruition: in fact, developing
and maintaining today's security machine learning systems can require
engineering resources that are comparable to that of signature-based detection
systems, due in part to the need to develop and continuously tune the
""features"" these machine learning systems look at as attacks evolve. Deep
learning, a subfield of machine learning, promises to change this by operating
on raw input signals and automating the process of feature design and
extraction. In this paper we propose the eXpose neural network, which uses a
deep learning approach we have developed to take generic, raw short character
strings as input (a common case for security inputs, which include artifacts
like potentially malicious URLs, file paths, named pipes, named mutexes, and
registry keys), and learns to simultaneously extract features and classify
using character-level embeddings and convolutional neural network. In addition
to completely automating the feature design and extraction process, eXpose
outperforms manual feature extraction based baselines on all of the intrusion
detection problems we tested it on, yielding a 5%-10% detection rate gain at
0.1% false positive rate compared to these baselines.
",1,0,0,0,0,0
19472,"A Software Reuse Approach and Its Effect On Software Quality, An Empirical Study for The Software Industry","  Software reusability has become much interesting because of increased quality
and reduce cost. A good process of software reuse leads to enhance the
reliability, productivity, quality and the reduction of time and cost. Current
reuse techniques focuses on the reuse of software artifact which grounded on
anticipated functionality whereas, the non-functional (quality) aspect are also
important. So, Software reusability used here to expand quality and
productivity of software. It improves overall quality of software in minimum
energy and time. Main objective of this study was to present a reuse approach
that discovered that how software reuse improves the quality in Software
Industry. The V&V technique used for this purpose which is part of software
quality management process, it checks the quality and correctness during the
software life cycle. A survey study conducted as QUESTIONAIR to find the impact
of reuse approach on quality attributes which are requirement specification and
design specification. Other quality enhancement techniques like ad hoc, CBSE,
MBSE, Product line, COTS reuse checked on existing software industry. Results
analyzed with the help of MATLAB tool as it provides effective data management,
wide range of options, better output organization, to check weather quality
enhancement technique is affected due to reusability and how quality will
improve.
",1,0,0,0,0,0
19473,Portfolio Optimization with Nondominated Priors and Unbounded Parameters,"  We consider classical Merton problem of terminal wealth maximization in
finite horizon. We assume that the drift of the stock is following
Ornstein-Uhlenbeck process and the volatility of it is following GARCH(1)
process. In particular, both mean and volatility are unbounded. We assume that
there is Knightian uncertainty on the parameters of both mean and volatility.
We take that the investor has logarithmic utility function, and solve the
corresponding utility maximization problem explicitly. To the best of our
knowledge, this is the first work on utility maximization with unbounded mean
and volatility in Knightian uncertainty under nondominated priors.
",0,0,0,0,0,1
19474,Measuring heterogeneity in urban expansion via spatial entropy,"  The lack of efficiency in urban diffusion is a debated issue, important for
biologists, urban specialists, planners and statisticians, both in developed
and new developing countries. Many approaches have been considered to measure
urban sprawl, i.e. chaotic urban expansion; such idea of chaos is here linked
to the concept of entropy. Entropy, firstly introduced in information theory,
rapidly became a standard tool in ecology, biology and geography to measure the
degree of heterogeneity among observations; in these contexts, entropy measures
should include spatial information. The aim of this paper is to employ a
rigorous spatial entropy based approach to measure urban sprawl associated to
the diffusion of metropolitan cities. In order to assess the performance of the
considered measures, a comparative study is run over alternative urban
scenarios; afterwards, measures are used to quantify the degree of disorder in
the urban expansion of three cities in Europe. Results are easily interpretable
and can be used both as an absolute measure of urban sprawl and for comparison
over space and time.
",0,0,0,1,0,0
19475,Bayesian Cluster Enumeration Criterion for Unsupervised Learning,"  We derive a new Bayesian Information Criterion (BIC) by formulating the
problem of estimating the number of clusters in an observed data set as
maximization of the posterior probability of the candidate models. Given that
some mild assumptions are satisfied, we provide a general BIC expression for a
broad class of data distributions. This serves as a starting point when
deriving the BIC for specific distributions. Along this line, we provide a
closed-form BIC expression for multivariate Gaussian distributed variables. We
show that incorporating the data structure of the clustering problem into the
derivation of the BIC results in an expression whose penalty term is different
from that of the original BIC. We propose a two-step cluster enumeration
algorithm. First, a model-based unsupervised learning algorithm partitions the
data according to a given set of candidate models. Subsequently, the number of
clusters is determined as the one associated with the model for which the
proposed BIC is maximal. The performance of the proposed two-step algorithm is
tested using synthetic and real data sets.
",1,0,1,1,0,0
19476,Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation,"  Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.
",1,0,0,0,0,0
19477,Is Life Most Likely Around Sun-like Stars?,"  We consider the habitability of Earth-analogs around stars of different
masses, which is regulated by the stellar lifetime, stellar wind-induced
atmospheric erosion, and biologically active ultraviolet (UV) irradiance. By
estimating the timescales associated with each of these processes, we show that
they collectively impose limits on the habitability of Earth-analogs. We
conclude that planets orbiting most M-dwarfs are not likely to host life, and
that the highest probability of complex biospheres is for planets around K- and
G-type stars. Our analysis suggests that the current existence of life near the
Sun is slightly unusual, but not significantly anomalous.
",0,1,0,0,0,0
19478,Maximum Entropy Generators for Energy-Based Models,"  Unsupervised learning is about capturing dependencies between variables and
is driven by the contrast between the probable vs. improbable configurations of
these variables, often either via a generative model that only samples probable
ones or with an energy function (unnormalized log-density) that is low for
probable ones and high for improbable ones. Here, we consider learning both an
energy function and an efficient approximate sampling mechanism. Whereas the
discriminator in generative adversarial networks (GANs) learns to separate data
and generator samples, introducing an entropy maximization regularizer on the
generator can turn the interpretation of the critic into an energy function,
which separates the training distribution from everything else, and thus can be
used for tasks like anomaly or novelty detection. Then, we show how Markov
Chain Monte Carlo can be done in the generator latent space whose samples can
be mapped to data space, producing better samples. These samples are used for
the negative phase gradient required to estimate the log-likelihood gradient of
the data space energy function. To maximize entropy at the output of the
generator, we take advantage of recently introduced neural estimators of mutual
information. We find that in addition to producing a useful scoring function
for anomaly detection, the resulting approach produces sharp samples while
covering the modes well, leading to high Inception and Frechet scores.
",1,0,0,1,0,0
19479,A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols,"  Convolutional neural networks (CNNs) have shown promising results on several
segmentation tasks in magnetic resonance (MR) images. However, the accuracy of
CNNs may degrade severely when segmenting images acquired with different
scanners and/or protocols as compared to the training data, thus limiting their
practical utility. We address this shortcoming in a lifelong multi-domain
learning setting by treating images acquired with different scanners or
protocols as samples from different, but related domains. Our solution is a
single CNN with shared convolutional filters and domain-specific batch
normalization layers, which can be tuned to new domains with only a few
($\approx$ 4) labelled images. Importantly, this is achieved while retaining
performance on the older domains whose training data may no longer be
available. We evaluate the method for brain structure segmentation in MR
images. Results demonstrate that the proposed method largely closes the gap to
the benchmark, which is training a dedicated CNN for each scanner.
",0,0,0,1,0,0
19480,Hybrid Kinematic Control for Rigid Body Pose Stabilization using Dual Quaternions,"  In this paper, we address the rigid body pose stabilization problem using
dual quaternion formalism. We propose a hybrid control strategy to design a
switching control law with hysteresis in such a way that the global asymptotic
stability of the closed-loop system is guaranteed and such that the global
attractivity of the stabilization pose does not exhibit chattering, a problem
that is present in all discontinuous-based feedback controllers. Using
numerical simulations, we illustrate the problems that arise from existing
results in the literature -- as unwinding and chattering -- and verify the
effectiveness of the proposed controller to solve the robust global pose
stability problem.
",1,0,1,0,0,0
19481,On 2d-4d motivic wall-crossing formulas,"  In this paper we propose definitions and examples of categorical enhancements
of the data involved in the $2d$-$4d$ wall-crossing formulas which generalize
both Cecotti-Vafa and Kontsevich-Soibelman motivic wall-crossing formulas.
",0,0,1,0,0,0
19482,On Quitting: Performance and Practice in Online Game Play,"  We study the relationship between performance and practice by analyzing the
activity of many players of a casual online game. We find significant
heterogeneity in the improvement of player performance, given by score, and
address this by dividing players into similar skill levels and segmenting each
player's activity into sessions, i.e., sequence of game rounds without an
extended break. After disaggregating data, we find that performance improves
with practice across all skill levels. More interestingly, players are more
likely to end their session after an especially large improvement, leading to a
peak score in their very last game of a session. In addition, success is
strongly correlated with a lower quitting rate when the score drops, and only
weakly correlated with skill, in line with psychological findings about the
value of persistence and ""grit"": successful players are those who persist in
their practice despite lower scores. Finally, we train an epsilon-machine, a
type of hidden Markov model, and find a plausible mechanism of game play that
can predict player performance and quitting the game. Our work raises the
possibility of real-time assessment and behavior prediction that can be used to
optimize human performance.
",1,0,0,0,0,0
19483,Video Labeling for Automatic Video Surveillance in Security Domains,"  Beyond traditional security methods, unmanned aerial vehicles (UAVs) have
become an important surveillance tool used in security domains to collect the
required annotated data. However, collecting annotated data from videos taken
by UAVs efficiently, and using these data to build datasets that can be used
for learning payoffs or adversary behaviors in game-theoretic approaches and
security applications, is an under-explored research question. This paper
presents VIOLA, a novel labeling application that includes (i) a workload
distribution framework to efficiently gather human labels from videos in a
secured manner; (ii) a software interface with features designed for labeling
videos taken by UAVs in the domain of wildlife security. We also present the
evolution of VIOLA and analyze how the changes made in the development process
relate to the efficiency of labeling, including when seemingly obvious
improvements did not lead to increased efficiency. VIOLA enables collecting
massive amounts of data with detailed information from challenging security
videos such as those collected aboard UAVs for wildlife security. VIOLA will
lead to the development of new approaches that integrate deep learning for
real-time detection and response.
",1,0,0,0,0,0
19484,On density of subgraphs of Cartesian products,"  In this paper, we extend two classical results about the density of subgraphs
of hypercubes to subgraphs $G$ of Cartesian products $G_1\times\cdots\times
G_m$ of arbitrary connected graphs. Namely, we show that
$\frac{|E(G)|}{|V(G)|}\le \lceil 2\max\{
\text{dens}(G_1),\ldots,\text{dens}(G_m)\} \rceil\log|V(G)|$, where
$\text{dens}(H)$ is the maximum ratio $\frac{|E(H')|}{|V(H')|}$ over all
subgraphs $H'$ of $H$. We introduce the notions of VC-dimension
$\text{VC-dim}(G)$ and VC-density $\text{VC-dens}(G)$ of a subgraph $G$ of a
Cartesian product $G_1\times\cdots\times G_m$, generalizing the classical
Vapnik-Chervonenkis dimension of set-families (viewed as subgraphs of
hypercubes). We prove that if $G_1,\ldots,G_m$ belong to the class ${\mathcal
G}(H)$ of all finite connected graphs not containing a given graph $H$ as a
minor, then for any subgraph $G$ of $G_1\times\cdots\times G_m$ a sharper
inequality $\frac{|E(G)|}{|V(G)|}\le \text{VC-dim}(G)\alpha(H)$ holds, where
$\alpha(H)$ is the density of the graphs from ${\mathcal G}(H)$. We refine and
sharpen those two results to several specific graph classes. We also derive
upper bounds (some of them polylogarithmic) for the size of adjacency labeling
schemes of subgraphs of Cartesian products.
",1,0,0,0,0,0
19485,Replica Analysis for Maximization of Net Present Value,"  In this paper, we use replica analysis to determine the investment strategy
that can maximize the net present value for portfolios containing multiple
development projects. Replica analysis was developed in statistical mechanical
informatics and econophysics to evaluate disordered systems, and here we use it
to formulate the maximization of the net present value as an optimization
problem under budget and investment concentration constraints. Furthermore, we
confirm that a common approach from operations research underestimates the true
maximal net present value as the maximal expected net present value by
comparing our results with the maximal expected net present value as derived in
operations research. Moreover, it is shown that the conventional method for
estimating the net present value does not consider variance in the cash flow.
",0,0,0,0,0,1
19486,On some properties of weak solutions to elliptic equations with divergence-free drifts,"  We discuss the local properties of weak solutions to the equation $-\Delta u
+ b\cdot\nabla u=0$. The corresponding theory is well-known in the case $b\in
L_n$, where $n$ is the dimension of the space. Our main interest is focused on
the case $b\in L_2$. In this case the structure assumption $\operatorname{div}
b=0$ turns out to be crucial.
",0,0,1,0,0,0
19487,The heptagon-wheel cocycle in the Kontsevich graph complex,"  The real vector space of non-oriented graphs is known to carry a differential
graded Lie algebra structure. Cocycles in the Kontsevich graph complex,
expressed using formal sums of graphs on $n$ vertices and $2n-2$ edges, induce
-- under the orientation mapping -- infinitesimal symmetries of classical
Poisson structures on arbitrary finite-dimensional affine real manifolds.
Willwacher has stated the existence of a nontrivial cocycle that contains the
$(2\ell+1)$-wheel graph with a nonzero coefficient at every
$\ell\in\mathbb{N}$. We present detailed calculations of the differential of
graphs; for the tetrahedron and pentagon-wheel cocycles, consisting at $\ell =
1$ and $\ell = 2$ of one and two graphs respectively, the cocycle condition
$d(\gamma) = 0$ is verified by hand. For the next, heptagon-wheel cocycle
(known to exist at $\ell = 3$), we provide an explicit representative: it
consists of 46 graphs on 8 vertices and 14 edges.
",0,0,1,0,0,0
19488,Maneuver Regulation for Accelerating Bodies in Atmospheric Environments,"  In order to address the need for an affordable reduced gravity test platform,
this work focuses on the analysis and implementation of atmospheric
acceleration tracking with an autonomous aerial vehicle. As proof of concept,
the vehicle is designed with the objective of flying accurate reduced-gravity
parabolas. Suggestions from both academia and industry were taken into account,
as well as requirements imposed by a regulatory agency. The novelty of this
work is the Proportional Integral Ramp Quadratic PIRQ controller, which is
employed to counteract the aerodynamic forces impeding the vehicles constant
acceleration during the maneuver. The stability of the free-fall maneuver under
this controller is studied in detail via the formation of the transverse
dynamics and the application of the circle criterion. The implementation of
such a controller is then outlined, and the PIRQ controller is validated
through a flight test, where the vehicle successfully tracks Martian gravity
0.378 G's with a standard deviation of 0.0426.
",1,0,0,0,0,0
19489,A Hybrid Deep Learning Approach for Texture Analysis,"  Texture classification is a problem that has various applications such as
remote sensing and forest species recognition. Solutions tend to be custom fit
to the dataset used but fails to generalize. The Convolutional Neural Network
(CNN) in combination with Support Vector Machine (SVM) form a robust selection
between powerful invariant feature extractor and accurate classifier. The
fusion of experts provides stability in classification rates among different
datasets.
",1,0,0,0,0,0
19490,Accounting Noise and the Pricing of CoCos,"  Contingent Convertible bonds (CoCos) are debt instruments that convert into
equity or are written down in times of distress. Existing pricing models assume
conversion triggers based on market prices and on the assumption that markets
can always observe all relevant firm information. But all Cocos issued so far
have triggers based on accounting ratios and/or regulatory intervention. We
incorporate that markets receive information through noisy accounting reports
issued at discrete time instants, which allows us to distinguish between market
and accounting values, and between automatic triggers and regulator-mandated
conversions. Our second contribution is to incorporate that coupon payments are
contingent too: their payment is conditional on the Maximum Distributable
Amount not being exceeded. We examine the impact of CoCo design parameters,
asset volatility and accounting noise on the price of a CoCo; and investigate
the interaction between CoCo design features, the capital structure of the
issuing bank and their implications for risk taking and investment incentives.
Finally, we use our model to explain the crash in CoCo prices after Deutsche
Bank's profit warning in February 2016.
",0,0,0,0,0,1
19491,An Improved Algorithm for E-Generalization,"  E-generalization computes common generalizations of given ground terms w.r.t.
a given equational background theory E. In 2005 [arXiv:1403.8118], we had
presented a computation approach based on standard regular tree grammar
algorithms, and a Prolog prototype implementation. In this report, we present
algorithmic improvements, prove them correct and complete, and give some
details of an efficiency-oriented implementation in C that allows us to handle
problems larger by several orders of magnitude.
",1,0,0,0,0,0
19492,On quartic double fivefolds and the matrix factorizations of exceptional quaternionic representations,"  We study quartic double fivefolds from the perspective of Fano manifolds of
Calabi-Yau type and that of exceptional quaternionic representations. We first
prove that the generic quartic double fivefold can be represented, in a finite
number of ways, as a double cover of P^5 ramified along a linear section of the
Sp 12-invariant quartic in P^31. Then, using the geometry of the Vinberg's type
II decomposition of some exceptional quaternionic representations, and backed
by some cohomological computations performed by Macaulay2, we prove the
existence of a spherical rank 6 vector bundle on such a generic quartic double
fivefold. We finally use the existence this vector bundle to prove that the
homological unit of the CY-3 category associated by Kuznetsov to the derived
category of a generic quartic double fivefold is C $\oplus$ C[3].
",0,0,1,0,0,0
19493,Atmospheric thermal tides and planetary spin I. The complex interplay between stratification and rotation,"  Thermal atmospheric tides can torque telluric planets away from spin-orbit
synchronous rotation, as observed in the case of Venus. They thus participate
to determine the possible climates and general circulations of the atmospheres
of these planets. In this work, we write the equations governing the dynamics
of thermal tides in a local vertically-stratified section of a rotating
planetary atmosphere by taking into account the effects of the complete
Coriolis acceleration on tidal waves. This allows us to derive analytically the
tidal torque and the tidally dissipated energy, which we use to discuss the
possible regimes of tidal dissipation and examine the key role played by
stratification.
In agreement with early studies, we find that the frequency dependence of the
thermal atmospheric tidal torque in the vicinity of synchronization can be
approximated by a Maxwell model. This behaviour corresponds to weakly stably
stratified or convective fluid layers, as observed in ADLM2016a. A strong
stable stratification allows gravity waves to propagate, which makes the tidal
torque become negligible. The transition is continuous between these two
regimes. The traditional approximation appears to be valid in thin atmospheres
and in regimes where the rotation frequency is dominated by the forcing or the
buoyancy frequencies.
Depending on the stability of their atmospheres with respect to convection,
observed exoplanets can be tidally driven toward synchronous or asynchronous
final rotation rates. The domain of applicability of the traditional
approximation is rigorously constrained by calculations.
",0,1,0,0,0,0
19494,Inferring Generative Model Structure with Static Analysis,"  Obtaining enough labeled data to robustly train complex discriminative models
is a major bottleneck in the machine learning pipeline. A popular solution is
combining multiple sources of weak supervision using generative models. The
structure of these models affects training label quality, but is difficult to
learn without any ground truth labels. We instead rely on these weak
supervision sources having some structure by virtue of being encoded
programmatically. We present Coral, a paradigm that infers generative model
structure by statically analyzing the code for these heuristics, thus reducing
the data required to learn structure significantly. We prove that Coral's
sample complexity scales quasilinearly with the number of heuristics and number
of relations found, improving over the standard sample complexity, which is
exponential in $n$ for identifying $n^{\textrm{th}}$ degree relations.
Experimentally, Coral matches or outperforms traditional structure learning
approaches by up to 3.81 F1 points. Using Coral to model dependencies instead
of assuming independence results in better performance than a fully supervised
model by 3.07 accuracy points when heuristics are used to label radiology data
without ground truth labels.
",1,0,0,1,0,0
19495,"Maslov, Chern-Weil and Mean Curvature","  We provide an integral formula for the Maslov index of a pair $(E,F)$ over a
surface $\Sigma$, where $E\rightarrow\Sigma$ is a complex vector bundle and
$F\subset E_{|\partial\Sigma}$ is a totally real subbundle. As in Chern-Weil
theory, this formula is written in terms of the curvature of $E$ plus a
boundary contribution.
When $(E,F)$ is obtained via an immersion of $(\Sigma,\partial\Sigma)$ into a
pair $(M,L)$ where $M$ is Kähler and $L$ is totally real, the formula allows
us to control the Maslov index in terms of the geometry of $(M,L)$. We exhibit
natural conditions on $(M,L)$ which lead to bounds and monotonicity results.
",0,0,1,0,0,0
19496,Tuning Goodness-of-Fit Tests,"  As modern precision cosmological measurements continue to show agreement with
the broad features of the standard $\Lambda$-Cold Dark Matter ($\Lambda$CDM)
cosmological model, we are increasingly motivated to look for small departures
from the standard model's predictions which might not be detected with standard
approaches. While searches for extensions and modifications of $\Lambda$CDM
have to date turned up no convincing evidence of beyond-the-standard-model
cosmology, the list of models compared against $\Lambda$CDM is by no means
complete and is often governed by readily-coded modifications to standard
Boltzmann codes. Also, standard goodness-of-fit methods such as a naive
$\chi^2$ test fail to put strong pressure on the null $\Lambda$CDM hypothesis,
since modern datasets have orders of magnitudes more degrees of freedom than
$\Lambda$CDM. Here we present a method of tuning goodness-of-fit tests to
detect potential sub-dominant extra-$\Lambda$CDM signals present in the data
through compressing observations in a way that maximizes extra-$\Lambda$CDM
signal variation over noise and $\Lambda$CDM variation. This method, based on a
Karhunen-Loève transformation of the data, is tuned to be maximally
sensitive to particular types of variations characteristic of the tuning model;
but, unlike direct model comparison, the test is also sensitive to features
that only partially mimic the tuning model. As an example of its use, we apply
this method in the context of a nonstandard primordial power spectrum compared
against the $2015$ $Planck$ CMB temperature and polarization power spectrum. We
find weak evidence of extra-$\Lambda$CDM physics, conceivably due to known
systematics in the 2015 Planck polarization release.
",0,1,0,0,0,0
19497,The Lubin-Tate stack and Gross-Hopkins duality,"  Morava $E$-theory $E$ is an $E_\infty$-ring with an action of the Morava
stabilizer group $\Gamma$. We study the derived stack $\operatorname{Spf}
E/\Gamma$. Descent-theoretic techniques allow us to deduce a theorem of
Hopkins-Mahowald-Sadofsky on the $K(n)$-local Picard group, as well as a recent
result of Barthel-Beaudry-Stojanoska on the Anderson duals of higher real
$K$-theories.
",0,0,1,0,0,0
19498,Generalized Probabilistic Bisection for Stochastic Root-Finding,"  We consider numerical schemes for root finding of noisy responses through
generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical
context where the sampling distribution is unknown and location-dependent. As
in standard PBA, we rely on a knowledge state for the approximate posterior of
the root location. To implement the corresponding Bayesian updating, we also
carry out inference of oracle accuracy, namely learning the probability of
correct response. To this end we utilize batched querying in combination with a
variety of frequentist and Bayesian estimators based on majority vote, as well
as the underlying functional responses, if available. For guiding sampling
selection we investigate both Information Directed sampling, as well as
Quantile sampling. Our numerical experiments show that these strategies perform
quite differently; in particular we demonstrate the efficiency of randomized
quantile sampling which is reminiscent of Thompson sampling. Our work is
motivated by the root-finding sub-routine in pricing of Bermudan financial
derivatives, illustrated in the last section of the paper.
",1,0,0,1,0,0
19499,Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic,"  Inspired by the recent evolution of deep neural networks (DNNs) in machine
learning, we explore their application to PL-related topics. This paper is the
first step towards this goal; we propose a proof-synthesis method for the
negation-free propositional logic in which we use a DNN to obtain a guide of
proof search. The idea is to view the proof-synthesis problem as a translation
from a proposition to its proof. We train seq2seq, which is a popular network
in neural machine translation, so that it generates a proof encoded as a
$\lambda$-term of a given proposition. We implement the whole framework and
empirically observe that a generated proof term is close to a correct proof in
terms of the tree edit distance of AST. This observation justifies using the
output from a trained seq2seq model as a guide for proof search.
",1,0,0,0,0,0
19500,Fibonacci words in hyperbolic Pascal triangles,"  The hyperbolic Pascal triangle ${\cal HPT}_{4,q}$ $(q\ge5)$ is a new
mathematical construction, which is a geometrical generalization of Pascal's
arithmetical triangle. In the present study we show that a natural pattern of
rows of ${\cal HPT}_{4,5}$ is almost the same as the sequence consisting of
every second term of the well-known Fibonacci words. Further, we give a
generalization of the Fibonacci words using the hyperbolic Pascal triangles.
The geometrical properties of a ${\cal HPT}_{4,q}$ imply a graph structure
between the finite Fibonacci words.
",1,0,0,0,0,0
19501,A Review on Quantile Regression for Stochastic Computer Experiments,"  We report on an empirical study of the main strategies for conditional
quantile estimation in the context of stochastic computer experiments. To
ensure adequate diversity, six metamodels are presented, divided into three
categories based on order statistics, functional approaches, and those of
Bayesian inspiration. The metamodels are tested on several problems
characterized by the size of the training set, the input dimension, the
quantile order and the value of the probability density function in the
neighborhood of the quantile. The metamodels studied reveal good contrasts in
our set of 480 experiments, enabling several patterns to be extracted. Based on
our results, guidelines are proposed to allow users to select the best method
for a given problem.
",1,0,0,1,0,0
19502,Scalability of Voltage-Controlled Filamentary and Nanometallic Resistance Memories,"  Much effort has been devoted to device and materials engineering to realize
nanoscale resistance random access memory (RRAM) for practical applications,
but there still lacks a rational physical basis to be relied on to design
scalable devices spanning many length scales. In particular, the critical
switching criterion is not clear for RRAM devices in which resistance changes
are limited to localized nanoscale filaments that experience concentrated heat,
electric current and field. Here, we demonstrate voltage-controlled resistance
switching for macro and nano devices in both filamentary RRAM and nanometallic
RRAM, the latter switches uniformly and does not require forming. As a result,
using a constant current density as the compliance, we have achieved
area-scalability for the low resistance state of the filamentary RRAM, and for
both the low and high resistance states of the nanometallic RRAM. This finding
will help design area-scalable RRAM at the nanoscale.
",0,1,0,0,0,0
19503,Functional geometry of protein-protein interaction networks,"  Motivation: Protein-protein interactions (PPIs) are usually modelled as
networks. These networks have extensively been studied using graphlets, small
induced subgraphs capturing the local wiring patterns around nodes in networks.
They revealed that proteins involved in similar functions tend to be similarly
wired. However, such simple models can only represent pairwise relationships
and cannot fully capture the higher-order organization of protein interactions,
including protein complexes. Results: To model the multi-sale organization of
these complex biological systems, we utilize simplicial complexes from
computational geometry. The question is how to mine these new representations
of PPI networks to reveal additional biological information. To address this,
we define simplets, a generalization of graphlets to simplicial complexes. By
using simplets, we define a sensitive measure of similarity between simplicial
complex network representations that allows for clustering them according to
their data types better than clustering them by using other state-of-the-art
measures, e.g., spectral distance, or facet distribution distance. We model
human and baker's yeast PPI networks as simplicial complexes that capture PPIs
and protein complexes as simplices. On these models, we show that our newly
introduced simplet-based methods cluster proteins by function better than the
clustering methods that use the standard PPI networks, uncovering the new
underlying functional organization of the cell. We demonstrate the existence of
the functional geometry in the PPI data and the superiority of our
simplet-based methods to effectively mine for new biological information hidden
in the complexity of the higher order organization of PPI networks.
",0,0,0,0,1,0
19504,A Two-Layer Component-Based Allocation for Embedded Systems with GPUs,"  Component-based development is a software engineering paradigm that can
facilitate the construction of embedded systems and tackle its complexities.
The modern embedded systems have more and more demanding requirements. One way
to cope with such versatile and growing set of requirements is to employ
heterogeneous processing power, i.e., CPU-GPU architectures. The new CPU-GPU
embedded boards deliver an increased performance but also introduce additional
complexity and challenges. In this work, we address the component-to-hardware
allocation for CPU-GPU embedded systems. The allocation for such systems is
much complex due to the increased amount of GPU-related information. For
example, while in traditional embedded systems the allocation mechanism may
consider only the CPU memory usage of components to find an appropriate
allocation scheme, in heterogeneous systems, the GPU memory usage needs also to
be taken into account in the allocation process. This paper aims at decreasing
the component-to-hardware allocation complexity by introducing a 2-layer
component-based architecture for heterogeneous embedded systems. The detailed
CPU-GPU information of the system is abstracted at a high-layer by compacting
connected components into single units that behave as regular components. The
allocator, based on the compacted information received from the high-level
layer, computes, with a decreased complexity, feasible allocation schemes. In
the last part of the paper, the 2-layer allocation method is evaluated using an
existing embedded system demonstrator; namely, an underwater robot.
",1,0,0,0,0,0
19505,Sales Forecast in E-commerce using Convolutional Neural Network,"  Sales forecast is an essential task in E-commerce and has a crucial impact on
making informed business decisions. It can help us to manage the workforce,
cash flow and resources such as optimizing the supply chain of manufacturers
etc. Sales forecast is a challenging problem in that sales is affected by many
factors including promotion activities, price changes, and user preferences
etc. Traditional sales forecast techniques mainly rely on historical sales data
to predict future sales and their accuracies are limited. Some more recent
learning-based methods capture more information in the model to improve the
forecast accuracy. However, these methods require case-by-case manual feature
engineering for specific commercial scenarios, which is usually a difficult,
time-consuming task and requires expert knowledge. To overcome the limitations
of existing methods, we propose a novel approach in this paper to learn
effective features automatically from the structured data using the
Convolutional Neural Network (CNN). When fed with raw log data, our approach
can automatically extract effective features from that and then forecast sales
using those extracted features. We test our method on a large real-world
dataset from CaiNiao.com and the experimental results validate the
effectiveness of our method.
",1,0,0,0,0,0
19506,On normalization of inconsistency indicators in pairwise comparisons,"  In this study, we provide mathematical and practice-driven justification for
using $[0,1]$ normalization of inconsistency indicators in pairwise
comparisons. The need for normalization, as well as problems with the lack of
normalization, are presented. A new type of paradox of infinity is described.
",1,0,0,0,0,0
19507,Gamma factors of intertwining periods and distinction for inner forms of $\GL(n)$,"  Let $F$ be a $p$-adic fied, $E$ be a quadratic extension of $F$, and $D$ be
an $F$-division algebra of odd index. Set $H=\mathrm{GL}m,D)$ and
$G=\mathrm{GL}(m,D\otimes_F E)$, we carry out a fine study of local
intertwining open periods attached to $H$-distinguished induced representations
of inner forms of $G$. These objects have been studied globally in \cite{JLR}
and \cite{LR}, and locally in \cite{BD08}. Here we give sufficient conditions
for the local intertwining periods to have singularities. By a local/global
method, we also compute in terms of Asai gamma factors the proportionality
constants involved in their functional equations with respect to certain
intertwining operators. As a consequence, we classify distinguished unitary and
ladder representations of $G$, extending respectively the results of \cite{M14}
and \cite{G15} for $D=F$, which both relied at some crucial step on the theory
of Bernstein-Zelevinsky derivatives. We make use of one of the main results of
\cite{BP17} in our setting, which in the case of the group $G$, asserts that
the Jacquet-Langlands correspondence preserves distinction. Such a result is
for discrete series representations, but our method in fact allows us to use it
only for cuspidal representations of $G$.
",0,0,1,0,0,0
19508,Spin-flip scattering selection in a controlled molecular junction,"  A simple double-decker molecule with magnetic anisotropy, nickelocene, is
attached to the metallic tip of a low-temperature scanning tunneling
microscope. In the presence of a Cu(100) surface, the conductance around the
Fermi energy is governed by spin-flip scattering, the nature of which is
determined by the tunneling barrier thickness. The molecular tip exhibits
inelastic spin-flip scattering in the tunneling regime, while in the contact
regime a Kondo ground state is stabilized causing an order of magnitude change
in the zero-bias conductance. First principle calculations show that
nickelocene reversibly switches from a spin 1 to 1/2 between the two transport
regimes.
",0,1,0,0,0,0
19509,Mean-Field Sparse Jurdjevic--Quinn Control,"  We consider nonlinear transport equations with non-local velocity, describing
the time-evolution of a measure, which in practice may represent the density of
a crowd. Such equations often appear by taking the mean-field limit of
finite-dimensional systems modelling collective dynamics. We first give a sense
to dissipativity of these mean-field equations in terms of Lie derivatives of a
Lyapunov function depending on the measure. Then, we address the problem of
controlling such equations by means of a time-varying bounded control action
localized on a time-varying control subset with bounded Lebesgue measure
(sparsity space constraint). Finite-dimensional versions are given by
control-affine systems, which can be stabilized by the well known
Jurdjevic--Quinn procedure. In this paper, assuming that the uncontrolled
dynamics are dissipative, we develop an approach in the spirit of the classical
Jurdjevic--Quinn theorem, showing how to steer the system to an invariant
sublevel of the Lyapunov function. The control function and the control domain
are designed in terms of the Lie derivatives of the Lyapunov function, and
enjoy sparsity properties in the sense that the control support is small.
Finally, we show that our result applies to a large class of kinetic equations
modelling multi-agent dynamics.
",0,0,1,0,0,0
19510,Sampling from Social Networks with Attributes,"  Sampling from large networks represents a fundamental challenge for social
network research. In this paper, we explore the sensitivity of different
sampling techniques (node sampling, edge sampling, random walk sampling, and
snowball sampling) on social networks with attributes. We consider the special
case of networks (i) where we have one attribute with two values (e.g., male
and female in the case of gender), (ii) where the size of the two groups is
unequal (e.g., a male majority and a female minority), and (iii) where nodes
with the same or different attribute value attract or repel each other (i.e.,
homophilic or heterophilic behavior). We evaluate the different sampling
techniques with respect to conserving the position of nodes and the visibility
of groups in such networks. Experiments are conducted both on synthetic and
empirical social networks. Our results provide evidence that different network
sampling techniques are highly sensitive with regard to capturing the expected
centrality of nodes, and that their accuracy depends on relative group size
differences and on the level of homophily that can be observed in the network.
We conclude that uninformed sampling from social networks with attributes thus
can significantly impair the ability of researchers to draw valid conclusions
about the centrality of nodes and the visibility or invisibility of groups in
social networks.
",1,1,0,0,0,0
19511,Exploiting Investors Social Network for Stock Prediction in China's Market,"  Recent works have shown that social media platforms are able to influence the
trends of stock price movements. However, existing works have majorly focused
on the U.S. stock market and lacked attention to certain emerging countries
such as China, where retail investors dominate the market. In this regard, as
retail investors are prone to be influenced by news or other social media,
psychological and behavioral features extracted from social media platforms are
thought to well predict stock price movements in the China's market. Recent
advances in the investor social network in China enables the extraction of such
features from web-scale data. In this paper, on the basis of tweets from
Xueqiu, a popular Chinese Twitter-like social platform specialized for
investors, we analyze features with regard to collective sentiment and
perception on stock relatedness and predict stock price movements by employing
nonlinear models. The features of interest prove to be effective in our
experiments.
",0,0,0,0,0,1
19512,Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks,"  Can we make a famous rap singer like Eminem sing whatever our favorite song?
Singing style transfer attempts to make this possible, by replacing the vocal
of a song from the source singer to the target singer. This paper presents a
method that learns from unpaired data for singing style transfer using
generative adversarial networks.
",1,0,0,0,0,0
19513,A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots,"  We present a robust deep learning based 6 degrees-of-freedom (DoF)
localization system for endoscopic capsule robots. Our system mainly focuses on
localization of endoscopic capsule robots inside the GI tract using only visual
information captured by a mono camera integrated to the robot. The proposed
system is a 23-layer deep convolutional neural network (CNN) that is capable to
estimate the pose of the robot in real time using a standard CPU. The dataset
for the evaluation of the system was recorded inside a surgical human stomach
model with realistic surface texture, softness, and surface liquid properties
so that the pre-trained CNN architecture can be transferred confidently into a
real endoscopic scenario. An average error of 7:1% and 3:4% for translation and
rotation has been obtained, respectively. The results accomplished from the
experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images
performs accurately inside the GI tract and is robust to various challenges
posed by reflection distortions, lens imperfections, vignetting, noise, motion
blur, low resolution, and lack of unique landmarks to track.
",1,0,0,0,0,0
19514,On a cross-diffusion system arising in image denosing,"  We study a generalization of a cross-diffusion problem deduced from a
nonlinear complex-variable diffusion model for signal and image denoising.
We prove the existence of weak solutions of the time-independent problem with
fidelity terms under mild conditions on the data problem. Then, we show that
this translates on the well-posedness of a quasi-steady state approximation of
the evolution problem, and also prove the existence of weak solutions of the
latter under more restrictive hypothesis.
We finally perform some numerical simulations for image denoising, comparing
the performance of the cross-diffusion model and its corresponding scalar
Perona-Malik equation.
",0,0,1,0,0,0
19515,"Locally Repairable Codes with Multiple $(r_{i}, δ_{i})$-Localities","  In distributed storage systems, locally repairable codes (LRCs) are
introduced to realize low disk I/O and repair cost. In order to tolerate
multiple node failures, the LRCs with \emph{$(r, \delta)$-locality} are further
proposed. Since hot data is not uncommon in a distributed storage system, both
Zeh \emph{et al.} and Kadhe \emph{et al.} focus on the LRCs with \emph{multiple
localities or unequal localities} (ML-LRCs) recently, which said that the
localities among the code symbols can be different. ML-LRCs are attractive and
useful in reducing repair cost for hot data. In this paper, we generalize the
ML-LRCs to the $(r,\delta)$-locality case of multiple node failures, and define
an LRC with multiple $(r_{i}, \delta_{i})_{i\in [s]}$ localities ($s\ge 2$),
where $r_{1}\leq r_{2}\leq\dots\leq r_{s}$ and
$\delta_{1}\geq\delta_{2}\geq\dots\geq\delta_{s}\geq2$. Such codes ensure that
some hot data could be repaired more quickly and have better failure-tolerance
in certain cases because of relatively smaller $r_{i}$ and larger $\delta_{i}$.
Then, we derive a Singleton-like upper bound on the minimum distance for the
proposed LRCs by employing the regenerating-set technique. Finally, we obtain a
class of explicit and structured constructions of optimal ML-LRCs, and further
extend them to the cases of multiple $(r_{i}, \delta)_{i\in [s]}$ localities.
",1,0,0,0,0,0
19516,Reconciling Enumerative and Symbolic Search in Syntax-Guided Synthesis,"  Syntax-guided synthesis aims to find a program satisfying semantic
specification as well as user-provided structural hypothesis. For syntax-guided
synthesis there are two main search strategies: concrete search, which
systematically or stochastically enumerates all possible solutions, and
symbolic search, which interacts with a constraint solver to solve the
synthesis problem. In this paper, we propose a concolic synthesis framework
which combines the best of the two worlds. Based on a decision tree
representation, our framework works by enumerating tree heights from the
smallest possible one to larger ones. For each fixed height, the framework
symbolically searches a solution through the counterexample-guided inductive
synthesis approach. To compensate the exponential blow-up problem with the
concolic synthesis framework, we identify two fragments of synthesis problems
and develop purely symbolic and more efficient procedures. The two fragments
are decidable as these procedures are terminating and complete. We implemented
our synthesis procedures and compared with state-of-the-art synthesizers on a
range of benchmarks. Experiments show that our algorithms are promising.
",1,0,0,0,0,0
19517,Periodic solutions of semilinear Duffing equations with impulsive effects,"  In this paper we are concerned with the existence of periodic solutions for
semilinear Duffing equations with impulsive effects. Firstly for the autonomous
one, basing on Poincaré-Birkhoff twist theorem, we prove the existence of
infinitely many periodic solutions. Secondly, as for the nonautonomous case,
the impulse brings us great challenges for the study, and there are only
finitely many periodic solutions, which is quite different from the
corresponding equation without impulses. Here, taking the autonomous one as an
auxiliary equation, we find the relation between these two equations and then
obtain the result also by Poincaré-Birkhoff twist theorem.
",0,1,1,0,0,0
19518,Spectral and scattering theory for perturbed block Toeplitz operators,"  We analyse spectral properties of a class of compact perturbations of block
Toeplitz operators associated with analytic symbols. In particular, a limiting
absorption principle and the absence of singular continuous spectrum are shown.
The existence and the completeness of wave operators are also obtained. Our
study is based on the construction of a conjugate operator in Mourre sense for
the corresponding Laurent operators.
",0,0,1,0,0,0
19519,Robust adaptive droop control for DC microgrids,"  There are tradeoffs between current sharing among distributed resources and
DC bus voltage stability when conventional droop control is used in DC
microgrids. As current sharing approaches the setpoint, bus voltage deviation
increases. Previous studies have suggested using secondary control utilizing
linear controllers to overcome drawbacks of droop control. However, linear
control design depends on an accurate model of the system. The derivation of
such a model is challenging because the noise and disturbances caused by the
coupling between sources, loads, and switches in microgrids are
under-represented. This under-representation makes linear modeling and control
insufficient. Hence, in this paper, we propose a robust adaptive control to
adjust droop characteristics to satisfy both current sharing and bus voltage
stability. First, the time-varying models of DC microgrids are derived. Second,
the improvements for the adaptive control method are presented. Third, the
application of the enhanced adaptive method to DC microgrids is presented to
satisfy the system objective. Fourth, simulation and experimental results on a
microgrid show that the adaptive method precisely shares current between two
distributed resources and maintains the nominal bus voltage. Last, the
comparative study validates the effectiveness of the proposed method over the
conventional method.
",0,0,1,0,0,0
19520,Off-diagonal asymptotic properties of Bergman kernels associated to analytic Kähler potentials,"  We prove a new off-diagonal asymptotic of the Bergman kernels associated to
tensor powers of a positive line bundle on a compact Kähler manifold. We show
that if the Kähler potential is real analytic, then the Bergman kernel
accepts a complete asymptotic expansion in a neighborhood of the diagonal of
shrinking size $k^{-\frac14}$. These improve the earlier results in the subject
for smooth potentials, where an expansion exists in a $k^{-\frac12}$
neighborhood of the diagonal. We obtain our results by finding upper bounds of
the form $C^m m!^{2}$ for the Bergman coefficients $b_m(x, \bar y)$, which is
an interesting problem on its own. We find such upper bounds using the method
of Berman-Berndtsson-Sjöstrand. We also show that sharpening these upper
bounds would improve the rate of shrinking neighborhoods of the diagonal $x=y$
in our results. In the special case of metrics with local constant holomorphic
sectional curvatures, we obtain off-diagonal asymptotic in a fixed (as $k \to
\infty$) neighborhood of the diagonal, which recovers a result of Berman [Ber]
(see Remark 3.5 of [Ber] for higher dimensions). In this case, we also find an
explicit formula for the Bergman kernel mod $O(e^{-k \delta} )$.
",0,0,1,0,0,0
19521,Network Backboning with Noisy Data,"  Networks are powerful instruments to study complex phenomena, but they become
hard to analyze in data that contain noise. Network backbones provide a tool to
extract the latent structure from noisy networks by pruning non-salient edges.
We describe a new approach to extract such backbones. We assume that edge
weights are drawn from a binomial distribution, and estimate the error-variance
in edge weights using a Bayesian framework. Our approach uses a more realistic
null model for the edge weight creation process than prior work. In particular,
it simultaneously considers the propensity of nodes to send and receive
connections, whereas previous approaches only considered nodes as emitters of
edges. We test our model with real world networks of different types (flows,
stocks, co-occurrences, directed, undirected) and show that our Noise-Corrected
approach returns backbones that outperform other approaches on a number of
criteria. Our approach is scalable, able to deal with networks with millions of
edges.
",1,1,0,0,0,0
19522,The 2017 DAVIS Challenge on Video Object Segmentation,"  We present the 2017 DAVIS Challenge on Video Object Segmentation, a public
dataset, benchmark, and competition specifically designed for the task of video
object segmentation. Following the footsteps of other successful initiatives,
such as ILSVRC and PASCAL VOC, which established the avenue of research in the
fields of scene classification and semantic segmentation, the DAVIS Challenge
comprises a dataset, an evaluation methodology, and a public competition with a
dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on
the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which
has fostered the development of several novel state-of-the-art video object
segmentation techniques. In this paper we describe the scope of the benchmark,
highlight the main characteristics of the dataset, define the evaluation
metrics of the competition, and present a detailed analysis of the results of
the participants to the challenge.
",1,0,0,0,0,0
19523,How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow,"  Context: The success of Stack Overflow and other community-based
question-and-answer (Q&A) sites depends mainly on the will of their members to
answer others' questions. In fact, when formulating requests on Q&A sites, we
are not simply seeking for information. Instead, we are also asking for other
people's help and feedback. Understanding the dynamics of the participation in
Q&A communities is essential to improve the value of crowdsourced knowledge.
Objective: In this paper, we investigate how information seekers can increase
the chance of eliciting a successful answer to their questions on Stack
Overflow by focusing on the following actionable factors: affect, presentation
quality, and time.
Method: We develop a conceptual framework of factors potentially influencing
the success of questions in Stack Overflow. We quantitatively analyze a set of
over 87K questions from the official Stack Overflow dump to assess the impact
of actionable factors on the success of technical requests. The information
seeker reputation is included as a control factor. Furthermore, to understand
the role played by affective states in the success of questions, we
qualitatively analyze questions containing positive and negative emotions.
Finally, a survey is conducted to understand how Stack Overflow users perceive
the guideline suggestions for writing questions.
Results: We found that regardless of user reputation, successful questions
are short, contain code snippets, and do not abuse with uppercase characters.
As regards affect, successful questions adopt a neutral emotional style.
Conclusion: We provide evidence-based guidelines for writing effective
questions on Stack Overflow that software engineers can follow to increase the
chance of getting technical help. As for the role of affect, we empirically
confirmed community guidelines that suggest avoiding rudeness in question
writing.
",1,0,0,0,0,0
19524,Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays,"  Massive multiple-input multiple-output (MIMO) systems, which utilize a large
number of antennas at the base station, are expected to enhance network
throughput by enabling improved multiuser MIMO techniques. To deploy many
antennas in reasonable form factors, base stations are expected to employ
antenna arrays in both horizontal and vertical dimensions, which is known as
full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
planar array (UPA), where antennas are placed in a grid pattern. To exploit the
full benefit of massive MIMO in frequency division duplexing (FDD), the
downlink channel state information (CSI) should be estimated, quantized, and
fed back from the receiver to the transmitter. However, it is difficult to
accurately quantize the channel in a computationally efficient manner due to
the high dimensionality of the massive MIMO channel. In this paper, we develop
both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
of realistic channels and the UPA into consideration. To improve quantization
quality, we focus on not only quantizing dominant radio paths in the channel,
but also combining the quantized beams. We also develop a hierarchical beam
search approach, which scans both vertical and horizontal domains jointly with
moderate computational complexity. Numerical simulations verify that the
performance of the proposed quantizers is better than that of previous CSI
quantization techniques.
",1,0,0,0,0,0
19525,A Novel Stretch Energy Minimization Algorithm for Equiareal Parameterizations,"  Surface parameterizations have been widely applied to computer graphics and
digital geometry processing. In this paper, we propose a novel stretch energy
minimization (SEM) algorithm for the computation of equiareal parameterizations
of simply connected open surfaces with a very small area distortion and a
highly improved computational efficiency. In addition, the existence of
nontrivial limit points of the SEM algorithm is guaranteed under some mild
assumptions of the mesh quality. Numerical experiments indicate that the
efficiency, accuracy, and robustness of the proposed SEM algorithm outperform
other state-of-the-art algorithms. Applications of the SEM on surface remeshing
and surface registration for simply connected open surfaces are demonstrated
thereafter. Thanks to the SEM algorithm, the computations for these
applications can be carried out efficiently and robustly.
",1,0,0,0,0,0
19526,Time Complexity Analysis of a Distributed Stochastic Optimization in a Non-Stationary Environment,"  In this paper, we consider a distributed stochastic optimization problem
where the goal is to minimize the time average of a cost function subject to a
set of constraints on the time averages of related stochastic processes called
penalties. We assume that the state of the system is evolving in an independent
and non-stationary fashion and the ""common information"" available at each node
is distributed and delayed. Such stochastic optimization is an integral part of
many important problems in wireless networks such as scheduling, routing,
resource allocation and crowd sensing. We propose an approximate distributed
Drift- Plus-Penalty (DPP) algorithm, and show that it achieves a time average
cost (and penalties) that is within epsilon > 0 of the optimal cost (and
constraints) with high probability. Also, we provide a condition on the
convergence time t for this result to hold. In particular, for any delay D >= 0
in the common information, we use a coupling argument to prove that the
proposed algorithm converges almost surely to the optimal solution. We use an
application from wireless sensor network to corroborate our theoretical
findings through simulation results.
",1,0,1,0,0,0
19527,Robust Counterfactual Inferences using Feature Learning and their Applications,"  In a wide variety of applications, including personalization, we want to
measure the difference in outcome due to an intervention and thus have to deal
with counterfactual inference. The feedback from a customer in any of these
situations is only 'bandit feedback' - that is, a partial feedback based on
whether we chose to intervene or not. Typically randomized experiments are
carried out to understand whether an intervention is overall better than no
intervention. Here we present a feature learning algorithm to learn from a
randomized experiment where the intervention in consideration is most effective
and where it is least effective rather than only focusing on the overall
impact, thus adding a context to our learning mechanism and extract more
information. From the randomized experiment, we learn the feature
representations which divide the population into subpopulations where we
observe statistically significant difference in average customer feedback
between those who were subjected to the intervention and those who were not,
with a level of significance l, where l is a configurable parameter in our
model. We use this information to derive the value of the intervention in
consideration for each instance in the population. With experiments, we show
that using this additional learning, in future interventions, the context for
each instance could be leveraged to decide whether to intervene or not.
",0,0,0,1,0,0
19528,Regularity of symbolic powers and Arboricity of matroids,"  Let $\Delta$ be a simplicial complex of a matroid $M$. In this paper, we
explicitly compute the regularity of all the symbolic powers of a
Stanley-Reisner ideal $I_\Delta$ in terms of combinatorial data of the matroid
$M$. In order to do that, we provide a sharp bound between the arboricity of
$M$ and the circumference of its dual $M^*$.
",0,0,1,0,0,0
19529,Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting,"  We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothing based evaluation approach is employed to measure keyword
spotting performance. Our experimental results show that LSTM models trained
using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
trained baseline feed-forward Deep Neural Network (DNN). In addition,
max-pooling loss trained LSTM with randomly initialized network performs better
compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
trained LSTM initialized with a cross-entropy pre-trained network shows the
best performance, which yields $67.6\%$ relative reduction compared to baseline
feed-forward DNN in Area Under the Curve (AUC) measure.
",1,0,0,1,0,0
19530,On Packet Scheduling with Adversarial Jamming and Speedup,"  In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes
arrive over time to be transmitted over a channel in which instantaneous
jamming errors occur at times chosen by the adversary and not known to the
algorithm. The transmission taking place at the time of jamming is corrupt, and
the algorithm learns this fact immediately. An online algorithm maximizes the
total size of packets it successfully transmits and the goal is to develop an
algorithm with the lowest possible asymptotic competitive ratio, where the
additive constant may depend on packet sizes.
Our main contribution is a universal algorithm that works for any speedup and
packet sizes and, unlike previous algorithms for the problem, it does not need
to know these properties in advance. We show that this algorithm guarantees
1-competitiveness with speedup 4, making it the first known algorithm to
maintain 1-competitiveness with a moderate speedup in the general setting of
arbitrary packet sizes. We also prove a lower bound of $\phi+1\approx 2.618$ on
the speedup of any 1-competitive deterministic algorithm, showing that our
algorithm is close to the optimum.
Additionally, we formulate a general framework for analyzing our algorithm
locally and use it to show upper bounds on its competitive ratio for speedups
in $[1,4)$ and for several special cases, recovering some previously known
results, each of which had a dedicated proof. In particular, our algorithm is
3-competitive without speedup, matching both the (worst-case) performance of
the algorithm by Jurdzinski et al. and the lower bound by Anta et al.
",1,0,0,0,0,0
19531,Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning,"  Despite the progress in high performance computing, Computational Fluid
Dynamics (CFD) simulations are still computationally expensive for many
practical engineering applications such as simulating large computational
domains and highly turbulent flows. One of the major reasons of the high
expense of CFD is the need for a fine grid to resolve phenomena at the relevant
scale, and obtain a grid-independent solution. The fine grid requirements often
drive the computational time step size down, which makes long transient
problems prohibitively expensive. In the research presented, the feasibility of
a Coarse Grid CFD (CG-CFD) approach is investigated by utilizing Machine
Learning (ML) algorithms. Relying on coarse grids increases the discretization
error. Hence, a method is suggested to produce a surrogate model that predicts
the CG-CFD local errors to correct the variables of interest. Given
high-fidelity data, a surrogate model is trained to predict the CG-CFD local
errors as a function of the coarse grid local features. ML regression
algorithms are utilized to construct a surrogate model that relates the local
error and the coarse grid features. This method is applied to a
three-dimensional flow in a lid driven cubic cavity domain. The performance of
the method was assessed by training the surrogate model on the flow full field
spatial data and tested on new data (from flows of different Reynolds number
and/or computed by different grid sizes). The proposed method maximizes the
benefit of the available data and shows potential for a good predictive
capability.
",0,1,0,0,0,0
19532,EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection,"  In this paper, we propose a deep learning based approach for facial action
unit detection by enhancing and cropping the regions of interest. The approach
is implemented by adding two novel nets (layers): the enhancing layers and the
cropping layers, to a pretrained CNN model. For the enhancing layers, we
designed an attention map based on facial landmark features and applied it to a
pretrained neural network to conduct enhanced learning (The E-Net). For the
cropping layers, we crop facial regions around the detected landmarks and
design convolutional layers to learn deeper features for each facial region
(C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and
Cropping (EAC) Net, which can learn both feature enhancing and region cropping
functions. Our approach shows significant improvement in performance compared
to the state-of-the-art methods applied to BP4D and DISFA AU datasets.
",1,0,0,0,0,0
19533,Influence of surface and bulk water ice on the reactivity of a water-forming reaction,"  On the surface of icy dust grains in the dense regions of the interstellar
medium a rich chemistry can take place. Due to the low temperature, reactions
that proceed via a barrier can only take place through tunneling. The reaction
H + H$_2$O$_2$ $\rightarrow$ H$_2$O + OH is such a case with a gas-phase
barrier of $\sim$26.5 kJ/mol. Still the reaction is known to be involved in
water formation on interstellar grains. Here, we investigate the influence of a
water ice surface and of bulk ice on the reaction rate constant. Rate constants
are calculated using instanton theory down to 74 K. The ice is taken into
account via multiscale modeling, describing the reactants and the direct
surrounding at the quantum mechanical level with density functional theory
(DFT), while the rest of the ice is modeled on the molecular mechanical level
with a force field. We find that H$_2$O$_2$ binding energies cannot be captured
by a single value, but rather depend on the number of hydrogen bonds with
surface molecules. In highly amorphous surroundings the binding site can block
the routes of attack and impede the reaction. Furthermore, the activation
energies do not correlate with the binding energies of the same sites. The
unimolecular rate constants related to the Langmuir-Hinshelwood mechanism
increase as the activation energy decreases. Thus, we provide a lower limit for
the rate constant and argue that rate constants can have values up to two order
of magnitude larger than this limit.
",0,1,0,0,0,0
19534,Impact of energy dissipation on interface shapes and on rates for dewetting from liquid substrates,"  We revisit the fundamental problem of liquid-liquid dewetting and perform a
detailed comparison of theoretical predictions based on thin-film models with
experimental measurements obtained by atomic force microscopy (AFM).
Specifically, we consider the dewetting of a liquid polystyrene (PS) layer from
a liquid polymethyl methacrylate (PMMA) layer, where the thicknesses and the
viscosities of PS and PMMA layers are similar. The excellent agreement of
experiment and theory reveals that dewetting rates for such systems follow no
universal power law, in contrast to dewetting scenarios on solid substrates.
Our new energetic approach allows to assess the physical importance of
different contributions to the energy-dissipation mechanism, for which we
analyze the local flow fields and the local dissipation rates.
",0,1,0,0,0,0
19535,Resonance control of graphene drum resonator in nonlinear regime by standing wave of light,"  We demonstrate the control of resonance characteristics of a drum type
graphene mechanical resonator in nonlinear oscillation regime by the
photothermal effect, which is induced by a standing wave of light between a
graphene and a substrate. Unlike the conventional Duffing type nonlinearity,
the resonance characteristics in nonlinear oscillation regime is modulated by
the standing wave of light despite a small variation amplitude. From numerical
calculations with a combination of equations of heat and motion with Duffing
type nonlinearity, this can be explained that the photothermal effect causes
delayed modulation of stress or tension of the graphene.
",0,1,0,0,0,0
19536,Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos,"  A surrogate model approximates a computationally expensive solver. Polynomial
Chaos is a method to construct surrogate models by summing combinations of
carefully chosen polynomials. The polynomials are chosen to respect the
probability distributions of the uncertain input variables (parameters); this
allows for both uncertainty quantification and global sensitivity analysis.
In this paper we apply these techniques to a commercial solver for the
estimation of peak gas rate and cumulative gas extraction from a coal seam gas
well. The polynomial expansion is shown to honour the underlying geophysics
with low error when compared to a much more complex and computationally slower
commercial solver. We make use of advanced numerical integration techniques to
achieve this accuracy using relatively small amounts of training data.
",0,0,1,0,0,0
19537,Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards,"  The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. However, the current algorithms
lack an effective exploration strategy to deal with sparse or misleading reward
scenarios: if they do not experience any state with a positive reward during
the initial random exploration, it is very unlikely to solve the problem. Here,
we propose a novel model-based policy search algorithm, Multi-DEX, that
leverages a learned dynamical model to efficiently explore the task space and
solve tasks with sparse rewards in a few episodes. To achieve this, we frame
the policy search problem as a multi-objective, model-based policy optimization
problem with three objectives: (1) generate maximally novel state trajectories,
(2) maximize the expected return and (3) keep the system in state-space regions
for which the model is as accurate as possible. We then optimize these
objectives using a Pareto-based multi-objective optimization algorithm. The
experiments show that Multi-DEX is able to solve sparse reward scenarios (with
a simulated robotic arm) in much lower interaction time than VIME, TRPO,
GEP-PG, CMA-ES and Black-DROPS.
",1,0,0,1,0,0
19538,Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification,"  This work explores the trade-off between the number of samples required to
accurately build models of dynamical systems and the degradation of performance
in various control objectives due to a coarse approximation. In particular, we
show that simple models can be easily fit from input/output data and are
sufficient for achieving various control objectives. We derive bounds on the
number of noisy input/output samples from a stable linear time-invariant system
that are sufficient to guarantee that the corresponding finite impulse response
approximation is close to the true system in the $\mathcal{H}_\infty$-norm. We
demonstrate that these demands are lower than those derived in prior art which
aimed to accurately identify dynamical models. We also explore how different
physical input constraints, such as power constraints, affect the sample
complexity. Finally, we show how our analysis fits within the established
framework of robust control, by demonstrating how a controller designed for an
approximate system provably meets performance objectives on the true system.
",1,0,1,0,0,0
19539,"Construction,sensitivity index, and synchronization speed of optimal networks","  The stability (or instability) of synchronization is important in a number of
real world systems, including the power grid, the human brain and biological
cells. For identical synchronization, the synchronizability of a network, which
can be measured by the range of coupling strength that admits stable
synchronization, can be optimized for a given number of nodes and links.
Depending on the geometric degeneracy of the Laplacian eigenvectors, optimal
networks can be classified into different sensitivity levels, which we define
as a network's sensitivity index. We introduce an efficient and explicit way to
construct optimal networks of arbitrary size over a wide range of sensitivity
and link densities. Using coupled chaotic oscillators, we study synchronization
dynamics on optimal networks, showing that cospectral optimal networks can have
drastically different speed of synchronization. Such difference in dynamical
stability is found to be closely related to the different structural
sensitivity of these networks: generally, networks with high sensitivity index
are slower to synchronize, and, surprisingly, may not synchronize at all,
despite being theoretically stable under linear stability analysis.
",0,1,1,0,0,0
19540,A Vorticity-Preserving Hydrodynamical Scheme for Modeling Accretion Disk Flows,"  Vortices, turbulence, and unsteady non-laminar flows are likely both
prominent and dynamically important features of astrophysical disks. Such
strongly nonlinear phenomena are often difficult, however, to simulate
accurately, and are generally amenable to analytic treatment only in idealized
form. In this paper, we explore the evolution of compressible two-dimensional
flows using an implicit dual-time hydrodynamical scheme that strictly conserves
vorticity (if applied to simulate inviscid flows for which Kelvin's Circulation
Theorem is applicable). The algorithm is based on the work of Lerat, Falissard
& Side (2007), who proposed it in the context of terrestrial applications such
as the blade-vortex interactions generated by helicopter rotors. We present
several tests of Lerat et al.'s vorticity-preserving approach, which we have
implemented to second-order accuracy, providing side-by-side comparisons with
other algorithms that are frequently used in protostellar disk simulations. The
comparison codes include one based on explicit, second-order van-Leer
advection, one based on spectral methods, and another that implements a
higher-order Godunov solver. Our results suggest that Lerat et al's algorithm
will be useful for simulations of astrophysical environments in which vortices
play a dynamical role, and where strong shocks are not expected.
",0,1,0,0,0,0
19541,Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method,"  Direct numerical simulation of liquid-gas-solid flows is uncommon due to the
considerable computational cost. As the grid spacing is determined by the
smallest involved length scale, large grid sizes become necessary -- in
particular if the bubble-particle aspect ratio is on the order of 10 or larger.
Hence, it arises the question of both feasibility and reasonability. In this
paper, we present a fully parallel, scalable method for direct numerical
simulation of bubble-particle interaction at a size ratio of 1-2 orders of
magnitude that makes simulations feasible on currently available
super-computing resources. With the presented approach, simulations of bubbles
in suspension columns consisting of more than $100\,000$ fully resolved
particles become possible. Furthermore, we demonstrate the significance of
particle-resolved simulations by comparison to previous unresolved solutions.
The results indicate that fully-resolved direct numerical simulation is indeed
necessary to predict the flow structure of bubble-particle interaction problems
correctly.
",0,1,0,0,0,0
19542,TALL: Temporal Activity Localization via Language Query,"  This paper focuses on temporal localization of actions in untrimmed videos.
Existing methods typically train classifiers for a pre-defined list of actions
and apply them in a sliding window fashion. However, activities in the wild
consist of a wide combination of actors, actions and objects; it is difficult
to design a proper activity list that meets users' needs. We propose to
localize activities by natural language queries. Temporal Activity Localization
via Language (TALL) is challenging as it requires: (1) suitable design of text
and video representations to allow cross-modal matching of actions and language
queries; (2) ability to locate actions accurately given features from sliding
windows of limited granularity. We propose a novel Cross-modal Temporal
Regression Localizer (CTRL) to jointly model text query and video clips, output
alignment scores and action boundary regression results for candidate clips.
For evaluation, we adopt TaCoS dataset, and build a new dataset for this task
on top of Charades by adding sentence temporal annotations, called
Charades-STA. We also build complex sentence queries in Charades-STA for test.
Experimental results show that CTRL outperforms previous methods significantly
on both datasets.
",1,0,0,0,0,0
19543,2D granular flows with the $μ(I)$ rheology and side walls friction: a well balanced multilayer discretization,"  We present here numerical modelling of granular flows with the $\mu(I)$
rheology in confined channels. The contribution is twofold: (i) a model to
approximate the Navier-Stokes equations with the $\mu(I)$ rheology through an
asymptotic analysis. Under the hypothesis of a one-dimensional flow, this model
takes into account side walls friction; (ii) a multilayer discretization
following Fernández-Nieto et al. (J. Fluid Mech., vol. 798, 2016, pp.
643-681). In this new numerical scheme, we propose an appropriate treatment of
the rheological terms through a hydrostatic reconstruction which allows this
scheme to be well-balanced and therefore to deal with dry areas. Based on
academic tests, we first evaluate the influence of the width of the channel on
the normal profiles of the downslope velocity thanks to the multilayer approach
that is intrinsically able to describe changes from Bagnold to S-shaped (and
vice versa) velocity profiles. We also check the well balance property of the
proposed numerical scheme. We show that approximating side walls friction using
single-layer models may lead to strong errors. Secondly, we compare the
numerical results with experimental data on granular collapses. We show that
the proposed scheme allows us to qualitatively reproduce the deposit in the
case of a rigid bed (i. e. dry area) and that the error made by replacing the
dry area by a small layer of material may be large if this layer is not thin
enough. The proposed model is also able to reproduce the time evolution of the
free surface and of the flow/no-flow interface. In addition, it reproduces the
effect of erosion for granular flows over initially static material lying on
the bed. This is possible when using a variable friction coefficient $\mu(I)$
but not with a constant friction coefficient.
",0,1,0,0,0,0
19544,A scientists' view of scientometrics: Not everything that counts can be counted,"  Like it or not, attempts to evaluate and monitor the quality of academic
research have become increasingly prevalent worldwide. Performance reviews
range from at the level of individuals, through research groups and
departments, to entire universities. Many of these are informed by, or
functions of, simple scientometric indicators and the results of such exercises
impact onto careers, funding and prestige. However, there is sometimes a
failure to appreciate that scientometrics are, at best, very blunt instruments
and their incorrect usage can be misleading. Rather than accepting the rise and
fall of individuals and institutions on the basis of such imprecise measures,
calls have been made for indicators be regularly scrutinised and for
improvements to the evidence base in this area. It is thus incumbent upon the
scientific community, especially the physics, complexity-science and
scientometrics communities, to scrutinise metric indicators. Here, we review
recent attempts to do this and show that some metrics in widespread use cannot
be used as reliable indicators research quality.
",1,1,0,0,0,0
19545,On analyzing and evaluating privacy measures for social networks under active attack,"  Widespread usage of complex interconnected social networks such as Facebook,
Twitter and LinkedIn in modern internet era has also unfortunately opened the
door for privacy violation of users of such networks by malicious entities. In
this article we investigate, both theoretically and empirically, privacy
violation measures of large networks under active attacks that was recently
introduced in (Information Sciences, 328, 403-417, 2016). Our theoretical
result indicates that the network manager responsible for prevention of privacy
violation must be very careful in designing the network if its topology does
not contain a cycle. Our empirical results shed light on privacy violation
properties of eight real social networks as well as a large number of synthetic
networks generated by both the classical Erdos-Renyi model and the scale-free
random networks generated by the Barabasi-Albert preferential-attachment model.
",1,0,0,0,0,0
19546,Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise,"  Recent developments have established the vulnerability of deep reinforcement
learning to policy manipulation attacks via intentionally perturbed inputs,
known as adversarial examples. In this work, we propose a technique for
mitigation of such attacks based on addition of noise to the parameter space of
deep reinforcement learners during training. We experimentally verify the
effect of parameter-space noise in reducing the transferability of adversarial
examples, and demonstrate the promising performance of this technique in
mitigating the impact of whitebox and blackbox attacks at both test and
training times.
",0,0,0,1,0,0
19547,Multi-Observation Elicitation,"  We study loss functions that measure the accuracy of a prediction based on
multiple data points simultaneously. To our knowledge, such loss functions have
not been studied before in the area of property elicitation or in machine
learning more broadly. As compared to traditional loss functions that take only
a single data point, these multi-observation loss functions can in some cases
drastically reduce the dimensionality of the hypothesis required. In
elicitation, this corresponds to requiring many fewer reports; in empirical
risk minimization, it corresponds to algorithms on a hypothesis space of much
smaller dimension. We explore some examples of the tradeoff between
dimensionality and number of observations, give some geometric
characterizations and intuition for relating loss functions and the properties
that they elicit, and discuss some implications for both elicitation and
machine-learning contexts.
",1,0,0,0,0,0
19548,"Next Stop ""NoOps"": Enabling Cross-System Diagnostics Through Graph-based Composition of Logs and Metrics","  Performing diagnostics in IT systems is an increasingly complicated task, and
it is not doable in satisfactory time by even the most skillful operators.
Systems and their architecture change very rapidly in response to business and
user demand. Many organizations see value in the maintenance and management
model of NoOps that stands for No Operations. One of the implementations of
this model is a system that is maintained automatically without any human
intervention. The path to NoOps involves not only precise and fast diagnostics
but also reusing as much knowledge as possible after the system is reconfigured
or changed. The biggest challenge is to leverage knowledge on one IT system and
reuse this knowledge for diagnostics of another, different system. We propose a
framework of weighted graphs which can transfer knowledge, and perform
high-quality diagnostics of IT systems. We encode all possible data in a graph
representation of a system state and automatically calculate weights of these
graphs. Then, thanks to the evaluation of similarity between graphs, we
transfer knowledge about failures from one system to another and use it for
diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop,
Kafka and Cassandra systems.
",1,0,0,0,0,0
19549,Davenport-Heilbronn Theorems for Quotients of Class Groups,"  We prove a generalization of the Davenport-Heilbronn theorem to quotients of
ideal class groups of quadratic fields by the primes lying above a fixed set of
rational primes $S$. Additionally, we obtain average sizes for the relaxed
Selmer group $\mathrm{Sel}_3^S(K)$ and for
$\mathcal{O}_{K,S}^\times/(\mathcal{O}_{K,S}^\times)^3$ as $K$ varies among
quadratic fields with a fixed signature ordered by discriminant.
",0,0,1,0,0,0
19550,Learning Universal Adversarial Perturbations with Generative Models,"  Neural networks are known to be vulnerable to adversarial examples, inputs
that have been intentionally perturbed to remain visually similar to the source
input, but cause a misclassification. It was recently shown that given a
dataset and classifier, there exists so called universal adversarial
perturbations, a single perturbation that causes a misclassification when
applied to any input. In this work, we introduce universal adversarial
networks, a generative network that is capable of fooling a target classifier
when it's generated output is added to a clean sample from a dataset. We show
that this technique improves on known universal adversarial attacks.
",1,0,0,1,0,0
19551,Large-Scale Classification using Multinomial Regression and ADMM,"  We present a novel method for learning the weights in multinomial logistic
regression based on the alternating direction method of multipliers (ADMM). In
each iteration, our algorithm decomposes the training into three steps; a
linear least-squares problem for the weights, a global variable update
involving a separable cross-entropy loss function, and a trivial dual variable
update The least-squares problem can be factorized in the off-line phase, and
the separability in the global variable update allows for efficient
parallelization, leading to faster convergence. We compare our method with
stochastic gradient descent for linear classification as well as for transfer
learning and show that the proposed ADMM-Softmax leads to improved
generalization and convergence.
",1,0,0,1,0,0
19552,First order dipolar phase transition in the Dicke model with infinitely coordinated frustrating interaction,"  We found analytically a first order quantum phase transition in the Cooper
pair box array of $N$ low-capacitance Josephson junctions capacitively coupled
to a resonant photon in a microwave cavity. The Hamiltonian of the system maps
on the extended Dicke Hamiltonian of $N$ spins one-half with infinitely
coordinated antiferromagnetic (frustrating) interaction. This interaction
arises from the gauge-invariant coupling of the Josephson junctions phases to
the vector potential of the resonant photon field. In $N \gg 1$ semiclassical
limit, we found a critical coupling at which ground state of the system
switches to the one with a net collective electric dipole moment of the Cooper
pair boxes coupled to superradiant equilibrium photonic condensate. This phase
transition changes from the first to second order if the frustrating
interaction is switched off. A self-consistently `rotating' Holstein-Primakoff
representation for the Cartesian components of the total superspin is proposed,
that enables to trace both the first and the second order quantum phase
transitions in the extended and standard Dicke models respectively.
",0,1,0,0,0,0
19553,Parsimonious Bayesian deep networks,"  Combining Bayesian nonparametrics and a forward model selection strategy, we
construct parsimonious Bayesian deep networks (PBDNs) that infer
capacity-regularized network architectures from the data and require neither
cross-validation nor fine-tuning when training the model. One of the two
essential components of a PBDN is the development of a special infinite-wide
single-hidden-layer neural network, whose number of active hidden units can be
inferred from the data. The other one is the construction of a greedy
layer-wise learning algorithm that uses a forward model selection criterion to
determine when to stop adding another hidden layer. We develop both Gibbs
sampling and stochastic gradient descent based maximum a posteriori inference
for PBDNs, providing state-of-the-art classification accuracy and interpretable
data subtypes near the decision boundaries, while maintaining low computational
complexity for out-of-sample prediction.
",0,0,0,1,0,0
19554,Learning to Play Othello with Deep Neural Networks,"  Achieving superhuman playing level by AlphaGo corroborated the capabilities
of convolutional neural architectures (CNNs) for capturing complex spatial
patterns. This result was to a great extent due to several analogies between Go
board states and 2D images CNNs have been designed for, in particular
translational invariance and a relatively large board. In this paper, we verify
whether CNN-based move predictors prove effective for Othello, a game with
significantly different characteristics, including a much smaller board size
and complete lack of translational invariance. We compare several CNN
architectures and board encodings, augment them with state-of-the-art
extensions, train on an extensive database of experts' moves, and examine them
with respect to move prediction accuracy and playing strength. The empirical
evaluation confirms high capabilities of neural move predictors and suggests a
strong correlation between prediction accuracy and playing strength. The best
CNNs not only surpass all other 1-ply Othello players proposed to date but
defeat (2-ply) Edax, the best open-source Othello player.
",1,0,0,1,0,0
19555,A causal modelling framework for reference-based imputation and tipping point analysis,"  We consider estimating the ""de facto"" or effectiveness estimand in a
randomised placebo-controlled or standard-of-care-controlled drug trial with
quantitative outcome, where participants who discontinue an investigational
treatment are not followed up thereafter. Carpenter et al (2013) proposed
reference-based imputation methods which use a reference arm to inform the
distribution of post-discontinuation outcomes and hence to inform an imputation
model. However, the reference-based imputation methods were not formally
justified. We present a causal model which makes an explicit assumption in a
potential outcomes framework about the maintained causal effect of treatment
after discontinuation. We show that the ""jump to reference"", ""copy reference""
and ""copy increments in reference"" reference-based imputation methods, with the
control arm as the reference arm, are special cases of the causal model with
specific assumptions about the causal treatment effect. Results from simulation
studies are presented. We also show that the causal model provides a flexible
and transparent framework for a tipping point sensitivity analysis in which we
vary the assumptions made about the causal effect of discontinued treatment. We
illustrate the approach with data from two longitudinal clinical trials.
",0,0,0,1,0,0
19556,"The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race","  Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.
",1,0,0,0,0,0
19557,Weakly-Supervised Spatial Context Networks,"  We explore the power of spatial context as a self-supervisory signal for
learning visual representations. In particular, we propose spatial context
networks that learn to predict a representation of one image patch from another
image patch, within the same image, conditioned on their real-valued relative
spatial offset. Unlike auto-encoders, that aim to encode and reconstruct
original image patches, our network aims to encode and reconstruct intermediate
representations of the spatially offset patches. As such, the network learns a
spatially conditioned contextual representation. By testing performance with
various patch selection mechanisms we show that focusing on object-centric
patches is important, and that using object proposal as a patch selection
mechanism leads to the highest improvement in performance. Further, unlike
auto-encoders, context encoders [21], or other forms of unsupervised feature
learning, we illustrate that contextual supervision (with pre-trained model
initialization) can improve on existing pre-trained model performance. We build
our spatial context networks on top of standard VGG_19 and CNN_M architectures
and, among other things, show that we can achieve improvements (with no
additional explicit supervision) over the original ImageNet pre-trained VGG_19
and CNN_M models in object categorization and detection on VOC2007.
",1,0,0,0,0,0
19558,Variation of ionizing continuum: the main driver of Broad Absorption Line Variability,"  We present a statistical analysis of the variability of broad absorption
lines (BALs) in quasars using the large multi-epoch spectroscopic dataset of
the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We divide the sample
into two groups according to the pattern of the variation of C iv BAL with
respect to that of continuum: the equivalent widths (EW) of the BAL decreases
(increases) when the continuum brightens (dims) as group T1; and the variation
of EW and continuum in the opposite relation as group T2. We find that T2 has
significantly (P_T<10-6 , Students T Test) higher EW ratios (R) of Si iv to C
iv BAL than T1. Our result agrees with the prediction of photoionization models
that C +3 column density increases (decreases) if there is a (or no) C +3
ionization front while R decreases with the incident continuum. We show that
BAL variabilities in at least 80% quasars are driven by the variation of
ionizing continuum while other models that predict uncorrelated BAL and
continuum variability contribute less than 20%. Considering large uncertainty
in the continuum flux calibration, the latter fraction may be much smaller.
When the sample is binned into different time interval between the two
observations, we find significant difference in the distribution of R between
T1 and T2 in all time-bins down to a deltaT < 6 days, suggesting that BAL
outflow in a fraction of quasars has a recombination time scale of only a few
days.
",0,1,0,0,0,0
19559,Low Dimensional Atomic Norm Representations in Line Spectral Estimation,"  The line spectral estimation problem consists in recovering the frequencies
of a complex valued time signal that is assumed to be sparse in the spectral
domain from its discrete observations. Unlike the gridding required by the
classical compressed sensing framework, line spectral estimation reconstructs
signals whose spectral supports lie continuously in the Fourier domain. If
recent advances have shown that atomic norm relaxation produces highly robust
estimates in this context, the computational cost of this approach remains,
however, the major flaw for its application to practical systems.
In this work, we aim to bridge the complexity issue by studying the atomic
norm minimization problem from low dimensional projection of the signal
samples. We derive conditions on the sub-sampling matrix under which the
partial atomic norm can be expressed by a low-dimensional semidefinite program.
Moreover, we illustrate the tightness of this relaxation by showing that it is
possible to recover the original signal in poly-logarithmic time for two
specific sub-sampling patterns.
",1,0,0,0,0,0
19560,The Trio Identity for Quasi-Monte Carlo Error,"  Monte Carlo methods approximate integrals by sample averages of integrand
values. The error of Monte Carlo methods may be expressed as a trio identity:
the product of the variation of the integrand, the discrepancy of the sampling
measure, and the confounding. The trio identity has different versions,
depending on whether the integrand is deterministic or Bayesian and whether the
sampling measure is deterministic or random. Although the variation and the
discrepancy are common in the literature, the confounding is relatively unknown
and under-appreciated. Theory and examples are used to show how the cubature
error may be reduced by employing the low discrepancy sampling that defines
quasi-Monte Carlo methods. The error may also be reduced by rewriting the
integral in terms of a different integrand. Finally, the confounding explains
why the cubature error might decay at a rate different from that of the
discrepancy.
",0,0,1,0,0,0
19561,Weak type operator Lipschitz and commutator estimates for commuting tuples,"  Let $f: \mathbb{R}^d \to\mathbb{R}$ be a Lipschitz function. If $B$ is a
bounded self-adjoint operator and if $\{A_k\}_{k=1}^d$ are commuting bounded
self-adjoint operators such that $[A_k,B]\in L_1(H),$ then
$$\|[f(A_1,\cdots,A_d),B]\|_{1,\infty}\leq
c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|[A_k,B]\|_1,$$ where $c(d)$ is
a constant independent of $f$, $\mathcal{M}$ and $A,B$ and
$\|\cdot\|_{1,\infty}$ denotes the weak $L_1$-norm. If $\{X_k\}_{k=1}^d$
(respectively, $\{Y_k\}_{k=1}^d$) are commuting bounded self-adjoint operators
such that $X_k-Y_k\in L_1(H),$ then
$$\|f(X_1,\cdots,X_d)-f(Y_1,\cdots,Y_d)\|_{1,\infty}\leq
c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|X_k-Y_k\|_1.$$
",0,0,1,0,0,0
19562,Fast Spectral Ranking for Similarity Search,"  Despite the success of deep learning on representing images for particular
object retrieval, recent studies show that the learned representations still
lie on manifolds in a high dimensional space. This makes the Euclidean nearest
neighbor search biased for this task. Exploring the manifolds online remains
expensive even if a nearest neighbor graph has been computed offline. This work
introduces an explicit embedding reducing manifold search to Euclidean search
followed by dot product similarity search. This is equivalent to linear graph
filtering of a sparse signal in the frequency domain. To speed up online
search, we compute an approximate Fourier basis of the graph offline. We
improve the state of art on particular object retrieval datasets including the
challenging Instre dataset containing small objects. At a scale of 10^5 images,
the offline cost is only a few hours, while query time is comparable to
standard similarity search.
",1,0,0,0,0,0
19563,Transition of multi-diffusive states in a biased periodic potential,"  We study a frequency-dependent damping model of hyper-diffusion within the
generalized Langevin equation. The model allows for the colored noise defined
by its spectral density, assumed to be proportional to $\omega^{\delta-1}$ at
low frequencies with $0<\delta<1$ (sub-Ohmic damping) or $1<\delta<2$
(super-Ohmic damping), where the frequency-dependent damping is deduced from
the noise by means of the fluctuation-dissipation theorem. It is shown that for
super-Ohmic damping and certain parameters, the diffusive process of the
particle in a titled periodic potential undergos sequentially four
time-regimes: thermalization, hyper-diffusion, collapse and asymptotical
restoration. For analysing transition phenomenon of multi-diffusive states, we
demonstrate that the first exist time of the particle escaping from the locked
state into the running state abides by an exponential distribution. The concept
of equivalent velocity trap is introduced in the present model, moreover,
reformation of ballistic diffusive system is also considered as a marginal
situation, however there does not exhibit the collapsed state of diffusion.
",0,1,0,0,0,0
19564,Deformable Classifiers,"  Geometric variations of objects, which do not modify the object class, pose a
major challenge for object recognition. These variations could be rigid as well
as non-rigid transformations. In this paper, we design a framework for training
deformable classifiers, where latent transformation variables are introduced,
and a transformation of the object image to a reference instantiation is
computed in terms of the classifier output, separately for each class. The
classifier outputs for each class, after transformation, are compared to yield
the final decision. As a by-product of the classification this yields a
transformation of the input object to a reference pose, which can be used for
downstream tasks such as the computation of object support. We apply a two-step
training mechanism for our framework, which alternates between optimizing over
the latent transformation variables and the classifier parameters to minimize
the loss function. We show that multilayer perceptrons, also known as deep
networks, are well suited for this approach and achieve state of the art
results on the rotated MNIST and the Google Earth dataset, and produce
competitive results on MNIST and CIFAR-10 when training on smaller subsets of
training data.
",0,0,0,1,0,0
19565,Energy Dissipation in Hamiltonian Chains of Rotators,"  We discuss, in the context of energy flow in high-dimensional systems and
Kolmogorov-Arnol'd-Moser (KAM) theory, the behavior of a chain of rotators
(rotors) which is purely Hamiltonian, apart from dissipation at just one end.
We derive bounds on the dissipation rate which become arbitrarily small in
certain physical regimes, and we present numerical evidence that these bounds
are sharp. We relate this to the decoupling of non-resonant terms as is known
in KAM problems.
",0,1,1,0,0,0
19566,"Computational complexity, torsion-freeness of homoclinic Floer homology, and homoclinic Morse inequalities","  Floer theory was originally devised to estimate the number of 1-periodic
orbits of Hamiltonian systems. In earlier works, we constructed Floer homology
for homoclinic orbits on two dimensional manifolds using combinatorial
techniques. In the present paper, we study theoretic aspects of computational
complexity of homoclinic Floer homology. More precisely, for finding the
homoclinic points and immersions that generate the homology and its boundary
operator, we establish sharp upper bounds in terms of iterations of the
underlying symplectomorphism. This prepares the ground for future numerical
works.
Although originally aimed at numerics, the above bounds provide also purely
algebraic applications, namely
1) Torsion-freeness of primary homoclinic Floer homology.
2) Morse type inequalities for primary homoclinic orbits.
",0,0,1,0,0,0
19567,Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models,"  Bayesian inference for stochastic volatility models using MCMC methods highly
depends on actual parameter values in terms of sampling efficiency. While draws
from the posterior utilizing the standard centered parameterization break down
when the volatility of volatility parameter in the latent state equation is
small, non-centered versions of the model show deficiencies for highly
persistent latent variable series. The novel approach of
ancillarity-sufficiency interweaving has recently been shown to aid in
overcoming these issues for a broad class of multilevel models. In this paper,
we demonstrate how such an interweaving strategy can be applied to stochastic
volatility models in order to greatly improve sampling efficiency for all
parameters and throughout the entire parameter range. Moreover, this method of
""combining best of different worlds"" allows for inference for parameter
constellations that have previously been infeasible to estimate without the
need to select a particular parameterization beforehand.
",0,0,0,1,0,0
19568,Simple root flows for Hitchin representations,"  We study simple root flows and Liouville currents for Hitchin
representations. We show that the Liouville current is associated to the
measure of maximal entropy for a simple root flow, derive a Liouville volume
rigidity result, and construct a Liouville pressure metric on the Hitchin
component.
",0,0,1,0,0,0
19569,On the Origin of Deep Learning,"  This paper is a review of the evolutionary history of deep learning models.
It covers from the genesis of neural networks when associationism modeling of
the brain is studied, to the models that dominate the last decade of research
in deep learning like convolutional neural networks, deep belief networks, and
recurrent neural networks. In addition to a review of these models, this paper
primarily focuses on the precedents of the models above, examining how the
initial ideas are assembled to construct the early models and how these
preliminary models are developed into their current forms. Many of these
evolutionary paths last more than half a century and have a diversity of
directions. For example, CNN is built on prior knowledge of biological vision
system; DBN is evolved from a trade-off of modeling power and computation
complexity of graphical models and many nowadays models are neural counterparts
of ancient linear models. This paper reviews these evolutionary paths and
offers a concise thought flow of how these models are developed, and aims to
provide a thorough background for deep learning. More importantly, along with
the path, this paper summarizes the gist behind these milestones and proposes
many directions to guide the future research of deep learning.
",1,0,0,1,0,0
19570,Control of Asynchronous Imitation Dynamics on Networks,"  Imitation is widely observed in populations of decision-making agents. Using
our recent convergence results for asynchronous imitation dynamics on networks,
we consider how such networks can be efficiently driven to a desired
equilibrium state by offering payoff incentives for using a certain strategy,
either uniformly or targeted to individuals. In particular, if for each
available strategy, agents playing that strategy receive maximum payoff when
their neighbors play that same strategy, we show that providing incentives to
agents in a network that is at equilibrium will result in convergence to a
unique new equilibrium. For the case when a uniform incentive can be offered to
all agents, this result allows the computation of the optimal incentive using a
binary search algorithm. When incentives can be targeted to individual agents,
we propose an algorithm to select which agents should be chosen based on
iteratively maximizing a ratio of the number of agents who adopt the desired
strategy to the payoff incentive required to get those agents to do so.
Simulations demonstrate that the proposed algorithm computes near-optimal
targeted payoff incentives for a range of networks and payoff distributions in
coordination games.
",1,1,0,0,0,0
19571,Finsler structures on holomorphic Lie algebroids,"  Complex Finsler vector bundles have been studied mainly by T. Aikou, who
defined complex Finsler structures on holomorphic vector bundles. In this
paper, we consider the more general case of a holomorphic Lie algebroid E and
we introduce Finsler structures, partial and Chern-Finsler connections on it.
First, we recall some basic notions on holomorphic Lie algebroids. Then, using
an idea from E. Martinez, we introduce the concept of complexified prolongation
of such an algebroid. Also, we study nonlinear and linear connections on the
tangent bundle of E and on the prolongation of E and we investigate the
relation between their coefficients. The analogue of the classical
Chern-Finsler connection is defined and studied in the paper for the case of
the holomorphic Lie algebroid.
",0,0,1,0,0,0
19572,The role of complex analysis in modeling economic growth,"  Development and growth are complex and tumultuous processes. Modern economic
growth theories identify some key determinants of economic growth. However, the
relative importance of the determinants remains unknown, and additional
variables may help clarify the directions and dimensions of the interactions.
The novel stream of literature on economic complexity goes beyond aggregate
measures of productive inputs, and considers instead a more granular and
structural view of the productive possibilities of countries, i.e. their
capabilities. Different endowments of capabilities are crucial ingredients in
explaining differences in economic performances. In this paper we employ
economic fitness, a measure of productive capabilities obtained through complex
network techniques. Focusing on the combined roles of fitness and some more
traditional drivers of growth, we build a bridge between economic growth
theories and the economic complexity literature. Our findings, in agreement
with other recent empirical studies, show that fitness plays a crucial role in
fostering economic growth and, when it is included in the analysis, can be
either complementary to traditional drivers of growth or can completely
overshadow them.
",0,0,0,0,0,1
19573,Inputs from Hell: Generating Uncommon Inputs from Common Samples,"  Generating structured input files to test programs can be performed by
techniques that produce them from a grammar that serves as the specification
for syntactically correct input files. Two interesting scenarios then arise for
effective testing. In the first scenario, software engineers would like to
generate inputs that are as similar as possible to the inputs in common usage
of the program, to test the reliability of the program. More interesting is the
second scenario where inputs should be as dissimilar as possible from normal
usage. This is useful for robustness testing and exploring yet uncovered
behavior. To provide test cases for both scenarios, we leverage a context-free
grammar to parse a set of sample input files that represent the program's
common usage, and determine probabilities for individual grammar production as
they occur during parsing the inputs. Replicating these probabilities during
grammar-based test input generation, we obtain inputs that are close to the
samples. Inverting these probabilities yields inputs that are strongly
dissimilar to common inputs, yet still valid with respect to the grammar. Our
evaluation on three common input formats (JSON, JavaScript, CSS) shows the
effectiveness of these approaches in obtaining instances from both sets of
inputs.
",1,0,0,0,0,0
19574,On equivariant formal deformation theory,"  Using the set-up of deformation categories of Talpo and Vistoli, we
re-interpret and generalize, in the context of cartesian morphisms in abstract
categories, some results of Rim concerning obstructions against extensions of
group actions in infinitesimal deformations. Furthermore, we observe that
finite étale coverings can be infinitesimally extended and the resulting
formal scheme is algebraizable. Finally, we show that pre-Tango structures
survive under pullbacks with respect to finite, generically étale surjections
$\pi:X\rightarrow Y$, and record some consequences regarding Kodaira vanishing
in degree one.
",0,0,1,0,0,0
19575,A self-consistent cloud model for brown dwarfs and young giant exoplanets: comparison with photometric and spectroscopic observations,"  We developed a simple, physical and self-consistent cloud model for brown
dwarfs and young giant exoplanets. We compared different parametrisations for
the cloud particle size, by either fixing particle radii, or fixing the mixing
efficiency (parameter fsed) or estimating particle radii from simple
microphysics. The cloud scheme with simple microphysics appears as the best
parametrisation by successfully reproducing the observed photometry and spectra
of brown dwarfs and young giant exoplanets. In particular, it reproduces the
L-T transition, due to the condensation of silicate and iron clouds below the
visible/near-IR photosphere. It also reproduces the reddening observed for
low-gravity objects, due to an increase of cloud optical depth for low gravity.
In addition, we found that the cloud greenhouse effect shifts chemical
equilibriums, increasing the abundances of species stable at high temperature.
This effect should significantly contribute to the strong variation of methane
abundance at the L-T transition and to the methane depletion observed on young
exoplanets. Finally, we predict the existence of a continuum of brown dwarfs
and exoplanets for absolute J magnitude=15-18 and J-K color=0-3, due to the
evolution of the L-T transition with gravity. This self-consistent model
therefore provides a general framework to understand the effects of clouds and
appears well-suited for atmospheric retrievals.
",0,1,0,0,0,0
19576,Consistency Between the Luminosity Function of Resolved Millisecond Pulsars and the Galactic Center Excess,"  Fermi Large Area Telescope data reveal an excess of GeV gamma rays from the
direction of the Galactic Center and bulge. Several explanations have been
proposed for this excess including an unresolved population of millisecond
pulsars (MSPs) and self-annihilating dark matter. It has been claimed that a
key discriminant for or against the MSP explanation can be extracted from the
properties of the luminosity function describing this source population.
Specifically, is the luminosity function of the putative MSPs in the Galactic
Center consistent with that characterizing the resolved MSPs in the Galactic
disk? To investigate this we have used a Bayesian Markov Chain Monte Carlo to
evaluate the posterior distribution of the parameters of the MSP luminosity
function describing both resolved MSPs and the Galactic Center excess. At
variance with some other claims, our analysis reveals that, within current
uncertainties, both data sets can be well fit with the same luminosity
function.
",0,1,0,0,0,0
19577,First functionality tests of a 64 x 64 pixel DSSC sensor module connected to the complete ladder readout,"  The European X-ray Free Electron Laser (XFEL.EU) will provide every 0.1 s a
train of 2700 spatially coherent ultrashort X-ray pulses at 4.5 MHz repetition
rate. The Small Quantum Systems (SQS) instrument and the Spectroscopy and
Coherent Scattering instrument (SCS) operate with soft X-rays between 0.5 keV -
6keV. The DEPFET Sensor with Signal Compression (DSSC) detector is being
developed to meet the requirements set by these two XFEL.EU instruments. The
DSSC imager is a 1 mega-pixel camera able to store up to 800 single-pulse
images per train. The so-called ladder is the basic unit of the DSSC detector.
It is the single unit out of sixteen identical-units composing the
DSSC-megapixel camera, containing all representative electronic components of
the full-size system and allows testing the full electronic chain. Each DSSC
ladder has a focal plane sensor with 128 x 512 pixels. The read-out ASIC
provides full-parallel readout of the sensor pixels. Every read-out channel
contains an amplifier and an analog filter, an up-to 9 bit ADC and the digital
memory. The ASIC amplifier have a double front-end to allow one to use either
DEPFET sensors or Mini-SDD sensors. In the first case, the signal compression
is a characteristic intrinsic of the sensor; in the second case, the
compression is implemented at the first amplification stage. The goal of signal
compression is to meet the requirement of single-photon detection capability
and wide dynamic range. We present the first results of measurements obtained
using a 64 x 64 pixel DEPFET sensor attached to the full final electronic and
data-acquisition chain.
",0,1,0,0,0,0
19578,Label Propagation on K-partite Graphs with Heterophily,"  In this paper, for the first time, we study label propagation in
heterogeneous graphs under heterophily assumption. Homophily label propagation
(i.e., two connected nodes share similar labels) in homogeneous graph (with
same types of vertices and relations) has been extensively studied before.
Unfortunately, real-life networks are heterogeneous, they contain different
types of vertices (e.g., users, images, texts) and relations (e.g.,
friendships, co-tagging) and allow for each node to propagate both the same and
opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite
label propagation model to handle the mystifying combination of heterogeneous
nodes/relations and heterophily propagation. With this model, we develop a
novel label inference algorithm framework with update rules in near-linear time
complexity. Since real networks change over time, we devise an incremental
approach, which supports fast updates for both new data and evidence (e.g.,
ground truth labels) with guaranteed efficiency. We further provide a utility
function to automatically determine whether an incremental or a re-modeling
approach is favored. Extensive experiments on real datasets have verified the
effectiveness and efficiency of our approach, and its superiority over the
state-of-the-art label propagation methods.
",1,0,0,0,0,0
19579,On LoRaWAN Scalability: Empirical Evaluation of Susceptibility to Inter-Network Interference,"  Appearing on the stage quite recently, the Low Power Wide Area Networks
(LPWANs) are currently getting much of attention. In the current paper we study
the susceptibility of one LPWAN technology, namely LoRaWAN, to the
inter-network interferences. By means of excessive empirical measurements
employing the certified commercial transceivers, we characterize the effect of
modulation coding schemes (known for LoRaWAN as data rates (DRs)) of a
transmitter and an interferer on probability of successful packet delivery
while operating in EU 868 MHz band. We show that in reality the transmissions
with different DRs in the same frequency channel can negatively affect each
other and that the high DRs are influenced by interferences more severely than
the low ones. Also, we show that the LoRa-modulated DRs are affected by the
interferences much less than the FSK-modulated one. Importantly, the presented
results provide insight into the network-level operation of the LoRa LPWAN
technology in general, and its scalability potential in particular. The results
can also be used as a reference for simulations and analyses or for defining
the communication parameters for real-life applications.
",1,0,0,0,0,0
19580,DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution,"  The impacts of climate change are felt by most critical systems, such as
infrastructure, ecological systems, and power-plants. However, contemporary
Earth System Models (ESM) are run at spatial resolutions too coarse for
assessing effects this localized. Local scale projections can be obtained using
statistical downscaling, a technique which uses historical climate observations
to learn a low-resolution to high-resolution mapping. Depending on statistical
modeling choices, downscaled projections have been shown to vary significantly
terms of accuracy and reliability. The spatio-temporal nature of the climate
system motivates the adaptation of super-resolution image processing techniques
to statistical downscaling. In our work, we present DeepSD, a generalized
stacked super resolution convolutional neural network (SRCNN) framework for
statistical downscaling of climate variables. DeepSD augments SRCNN with
multi-scale input channels to maximize predictability in statistical
downscaling. We provide a comparison with Bias Correction Spatial
Disaggregation as well as three Automated-Statistical Downscaling approaches in
downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km)
over the Continental United States. Furthermore, a framework using the NASA
Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM
models with multiple emission scenarios.
",1,0,0,0,0,0
19581,Statistical analysis of the ambiguities in the asteroid period determinations,"  Among asteroids there exist ambiguities in their rotation period
determinations. They are due to incomplete coverage of the rotation, noise
and/or aliases resulting from gaps between separate lightcurves. To help to
remove such uncertainties, basic characteristic of the lightcurves resulting
from constraints imposed by the asteroid shapes and geometries of observations
should be identified. We simulated light variations of asteroids which shapes
were modelled as Gaussian random spheres, with random orientations of spin
vectors and phase angles changed every $5^\circ$ from $0^\circ$ to $65^\circ$.
This produced 1.4 mln lightcurves. For each simulated lightcurve Fourier
analysis has been made and the harmonic of the highest amplitude was recorded.
From the statistical point of view, all lightcurves observed at phase angles
$\alpha < 30^\circ$, with peak-to-peak amplitudes $A>0.2$ mag are bimodal.
Second most frequently dominating harmonic is the first one, with the 3rd
harmonic following right after. For 1% of lightcurves with amplitudes $A < 0.1$
mag and phase angles $\alpha < 40^\circ$ 4th harmonic dominates.
",0,1,0,0,0,0
19582,The Assistive Multi-Armed Bandit,"  Learning preferences implicit in the choices humans make is a well studied
problem in both economics and computer science. However, most work makes the
assumption that humans are acting (noisily) optimally with respect to their
preferences. Such approaches can fail when people are themselves learning about
what they want. In this work, we introduce the assistive multi-armed bandit,
where a robot assists a human playing a bandit task to maximize cumulative
reward. In this problem, the human does not know the reward function but can
learn it through the rewards received from arm pulls; the robot only observes
which arms the human pulls but not the reward associated with each pull. We
offer sufficient and necessary conditions for successfully assisting the human
in this framework. Surprisingly, better human performance in isolation does not
necessarily lead to better performance when assisted by the robot: a human
policy can do better by effectively communicating its observed rewards to the
robot. We conduct proof-of-concept experiments that support these results. We
see this work as contributing towards a theory behind algorithms for
human-robot interaction.
",1,0,0,1,0,0
19583,Localized Manifold Harmonics for Spectral Shape Analysis,"  The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer
graphics and geometry processing applications. In particular, Laplacian
eigenbases allow generalizing the classical Fourier analysis to manifolds. A
key drawback of such bases is their inherently global nature, as the Laplacian
eigenfunctions carry geometric and topological structure of the entire
manifold. In this paper, we introduce a new framework for local spectral shape
analysis. We show how to efficiently construct localized orthogonal bases by
solving an optimization problem that in turn can be posed as the
eigendecomposition of a new operator obtained by a modification of the standard
Laplacian. We study the theoretical and computational aspects of the proposed
framework and showcase our new construction on the classical problems of shape
approximation and correspondence. We obtain significant improvement compared to
classical Laplacian eigenbases as well as other alternatives for constructing
localized bases.
",1,0,0,0,0,0
19584,How to Search the Internet Archive Without Indexing It,"  Significant parts of cultural heritage are produced on the web during the
last decades. While easy accessibility to the current web is a good baseline,
optimal access to the past web faces several challenges. This includes dealing
with large-scale web archive collections and lacking of usage logs that contain
implicit human feedback most relevant for today's web search. In this paper, we
propose an entity-oriented search system to support retrieval and analytics on
the Internet Archive. We use Bing to retrieve a ranked list of results from the
current web. In addition, we link retrieved results to the WayBack Machine;
thus allowing keyword search on the Internet Archive without processing and
indexing its raw archived content. Our search system complements existing web
archive search tools through a user-friendly interface, which comes close to
the functionalities of modern web search engines (e.g., keyword search, query
auto-completion and related query suggestion), and provides a great benefit of
taking user feedback on the current web into account also for web archive
search. Through extensive experiments, we conduct quantitative and qualitative
analyses in order to provide insights that enable further research on and
practical applications of web archives.
",1,0,0,0,0,0
19585,Quantum Dot at a Luttinger liquid edge - Exact solution via Bethe Ansatz,"  We study a system consisting of a Luttinger liquid coupled to a quantum dot
on the boundary. The Luttinger liquid is expressed in terms of fermions
interacting via density-density coupling and the dot is modeled as an
interacting resonant level on to which the bulk fermions can tunnel. We solve
the Hamiltonian exactly and construct all eigenstates. We study both the zero
and finite temperature properties of the system, in particular we compute the
exact dot occupation as a function of the dot energy in all parameter regimes.
The system is seen to flow from weak to to strong coupling for all values of
the bulk interaction, with the flow characterized by a non-perturbative Kondo
scale. We identify the critical exponents at the weak and strong coupling
regimes.
",0,1,0,0,0,0
19586,Optimal Jittered Sampling for two Points in the Unit Square,"  Jittered Sampling is a refinement of the classical Monte Carlo sampling
method. Instead of picking $n$ points randomly from $[0,1]^2$, one partitions
the unit square into $n$ regions of equal measure and then chooses a point
randomly from each partition. Currently, no good rules for how to partition the
space are available. In this paper, we present a solution for the special case
of subdividing the unit square by a decreasing function into two regions so as
to minimize the expected squared $\mathcal{L}_2-$discrepancy. The optimal
partitions are given by a \textit{highly} nonlinear integral equation for which
we determine an approximate solution. In particular, there is a break of
symmetry and the optimal partition is not into two sets of equal measure. We
hope this stimulates further interest in the construction of good partitions.
",0,0,1,0,0,0
19587,Brain structural connectivity atrophy in Alzheimer's disease,"  Analysis and quantification of brain structural changes, using Magnetic
resonance imaging (MRI), are increasingly used to define novel biomarkers of
brain pathologies, such as Alzheimer's disease (AD). Network-based models of
the brain have shown that both local and global topological properties can
reveal patterns of disease propagation. On the other hand, intra-subject
descriptions cannot exploit the whole information context, accessible through
inter-subject comparisons. To address this, we developed a novel approach,
which models brain structural connectivity atrophy with a multiplex network and
summarizes it within a classification score. On an independent dataset
multiplex networks were able to correctly segregate, from normal controls (NC),
AD patients and subjects with mild cognitive impairment that will convert to AD
(cMCI) with an accuracy of, respectively, $0.86 \pm 0.01$ and $0.84 \pm 0.01$.
The model also shows that illness effects are maximally detected by parceling
the brain in equal volumes of $3000$ $mm^3$ (""patches""), without any $a$
$priori$ segmentation based on anatomical features. A direct comparison to
standard voxel-based morphometry on the same dataset showed that the multiplex
network approach had higher sensitivity. This method is general and can have
twofold potential applications: providing a reliable tool for clinical trials
and a disease signature of neurodegenerative pathologies.
",0,1,0,0,0,0
19588,Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry,"  This work proposes a visual odometry method that combines points and plane
primitives, extracted from a noisy depth camera. Depth measurement uncertainty
is modelled and propagated through the extraction of geometric primitives to
the frame-to-frame motion estimation, where pose is optimized by weighting the
residuals of 3D point and planes matches, according to their uncertainties.
Results on an RGB-D dataset show that the combination of points and planes,
through the proposed method, is able to perform well in poorly textured
environments, where point-based odometry is bound to fail.
",1,0,0,0,0,0
19589,Autoignition of Butanol Isomers at Low to Intermediate Temperature and Elevated Pressure,"  Autoignition delay experiments for the isomers of butanol, including n-,
sec-, tert-, and iso-butanol, have been performed using a heated rapid
compression machine. For a compressed pressure of 15 bar, the compressed
temperatures have been varied in the range of 725-855 K for all the
stoichiometric fuel/oxidizer mixtures. Over the conditions investigated in this
study, the ignition delay decreases monotonically as temperature increases and
exhibits single-stage characteristics. Experimental ignition delays are also
compared to simulations computed using three kinetic mechanisms available in
the literature. Reasonable agreement is found for three isomers (tert-, iso-,
and n-butanol).
",0,1,0,0,0,0
19590,On the $k$-abelian complexity of the Cantor sequence,"  In this paper, we prove that for every integer $k \geq 1$, the $k$-abelian
complexity function of the Cantor sequence $\mathbf{c} = 101000101\cdots$ is a
$3$-regular sequence.
",1,0,1,0,0,0
19591,Sharp rates of convergence for accumulated spectrograms,"  We investigate an inverse problem in time-frequency localization: the
approximation of the symbol of a time-frequency localization operator from
partial spectral information by the method of accumulated spectrograms (the sum
of the spectrograms corresponding to large eigenvalues). We derive a sharp
bound for the rate of convergence of the accumulated spectrogram, improving on
recent results.
",0,0,1,0,0,0
19592,Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers,"  In this paper, we present a system that associates faces with voices in a
video by fusing information from the audio and visual signals. The thesis
underlying our work is that an extremely simple approach to generating (weak)
speech clusters can be combined with visual signals to effectively associate
faces and voices by aggregating statistics across a video. This approach does
not need any training data specific to this task and leverages the natural
coherence of information in the audio and visual streams. It is particularly
applicable to tracking speakers in videos on the web where a priori information
about the environment (e.g., number of speakers, spatial signals for
beamforming) is not available. We performed experiments on a real-world dataset
using this analysis framework to determine the speaker in a video. Given a
ground truth labeling determined by human rater consensus, our approach had
~71% accuracy.
",1,0,0,0,0,0
19593,Topological Interplay between Knots and Entangled Vortex-Membranes,"  In this paper, the Kelvin wave and knot dynamics are studied on three
dimensional smoothly deformed entangled vortex-membranes in five dimensional
space. Owing to the existence of local Lorentz invariance and diffeomorphism
invariance, in continuum limit gravity becomes an emergent phenomenon on 3+1
dimensional zero-lattice (a lattice of projected zeroes): On the one hand, the
deformed zero-lattice can be denoted by curved space-time for knots; on the
other hand, the knots as topological defect of 3+1 dimensional zero-lattice
indicates matter may curve space-time. This work would help researchers to
understand the mystery in gravity.
",0,1,0,0,0,0
19594,The first and second fundamental theorems of invariant theory for the quantum general linear supergroup,"  We develop the non-commutative polynomial version of the invariant theory for
the quantum general linear supergroup ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$. A
non-commutative ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-module superalgebra
$\mathcal{P}^{k|l}_{\,r|s}$ is constructed, which is the quantum analogue of
the supersymmetric algebra over $\mathbb{C}^{k|l}\otimes \mathbb{C}^{m|n}\oplus
\mathbb{C}^{r|s}\otimes (\mathbb{C}^{m|n})^{\ast}$. We analyse the structure of
the subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
$\mathcal{P}^{k|l}_{\,r|s}$ by using the quantum super analogue of Howe
duality.
The subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
$\mathcal{P}^{k|l}_{\,r|s}$ is shown to be finitely generated. We determine its
generators and establish a surjective superalgebra homomorphism from a braided
supersymmetric algebra onto it. This establishes the first fundamental theorem
of invariant theory for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
We show that the above mentioned superalgebra homomorphism is an isomorphism
if and only if $m\geq \min\{k,r\}$ and $n\geq \min\{l,s\}$, and obtain a
monomial basis for the subalgebra of invariants in this case. When the
homomorphism is not injective, we give a representation theoretical description
of the generating elements of the kernel associated to the partition
$((m+1)^{n+1})$, producing the second fundamental theorem of invariant theory
for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
We consider two applications of our results. A complete treatment of the
non-commutative polynomial version of invariant theory for ${\rm{
U}}_q(\mathfrak{gl}_{m})$ is obtained as the special case with $n=0$, where an
explicit SFT is proved, which we believe to be new. The FFT and SFT of the
invariant theory for the general linear superalgebra are recovered from the
classical (i.e., $q\to 1$) limit of our results.
",0,0,1,0,0,0
19595,Poly-Spline Finite Element Method,"  We introduce an integrated meshing and finite element method pipeline
enabling black-box solution of partial differential equations in the volume
enclosed by a boundary representation. We construct a hybrid
hexahedral-dominant mesh, which contains a small number of star-shaped
polyhedra, and build a set of high-order basis on its elements, combining
triquadratic B-splines, triquadratic hexahedra (27 degrees of freedom), and
harmonic elements. We demonstrate that our approach converges cubically under
refinement, while requiring around 50% of the degrees of freedom than a
similarly dense hexahedral mesh composed of triquadratic hexahedra. We validate
our approach solving Poisson's equation on a large collection of models, which
are automatically processed by our algorithm, only requiring the user to
provide boundary conditions on their surface.
",1,0,0,0,0,0
19596,"Turing Completeness of Finite, Epistemic Programs","  In this note, we show the class of finite, epistemic programs to be Turing
complete. Epistemic programs is a widely used update mechanism used in
epistemic logic, where it such are a special type of action models: One which
does not contain postconditions.
",1,0,0,0,0,0
19597,Spontaneous domain formation in disordered copolymers as a mechanism for chromosome structuring,"  Motivated by the problem of domain formation in chromosomes, we studied a
co--polymer model where only a subset of the monomers feel attractive
interactions. These monomers are displaced randomly from a regularly-spaced
pattern, thus introducing some quenched disorder in the system. Previous work
has shown that in the case of regularly-spaced interacting monomers this chain
can fold into structures characterized by multiple distinct domains of
consecutive segments. In each domain, attractive interactions are balanced by
the entropy cost of forming loops. We show by advanced replica-exchange
simulations that adding disorder in the position of the interacting monomers
further stabilizes these domains. The model suggests that the partitioning of
the chain into well-defined domains of consecutive monomers is a spontaneous
property of heteropolymers. In the case of chromosomes, evolution could have
acted on the spacing of interacting monomers to modulate in a simple way the
underlying domains for functional reasons.
",0,0,0,0,1,0
19598,Reconstruction of Correlated Sources with Energy Harvesting Constraints in Delay-constrained and Delay-tolerant Communication Scenarios,"  In this paper, we investigate the reconstruction of time-correlated sources
in a point-to-point communications scenario comprising an energy-harvesting
sensor and a Fusion Center (FC). Our goal is to minimize the average distortion
in the reconstructed observations by using data from previously encoded sources
as side information. First, we analyze a delay-constrained scenario, where the
sources must be reconstructed before the next time slot. We formulate the
problem in a convex optimization framework and derive the optimal transmission
(i.e., power and rate allocation) policy. To solve this problem, we propose an
iterative algorithm based on the subgradient method. Interestingly, the
solution to the problem consists of a coupling between a two-dimensional
directional water-filling algorithm (for power allocation) and a reverse
water-filling algorithm (for rate allocation). Then we find a more general
solution to this problem in a delay-tolerant scenario where the time horizon
for source reconstruction is extended to multiple time slots. Finally, we
provide some numerical results that illustrate the impact of delay and
correlation in the power and rate allocation policies, and in the resulting
reconstruction distortion. We also discuss the performance gap exhibited by a
heuristic online policy derived from the optimal (offline) one.
",1,0,0,0,0,0
19599,Future of Flexible Robotic Endoscopy Systems,"  Robotics enables a variety of unconventional actuation strategies to be used
for endoscopes, resulting in reduced trauma to the GI tract. For transmission
of force to distally mounted endoscopic instruments, robotically actuated
tendon sheath mechanisms are the current state of the art. Robotics in surgical
endoscopy enables an ergonomic mapping of the surgeon movements to remotely
controlled slave arms, facilitating tissue manipulation. The learning curve for
difficult procedures such as endoscopic submucosal dissection and
full-thickness resection can be significantly reduced. Improved surgical
outcomes are also observed from clinical and pre-clinical trials. The
technology behind master-slave surgical robotics will continue to mature, with
the addition of position and force sensors enabling better control and tactile
feedback. More robotic assisted GI luminal and NOTES surgeries are expected to
be conducted in future, and gastroenterologists will have a key collaborative
role to play.
",1,1,0,0,0,0
19600,Shear-driven parametric instability in a precessing sphere,"  The present numerical study aims at shedding light on the mechanism
underlying the precessional instability in a sphere. Precessional instabilities
in the form of parametric resonance due to topographic coupling have been
reported in a spheroidal geometry both analytically and numerically. We show
that such parametric resonances can also develop in spherical geometry due to
the conical shear layers driven by the Ekman pumping singularities at the
critical latitudes. Scaling considerations lead to a stability criterion of the
form, $|P_o|>O(E^{4/5})$, where $P_o$ represents the Poincaré number and $E$
the Ekman number. The predicted threshold is consistent with our numerical
simulations as well as previous experimental results. When the precessional
forcing is supercriticial, our simulations show evidence of an inverse cascade,
i.e. small scale flows merging into large scale cyclones with a retrograde
drift. Finally, it is shown that this instability mechanism may be relevant to
precessing celestial bodies such as the Earth and Earth's moon.
",0,1,0,0,0,0
19601,"Linear-Size Hopsets with Small Hopbound, and Distributed Routing with Low Memory","  For a positive parameter $\beta$, the $\beta$-bounded distance between a pair
of vertices $u,v$ in a weighted undirected graph $G = (V,E,\omega)$ is the
length of the shortest $u-v$ path in $G$ with at most $\beta$ edges, aka {\em
hops}. For $\beta$ as above and $\epsilon>0$, a {\em $(\beta,\epsilon)$-hopset}
of $G = (V,E,\omega)$ is a graph $G' =(V,H,\omega_H)$ on the same vertex set,
such that all distances in $G$ are $(1+\epsilon)$-approximated by
$\beta$-bounded distances in $G\cup G'$.
Hopsets are a fundamental graph-theoretic and graph-algorithmic construct,
and they are widely used for distance-related problems in a variety of
computational settings. Currently existing constructions of hopsets produce
hopsets either with $\Omega(n \log n)$ edges, or with a hopbound
$n^{\Omega(1)}$. In this paper we devise a construction of {\em linear-size}
hopsets with hopbound $(\log n)^{\log^{(3)}n+O(1)}$. This improves the previous
bound almost exponentially.
We also devise efficient implementations of our construction in PRAM and
distributed settings. The only existing PRAM algorithm \cite{EN16} for
computing hopsets with a constant (i.e., independent of $n$) hopbound requires
$n^{\Omega(1)}$ time. We devise a PRAM algorithm with polylogarithmic running
time for computing hopsets with a constant hopbound, i.e., our running time is
exponentially better than the previous one. Moreover, these hopsets are also
significantly sparser than their counterparts from \cite{EN16}.
We use our hopsets to devise a distributed routing scheme that exhibits
near-optimal tradeoff between individual memory requirement
$\tilde{O}(n^{1/k})$ of vertices throughout preprocessing and routing phases of
the algorithm, and stretch $O(k)$, along with a near-optimal construction time
$\approx D + n^{1/2 + 1/k}$, where $D$ is the hop-diameter of the input graph.
",1,0,0,0,0,0
19602,The Openpipeflow Navier--Stokes Solver,"  Pipelines are used in a huge range of industrial processes involving fluids,
and the ability to accurately predict properties of the flow through a pipe is
of fundamental engineering importance. Armed with parallel MPI, Arnoldi and
Newton--Krylov solvers, the Openpipeflow code can be used in a range of
settings, from large-scale simulation of highly turbulent flow, to the detailed
analysis of nonlinear invariant solutions (equilibria and periodic orbits) and
their influence on the dynamics of the flow.
",0,1,0,0,0,0
19603,Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems,"  The quadratic unconstrained binary optimization (QUBO) problem arises in
diverse optimization applications ranging from Ising spin problems to classical
problems in graph theory and binary discrete optimization. The use of
preprocessing to transform the graph representing the QUBO problem into a
smaller equivalent graph is important for improving solution quality and time
for both exact and metaheuristic algorithms and is a step towards mapping large
scale QUBO to hardware graphs used in quantum annealing computers. In an
earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
achieved significant QUBO reductions as verified through computational testing.
Here this work is extended with additional rules that provide further
reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
An algorithm and associated data structures to efficiently implement the entire
set of rules is detailed and computational experiments are reported that
demonstrate their efficacy.
",1,0,0,0,0,0
19604,Optimal Decentralized Economical-sharing Criterion and Scheme for Microgrid,"  In order to address the economical dispatch problem in islanded microgrid,
this letter proposes an optimal criterion and two decentralized
economical-sharing schemes. The criterion is to judge whether global optimal
economical-sharing can be realized via a decentralized manner. On the one hand,
if the system cost functions meet this criterion, the corresponding
decentralized droop method is proposed to achieve the global optimal dispatch.
Otherwise, if the system does not meet this criterion, a modified method to
achieve suboptimal dispatch is presented. The advantages of these methods are
convenient,effective and communication-less.
",1,0,0,0,0,0
19605,Unbiased inference for discretely observed hidden Markov model diffusions,"  We develop an importance sampling (IS) type estimator for Bayesian joint
inference on the model parameters and latent states of a class of hidden Markov
models. The hidden state dynamics is a diffusion process and noisy observations
are obtained at discrete points in time. We suppose that the diffusion dynamics
can not be simulated exactly and hence one must time-discretise the diffusion.
Our approach is based on particle marginal Metropolis--Hastings, particle
filters, and multilevel Monte Carlo. The resulting IS type estimator leads to
inference without a bias from the time-discretisation. We give convergence
results and recommend allocations for algorithm inputs. In contrast to existing
unbiased methods requiring strong conditions on the diffusion and tailored
solutions, our method relies on standard Euler approximations of the diffusion.
Our method is parallelisable, and can be computationally efficient. The
user-friendly approach is illustrated with two examples.
",0,0,0,1,0,0
19606,Electric propulsion reliability: statistical analysis of on-orbit anomalies and comparative analysis of electric versus chemical propulsion failure rates,"  With a few hundred spacecraft launched to date with electric propulsion (EP),
it is possible to conduct an epidemiological study of EP on orbit reliability.
The first objective of the present work was to undertake such a study and
analyze EP track record of on orbit anomalies and failures by different
covariates. The second objective was to provide a comparative analysis of EP
failure rates with those of chemical propulsion. After a thorough data
collection, 162 EP-equipped satellites launched between January 1997 and
December 2015 were included in our dataset for analysis. Several statistical
analyses were conducted, at the aggregate level and then with the data
stratified by severity of the anomaly, by orbit type, and by EP technology.
Mean Time To Anomaly (MTTA) and the distribution of the time to anomaly were
investigated, as well as anomaly rates. The important findings in this work
include the following: (1) Post-2005, EP reliability has outperformed that of
chemical propulsion; (2) Hall thrusters have robustly outperformed chemical
propulsion, and they maintain a small but shrinking reliability advantage over
gridded ion engines. Other results were also provided, for example the
differentials in MTTA of minor and major anomalies for gridded ion engines and
Hall thrusters. It was shown that: (3) Hall thrusters exhibit minor anomalies
very early on orbit, which might be indicative of infant anomalies, and thus
would benefit from better ground testing and acceptance procedures; (4) Strong
evidence exists that EP anomalies (onset and likelihood) and orbit type are
dependent, a dependence likely mediated by either the space environment or
differences in thrusters duty cycles; (5) Gridded ion thrusters exhibit both
infant and wear-out failures, and thus would benefit from a reliability growth
program that addresses both these types of problems.
",0,1,0,1,0,0
19607,A Bayesian Framework for Cosmic String Searches in CMB Maps,"  There exists various proposals to detect cosmic strings from Cosmic Microwave
Background (CMB) or 21 cm temperature maps. Current proposals do not aim to
find the location of strings on sky maps, all of these approaches can be
thought of as a statistic on a sky map. We propose a Bayesian interpretation of
cosmic string detection and within that framework, we derive a connection
between estimates of cosmic string locations and cosmic string tension $G\mu$.
We use this Bayesian framework to develop a machine learning framework for
detecting strings from sky maps and outline how to implement this framework
with neural networks. The neural network we trained was able to detect and
locate cosmic strings on noiseless CMB temperature map down to a string tension
of $G\mu=5 \times10^{-9}$ and when analyzing a CMB temperature map that does
not contain strings, the neural network gives a 0.95 probability that
$G\mu\leq2.3\times10^{-9}$.
",0,1,0,0,0,0
19608,Constraining the giant planets' initial configuration from their evolution: implications for the timing of the planetary instability,"  Recent works on planetary migration show that the orbital structure of the
Kuiper belt can be very well reproduced if before the onset of the planetary
instability Neptune underwent a long-range planetesimal-driven migration up to
$\sim$28 au. However, considering that all giant planets should have been
captured in mean motion resonances among themselves during the gas-disk phase,
it is not clear whether such a very specific evolution for Neptune is possible,
nor whether the instability could have happened at late times. Here, we first
investigate which initial resonant configuration of the giant planets can be
compatible with Neptune being extracted from the resonant chain and migrating
to $\sim$28 au before that the planetary instability happened. We address the
late instability issue by investigating the conditions where the planets can
stay in resonance for about 400 My. Our results indicate that this can happen
only in the case where the planetesimal disk is beyond a specific minimum
distance $\delta_{stab}$ from Neptune. Then, if there is a sufficient amount of
dust produced in the planetesimal disk, that drifts inwards, Neptune can enter
in a slow dust-driven migration phase for hundreds of Mys until it reaches a
critical distance $\delta_{mig}$ from the disk. From that point, faster
planetesimal-driven migration takes over and Neptune continues migrating
outward until the instability happens. We conclude that, although an early
instability reproduces more easily the evolution of Neptune required to explain
the structure of the Kuiper belt, such evolution is also compatible with a late
instability.
",0,1,0,0,0,0
19609,General AI Challenge - Round One: Gradual Learning,"  The General AI Challenge is an initiative to encourage the wider artificial
intelligence community to focus on important problems in building intelligent
machines with more general scope than is currently possible. The challenge
comprises of multiple rounds, with the first round focusing on gradual
learning, i.e. the ability to re-use already learned knowledge for efficiently
learning to solve subsequent problems. In this article, we will present details
of the first round of the challenge, its inspiration and aims. We also outline
a more formal description of the challenge and present a preliminary analysis
of its curriculum, based on ideas from computational mechanics. We believe,
that such formalism will allow for a more principled approach towards
investigating tasks in the challenge, building new curricula and for
potentially improving consequent challenge rounds.
",1,0,0,0,0,0
19610,DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks,"  A key enabler for optimizing business processes is accurately estimating the
probability distribution of a time series future given its past. Such
probabilistic forecasts are crucial for example for reducing excess inventory
in supply chains. In this paper we propose DeepAR, a novel methodology for
producing accurate probabilistic forecasts, based on training an
auto-regressive recurrent network model on a large number of related time
series. We show through extensive empirical evaluation on several real-world
forecasting data sets that our methodology is more accurate than
state-of-the-art models, while requiring minimal feature engineering.
",1,0,0,1,0,0
19611,Algebraic and logistic investigations on free lattices,"  Lorenzen's ""Algebraische und logistische Untersuchungen über freie
Verbände"" appeared in 1951 in The journal of symbolic logic. These
""Investigations"" have immediately been recognised as a landmark in the history
of infinitary proof theory, but their approach and method of proof have not
been incorporated into the corpus of proof theory. More precisely, Lorenzen
proves the admissibility of cut by double induction, on the cut formula and on
the complexity of the derivations, without using any ordinal assignment,
contrary to the presentation of cut elimination in most standard texts on proof
theory. This translation has the intent of giving a new impetus to their
reception.
The ""Investigations"" are best known for providing a constructive proof of
consistency for ramified type theory without axiom of reducibility. They do so
by showing that it is a part of a trivially consistent ""inductive calculus""
that describes our knowledge of arithmetic without detour. The proof resorts
only to the inductive definition of formulas and theorems.
They propose furthermore a definition of a semilattice, of a distributive
lattice, of a pseudocomplemented semilattice, and of a countably complete
boolean lattice as deductive calculuses, and show how to present them for
constructing the respective free object over a given preordered set.
This translation is published with the kind permission of Lorenzen's
daughter, Jutta Reinhardt.
",0,0,1,0,0,0
19612,Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms,"  Online interactive recommender systems strive to promptly suggest to
consumers appropriate items (e.g., movies, news articles) according to the
current context including both the consumer and item content information.
However, such context information is often unavailable in practice for the
recommendation, where only the users' interaction data on items can be
utilized. Moreover, the lack of interaction records, especially for new users
and items, worsens the performance of recommendation further. To address these
issues, collaborative filtering (CF), one of the recommendation techniques
relying on the interaction data only, as well as the online multi-armed bandit
mechanisms, capable of achieving the balance between exploitation and
exploration, are adopted in the online interactive recommendation settings, by
assuming independent items (i.e., arms). Nonetheless, the assumption rarely
holds in reality, since the real-world items tend to be correlated with each
other (e.g., two articles with similar topics). In this paper, we study online
interactive collaborative filtering problems by considering the dependencies
among items. We explicitly formulate the item dependencies as the clusters on
arms, where the arms within a single cluster share the similar latent topics.
In light of the topic modeling techniques, we come up with a generative model
to generate the items from their underlying topics. Furthermore, an efficient
online algorithm based on particle learning is developed for inferring both
latent parameters and states of our model. Additionally, our inferred model can
be naturally integrated with existing multi-armed selection strategies in the
online interactive collaborating setting. Empirical studies on two real-world
applications, online recommendations of movies and news, demonstrate both the
effectiveness and efficiency of the proposed approach.
",1,0,0,0,0,0
19613,Learning Latent Features with Pairwise Penalties in Matrix Completion,"  Low-rank matrix completion (MC) has achieved great success in many real-world
data applications. A latent feature model formulation is usually employed and,
to improve prediction performance, the similarities between latent variables
can be exploited by pairwise learning, e.g., the graph regularized matrix
factorization (GRMF) method. However, existing GRMF approaches often use a
squared L2 norm to measure the pairwise difference, which may be overly
influenced by dissimilar pairs and lead to inferior prediction. To fully
empower pairwise learning for matrix completion, we propose a general
optimization framework that allows a rich class of (non-)convex pairwise
penalty functions. A new and efficient algorithm is further developed to
uniformly solve the optimization problem, with a theoretical convergence
guarantee. In an important situation where the latent variables form a small
number of subgroups, its statistical guarantee is also fully characterized. In
particular, we theoretically characterize the complexity-regularized maximum
likelihood estimator, as a special case of our framework. It has a better error
bound when compared to the standard trace-norm regularized matrix completion.
We conduct extensive experiments on both synthetic and real datasets to
demonstrate the superior performance of this general framework.
",0,0,0,1,0,0
19614,Renewal theorems and mixing for non Markov flows with infinite measure,"  We obtain results on mixing for a large class of (not necessarily Markov)
infinite measure semiflows and flows. Erickson proved, amongst other things, a
strong renewal theorem in the corresponding i.i.d. setting. Using operator
renewal theory, we extend Erickson's methods to the deterministic (i.e.
non-i.i.d.) continuous time setting and obtain results on mixing as a
consequence.
Our results apply to intermittent semiflows and flows of Pomeau-Manneville
type (both Markov and nonMarkov), and to semiflows and flows over
Collet-Eckmann maps with nonintegrable roof function.
",0,0,1,0,0,0
19615,Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods,"  Many cognitive, sensory and motor processes have correlates in oscillatory
neural sources, which are embedded as a subspace into the recorded brain
signals. Decoding such processes from noisy
magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the
use of data-driven analysis methods. The objective evaluation of such decoding
algorithms on experimental raw signals, however, is a challenge: the amount of
available M/EEG data typically is limited, labels can be unreliable, and raw
signals often are contaminated with artifacts. The latter is specifically
problematic, if the artifacts stem from behavioral confounds of the oscillatory
neural processes of interest.
To overcome some of these problems, simulation frameworks have been
introduced for benchmarking decoding methods. Generating artificial brain
signals, however, most simulation frameworks make strong and partially
unrealistic assumptions about brain activity, which limits the generalization
of obtained results to real-world conditions.
In the present contribution, we thrive to remove many shortcomings of current
simulation frameworks and propose a versatile alternative, that allows for
objective evaluation and benchmarking of novel data-driven decoding methods for
neural signals. Its central idea is to utilize post-hoc labelings of arbitrary
M/EEG recordings. This strategy makes it paradigm-agnostic and allows to
generate comparatively large datasets with noiseless labels. Source code and
data of the novel simulation approach are made available for facilitating its
adoption.
",1,0,0,1,0,0
19616,"Sums of two cubes as twisted perfect powers, revisited","  In this paper, we sharpen earlier work of the first author, Luca and
Mulholland, showing that the Diophantine equation $$ A^3+B^3 = q^\alpha C^p, \,
\, ABC \neq 0, \, \, \gcd (A,B) =1, $$ has, for ""most"" primes $q$ and suitably
large prime exponents $p$, no solutions. We handle a number of (presumably
infinite) families where no such conclusion was hitherto known. Through further
application of certain {\it symplectic criteria}, we are able to make some
conditional statements about still more values of $q$, a sample such result is
that, for all but $O(\sqrt{x}/\log x)$ primes $q$ up to $x$, the equation $$
A^3 + B^3 = q C^p. $$ has no solutions in coprime, nonzero integers $A, B$
and $C$, for a positive proportion of prime exponents $p$.
",0,0,1,0,0,0
19617,A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications,"  Bilevel optimization is defined as a mathematical program, where an
optimization problem contains another optimization problem as a constraint.
These problems have received significant attention from the mathematical
programming community. Only limited work exists on bilevel problems using
evolutionary computation techniques; however, recently there has been an
increasing interest due to the proliferation of practical applications and the
potential of evolutionary algorithms in tackling these problems. This paper
provides a comprehensive review on bilevel optimization from the basic
principles to solution strategies; both classical and evolutionary. A number of
potential application problems are also discussed. To offer the readers
insights on the prominent developments in the field of bilevel optimization, we
have performed an automated text-analysis of an extended list of papers
published on bilevel optimization to date. This paper should motivate
evolutionary computation researchers to pay more attention to this practical
yet challenging area.
",1,0,1,0,0,0
19618,"Speaker Recognition with Cough, Laugh and ""Wei""","  This paper proposes a speaker recognition (SRE) task with trivial speech
events, such as cough and laugh. These trivial events are ubiquitous in
conversations and less subjected to intentional change, therefore offering
valuable particularities to discover the genuine speaker from disguised speech.
However, trivial events are often short and idiocratic in spectral patterns,
making SRE extremely difficult. Fortunately, we found a very powerful deep
feature learning structure that can extract highly speaker-sensitive features.
By employing this tool, we studied the SRE performance on three types of
trivial events: cough, laugh and ""Wei"" (a short Chinese ""Hello""). The results
show that there is rich speaker information within these trivial events, even
for cough that is intuitively less speaker distinguishable. With the deep
feature approach, the EER can reach 10%-14% with the three trivial events,
despite their extremely short durations (0.2-1.0 seconds).
",1,0,0,0,0,0
19619,Explicit equations for two-dimensional water waves with constant vorticity,"  Governing equations for two-dimensional inviscid free-surface flows with
constant vorticity over arbitrary non-uniform bottom profile are presented in
exact and compact form using conformal variables. An efficient and very
accurate numerical method for this problem is developed.
",0,1,0,0,0,0
19620,On the Global Limiting Absorption Principle for Massless Dirac Operators,"  We prove a global limiting absorption principle on the entire real line for
free, massless Dirac operators $H_0 = \alpha \cdot (-i \nabla)$ for all space
dimensions $n \in \mathbb{N}$, $n \geq 2$. This is a new result for all
dimensions other than three, in particular, it applies to the two-dimensional
case which is known to be of some relevance in applications to graphene.
We also prove an essential self-adjointness result for first-order
matrix-valued differential operators with Lipschitz coefficients.
",0,0,1,0,0,0
19621,Rotation Averaging and Strong Duality,"  In this paper we explore the role of duality principles within the problem of
rotation averaging, a fundamental task in a wide range of computer vision
applications. In its conventional form, rotation averaging is stated as a
minimization over multiple rotation constraints. As these constraints are
non-convex, this problem is generally considered challenging to solve globally.
We show how to circumvent this difficulty through the use of Lagrangian
duality. While such an approach is well-known it is normally not guaranteed to
provide a tight relaxation. Based on spectral graph theory, we analytically
prove that in many cases there is no duality gap unless the noise levels are
severe. This allows us to obtain certifiably global solutions to a class of
important non-convex problems in polynomial time.
We also propose an efficient, scalable algorithm that out-performs general
purpose numerical solvers and is able to handle the large problem instances
commonly occurring in structure from motion settings. The potential of this
proposed method is demonstrated on a number of different problems, consisting
of both synthetic and real-world data.
",1,0,0,0,0,0
19622,Active Learning with Gaussian Processes for High Throughput Phenotyping,"  A looming question that must be solved before robotic plant phenotyping
capabilities can have significant impact to crop improvement programs is
scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
analyze crops in order to determine species with favorable traits, however, the
current practices rely on exhaustive coverage and data collection from the
entire crop field being monitored under the breeding experiment. This works
well in relatively small agricultural fields but can not be scaled to the
larger ones, thus limiting the progress of genetics research. In this work, we
propose an active learning algorithm to enable an autonomous system to collect
the most informative samples in order to accurately learn the distribution of
phenotypes in the field with the help of a Gaussian Process model. We
demonstrate the superior performance of our proposed algorithm compared to the
current practices on sorghum phenotype data collection.
",1,0,0,1,0,0
19623,Comparative Autoignition Trends in the Butanol Isomers at Elevated Pressure,"  Autoignition experiments of stoichiometric mixtures of s-, t-, and i-butanol
in air have been performed using a heated rapid compression machine (RCM). At
compressed pressures of 15 and 30 bar and for compressed temperatures in the
range of 715-910 K, no evidence of a negative temperature coefficient region in
terms of ignition delay response is found. The present experimental results are
also compared with previously reported RCM data of n-butanol in air. The order
of reactivity of the butanols is
n-butanol>s-butanol$\approx$i-butanol>t-butanol at the lower pressure, but
changes to n-butanol>t-butanol>s-butanol>i-butanol at higher pressure. In
addition, t-butanol shows pre-ignition heat release behavior, which is
especially evident at higher pressures. To help identify the controlling
chemistry leading to this pre-ignition heat release, off-stoichiometric
experiments are further performed at 30 bar compressed pressure, for t-butanol
at $\phi$ = 0.5 and $\phi$ = 2.0 in air. For these experiments, higher fuel
loading (i.e. $\phi$ = 2.0) causes greater pre-ignition heat release (as
indicated by greater pressure rise) than the stoichiometric or $\phi$ = 0.5
cases. Comparison of the experimental ignition delays with the simulated
results using two literature kinetic mechanisms shows generally good agreement,
and one mechanism is further used to explore and compare the fuel decomposition
pathways of the butanol isomers. Using this mechanism, the importance of peroxy
chemistry in the autoignition of the butanol isomers is highlighted and
discussed.
",0,1,0,0,0,0
19624,A cross-vendor and cross-state analysis of the GPS-probe data latency,"  Crowdsourced GPS probe data has become a major source of real-time traffic
information applications. In addition to traditional traveler advisory systems
such as dynamic message signs (DMS) and 511 systems, probe data is being used
for automatic incident detection, Integrated Corridor Management (ICM), end of
queue warning systems, and mobility-related smartphone applications. Several
private sector vendors offer minute by minute network-wide travel time and
speed probe data. The quality of such data in terms of deviation of the
reported travel time and speeds from ground-truth has been extensively studied
in recent years, and as a result concerns over the accuracy of probe data has
mostly faded away. However, the latency of probe data, defined as the lag
between the time that disturbance in traffic speed is reported in the
outsourced data feed, and the time that the traffic is perturbed, has become a
subject of interest. The extent of latency of probe data for real-time
applications is critical, so it is important to have a good understanding of
the amount of latency and its influencing factors. This paper uses high-quality
independent Bluetooth/Wi-Fi re-identification data collected on multiple
freeway segments in three different states, to measure the latency of the
vehicle probe data provided by three major vendors. The statistical
distribution of the latency and its sensitivity to speed slowdown and recovery
periods are discussed.
",0,0,0,1,0,0
19625,Determining Phonon Coherence Using Photon Sideband Detection,"  Generating and detection coherent high-frequency heat-carrying phonons has
been a great topic of interest in recent years. While there have been
successful attempts in generating and observing coherent phonons, rigorous
techniques to characterize and detect these phonon coherence in a crystalline
material have been lagging compared to what has been achieved for photons. One
main challenge is a lack of detailed understanding of how detection signals for
phonons can be related to coherence. The quantum theory of photoelectric
detection has greatly advanced the ability to characterize photon coherence in
the last century and a similar theory for phonon detection is necessary. Here,
we re-examine the optical sideband fluorescence technique that has been used
detect high frequency phonons in materials with optically active defects. We
apply the quantum theory of photodetection to the sideband technique and
propose signatures in sideband photon-counting statistics and second-order
correlation measurement of sideband signals that indicates the degree of phonon
coherence. Our theory can be implemented in recently performed experiments to
bridge the gap of determining phonon coherence to be on par with that of
photons.
",0,1,0,0,0,0
19626,Landscape of Configurational Density of States for Discrete Large Systems,"  For classical many-body systems, our recent study reveals that expectation
value of internal energy, structure, and free energy can be well characterized
by a single specially-selected microscopic structure. This finding relies on
the fact that configurational density of states (CDOS) for typical classical
system before applying interatomic interaction can be well characterized by
multidimensional gaussian distribution. Although gaussian distribution is an
well-known and widely-used function in diverse fields, it is quantitatively
unclear why the CDOS takes gaussian when system size gets large, even for
projected CDOS onto a single chosen coordination. Here we demonstrate that for
equiatomic binary system, one-dimensional CDOS along coordination of pair
correlation can be reasonably described by gaussian distribution under an
appropriate condition, whose deviation from real CDOS mainly reflects the
existence of triplet closed link consisting of the pair figure considered. The
present result thus significantly makes advance in analytic determination of
the special microscopic states to characterized macroscopic physical property
in equilibrium state.
",0,1,0,0,0,0
19627,On the domain of Dirac and Laplace type operators on stratified spaces,"  We consider a generalized Dirac operator on a compact stratified space with
an iterated cone-edge metric. Assuming a spectral Witt condition, we prove its
essential self-adjointness and identify its domain and the domain of its square
with weighted edge Sobolev spaces. This sharpens previous results where the
minimal domain is shown only to be a subset of an intersection of weighted edge
Sobolev spaces. Our argument does not rely on microlocal techniques and is very
explicit. The novelty of our approach is the use of an abstract functional
analytic notion of interpolation scales. Our results hold for the Gauss-Bonnet
and spin Dirac operators satisfying a spectral Witt condition.
",0,0,1,0,0,0
19628,Modeling Influence with Semantics in Social Networks: a Survey,"  The discovery of influential entities in all kinds of networks (e.g. social,
digital, or computer) has always been an important field of study. In recent
years, Online Social Networks (OSNs) have been established as a basic means of
communication and often influencers and opinion makers promote politics,
events, brands or products through viral content. In this work, we present a
systematic review across i) online social influence metrics, properties, and
applications and ii) the role of semantic in modeling OSNs information. We end
up with the conclusion that both areas can jointly provide useful insights
towards the qualitative assessment of viral user-generated content, as well as
for modeling the dynamic properties of influential content and its flow
dynamics.
",1,0,0,0,0,0
19629,Differentiable Submodular Maximization,"  We consider learning of submodular functions from data. These functions are
important in machine learning and have a wide range of applications, e.g. data
summarization, feature selection and active learning. Despite their
combinatorial nature, submodular functions can be maximized approximately with
strong theoretical guarantees in polynomial time. Typically, learning the
submodular function and optimization of that function are treated separately,
i.e. the function is first learned using a proxy objective and subsequently
maximized. In contrast, we show how to perform learning and optimization
jointly. By interpreting the output of greedy maximization algorithms as
distributions over sequences of items and smoothening these distributions, we
obtain a differentiable objective. In this way, we can differentiate through
the maximization algorithms and optimize the model to work well with the
optimization algorithm. We theoretically characterize the error made by our
approach, yielding insights into the tradeoff of smoothness and accuracy. We
demonstrate the effectiveness of our approach for jointly learning and
optimizing on synthetic maximum cut data, and on real world applications such
as product recommendation and image collection summarization.
",0,0,0,1,0,0
19630,On the Support of Weight Modules for Affine Kac-Moody-Algebras,"  An irreducible weight module of an affine Kac-Moody algebra $\mathfrak{g}$ is
called dense if its support is equal to a coset in $\mathfrak{h}^{*}/Q$.
Following a conjecture of V. Futorny about affine Kac-Moody algebras
$\mathfrak{g}$, an irreducible weight $\mathfrak{g}$-module is dense if and
only if it is cuspidal (i.e. not a quotient of an induced module). The
conjecture is confirmed for $\mathfrak{g}=A_{2}^{\left(1\right)}$,
$A_{3}^{\left(1\right)}$ and$A_{4}^{\left(1\right)}$ and a classification of
the supports of the irreducible weight $\mathfrak{g}$-modules obtained. For all
$A_{n}^{\left(1\right)}$ the problem is reduced to finding primitive elements
for only finitely many cases, all lying below a certain bound. For the
left-over finitely many cases an algorithm is proposed, which leads to the
solution of Futorny's conjecture for the cases $A_{2}^{\left(1\right)}$ and
$A_{3}^{\left(1\right)}$. Yet, the solution of the case
$A_{4}^{\left(1\right)}$ required additional combinatorics.
For the proofs, a new category of hypoabelian Lie subalgebras,
pre-prosolvable subalgebras, and a subclass thereof, quasicone subalgebras, is
introduced and its tropical matrix algebra structure outlined.
",0,0,1,0,0,0
19631,The middle-scale asymptotics of Wishart matrices,"  We study the behavior of a real $p$-dimensional Wishart random matrix with
$n$ degrees of freedom when $n,p\rightarrow\infty$ but $p/n\rightarrow 0$. We
establish the existence of phase transitions when $p$ grows at the order
$n^{(K+1)/(K+3)}$ for every $k\in\mathbb{N}$, and derive expressions for
approximating densities between every two phase transitions. To do this, we
make use of a novel tool we call the G-transform of a distribution, which is
closely related to the characteristic function. We also derive an extension of
the $t$-distribution to the real symmetric matrices, which naturally appears as
the conjugate distribution to the Wishart under a G-transformation, and show
its empirical spectral distribution obeys a semicircle law when $p/n\rightarrow
0$. Finally, we discuss how the phase transitions of the Wishart distribution
might originate from changes in rates of convergence of symmetric $t$
statistics.
",0,0,1,1,0,0
19632,Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping,"  In this paper, we propose an online learning algorithm based on a
Rao-Blackwellized particle filter for spatial concept acquisition and mapping.
We have proposed a nonparametric Bayesian spatial concept acquisition model
(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in
the theoretical framework of the Bayesian generative model. The proposed method
can simultaneously learn place categories and lexicons while incrementally
generating an environmental map. Furthermore, the proposed method has scene
image features and a language model added to SpCoA. In the experiments, we
tested online learning of spatial concepts and environmental maps in a novel
environment of which the robot did not have a map. Then, we evaluated the
results of online learning of spatial concepts and lexical acquisition. The
experimental results demonstrated that the robot was able to more accurately
learn the relationships between words and the place in the environmental map
incrementally by using the proposed method.
",1,0,0,0,0,0
19633,The Method of Arbitrarily Large Moments to Calculate Single Scale Processes in Quantum Field Theory,"  We device a new method to calculate a large number of Mellin moments of
single scale quantities using the systems of differential and/or difference
equations obtained by integration-by-parts identities between the corresponding
Feynman integrals of loop corrections to physical quantities. These scalar
quantities have a much simpler mathematical structure than the complete
quantity. A sufficiently large set of moments may even allow the analytic
reconstruction of the whole quantity considered, holding in case of first order
factorizing systems. In any case, one may derive highly precise numerical
representations in general using this method, which is otherwise completely
analytic.
",1,0,1,0,0,0
19634,Unraveling the escape dynamics and the nature of the normally hyperbolic invariant manifolds in tidally limited star clusters,"  The escape mechanism of orbits in a star cluster rotating around its parent
galaxy in a circular orbit is investigated. A three degrees of freedom model is
used for describing the dynamical properties of the Hamiltonian system. The
gravitational field of the star cluster is represented by a smooth and
spherically symmetric Plummer potential. We distinguish between ordered and
chaotic orbits as well as between trapped and escaping orbits, considering only
unbounded motion for several energy levels. The Smaller Alignment Index (SALI)
method is used for determining the regular or chaotic nature of the orbits. The
basins of escape are located and they are also correlated with the
corresponding escape time of the orbits. Areas of bounded regular or chaotic
motion and basins of escape were found to coexist in the $(x,z)$ plane. The
properties of the normally hyperbolic invariant manifolds (NHIMs), located in
the vicinity of the index-1 Lagrange points $L_1$ and $L_2$, are also explored.
These manifolds are of paramount importance as they control the flow of stars
over the saddle points, while they also trigger the formation of tidal tails
observed in star clusters. Bifurcation diagrams of the Lyapunov periodic orbits
as well as restrictions of the Poincaré map to the NHIMs are deployed for
elucidating the dynamics in the neighbourhood of the saddle points. The
extended tidal tails, or tidal arms, formed by stars with low velocity which
escape through the Lagrange points are monitored. The numerical results of this
work are also compared with previous related work.
",0,1,0,0,0,0
19635,The ALMA Early Science View of FUor/EXor objects. III. The Slow and Wide Outflow of V883 Ori,"  We present Atacama Large Millimeter/ sub-millimeter Array (ALMA) observations
of V883 Ori, an FU Ori object. We describe the molecular outflow and envelope
of the system based on the $^{12}$CO and $^{13}$CO emissions, which together
trace a bipolar molecular outflow. The C$^{18}$O emission traces the rotational
motion of the circumstellar disk. From the $^{12}$CO blue-shifted emission, we
estimate a wide opening angle of $\sim$ 150$^{^{\circ}}$ for the outflow
cavities. Also, we find that the outflow is very slow (characteristic velocity
of only 0.65 km~s$^{-1}$), which is unique for an FU Ori object. We calculate
the kinematic properties of the outflow in the standard manner using the
$^{12}$CO and $^{13}$CO emissions. In addition, we present a P Cygni profile
observed in the high-resolution optical spectrum, evidence of a wind driven by
the accretion and being the cause for the particular morphology of the
outflows. We discuss the implications of our findings and the rise of these
slow outflows during and/or after the formation of a rotationally supported
disk.
",0,1,0,0,0,0
19636,Hydrodynamical models of cometary HII regions,"  We have modelled the evolution of cometary HII regions produced by zero-age
main-sequence stars of O and B spectral types, which are driving strong winds
and are born off-centre from spherically symmetric cores with power-law
($\alpha = 2$) density slopes. A model parameter grid was produced that spans
stellar mass, age and core density. Exploring this parameter space we
investigated limb-brightening, a feature commonly seen in cometary HII regions.
We found that stars with mass $M_\star \geq 12\, \mathrm{M}_\odot$ produce this
feature. Our models have a cavity bounded by a contact discontinuity separating
hot shocked wind and ionised ambient gas that is similar in size to the
surrounding HII region. Due to early pressure confinement we did not see shocks
outside of the contact discontinuity for stars with $M_\star \leq 40\,
\mathrm{M}_\odot$, but the cavities were found to continue to grow. The cavity
size in each model plateaus as the HII region stagnates. The spectral energy
distributions of our models are similar to those from identical stars evolving
in uniform density fields. The turn-over frequency is slightly lower in our
power-law models due to a higher proportion of low density gas covered by the
HII regions.
",0,1,0,0,0,0
19637,Feature Selection based on the Local Lift Dependence Scale,"  This paper uses a classical approach to feature selection: minimization of a
cost function applied on estimated joint distributions. However, the search
space in which such minimization is performed is extended. In the original
formulation, the search space is the Boolean lattice of features sets (BLFS),
while, in the present formulation, it is a collection of Boolean lattices of
ordered pairs (features, associated value) (CBLOP), indexed by the elements of
the BLFS. In this approach, we may not only select the features that are most
related to a variable Y, but also select the values of the features that most
influence the variable or that are most prone to have a specific value of Y. A
local formulation of Shanon's mutual information is applied on a CBLOP to
select features, namely, the Local Lift Dependence Scale, an scale for
measuring variable dependence in multiple resolutions. The main contribution of
this paper is to define and apply this local measure, which permits to analyse
local properties of joint distributions that are neglected by the classical
Shanon's global measure. The proposed approach is applied to a dataset
consisting of student performances on a university entrance exam, as well as on
undergraduate courses. The approach is also applied to two datasets of the UCI
Machine Learning Repository.
",1,0,0,1,0,0
19638,Weyl Rings and enhanced susceptibilities in Pyrochlore Iridates: $k\cdot p$ Analysis of Cluster Dynamical Mean-Field Theory Results,"  We match analytic results to numerical calculations to provide a detailed
picture of the metal-insulator and topological transitions found in density
functional plus cluster dynamical mean-field calculations of pyrochlore
iridates. We discuss the transition from Weyl metal to Weyl semimetal regimes,
and then analyse in detail the properties of the Weyl semimetal phase and its
evolution into the topologically trivial insulator. The energy scales in the
Weyl semimetal phase are found to be very small, as are the anisotropy
parameters. The electronic structure can to a good approximation be described
as `Weyl rings' and one of the two branches that contributes to the Weyl bands
is essentially flat, leading to enhanced susceptibilities. The optical
longitudinal and Hall conductivities are determined; the frequency dependence
includes pronounced features that reveal the basic energy scales of the Weyl
semimetal phase.
",0,1,0,0,0,0
19639,Inconsistency of Template Estimation by Minimizing of the Variance/Pre-Variance in the Quotient Space,"  We tackle the problem of template estimation when data have been randomly
deformed under a group action in the presence of noise. In order to estimate
the template, one often minimizes the variance when the influence of the
transformations have been removed (computation of the Fr{é}chet mean in the
quotient space). The consistency bias is defined as the distance (possibly
zero) between the orbit of the template and the orbit of one element which
minimizes the variance. In the first part, we restrict ourselves to isometric
group action, in this case the Hilbertian distance is invariant under the group
action. We establish an asymptotic behavior of the consistency bias which is
linear with respect to the noise level. As a result the inconsistency is
unavoidable as soon as the noise is enough. In practice, template estimation
with a finite sample is often done with an algorithm called ""max-max"". In the
second part, also in the case of isometric group finite, we show the
convergence of this algorithm to an empirical Karcher mean. Our numerical
experiments show that the bias observed in practice can not be attributed to
the small sample size or to a convergence problem but is indeed due to the
previously studied inconsistency. In a third part, we also present some
insights of the case of a non invariant distance with respect to the group
action. We will see that the inconsistency still holds as soon as the noise
level is large enough. Moreover we prove the inconsistency even when a
regularization term is added.
",0,0,1,1,0,0
19640,Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Erro,"  Bayesian inverse modeling is important for a better understanding of
hydrological processes. However, this approach can be computationally demanding
as it usually requires a large number of model evaluations. To address this
issue, one can take advantage of surrogate modeling techniques. Nevertheless,
when approximation error of the surrogate model is neglected in inverse
modeling, the inversion result will be biased. In this paper, we develop a
surrogate-based Bayesian inversion framework that explicitly quantifies and
gradually reduces the approximation error of the surrogate. Specifically, two
strategies are proposed and compared. The first strategy works by obtaining an
ensemble of sparse polynomial chaos expansion (PCE) surrogates with Markov
chain Monte Carlo sampling, while the second one uses Gaussian process (GP) to
simulate the approximation error of a single sparse PCE surrogate. The two
strategies can also be applied with other surrogates, thus they have general
applicability. By adaptively refining the surrogate over the posterior
distribution, we can gradually reduce the surrogate approximation error to a
small level. Demonstrated with three case studies involving
high-dimensionality, multi-modality and a real-world application, respectively,
it is found that both strategies can reduce the bias introduced by surrogate
modeling, while the second strategy has a better performance as it integrates
two methods (i.e., sparse PCE and GP) that complement each other.
",0,0,0,1,0,0
19641,On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox,"  One-Class Classification (OCC) has been prime concern for researchers and
effectively employed in various disciplines. But, traditional methods based
one-class classifiers are very time consuming due to its iterative process and
various parameters tuning. In this paper, we present six OCC methods based on
extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed
classifiers mainly lie in two categories: reconstruction based and boundary
based, which supports both types of learning viz., online and offline learning.
Out of various proposed methods, four are offline and remaining two are online
methods. Out of four offline methods, two methods perform random feature
mapping and two methods perform kernel feature mapping. Kernel feature mapping
based approaches have been tested with RBF kernel and online version of
one-class classifiers are tested with both types of nodes viz., additive and
RBF. It is well known fact that threshold decision is a crucial factor in case
of OCC, so, three different threshold deciding criteria have been employed so
far and analyses the effectiveness of one threshold deciding criteria over
another. Further, these methods are tested on two artificial datasets to check
there boundary construction capability and on eight benchmark datasets from
different discipline to evaluate the performance of the classifiers. Our
proposed classifiers exhibit better performance compared to ten traditional
one-class classifiers and ELM based two one-class classifiers. Through proposed
one-class classifiers, we intend to expand the functionality of the most used
toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with
all the present features of the toolbox.
",1,0,0,1,0,0
19642,2nd order PDEs: geometric and functional considerations,"  INTRODUCTION
This papers deals with partial differential equations of second order,
linear, with constant and not constant coefficients, in two variables, which
admit real characteristics. I face the study of PDEs with the mentality of the
applied physicist, but with a weakness for formalization: look inside the black
box of the formulas, try to compact them (for example, proceeding from an
inverse transformation of coordinates) and make them smart (in the context,
reformulating the theory by means of differential operators and related
invariants), applying them with awareness and then connecting them to geometry
or to spatial categories, which are in mathematics what is closest to the
sensible reality. Finally, proposing examples that are exercise and
corroborating for theory.
TOPICS
The geometric meaning of invariant to a differential operator. Operator
Principal Part and its factorization: commutativity and product with and
without residues(first order terms). Related conditions by operators and
invariants derivatives. Coordinate transformation by invariants and expression
of the hyperbolic and parabolic operators in the new coordinates. Properties of
the Jacobian Matrix and relations between invariants derivatives and inverse
coordinates transformation or the initial variables derivatives. Commutativity
conditions and product without residues in terms of inverse coordinate
transformations that allow to build commutative differential operators or whose
product is without residues (or both). Diffeomorphisms and plane
transformations: new operators and invariants in the new coordinate space which
lead to the chain rule in compact form. Conclusive considerations and examples
who compares different methods of solution.
",0,0,1,0,0,0
19643,Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression,"  Machine Learning models have been shown to be vulnerable to adversarial
examples, ie. the manipulation of data by a attacker to defeat a defender's
classifier at test time. We present a novel probabilistic definition of
adversarial examples in perfect or limited knowledge setting using prior
probability distributions on the defender's classifier. Using the asymptotic
properties of the logistic regression, we derive a closed-form expression of
the intensity of any adversarial perturbation, in order to achieve a given
expected misclassification rate. This technique is relevant in a threat model
of known model specifications and unknown training data. To our knowledge, this
is the first method that allows an attacker to directly choose the probability
of attack success. We evaluate our approach on two real-world datasets.
",0,0,0,1,0,0
19644,Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE,"  In cellular massive Machine-Type Communications (MTC), a device can transmit
directly to the base station (BS) or through an aggregator (intermediate node).
While direct device-BS communication has recently been in the focus of 5G/3GPP
research and standardization efforts, the use of aggregators remains a less
explored topic. In this paper we analyze the deployment scenarios in which
aggregators can perform cellular access on behalf of multiple MTC devices. We
study the effect of packet bundling at the aggregator, which alleviates
overhead and resource waste when sending small packets. The aggregators give
rise to a tradeoff between access congestion and resource starvation and we
show that packet bundling can minimize resource starvation, especially for
smaller numbers of aggregators. Under the limitations of the considered model,
we investigate the optimal settings of the network parameters, in terms of
number of aggregators and packet-bundle size. Our results show that, in
general, data aggregation can benefit the uplink massive MTC in LTE, by
reducing the signalling overhead.
",1,0,0,0,0,0
19645,The GTC exoplanet transit spectroscopy survey. VII. Detection of sodium in WASP-52b's cloudy atmosphere,"  We report the first detection of sodium absorption in the atmosphere of the
hot Jupiter WASP-52b. We observed one transit of WASP-52b with the
low-resolution Optical System for Imaging and low-Intermediate-Resolution
Integrated Spectroscopy (OSIRIS) at the 10.4 m Gran Telescopio Canarias (GTC).
The resulting transmission spectrum, covering the wavelength range from 522 nm
to 903 nm, is flat and featureless, except for the significant narrow
absorption signature at the sodium doublet, which can be explained by an
atmosphere in solar composition with clouds at 1 mbar. A cloud-free atmosphere
is stringently ruled out. By assessing the absorption depths of sodium in
various bin widths, we find that temperature increases towards lower
atmospheric pressure levels, with a positive temperature gradient of 0.88 +/-
0.65 K/km, possibly indicative of upper atmospheric heating and a temperature
inversion.
",0,1,0,0,0,0
19646,That's Enough: Asynchrony with Standard Choreography Primitives,"  Choreographies are widely used for the specification of concurrent and
distributed software architectures. Since asynchronous communications are
ubiquitous in real-world systems, previous works have proposed different
approaches for the formal modelling of asynchrony in choreographies. Such
approaches typically rely on ad-hoc syntactic terms or semantics for capturing
the concept of messages in transit, yielding different formalisms that have to
be studied separately.
In this work, we take a different approach, and show that such extensions are
not needed to reason about asynchronous communications in choreographies.
Rather, we demonstrate how a standard choreography calculus already has all the
needed expressive power to encode messages in transit (and thus asynchronous
communications) through the primitives of process spawning and name mobility.
The practical consequence of our results is that we can reason about real-world
systems within a choreography formalism that is simpler than those hitherto
proposed.
",1,0,0,0,0,0
19647,Doing Things Twice (Or Differently): Strategies to Identify Studies for Targeted Validation,"  The ""reproducibility crisis"" has been a highly visible source of scientific
controversy and dispute. Here, I propose and review several avenues for
identifying and prioritizing research studies for the purpose of targeted
validation. Of the various proposals discussed, I identify scientific data
science as being a strategy that merits greater attention among those
interested in reproducibility. I argue that the tremendous potential of
scientific data science for uncovering high-value research studies is a
significant and rarely discussed benefit of the transition to a fully
open-access publishing model.
",1,1,0,0,0,0
19648,SciSports: Learning football kinematics through two-dimensional tracking data,"  SciSports is a Dutch startup company specializing in football analytics. This
paper describes a joint research effort with SciSports, during the Study Group
Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main
challenge that we addressed was to automatically process empirical football
players' trajectories, in order to extract useful information from them. The
data provided to us was two-dimensional positional data during entire matches.
We developed methods based on Newtonian mechanics and the Kalman filter,
Generative Adversarial Nets and Variational Autoencoders. In addition, we
trained a discriminator network to recognize and discern different movement
patterns of players. The Kalman-filter approach yields an interpretable model,
in which a small number of player-dependent parameters can be fit; in theory
this could be used to distinguish among players. The
Generative-Adversarial-Nets approach appears promising in theory, and some
initial tests showed an improvement with respect to the baseline, but the
limits in time and computational power meant that we could not fully explore
it. We also trained a Discriminator network to distinguish between two players
based on their trajectories; after training, the network managed to distinguish
between some pairs of players, but not between others. After training, the
Variational Autoencoders generated trajectories that are difficult to
distinguish, visually, from the data. These experiments provide an indication
that deep generative models can learn the underlying structure and statistics
of football players' trajectories. This can serve as a starting point for
determining player qualities based on such trajectory data.
",0,0,0,1,0,0
19649,A Model-based Projection Technique for Segmenting Customers,"  We consider the problem of segmenting a large population of customers into
non-overlapping groups with similar preferences, using diverse preference
observations such as purchases, ratings, clicks, etc. over subsets of items. We
focus on the setting where the universe of items is large (ranging from
thousands to millions) and unstructured (lacking well-defined attributes) and
each customer provides observations for only a few items. These data
characteristics limit the applicability of existing techniques in marketing and
machine learning. To overcome these limitations, we propose a model-based
projection technique, which transforms the diverse set of observations into a
more comparable scale and deals with missing data by projecting the transformed
data onto a low-dimensional space. We then cluster the projected data to obtain
the customer segments. Theoretically, we derive precise necessary and
sufficient conditions that guarantee asymptotic recovery of the true customer
segments. Empirically, we demonstrate the speed and performance of our method
in two real-world case studies: (a) 84% improvement in the accuracy of new
movie recommendations on the MovieLens data set and (b) 6% improvement in the
performance of similar item recommendations algorithm on an offline dataset at
eBay. We show that our method outperforms standard latent-class and
demographic-based techniques.
",1,0,0,1,0,0
19650,Distinct Effects of Cr Bulk Doping and Surface Deposition on the Chemical Environment and Electronic Structure of the Topological Insulator Bi2Se3,"  In this report, it is shown that Cr doped into the bulk and Cr deposited on
the surface of Bi2Se3 films produced by molecular beam epitaxy (MBE) have
strikingly different effects on both the electronic structure and chemical
environment.
",0,1,0,0,0,0
19651,High-Performance Code Generation though Fusion and Vectorization,"  We present a technique for automatically transforming kernel-based
computations in disparate, nested loops into a fused, vectorized form that can
reduce intermediate storage needs and lead to improved performance on
contemporary hardware.
We introduce representations for the abstract relationships and data
dependencies of kernels in loop nests and algorithms for manipulating them into
more efficient form; we similarly introduce techniques for determining data
access patterns for stencil-like array accesses and show how this can be used
to elide storage and improve vectorization.
We discuss our prototype implementation of these ideas---named HFAV---and its
use of a declarative, inference-based front-end to drive transformations, and
we present results for some prominent codes in HPC.
",1,0,0,0,0,0
19652,Language Model Pre-training for Hierarchical Document Representations,"  Hierarchical neural architectures are often used to capture long-distance
dependencies and have been applied to many document-level tasks such as
summarization, document segmentation, and sentiment analysis. However,
effective usage of such a large context can be difficult to learn, especially
in the case where there is limited labeled data available. Building on the
recent success of language model pretraining methods for learning flat
representations of text, we propose algorithms for pre-training hierarchical
document representations from unlabeled data. Unlike prior work, which has
focused on pre-training contextual token representations or context-independent
{sentence/paragraph} representations, our hierarchical document representations
include fixed-length sentence/paragraph representations which integrate
contextual information from the entire documents. Experiments on document
segmentation, document-level question answering, and extractive document
summarization demonstrate the effectiveness of the proposed pre-training
algorithms.
",1,0,0,0,0,0
19653,Interpretation of Semantic Tweet Representations,"  Research in analysis of microblogging platforms is experiencing a renewed
surge with a large number of works applying representation learning models for
applications like sentiment analysis, semantic textual similarity computation,
hashtag prediction, etc. Although the performance of the representation
learning models has been better than the traditional baselines for such tasks,
little is known about the elementary properties of a tweet encoded within these
representations, or why particular representations work better for certain
tasks. Our work presented here constitutes the first step in opening the
black-box of vector embeddings for tweets. Traditional feature engineering
methods for high-level applications have exploited various elementary
properties of tweets. We believe that a tweet representation is effective for
an application because it meticulously encodes the application-specific
elementary properties of tweets. To understand the elementary properties
encoded in a tweet representation, we evaluate the representations on the
accuracy to which they can model each of those properties such as tweet length,
presence of particular words, hashtags, mentions, capitalization, etc. Our
systematic extensive study of nine supervised and four unsupervised tweet
representations against most popular eight textual and five social elementary
properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
(STV) best encode the textual and social properties of tweets respectively.
FastText is the best model for low resource settings, providing very little
degradation with reduction in embedding size. Finally, we draw interesting
insights by correlating the model performance obtained for elementary property
prediction tasks with the highlevel downstream applications.
",1,0,0,0,0,0
19654,LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report,"  Logic-based paradigms are nowadays widely used in many different fields, also
thank to the availability of robust tools and systems that allow the
development of real-world and industrial applications.
In this work we present LoIDE, an advanced and modular web-editor for
logic-based languages that also integrates with state-of-the-art solvers.
",1,0,0,0,0,0
19655,Introduction to finite mixtures,"  Mixture models have been around for over 150 years, as an intuitively simple
and practical tool for enriching the collection of probability distributions
available for modelling data. In this chapter we describe the basic ideas of
the subject, present several alternative representations and perspectives on
these models, and discuss some of the elements of inference about the unknowns
in the models. Our focus is on the simplest set-up, of finite mixture models,
but we discuss also how various simplifying assumptions can be relaxed to
generate the rich landscape of modelling and inference ideas traversed in the
rest of this book.
",0,0,0,1,0,0
19656,On the complexity of the projective splitting and Spingarn's methods for the sum of two maximal monotone operators,"  In this work we study the pointwise and ergodic iteration-complexity of a
family of projective splitting methods proposed by Eckstein and Svaiter, for
finding a zero of the sum of two maximal monotone operators. As a consequence
of the complexity analysis of the projective splitting methods, we obtain
complexity bounds for the two-operator case of Spingarn's partial inverse
method. We also present inexact variants of two specific instances of this
family of algorithms, and derive corresponding convergence rate results.
",0,0,1,0,0,0
19657,Demystifying Relational Latent Representations,"  Latent features learned by deep learning approaches have proven to be a
powerful tool for machine learning. They serve as a data abstraction that makes
learning easier by capturing regularities in data explicitly. Their benefits
motivated their adaptation to relational learning context. In our previous
work, we introduce an approach that learns relational latent features by means
of clustering instances and their relations. The major drawback of latent
representations is that they are often black-box and difficult to interpret.
This work addresses these issues and shows that (1) latent features created by
clustering are interpretable and capture interesting properties of data; (2)
they identify local regions of instances that match well with the label, which
partially explains their benefit; and (3) although the number of latent
features generated by this approach is large, often many of them are highly
redundant and can be removed without hurting performance much.
",1,0,0,1,0,0
19658,A direct method to compute the galaxy count angular correlation function including redshift-space distortions,"  In the near future, cosmology will enter the wide and deep galaxy survey area
allowing high-precision studies of the large scale structure of the universe in
three dimensions. To test cosmological models and determine their parameters
accurately, it is natural to confront data with exact theoretical expectations
expressed in the observational parameter space (angles and redshift). The
data-driven galaxy number count fluctuations on redshift shells, can be used to
build correlation functions $C(\theta; z_1, z_2)$ on and between shells which
can probe the baryonic acoustic oscillations, the distance-redshift distortions
as well as gravitational lensing and other relativistic effects. Transforming
the model to the data space usually requires the computation of the angular
power spectrum $C_\ell(z_1, z_2)$ but this appears as an artificial and
inefficient step plagued by apodization issues. In this article we show that it
is not necessary and present a compact expression for $C(\theta; z_1, z_2)$
that includes directly the leading density and redshift space distortions terms
from the full linear theory. It can be evaluated using a fast integration
method based on Clenshaw-Curtis quadrature and Chebyshev polynomial series.
This new method to compute the correlation functions without any Limber
approximation, allows us to produce and discuss maps of the correlation
function directly in the observable space and is a significant step towards
disentangling the data from the tested models.
",0,1,0,0,0,0
19659,Contributors profile modelization in crowdsourcing platforms,"  The crowdsourcing consists in the externalisation of tasks to a crowd of
people remunerated to execute this ones. The crowd, usually diversified, can
include users without qualification and/or motivation for the tasks. In this
paper we will introduce a new method of user expertise modelization in the
crowdsourcing platforms based on the theory of belief functions in order to
identify serious and qualificated users.
",1,0,0,0,0,0
19660,Discrete versions of the Li-Yau gradient estimate,"  We study positive solutions to the heat equation on graphs. We prove variants
of the Li-Yau gradient estimate and the differential Harnack inequality. For
some graphs, we can show the estimates to be sharp. We establish new
computation rules for differential operators on discrete spaces and introduce a
relaxation function that governs the time dependency in the differential
Harnack estimate.
",0,0,1,0,0,0
19661,Efficient Modelling & Forecasting with range based volatility models and application,"  This paper considers an alternative method for fitting CARR models using
combined estimating functions (CEF) by showing its usefulness in applications
in economics and quantitative finance. The associated information matrix for
corresponding new estimates is derived to calculate the standard errors. A
simulation study is carried out to demonstrate its superiority relative to
other two competitors: linear estimating functions (LEF) and the maximum
likelihood (ML). Results show that CEF estimates are more efficient than LEF
and ML estimates when the error distribution is mis-specified. Taking a real
data set from financial economics, we illustrate the usefulness and
applicability of the CEF method in practice and report reliable forecast values
to minimize the risk in the decision making process.
",0,0,1,1,0,0
19662,A Constrained Shortest Path Scheme for Virtual Network Service Management,"  Virtual network services that span multiple data centers are important to
support emerging data-intensive applications in fields such as bioinformatics
and retail analytics. Successful virtual network service composition and
maintenance requires flexible and scalable 'constrained shortest path
management' both in the management plane for virtual network embedding (VNE) or
network function virtualization service chaining (NFV-SC), as well as in the
data plane for traffic engineering (TE). In this paper, we show analytically
and empirically that leveraging constrained shortest paths within recent VNE,
NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%)
and higher energy efficiency. The management of complex VNE, NFV-SC and TE
algorithms can be, however, intractable for large scale substrate networks due
to the NP-hardness of the constrained shortest path problem. To address such
scalability challenges, we propose a novel, exact constrained shortest path
algorithm viz., 'Neighborhoods Method' (NM). Our NM uses novel search space
reduction techniques and has a theoretical quadratic speed-up making it
practically faster (by an order of magnitude) than recent branch-and-bound
exhaustive search solutions. Finally, we detail our NM-based SDN controller
implementation in a real-world testbed to further validate practical NM
benefits for virtual network services.
",1,0,0,0,0,0
19663,Tunneling of Glashow-Weinberg-Salam model particles from Black Hole Solutions in Rastall Theory,"  Using the semiclassical WKB approximation and Hamilton-Jacobi method, we
solve an equation of motion for the Glashow-Weinberg-Salam model, which is
important for understanding the unified gauge-theory of weak and
electromagnetic interactions. We calculate the tunneling rate of the massive
charged W-bosons in a background of electromagnetic field to investigate the
Hawking temperature of black holes surrounded by perfect fluid in Rastall
theory. Then, we study the quantum gravity effects on the generalized Proca
equation with generalized uncertainty principle (GUP) on this background. We
show that quantum gravity effects leave the remnants on the Hawking temperature
and the Hawking radiation becomes nonthermal.
",0,1,0,0,0,0
19664,"Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey","  Natural language and symbols are intimately correlated. Recent advances in
machine learning (ML) and in natural language processing (NLP) seem to
contradict the above intuition: symbols are fading away, erased by vectors or
tensors called distributed and distributional representations. However, there
is a strict link between distributed/distributional representations and
symbols, being the first an approximation of the second. A clearer
understanding of the strict link between distributed/distributional
representations and symbols will certainly lead to radically new deep learning
networks. In this paper we make a survey that aims to draw the link between
symbolic representations and distributed/distributional representations. This
is the right time to revitalize the area of interpreting how symbols are
represented inside neural networks.
",1,0,0,0,0,0
19665,Joint Embedding of Graphs,"  Feature extraction and dimension reduction for networks is critical in a wide
variety of domains. Efficiently and accurately learning features for multiple
graphs has important applications in statistical inference on graphs. We
propose a method to jointly embed multiple undirected graphs. Given a set of
graphs, the joint embedding method identifies a linear subspace spanned by rank
one symmetric matrices and projects adjacency matrices of graphs into this
subspace. The projection coefficients can be treated as features of the graphs.
We also propose a random graph model which generalizes classical random graph
model and can be used to model multiple graphs. We show through theory and
numerical experiments that under the model, the joint embedding method produces
estimates of parameters with small errors. Via simulation experiments, we
demonstrate that the joint embedding method produces features which lead to
state of the art performance in classifying graphs. Applying the joint
embedding method to human brain graphs, we find it extract interpretable
features that can be used to predict individual composite creativity index.
",1,0,0,1,0,0
19666,Tuples of polynomials over finite fields with pairwise coprimality conditions,"  Let $q$ be a prime power. We estimate the number of tuples of degree bounded
monic polynomials $(Q_1,\ldots,Q_v) \in (\mathbb{F}_q[z])^v$ that satisfy given
pairwise coprimality conditions. We show how this generalises from monic
polynomials in finite fields to Dedekind domains with finite norms.
",0,0,1,0,0,0
19667,Pythagorean theorem of Sharpe ratio,"  In the present paper, using a replica analysis, we examine the portfolio
optimization problem handled in previous work and discuss the minimization of
investment risk under constraints of budget and expected return for the case
that the distribution of the hyperparameters of the mean and variance of the
return rate of each asset are not limited to a specific probability family.
Findings derived using our proposed method are compared with those in previous
work to verify the effectiveness of our proposed method. Further, we derive a
Pythagorean theorem of the Sharpe ratio and macroscopic relations of
opportunity loss. Using numerical experiments, the effectiveness of our
proposed method is demonstrated for a specific situation.
",0,1,1,0,0,0
19668,Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models,"  Researchers often summarize their work in the form of scientific posters.
Posters provide a coherent and efficient way to convey core ideas expressed in
scientific papers. Generating a good scientific poster, however, is a complex
and time consuming cognitive task, since such posters need to be readable,
informative, and visually aesthetic. In this paper, for the first time, we
study the challenging problem of learning to generate posters from scientific
papers. To this end, a data-driven framework, that utilizes graphical models,
is proposed. Specifically, given content to display, the key elements of a good
poster, including attributes of each panel and arrangements of graphical
elements are learned and inferred from data. During the inference stage, an MAP
inference framework is employed to incorporate some design principles. In order
to bridge the gap between panel attributes and the composition within each
panel, we also propose a recursive page splitting algorithm to generate the
panel layout for a poster. To learn and validate our model, we collect and
release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which
consists of scientific papers and corresponding posters with exhaustively
labelled panels and attributes. Qualitative and quantitative results indicate
the effectiveness of our approach.
",1,0,0,0,0,0
19669,Transformable Biomimetic Liquid Metal Chameleon,"  Liquid metal (LM) is of current core interest for a wide variety of newly
emerging areas. However, the functional materials thus made so far by LM only
could display a single silver-white appearance. Here in this study, the new
conceptual colorful LM marbles working like transformable biomimetic chameleons
were proposed and fabricated from LM droplets through encasing them with
fluorescent nano-particles. We demonstrated that this unique LM marble can be
manipulated into various stable magnificent appearances as one desires. And it
can also splitt and merge among different colors. Such multifunctional LM
chameleon is capable of responding to the outside electric-stimulus and
realizing shape transformation and discoloration behaviors as well. Further
more, the electric-stimuli has been disclosed to be an easy going way to
trigger the release of nano/micro-particles from the LM. The present
fluorescent biomimetic liquid metal chameleon is expected to offer important
opportunities for diverse unconventional applications, especially in a wide
variety of functional smart material and color changeable soft robot areas.
",0,1,0,0,0,0
19670,Local Private Hypothesis Testing: Chi-Square Tests,"  The local model for differential privacy is emerging as the reference model
for practical applications collecting and sharing sensitive information while
satisfying strong privacy guarantees. In the local model, there is no trusted
entity which is allowed to have each individual's raw data as is assumed in the
traditional curator model for differential privacy. So, individuals' data are
usually perturbed before sharing them.
We explore the design of private hypothesis tests in the local model, where
each data entry is perturbed to ensure the privacy of each participant.
Specifically, we analyze locally private chi-square tests for goodness of fit
and independence testing, which have been studied in the traditional, curator
model for differential privacy.
",1,0,1,1,0,0
19671,Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation,"  A low-cost, robust, and simple mechanism to measure hemoglobin would play a
critical role in the modern health infrastructure. Consistent sample
acquisition has been a long-standing technical hurdle for photometer-based
portable hemoglobin detectors which rely on micro cuvettes and dry chemistry.
Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a
cuvette's sensing area drastically impact optical absorption profile, and
commercial hemoglobinometers lack the ability to automatically detect faulty
samples. We present the ground-up development of a portable, low-cost and open
platform with equivalent accuracy to medical-grade devices, with the addition
of CNN-based image processing for rapid sample viability prechecks. The
developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of
hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported
in literature, and a 97% detection accuracy of poorly-prepared samples. We see
the developed hemoglobin device/ML platform having massive implications in
rural medicine, and consider it an excellent springboard for robust deep
learning optical spectroscopy: a currently untapped source of data for
detection of countless analytes.
",0,1,0,1,0,0
19672,Families of sets with no matchings of sizes 3 and 4,"  In this paper, we study the following classical question of extremal set
theory: what is the maximum size of a family of subsets of $[n]$ such that no
$s$ sets from the family are pairwise disjoint? This problem was first posed by
Erd\H os and resolved for $n\equiv 0, -1\ (\mathrm{mod }\ s)$ by Kleitman in
the 60s. Very little progress was made on the problem until recently. The only
result was a very lengthy resolution of the case $s=3,\ n\equiv 1\ (\mathrm{mod
}\ 3)$ by Quinn, which was written in his PhD thesis and never published in a
refereed journal. In this paper, we give another, much shorter proof of Quinn's
result, as well as resolve the case $s=4,\ n\equiv 2\ (\mathrm{mod }\ 4)$. This
complements the results in our recent paper, where, in particular, we answered
the question in the case $n\equiv -2\ (\mathrm{mod }\ s)$ for $s\ge 5$.
",1,0,1,0,0,0
19673,Light fields in complex media: mesoscopic scattering meets wave control,"  The newly emerging field of wave front shaping in complex media has recently
seen enormous progress. The driving force behind these advances has been the
experimental accessibility of the information stored in the scattering matrix
of a disordered medium, which can nowadays routinely be exploited to focus
light as well as to image or to transmit information even across highly turbid
scattering samples. We will provide an overview of these new techniques, of
their experimental implementations as well as of the underlying theoretical
concepts following from mesoscopic scattering theory. In particular, we will
highlight the intimate connections between quantum transport phenomena and the
scattering of light fields in disordered media, which can both be described by
the same theoretical concepts. We also put particular emphasis on how the above
topics relate to application-oriented research fields such as optical imaging,
sensing and communication.
",0,1,0,0,0,0
19674,Tailoring Product Ownership in Large-Scale Agile,"  In large-scale agile projects, product owners undertake a range of
challenging and varied activities beyond those conventionally associated with
that role. Using in-depth research interviews from 93 practitioners working in
cross-border teams, from 21 organisations, our rich empirical data offers a
unique international perspective into product owner activities. We found that
the leaders of large-scale agile projects create product owner teams. Product
owner team members undertake sponsor, intermediary and release plan master
activities to manage scale. They undertake communicator and traveller
activities to manage distance and technical architect, governor and risk
assessor activities to manage governance. Based on our findings, we describe
product owner behaviors that are valued by experienced product owners and their
line managers.
",1,0,0,0,0,0
19675,"Gravitational wave, collider and dark matter signals from a scalar singlet electroweak baryogenesis","  We analyse a simple extension of the SM with just an additional scalar
singlet coupled to the Higgs boson. We discuss the possible probes for
electroweak baryogenesis in this model including collider searches,
gravitational wave and direct dark matter detection signals. We show that a
large portion of the model parameter space exists where the observation of
gravitational waves would allow detection while the indirect collider searches
would not.
",0,1,0,0,0,0
19676,Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case,"  Consider a Gaussian vector $\mathbf{z}=(\mathbf{x}',\mathbf{y}')'$,
consisting of two sub-vectors $\mathbf{x}$ and $\mathbf{y}$ with dimensions $p$
and $q$ respectively, where both $p$ and $q$ are proportional to the sample
size $n$. Denote by $\Sigma_{\mathbf{u}\mathbf{v}}$ the population
cross-covariance matrix of random vectors $\mathbf{u}$ and $\mathbf{v}$, and
denote by $S_{\mathbf{u}\mathbf{v}}$ the sample counterpart. The canonical
correlation coefficients between $\mathbf{x}$ and $\mathbf{y}$ are known as the
square roots of the nonzero eigenvalues of the canonical correlation matrix
$\Sigma_{\mathbf{x}\mathbf{x}}^{-1}\Sigma_{\mathbf{x}\mathbf{y}}\Sigma_{\mathbf{y}\mathbf{y}}^{-1}\Sigma_{\mathbf{y}\mathbf{x}}$.
In this paper, we focus on the case that $\Sigma_{\mathbf{x}\mathbf{y}}$ is of
finite rank $k$, i.e. there are $k$ nonzero canonical correlation coefficients,
whose squares are denoted by $r_1\geq\cdots\geq r_k>0$. We study the sample
counterparts of $r_i,i=1,\ldots,k$, i.e. the largest $k$ eigenvalues of the
sample canonical correlation matrix
$§_{\mathbf{x}\mathbf{x}}^{-1}§_{\mathbf{x}\mathbf{y}}§_{\mathbf{y}\mathbf{y}}^{-1}§_{\mathbf{y}\mathbf{x}}$,
denoted by $\lambda_1\geq\cdots\geq \lambda_k$. We show that there exists a
threshold $r_c\in(0,1)$, such that for each $i\in\{1,\ldots,k\}$, when $r_i\leq
r_c$, $\lambda_i$ converges almost surely to the right edge of the limiting
spectral distribution of the sample canonical correlation matrix, denoted by
$d_{+}$. When $r_i>r_c$, $\lambda_i$ possesses an almost sure limit in
$(d_{+},1]$. We also obtain the limiting distribution of $\lambda_i$'s under
appropriate normalization. Specifically, $\lambda_i$ possesses Gaussian type
fluctuation if $r_i>r_c$, and follows Tracy-Widom distribution if $r_i<r_c$.
Some applications of our results are also discussed.
",0,0,1,1,0,0
19677,Synthesizing Normalized Faces from Facial Identity Features,"  We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar.
",1,0,0,1,0,0
19678,One-Shot Coresets: The Case of k-Clustering,"  Scaling clustering algorithms to massive data sets is a challenging task.
Recently, several successful approaches based on data summarization methods,
such as coresets and sketches, were proposed. While these techniques provide
provably good and small summaries, they are inherently problem dependent - the
practitioner has to commit to a fixed clustering objective before even
exploring the data. However, can one construct small data summaries for a wide
range of clustering problems simultaneously? In this work, we affirmatively
answer this question by proposing an efficient algorithm that constructs such
one-shot summaries for k-clustering problems while retaining strong theoretical
guarantees.
",1,0,0,1,0,0
19679,Multiple nodal solutions of nonlinear Choquard equations,"  In this paper, we consider the existence of multiple nodal solutions of the
nonlinear Choquard equation \begin{equation*} \ \ \ \ (P)\ \ \ \ \begin{cases}
-\Delta u+u=(|x|^{-1}\ast|u|^p)|u|^{p-2}u \ \ \ \text{in}\ \mathbb{R}^3, \ \ \
\ \\ u\in H^1(\mathbb{R}^3),\\ \end{cases} \end{equation*} where $p\in
(\frac{5}{2},5)$. We show that for any positive integer $k$, problem $(P)$ has
at least a radially symmetrical solution changing sign exactly $k$-times.
",0,0,1,0,0,0
19680,"Computational Sufficiency, Reflection Groups, and Generalized Lasso Penalties","  We study estimators with generalized lasso penalties within the computational
sufficiency framework introduced by Vu (2018, arXiv:1807.05985). By
representing these penalties as support functions of zonotopes and more
generally Minkowski sums of line segments and rays, we show that there is a
natural reflection group associated with the underlying optimization problem. A
consequence of this point of view is that for large classes of estimators
sharing the same penalty, the penalized least squares estimator is
computationally minimal sufficient. This means that all such estimators can be
computed by refining the output of any algorithm for the least squares case. An
interesting technical component is our analysis of coordinate descent on the
dual problem. A key insight is that the iterates are obtained by reflecting and
averaging, so they converge to an element of the dual feasible set that is
minimal with respect to a ordering induced by the group associated with the
penalty. Our main application is fused lasso/total variation denoising and
isotonic regression on arbitrary graphs. In those cases the associated group is
a permutation group.
",0,0,0,1,0,0
19681,Driving Simulator Platform for Development and Evaluation of Safety and Emergency Systems,"  According to data from the United Nations, more than 3000 people have died
each day in the world due to road traffic collision. Considering recent
researches, the human error may be considered as the main responsible for these
fatalities. Because of this, researchers seek alternatives to transfer the
vehicle control from people to autonomous systems. However, providing this
technological innovation for the people may demand complex challenges in the
legal, economic and technological areas. Consequently, carmakers and
researchers have divided the driving automation in safety and emergency systems
that improve the driver perception on the road. This may reduce the human
error. Therefore, the main contribution of this study is to propose a driving
simulator platform to develop and evaluate safety and emergency systems, in the
first design stage. This driving simulator platform has an advantage: a
flexible software structure.This allows in the simulation one adaptation for
development or evaluation of a system. The proposed driving simulator platform
was tested in two applications: cooperative vehicle system development and the
influence evaluation of a Driving Assistance System (\textit{DAS}) on a driver.
In the cooperative vehicle system development, the results obtained show that
the increment of the time delay in the communication among vehicles ($V2V$) is
determinant for the system performance. On the other hand, in the influence
evaluation of a \textit{DAS} in a driver, it was possible to conclude that the
\textit{DAS'} model does not have the level of influence necessary in a driver
to avoid an accident.
",1,0,0,0,0,0
19682,Electrical transient laws in neuronal microdomains based on electro-diffusion,"  The current-voltage (I-V) conversion characterizes the physiology of cellular
microdomains and reflects cellular communication, excitability, and electrical
transduction. Yet deriving such I-V laws remains a major challenge in most
cellular microdomains due to their small sizes and the difficulty of accessing
voltage with a high nanometer precision. We present here novel analytical
relations derived for different numbers of ionic species inside a neuronal
micro/nano-domains, such as dendritic spines. When a steady-state current is
injected, we find a large deviation from the classical Ohm's law, showing that
the spine neck resistance is insuficent to characterize electrical properties.
For a constricted spine neck, modeled by a hyperboloid, we obtain a new I-V law
that illustrates the consequences of narrow passages on electrical conduction.
Finally, during a fast current transient, the local voltage is modulated by the
distance between activated voltage-gated channels. To conclude,
electro-diffusion laws can now be used to interpret voltage distribution in
neuronal microdomains.
",0,0,0,0,1,0
19683,Characterization of a Deuterium-Deuterium Plasma Fusion Neutron Generator,"  We characterize the neutron output of a deuterium-deuterium plasma fusion
neutron generator, model 35-DD-W-S, manufactured by NSD/Gradel-Fusion. The
measured energy spectrum is found to be dominated by neutron peaks at 2.2 MeV
and 2.7 MeV. A detailed GEANT4 simulation accurately reproduces the measured
energy spectrum and confirms our understanding of the fusion process in this
generator. Additionally, a contribution of 14.1 MeV neutrons from
deuterium-tritium fusion is found at a level of~$3.5\%$, from tritium produced
in previous deuterium-deuterium reactions. We have measured both the absolute
neutron flux as well as its relative variation on the operational parameters of
the generator. We find the flux to be proportional to voltage $V^{3.32 \pm
0.14}$ and current $I^{0.97 \pm 0.01}$. Further, we have measured the angular
dependence of the neutron emission with respect to the polar angle. We conclude
that it is well described by isotropic production of neutrons within the
cathode field cage.
",0,1,0,0,0,0
19684,Turbulent Mass Inhomogeneities induced by a point-source,"  We describe how turbulence distributes tracers away from a localized source
of injection, and analyse how the spatial inhomogeneities of the concentration
field depend on the amount of randomness in the injection mechanism. For that
purpose, we contrast the mass correlations induced by purely random injections
with those induced by continuous injections in the environment. Using the
Kraichnan model of turbulent advection, whereby the underlying velocity field
is assumed to be shortly correlated in time, we explicitly identify scaling
regions for the statistics of the mass contained within a shell of radius $r$
and located at a distance $\rho$ away from the source. The two key parameters
are found to be (i) the ratio $s^2$ between the absolute and the relative
timescales of dispersion and (ii) the ratio $\Lambda$ between the size of the
cloud and its distance away from the source. When the injection is random, only
the former is relevant, as previously shown by Celani, Martins-Afonso $\&$
Mazzino, $J. Fluid. Mech$, 2007 in the case of an incompressible fluid. It is
argued that the space partition in terms of $s^2$ and $\Lambda$ is a robust
feature of the injection mechanism itself, which should remain relevant beyond
the Kraichnan model. This is for instance the case in a generalised version of
the model, where the absolute dispersion is prescribed to be ballistic rather
than diffusive.
",0,1,0,0,0,0
19685,Berezin-toeplitz quantization and complex weyl quantization of the torus t${}^2$,"  In this paper, we give a correspondence between the Berezin-Toeplitz and the
complex Weyl quantizations of the torus $ \mathbb{T}^2$. To achieve this, we
use the correspondence between the Berezin-Toeplitz and the complex Weyl
quantizations of the complex plane and a relation between the Berezin-Toeplitz
quantization of a periodic symbol on the real phase space $\mathbb{R}^2$ and
the Berezin-Toeplitz quantization of a symbol on the torus $ \mathbb{T}^2 $.
",0,0,1,0,0,0
19686,Conformal predictive distributions with kernels,"  This paper reviews the checkered history of predictive distributions in
statistics and discusses two developments, one from recent literature and the
other new. The first development is bringing predictive distributions into
machine learning, whose early development was so deeply influenced by two
remarkable groups at the Institute of Automation and Remote Control. The second
development is combining predictive distributions with kernel methods, which
were originated by one of those groups, including Emmanuel Braverman.
",1,0,0,1,0,0
19687,Degree bound for toric envelope of a linear algebraic group,"  Algorithms working with linear algebraic groups often represent them via
defining polynomial equations. One can always choose defining equations for an
algebraic group to be of the degree at most the degree of the group as an
algebraic variety. However, the degree of a linear algebraic group $G \subset
\mathrm{GL}_n(C)$ can be arbitrarily large even for $n = 1$. One of the key
ingredients of Hrushovski's algorithm for computing the Galois group of a
linear differential equation was an idea to `approximate' every algebraic
subgroup of $\mathrm{GL}_n(C)$ by a `similar' group so that the degree of the
latter is bounded uniformly in $n$. Making this uniform bound computationally
feasible is crucial for making the algorithm practical.
In this paper, we derive a single-exponential degree bound for such an
approximation (we call it toric envelope), which is qualitatively optimal. As
an application, we improve the quintuply exponential bound for the first step
of the Hrushovski's algorithm due to Feng to a single-exponential bound. For
the cases $n = 2, 3$ often arising in practice, we further refine our general
bound.
",1,0,0,0,0,0
19688,The Modified Lommel functions: monotonic pattern and inequalities,"  This article studies the monotonicity, log-convexity of the modified Lommel
functions by using its power series and infinite product representation. Same
properties for the ratio of the modified Lommel functions with the Lommel
function, $\sinh$ and $\cosh$ are also discussed. As a consequence, some
Turán type and reverse Turán type inequalities are given. A Rayleigh type
function for the Lommel functions are derived and as an application, we obtain
the Redheffer-type inequality.
",0,0,1,0,0,0
19689,Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem,"  Epilepsy is a neurological disorder arising from anomalies of the electrical
activity in the brain, affecting about 0.5--0.8\% of the world population.
Several studies investigated the relationship between seizures and brainwave
synchronization patterns, pursuing the possibility of identifying interictal,
preictal, ictal and postictal states. In this work, we introduce a graph-based
model of the brain interactions developed to study synchronization patterns in
the electroencephalogram (EEG) signals. The aim is to develop a
patient-specific approach, also for a real-time use, for the prediction of
epileptic seizures' occurrences. Different synchronization measures of the EEG
signals and easily computable functions able to capture in real-time the
variations of EEG synchronization have been considered. Both standard and
ad-hoc classification algorithms have been developed and used. Results on scalp
EEG signals show that this simple and computationally viable processing is able
to highlight the changes in the synchronization corresponding to the preictal
state.
",0,0,0,0,1,0
19690,On distances in lattices from algebraic number fields,"  In this paper, we study a classical construction of lattices from number
fields and obtain a series of new results about their minimum distance and
other characteristics by introducing a new measure of algebraic numbers. In
particular, we show that when the number fields have few complex embeddings,
the minimum distances of these lattices can be computed exactly.
",0,0,1,0,0,0
19691,"Significance of distinct electron correlation effects in determining the P,T-odd electric dipole moment of $^{171}$Yb","  Parity and time-reversal violating electric dipole moment (EDM) of $^{171}$Yb
is calculated accounting for the electron correlation effects over the
Dirac-Hartree-Fock (DHF) method in the relativistic Rayleigh-Schrödinger
many-body perturbation theory, with the second (MBPT(2) method) and third order
(MBPT(3) method) approximations, and two variants of all-order relativistic
many-body approaches, in the random phase approximation (RPA) and
coupled-cluster (CC) method with singles and doubles (CCSD method) framework.
We consider electron-nucleus tensor-pseudotensor (T-PT) and nuclear Schiff
moment (NSM) interactions as the predominant sources that induce EDM in a
diamagnetic atomic system. Our results from the CCSD method to EDM ($d_a$) of
$^{171}$Yb due to the T-PT and NSM interactions are found to be $d_a = 4.85(6)
\times 10^{-20} \langle \sigma \rangle C_T \ |e| \ cm$ and $d_a=2.89(4) \times
10^{-17} {S/(|e|\ fm^3)}$, respectively, where $C_T$ is the T-PT coupling
constant and $S$ is the NSM. These values differ significantly from the earlier
calculations. The reason for the same has been attributed to large correlation
effects arising through non-RPA type of interactions among the electrons in
this atom that are observed by analyzing the differences in the RPA and CCSD
results. This has been further scrutinized from the MBPT(2) and MBPT(3) results
and their roles have been demonstrated explicitly.
",0,1,0,0,0,0
19692,Facilitating information system development with Panoramic view on data,"  The increasing amount of information and the absence of an effective tool for
assisting users with minimal technical knowledge lead us to use associative
thinking paradigm for implementation of a software solution - Panorama. In this
study, we present object recognition process, based on context + focus
information visualization techniques, as a foundation for realization of
Panorama. We show that user can easily define data vocabulary of selected
domain that is furthermore used as the application framework. The purpose of
Panorama approach is to facilitate software development of certain problem
domains by shortening the Software Development Life Cycle with minimizing the
impact of implementation, review and maintenance phase. Our approach is focused
on using and updating data vocabulary by users without extensive programming
skills. Panorama therefore facilitates traversing through data by following
associations where user does not need to be familiar with the query language,
the data structure and does not need to know the problem domain fully. Our
approach has been verified by detailed comparison to existing approaches and in
an experiment by implementing selected use cases. The results confirmed that
Panorama fits problem domains with emphasis on data oriented rather than ones
with process oriented aspects. In such cases the development of selected
problem domains is shortened up to 25%, where emphasis is mainly on analysis,
logical design and testing, while omitting physical design and programming,
which is performed automatically by Panorama tool.
",1,0,0,0,0,0
19693,Neutron Diffraction and $μ$SR Studies of Two Polymorphs of Nickel Niobate (NiNb$_2$O$_6$),"  Neutron diffraction and muon spin relaxation ($\mu$SR) studies are presented
for the newly characterized polymorph of NiNb$_2$O$_6$ ($\beta$-NiNb$_2$O$_6$)
with space group P4$_2$/n and $\mu$SR data only for the previously known
columbite structure polymorph with space group Pbcn. The magnetic structure of
the P4$_2$/n form was determined from neutron diffraction using both powder and
single crystal data. Powder neutron diffraction determined an ordering wave
vector $\vec{k}$ = ($\frac{1}{2},\frac{1}{2},\frac{1}{2}$). Single crystal data
confirmed the same $\vec{k}$-vector and showed that the correct magnetic
structure consists of antiferromagnetically-coupled chains running along the a
or b-axes in adjacent Ni$^{2+}$ layers perpendicular to the c-axis, which is
consistent with the expected exchange interaction hierarchy in this system. The
refined magnetic structure is compared with the known magnetic structures of
the closely related tri-rutile phases, NiSb$_2$O$_6$ and NiTa$_2$O$_6$. $\mu$SR
data finds a transition temperature of $T_N \sim$ 15 K for this system, while
the columbite polymorph exhibits a lower $T_N =$ 5.7(3) K. Our $\mu$SR
measurements also allowed us to estimate the critical exponent of the order
parameter $\beta$ for each polymorph. We found $\beta =$ 0.25(3) and 0.16(2)
for the $\beta$ and columbite polymorphs respectively. The single crystal
neutron scattering data gives a value for the critical exponent $\beta
=$~0.28(3) for $\beta$-NiNb$_2$O$_6$, in agreement with the $\mu$SR value.
While both systems have $\beta$ values less than 0.3, which is indicative of
reduced dimensionality, this effect appears to be much stronger for the
columbite system. In other words, although both systems appear to
well-described by $S = 1$ spin chains, the interchain interactions in the
$\beta$-polymorph are likely much larger.
",0,1,0,0,0,0
19694,Semiclassical Propagation: Hilbert Space vs. Wigner Representation,"  A unified viewpoint on the van Vleck and Herman-Kluk propagators in Hilbert
space and their recently developed counterparts in Wigner representation is
presented. It is shown that the numerical protocol for the Herman-Kluk
propagator, which contains the van Vleck one as a particular case, coincides in
both representations. The flexibility of the Wigner version in choosing the
Gaussians' width for the underlying coherent states, being not bound to minimal
uncertainty, is investigated numerically on prototypical potentials. Exploiting
this flexibility provides neither qualitative nor quantitative improvements.
Thus, the well-established Herman-Kluk propagator in Hilbert space remains the
best choice to date given the large number of semiclassical developments and
applications based on it.
",0,1,0,0,0,0
19695,Dynamic analysis and control PID path of a model type gantry crane,"  This paper presents an alternate form for the dynamic modelling of a
mechanical system that simulates in real life a gantry crane type, using
Euler's classical mechanics and Lagrange formalism, which allows find the
equations of motion that our model describe. Moreover, it has a basic model
design system using the SolidWorks software, based on the material and
dimensions of the model provides some physical variables necessary for
modelling. In order to verify the theoretical results obtained, a contrast was
made between solutions obtained by simulation in SimMechanics-Matlab and
Euler-Lagrange equations system, has been solved through Matlab libraries for
solving equation's systems of the type and order obtained. The force is
determined, but not as exerted by the spring, as this will be the control
variable. The objective to bring the mass of the pendulum from one point to
another with a specified distance without the oscillation from it, so that, the
answer is overdamped. This article includes an analysis of PID control in which
the equations of motion of Euler-Lagrange are rewritten in the state space,
once there, they were implemented in Simulink to get the natural response of
the system to a step input in F and then draw the desired trajectories.
",1,1,0,0,0,0
19696,Dynamic Minimum Spanning Forest with Subpolynomial Worst-case Update Time,"  We present a Las Vegas algorithm for dynamically maintaining a minimum
spanning forest of an $n$-node graph undergoing edge insertions and deletions.
Our algorithm guarantees an $O(n^{o(1)})$ worst-case update time with high
probability. This significantly improves the two recent Las Vegas algorithms by
Wulff-Nilsen [STOC'17] with update time $O(n^{0.5-\epsilon})$ for some constant
$\epsilon>0$ and, independently, by Nanongkai and Saranurak [STOC'17] with
update time $O(n^{0.494})$ (the latter works only for maintaining a spanning
forest).
Our result is obtained by identifying the common framework that both two
previous algorithms rely on, and then improve and combine the ideas from both
works. There are two main algorithmic components of the framework that are
newly improved and critical for obtaining our result. First, we improve the
update time from $O(n^{0.5-\epsilon})$ in Wulff-Nilsen [STOC'17] to
$O(n^{o(1)})$ for decrementally removing all low-conductance cuts in an
expander undergoing edge deletions. Second, by revisiting the ""contraction
technique"" by Henzinger and King [1997] and Holm et al. [STOC'98], we show a
new approach for maintaining a minimum spanning forest in connected graphs with
very few (at most $(1+o(1))n$) edges. This significantly improves the previous
approach in [Wulff-Nilsen STOC'17] and [Nanongkai and Saranurak STOC'17] which
is based on Frederickson's 2-dimensional topology tree and illustrates a new
application to this old technique.
",1,0,0,0,0,0
19697,Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,"  The rising popularity of intelligent mobile devices and the daunting
computational cost of deep learning-based models call for efficient and
accurate on-device inference schemes. We propose a quantization scheme that
allows inference to be carried out using integer-only arithmetic, which can be
implemented more efficiently than floating point inference on commonly
available integer-only hardware. We also co-design a training procedure to
preserve end-to-end model accuracy post quantization. As a result, the proposed
quantization scheme improves the tradeoff between accuracy and on-device
latency. The improvements are significant even on MobileNets, a model family
known for run-time efficiency, and are demonstrated in ImageNet classification
and COCO detection on popular CPUs.
",1,0,0,1,0,0
19698,airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution,"  Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants
regulated by the Environmental Protection Agency in the United States. There is
strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of
mortality and hospitalization. Large scale epidemiological studies on the
health effects of PM$_{2.5}$ provide the necessary evidence base for lowering
the safety standards and inform regulatory policy. However, ambient monitors of
PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located
across the U.S., and therefore studies based only on the levels of PM$_{2.5}$
measured from the monitors would inevitably exclude large amounts of the
population. One approach to resolving this issue has been developing models to
predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological,
and land use data. This process typically relies developing a prediction model
that relies on large amounts of input data and is highly computationally
intensive to predict levels of air pollution in unmonitored areas. We have
developed a flexible R package that allows for environmental health researchers
to design and train spatio-temporal models capable of predicting multiple
pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data
platform, to achieve both performance and scalability when used in conjunction
with cloud or cluster computing systems.
",0,0,0,1,0,0
19699,Accurate and Efficient Profile Matching in Knowledge Bases,"  A profile describes a set of properties, e.g. a set of skills a person may
have, a set of skills required for a particular job, or a set of abilities a
football player may have with respect to a particular team strategy. Profile
matching aims to determine how well a given profile fits to a requested
profile. The approach taken in this article is grounded in a matching theory
that uses filters in lattices to represent profiles, and matching values in the
interval [0,1]: the higher the matching value the better is the fit. Such
lattices can be derived from knowledge bases exploiting description logics to
represent the knowledge about profiles. An interesting first question is, how
human expertise concerning the matching can be exploited to obtain most
accurate matchings. It will be shown that if a set of filters together with
matching values by some human expert is given, then under some mild
plausibility assumptions a matching measure can be determined such that the
computed matching values preserve the rankings given by the expert. A second
question concerns the efficient querying of databases of profile instances. For
matching queries that result in a ranked list of profile instances matching a
given one it will be shown how corresponding top-k queries can be evaluated on
grounds of pre-computed matching values, which in turn allows the maintenance
of the knowledge base to be decoupled from the maintenance of profile
instances. In addition, it will be shown how the matching queries can be
exploited for gap queries that determine how profile instances need to be
extended in order to improve in the rankings. Finally, the theory of matching
will be extended beyond the filters, which lead to a matching theory that
exploits fuzzy sets or probabilistic logic with maximum entropy semantics. It
will be shown that added fuzzy links can be captured by extending the
underlying lattice.
",1,0,0,0,0,0
19700,Boötes-HiZELS: an optical to near-infrared survey of emission-line galaxies at $\bf z=0.4-4.7$,"  We present a sample of $\sim 1000$ emission line galaxies at $z=0.4-4.7$ from
the $\sim0.7$deg$^2$ High-$z$ Emission Line Survey (HiZELS) in the Boötes
field identified with a suite of six narrow-band filters at $\approx 0.4-2.1$
$\mu$m. These galaxies have been selected on their Ly$\alpha$ (73), {\sc [Oii]}
(285), H$\beta$/{\sc [Oiii]} (387) or H$\alpha$ (362) emission-line, and have
been classified with optical to near-infrared colours. A subsample of 98
sources have reliable redshifts from multiple narrow-band (e.g. [O{\sc
ii}]-H$\alpha$) detections and/or spectroscopy. In this survey paper, we
present the observations, selection and catalogs of emitters. We measure number
densities of Ly$\alpha$, [O{\sc ii}], H$\beta$/{\sc [Oiii]} and H$\alpha$ and
confirm strong luminosity evolution in star-forming galaxies from $z\sim0.4$ to
$\sim 5$, in agreement with previous results. To demonstrate the usefulness of
dual-line emitters, we use the sample of dual [O{\sc ii}]-H$\alpha$ emitters to
measure the observed [O{\sc ii}]/H$\alpha$ ratio at $z=1.47$. The observed
[O{\sc ii}]/H$\alpha$ ratio increases significantly from 0.40$\pm0.01$ at
$z=0.1$ to 0.52$\pm0.05$ at $z=1.47$, which we attribute to either decreasing
dust attenuation with redshift, or due to a bias in the (typically)
fiber-measurements in the local Universe which only measure the central kpc
regions. At the bright end, we find that both the H$\alpha$ and Ly$\alpha$
number densities at $z\approx2.2$ deviate significantly from a Schechter form,
following a power-law. We show that this is driven entirely by an increasing
X-ray/AGN fraction with line-luminosity, which reaches $\approx 100$ \% at
line-luminosities $L\gtrsim3\times10^{44}$ erg s$^{-1}$.
",0,1,0,0,0,0
19701,Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting,"  Tensor factorization with hard and/or soft constraints has played an
important role in signal processing and data analysis. However, existing
algorithms for constrained tensor factorization have two drawbacks: (i) they
require matrix-inversion; and (ii) they cannot (or at least is very difficult
to) handle structured regularizations. We propose a new tensor factorization
algorithm that circumvents these drawbacks. The proposed method is built upon
alternating optimization, and each subproblem is solved by a primal-dual
splitting algorithm, yielding an efficient and flexible algorithmic framework
to constrained tensor factorization. The advantages of the proposed method over
a state-of-the-art constrained tensor factorization algorithm, called AO-ADMM,
are demonstrated on regularized nonnegative tensor factorization.
",1,0,0,0,0,0
19702,Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes,"  In this paper, we propose a replay attack spoofing detection system for
automatic speaker verification using multitask learning of noise classes. We
define the noise that is caused by the replay attack as replay noise. We
explore the effectiveness of training a deep neural network simultaneously for
replay attack spoofing detection and replay noise classification. The
multi-task learning includes classifying the noise of playback devices,
recording environments, and recording devices as well as the spoofing
detection. Each of the three types of the noise classes also includes a genuine
class. The experiment results on the ASVspoof2017 datasets demonstrate that the
performance of our proposed system is improved by 30% relatively on the
evaluation set.
",1,0,0,1,0,0
19703,On One Property of Tikhonov Regularization Algorithm,"  For linear inverse problem with Gaussian random noise we show that Tikhonov
regularization algorithm is minimax in the class of linear estimators and is
asymptotically minimax in the sense of sharp asymptotic in the class of all
estimators. The results are valid if some a priori information on a Fourier
coefficients of solution is provided. For trigonometric basis this a priori
information implies that the solution belongs to a ball in Besov space
$B^r_{2\infty}$.
",0,0,1,1,0,0
19704,Observation of Extra Photon Recoil in a Distorted Optical Field,"  Light carries momentum which induces on atoms a recoil for each photon
absorbed. In vacuum, for a monochromatic beam of frequency $\nu$, the global
momentum per photon is bounded by general principles and is smaller than $h
\nu/c$ leading to a limit on the recoil. However, locally this limit can be
broken. In this paper, we give a general formula to calculate the recoil in
vacuum. We show that in a laser beam with a distorted optical field, there are
regions where the recoil can be higher than this limit. Using atoms placed in
those regions we are able to measure directly the extra recoil.
",0,1,0,0,0,0
19705,Weak Poincaré Inequalities for Convergence Rate of Degenerate Diffusion Processes,"  For a contraction $C_0$-semigroup on a separable Hilbert space, the decay
rate is estimated by using the weak Poincaré inequalities for the symmetric
and anti-symmetric part of the generator. As applications, non-exponential
convergence rate is characterized for a class of degenerate diffusion
processes, so that the study of hypocoercivity is extended. Concrete examples
are presented.
",0,0,1,0,0,0
19706,rTraceroute: Réunion Traceroute Visualisation,"  Traceroute is the main tools to explore Internet path. It provides limited
information about each node along the path. However, Traceroute cannot go
further in statistics analysis, or \emph{Man-Machine Interface (MMI)}.
Indeed, there are no graphical tool that is able to draw all paths used by IP
routes. We present a new tool that can handle more than 1,000 Traceroute
results, map them, identify graphically MPLS links, get information of usage of
all routes (in percent) to improve the knowledge between countries' links.
rTraceroute want to go deeper in usage of atomic traces. In this paper, we will
discuss the concept of rTraceroute and present some example of usage.
",1,0,0,0,0,0
19707,Effects of heterogeneity in site-site couplings for tight-binding models on scale-invariant structures,"  We studied the thermodynamic behaviors of non-interacting bosons and fermions
trapped by a scale-invariant branching structure of adjustable degree of
heterogeneity. The full energy spectrum in tight-binding approximation was
analytically solved . We found that the log-periodic oscillation of the
specific heat for Fermi gas depended on the heterogeneity of hopping. Also, low
dimensional Bose-Einstein condensation occurred only for non-homogeneous setup.
",0,1,0,0,0,0
19708,Are generative deep models for novelty detection truly better?,"  Many deep models have been recently proposed for anomaly detection. This
paper presents comparison of selected generative deep models and classical
anomaly detection methods on an extensive number of non--image benchmark
datasets. We provide statistical comparison of the selected models, in many
configurations, architectures and hyperparamaters. We arrive to conclusion that
performance of the generative models is determined by the process of selection
of their hyperparameters. Specifically, performance of the deep generative
models deteriorates with decreasing amount of anomalous samples used in
hyperparameter selection. In practical scenarios of anomaly detection, none of
the deep generative models systematically outperforms the kNN.
",0,0,0,1,0,0
19709,Minimizing the waiting time for a one-way shuttle service,"  Consider a terminal in which users arrive continuously over a finite period
of time at a variable rate known in advance. A fleet of shuttles has to carry
the users over a fixed trip. What is the shuttle schedule that minimizes their
waiting time? This is the question addressed in the present paper. We propose
efficient algorithms for several variations of this question with proven
performance guarantees. The techniques used are of various types (convex
optimization, shortest paths,...). The paper ends with numerical experiments
showing that most of our algorithms behave also well in practice.
",0,0,1,0,0,0
19710,Two weight bump conditions for matrix weights,"  In this paper we extend the theory of two weight, $A_p$ bump conditions to
the setting of matrix weights. We prove two matrix weight inequalities for
fractional maximal operators, fractional and singular integrals, sparse
operators and averaging operators. As applications we prove quantitative, one
weight estimates, in terms of the matrix $A_p$ constant, for singular
integrals, and prove a Poincaré inequality related to those that appear in
the study of degenerate elliptic PDEs.
",0,0,1,0,0,0
19711,Legendrian ribbons and strongly quasipositive links in an open book,"  We show that a link in an open book can be realized as a strongly
quasipositive braid if and only if it bounds a Legendrian ribbon with respect
to the associated contact structure. This generalizes a result due to Baader
and Ishikawa for links in the three-sphere. We highlight some related
techniques for determining whether or not a link is strongly quasipositive,
emphasizing applications to fibered links and satellites.
",0,0,1,0,0,0
19712,Δ-cumulants in terms of moments,"  The \Delta-convolution of real probability measures, introduced by Bożejko,
generalizes both free and boolean convolutions. It is linearized by the
\Delta-cumulants, and Yoshida gave a combinatorial formula for moments in terms
of \Delta-cumulants, that implicitly defines the latter. It relies on the
definition of an appropriate weight on noncrossing partitions. We give here two
different expressions for the \Delta-cumulants: the first one is a simple
variant of Lagrange inversion formula, and the second one is a combinatorial
inversion of Yoshida's formula involving Schröder trees.
",0,0,1,0,0,0
19713,A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds,"  When classifying point clouds, a large amount of time is devoted to the
process of engineering a reliable set of features which are then passed to a
classifier of choice. Generally, such features - usually derived from the
3D-covariance matrix - are computed using the surrounding neighborhood of
points. While these features capture local information, the process is usually
time-consuming, and requires the application at multiple scales combined with
contextual methods in order to adequately describe the diversity of objects
within a scene. In this paper we present a 1D-fully convolutional network that
consumes terrain-normalized points directly with the corresponding spectral
data,if available, to generate point-wise labeling while implicitly learning
contextual features in an end-to-end fashion. Our method uses only the
3D-coordinates and three corresponding spectral features for each point.
Spectral features may either be extracted from 2D-georeferenced images, as
shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted
directly for passive-derived point clouds,i.e. from muliple-view imagery. We
train our network by splitting the data into square regions, and use a pooling
layer that respects the permutation-invariance of the input points. Evaluated
using the ISPRS 3D Semantic Labeling Contest, our method scored second place
with an overall accuracy of 81.6%. We ranked third place with a mean F1-score
of 63.32%, surpassing the F1-score of the method with highest accuracy by
1.69%. In addition to labeling 3D-point clouds, we also show that our method
can be easily extended to 2D-semantic segmentation tasks, with promising
initial results.
",1,0,0,1,0,0
19714,Boundedness properties in function spaces,"  Some boundedness properties of function spaces (considered as topological
groups) are studied.
",0,0,1,0,0,0
19715,Linear-size CDAWG: new repetition-aware indexing and grammar compression,"  In this paper, we propose a novel approach to combine \emph{compact directed
acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to
an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be
represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log
n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m
\log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of
all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the
lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and
$occ$ is the number of occurrences of the pattern in $T$. The repetitiveness
measure $\tilde e_T$ is known to be much smaller than the text length $n$ for
highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m +
occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which
improves the pattern matching time of Belazzougui et al.'s run-length
BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here,
$e_T^r$ is the number of right extensions of maximal repeats in $T$. As a
byproduct, our result gives a way of constructing an SLP of size $O(\tilde
e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.
",1,0,0,0,0,0
19716,Topic Lifecycle on Social Networks: Analyzing the Effects of Semantic Continuity and Social Communities,"  Topic lifecycle analysis on Twitter, a branch of study that investigates
Twitter topics from their birth through lifecycle to death, has gained immense
mainstream research popularity. In the literature, topics are often treated as
one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
in a short time span or (c) a latent concept space captured by advanced text
analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
two approaches are not capable of recognizing topics where different users use
different hashtags to express the same concept (semantically related), while
the third approach misses out the user's explicit intent expressed via
hashtags. In our work, we use a word embedding based approach to cluster
different hashtags together, and the temporal concurrency of the hashtag
usages, thus forming topics (a semantically and temporally related group of
hashtags).We present a novel analysis of topic lifecycles with respect to
communities. We characterize the participation of social communities in the
topic clusters, and analyze the lifecycle of topic clusters with respect to
such participation. We derive first-of-its-kind novel insights with respect to
the complex evolution of topics over communities and time: temporal morphing of
topics over hashtags within communities, how the hashtags die in some
communities but morph into some other hashtags in some other communities (that,
it is a community-level phenomenon), and how specific communities adopt to
specific hashtags. Our work is fundamental in the space of topic lifecycle
modeling and understanding in communities: it redefines our understanding of
topic lifecycles and shows that the social boundaries of topic lifecycles are
deeply ingrained with community behavior.
",1,0,0,0,0,0
19717,Unstable Footwear as a Speed-Dependent Noise-Based Training Gear to Exercise Inverted Pendulum Motion During Walking,"  Previous research on unstable footwear has suggested that it may induce
plantar mechanical noise during walking. The purpose of this study was to
explore whether unstable footwear could be considered as a noise-based training
gear to exercise body center of mass (CoM) motion during walking or not. Ground
reaction forces were collected among 24 healthy young women walking at speeds
between 3 and 6 km h-1 with control running shoes and unstable rocker-bottom
shoes. The external mechanical work, the recovery of mechanical energy of the
CoM during and within the step cycles, and the phase shift between potential
and kinetic energy curves of the CoM were computed. Our findings support the
idea that unstable rocker-bottom footwear could serve as a speed-dependent
noise- based training gear to exercise CoM motion during walking. At slow
speed, it acts as a stochastic resonance or facilitator, whereas at brisk speed
it acts as a constraint.
",0,1,0,0,0,0
19718,Gaussian autoregressive process with dependent innovations. Some asymptotic results,"  In this paper we introduce a modified version of a gaussian standard
first-order autoregressive process where we allow for a dependence structure
between the state variable $Y_{t-1}$ and the next innovation $\xi_t$. We call
this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze
the moment and temporal dependence properties of the new model. After proving
that the OLS estimator does not consistently estimate the autoregressive
parameter, we introduce an infeasible estimator and we provide its
$\sqrt{T}$-asymptotic normality.
",0,0,1,1,0,0
19719,Structured Peer Learning Program - An Innovative Approach to Computer Science Education,"  Structured Peer Learning (SPL) is a form of peer-based supplemental
instruction that focuses on mentoring, guidance, and development of technical,
communication, and social skills in both the students receiving assistance and
the students in teaching roles. This paper explores the methodology, efficacy,
and reasoning behind the practical realization of a SPL program designed to
increase student knowledge and success in undergraduate Computer Science
courses. Students expressed an increased level of comfort when asking for help
from student teachers versus traditional educational resources, historically
showed an increased average grade in lower-level courses, and felt that the
program positively impacted their desire to continue in or switch to a Computer
major. Additionally, results indicated that advances in programming, analytical
thinking, and abstract analysis skills were evident in not only the students
but also the student teachers, suggesting a strong bidirectional flow of
knowledge.
",1,0,0,0,0,0
19720,Analysis of the possibility for time-optimal control of the scanning system of the GREEN-WAKE's project lidar,"  The monograph represents analysis of the possibility for time-optimal control
of a prototype of an aircraft-mounted scanning-imaging system of a Light
Detection and Ranging based wake vortex detection system. The study is a part
of the research project ""Demonstration of Light Detection and Ranging based
wake vortex detection system incorporating an atmospheric hazard map"" or
GREEN-WAKE (Project ID 213254) of the European Union Seventh Framework Program
for Research and Technological Development. The scanning system comprises two
light mirror actuators. The study is decomposed into several group of problems.
The first and second groups consider the mathematical models of the scanning
system and the mirror actuators. The third group of problems deals with the
design of closed loop tracking control systems of both the large and small
mirror actuators. The control of each one system is synthesized as a near
time-optimal control of the precise linear model of the mirror actuator. The
control algorithms realize the state of the art method for synthesis of
time-optimal control of any order for a class of linear time-optimal control
problems developed in the author's dissertation. The last discovers and
theoretically proves new properties of a class of linear time-optimal control
problems. From the point of view of the control synthesis algorithms the main
advantage is that the time-optimal control is produced by a multistage
procedure within the class of problems but without the need of describing the
hyper-surfaces of switching over. The fourth group of problems considers
modeling the real scan picture but with inclusion also of the Coulomb's
friction model. The fifth group of problems investigates the ways of
improvement of the real scan picture. As a result an excellent repetition and
clearness of the scanning alongside with symmetry and matching the scan pattern
are seen.
",1,0,0,0,0,0
19721,Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples,"  Recently, researchers have discovered that the state-of-the-art object
classifiers can be fooled easily by small perturbations in the input
unnoticeable to human eyes. It is also known that an attacker can generate
strong adversarial examples if she knows the classifier parameters. Conversely,
a defender can robustify the classifier by retraining if she has access to the
adversarial examples. We explain and formulate this adversarial example problem
as a two-player continuous zero-sum game, and demonstrate the fallacy of
evaluating a defense or an attack as a static problem. To find the best
worst-case defense against whitebox attacks, we propose a continuous minimax
optimization algorithm. We demonstrate the minimax defense with two types of
attack classes -- gradient-based and neural network-based attacks. Experiments
with the MNIST and the CIFAR-10 datasets demonstrate that the defense found by
numerical minimax optimization is indeed more robust than non-minimax defenses.
We discuss directions for improving the result toward achieving robustness
against multiple types of attack classes.
",1,0,0,1,0,0
19722,From Attention to Participation: Reviewing and Modelling Engagement with Computers,"  Over the last decades, the Internet and mobile technology have consolidated
the digital as a public sphere of life. Designers are asked to create engaging
digital experiences. However, in some cases engagement is seen as a
psychological state, while in others it emphasizes a participative vein. In
this paper, I review and discuss both and propose a new definition to clarify
the concept engagement with computers. Thus, engagement is a quality of an
active connection between a user and a computing product, either a website or a
mobile phone app. Studying it requires understanding a set of aspects like the
user's affect, motivation and attention, as well as the product's design,
content and composition. Finally, I propose explaining these concepts aligned
with engagement and integrate them into a preliminary model to measure the
manifestations.
",1,0,0,0,0,0
19723,"Circularizing Planet Nine through dynamical friction with an extended, cold planetesimal belt","  Unexpected clustering in the orbital elements of minor bodies beyond the
Kuiper belt has led to speculations that our solar system actually hosts nine
planets, the eight established plus a hypothetical ""Planet Nine"". Several
recent studies have shown that a planet with a mass of about 10 Earth masses on
a distant eccentric orbit with perihelion far beyond the Kuiper belt could
create and maintain this clustering. The evolutionary path resulting in an
orbit such as the one suggested for Planet Nine is nevertheless not easily
explained. Here we investigate whether a planet scattered away from the
giant-planet region could be lifted to an orbit similar to the one suggested
for Planet Nine through dynamical friction with a cold, distant planetesimal
belt. Recent simulations of planetesimal formation via the streaming
instability suggest that planetesimals can readily form beyond 100au. We
explore this circularisation by dynamical friction with a set of numerical
simulations. We find that a planet that is scattered from the region close to
Neptune onto an eccentric orbit has a 20-30% chance of obtaining an orbit
similar to that of Planet Nine after 4.6Gyr. Our simulations also result in
strong or partial clustering of the planetesimals; however, whether or not this
clustering is observable depends on the location of the inner edge of the
planetesimal belt. If the inner edge is located at 200au the degree of
clustering amongst observable objects is significant.
",0,1,0,0,0,0
19724,Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,"  We introduce two tactics to attack agents trained by deep reinforcement
learning algorithms using adversarial examples, namely the strategically-timed
attack and the enchanting attack. In the strategically-timed attack, the
adversary aims at minimizing the agent's reward by only attacking the agent at
a small subset of time steps in an episode. Limiting the attack activity to
this subset helps prevent detection of the attack by the agent. We propose a
novel method to determine when an adversarial example should be crafted and
applied. In the enchanting attack, the adversary aims at luring the agent to a
designated target state. This is achieved by combining a generative model and a
planning algorithm: while the generative model predicts the future states, the
planning algorithm generates a preferred sequence of actions for luring the
agent. A sequence of adversarial examples is then crafted to lure the agent to
take the preferred sequence of actions. We apply the two tactics to the agents
trained by the state-of-the-art deep reinforcement learning algorithm including
DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much
reward as the uniform attack (i.e., attacking at every time step) does by
attacking the agent 4 times less often. Our enchanting attack lures the agent
toward designated target states with a more than 70% success rate. Videos are
available at this http URL
",1,0,0,1,0,0
19725,Differentially Private Distributed Learning for Language Modeling Tasks,"  One of the big challenges in machine learning applications is that training
data can be different from the real-world data faced by the algorithm. In
language modeling, users' language (e.g. in private messaging) could change in
a year and be completely different from what we observe in publicly available
data. At the same time, public data can be used for obtaining general knowledge
(i.e. general model of English). We study approaches to distributed fine-tuning
of a general model on user private data with the additional requirements of
maintaining the quality on the general data and minimization of communication
costs. We propose a novel technique that significantly improves prediction
quality on users' language compared to a general model and outperforms gradient
compression methods in terms of communication efficiency. The proposed
procedure is fast and leads to an almost 70% perplexity reduction and 8.7
percentage point improvement in keystroke saving rate on informal English
texts. We also show that the range of tasks our approach is applicable to is
not limited by language modeling only. Finally, we propose an experimental
framework for evaluating differential privacy of distributed training of
language models and show that our approach has good privacy guarantees.
",1,0,0,0,0,0
19726,Tensor Train Neighborhood Preserving Embedding,"  In this paper, we propose a Tensor Train Neighborhood Preserving Embedding
(TTNPE) to embed multi-dimensional tensor data into low dimensional tensor
subspace. Novel approaches to solve the optimization problem in TTNPE are
proposed. For this embedding, we evaluate novel trade-off gain among
classification, computation, and dimensionality reduction (storage) for
supervised learning. It is shown that compared to the state-of-the-arts tensor
embedding methods, TTNPE achieves superior trade-off in classification,
computation, and dimensionality reduction in MNIST handwritten digits and
Weizmann face datasets.
",1,0,0,1,0,0
19727,Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach,"  Three dimensional (3D) topology optimization problems always involve huge
numbers of Degrees of Freedom (DOFs) in finite element analysis (FEA) and
design variables in numerical optimization, respectively. This will inevitably
lead to large computational efforts in the solution process. In the present
paper, an efficient and explicit topology optimization approach which can
reduce not only the number of design variables but also the number of degrees
of freedom in FEA is proposed based on the Moving Morphable Voids (MMVs)
solution framework. This is achieved by introducing a set of geometry
parameters (e.g., control points of B-spline surfaces) to describe the boundary
of a structure explicitly and removing the unnecessary DOFs from the FE model
at every step of numerical optimization. Numerical examples demonstrate that
the proposed approach does can overcome the bottleneck problems associated with
a 3D topology optimization problem in a straightforward way and enhance the
solution efficiency significantly.
",0,1,0,0,0,0
19728,The index of compact simple Lie groups,"  Let M be an irreducible Riemannian symmetric space. The index i(M) of M is
the minimal codimension of a (non-trivial) totally geodesic submanifold of M.
The purpose of this note is to determine the index i(M) for all irreducible
Riemannian symmetric spaces M of type (II) and (IV).
",0,0,1,0,0,0
19729,Cell Tracking via Proposal Generation and Selection,"  Microscopy imaging plays a vital role in understanding many biological
processes in development and disease. The recent advances in automation of
microscopes and development of methods and markers for live cell imaging has
led to rapid growth in the amount of image data being captured. To efficiently
and reliably extract useful insights from these captured sequences, automated
cell tracking is essential. This is a challenging problem due to large
variation in the appearance and shapes of cells depending on many factors
including imaging methodology, biological characteristics of cells, cell matrix
composition, labeling methodology, etc. Often cell tracking methods require a
sequence-specific segmentation method and manual tuning of many tracking
parameters, which limits their applicability to sequences other than those they
are designed for. In this paper, we propose 1) a deep learning based cell
proposal method, which proposes candidates for cells along with their scores,
and 2) a cell tracking method, which links proposals in adjacent frames in a
graphical model using edges representing different cellular events and poses
joint cell detection and tracking as the selection of a subset of cell and edge
proposals. Our method is completely automated and given enough training data
can be applied to a wide variety of microscopy sequences. We evaluate our
method on multiple fluorescence and phase contrast microscopy sequences
containing cells of various shapes and appearances from ISBI cell tracking
challenge, and show that our method outperforms existing cell tracking methods.
Code is available at: this https URL
",1,0,0,0,0,0
19730,Geometric Properties of Isostables and Basins of Attraction of Monotone Systems,"  In this paper, we study geometric properties of basins of attraction of
monotone systems. Our results are based on a combination of monotone systems
theory and spectral operator theory. We exploit the framework of the Koopman
operator, which provides a linear infinite-dimensional description of nonlinear
dynamical systems and spectral operator-theoretic notions such as eigenvalues
and eigenfunctions. The sublevel sets of the dominant eigenfunction form a
family of nested forward-invariant sets and the basin of attraction is the
largest of these sets. The boundaries of these sets, called isostables, allow
studying temporal properties of the system. Our first observation is that the
dominant eigenfunction is increasing in every variable in the case of monotone
systems. This is a strong geometric property which simplifies the computation
of isostables. We also show how variations in basins of attraction can be
bounded under parametric uncertainty in the vector field of monotone systems.
Finally, we study the properties of the parameter set for which a monotone
system is multistable. Our results are illustrated on several systems of two to
four dimensions.
",1,0,1,0,0,0
19731,Elongation and shape changes in organisms with cell walls: a dialogue between experiments and models,"  The generation of anisotropic shapes occurs during morphogenesis of almost
all organisms. With the recent renewal of the interest in mechanical aspects of
morphogenesis, it has become clear that mechanics contributes to anisotropic
forms in a subtle interaction with various molecular actors. Here, we consider
plants, fungi, oomycetes, and bacteria, and we review the mechanisms by which
elongated shapes are generated and maintained. We focus on theoretical models
of the interplay between growth and mechanics, in relation with experimental
data, and discuss how models may help us improve our understanding of the
underlying biological mechanisms.
",0,0,0,0,1,0
19732,Localic completion of uniform spaces,"  We extend the notion of localic completion of generalised metric spaces by
Steven Vickers to the setting of generalised uniform spaces. A generalised
uniform space (gus) is a set X equipped with a family of generalised metrics on
X, where a generalised metric on X is a map from the product of X to the upper
reals satisfying zero self-distance law and triangle inequality.
For a symmetric generalised uniform space, the localic completion lifts its
generalised uniform structure to a point-free generalised uniform structure.
This point-free structure induces a complete generalised uniform structure on
the set of formal points of the localic completion that gives the standard
completion of the original gus with Cauchy filters.
We extend the localic completion to a full and faithful functor from the
category of locally compact uniform spaces into that of overt locally compact
completely regular formal topologies. Moreover, we give an elementary
characterisation of the cover of the localic completion of a locally compact
uniform space that simplifies the existing characterisation for metric spaces.
These results generalise the corresponding results for metric spaces by Erik
Palmgren.
Furthermore, we show that the localic completion of a symmetric gus is
equivalent to the point-free completion of the uniform formal topology
associated with the gus.
We work in Aczel's constructive set theory CZF with the Regular Extension
Axiom. Some of our results also require Countable Choice.
",1,0,1,0,0,0
19733,Kinetic inhibition of MHD-shocks in the vicinity of a parallel magnetic field,"  According to magnetohydrodynamics (MHD), the encounter of two collisional
magnetized plasmas at high velocity gives rise to shock waves. Investigations
conducted so far have found that the same conclusion still holds in the case of
collisionless plasmas. For the case of a flow-aligned field, MHD stipulates
that the field and the fluid are disconnected, so that the shock produced is
independent of the field. We present a violation of this MHD prediction when
considering the encounter of two cold pair plasmas along a flow-aligned
magnetic field. As the guiding magnetic field grows, isotropization is
progressively suppressed, resulting in a strong influence of the field on the
resulting structure. A micro-physics analysis allows to understand the
mechanisms at work. Particle-in-cell simulations also support our conclusions
and show that the results are not restricted to a strictly parallel field.
",0,1,0,0,0,0
19734,Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things,"  In this paper, we propose Squeezed Convolutional Variational AutoEncoder
(SCVAE) for anomaly detection in time series data for Edge Computing in
Industrial Internet of Things (IIoT). The proposed model is applied to labeled
time series data from UCI datasets for exact performance evaluation, and
applied to real world data for indirect model performance comparison. In
addition, by comparing the models before and after applying Fire Modules from
SqueezeNet, we show that model size and inference times are reduced while
similar levels of performance is maintained.
",1,0,0,0,0,0
19735,Global Weisfeiler-Lehman Graph Kernels,"  Most state-of-the-art graph kernels only take local graph properties into
account, i.e., the kernel is computed with regard to properties of the
neighborhood of vertices or other small substructures. On the other hand,
kernels that do take global graph propertiesinto account may not scale well to
large graph databases. Here we propose to start exploring the space between
local and global graph kernels, striking the balance between both worlds.
Specifically, we introduce a novel graph kernel based on the $k$-dimensional
Weisfeiler-Lehman algorithm. Unfortunately, the $k$-dimensional
Weisfeiler-Lehman algorithm scales exponentially in $k$. Consequently, we
devise a stochastic version of the kernel with provable approximation
guarantees using conditional Rademacher averages. On bounded-degree graphs, it
can even be computed in constant time. We support our theoretical results with
experiments on several graph classification benchmarks, showing that our
kernels often outperform the state-of-the-art in terms of classification
accuracies.
",1,0,0,1,0,0
19736,An analytical Model which Determines the Apparent T1 for Modified Look-Locker Inversion Recovery (MOLLI) -- Analysis of the Longitudinal Relaxation under the Influence of Discontinuous Balanced and Spoiled Gradient Echo Readouts,"  Quantitative nuclear magnetic resonance imaging (MRI) shifts more and more
into the focus of clinical research. Especially determination of relaxation
times without/and with contrast agents becomes the foundation of tissue
characterization, e.g. in cardiac MRI for myocardial fibrosis. Techniques which
assess longitudinal relaxation times rely on repetitive application of readout
modules, which are interrupted by free relaxation periods, e.g. the Modified
Look-Locker Inversion Recovery = MOLLI sequence. These discontinuous sequences
reveal an apparent relaxation time, and, by techniques extrapolated from
continuous readout sequences, the real T1 is determined. What is missing is a
rigorous analysis of the dependence of the apparent relaxation time on its real
partner, readout sequence parameters and biological parameters as heart rate.
This is provided in this paper for the discontinuous balanced steady state free
precession (bSSFP) and spoiled gradient echo readouts. It turns out that the
apparente longitudinal relaxation rate is the time average of the relaxation
rates during the readout module, and free relaxation period. Knowing the heart
rate our results vice versa allow to determine the real T1 from its measured
apparent partner.
",0,1,0,0,0,0
19737,Updated Constraints on the Dark Matter Interpretation of CDMS-II-Si Data,"  We present an updated halo-dependent and halo-independent analysis of viable
light WIMP dark matter candidates which could account for the excess observed
in CDMS-II-Si. We include recent constraints from LUX, PandaX-II, and PICO-60,
as well as projected sensitivities for XENON1T, SuperCDMS SNOLAB, LZ, DARWIN,
DarkSide-20k, and PICO-250, on candidates with spin-independent isospin
conserving and isospin-violating interactions, and either elastic or exothermic
scattering. We show that there exist dark matter candidates which can explain
the CDMS-II-Si data and remain very marginally consistent with the null results
of all current experiments, however such models are highly tuned, making a dark
matter interpretation of CDMS-II-Si very unlikely. We find that these models
can only be ruled out in the future by an experiment comparable to LZ or
PICO-250.
",0,1,0,0,0,0
19738,"Emulating Simulations of Cosmic Dawn for 21cm Power Spectrum Constraints on Cosmology, Reionization, and X-ray Heating","  Current and upcoming radio interferometric experiments are aiming to make a
statistical characterization of the high-redshift 21cm fluctuation signal
spanning the hydrogen reionization and X-ray heating epochs of the universe.
However, connecting 21cm statistics to underlying physical parameters is
complicated by the theoretical challenge of modeling the relevant physics at
computational speeds quick enough to enable exploration of the high dimensional
and weakly constrained parameter space. In this work, we use machine learning
algorithms to build a fast emulator that mimics expensive simulations of the
21cm signal across a wide parameter space to high precision. We embed our
emulator within a Markov-Chain Monte Carlo framework, enabling it to explore
the posterior distribution over a large number of model parameters, including
those that govern the Epoch of Reionization, the Epoch of X-ray Heating, and
cosmology. As a worked example, we use our emulator to present an updated
parameter constraint forecast for the Hydrogen Epoch of Reionization Array
experiment, showing that its characterization of a fiducial 21cm power spectrum
will considerably narrow the allowed parameter space of reionization and
heating parameters, and could help strengthen Planck's constraints on
$\sigma_8$. We provide both our generalized emulator code and its
implementation specifically for 21cm parameter constraints as publicly
available software.
",0,1,0,0,0,0
19739,Fluctuations and Noise Signatures of Driven Magnetic Skyrmions,"  Magnetic skyrmions are particle-like objects with topologically-protected
stability which can be set into motion with an applied current. Using a
particle-based model we simulate current-driven magnetic skyrmions interacting
with random quenched disorder and examine the skyrmion velocity fluctuations
parallel and perpendicular to the direction of motion as a function of
increasing drive. We show that the Magnus force contribution to skyrmion
dynamics combined with the random pinning produces an isotropic effective
shaking temperature. As a result, the skyrmions form a moving crystal at large
drives instead of the moving smectic state observed in systems with a
negligible Magnus force where the effective shaking temperature is anisotropic.
We demonstrate that spectral analysis of the velocity noise fluctuations can be
used to identify dynamical phase transitions and to extract information about
the different dynamic phases, and show how the velocity noise fluctuations are
correlated with changes in the skyrmion Hall angle, transport features, and
skyrmion lattice structure.
",0,1,0,0,0,0
19740,Metallic maps between metallic Riemannian manifolds and constancy of certain maps,"  In this paper, we introduce metallic maps between metallic Riemannian
manifolds, provide an example and obtain certain conditions for such maps to be
totally geodesic. We also give a sufficient condition for a map between
metallic Riemannian manifolds to be harmonic map. Then we investigate the
constancy of certain maps between metallic Riemannian manifolds and various
manifolds by imposing the holomorphic-like condition. Moreover, we check the
reverse case and show that some such maps are constant if there is a condition
for this.
",0,0,1,0,0,0
19741,Hashing as Tie-Aware Learning to Rank,"  Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.
",1,0,0,1,0,0
19742,Models of compact stars on paraboloidal spacetime satisfying Karmarkar condition,"  A new exact solution of Einstein's field equations on the background of
paraboloidal spacetime using Karmarkar condition is reported. The physical
acceptability conditions of the model are investigated and found that the model
is compatible with a number of compact star candidates like Her X-1, LMC X-4,
EXO 1785-248, PSR J1903+327, Vela X-1 and PSR J1614-2230. A noteworthy feature
of the model is that it is geometrically significant and simple in form.
",0,1,0,0,0,0
19743,Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification,"  Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.
",1,0,0,1,0,0
19744,An Input Reconstruction Approach for Command Following in Linear MIMO Systems,"  The idea of posing a command following or tracking control problem as an
input reconstruction problem is explored in the paper. For a class of square
MIMO systems with known dynamics, by pretending that reference commands are
actual outputs of the system, input reconstruction methods can be used to
determine control action that will result in a system following desired
reference commands. A feedback controller which is a combination of an unbiased
state estimator and an input reconstructor that ensures unbiased tracking of
reference commands is proposed. Simulations and real-time implementation are
presented to demonstrate utility of the proposed idea. Conditions under which
proposed controller may be used for non-square systems are also discussed.
",1,0,0,0,0,0
19745,Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module,"  Wireless communication plays a vital role in the promising performance of
connected and automated vehicle (CAV) technology. This paper proposes a
Vissim-based microscopic traffic simulation framework with an analytical
dedicated short-range communication (DSRC) module for packet reception. Being
derived from ns-2, a packet-level network simulator, the DSRC probability
module takes into account the imperfect wireless communication that occurs in
real-world deployment. Four managed lane deployment strategies are evaluated
using the proposed framework. While the average packet reception rate is above
93\% among all tested scenarios, the results reveal that the reliability of the
vehicle-to-vehicle (V2V) communication can be influenced by the deployment
strategies. Additionally, the proposed framework exhibits desirable scalability
for traffic simulation and it is able to evaluate transportation-network-level
deployment strategies in the near future for CAV technologies.
",1,0,0,0,0,0
19746,Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network,"  This paper studies the synchronization of a finite number of Kuramoto
oscillators in a frequency-dependent bidirectional tree network. We assume that
the coupling strength of each link in each direction is equal to the product of
a common coefficient and the exogenous frequency of its corresponding head
oscillator. We derive a sufficient condition for the common coupling strength
in order to guarantee frequency synchronization in tree networks. Moreover, we
discuss the dependency of the obtained bound on both the graph structure and
the way that exogenous frequencies are distributed. Further, we present an
application of the obtained result by means of an event-triggered algorithm for
achieving frequency synchronization in a star network assuming that the common
coupling coefficient is given.
",1,0,0,0,0,0
19747,On Exchangeability in Network Models,"  We derive representation theorems for exchangeable distributions on finite
and infinite graphs using elementary arguments based on geometric and
graph-theoretic concepts. Our results elucidate some of the key differences,
and their implications, between statistical network models that are finitely
exchangeable and models that define a consistent sequence of probability
distributions on graphs of increasing size.
",0,0,1,1,0,0
19748,Replicator equation on networks with degree regular communities,"  The replicator equation is one of the fundamental tools to study evolutionary
dynamics in well-mixed populations. This paper contributes to the literature on
evolutionary graph theory, providing a version of the replicator equation for a
family of connected networks with communities, where nodes in the same
community have the same degree. This replicator equation is applied to the
study of different classes of games, exploring the impact of the graph
structure on the equilibria of the evolutionary dynamics.
",0,0,0,0,1,0
19749,Go game formal revealing by Ising model,"  Go gaming is a struggle for territory control between rival, black and white,
stones on a board. We model the Go dynamics in a game by means of the Ising
model whose interaction coefficients reflect essential rules and tactics
employed in Go to build long-term strategies. At any step of the game, the
energy functional of the model provides the control degree (strength) of a
player over the board. A close fit between predictions of the model with actual
games is obtained.
",1,0,0,0,0,0
19750,High resolution structural characterisation of laser-induced defect clusters inside diamond,"  Laser writing with ultrashort pulses provides a potential route for the
manufacture of three-dimensional wires, waveguides and defects within diamond.
We present a transmission electron microscopy (TEM) study of the intrinsic
structure of the laser modifications and reveal a complex distribution of
defects. Electron energy loss spectroscopy (EELS) indicates that the majority
of the irradiated region remains as $sp^3$ bonded diamond.
Electrically-conductive paths are attributed to the formation of multiple
nano-scale, $sp^2$-bonded graphitic wires and a network of strain-relieving
micro-cracks.
",0,1,0,0,0,0
19751,Neural networks for post-processing ensemble weather forecasts,"  Ensemble weather predictions require statistical post-processing of
systematic errors to obtain reliable and accurate probabilistic forecasts.
Traditionally, this is accomplished with distributional regression models in
which the parameters of a predictive distribution are estimated from a training
period. We propose a flexible alternative based on neural networks that can
incorporate nonlinear relationships between arbitrary predictor variables and
forecast distribution parameters that are automatically learned in a
data-driven way rather than requiring pre-specified link functions. In a case
study of 2-meter temperature forecasts at surface stations in Germany, the
neural network approach significantly outperforms benchmark post-processing
methods while being computationally more affordable. Key components to this
improvement are the use of auxiliary predictor variables and station-specific
information with the help of embeddings. Furthermore, the trained neural
network can be used to gain insight into the importance of meteorological
variables thereby challenging the notion of neural networks as uninterpretable
black boxes. Our approach can easily be extended to other statistical
post-processing and forecasting problems. We anticipate that recent advances in
deep learning combined with the ever-increasing amounts of model and
observation data will transform the post-processing of numerical weather
forecasts in the coming decade.
",0,0,0,1,0,0
19752,Power maps in finite groups,"  In recent work, Pomerance and Shparlinski have obtained results on the number
of cycles in the functional graph of the map $x \mapsto x^a$ in
$\mathbb{F}_p^*$. We prove similar results for other families of finite groups.
In particular, we obtain estimates for the number of cycles for cyclic groups,
symmetric groups, dihedral groups and $SL_2(\mathbb{F}_q)$. We also show that
the cyclic group of order $n$ minimizes the number of cycles among all
nilpotent groups of order $n$ for a fixed exponent. Finally, we pose several
problems.
",0,0,1,0,0,0
19753,"Permutation polynomials, fractional polynomials, and algebraic curves","  In this note we prove a conjecture by Li, Qu, Li, and Fu on permutation
trinomials over $\mathbb{F}_3^{2k}$. In addition, new examples and
generalizations of some families of permutation polynomials of
$\mathbb{F}_{3^k}$ and $\mathbb{F}_{5^k}$ are given. We also study permutation
quadrinomials of type $Ax^{q(q-1)+1} + Bx^{2(q-1)+1} + Cx^{q} + x$. Our method
is based on the investigation of an algebraic curve associated with a
{fractional polynomial} over a finite field.
",0,0,1,0,0,0
19754,Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition,"  Recently in speaker recognition, performance degradation due to the channel
domain mismatched condition has been actively addressed. However, the
mismatches arising from language is yet to be sufficiently addressed. This
paper proposes an approach which employs recursive whitening transformation to
mitigate the language mismatched condition. The proposed method is based on the
multiple whitening transformation, which is intended to remove un-whitened
residual components in the dataset associated with i-vector length
normalization. The experiments were conducted on the Speaker Recognition
Evaluation 2016 trials of which the task is non-English speaker recognition
using development dataset consist of both a large scale out-of-domain (English)
dataset and an extremely low-quantity in-domain (non-English) dataset. For
performance comparison, we develop a state-of- the-art system using deep neural
network and bottleneck feature, which is based on a phonetically aware model.
From the experimental results, along with other prior studies, effectiveness of
the proposed method on language mismatched condition is validated.
",1,0,0,0,0,0
19755,Learning Representations for Soft Skill Matching,"  Employers actively look for talents having not only specific hard skills but
also various soft skills. To analyze the soft skill demands on the job market,
it is important to be able to detect soft skill phrases from job advertisements
automatically. However, a naive matching of soft skill phrases can lead to
false positive matches when a soft skill phrase, such as friendly, is used to
describe a company, a team, or another entity, rather than a desired candidate.
In this paper, we propose a phrase-matching-based approach which
differentiates between soft skill phrases referring to a candidate vs.
something else. The disambiguation is formulated as a binary text
classification problem where the prediction is made for the potential soft
skill based on the context where it occurs. To inform the model about the soft
skill for which the prediction is made, we develop several approaches,
including soft skill masking and soft skill tagging.
We compare several neural network based approaches, including CNN, LSTM and
Hierarchical Attention Model. The proposed tagging-based input representation
using LSTM achieved the highest recall of 83.92% on the job dataset when fixing
a precision to 95%.
",0,0,0,1,0,0
19756,Time-reversal and spatial reflection symmetry localization anomalies in (2+1)D topological phases of matter,"  We study a class of anomalies associated with time-reversal and spatial
reflection symmetry in (2+1)D topological phases of matter. In these systems,
the topological quantum numbers of the quasiparticles, such as the fusion rules
and braiding statistics, possess a $\mathbb{Z}_2$ symmetry which can be
associated with either time-reversal (denoted $\mathbb{Z}_2^{\bf T})$ or
spatial reflections. Under this symmetry, correlation functions of all Wilson
loop operators in the low energy topological quantum field theory (TQFT) are
invariant. However, the theories that we study possess a severe anomaly
associated with the failure to consistently localize the symmetry action to the
quasiparticles, precluding even defining a notion of symmetry
fractionalization. We present simple sufficient conditions which determine when
$\mathbb{Z}_2^{\bf T}$ symmetry localization anomalies exist. We present an
infinite series of TQFTs with such anomalies, some examples of which include
USp$(4)_2$ and SO$(4)_4$ Chern-Simons (CS) theory. The theories that we find
with these $\mathbb{Z}_2^{\bf T}$ anomalies can be obtained by gauging the
unitary $\mathbb{Z}_2$ subgroup of a different TQFT with a $\mathbb{Z}_4^{\bf
T}$ symmetry. We show that the anomaly can be resolved in several ways: (1) the
true symmetry of the theory is $\mathbb{Z}_4^{\bf T}$, or (2) the theory can be
considered to be a theory of fermions, with ${\bf T}^2 = (-1)^{N_f}$
corresponding to fermion parity. Finally, we demonstrate that theories with the
$\mathbb{Z}_2^{\bf T}$ localization anomaly can be compatible with
$\mathbb{Z}_2^{\bf T}$ if they are ""pseudo-realized"" at the surface of a (3+1)D
symmetry-enriched topological phase. The ""pseudo-realization"" refers to the
fact that the bulk (3+1)D system is described by a dynamical $\mathbb{Z}_2$
gauge theory and thus only a subset of the quasiparticles are confined to the
surface.
",0,1,0,0,0,0
19757,Extending the modeling of the anisotropic galaxy power spectrum to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,"  We present a new model for the redshift-space power spectrum of galaxies and
demonstrate its accuracy in modeling the monopole, quadrupole, and hexadecapole
of the galaxy density field down to scales of $k = 0.4 \ h\mathrm{Mpc}^{-1}$.
The model describes the clustering of galaxies in the context of a halo model
and the clustering of the underlying halos in redshift space using a
combination of Eulerian perturbation theory and $N$-body simulations. The
modeling of redshift-space distortions is done using the so-called distribution
function approach. The final model has 13 free parameters, and each parameter
is physically motivated rather than a nuisance parameter, which allows the use
of well-motivated priors. We account for the Finger-of-God effect from centrals
and both isolated and non-isolated satellites rather than using a single
velocity dispersion to describe the combined effect. We test and validate the
accuracy of the model on several sets of high-fidelity $N$-body simulations, as
well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data
set. The suite of simulations covers a range of cosmologies and galaxy bias
models, providing a rigorous test of the level of theoretical systematics
present in the model. The level of bias in the recovered values of $f \sigma_8$
is found to be small. When including scales to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,
we find 15-30\% gains in the statistical precision of $f \sigma_8$ relative to
$k = 0.2 \ h\mathrm{Mpc}^{-1}$ and a roughly 10-15\% improvement for the
perpendicular Alcock-Paczynski parameter $\alpha_\perp$. Using the BOSS DR12
CMASS mocks as a benchmark for comparison, we estimate an uncertainty on $f
\sigma_8$ that is $\sim$10-20\% larger than other similar Fourier-space RSD
models in the literature that use $k \leq 0.2 \ h\mathrm{Mpc}^{-1}$, suggesting
that these models likely have a too-limited parametrization.
",0,1,0,0,0,0
19758,Finite rank and pseudofinite groups,"  It is proven that an infinite finitely generated group cannot be elementarily
equivalent to an ultraproduct of finite groups of a given Prüfer rank.
Furthermore, it is shown that an infinite finitely generated group of finite
Prüfer rank is not pseudofinite.
",0,0,1,0,0,0
19759,Thermographic measurements of the spin Peltier effect in metal/yttrium-iron-garnet junction systems,"  The spin Peltier effect (SPE), heat-current generation due to spin-current
injection, in various metal (Pt, W, and Au single layers and Pt/Cu
bilayer)/ferrimagnetic insulator (yttrium iron garnet: YIG) junction systems
has been investigated by means of a lock-in thermography (LIT) method. The SPE
is excited by a spin current across the metal/YIG interface, which is generated
by applying a charge current to the metallic layer via the spin Hall effect.
The LIT method enables the thermal imaging of the SPE free from the
Joule-heating contribution. Importantly, we observed spin-current-induced
temperature modulation not only in the Pt/YIG and W/YIG systems but also in the
Au/YIG and Pt/Cu/YIG systems, excluding the possible contamination by anomalous
Ettingshausen effects due to proximity-induced ferromagnetism near the
metal/YIG interface. As demonstrated in our previous study, the SPE signals are
confined only in the vicinity of the metal/YIG interface; we buttress this
conclusion by reducing a spatial blur due to thermal diffusion in an infrared
emission layer on the sample surface used for the LIT measurements. We also
found that the YIG-thickness dependence of the SPE is similar to that of the
spin Seebeck effect measured in the same Pt/YIG sample, implying the reciprocal
relation between them.
",0,1,0,0,0,0
19760,"Number-theoretic aspects of 1D localization: ""popcorn function"" with Lifshitz tails and its continuous approximation by the Dedekind $η$","  We discuss the number-theoretic properties of distributions appearing in
physical systems when an observable is a quotient of two independent
exponentially weighted integers. The spectral density of ensemble of linear
polymer chains distributed with the law $\sim f^L$ ($0<f<1$), where $L$ is the
chain length, serves as a particular example. At $f\to 1$, the spectral density
can be expressed through the discontinuous at all rational points, Thomae
(""popcorn"") function. We suggest a continuous approximation of the popcorn
function, based on the Dedekind $\eta$-function near the real axis. Moreover,
we provide simple arguments, based on the ""Euclid orchard"" construction, that
demonstrate the presence of Lifshitz tails, typical for the 1D Anderson
localization, at the spectral edges. We emphasize that the ultrametric
structure of the spectral density is ultimately connected with number-theoretic
relations on asymptotic modular functions. We also pay attention to connection
of the Dedekind $\eta$-function near the real axis to invariant measures of
some continued fractions studied by Borwein and Borwein in 1993.
",0,1,1,0,0,0
19761,Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates,"  In this paper, we describe a phenomenon, which we named ""super-convergence"",
where neural networks can be trained an order of magnitude faster than with
standard training methods. The existence of super-convergence is relevant to
understanding why deep networks generalize well. One of the key elements of
super-convergence is training with one learning rate cycle and a large maximum
learning rate. A primary insight that allows super-convergence training is that
large learning rates regularize the training, hence requiring a reduction of
all other forms of regularization in order to preserve an optimal
regularization balance. We also derive a simplification of the Hessian Free
optimization method to compute an estimate of the optimal learning rate.
Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
datasets, and resnet, wide-resnet, densenet, and inception architectures. In
addition, we show that super-convergence provides a greater boost in
performance relative to standard training when the amount of labeled training
data is limited. The architectures and code to replicate the figures in this
paper are available at github.com/lnsmith54/super-convergence. See
this http URL for an application of
super-convergence to win the DAWNBench challenge (see
this https URL).
",1,0,0,1,0,0
19762,Space of initial conditions for a cubic Hamiltonian system,"  In this paper we perform the analysis that leads to the space of initial
conditions for the Hamiltonian system $q' = p^2 + zq + \alpha$, $p' = -q^2 - zp
- \beta$, studied by the author in an earlier article. By compactifying the
phase space of the system from $\mathbb{C}^2$ to $\mathbb{CP}^2$ three base
points arise in the standard coordinate charts covering the complex projective
space. Each of these is removed by a sequence of three blow-ups, a construction
to regularise the system at these points. The resulting space, where the
exceptional curves introduced after the first and second blow-up are removed,
is the so-called Okamoto's space of initial conditions for this system which,
at every point, defines a regular initial value problem in some coordinate
chart of the space.
",0,1,1,0,0,0
19763,Electronic properties of WS$_2$ on epitaxial graphene on SiC(0001),"  This work reports an electronic and micro-structural study of an appealing
system for optoelectronics: tungsten disulphide WS$_2$ on epitaxial graphene
(EG) on SiC(0001). The WS$_2$ is grown via chemical vapor deposition (CVD) onto
the EG. Low-energy electron diffraction (LEED) measurements assign the
zero-degree orientation as the preferential azimuthal alignment for WS$_2$/EG.
The valence-band (VB) structure emerging from this alignment is investigated by
means of photoelectron spectroscopy measurements, with both high space and
energy resolution. We find that the spin-orbit splitting of monolayer WS$_2$ on
graphene is of 462 meV, larger than what is reported to date for other
substrates. We determine the value of the work function for the WS$_2$/EG to be
4.5$\pm$0.1 eV. A large shift of the WS$_2$ VB maximum is observed as well ,
due to the lowering of the WS$_2$ work function caused by the donor-like
interfacial states of EG. Density functional theory (DFT) calculations carried
out on a coincidence supercell confirm the experimental band structure to an
excellent degree. X-ray photoemission electron microscopy (XPEEM) measurements
performed on single WS$_2$ crystals confirm the van der Waals nature of the
interface coupling between the two layers. In virtue of its band alignment and
large spin-orbit splitting, this system gains strong appeal for optical
spin-injection experiments and opto-spintronic applications in general.
",0,1,0,0,0,0
19764,Electrical transport and optical band gap of NiFe$_\textrm{2}$O$_\textrm{x}$ thin films,"  We fabricated NiFe$_\textrm{2}$O$_\textrm{x}$ thin films on
MgAl$_2$O$_4$(001) substrates by reactive dc magnetron co-sputtering varying
the oxygen partial pressure during deposition. The fabrication of a variable
material with oxygen deficiency leads to controllable electrical and optical
properties which would be beneficial for the investigations of the transport
phenomena and would, therefore, promote the use of such materials in spintronic
and spin caloritronic applications. We used several characterization techniques
in order to investigate the film properties, focusing on their structural,
magnetic, electrical, and optical properties. From the electrical resistivity
measurements we obtained the conduction mechanisms that govern the systems in
high and low temperature regimes, extracting low thermal activation energies
which unveil extrinsic transport mechanisms. The thermal activation energy
decreases in the less oxidized samples revealing the pronounced contribution of
a large amount of electronic states localized in the band gap to the electrical
conductivity. Hall effect measurements showed the mixed-type semiconducting
character of our films. The optical band gaps were determined via
ultraviolet-visible spectroscopy. They follow a similar trend as the thermal
activation energy, with lower band gap values in the less oxidized samples.
",0,1,0,0,0,0
19765,Towards parallelizable sampling-based Nonlinear Model Predictive Control,"  This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
""optimal"" solution, even when using a small number of samples.
",1,0,0,0,0,0
19766,Quantum decoherence from entanglement during inflation,"  We study the primary entanglement effect on the decoherence of fields reduced
density matrix which are in interaction with another fields or independent mode
functions. We show that the primary entanglement has a significant role in
decoherence of the system quantum state. We find that the existence of
entanglement could couple dynamical equations coming from Schrödinger
equation. We show if one wants to see no effect of the entanglement parameter
in decoherence then interaction terms in Hamiltonian can not be independent
from each other. Generally, including the primary entanglement destroys the
independence of the interaction terms. Our results could be generalized to
every scalar quantum field theory with a well defined quantization in a given
curved space time.
",0,1,0,0,0,0
19767,Breast density classification with deep convolutional neural networks,"  Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the limits of this task with a data set coming from over
200,000 breast cancer screening exams. We use this data to train and evaluate a
strong convolutional neural network classifier. In a reader study, we find that
our model can perform this task comparably to a human expert.
",1,0,0,1,0,0
19768,Spatial Integration by a Dielectric Slab Waveguide and its Planar Graphene-based Counterpart,"  Motivated by the recent progress in analog computing [Science 343, 160
(2014)], a new approach to perform spatial integration is presented using a
dielectric slab waveguide. Our approach is indeed based on the fact that the
transmission coefficient of a simple dielectric slab waveguide at its mode
excitation angle matches the Green's function of first order integration.
Inspired by the mentioned dielectric-based integrator, we further demonstrate
its graphene-based counterpart. The latter is not only reconfigurable but also
highly miniaturized in contrast to the previously reported designs [Opt.
Commun. 338, 457 (2015)]. Such integrators have the potential to be used in
ultrafast analog computation and signal processing.
",0,1,0,0,0,0
19769,Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,"  A crucial challenge in image-based modeling of biomedical data is to identify
trends and features that separate normality and pathology. In many cases, the
morphology of the imaged object exhibits continuous change as it deviates from
normality, and thus a generative model can be trained to model this
morphological continuum. Moreover, given side information that correlates to
certain trend in morphological change, a latent variable model can be
regularized such that its latent representation reflects this side information.
In this work, we use the Wasserstein Auto-encoder to model this pathology
continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to
enforce dependency between certain latent features and the provided side
information. We experimentally show that the model can provide disentangled and
interpretable latent representations and also generate a continuum of
morphological changes that corresponds to change in the side information.
",1,0,0,1,0,0
19770,Control Problems with Vanishing Lie Bracket Arising from Complete Odd Circulant Evolutionary Games,"  We study an optimal control problem arising from a generalization of
rock-paper-scissors in which the number of strategies may be selected from any
positive odd number greater than 1 and in which the payoff to the winner is
controlled by a control variable $\gamma$. Using the replicator dynamics as the
equations of motion, we show that a quasi-linearization of the problem admits a
special optimal control form in which explicit dynamics for the controller can
be identified. We show that all optimal controls must satisfy a specific second
order differential equation parameterized by the number of strategies in the
game. We show that as the number of strategies increases, a limiting case
admits a closed form for the open-loop optimal control. In performing our
analysis we show necessary conditions on an optimal control problem that allow
this analytic approach to function.
",1,0,0,0,0,0
19771,Teaching DevOps in Corporate Environments: An experience report,"  This paper describes our experience of training a team of developers of an
East-European phone service provider. The training experience was structured in
two sessions of two days each conducted in different weeks with a gap of about
fifteen days. The first session was dedicated to the Continuous Integration
Delivery Pipeline, and the second on Agile methods. We summarize the activity,
its preparation and delivery and draw some conclusions out of it on our
mistakes and how future session should be addressed.
",1,0,0,0,0,0
19772,WKB solutions of difference equations and reconstruction by the topological recursion,"  The purpose of this article is to analyze the connection between
Eynard-Orantin topological recursion and formal WKB solutions of a
$\hbar$-difference equation: $\Psi(x+\hbar)=\left(e^{\hbar\frac{d}{dx}}\right)
\Psi(x)=L(x;\hbar)\Psi(x)$ with $L(x;\hbar)\in GL_2( (\mathbb{C}(x))[\hbar])$.
In particular, we extend the notion of determinantal formulas and topological
type property proposed for formal WKB solutions of $\hbar$-differential systems
to this setting. We apply our results to a specific $\hbar$-difference system
associated to the quantum curve of the Gromov-Witten invariants of
$\mathbb{P}^1$ for which we are able to prove that the correlation functions
are reconstructed from the Eynard-Orantin differentials computed from the
topological recursion applied to the spectral curve $y=\cosh^{-1}\frac{x}{2}$.
Finally, identifying the large $x$ expansion of the correlation functions,
proves a recent conjecture made by B. Dubrovin and D. Yang regarding a new
generating series for Gromov-Witten invariants of $\mathbb{P}^1$.
",0,1,1,0,0,0
19773,Diagrammatic Exciton Basis Theory of the Photophysics of Pentacene Dimers,"  Covalently linked acene dimers are of interest as candidates for
intramolecular singlet fission. We report many-electron calculations of the
energies and wavefunctions of the optical singlets, the lowest triplet exciton
and the triplet-triplet biexciton, as well as the final states of excited state
absorptions from these states in a family of phenyl-linked pentacene dimers.
While it is difficult to distinguish between the triplet and the
triplet-triplet from their transient absorptions in the 500-600 nm region, by
comparing theoretical transient absorption spectra against published and
unpublished experimental transient absorptions in the near and mid infrared we
conclude that the end product of photoexcitation in these particular
bipentacenes is the bound triplet-triplet and not free triplets. We predict
additional transient absorptions at even longer wavelengths, beyond 1500 nm, to
the equivalent of the classic 2$^1$A$_g^-$ in linear polyenes.
",0,1,0,0,0,0
19774,Developing and evaluating an interactive tutorial on degenerate perturbation theory,"  We discuss an investigation of student difficulties with degenerate
perturbation theory (DPT) carried out in advanced quantum mechanics courses by
administering free-response and multiple-choice questions and conducting
individual interviews with students. We find that students share many common
difficulties related to this topic. We used the difficulties found via research
as resources to develop and evaluate a Quantum Interactive Learning Tutorial
(QuILT) which strives to help students develop a functional understanding of
DPT. We discuss the development of the DPT QuILT and its preliminary evaluation
in the undergraduate and graduate courses.
",0,1,0,0,0,0
19775,Time optimal sampled-data controls for heat equations,"  In this paper, we first design a time optimal control problem for the heat
equation with sampled-data controls, and then use it to approximate a time
optimal control problem for the heat equation with distributed controls. Our
design is reasonable from perspective of sampled-data controls. And it might
provide a right way for the numerical approach of a time optimal distributed
control problem, via the corresponding semi-discretized (in time variable) time
optimal control problem.
The study of such a time optimal sampled-data control problem is not easy,
because it may have infinitely many optimal controls. We find connections among
this problem, a minimal norm sampled-data control problem and a minimization
problem. And obtain some properties on these problems. Based on these, we not
only build up error estimates for optimal time and optimal controls between the
time optimal sampled-data control problem and the time optimal distributed
control problem, in terms of the sampling period, but also prove that such
estimates are optimal in some sense.
",0,0,1,0,0,0
19776,Malware distributions and graph structure of the Web,"  Knowledge about the graph structure of the Web is important for understanding
this complex socio-technical system and for devising proper policies supporting
its future development. Knowledge about the differences between clean and
malicious parts of the Web is important for understanding potential treats to
its users and for devising protection mechanisms. In this study, we conduct
data science methods on a large crawl of surface and deep Web pages with the
aim to increase such knowledge. To accomplish this, we answer the following
questions. Which theoretical distributions explain important local
characteristics and network properties of websites? How are these
characteristics and properties different between clean and malicious
(malware-affected) websites? What is the prediction power of local
characteristics and network properties to classify malware websites? To the
best of our knowledge, this is the first large-scale study describing the
differences in global properties between malicious and clean parts of the Web.
In other words, our work is building on and bridging the gap between
\textit{Web science} that tackles large-scale graph representations and
\textit{Web cyber security} that is concerned with malicious activities on the
Web. The results presented herein can also help antivirus vendors in devising
approaches to improve their detection algorithms.
",1,0,0,0,0,0
19777,Depth creates no more spurious local minima,"  We show that for any convex differentiable loss function, a deep linear
network has no spurious local minima as long as it is true for the two layer
case. When applied to the quadratic loss, our result immediately implies the
powerful result in [Kawaguchi 2016] that there is no spurious local minima in
deep linear networks. Further, with the recent work [Zhou and Liang 2018], we
can remove all the assumptions in [Kawaguchi 2016]. Our proof is short and
elementary. It builds on the recent work of [Laurent and von Brecht 2018] and
uses a new rank one perturbation argument.
",1,0,0,1,0,0
19778,On the Application of ISO 26262 in Control Design for Automated Vehicles,"  Research on automated vehicles has experienced an explosive growth over the
past decade. A main obstacle to their practical realization, however, is a
convincing safety concept. This question becomes ever more important as more
sophisticated algorithms are used and the vehicle automation level increases.
The field of functional safety offers a systematic approach to identify
possible sources of risk and to improve the safety of a vehicle. It is based on
practical experience across the aerospace, process and other industries over
multiple decades. This experience is compiled in the functional safety standard
for the automotive domain, ISO 26262, which is widely adopted throughout the
automotive industry. However, its applicability and relevance for highly
automated vehicles is subject to a controversial debate. This paper takes a
critical look at the discussion and summarizes the main steps of ISO 26262 for
a safe control design for automated vehicles.
",1,0,0,0,0,0
19779,Semi-wavefront solutions in models of collective movements with density-dependent diffusivity,"  This paper deals with a nonhomogeneous scalar parabolic equation with
possibly degenerate diffusion term; the process has only one stationary state.
The equation can be interpreted as modeling collective movements (crowd
dynamics, for instance). We first prove the existence of semi-wavefront
solutions for every wave speed; their properties are investigated. Then, a
family of travelling wave solutions is constructed by a suitable combination of
the previous semi-wavefront solutions. Proofs exploit comparison-type
techniques and are carried out in the case of one spatial variable; the
extension to the general case is straightforward.
",0,0,1,0,0,0
19780,Self-Regulated Transport in Photonic Crystals with Phase-Changing Defects,"  Phase changing materials (PCM) are widely used for optical data recording,
sensing, all-optical switching, and optical limiting. Our focus here is on the
case when the change in the transmission characteristics of the optical
material is caused by the input light itself. Specifically, the light-induced
heating triggers the phase transition in the PCM. In this paper, using a
numerical example, we demonstrate that incorporating the PCM in a photonic
structure can lead to a dramatic modification of the effects of light-induced
phase transition, as compared to a stand-alone sample of the same PCM. Our
focus is on short pulses. We discuss some possible applications of such
phase-changing photonic structures for optical sensing and limiting.
",0,1,0,0,0,0
19781,Assessing randomness in case assignment: the case study of the Brazilian Supreme Court,"  Sortition, i.e., random appointment for public duty, has been employed by
societies throughout the years, especially for duties related to the judicial
system, as a firewall designated to prevent illegitimate interference between
parties in a legal case and agents of the legal system. In judicial systems of
modern western countries, random procedures are mainly employed to select the
jury, the court and/or the judge in charge of judging a legal case, so that
they have a significant role in the course of a case. Therefore, these random
procedures must comply with some principles, as statistical soundness; complete
auditability; open-source programming; and procedural, cryptographical and
computational security. Nevertheless, some of these principles are neglected by
some random procedures in judicial systems, that are, in some cases, performed
in secrecy and are not auditable by the involved parts. The assignment of cases
in the Brazilian Supreme Court (Supremo Tribunal Federal) is an example of such
procedures, for it is performed by a closed-source algorithm, unknown to the
public and to the parts involved in the judicial cases, that allegedly assign
the cases randomly to the justice chairs based on their caseload.
In this context, this article presents a review of how sortition has been
employed historically by societies, and discusses how Mathematical Statistics
may be applied to random procedures of the judicial system, as it has been
applied for almost a century on clinical trials, for example. Based on this
discussion, a statistical model for assessing randomness in case assignment is
proposed and applied to the Brazilian Supreme Court in order to shed light on
how this assignment process is performed by the closed-source algorithm.
Guidelines for random procedures are outlined and topics for further researches
presented.
",0,0,0,1,0,0
19782,Particle picture representation of the non-symmetric Rosenblatt process and Hermite processes of any order,"  We provide a particle picture representation for the non-symmetric Rosenblatt
process and for Hermite processes of any order, extending the result of
Bojdecki, Gorostiza and Talarczyk in~\cite{FILT}. We show that these processes
can be obtained as limits in the sense of finite-dimensional distributions of
certain functionals of a system of particles evolving according to symmetric
stable Lévy motions. In the case of $k$-Hermite processes the corresponding
functional involves $k$-intersection local time of symmetric stable Lévy
processes
",0,0,1,0,0,0
19783,Houdini: Fooling Deep Structured Prediction Models,"  Generating adversarial examples is a critical step for evaluating and
improving the robustness of learning machines. So far, most existing methods
only work for classification and are not designed to alter the true performance
measure of the problem at hand. We introduce a novel flexible approach named
Houdini for generating adversarial examples specifically tailored for the final
performance measure of the task considered, be it combinatorial and
non-decomposable. We successfully apply Houdini to a range of applications such
as speech recognition, pose estimation and semantic segmentation. In all cases,
the attacks based on Houdini achieve higher success rate than those based on
the traditional surrogates used to train the models while using a less
perceptible adversarial perturbation.
",1,0,0,1,0,0
19784,Don't relax: early stopping for convex regularization,"  We consider the problem of designing efficient regularization algorithms when
regularization is encoded by a (strongly) convex functional. Unlike classical
penalization methods based on a relaxation approach, we propose an iterative
method where regularization is achieved via early stopping. Our results show
that the proposed procedure achieves the same recovery accuracy as penalization
methods, while naturally integrating computational considerations. An empirical
analysis on a number of problems provides promising results with respect to the
state of the art.
",1,0,1,0,0,0
19785,Interplay between social influence and competitive strategical games in multiplex networks,"  We present a model that takes into account the coupling between evolutionary
game dynamics and social influence. Importantly, social influence and game
dynamics take place in different domains, which we model as different layers of
a multiplex network. We show that the coupling between these dynamical
processes can lead to cooperation in scenarios where the pure game dynamics
predicts defection. In addition, we show that the structure of the network
layers and the relation between them can further increase cooperation.
Remarkably, if the layers are related in a certain way, the system can reach a
polarized metastable state.These findings could explain the prevalence of
polarization observed in many social dilemmas.
",1,1,0,0,0,0
19786,Data Augmentation for Low-Resource Neural Machine Translation,"  The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequency words by generating new sentence pairs containing rare words in
new, synthetically created contexts. Experimental results on simulated
low-resource settings show that our method improves translation quality by up
to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
",1,0,0,0,0,0
19787,A Lagrangian approach to modeling heat flux driven close-contact melting,"  Close-contact melting refers to the process of a heat source melting its way
into a phase-change material. Of special interest is the close-contact melting
velocity, or more specifically the relative velocity between the heat source
and the phase-change material. In this work, we present a novel numerical
approach to simulate quasi-steady, heat flux driven close-contact melting. It
extends existing approaches found in the literature, and, for the first time,
allows to study the impact of a spatially varying heat flux distribution. We
will start by deriving the governing equations in a Lagrangian reference frame
fixed to the heat source. Exploiting the narrowness of the melt film enables us
to reduce the momentum balance to the Reynolds equation, which is coupled to
the energy balance via the velocity field. We particularize our derivation for
two simple, yet technically relevant geometries, namely a 3d circular disc and
a 2d planar heat source. An iterative solution procedure for the coupled system
is described in detail and discussed on the basis of a convergence study.
Furthermore, we present an extension to allow for rotational melting modes.
Various test cases demonstrate the proficiency of our method. In particular, we
will utilize the method to assess the efficiency of the close-contact melting
process and to quantify the model error introduced if convective losses are
neglected. Finally, we will draw conclusions and present an outlook to future
work.
",0,1,0,0,0,0
19788,Spectral Graph Analysis: A Unified Explanation and Modern Perspectives,"  Complex networks or graphs are ubiquitous in sciences and engineering:
biological networks, brain networks, transportation networks, social networks,
and the World Wide Web, to name a few. Spectral graph theory provides a set of
useful techniques and models for understanding `patterns of interconnectedness'
in a graph. Our prime focus in this paper is on the following question: Is
there a unified explanation and description of the fundamental spectral graph
methods? There are at least two reasons to be interested in this question.
Firstly, to gain a much deeper and refined understanding of the basic
foundational principles, and secondly, to derive rich consequences with
practical significance for algorithm design. However, despite half a century of
research, this question remains one of the most formidable open issues, if not
the core problem in modern network science. The achievement of this paper is to
take a step towards answering this question by discovering a simple, yet
universal statistical logic of spectral graph analysis. The prescribed
viewpoint appears to be good enough to accommodate almost all existing spectral
graph techniques as a consequence of just one single formalism and algorithm.
",0,0,1,1,0,0
19789,ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting,"  Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.
",0,0,0,1,0,0
19790,Extreme Dimension Reduction for Handling Covariate Shift,"  In the covariate shift learning scenario, the training and test covariate
distributions differ, so that a predictor's average loss over the training and
test distributions also differ. In this work, we explore the potential of
extreme dimension reduction, i.e. to very low dimensions, in improving the
performance of importance weighting methods for handling covariate shift, which
fail in high dimensions due to potentially high train/test covariate divergence
and the inability to accurately estimate the requisite density ratios. We first
formulate and solve a problem optimizing over linear subspaces a combination of
their predictive utility and train/test divergence within. Applying it to
simulated and real data, we show extreme dimension reduction helps sometimes
but not always, due to a bias introduced by dimension reduction.
",1,0,0,1,0,0
19791,Landau-Zener transitions for Majorana fermions,"  One-dimensional systems obtained as low-energy limits of hybrid
superconductor-topological insulator devices provide means of production,
transport, and destruction of Majorana bound states (MBSs) by variations of the
magnetic flux. When two or more pairs of MBSs are present in the intermediate
state, there is a possibility of a Landau-Zener transition, wherein even a slow
variation of the flux leads to production of a quasiparticle pair. We study
numerically a version of this process, with four MBSs produced and subsequently
destroyed, and find that, quite universally, the probability of quasiparticle
production in it is 50%. This implies that the effect may be a limiting factor
in applications requiring a high degree of quantum coherence.
",0,1,0,0,0,0
19792,Deep Hyperspherical Learning,"  Convolution as inner product has been the founding basis of convolutional
neural networks (CNNs) and the key to end-to-end visual representation
learning. Benefiting from deeper architectures, recent CNNs have demonstrated
increasingly strong representation abilities. Despite such improvement, the
increased depth and larger parameter space have also led to challenges in
properly training a network. In light of such challenges, we propose
hyperspherical convolution (SphereConv), a novel learning framework that gives
angular representations on hyperspheres. We introduce SphereNet, deep
hyperspherical convolution networks that are distinct from conventional inner
product based convolutional networks. In particular, SphereNet adopts
SphereConv as its basic convolution operator and is supervised by generalized
angular softmax loss - a natural loss formulation under SphereConv. We show
that SphereNet can effectively encode discriminative representation and
alleviate training difficulty, leading to easier optimization, faster
convergence and comparable (even better) classification accuracy over
convolutional counterparts. We also provide some theoretical insights for the
advantages of learning on hyperspheres. In addition, we introduce the learnable
SphereConv, i.e., a natural improvement over prefixed SphereConv, and
SphereNorm, i.e., hyperspherical learning as a normalization method.
Experiments have verified our conclusions.
",1,0,0,1,0,0
19793,Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching,"  This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at this http URL
",1,0,0,0,0,0
19794,Toward an enumerative geometry with quadratic forms,"  We develop various aspects of classical enumerative geometry, including Euler
characteristics and formulas for counting degenerate fibres in a pencil, with
the classical numerical formulas being replaced by identitites in the
Grothendieck-Witt group of quadratic forms with coefficients in the base-field.
",0,0,1,0,0,0
19795,Robust reputation-based ranking on multipartite rating networks,"  The spread of online reviews, ratings and opinions and its growing influence
on people's behavior and decisions boosted the interest to extract meaningful
information from this data deluge. Hence, crowdsourced ratings of products and
services gained a critical role in business, governments, and others. We
propose a new reputation-based ranking system utilizing multipartite rating
subnetworks, that clusters users by their similarities, using Kolmogorov
complexity. Our system is novel in that it reflects a diversity of
opinions/preferences by assigning possibly distinct rankings, for the same
item, for different groups of users. We prove the convergence and efficiency of
the system and show that it copes better with spamming/spurious users, and it
is more robust to attacks than state-of-the-art approaches.
",1,0,0,0,0,0
19796,Duality and upper bounds in optimal stochastic control governed by partial differential equations,"  A dual control problem is presented for the optimal stochastic control of a
system governed by partial differential equations. Relationships between the
optimal values of the original and the dual problems are investigated and two
duality theorems are proved. The dual problem serves to provide upper bounds
for the optimal and maximum value of the original one or even to give the
optimal value.
",0,0,1,0,0,0
19797,On Sampling from Massive Graph Streams,"  We propose Graph Priority Sampling (GPS), a new paradigm for order-based
reservoir sampling from massive streams of graph edges. GPS provides a general
way to weight edge sampling according to auxiliary and/or size variables so as
to accomplish various estimation goals of graph properties. In the context of
subgraph counting, we show how edge sampling weights can be chosen so as to
minimize the estimation variance of counts of specified sets of subgraphs. In
distinction with many prior graph sampling schemes, GPS separates the functions
of edge sampling and subgraph estimation. We propose two estimation frameworks:
(1) Post-Stream estimation, to allow GPS to construct a reference sample of
edges to support retrospective graph queries, and (2) In-Stream estimation, to
allow GPS to obtain lower variance estimates by incrementally updating the
subgraph count estimates during stream processing. Unbiasedness of subgraph
estimators is established through a new Martingale formulation of graph stream
order sampling, which shows that subgraph estimators, written as a product of
constituent edge estimators are unbiased, even when computed at different
points in the stream. The separation of estimation and sampling enables
significant resource savings relative to previous work. We illustrate our
framework with applications to triangle and wedge counting. We perform a
large-scale experimental study on real-world graphs from various domains and
types. GPS achieves high accuracy with less than 1% error for triangle and
wedge counting, while storing a small fraction of the graph with average update
times of a few microseconds per edge. Notably, for a large Twitter graph with
more than 260M edges, GPS accurately estimates triangle counts with less than
1% error, while storing only 40K edges.
",1,0,1,1,0,0
19798,"Optimal Investment, Demand and Arbitrage under Price Impact","  This paper studies the optimal investment problem with random endowment in an
inventory-based price impact model with competitive market makers. Our goal is
to analyze how price impact affects optimal policies, as well as both pricing
rules and demand schedules for contingent claims. For exponential market makers
preferences, we establish two effects due to price impact: constrained trading,
and non-linear hedging costs. To the former, wealth processes in the impact
model are identified with those in a model without impact, but with constrained
trading, where the (random) constraint set is generically neither closed nor
convex. Regarding hedging, non-linear hedging costs motivate the study of
arbitrage free prices for the claim. We provide three such notions, which
coincide in the frictionless case, but which dramatically differ in the
presence of price impact. Additionally, we show arbitrage opportunities, should
they arise from claim prices, can be exploited only for limited position sizes,
and may be ignored if outweighed by hedging considerations. We also show that
arbitrage inducing prices may arise endogenously in equilibrium, and that
equilibrium positions are inversely proportional to the market makers'
representative risk aversion. Therefore, large positions endogenously arise in
the limit of either market maker risk neutrality, or a large number of market
makers.
",0,0,0,0,0,1
19799,Amplitude Mediated Chimera States with Active and Inactive Oscillators,"  The emergence and nature of amplitude mediated chimera states,
spatio-temporal patterns of co-existing coherent and incoherent regions, are
investigated for a globally coupled system of active and inactive
Ginzburg-Landau oscillators. The existence domain of such states is found to
shrink and shift in parametric space as the fraction of inactive oscillators is
increased. The role of inactive oscillators is found to be two fold - they get
activated to form a separate region of coherent oscillations and in addition
decrease the common collective frequency of the coherent regions by their
presence. The dynamical origin of these effects is delineated through a
detailed bifurcation analysis of a reduced model equation that is based on a
mean field approximation. Our results may have practical implications for the
robustness of such states in biological or physical systems where age related
deterioration in the functionality of components can occur.
",0,1,0,0,0,0
19800,Astronomical random numbers for quantum foundations experiments,"  Photons from distant astronomical sources can be used as a classical source
of randomness to improve fundamental tests of quantum nonlocality,
wave-particle duality, and local realism through Bell's inequality and
delayed-choice quantum eraser tests inspired by Wheeler's cosmic-scale
Mach-Zehnder interferometer gedankenexperiment. Such sources of random numbers
may also be useful for information-theoretic applications such as key
distribution for quantum cryptography. Building on the design of an
""astronomical random-number generator"" developed for the recent ""cosmic Bell""
experiment [Handsteiner et al., Phys. Rev. Lett. 118, 060401 (2017)], in this
paper we report on the design and characterization of a device that, with
20-nanosecond latency, outputs a bit based on whether the wavelength of an
incoming photon is greater than or less than 700 nm. Using the one-meter
telescope at the Jet Propulsion Laboratory (JPL) Table Mountain Observatory, we
generated random bits from astronomical photons in both color channels from 50
stars of varying color and magnitude, and from 12 quasars with redshifts up to
$z = 3.9$. With stars, we achieved bit rates of $\sim 1 \times 10^6$ Hz /
m$^2$, limited by saturation for our single-photon detectors, and with quasars
of magnitudes between 12.9 and 16, we achieved rates between $\sim 10^2$ and $2
\times 10^3$ Hz /m$^2$. For bright quasars, the resulting bitstreams exhibit
sufficiently low amounts of statistical predictability as quantified by the
mutual information. In addition, a sufficiently high fraction of bits generated
are of true astronomical origin in order to address both the locality and
freedom-of-choice loopholes when used to set the measurement settings in a test
of the Bell-CHSH inequality.
",0,1,0,0,0,0
19801,Application of generative autoencoder in de novo molecular design,"  A major challenge in computational chemistry is the generation of novel
molecular structures with desirable pharmacological and physiochemical
properties. In this work, we investigate the potential use of autoencoder, a
deep learning methodology, for de novo molecular design. Various generative
autoencoders were used to map molecule structures into a continuous latent
space and vice versa and their performance as structure generator was assessed.
Our results show that the latent space preserves chemical similarity principle
and thus can be used for the generation of analogue structures. Furthermore,
the latent space created by autoencoders were searched systematically to
generate novel compounds with predicted activity against dopamine receptor type
2 and compounds similar to known active compounds not included in the training
set were identified.
",1,0,0,1,0,0
19802,Prospects for Measuring Cosmic Microwave Background Spectral Distortions in the Presence of Foregrounds,"  Measurements of cosmic microwave background spectral distortions have
profound implications for our understanding of physical processes taking place
over a vast window in cosmological history. Foreground contamination is
unavoidable in such measurements and detailed signal-foreground separation will
be necessary to extract cosmological science. We present MCMC-based spectral
distortion detection forecasts in the presence of Galactic and extragalactic
foregrounds for a range of possible experimental configurations, focusing on
the Primordial Inflation Explorer (PIXIE) as a fiducial concept. We consider
modifications to the baseline PIXIE mission (operating 12 months in distortion
mode), searching for optimal configurations using a Fisher approach. Using only
spectral information, we forecast an extended PIXIE mission to detect the
expected average non-relativistic and relativistic thermal Sunyaev-Zeldovich
distortions at high significance (194$\sigma$ and 11$\sigma$, respectively),
even in the presence of foregrounds. The $\Lambda$CDM Silk damping $\mu$-type
distortion is not detected without additional modifications of the instrument
or external data. Galactic synchrotron radiation is the most problematic source
of contamination in this respect, an issue that could be mitigated by combining
PIXIE data with future ground-based observations at low frequencies ($\nu <
15-30$GHz). Assuming moderate external information on the synchrotron spectrum,
we project an upper limit of $|\mu| < 3.6\times 10^{-7}$ (95\% c.l.), slightly
more than one order of magnitude above the fiducial $\Lambda$CDM signal from
the damping of small-scale primordial fluctuations, but a factor of $\simeq
250$ improvement over the current upper limit from COBE/FIRAS. This limit could
be further reduced to $|\mu| < 9.4\times 10^{-8}$ (95\% c.l.) with more
optimistic assumptions about low-frequency information. (Abridged)
",0,1,0,0,0,0
19803,The Fog of War: A Machine Learning Approach to Forecasting Weather on Mars,"  For over a decade, scientists at NASA's Jet Propulsion Laboratory (JPL) have
been recording measurements from the Martian surface as a part of the Mars
Exploration Rovers mission. One quantity of interest has been the opacity of
Mars's atmosphere for its importance in day-to-day estimations of the amount of
power available to the rover from its solar arrays. This paper proposes the use
of neural networks as a method for forecasting Martian atmospheric opacity that
is more effective than the current empirical model. The more accurate
prediction provided by these networks would allow operators at JPL to make more
accurate predictions of the amount of energy available to the rover when they
plan activities for coming sols.
",1,1,0,0,0,0
19804,Substrate inhibition imposes fitness penalty at high protein stability,"  Proteins are only moderately stable. It has long been debated whether this
narrow range of stabilities is solely a result of neutral drift towards lower
stability or purifying selection against excess stability is also at work - for
which no experimental evidence was found so far. Here we show that mutations
outside the active site in the essential E. coli enzyme adenylate kinase result
in stability-dependent increase in substrate inhibition by AMP, thereby
impairing overall enzyme activity at high stability. Such inhibition caused
substantial fitness defects not only in the presence of excess substrate but
also under physiological conditions. In the latter case, substrate inhibition
caused differential accumulation of AMP in the stationary phase for the
inhibition prone mutants. Further, we show that changes in flux through Adk
could accurately describe the variation in fitness effects. Taken together,
these data suggest that selection against substrate inhibition and hence excess
stability may have resulted in a narrow range of optimal stability observed for
modern proteins.
",0,0,0,0,1,0
19805,Exact relativistic Toda chain eigenfunctions from Separation of Variables and gauge theory,"  We provide a proposal, motivated by Separation of Variables and gauge theory
arguments, for constructing exact solutions to the quantum Baxter equation
associated to the $N$-particle relativistic Toda chain and test our proposal
against numerical results. Quantum Mechanical non-perturbative corrections,
essential in order to obtain a sensible solution, are taken into account in our
gauge theory approach by considering codimension two defects on curved
backgrounds (squashed $S^5$ and degenerate limits) rather than flat space; this
setting also naturally incorporates exact quantization conditions and energy
spectrum of the relativistic Toda chain as well as its modular dual structure.
",0,1,0,0,0,0
19806,Simple Compact Monotone Tree Drawings,"  A monotone drawing of a graph G is a straight-line drawing of G such that
every pair of vertices is connected by a path that is monotone with respect to
some direction.
Trees, as a special class of graphs, have been the focus of several papers
and, recently, He and He~\cite{mt:4} showed how to produce a monotone drawing
of an arbitrary $n$-vertex tree that is contained in a $12n \times 12n$ grid.
All monotone tree drawing algorithms that have appeared in the literature
consider rooted ordered trees and they draw them so that (i) the root of the
tree is drawn at the origin of the drawing, (ii) the drawing is confined in the
first quadrant, and (iii) the ordering/embedding of the tree is respected. In
this paper, we provide a simple algorithm that has the exact same
characteristics and, given an $n$-vertex rooted tree $T$, it outputs a monotone
drawing of $T$ that fits on a $n \times n$ grid.
For unrooted ordered trees, we present an algorithms that produces monotone
drawings that respect the ordering and fit in an $(n+1) \times (\frac{n}{2}
+1)$ grid, while, for unrooted non-ordered trees we produce monotone drawings
of good aspect ratio which fit on a grid of size at most $\left\lfloor
\frac{3}{4} \left(n+2\right)\right\rfloor \times \left\lfloor \frac{3}{4}
\left(n+2\right)\right\rfloor$.
",1,0,0,0,0,0
19807,Context-Free Path Querying by Matrix Multiplication,"  Graph data models are widely used in many areas, for example, bioinformatics,
graph databases. In these areas, it is often required to process queries for
large graphs. Some of the most common graph queries are navigational queries.
The result of query evaluation is a set of implicit relations between nodes of
the graph, i.e. paths in the graph. A natural way to specify these relations is
by specifying paths using formal grammars over the alphabet of edge labels. An
answer to a context-free path query in this approach is usually a set of
triples (A, m, n) such that there is a path from the node m to the node n,
whose labeling is derived from a non-terminal A of the given context-free
grammar. This type of queries is evaluated using the relational query
semantics. Another example of path query semantics is the single-path query
semantics which requires presenting a single path from the node m to the node
n, whose labeling is derived from a non-terminal A for all triples (A, m, n)
evaluated using the relational query semantics. There is a number of algorithms
for query evaluation which use these semantics but all of them perform poorly
on large graphs. One of the most common technique for efficient big data
processing is the use of a graphics processing unit (GPU) to perform
computations, but these algorithms do not allow to use this technique
efficiently. In this paper, we show how the context-free path query evaluation
using these query semantics can be reduced to the calculation of the matrix
transitive closure. Also, we propose an algorithm for context-free path query
evaluation which uses relational query semantics and is based on matrix
operations that make it possible to speed up computations by using a GPU.
",1,0,0,0,0,0
19808,Equivalence of sparse and Carleson coefficients for general sets,"  We remark that sparse and Carleson coefficients are equivalent for every
countable collection of Borel sets and hence, in particular, for dyadic
rectangles, the case relevant to the theory of bi-parameter singular integrals.
The key observation is that a dual refomulation by I. E. Verbitsky for
Carleson coefficients over dyadic cubes holds also for Carleson coefficients
over general sets. We give a simple proof for this reformulation.
",0,0,1,0,0,0
19809,Discovering Business Rules from Business Process Models,"  Discovering business rules from business process models are of advantage to
ensure the compliance of business processes with business rules. Furthermore it
provides the agility of business processes in case of business rules evolution.
Current approaches are limited on types of rules that can be discovered. This
paper analyses the expression power of some popular business process modelling
languages in embedding business rules in its presentation and provides
indicators to extract various types of business rules from business process
models.
",1,0,0,0,0,0
19810,Fast Learning and Prediction for Object Detection using Whitened CNN Features,"  We combine features extracted from pre-trained convolutional neural networks
(CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of
both: the high detection performance of CNNs, automatic feature engineering,
fast model learning from few training samples and efficient sliding-window
detection. The Adaptive Real-Time Object Detection System (ARTOS) has been
refactored broadly to be used in combination with Caffe for the experimental
studies reported in this work.
",1,0,0,0,0,0
19811,N-body simulations of planet formation via pebble accretion I: First Results,"  Context. Planet formation with pebbles has been proposed to solve a couple of
long-standing issues in the classical formation model. Some sophisticated
simulations have been done to confirm the efficiency of pebble accretion.
However, there has not been any global N-body simulations that compare the
outcomes of planet formation via pebble accretion with observed extrasolar
planetary systems. Aims. In this paper, we study the effects of a range of
initial parameters of planet formation via pebble accretion, and present the
first results of our simulations. Methods. We incorporate the pebble accretion
model by Ida et al. (2016) in the N-body code SyMBA (Duncan et al. 1998), along
with the effects of gas accretion, eccentricity and inclination damping and
planet migration in the disc. Results. We confirm that pebble accretion leads
to a variety of planetary systems, but have difficulty in reproducing observed
properties of exoplanetary systems, such as planetary mass, semimajor axis, and
eccentricity distributions. The main reason behind this is a too-efficient type
I migration, which sensitively depends on the disc model. However, our
simulations also lead to a few interesting predictions. First, we find that
formation efficiencies of planets depend on the stellar metallicities, not only
for giant planets, but also for Earths (Es) and Super-Earths (SEs). The
dependency for Es/SEs is subtle. Although higher metallicity environments lead
to faster formation of a larger number of Es/SEs, they also tend to be lost
later via dynamical instability. Second, our results indicate that a wide range
of bulk densities observed for Es and SEs is a natural consequence of dynamical
evolution of planetary systems. Third, the ejection trend of our simulations
suggest that one free-floating E/SE may be expected for two smaller-mass
planets.
",0,1,0,0,0,0
19812,Local character of Kim-independence,"  We show that NSOP$_{1}$ theories are exactly the theories in which
Kim-independence satisfies a form of local character. In particular, we show
that if $T$ is NSOP$_{1}$, $M\models T$, and $p$ is a type over $M$, then the
collection of elementary substructures of size $\left|T\right|$ over which $p$
does not Kim-fork is a club of $\left[M\right]^{\left|T\right|}$ and that this
characterizes NSOP$_{1}$.
We also present a new phenomenon we call dual local-character for
Kim-independence in NSOP$_{1}$-theories.
",0,0,1,0,0,0
19813,A Further Analysis of The Role of Heterogeneity in Coevolutionary Spatial Games,"  Heterogeneity has been studied as one of the most common explanations of the
puzzle of cooperation in social dilemmas. A large number of papers have been
published discussing the effects of increasing heterogeneity in structured
populations of agents, where it has been established that heterogeneity may
favour cooperative behaviour if it supports agents to locally coordinate their
strategies. In this paper, assuming an existing model of a heterogeneous
weighted network, we aim to further this analysis by exploring the relationship
(if any) between heterogeneity and cooperation. We adopt a weighted network
which is fully populated by agents playing both the Prisoner's Dilemma or the
Optional Prisoner's Dilemma games with coevolutionary rules, i.e., not only the
strategies but also the link weights evolve over time. Surprisingly, results
show that the heterogeneity of link weights (states) on their own does not
always promote cooperation; rather cooperation is actually favoured by the
increase in the number of overlapping states and not by the heterogeneity
itself. We believe that these results can guide further research towards a more
accurate analysis of the role of heterogeneity in social dilemmas.
",1,0,0,0,0,0
19814,Transport properties of the Azimuthal Magnetorotational Instability,"  The magnetorotational instability (MRI) is thought to be a powerful source of
turbulence in Keplerian accretion disks. Motivated by recent laboratory
experiments, we study the MRI driven by an azimuthal magnetic field in an
electrically conducting fluid sheared between two concentric rotating
cylinders. By adjusting the rotation rates of the cylinders, we approximate
angular velocity profiles $\omega \propto r^{q}$. We perform direct numerical
simulations of a steep profile close to the Rayleigh line $q \gtrsim -2 $ and a
quasi-Keplerian profile $q \approx -3/2$ and cover wide ranges of Reynolds
($Re\le 4\cdot10^4$) and magnetic Prandtl numbers ($0\le Pm \le 1$). In the
quasi-Keplerian case, the onset of instability depends on the magnetic Reynolds
number, with $Rm_c \approx 50$, and angular momentum transport scales as
$\sqrt{Pm} Re^2$ in the turbulent regime. The ratio of Maxwell to Reynolds
stresses is set by $Rm$. At the onset of instability both stresses have similar
magnitude, whereas the Reynolds stress vanishes or becomes even negative as
$Rm$ increases. For the profile close to the Rayleigh line, the instability
shares these properties as long as $Pm\gtrsim0.1$, but exhibits a markedly
different character if $Pm\rightarrow 0$, where the onset of instability is
governed by the Reynolds number, with $Re_c \approx 1250$, transport is via
Reynolds stresses and scales as $Re^2$. At intermediate $Pm=0.01$ we observe a
continuous transition from one regime to the other, with a crossover at
$Rm=\mathcal{O}(100)$. Our results give a comprehensive picture of angular
momentum transport of the MRI with an imposed azimuthal field.
",0,1,0,0,0,0
19815,New bounds for the Probability of Causation in Mediation Analysis,"  An individual has been subjected to some exposure and has developed some
outcome. Using data on similar individuals, we wish to evaluate, for this case,
the probability that the outcome was in fact caused by the exposure. Even with
the best possible experimental data on exposure and outcome, we typically can
not identify this ""probability of causation"" exactly, but we can provide
information in the form of bounds for it. Under appropriate assumptions, these
bounds can be tightened if we can make other observations (e.g., on
non-experimental cases), measure additional variables (e.g., covariates) or
measure complete mediators. In this work we propose new bounds for the case
that a third variable mediates partially the effect of the exposure on the
outcome.
",0,0,1,1,0,0
19816,Searching for a cosmological preferred direction with 147 rotationally supported galaxies,"  It is well known that the Milgrom's MOND (modified Newtonian dynamics)
explains well the mass discrepancy problem in galaxy rotation curves. The MOND
predicts a universal acceleration scale below which the Newtonian dynamics is
invalid yet. The universal acceleration scale we got from the SPARC dataset is
$g_†=1.02\times10^{-10} \rm m~s^{-2}$. Milgrom suggested that the
acceleration scale may be a fingerprint of cosmology on local dynamics and
related with the Hubble constant $g_†\sim cH_0$. In this paper, we use the
hemisphere comparison method with the SPARC dataset to investigate the spatial
anisotropy on the acceleration scale. We find that the hemisphere of the
maximum acceleration scale is in the direction $(l,b) =
({175.5^\circ}^{+6^\circ}_{-10^\circ}, {-6.5^\circ}^{+8^\circ}_{-3^\circ})$
with $g_{†,max}=1.10\times10^{-10} \rm m~s^{-2}$, while the hemisphere of
the minimum acceleration scale is in the opposite direction $(l,b) =
({355.5^\circ}^{+6^\circ}_{-10^\circ}, {6.5^\circ}^{+3^\circ}_{-8^\circ})$ with
$g_{†,min}=0.76\times10^{-10} \rm m~s^{-2}$. The maximum anisotropy level
reaches up to $0.37\pm0.04$. Robust tests present that such a level of
anisotropy can't be reproduced by a statistically isotropic data. In addition,
we show that the spatial anisotropy on the acceleration scale has little
correlation with the non-uniform distribution of the SPARC data points in sky.
We also find that the maximum anisotropy direction is close with other
cosmological preferred directions, especially the direction of the ""Australia
dipole"" for the fine structure constant.
",0,1,0,0,0,0
19817,MultiFIT: A Multivariate Multiscale Framework for Independence Tests,"  We present a framework for testing independence between two random vectors
that is scalable to massive data. We break down the multivariate test into
univariate tests of independence on a collection of $2\times 2$ contingency
tables, constructed by sequentially discretizing the sample space. This
transforms a complex problem that traditionally requires quadratic
computational complexity with respect to the sample size into one that scales
almost linearly with the sample size. We further consider the scenario when the
dimensionality of the random vectors grows large, in which case the curse of
dimensionality arises in the proposed framework through an explosion in the
number of univariate tests to be completed. To overcome this difficulty we
propose a data-adaptive version of our method that completes a fraction of the
univariate tests judged to be more likely to contain evidence for dependency.
We demonstrate the tremendous computational advantage of the algorithm in
comparison to existing approaches while achieving desirable statistical power
through an extensive simulation study. In addition, we illustrate how our
method can be used for learning the nature of the underlying dependency. We
demonstrate the use of our method through analyzing a data set from flow
cytometry.
",0,0,0,1,0,0
19818,Stability of algebraic varieties and Kahler geometry,"  This is a survey article, based on the author's lectures in the 2015 AMS
Summer Research Institute in Algebraic Geometry, and to appear in the
Proceedings.
",0,0,1,0,0,0
19819,Scratch iridescence: Wave-optical rendering of diffractive surface structure,"  The surface of metal, glass and plastic objects is often characterized by
microscopic scratches caused by manufacturing and/or wear. A closer look onto
such scratches reveals iridescent colors with a complex dependency on viewing
and lighting conditions. The physics behind this phenomenon is well understood;
it is caused by diffraction of the incident light by surface features on the
order of the optical wavelength. Existing analytic models are able to reproduce
spatially unresolved microstructure such as the iridescent appearance of
compact disks and similar materials. Spatially resolved scratches, on the other
hand, have proven elusive due to the highly complex wave-optical light
transport simulations needed to account for their appearance. In this paper, we
propose a wave-optical shading model based on non-paraxial scalar diffraction
theory to render this class of effects. Our model expresses surface roughness
as a collection of line segments. To shade a point on the surface, the
individual diffraction patterns for contributing scratch segments are computed
analytically and superimposed coherently. This provides natural transitions
from localized glint-like iridescence to smooth BRDFs representing the
superposition of many reflections at large viewing distances. We demonstrate
that our model is capable of recreating the overall appearance as well as
characteristic detail effects observed on real-world examples.
",1,0,0,0,0,0
19820,An oscillation criterion for delay differential equations with several non-monotone arguments,"  The oscillatory behavior of the solutions to a differential equation with
several non-monotone delay arguments and non-negative coefficients is studied.
A new sufficient oscillation condition, involving lim sup, is obtained. An
example illustrating the significance of the result is also given.
",0,0,1,0,0,0
19821,Item Recommendation with Variational Autoencoders and Heterogenous Priors,"  In recent years, Variational Autoencoders (VAEs) have been shown to be highly
effective in both standard collaborative filtering applications and extensions
such as incorporation of implicit feedback. We extend VAEs to collaborative
filtering with side information, for instance when ratings are combined with
explicit text feedback from the user. Instead of using a user-agnostic standard
Gaussian prior, we incorporate user-dependent priors in the latent VAE space to
encode users' preferences as functions of the review text. Taking into account
both the rating and the text information to represent users in this multimodal
latent space is promising to improve recommendation quality. Our proposed model
is shown to outperform the existing VAE models for collaborative filtering (up
to 29.41% relative improvement in ranking metric) along with other baselines
that incorporate both user ratings and text for item recommendation.
",0,0,0,1,0,0
19822,Boltzmann Exploration Done Right,"  Boltzmann exploration is a classic strategy for sequential decision-making
under uncertainty, and is one of the most standard tools in Reinforcement
Learning (RL). Despite its widespread use, there is virtually no theoretical
understanding about the limitations or the actual benefits of this exploration
scheme. Does it drive exploration in a meaningful way? Is it prone to
misidentifying the optimal actions or spending too much time exploring the
suboptimal ones? What is the right tuning for the learning rate? In this paper,
we address several of these questions in the classic setup of stochastic
multi-armed bandits. One of our main results is showing that the Boltzmann
exploration strategy with any monotone learning-rate sequence will induce
suboptimal behavior. As a remedy, we offer a simple non-monotone schedule that
guarantees near-optimal performance, albeit only when given prior access to key
problem parameters that are typically not available in practical situations
(like the time horizon $T$ and the suboptimality gap $\Delta$). More
importantly, we propose a novel variant that uses different learning rates for
different arms, and achieves a distribution-dependent regret bound of order
$\frac{K\log^2 T}{\Delta}$ and a distribution-independent bound of order
$\sqrt{KT}\log K$ without requiring such prior knowledge. To demonstrate the
flexibility of our technique, we also propose a variant that guarantees the
same performance bounds even if the rewards are heavy-tailed.
",1,0,0,1,0,0
19823,Deep reinforcement learning from human preferences,"  For sophisticated reinforcement learning (RL) systems to interact usefully
with real-world environments, we need to communicate complex goals to these
systems. In this work, we explore goals defined in terms of (non-expert) human
preferences between pairs of trajectory segments. We show that this approach
can effectively solve complex RL tasks without access to the reward function,
including Atari games and simulated robot locomotion, while providing feedback
on less than one percent of our agent's interactions with the environment. This
reduces the cost of human oversight far enough that it can be practically
applied to state-of-the-art RL systems. To demonstrate the flexibility of our
approach, we show that we can successfully train complex novel behaviors with
about an hour of human time. These behaviors and environments are considerably
more complex than any that have been previously learned from human feedback.
",1,0,0,1,0,0
19824,Entanglement spectroscopy on a quantum computer,"  We present a quantum algorithm to compute the entanglement spectrum of
arbitrary quantum states. The interesting universal part of the entanglement
spectrum is typically contained in the largest eigenvalues of the density
matrix which can be obtained from the lower Renyi entropies through the
Newton-Girard method. Obtaining the $p$ largest eigenvalues
($\lambda_1>\lambda_2\ldots>\lambda_p$) requires a parallel circuit depth of
$\mathcal{O}(p(\lambda_1/\lambda_p)^p)$ and $\mathcal{O}(p\log(N))$ qubits
where up to $p$ copies of the quantum state defined on a Hilbert space of size
$N$ are needed as the input. We validate this procedure for the entanglement
spectrum of the topologically-ordered Laughlin wave function corresponding to
the quantum Hall state at filling factor $\nu=1/3$. Our scaling analysis
exposes the tradeoffs between time and number of qubits for obtaining the
entanglement spectrum in the thermodynamic limit using finite-size digital
quantum computers. We also illustrate the utility of the second Renyi entropy
in predicting a topological phase transition and in extracting the localization
length in a many-body localized system.
",0,1,0,0,0,0
19825,Light Attenuation Length of High Quality Linear Alkyl Benzene as Liquid Scintillator Solvent for the JUNO Experiment,"  The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose
neutrino experiment with a 20 kt liquid scintillator detector designed to
determine the neutrino mass hierarchy, and measure the neutrino oscillation
parameters. Linear alkyl benzene (LAB) will be used as the solvent for the
liquid scintillation system in the central detector of JUNO. For this purpose,
we have prepared LAB samples, and have measured their light attenuation
lengths, with one achieving a length of 25.8 m, comparable to the diameter of
the JUNO detector.
",0,1,0,0,0,0
19826,OIL: Observational Imitation Learning,"  Recent work has explored the problem of autonomous navigation by imitating a
teacher and learning an end-to-end policy, which directly predicts controls
from raw images. However, these approaches tend to be sensitive to mistakes by
the teacher and do not scale well to other environments or vehicles. To this
end, we propose Observational Imitation Learning (OIL), a novel imitation
learning variant that supports online training and automatic selection of
optimal behavior by observing multiple imperfect teachers. We apply our
proposed methodology to the challenging problems of autonomous driving and UAV
racing. For both tasks, we utilize the Sim4CV simulator that enables the
generation of large amounts of synthetic training data and also allows for
online learning and evaluation. We train a perception network to predict
waypoints from raw image data and use OIL to train another network to predict
controls from these waypoints. Extensive experiments demonstrate that our
trained network outperforms its teachers, conventional imitation learning (IL)
and reinforcement learning (RL) baselines and even humans in simulation. The
project website is available at this https URL
",1,0,0,0,0,0
19827,A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation,"  State space models in which the system state is a finite set--called the
multi-object state--have generated considerable interest in recent years.
Smoothing for state space models provides better estimation performance than
filtering by using the full posterior rather than the filtering density. In
multi-object state estimation, the Bayes multi-object filtering recursion
admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
the multi-object posterior. We also propose an implementation of this so-called
multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
implementation.
",0,0,0,1,0,0
19828,On the Complexity of Sampling Nodes Uniformly from a Graph,"  We study a number of graph exploration problems in the following natural
scenario: an algorithm starts exploring an undirected graph from some seed
node; the algorithm, for an arbitrary node $v$ that it is aware of, can ask an
oracle to return the set of the neighbors of $v$. (In social network analysis,
a call to this oracle corresponds to downloading the profile page of user $v$
in a social network.) The goal of the algorithm is to either learn something
(e.g., average degree) about the graph, or to return some random function of
the graph (e.g., a uniform-at-random node), while accessing/downloading as few
nodes of the graph as possible. Motivated by practical applications, we study
the complexities of a variety of problems in terms of the graph's mixing time
and average degree -- two measures that are believed to be quite small in
real-world social networks, and that have often been used in the applied
literature to bound the performance of online exploration algorithms. Our main
result is that the algorithm has to access $\Omega\left(t_{\rm mix} d_{\rm avg}
\epsilon^{-2} \ln \delta^{-1}\right)$ nodes to obtain, with probability at
least $1-\delta$, an $\epsilon$-additive approximation of the average of a
bounded function on the nodes of a graph -- this lower bound matches the
performance of an algorithm that was proposed in the literature. We also give
tight bounds for the problem of returning a close-to-uniform-at-random node
from the graph. Finally, we give lower bounds for the problems of estimating
the average degree of the graph, and the number of nodes of the graph.
",1,0,0,0,0,0
19829,A structure theorem for almost low-degree functions on the slice,"  The Fourier-Walsh expansion of a Boolean function $f \colon \{0,1\}^n
\rightarrow \{0,1\}$ is its unique representation as a multilinear polynomial.
The Kindler-Safra theorem (2002) asserts that if in the expansion of $f$, the
total weight on coefficients beyond degree $k$ is very small, then $f$ can be
approximated by a Boolean-valued function depending on at most $O(2^k)$
variables.
In this paper we prove a similar theorem for Boolean functions whose domain
is the `slice' ${{[n]}\choose{pn}} = \{x \in \{0,1\}^n\colon \sum_i x_i =
pn\}$, where $0 \ll p \ll 1$, with respect to their unique representation as
harmonic multilinear polynomials. We show that if in the representation of
$f\colon {{[n]}\choose{pn}} \rightarrow \{0,1\}$, the total weight beyond
degree $k$ is at most $\epsilon$, where $\epsilon = \min(p, 1-p)^{O(k)}$, then
$f$ can be $O(\epsilon)$-approximated by a degree-$k$ Boolean function on the
slice, which in turn depends on $O(2^{k})$ coordinates. This proves a
conjecture of Filmus, Kindler, Mossel, and Wimmer (2015). Our proof relies on
hypercontractivity, along with a novel kind of a shifting procedure.
In addition, we show that the approximation rate in the Kindler-Safra theorem
can be improved from $\epsilon + \exp(O(k)) \epsilon^{1/4}$ to
$\epsilon+\epsilon^2 (2\ln(1/\epsilon))^k/k!$, which is tight in terms of the
dependence on $\epsilon$ and misses at most a factor of $2^{O(k)}$ in the
lower-order term.
",1,0,0,0,0,0
19830,Oxygen-vacancy driven electron localization and itinerancy in rutile-based TiO$_2$,"  Oxygen-deficient TiO$_2$ in the rutile structure as well as the Ti$_3$O$_5$
Magn{é}li phase is investigated within the charge self-consistent combination
of density functional theory (DFT) with dynamical mean-field theory (DMFT). It
is shown that an isolated oxygen vacancy (V$_{\rm O}$) in titanium dioxide is
not sufficient to metallize the system at low temperatures. In a semiconducting
phase, an in-gap state is identified at $\varepsilon_{\rm IG}^{\hfill}\sim
-0.75\,$eV\, in excellent agreement with experimental data. Band-like impurity
levels, resulting from a threefold V$_{\rm O}$-Ti coordination as well as
entangled $(t_{2g},e_g)$ states, become localized due to site-dependent
electronic correlations. Charge localization and strong orbital polarization
occur in the V$_{\rm O}$-near Ti ions, which details can be modified by a
variation of the correlated subspace. At higher oxygen vacancy concentration, a
correlated metal is stabilized in the Magn{é}li phase. A V$_{\rm O}$-defect
rutile structure of identical stoichiometry shows key differences in the
orbital-resolved character and the spectral properties. Charge
disproportionation is vital in the oxygen-deficient compounds, but obvious
metal-insulator transitions driven or sustained by charge order are not
identified.
",0,1,0,0,0,0
19831,3D printable multimaterial cellular auxetics with tunable stiffness,"  Auxetic materials are a novel class of mechanical metamaterials which exhibit
an interesting property of negative Poisson ratio by virtue of their
architecture rather than composition. It has been well established that a wide
range of negative Poisson ratio can be obtained by varying the geometry and
architecture of the cellular materials. However, the limited range of stiffness
values obtained from a given geometry restricts their applications. Research
trials have revealed that multi-material cellular designs have the capability
to generate range of stiffness values as per the requirement of application.
With the advancements in 3D printing, multi-material cellular designs can be
realized in practice. In this work, multi-material cellular designs are
investigated using finite element method. It was observed that introduction of
material gradient/distribution in the cell provides a means to tune cellular
stiffness as per the specific requirement. These results will aid in the design
of wearable auxetic impact protection devices which rely on stiffness gradients
and variable auxeticity.
",0,1,0,0,0,0
19832,MACS J0416.1-2403: Impact of line-of-sight structures on strong gravitational lensing modelling of galaxy clusters,"  Exploiting the powerful tool of strong gravitational lensing by galaxy
clusters to study the highest-redshift Universe and cluster mass distributions
relies on precise lens mass modelling. In this work, we present the first
attempt at modelling line-of-sight mass distribution in addition to that of the
cluster, extending previous modelling techniques that assume mass distributions
to be on a single lens plane. We focus on the Hubble Frontier Field cluster
MACS J0416.1-2403, and our multi-plane model reproduces the observed image
positions with a rms offset of ~0.53"". Starting from this best-fitting model,
we simulate a mock cluster that resembles MACS J0416.1-2403 in order to explore
the effects of line-of-sight structures on cluster mass modelling. By
systematically analysing the mock cluster under different model assumptions, we
find that neglecting the lensing environment has a significant impact on the
reconstruction of image positions (rms ~0.3""); accounting for line-of-sight
galaxies as if they were at the cluster redshift can partially reduce this
offset. Moreover, foreground galaxies are more important to include into the
model than the background ones. While the magnification factors of the lensed
multiple images are recovered within ~10% for ~95% of them, those ~5% that lie
near critical curves can be significantly affected by the exclusion of the
lensing environment in the models (up to a factor of ~200). In addition,
line-of-sight galaxies cannot explain the apparent discrepancy in the
properties of massive subhalos between MACS J0416.1-2403 and N-body simulated
clusters. Since our model of MACS J0416.1-2403 with line-of-sight galaxies only
reduced modestly the rms offset in the image positions, we conclude that
additional complexities, such as more flexible halo shapes, would be needed in
future models of MACS J0416.1-2403.
",0,1,0,0,0,0
19833,What can you do with a rock? Affordance extraction via word embeddings,"  Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose.
",1,0,0,0,0,0
19834,On Generalized Gibbs Ensembles with an infinite set of conserved charges,"  We revisit the question of whether and how the steady states arising after
non-equilibrium time evolution in integrable models (and in particular in the
XXZ spin chain) can be described by the so-called Generalized Gibbs Ensemble
(GGE). It is known that the micro-canonical ensemble built on a complete set of
charges correctly describes the long-time limit of local observables, and
recently a canonical ensemble was built by Ilievski et. al. using particle
occupation number operators. Here we provide an alternative construction by
considering truncated GGE's (tGGE's) that only include a finite number of well
localized conserved operators. It is shown that the tGGE's can approximate the
steady states with arbitrary precision, i.e. all physical observables are
exactly reproduced in the infinite truncation limit. In addition, we show that
a complete canonical ensemble can in fact be built in terms of a new (discrete)
set of charges built as linear combinations of the standard ones.
Our general arguments are applied to concrete quench situations in the XXZ
chain, where the initial states are simple two-site or four-site product
states. Depending on the quench we find that numerical results for the local
correlators can be obtained with remarkable precision using truncated GGE's
with only 10-100 charges.
",0,1,0,0,0,0
19835,High resolution ion trap time-of-flight mass spectrometer for cold trapped ion experiments,"  Trapping molecular ions that have been sympathetically cooled with
laser-cooled atomic ions is a useful platform for exploring cold ion chemistry.
We designed and characterized a new experimental apparatus for probing chemical
reaction dynamics between molecular cations and neutral radicals at
temperatures below 1 K. The ions are trapped in a linear quadrupole
radio-frequency trap and sympathetically cooled by co-trapped, laser-cooled,
atomic ions. The ion trap is coupled to a time-of-flight mass spectrometer to
readily identify product ion species, as well as to accurately determine
trapped ion numbers. We discuss, and present in detail, the design of this ion
trap time-of-flight mass spectrometer, as well as the electronics required for
driving the trap and mass spectrometer. Furthermore, we measure the performance
of this system, which yields mass resolutions of $m/\Delta{}m \geq 1100$ over a
wide mass range, and discuss its relevance for future measurements in chemical
reaction kinetics and dynamics.
",0,1,0,0,0,0
19836,The role of local-geometrical-orders on the growth of dynamic-length-scales in glass-forming liquids,"  The precise nature of complex structural relaxation as well as an explanation
for the precipitous growth of relaxation time in cooling glass-forming liquids
are essential to the understanding of vitrification of liquids. The dramatic
increase of relaxation time is believed to be caused by the growth of one or
more correlation lengths, which has received much attention recently. Here, we
report a direct link between the growth of a specific local-geometrical-order
and an increase of dynamic-length-scale as the atomic dynamics in metallic
glass-forming liquids slow down. Although several types of local
geometrical-orders are present in these metallic liquids, the growth of
icosahedral ordering is found to be directly related to the increase of the
dynamic-length-scale. This finding suggests an intriguing scenario that the
transient icosahedral ordering could be the origin of the dynamic-length-scale
in metallic glass-forming liquids.
",0,1,0,0,0,0
19837,Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs,"  A regularized optimization problem over a large unstructured graph is
studied, where the regularization term is tied to the graph geometry. Typical
regularization examples include the total variation and the Laplacian
regularizations over the graph. When applying the proximal gradient algorithm
to solve this problem, there exist quite affordable methods to implement the
proximity operator (backward step) in the special case where the graph is a
simple path without loops. In this paper, an algorithm, referred to as ""Snake"",
is proposed to solve such regularized problems over general graphs, by taking
benefit of these fast methods. The algorithm consists in properly selecting
random simple paths in the graph and performing the proximal gradient algorithm
over these simple paths. This algorithm is an instance of a new general
stochastic proximal gradient algorithm, whose convergence is proven.
Applications to trend filtering and graph inpainting are provided among others.
Numerical experiments are conducted over large graphs.
",1,0,0,1,0,0
19838,Stability of the coexistent superconducting-nematic phase under the presence of intersite interactions,"  We analyze the effect of intersite-interaction terms on the stability of the
coexisting superconucting-nematic phase (SC+N) within the extended Hubbard and
$t$-$J$-$U$ models on the square lattice. In order to take into account the
correlation effects with a proper precision, we use the approach based on the
\textit{diagrammatic expansion of the Gutzwiller wave function} (DE-GWF), which
goes beyond the renormalized mean field theory (RMFT) in a systematic manner.
As a starting point of our analysis we discuss the stability region of the SC+N
phase on the intrasite Coulomb repulsion-hole doping plane for the case of the
Hubbard model. Next, we show that the exchange interaction term enhances
superconductivity while suppresses the nematicity, whereas the intersite
Coulomb repulsion term acts in the opposite manner. The competing character of
the SC and N phases interplay is clearly visible throughout the analysis. A
universal conclusion is that the nematic phase does not survive within the
$t$-$J$-$U$ model with the value of $J$ integral typical for the high-T$_C$
cuprates ($J\approx 0.1$eV). For the sake of completeness, the effect of the
correlated hopping term is also analyzed. Thus the present discussion contains
all relevant two-site interaction terms which appear in the parametrized
one-band model within the second quantization scheme. At the end, the influence
of the higher-order terms of the diagrammatic expansion on the rotational
symmetry breaking is also shown by comparing the DE-GWF results with those
corresponding to the RMFT.
",0,1,0,0,0,0
19839,Multi-Advisor Reinforcement Learning,"  We consider tackling a single-agent RL problem by distributing it to $n$
learners. These learners, called advisors, endeavour to solve the problem from
a different focus. Their advice, taking the form of action values, is then
communicated to an aggregator, which is in control of the system. We show that
the local planning method for the advisors is critical and that none of the
ones found in the literature is flawless: the egocentric planning overestimates
values of states where the other advisors disagree, and the agnostic planning
is inefficient around danger zones. We introduce a novel approach called
empathic and discuss its theoretical aspects. We empirically examine and
validate our theoretical findings on a fruit collection task.
",1,0,0,1,0,0
19840,Using CMB spectral distortions to distinguish between dark matter solutions to the small-scale crisis,"  The dissipation of small-scale perturbations in the early universe produces a
distortion in the blackbody spectrum of cosmic microwave background photons. In
this work, we propose to use these distortions as a probe of the microphysics
of dark matter on scales $1\,\textrm{Mpc}^{-1}\lesssim k \lesssim
10^{4}\,\textrm{Mpc}^{-1}$. We consider in particular models in which the dark
matter is kinetically coupled to either neutrinos or photons until shortly
before recombination, and compute the photon heating rate and the resultant
$\mu$-distortion in both cases. We show that the $\mu$-parameter is generally
enhanced relative to $\Lambda$CDM for interactions with neutrinos, and may be
either enhanced or suppressed in the case of interactions with photons. The
deviations from the $\Lambda$CDM signal are potentially within the sensitivity
reach of a PRISM-like experiment if $\sigma_{\textrm{DM}-\gamma} \gtrsim
1.1\times10^{-30} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$
and $\sigma_{\textrm{DM}-\nu} \gtrsim 4.8\times 10^{-32}
\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for time-independent
cross sections, and $\sigma^{0}_{\textrm{DM}-\gamma} \gtrsim 1.8 \times
10^{-40} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ and
$\sigma^{0}_{\textrm{DM}-\nu} \gtrsim 2.5 \times 10^{-47}
\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for cross sections
scaling as temperature squared, coinciding with the parameter regions in which
late kinetic decoupling may serve as a solution to the small-scale crisis.
Furthermore, these $\mu$-distortion signals differ from those of warm dark
matter (no deviation from $\Lambda$CDM) and a suppressed primordial power
spectrum (strongly suppressed or a negative $\mu$-parameter), demonstrating
that CMB spectral distortion can potentially be used to distinguish between
solutions to the small-scale crisis.
",0,1,0,0,0,0
19841,Gradient Hyperalignment for multi-subject fMRI data alignment,"  Multi-subject fMRI data analysis is an interesting and challenging problem in
human brain decoding studies. The inherent anatomical and functional
variability across subjects make it necessary to do both anatomical and
functional alignment before classification analysis. Besides, when it comes to
big data, time complexity becomes a problem that cannot be ignored. This paper
proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional
alignment method that is suitable for multi-subject fMRI datasets with large
amounts of samples and voxels. The advantage of Gradient-HA is that it can
solve independence and high dimension problems by using Independent Component
Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using
multi-classification tasks on big data demonstrates that Gradient-HA method has
less time complexity and better or comparable performance compared with other
state-of-the-art functional alignment methods.
",0,0,0,1,1,0
19842,Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer,"  Knowledge transfer between tasks can improve the performance of learned
models, but requires an accurate estimate of the inter-task relationships to
identify the relevant knowledge to transfer. These inter-task relationships are
typically estimated based on training data for each task, which is inefficient
in lifelong learning settings where the goal is to learn each consecutive task
rapidly from as little data as possible. To reduce this burden, we develop a
lifelong learning method based on coupled dictionary learning that utilizes
high-level task descriptions to model the inter-task relationships. We show
that using task descriptors improves the performance of the learned task
policies, providing both theoretical justification for the benefit and
empirical demonstration of the improvement across a variety of learning
problems. Given only the descriptor for a new task, the lifelong learner is
also able to accurately predict a model for the new task through zero-shot
learning using the coupled dictionary, eliminating the need to gather training
data before addressing the task.
",1,0,0,1,0,0
19843,Giant Unification Theory of the Grand Unification and Gravitation Theories,"  Because the grand unification theory of gauge theories of strong, weak and
electromagnetic interactions is based on principal bundle theory, and
gravitational theory is based on the tangent vector bundle theory, so people
cannot unify the these four basic interactions in principal bundle theory. This
Letter discovers and gives giant unification theory of the grand unification
theory and gravitation theory, i.e., the giant unification theory of strong,
weak, electromagnetic and gravitational interactions according to the general
fiber bundle theory, symmetry, quantitative causal principle (QCP) and so on.
Consequently, the research of this Letter is based on the rigorous scientific
bases of mathematics and physics. The Lagrangians of the well-known fundamental
physics interactions are unifiedly deduced from QCP and satisfy the gauge
invariant principle of general gauge fields interacting with Fermion and/or
boson fields. The geometry and physics meanings of gauge invariant property of
different physical systems are revealed, and it is discovered that all the
Lagrangians of the well-known fundamental physics interactions are composed of
the invariant quantities in corresponding spacetime structures. The
difficulties that fundamental physics interactions and Noether theorem are not
able to be unifiedly given and investigated are overcome, the unified
description and origin of the fundamental physics interactions and Noether
theorem are shown by QCP, their two-order general Euler-Lagrange Equations and
corresponding Noether conservation currents are derived in general curved
spacetime. Therefore, using the new unification theory, a lot of research works
about different branches of physics etc can be renewedly done and expressed
simpler with clear quantitative causal physical meanings.
",0,1,0,0,0,0
19844,Generalized Rich-Club Ordering in Networks,"  Rich-club ordering refers to the tendency of nodes with a high degree to be
more interconnected than expected. In this paper we consider the concept of
rich-club ordering when generalized to structural measures that differ from the
node degree and to non-structural measures (i.e. to node metadata). The
differences in considering rich-club ordering (RCO) with respect to both
structural and non-structural measures is then discussed in terms of employed
coefficients and of appropriate null models (link rewiring vs metadata
reshuffling). Once a framework for the evaluation of generalized rich-club
ordering (GRCO) is defined, we investigate such a phenomenon in real networks
provided with node metadata. By considering different notions of node richness,
we compare structural and non-structural rich-club ordering, observing how
external information about the network nodes is able to validate the presence
of rich-clubs in networked systems.
",1,0,0,0,0,0
19845,Integrating Proactive Mode Changes in Mixed Criticality Systems,"  In this work, we propose to integrate prediction algorithms to the scheduling
of mode changes under the Earliest-Deadline-First and Fixed-priority scheduling
in mixed-criticality real-time systems. The method proactively schedules a mode
change in the system based on state variables such as laxity, to the percentage
difference in the temporal distance between the completion time of the instance
of a task and its respective deadline, by the deadline (D) stipulated for the
task, in order to minimize deadline misses. The simulation model was validated
against an analytical model prior to the logical integration of the
Kalman-based prediction algorithm. Two study cases were presented, one covering
earliest-deadline first and the other the fixed-priority scheduling approach.
The results showed the gains in the adoption of the prediction approach for
both scheduling paradigms by presenting a significant reduction of the number
of missed deadlines for low-criticality tasks.
",1,0,0,0,0,0
19846,Leveraging Continuous Material Averaging for Inverse Electromagnetic Design,"  Inverse electromagnetic design has emerged as a way of efficiently designing
active and passive electromagnetic devices. This maturing strategy involves
optimizing the shape or topology of a device in order to improve a figure of
merit--a process which is typically performed using some form of steepest
descent algorithm. Naturally, this requires that we compute the gradient of a
figure of merit which describes device performance, potentially with respect to
many design variables. In this paper, we introduce a new strategy based on
smoothing abrupt material interfaces which enables us to efficiently compute
these gradients with high accuracy irrespective of the resolution of the
underlying simulation. This has advantages over previous approaches to shape
and topology optimization in nanophotonics which are either prone to gradient
errors or place important constraints on the shape of the device. As a
demonstration of this new strategy, we optimize a non-adiabatic waveguide taper
between a narrow and wide waveguide. This optimization leads to a non-intuitive
design with a very low insertion loss of only 0.041 dB at 1550 nm.
",0,1,0,0,0,0
19847,Robust Speech Recognition Using Generative Adversarial Networks,"  This paper describes a general, scalable, end-to-end framework that uses the
generative adversarial network (GAN) objective to enable robust speech
recognition. Encoders trained with the proposed approach enjoy improved
invariance by learning to map noisy audio to the same embedding space as that
of clean audio. Unlike previous methods, the new framework does not rely on
domain expertise or simplifying assumptions as are often needed in signal
processing, and directly encourages robustness in a data-driven way. We show
the new approach improves simulated far-field speech recognition of vanilla
sequence-to-sequence models without specialized front-ends or preprocessing.
",1,0,0,0,0,0
19848,Minimal inequalities for an infinite relaxation of integer programs,"  We show that maximal $S$-free convex sets are polyhedra when $S$ is the set
of integral points in some rational polyhedron of $\mathbb{R}^n$. This result
extends a theorem of Lovász characterizing maximal lattice-free convex sets.
Our theorem has implications in integer programming. In particular, we show
that maximal $S$-free convex sets are in one-to-one correspondence with minimal
inequalities.
",0,0,1,0,0,0
19849,The population of SNe/SNRs in the starburst galaxy Arp 220. A self-consistent analysis of 20 years of VLBI monitoring,"  The nearby ultra-luminous infrared galaxy (ULIRG) Arp 220 is an excellent
laboratory for studies of extreme astrophysical environments. For 20 years,
Very Long Baseline Interferometry (VLBI) has been used to monitor a population
of compact sources thought to be supernovae (SNe), supernova remnants (SNRs)
and possibly active galactic nuclei (AGNs). Using new and archival VLBI data
spanning 20 years, we obtain 23 high-resolution radio images of Arp 220 at
wavelengths from 18 cm to 2 cm. From model-fitting to the images we obtain
estimates of flux densities and sizes of all detected sources. We detect radio
continuum emission from 97 compact sources and present flux densities and sizes
for all analysed observation epochs. We find evidence for a LD-relation within
Arp 220, with larger sources being less luminous. We find a compact source LF
$n(L)\propto L^\beta$ with $\beta=-2.02\pm0.11$, similar to SNRs in normal
galaxies. Based on simulations we argue that there are many relatively large
and weak sources below our detection threshold. The rapidly declining object
0.2227+0.482 is proposed as a possible AGN candidate. The observations can be
explained by a mixed population of SNe and SNRs, where the former expand in a
dense circumstellar medium (CSM) and the latter interact with the surrounding
interstellar medium (ISM). Several sources (9 of the 94 with fitted sizes) are
likely luminous, type IIn SNe. This number of luminous SNe correspond to few
percent of the total number of SNe in Arp 220 which is consistent with a total
SN-rate of 4 yr$^{-1}$ as inferred from the total radio emission given a normal
stellar initial mass function (IMF). Based on the fitted luminosity function,
we argue that emission from all compact sources, also below our detection
threshold, make up at most 20\% of the total radio emission at GHz frequencies.
",0,1,0,0,0,0
19850,Dirac and Chiral Quantum Spin Liquids on the Honeycomb Lattice in a Magnetic Field,"  Motivated by recent experimental observations in $\alpha$-RuCl$_3$, we study
the $K$-$\Gamma$ model on the honeycomb lattice in an external magnetic field.
By a slave-particle representation and Variational Monte Carlo calculations, we
reproduce the phase transition from zigzag magnetic order to a field-induced
disordered phase. The nature of this state depends crucially on the field
orientation. For particular field directions in the honeycomb plane, we find a
gapless Dirac spin liquid, in agreement with recent experiments on
$\alpha$-RuCl$_3$. For a range of out-of-plane fields, we predict the existence
of a Kalmeyer-Laughlin-type chiral spin liquid, which would show an
integer-quantized thermal Hall effect.
",0,1,0,0,0,0
19851,Secular Dynamics of an Exterior Test Particle: The Inverse Kozai and Other Eccentricity-Inclination Resonances,"  The behavior of an interior test particle in the secular 3-body problem has
been studied extensively. A well-known feature is the Lidov-Kozai resonance in
which the test particle's argument of periapse librates about $\pm 90^\circ$
and large oscillations in eccentricity and inclination are possible. Less
explored is the inverse problem: the dynamics of an exterior test particle and
an interior perturber. We survey numerically the inverse secular problem,
expanding the potential to hexadecapolar order and correcting an error in the
published expansion. Four secular resonances are uncovered that persist in full
$N$-body treatments (in what follows, $\varpi$ and $\Omega$ are the longitudes
of periapse and of ascending node, $\omega$ is the argument of periapse, and
subscripts 1 and 2 refer to the inner perturber and outer test particle): (i)
an orbit-flipping quadrupole resonance requiring a non-zero perturber
eccentricity $e_1$, in which $\Omega_2-\varpi_1$ librates about $\pm 90^\circ$;
(ii) a hexadecapolar resonance (the ""inverse Kozai"" resonance) for perturbers
that are circular or nearly so and inclined by $I \simeq 63^\circ/117^\circ$,
in which $\omega_2$ librates about $\pm 90^\circ$ and which can vary the
particle eccentricity by $\Delta e_2 \simeq 0.2$ and lead to orbit crossing;
(iii) an octopole ""apse-aligned"" resonance at $I \simeq 46^\circ/107^\circ$
wherein $\varpi_2 - \varpi_1$ librates about $0^\circ$ and $\Delta e_2$ grows
with $e_1$; and (iv) an octopole resonance at $I \simeq 73^\circ/134^\circ$
wherein $\varpi_2 + \varpi_1 - 2 \Omega_2$ librates about $0^\circ$ and $\Delta
e_2$ can be as large as 0.3 for small $e_1 \neq 0$. The more eccentric the
perturber, the more the particle's eccentricity and inclination vary; also,
more polar orbits are more chaotic. Our inverse solutions may be applied to the
Kuiper belt and debris disks, circumbinary planets, and stellar systems.
",0,1,0,0,0,0
19852,"A pulsed, mono-energetic and angular-selective UV photo-electron source for the commissioning of the KATRIN experiment","  The KATRIN experiment aims to determine the neutrino mass scale with a
sensitivity of 200 meV/c^2 (90% C.L.) by a precision measurement of the shape
of the tritium $\beta$-spectrum in the endpoint region. The energy analysis of
the decay electrons is achieved by a MAC-E filter spectrometer. To determine
the transmission properties of the KATRIN main spectrometer, a mono-energetic
and angular-selective electron source has been developed. In preparation for
the second commissioning phase of the main spectrometer, a measurement phase
was carried out at the KATRIN monitor spectrometer where the device was
operated in a MAC-E filter setup for testing. The results of these measurements
are compared with simulations using the particle-tracking software
""Kassiopeia"", which was developed in the KATRIN collaboration over recent
years.
",0,1,0,0,0,0
19853,Carrier frequency modulation of an acousto-optic modulator for laser stabilization,"  The stabilization of lasers to absolute frequency references is a fundamental
requirement in several areas of atomic, molecular and optical physics. A range
of techniques are available to produce a suitable reference onto which one can
'lock' the laser, many of which depend on the specific internal structure of
the reference or are sensitive to laser intensity noise. We present a novel
method using the frequency modulation of an acousto-optic modulator's carrier
(drive) signal to generate two spatially separated beams, with a frequency
difference of only a few MHz. These beams are used to probe a narrow absorption
feature and the difference in their detected signals leads to a dispersion-like
feature suitable for wavelength stabilization of a diode laser. This simple and
versatile method only requires a narrow absorption line and is therefore
suitable for both atomic and cavity based stabilization schemes. To demonstrate
the suitability of this method we lock an external cavity diode laser near the
$^{85}\mathrm{Rb}\,5S_{1/2}\rightarrow5P_{3/2}, F=3\rightarrow F^{\prime}=4$
using sub-Doppler pump probe spectroscopy and also demonstrate excellent
agreement between the measured signal and a theoretical model.
",0,1,0,0,0,0
19854,Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method,"  In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-sized bin in many real business scenarios and the cost of a
bin is proportional to its surface area. Our research shows that this problem
is NP-hard. Based on previous research on 3D BPP, the surface area is
determined by the sequence, spatial locations and orientations of items. Among
these factors, the sequence of items plays a key role in minimizing the surface
area. Inspired by recent achievements of deep reinforcement learning (DRL)
techniques, especially Pointer Network, on combinatorial optimization problems
such as TSP, a DRL-based method is applied to optimize the sequence of items to
be packed into the bin. Numerical results show that the method proposed in this
paper achieve about 5% improvement than heuristic method.
",1,0,0,0,0,0
19855,Entendendo o Pensamento Computacional,"  The goal of this article is to clarify the meaning of Computational Thinking.
We differentiate logical from computational reasoning and discuss the
importance of Computational Thinking in solving problems. The three pillars of
Computational Thinking - Abstraction, Automation and Analysis - are outlined,
highlighting the role of each one in developing the skills needed for the
problem-solving process.
-----
O objetivo deste artigo é esclarecer o significado de Pensamento
Computacional. Diferencia-se o raciocínio lógico do computacional e
discute-se a importância do Pensamento Computacional na resolução de
problemas. Os três pilares do Pensamento Computacional - Abstração,
Automação e Análise - são delineados, destacando-se o papel de cada
um deles no desenvolvimento das habilidades necessárias para o processo de
solução de problemas.
",1,0,0,0,0,0
19856,Estimation of samples relevance by their histograms,"  The problem of the estimation of relevance to a set of histograms generated
by samples of a discrete time process is discussed on the base of the
variational principles proposed in the previous paper [1]. Some conditions for
dimension reduction of corresponding linear programming problems are presented
also.
",0,0,1,0,0,0
19857,On the synthesis of acoustic sources with controllable near fields,"  In this paper we present a strategy for the the synthesis of acoustic sources
with controllable near fields in free space and finite depth homogeneous ocean
environments. We first present the theoretical results at the basis of our
discussion and then, to illustrate our findings we focus on the following three
particular examples:
1. acoustic source approximating a prescribed field pattern in a given
bounded sub- region of its near field. 2. acoustic source approximating
different prescribed field patterns in given disjoint bounded near field
sub-regions. 3. acoustic source approximating a prescribed back-propagating
field in a given bounded near field sub-region while maintaining a very low far
field signature.
For each of these three examples, we discuss the optimization scheme used to
approx- imate their solutions and support our claims through relevant numerical
simulations.
",0,0,1,0,0,0
19858,Inverse Protein Folding Problem via Quadratic Programming,"  This paper presents a method of reconstruction a primary structure of a
protein that folds into a given geometrical shape. This method predicts the
primary structure of a protein and restores its linear sequence of amino acids
in the polypeptide chain using the tertiary structure of a molecule. Unknown
amino acids are determined according to the principle of energy minimization.
This study represents inverse folding problem as a quadratic optimization
problem and uses different relaxation techniques to reduce it to the problem of
convex optimizations. Computational experiment compares the quality of these
approaches on real protein structures.
",0,0,1,0,0,0
19859,Spoken Language Understanding on the Edge,"  We consider the problem of performing Spoken Language Understanding (SLU) on
small devices typical of IoT applications. Our contributions are twofold.
First, we outline the design of an embedded, private-by-design SLU system and
show that it has performance on par with cloud-based commercial solutions.
Second, we release the datasets used in our experiments in the interest of
reproducibility and in the hope that they can prove useful to the SLU
community.
",1,0,0,0,0,0
19860,On the link between column density distribution and density scaling relation in star formation regions,"  We present a method to derive the density scaling relation $\langle n\rangle
\propto L^{-\alpha}$ in regions of star formation or in their turbulent
vicinities from straightforward binning of the column-density distribution
($N$-pdf). The outcome of the method is studied for three types of $N$-pdf:
power law ($7/5\le\alpha\le5/3$), lognormal ($0.7\lesssim\alpha\lesssim1.4$)
and combination of lognormals. In the last case, the method of Stanchev et al.
(2015) was also applied for comparison and a very weak (or close to zero)
correlation was found. We conclude that the considered `binning approach'
reflects rather the local morphology of the $N$-pdf with no reference to the
physical conditions in a considered region. The rough consistency of the
derived slopes with the widely adopted Larson's (1981) value $\alpha\sim1.1$ is
suggested to support claims that the density-size relation in molecular clouds
is indeed an artifact of the observed $N$-pdf.
",0,1,0,0,0,0
19861,Measuring Effectiveness of Video Advertisements,"  Advertisements are unavoidable in modern society. Times Square is notorious
for its incessant display of advertisements. Its popularity is worldwide and
smaller cities possess miniature versions of the display, such as Pittsburgh
and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district
recently rose to popularity due to its upscale shops and constant onslaught of
advertisements to pedestrians. Advertisements arise in other mediums as well.
For example, they help popular streaming services, such as Spotify, Hulu, and
Youtube TV gather significant streams of revenue to reduce the cost of monthly
subscriptions for consumers. Ads provide an additional source of money for
companies and entire industries to allocate resources toward alternative
business motives. They are attractive to companies and nearly unavoidable for
consumers. One challenge for advertisers is examining a advertisement's
effectiveness or usefulness in conveying a message to their targeted
demographics. Rather than constructing a single, static image of content, a
video advertisement possesses hundreds of frames of data with varying scenes,
actors, objects, and complexity. Therefore, measuring effectiveness of video
advertisements is important to impacting a billion-dollar industry. This paper
explores the combination of human-annotated features and common video
processing techniques to predict effectiveness ratings of advertisements
collected from Youtube. This task is seen as a binary (effective vs.
non-effective), four-way, and five-way machine learning classification task.
The first findings in terms of accuracy and inference on this dataset, as well
as some of the first ad research, on a small dataset are presented. Accuracies
of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks
respectively.
",1,0,0,0,0,0
19862,Multilinear compressive sensing and an application to convolutional linear networks,"  We study a deep linear network endowed with a structure. It takes the form of
a matrix $X$ obtained by multiplying $K$ matrices (called factors and
corresponding to the action of the layers). The action of each layer (i.e. a
factor) is obtained by applying a fixed linear operator to a vector of
parameters satisfying a constraint. The number of layers is not limited.
Assuming that $X$ is given and factors have been estimated, the error between
the product of the estimated factors and $X$ (i.e. the reconstruction error) is
either the statistical or the empirical risk. In this paper, we provide
necessary and sufficient conditions on the network topology under which a
stability property holds. The stability property requires that the error on the
parameters defining the factors (i.e. the stability of the recovered
parameters) scales linearly with the reconstruction error (i.e. the risk).
Therefore, under these conditions on the network topology, any successful
learning task leads to stably defined features and therefore interpretable
layers/network.In order to do so, we first evaluate how the Segre embedding and
its inverse distort distances. Then, we show that any deep structured linear
network can be cast as a generic multilinear problem (that uses the Segre
embedding). This is the {\em tensorial lifting}. Using the tensorial lifting,
we provide necessary and sufficient conditions for the identifiability of the
factors (up to a scale rearrangement). We finally provide the necessary and
sufficient condition called \NSPlong~(because of the analogy with the usual
Null Space Property in the compressed sensing framework) which guarantees that
the stability property holds. We illustrate the theory with a practical example
where the deep structured linear network is a convolutional linear network. As
expected, the conditions are rather strong but not empty. A simple test on the
network topology can be implemented to test if the condition holds.
",0,0,1,1,0,0
19863,Normalization of closed Ekedahl-Oort strata,"  We apply our theory of partial flag spaces developed in previous articles to
study a group-theoretical generalization of the canonical filtration of a
truncated Barsotti-Tate group of level 1. As an application, we determine
explicitly the normalization of the Zariski closures of Ekedahl-Oort strata of
Shimura varieties of Hodge-type as certain closed coarse strata in the
associated partial flag spaces.
",0,0,1,0,0,0
19864,Robustness of functional networks at criticality against structural defects,"  The robustness of dynamical properties of neuronal networks against
structural damages is a central problem in computational and experimental
neuroscience. Research has shown that the cortical network of a healthy brain
works near a critical state, and moreover, that functional neuronal networks
often have scale-free and small-world properties. In this work, we study how
the robustness of simple functional networks at criticality is affected by
structural defects. In particular, we consider a 2D Ising model at the critical
temperature and investigate how its functional network changes with the
increasing degree of structural defects. We show that the scale-free and
small-world properties of the functional network at criticality are robust
against large degrees of structural lesions while the system remains below the
percolation limit. Although the Ising model is only a conceptual description of
a two-state neuron, our research reveals fundamental robustness properties of
functional networks derived from classical statistical mechanics models.
",0,0,0,0,1,0
19865,Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning,"  Maximizing the area under the receiver operating characteristic curve (AUC)
is a standard approach to imbalanced classification. So far, various supervised
AUC optimization methods have been developed and they are also extended to
semi-supervised scenarios to cope with small sample problems. However, existing
semi-supervised AUC optimization methods rely on strong distributional
assumptions, which are rarely satisfied in real-world problems. In this paper,
we propose a novel semi-supervised AUC optimization method that does not
require such restrictive assumptions. We first develop an AUC optimization
method based only on positive and unlabeled data (PU-AUC) and then extend it to
semi-supervised learning by combining it with a supervised AUC optimization
method. We theoretically prove that, without the restrictive distributional
assumptions, unlabeled data contribute to improving the generalization
performance in PU and semi-supervised AUC optimization methods. Finally, we
demonstrate the practical usefulness of the proposed methods through
experiments.
",1,0,0,1,0,0
19866,Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering,"  Motivated by problems in data clustering, we establish general conditions
under which families of nonparametric mixture models are identifiable, by
introducing a novel framework involving clustering overfitted \emph{parametric}
(i.e. misspecified) mixture models. These identifiability conditions generalize
existing conditions in the literature, and are flexible enough to include for
example mixtures of Gaussian mixtures. In contrast to the recent literature on
estimating nonparametric mixtures, we allow for general nonparametric mixture
components, and instead impose regularity assumptions on the underlying mixing
measure. As our primary application, we apply these results to partition-based
clustering, generalizing the notion of a Bayes optimal partition from classical
parametric model-based clustering to nonparametric settings. Furthermore, this
framework is constructive so that it yields a practical algorithm for learning
identified mixtures, which is illustrated through several examples on real
data. The key conceptual device in the analysis is the convex, metric geometry
of probability measures on metric spaces and its connection to the Wasserstein
convergence of mixing measures. The result is a flexible framework for
nonparametric clustering with formal consistency guarantees.
",0,0,0,1,0,0
19867,The geometric classification of Leibniz algebras,"  We describe all rigid algebras and all irreducible components in the variety
of four dimensional Leibniz algebras $\mathfrak{Leib}_4$ over $\mathbb{C}.$ In
particular, we prove that the Grunewald--O'Halloran conjecture is not valid and
the Vergne conjecture is valid for $\mathfrak{Leib}_4.$
",0,0,1,0,0,0
19868,Discrete fundamental groups of Warped Cones and expanders,"  In this paper we compute the discrete fundamental groups of warped cones. As
an immediate consequence, this allows us to show that there exist coarsely
simply-connected expanders and superexpanders. This also provides a strong
coarse invariant of warped cones and implies that many warped cones cannot be
coarsely equivalent to any box space.
",0,0,1,0,0,0
19869,Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning,"  We investigate the theoretical foundations of the simulated tempering method
and use our findings to design efficient algorithms. Employing a large
deviation argument first used for replica exchange molecular dynamics [Plattner
et al., J. Chem. Phys. 135:134111 (2011)], we demonstrate that the most
efficient approach to simulated tempering is to vary the temperature infinitely
rapidly. In this limit, we can replace the equations of motion for the
temperature and physical variables by averaged equations for the latter alone,
with the forces rescaled according to a position-dependent function defined in
terms of temperature weights. The averaged equations are similar to those used
in Gao's integrated-over-temperature method, except that we show that it is
better to use a continuous rather than a discrete set of temperatures. We give
a theoretical argument for the choice of the temperature weights as the
reciprocal partition function, thereby relating simulated tempering to
Wang-Landau sampling. Finally, we describe a self-consistent algorithm for
simultaneously sampling the canonical ensemble and learning the weights during
simulation. This algorithm is tested on a system of harmonic oscillators as
well as a continuous variant of the Curie-Weiss model, where it is shown to
perform well and to accurately capture the second-order phase transition
observed in this model.
",0,0,0,1,0,0
19870,Analysis and Modeling of 3D Indoor Scenes,"  We live in a 3D world, performing activities and interacting with objects in
the indoor environments everyday. Indoor scenes are the most familiar and
essential environments in everyone's life. In the virtual world, 3D indoor
scenes are also ubiquitous in 3D games and interior design. With the fast
development of VR/AR devices and the emerging applications, the demand of
realistic 3D indoor scenes keeps growing rapidly. Currently, designing detailed
3D indoor scenes requires proficient 3D designing and modeling skills and is
often time-consuming. For novice users, creating realistic and complex 3D
indoor scenes is even more difficult and challenging.
Many efforts have been made in different research communities, e.g. computer
graphics, vision and robotics, to capture, analyze and generate the 3D indoor
data. This report mainly focuses on the recent research progress in graphics on
geometry, structure and semantic analysis of 3D indoor data and different
modeling techniques for creating plausible and realistic indoor scenes. We
first review works on understanding and semantic modeling of scenes from
captured 3D data of the real world. Then, we focus on the virtual scenes
composed of 3D CAD models and study methods for 3D scene analysis and
processing. After that, we survey various modeling paradigms for creating 3D
indoor scenes and investigate human-centric scene analysis and modeling, which
bridge indoor scene studies of graphics, vision and robotics. At last, we
discuss open problems in indoor scene processing that might bring interests to
graphics and all related communities.
",1,0,0,0,0,0
19871,Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy,"  Advances in astronomy are intimately linked to advances in digital signal
processing (DSP). This special issue is focused upon advances in DSP within
radio astronomy. The trend within that community is to use off-the-shelf
digital hardware where possible and leverage advances in high performance
computing. In particular, graphics processing units (GPUs) and field
programmable gate arrays (FPGAs) are being used in place of
application-specific circuits (ASICs); high-speed Ethernet and Infiniband are
being used for interconnect in place of custom backplanes. Further, to lower
hurdles in digital engineering, communities have designed and released
general-purpose FPGA-based DSP systems, such as the CASPER ROACH board, ASTRON
Uniboard and CSIRO Redback board. In this introductory article, we give a brief
historical overview, a summary of recent trends, and provide an outlook on
future directions.
",0,1,0,0,0,0
19872,HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud,"  Eliminating duplicate data in primary storage of clouds increases the
cost-efficiency of cloud service providers as well as reduces the cost of users
for using cloud services. Existing primary deduplication techniques either use
inline caching to exploit locality in primary workloads or use post-processing
deduplication running in system idle time to avoid the negative impact on I/O
performance. However, neither of them works well in the cloud servers running
multiple services or applications for the following two reasons: Firstly, the
temporal locality of duplicate data writes may not exist in some primary
storage workloads thus inline caching often fails to achieve good deduplication
ratio. Secondly, the post-processing deduplication allows duplicate data to be
written into disks, therefore does not provide the benefit of I/O deduplication
and requires high peak storage capacity. This paper presents HPDedup, a Hybrid
Prioritized data Deduplication mechanism to deal with the storage system shared
by applications running in co-located virtual machines or containers by fusing
an inline and a post-processing process for exact deduplication. In the inline
deduplication phase, HPDedup gives a fingerprint caching mechanism that
estimates the temporal locality of duplicates in data streams from different
VMs or applications and prioritizes the cache allocation for these streams
based on the estimation. HPDedup also allows different deduplication threshold
for streams based on their spatial locality to reduce the disk fragmentation.
The post-processing phase removes duplicates whose fingerprints are not able to
be cached due to the weak temporal locality from disks. Our experimental
results show that HPDedup clearly outperforms the state-of-the-art primary
storage deduplication techniques in terms of inline cache efficiency and
primary deduplication efficiency.
",1,0,0,0,0,0
19873,Two-domain and three-domain limit cycles in a typical aeroelastic system with freeplay in pitch,"  Freeplay is a significant source of nonlinearity in aeroelastic systems and
is strictly regulated by airworthiness authorities. It splits the phase plane
of such systems into three piecewise linear subdomains. Depending on the
location of the freeplay, limit cycle oscillations can result that span either
two or three of these subdomains. The purpose of this work is to demonstrate
the existence of two-domain cycles both theoretically and experimentally. A
simple aeroelastic system with pitch, plunge and control deflection degrees of
freedom is investigated in the presence of freeplay in pitch. It is shown that
two-domain and three-domain cycles can result from a grazing bifurcation and
propagate in the decreasing airspeed direction. Close to the bifurcation, the
two limit cycle branches interact with each other and aperiodic oscillations
ensue. Equivalent linearization is used to derive the conditions of existence
of each type of limit cycle and to predict their amplitudes and frequencies.
Comparisons with measurements from wind tunnel experiments demonstrate that the
theory describes these phenomena with accuracy.
",0,1,0,0,0,0
19874,Preference Modeling by Exploiting Latent Components of Ratings,"  Understanding user preference is essential to the optimization of recommender
systems. As a feedback of user's taste, rating scores can directly reflect the
preference of a given user to a given product. Uncovering the latent components
of user ratings is thus of significant importance for learning user interests.
In this paper, a new recommendation approach, called LCR, was proposed by
investigating the latent components of user ratings. The basic idea is to
decompose an existing rating into several components via a cost-sensitive
learning strategy. Specifically, each rating is assigned to several latent
factor models and each model is updated according to its predictive errors.
Afterwards, these accumulated predictive errors of models are utilized to
decompose a rating into several components, each of which is treated as an
independent part to retrain the latent factor models. Finally, all latent
factor models are combined linearly to estimate predictive ratings for users.
In contrast to existing methods, LCR provides an intuitive preference modeling
strategy via multiple component analysis at an individual perspective.
Meanwhile, it is verified by the experimental results on several benchmark
datasets that the proposed method is superior to the state-of-art methods in
terms of recommendation accuracy.
",1,0,0,0,0,0
19875,Inductive Representation Learning on Large Graphs,"  Low-dimensional embeddings of nodes in large graphs have proved extremely
useful in a variety of prediction tasks, from content recommendation to
identifying protein functions. However, most existing approaches require that
all nodes in the graph are present during training of the embeddings; these
previous approaches are inherently transductive and do not naturally generalize
to unseen nodes. Here we present GraphSAGE, a general, inductive framework that
leverages node feature information (e.g., text attributes) to efficiently
generate node embeddings for previously unseen data. Instead of training
individual embeddings for each node, we learn a function that generates
embeddings by sampling and aggregating features from a node's local
neighborhood. Our algorithm outperforms strong baselines on three inductive
node-classification benchmarks: we classify the category of unseen nodes in
evolving information graphs based on citation and Reddit post data, and we show
that our algorithm generalizes to completely unseen graphs using a multi-graph
dataset of protein-protein interactions.
",1,0,0,1,0,0
19876,Milnor and Tjurina numbers for a hypersurface germ with isolated singularity,"  Assume that $f:(\mathbb{C}^n,0) \to (\mathbb{C},0)$ is an analytic function
germ at the origin with only isolated singularity. Let $\mu$ and $\tau$ be the
corresponding Milnor and Tjurina numbers. We show that $\dfrac{\mu}{\tau} \leq
n$. As an application, we give a lower bound for the Tjurina number in terms of
$n$ and the multiplicity of $f$ at the origin.
",0,0,1,0,0,0
19877,Analysis of the Gibbs Sampler for Gaussian hierarchical models via multigrid decomposition,"  We study the convergence properties of the Gibbs Sampler in the context of
posterior distributions arising from Bayesian analysis of Gaussian hierarchical
models. We consider centred and non-centred parameterizations as well as their
hybrids including the full family of partially non-centred parameterizations.
We develop a novel methodology based on multi-grid decompositions to derive
analytic expressions for the convergence rates of the algorithm for an
arbitrary number of layers in the hierarchy, while previous work was typically
limited to the two-level case. Our work gives a complete understanding for the
three-level symmetric case and this gives rise to approximations for the
non-symmetric case. We also give analogous, if less explicit, results for
models of arbitrary level. This theory gives rise to simple and
easy-to-implement guidelines for the practical implementation of Gibbs samplers
on conditionally Gaussian hierarchical models.
",0,0,0,1,0,0
19878,A Cosmic Selection Rule for Glueball Dark Matter Relic Density,"  We point out a unique mechanism to produce the relic abundance for glueball
dark matter from a gauged $SU(N)_d$ hidden sector which is bridged to the
standard model sector through heavy vectorlike quarks colored under gauge
interactions from both sides. A necessary ingredient of our assumption is that
the vectorlike quarks, produced either thermally or non-thermally, are abundant
enough to dominate the universe for some time in the early universe. They later
undergo dark color confinement and form unstable vectorlike-quarkonium states
which annihilate decay and reheat the visible and dark sectors. The ratio of
entropy dumped into two sectors and the final energy budget in the dark
glueballs is only determined by low energy parameters, including the intrinsic
scale of the dark $SU(N)_d$, $\Lambda_d$, and number of dark colors, $N_d$, but
depend weakly on parameters in the ultraviolet such as the vectorlike quark
mass or the initial condition. We call this a cosmic selection rule for the
glueball dark matter relic density.
",0,1,0,0,0,0
19879,Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network,"  In this paper, we propose a method using a three dimensional convolutional
neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral
(HS) images to obtain a high resolution hyperspectral image. Dimensionality
reduction of the hyperspectral image is performed prior to fusion in order to
significantly reduce the computational time and make the method more robust to
noise. Experiments are performed on a data set simulated using a real
hyperspectral image. The results obtained show that the proposed approach is
very promising when compared to conventional methods. This is especially true
when the hyperspectral image is corrupted by additive noise.
",1,0,0,1,0,0
19880,The fundamental Lepage form in variational theory for submanifolds,"  A setting for global variational geometry on Grassmann fibrations is
presented. The integral variational functionals for finite dimensional immersed
submanifolds are studied by means of the fundamental Lepage equivalent of a
homogeneous Lagrangian, which can be regarded as a generalization of the
well-known Hilbert form in the classical mechanics. Prolongations of
immersions, diffeomorphisms and vector fields to the Grassmann fibrations are
introduced as geometric tools for the variations of immersions. The first
infinitesimal variation formula together with its consequences, the
Euler-Lagrange equations for extremal submanifolds and the Noether theorem for
invariant variational functionals are proved. The theory is illustrated on the
variational functional for minimal submanifolds.
",0,0,1,0,0,0
19881,Adding educational funcionalities to classic board games,"  In this paper we revisit some classic board games like Pachisi or the Game of
Gosse. The main contribution of the paper is to design and add some
functionalities to the games in order to transform them in serious games, that
is, in games with learning and educational purposes. To do that, at the
beginning of the game, players choose one or several topics and during the
game, players have to anwers questions on these topics in order to move their
markers. We choose classic board games because a lot of people are familiar
with them so it is very easy to start to play without wasting time learning
game rules and, we think that this is an important element to make the game
more attractive to people. To enlarge the number of potential users we have
implement the games just using html and javascript and the games can be used in
any web browser, in any computer (including tablets) , in any computer
arquitecture (Windows, Mac, Linux) and no internet/server conexion is required.
Associated software is distributed under Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 licence and can be obtained at
this http URL
",1,0,0,0,0,0
19882,Conceptual Frameworks for Building Online Citizen Science Projects,"  In recent years, citizen science has grown in popularity due to a number of
reasons, including the emphasis on informal learning and creativity potential
associated with these initiatives. Citizen science projects address research
questions from various domains, ranging from Ecology to Astronomy. Due to the
advancement of communication technologies, which makes outreach and engagement
of wider communities easier, scientists are keen to turn their own research
into citizen science projects. However, the development, deployment and
management of these projects remains challenging. One of the most important
challenges is building the project itself. There is no single tool or
framework, which guides the step-by-step development of the project, since
every project has specific characteristics, such as geographical constraints or
volunteers' mode of participation. Therefore, in this article, we present a
series of conceptual frameworks for categorisation, decision and deployment,
which guide a citizen science project creator in every step of creating a new
project starting from the research question to project deployment. The
frameworks are designed with consideration to the properties of already
existing citizen science projects and could be easily extended to include other
dimensions, which are not currently perceived.
",1,0,0,0,0,0
19883,SDN Architecture and Southbound APIs for IPv6 Segment Routing Enabled Wide Area Networks,"  The SRv6 architecture (Segment Routing based on IPv6 data plane) is a
promising solution to support services like Traffic Engineering, Service
Function Chaining and Virtual Private Networks in IPv6 backbones and
datacenters. The SRv6 architecture has interesting scalability properties as it
reduces the amount of state information that needs to be configured in the
nodes to support the network services. In this paper, we describe the
advantages of complementing the SRv6 technology with an SDN based approach in
backbone networks. We discuss the architecture of a SRv6 enabled network based
on Linux nodes. In addition, we present the design and implementation of the
Southbound API between the SDN controller and the SRv6 device. We have defined
a data-model and four different implementations of the API, respectively based
on gRPC, REST, NETCONF and remote Command Line Interface (CLI). Since it is
important to support both the development and testing aspects we have realized
an Intent based emulation system to build realistic and reproducible
experiments. This collection of tools automate most of the configuration
aspects relieving the experimenter from a significant effort. Finally, we have
realized an evaluation of some performance aspects of our architecture and of
the different variants of the Southbound APIs and we have analyzed the effects
of the configuration updates in the SRv6 enabled nodes.
",1,0,0,0,0,0
19884,Topology-optimized Dual-Polarization Dirac Cones,"  We apply a large-scale computational technique, known as topology
optimization, to the inverse design of photonic Dirac cones. In particular, we
report on a variety of photonic crystal geometries, realizable in simple
isotropic dielectric materials, which exhibit dual-polarization and
dual-wavelength Dirac cones. We demonstrate the flexibility of this technique
by designing photonic crystals of different symmetry types, such as ones with
four-fold and six-fold rotational symmetry, which possess Dirac cones at
different points within the Brillouin zone. The demonstrated and related
optimization techniques could open new avenues to band-structure engineering
and manipulating the propagation of light in periodic media, with possible
applications in exotic optical phenomena such as effective zero-index media and
topological photonics.
",0,1,0,0,0,0
19885,Reconstruction of Word Embeddings from Sub-Word Parameters,"  Pre-trained word embeddings improve the performance of a neural model at the
cost of increasing the model size. We propose to benefit from this resource
without paying the cost by operating strictly at the sub-lexical level. Our
approach is quite simple: before task-specific training, we first optimize
sub-word parameters to reconstruct pre-trained word embeddings using various
distance measures. We report interesting results on a variety of tasks: word
similarity, word analogy, and part-of-speech tagging.
",1,0,0,0,0,0
19886,Deep Submodular Functions,"  We start with an overview of a class of submodular functions called SCMMs
(sums of concave composed with non-negative modular functions plus a final
arbitrary modular). We then define a new class of submodular functions we call
{\em deep submodular functions} or DSFs. We show that DSFs are a flexible
parametric family of submodular functions that share many of the properties and
advantages of deep neural networks (DNNs). DSFs can be motivated by considering
a hierarchy of descriptive concepts over ground elements and where one wishes
to allow submodular interaction throughout this hierarchy. Results in this
paper show that DSFs constitute a strictly larger class of submodular functions
than SCMMs. We show that, for any integer $k>0$, there are $k$-layer DSFs that
cannot be represented by a $k'$-layer DSF for any $k'<k$. This implies that,
like DNNs, there is a utility to depth, but unlike DNNs, the family of DSFs
strictly increase with depth. Despite this, we show (using a ""backpropagation""
like method) that DSFs, even with arbitrarily large $k$, do not comprise all
submodular functions. In offering the above results, we also define the notion
of an antitone superdifferential of a concave function and show how this
relates to submodular functions (in general), DSFs (in particular), negative
second-order partial derivatives, continuous submodularity, and concave
extensions. To further motivate our analysis, we provide various special case
results from matroid theory, comparing DSFs with forms of matroid rank, in
particular the laminar matroid. Lastly, we discuss strategies to learn DSFs,
and define the classes of deep supermodular functions, deep difference of
submodular functions, and deep multivariate submodular functions, and discuss
where these can be useful in applications.
",1,0,0,0,0,0
19887,Inverse problems for the wave equation with under-determined data,"  We consider the inverse problems of determining the potential or the damping
coefficient appearing in the wave equation. We will prove the unique
determination of these coefficients from the one point measurement. Since our
problem is under-determined, so some extra assumption on the coefficients is
required to prove the uniqueness.
",0,0,1,0,0,0
19888,Spatial interactions and oscillatory tragedies of the commons,"  A tragedy of the commons (TOC) occurs when individuals acting in their own
self-interest deplete commonly-held resources, leading to a worse outcome than
had they cooperated. Over time, the depletion of resources can change
incentives for subsequent actions. Here, we investigate long-term feedback
between game and environment across a continuum of incentives in an
individual-based framework. We identify payoff-dependent transition rules that
lead to oscillatory TOC-s in stochastic simulations and the mean field limit.
Further extending the stochastic model, we find that spatially explicit
interactions can lead to emergent, localized dynamics, including the
propagation of cooperative wave fronts and cluster formation of both social
context and resources. These dynamics suggest new mechanisms underlying how
TOCs arise and how they might be averted.
",0,0,0,0,1,0
19889,An upper bound on transport,"  The linear growth of operators in local quantum systems leads to an effective
lightcone even if the system is non-relativistic. We show that consistency of
diffusive transport with this lightcone places an upper bound on the
diffusivity: $D \lesssim v^2 \tau_\text{eq}$. The operator growth velocity $v$
defines the lightcone and $\tau_\text{eq}$ is the local equilibration
timescale, beyond which the dynamics of conserved densities is diffusive. We
verify that the bound is obeyed in various weakly and strongly interacting
theories. In holographic models this bound establishes a relation between the
hydrodynamic and leading non-hydrodynamic quasinormal modes of planar black
holes. Our bound relates transport data --- including the electrical
resistivity and the shear viscosity --- to the local equilibration time, even
in the absence of a quasiparticle description. In this way, the bound sheds
light on the observed $T$-linear resistivity of many unconventional metals, the
shear viscosity of the quark-gluon plasma and the spin transport of unitary
fermions.
",0,1,0,0,0,0
19890,Taylor expansion in linear logic is invertible,"  Each Multiplicative Exponential Linear Logic (MELL) proof-net can be expanded
into a differential net, which is its Taylor expansion. We prove that two
different MELL proof-nets have two different Taylor expansions. As a corollary,
we prove a completeness result for MELL: We show that the relational model is
injective for MELL proof-nets, i.e. the equality between MELL proof-nets in the
relational model is exactly axiomatized by cut-elimination.
",1,0,0,0,0,0
19891,Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning,"  Multi robot systems have the potential to be utilized in a variety of
applications. In most of the previous works, the trajectory generation for
multi robot systems is implemented in known environments. To overcome that we
present an online trajectory optimization algorithm that utilizes communication
of robots' current states to account to the other robots while using local
object based maps for identifying obstacles. Based upon this data, we predict
the trajectory expected to be traversed by the robots and utilize that to avoid
collisions by formulating regions of free space that the robot can be without
colliding with other robots and obstacles. A trajectory is optimized
constraining the robot to remain within this region.The proposed method is
tested in simulations on Gazebo using ROS.
",1,0,0,0,0,0
19892,On the bottom of spectra under coverings,"  For a Riemannian covering $M_1\to M_0$ of complete Riemannian manifolds with
boundary (possibly empty) and respective fundamental groups
$\Gamma_1\subseteq\Gamma_0$, we show that the bottoms of the spectra of $M_0$
and $M_1$ coincide if the right action of $\Gamma_0$ on
$\Gamma_1\backslash\Gamma_0$ is amenable.
",0,0,1,0,0,0
19893,Theory of ground states for classical Heisenberg spin systems IV,"  We extend the theory of ground states of classical Heisenberg spin systems
previously published to the case where the interaction with an external
magnetic field is described by a Zeeman term. The ground state problem for the
Heisenberg-Zeeman Hamiltonian can be reduced first to the relative ground state
problem, and, in a second step, to the absolute ground state problem for pure
Heisenberg Hamiltonians depending on an additional Lagrange parameter. We
distinguish between continuous and discontinuous reduction. Moreover, there are
various general statements about Heisenberg-Zeeman systems that will be proven
under most general assumptions. One topic is the connection between the minimal
energy functions $E_{min}$ for the Heisenberg energy and $H_{min}$ for the
Heisenberg-Zeeman energy which turn out to be essentially mutual
Legendre-Fenchel transforms. This generalization of the traditional Legendre
transform is especially suited to cope with situations where the function
$E_{min}$ is not convex and consequently there is a magnetization jump at a
critical field. Another topic is magnetization and the occurrence of threshold
fields $B_{thr}$ and saturation fields $B_{sat}$, where we provide a general
formula for the latter. We suggest a distinction between ferromagnetic and
anti-ferromagnetic systems based on the vanishing of $B_{sat}$ for the former
ones. Parabolic systems are defined in such a way that $E_{min}$ and $H_{min}$
have a particularly simple form and studied in detail. For a large class of
parabolic systems the relative ground states can be constructed from the
absolute ground state by means of a so-called umbrella family. Finally we
provide a counter-example of a parabolic system where this construction is not
possible.
",0,1,0,0,0,0
19894,Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model,"  We present a framework for vision-based model predictive control (MPC) for
the task of aggressive, high-speed autonomous driving. Our approach uses deep
convolutional neural networks to predict cost functions from input video which
are directly suitable for online trajectory optimization with MPC. We
demonstrate the method in a high speed autonomous driving scenario, where we
use a single monocular camera and a deep convolutional neural network to
predict a cost map of the track in front of the vehicle. Results are
demonstrated on a 1:5 scale autonomous vehicle given the task of high speed,
aggressive driving.
",1,0,0,0,0,0
19895,K-theory of line bundles and smooth varieties,"  We give a $K$-theoretic criterion for a quasi-projective variety to be
smooth. If $\mathbb{L}$ is a line bundle corresponding to an ample invertible
sheaf on $X$, it suffices that $K_q(X) = K_q(\mathbb{L})$ for all
$q\le\dim(X)+1$.
",0,0,1,0,0,0
19896,Local Two-Sample Testing: A New Tool for Analysing High-Dimensional Astronomical Data,"  Modern surveys have provided the astronomical community with a flood of
high-dimensional data, but analyses of these data often occur after their
projection to lower-dimensional spaces. In this work, we introduce a local
two-sample hypothesis test framework that an analyst may directly apply to data
in their native space. In this framework, the analyst defines two classes based
on a response variable of interest (e.g. higher-mass galaxies versus lower-mass
galaxies) and determines at arbitrary points in predictor space whether the
local proportions of objects that belong to the two classes significantly
differs from the global proportion.
Our framework has a potential myriad of uses throughout astronomy; here, we
demonstrate its efficacy by applying it to a sample of 2487 i-band-selected
galaxies observed by the HST ACS in four of the CANDELS program fields. For
each galaxy, we have seven morphological summary statistics along with an
estimated stellar mass and star-formation rate. We perform two studies: one in
which we determine regions of the seven-dimensional space of morphological
statistics where high-mass galaxies are significantly more numerous than
low-mass galaxies, and vice-versa, and another study where we use SFR in place
of mass. We find that we are able to identify such regions, and show how
high-mass/low-SFR regions are associated with concentrated and undisturbed
galaxies while galaxies in low-mass/high-SFR regions appear more extended
and/or disturbed than their high-mass/low-SFR counterparts.
",0,1,0,0,0,0
19897,Are You Tampering With My Data?,"  We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary.
",0,0,0,1,0,0
19898,Answering Spatial Multiple-Set Intersection Queries Using 2-3 Cuckoo Hash-Filters,"  We show how to answer spatial multiple-set intersection queries in O(n(log
w)/w + kt) expected time, where n is the total size of the t sets involved in
the query, w is the number of bits in a memory word, k is the output size, and
c is any fixed constant. This improves the asymptotic performance over previous
solutions and is based on an interesting data structure, known as 2-3 cuckoo
hash-filters. Our results apply in the word-RAM model (or practical RAM model),
which allows for constant-time bit-parallel operations, such as bitwise AND,
OR, NOT, and MSB (most-significant 1-bit), as exist in modern CPUs and GPUs.
Our solutions apply to any multiple-set intersection queries in spatial data
sets that can be reduced to one-dimensional range queries, such as spatial join
queries for one-dimensional points or sets of points stored along space-filling
curves, which are used in GIS applications.
",1,0,0,0,0,0
19899,A framework for cascade size calculations on random networks,"  We present a framework to calculate the cascade size evolution for a large
class of cascade models on random network ensembles in the limit of infinite
network size. Our method is exact and applies to network ensembles with almost
arbitrary degree distribution, degree-degree correlations and, in case of
threshold models, with arbitrary threshold distribution. With our approach, we
shift the perspective from the known branching process approximations to the
iterative update of suitable probability distributions. Such distributions are
key to capture cascade dynamics that involve possibly continuous quantities and
that depend on the cascade history, e.g. if load is accumulated over time.
These distributions respect the Markovian nature of the studied random
processes. Random variables capture the impact of nodes that have failed at any
point in the past on their neighborhood. As a proof of concept, we provide two
examples: (a) Constant load models that cover many of the analytically
tractable cascade models, and, as a highlight, (b) a fiber bundle model that
was not tractable by branching process approximations before. Our derivations
cover the whole cascade dynamics, not only their steady state. This allows to
include interventions in time or further model complexity in the analysis.
",1,1,0,0,0,0
19900,A Gaussian Process Regression Model for Distribution Inputs,"  Monge-Kantorovich distances, otherwise known as Wasserstein distances, have
received a growing attention in statistics and machine learning as a powerful
discrepancy measure for probability distributions. In this paper, we focus on
forecasting a Gaussian process indexed by probability distributions. For this,
we provide a family of positive definite kernels built using transportation
based distances. We provide a probabilistic understanding of these kernels and
characterize the corresponding stochastic processes. We prove that the Gaussian
processes indexed by distributions corresponding to these kernels can be
efficiently forecast, opening new perspectives in Gaussian process modeling.
",0,0,1,1,0,0
19901,The intrinsic stable normal cone,"  We construct an analog of the intrinsic normal cone of Behrend-Fantechi in
the equivariant motivic stable homotopy category over a base-scheme B and
construct a fundament class in E-cohomology for any cohomology theory E in
SH(B). For affine B, a perfect obstruction theory gives rise to a virtual
fundamental class in a twisted Borel-Moore E-homology for arbitrary E. This
includes motivic cohomology (homotopy invariant) K-theory algebraic cobordism
and the oriented Chow groups of Barge-Morel and Fasel. In the case of motivic
cohomology, we recover the constructions of Behrend-Fantechi, with values in
the Chow group.
",0,0,1,0,0,0
19902,Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes,"  Most old globular clusters (GCs) in the Galaxy are observed to have internal
chemical abundance spreads in light elements. We discuss a new GC formation
scenario based on hierarchical star formation within fractal molecular clouds.
In the new scenario, a cluster of bound and unbound star clusters (`star
cluster complex', SCC) that have a power-law cluster mass function with a slope
(beta) of 2 is first formed from a massive gas clump developed in a dwarf
galaxy. Such cluster complexes and beta=2 are observed and expected from
hierarchical star formation. The most massive star cluster (`main cluster'),
which is the progenitor of a GC, can accrete gas ejected from asymptotic giant
branch (AGB) stars initially in the cluster and other low-mass clusters before
the clusters are tidally stripped or destroyed to become field stars in the
dwarf. The SCC is initially embedded in a giant gas hole created by numerous
supernovae of the SCC so that cold gas outside the hole can be accreted onto
the main cluster later. New stars formed from the accreted gas have chemical
abundances that are different from those of the original SCC. Using
hydrodynamical simulations of GC formation based on this scenario, we show that
the main cluster with the initial mass as large as [2-5]x10^5 Msun can accrete
more than 10^5 Msun gas from AGB stars of the SCC. We suggest that merging of
hierarchical star cluster complexes can play key roles in stellar halo
formation around GCs and self-enrichment processes of GCs.
",0,1,0,0,0,0
19903,Multiple Source Domain Adaptation with Adversarial Training of Neural Networks,"  While domain adaptation has been actively researched in recent years, most
theoretical results and algorithms focus on the single-source-single-target
adaptation setting. Naive application of such algorithms on multiple source
domain adaptation problem may lead to suboptimal solutions. As a step toward
bridging the gap, we propose a new generalization bound for domain adaptation
when there are multiple source domains with labeled instances and one target
domain with unlabeled instances. Compared with existing bounds, the new bound
does not require expert knowledge about the target distribution, nor the
optimal combination rule for multisource domains. Interestingly, our theory
also leads to an efficient learning strategy using adversarial neural networks:
we show how to interpret it as learning feature representations that are
invariant to the multiple domain shifts while still being discriminative for
the learning task. To this end, we propose two models, both of which we call
multisource domain adversarial networks (MDANs): the first model optimizes
directly our bound, while the second model is a smoothed approximation of the
first one, leading to a more data-efficient and task-adaptive model. The
optimization tasks of both models are minimax saddle point problems that can be
optimized by adversarial training. To demonstrate the effectiveness of MDANs,
we conduct extensive experiments showing superior adaptation performance on
three real-world datasets: sentiment analysis, digit classification, and
vehicle counting.
",1,0,0,1,0,0
19904,OSSOS: V. Diffusion in the orbit of a high-perihelion distant Solar System object,"  We report the discovery of the minor planet 2013 SY$_{99}$, on an
exceptionally distant, highly eccentric orbit. With a perihelion of 50.0 au,
2013 SY$_{99}$'s orbit has a semi-major axis of $730 \pm 40$ au, the largest
known for a high-perihelion trans-Neptunian object (TNO), well beyond those of
(90377) Sedna and 2012 VP$_{113}$. Yet, with an aphelion of $1420 \pm 90$ au,
2013 SY$_{99}$'s orbit is interior to the region influenced by Galactic tides.
Such TNOs are not thought to be produced in the current known planetary
architecture of the Solar System, and they have informed the recent debate on
the existence of a distant giant planet. Photometry from the
Canada-France-Hawaii Telescope, Gemini North and Subaru indicate 2013 SY$_{99}$
is $\sim 250$ km in diameter and moderately red in colour, similar to other
dynamically excited TNOs. Our dynamical simulations show that Neptune's weak
influence during 2013 SY$_{99}$'s perihelia encounters drives diffusion in its
semi-major axis of hundreds of astronomical units over 4 Gyr. The overall
symmetry of random walks in semi-major axis allow diffusion to populate 2013
SY$_{99}$'s orbital parameter space from the 1000-2000 au inner fringe of the
Oort cloud. Diffusion affects other known TNOs on orbits with perihelia of 45
to 49 au and semi-major axes beyond 250 au, providing a formation mechanism
that implies an extended population, gently cycling into and returning from the
inner fringe of the Oort cloud.
",0,1,0,0,0,0
19905,Solution of the Lindblad equation for spin helix states,"  Using Lindblad dynamics we study quantum spin systems with dissipative
boundary dynamics that generate a stationary nonequilibrium state with a
non-vanishing spin current that is locally conserved except at the boundaries.
We demonstrate that with suitably chosen boundary target states one can solve
the many-body Lindblad equation exactly in any dimension. As solution we obtain
pure states at any finite value of the dissipation strength and any system
size. They are characterized by a helical stationary magnetization profile and
a superdiffusive ballistic current of order one, independent of system size
even when the quantum spin system is not integrable. These results are derived
in explicit form for the one-dimensional spin-1/2 Heisenberg chain and its
higher-spin generalizations (which include for spin-1 the integrable
Zamolodchikov-Fateev model and the bi-quadratic Heisenberg chain). The
extension of the results to higher dimensions is straightforward.
",0,1,0,0,0,0
19906,Global bifurcation map of the homogeneus states in the Gray-Scott model,"  We study the spatially homogeneous time dependent solutions and their
bifurcations of the Gray-Scott model. We find the global map of bifurcations by
a combination of rigorous verification of the existence of Takens Bogdanov and
a Bautin bifurcations, in the space of two parameters k and F. With the aid of
numerical continuation of local bifurcation curves we give a global description
of all the possible bifurcations
",0,0,1,0,0,0
19907,Stochastic Composite Least-Squares Regression with convergence rate O(1/n),"  We consider the minimization of composite objective functions composed of the
expectation of quadratic functions and an arbitrary convex function. We study
the stochastic dual averaging algorithm with a constant step-size, showing that
it leads to a convergence rate of O(1/n) without strong convexity assumptions.
This thus extends earlier results on least-squares regression with the
Euclidean geometry to (a) all convex regularizers and constraints, and (b) all
geome-tries represented by a Bregman divergence. This is achieved by a new
proof technique that relates stochastic and deterministic recursions.
",0,0,1,1,0,0
19908,Fisher GAN,"  Generative Adversarial Networks (GANs) are powerful models for learning
complex distributions. Stable training of GANs has been addressed in many
recent works which explore different metrics between distributions. In this
paper we introduce Fisher GAN which fits within the Integral Probability
Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a
data dependent constraint on its second order moments. We show in this paper
that Fisher GAN allows for stable and time efficient training that does not
compromise the capacity of the critic, and does not need data independent
constraints such as weight clipping. We analyze our Fisher IPM theoretically
and provide an algorithm based on Augmented Lagrangian for Fisher GAN. We
validate our claims on both image sample generation and semi-supervised
classification using Fisher GAN.
",1,0,0,1,0,0
19909,Language as a matrix product state,"  We propose a statistical model for natural language that begins by
considering language as a monoid, then representing it in complex matrices with
a compatible translation invariant probability measure. We interpret the
probability measure as arising via the Born rule from a translation invariant
matrix product state.
",1,1,0,1,0,0
19910,On the application of Mattis-Bardeen theory in strongly disordered superconductors,"  The low energy optical conductivity of conventional superconductors is
usually well described by Mattis-Bardeen (MB) theory which predicts the onset
of absorption above an energy corresponding to twice the superconducing (SC)
gap parameter Delta. Recent experiments on strongly disordered superconductors
have challenged the application of the MB formulas due to the occurrence of
additional spectral weight at low energies below 2Delta. Here we identify three
crucial items which have to be included in the analysis of optical-conductivity
data for these systems: (a) the correct identification of the optical threshold
in the Mattis-Bardeen theory, and its relation with the gap value extracted
from the measured density of states, (b) the gauge-invariant evaluation of the
current-current response function, needed to account for the optical absorption
by SC collective modes, and (c) the inclusion into the MB formula of the energy
dependence of the density of states present already above Tc. By computing the
optical conductvity in the disordered attractive Hubbard model we analyze the
relevance of all these items, and we provide a compelling scheme for the
analysis and interpretation of the optical data in real materials.
",0,1,0,0,0,0
19911,Nanoscale Magnetic Imaging using Circularly Polarized High-Harmonic Radiation,"  This work demonstrates nanoscale magnetic imaging using bright circularly
polarized high-harmonic radiation. We utilize the magneto-optical contrast of
worm-like magnetic domains in a Co/Pd multilayer structure, obtaining
quantitative amplitude and phase maps by lensless imaging. A
diffraction-limited spatial resolution of 49 nm is achieved with iterative
phase reconstruction enhanced by a holographic mask. Harnessing the unique
coherence of high harmonics, this approach will facilitate quantitative,
element-specific and spatially-resolved studies of ultrafast magnetization
dynamics, advancing both fundamental and applied aspects of nanoscale
magnetism.
",0,1,0,0,0,0
19912,Towards integrated superconducting detectors on lithium niobate waveguides,"  Superconducting detectors are now well-established tools for low-light
optics, and in particular quantum optics, boasting high-efficiency, fast
response and low noise. Similarly, lithium niobate is an important platform for
integrated optics given its high second-order nonlinearity, used for high-speed
electro-optic modulation and polarization conversion, as well as frequency
conversion and sources of quantum light. Combining these technologies addresses
the requirements for a single platform capable of generating, manipulating and
measuring quantum light in many degrees of freedom, in a compact and
potentially scalable manner. We will report on progress integrating tungsten
transition-edge sensors (TESs) and amorphous tungsten silicide superconducting
nanowire single-photon detectors (SNSPDs) on titanium in-diffused lithium
niobate waveguides. The travelling-wave design couples the evanescent field
from the waveguides into the superconducting absorber. We will report on
simulations and measurements of the absorption, which we can characterize at
room temperature prior to cooling down the devices. Independently, we show how
the detectors respond to flood illumination, normally incident on the devices,
demonstrating their functionality.
",0,1,0,0,0,0
19913,The Hilbert scheme of 11 points in A^3 is irreducible,"  We prove that the Hilbert scheme of 11 points on a smooth threefold is
irreducible. In the course of the proof, we present several known and new
techniques for producing curves on the Hilbert scheme.
",0,0,1,0,0,0
19914,Dynamically reconfigurable metal-semiconductor Yagi-Uda nanoantenna,"  We propose a novel type of tunable Yagi-Uda nanoantenna composed of
metal-dielectric (Ag-Ge) core-shell nanoparticles. We show that, due to the
combination of two types of resonances in each nanoparticle, such hybrid
Yagi-Uda nanoantenna can operate in two different regimes. Besides the
conventional nonresonant operation regime at low frequencies, characterized by
highly directive emission in the forward direction, there is another one at
higher frequencies caused by hybrid magneto-electric response of the core-shell
nanoparticles. This regime is based on the excitation of the van Hove
singularity, and emission in this regime is accompanied by high values of
directivity and Purcell factor within the same narrow frequency range. Our
analysis reveals the possibility of flexible dynamical tuning of the hybrid
nanoantenna emission pattern via electron-hole plasma excitation by 100
femtosecond pump pulse with relatively low peak intensities $\sim$200
MW/cm$^2$.
",0,1,0,0,0,0
19915,Synthesis and In Situ Modification of Hierarchical SAPO-34 by PEG with Different Molecular Weights; Application in MTO Process,"  Modified structures of SAPO-34 were prepared using polyethylene glycol as the
mesopores generating agent. The synthesized catalysts were applied in
methanol-to-olefins (MTO) process. All modified synthesized catalysts were
characterized via XRD, XRF, FESEM, FTIR, N2 adsorption-desorption techniques,
and temperature-programmed NH3 desorption and they were compared with
conventional microporous SAPO-34. Introduction of non-ionic PEG capping agent
affected the degree of homogeneity and integrity of the synthesis media and
thus reduced the number of nuclei and order of coordination structures
resulting in larger and less crystalline particles compared with the
conventional sample. During the calcination process, decomposition of absorbed
PEG moieties among the piled up SAPO patches formed a great portion of tuned
mesopores into the microporous matrix. These tailored mesopores were served as
auxiliary diffusion pathways in MTO reaction. The effects of molecular weight
of PEG and PEG/Al molar ratio on the properties of the synthesized materials
were investigated in order to optimize their MTO reaction performance. It was
revealed that both of these two parameters can significantly change the
structural composition and physicochemical properties of resultant products.
Using PEG with MW of 6000 has led to the formation of RHO and CHA structural
frameworks i.e. DNL-6 and SAPO-34, simultaneously, while addition of PEG with
MW of 4000 resulted the formation of pure SAPO-34 phase. Altering the PEG/Al
molar ratio in the precursor significantly influenced the porosity and acidity
of the synthesized silicoaluminophosphate products. SAPO-34 impregnated with
PEG molecular weight of 4000 and PEG/Al molar ratio of 0.0125 showed superior
catalytic stability in MTO reaction because of the tuned bi-modal porosity and
tailored acidity pattern.
",0,1,0,0,0,0
19916,Support Feature Machines,"  Support Vector Machines (SVMs) with various kernels have played dominant role
in machine learning for many years, finding numerous applications. Although
they have many attractive features interpretation of their solutions is quite
difficult, the use of a single kernel type may not be appropriate in all areas
of the input space, convergence problems for some kernels are not uncommon, the
standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space
complexity for $m$ training patterns. Kernel methods work because they
implicitly provide new, useful features. Such features, derived from various
kernels and other vector transformations, may be used directly in any machine
learning algorithm, facilitating multiresolution, heterogeneous models of data.
Therefore Support Feature Machines (SFM) based on linear models in the extended
feature spaces, enabling control over selection of support features, give at
least as good results as any kernel-based SVMs, removing all problems related
to interpretation, scaling and convergence. This is demonstrated for a number
of benchmark datasets analyzed with linear discrimination, SVM, decision trees
and nearest neighbor methods.
",1,0,0,1,0,0
19917,Voltage Control Using Eigen Value Decomposition of Fast Decoupled Load Flow Jacobian,"  Voltage deviations occur frequently in power systems. If the violation at
some buses falls outside the prescribed range, it will be necessary to correct
the problem by controlling reactive power resources. In this paper, an optimal
algorithm is proposed to solve this problem by identifying the voltage buses,
that will have a maximum effect on the affected buses, and setting their new
set-points. This algorithm is based on the Eigen-Value Decomposition of the
fast decoupled load flow Jacobian matrix. Different Case studies including IEEE
9, 14, 30 and 57 bus systems have been used to verify the method.
",1,0,0,0,0,0
19918,Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer,"  Convolutional Neural Networks (CNNs) are a popular deep learning architecture
widely applied in different domains, in particular in classifying over images,
for which the concept of convolution with a filter comes naturally.
Unfortunately, the requirement of a distance (or, at least, of a neighbourhood
function) in the input feature space has so far prevented its direct use on
data types such as omics data. However, a number of omics data are metrizable,
i.e., they can be endowed with a metric structure, enabling to adopt a
convolutional based deep learning framework, e.g., for prediction. We propose a
generalized solution for CNNs on omics data, implemented through a dedicated
Keras layer. In particular, for metagenomics data, a metric can be derived from
the patristic distance on the phylogenetic tree. For transcriptomics data, we
combine Gene Ontology semantic similarity and gene co-expression to define a
distance; the function is defined through a multilayer network where 3 layers
are defined by the GO mutual semantic similarity while the fourth one by gene
co-expression. As a general tool, feature distance on omics data is enabled by
OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning
framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for
Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a
metagenomics collection of gut microbiota of 222 IBD patients.
",0,0,0,1,0,0
19919,Neural Network Multitask Learning for Traffic Flow Forecasting,"  Traditional neural network approaches for traffic flow forecasting are
usually single task learning (STL) models, which do not take advantage of the
information provided by related tasks. In contrast to STL, multitask learning
(MTL) has the potential to improve generalization by transferring information
in training signals of extra tasks. In this paper, MTL based neural networks
are used for traffic flow forecasting. For neural network MTL, a
backpropagation (BP) network is constructed by incorporating traffic flows at
several contiguous time instants into an output layer. Nodes in the output
layer can be seen as outputs of different but closely related STL tasks.
Comprehensive experiments on urban vehicular traffic flow data and comparisons
with STL show that MTL in BP neural networks is a promising and effective
approach for traffic flow forecasting.
",1,0,0,0,0,0
19920,A note on a new paradox in superluminal signalling,"  The Tolman paradox is well known as a base for demonstrating the causality
violation by faster-than-light signals within special relativity. It is
constructed using a two-way exchange of faster-than-light signals between two
inertial observers who are in a relative motion receding one from another.
Recently a one-way superluminal signalling arrangement was suggested as a
possible construction of a causal paradox. In this note we show that this
suggestion is not correct, and no causality principle violation can occur in
any one-way signalling by the use of faster-than-light particles and signals.
",0,1,0,0,0,0
19921,The Compressed Overlap Index,"  For analysing text algorithms, for computing superstrings, or for testing
random number generators, one needs to compute all overlaps between any pairs
of words in a given set. The positions of overlaps of a word onto itself, or of
two words, are needed to compute the absence probability of a word in a random
text, or the numbers of common words shared by two random texts. In all these
contexts, one needs to compute or to query overlaps between pairs of words in a
given set. For this sake, we designed COvI, a compressed overlap index that
supports multiple queries on overlaps: like computing the correlation of two
words, or listing pairs of words whose longest overlap is maximal among all
possible pairs. COvI stores overlaps in a hierarchical and non-redundant
manner. We propose an implementation that can handle datasets of millions of
words and still answer queries efficiently. Comparison with a baseline solution
- called FullAC - relying on the Aho-Corasick automaton shows that COvI
provides significant advantages. For similar construction times, COvI requires
half the memory FullAC, and still solves complex queries much faster.
",1,0,0,0,0,0
19922,An intuitive approach to the unified theory of spin-relaxation,"  Spin-relaxation is conventionally discussed using two different approaches
for materials with and without inversion symmetry. The former is known as the
Elliott-Yafet (EY) theory and for the latter the D'yakonov-Perel' (DP) theory
applies, respectively. We discuss herein a simple and intuitive approach to
demonstrate that the two seemingly disparate mechanisms are closely related. A
compelling analogy between the respective Hamiltonian is presented and that the
usual derivation of spin-relaxation times, in the respective frameworks of the
two theories, can be performed. The result also allows to obtain the less
canonical spin-relaxation regimes; the generalization of the EY when the
material has a large quasiparticle broadening and the DP mechanism in ultrapure
semiconductors. The method also allows a practical and intuitive numerical
implementation of the spin-relaxation calculation, which is demonstrated for
MgB$_2$ that has anomalous spin-relaxation properties.
",0,1,0,0,0,0
19923,Weighted Random Walk Sampling for Multi-Relational Recommendation,"  In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency.
",1,0,0,0,0,0
19924,Understanding the Twitter Usage of Humanities and Social Sciences Academic Journals,"  Scholarly communication has the scope to transcend the limitations of the
physical world through social media extended coverage and shortened information
paths. Accordingly, publishers have created profiles for their journals in
Twitter to promote their publications and to initiate discussions with public.
This paper investigates the Twitter presence of humanities and social sciences
(HSS) journal titles obtained from mainstream citation indices, by analysing
the interaction and communication patterns. This study utilizes webometric data
collection, descriptive analysis, and social network analysis. Findings
indicate that the presence of HSS journals in Twitter across disciplines is not
yet substantial. Sharing of general websites appears to be the key activity
performed by HSS journals in Twitter. Among them, web content from news portals
and magazines are highly disseminated. Sharing of research articles and
retweeting was not majorly observed. Inter-journal communication is apparent
within the same citation index, but it is very minimal with journals from the
other index. However, there seems to be an effort to broaden communication
beyond the research community, reaching out to connect with the public.
",1,0,0,0,0,0
19925,Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting,"  Machine learning algorithms, when applied to sensitive data, pose a distinct
threat to privacy. A growing body of prior work demonstrates that models
produced by these algorithms may leak specific private information in the
training data to an attacker, either through the models' structure or their
observable behavior. However, the underlying cause of this privacy risk is not
well understood beyond a handful of anecdotal accounts that suggest overfitting
and influence might play a role.
This paper examines the effect that overfitting and influence have on the
ability of an attacker to learn information about the training data from
machine learning models, either through training set membership inference or
attribute inference attacks. Using both formal and empirical analyses, we
illustrate a clear relationship between these factors and the privacy risk that
arises in several popular machine learning algorithms. We find that overfitting
is sufficient to allow an attacker to perform membership inference and, when
the target attribute meets certain conditions about its influence, attribute
inference attacks. Interestingly, our formal analysis also shows that
overfitting is not necessary for these attacks and begins to shed light on what
other factors may be in play. Finally, we explore the connection between
membership inference and attribute inference, showing that there are deep
connections between the two that lead to effective new attacks.
",1,0,0,1,0,0
19926,Nanoplatelets as material system between strong confinement and weak confinement,"  Recently, the fabrication of CdSe nanoplatelets became an important research
topic. Nanoplatelets are often described as having a similar electronic
structure as 2D dimensional quantum wells and are promoted as colloidal quantum
wells with monolayer precision width. In this paper, we show, that
nanoplatelets are not ideal quantum wells, but cover depending on the size: the
strong confinement regime, an intermediate regime and a Coulomb dominated
regime. Thus, nanoplatelets are an ideal platform to study the physics in these
regimes. Therefore, the exciton states of the nanoplatelets are numerically
calculated by solving the full four dimensional Schrödinger equation. We
compare the results with approximate solutions from semiconductor quantum well
and quantum dot theory. The paper can also act as review of these concepts for
the colloidal nanoparticle community.
",0,1,0,0,0,0
19927,Combined Thermal Control and GNC: An Enabling Technology for CubeSat Surface Probes and Small Robots,"  Advances in GNC, particularly from miniaturized control electronics,
reaction-wheels and attitude determination sensors make it possible to design
surface probes and small robots to perform surface exploration and science on
low-gravity environments. These robots would use their reaction wheels to roll,
hop and tumble over rugged surfaces. These robots could provide 'Google
Streetview' quality images of off-world surfaces and perform some unique
science using penetrometers. These systems can be powered by high-efficiency
fuel cells that operate at 60-65 % and utilize hydrogen and oxygen electrolyzed
from water. However, one of the major challenges that prevent these probes and
robots from performing long duration surface exploration and science is thermal
design and control. In the inner solar system, during the day time, there is
often enough solar-insolation to keep these robots warm and power these
devices, but during eclipse the temperatures falls well below storage
temperature. We have developed a thermal control system that utilizes chemicals
to store and dispense heat when needed. The system takes waste products, such
as water from these robots and transfers them to a thermochemical storage
system. These thermochemical storage systems when mixed with water (a waste
product from a PEM fuel cell) releases heat. Under eclipse, the heat from the
thermochemical storage system is released to keep the probe warm enough to
survive. In sunlight, solar photovoltaics are used to electrolyze the water and
reheat the thermochemical storage system to release the water. Our research has
showed thermochemical storage systems are a feasible solution for use on
surface probes and robots for applications on the Moon, Mars and asteroids.
",1,1,0,0,0,0
19928,Denoising Adversarial Autoencoders,"  Unsupervised learning is of growing interest because it unlocks the potential
held in vast amounts of unlabelled data to learn useful representations for
inference. Autoencoders, a form of generative model, may be trained by learning
to reconstruct unlabelled input data from a latent representation space. More
robust representations may be produced by an autoencoder if it learns to
recover clean input samples from corrupted ones. Representations may be further
improved by introducing regularisation during training to shape the
distribution of the encoded data in latent space. We suggest denoising
adversarial autoencoders, which combine denoising and regularisation, shaping
the distribution of latent space using adversarial training. We introduce a
novel analysis that shows how denoising may be incorporated into the training
and sampling of adversarial autoencoders. Experiments are performed to assess
the contributions that denoising makes to the learning of representations for
classification and sample synthesis. Our results suggest that autoencoders
trained using a denoising criterion achieve higher classification performance,
and can synthesise samples that are more consistent with the input data than
those trained without a corruption process.
",1,0,0,1,0,0
19929,Monodromy map for tropical Dolbeault cohomology,"  We define monodromy maps for tropical Dolbeault cohomology of algebraic
varieties over non-Archimedean fields. We propose a conjecture of Hodge
isomorphisms via monodromy maps, and provide some evidence.
",0,0,1,0,0,0
19930,Randomness Evaluation with the Discrete Fourier Transform Test Based on Exact Analysis of the Reference Distribution,"  In this paper, we study the problems in the discrete Fourier transform (DFT)
test included in NIST SP 800-22 released by the National Institute of Standards
and Technology (NIST), which is a collection of tests for evaluating both
physical and pseudo-random number generators for cryptographic applications.
The most crucial problem in the DFT test is that its reference distribution of
the test statistic is not derived mathematically but rather numerically
estimated, the DFT test for randomness is based on a pseudo-random number
generator (PRNG). Therefore, the present DFT test should not be used unless the
reference distribution is mathematically derived. Here, we prove that a power
spectrum, which is a component of the test statistic, follows a chi-squared
distribution with 2 degrees of freedom. Based on this fact, we propose a test
whose reference distribution of the test statistic is mathematically derived.
Furthermore, the results of testing non-random sequences and several PRNGs
showed that the proposed test is more reliable and definitely more sensitive
than the present DFT test.
",1,0,0,1,0,0
19931,Analogies Explained: Towards Understanding Word Embeddings,"  Word embeddings generated by neural network methods such as word2vec (W2V)
are well known to exhibit seemingly linear behaviour, e.g. the embeddings of
analogy ""woman is to queen as man is to king"" approximately describe a
parallelogram. This property is particularly intriguing since the embeddings
are not trained to achieve it. Several explanations have been proposed, but
each introduces assumptions that do not hold in practice. We derive a
probabilistically grounded definition of paraphrasing and show it can be
re-interpreted as word transformation, a mathematical description of ""$w_x$ is
to $w_y$"". From these concepts we prove existence of the linear relationship
between W2V-type embeddings that underlies the analogical phenomenon, and
identify explicit error terms in the relationship.
",1,0,0,1,0,0
19932,Zero sum partition into sets of the same order and its applications,"  We will say that an Abelian group $\Gamma$ of order $n$ has the
$m$-\emph{zero-sum-partition property} ($m$-\textit{ZSP-property}) if $m$
divides $n$, $m\geq 2$ and there is a partition of $\Gamma$ into pairwise
disjoint subsets $A_1, A_2,\ldots , A_t$, such that $|A_i| = m$ and $\sum_{a\in
A_i}a = g_0$ for $1 \leq i \leq t$, where $g_0$ is the identity element of
$\Gamma$.
In this paper we study the $m$-ZSP property of $\Gamma$. We show that
$\Gamma$ has $m$-ZSP if and only if $|\Gamma|$ is odd or $m\geq 3$ and $\Gamma$
has more than one involution. We will apply the results to the study of group
distance magic graphs as well as to generalized Kotzig arrays.
",0,0,1,0,0,0
19933,Strongly Coupled Dark Energy with Warm dark matter vs. LCDM,"  Cosmologies including strongly Coupled (SC) Dark Energy (DE) and Warm dark
matter (SCDEW) are based on a conformally invariant (CI) attractor solution
modifying the early radiative expansion. Then, aside of radiation, a kinetic
field $\Phi$ and a DM component account for a stationary fraction, $\sim 1\,
\%$, of the total energy. Most SCDEW predictions are hardly distinguishable
from LCDM, while SCDEW alleviates quite a few LCDM conceptual problems, as well
as its difficulties to meet data below the average galaxy scale. The CI
expansion begins at the inflation end, when $\Phi$ (future DE) possibly plays a
role in reheating, and ends at the Higgs' scale. Afterwards, a number of viable
options is open, allowing for the transition from the CI expansion to the
present Universe. In this paper: (i) We show how the attractor is recovered
when the spin degrees of freedom decreases. (ii) We perform a detailed
comparison of CMB anisotropy and polarization spectra for SCDEW and LCDM,
including tensor components, finding negligible discrepancies. (iii) Linear
spectra exhibit a greater parameter dependence at large $k$'s, but are still
consistent with data for suitable parameter choices. (iv) We also compare
previous simulation results with fresh data on galaxy concentration. Finally,
(v) we outline numerical difficulties at high $k$. This motivates a second
related paper, where such problems are treated in a quantitative way.
",0,1,0,0,0,0
19934,Clausal Analysis of First-order Proof Schemata,"  Proof schemata are a variant of LK-proofs able to simulate various induction
schemes in first-order logic by adding so called proof links to the standard
first-order LK-calculus. Proof links allow proofs to reference proofs thus
giving proof schemata a recursive structure. Unfortunately, applying reductive
cut- elimination is non-trivial in the presence of proof links. Borrowing the
concept of lazy instantiation from functional programming, we evaluate proof
links locally allowing reductive cut-elimination to proceed past them. Though,
this method cannot be used to obtain cut-free proof schemata, we nonetheless
obtain important results concerning the schematic CERES method, that is a
method of cut-elimination for proof schemata based on resolution. In ""Towards a
clausal analysis of cut-elimination"", it was shown that reductive
cut-elimination transforms a given LK-proof in such a way that a subsumption
relation holds between the pre- and post-transformation characteristic clause
sets, i.e. the clause set representing the cut-structure of an LK-proof. Let
CL(A') be the characteristic clause set of a normal form A' of an LK-proof A
that is reached by performing reductive cut-elimination on A without atomic cut
elimination. Then CL(A') is subsumed by all characteristic clause sets
extractable from any application of reductive cut-elimination to A. Such a
normal form is referred to as an ACNF top and plays an essential role in
methods of cut-elimination by resolution. These results can be extended to
proof schemata through our ""lazy instantiation"" of proof links, and provides an
essential step toward a complete cut-elimination method for proof schemata.
",1,0,1,0,0,0
19935,Emotion Intensities in Tweets,"  This paper examines the task of detecting intensity of emotion from text. We
create the first datasets of tweets annotated for anger, fear, joy, and sadness
intensities. We use a technique called best--worst scaling (BWS) that improves
annotation consistency and obtains reliable fine-grained scores. We show that
emotion-word hashtags often impact emotion intensity, usually conveying a more
intense emotion. Finally, we create a benchmark regression system and conduct
experiments to determine: which features are useful for detecting emotion
intensity, and, the extent to which two emotions are similar in terms of how
they manifest in language.
",1,0,0,0,0,0
19936,Modelling the descent of nitric oxide during the elevated stratopause event of January 2013,"  Using simulations with a whole-atmosphere chemistry-climate model nudged by
meteorological analyses, global satellite observations of nitrogen oxide (NO)
and water vapour by the Sub-Millimetre Radiometer instrument (SMR), of
temperature by the Microwave Limb Sounder (MLS), as well as local radar
observations, this study examines the recent major stratospheric sudden warming
accompanied by an elevated stratopause event (ESE) that occurred in January
2013. We examine dynamical processes during the ESE, including the role of
planetary wave, gravity wave and tidal forcing on the initiation of the descent
in the mesosphere-lower thermosphere (MLT) and its continuation throughout the
mesosphere and stratosphere, as well as the impact of model eddy diffusion. We
analyse the transport of NO and find the model underestimates the large descent
of NO compared to SMR observations. We demonstrate that the discrepancy arises
abruptly in the MLT region at a time when the resolved wave forcing and the
planetary wave activity increase, just before the elevated stratopause reforms.
The discrepancy persists despite doubling the model eddy diffusion. While the
simulations reproduce an enhancement of the semi-diurnal tide following the
onset of the 2013 SSW, corroborating new meteor radar observations at high
northern latitudes over Trondheim (63.4$^{\circ}$N), the modelled tidal
contribution to the forcing of the mean meridional circulation and to the
descent is a small portion of the resolved wave forcing, and lags it by about
ten days.
",0,1,0,0,0,0
19937,Optimal Evidence Accumulation on Social Networks,"  A fundamental question in biology is how organisms integrate sensory and
social evidence to make decisions. However, few models describe how both these
streams of information can be combined to optimize choices. Here we develop a
normative model for collective decision making in a network of agents
performing a two-alternative forced choice task. We assume that rational
(Bayesian) agents in this network make private measurements, and observe the
decisions of their neighbors until they accumulate sufficient evidence to make
an irreversible choice. As each agent communicates its decision to those
observing it, the flow of social information is described by a directed graph.
The decision-making process in this setting is intuitive, but can be complex.
We describe when and how the absence of a decision of a neighboring agent
communicates social information, and how an agent must marginalize over all
unobserved decisions. We also show how decision thresholds and network
connectivity affect group evidence accumulation, and describe the dynamics of
decision making in social cliques. Our model provides a bridge between the
abstractions used in the economics literature and the evidence accumulator
models used widely in neuroscience and psychology.
",1,0,0,0,1,0
19938,Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm,"  Conventional seismic techniques for detecting the subsurface geologic
features are challenged by limited data coverage, computational inefficiency,
and subjective human factors. We developed a novel data-driven geological
feature detection approach based on pre-stack seismic measurements. Our
detection method employs an efficient and accurate machine-learning detection
approach to extract useful subsurface geologic features automatically.
Specifically, our method is based on kernel ridge regression model. The
conventional kernel ridge regression can be computationally prohibited because
of the large volume of seismic measurements. We employ a data reduction
technique in combination with the conventional kernel ridge regression method
to improve the computational efficiency and reduce memory usage. In particular,
we utilize a randomized numerical linear algebra technique, named Nyström
method, to effectively reduce the dimensionality of the feature space without
compromising the information content required for accurate detection. We
provide thorough computational cost analysis to show efficiency of our new
geological feature detection methods. We further validate the performance of
our new subsurface geologic feature detection method using synthetic surface
seismic data for 2D acoustic and elastic velocity models. Our numerical
examples demonstrate that our new detection method significantly improves the
computational efficiency while maintaining comparable accuracy. Interestingly,
we show that our method yields a speed-up ratio on the order of $\sim10^2$ to
$\sim 10^3$ in a multi-core computational environment.
",1,0,0,1,0,0
19939,Metastable Modular Metastructures for On-Demand Reconfiguration of Band Structures and Non-Reciprocal Wave Propagation,"  We present a novel approach to achieve adaptable band structures and
non-reciprocal wave propagation by exploring and exploiting the concept of
metastable modular metastructures. Through studying the dynamics of wave
propagation in a chain composed of finite metastable modules, we provide
experimental and analysis results on non-reciprocal wave propagation and unveil
the underlying mechanisms in accomplishing such unidirectional energy
transmission. Utilizing the property adaptation feature afforded via
transitioning amongst metastable states, we uncovered an unprecedented bandgap
reconfiguration characteristic, which enables the adaptivity of wave
propagation within the metastructure. Overall, this investigation elucidates
the rich dynamics attainable by periodicity, nonlinearity, asymmetry, and
metastability, and creates a new class of adaptive structural and material
systems capable of realizing tunable bandgaps and non-reciprocal wave
transmissions.
",0,1,0,0,0,0
19940,Learning to Plan Chemical Syntheses,"  From medicines to materials, small organic molecules are indispensable for
human well-being. To plan their syntheses, chemists employ a problem solving
technique called retrosynthesis. In retrosynthesis, target molecules are
recursively transformed into increasingly simpler precursor compounds until a
set of readily available starting materials is obtained. Computer-aided
retrosynthesis would be a highly valuable tool, however, past approaches were
slow and provided results of unsatisfactory quality. Here, we employ Monte
Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS
was combined with an expansion policy network that guides the search, and an
""in-scope"" filter network to pre-select the most promising retrosynthetic
steps. These deep neural networks were trained on 12 million reactions, which
represents essentially all reactions ever published in organic chemistry. Our
system solves almost twice as many molecules and is 30 times faster in
comparison to the traditional search method based on extracted rules and
hand-coded heuristics. Finally after a 60 year history of computer-aided
synthesis planning, chemists can no longer distinguish between routes generated
by a computer system and real routes taken from the scientific literature. We
anticipate that our method will accelerate drug and materials discovery by
assisting chemists to plan better syntheses faster, and by enabling fully
automated robot synthesis.
",1,1,0,0,0,0
19941,Projective embedding of pairs and logarithmic K-stability,"  Let $\hat{L}$ be the projective completion of an ample line bundle $L$ over
$D$, a smooth projective manifold. Hwang-Singer \cite{HwangS} have constructed
complete CSCK metric on $\hat{L}\backslash D$. When the corresponding \kahler
form is in the cohomology class of a rational divisor $A$ and when $L$ has
negative CSCK metric on $D$, we show that the Kodaira embedding induced by
orthonormal basis of the Bergman space of $kA$ is almost balanced. As a
corollary, $(\hat{L},D,cA,0)$ is K-semistable.
",0,0,1,0,0,0
19942,Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network,"  To ensure that a robot is able to accomplish an extensive range of tasks, it
is necessary to achieve a flexible combination of multiple behaviors. This is
because the design of task motions suited to each situation would become
increasingly difficult as the number of situations and the types of tasks
performed by them increase. To handle the switching and combination of multiple
behaviors, we propose a method to design dynamical systems based on point
attractors that accept (i) ""instruction signals"" for instruction-driven
switching. We incorporate the (ii) ""instruction phase"" to form a point
attractor and divide the target task into multiple subtasks. By forming an
instruction phase that consists of point attractors, the model embeds a subtask
in the form of trajectory dynamics that can be manipulated using sensory and
instruction signals. Our model comprises two deep neural networks: a
convolutional autoencoder and a multiple time-scale recurrent neural network.
In this study, we apply the proposed method to manipulate soft materials. To
evaluate our model, we design a cloth-folding task that consists of four
subtasks and three patterns of instruction signals, which indicate the
direction of motion. The results depict that the robot can perform the required
task by combining subtasks based on sensory and instruction signals. And, our
model determined the relations among these signals using its internal dynamics.
",1,0,0,0,0,0
19943,The evolution of magnetic fields in hot stars,"  Over the last decade, tremendous strides have been achieved in our
understanding of magnetism in main sequence hot stars. In particular, the
statistical occurrence of their surface magnetism has been established (~10%)
and the field origin is now understood to be fossil. However, fundamental
questions remain: how do these fossil fields evolve during the post-main
sequence phases, and how do they influence the evolution of hot stars from the
main sequence to their ultimate demise? Filling the void of known magnetic
evolved hot (OBA) stars, studying the evolution of their fossil magnetic fields
along stellar evolution, and understanding the impact of these fields on the
angular momentum, rotation, mass loss, and evolution of the star itself, is
crucial to answering these questions, with far reaching consequences, in
particular for the properties of the precursors of supernovae explosions and
stellar remnants. In the framework of the BRITE spectropolarimetric survey and
LIFE project, we have discovered the first few magnetic hot supergiants. Their
longitudinal surface magnetic field is very weak but their configuration
resembles those of main sequence hot stars. We present these first
observational results and propose to interpret them at first order in the
context of magnetic flux conservation as the radius of the star expands with
evolution. We then also consider the possible impact of stellar structure
changes along evolution.
",0,1,0,0,0,0
19944,Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees,"  We develop polynomial-time heuristic methods to solve unimodular quadratic
programs (UQPs) approximately, which are known to be NP-hard. In the UQP
framework, we maximize a quadratic function of a vector of complex variables
with unit modulus. Several problems in active sensing and wireless
communication applications boil down to UQP. With this motivation, we present
three new heuristic methods with polynomial-time complexity to solve the UQP
approximately. The first method is called dominant-eigenvector-matching; here
the solution is picked that matches the complex arguments of the dominant
eigenvector of the Hermitian matrix in the UQP formulation. We also provide a
performance guarantee for this method. The second method, a greedy strategy, is
shown to provide a performance guarantee of (1-1/e) with respect to the optimal
objective value given that the objective function possesses a property called
string submodularity. The third heuristic method is called row-swap greedy
strategy, which is an extension to the greedy strategy and utilizes certain
properties of the UQP to provide a better performance than the greedy strategy
at the expense of an increase in computational complexity. We present numerical
results to demonstrate the performance of these heuristic methods, and also
compare the performance of these methods against a standard heuristic method
called semidefinite relaxation.
",1,0,1,0,0,0
19945,Can the Wild Bootstrap be Tamed into a General Analysis of Covariance Model?,"  It is well known that the F test is severly affected by heteroskedasticity in
unbalanced analysis of covariance (ANCOVA) models. Currently available remedies
for such a scenario are either based on heteroskedasticity-consistent
covariance matrix estimation (HCCME) or bootstrap techniques. However, the
HCCME approach tends to be liberal in small samples. Therefore, we propose a
combination of HCCME and a wild bootstrap technique. We prove the theoretical
validity of our approach and investigate its performance in an extensive
simulation study in comparison to existing procedures. The results indicate
that our proposed test remedies all problems of the ANCOVA F test and its
heteroskedasticityconsistent alternatives. Our test only requires very general
conditions, thus being applicable in a broad range of real-life settings.
",0,0,0,1,0,0
19946,Efficient K-Shot Learning with Regularized Deep Networks,"  Feature representations from pre-trained deep neural networks have been known
to exhibit excellent generalization and utility across a variety of related
tasks. Fine-tuning is by far the simplest and most widely used approach that
seeks to exploit and adapt these feature representations to novel tasks with
limited data. Despite the effectiveness of fine-tuning, itis often sub-optimal
and requires very careful optimization to prevent severe over-fitting to small
datasets. The problem of sub-optimality and over-fitting, is due in part to the
large number of parameters used in a typical deep convolutional neural network.
To address these problems, we propose a simple yet effective regularization
method for fine-tuning pre-trained deep networks for the task of k-shot
learning. To prevent overfitting, our key strategy is to cluster the model
parameters while ensuring intra-cluster similarity and inter-cluster diversity
of the parameters, effectively regularizing the dimensionality of the parameter
search space. In particular, we identify groups of neurons within each layer of
a deep network that shares similar activation patterns. When the network is to
be fine-tuned for a classification task using only k examples, we propagate a
single gradient to all of the neuron parameters that belong to the same group.
The grouping of neurons is non-trivial as neuron activations depend on the
distribution of the input data. To efficiently search for optimal groupings
conditioned on the input data, we propose a reinforcement learning search
strategy using recurrent networks to learn the optimal group assignments for
each network layer. Experimental results show that our method can be easily
applied to several popular convolutional neural networks and improve upon other
state-of-the-art fine-tuning based k-shot learning strategies by more than10%
",1,0,0,1,0,0
19947,The cohomology ring of some Hopf algebras,"  Let p be a prime, and k be a field of characteristic p. We investigate the
algebra structure and the structure of the cohomology ring for the connected
Hopf algebras of dimension p^3, which appear in the classification obtained in
[V.C. Nguyen, L.-H. Wang and X.-T. Wang, Classification of connected Hopf
algebras of dimension p^3, J. Algebra 424 (2015), 473-505]. The list consists
of 23 algebras together with two infinite families. We identify the Morita type
of the algebra, and in almost all cases this is sufficient to clarify the
structure of the cohomology ring.
",0,0,1,0,0,0
19948,Eckart ro-vibrational Hamiltonians via the gateway Hamilton operator: theory and practice,"  Recently, a general expression for Eckart-frame Hamilton operators has been
obtained by the gateway Hamiltonian method ({\it J. Chem. Phys.} {\bf 142},
174107 (2015); {\it ibid.} {\bf 143}, 064104 (2015)). The kinetic energy
operator in this general Hamiltonian is nearly identical with that of the
Eckart-Watson operator even when curvilinear vibrational coordinates are
employed. Its different realizations correspond to different methods of
calculating Eckart displacements. There are at least two different methods for
calculating such displacements: rotation and projection. In this communication
the application of Eckart Hamiltonian operators constructed by rotation and
projection, respectively, is numerically demonstrated in calculating
vibrational energy levels. The numerical examples confirm that there is no need
for rotation to construct an Eckart ro-vibrational Hamiltonian. The application
of the gateway method is advantageous even when rotation is used, since it
obviates the need for differentiation of the matrix rotating into the Eckart
frame. Simple geometrical arguments explain that there are infinitely many
different methods for calculating Eckart displacements. The geometrical picture
also suggests that a unique Eckart displacement vector may be defined as the
shortest (mass-weighted) Eckart displacement vector among Eckart displacement
vectors corresponding to configurations related by rotation. Its length, as
shown analytically and demonstrated by way of numerical examples, is equal to
or less than that of the Eckart displacement vector one can obtain by rotation
to the Eckart frame.
",0,1,0,0,0,0
19949,Optimal Timing to Trade Along a Randomized Brownian Bridge,"  This paper studies an optimal trading problem that incorporates the trader's
market view on the terminal asset price distribution and uninformative noise
embedded in the asset price dynamics. We model the underlying asset price
evolution by an exponential randomized Brownian bridge (rBb) and consider
various prior distributions for the random endpoint. We solve for the optimal
strategies to sell a stock, call, or put, and analyze the associated delayed
liquidation premia. We solve for the optimal trading strategies numerically and
compare them across different prior beliefs. Among our results, we find that
disconnected continuation/exercise regions arise when the trader prescribe a
two-point discrete distribution and double exponential distribution.
",0,0,0,0,0,1
19950,Inflationary Features and Shifts in Cosmological Parameters from Planck 2015 Data,"  We explore the relationship between features in the Planck 2015 temperature
and polarization data, shifts in the cosmological parameters, and features from
inflation. Residuals in the temperature data at low multipole $\ell$, which are
responsible for the high $H_0\approx 70$ km s$^{-1}$Mpc$^{-1}$ and low
$\sigma_8\Omega_m^{1/2}$ values from $\ell<1000$ in power-law $\Lambda$CDM
models, are better fit to inflationary features with a $1.9\sigma$ preference
for running of the running of the tilt or a stronger $99\%$ CL local
significance preference for a sharp drop in power around $k=0.004$ Mpc$^{-1}$
in generalized slow roll and a lower $H_0\approx 67$ km s$^{-1}$Mpc$^{-1}$. The
same in-phase acoustic residuals at $\ell>1000$ that drive the global $H_0$
constraints and appear as a lensing anomaly also favor running parameters which
allow even lower $H_0$, but not once lensing reconstruction is considered.
Polarization spectra are intrinsically highly sensitive to these parameter
shifts, and even more so in the Planck 2015 TE data due to an outlier at $\ell
\approx 165$, which disfavors the best fit $H_0$ $\Lambda$CDM solution by more
than $2\sigma$, and high $H_0$ value at almost $3\sigma$. Current polarization
data also slightly enhance the significance of a sharp suppression of
large-scale power but leave room for large improvements in the future with
cosmic variance limited $E$-mode measurements.
",0,1,0,0,0,0
19951,Tree based weighted learning for estimating individualized treatment rules with censored data,"  Estimating individualized treatment rules is a central task for personalized
medicine. [zhao2012estimating] and [zhang2012robust] proposed outcome weighted
learning to estimate individualized treatment rules directly through maximizing
the expected outcome without modeling the response directly. In this paper, we
extend the outcome weighted learning to right censored survival data without
requiring either an inverse probability of censoring weighting or a
semiparametric modeling of the censoring and failure times as done in
[zhao2015doubly]. To accomplish this, we take advantage of the tree based
approach proposed in [zhu2012recursively] to nonparametrically impute the
survival time in two different ways. The first approach replaces the reward of
each individual by the expected survival time, while in the second approach
only the censored observations are imputed by their conditional expected
failure times. We establish consistency and convergence rates for both
estimators. In simulation studies, our estimators demonstrate improved
performance compared to existing methods. We also illustrate the proposed
method on a phase III clinical trial of non-small cell lung cancer.
",0,0,1,1,0,0
19952,Fine-Tuning in the Context of Bayesian Theory Testing,"  Fine-tuning in physics and cosmology is often used as evidence that a theory
is incomplete. For example, the parameters of the standard model of particle
physics are ""unnaturally"" small (in various technical senses), which has driven
much of the search for physics beyond the standard model. Of particular
interest is the fine-tuning of the universe for life, which suggests that our
universe's ability to create physical life forms is improbable and in need of
explanation, perhaps by a multiverse. This claim has been challenged on the
grounds that the relevant probability measure cannot be justified because it
cannot be normalized, and so small probabilities cannot be inferred. We show
how fine-tuning can be formulated within the context of Bayesian theory testing
(or \emph{model selection}) in the physical sciences. The normalizability
problem is seen to be a general problem for testing any theory with free
parameters, and not a unique problem for fine-tuning. Physical theories in fact
avoid such problems in one of two ways. Dimensional parameters are bounded by
the Planck scale, avoiding troublesome infinities, and we are not compelled to
assume that dimensionless parameters are distributed uniformly, which avoids
non-normalizability.
",0,1,0,0,0,0
19953,Nuclear physics insights for new-physics searches using nuclei: Neutrinoless $ββ$ decay and dark matter direct detection,"  Experiments using nuclei to probe new physics beyond the Standard Model, such
as neutrinoless $\beta\beta$ decay searches testing whether neutrinos are their
own antiparticle, and direct detection experiments aiming to identify the
nature of dark matter, require accurate nuclear physics input for optimizing
their discovery potential and for a correct interpretation of their results.
This demands a detailed knowledge of the nuclear structure relevant for these
processes. For instance, neutrinoless $\beta\beta$ decay nuclear matrix
elements are very sensitive to the nuclear correlations in the initial and
final nuclei, and the spin-dependent nuclear structure factors of dark matter
scattering depend on the subtle distribution of the nuclear spin among all
nucleons. In addition, nucleons are composite and strongly interacting, which
implies that many-nucleon processes are necessary for a correct description of
nuclei and their interactions. It is thus crucial that theoretical studies and
experimental analyses consider $\beta$ decays and dark matter interactions with
a coupling to two nucleons, called two-nucleon currents.
",0,1,0,0,0,0
19954,On the (Statistical) Detection of Adversarial Examples,"  Machine Learning (ML) models are applied in a variety of tasks such as
network intrusion detection or Malware classification. Yet, these models are
vulnerable to a class of malicious inputs known as adversarial examples. These
are slightly perturbed inputs that are classified incorrectly by the ML model.
The mitigation of these adversarial inputs remains an open problem. As a step
towards understanding adversarial examples, we show that they are not drawn
from the same distribution than the original data, and can thus be detected
using statistical tests. Using thus knowledge, we introduce a complimentary
approach to identify specific inputs that are adversarial. Specifically, we
augment our ML model with an additional output, in which the model is trained
to classify all adversarial inputs. We evaluate our approach on multiple
adversarial example crafting methods (including the fast gradient sign and
saliency map methods) with several datasets. The statistical test flags sample
sets containing adversarial inputs confidently at sample sizes between 10 and
100 data points. Furthermore, our augmented model either detects adversarial
examples as outliers with high accuracy (> 80%) or increases the adversary's
cost - the perturbation added - by more than 150%. In this way, we show that
statistical properties of adversarial examples are essential to their
detection.
",1,0,0,1,0,0
19955,Search Rank Fraud De-Anonymization in Online Systems,"  We introduce the fraud de-anonymization problem, that goes beyond fraud
detection, to unmask the human masterminds responsible for posting search rank
fraud in online systems. We collect and study search rank fraud data from
Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters
recruited from 6 crowdsourcing sites. We propose Dolos, a fraud
de-anonymization system that leverages traits and behaviors extracted from
these studies, to attribute detected fraud to crowdsourcing site fraudsters,
thus to real identities and bank accounts. We introduce MCDense, a min-cut
dense component detection algorithm to uncover groups of user accounts
controlled by different fraudsters, and leverage stylometry and deep learning
to attribute them to crowdsourcing site profiles. Dolos correctly identified
the owners of 95% of fraudster-controlled communities, and uncovered fraudsters
who promoted as many as 97.5% of fraud apps we collected from Google Play. When
evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6
months, Dolos identified 1,056 apps with suspicious reviewer groups. We report
orthogonal evidence of their fraud, including fraud duplicates and fraud
re-posts.
",1,0,0,0,0,0
19956,Instabilities in Interacting Binary Stars,"  The types of instability in the interacting binary stars are reviewed. The
project ""Inter-Longitude Astronomy"" is a series of smaller projects on concrete
stars or groups of stars. It has no special funds, and is supported from
resources and grants of participating organizations, when informal working
groups are created. Totally we studied 1900+ variable stars of different types.
The characteristic timescale is from seconds to decades and (extrapolating)
even more. The monitoring of the first star of our sample AM Her was initiated
by Prof. V.P. Tsesevich (1907-1983). Since more than 358 ADS papers were
published. Some highlights of our photometric and photo-polarimetric monitoring
and mathematical modelling of interacting binary stars of different types are
presented: classical, asynchronous, intermediate polars and magnetic dwarf
novae (DO Dra) with 25 timescales corresponding to different physical
mechanisms and their combinations (part ""Polar""); negative and positive
superhumpers in nova-like and many dwarf novae stars (""Superhumper""); eclipsing
""non-magnetic"" cataclysmic variables; symbiotic systems (""Symbiosis"");
super-soft sources (SSS, QR And); spotted (and not spotted) eclipsing variables
with (and without) evidence for a current mass transfer (""Eclipser"") with a
special emphasis on systems with a direct impact of the stream into the gainer
star's atmosphere, or V361 Lyr-type stars. Other parts of the ILA project are
""Stellar Bell"" (interesting pulsating variables of different types and periods
- M, SR, RV Tau, RR Lyr, Delta Sct) and ""Novice""(=""New Variable"") discoveries
and classification with a subsequent monitoring for searching and studying
possible multiple components of variability. Special mathematical methods have
been developed to create a set of complementary software for statistically
optimal modelling of variable stars of different types.
",0,1,0,0,0,0
19957,Interaction blockade for bosons in an asymmetric double well,"  The interaction blockade phenomenon isolates the motion of a single quantum
particle within a multi-particle system, in particular for coherent
oscillations in and out of a region affected by the blockade mechanism. For
identical quantum particles with Bose statistics, the presence of the other
particles is still felt by a bosonic stimulation factor $\sqrt{N}$ that speeds
up the coherent oscillations, where $N$ is the number of bosons. Here we
propose an experiment to observe this enhancement factor with a small number of
bosonic atoms. The proposed protocol realises an asymmetric double well
potential with multiple optical tweezer laser beams. The ability to adjust bias
independently of the coherent coupling between the wells allows the potential
to be loaded with different particle numbers while maintaining the resonance
condition needed for coherent oscillations. Numerical simulations with up to
three bosons in a realistic potential generated by three optical tweezers
predict that the relevant avoided level crossing can be probed and the expected
bosonic enhancement factor observed.
",0,1,0,0,0,0
19958,SPIDER: CMB polarimetry from the edge of space,"  SPIDER is a balloon-borne instrument designed to map the polarization of the
millimeter-wave sky at large angular scales. SPIDER targets the B-mode
signature of primordial gravitational waves in the cosmic microwave background
(CMB), with a focus on mapping a large sky area with high fidelity at multiple
frequencies. SPIDER's first longduration balloon (LDB) flight in January 2015
deployed a total of 2400 antenna-coupled Transition Edge Sensors (TESs) at 90
GHz and 150 GHz. In this work we review the design and in-flight performance of
the SPIDER instrument, with a particular focus on the measured performance of
the detectors and instrument in a space-like loading and radiation environment.
SPIDER's second flight in December 2018 will incorporate payload upgrades and
new receivers to map the sky at 285 GHz, providing valuable information for
cleaning polarized dust emission from CMB maps.
",0,1,0,0,0,0
19959,Wind Shear and Turbulence on Titan : Huygens Analysis,"  Wind shear measured by Doppler tracking of the Huygens probe is evaluated,
and found to be within the range anticipated by pre-flight assessments (namely
less than two times the Brunt-Vaisala frequency). The strongest large-scale
shear encountered was ~5 m/s/km, a level associated with 'Light' turbulence in
terrestrial aviation. Near-surface winds (below 4km) have small-scale
fluctuations of ~0.2 m/s , indicated both by probe tilt and Doppler tracking,
and the characteristics of the fluctuation, of interest for future missions to
Titan, can be reproduced with a simple autoregressive (AR(1)) model. The
turbulent dissipation rate at an altitude of ~500m is found to be 16 cm2/sec3,
which may be a useful benchmark for atmospheric circulation models.
",0,1,0,0,0,0
19960,Composable security in relativistic quantum cryptography,"  Relativistic protocols have been proposed to overcome some impossibility
results in classical and quantum cryptography. In such a setting, one takes the
location of honest players into account, and uses the fact that information
cannot travel faster than the speed of light to limit the abilities of
dishonest agents. For example, various relativistic bit commitment protocols
have been proposed. Although it has been shown that bit commitment is
sufficient to construct oblivious transfer and thus multiparty computation,
composing specific relativistic protocols in this way is known to be insecure.
A composable framework is required to perform such a modular security analysis
of construction schemes, but no known frameworks can handle models of
computation in Minkowski space.
By instantiating the systems model from the Abstract Cryptography framework
with Causal Boxes, we obtain such a composable framework, in which messages are
assigned a location in Minkowski space (or superpositions thereof). This allows
us to analyse relativistic protocols and to derive novel possibility and
impossibility results. We show that (1) coin flipping can be constructed from
the primitive channel with delay, (2) biased coin flipping, bit commitment and
channel with delay are all impossible without further assumptions, and (3) it
is impossible to improve a channel with delay. Note that the impossibility
results also hold in the computational and bounded storage settings. This
implies in particular non-composability of all proposed relativistic bit
commitment protocols, of bit commitment in the bounded storage model, and of
biased coin flipping.
",1,0,0,0,0,0
19961,Tensor networks demonstrate the robustness of localization and symmetry protected topological phases,"  We prove that all eigenstates of many-body localized symmetry protected
topological systems with time reversal symmetry have four-fold degenerate
entanglement spectra in the thermodynamic limit. To that end, we employ unitary
quantum circuits where the number of sites the gates act on grows linearly with
the system size. We find that the corresponding matrix product operator
representation has similar local symmetries as matrix product ground states of
symmetry protected topological phases. Those local symmetries give rise to a
$\mathbb{Z}_2$ topological index, which is robust against arbitrary
perturbations so long as they do not break time reversal symmetry or drive the
system out of the fully many-body localized phase.
",0,1,0,0,0,0
19962,Formation of Intermediate-Mass Black Holes through Runaway Collisions in the First Star Clusters,"  We study the formation of massive black holes in the first star clusters. We
first locate star-forming gas clouds in proto-galactic haloes of $\gtrsim
\!10^7\,{\rm M}_{\odot}$ in cosmological hydrodynamics simulations and use them
to generate the initial conditions for star clusters with masses of $\sim
\!10^5\,{\rm M}_{\odot}$. We then perform a series of direct-tree hybrid
$N$-body simulations to follow runaway stellar collisions in the dense star
clusters. In all the cluster models except one, runaway collisions occur within
a few million years, and the mass of the central, most massive star reaches
$\sim \!400-1900\,{\rm M}_{\odot}$. Such very massive stars collapse to leave
intermediate-mass black holes (IMBHs). The diversity of the final masses may be
attributed to the differences in a few basic properties of the host haloes such
as mass, central gas velocity dispersion, and mean gas density of the central
core. Finally, we derive the IMBH mass to cluster mass ratios, and compare them
with the observed black hole to bulge mass ratios in the present-day Universe.
",0,1,0,0,0,0
19963,Mean Reverting Portfolios via Penalized OU-Likelihood Estimation,"  We study an optimization-based approach to con- struct a mean-reverting
portfolio of assets. Our objectives are threefold: (1) design a portfolio that
is well-represented by an Ornstein-Uhlenbeck process with parameters estimated
by maximum likelihood, (2) select portfolios with desirable characteristics of
high mean reversion and low variance, and (3) select a parsimonious portfolio,
i.e. find a small subset of a larger universe of assets that can be used for
long and short positions. We present the full problem formulation, a
specialized algorithm that exploits partial minimization, and numerical
examples using both simulated and empirical price data.
",0,0,0,1,0,1
19964,When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies,"  We show how third-party web trackers can deanonymize users of
cryptocurrencies. We present two distinct but complementary attacks. On most
shopping websites, third party trackers receive information about user
purchases for purposes of advertising and analytics. We show that, if the user
pays using a cryptocurrency, trackers typically possess enough information
about the purchase to uniquely identify the transaction on the blockchain, link
it to the user's cookie, and further to the user's real identity. Our second
attack shows that if the tracker is able to link two purchases of the same user
to the blockchain in this manner, it can identify the user's entire cluster of
addresses and transactions on the blockchain, even if the user employs
blockchain anonymity techniques such as CoinJoin. The attacks are passive and
hence can be retroactively applied to past purchases. We discuss several
mitigations, but none are perfect.
",1,0,0,0,0,0
19965,Performance Evaluation of Container-based Virtualization for High Performance Computing Environments,"  Virtualization technologies have evolved along with the development of
computational environments since virtualization offered needed features at that
time such as isolation, accountability, resource allocation, resource fair
sharing and so on. Novel processor technologies bring to commodity computers
the possibility to emulate diverse environments where a wide range of
computational scenarios can be run. Along with processors evolution, system
developers have created different virtualization mechanisms where each new
development enhanced the performance of previous virtualized environments.
Recently, operating system-based virtualization technologies captured the
attention of communities abroad (from industry to academy and research) because
their important improvements on performance area.
In this paper, the features of three container-based operating systems
virtualization tools (LXC, Docker and Singularity) are presented. LXC, Docker,
Singularity and bare metal are put under test through a customized single node
HPL-Benchmark and a MPI-based application for the multi node testbed. Also the
disk I/O performance, Memory (RAM) performance, Network bandwidth and GPU
performance are tested for the COS technologies vs bare metal. Preliminary
results and conclusions around them are presented and discussed.
",1,0,0,0,0,0
19966,An integral formula for the $Q$-prime curvature in 3-dimensional CR geometry,"  We give an integral formula for the total $Q^\prime$-curvature of a
three-dimensional CR manifold with positive CR Yamabe constant and nonnegative
Paneitz operator. Our derivation includes a relationship between the Green's
functions of the CR Laplacian and the $P^\prime$-operator.
",0,0,1,0,0,0
19967,Intelligent Device Discovery in the Internet of Things - Enabling the Robot Society,"  The Internet of Things (IoT) is continuously growing to connect billions of
smart devices anywhere and anytime in an Internet-like structure, which enables
a variety of applications, services and interactions between human and objects.
In the future, the smart devices are supposed to be able to autonomously
discover a target device with desired features and generate a set of entirely
new services and applications that are not supervised or even imagined by human
beings. The pervasiveness of smart devices, as well as the heterogeneity of
their design and functionalities, raise a major concern: How can a smart device
efficiently discover a desired target device? In this paper, we propose a
Social-Aware and Distributed (SAND) scheme that achieves a fast, scalable and
efficient device discovery in the IoT. The proposed SAND scheme adopts a novel
device ranking criteria that measures the device's degree, social relationship
diversity, clustering coefficient and betweenness. Based on the device ranking
criteria, the discovery request can be guided to travel through critical
devices that stand at the major intersections of the network, and thus quickly
reach the desired target device by contacting only a limited number of
intermediate devices. With the help of such an intelligent device discovery as
SAND, the IoT devices, as well as other computing facilities, software and data
on the Internet, can autonomously establish new social connections with each
other as human being do. They can formulate self-organized computing groups to
perform required computing tasks, facilitate a fusion of a variety of computing
service, network service and data to generate novel applications and services,
evolve from the individual aritificial intelligence to the collaborative
intelligence, and eventually enable the birth of a robot society.
",1,0,0,0,0,0
19968,Super-blockers and the effect of network structure on information cascades,"  Modelling information cascades over online social networks is important in
fields from marketing to civil unrest prediction, however the underlying
network structure strongly affects the probability and nature of such cascades.
Even with simple cascade dynamics the probability of large cascades are almost
entirely dictated by network properties, with well-known networks such as
Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the
same model. Indeed, the notion of 'superspreaders' has arisen to describe
highly influential nodes promoting global cascades in a social network. Here we
use a simple model of global cascades to show that the presence of locality in
the network increases the probability of a global cascade due to the increased
vulnerability of connecting nodes. Rather than 'super-spreaders', we find that
the presence of these highly connected 'super-blockers' in heavy-tailed
networks in fact reduces the probability of global cascades, while promoting
information spread when targeted as the initial spreader.
",1,0,0,0,0,0
19969,"Super-Gaussian, super-diffusive transport of multi-mode active matter","  Living cells exhibit multi-mode transport that switches between an active,
self-propelled motion and a seemingly passive, random motion. Cellular
decision-making over transport mode switching is a stochastic process that
depends on the dynamics of the intracellular chemical network regulating the
cell migration process. Here, we propose a theory and an exactly solvable model
of multi-mode active matter. Our exact model study shows that the reversible
transition between a passive mode and an active mode is the origin of the
anomalous, super-Gaussian transport dynamics, which has been observed in
various experiments for multi-mode active matter. We also present the
generalization of our model to encompass complex multi-mode matter with
arbitrary internal state chemical dynamics and internal state dependent
transport dynamics.
",0,1,0,0,0,0
19970,Height functions for motives,"  We define various height functions for motives over number fields. We compare
these height functions with classical height functions on algebraic varieties,
and also with analogous height functions for variations of Hodge structures on
curves over C. These comparisons provide new questions on motives over number
fields.
",0,0,1,0,0,0
19971,On the dimension effect of regularized linear discriminant analysis,"  This paper studies the dimension effect of the linear discriminant analysis
(LDA) and the regularized linear discriminant analysis (RLDA) classifiers for
large dimensional data where the observation dimension $p$ is of the same order
as the sample size $n$. More specifically, built on properties of the Wishart
distribution and recent results in random matrix theory, we derive explicit
expressions for the asymptotic misclassification errors of LDA and RLDA
respectively, from which we gain insights of how dimension affects the
performance of classification and in what sense. Motivated by these results, we
propose adjusted classifiers by correcting the bias brought by the unequal
sample sizes. The bias-corrected LDA and RLDA classifiers are shown to have
smaller misclassification rates than LDA and RLDA respectively. Several
interesting examples are discussed in detail and the theoretical results on
dimension effect are illustrated via extensive simulation studies.
",0,0,1,1,0,0
19972,Plasma Wake Accelerators: Introduction and Historical Overview,"  Fundamental questions on the nature of matter and energy have found answers
thanks to the use of particle accelerators. Societal applications, such as
cancer treatment or cancer imaging, illustrate the impact of accelerators in
our current life. Today, accelerators use metallic cavities that sustain
electricfields with values limited to about 100 MV/m. Because of their ability
to support extreme accelerating gradients, the plasma medium has recently been
proposed for future cavity-like accelerating structures. This contribution
highlights the tremendous evolution of plasma accelerators driven by either
laser or particle beams that allow the production of high quality particle
beams with a degree of tunability and a set of parameters that make them very
pertinent for many applications.
",0,1,0,0,0,0
19973,$R$-triviality of some exceptional groups,"  The main aim of this paper is to prove $R$-triviality for simple, simply
connected algebraic groups with Tits index $E_{8,2}^{78}$ or $E_{7,1}^{78}$,
defined over a field $k$ of arbitrary characteristic. Let $G$ be such a group.
We prove that there exists a quadratic extension $K$ of $k$ such that $G$ is
$R$-trivial over $K$, i.e., for any extension $F$ of $K$, $G(F)/R=\{1\}$, where
$G(F)/R$ denotes the group of $R$-equivalence classes in $G(F)$, in the sense
of Manin (see \cite{M}). As a consequence, it follows that the variety $G$ is
retract $K$-rational and that the Kneser-Tits conjecture holds for these groups
over $K$. Moreover, $G(L)$ is projectively simple as an abstract group for any
field extension $L$ of $K$. In their monograph (\cite{TW}) J. Tits and Richard
Weiss conjectured that for an Albert division algebra $A$ over a field $k$, its
structure group $Str(A)$ is generated by scalar homotheties and its
$U$-operators. This is known to be equivalent to the Kneser-Tits conjecture for
groups with Tits index $E_{8,2}^{78}$. We settle this conjecture for Albert
division algebras which are first constructions, in affirmative. These results
are obtained as corollaries to the main result, which shows that if $A$ is an
Albert division algebra which is a first construction and $\Gamma$ its
structure group, i.e., the algebraic group of the norm similarities of $A$,
then $\Gamma(F)/R=\{1\}$ for any field extension $F$ of $k$, i.e., $\Gamma$ is
$R$-trivial.
",0,0,1,0,0,0
19974,Aperture synthesis imaging of the carbon AGB star R Sculptoris: Detection of a complex structure and a dominating spot on the stellar disk,"  We present near-infrared interferometry of the carbon-rich asymptotic giant
branch (AGB) star R Sculptoris.
The visibility data indicate a broadly circular resolved stellar disk with a
complex substructure. The observed AMBER squared visibility values show drops
at the positions of CO and CN bands, indicating that these lines form in
extended layers above the photosphere. The AMBER visibility values are best fit
by a model without a wind. The PIONIER data are consistent with the same model.
We obtain a Rosseland angular diameter of 8.9+-0.3 mas, corresponding to a
Rosseland radius of 355+-55 Rsun, an effective temperature of 2640+-80 K, and a
luminosity of log L/Lsun=3.74+-0.18. These parameters match evolutionary tracks
of initial mass 1.5+-0.5 Msun and current mass 1.3+-0.7 Msun. The reconstructed
PIONIER images exhibit a complex structure within the stellar disk including a
dominant bright spot located at the western part of the stellar disk. The spot
has an H-band peak intensity of 40% to 60% above the average intensity of the
limb-darkening-corrected stellar disk. The contrast between the minimum and
maximum intensity on the stellar disk is about 1:2.5.
Our observations are broadly consistent with predictions by dynamic
atmosphere and wind models, although models with wind appear to have a
circumstellar envelope that is too extended compared to our observations. The
detected complex structure within the stellar disk is most likely caused by
giant convection cells, resulting in large-scale shock fronts, and their
effects on clumpy molecule and dust formation seen against the photosphere at
distances of 2-3 stellar radii.
",0,1,0,0,0,0
19975,Intrinsically Sparse Long Short-Term Memory Networks,"  Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a
wide range of tasks. Its outstanding performance is guaranteed by the long-term
memory ability which matches the sequential data perfectly and the gating
structure controlling the information flow. However, LSTMs are prone to be
memory-bandwidth limited in realistic applications and need an unbearable
period of training and inference time as the model size is ever-increasing. To
tackle this problem, various efficient model compression methods have been
proposed. Most of them need a big and expensive pre-trained model which is a
nightmare for resource-limited devices where the memory budget is strictly
limited. To remedy this situation, in this paper, we incorporate the Sparse
Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed
SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM
has a sparse topology and dramatically fewer parameters in both phases,
training and inference. Considering the specific architecture of LSTMs, we
replace the LSTM cells and embedding layers with sparse structures and further
on, use an evolutionary strategy to adapt the sparse connectivity to the data.
Additionally, we find that SET-LSTM can provide many different good
combinations of sparse connectivity to substitute the overparameterized
optimization problem of dense neural networks. Evaluated on four sentiment
analysis classification datasets, the results demonstrate that our proposed
model is able to achieve usually better performance than its fully connected
counterpart while having less than 4\% of its parameters.
",1,0,0,0,0,0
19976,Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments',"  I welcome the contribution from Falessi et al. [1] hereafter referred to as
F++ , and the ensuing debate. Experimentation is an important tool within
empirical software engineering, so how we select participants is clearly a
relevant question. Moreover as F++ point out, the question is considerably more
nuanced than the simple dichotomy it might appear to be at first sight.
This commentary is structured as follows. In Section 2 I briefly summarise
the arguments of F++ and comment on their approach. Next, in Section 3, I take
a step back to consider the nature of representativeness in inferential
arguments and the need for careful definition. Then I give three examples of
using different types of participant to consider impact. I conclude by arguing,
largely in agreement with F++, that the question of whether student
participants are representative or not depends on the target population.
However, we need to give careful consideration to defining that population and,
in particular, not to overlook the representativeness of tasks and environment.
This is facilitated by explicit description of the target populations.
",1,0,0,0,0,0
19977,NoScope: Optimizing Neural Network Queries over Video at Scale,"  Recent advances in computer vision-in the form of deep neural networks-have
made it possible to query increasing volumes of video data with high accuracy.
However, neural network inference is computationally expensive at scale:
applying a state-of-the-art object detector in real time (i.e., 30+ frames per
second) to a single video requires a $4000 GPU. In response, we present
NoScope, a system for querying videos that can reduce the cost of neural
network video analysis by up to three orders of magnitude via
inference-optimized model search. Given a target video, object to detect, and
reference neural network, NoScope automatically searches for and trains a
sequence, or cascade, of models that preserves the accuracy of the reference
network but is specialized to the target video and are therefore far less
computationally expensive. NoScope cascades two types of models: specialized
models that forego the full generality of the reference model but faithfully
mimic its behavior for the target video and object; and difference detectors
that highlight temporal differences across frames. We show that the optimal
cascade architecture differs across videos and objects, so NoScope uses an
efficient cost-based optimizer to search across models and cascades. With this
approach, NoScope achieves two to three order of magnitude speed-ups
(265-15,500x real-time) on binary classification tasks over fixed-angle webcam
and surveillance video while maintaining accuracy within 1-5% of
state-of-the-art neural networks.
",1,0,0,0,0,0
19978,TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization,"  Recent research implies that training and inference of deep neural networks
(DNN) can be computed with low precision numerical representations of the
training/test data, weights and gradients without a general loss in accuracy.
The benefit of such compact representations is twofold: they allow a
significant reduction of the communication bottleneck in distributed DNN
training and faster neural network implementations on hardware accelerators
like FPGAs. Several quantization methods have been proposed to map the original
32-bit floating point problem to low-bit representations. While most related
publications validate the proposed approach on a single DNN topology, it
appears to be evident, that the optimal choice of the quantization method and
number of coding bits is topology dependent. To this end, there is no general
theory available, which would allow users to derive the optimal quantization
during the design of a DNN topology. In this paper, we present a quantization
tool box for the TensorFlow framework. TensorQuant allows a transparent
quantization simulation of existing DNN topologies during training and
inference. TensorQuant supports generic quantization methods and allows
experimental evaluation of the impact of the quantization on single layers as
well as on the full topology. In a first series of experiments with
TensorQuant, we show an analysis of fix-point quantizations of popular CNN
topologies.
",1,0,0,1,0,0
19979,Information-geometrical characterization of statistical models which are statistically equivalent to probability simplexes,"  The probability simplex is the set of all probability distributions on a
finite set and is the most fundamental object in the finite probability theory.
In this paper we give a characterization of statistical models on finite sets
which are statistically equivalent to probability simplexes in terms of
$\alpha$-families including exponential families and mixture families. The
subject has a close relation to some fundamental aspects of information
geometry such as $\alpha$-connections and autoparallelity.
",1,0,1,1,0,0
19980,The Network Nullspace Property for Compressed Sensing of Big Data over Networks,"  We present a novel condition, which we term the net- work nullspace property,
which ensures accurate recovery of graph signals representing massive
network-structured datasets from few signal values. The network nullspace
property couples the cluster structure of the underlying network-structure with
the geometry of the sampling set. Our results can be used to design efficient
sampling strategies based on the network topology.
",1,0,0,1,0,0
19981,On physically redundant and irrelevant features when applying Lie-group symmetry analysis to hydrodynamic stability analysis,"  Every linear system of partial differential equations (PDEs) admits a scaling
symmetry in its dependent variables. In conjunction with other admitted
symmetries of linear type, the associated invariant solution condition poses a
linear eigenvalue problem. If this problem is structured such that the spectral
theorem applies, then the general solution of the considered linear PDE system
is obtained by summing or integrating the invariant eigenfunctions (modes) over
all eigenvalues, depending on whether the spectrum of the operator is discrete
or continuous. By first studying the 1-D diffusion equation as a demonstrating
example, this method is then applied to a relevant 2-D problem from
hydrodynamic stability analysis. The aim of this study is to draw attention to
the following two independent facts that need to be addressed in future studies
when constructing solutions for linear PDEs with the method of Lie-symmetries:
(i) Although each new symmetry leads to a mathematically different spectral
decomposition, they may all be physically redundant to standard ones and do not
reveal a new physical mechanism behind the overall considered dynamical
process, as incorrectly asserted, for example, in the recent studies by the
group of Oberlack et al. Hence, with regard to linear stability analysis, no
physically ""new"" or more ""general"" modes are generated by this method than the
ones already established. (ii) Next to the eigenvalue parameters, each single
mode can also acquire non-system parameters, depending on the choice of its
underlying symmetry. These symmetry-induced parameters, however, are all
physically irrelevant, since their effect on a single mode will cancel when
considering all modes collectively. In particular, the collective action of all
single modes is identical for all symmetry-based decompositions and thus
indistinguishable when considering the full physical fields.
",0,1,0,0,0,0
19982,Transforming Musical Signals through a Genre Classifying Convolutional Neural Network,"  Convolutional neural networks (CNNs) have been successfully applied on both
discriminative and generative modeling for music-related tasks. For a
particular task, the trained CNN contains information representing the decision
making or the abstracting process. One can hope to manipulate existing music
based on this 'informed' network and create music with new features
corresponding to the knowledge obtained by the network. In this paper, we
propose a method to utilize the stored information from a CNN trained on
musical genre classification task. The network was composed of three
convolutional layers, and was trained to classify five-second song clips into
five different genres. After training, randomly selected clips were modified by
maximizing the sum of outputs from the network layers. In addition to the
potential of such CNNs to produce interesting audio transformation, more
information about the network and the original music could be obtained from the
analysis of the generated features since these features indicate how the
network 'understands' the music.
",1,0,0,0,0,0
19983,AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture,"  Dominant approaches to action detection can only provide sub-optimal
solutions to the problem, as they rely on seeking frame-level detections, to
later compose them into ""action tubes"" in a post-processing step. With this
paper we radically depart from current practice, and take a first step towards
the design and implementation of a deep network architecture able to classify
and regress whole video subsets, so providing a truly optimal solution of the
action detection problem. In this work, in particular, we propose a novel deep
net framework able to regress and classify 3D region proposals spanning two
successive video frames, whose core is an evolution of classical region
proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode
the temporal aspect of actions by purely exploiting appearance, as opposed to
methods which heavily rely on expensive flow maps. The proposed model is
end-to-end trainable and can be jointly optimised for action localisation and
classification in a single step. At test time the network predicts
""micro-tubes"" encompassing two successive frames, which are linked up into
complete action tubes via a new algorithm which exploits the temporal encoding
learned by the network and cuts computation time by 50%. Promising results on
the J-HMDB-21 and UCF-101 action detection datasets show that our model does
outperform the state-of-the-art when relying purely on appearance.
",1,0,0,0,0,0
19984,Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering,"  This work introduces a novel estimation method, called LOVE, of the entries
and structure of a loading matrix A in a sparse latent factor model X = AZ + E,
for an observable random vector X in Rp, with correlated unobservable factors Z
\in RK, with K unknown, and independent noise E. Each row of A is scaled and
sparse. In order to identify the loading matrix A, we require the existence of
pure variables, which are components of X that are associated, via A, with one
and only one latent factor. Despite the fact that the number of factors K, the
number of the pure variables, and their location are all unknown, we only
require a mild condition on the covariance matrix of Z, and a minimum of only
two pure variables per latent factor to show that A is uniquely defined, up to
signed permutations. Our proofs for model identifiability are constructive, and
lead to our novel estimation method of the number of factors and of the set of
pure variables, from a sample of size n of observations on X. This is the first
step of our LOVE algorithm, which is optimization-free, and has low
computational complexity of order p2. The second step of LOVE is an easily
implementable linear program that estimates A. We prove that the resulting
estimator is minimax rate optimal up to logarithmic factors in p. The model
structure is motivated by the problem of overlapping variable clustering,
ubiquitous in data science. We define the population level clusters as groups
of those components of X that are associated, via the sparse matrix A, with the
same unobservable latent factor, and multi-factor association is allowed.
Clusters are respectively anchored by the pure variables, and form overlapping
sub-groups of the p-dimensional random vector X. The Latent model approach to
OVErlapping clustering is reflected in the name of our algorithm, LOVE.
",0,0,1,1,0,0
19985,Asymptotic profile of solutions for some wave equations with very strong structural damping,"  We consider the Cauchy problem in R^n for some types of damped wave
equations. We derive asymptotic profiles of solutions with weighted
L^{1,1}(R^n) initial data by employing a simple method introduced by the first
author. The obtained results will include regularity loss type estimates, which
are essentially new in this kind of equations.
",0,0,1,0,0,0
19986,Spontaneous generation of fractional vortex-antivortex pairs at single edges of high-Tc superconductors,"  Unconventional d-wave superconductors with pair-breaking edges are predicted
to have ground states with spontaneously broken time-reversal and translational
symmetries. We use the quasiclassical theory of superconductivity to
demonstrate that such phases can exist at any single pair-breaking facet. This
implies that a greater variety of systems, not necessarily mesoscopic in size,
should be unstable to such symmetry breaking. The density of states averaged
over the facet displays a broad peak centered at zero energy, which is
consistent with experimental findings of a broad zero-bias conductance peak
with a temperature-independent width at low temperatures.
",0,1,0,0,0,0
19987,A Survey of Neuromorphic Computing and Neural Networks in Hardware,"  Neuromorphic computing has come to refer to a variety of brain-inspired
computers, devices, and models that contrast the pervasive von Neumann computer
architecture. This biologically inspired approach has created highly connected
synthetic neurons and synapses that can be used to model neuroscience theories
as well as solve challenging machine learning problems. The promise of the
technology is to create a brain-like ability to learn and adapt, but the
technical challenges are significant, starting with an accurate neuroscience
model of how the brain works, to finding materials and engineering
breakthroughs to build devices to support these models, to creating a
programming framework so the systems can learn, to creating applications with
brain-like capabilities. In this work, we provide a comprehensive survey of the
research and motivations for neuromorphic computing over its history. We begin
with a 35-year review of the motivations and drivers of neuromorphic computing,
then look at the major research areas of the field, which we define as
neuro-inspired models, algorithms and learning approaches, hardware and
devices, supporting systems, and finally applications. We conclude with a broad
discussion on the major research topics that need to be addressed in the coming
years to see the promise of neuromorphic computing fulfilled. The goals of this
work are to provide an exhaustive review of the research conducted in
neuromorphic computing since the inception of the term, and to motivate further
work by illuminating gaps in the field where new research is needed.
",1,0,0,0,0,0
19988,Junctions of refined Wilson lines and one-parameter deformation of quantum groups,"  We study junctions of Wilson lines in refined SU(N) Chern-Simons theory and
their local relations. We focus on junctions of Wilson lines in antisymmetric
and symmetric powers of the fundamental representation and propose a set of
local relations which realize one-parameter deformations of quantum groups
$\dot{U}_{q}(\mathfrak{sl}_{m})$ and $\dot{U}_{q}(\mathfrak{sl}_{n|m})$.
",0,0,1,0,0,0
19989,Online estimation of the asymptotic variance for averaged stochastic gradient algorithms,"  Stochastic gradient algorithms are more and more studied since they can deal
efficiently and online with large samples in high dimensional spaces. In this
paper, we first establish a Central Limit Theorem for these estimates as well
as for their averaged version in general Hilbert spaces. Moreover, since having
the asymptotic normality of estimates is often unusable without an estimation
of the asymptotic variance, we introduce a new recursive algorithm for
estimating this last one, and we establish its almost sure rate of convergence
as well as its rate of convergence in quadratic mean. Finally, two examples
consisting in estimating the parameters of the logistic regression and
estimating geometric quantiles are given.
",0,0,1,1,0,0
19990,The response of the terrestrial bow shock and magnetopause of the long term decline in solar polar fields,"  The location of the terrestrial magnetopause (MP) and it's subsolar stand-off
distance depends not only on the solar wind dynamic pressure and the
interplanetary magnetic field (IMF), both of which play a crucial role in
determining it's shape, but also on the nature of the processes involved in the
interaction between the solar wind and the magnetosphere. The stand-off
distance of the earth's MP and bow shock (BS) also define the extent of
terrestrial magnetic fields into near-earth space on the sunward side and have
important consequences for space weather. However, asymmetries due to the
direction of the IMF are hard to account for, making it nearly impossible to
favour any specific model over the other in estimating the extent of the MP or
BS. Thus, both numerical and empirical models have been used and compared to
estimate the BS and MP stand-off distances as well as the MP shape, in the
period Jan. 1975-Dec. 2016, covering solar cycles 21-24. The computed MP and BS
stand-off distances have been found to be increasing steadily over the past two
decades, since ~1995, spanning solar cycles 23 and 24. The increasing trend is
consistent with earlier reported studies of a long term and steady decline in
solar polar magnetic fields and solar wind micro-turbulence levels. The present
study, thus, highlights the response of the terrestrial magnetosphere to the
long term global changes in both solar and solar wind activity, through a
detailed study of the extent and shape of the terrestrial MP and BS over the
past four solar cycles, a period spanning the last four decades.
",0,1,0,0,0,0
19991,Superconductivity of barium-VI synthesized via compression at low temperatures,"  Using a membrane-driven diamond anvil cell and both ac magnetic
susceptibility and electrical resistivity measurements, we have characterized
the superconducting phase diagram of elemental barium to pressures as high as
65 GPa. We have determined the superconducting properties of the recently
discovered Ba-VI crystal structure, which can only be accessed via the
application of pressure at low temperature. We find that Ba-VI exhibits a
maximum Tc near 8 K, which is substantially higher than the maximum Tc found
when pressure is applied at room temperature.
",0,1,0,0,0,0
19992,Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation,"  Everyday robotics are challenged to deal with autonomous product handling in
applications like logistics or retail, possibly causing damage on the items
during manipulation. Traditionally, most approaches try to minimize physical
interaction with goods. However, we propose to take into account any unintended
motion of objects in the scene and to learn manipulation strategies in a
self-supervised way which minimize the potential damage. The presented approach
consists of a planning method that determines the optimal sequence to
manipulate a number of objects in a scene with respect to possible damage by
simulating interaction and hence anticipating scene dynamics. The planned
manipulation sequences are taken as input to a machine learning process which
generalizes to new, unseen scenes in the same application scenario. This
learned manipulation strategy is continuously refined in a self-supervised
optimization cycle dur- ing load-free times of the system. Such a
simulation-in-the-loop setup is commonly known as mental simulation and allows
for efficient, fully automatic generation of training data as opposed to
classical supervised learning approaches. In parallel, the generated
manipulation strategies can be deployed in near-real time in an anytime
fashion. We evaluate our approach on one industrial scenario (autonomous
container unloading) and one retail scenario (autonomous shelf replenishment).
",1,0,0,0,0,0
19993,Adversarial Deep Learning for Robust Detection of Binary Encoded Malware,"  Malware is constantly adapting in order to avoid detection. Model based
malware detectors, such as SVM and neural networks, are vulnerable to so-called
adversarial examples which are modest changes to detectable malware that allows
the resulting malware to evade detection. Continuous-valued methods that are
robust to adversarial examples of images have been developed using saddle-point
optimization formulations. We are inspired by them to develop similar methods
for the discrete, e.g. binary, domain which characterizes the features of
malware. A specific extra challenge of malware is that the adversarial examples
must be generated in a way that preserves their malicious functionality. We
introduce methods capable of generating functionally preserved adversarial
malware examples in the binary domain. Using the saddle-point formulation, we
incorporate the adversarial examples into the training of models that are
robust to them. We evaluate the effectiveness of the methods and others in the
literature on a set of Portable Execution~(PE) files. Comparison prompts our
introduction of an online measure computed during training to assess general
expectation of robustness.
",0,0,0,1,0,0
19994,"Simple closed curves, finite covers of surfaces, and power subgroups of Out(F_n)","  We construct examples of finite covers of punctured surfaces where the first
rational homology is not spanned by lifts of simple closed curves. More
generally, for any set $\mathcal{O} \subset F_n$ which is contained in the
union of finitely many $Aut(F_n)$-orbits, we construct finite-index normal
subgroups of $F_n$ whose first rational homology is not spanned by powers of
elements of $\mathcal{O}$. These examples answer questions of Farb-Hensel,
Looijenga, and Marche. We also show that the quotient of $Out(F_n)$ by the
subgroup generated by kth powers of transvections often contains infinite order
elements, strengthening a result of Bridson-Vogtmann saying that it is often
infinite. Finally, for any set $\mathcal{O} \subset F_n$ which is contained in
the union of finitely many $Aut(F_n)$-orbits, we construct integral linear
representations of free groups that have infinite image and map all elements of
$\mathcal{O}$ to torsion elements.
",0,0,1,0,0,0
19995,Hard Mixtures of Experts for Large Scale Weakly Supervised Vision,"  Training convolutional networks (CNN's) that fit on a single GPU with
minibatch stochastic gradient descent has become effective in practice.
However, there is still no effective method for training large CNN's that do
not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
this work we show that a simple hard mixture of experts model can be
efficiently trained to good effect on large scale hashtag (multilabel)
prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991,
Collobert et. al. 2003), but in the past, researchers have had to devise
sophisticated methods to deal with data fragmentation. We show empirically that
modern weakly supervised data sets are large enough to support naive
partitioning schemes where each data point is assigned to a single expert.
Because the experts are independent, training them in parallel is easy, and
evaluation is cheap for the size of the model. Furthermore, we show that we can
use a single decoding layer for all the experts, allowing a unified feature
embedding space. We demonstrate that it is feasible (and in fact relatively
painless) to train far larger models than could be practically trained with
standard CNN architectures, and that the extra capacity can be well used on
current datasets.
",1,0,0,1,0,0
19996,Single-hole GPR reflection imaging of solute transport in a granitic aquifer,"  Identifying transport pathways in fractured rock is extremely challenging as
flow is often organized in a few fractures that occupy a very small portion of
the rock volume. We demonstrate that saline tracer experiments combined with
single-hole ground penetrating radar (GPR) reflection imaging can be used to
monitor saline tracer movement within mm-aperture fractures. A dipole tracer
test was performed in a granitic aquifer by injecting a saline solution in a
known fracture, while repeatedly acquiring single-hole GPR sections in the
pumping borehole located 6 m away. The final depth-migrated difference sections
make it possible to identify consistent temporal changes over a 30 m depth
interval at locations corresponding to fractures previously imaged in GPR
sections acquired under natural flow and tracer-free conditions. The experiment
allows determining the dominant flow paths of the injected tracer and the
velocity (0.4-0.7 m/min) of the tracer front.
",0,1,0,0,0,0
19997,Episodic Torque-Luminosity Correlations and Anticorrelations of GX 1+4,"  We analyse archival CGRO-BATSE X-ray flux and spin frequency measurements of
GX 1+4 over a time span of 3000 days. We systematically search for time
dependent variations of torque luminosity correlation. Our preliminary results
indicate that the correlation shifts from being positive to negative on time
scales of few 100 days.
",0,1,0,0,0,0
19998,Applications of Fractional Calculus to Newtonian Mechanics,"  We investigate some basic applications of Fractional Calculus (FC) to
Newtonian mechanics. After a brief review of FC, we consider a possible
generalization of Newton's second law of motion and apply it to the case of a
body subject to a constant force. In our second application of FC to Newtonian
gravity, we consider a generalized fractional gravitational potential and
derive the related circular orbital velocities. This analysis might be used as
a tool to model galactic rotation curves, in view of the dark matter problem.
Both applications have a pedagogical value in connecting fractional calculus to
standard mechanics and can be used as a starting point for a more advanced
treatment of fractional mechanics.
",0,1,0,0,0,0
19999,Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti,"  We present ~0.4 resolution images of CO(3-2) and associated continuum
emission from the gas-bearing debris disk around the nearby A star 49 Ceti,
observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We
analyze the ALMA visibilities in tandem with the broad-band spectral energy
distribution to measure the radial surface density profiles of dust and gas
emission from the system. The dust surface density decreases with radius
between ~100 and 310 au, with a marginally significant enhancement of surface
density at a radius of ~110 au. The SED requires an inner disk of small grains
in addition to the outer disk of larger grains resolved by ALMA. The gas disk
exhibits a surface density profile that increases with radius, contrary to most
previous spatially resolved observations of circumstellar gas disks. While ~80%
of the CO flux is well described by an axisymmetric power-law disk in Keplerian
rotation about the central star, residuals at ~20% of the peak flux exhibit a
departure from axisymmetry suggestive of spiral arms or a warp in the gas disk.
The radial extent of the gas disk (~220 au) is smaller than that of the dust
disk (~300 au), consistent with recent observations of other gas-bearing debris
disks. While there are so far only three broad debris disks with well
characterized radial dust profiles at millimeter wavelengths, 49 Ceti's disk
shows a markedly different structure from two radially resolved gas-poor debris
disks, implying that the physical processes generating and sculpting the gas
and dust are fundamentally different.
",0,1,0,0,0,0
20000,Spontaneously broken translational symmetry at edges of high-temperature superconductors: thermodynamics in magnetic field,"  We investigate equilibrium properties, including structure of the order
parameter, superflow patterns, and thermodynamics of low-temperature surface
phases of layered d_{x^2-y^2}-wave superconductors in magnetic field. At zero
external magnetic field, time-reversal symmetry and continuous translational
symmetry along the edge are broken spontaneously in a second order phase
transition at a temperature $T^*\approx 0.18 T_c$, where $T_c$ is the
superconducting transition temperature. At the phase transition there is a jump
in the specific heat that scales with the ratio between the edge length $D$ and
layer area ${\cal A}$ as $(D\xi_0/{\cal A})\Delta C_d$, where $\Delta C_d$ is
the jump in the specific heat at the d-wave superconducting transition and
$\xi_0$ is the superconducting coherence length. The phase with broken symmetry
is characterized by a gauge invariant superfluid momentum ${\bf p}_s$ that
forms a non-trivial planar vector field with a chain of sources and sinks along
the edges with a period of approximately $12\xi_0$, and saddle point
disclinations in the interior. To find out the relative importance of
time-reversal and translational symmetry breaking we apply an external field
that breaks time-reversal symmetry explicitly. We find that the phase
transition into the state with the non-trivial ${\bf p}_s$ vector field keeps
its main signatures, and is still of second order. In the external field, the
saddle point disclinations are pushed towards the edges, and thereby a chain of
edge motifs are formed, where each motif contains a source, a sink, and a
saddle point. Due to a competing paramagnetic response at the edges, the phase
transition temperature $T^*$ is slowly suppressed with increasing magnetic
field strength, but the phase with broken symmetry survives into the mixed
state.
",0,1,0,0,0,0
20001,Asymmetric Spin-wave Dispersion on Fe(110): Direct Evidence of Dzyaloshinskii--Moriya Interaction,"  The influence of the Dzyaloshinskii-Moriya interaction on the spin-wave
dispersion in an Fe double layer grown on W(110) is measured for the first
time. It is demonstrated that the Dzyaloshinskii-Moriya interaction breaks the
degeneracy of spin waves and leads to an asymmetric spin-wave dispersion
relation. An extended Heisenberg spin Hamiltonian is employed to obtain the
longitudinal component of the Dzyaloshinskii-Moriya vectors from the
experimentally measured energy asymmetry.
",0,1,0,0,0,0
20002,Consumption smoothing in the working-class households of interwar Japan,"  I analyze Osaka factory worker households in the early 1920s, whether
idiosyncratic income shocks were shared efficiently, and which consumption
categories were robust to shocks. While the null hypothesis of full
risk-sharing of total expenditures was rejected, factory workers maintained
their households, in that they paid for essential expenditures (rent,
utilities, and commutation) during economic hardship. Additionally, children's
education expenditures were possibly robust to idiosyncratic income shocks. The
results suggest that temporary income is statistically significantly increased
if disposable income drops due to idiosyncratic shocks. Historical documents
suggest microfinancial lending and saving institutions helped mitigate
risk-based vulnerabilities.
",0,0,0,0,0,1
20003,Observations and Modelling of the Pre-Flare Period of the 29 March 2014 X1 Flare,"  On the 29 March 2014 NOAA active region (AR) 12017 produced an X1 flare which
was simultaneously observed by an unprecedented number of observatories. We
have investigated the pre-flare period of this flare from 14:00 UT until 19:00
UT using joint observations made by the Interface Region Imaging Spectrometer
(IRIS) and the Hinode Extreme Ultraviolet Imaging Spectrometer (EIS). Spectral
lines providing coverage of the solar atmosphere from chromosphere to the
corona were analysed to investigate pre-flare activity within the AR. The
results of the investigation have revealed evidence of strongly blue-shifted
plasma flows, with velocities up to 200 km/s, being observed 40 minutes prior
to flaring. These flows are located along the filament present in the active
region and are both spatially discrete and transient. In order to constrain the
possible explanations for this activity, we undertake non-potential magnetic
field modelling of the active region. This modelling indicates the existence of
a weakly twisted flux rope along the polarity inversion line in the region
where a filament and the strong pre-flare flows are observed. We then discuss
how these observations relate to the current models of flare triggering. We
conclude that the most likely drivers of the observed activity are internal
reconnection in the flux rope, early onset of the flare reconnection, or tether
cutting reconnection along the filament.
",0,1,0,0,0,0
20004,"Wild ramification and K(pi, 1) spaces","  We prove that every connected affine scheme of positive characteristic is a
K(pi, 1) space for the etale topology. The main ingredient is the special case
of the affine space over a field k. This is dealt with by induction on n, using
a key ""Bertini-type""' statement regarding the wild ramification of l-adic local
systems on affine spaces, which might be of independent interest. Its proof
uses in an essential way recent advances in higher ramification theory due to
T. Saito. We also give rigid analytic and mixed characteristic versions of the
main result.
",0,0,1,0,0,0
20005,A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading,"  Deep neural network models have been proven to be very successful in image
classification tasks, also for medical diagnosis, but their main concern is its
lack of interpretability. They use to work as intuition machines with high
statistical confidence but unable to give interpretable explanations about the
reported results. The vast amount of parameters of these models make difficult
to infer a rationale interpretation from them. In this paper we present a
diabetic retinopathy interpretable classifier able to classify retine images
into the different levels of disease severity and of explaining its results by
assigning a score for every point in the hidden and input space, evaluating its
contribution to the final classification in a linear way. The generated visual
maps can be interpreted by an expert in order to compare its own knowledge with
the interpretation given by the model.
",1,0,0,1,0,0
20006,Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM,"  Arabic word segmentation is essential for a variety of NLP applications such
as machine translation and information retrieval. Segmentation entails breaking
words into their constituent stems, affixes and clitics. In this paper, we
compare two approaches for segmenting four major Arabic dialects using only
several thousand training examples for each dialect. The two approaches involve
posing the problem as a ranking problem, where an SVM ranker picks the best
segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled
with CRF determines where best to segment words. We are able to achieve solid
segmentation results for all dialects using rather limited training data. We
also show that employing Modern Standard Arabic data for domain adaptation and
assuming context independence improve overall results.
",1,0,0,0,0,0
20007,Iterative bidding in electricity markets: rationality and robustness,"  This paper studies an electricity market consisting of an independent system
operator (ISO) and a group of generators. The goal is to solve the DC optimal
power flow (DC-OPF) problem: have the generators collectively meet the power
demand while minimizing the aggregate generation cost and respecting line flow
limits in the network. The ISO by itself cannot solve the DC-OPF problem as
generators are strategic and do not share their cost functions. Instead, each
generator submits to the ISO a bid, consisting of the price per unit of
electricity at which it is willing to provide power. Based on the bids, the ISO
decides how much production to allocate to each generator to minimize the total
payment while meeting the load and satisfying the line limits. We provide a
provably correct, decentralized iterative scheme, termed BID ADJUSTMENT
ALGORITHM, for the resulting Bertrand competition game. Regarding convergence,
we show that the algorithm takes the generators' bids to any desired
neighborhood of the efficient Nash equilibrium at a linear convergence rate. As
a consequence, the optimal production of the generators converges to the
optimizer of the DC-OPF problem. Regarding robustness, we show that the
algorithm is robust to affine perturbations in the bid adjustment scheme and
that there is no incentive for any individual generator to deviate from the
algorithm by using an alternative bid update scheme. We also establish the
algorithm robustness to collusion, i.e., we show that, as long as each bus with
generation has a generator following the strategy, there is no incentive for
any group of generators to share information with the intent of tricking the
system to obtain a higher payoff. Simulations illustrate our results.
",1,0,1,0,0,0
20008,Short-baseline electron antineutrino disappearance study by using neutrino sources from $^{13}$C + $^{9}$Be reaction,"  To investigate the existence of sterile neutrino, we propose a new neutrino
production method using $^{13}$C beams and a $^{9}$Be target for short-baseline
electron antineutrino (${\bar{\nu}}_{e}$) disappearance study. The production
of secondary unstable isotopes which can emit neutrinos from the $^{13}$C +
$^{9}$Be reaction is calculated with three different nucleus-nucleus (AA)
reaction models. Different isotope yields are obtained using these models, but
the results of the neutrino flux are found to have unanimous similarities. This
feature gives an opportunity to study neutrino oscillation through shape
analysis. In this work, expected neutrino flux and event rates are discussed in
detail through intensive simulation of the light ion collision reaction and the
neutrino flux from the beta decay of unstable isotopes followed by this
collision. Together with the reactor and accelerator anomalies, the present
proposed ${\bar{\nu}}_{e}$ source is shown to be a practically alternative test
of the existence of the $\Delta m^{2}$ $\sim$ 1 eV$^{2}$ scale sterile
neutrino.
",0,1,0,0,0,0
20009,Temperature-dependent non-covalent protein-protein interactions explain normal and inverted solubility in a mutant of human gamma D-crystallin,"  Protein crystal production is a major bottleneck for the structural
characterisation of proteins. To advance beyond large-scale screening, rational
strategies for protein crystallization are crucial. Understanding how chemical
anisotropy (or patchiness) of the protein surface due to the variety of amino
acid side chains in contact with solvent, contributes to protein protein
contact formation in the crystal lattice is a major obstacle to predicting and
optimising crystallization. The relative scarcity of sophisticated theoretical
models that include sufficient detail to link collective behaviour, captured in
protein phase diagrams, and molecular level details, determined from
high-resolution structural information is a further barrier. Here we present
two crystals structures for the P23TR36S mutant of gamma D-crystallin, each
with opposite solubility behaviour, one melts when heated, the other when
cooled. When combined with the protein phase diagram and a tailored patchy
particle model we show that a single temperature dependent interaction is
sufficient to stabilise the inverted solubility crystal. This contact, at the
P23T substitution site, relates to a genetic cataract and reveals at a
molecular level, the origin of the lowered and retrograde solubility of the
protein. Our results show that the approach employed here may present an
alternative strategy for the rationalization of protein crystallization.
",0,0,0,0,1,0
20010,Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25,"  We present the Kepler Object of Interest (KOI) catalog of transiting
exoplanets based on searching four years of Kepler time series photometry (Data
Release 25, Q1-Q17). The catalog contains 8054 KOIs of which 4034 are planet
candidates with periods between 0.25 and 632 days. Of these candidates, 219 are
new and include two in multi-planet systems (KOI-82.06 and KOI-2926.05), and
ten high-reliability, terrestrial-size, habitable zone candidates. This catalog
was created using a tool called the Robovetter which automatically vets the
DR25 Threshold Crossing Events (TCEs, Twicken et al. 2016). The Robovetter also
vetted simulated data sets and measured how well it was able to separate TCEs
caused by noise from those caused by low signal-to-noise transits. We discusses
the Robovetter and the metrics it uses to sort TCEs. For orbital periods less
than 100 days the Robovetter completeness (the fraction of simulated transits
that are determined to be planet candidates) across all observed stars is
greater than 85%. For the same period range, the catalog reliability (the
fraction of candidates that are not due to instrumental or stellar noise) is
greater than 98%. However, for low signal-to-noise candidates between 200 and
500 days around FGK dwarf stars, the Robovetter is 76.7% complete and the
catalog is 50.5% reliable. The KOI catalog, the transit fits and all of the
simulated data used to characterize this catalog are available at the NASA
Exoplanet Archive.
",0,1,0,0,0,0
20011,Dark Photons from Captured Inelastic Dark Matter Annihilation: Charged Particle Signatures,"  The dark sector may contain a dark photon that kinetically mixes with the
Standard Model photon, allowing dark matter to interact weakly with normal
matter. In previous work we analyzed the implications of this scenario for dark
matter capture by the Sun. Dark matter will gather in the core of the Sun and
annihilate to dark photons. These dark photons travel outwards from the center
of the Sun and may decay to produce positrons that can be detected by the Alpha
Magnetic Spectrometer (AMS-02) on the ISS. We found that the dark photon
parameter space accessible to this analysis is largely constrained by strong
limits on the spin-independent WIMP-nucleon cross section from direct detection
experiments. In this paper we build upon previous work by considering the case
where the dark sector contains two species of Dirac fermion that are nearly
degenerate in mass and couple inelastically to the dark photon. We find that
for small values of the mass splitting $\Delta \sim 100 ~\text{keV}$, the
predicted positron signal at AMS-02 remains largely unchanged from the
previously considered elastic case while constraints from direct detection are
relaxed, leaving a region of parameter space with dark matter mass $100
~\text{GeV} \lesssim m_X \lesssim 10 ~\text{TeV}$, dark photon mass $1
~\text{MeV} \lesssim m_{A'} \lesssim 100 ~\text{MeV}$, and kinetic mixing
parameter $10^{-9} \lesssim \varepsilon \lesssim 10^{-8}$ that is untouched by
supernova observations and fixed target experiments but where an inelastic dark
sector may still be discovered using existing AMS-02 data.
",0,1,0,0,0,0
20012,A Polya Contagion Model for Networks,"  A network epidemics model based on the classical Polya urn scheme is
investigated. Temporal contagion processes are generated on the network nodes
using a modified Polya sampling scheme that accounts for spatial infection
among neighbouring nodes. The stochastic properties and the asymptotic
behaviour of the resulting network contagion process are analyzed. Unlike the
classical Polya process, the network process is noted to be non-stationary in
general, although it is shown to be time-invariant in its first and some of its
second-order statistics and to satisfy martingale convergence properties under
certain conditions. Three classical Polya processes, one computational and two
analytical, are proposed to statistically approximate the contagion process of
each node, showing a good fit for a range of system parameters. Finally,
empirical results compare and contrast our model with the well-known discrete
time SIS model.
",1,1,1,0,0,0
20013,Compressive Statistical Learning with Random Feature Moments,"  We describe a general framework --compressive statistical learning-- for
resource-efficient large-scale learning: the training collection is compressed
in one pass into a low-dimensional sketch (a vector of random empirical
generalized moments) that captures the information relevant to the considered
learning task. A near-minimizer of the risk is computed from the sketch through
the solution of a nonlinear least squares problem. We investigate sufficient
sketch sizes to control the generalization error of this procedure. The
framework is illustrated on compressive clustering, compressive Gaussian
mixture Modeling with fixed known variance, and compressive PCA.
",1,0,1,1,0,0
20014,Bandit Structured Prediction for Neural Sequence-to-Sequence Learning,"  Bandit structured prediction describes a stochastic optimization framework
where learning is performed from partial feedback. This feedback is received in
the form of a task loss evaluation to a predicted output structure, without
having access to gold standard structures. We advance this framework by lifting
linear bandit learning to neural sequence-to-sequence learning problems using
attention-based recurrent neural networks. Furthermore, we show how to
incorporate control variates into our learning algorithms for variance
reduction and improved generalization. We present an evaluation on a neural
machine translation task that shows improvements of up to 5.89 BLEU points for
domain adaptation from simulated bandit feedback.
",1,0,0,1,0,0
20015,Dynamic Input Structure and Network Assembly for Few-Shot Learning,"  The ability to learn from a small number of examples has been a difficult
problem in machine learning since its inception. While methods have succeeded
with large amounts of training data, research has been underway in how to
accomplish similar performance with fewer examples, known as one-shot or more
generally few-shot learning. This technique has been shown to have promising
performance, but in practice requires fixed-size inputs making it impractical
for production systems where class sizes can vary. This impedes training and
the final utility of few-shot learning systems. This paper describes an
approach to constructing and training a network that can handle arbitrary
example sizes dynamically as the system is used.
",1,0,0,1,0,0
20016,Face R-CNN,"  Faster R-CNN is one of the most representative and successful methods for
object detection, and has been becoming increasingly popular in various
objection detection applications. In this report, we propose a robust deep face
detection approach based on Faster R-CNN. In our approach, we exploit several
new techniques including new multi-task loss function design, online hard
example mining, and multi-scale training strategy to improve Faster R-CNN in
multiple aspects. The proposed approach is well suited for face detection, so
we call it Face R-CNN. Extensive experiments are conducted on two most popular
and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate
the superiority of the proposed approach over state-of-the-arts.
",1,0,0,0,0,0
20017,Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach,"  Data-target pairing is an important step towards multi-target localization
for the intelligent operation of unmanned systems. Target localization plays a
crucial role in numerous applications, such as search, and rescue missions,
traffic management and surveillance. The objective of this paper is to present
an innovative target location learning approach, where numerous machine
learning approaches, including K-means clustering and supported vector machines
(SVM), are used to learn the data pattern across a list of spatially
distributed sensors. To enable the accurate data association from different
sensors for accurate target localization, appropriate data pre-processing is
essential, which is then followed by the application of different machine
learning algorithms to appropriately group data from different sensors for the
accurate localization of multiple targets. Through simulation examples, the
performance of these machine learning algorithms is quantified and compared.
",1,0,0,1,0,0
20018,Calipso: Physics-based Image and Video Editing through CAD Model Proxies,"  We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.
",1,0,0,0,0,0
20019,"Maximal (120,8)-arcs in projective planes of order 16 and related designs","  The resolutions and maximal sets of compatible resolutions of all 2-(120,8,1)
designs arising frommaximal (120,8)-arcs in the known projective planes of
order 16 are computed. It is shown that each of these designs is embeddable in
a unique way in a projective plane of order 16.
",0,0,1,0,0,0
20020,First order sentences about random graphs: small number of alternations,"  Spectrum of a first order sentence is the set of all $\alpha$ such that $G(n,
n^{-\alpha})$ does not obey zero-one law w.r.t. this sentence. We have proved
that the minimal number of quantifier alternations of a first order sentence
with an infinite spectrum equals 3. We have also proved that the spectrum of a
first order sentence with a quantifier depth 4 has no limit points except
possibly the points 1/2 and 3/5.
",0,0,1,0,0,0
20021,New Methods for Metadata Extraction from Scientific Literature,"  Within the past few decades we have witnessed digital revolution, which moved
scholarly communication to electronic media and also resulted in a substantial
increase in its volume. Nowadays keeping track with the latest scientific
achievements poses a major challenge for the researchers. Scientific
information overload is a severe problem that slows down scholarly
communication and knowledge propagation across the academia. Modern research
infrastructures facilitate studying scientific literature by providing
intelligent search tools, proposing similar and related documents, visualizing
citation and author networks, assessing the quality and impact of the articles,
and so on. In order to provide such high quality services the system requires
the access not only to the text content of stored documents, but also to their
machine-readable metadata. Since in practice good quality metadata is not
always available, there is a strong demand for a reliable automatic method of
extracting machine-readable metadata directly from source documents. This
research addresses these problems by proposing an automatic, accurate and
flexible algorithm for extracting wide range of metadata directly from
scientific articles in born-digital form. Extracted information includes basic
document metadata, structured full text and bibliography section. Designed as a
universal solution, proposed algorithm is able to handle a vast variety of
publication layouts with high precision and thus is well-suited for analyzing
heterogeneous document collections. This was achieved by employing supervised
and unsupervised machine-learning algorithms trained on large, diverse
datasets. The evaluation we conducted showed good performance of proposed
metadata extraction algorithm. The comparison with other similar solutions also
proved our algorithm performs better than competition for most metadata types.
",1,0,0,0,0,0
20022,Unexpected biases in prime factorizations and Liouville functions for arithmetic progressions,"  We introduce a refinement of the classical Liouville function to primes in
arithmetic progressions. Using this, we discover new biases in the appearances
of primes in a given arithmetic progression in the prime factorizations of
integers. For example, we observe that the primes of the form $4k+1$ tend to
appear an even number of times in the prime factorization of a given integer,
more so than for primes of the form $4k+3$. We are led to consider variants of
Pólya's conjecture, supported by extensive numerical evidence, and its
relation to other conjectures.
",0,0,1,0,0,0
20023,Nonconvection and uniqueness in Navier-Stokes equation,"  In the presence of a certain class of functions we show that there exists a
smooth solution to Navier-Stokes equation. This solution entertains the
property of being nonconvective. We introduce a definition for any possible
solution to the problem with minimum assumptions on the existence and the
regularity of such solution. Then we prove that the proposed class of functions
represents the unique solution to the problem and consequently we conclude that
there exists no convective solutions to the problem in the sense of the given
definition.
",0,0,1,0,0,0
20024,An alternative approach for compatibility of two discrete conditional distributions,"  Conditional specification of distributions is a developing area with
increasing applications. In the finite discrete case, a variety of compatible
conditions can be derived. In this paper, we propose an alternative approach to
study the compatibility of two conditional probability distributions under the
finite discrete setup. A technique based on rank-based criterion is shown to be
particularly convenient for identifying compatible distributions corresponding
to complete conditional specification including the case with zeros.The
proposed methods are illustrated with several examples.
",0,0,1,1,0,0
20025,System of unbiased representatives for a collection of bicolorings,"  Let $\mathcal{B}$ denote a set of bicolorings of $[n]$, where each bicoloring
is a mapping of the points in $[n]$ to $\{-1,+1\}$.
For each $B \in \mathcal{B}$, let $Y_B=(B(1),\ldots,B(n))$.
For each $A \subseteq [n]$, let $X_A \in \{0,1\}^n$ denote the incidence
vector of $A$.
A non-empty set $A$ is said to be an `unbiased representative' for a
bicoloring $B \in \mathcal{B}$ if $\left\langle X_A,Y_B\right\rangle =0$.
Given a set $\mathcal{B}$ of bicolorings, we study the minimum cardinality of
a family $\mathcal{A}$ consisting of subsets of $[n]$ such that every
bicoloring in $\mathcal{B}$ has an unbiased representative in $\mathcal{A}$.
",1,0,0,0,0,0
20026,Stability and instability in saddle point dynamics Part II: The subgradient method,"  In part I we considered the problem of convergence to a saddle point of a
concave-convex function via gradient dynamics and an exact characterization was
given to their asymptotic behaviour. In part II we consider a general class of
subgradient dynamics that provide a restriction in an arbitrary convex domain.
We show that despite the nonlinear and non-smooth character of these dynamics
their $\omega$-limit set is comprised of solutions to only linear ODEs. In
particular, we show that the latter are solutions to subgradient dynamics on
affine subspaces which is a smooth class of dynamics the asymptotic properties
of which have been exactly characterized in part I. Various convergence
criteria are formulated using these results and several examples and
applications are also discussed throughout the manuscript.
",1,0,1,0,0,0
20027,"Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly","  JavaScript systems are becoming increasingly complex and large. To tackle the
challenges involved in implementing these systems, the language is evolving to
include several constructions for programming- in-the-large. For example,
although the language is prototype-based, the latest JavaScript standard, named
ECMAScript 6 (ES6), provides native support for implementing classes. Even
though most modern web browsers support ES6, only a very few applications use
the class syntax. In this paper, we analyze the process of migrating structures
that emulate classes in legacy JavaScript code to adopt the new syntax for
classes introduced by ES6. We apply a set of migration rules on eight legacy
JavaScript systems. In our study, we document: (a) cases that are
straightforward to migrate (the good parts); (b) cases that require manual and
ad-hoc migration (the bad parts); and (c) cases that cannot be migrated due to
limitations and restrictions of ES6 (the ugly parts). Six out of eight systems
(75%) contain instances of bad and/or ugly cases. We also collect the
perceptions of JavaScript developers about migrating their code to use the new
syntax for classes.
",1,0,0,0,0,0
20028,Parylene-C microfibrous thin films as phononic crystals,"  Phononic bandgaps of Parylene-C microfibrous thin films (muFTFs) were
computationally determined by treating them as phononic crystals comprising
identical microfibers arranged either on a square or a hexagonal lattice. The
microfibers could be columnar,chevronic, or helical in shape, and the host
medium could be either water or air. All bandgaps were observed to lie in the
0.01-to-162.9-MHz regime, for microfibers of realistically chosen dimensions.
The upper limit of the frequency of bandgaps was the highest for the columnar
muFTF and the lowest for the chiral muFTF. More bandgaps exist when the host
medium is water than air. Complete bandgaps were observed for the columnar
muFTF with microfibers arranged on a hexagonal lattice in air, the chevronic
muFTF with microfibers arranged on a square lattice in water, and the chiral
muFTF with microfibers arranged on a hexagonal lattice in either air or water.
The softness of the Parylene-C muFTFs makes them mechanically tunable, and
their bandgaps can be exploited in multiband ultrasonic filters.
",0,1,0,0,0,0
20029,A General Theory for Training Learning Machine,"  Though the deep learning is pushing the machine learning to a new stage,
basic theories of machine learning are still limited. The principle of
learning, the role of the a prior knowledge, the role of neuron bias, and the
basis for choosing neural transfer function and cost function, etc., are still
far from clear. In this paper, we present a general theoretical framework for
machine learning. We classify the prior knowledge into common and
problem-dependent parts, and consider that the aim of learning is to maximally
incorporate them. The principle we suggested for maximizing the former is the
design risk minimization principle, while the neural transfer function, the
cost function, as well as pretreatment of samples, are endowed with the role
for maximizing the latter. The role of the neuron bias is explained from a
different angle. We develop a Monte Carlo algorithm to establish the
input-output responses, and we control the input-output sensitivity of a
learning machine by controlling that of individual neurons. Applications of
function approaching and smoothing, pattern recognition and classification, are
provided to illustrate how to train general learning machines based on our
theory and algorithm. Our method may in addition induce new applications, such
as the transductive inference.
",1,0,0,1,0,0
20030,Semiparametric spectral modeling of the Drosophila connectome,"  We present semiparametric spectral modeling of the complete larval Drosophila
mushroom body connectome. Motivated by a thorough exploratory data analysis of
the network via Gaussian mixture modeling (GMM) in the adjacency spectral
embedding (ASE) representation space, we introduce the latent structure model
(LSM) for network modeling and inference. LSM is a generalization of the
stochastic block model (SBM) and a special case of the random dot product graph
(RDPG) latent position model, and is amenable to semiparametric GMM in the ASE
representation space. The resulting connectome code derived via semiparametric
GMM composed with ASE captures latent connectome structure and elucidates
biologically relevant neuronal properties.
",0,0,0,1,0,0
20031,A Large Term Rewrite System Modelling a Pioneering Cryptographic Algorithm,"  We present a term rewrite system that formally models the Message
Authenticator Algorithm (MAA), which was one of the first cryptographic
functions for computing a Message Authentication Code and was adopted, between
1987 and 2001, in international standards (ISO 8730 and ISO 8731-2) to ensure
the authenticity and integrity of banking transactions. Our term rewrite system
is large (13 sorts, 18 constructors, 644 non-constructors, and 684 rewrite
rules), confluent, and terminating. Implementations in thirteen different
languages have been automatically derived from this model and used to validate
200 official test vectors for the MAA.
",1,0,0,0,0,0
20032,Malleability of complex networks,"  Most complex networks are not static, but evolve along time. Given a specific
configuration of one such changing network, it becomes a particularly
interesting issue to quantify the diversity of possible unfoldings of its
topology. In this work, we suggest the concept of malleability of a network,
which is defined as the exponential of the entropy of the probabilities of each
possible unfolding with respect to a given configuration. We calculate the
malleability with respect to specific measurements of the involved topologies.
More specifically, we identify the possible topologies derivable from a given
configuration and calculate some topological measurement of them (e.g.
clustering coefficient, shortest path length, assortativity, etc.), leading to
respective probabilities being associated to each possible measurement value.
Though this approach implies some level of degeneracy in the mapping from
topology to measurement space, it still paves the way to inferring the
malleability of specific network types with respect to given topological
measurements. We report that the malleability, in general, depends on each
specific measurement, with the average shortest path length and degree
assortativity typically leading to large malleability values. The maximum
malleability was observed for the Wikipedia network and the minimum for the
Watts-Strogatz model.
",1,0,0,0,0,0
20033,Identifying Critical Risks of Cascading Failures in Power Systems,"  Potential critical risks of cascading failures in power systems can be
identified by exposing those critical electrical elements on which certain
initial disturbances may cause maximum disruption to power transmission
networks. In this work, we investigate cascading failures in power systems
described by the direct current (DC) power flow equations, while initial
disturbances take the form of altering admittance of elements. The disruption
is quantified with the remaining transmission power at the end of cascading
process. In particular, identifying the critical elements and the corresponding
initial disturbances causing the worst-case cascading blackout is formulated as
a dynamic optimization problem (DOP) in the framework of optimal control
theory, where the entire propagation process of cascading failures is put under
consideration. An Identifying Critical Risk Algorithm (ICRA) based on the
maximum principle is proposed to solve the DOP. Simulation results on the IEEE
9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the
effectiveness of the algorithm.
",1,0,0,0,0,0
20034,Radiation Hardness Test of Eljen EJ-500 Optical Cement,"  We present a comprehensive account of the proton radiation hardness of Eljen
Technology's EJ-500 optical cement used in the construction of experiment
detectors. The cement was embedded into five plastic scintillator tiles which
were each exposed to one of five different levels of radiation by a 50 MeV
proton beam produced at the 88-Inch Cyclotron at Lawrence Berkeley National
Laboratory. A cosmic ray telescope setup was used to measure signal amplitudes
before and after irradiation. Another post-radiation measurement was taken four
months after the experiment to investigate whether the radiation damage to the
cement recovers after a short amount of time. We verified that the radiation
damage to the tiles increased with increasing dose but showed significant
improvement after the four months time interval.
",0,1,0,0,0,0
20035,Triviality of the ground-state metastate in long-range Ising spin glasses in one dimension,"  We consider the one-dimensional model of a spin glass with independent
Gaussian-distributed random interactions, that have mean zero and variance
$1/|i-j|^{2\sigma}$, between the spins at sites $i$ and $j$ for all $i\neq j$.
It is known that, for $\sigma>1$, there is no phase transition at any non-zero
temperature in this model. We prove rigorously that, for $\sigma>3/2$, any
Newman-Stein metastate for the ground states (i.e.\ the frequencies with which
distinct ground states are observed in finite size samples in the limit of
infinite size, for given disorder) is trivial and unique. In other words, for
given disorder and asymptotically at large sizes, the same ground state, or its
global spin flip, is obtained (almost) always. The proof consists of two parts:
one is a theorem (based on one by Newman and Stein for short-range
two-dimensional models), valid for all $\sigma>1$, that establishes triviality
under a convergence hypothesis on something similar to the energies of domain
walls, and the other (based on older results for the one-dimensional model)
establishes that the hypothesis is true for $\sigma>3/2$. In addition, we
derive heuristic scaling arguments and rigorous exponent inequalities which
tend to support the validity of the hypothesis under broader conditions. The
constructions of various metastates are extended to all values $\sigma>1/2$.
Triviality of the metastate in bond-diluted power-law models for $\sigma>1$ is
proved directly.
",0,1,0,0,0,0
20036,Thermodynamics of BTZ Black Holes in Gravity's Rainbow,"  In this paper, we deform the thermodynamics of a BTZ black hole from rainbow
functions in gravity's rainbow. The rainbow functions will be motivated from
results in loop quantum gravity and Noncommutative geometry. It will be
observed that the thermodynamics gets deformed due to these rainbow functions,
indicating the existence of a remnant. However, the Gibbs free energy does not
get deformed due to these rainbow functions, and so the critical behaviour from
Gibbs does not change by this deformation.This is because the deformation in
the entropy cancel's out the temperature deformation.
",0,1,0,0,0,0
20037,Nondegeneracy and the Jacobi fields of rotationally symmetric solutions to the Cahn-Hillard equation,"  In this paper we study rotationally symmetric solutions of the Cahn-Hilliard
equation in $\mathbb R^3$ constructed by the authors. These solutions form a
one parameter family analog to the family of Delaunay surfaces and in fact the
zero level sets of their blowdowns approach these surfaces. Presently we go a
step further and show that their stability properties are inherited from the
stability properties of the Delaunay surfaces. Our main result states that the
rotationally symmetric solutions are non degenerate and that they have exactly
$6$ Jacobi fields of temperate growth coming from the natural invariances of
the problem (3 translations and 2 rotations) and the variation of the Delaunay
parameter.
",0,0,1,0,0,0
20038,Quasiflats in hierarchically hyperbolic spaces,"  The rank of a hierarchically hyperbolic space is the maximal number of
unbounded factors of standard product regions; this coincides with the maximal
dimension of a quasiflat for hierarchically hyperbolic groups. Noteworthy
examples where the rank coincides with familiar quantities include: the
dimension of maximal Dehn twist flats for mapping class groups, the maximal
rank of a free abelian subgroup for right-angled Coxeter groups and
right-angled Artin groups (in the latter this coincides with the clique number
of the defining graph), and, for the Weil-Petersson metric the rank is half the
complex dimension of Teichmuller space.
We prove that in a HHS, any quasiflat of dimension equal to the rank lies
within finite distance of a union of standard orthants (under a very mild
condition satisfied by all natural examples). This resolves outstanding
conjectures when applied to a number of different groups and spaces. The
mapping class group case resolves a conjecture of Farb, in Teichmuller space
this resolves a question of Brock, and in the context of CAT(0) cubical groups
it strengthens previous results (so as to handle, for example, the right-angled
Coxeter case).
An important ingredient, is our proof that the hull of any finite set in an
HHS is quasi-isometric to a cube complex of dimension equal to the rank.
We deduce a number of applications; for instance we show that any
quasi-isometry between HHS induces a quasi-isometry between certain simpler
HHS. This allows one, for example, to distinguish quasi-isometry classes of
right-angled Artin/Coxeter groups.
Another application is that our tools, in many cases, allow one to reduce the
problem of quasi-isometric rigidity for a given HHG to a combinatorial problem.
As a template, we give a new proof of quasi-isometric rigidity of mapping class
groups, using simpler combinatorial arguments than in previous proofs.
",0,0,1,0,0,0
20039,Learning to Skim Text,"  Recurrent Neural Networks are showing much promise in many sub-areas of
natural language processing, ranging from document classification to machine
translation to automatic question answering. Despite their promise, many
recurrent models have to read the whole text word by word, making it slow to
handle long documents. For example, it is difficult to use a recurrent network
to read a book and answer questions about it. In this paper, we present an
approach of reading text while skipping irrelevant information if needed. The
underlying model is a recurrent network that learns how far to jump after
reading a few words of the input text. We employ a standard policy gradient
method to train the model to make discrete jumping decisions. In our benchmarks
on four different tasks, including number prediction, sentiment analysis, news
article classification and automatic Q\&A, our proposed model, a modified LSTM
with jumping, is up to 6 times faster than the standard sequential LSTM, while
maintaining the same or even better accuracy.
",1,0,0,0,0,0
20040,Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications,"  Statistical analysis (SA) is a complex process to deduce population
properties from analysis of data. It usually takes a well-trained analyst to
successfully perform SA, and it becomes extremely challenging to apply SA to
big data applications. We propose to use deep neural networks to automate the
SA process. In particular, we propose to construct convolutional neural
networks (CNNs) to perform automatic model selection and parameter estimation,
two most important SA tasks. We refer to the resulting CNNs as the neural model
selector and the neural model estimator, respectively, which can be properly
trained using labeled data systematically generated from candidate models.
Simulation study shows that both the selector and estimator demonstrate
excellent performances. The idea and proposed framework can be further extended
to automate the entire SA process and have the potential to revolutionize how
SA is performed in big data analytics.
",1,0,0,1,0,0
20041,Duty to Delete on Non-Volatile Memory,"  We firstly suggest new cache policy applying the duty to delete invalid cache
data on Non-volatile Memory (NVM). This cache policy includes generating random
data and overwriting the random data into invalid cache data. Proposed cache
policy is more economical and effective regarding perfect deletion of data. It
is ensure that the invalid cache data in NVM is secure against malicious
hackers.
",1,0,0,0,0,0
20042,Geometry of Log-Concave Density Estimation,"  Shape-constrained density estimation is an important topic in mathematical
statistics. We focus on densities on $\mathbb{R}^d$ that are log-concave, and
we study geometric properties of the maximum likelihood estimator (MLE) for
weighted samples. Cule, Samworth, and Stewart showed that the logarithm of the
optimal log-concave density is piecewise linear and supported on a regular
subdivision of the samples. This defines a map from the space of weights to the
set of regular subdivisions of the samples, i.e. the face poset of their
secondary polytope. We prove that this map is surjective. In fact, every
regular subdivision arises in the MLE for some set of weights with positive
probability, but coarser subdivisions appear to be more likely to arise than
finer ones. To quantify these results, we introduce a continuous version of the
secondary polytope, whose dual we name the Samworth body. This article
establishes a new link between geometric combinatorics and nonparametric
statistics, and it suggests numerous open problems.
",0,0,0,1,0,0
20043,Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength is not Enough,"  The aim of this paper is to analyze the array synthesis for 5 G massive MIMO
systems in the line-of-sight working condition. The main result of the
numerical investigation performed is that non-uniform arrays are the natural
choice in this kind of application. In particular, by using non-equispaced
arrays, we show that it is possible to achieve a better average condition
number of the channel matrix and a significantly higher spectral efficiency.
Furthermore, we verify that increasing the array size is beneficial also for
circular arrays, and we provide some useful rules-of-thumb for antenna array
design for massive MIMO applications. These results are in contrast to the
widely-accepted idea in the 5 G massive MIMO literature, in which the
half-wavelength linear uniform array is universally adopted.
",1,0,0,0,0,0
20044,Gravitational octree code performance evaluation on Volta GPU,"  In this study, the gravitational octree code originally optimized for the
Fermi, Kepler, and Maxwell GPU architectures is adapted to the Volta
architecture. The Volta architecture introduces independent thread scheduling
requiring either the insertion of the explicit synchronizations at appropriate
locations or the enforcement of the same implicit synchronizations as do the
Pascal or earlier architectures by specifying \texttt{-gencode
arch=compute\_60,code=sm\_70}. The performance measurements on Tesla V100, the
current flagship GPU by NVIDIA, revealed that the $N$-body simulations of the
Andromeda galaxy model with $2^{23} = 8388608$ particles took $3.8 \times
10^{-2}$~s or $3.3 \times 10^{-2}$~s per step for each case. Tesla V100
achieves a 1.4 to 2.2-fold acceleration in comparison with Tesla P100, the
flagship GPU in the previous generation. The observed speed-up of 2.2 is
greater than 1.5, which is the ratio of the theoretical peak performance of the
two GPUs. The independence of the units for integer operations from those for
floating-point number operations enables the overlapped execution of integer
and floating-point number operations. It hides the execution time of the
integer operations leading to the speed-up rate above the theoretical peak
performance ratio. Tesla V100 can execute $N$-body simulation with up to $25
\times 2^{20} = 26214400$ particles, and it took $2.0 \times 10^{-1}$~s per
step. It corresponds to $3.5$~TFlop/s, which is 22\% of the single-precision
theoretical peak performance.
",1,0,0,0,0,0
20045,"Instanton bundles on the flag variety F(0,1,2)","  Instanton bundles on $\mathbb{P}^3$ have been at the core of the research in
Algebraic Geometry during the last thirty years. Motivated by the recent
extension of their definition to other Fano threefolds of Picard number one, we
develop the theory of instanton bundles on the complete flag variety
$F:=F(0,1,2)$ of point-lines on $\mathbb{P}^2$. After giving for them two
different monadic presentations, we use it to show that the moduli space
$MI_F(k)$ of instanton bundles of charge $k$ is a geometric GIT quotient with a
generically smooth component of dim $8k-3$. Finally we study their locus of
jumping conics.
",0,0,1,0,0,0
20046,Non-linear motor control by local learning in spiking neural networks,"  Learning weights in a spiking neural network with hidden neurons, using
local, stable and online rules, to control non-linear body dynamics is an open
problem. Here, we employ a supervised scheme, Feedback-based Online Local
Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
neurons with hidden layers, to control a two-link arm so as to reproduce a
desired state trajectory. The network first learns an inverse model of the
non-linear dynamics, i.e. from state trajectory as input to the network, it
learns to infer the continuous-time command that produced the trajectory.
Connection weights are adjusted via a local plasticity rule that involves
pre-synaptic firing and post-synaptic feedback of the error in the inferred
command. We choose a network architecture, termed differential feedforward,
that gives the lowest test error from different feedforward and recurrent
architectures. The learned inverse model is then used to generate a
continuous-time motor command to control the arm, given a desired trajectory.
",1,0,0,1,0,0
20047,Training Deep Neural Networks via Optimization Over Graphs,"  In this work, we propose to train a deep neural network by distributed
optimization over a graph. Two nonlinear functions are considered: the
rectified linear unit (ReLU) and a linear unit with both lower and upper
cutoffs (DCutLU). The problem reformulation over a graph is realized by
explicitly representing ReLU or DCutLU using a set of slack variables. We then
apply the alternating direction method of multipliers (ADMM) to update the
weights of the network layerwise by solving subproblems of the reformulated
problem. Empirical results suggest that the ADMM-based method is less sensitive
to overfitting than the stochastic gradient descent (SGD) and Adam methods.
",1,0,0,0,0,0
20048,Calculation of Effective Interaction Potential During Positron Channeling in Ionic Crystals,"  An analytical expression is received for the effective interaction potential
of a fast charged particle with the ionic crystal CsCl near the direction of
<100> axis as a function of the temperature of the medium. By numerical
analysis it is shown that the effective potential of axial channeling of
positrons along the axis <100> of negatively charged ions practically does not
depend on temperature of the media
",0,1,0,0,0,0
20049,The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count,"  Online Social Networks (OSN) are increasingly being used as platform for an
effective communication, to engage with other users, and to create a social
worth via number of likes, followers and shares. Such metrics and crowd-sourced
ratings give the OSN user a sense of social reputation which she tries to
maintain and boost to be more influential. Users artificially bolster their
social reputation via black-market web services. In this work, we identify
users which manipulate their projected follower count using an unsupervised
local neighborhood detection method. We identify a neighborhood of the user
based on a robust set of features which reflect user similarity in terms of the
expected follower count. We show that follower count estimation using our
method has 84.2% accuracy with a low error rate. In addition, we estimate the
follower count of the user under suspicion by finding its neighborhood drawn
from a large random sample of Twitter. We show that our method is highly
tolerant to synthetic manipulation of followers. Using the deviation of
predicted follower count from the displayed count, we are also able to detect
customers with a high precision of 98.62%
",1,0,0,0,0,0
20050,Bergman kernel estimates and Toeplitz operators on holomorphic line bundles,"  We characterize operator-theoretic properties (boundedness, compactness, and
Schatten class membership) of Toeplitz operators with positive measure symbols
on Bergman spaces of holomorphic hermitian line bundles over Kähler
Cartan-Hadamard manifolds in terms of geometric or operator-theoretic
properties of measures.
",0,0,1,0,0,0
20051,HST PanCET program: A Cloudy Atmosphere for the promising JWST target WASP-101b,"  We present results from the first observations of the Hubble Space Telescope
(HST) Panchromatic Comparative Exoplanet Treasury (PanCET) program for
WASP-101b, a highly inflated hot Jupiter and one of the community targets
proposed for the James Webb Space Telescope (JWST) Early Release Science (ERS)
program. From a single HST Wide Field Camera 3 (WFC3) observation, we find that
the near-infrared transmission spectrum of WASP-101b contains no significant
H$_2$O absorption features and we rule out a clear atmosphere at 13{\sigma}.
Therefore, WASP-101b is not an optimum target for a JWST ERS program aimed at
observing strong molecular transmission features. We compare WASP-101b to the
well studied and nearly identical hot Jupiter WASP-31b. These twin planets show
similar temperature-pressure profiles and atmospheric features in the
near-infrared. We suggest exoplanets in the same parameter space as WASP-101b
and WASP-31b will also exhibit cloudy transmission spectral features. For
future HST exoplanet studies, our analysis also suggests that a lower count
limit needs to be exceeded per pixel on the detector in order to avoid unwanted
instrumental systematics.
",0,1,0,0,0,0
20052,Quantum gauge symmetry of reducible gauge theory,"  We derive the gaugeon formalism of the Kalb-Ramond field theory, a reducible
gauge theory, which discusses the quantum gauge freedom. In gaugeon formalism,
theory admits quantum gauge symmetry which leaves the action form-invariant.
The BRST symmetric gaugeon formalism is also studied which introduces the
gaugeon ghost fields and gaugeon ghosts of ghosts fields. To replace the
Yokoyama subsidiary conditions by a single Kugo-Ojima type condition the virtue
of BRST symmetry is utilized. Under generalized BRST transformations, we show
that the gaugeon fields appear naturally in the reducible gauge theory.
",0,1,0,0,0,0
20053,The Tian Pseudo-Atom Method,"  In this work, the authors give a new method for phase determination, the Tian
pseudo atom method (TPAM) or pseudo atom method (PAM) for short. In this new
method, the figure of merit function, Rtian, replaces Rcf in the charge
flipping algorithm. The key difference between Rcf and Rtian is the oberved
structure factor was replaced by the pseudo structure factor. The test results
show that Rtian is more powerful and robust than Rcf to estimate the correct
structure especially with low resolution data. Therefore, the pseudo atom
method could overcome the charge flipping method's defeat to some extent. In
theory, the pseudo atom method could deal with quite low resolution data but it
needs a further test.
",0,1,0,0,0,0
20054,"The Phenotypes of Fluctuating Flow: Development of Distribution Networks in Biology and the Trade-off between Efficiency, Cost, and Resilience","  Complex distribution networks are pervasive in biology. Examples include
nutrient transport in the slime mold $Physarum$ $polycephalum$ as well as
mammalian and plant venation. Adaptive rules are believed to guide development
of these networks and lead to a reticulate, hierarchically nested topology that
is both efficient and resilient against perturbations. However, as of yet no
mechanism is known that can generate such networks on all scales. We show how
hierarchically organized reticulation can be generated and maintained through
spatially collective load fluctuations on a particular length scale. We
demonstrate that the resulting network topologies represent a trade-off between
optimizing power dissipation, construction cost, and damage robustness and
identify the Pareto-efficient front that evolution is expected to favor and
select for. We show that the typical fluctuation length scale controls the
position of the networks on the Pareto front and thus on the spectrum of
venation phenotypes. We compare the Pareto archetypes predicted by our model
with examples of real leaf networks.
",0,1,0,0,0,0
20055,Recent Trends in Deep Learning Based Natural Language Processing,"  Deep learning methods employ multiple processing layers to learn hierarchical
representations of data and have produced state-of-the-art results in many
domains. Recently, a variety of model designs and methods have blossomed in the
context of natural language processing (NLP). In this paper, we review
significant deep learning related models and methods that have been employed
for numerous NLP tasks and provide a walk-through of their evolution. We also
summarize, compare and contrast the various models and put forward a detailed
understanding of the past, present and future of deep learning in NLP.
",1,0,0,0,0,0
20056,Volatile memory forensics for the Robot Operating System,"  The increasing impact of robotics on industry and on society will unavoidably
lead to the involvement of robots in incidents and mishaps. In such cases,
forensic analyses are key techniques to provide useful evidence on what
happened, and try to prevent future incidents. This article discusses volatile
memory forensics for the Robot Operating System (ROS). The authors start by
providing a general overview of forensic techniques in robotics and then
present a robotics-specific Volatility plugin named linux_rosnode, packaged
within the ros_volatility project and aimed to extract evidence from robot's
volatile memory. They demonstrate how this plugin can be used to detect a
specific attack pattern on ROS, where a publisher node is unregistered
externally, leading to denial of service and disruption of robotic behaviors.
Step-by-step, common practices are introduced for performing forensic analysis
and several techniques to capture memory are described. The authors finalize by
introducing some future remarks while providing references to reproduce their
work.
",1,0,0,0,0,0
20057,HYDRA: HYbrid Design for Remote Attestation (Using a Formally Verified Microkernel),"  Remote Attestation (RA) allows a trusted entity (verifier) to securely
measure internal state of a remote untrusted hardware platform (prover). RA can
be used to establish a static or dynamic root of trust in embedded and
cyber-physical systems. It can also be used as a building block for other
security services and primitives, such as software updates and patches,
verifiable deletion and memory resetting. There are three major classes of RA
designs: hardware-based, software-based, and hybrid, each with its own set of
benefits and drawbacks. This paper presents the first hybrid RA design, called
HYDRA, that builds upon formally verified software components that ensure
memory isolation and protection, as well as enforce access control to memory
and other resources. HYDRA obtains these properties by using the formally
verified seL4 microkernel. (Until now, this was only attainable with purely
hardware-based designs.) Using seL4 requires fewer hardware modifications to
the underlying microprocessor. Building upon a formally verified software
component increases confidence in security of the overall design of HYDRA and
its implementation. We instantiate HYDRA on two commodity hardware platforms
and assess the performance and overhead of performing RA on such platforms via
experimentation; we show that HYDRA can attest 10MB of memory in less than
500msec when using a Speck-based message authentication code (MAC) to compute a
cryptographic checksum over the memory to be attested.
",1,0,0,0,0,0
20058,Social Robots for People with Developmental Disabilities: A User Study on Design Features of a Graphical User Interface,"  Social robots, also known as service or assistant robots, have been developed
to improve the quality of human life in recent years. The design of socially
capable and intelligent robots can vary, depending on the target user groups.
In this work, we assess the effect of social robots' roles, functions, and
communication approaches in the context of a social agent providing service or
entertainment to users with developmental disabilities. In this paper, we
describe an exploratory study of interface design for a social robot that
assists people suffering from developmental disabilities. We developed series
of prototypes and tested one in a user study that included three residents with
various function levels. This entire study had been recorded for the following
qualitative data analysis. Results show that each design factor played a
different role in delivering information and in increasing engagement. We also
note that some of the fundamental design principles that would work for
ordinary users did not apply to our target user group. We conclude that social
robots could benefit our target users, and acknowledge that these robots were
not suitable for certain scenarios based on the feedback from our users.
",1,0,0,0,0,0
20059,Inverse problem for multi-species mean field models in the low temperature phase,"  In this paper we solve the inverse problem for a class of mean field models
(Curie-Weiss model and its multi-species version) when multiple thermodynamic
states are present, as in the low temperature phase where the phase space is
clustered. The inverse problem consists in reconstructing the model parameters
starting from configuration data generated according to the distribution of the
model. We show that the application of the inversion procedure without taking
into account the presence of many states produces very poor inference results.
This problem is overcomed using the clustering algorithm. When the system has
two symmetric states of positive and negative magnetization, the parameter
reconstruction can be also obtained with smaller computational effort simply by
flipping the sign of the magnetizations from positive to negative (or
viceversa). The parameter reconstruction fails when the system is critical: in
this case we give the correct inversion formulas for the Curie-Weiss model and
we show that they can be used to measuring how much the system is close to
criticality.
",0,1,0,0,0,0
20060,Inertial Odometry on Handheld Smartphones,"  Building a complete inertial navigation system using the limited quality data
provided by current smartphones has been regarded challenging, if not
impossible. This paper shows that by careful crafting and accounting for the
weak information in the sensor samples, smartphones are capable of pure
inertial navigation. We present a probabilistic approach for orientation and
use-case free inertial odometry, which is based on double-integrating rotated
accelerations. The strength of the model is in learning additive and
multiplicative IMU biases online. We are able to track the phone position,
velocity, and pose in real-time and in a computationally lightweight fashion by
solving the inference with an extended Kalman filter. The information fusion is
completed with zero-velocity updates (if the phone remains stationary),
altitude correction from barometric pressure readings (if available), and
pseudo-updates constraining the momentary speed. We demonstrate our approach
using an iPad and iPhone in several indoor dead-reckoning applications and in a
measurement tool setup.
",1,0,0,1,0,0
20061,Asymptotic network models of subwavelength metamaterials formed by closely packed photonic and phononic crystals,"  We demonstrate that photonic and phononic crystals consisting of closely
spaced inclusions constitute a versatile class of subwavelength metamaterials.
Intuitively, the voids and narrow gaps that characterise the crystal form an
interconnected network of Helmholtz-like resonators. We use this intuition to
argue that these continuous photonic (phononic) crystals are in fact
asymptotically equivalent, at low frequencies, to discrete capacitor-inductor
(mass-spring) networks whose lumped parameters we derive explicitly. The
crystals are tantamount to metamaterials as their entire acoustic branch, or
branches when the discrete analogue is polyatomic, is squeezed into a
subwavelength regime where the ratio of wavelength to period scales like the
ratio of period to gap width raised to the power 1/4; at yet larger wavelengths
we accordingly find a comparably large effective refractive index. The fully
analytical dispersion relations predicted by the discrete models yield
dispersion curves that agree with those from finite-element simulations of the
continuous crystals. The insight gained from the network approach is used to
show that, surprisingly, the continuum created by a closely packed hexagonal
lattice of cylinders is represented by a discrete honeycomb lattice. The
analogy is utilised to show that the hexagonal continuum lattice has a
Dirac-point degeneracy that is lifted in a controlled manner by specifying the
area of a symmetry-breaking defect.
",0,1,0,0,0,0
20062,The Hydrogen Epoch of Reionization Array Dish III: Measuring Chromaticity of Prototype Element with Reflectometry,"  The experimental efforts to detect the redshifted 21 cm signal from the Epoch
of Reionization (EoR) are limited predominantly by the chromatic instrumental
systematic effect. The delay spectrum methodology for 21 cm power spectrum
measurements brought new attention to the critical impact of an antenna's
chromaticity on the viability of making this measurement. This methodology
established a straightforward relationship between time-domain response of an
instrument and the power spectrum modes accessible to a 21 cm EoR experiment.
We examine the performance of a prototype of the Hydrogen Epoch of Reionization
Array (HERA) array element that is currently observing in Karoo desert, South
Africa. We present a mathematical framework to derive the beam integrated
frequency response of a HERA prototype element in reception from the return
loss measurements between 100-200 MHz and determined the extent of additional
foreground contamination in the delay space. The measurement reveals excess
spectral structures in comparison to the simulation studies of the HERA
element. Combined with the HERA data analysis pipeline that incorporates
inverse covariance weighting in optimal quadratic estimation of power spectrum,
we find that in spite of its departure from the simulated response, HERA
prototype element satisfies the necessary criteria posed by the foreground
attenuation limits and potentially can measure the power spectrum at spatial
modes as low as $k_{\parallel} > 0.1h$~Mpc$^{-1}$. The work highlights a
straightforward method for directly measuring an instrument response and
assessing its impact on 21 cm EoR power spectrum measurements for future
experiments that will use reflector-type antenna.
",0,1,0,0,0,0
20063,Unifying the micro and macro properties of AGN feeding and feedback,"  We unify the feeding and feedback of supermassive black holes with the global
properties of galaxies, groups, and clusters, by linking for the first time the
physical mechanical efficiency at the horizon and Mpc scale. The macro hot halo
is tightly constrained by the absence of overheating and overcooling as probed
by X-ray data and hydrodynamic simulations ($\varepsilon_{\rm BH} \simeq$
10$^{-3}\, T_{\rm x,7.4}$). The micro flow is shaped by general relativistic
effects tracked by state-of-the-art GR-RMHD simulations ($\varepsilon_\bullet
\simeq$ 0.03). The SMBH properties are tied to the X-ray halo temperature
$T_{\rm x}$, or related cosmic scaling relation (as $L_{\rm x}$). The model is
minimally based on first principles, as conservation of energy and mass
recycling. The inflow occurs via chaotic cold accretion (CCA), the rain of cold
clouds condensing out of the quenched cooling flow and recurrently funneled via
inelastic collisions. Within 100s gravitational radii, the accretion energy is
transformed into ultrafast 10$^4$ km s$^{-1}$ outflows (UFOs) ejecting most of
the inflowing mass. At larger radii the energy-driven outflow entrains
progressively more mass: at roughly kpc scale, the velocities of the
hot/warm/cold outflows are a few 10$^3$, 1000, 500 km s$^{-1}$, with median
mass rates ~10, 100, several 100 M$_\odot$ yr$^{-1}$, respectively. The unified
CCA model is consistent with the observations of nuclear UFOs, and ionized,
neutral, and molecular macro outflows. We provide step-by-step implementation
for subgrid simulations, (semi)analytic works, or observational interpretations
which require self-regulated AGN feedback at coarse scales, avoiding the
a-posteriori fine-tuning of efficiencies.
",0,1,0,0,0,0
20064,Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning,"  The inherent noise in the observed (e.g., scanned) binary document image
degrades the image quality and harms the compression ratio through breaking the
pattern repentance and adding entropy to the document images. In this paper, we
design a cost function in Bayesian framework with dictionary learning.
Minimizing our cost function produces a restored image which has better quality
than that of the observed noisy image, and a dictionary for representing and
encoding the image. After the restoration, we use this dictionary (from the
same cost function) to encode the restored image following the
symbol-dictionary framework by JBIG2 standard with the lossless mode.
Experimental results with a variety of document images demonstrate that our
method improves the image quality compared with the observed image, and
simultaneously improves the compression ratio. For the test images with
synthetic noise, our method reduces the number of flipped pixels by 48.2% and
improves the compression ratio by 36.36% as compared with the best encoding
methods. For the test images with real noise, our method visually improves the
image quality, and outperforms the cutting-edge method by 28.27% in terms of
the compression ratio.
",1,0,0,0,0,0
20065,A First Look at Ad Blocking Apps on Google Play,"  Online advertisers and analytics services (or trackers), are constantly
tracking users activities as they access web services either through browsers
or a mobile apps. Numerous tools such as browser plugins and specialized mobile
apps have been proposed to limit intrusive advertisements and prevent tracking
on desktop computing and mobile phones. For desktop computing, browser plugins
are heavily studied for their usability and efficiency issues, however, tools
that block ads and prevent trackers in mobile platforms, have received the
least or no attention.
In this paper, we present a first look at 97 Android adblocking apps (or
adblockers), extracted from more than 1.5 million apps from Google Play, that
promise to block advertisements and analytics services. With our data
collection and analysis pipeline of the Android adblockers, we reveal the
presences of third-party tracking libraries and sensitive permissions for
critical resources on user mobile devices as well as have malware in the source
codes. We analyze users' reviews for the in-effectiveness of adblockers in
terms of not blocking ads and trackers. We found that a significant fraction of
adblockers are not fulfilling their advertised functionality.
",1,0,0,0,0,0
20066,Nondegeneracy of the traveling lump solution to the $2+1$ Toda lattice,"  We consider the $2+1$ Toda system \[ \frac{1}{4}\Delta
q_{n}=e^{q_{n-1}-q_{n}}-e^{q_{n}-q_{n+1}}\text{ in }\mathbb{R}^{2},\
n\in\mathbb{Z}. \] It has a traveling wave type solution $\left\{ Q_{n}\right\}
$ satisfying $Q_{n+1}(x,y)=Q_{n}(x+\frac{1}{2\sqrt{2}},y)$, and is explicitly
given by \[ Q_{n}\left( x,y\right) =\ln\frac{\frac{1}{4}+\left(
n-1+2\sqrt{2}x\right) ^{2}+4y^{2}}{\frac{1}{4}+\left( n+2\sqrt{2}x\right)
^{2}+4y^{2}}. \] In this paper we prove that \{$Q_{n}$\} is nondegenerate.
",0,0,1,0,0,0
20067,One side continuity of meromorphic mappings between real analytic hypersurfaces,"  We prove that a meromorphic mapping, which sends a peace of a real analytic
strictly pseudoconvex hypersurface in $\cc^2$ to a compact subset of $\cc^N$
which doesn't contain germs of non-constant complex curves is continuous from
the concave side of the hypersurface. This implies the analytic continuability
along CR-paths of germs of holomorphic mappings from real analytic
hypersurfaces with non-vanishing Levi form to the locally spherical ones in all
dimensions.
",0,0,1,0,0,0
20068,Generic Camera Attribute Control using Bayesian Optimization,"  Cameras are the most widely exploited sensor in both robotics and computer
vision communities. Despite their popularity, two dominant attributes (i.e.,
gain and exposure time) have been determined empirically and images are
captured in very passive manner. In this paper, we present an active and
generic camera attribute control scheme using Bayesian optimization. We extend
from our previous work [1] in two aspects. First, we propose a method that
jointly controls camera gain and exposure time. Secondly, to speed up the
Bayesian optimization process, we introduce image synthesis using the camera
response function (CRF). These synthesized images allowed us to diminish the
image acquisition time during the Bayesian optimization phase, substantially
improving overall control performance. The proposed method is validated both in
an indoor and an outdoor environment where light condition rapidly changes.
Supplementary material is available at this https URL .
",1,0,0,0,0,0
20069,Far-from-equilibrium energy flow and entanglement entropy,"  The time evolution of the energy transport triggered in a strongly coupled
system by a temperature gradient is holographically related to the evolution of
an asymptotically AdS black brane. We study the far-from-equilibrium properties
of such a system by using the AdS/CFT correspondence. In particular, we
describe the appearance of a steady state, and study the information flow by
computing the time evolution of the holographic entanglement entropy. Some
universal properties of the quenching process are presented.
",0,1,0,0,0,0
20070,Algebraic Aspects of Conditional Independence and Graphical Models,"  This chapter of the forthcoming Handbook of Graphical Models contains an
overview of basic theorems and techniques from algebraic geometry and how they
can be applied to the study of conditional independence and graphical models.
It also introduces binomial ideals and some ideas from real algebraic geometry.
When random variables are discrete or Gaussian, tools from computational
algebraic geometry can be used to understand implications between conditional
independence statements. This is accomplished by computing primary
decompositions of conditional independence ideals. As examples the chapter
presents in detail the graphical model of a four cycle and the intersection
axiom, a certain implication of conditional independence statements. Another
important problem in the area is to determine all constraints on a graphical
model, for example, equations determined by trek separation. The full set of
equality constraints can be determined by computing the model's vanishing
ideal. The chapter illustrates these techniques and ideas with examples from
the literature and provides references for further reading.
",0,0,1,1,0,0
20071,A Survey of Riccati Equation Results in Negative Imaginary Systems Theory and Quantum Control Theory,"  This paper presents a survey of some new applications of algebraic Riccati
equations. In particular, the paper surveys some recent results on the use of
algebraic Riccati equations in testing whether a system is negative imaginary
and in synthesizing state feedback controllers which make the closed loop
system negative imaginary. The paper also surveys the use of Riccati equation
methods in the control of quantum linear systems including coherent $H^\infty$
control.
",1,0,1,0,0,0
20072,"The circumstellar disk HD$\,$169142: gas, dust and planets acting in concert?","  HD$\,$169142 is an excellent target to investigate signs of planet-disk
interaction due to the previous evidence of gap structures. We performed J-band
(~1.2{\mu}m) polarized intensity imaging of HD169142 with VLT/SPHERE. We
observe polarized scattered light down to 0.16"" (~19 au) and find an inner gap
with a significantly reduced scattered light flux. We confirm the previously
detected double ring structure peaking at 0.18"" (~21 au) and 0.56"" (~66 au),
and marginally detect a faint third gap at 0.70""-0.73"" (~82-85 au). We explore
dust evolution models in a disk perturbed by two giant planets, as well as
models with a parameterized dust size distribution. The dust evolution model is
able to reproduce the ring locations and gap widths in polarized intensity, but
fails to reproduce their depths. It, however, gives a good match with the ALMA
dust continuum image at 1.3 mm. Models with a parameterized dust size
distribution better reproduce the gap depth in scattered light, suggesting that
dust filtration at the outer edges of the gaps is less effective. The pile-up
of millimeter grains in a dust trap and the continuous distribution of small
grains throughout the gap likely require a more efficient dust fragmentation
and dust diffusion in the dust trap. Alternatively, turbulence or charging
effects might lead to a reservoir of small grains at the surface layer that is
not affected by the dust growth and fragmentation cycle dominating the dense
disk midplane. The exploration of models shows that extracting planet
properties such as mass from observed gap profiles is highly degenerate.
",0,1,0,0,0,0
20073,Quantitative analysis of the influence of keV He ion bombardment on exchange bias layer systems,"  The mechanism of ion bombardment induced magnetic patterning of exchange bias
layer systems for creating engineered magnetic stray field landscapes is still
unclear. We compare results from vectorial magneto-optic Kerr effect
measurements to a recently proposed model with time dependent rotatable
magnetic anisotropy. Results show massive reduction of rotational magnetic
anisotropy compared to all other magnetic anisotropies. We disprove the
assumption of comparable weakening of all magnetic anisotropies and show that
ion bombardment mainly influences smaller grains in the antiferromagnet.
",0,1,0,0,0,0
20074,Automatic classification of automorphisms of lower-dimensional Lie algebras,"  We implement two algorithms in MATHEMATICA for classifying automorphisms of
lower-dimensional non-commutative Lie algebras. The first algorithm is a
brute-force approach whereas the second is an evolutionary strategy. These
algorithms are delivered as the MATHEMATICA package cwsAutoClass. In order to
facilitate the application of this package to symmetry Lie algebras of
differential equations, we also provide a package, cwsLieSymTools, for
manipulating finite-dimensional Lie algebras of vector fields. In particular,
this package allows the computations of Lie brackets, structure constants, and
the visualization of commutator tables. Several examples are provided to
illustrate the pertinence of our approach.
",0,0,1,0,0,0
20075,Information-Theoretic Understanding of Population Risk Improvement with Model Compression,"  We show that model compression can improve the population risk of a
pre-trained model, by studying the tradeoff between the decrease in the
generalization error and the increase in the empirical risk with model
compression. We first prove that model compression reduces an
information-theoretic bound on the generalization error; this allows for an
interpretation of model compression as a regularization technique to avoid
overfitting. We then characterize the increase in empirical risk with model
compression using rate distortion theory. These results imply that the
population risk could be improved by model compression if the decrease in
generalization error exceeds the increase in empirical risk. We show through a
linear regression example that such a decrease in population risk due to model
compression is indeed possible. Our theoretical results further suggest that
the Hessian-weighted $K$-means clustering compression approach can be improved
by regularizing the distance between the clustering centers. We provide
experiments with neural networks to support our theoretical assertions.
",1,0,0,1,0,0
20076,Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries,"  This paper presents a procedure to retrieve subsets of relevant documents
from large text collections for Content Analysis, e.g. in social sciences.
Document retrieval for this purpose needs to take account of the fact that
analysts often cannot describe their research objective with a small set of key
terms, especially when dealing with theoretical or rather abstract research
interests. Instead, it is much easier to define a set of paradigmatic documents
which reflect topics of interest as well as targeted manner of speech. Thus, in
contrast to classic information retrieval tasks we employ manually compiled
collections of reference documents to compose large queries of several hundred
key terms, called dictionaries. We extract dictionaries via Topic Models and
also use co-occurrence data from reference collections. Evaluations show that
the procedure improves retrieval results for this purpose compared to
alternative methods of key term extraction as well as neglecting co-occurrence
data.
",1,0,0,0,0,0
20077,Cepheids with the eyes of photometric space telescopes,"  Space photometric missions have been steadily accumulating observations of
Cepheids in recent years, leading to a flow of new discoveries. In this short
review we summarize the findings provided by the early missions such as WIRE,
MOST, and CoRoT, and the recent results of the Kepler and K2 missions. The
surprising and fascinating results from the high-precision, quasi-continuous
data include the detection of the amplitude increase of Polaris, and exquisite
details about V1154 Cyg within the original Kepler field of view. We also
briefly discuss the current opportunities with the K2 mission, and the
prospects of the TESS space telescope regarding Cepheids.
",0,1,0,0,0,0
20078,Explicitly correlated formalism for second-order single-particle Green's function,"  We present an explicitly correlated formalism for the second-order
single-particle Green's function method (GF2-F12) that does not assume the
popular diagonal approximation, and describes the energy dependence of the
explicitly correlated terms. For small and medium organic molecules the basis
set errors of ionization potentials of GF2-F12 are radically improved relative
to GF2: the performance of GF2-F12/aug- cc-pVDZ is better than that of
GF2/aug-cc-pVQZ, at a significantly lower cost.
",0,1,0,0,0,0
20079,Relative stability associated to quantised extremal Kähler metrics,"  We study algebro-geometric consequences of the quantised extremal Kähler
metrics, introduced in the previous work of the author. We prove that the
existence of quantised extremal metrics implies weak relative Chow
polystability. As a consequence, we obtain asymptotic weak relative Chow
polystability and $K$-semistability of extremal manifolds by using quantised
extremal metrics; this gives an alternative proof of the results of Mabuchi and
Stoppa--Székelyhidi. In proving them, we further provide an explicit local
density formula for the equivariant Riemann--Roch theorem.
",0,0,1,0,0,0
20080,Beyond the EULA: Improving consent for data mining,"  Companies and academic researchers may collect, process, and distribute large
quantities of personal data without the explicit knowledge or consent of the
individuals to whom the data pertains. Existing forms of consent often fail to
be appropriately readable and ethical oversight of data mining may not be
sufficient. This raises the question of whether existing consent instruments
are sufficient, logistically feasible, or even necessary, for data mining. In
this chapter, we review the data collection and mining landscape, including
commercial and academic activities, and the relevant data protection concerns,
to determine the types of consent instruments used. Using three case studies,
we use the new paradigm of human-data interaction to examine whether these
existing approaches are appropriate. We then introduce an approach to consent
that has been empirically demonstrated to improve on the state of the art and
deliver meaningful consent. Finally, we propose some best practices for data
collectors to ensure their data mining activities do not violate the
expectations of the people to whom the data relate.
",1,0,0,0,0,0
20081,Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations,"  A fundamental problem in neuroscience is to characterize the dynamics of
spiking from the neurons in a circuit that is involved in learning about a
stimulus or a contingency. A key limitation of current methods to analyze
neural spiking data is the need to collapse neural activity over time or
trials, which may cause the loss of information pertinent to understanding the
function of a neuron or circuit. We introduce a new method that can determine
not only the trial-to-trial dynamics that accompany the learning of a
contingency by a neuron, but also the latency of this learning with respect to
the onset of a conditioned stimulus. The backbone of the method is a separable
two-dimensional (2D) random field (RF) model of neural spike rasters, in which
the joint conditional intensity function of a neuron over time and trials
depends on two latent Markovian state sequences that evolve separately but in
parallel. Classical tools to estimate state-space models cannot be applied
readily to our 2D separable RF model. We develop efficient statistical and
computational tools to estimate the parameters of the separable 2D RF model. We
apply these to data collected from neurons in the pre-frontal cortex (PFC) in
an experiment designed to characterize the neural underpinnings of the
associative learning of fear in mice. Overall, the separable 2D RF model
provides a detailed, interpretable, characterization of the dynamics of neural
spiking that accompany the learning of a contingency.
",1,0,0,1,0,0
20082,Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms,"  Deep reinforcement learning enables algorithms to learn complex behavior,
deal with continuous action spaces and find good strategies in environments
with high dimensional state spaces. With deep reinforcement learning being an
active area of research and many concurrent inventions, we decided to focus on
a relatively simple robotic task to evaluate a set of ideas that might help to
solve recent reinforcement learning problems. We test a newly created
combination of two commonly used reinforcement learning methods, whether it is
able to learn more effectively than a baseline. We also compare different ideas
to preprocess information before it is fed to the reinforcement learning
algorithm. The goal of this strategy is to reduce training time and eventually
help the algorithm to converge. The concluding evaluation proves the general
applicability of the described concepts by testing them using a simulated
environment. These concepts might be reused for future experiments.
",1,0,0,0,0,0
20083,The First Detection of Gravitational Waves,"  This article deals with the first detection of gravitational waves by the
advanced Laser Interferometer Gravitational Wave Observatory (LIGO) detectors
on 14 September 2015, where the signal was generated by two stellar mass black
holes with masses 36 $ M_{\odot}$ and 29 $ M_{\odot}$ that merged to form a 62
$ M_{\odot}$ black hole, releasing 3 $M_{\odot}$ energy in gravitational waves,
almost 1.3 billion years ago. We begin by providing a brief overview of
gravitational waves, their sources and the gravitational wave detectors. We
then describe in detail the first detection of gravitational waves from a
binary black hole merger. We then comment on the electromagnetic follow up of
the detection event with various telescopes. Finally, we conclude with the
discussion on the tests of gravity and fundamental physics with the first
gravitational wave detection event.
",0,1,0,0,0,0
20084,Remarkably strong chemisorption of nitric oxide on insulating oxide films promoted by hybrid structure,"  The remarkably strong chemical adsorption behaviors of nitric oxide on
magnesia (001) film deposited on metal substrate have been investigated by
employing periodic density functional calculations with Van der Waals
corrections. The molybdenum supported magnesia (001) show significantly
enhanced adsorption properties and the nitric oxide is chemisorbed strongly and
preferably trapped in flat adsorption configuration on metal supported oxide
film, due to the substantially large adsorption energies and transformation
barriers. The analysis of Bader charges, projected density of states,
differential charge densities, electron localization function, highest occupied
orbital and particular orbital with largest Mg-NO-Mg bonding coefficients, are
applied to reveal the electronic adsorption properties and characteristics of
bonding between nitric oxide and surface as well as the bonding within the
hybrid structure. The strong chemical binding of nitric oxide on magnesia
deposited on molybdenum slab offers new opportunities for toxic gas detection
and treatment. We anticipate that hybrid structure promoted remarkable chemical
adsorption of nitric oxide on magnesia in this study will provide versatile
strategy for enhancing chemical reactivity and properties of insulating oxide.
",0,1,0,0,0,0
20085,Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes,"  In this work we propose approaches to effectively transfer knowledge from
weakly labeled web audio data. We first describe a convolutional neural network
(CNN) based framework for sound event detection and classification using weakly
labeled audio data. Our model trains efficiently from audios of variable
lengths; hence, it is well suited for transfer learning. We then propose
methods to learn representations using this model which can be effectively used
for solving the target task. We study both transductive and inductive transfer
learning tasks, showing the effectiveness of our methods for both domain and
task adaptation. We show that the learned representations using the proposed
CNN model generalizes well enough to reach human level accuracy on ESC-50 sound
events dataset and set state of art results on this dataset. We further use
them for acoustic scene classification task and once again show that our
proposed approaches suit well for this task as well. We also show that our
methods are helpful in capturing semantic meanings and relations as well.
Moreover, in this process we also set state-of-art results on Audioset dataset,
relying on balanced training set.
",1,0,0,0,0,0
20086,A priori estimates for the free-boundary Euler equations with surface tension in three dimensions,"  We derive a priori estimates for the incompressible free-boundary Euler
equations with surface tension in three spatial dimensions. Working in
Lagrangian coordinates, we provide a priori estimates for the local existence
when the initial velocity, which is rotational, belongs to $H^3$ and the trace
of initial velocity on the free boundary to $H^{3.5}$, thus lowering the
requirement on the regularity of initial data in the Lagrangian setting. Our
methods are direct and involve three key elements: estimates for the pressure,
the boundary regularity provided by the mean curvature, and the Cauchy
invariance.
",0,0,1,0,0,0
20087,Koszul duality via suspending Lefschetz fibrations,"  Let $M$ be a Liouville 6-manifold which is the smooth fiber of a Lefschetz
fibration on $\mathbb{C}^4$ constructed by suspending a Lefschetz fibration on
$\mathbb{C}^3$. We prove that for many examples including stabilizations of
Milnor fibers of hypersurface cusp singularities, the compact Fukaya category
$\mathcal{F}(M)$ and the wrapped Fukaya category $\mathcal{W}(M)$ are related
through $A_\infty$-Koszul duality, by identifying them with cyclic and
Calabi-Yau completions of the same quiver algebra. This implies the
split-generation of the compact Fukaya category $\mathcal{F}(M)$ by vanishing
cycles. Moreover, new examples of Liouville manifolds which admit
quasi-dilations in the sense of Seidel-Solomon are obtained.
",0,0,1,0,0,0
20088,Attractor of Cantor Type with Positive Measure,"  We construct an iterated function system consisting of strictly increasing
contractions $f,g\colon [0,1]\to [0,1]$ with $f([0,1])\cap g([0,1])=\emptyset$
and such that its attractor has positive Lebesgue measure.
",0,0,1,0,0,0
20089,Generating online social networks based on socio-demographic attributes,"  Recent years have seen tremendous growth of many online social networks such
as Facebook, LinkedIn and MySpace. People connect to each other through these
networks forming large social communities providing researchers rich datasets
to understand, model and predict social interactions and behaviors. New
contacts in these networks can be formed due to an individual's demographic
attributes such as age group, gender, geographic location, or due to a
network's structural dynamics such as triadic closure and preferential
attachment, or a combination of both demographic and structural
characteristics.
A number of network generation models have been proposed in the last decade
to explain the structure, evolution and processes taking place in different
types of networks, and notably social networks. Network generation models
studied in the literature primarily consider structural properties, and in some
cases an individual's demographic profile in the formation of new social
contacts. These models do not present a mechanism to combine both structural
and demographic characteristics for the formation of new links. In this paper,
we propose a new network generation algorithm which incorporates both these
characteristics to model network formation. We use different publicly available
Facebook datasets as benchmarks to demonstrate the correctness of the proposed
network generation model. The proposed model is flexible and thus can generate
networks with varying demographic and structural properties.
",1,1,0,0,0,0
20090,Optimal control of diffuser shapes for confined turbulent shear flows,"  A model for the development of turbulent shear flows, created by non-uniform
parallel flows in a confining channel, is used to identify the diffuser shape
that maximises pressure recovery when the inflow is non-uniform. Wide diffuser
angles tend to accentuate the non- uniform flow, causing poor pressure
recovery. On the other hand, shallow diffuser angles create longer regions with
large wall drag, which is also detrimental to pressure recovery. Thus, optimal
diffuser shapes strike a balance between the two effects. We use a simple model
which describes the evolution of an approximate flow profile and pressure in
the diffuser. The model equations form the dynamics of an optimal control
problem where the control is the diffuser channel shape. A numerical
optimisation approach is used to solve the optimal control problem and we use
analytical results to interpret the numerics in some limiting cases. The
results of the optimisation are compared to calculations from computational
fluid dynamics.
",0,1,0,0,0,0
20091,Criteria for Solar Car Optimized Route Estimation,"  This paper gives a thorough overview of Solar Car Optimized Route Estimation
(SCORE), novel route optimization scheme for solar vehicles based on solar
irradiance and target distance. In order to conduct the optimization, both data
collection and the optimization algorithm itself have to be performed using
appropriate hardware. Here we give an insight to both stages, hardware and
software used and present some results of the SCORE system together with
certain improvements of its fusion and optimization criteria. Results and the
limited applicability of SCORE are discussed together with an overview of
future research plans and comparison with state-of-the-art solar vehicle
optimization solutions.
",1,0,1,0,0,0
20092,Dykes for filtering ocean waves using c-shaped vertical cylinders,"  The present study investigates a way to design dykes which can filter the
wavelengths of ocean surface waves. This offers the possibility to achieve a
structure that can attenuate waves associated with storm swell, without
affecting coastline in other conditions. Our approach is based on low frequency
resonances in metamaterials combined with Bragg frequencies for which waves
cannot propagate in periodic lattices.
",0,1,0,0,0,0
20093,Origin of Non-axisymmetric Features of Virgo Cluster Early-type Dwarf Galaxies. I. Bar Formation and Recurrent Buckling,"  A fraction of early-type dwarf galaxies in the Virgo cluster have a disk
component and even possess disk features such as bar, lens, and spiral arms. In
this study, we construct 15 galaxy models that resemble VCC856, considered to
be an infalling progenitor of disk dwarf galaxies, within observational error
ranges, and use $N$-body simulations to study their long-term dynamical
evolution in isolation as well as the formation of bar in them. We find that
dwarf disk galaxies readily form bars unless they have an excessively
concentrated halo or a hot disk. This suggests that infalling dwarf disk
galaxies are intrinsically unstable to bar formation, even without any external
perturbation, accounting for a population of barred dwarf galaxies in the
outskirts of the Virgo cluster. The bars form earlier and stronger in galaxies
with a lower fraction of counter-streaming motions, lower halo concentration,
lower velocity anisotropy, and thinner disk. Similarly to normal disk galaxies,
dwarf disk galaxies also undergo recurrent buckling instabilities. The first
buckling instability tends to shorten the bar and to thicken the disk, and
drives a dynamical transition in the bar pattern speed as well as mass inflow
rate. In nine models, the bars regrow after the mild first buckling instability
due to the efficient transfer of disk angular momentum to the halo, and are
subject to recurrent buckling instabilities to turn into X-shaped bulges.
",0,1,0,0,0,0
20094,First results from the DEAP-3600 dark matter search with argon at SNOLAB,"  This paper reports the first results of a direct dark matter search with the
DEAP-3600 single-phase liquid argon (LAr) detector. The experiment was
performed 2 km underground at SNOLAB (Sudbury, Canada) utilizing a large target
mass, with the LAr target contained in a spherical acrylic vessel of 3600 kg
capacity. The LAr is viewed by an array of PMTs, which would register
scintillation light produced by rare nuclear recoil signals induced by dark
matter particle scattering. An analysis of 4.44 live days (fiducial exposure of
9.87 tonne-days) of data taken with the nearly full detector during the initial
filling phase demonstrates the detector performance and the best electronic
recoil rejection using pulse-shape discrimination in argon, with leakage
$<1.2\times 10^{-7}$ (90% C.L.) between 16 and 33 keV$_{ee}$. No candidate
signal events are observed, which results in the leading limit on WIMP-nucleon
spin-independent cross section on argon, $<1.2\times 10^{-44}$ cm$^2$ for a 100
GeV/c$^2$ WIMP mass (90% C.L.).
",0,1,0,0,0,0
20095,Camera Calibration by Global Constraints on the Motion of Silhouettes,"  We address the problem of epipolar geometry using the motion of silhouettes.
Such methods match epipolar lines or frontier points across views, which are
then used as the set of putative correspondences. We introduce an approach that
improves by two orders of magnitude the performance over state-of-the-art
methods, by significantly reducing the number of outliers in the putative
matching. We model the frontier points' correspondence problem as constrained
flow optimization, requiring small differences between their coordinates over
consecutive frames. Our approach is formulated as a Linear Integer Program and
we show that due to the nature of our problem, it can be solved efficiently in
an iterative manner. Our method was validated on four standard datasets
providing accurate calibrations across very different viewpoints.
",1,0,0,0,0,0
20096,Ann: A domain-specific language for the effective design and validation of Java annotations,"  This paper describes a new modelling language for the effective design and
validation of Java annotations. Since their inclusion in the 5th edition of
Java, annotations have grown from a useful tool for the addition of meta-data
to play a central role in many popular software projects. Usually they are not
conceived in isolation, but in groups, with dependency and integrity
constraints between them. However, the native support provided by Java for
expressing this design is very limited.
To overcome its deficiencies and make explicit the rich conceptual model
which lies behind a set of annotations, we propose a domain-specific modelling
language. The proposal has been implemented as an Eclipse plug-in, including an
editor and an integrated code generator that synthesises annotation processors.
The environment also integrates a model finder, able to detect unsatisfiable
constraints between different annotations, and to provide examples of correct
annotation usages for validation. The language has been tested using a real set
of annotations from the Java Persistence API (JPA). Within this subset we have
found enough rich semantics expressible with Ann and omitted nowadays by the
Java language, which shows the benefits of Ann in a relevant field of
application.
",1,0,0,0,0,0
20097,Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery,"  The new wave of successful generative models in machine learning has
increased the interest in deep learning driven de novo drug design. However,
assessing the performance of such generative models is notoriously difficult.
Metrics that are typically used to assess the performance of such generative
models are the percentage of chemically valid molecules or the similarity to
real molecules in terms of particular descriptors, such as the partition
coefficient (logP) or druglikeness. However, method comparison is difficult
because of the inconsistent use of evaluation metrics, the necessity for
multiple metrics, and the fact that some of these measures can easily be
tricked by simple rule-based systems. We propose a novel distance measure
between two sets of molecules, called Fréchet ChemNet distance (FCD), that
can be used as an evaluation metric for generative models. The FCD is similar
to a recently established performance metric for comparing image generation
methods, the Fréchet Inception Distance (FID). Whereas the FID uses one of
the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
deep neural network called ChemNet, which was trained to predict drug
activities. Thus, the FCD metric takes into account chemically and biologically
relevant information about molecules, and also measures the diversity of the
set via the distribution of generated molecules. The FCD's advantage over
previous metrics is that it can detect if generated molecules are a) diverse
and have similar b) chemical and c) biological properties as real molecules. We
further provide an easy-to-use implementation that only requires the SMILES
representation of the generated molecules as input to calculate the FCD.
Implementations are available at: this https URL
",0,0,0,1,1,0
20098,RANSAC Algorithms for Subspace Recovery and Subspace Clustering,"  We consider the RANSAC algorithm in the context of subspace recovery and
subspace clustering. We derive some theory and perform some numerical
experiments. We also draw some correspondences with the methods of Hardt and
Moitra (2013) and Chen and Lerman (2009b).
",0,0,1,1,0,0
20099,Convex Formulations for Fair Principal Component Analysis,"  Though there is a growing body of literature on fairness for supervised
learning, the problem of incorporating fairness into unsupervised learning has
been less well-studied. This paper studies fairness in the context of principal
component analysis (PCA). We first present a definition of fairness for
dimensionality reduction, and our definition can be interpreted as saying that
a reduction is fair if information about a protected class (e.g., race or
gender) cannot be inferred from the dimensionality-reduced data points. Next,
we develop convex optimization formulations that can improve the fairness (with
respect to our definition) of PCA and kernel PCA. These formulations are
semidefinite programs (SDP's), and we demonstrate the effectiveness of our
formulations using several datasets. We conclude by showing how our approach
can be used to perform a fair (with respect to age) clustering of health data
that may be used to set health insurance rates.
",0,0,0,1,0,0
20100,Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture,"  In the research of the impact of gestures using by a lecturer, one
challenging task is to infer the attention of a group of audiences. Two
important measurements that can help infer the level of attention are eye
movement data and Electroencephalography (EEG) data. Under the fundamental
assumption that a group of people would look at the same place if they all pay
attention at the same time, we apply a method, ""Time Warp Edit Distance"", to
calculate the similarity of their eye movement trajectories. Moreover, we also
cluster eye movement pattern of audiences based on these pair-wised similarity
metrics. Besides, since we don't have a direct metric for the ""attention""
ground truth, a visual assessment would be beneficial to evaluate the
gesture-attention relationship. Thus we also implement a visualization tool.
",1,0,0,0,0,0
20101,Infinite symmetric ergodic index and related examples in infinite measure,"  We define an infinite measure-preserving transformation to have infinite
symmetric ergodic index if all finite Cartesian products of the transformation
and its inverse are ergodic, and show that infinite symmetric ergodic index
does not imply that all products of powers are conservative, so does not imply
power weak mixing. We provide a sufficient condition for $k$-fold and infinite
symmetric ergodic index and use it to answer a question on the relationship
between product conservativity and product ergodicity. We also show that a
class of rank-one transformations that have infinite symmetric ergodic index
are not power weakly mixing, and precisely characterize a class of power weak
transformations that generalizes existing examples.
",0,0,1,0,0,0
20102,Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature,"  An exciting branch of machine learning research focuses on methods for
learning, optimizing, and integrating unknown functions that are difficult or
costly to evaluate. A popular Bayesian approach to this problem uses a Gaussian
process (GP) to construct a posterior distribution over the function of
interest given a set of observed measurements, and selects new points to
evaluate using the statistics of this posterior. Here we extend these methods
to exploit derivative information from the unknown function. We describe
methods for Bayesian optimization (BO) and Bayesian quadrature (BQ) in settings
where first and second derivatives may be evaluated along with the function
itself. We perform sampling-based inference in order to incorporate uncertainty
over hyperparameters, and show that both hyperparameter and function
uncertainty decrease much more rapidly when using derivative information.
Moreover, we introduce techniques for overcoming ill-conditioning issues that
have plagued earlier methods for gradient-enhanced Gaussian processes and
kriging. We illustrate the efficacy of these methods using applications to real
and simulated Bayesian optimization and quadrature problems, and show that
exploting derivatives can provide substantial gains over standard methods.
",0,0,0,1,0,0
20103,Permanency of the age-structured population model on several temporally variable patches,"  We consider a system of nonlinear partial differential equations that
describes an age-structured population inhabiting several temporally varying
patches. We prove existence and uniqueness of solution and analyze its
large-time behavior in cases when the environment is constant and when it
changes periodically. A pivotal assumption is that individuals can disperse and
that each patch can be reached from every other patch, directly or through
several intermediary patches. We introduce the net reproductive operator and
characteristic equations for time-independent and periodical models and prove
that permanency is defined by the net reproductive rate for the whole system.
If the net reproductive rate is less or equal to one, extinction on all patches
is imminent. Otherwise, permanency on all patches is guaranteed. The proof is
based on a new approach to analysis of large-time stability.
",0,1,1,0,0,0
20104,Rapid laser-induced photochemical conversion of sol-gel precursors to In2O3 layers and their application in thin-film transistors,"  We report the development of indium oxide (In2O3) transistors via a single
step laser-induced photochemical conversion process of a sol-gel metal oxide
precursor. Through careful optimization of the laser annealing conditions we
demonstrated successful conversion of the precursor to In2O3 and its subsequent
implementation in n-channel transistors with electron mobility up to 13 cm2/Vs.
Importantly, the process does not require thermal annealing making it
compatible with temperature sensitive materials such as plastic. On the other
hand, the spatial conversion/densification of the sol-gel layer eliminates
additional process steps associated with semiconductor patterning and hence
significantly reduces fabrication complexity and cost. Our work demonstrates
unambiguously that laser-induced photochemical conversion of sol-gel metal
oxide precursors can be rapid and compatible with large-area electronics
manufacturing.
",0,1,0,0,0,0
20105,Testing for Change in Stochastic Volatility with Long Range Dependence,"  In this paper, change-point problems for long memory stochastic volatility
models are considered. A general testing problem which includes various
alternative hypotheses is discussed. Under the hypothesis of stationarity the
limiting behavior of CUSUM- and Wilcoxon-type test statistics is derived. In
this context, a limit theorem for the two-parameter empirical process of long
memory stochastic volatility time series is proved. In particular, it is shown
that the asymptotic distribution of CUSUM test statistics may not be affected
by long memory, unlike Wilcoxon test statistics which are typically influenced
by long range dependence. To avoid the estimation of nuisance parameters in
applications, the usage of self-normalized test statistics is proposed. The
theoretical results are accompanied by simulation studies which characterize
the finite sample behavior of the considered testing procedures when testing
for changes in mean, in variance, and in the tail index.
",0,0,1,1,0,0
20106,Kinematics effects of atmospheric friction in spacecraft flybys,"  Gravity assist manoeuvres are one of the most succesful techniques in
astrodynamics. In these trajectories the spacecraft comes very close to the
surface of the Earth, or other Solar system planets or moons, and, as a
consequence, it experiences the effect of atmospheric friction by the outer
layers of the Earth's atmosphere or ionosphere.
In this paper we analyze a standard atmospheric model to estimate the density
profile during the two Galileo flybys, the NEAR and the Juno flyby. We show
that, even allowing for a margin of uncertainty in the spacecraft cross-section
and the drag coefficient, the observed -8 mm/sec anomalous velocity decrease
during the second Galileo flyby of December, 8th, 1992 cannot be attributed
only to atmospheric friction. On the other hand, for perigees on the border
between the termosphere and the exosphere the friction only accounts for a
fraction of a millimeter per second in the final asymptotic velocity.
",0,1,0,0,0,0
20107,Interval vs. Point Temporal Logic Model Checking: an Expressiveness Comparison,"  In the last years, model checking with interval temporal logics is emerging
as a viable alternative to model checking with standard point-based temporal
logics, such as LTL, CTL, CTL*, and the like. The behavior of the system is
modeled by means of (finite) Kripke structures, as usual. However, while
temporal logics which are interpreted ""point-wise"" describe how the system
evolves state-by-state, and predicate properties of system states, those which
are interpreted ""interval-wise"" express properties of computation stretches,
spanning a sequence of states. A proposition letter is assumed to hold over a
computation stretch (interval) if and only if it holds over each component
state (homogeneity assumption). A natural question arises: is there any
advantage in replacing points by intervals as the primary temporal entities, or
is it just a matter of taste?
In this paper, we study the expressiveness of Halpern and Shoham's interval
temporal logic (HS) in model checking, in comparison with those of LTL, CTL,
and CTL*. To this end, we consider three semantic variants of HS: the
state-based one, introduced by Montanari et al., that allows time to branch
both in the past and in the future, the computation-tree-based one, that allows
time to branch in the future only, and the trace-based variant, that disallows
time to branch. These variants are compared among themselves and to the
aforementioned standard logics, getting a complete picture. In particular, we
show that HS with trace-based semantics is equivalent to LTL (but at least
exponentially more succinct), HS with computation-tree-based semantics is
equivalent to finitary CTL*, and HS with state-based semantics is incomparable
with all of them (LTL, CTL, and CTL*).
",1,0,0,0,0,0
20108,Calculations for electron-impact ionization of magnesium and calcium atoms in the method of interacting configurations in the complex number representation,"  Next investigations in our program of transition from the He atom to the
complex atoms description have been presented. The method of interacting
configurations in the complex number representation is under consideration. The
spectroscopic characteristics of the Mg and Ca atoms in the problem of the
electron-impact ionization of these atoms are investigated. The energies and
the widths of the lowest autoionizing states of Mg and Ca atoms are calculated.
Few results in the photoionization problem on the autoionizing states above the
n=2 threshold of helium-like Be ion are presented.
",0,1,0,0,0,0
20109,An Information Theoretic Approach to Sample Acquisition and Perception in Planetary Robotics,"  An important and emerging component of planetary exploration is sample
retrieval and return to Earth. Obtaining and analyzing rock samples can provide
unprecedented insight into the geology, geo-history and prospects for finding
past life and water. Current methods of exploration rely on mission scientists
to identify objects of interests and this presents major operational
challenges. Finding objects of interests will require systematic and efficient
methods to quickly and correctly evaluate the importance of hundreds if not
thousands of samples so that the most interesting are saved for further
analysis by the mission scientists. In this paper, we propose an automated
information theoretic approach to identify shapes of interests using a library
of predefined interesting shapes. These predefined shapes maybe human input or
samples that are then extrapolated by the shape matching system using the
Superformula to judge the importance of newly obtained objects. Shape samples
are matched to a library of shapes using the eigenfaces approach enabling
categorization and prioritization of the sample. The approach shows robustness
to simulated sensor noise of up to 20%. The effect of shape parameters and
rotational angle on shape matching accuracy has been analyzed. The approach
shows significant promise and efforts are underway in testing the algorithm
with real rock samples.
",1,1,0,0,0,0
20110,Global solvability of the Navier-Stokes equations with a free surface in the maximal $L_p\text{-}L_q$ regularity class,"  We consider the motion of incompressible viscous fluids bounded above by a
free surface and below by a solid surface in the $N$-dimensional Euclidean
space for $N\geq 2$ when the gravity is not taken into account. The aim of this
paper is to show the global solvability of the Naiver-Stokes equations with a
free surface, describing the above-mentioned motion, in the maximal
$L_p\text{-}L_q$ regularity class. Our approach is based on the maximal
$L_p\text{-}L_q$ regularity with exponential stability for the linearized
equations, and solutions to the original nonlinear problem are also
exponentially stable.
",0,0,1,0,0,0
20111,The fundamental group of the complement of the singular locus of Lauricella's $F_C$,"  We study the fundamental group of the complement of the singular locus of
Lauricella's hypergeometric function $F_C$ of $n$ variables. The singular locus
consists of $n$ hyperplanes and a hypersurface of degree $2^{n-1}$ in the
complex $n$-space. We derive some relations that holds for general $n\geq 3$.
We give an explicit presentation of the fundamental groupin the
three-dimensional case. We also consider a presentation of the fundamental
group of $2^3$-covering of this space.
In the version 2, we omit some of the calculations. For all the calculations,
refer to the version 1 (arXiv:1710.09594v1) of this article.
",0,0,1,0,0,0
20112,Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis,"  Spectral Clustering (SC) is a widely used data clustering method which first
learns a low-dimensional embedding $U$ of data by computing the eigenvectors of
the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get
the final clustering result. The Sparse Spectral Clustering (SSC) method
extends SC with a sparse regularization on $UU^\top$ by using the block
diagonal structure prior of $UU^\top$ in the ideal case. However, encouraging
$UU^\top$ to be sparse leads to a heavily nonconvex problem which is
challenging to solve and the work (Lu, Yan, and Lin 2016) proposes a convex
relaxation in the pursuit of this aim indirectly. However, the convex
relaxation generally leads to a loose approximation and the quality of the
solution is not clear. This work instead considers to solve the nonconvex
formulation of SSC which directly encourages $UU^\top$ to be sparse. We propose
an efficient Alternating Direction Method of Multipliers (ADMM) to solve the
nonconvex SSC and provide the convergence guarantee. In particular, we prove
that the sequences generated by ADMM always exist a limit point and any limit
point is a stationary point. Our analysis does not impose any assumptions on
the iterates and thus is practical. Our proposed ADMM for nonconvex problems
allows the stepsize to be increasing but upper bounded, and this makes it very
efficient in practice. Experimental analysis on several real data sets verifies
the effectiveness of our method.
",1,0,0,0,0,0
20113,Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings,"  A hyperbolic space has been shown to be more capable of modeling complex
networks than a Euclidean space. This paper proposes an explicit update rule
along geodesics in a hyperbolic space. The convergence of our algorithm is
theoretically guaranteed, and the convergence rate is better than the
conventional Euclidean gradient descent algorithm. Moreover, our algorithm
avoids the ""bias"" problem of existing methods using the Riemannian gradient.
Experimental results demonstrate the good performance of our algorithm in the
\Poincare embeddings of knowledge base data.
",0,0,0,1,0,0
20114,A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches,"  We present a parameterized approach to produce personalized variable length
summaries of soccer matches. Our approach is based on temporally segmenting the
soccer video into 'plays', associating a user-specifiable 'utility' for each
type of play and using 'bin-packing' to select a subset of the plays that add
up to the desired length while maximizing the overall utility (volume in
bin-packing terms). Our approach systematically allows a user to override the
default weights assigned to each type of play with individual preferences and
thus see a highly personalized variable length summarization of soccer matches.
We demonstrate our approach based on the output of an end-to-end pipeline that
we are building to produce such summaries. Though aspects of the overall
end-to-end pipeline are human assisted at present, the results clearly show
that the proposed approach is capable of producing semantically meaningful and
compelling summaries. Besides the obvious use of producing summaries of
superior league matches for news broadcasts, we anticipate our work to promote
greater awareness of the local matches and junior leagues by producing
consumable summaries of them.
",1,0,0,0,0,0
20115,Towards Robust Neural Networks via Random Self-ensemble,"  Recent studies have revealed the vulnerability of deep neural networks: A
small adversarial perturbation that is imperceptible to human can easily make a
well-trained deep neural network misclassify. This makes it unsafe to apply
neural networks in security-critical applications. In this paper, we propose a
new defense algorithm called Random Self-Ensemble (RSE) by combining two
important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted
model, RSE adds random noise layers to the neural network to prevent the strong
gradient-based attacks, and ensembles the prediction over random noises to
stabilize the performance. We show that our algorithm is equivalent to ensemble
an infinite number of noisy models $f_\epsilon$ without any additional memory
overhead, and the proposed training procedure based on noisy stochastic
gradient descent can ensure the ensemble model has a good predictive
capability. Our algorithm significantly outperforms previous defense techniques
on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\%
accuracy without any attack), under the strong C\&W attack within a certain
distortion tolerance, the accuracy of unprotected model drops to less than
10\%, the best previous defense technique has $48\%$ accuracy, while our method
still has $86\%$ prediction accuracy under the same level of attack. Finally,
our method is simple and easy to integrate into any neural network.
",1,0,0,1,0,0
20116,Prospects for detection of intermediate-mass black holes in globular clusters using integrated-light spectroscopy,"  The detection of intermediate mass black holes (IMBHs) in Galactic globular
clusters (GCs) has so far been controversial. In order to characterize the
effectiveness of integrated-light spectroscopy through integral field units, we
analyze realistic mock data generated from state-of-the-art Monte Carlo
simulations of GCs with a central IMBH, considering different setups and
conditions varying IMBH mass, cluster distance, and accuracy in determination
of the center. The mock observations are modeled with isotropic Jeans models to
assess the success rate in identifying the IMBH presence, which we find to be
primarily dependent on IMBH mass. However, even for a IMBH of considerable mass
(3% of the total GC mass), the analysis does not yield conclusive results in 1
out of 5 cases, because of shot noise due to bright stars close to the IMBH
line-of-sight. This stochastic variability in the modeling outcome grows with
decreasing BH mass, with approximately 3 failures out of 4 for IMBHs with 0.1%
of total GC mass. Finally, we find that our analysis is generally unable to
exclude at 68% confidence an IMBH with mass of $10^3~M_\odot$ in snapshots
without a central BH. Interestingly, our results are not sensitive to GC
distance within 5-20 kpc, nor to mis-identification of the GC center by less
than 2'' (<20% of the core radius). These findings highlight the value of
ground-based integral field spectroscopy for large GC surveys, where systematic
failures can be accounted for, but stress the importance of discrete kinematic
measurements that are less affected by stochasticity induced by bright stars.
",0,1,0,0,0,0
20117,A Multi-frequency analysis of possible Dark Matter Contributions to M31 Gamma-Ray Emissions,"  We examine the possibility of a dark matter (DM) contribution to the recently
observed gamma-ray spectrum seen in the M31 galaxy. In particular, we apply
limits on Weakly Interacting Massive Particle DM annihilation cross-sections
derived from the Coma galaxy cluster and the Reticulum II dwarf galaxy to
determine the maximal flux contribution by DM annihilation to both the M31
gamma-ray spectrum and that of the Milky-Way galactic centre. We limit the
energy range between 1 and 12 GeV in M31 and galactic centre spectra due to the
limited range of former's data, as well as to encompass the high-energy
gamma-ray excess observed in the latter target. In so doing, we will make use
of Fermi-LAT data for all mentioned targets, as well as diffuse radio data for
the Coma cluster. The multi-target strategy using both Coma and Reticulum II to
derive cross-section limits, as well as multi-frequency data, ensures that our
results are robust against the various uncertainties inherent in modelling of
indirect DM emissions.
Our results indicate that, when a Navarro-Frenk-White (or shallower) radial
density profile is assumed, severe constraints can be imposed upon the fraction
of the M31 and galactic centre spectra that can be accounted for by DM, with
the best limits arising from cross-section constraints from Coma radio data and
Reticulum II gamma-ray limits. These particular limits force all the studied
annihilation channels to contribute 1% or less to the total integrated
gamma-ray flux within both M31 and galactic centre targets. In contrast,
considerably more, 10-100%, of the flux can be attributed to DM when a
contracted Navarro-Frenk-White profile is assumed. This demonstrates how
sensitive DM contributions to gamma-ray emissions are to the possibility of
cored profiles in galaxies.
",0,1,0,0,0,0
20118,Tensor Decompositions for Modeling Inverse Dynamics,"  Modeling inverse dynamics is crucial for accurate feedforward robot control.
The model computes the necessary joint torques, to perform a desired movement.
The highly non-linear inverse function of the dynamical system can be
approximated using regression techniques. We propose as regression method a
tensor decomposition model that exploits the inherent three-way interaction of
positions x velocities x accelerations. Most work in tensor factorization has
addressed the decomposition of dense tensors. In this paper, we build upon the
decomposition of sparse tensors, with only small amounts of nonzero entries.
The decomposition of sparse tensors has successfully been used in relational
learning, e.g., the modeling of large knowledge graphs. Recently, the approach
has been extended to multi-class classification with discrete input variables.
Representing the data in high dimensional sparse tensors enables the
approximation of complex highly non-linear functions. In this paper we show how
the decomposition of sparse tensors can be applied to regression problems.
Furthermore, we extend the method to continuous inputs, by learning a mapping
from the continuous inputs to the latent representations of the tensor
decomposition, using basis functions. We evaluate our proposed model on a
dataset with trajectories from a seven degrees of freedom SARCOS robot arm. Our
experimental results show superior performance of the proposed functional
tensor model, compared to challenging state-of-the art methods.
",1,0,0,1,0,0
20119,Synthesis and Hydrogen Sorption Characteristics of Mechanically Alloyed Mg(NixMn1-x)2 Intermetallics,"  New ternary Mg-Ni-Mn intermetallics have been successfully synthesized by
High Energy Ball Milling (HEBM) and have been studied as possible materials for
efficient hydrogen storage applications. The microstructures of the as-cast and
milled alloys were characterized by means of X-ray Powder Diffraction (XRD) and
Scanning Electron Microscopy (SEM) both prior and after the hydrogenation
process, while the hydrogen storage characteristics (P-c-T) and the kinetics
were measured by using a commercial and automatically controlled Sievert-type
apparatus. The hydrogenation and dehydrogenation measurements were performed at
four different temperatures 150-200-250-300oC and the results showed that the
kinetics for both the hydrogenation and dehydrogenation process are very fast
for operation temperatures 250 and 300oC, but for temperatures below 200oC the
hydrogenation process becomes very slow and the dehydrogenation process cannot
be achieved.
",0,1,0,0,0,0
20120,Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective,"  Recent advances in the field of network embedding have shown the
low-dimensional network representation is playing a critical role in network
analysis. However, most of the existing principles of network embedding do not
incorporate auxiliary information such as content and labels of nodes flexibly.
In this paper, we take a matrix factorization perspective of network embedding,
and incorporate structure, content and label information of the network
simultaneously. For structure, we validate that the matrix we construct
preserves high-order proximities of the network. Label information can be
further integrated into the matrix via the process of random walk sampling to
enhance the quality of embedding in an unsupervised manner, i.e., without
leveraging downstream classifiers. In addition, we generalize the Skip-Gram
Negative Sampling model to integrate the content of the network in a matrix
factorization framework. As a consequence, network embedding can be learned in
a unified framework integrating network structure and node content as well as
label information simultaneously. We demonstrate the efficacy of the proposed
model with the tasks of semi-supervised node classification and link prediction
on a variety of real-world benchmark network datasets.
",1,0,0,1,0,0
20121,Incremental Principal Component Analysis Exact implementation and continuity corrections,"  This paper describes some applications of an incremental implementation of
the principal component analysis (PCA). The algorithm updates the
transformation coefficients matrix on-line for each new sample, without the
need to keep all the samples in memory. The algorithm is formally equivalent to
the usual batch version, in the sense that given a sample set the
transformation coefficients at the end of the process are the same. The
implications of applying the PCA in real time are discussed with the help of
data analysis examples. In particular we focus on the problem of the continuity
of the PCs during an on-line analysis.
",1,0,0,1,0,0
20122,More investment in Research and Development for better Education in the future?,"  The question in this paper is whether R&D efforts affect education
performance in small classes. Merging two datasets collected from the PISA
studies and the World Development Indicators and using Learning Bayesian
Networks, we prove the existence of a statistical causal relationship between
investment in R&D of a country and its education performance (PISA scores). We
also prove that the effect of R\&D on Education is long term as a country has
to invest at least 10 years before beginning to improve the level of young
pupils.
",0,0,0,1,0,0
20123,Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades,"  The global dynamics of event cascades are often governed by the local
dynamics of peer influence. However, detecting social influence from
observational data is challenging, due to confounds like homophily and
practical issues like missing data. In this work, we propose a novel
discriminative method to detect influence from observational data. The core of
the approach is to train a ranking algorithm to predict the source of the next
event in a cascade, and compare its out-of-sample accuracy against a
competitive baseline which lacks access to features corresponding to social
influence. Using synthetically generated data, we provide empirical evidence
that this method correctly identifies influence in the presence of confounds,
and is robust to both missing data and misspecification --- unlike popular
alternatives. We also apply the method to two real-world datasets: (1) cascades
of co-sponsorship of legislation in the U.S. House of Representatives, on a
social network of shared campaign donors; (2) rumors about the Higgs boson
discovery, on a follower network of $10^5$ Twitter accounts. Our model
identifies the role of peer influence in these scenarios, and uses it to make
more accurate predictions about the future trajectory of cascades.
",1,0,0,0,0,0
20124,Online codes for analog signals,"  We revisit a classical scenario in communication theory: a source is
generating a waveform which we sample at regular intervals; we wish to
transform the signal in such a way as to minimize distortion in its
reconstruction, despite noise. The transformation must be online (also called
causal), in order to enable real-time signaling. The noise model we consider is
adversarial $\ell_1$-bounded; this is the ""atomic norm"" convex relaxation of
the standard adversary model in discrete-alphabet communications, namely
sparsity (low Hamming weight). We require that our encoding not increase the
power of the original signal.
In the ""block coding"" setting such encoding is possible due to the existence
of large almost-Euclidean sections in $\ell_1$ spaces (established in the work
of Dvoretzky, Milman, Kašin, and Figiel, Lindenstrauss and Milman).
Our main result is that an analogous result is achievable even online.
Equivalently, we show a ""lower triangular"" version of $\ell_1$ Dvoretzky
theorems. In terms of communication, the result has the following form: If the
signal is a stream of reals $x_1,\ldots$, one per unit time, which we encode
causally into $\rho$ (a constant) reals per unit time (forming altogether an
output stream $\mathcal{E}(x)$), and if the adversarial noise added to this
encoded stream up to time $s$ is a vector $\vec{y}$, then at time $s$ the
decoder's reconstruction of the input prefix $x_{[s]}$ is accurate in a
time-weighted $\ell_2$ norm, to within $s^{-1/2+\delta}$ (any $\delta>0$) times
the adversary's noise as measured in a time-weighted $\ell_1$ norm. The
time-weighted decoding norm forces increasingly accurate reconstruction of the
distant past, while the time-weighted noise norm permits only vanishing effect
from noise in the distant past.
Encoding is linear, and decoding is performed by an LP analogous to those
used in compressed sensing.
",1,0,0,0,0,0
20125,Statistical test for fractional Brownian motion based on detrending moving average algorithm,"  Motivated by contemporary and rich applications of anomalous diffusion
processes we propose a new statistical test for fractional Brownian motion,
which is one of the most popular models for anomalous diffusion systems. The
test is based on detrending moving average statistic and its probability
distribution. Using the theory of Gaussian quadratic forms we determined it as
a generalized chi-squared distribution. The proposed test could be generalized
for statistical testing of any centered non-degenerate Gaussian process.
Finally, we examine the test via Monte Carlo simulations for two exemplary
scenarios of subdiffusive and superdiffusive dynamics.
",0,0,0,1,0,0
20126,Fast Radio Map Construction and Position Estimation via Direct Mapping for WLAN Indoor Localization System,"  The main limitation that constrains the fast and comprehensive application of
Wireless Local Area Network (WLAN) based indoor localization systems with
Received Signal Strength (RSS) positioning algorithms is the building of the
fingerprinting radio map, which is time-consuming especially when the indoor
environment is large and/or with high frequent changes. Different approaches
have been proposed to reduce workload, including fingerprinting deployment and
update efforts, but the performance degrades greatly when the workload is
reduced below a certain level. In this paper, we propose an indoor localization
scenario that applies metric learning and manifold alignment to realize direct
mapping localization (DML) using a low resolution radio map with single sample
of RSS that reduces the fingerprinting workload by up to 87\%. Compared to
previous work. The proposed two localization approaches, DML and $k$ nearest
neighbors based on reconstructed radio map (reKNN), were shown to achieve less
than 4.3\ m and 3.7\ m mean localization error respectively in a typical office
environment with an area of approximately 170\ m$^2$, while the unsupervised
localization with perturbation algorithm was shown to achieve 4.7\ m mean
localization error with 8 times more workload than the proposed methods. As for
the room level localization application, both DML and reKNN can meet the
requirement with at most 9\ m of localization error which is enough to tell
apart different rooms with over 99\% accuracy.
",1,0,0,1,0,0
20127,Lagrangian solutions to the Vlasov-Poisson system with a point charge,"  We consider the Cauchy problem for the repulsive Vlasov-Poisson system in the
three dimensional space, where the initial datum is the sum of a diffuse
density, assumed to be bounded and integrable, and a point charge. Under some
decay assumptions for the diffuse density close to the point charge, under
bounds on the total energy, and assuming that the initial total diffuse charge
is strictly less than one, we prove existence of global Lagrangian solutions.
Our result extends the Eulerian theory of [16], proving that solutions are
transported by the flow trajectories. The proof is based on the ODE theory
developed in [8] in the setting of vector fields with anisotropic regularity,
where some components of the gradient of the vector field is a singular
integral of a measure.
",0,0,1,0,0,0
20128,MITHRIL: Mining Sporadic Associations for Cache Prefetching,"  The growing pressure on cloud application scalability has accentuated storage
performance as a critical bottle- neck. Although cache replacement algorithms
have been extensively studied, cache prefetching - reducing latency by
retrieving items before they are actually requested remains an underexplored
area. Existing approaches to history-based prefetching, in particular, provide
too few benefits for real systems for the resources they cost. We propose
MITHRIL, a prefetching layer that efficiently exploits historical patterns in
cache request associations. MITHRIL is inspired by sporadic association rule
mining and only relies on the timestamps of requests. Through evaluation of 135
block-storage traces, we show that MITHRIL is effective, giving an average of a
55% hit ratio increase over LRU and PROBABILITY GRAPH, a 36% hit ratio gain
over AMP at reasonable cost. We further show that MITHRIL can supplement any
cache replacement algorithm and be readily integrated into existing systems.
Furthermore, we demonstrate the improvement comes from MITHRIL being able to
capture mid-frequency blocks.
",1,0,0,0,0,0
20129,On 2-level polytopes arising in combinatorial settings,"  2-level polytopes naturally appear in several areas of pure and applied
mathematics, including combinatorial optimization, polyhedral combinatorics,
communication complexity, and statistics. In this paper, we present a study of
some 2-level polytopes arising in combinatorial settings. Our first
contribution is proving that v(P)*f(P) is upper bounded by d*2^(d+1), for a
large collection of families of such polytopes P. Here v(P) (resp. f(P)) is the
number of vertices (resp. facets) of P, and d is its dimension. Whether this
holds for all 2-level polytopes was asked in [Bohn et al., ESA 2015], and
experimental results from [Fiorini et al., ISCO 2016] showed it true up to
dimension 7. The key to most of our proofs is a deeper understanding of the
relations among those polytopes and their underlying combinatorial structures.
This leads to a number of results that we believe to be of independent
interest: a trade-off formula for the number of cliques and stable sets in a
graph; a description of stable matching polytopes as affine projections of
certain order polytopes; and a linear-size description of the base polytope of
matroids that are 2-level in terms of cuts of an associated tree.
",1,0,1,0,0,0
20130,Learned Optimizers that Scale and Generalize,"  Learning to learn has emerged as an important direction for achieving
artificial intelligence. Two of the primary barriers to its adoption are an
inability to scale to larger problems and a limited ability to generalize to
new tasks. We introduce a learned gradient descent optimizer that generalizes
well to new tasks, and which has significantly reduced memory and computation
overhead. We achieve this by introducing a novel hierarchical RNN architecture,
with minimal per-parameter overhead, augmented with additional architectural
features that mirror the known structure of optimization tasks. We also develop
a meta-training ensemble of small, diverse optimization tasks capturing common
properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM
on problems in this corpus. More importantly, it performs comparably or better
when applied to small convolutional neural networks, despite seeing no neural
networks in its meta-training set. Finally, it generalizes to train Inception
V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps,
optimization problems that are of a vastly different scale than those it was
trained on. We release an open source implementation of the meta-training
algorithm.
",1,0,0,1,0,0
20131,Toward Optimal Run Racing: Application to Deep Learning Calibration,"  This paper aims at one-shot learning of deep neural nets, where a highly
parallel setting is considered to address the algorithm calibration problem -
selecting the best neural architecture and learning hyper-parameter values
depending on the dataset at hand. The notoriously expensive calibration problem
is optimally reduced by detecting and early stopping non-optimal runs. The
theoretical contribution regards the optimality guarantees within the multiple
hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki
benchmarks demonstrate the relevance of the approach with a principled and
consistent improvement on the state of the art with no extra hyper-parameter.
",1,0,0,0,0,0
20132,Bernstein - von Mises theorems for statistical inverse problems I: Schrödinger equation,"  The inverse problem of determining the unknown potential $f>0$ in the partial
differential equation $$\frac{\Delta}{2} u - fu =0 \text{ on } \mathcal O
~~\text{s.t. } u = g \text { on } \partial \mathcal O,$$ where $\mathcal O$ is
a bounded $C^\infty$-domain in $\mathbb R^d$ and $g>0$ is a given function
prescribing boundary values, is considered. The data consist of the solution
$u$ corrupted by additive Gaussian noise. A nonparametric Bayesian prior for
the function $f$ is devised and a Bernstein - von Mises theorem is proved which
entails that the posterior distribution given the observations is approximated
in a suitable function space by an infinite-dimensional Gaussian measure that
has a `minimal' covariance structure in an information-theoretic sense. As a
consequence the posterior distribution performs valid and optimal frequentist
statistical inference on $f$ in the small noise limit.
",0,0,1,1,0,0
20133,Tailoring Architecture Centric Design Method with Rapid Prototyping,"  Many engineering processes exist in the industry, text books and
international standards. However, in practice rarely any of the processes are
followed consistently and literally. It is observed across industries the
processes are altered based on the requirements of the projects. Two features
commonly lacking from many engineering processes are, 1) the formal capacity to
rapidly develop prototypes in the rudimentary stage of the project, 2)
transitioning of requirements into architectural designs, when and how to
evaluate designs and how to use the throw away prototypes throughout the system
lifecycle. Prototypes are useful for eliciting requirements, generating
customer feedback and identifying, examining or mitigating risks in a project
where the product concept is at a cutting edge or not fully perceived. Apart
from the work that the product is intended to do, systemic properties like
availability, performance and modifiability matter as much as functionality.
Architects must even these concerns with the method they select to promote
these systemic properties and at the same time equip the stakeholders with the
desired functionality. Architectural design and prototyping is one of the key
ways to build the right product embedded with the desired systemic properties.
Once the product is built it can be almost impossible to retrofit the system
with the desired attributes. This paper customizes the architecture centric
development method with rapid prototyping to achieve the above-mentioned goals
and reducing the number of iterations across the stages of ACDM.
",1,0,0,0,0,0
20134,Easy High-Dimensional Likelihood-Free Inference,"  We introduce a framework using Generative Adversarial Networks (GANs) for
likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC)
where we replace the black-box simulator model with an approximator network and
generate a rich set of summary features in a data driven fashion. On benchmark
data sets, our approach improves on others with respect to scalability, ability
to handle high dimensional data and complex probability distributions.
",1,0,0,1,0,0
20135,Narrow-line Laser Cooling by Adiabatic Transfer,"  We propose and demonstrate a novel laser cooling mechanism applicable to
particles with narrow-linewidth optical transitions. By sweeping the frequency
of counter-propagating laser beams in a sawtooth manner, we cause adiabatic
transfer back and forth between the ground state and a long-lived optically
excited state. The time-ordering of these adiabatic transfers is determined by
Doppler shifts, which ensures that the associated photon recoils are in the
opposite direction to the particle's motion. This ultimately leads to a robust
cooling mechanism capable of exerting large forces via a weak transition and
with reduced reliance on spontaneous emission. We present a simple intuitive
model for the resulting frictional force, and directly demonstrate its efficacy
for increasing the total phase-space density of an atomic ensemble. We rely on
both simulation and experimental studies using the 7.5~kHz linewidth $^1$S$_0$
to $^3$P$_1$ transition in $^{88}$Sr. The reduced reliance on spontaneous
emission may allow this adiabatic sweep method to be a useful tool for cooling
particles that lack closed cycling transitions, such as molecules.
",0,1,0,0,0,0
20136,Assessing the Effect of Stellar Companions from High-Resolution Imaging of Kepler Objects of Interest,"  We report on 176 close (<2"") stellar companions detected with high-resolution
imaging near 170 hosts of Kepler Objects of Interest. These Kepler targets were
prioritized for imaging follow-up based on the presence of small planets, so
most of the KOIs in these systems (176 out of 204) have nominal radii <6 R_E .
Each KOI in our sample was observed in at least 2 filters with adaptive optics,
speckle imaging, lucky imaging, or HST. Multi-filter photometry provides color
information on the companions, allowing us to constrain their stellar
properties and assess the probability that the companions are physically bound.
We find that 60 -- 80% of companions within 1"" are bound, and the bound
fraction is >90% for companions within 0.5""; the bound fraction decreases with
increasing angular separation. This picture is consistent with simulations of
the binary and background stellar populations in the Kepler field. We also
reassess the planet radii in these systems, converting the observed
differential magnitudes to a contamination in the Kepler bandpass and
calculating the planet radius correction factor, $X_R = R_p (true) / R_p
(single)$. Under the assumption that planets in bound binaries are equally
likely to orbit the primary or secondary, we find a mean radius correction
factor for planets in stellar multiples of $X_R = 1.65$. If stellar
multiplicity in the Kepler field is similar to the solar neighborhood, then
nearly half of all Kepler planets may have radii underestimated by an average
of 65%, unless vetted using high resolution imaging or spectroscopy.
",0,1,0,0,0,0
20137,Semi-Supervised Learning via New Deep Network Inversion,"  We exploit a recently derived inversion scheme for arbitrary deep neural
networks to develop a new semi-supervised learning framework that applies to a
wide range of systems and problems. The approach outperforms current
state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while
using $5$ labeled examples per class. Experiments with one-dimensional signals
highlight the generality of the method. Importantly, our approach is simple,
efficient, and requires no change in the deep network architecture.
",1,0,0,1,0,0
20138,Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora,"  The effectiveness of a statistical machine translation system (SMT) is very
dependent upon the amount of parallel corpus used in the training phase. For
low-resource language pairs there are not enough parallel corpora to build an
accurate SMT. In this paper, a novel approach is presented to extract bilingual
Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
this study, English is used as the pivot language to compute the matching
scores between source and target sentences and candidate selection phase.
Additionally, a new monolingual sentence similarity metric, Normalized Google
Distance (NGD) is proposed to improve the matching process. Moreover, some
extensions of the baseline system are applied to improve the quality of
extracted sentences measured with BLEU. Experimental results show that using
the new pivot based extraction can increase the quality of bilingual corpus
significantly and consequently improves the performance of the Persian-Italian
SMT system.
",1,0,0,0,0,0
20139,Intrinsic geometry and analysis of Finsler structures,"  In this short note, we prove that if $F$ is a weak upper semicontinuous
admissible Finsler structure on a domain in $\mathbb{R}^n$, $n\geq 2$, then the
intrinsic distance and differential structures coincide.
",0,0,1,0,0,0
20140,Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games,"  Monte Carlo Tree Search (MCTS) has been extended to many imperfect
information games. However, due to the added complexity that uncertainty
introduces, these adaptations have not reached the same level of practical
success as their perfect information counterparts. In this paper we consider
the development of agents that perform well against humans in imperfect
information games with partially observable actions. We introduce the
Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS
algorithm (ISMCTS). More specifically, SDMCTS generates a predictive model of
the unobservable portion of the opponent's actions from historical behavioral
data. Next, SDMCTS performs simulations on an instance of the game where the
unobservable portion of the opponent's actions are determined. Thereby, it
facilitates the use of the predictive model in order to decrease uncertainty.
We present an implementation of the SDMCTS applied to the Cheat Game, a
well-known card game, with partially observable (and often deceptive) actions.
Results from experiments with 120 subjects playing a head-to-head Cheat Game
against our SDMCTS agents suggest that SDMCTS performs well against humans, and
its performance improves as the predictive model's accuracy increases.
",1,0,0,0,0,0
20141,The KLASH Proposal,"  We propose a search of galactic axions with mass about 0.2 microeV using a
large volume resonant cavity, about 50 m^3, cooled down to 4 K and immersed in
a moderate axial magnetic field of about 0.6 T generated inside the
superconducting magnet of the KLOE experiment located at the National
Laboratory of Frascati of INFN. This experiment, called KLASH (KLoe magnet for
Axion SearcH) in the following, has a potential sensitivity on the
axion-to-photon coupling, g_agg, of about 6x10^-17 GeV-1, reaching the region
predicted by KSVZ and DFSZ models of QCD axions.
",0,1,0,0,0,0
20142,Dimensionality-Driven Learning with Noisy Labels,"  Datasets with significant proportions of noisy (incorrect) class labels
present challenges for training accurate Deep Neural Networks (DNNs). We
propose a new perspective for understanding DNN generalization for such
datasets, by investigating the dimensionality of the deep representation
subspace of training samples. We show that from a dimensionality perspective,
DNNs exhibit quite distinctive learning styles when trained with clean labels
versus when trained with a proportion of noisy labels. Based on this finding,
we develop a new dimensionality-driven learning strategy, which monitors the
dimensionality of subspaces during training and adapts the loss function
accordingly. We empirically demonstrate that our approach is highly tolerant to
significant proportions of noisy labels, and can effectively learn
low-dimensional local subspaces that capture the data distribution.
",0,0,0,1,0,0
20143,Delving into adversarial attacks on deep policies,"  Adversarial examples have been shown to exist for a variety of deep learning
architectures. Deep reinforcement learning has shown promising results on
training agent policies directly on raw inputs such as image pixels. In this
paper we present a novel study into adversarial attacks on deep reinforcement
learning polices. We compare the effectiveness of the attacks using adversarial
examples vs. random noise. We present a novel method for reducing the number of
times adversarial examples need to be injected for a successful attack, based
on the value function. We further explore how re-training on random noise and
FGSM perturbations affects the resilience against adversarial examples.
",1,0,0,1,0,0
20144,On two functions arising in the study of the Euler and Carmichael quotients,"  We investigate two arithmetic functions naturally occurring in the study of
the Euler and Carmichael quotients. The functions are related to the frequency
of vanishing of the Euler and Carmichael quotients. We obtain several results
concerning the relations between these functions as well as their typical and
extreme values.
",0,0,1,0,0,0
20145,Lurking Variable Detection via Dimensional Analysis,"  Lurking variables represent hidden information, and preclude a full
understanding of phenomena of interest. Detection is usually based on
serendipity -- visual detection of unexplained, systematic variation. However,
these approaches are doomed to fail if the lurking variables do not vary. In
this article, we address these challenges by introducing formal hypothesis
tests for the presence of lurking variables, based on Dimensional Analysis.
These procedures utilize a modified form of the Buckingham Pi theorem to
provide structure for a suitable null hypothesis. We present analytic tools for
reasoning about lurking variables in physical phenomena, construct procedures
to handle cases of increasing complexity, and present examples of their
application to engineering problems. The results of this work enable
algorithm-driven lurking variable detection, complementing a traditionally
inspection-based approach.
",0,0,0,1,0,0
20146,System calibration method for Fourier ptychographic microscopy,"  Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
phase imaging technique with high resolution and wide field-of-view (FOV). In
current FPM imaging platforms, systematic error sources come from the
aberrations, LED intensity fluctuation, parameter imperfections and noise,
which will severely corrupt the reconstruction results with artifacts. Although
these problems have been researched and some special methods have been proposed
respectively, there is no method to solve all of them. However, the systematic
error is a mixture of various sources in the real situation. It is difficult to
distinguish a kind of error source from another due to the similar artifacts.
To this end, we report a system calibration procedure, termed SC-FPM, based on
the simulated annealing (SA) algorithm, LED intensity correction and adaptive
step-size strategy, which involves the evaluation of an error matric at each
iteration step, followed by the re-estimation of accurate parameters. The great
performance has been achieved both in simulation and experiments. The reported
system calibration scheme improves the robustness of FPM and relaxes the
experiment conditions, which makes the FPM more pragmatic.
",0,1,0,0,0,0
20147,Categories for Dynamic Epistemic Logic,"  The primary goal of this paper is to recast the semantics of modal logic, and
dynamic epistemic logic (DEL) in particular, in category-theoretic terms. We
first review the category of relations and categories of Kripke frames, with
particular emphasis on the duality between relations and adjoint homomorphisms.
Using these categories, we then reformulate the semantics of DEL in a more
categorical and algebraic form. Several virtues of the new formulation will be
demonstrated: The DEL idea of updating a model into another is captured
naturally by the categorical perspective -- which emphasizes a family of
objects and structural relationships among them, as opposed to a single object
and structure on it. Also, the categorical semantics of DEL can be merged
straightforwardly with a standard categorical semantics for first-order logic,
providing a semantics for first-order DEL.
",1,0,1,0,0,0
20148,Accurate Pouring with an Autonomous Robot Using an RGB-D Camera,"  Robotic assistants in a home environment are expected to perform various
complex tasks for their users. One particularly challenging task is pouring
drinks into cups, which for successful completion, requires the detection and
tracking of the liquid level during a pour to determine when to stop. In this
paper, we present a novel approach to autonomous pouring that tracks the liquid
level using an RGB-D camera and adapts the rate of pouring based on the liquid
level feedback. We thoroughly evaluate our system on various types of liquids
and under different conditions, conducting over 250 pours with a PR2 robot. The
results demonstrate that our approach is able to pour liquids to a target
height with an accuracy of a few millimeters.
",1,0,0,0,0,0
20149,Restriction of Odd Degree Characters of $\mathfrak{S}_n$,"  Let $n$ and $k$ be natural numbers such that $2^k < n$. We study the
restriction to $\mathfrak{S}_{n-2^k}$ of odd-degree irreducible characters of
the symmetric group $\mathfrak{S}_n$. This analysis completes the study begun
in [Ayyer A., Prasad A., Spallone S., Sem. Lothar. Combin. 75 (2015), Art.
B75g, 13 pages] and recently developed in [Isaacs I.M., Navarro G., Olsson
J.B., Tiep P.H., J. Algebra 478 (2017), 271-282].
",0,0,1,0,0,0
20150,CSGNet: Neural Shape Parser for Constructive Solid Geometry,"  We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.
",1,0,0,0,0,0
20151,Composite Weyl nodes stabilized by screw symmetry with and without time reversal,"  We classify the band degeneracies in 3D crystals with screw symmetry $n_m$
and broken $\mathcal P*\mathcal T$ symmetry, where $\mathcal P$ stands for
spatial inversion and $\mathcal T$ for time reversal. The generic degeneracies
along symmetry lines are Weyl nodes: Chiral contact points between pairs of
bands. They can be single nodes with a chiral charge of magnitude $|\chi|=1$ or
composite nodes with $|\chi|=2$ or $3$, and the possible $\chi$ values only
depend on the order $n$ of the axis, not on the pitch $m/n$ of the screw.
Double Weyl nodes require $n=4$ or 6, and triple nodes require $n=6$. In all
cases the bands split linearly along the axis, and for composite nodes the
splitting is quadratic on the orthogonal plane. This is true for triple as well
as double nodes, due to the presence in the effective two-band Hamiltonian of a
nonchiral quadratic term that masks the chiral cubic dispersion. If $\mathcal
T$ symmetry is present and $\mathcal P$ is broken there may exist on some
symmetry lines Weyl nodes pinned to $\mathcal T$-invariant momenta, which in
some cases are unavoidable. In the absence of other symmetries their
classification depends on $n$, $m$, and the type of $\mathcal T$ symmetry. With
spinless $\mathcal T$ such $\mathcal T$-invariant Weyl nodes are always double
nodes, while with spinful $\mathcal T$ they can be single or triple nodes.
$\mathcal T$-invariant triples nodes can occur not only on 6-fold axes but also
on 3-fold ones, and their in-plane band splitting is cubic, not quadratic as in
the case of generic triple nodes. These rules are illustrated by means of
first-principles calculations for hcp cobalt, a $\mathcal T$-broken, $\mathcal
P$-invariant crystal with $6_3$ symmetry, and for trigonal tellurium and
hexagonal NbSi$_2$, which are $\mathcal T$-invariant, $\mathcal P$-broken
crystals with 3-fold and 6-fold screw symmetry respectively.
",0,1,0,0,0,0
20152,Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study,"  Deep Neural Networks (DNNs) are very popular these days, and are the subject
of a very intense investigation. A DNN is made by layers of internal units (or
neurons), each of which computes an affine combination of the output of the
units in the previous layer, applies a nonlinear operator, and outputs the
corresponding value (also known as activation). A commonly-used nonlinear
operator is the so-called rectified linear unit (ReLU), whose output is just
the maximum between its input value and zero. In this (and other similar cases
like max pooling, where the max operation involves more than one input value),
one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where
the continuous variables correspond to the output values of each unit, and a
binary variable is associated with each ReLU to model its yes/no nature. In
this paper we discuss the peculiarity of this kind of 0-1 MILP models, and
describe an effective bound-tightening technique intended to ease its solution.
We also present possible applications of the 0-1 MILP model arising in feature
visualization and in the construction of adversarial examples. Preliminary
computational results are reported, aimed at investigating (on small DNNs) the
computational performance of a state-of-the-art MILP solver when applied to a
known test case, namely, hand-written digit recognition.
",1,0,0,0,0,0
20153,Directionality Fields generated by a Local Hilbert Transform,"  We propose a new approach based on a local Hilbert transform to design
non-Hermitian potentials generating arbitrary vector fields of directionality,
p(r), with desired shapes and topologies. We derive a local Hilbert transform
to systematically build such potentials, by modifying background potentials
(being either regular or random, extended or localized). In particular, we
explore particular directionality fields, for instance in the form of a focus
to create sinks for probe fields (which could help to increase absorption at
the sink), or to generate vortices in the probe fields. Physically, the
proposed directionality fields provide a flexible new mechanism for dynamically
shaping and precise control over probe fields leading to novel effects in wave
dynamics.
",0,1,0,0,0,0
20154,Bulk Eigenvalue Correlation Statistics of Random Biregular Bipartite Graphs,"  This paper is the second chapter of three of the author's undergraduate
thesis. In this paper, we consider the random matrix ensemble given by $(d_b,
d_w)$-regular graphs on $M$ black vertices and $N$ white vertices, where $d_b
\in [N^{\gamma}, N^{2/3 - \gamma}]$ for any $\gamma > 0$. We simultaneously
prove that the bulk eigenvalue correlation statistics for both normalized
adjacency matrices and their corresponding covariance matrices are stable for
short times. Combined with an ergodicity analysis of the Dyson Brownian motion
in another paper, this proves universality of bulk eigenvalue correlation
statistics, matching normalized adjacency matrices with the GOE and the
corresponding covariance matrices with the Gaussian Wishart Ensemble.
",0,0,1,1,0,0
20155,A local weighted Axler-Zheng theorem in $\mathbb{C}^n$,"  The well-known Axler-Zheng theorem characterizes compactness of finite sums
of finite products of Toeplitz operators on the unit disk in terms of the
Berezin transform of these operators. Subsequently this theorem was generalized
to other domains and appeared in different forms, including domains in
$\mathbb{C}^n$ on which the $\overline{\partial}$-Neumann operator $N$ is
compact. In this work we remove the assumption on $N$, and we study weighted
Bergman spaces on smooth bounded pseudoconvex domains. We prove a local version
of the Axler-Zheng theorem characterizing compactness of Toeplitz operators in
the algebra generated by symbols continuous up to the boundary in terms of the
behavior of the Berezin transform at strongly pseudoconvex points. We employ a
Forelli-Rudin type inflation method to handle the weights.
",0,0,1,0,0,0
20156,Spatial modulation of Joule losses to increase the normal zone propagation velocity in (RE)BaCuO tapes,"  This paper presents a simple approach to increase the normal zone propagation
velocity in (RE)BaCuO thin films grown on a flexible metallic substrate, also
called superconducting tapes. The key idea behind this approach is to use a
specific geometry of the silver thermal stabilizer that surrounds the
superconducting tape. More specifically, a very thin layer of silver stabilizer
is deposited on top of the superconductor layer, typically less than 100 nm,
while the remaining stabilizer (still silver) is deposited on the substrate
side. Normal zone propagation velocities up to 170 cm/s have been measured
experimentally, corresponding to a stabilizer thickness of 20 nm on top of the
superconductor layer. This is one order of magnitude faster than the speed
measured on actual commercial tapes. Our results clearly demonstrate that a
very thin stabilizer on top of the superconductor layer leads to high normal
zone propagation velocities. The experimental values are in good agreement with
predictions realized by finite element simulations. Furthermore, the
propagation of the normal zone during the quench was recorded in situ and in
real time using a high-speed camera. Due to high Joule losses generated on both
edges of the tape sample, a ""U-shaped"" profile could be observed at the
boundaries between the superconducting and the normal zones, which matches very
closely the profile predicted by the simulations.
",0,1,0,0,0,0
20157,Vortex Thermometry for Turbulent Two-Dimensional Fluids,"  We introduce a new method of statistical analysis to characterise the
dynamics of turbulent fluids in two dimensions. We establish that, in
equilibrium, the vortex distributions can be uniquely connected to the
temperature of the vortex gas, and apply this vortex thermometry to
characterise simulations of decaying superfluid turbulence. We confirm the
hypothesis of vortex evaporative heating leading to Onsager vortices proposed
in Phys. Rev. Lett. 113, 165302 (2014), and find previously unidentified vortex
power-law distributions that emerge from the dynamics.
",0,1,0,0,0,0
20158,The Hurwitz-type theorem for the regular Coulomb wave function via Hankel determinants,"  We derive a closed formula for the determinant of the Hankel matrix whose
entries are given by sums of negative powers of the zeros of the regular
Coulomb wave function. This new identity applied together with results of
Grommer and Chebotarev allows us to prove a Hurwitz-type theorem about the
zeros of the regular Coulomb wave function. As a particular case, we obtain a
new proof of the classical Hurwitz's theorem from the theory of Bessel
functions that is based on algebraic arguments. In addition, several Hankel
determinants with entries given by the Rayleigh function and Bernoulli numbers
are also evaluated.
",0,0,1,0,0,0
20159,An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients,"  In this technical report, we consider an approach that combines the PPO
objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
We perform a range of empirical analysis on various aspects of the algorithm,
such as sample complexity, training speed, and sensitivity to batch size and
training epochs. We observe that PPOKFAC is able to outperform PPO in terms of
sample complexity and speed in a range of MuJoCo environments, while being
scalable in terms of batch size. In spite of this, it seems that adding more
epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to
be worse than its A2C counterpart, ACKTR.
",0,0,0,1,0,0
20160,On the density of sets avoiding parallelohedron distance 1,"  The maximal density of a measurable subset of R^n avoiding Euclidean
distance1 is unknown except in the trivial case of dimension 1. In this paper,
we consider thecase of a distance associated to a polytope that tiles space,
where it is likely that the setsavoiding distance 1 are of maximal density
2^-n, as conjectured by Bachoc and Robins. We prove that this is true for n =
2, and for the Voronoï regions of the lattices An, n >= 2.
",0,0,1,0,0,0
20161,Adversarial Deep Structured Nets for Mass Segmentation from Mammograms,"  Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{this https URL}
",1,0,0,0,0,0
20162,The downward directed grounds hypothesis and very large cardinals,"  A transitive model $M$ of ZFC is called a ground if the universe $V$ is a set
forcing extension of $M$. We show that the grounds of $V$ are downward
set-directed. Consequently, we establish some fundamental theorems on the
forcing method and the set-theoretic geology. For instance, (1) the mantle, the
intersection of all grounds, must be a model of ZFC. (2) $V$ has only set many
grounds if and only if the mantle is a ground. We also show that if the
universe has some very large cardinal, then the mantle must be a ground.
",0,0,1,0,0,0
20163,A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking,"  Wild-land fire fighting is a hazardous job. A key task for firefighters is to
observe the ""fire front"" to chart the progress of the fire and areas that will
likely spread next. Lack of information of the fire front causes many
accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising
because it can replace humans in hazardous fire tracking and significantly
reduce operation costs. In this paper we propose a distributed control
framework designed for a team of UAVs that can closely monitor a wildfire in
open space, and precisely track its development. The UAV team, designed for
flexible deployment, can effectively avoid in-flight collisions and cooperate
well with neighbors. They can maintain a certain height level to the ground for
safe flight above fire. Experimental results are conducted to demonstrate the
capabilities of the UAV team in covering a spreading wildfire.
",1,0,0,0,0,0
20164,Rayleigh-Brillouin light scattering spectroscopy of nitrous oxide (N$_2$O),"  High signal-to-noise and high-resolution light scattering spectra are
measured for nitrous oxide (N$_2$O) gas at an incident wavelength of 403.00 nm,
at 90$^\circ$ scattering, at room temperature and at gas pressures in the range
$0.5-4$ bar. The resulting Rayleigh-Brillouin light scattering spectra are
compared to a number of models describing in an approximate manner the
collisional dynamics and energy transfer in this gaseous medium of this
polyatomic molecular species. The Tenti-S6 model, based on macroscopic gas
transport coefficients, reproduces the scattering profiles in the entire
pressure range at less than 2\% deviation at a similar level as does the
alternative kinetic Grad's 6-moment model, which is based on the internal
collisional relaxation as a decisive parameter. A hydrodynamic model fails to
reproduce experimental spectra for the low pressures of 0.5-1 bar, but yields
very good agreement ($< 1$\%) in the pressure range $2-4$ bar. While these
three models have a different physical basis the internal molecular relaxation
derived can for all three be described in terms of a bulk viscosity of $\eta_b
\sim (6 \pm 2) \times 10^{-5}$ Pa$\cdot$s. A 'rough-sphere' model, previously
shown to be effective to describe light scattering in SF$_6$ gas, is not found
to be suitable, likely in view of the non-sphericity and asymmetry of the N-N-O
structured linear polyatomic molecule.
",0,1,0,0,0,0
20165,Competition between Chaotic and Non-Chaotic Phases in a Quadratically Coupled Sachdev-Ye-Kitaev Model,"  The Sachdev-Ye-Kitaev (SYK) model is a concrete solvable model to study
non-Fermi liquid properties, holographic duality and maximally chaotic
behavior. In this work, we consider a generalization of the SYK model that
contains two SYK models with different number of Majorana modes coupled by
quadratic terms. This model is also solvable, and the solution shows a
zero-temperature quantum phase transition between two non-Fermi liquid chaotic
phases. This phase transition is driven by tuning the ratio of two mode
numbers, and a Fermi liquid non-chaotic phase sits at the critical point with
equal mode number. At finite temperature, the Fermi liquid phase expands to a
finite regime. More intriguingly, a different non-Fermi liquid phase emerges at
finite temperature. We characterize the phase diagram in term of the spectral
function, the Lyapunov exponent and the entropy. Our results illustrate a
concrete example of quantum phase transition and critical regime between two
non-Fermi liquid phases.
",0,1,0,0,0,0
20166,Symmetry Realization via a Dynamical Inverse Higgs Mechanism,"  The Ward identities associated with spontaneously broken symmetries can be
saturated by Goldstone bosons. However, when space-time symmetries are broken,
the number of Goldstone bosons necessary to non-linearly realize the symmetry
can be less than the number of broken generators. The loss of Goldstones may be
due to a redundancy or the generation of a gap. This phenomena is called an
Inverse Higgs Mechanism (IHM). However, there are cases when a Goldstone boson
associated with a broken generator does not appear in the low energy theory
despite the lack of the existence of an associated IHM. In this paper we will
show that in such cases the relevant broken symmetry can be realized, without
the aid of an associated Goldstone, if there exists a proper set of operator
constraints, which we call a Dynamical Inverse Higgs Mechanism (DIHM). We
consider the spontaneous breaking of boosts, rotations and conformal
transformations in the context of Fermi liquids, finding three possible paths
to symmetry realization: pure Goldstones, no Goldstones and DIHM, or some
mixture thereof. We show that in the two dimensional degenerate electron system
the DIHM route is the only consistent way to realize spontaneously broken
boosts and dilatations, while in three dimensions these symmetries could just
as well be realized via the inclusion of non-derivatively coupled Goldstone
bosons. We have present the action, including the leading order
non-linearities, for the rotational Goldstone (angulon), and discuss the
constraint associated with the possible DIHM that would need to be imposed to
remove it from the spectrum. Finally we discuss the conditions under which
Goldstone bosons are non-derivatively coupled, a necessary condition for the
existence of a Dynamical Inverse Higgs Constraint (DIHC), generalizaing the
results for Vishwanath and Wantanabe.
",0,1,0,0,0,0
20167,Growth of strontium ruthenate films by hybrid molecular beam epitaxy,"  We report on the growth of epitaxial Sr2RuO4 films using a hybrid molecular
beam epitaxy approach in which a volatile precursor containing RuO4 is used to
supply ruthenium and oxygen. The use of the precursor overcomes a number of
issues encountered in traditional MBE that uses elemental metal sources.
Phase-pure, epitaxial thin films of Sr2RuO4 are obtained. At high substrate
temperatures, growth proceeds in a layer-by-layer mode with intensity
oscillations observed in reflection high-energy electron diffraction. Films are
of high structural quality, as documented by x-ray diffraction, atomic force
microscopy, and transmission electron microscopy. The method should be suitable
for the growth of other complex oxides containing ruthenium, opening up
opportunities to investigate thin films that host rich exotic ground states.
",0,1,0,0,0,0
20168,Possible Evidence for the Stochastic Acceleration of Secondary Antiprotons by Supernova Remnants,"  The antiproton-to-proton ratio in the cosmic-ray spectrum is a sensitive
probe of new physics. Using recent measurements of the cosmic-ray antiproton
and proton fluxes in the energy range of 1-1000 GeV, we study the contribution
to the $\bar{p}/p$ ratio from secondary antiprotons that are produced and
subsequently accelerated within individual supernova remnants. We consider
several well-motivated models for cosmic-ray propagation in the interstellar
medium and marginalize our results over the uncertainties related to the
antiproton production cross section and the time-, charge-, and
energy-dependent effects of solar modulation. We find that the increase in the
$\bar{p}/p$ ratio observed at rigidities above $\sim$ 100 GV cannot be
accounted for within the context of conventional cosmic-ray propagation models,
but is consistent with scenarios in which cosmic-ray antiprotons are produced
and subsequently accelerated by shocks within a given supernova remnant. In
light of this, the acceleration of secondary cosmic rays in supernova remnants
is predicted to substantially contribute to the cosmic-ray positron spectrum,
accounting for a significant fraction of the observed positron excess.
",0,1,0,0,0,0
20169,The Host Galaxy and Redshift of the Repeating Fast Radio Burst FRB 121102,"  The precise localization of the repeating fast radio burst (FRB 121102) has
provided the first unambiguous association (chance coincidence probability
$p\lesssim3\times10^{-4}$) of an FRB with an optical and persistent radio
counterpart. We report on optical imaging and spectroscopy of the counterpart
and find that it is an extended ($0.6^{\prime\prime}-0.8^{\prime\prime}$)
object displaying prominent Balmer and [OIII] emission lines. Based on the
spectrum and emission line ratios, we classify the counterpart as a
low-metallicity, star-forming, $m_{r^\prime} = 25.1$ AB mag dwarf galaxy at a
redshift of $z=0.19273(8)$, corresponding to a luminosity distance of 972 Mpc.
From the angular size, the redshift, and luminosity, we estimate the host
galaxy to have a diameter $\lesssim4$ kpc and a stellar mass of
$M_*\sim4-7\times 10^{7}\,M_\odot$, assuming a mass-to-light ratio between 2 to
3$\,M_\odot\,L_\odot^{-1}$. Based on the H$\alpha$ flux, we estimate the star
formation rate of the host to be $0.4\,M_\odot\,\mathrm{yr^{-1}}$ and a
substantial host dispersion measure depth $\lesssim 324\,\mathrm{pc\,cm^{-3}}$.
The net dispersion measure contribution of the host galaxy to FRB 121102 is
likely to be lower than this value depending on geometrical factors. We show
that the persistent radio source at FRB 121102's location reported by Marcote
et al (2017) is offset from the galaxy's center of light by $\sim$200 mas and
the host galaxy does not show optical signatures for AGN activity. If FRB
121102 is typical of the wider FRB population and if future interferometric
localizations preferentially find them in dwarf galaxies with low metallicities
and prominent emission lines, they would share such a preference with long
gamma ray bursts and superluminous supernovae.
",0,1,0,0,0,0
20170,Thicket Density,"  Thicket density is a new measure of the complexity of a set system, having
the same relationship to stable formulas that VC density has to NIP formulas.
It satisfies a Sauer-Shelah type dichotomy that has applications in both model
theory and the theory of algorithms
",0,0,1,0,0,0
20171,"Uniformizations of stable $(γ,n)$-gonal Riemann surfaces","  A $(\gamma,n)$-gonal pair is a pair $(S,f)$, where $S$ is a closed Riemann
surface and $f:S \to R$ is a degree $n$ holomorphic map onto a closed Riemann
surface $R$ of genus $\gamma$. If the signature of $(S,f)$ is of hyperbolic
type, then there is pair $(\Gamma,G)$, called an uniformization of $(S,f)$,
where $G$ is a Fuchsian group acting on the unit disc ${\mathbb D}$ containing
$\Gamma$ as an index $n$ subgroup, so that $f$ is induced by the inclusion of
$\Gamma <G$. The uniformization is uniquely determined by $(S,f)$, up to
conjugation by holomorphic automorphisms of ${\mathbb D}$, and it permits to
provide natural complex orbifold structures on the Hurwitz spaces parametrizing
(twisted) isomorphic classes of pairs topologically equivalent to $(S,f)$. In
order to produce certain compactifications of these Hurwitz spaces, one needs
to consider the so called stable $(\gamma,n)$-gonal pairs, which are natural
geometrical deformations of $(\gamma,n)$-gonal pairs. Due to the above, it
seems interesting to search for uniformizations of stable $(\gamma,n)$-gonal
pairs, in terms of certain class of Kleinian groups. In this paper we review
such uniformizations by using noded Fuchsian groups, which are (geometric)
limits of quasiconformal deformations of Fuchsian groups, and which provide
uniformizations of stable Riemann orbifolds. These uniformizations permit to
obtain a compactification of the Hurwitz spaces with a complex orbifold
structure, these being quotients of the augmented Teichmüller space of $G$ by
a suitable finite index subgroup of its modular group.
",0,0,1,0,0,0
20172,"If you are not paying for it, you are the product: How much do advertisers pay to reach you?","  Online advertising is progressively moving towards a programmatic model in
which ads are matched to actual interests of individuals collected as they
browse the web. Letting the huge debate around privacy aside, a very important
question in this area, for which little is known, is: How much do advertisers
pay to reach an individual? In this study, we develop a first of its kind
methodology for computing exactly that -- the price paid for a web user by the
ad ecosystem -- and we do that in real time. Our approach is based on tapping
on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted
prices for winning bids paid by advertisers in order to place targeted ads. Our
main technical contribution is a method for tallying winning bids even when
they are encrypted. We achieve this by training a model using as ground truth
prices obtained by running our own ""probe"" ad-campaigns. We design our
methodology through a browser extension and a back-end server that provides it
with fresh models for encrypted bids. We validate our methodology using a one
year long trace of 1600 mobile users and demonstrate that it can estimate a
user's advertising worth with more than 82% accuracy.
",1,0,0,0,0,0
20173,Circularly polarized vacuum field in three-dimensional chiral photonic crystals probed by quantum dot emission,"  The quantum nature of light-matter interactions in a circularly polarized
vacuum field was probed by spontaneous emission from quantum dots in
three-dimensional chiral photonic crystals. Due to the circularly polarized
eigenmodes along the helical axis in the GaAs-based mirror-asymmetric
structures we studied, we observed highly circularly polarized emission from
the quantum dots. Both spectroscopic and time-resolved measurements confirmed
that the obtained circularly polarized light was influenced by a large
difference in the photonic density of states between the orthogonal components
of the circular polarization in the vacuum field.
",0,1,0,0,0,0
20174,Critical behavior of quasi-two-dimensional semiconducting ferromagnet CrGeTe$_3$,"  The critical properties of the single-crystalline semiconducting ferromagnet
CrGeTe$_3$ were investigated by bulk dc magnetization around the paramagnetic
to ferromagnetic phase transition. Critical exponents $\beta = 0.200\pm0.003$
with critical temperature $T_c = 62.65\pm0.07$ K and $\gamma = 1.28\pm0.03$
with $T_c = 62.75\pm0.06$ K are obtained by the Kouvel-Fisher method whereas
$\delta = 7.96\pm0.01$ is obtained by the critical isotherm analysis at $T_c =
62.7$ K. These critical exponents obey the Widom scaling relation $\delta =
1+\gamma/\beta$, indicating self-consistency of the obtained values. With these
critical exponents the isotherm $M(H)$ curves below and above the critical
temperatures collapse into two independent universal branches, obeying the
single scaling equation $m = f_\pm(h)$, where $m$ and $h$ are renormalized
magnetization and field, respectively. The determined exponents match well with
those calculated from the results of renormalization group approach for a
two-dimensional Ising system coupled with long-range interaction between spins
decaying as $J(r)\approx r^{-(d+\sigma)}$ with $\sigma=1.52$.
",0,1,0,0,0,0
20175,Scale-free Monte Carlo method for calculating the critical exponent $γ$ of self-avoiding walks,"  We implement a scale-free version of the pivot algorithm and use it to sample
pairs of three-dimensional self-avoiding walks, for the purpose of efficiently
calculating an observable that corresponds to the probability that pairs of
self-avoiding walks remain self-avoiding when they are concatenated. We study
the properties of this Markov chain, and then use it to find the critical
exponent $\gamma$ for self-avoiding walks to unprecedented accuracy. Our final
estimate for $\gamma$ is $1.15695300(95)$.
",0,1,1,0,0,0
20176,The quadratic M-convexity testing problem,"  M-convex functions, which are a generalization of valuated matroids, play a
central role in discrete convex analysis. Quadratic M-convex functions
constitute a basic and important subclass of M-convex functions, which has a
close relationship with phylogenetics as well as valued constraint satisfaction
problems. In this paper, we consider the quadratic M-convexity testing problem
(QMCTP), which is the problem of deciding whether a given quadratic function on
$\{0,1\}^n$ is M-convex. We show that QMCTP is co-NP-complete in general, but
is polynomial-time solvable under a natural assumption. Furthermore, we propose
an $O(n^2)$-time algorithm for solving QMCTP in the polynomial-time solvable
case.
",0,0,1,0,0,0
20177,Distributed Bayesian Matrix Factorization with Limited Communication,"  Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank
representations of matrices and for predicting missing values and providing
confidence intervals. Scaling up the posterior inference for massive-scale
matrices is challenging and requires distributing both data and computation
over many workers, making communication the main computational bottleneck.
Embarrassingly parallel inference would remove the communication needed, by
using completely independent computations on different data subsets, but it
suffers from the inherent unidentifiability of BMF solutions. We introduce a
hierarchical decomposition of the joint posterior distribution, which couples
the subset inferences, allowing for embarrassingly parallel computations in a
sequence of at most three stages. Using an efficient approximate
implementation, we show improvements empirically on both real and simulated
data. Our distributed approach is able to achieve a speed-up of almost an order
of magnitude over the full posterior, with a negligible effect on predictive
accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC
methods in accuracy, and achieves results competitive to other available
distributed and parallel implementations of BMF.
",1,0,0,1,0,0
20178,Optimized Certificate Revocation List Distribution for Secure V2X Communications,"  The successful deployment of safe and trustworthy Connected and Autonomous
Vehicles (CAVs) will highly depend on the ability to devise robust and
effective security solutions to resist sophisticated cyber attacks and patch up
critical vulnerabilities. Pseudonym Public Key Infrastructure (PPKI) is a
promising approach to secure vehicular networks as well as ensure data and
location privacy, concealing the vehicles' real identities. Nevertheless,
pseudonym distribution and management affect PPKI scalability due to the
significant number of digital certificates required by a single vehicle. In
this paper, we focus on the certificate revocation process and propose a
versatile and low-complexity framework to facilitate the distribution of the
Certificate Revocation Lists (CRL) issued by the Certification Authority (CA).
CRL compression is achieved through optimized Bloom filters, which guarantee a
considerable overhead reduction with a configurable rate of false positives.
Our results show that the distribution of compressed CRLs can significantly
enhance the system scalability without increasing the complexity of the
revocation process.
",1,0,0,0,0,0
20179,Optical response of highly reflective film used in the water Cherenkov muon veto of the XENON1T dark matter experiment,"  The XENON1T experiment is the most recent stage of the XENON Dark Matter
Search, aiming for the direct detection of Weakly Interacting Massive Particles
(WIMPs). To reach its projected sensitivity, the background has to be reduced
by two orders of magnitude compared to its predecessor XENON100. This requires
a water Cherenkov muon veto surrounding the XENON1T TPC, both to shield
external backgrounds and to tag muon-induced energetic neutrons through
detection of a passing muon or the secondary shower induced by a muon
interacting in the surrounding rock. The muon veto is instrumented with $84$
$8""$ PMTs with high quantum efficiency (QE) in the Cherenkov regime and the
walls of the watertank are clad with the highly reflective DF2000MA foil by 3M.
Here, we present a study of the reflective properties of this foil, as well as
the measurement of its wavelength shifting (WLS) properties. Further, we
present the impact of reflectance and WLS on the detection efficiency of the
muon veto, using a Monte Carlo simulation carried out with Geant4. The
measurements yield a specular reflectance of $\approx100\%$ for wavelengths
larger than $400\,$nm, while $\approx90\%$ of the incoming light below
$370\,$nm is absorbed by the foil. Approximately $3-7.5\%$ of the light hitting
the foil within the wavelength range $250\,$nm $\leq \lambda \leq 390\,$nm is
used for the WLS process. The intensity of the emission spectrum of the WLS
light is slightly dependent on the absorbed wavelength and shows the shape of a
rotational-vibrational fluorescence spectrum, peaking at around $\lambda
\approx 420\,$nm. Adjusting the reflectance values to the measured ones in the
Monte Carlo simulation originally used for the muon veto design, the veto
detection efficiency remains unchanged. Including the wavelength shifting in
the Monte Carlo simulation leads to an increase of the efficiency of
approximately $0.5\%$.
",0,1,0,0,0,0
20180,On spectral partitioning of signed graphs,"  We argue that the standard graph Laplacian is preferable for spectral
partitioning of signed graphs compared to the signed Laplacian. Simple examples
demonstrate that partitioning based on signs of components of the leading
eigenvectors of the signed Laplacian may be meaningless, in contrast to
partitioning based on the Fiedler vector of the standard graph Laplacian for
signed graphs. We observe that negative eigenvalues are beneficial for spectral
partitioning of signed graphs, making the Fiedler vector easier to compute.
",1,0,1,1,0,0
20181,Sensor Fusion for Public Space Utilization Monitoring in a Smart City,"  Public space utilization is crucial for urban developers to understand how
efficient a place is being occupied in order to improve existing or future
infrastructures. In a smart cities approach, implementing public space
monitoring with Internet-of-Things (IoT) sensors appear to be a viable
solution. However, choice of sensors often is a challenging problem and often
linked with scalability, coverage, energy consumption, accuracy, and privacy.
To get the most from low cost sensor with aforementioned design in mind, we
proposed data processing modules for capturing public space utilization with
Renewable Wireless Sensor Network (RWSN) platform using pyroelectric infrared
(PIR) and analog sound sensor. We first proposed a calibration process to
remove false alarm of PIR sensor due to the impact of weather and environment.
We then demonstrate how the sounds sensor can be processed to provide various
insight of a public space. Lastly, we fused both sensors and study a particular
public space utilization based on one month data to unveil its usage.
",1,0,0,1,0,0
20182,Symplectic integrators for second-order linear non-autonomous equations,"  Two families of symplectic methods specially designed for second-order
time-dependent linear systems are presented. Both are obtained from the Magnus
expansion of the corresponding first-order equation, but otherwise they differ
in significant aspects. The first family is addressed to problems with low to
moderate dimension, whereas the second is more appropriate when the dimension
is large, in particular when the system corresponds to a linear wave equation
previously discretised in space. Several numerical experiments illustrate the
main features of the new schemes.
",0,0,1,0,0,0
20183,"Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations","  The recent breakthroughs of deep reinforcement learning (DRL) technique in
Alpha Go and playing Atari have set a good example in handling large state and
actions spaces of complicated control problems. The DRL technique is comprised
of (i) an offline deep neural network (DNN) construction phase, which derives
the correlation between each state-action pair of the system and its value
function, and (ii) an online deep Q-learning phase, which adaptively derives
the optimal action and updates value estimates. In this paper, we first present
the general DRL framework, which can be widely utilized in many applications
with different optimization objectives. This is followed by the introduction of
three specific applications: the cloud computing resource allocation problem,
the residential smart grid task scheduling problem, and building HVAC system
optimal control problem. The effectiveness of the DRL technique in these three
cyber-physical applications have been validated. Finally, this paper
investigates the stochastic computing-based hardware implementations of the DRL
framework, which consumes a significant improvement in area efficiency and
power consumption compared with binary-based implementation counterparts.
",1,0,0,0,0,0
20184,Experimental observation of node-line-like surface states in LaBi,"  In a Dirac nodal line semimetal, the bulk conduction and valence bands touch
at extended lines in the Brillouin zone. To date, most of the theoretically
predicted and experimentally discovered nodal lines derive from the bulk bands
of two- and three-dimensional materials. Here, based on combined angle-resolved
photoemission spectroscopy measurements and first-principles calculations, we
report the discovery of node-line-like surface states on the (001) surface of
LaBi. These bands derive from the topological surface states of LaBi and bridge
the band gap opened by spin-orbit coupling and band inversion. Our
first-principles calculations reveal that these ""nodal lines"" have a tiny gap,
which is beyond typical experimental resolution. These results may provide
important information to understand the extraordinary physical properties of
LaBi, such as the extremely large magnetoresistance and resistivity plateau.
",0,1,0,0,0,0
20185,Manin's conjecture for a class of singular cubic hypersurfaces,"  Let $n$ be a positive multiple of $4$. We establish an asymptotic formula for
the number of rational points of bounded height on singular cubic hypersurfaces
$S_n$ defined by $$ x^3=(y_1^2 + \cdots + y_n^2)z . $$ This result is new in
two aspects: first, it can be viewed as a modest start on the study of density
of rational points on those singular cubic hypersurfaces which are not covered
by the classical theorems of Davenport or Heath-Brown; second, it proves
Manin's conjecture for singular cubic hypersurfaces $S_n$ defined above.
",0,0,1,0,0,0
20186,A Flexible Approach to Automated RNN Architecture Generation,"  The process of designing neural architectures requires expert knowledge and
extensive trial and error. While automated architecture search may simplify
these requirements, the recurrent neural network (RNN) architectures generated
by existing methods are limited in both flexibility and components. We propose
a domain-specific language (DSL) for use in automated architecture search which
can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough
to define standard architectures such as the Gated Recurrent Unit and Long
Short Term Memory and allows the introduction of non-standard RNN components
such as trigonometric curves and layer normalization. Using two different
candidate generation techniques, random search with a ranking function and
reinforcement learning, we explore the novel architectures produced by the RNN
DSL for language modeling and machine translation domains. The resulting
architectures do not follow human intuition yet perform well on their targeted
tasks, suggesting the space of usable RNN architectures is far larger than
previously assumed.
",1,0,0,1,0,0
20187,Convexification of Neural Graph,"  Traditionally, most complex intelligence architectures are extremely
non-convex, which could not be well performed by convex optimization. However,
this paper decomposes complex structures into three types of nodes: operators,
algorithms and functions. Iteratively, propagating from node to node along
edge, we prove that ""regarding the tree-structured neural graph, it is nearly
convex in each variable, when the other variables are fixed."" In fact, the
non-convex properties stem from circles and functions, which could be
transformed to be convex with our proposed \textit{\textbf{scale mechanism}}.
Experimentally, we justify our theoretical analysis by two practical
applications.
",0,0,0,1,0,0
20188,Pseudogaps in strongly interacting Fermi gases,"  A central challenge in modern condensed matter physics is developing the
tools for understanding nontrivial yet unordered states of matter. One
important idea to emerge in this context is that of a ""pseudogap"": the fact
that under appropriate circumstances the normal state displays a suppression of
the single particle spectral density near the Fermi level, reminiscent of the
gaps seen in ordered states of matter. While these concepts arose in a solid
state context, it is now being explored in cold gases. This article reviews the
current experimental and theoretical understanding of the normal state of
strongly interacting Fermi gases, with particular focus on the phenomonology
which is traditionally associated with the pseudogap.
",0,1,0,0,0,0
20189,Resolution-Exact Planner for Thick Non-Crossing 2-Link Robots,"  We consider the path planning problem for a 2-link robot amidst polygonal
obstacles. Our robot is parametrizable by the lengths $\ell_1, \ell_2>0$ of its
two links, the thickness $\tau \ge 0$ of the links, and an angle $\kappa$ that
constrains the angle between the 2 links to be strictly greater than $\kappa$.
The case $\tau>0$ and $\kappa \ge 0$ corresponds to ""thick non-crossing""
robots. This results in a novel 4DOF configuration space ${\mathbb R}^2\times
({\mathbb T}\setminus\Delta(\kappa))$ where ${\mathbb T}$ is the torus and
$\Delta(\kappa)$ the diagonal band of width $\kappa$. We design a
resolution-exact planner for this robot using the framework of Soft Subdivision
Search (SSS). First, we provide an analysis of the space of forbidden angles,
leading to a soft predicate for classifying configuration boxes. We further
exploit the T/R splitting technique which was previously introduced for
self-crossing thin 2-link robots. Our open-source implementation in Core
Library achieves real-time performance for a suite of combinatorially
non-trivial obstacle sets. Experimentally, our algorithm is significantly
better than any of the state-of-art sampling algorithms we looked at, in timing
and in success rate.
",1,0,0,0,0,0
20190,Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks,"  Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. In the meantime, network embedding has emerged as a convenient
tool to mine and learn from networked data. As a result, it is of interest to
develop HIN embedding methods. However, the heterogeneity in HINs introduces
not only rich information but also potentially incompatible semantics, which
poses special challenges to embedding learning in HINs. With the intention to
preserve the rich yet potentially incompatible information in HIN embedding, we
propose to study the problem of comprehensive transcription of heterogeneous
information networks. The comprehensive transcription of HINs also provides an
easy-to-use approach to unleash the power of HINs, since it requires no
additional supervision, expertise, or feature engineering. To cope with the
challenges in the comprehensive transcription of HINs, we propose the HEER
algorithm, which embeds HINs via edge representations that are further coupled
with properly-learned heterogeneous metrics. To corroborate the efficacy of
HEER, we conducted experiments on two large-scale real-words datasets with an
edge reconstruction task and multiple case studies. Experiment results
demonstrate the effectiveness of the proposed HEER model and the utility of
edge representations and heterogeneous metrics. The code and data are available
at this https URL.
",1,0,0,0,0,0
20191,Generic Dynamical Phase Transition in One-Dimensional Bulk-Driven Lattice Gases with Exclusion,"  Dynamical phase transitions are crucial features of the fluctuations of
statistical systems, corresponding to boundaries between qualitatively
different mechanisms of maintaining unlikely values of dynamical observables
over long periods of time. They manifest themselves in the form of
non-analyticities in the large deviation function of those observables. In this
paper, we look at bulk-driven exclusion processes with open boundaries. It is
known that the standard asymmetric simple exclusion process exhibits a
dynamical phase transition in the large deviations of the current of particles
flowing through it. That phase transition has been described thanks to specific
calculation methods relying on the model being exactly solvable, but more
general methods have also been used to describe the extreme large deviations of
that current, far from the phase transition. We extend those methods to a large
class of models based on the ASEP, where we add arbitrary spatial
inhomogeneities in the rates and short-range potentials between the particles.
We show that, as for the regular ASEP, the large deviation function of the
current scales differently with the size of the system if one considers very
high or very low currents, pointing to the existence of a dynamical phase
transition between those two regimes: high current large deviations are
extensive in the system size, and the typical states associated to them are
Coulomb gases, which are correlated ; low current large deviations do not
depend on the system size, and the typical states associated to them are
anti-shocks, consistently with a hydrodynamic behaviour. Finally, we illustrate
our results numerically on a simple example, and we interpret the transition in
terms of the current pushing beyond its maximal hydrodynamic value, as well as
relate it to the appearance of Tracy-Widom distributions in the relaxation
statistics of such models.
",0,1,0,0,0,0
20192,Semianalytical calculation of the zonal-flow oscillation frequency in stellarators,"  Due to their capability to reduce turbulent transport in magnetized plasmas,
understanding the dynamics of zonal flows is an important problem in the fusion
programme. Since the pioneering work by Rosenbluth and Hinton in axisymmetric
tokamaks, it is known that studying the linear and collisionless relaxation of
zonal flow perturbations gives valuable information and physical insight.
Recently, the problem has been investigated in stellarators and it has been
found that in these devices the relaxation process exhibits a characteristic
feature: a damped oscillation. The frequency of this oscillation might be a
relevant parameter in the regulation of turbulent transport, and therefore its
efficient and accurate calculation is important. Although an analytical
expression can be derived for the frequency, its numerical evaluation is not
simple and has not been exploited systematically so far. Here, a numerical
method for its evaluation is considered, and the results are compared with
those obtained by calculating the frequency from gyrokinetic simulations. This
""semianalytical"" approach for the determination of the zonal-flow frequency
reveals accurate and faster than the one based on gyrokinetic simulations.
",0,1,0,0,0,0
20193,Geometric phase of a moving dipole under a magnetic field at a distance,"  We predict a geometric quantum phase shift of a moving electric dipole in the
presence of an external magnetic field at a distance. On the basis of the
Lorentz-covariant field interaction approach, we show that a geometric phase
appears under the condition that the dipole is moving in the field-free region,
which is distinct from the topological He-McKellar-Wilkens phase generated by a
direct overlap of the dipole and the field. We discuss the experimental
feasibility of detecting this phase with atomic interferometry and argue that
detection of this phase would result in a deeper understanding of the locality
in quantum electromagnetic interaction.
",0,1,0,0,0,0
20194,A refined count of Coxeter element factorizations,"  For well-generated complex reflection groups, Chapuy and Stump gave a simple
product for a generating function counting reflection factorizations of a
Coxeter element by their length. This is refined here to record the number of
reflections used from each orbit of hyperplanes. The proof is case-by-case via
the classification of well-generated groups. It implies a new expression for
the Coxeter number, expressed via data coming from a hyperplane orbit; a
case-free proof of this due to J. Michel is included.
",0,0,1,0,0,0
20195,Tuning the magnetism of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001): First-principles study,"  The magnetic properties of BaFe$_{2}$As$_{2}$(001) surface have been studied
by using first-principles electronic structure calculations. We find that for
As-terminated surface the magnetic ground state of the top-layer FeAs is in the
staggered dimer antiferromagnetic (AFM) order, while for Ba-terminated surface
the collinear (single stripe) AFM order is the most stable. When a certain
coverage of Ba or K atoms are deposited onto the As-terminated surface, the
calculated energy differences among different AFM orders for the top-layer FeAs
on BaFe$_{2}$As$_{2}$(001) can be much reduced, indicating enhanced spin
fluctuations. To identify the novel staggered dimer AFM order for the As
termination, we have simulated the scanning tunneling microscopy (STM) image
for this state, which shows a different $\sqrt{2}\times\sqrt{2}$ pattern from
the case of half Ba coverage. Our results suggest: i) the magnetic properties
of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001) can be tuned effectively by
surface doping; ii) both the surface termination and the AFM order in the
top-layer FeAs can affect the STM image of BaFe$_{2}$As$_{2}$(001).
",0,1,0,0,0,0
20196,Cubature methods to solve BSDEs: Error expansion and complexity control,"  We obtain an explicit error expansion for the solution of Backward Stochastic
Differential Equations (BSDEs) using the cubature on Wiener spaces method. The
result is proved under a mild strengthening of the assumptions needed for the
application of the cubature method. The explicit expansion can then be used to
construct implementable higher order approximations via Richardson-Romberg
extrapolation. To allow for an effective efficiency improvement of the
interpolated algorithm, we introduce an additional projection on sparse grids,
and study the resulting complexity reduction. Numerical examples are provided
to illustrate our results.
",0,0,1,0,0,0
20197,An Earth-mass Planet in a 1-AU Orbit around an Ultracool Dwarf,"  We combine $Spitzer$ and ground-based KMTNet microlensing observations to
identify and precisely measure an Earth-mass ($1.43^{+0.45}_{-0.32} M_\oplus$)
planet OGLE-2016-BLG-1195Lb at $1.16^{+0.16}_{-0.13}$ AU orbiting a
$0.078^{+0.016}_{-0.012} M_\odot$ ultracool dwarf. This is the lowest-mass
microlensing planet to date. At $3.91^{+0.42}_{-0.46}$ kpc, it is the third
consecutive case among the $Spitzer$ ""Galactic distribution"" planets toward the
Galactic bulge that lies in the Galactic disk as opposed to the bulge itself,
hinting at a skewed distribution of planets. Together with previous
microlensing discoveries, the seven Earth-size planets orbiting the ultracool
dwarf TRAPPIST-1, and the detection of disks around young brown dwarfs,
OGLE-2016-BLG-1195Lb suggests that such planets might be common around
ultracool dwarfs. It therefore sheds light on the formation of both ultracool
dwarfs and planetary systems at the limit of low-mass protoplanetary disks.
",0,1,0,0,0,0
20198,Distributed Statistical Estimation and Rates of Convergence in Normal Approximation,"  This paper presents a class of new algorithms for distributed statistical
estimation that exploit divide-and-conquer approach. We show that one of the
key benefits of the divide-and-conquer strategy is robustness, an important
characteristic for large distributed systems. We establish connections between
performance of these distributed algorithms and the rates of convergence in
normal approximation, and prove non-asymptotic deviations guarantees, as well
as limit theorems, for the resulting estimators. Our techniques are illustrated
through several examples: in particular, we obtain new results for the
median-of-means estimator, as well as provide performance guarantees for
distributed maximum likelihood estimation.
",0,0,1,1,0,0
20199,Connecting HL Tau to the Observed Exoplanet Sample,"  The Atacama Large Millimeter/submilimeter Array (ALMA) recently revealed a
set of nearly concentric gaps in the protoplanetary disk surrounding the young
star HL Tau. If these are carved by forming gas giants, this provides the first
set of orbital initial conditions for planets as they emerge from their birth
disks. Using N-body integrations, we have followed the evolution of the system
for 5 Gyr to explore the possible outcomes. We find that HL Tau initial
conditions scaled down to the size of typically observed exoplanet orbits
naturally produce several populations in the observed exoplanet sample. First,
for a plausible range of planetary masses, we can match the observed
eccentricity distribution of dynamically excited radial velocity giant planets
with eccentricities $>$ 0.2. Second, we roughly obtain the observed rate of hot
Jupiters around FGK stars. Finally, we obtain a large efficiency of planetary
ejections of $\approx 2$ per HL Tau-like system, but the small fraction of
stars observed to host giant planets makes it hard to match the rate of
free-floating planets inferred from microlensing observations. In view of
upcoming GAIA results, we also provide predictions for the expected mutual
inclination distribution, which is significantly broader than the absolute
inclination distributions typically considered by previous studies.
",0,1,0,0,0,0
20200,How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics,"  Endowing robots with the capability of assessing risk and making risk-aware
decisions is widely considered a key step toward ensuring safety for robots
operating under uncertainty. But, how should a robot quantify risk? A natural
and common approach is to consider the framework whereby costs are assigned to
stochastic outcomes - an assignment captured by a cost random variable.
Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping
from the cost random variable to a real number. Yet, the question of what
constitutes a ""good"" risk metric has received little attention within the
robotics community. The goal of this paper is to explore and partially address
this question by advocating axioms that risk metrics in robotics applications
should satisfy in order to be employed as rational assessments of risk. We
discuss general representation theorems that precisely characterize the class
of metrics that satisfy these axioms (referred to as distortion risk metrics),
and provide instantiations that can be used in applications. We further discuss
pitfalls of commonly used risk metrics in robotics, and discuss additional
properties that one must consider in sequential decision making tasks. Our hope
is that the ideas presented here will lead to a foundational framework for
quantifying risk (and hence safety) in robotics applications.
",1,0,0,0,0,0
20201,Pitfalls and Best Practices in Algorithm Configuration,"  Good parameter settings are crucial to achieve high performance in many areas
of artificial intelligence (AI), such as propositional satisfiability solving,
AI planning, scheduling, and machine learning (in particular deep learning).
Automated algorithm configuration methods have recently received much attention
in the AI community since they replace tedious, irreproducible and error-prone
manual parameter tuning and can lead to new state-of-the-art performance.
However, practical applications of algorithm configuration are prone to several
(often subtle) pitfalls in the experimental design that can render the
procedure ineffective. We identify several common issues and propose best
practices for avoiding them. As one possibility for automatically handling as
many of these as possible, we also propose a tool called GenericWrapper4AC.
",1,0,0,0,0,0
20202,Critical behaviors in contagion dynamics,"  We study the critical behavior of a general contagion model where nodes are
either active (e.g. with opinion A, or functioning) or inactive (e.g. with
opinion B, or damaged). The transitions between these two states are determined
by (i) spontaneous transitions independent of the neighborhood, (ii)
transitions induced by neighboring nodes and (iii) spontaneous reverse
transitions. The resulting dynamics is extremely rich including limit cycles
and random phase switching. We derive a unifying mean-field theory.
Specifically, we analytically show that the critical behavior of systems whose
dynamics is governed by processes (i-iii) can only exhibit three distinct
regimes: (a) uncorrelated spontaneous transition dynamics (b) contact process
dynamics and (c) cusp catastrophes. This ends a long-standing debate on the
universality classes of complex contagion dynamics in mean-field and
substantially deepens its mathematical understanding.
",0,1,1,0,0,0
20203,Machine Learning in Appearance-based Robot Self-localization,"  An appearance-based robot self-localization problem is considered in the
machine learning framework. The appearance space is composed of all possible
images, which can be captured by a robot's visual system under all robot
localizations. Using recent manifold learning and deep learning techniques, we
propose a new geometrically motivated solution based on training data
consisting of a finite set of images captured in known locations of the robot.
The solution includes estimation of the robot localization mapping from the
appearance space to the robot localization space, as well as estimation of the
inverse mapping for modeling visual image features. The latter allows solving
the robot localization problem as the Kalman filtering problem.
",1,0,0,1,0,0
20204,Towards Communication-Aware Robust Topologies,"  We currently witness the emergence of interesting new network topologies
optimized towards the traffic matrices they serve, such as demand-aware
datacenter interconnects (e.g., ProjecToR) and demand-aware overlay networks
(e.g., SplayNets). This paper introduces a formal framework and approach to
reason about and design such topologies. We leverage a connection between the
communication frequency of two nodes and the path length between them in the
network, which depends on the entropy of the communication matrix. Our main
contribution is a novel robust, yet sparse, family of network topologies which
guarantee an expected path length that is proportional to the entropy of the
communication patterns.
",1,0,0,0,0,0
20205,Zero-shot Domain Adaptation without Domain Semantic Descriptors,"  We propose a method to infer domain-specific models such as classifiers for
unseen domains, from which no data are given in the training phase, without
domain semantic descriptors. When training and test distributions are
different, standard supervised learning methods perform poorly. Zero-shot
domain adaptation attempts to alleviate this problem by inferring models that
generalize well to unseen domains by using training data in multiple source
domains. Existing methods use observed semantic descriptors characterizing
domains such as time information to infer the domain-specific models for the
unseen domains. However, it cannot always be assumed that such metadata can be
used in real-world applications. The proposed method can infer appropriate
domain-specific models without any semantic descriptors by introducing the
concept of latent domain vectors, which are latent representations for the
domains and are used for inferring the models. The latent domain vector for the
unseen domain is inferred from the set of the feature vectors in the
corresponding domain, which is given in the testing phase. The domain-specific
models consist of two components: the first is for extracting a representation
of a feature vector to be predicted, and the second is for inferring model
parameters given the latent domain vector. The posterior distributions of the
latent domain vectors and the domain-specific models are parametrized by neural
networks, and are optimized by maximizing the variational lower bound using
stochastic gradient descent. The effectiveness of the proposed method was
demonstrated through experiments using one regression and two classification
tasks.
",0,0,0,1,0,0
20206,Network modelling of topological domains using Hi-C data,"  Genome-wide chromosome conformation capture techniques such as Hi-C enable
the generation of 3D genome contact maps and offer new pathways toward
understanding the spatial organization of genome. One specific feature of the
3D organization is known as topologically associating domains (TADs), which are
densely interacting, contiguous chromatin regions playing important roles in
regulating gene expression. A few algorithms have been proposed to detect TADs.
In particular, the structure of Hi-C data naturally inspires application of
community detection methods. However, one of the drawbacks of community
detection is that most methods take exchangeability of the nodes in the network
for granted; whereas the nodes in this case, i.e. the positions on the
chromosomes, are not exchangeable. We propose a network model for detecting
TADs using Hi-C data that takes into account this non-exchangeability. In
addition, our model explicitly makes use of cell-type specific CTCF binding
sites as biological covariates and can be used to identify conserved TADs
across multiple cell types. The model leads to a likelihood objective that can
be efficiently optimized via relaxation. We also prove that when suitably
initialized, this model finds the underlying TAD structure with high
probability. Using simulated data, we show the advantages of our method and the
caveats of popular community detection methods, such as spectral clustering, in
this application. Applying our method to real Hi-C data, we demonstrate the
domains identified have desirable epigenetic features and compare them across
different cell types. The code is available upon request.
",0,0,0,1,0,0
20207,On a Possible Giant Impact Origin for the Colorado Plateau,"  It is proposed and substantiated that an extraterrestrial object of the
approximate size and mass of Planet Mars, impacting the Earth in an oblique
angle along an approximately NE-SW route (with respect to the current
orientation of the North America continent) around 750 million years ago (750
Ma), is likely to be the direct cause of a chain of events which led to the
rifting of the Rodinia supercontinent and the severing of the foundation of the
Colorado Plateau from its surrounding craton.
It is further argued that the impactor most likely originated as a rouge
exoplanet produced during one of the past crossings of our Solar System through
the Galactic spiral arms in its orbital motion around the center of the Milky
Way Galaxy. Recent work has shown that the sites of galactic spiral arms are
locations of density-wave collisionless shocks. The perturbations from such
shock are known lead to the formation of massive stars, which evolve quickly
and die as supernovae. The blastwaves from supernova explosions, in addition to
the collisionless shocks at the spiral arms, can perturb the orbits of the
streaming disk matter, occasionally producing rogue exoplanets that can reach
the inner confines of our Solar System. The similarity between the period of
spiral-arm crossings of our Solar System to the period of major extinction
events in the Phanerozoic Eon of the Earth's history, as well as to the period
of the supercontinent cycle (the so-called Wilson Cycle), indicates that the
global environment of the Milky Way Galaxy may have played a major role in
initiating Earth's past tectonic activities.
",0,1,0,0,0,0
20208,Reversing Parallel Programs with Blocks and Procedures,"  We show how to reverse a while language extended with blocks, local
variables, procedures and the interleaving parallel composition. Annotation is
defined along with a set of operational semantics capable of storing necessary
reversal information, and identifiers are introduced to capture the
interleaving order of an execution. Inversion is defined with a set of
operational semantics that use saved information to undo an execution. We prove
that annotation does not alter the behaviour of the original program, and that
inversion correctly restores the initial program state.
",1,0,0,0,0,0
20209,Detection of the Stellar Intracluster Medium in Perseus (Abell 426),"  Hubble Space Telescope photometry from the ACS/WFC and WFPC2 cameras is used
to detect and measure globular clusters (GCs) in the central region of the rich
Perseus cluster of galaxies. A detectable population of Intragalactic GCs is
found extending out to at least 500 kpc from the cluster center. These objects
display luminosity and color (metallicity) distributions that are entirely
normal for GC populations. Extrapolating from the limited spatial coverage of
the HST fields, we estimate very roughly that the entire Perseus cluster should
contain ~50000 or more IGCs, but a targetted wide-field survey will be needed
for a more definitive answer. Separate brief results are presented for the rich
GC systems in NGC 1272 and NGC 1275, the two largest Perseus ellipticals. For
NGC 1272 we find a specific frequency S_N = 8, while for the central giant NGC
1275, S_N ~ 12. In both these giant galaxies, the GC colors are well matched by
bimodal distributions, with the majority in the blue (metal-poor) component.
This preliminary study suggests that Perseus is a prime target for a more
comprehensive deep imaging survey of Intragalactic GCs.
",0,1,0,0,0,0
20210,Binets: fundamental building blocks for phylogenetic networks,"  Phylogenetic networks are a generalization of evolutionary trees that are
used by biologists to represent the evolution of organisms which have undergone
reticulate evolution. Essentially, a phylogenetic network is a directed acyclic
graph having a unique root in which the leaves are labelled by a given set of
species. Recently, some approaches have been developed to construct
phylogenetic networks from collections of networks on 2- and 3-leaved networks,
which are known as binets and trinets, respectively. Here we study in more
depth properties of collections of binets, one of the simplest possible types
of networks into which a phylogenetic network can be decomposed. More
specifically, we show that if a collection of level-1 binets is compatible with
some binary network, then it is also compatible with a binary level-1 network.
Our proofs are based on useful structural results concerning lowest stable
ancestors in networks. In addition, we show that, although the binets do not
determine the topology of the network, they do determine the number of
reticulations in the network, which is one of its most important parameters. We
also consider algorithmic questions concerning binets. We show that deciding
whether an arbitrary set of binets is compatible with some network is at least
as hard as the well-known Graph Isomorphism problem. However, if we restrict to
level-1 binets, it is possible to decide in polynomial time whether there
exists a binary network that displays all the binets. We also show that to find
a network that displays a maximum number of the binets is NP-hard, but that
there exists a simple polynomial-time 1/3-approximation algorithm for this
problem. It is hoped that these results will eventually assist in the
development of new methods for constructing phylogenetic networks from
collections of smaller networks.
",1,0,1,0,0,0
20211,G-Deformations of maps into projective space,"  $G$-deformability of maps into projective space is characterised by the
existence of certain Lie algebra valued 1-forms. This characterisation gives a
unified way to obtain well known results regarding deformability in different
geometries.
",0,0,1,0,0,0
20212,Cloudless atmospheres for young low-gravity substellar objects,"  Atmospheric modeling of low-gravity (VL-G) young brown dwarfs remains a
challenge. The presence of very thick clouds has been suggested because of
their extremely red near-infrared (NIR) spectra, but no cloud models provide a
good fit to the data with a radius compatible with evolutionary models for
these objects. We show that cloudless atmospheres assuming a temperature
gradient reduction caused by fingering convection provides a very good model to
match the observed VL-G NIR spectra. The sequence of extremely red colors in
the NIR for atmospheres with effective temperature from ~2000 K down to ~1200 K
is very well reproduced with predicted radii typical of young low-gravity
objects. Future observations with NIRSPEC and MIRI on the James Webb Space
Telescope (JWST) will provide more constrains in the mid-infrared, helping to
confirm/refute whether or not the NIR reddening is caused by fingering
convection. We suggest that the presence/absence of clouds will be directly
determined by the silicate absorption features that can be observed with MIRI.
JWST will therefore be able to better characterize the atmosphere of these hot
young brown dwarfs and their low-gravity exoplanet analogues.
",0,1,0,0,0,0
20213,On the State of the Art of Evaluation in Neural Language Models,"  Ongoing innovations in recurrent neural network architectures have provided a
steady influx of apparently state-of-the-art results on language modelling
benchmarks. However, these have been evaluated using differing code bases and
limited computational resources, which represent uncontrolled sources of
experimental variation. We reevaluate several popular architectures and
regularisation methods with large-scale automatic black-box hyperparameter
tuning and arrive at the somewhat surprising conclusion that standard LSTM
architectures, when properly regularised, outperform more recent models. We
establish a new state of the art on the Penn Treebank and Wikitext-2 corpora,
as well as strong baselines on the Hutter Prize dataset.
",1,0,0,0,0,0
20214,Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning,"  Separating audio mixtures into individual instrument tracks has been a long
standing challenging task. We introduce a novel weakly supervised audio source
separation approach based on deep adversarial learning. Specifically, our loss
function adopts the Wasserstein distance which directly measures the
distribution distance between the separated sources and the real sources for
each individual source. Moreover, a global regularization term is added to
fulfill the spectrum energy preservation property regardless separation. Unlike
state-of-the-art weakly supervised models which often involve deliberately
devised constraints or careful model selection, our approach need little prior
model specification on the data, and can be straightforwardly learned in an
end-to-end fashion. We show that the proposed method performs competitively on
public benchmark against state-of-the-art weakly supervised methods.
",1,0,0,0,0,0
20215,Proof of a conjecture of Kløve on permutation codes under the Chebychev distance,"  Let $d$ be a positive integer and $x$ a real number. Let $A_{d, x}$ be a
$d\times 2d$ matrix with its entries $$ a_{i,j}=\left\{ \begin{array}{ll} x\ \
& \mbox{for} \ 1\leqslant j\leqslant d+1-i, 1\ \ & \mbox{for} \ d+2-i\leqslant
j\leqslant d+i, 0\ \ & \mbox{for} \ d+1+i\leqslant j\leqslant 2d. \end{array}
\right. $$ Further, let $R_d$ be a set of sequences of integers as follows:
$$R_d=\{(\rho_1, \rho_2,\ldots, \rho_d)|1\leqslant \rho_i\leqslant d+i,
1\leqslant i \leqslant d,\ \mbox{and}\ \rho_r\neq \rho_s\ \mbox{for}\ r\neq
s\}.$$ and define $$\Omega_d(x)=\sum_{\rho\in R_d}a_{1,\rho_1}a_{2,
\rho_2}\ldots a_{d,\rho_d}.$$ In order to give a better bound on the size of
spheres of permutation codes under the Chebychev distance, Kl{\o}ve introduced
the above function and conjectured that $$\Omega_d(x)=\sum_{m=0}^d{d\choose
m}(m+1)^d(x-1)^{d-m}.$$ In this paper, we settle down this conjecture
positively.
",1,0,0,0,0,0
20216,Herschel observations of the Galactic HII region RCW 79,"  Triggered star formation around HII regions could be an important process.
The Galactic HII region RCW 79 is a prototypical object for triggered high-mass
star formation. We take advantage of Herschel data from the surveys HOBYS,
""Evolution of Interstellar Dust"", and Hi-Gal to extract compact sources in this
region, complemented with archival 2MASS, Spitzer, and WISE data to determine
the physical parameters of the sources (e.g., envelope mass, dust temperature,
and luminosity) by fitting the spectral energy distribution. We obtained a
sample of 50 compact sources, 96% of which are situated in the
ionization-compressed layer of cold and dense gas that is characterized by the
column density PDF with a double-peaked lognormal distribution. The 50 sources
have sizes of 0.1-0.4 pc with a typical value of 0.2 pc, temperatures of 11-26
K, envelope masses of 6-760 $M_\odot$, densities of 0.1-44 $\times$ $10^5$
cm$^{-3}$, and luminosities of 19-12712 $L_\odot$. The sources are classified
into 16 class 0, 19 intermediate, and 15 class I objects. Their distribution
follows the evolutionary tracks in the diagram of bolometric luminosity versus
envelope mass (Lbol-Menv) well. A mass threshold of 140 $M_\odot$, determined
from the Lbol-Menv diagram, yields 12 candidate massive dense cores that may
form high-mass stars. The core formation efficiency (CFE) for the 8 massive
condensations shows an increasing trend of the CFE with density. This suggests
that the denser the condensation, the higher the fraction of its mass
transformation into dense cores, as previously observed in other high-mass
star-forming regions.
",0,1,0,0,0,0
20217,Software-Defined Robotics -- Idea & Approach,"  The methodology of Software-Defined Robotics hierarchical-based and
stand-alone framework can be designed and implemented to program and control
different sets of robots, regardless of their manufacturers' parameters and
specifications, with unified commands and communications. This framework
approach will increase the capability of (re)programming a specific group of
robots during the runtime without affecting the others as desired in the
critical missions and industrial operations, expand the shared bandwidth,
enhance the reusability of code, leverage the computational processing power,
decrease the unnecessary analyses of vast supplemental electrical components
for each robot, as well as get advantages of the most state-of-the-art
industrial trends in the cloud-based computing, Virtual Machines (VM), and
Robot-as-a-Service (RaaS) technologies.
",1,0,0,0,0,0
20218,Stability interchanges in a curved Sitnikov problem,"  We consider a curved Sitnikov problem, in which an infinitesimal particle
moves on a circle under the gravitational influence of two equal masses in
Keplerian motion within a plane perpendicular to that circle. There are two
equilibrium points, whose stability we are studying. We show that one of the
equilibrium points undergoes stability interchanges as the semi-major axis of
the Keplerian ellipses approaches the diameter of that circle. To derive this
result, we first formulate and prove a general theorem on stability
interchanges, and then we apply it to our model. The motivation for our model
resides with the $n$-body problem in spaces of constant curvature.
",0,1,1,0,0,0
20219,Hardy Spaces over Half-strip Domains,"  We define Hardy spaces $H^p(\Omega_\pm)$ on half-strip domain~$\Omega_+$ and
$\Omega_-= \mathbb{C}\setminus\overline{\Omega_+}$, where $0<p<\infty$, and
prove that functions in $H^p(\Omega_\pm)$ has non-tangential boundary limit
a.e. on $\Gamma$, the common boundary of $\Omega_\pm$. We then prove that
Cauchy integral of functions in $L^p(\Gamma)$ are in $H^p(\Omega_\pm)$, where
$1<p<\infty$, that is, Cauchy transform is bounded. Besides, if $1\leqslant
p<\infty$, then $H^p(\Omega_\pm)$ functions are the Cauchy integral of their
non-tangential boundary limits. We also establish an isomorphism between
$H^p(\Omega_\pm)$ and $H^p(\mathbb{C}_\pm)$, the classical Hardy spaces over
upper and lower half complex planes.
",0,0,1,0,0,0
20220,An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming,"  In recent years, numerous vision and learning tasks have been (re)formulated
as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have
been proposed for particular problems, designing fast and flexible optimization
schemes with theoretical guarantee is a challenging task for general NNPs. It
has been investigated that performing inexact inner iterations often benefit to
special applications case by case, but their convergence behaviors are still
unclear. Motivated by these practical experiences, this paper designs a novel
algorithmic framework, named inexact proximal alternating direction method
(IPAD) for solving general NNPs. We demonstrate that any numerical algorithms
can be incorporated into IPAD for solving subproblems and the convergence of
the resulting hybrid schemes can be consistently guaranteed by a series of
simple error conditions. Beyond the guarantee in theory, numerical experiments
on both synthesized and real-world data further demonstrate the superiority and
flexibility of our IPAD framework for practical use.
",1,0,1,0,0,0
20221,Attitude and angular velocity tracking for a rigid body using geometric methods on the two-sphere,"  The control task of tracking a reference pointing direction (the attitude
about the pointing direction is irrelevant) while obtaining a desired angular
velocity (PDAV) around the pointing direction using geometric techniques is
addressed here. Existing geometric controllers developed on the two-sphere only
address the tracking of a reference pointing direction while driving the
angular velocity about the pointing direction to zero. In this paper a tracking
controller on the two-sphere, able to address the PDAV control task, is
developed globally in a geometric frame work, to avoid problems related to
other attitude representations such as unwinding (quaternions) or singularities
(Euler angles). An attitude error function is constructed resulting in a
control system with desired tracking performance for rotational maneuvers with
large initial attitude/angular velocity errors and the ability to negotiate
bounded modeling inaccuracies. The tracking ability of the developed control
system is evaluated by comparing its performance with an existing geometric
controller on the two-sphere and by numerical simulations, showing improved
performance for large initial attitude errors, smooth transitions between
desired angular velocities and the ability to negotiate bounded modeling
inaccuracies.
",1,0,1,0,0,0
20222,Miscomputation in software: Learning to live with errors,"  Computer programs do not always work as expected. In fact, ominous warnings
about the desperate state of the software industry continue to be released with
almost ritualistic regularity. In this paper, we look at the 60 years history
of programming and at the different practical methods that software community
developed to live with programming errors. We do so by observing a class of
students discussing different approaches to programming errors. While learning
about the different methods for dealing with errors, we uncover basic
assumptions that proponents of different paradigms follow. We learn about the
mathematical attempt to eliminate errors through formal methods, scientific
method based on testing, a way of building reliable systems through engineering
methods, as well as an artistic approach to live coding that accepts errors as
a creative inspiration. This way, we can explore the differences and
similarities among the different paradigms. By inviting proponents of different
methods into a single discussion, we hope to open potential for new thinking
about errors. When should we use which of the approaches? And what can software
development learn from mathematics, science, engineering and art? When
programming or studying programming, we are often enclosed in small communities
and we take our basic assumptions for granted. Through the discussion in this
paper, we attempt to map the large and rich space of programming ideas and
provide reference points for exploring, perhaps foreign, ideas that can
challenge some of our assumptions.
",1,0,0,0,0,0
20223,The Combinatorics of Weighted Vector Compositions,"  A vector composition of a vector $\mathbf{\ell}$ is a matrix $\mathbf{A}$
whose rows sum to $\mathbf{\ell}$. We define a weighted vector composition as a
vector composition in which the column values of $\mathbf{A}$ may appear in
different colors. We study vector compositions from different viewpoints: (1)
We show how they are related to sums of random vectors and (2) how they allow
to derive formulas for partial derivatives of composite functions. (3) We study
congruence properties of the number of weighted vector compositions, for fixed
and arbitrary number of parts, many of which are analogous to those of ordinary
binomial coefficients and related quantities. Via the Central Limit Theorem and
their multivariate generating functions, (4) we also investigate the asymptotic
behavior of several special cases of numbers of weighted vector compositions.
Finally, (5) we conjecture an extension of a primality criterion due to Mann
and Shanks in the context of weighted vector compositions.
",1,0,1,0,0,0
20224,Fast Linear Model for Knowledge Graph Embeddings,"  This paper shows that a simple baseline based on a Bag-of-Words (BoW)
representation learns surprisingly good knowledge graph embeddings. By casting
knowledge base completion and question answering as supervised classification
problems, we observe that modeling co-occurences of entities and relations
leads to state-of-the-art performance with a training time of a few minutes
using the open sourced library fastText.
",1,0,0,1,0,0
20225,Deep learning for plasma tomography using the bolometer system at JET,"  Deep learning is having a profound impact in many fields, especially those
that involve some form of image processing. Deep neural networks excel in
turning an input image into a set of high-level features. On the other hand,
tomography deals with the inverse problem of recreating an image from a number
of projections. In plasma diagnostics, tomography aims at reconstructing the
cross-section of the plasma from radiation measurements. This reconstruction
can be computed with neural networks. However, previous attempts have focused
on learning a parametric model of the plasma profile. In this work, we use a
deep neural network to produce a full, pixel-by-pixel reconstruction of the
plasma profile. For this purpose, we use the overview bolometer system at JET,
and we introduce an up-convolutional network that has been trained and tested
on a large set of sample tomograms. We show that this network is able to
reproduce existing reconstructions with a high level of accuracy, as measured
by several metrics.
",0,1,0,1,0,0
20226,Assembly Bias and Splashback in Galaxy Clusters,"  We use publicly available data for the Millennium Simulation to explore the
implications of the recent detection of assembly bias and splashback signatures
in a large sample of galaxy clusters. These were identified in the SDSS/DR8
photometric data by the redMaPPer algorithm and split into high- and
low-concentration subsamples based on the projected positions of cluster
members. We use simplified versions of these procedures to build cluster
samples of similar size from the simulation data. These match the observed
samples quite well and show similar assembly bias and splashback signals.
Previous theoretical work has found the logarithmic slope of halo density
profiles to have a well-defined minimum whose depth decreases and whose radius
increases with halo concentration. Projected profiles for the observed and
simulated cluster samples show trends with concentration which are opposite to
these predictions. In addition, for high-concentration clusters the minimum
slope occurs at significantly smaller radius than predicted. We show that these
discrepancies all reflect confusion between splashback features and features
imposed on the profiles by the cluster identification and concentration
estimation procedures. The strong apparent assembly bias is not reflected in
the three-dimensional distribution of matter around clusters. Rather it is a
consequence of the preferential contamination of low-concentration clusters by
foreground or background groups.
",0,1,0,0,0,0
20227,Accelerating Kernel Classifiers Through Borders Mapping,"  Support vector machines (SVM) and other kernel techniques represent a family
of powerful statistical classification methods with high accuracy and broad
applicability. Because they use all or a significant portion of the training
data, however, they can be slow, especially for large problems. Piecewise
linear classifiers are similarly versatile, yet have the additional advantages
of simplicity, ease of interpretation and, if the number of component linear
classifiers is not too large, speed. Here we show how a simple, piecewise
linear classifier can be trained from a kernel-based classifier in order to
improve the classification speed. The method works by finding the root of the
difference in conditional probabilities between pairs of opposite classes to
build up a representation of the decision boundary. When tested on 17 different
datasets, it succeeded in improving the classification speed of a SVM for 9 of
them by factors as high as 88 times or more. The method is best suited to
problems with continuum features data and smooth probability functions. Because
the component linear classifiers are built up individually from an existing
classifier, rather than through a simultaneous optimization procedure, the
classifier is also fast to train.
",1,0,0,1,0,0
20228,Information Criterion for Minimum Cross-Entropy Model Selection,"  This paper considers the problem of approximating a density when it can be
evaluated up to a normalizing constant at a finite number of points. This
density approximation problem is ubiquitous in machine learning, such as
approximating a posterior density for Bayesian inference and estimating an
optimal density for importance sampling. Approximating the density with a
parametric model can be cast as a model selection problem. This problem cannot
be addressed with traditional approaches that maximize the (marginal)
likelihood of a model, for example, using the Akaike information criterion
(AIC) or Bayesian information criterion (BIC). We instead aim to minimize the
cross-entropy that gauges the deviation of a parametric model from the target
density. We propose a novel information criterion called the cross-entropy
information criterion (CIC) and prove that the CIC is an asymptotically
unbiased estimator of the cross-entropy (up to a multiplicative constant) under
some regularity conditions. We propose an iterative method to approximate the
target density by minimizing the CIC. We demonstrate that the proposed method
selects a parametric model that well approximates the target density.
",0,0,0,1,0,0
20229,Scaling relations in large-Prandtl-number natural thermal convection,"  In this study we follow Grossmann and Lohse, Phys. Rev. Lett. 86 (2001), who
derived various scalings regimes for the dependence of the Nusselt number $Nu$
and the Reynolds number $Re$ on the Rayleigh number $Ra$ and the Prandtl number
$Pr$. We focus on theoretical arguments as well as on numerical simulations for
the case of large-$Pr$ natural thermal convection. Based on an analysis of
self-similarity of the boundary layer equations, we derive that in this case
the limiting large-$Pr$ boundary-layer dominated regime is I$_\infty^<$,
introduced and defined in [1], with the scaling relations $Nu\sim
Pr^0\,Ra^{1/3}$ and $Re\sim Pr^{-1}\,Ra^{2/3}$. Our direct numerical
simulations for $Ra$ from $10^4$ to $10^9$ and $Pr$ from 0.1 to 200 show that
the regime I$_\infty^<$ is almost indistinguishable from the regime
III$_\infty$, where the kinetic dissipation is bulk-dominated. With increasing
$Ra$, the scaling relations undergo a transition to those in IV$_u$ of
reference [1], where the thermal dissipation is determined by its bulk
contribution.
",0,1,0,0,0,0
20230,"The renormalization method from continuous to discrete dynamical systems: asymptotic solutions, reductions and invariant manifolds","  The renormalization method based on the Taylor expansion for asymptotic
analysis of differential equations is generalized to difference equations. The
proposed renormalization method is based on the Newton-Maclaurin expansion.
Several basic theorems on the renormalization method are proven. Some
interesting applications are given, including asymptotic solutions of quantum
anharmonic oscillator and discrete boundary layer, the reductions and invariant
manifolds of some discrete dynamics systems. Furthermore, the homotopy
renormalization method based on the Newton-Maclaurin expansion is proposed and
applied to those difference equations including no a small parameter.
",0,0,1,0,0,0
20231,A Stress/Displacement Virtual Element Method for Plane Elasticity Problems,"  The numerical approximation of 2D elasticity problems is considered, in the
framework of the small strain theory and in connection with the mixed
Hellinger-Reissner variational formulation. A low-order Virtual Element Method
(VEM) with a-priori symmetric stresses is proposed. Several numerical tests are
provided, along with a rigorous stability and convergence analysis.
",0,0,1,0,0,0
20232,Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions,"  In this paper, we consider the problem of estimating the lead-lag parameter
between two stochastic processes driven by fractional Brownian motions (fBMs)
of the Hurst parameter greater than 1/2. First we propose a lead-lag model
between two stochastic processes involving fBMs, and then construct a
consistent estimator of the lead-lag parameter with possible convergence rate.
Our estimator has the following two features. Firstly, we can construct the
lead-lag estimator without using the Hurst parameters of the underlying fBMs.
Secondly, our estimator can deal with some non-synchronous and irregular
observations. We explicitly calculate possible convergence rate when the
observation times are (1) synchronous and equidistant, and (2) given by the
Poisson sampling scheme. We also present numerical simulations of our results
using the R package YUIMA.
",0,0,1,1,0,0
20233,Multi-resolution polymer Brownian dynamics with hydrodynamic interactions,"  A polymer model given in terms of beads, interacting through Hookean springs
and hydrodynamic forces, is studied. Brownian dynamics description of this
bead-spring polymer model is extended to multiple resolutions. Using this
multiscale approach, a modeller can efficiently look at different regions of
the polymer in different spatial and temporal resolutions with scalings given
for the number of beads, statistical segment length and bead radius in order to
maintain macro-scale properties of the polymer filament. The Boltzmann
distribution of a Gaussian chain for differing statistical segment lengths
gives a Langevin equation for the multi-resolution model with a mobility tensor
for different bead sizes. Using the pre-averaging approximation, the
translational diffusion coefficient is obtained as a function of the inverse of
a matrix and then in closed form in the long-chain limit. This is then
confirmed with numerical experiments.
",0,1,0,0,0,0
20234,Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network,"  Distributed learning is an effective way to analyze big data. In distributed
regression, a typical approach is to divide the big data into multiple blocks,
apply a base regression algorithm on each of them, and then simply average the
output functions learnt from these blocks. Since the average process will
decrease the variance, not the bias, bias correction is expected to improve the
learning performance if the base regression algorithm is a biased one.
Regularization kernel network is an effective and widely used method for
nonlinear regression analysis. In this paper we will investigate a bias
corrected version of regularization kernel network. We derive the error bounds
when it is applied to a single data set and when it is applied as a base
algorithm in distributed regression. We show that, under certain appropriate
conditions, the optimal learning rates can be reached in both situations.
",1,0,0,1,0,0
20235,Cluster-based Kriging Approximation Algorithms for Complexity Reduction,"  Kriging or Gaussian Process Regression is applied in many fields as a
non-linear regression model as well as a surrogate model in the field of
evolutionary computation. However, the computational and space complexity of
Kriging, that is cubic and quadratic in the number of data points respectively,
becomes a major bottleneck with more and more data available nowadays. In this
paper, we propose a general methodology for the complexity reduction, called
cluster Kriging, where the whole data set is partitioned into smaller clusters
and multiple Kriging models are built on top of them. In addition, four Kriging
approximation algorithms are proposed as candidate algorithms within the new
framework. Each of these algorithms can be applied to much larger data sets
while maintaining the advantages and power of Kriging. The proposed algorithms
are explained in detail and compared empirically against a broad set of
existing state-of-the-art Kriging approximation methods on a well-defined
testing framework. According to the empirical study, the proposed algorithms
consistently outperform the existing algorithms. Moreover, some practical
suggestions are provided for using the proposed algorithms.
",1,0,0,1,0,0
20236,How to model fake news,"  Over the past three years it has become evident that fake news is a danger to
democracy. However, until now there has been no clear understanding of how to
define fake news, much less how to model it. This paper addresses both these
issues. A definition of fake news is given, and two approaches for the
modelling of fake news and its impact in elections and referendums are
introduced. The first approach, based on the idea of a representative voter, is
shown to be suitable to obtain a qualitative understanding of phenomena
associated with fake news at a macroscopic level. The second approach, based on
the idea of an election microstructure, describes the collective behaviour of
the electorate by modelling the preferences of individual voters. It is shown
through a simulation study that the mere knowledge that pieces of fake news may
be in circulation goes a long way towards mitigating the impact of fake news.
",1,0,0,0,0,1
20237,Analysis of Dropout in Online Learning,"  Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of layers
and a huge number of units and connections. Therefore, overfitting is a serious
problem with it, and the dropout which is a kind of regularization tool is
used. However, in online learning, the effect of dropout is not well known.
This paper presents our investigation on the effect of dropout in online
learning. We analyzed the effect of dropout on convergence speed near the
singular point. Our results indicated that dropout is effective in online
learning. Dropout tends to avoid the singular point for convergence speed near
that point.
",1,0,0,1,0,0
20238,TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease,"  The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
Challenge compares the performance of algorithms at predicting future evolution
of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants
train their models and algorithms on historical data from the Alzheimer's
Disease Neuroimaging Initiative (ADNI) study or any other datasets to which
they have access. Participants are then required to make monthly forecasts over
a period of 5 years from January 2018, of three key outcomes for ADNI-3
rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale
Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These
individual forecasts are later compared with the corresponding future
measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The
first submission phase of TADPOLE was open for prize-eligible submissions
between 15 June and 15 November 2017. The submission system remains open via
the website: this https URL, although since 15 November
2017 submissions are not eligible for the first round of prizes. This paper
describes the design of the TADPOLE Challenge.
",0,0,0,1,1,0
20239,A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction,"  The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian undersampling of 2D cardiac MR images, the proposed
method outperforms the state-of-the-art compressed sensing approaches, such as
dictionary learning-based MRI (DLMRI) reconstruction, in terms of
reconstruction error, perceptual quality and reconstruction speed for both
3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
method proposed is approximately twice as small, allowing to preserve
anatomical structures more faithfully. Using our method, each image can be
reconstructed in 23 ms, which is fast enough to enable real-time applications.
",1,0,0,0,0,0
20240,Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM,"  We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
model. We learn to listen and write characters with a joint Connectionist
Temporal Classification (CTC) and attention-based encoder-decoder network. The
encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
The CTC network sits on top of the encoder and is jointly trained with the
attention-based decoder. During the beam search process, we combine the CTC
predictions, the attention-based decoder predictions and a separately trained
LSTM language model. We achieve a 5-10\% error reduction compared to prior
systems on spontaneous Japanese and Chinese speech, and our end-to-end model
beats out traditional hybrid ASR systems.
",1,0,0,0,0,0
20241,The Social and Work Structure of an Afterschool Math Club,"  This study focuses on the social structure and interpersonal dynamics of an
afterschool math club for middle schoolers. Using social network analysis, two
networks were formed and analyzed: The network of friendship relationships and
the network of working relationships. The interconnections and correlations
between friendship relationships, working relationships, and student opinion
surveys are studied. A core working group of talented students emerged from the
network of working relations. This group acted a central go-between for other
members in the club. This core working group expanded into largest friendship
group in the friendship network. Although there were working isolates, they
were not found to be socially isolated. Students who were less popular tended
to report a greater favorable impact from club participation. Implications for
the study of the social structure of afterschool STEM clubs and classrooms are
discussed.
",0,0,1,0,0,0
20242,Fourier Transform of Schwartz Algebras on Groups in the Harish-Chandra class,"  It is well-known that the Harish-Chandra transform, $f\mapsto\mathcal{H}f,$
is a topological isomorphism of the spherical (Schwartz) convolution algebra
$\mathcal{C}^{p}(G//K)$ (where $K$ is a maximal compact subgroup of any
arbitrarily chosen group $G$ in the Harish-Chandra class and $0<p\leq2$) onto
the (Schwartz) multiplication algebra
$\bar{\mathcal{Z}}({\mathfrak{F}}^{\epsilon})$ (of $\mathfrak{w}-$invariant
members of $\mathcal{Z}({\mathfrak{F}}^{\epsilon}),$ with $\epsilon=(2/p)-1$).
The same cannot however be said of the full Schwartz convolution algebra
$\mathcal{C}^{p}(G),$ except for few specific examples of groups (notably
$G=SL(2,\mathbb{R})$) and for some notable values of $p$ (with restrictions on
$G$ and/or on $\mathcal{C}^{p}(G)$). Nevertheless the full Harish-Chandra
Plancherel formula on $G$ is known for all of
$\mathcal{C}^{2}(G)=:\mathcal{C}(G).$ In order to then understand the structure
of Harish-Chandra transform more clearly and to compute the image of
$\mathcal{C}^{p}(G)$ under it (without any restriction) we derive an absolutely
convergent series expansion (in terms of known functions) for the
Harish-Chandra transform by an application of the full Plancherel formula on
$G.$ This leads to a computation of the image of $\mathcal{C}(G)$ under the
Harish-Chandra transform which may be seen as a concrete realization of
Arthur's result and be easily extended to all of $\mathcal{C}^{p}(G)$ in much
the same way as it is known in the work of Trombi and Varadarajan.
",0,0,1,0,0,0
20243,Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation,"  We consider the estimation of a signal from the knowledge of its noisy linear
random Gaussian projections. A few examples where this problem is relevant are
compressed sensing, sparse superposition codes, and code division multiple
access. There has been a number of works considering the mutual information for
this problem using the replica method from statistical physics. Here we put
these considerations on a firm rigorous basis. First, we show, using a
Guerra-Toninelli type interpolation, that the replica formula yields an upper
bound to the exact mutual information. Secondly, for many relevant practical
cases, we present a converse lower bound via a method that uses spatial
coupling, state evolution analysis and the I-MMSE theorem. This yields a single
letter formula for the mutual information and the minimal-mean-square error for
random Gaussian linear estimation of all discrete bounded signals. In addition,
we prove that the low complexity approximate message-passing algorithm is
optimal outside of the so-called hard phase, in the sense that it
asymptotically reaches the minimal-mean-square error. In this work spatial
coupling is used primarily as a proof technique. However our results also prove
two important features of spatially coupled noisy linear random Gaussian
estimation. First there is no algorithmically hard phase. This means that for
such systems approximate message-passing always reaches the minimal-mean-square
error. Secondly, in a proper limit the mutual information associated to such
systems is the same as the one of uncoupled linear random Gaussian estimation.
",1,1,1,0,0,0
20244,A Memristor-Based Optimization Framework for AI Applications,"  Memristors have recently received significant attention as ubiquitous
device-level components for building a novel generation of computing systems.
These devices have many promising features, such as non-volatility, low power
consumption, high density, and excellent scalability. The ability to control
and modify biasing voltages at the two terminals of memristors make them
promising candidates to perform matrix-vector multiplications and solve systems
of linear equations. In this article, we discuss how networks of memristors
arranged in crossbar arrays can be used for efficiently solving optimization
and machine learning problems. We introduce a new memristor-based optimization
framework that combines the computational merit of memristor crossbars with the
advantages of an operator splitting method, alternating direction method of
multipliers (ADMM). Here, ADMM helps in splitting a complex optimization
problem into subproblems that involve the solution of systems of linear
equations. The capability of this framework is shown by applying it to linear
programming, quadratic programming, and sparse optimization. In addition to
ADMM, implementation of a customized power iteration (PI) method for
eigenvalue/eigenvector computation using memristor crossbars is discussed. The
memristor-based PI method can further be applied to principal component
analysis (PCA). The use of memristor crossbars yields a significant speed-up in
computation, and thus, we believe, has the potential to advance optimization
and machine learning research in artificial intelligence (AI).
",1,0,0,1,0,0
20245,Estimating parameters of a directed weighted graph model with beta-distributed edge-weights,"  We introduce a directed, weighted random graph model, where the edge-weights
are independent and beta-distributed with parameters depending on their
endpoints. We will show that the row- and column-sums of the transformed
edge-weight matrix are sufficient statistics for the parameters, and use the
theory of exponential families to prove that the ML estimate of the parameters
exists and is unique. Then an algorithm to find this estimate is introduced
together with convergence proof that uses properties of the digamma function.
Simulation results and applications are also presented.
",0,0,1,1,0,0
20246,On the Performance of Reduced-Complexity Transmit/Receive Diversity Systems over MIMO-V2V Channel Model,"  In this letter, we investigate the performance of multiple-input
multiple-output techniques in a vehicle-to-vehicle communication system. We
consider both transmit antenna selection with maximal-ratio combining and
transmit antenna selection with selection combining. The channel propagation
model between two vehicles is represented as n*Rayleigh distribution, which has
been shown to be a realistic model for vehicle-to-vehicle communication
scenarios. We derive tight analytical expressions for the outage probability
and amount of fading of the post-processing signal-to-noise ratio.
",1,0,0,0,0,0
20247,Internal migration and education: A cross-national comparison,"  Migration the main process shaping patterns of human settlement within and
between countries. It is widely acknowledged to be integral to the process of
human development as it plays a significant role in enhancing educational
outcomes. At regional and national levels, internal migration underpins the
efficient functioning of the economy by bringing knowledge and skills to the
locations where they are needed. It is the multi-dimensional nature of
migration that underlines its significance in the process of human development.
Human mobility extends in the spatial domain from local travel to international
migration, and in the temporal dimension from short-term stays to permanent
relocations. Classification and measurement of such phenomena is inevitably
complex, which has severely hindered progress in comparative research, with
very few large-scale cross-national comparisons of migration. The linkages
between migration and education have been explored in a separate line of
inquiry that has predominantly focused on country-specific analyses as to the
ways in which migration affects educational outcomes and how educational
attainment affects migration behaviour. A recurrent theme has been the
educational selectivity of migrants, which in turn leads to an increase of
human capital in some regions, primarily cities, at the expense of others.
Questions have long been raised as to the links between education and migration
in response to educational expansion, but have not yet been fully answered
because of the absence, until recently, of adequate data for comparative
analysis of migration. In this paper, we bring these two separate strands of
research together to systematically explore links between internal migration
and education across a global sample of 57 countries at various stages of
development, using data drawn from the IPUMS database.
",0,0,0,0,0,1
20248,Free differential Lie Rota-Baxter algebras and Gröbner-Shirshov bases,"  We establish the Gröbner-Shirshov bases theory for differential Lie
$\Omega$-algebras. As an application, we give a linear basis of a free
differential Lie Rota-Baxter algebra on a set.
",0,0,1,0,0,0
20249,Conversion Rate Optimization through Evolutionary Computation,"  Conversion optimization means designing a web interface so that as many users
as possible take a desired action on it, such as register or purchase. Such
design is usually done by hand, testing one change at a time through A/B
testing, or a limited number of combinations through multivariate testing,
making it possible to evaluate only a small fraction of designs in a vast
design space. This paper describes Sentient Ascend, an automatic conversion
optimization system that uses evolutionary optimization to create effective web
interface designs. Ascend makes it possible to discover and utilize
interactions between the design elements that are difficult to identify
otherwise. Moreover, evaluation of design candidates is done in parallel
online, i.e. with a large number of real users interacting with the system. A
case study on an existing media site shows that significant improvements (i.e.
over 43%) are possible beyond human design. Ascend can therefore be seen as an
approach to massively multivariate conversion optimization, based on a
massively parallel interactive evolution.
",1,0,0,0,0,0
20250,Emergence of spatial curvature,"  This paper investigates the phenomenon of emergence of spatial curvature.
This phenomenon is absent in the Standard Cosmological Model, which has a flat
and fixed spatial curvature (small perturbations are considered in the Standard
Cosmological Model but their global average vanishes, leading to spatial
flatness at all times). This paper shows that with the nonlinear growth of
cosmic structures the global average deviates from zero. The analysis is based
on the {\em silent universes} (a wide class of inhomogeneous cosmological
solutions of the Einstein equations). The initial conditions are set in the
early universe as perturbations around the $\Lambda$CDM model with $\Omega_m =
0.31$, $\Omega_\Lambda = 0.69$, and $H_0 = 67.8$ km s$^{-1}$ Mpc$^{-1}$. As the
growth of structures becomes nonlinear, the model deviates from the
$\Lambda$CDM model, and at the present instant if averaged over a domain ${\cal
D}$ with volume $V = (2150\,{\rm Mpc})^3$ (at these scales the cosmic variance
is negligibly small) gives: $\Omega_m^{\cal D} = 0.22$, $\Omega_\Lambda^{\cal
D} = 0.61$, $\Omega_{\cal R}^{\cal D} = 0.15$ (in the FLRW limit $\Omega_{\cal
R}^{\cal D} \to \Omega_k$), and $\langle H \rangle_{\cal D} = 72.2$ km s$^{-1}$
Mpc$^{-1}$. Given the fact that low-redshift observations favor higher values
of the Hubble constant and lower values of matter density, compared to the CMB
constraints, the emergence of the spatial curvature in the low-redshift
universe could be a possible solution to these discrepancies.
",0,1,0,0,0,0
20251,Asymptotic generalized bivariate extreme with random index,"  In many biological, agricultural, military activity problems and in some
quality control problems, it is almost impossible to have a fixed sample size,
because some observations are always lost for various reasons. Therefore, the
sample size itself is considered frequently to be a random variable (rv). The
class of limit distribution functions (df's) of the random bivariate extreme
generalized order statistics (GOS) from independent and identically distributed
RV's are fully characterized. When the random sample size is assumed to be
independent of the basic variables and its df is assumed to converge weakly to
a non-degenerate limit, the necessary and sufficient conditions for the weak
convergence of the random bivariate extreme GOS are obtained. Furthermore, when
the interrelation of the random size and the basic rv's is not restricted,
sufficient conditions for the convergence and the forms of the limit df's are
deduced. Illustrative examples are given which lend further support to our
theoretical results.
",0,0,1,1,0,0
20252,On problems in the calculus of variations in increasingly elongated domains,"  We consider minimization problems in the calculus of variations set in a
sequence of domains the size of which tends to infinity in certain directions
and such that the data only depend on the coordinates in the directions that
remain constant. We study the asymptotic behavior of minimizers in various
situations and show that they converge in an appropriate sense toward
minimizers of a related energy functional in the constant directions.
",0,0,1,0,0,0
20253,Inequalities related to Symmetrized Harmonic Convex Functions,"  In this paper, we extend the Hermite-Hadamard type $\dot{I}$scan inequality
to the class of symmetrized harmonic convex functions. The corresponding
version for harmonic h-convex functions is also investigated. Furthermore, we
establish Hermite-Hadamard type inequalites for the product of a harmonic
convex function with a symmetrized harmonic convex function.
",0,0,1,0,0,0
20254,Parameter estimation for fractional Ornstein-Uhlenbeck processes of general Hurst parameter,"  This paper provides several statistical estimators for the drift and
volatility parameters of an Ornstein-Uhlenbeck process driven by fractional
Brownian motion, whose observations can be made either continuously or at
discrete time instants. First and higher order power variations are used to
estimate the volatility parameter. The almost sure convergence of the
estimators and the corresponding central limit theorems are obtained for all
the Hurst parameter range $H\in (0, 1)$. The least squares estimator is used
for the drift parameter. A central limit theorem is proved when the Hurst
parameter $H \in (0, 1/2)$ and a noncentral limit theorem is proved for
$H\in[3/4, 1)$. Thus, the open problem left in the paper by Hu and Nualart
(2010) is completely solved, where a central limit theorem for least squares
estimator is proved for $H\in [1/2, 3/4)$.
",0,0,1,1,0,0
20255,"E-polynomials of $PGL(2,\mathbb{C})$-character varieties of surface groups","  In this paper, we compute the E-polynomials of the
$PGL(2,\mathbb{C})$-character varieties associated to surfaces of genus $g$
with one puncture, for any holonomy around it, and compare it with its
Langlands dual case, $SL(2,\mathbb{C})$. The study is based on the
stratification of the space of representations and on the analysis of the
behaviour of the E-polynomial under fibrations.
",0,0,1,0,0,0
20256,Security Analysis of Cache Replacement Policies,"  Modern computer architectures share physical resources between different
programs in order to increase area-, energy-, and cost-efficiency.
Unfortunately, sharing often gives rise to side channels that can be exploited
for extracting or transmitting sensitive information. We currently lack
techniques for systematic reasoning about this interplay between security and
efficiency. In particular, there is no established way for quantifying security
properties of shared caches.
In this paper, we propose a novel model that enables us to characterize
important security properties of caches. Our model encompasses two aspects: (1)
The amount of information that can be absorbed by a cache, and (2) the amount
of information that can effectively be extracted from the cache by an
adversary. We use our model to compute both quantities for common cache
replacement policies (FIFO, LRU, and PLRU) and to compare their isolation
properties. We further show how our model for information extraction leads to
an algorithm that can be used to improve the bounds delivered by the CacheAudit
static analyzer.
",1,0,0,0,0,0
20257,Supercongruences related to ${}_3F_2(1)$ involving harmonic numbers,"  We show various supercongruences for truncated series which involve central
binomial coefficients and harmonic numbers. The corresponding infinite series
are also evaluated.
",0,0,1,0,0,0
20258,Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition,"  Layer normalization is a recently introduced technique for normalizing the
activities of neurons in deep neural networks to improve the training speed and
stability. In this paper, we introduce a new layer normalization technique
called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling
in speech recognition. By dynamically generating the scaling and shifting
parameters in layer normalization, DLN adapts neural acoustic models to the
acoustic variability arising from various factors such as speakers, channel
noises, and environments. Unlike other adaptive acoustic models, our proposed
approach does not require additional adaptation data or speaker information
such as i-vectors. Moreover, the model size is fixed as it dynamically
generates adaptation parameters. We apply our proposed DLN to deep
bidirectional LSTM acoustic models and evaluate them on two benchmark datasets
for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The
experimental results show that our DLN improves neural acoustic models in terms
of transcription accuracy by dynamically adapting to various speakers and
environments.
",1,0,0,0,0,0
20259,First-Order vs. Second-Order Encodings for LTLf-to-Automata Translation,"  Translating formulas of Linear Temporal Logic (LTL) over finite traces, or
LTLf, to symbolic Deterministic Finite Automata (DFA) plays an important role
not only in LTLf synthesis, but also in synthesis for Safety LTL formulas. The
translation is enabled by using MONA, a powerful tool for symbolic, BDD-based,
DFA construction from logic specifications. Recent works used a first-order
encoding of LTLf formulas to translate LTLf to First Order Logic (FOL), which
is then fed to MONA to get the symbolic DFA. This encoding was shown to perform
well, but other encodings have not been studied. Specifically, the natural
question of whether second-order encoding, which has significantly simpler
quantificational structure, can outperform first-order encoding remained open.
In this paper we address this challenge and study second-order encodings for
LTLf formulas. We first introduce a specific MSO encoding that captures the
semantics of LTLf in a natural way and prove its correctness. We then explore
is a Compact MSO encoding, which benefits from automata-theoretic minimization,
thus suggesting a possible practical advantage. To that end, we propose a
formalization of symbolic DFA in second-order logic, thus developing a novel
connection between BDDs and MSO. We then show by empirical evaluations that the
first-order encoding does perform better than both second-order encodings. The
conclusion is that first-order encoding is a better choice than second-order
encoding in LTLf-to-Automata translation.
",1,0,0,0,0,0
20260,Regularity results and parametrices of semi-linear boundary problems of product type,"  This short note describes the benefit one obtains from a specific
construction of a family of parametrices for a class of elliptic boundary value
problems perturbed by non-linear terms of product type. The construction is
based on the Boutet de Monvel calculus of pseudo-differential boundary
operators for the linear elliptic parts, and on paradifferential operators for
the product terms.
",0,0,1,0,0,0
20261,"$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds","  In this paper, we introduce a new concept of stability for cross-validation,
called the $\left( \beta, \varpi \right)$-stability, and use it as a new
perspective to build the general theory for cross-validation. The $\left(
\beta, \varpi \right)$-stability mathematically connects the generalization
ability and the stability of the cross-validated model via the Rademacher
complexity. Our result reveals mathematically the effect of cross-validation
from two sides: on one hand, cross-validation picks the model with the best
empirical generalization ability by validating all the alternatives on test
sets; on the other hand, cross-validation may compromise the stability of the
model selection by causing subsampling error. Moreover, the difference between
training and test errors in q\textsuperscript{th} round, sometimes referred to
as the generalization error, might be autocorrelated on q. Guided by the ideas
above, the $\left( \beta, \varpi \right)$-stability help us derivd a new class
of Rademacher bounds, referred to as the one-round/convoluted Rademacher
bounds, for the stability of cross-validation in both the i.i.d.\ and
non-i.i.d.\ cases. For both light-tail and heavy-tail losses, the new bounds
quantify the stability of the one-round/average test error of the
cross-validated model in terms of its one-round/average training error, the
sample sizes $n$, number of folds $K$, the tail property of the loss (encoded
as Orlicz-$\Psi_\nu$ norms) and the Rademacher complexity of the model class
$\Lambda$. The new class of bounds not only quantitatively reveals the
stability of the generalization ability of the cross-validated model, it also
shows empirically the optimal choice for number of folds $K$, at which the
upper bound of the one-round/average test error is lowest, or, to put it in
another way, where the test error is most stable.
",1,0,1,1,0,0
20262,Directional convexity of harmonic mappings,"  The convolution properties are discussed for the complex-valued harmonic
functions in the unit disk $\mathbb{D}$ constructed from the harmonic shearing
of the analytic function $\phi(z):=\int_0^z
(1/(1-2\xi\textit{e}^{\textit{i}\mu}\cos\nu+\xi^2\textit{e}^{2\textit{i}\mu}))\textit{d}\xi$,
where $\mu$ and $\nu$ are real numbers. For any real number $\alpha$ and
harmonic function $f=h+\overline{g}$, define an analytic function
$f_{\alpha}:=h+\textit{e}^{-2\textit{i}\alpha}g$. Let $\mu_1$ and $\mu_2$
$(\mu_1+\mu_2=\mu)$ be real numbers, and $f=h+\overline{g}$ and
$F=H+\overline{G}$ be locally-univalent and sense-preserving harmonic functions
such that $f_{\mu_1}*F_{\mu_2}=\phi$. It is shown that the convolution $f*F$ is
univalent and convex in the direction of $-\mu$, provided it is locally
univalent and sense-preserving. Also, local-univalence of the above convolution
$f*F$ is shown for some specific analytic dilatations of $f$ and $F$.
Furthermore, if $g\equiv0$ and both the analytic functions $f_{\mu_1}$ and
$F_{\mu_2}$ are convex, then the convolution $f*F$ is shown to be convex. These
results extends the work done by Dorff \textit{et al.} to a larger class of
functions.
",0,0,1,0,0,0
20263,Optimal Non-uniform Deployments in Ultra-Dense Finite-Area Cellular Networks,"  Network densification and heterogenisation through the deployment of small
cellular access points (picocells and femtocells) are seen as key mechanisms in
handling the exponential increase in cellular data traffic. Modelling such
networks by leveraging tools from Stochastic Geometry has proven particularly
useful in understanding the fundamental limits imposed on network coverage and
capacity by co-channel interference. Most of these works however assume
infinite sized and uniformly distributed networks on the Euclidean plane. In
contrast, we study finite sized non-uniformly distributed networks, and find
the optimal non-uniform distribution of access points which maximises network
coverage for a given non-uniform distribution of mobile users, and vice versa.
",1,0,0,0,0,0
20264,"Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder","  We investigate the integration of a planning mechanism into an
encoder-decoder architecture with an explicit alignment for character-level
machine translation. We develop a model that plans ahead when it computes
alignments between the source and target sequences, constructing a matrix of
proposed future alignments and a commitment vector that governs whether to
follow or recompute the plan. This mechanism is inspired by the strategic
attentive reader and writer (STRAW) model. Our proposed model is end-to-end
trainable with fully differentiable operations. We show that it outperforms a
strong baseline on three character-level decoder neural machine translation on
WMT'15 corpus. Our analysis demonstrates that our model can compute
qualitatively intuitive alignments and achieves superior performance with fewer
parameters.
",1,0,0,0,0,0
20265,Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding,"  In this paper, we propose a novel recurrent neural network architecture for
speech separation. This architecture is constructed by unfolding the iterations
of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
optimization problem for sparse nonnegative matrix factorization (NMF) of
spectrograms. We name this network architecture deep recurrent NMF (DR-NMF).
The proposed DR-NMF network has three distinct advantages. First, DR-NMF
provides better interpretability than other deep architectures, since the
weights correspond to NMF model parameters, even after training. This
interpretability also provides principled initializations that enable faster
training and convergence to better solutions compared to conventional random
initialization. Second, like many deep networks, DR-NMF is an order of
magnitude faster at test time than NMF, since computation of the network output
only requires evaluating a few layers at each time step. Third, when a limited
amount of training data is available, DR-NMF exhibits stronger generalization
and separation performance compared to sparse NMF and state-of-the-art
long-short term memory (LSTM) networks. When a large amount of training data is
available, DR-NMF achieves lower yet competitive separation performance
compared to LSTM networks.
",1,0,0,1,0,0
20266,Birth of isolated nested cylinders and limit cycles in 3D piecewise smooth vector fields with symmetry,"  Our start point is a 3D piecewise smooth vector field defined in two zones
and presenting a shared fold curve for the two smooth vector fields considered.
Moreover, these smooth vector fields are symmetric relative to the fold curve,
giving raise to a continuum of nested topological cylinders such that each
orthogonal section of these cylinders is filled by centers. First we prove that
the normal form considered represents a whole class of piecewise smooth vector
fields. After we perturb the initial model in order to obtain exactly
$\mathcal{L}$ invariant planes containing centers. A second perturbation of the
initial model also is considered in order to obtain exactly $k$ isolated
cylinders filled by periodic orbits. Finally, joining the two previous
bifurcations we are able to exhibit a model, preserving the symmetry relative
to the fold curve, and having exactly $k.\mathcal{L}$ limit cycles.
",0,0,1,0,0,0
20267,The difficulty of folding self-folding origami,"  Why is it difficult to refold a previously folded sheet of paper? We show
that even crease patterns with only one designed folding motion inevitably
contain an exponential number of `distractor' folding branches accessible from
a bifurcation at the flat state. Consequently, refolding a sheet requires
finding the ground state in a glassy energy landscape with an exponential
number of other attractors of higher energy, much like in models of protein
folding (Levinthal's paradox) and other NP-hard satisfiability (SAT) problems.
As in these problems, we find that refolding a sheet requires actuation at
multiple carefully chosen creases. We show that seeding successful folding in
this way can be understood in terms of sub-patterns that fold when cut out
(`folding islands'). Besides providing guidelines for the placement of active
hinges in origami applications, our results point to fundamental limits on the
programmability of energy landscapes in sheets.
",0,1,0,0,0,0
20268,Integrable Trotterization: Local Conservation Laws and Boundary Driving,"  We discuss a general procedure to construct an integrable real-time
trotterization of interacting lattice models. As an illustrative example we
consider a spin-$1/2$ chain, with continuous time dynamics described by the
isotropic ($XXX$) Heisenberg Hamiltonian. For periodic boundary conditions
local conservation laws are derived from an inhomogeneous transfer matrix and a
boost operator is constructed. In the continuous time limit these local charges
reduce to the known integrals of motion of the Heisenberg chain. In a simple
Kraus representation we also examine the nonequilibrium setting, where our
integrable cellular automaton is driven by stochastic processes at the
boundaries. We show explicitly, how an exact nonequilibrium steady state
density matrix can be written in terms of a staggered matrix product ansatz.
This simple trotterization scheme, in particular in the open system framework,
could prove to be a useful tool for experimental simulations of the lattice
models in terms of trapped ion and atom optics setups.
",0,1,0,0,0,0
20269,The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation,"  The Hawkes process is a class of point processes whose future depends on its
own history. Previous theoretical work on the Hawkes process is limited to the
case of a mutually-exciting process, in which a past event can only increase
the occurrence of future events. However, in neuronal networks and other
real-world applications, inhibitory relationships may be present. In this
paper, we develop a new approach for establishing the properties of the Hawkes
process without the restriction to mutual excitation. To this end, we employ a
thinning process representation and a coupling construction to bound the
dependence coefficient of the Hawkes process. Using recent developments on
weakly dependent sequences, we establish a concentration inequality for
second-order statistics of the Hawkes process. We apply this concentration
inequality in order to establish theoretical results for penalized regression
and clustering analysis in the high-dimensional regime. Our theoretical results
are corroborated by simulation studies and an application to a neuronal spike
train data set.
",0,0,0,1,0,0
20270,Aerial-Ground collaborative sensing: Third-Person view for teleoperation,"  Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.
",1,0,0,0,0,0
20271,A universal thin film model for Ginzburg-Landau energy with dipolar interaction,"  We present an analytical treatment of a three-dimensional variational model
of a system that exhibits a second-order phase transition in the presence of
dipolar interactions. Within the framework of Ginzburg-Landau theory, we
concentrate on the case in which the domain occupied by the sample has the
shape of a flat thin film and obtain a reduced two-dimensional, non-local
variational model that describes the energetics of the system in terms of the
order parameter averages across the film thickness. Namely, we show that the
reduced two-dimensional model is in a certain sense asymptotically equivalent
to the original three-dimensional model for small film thicknesses. Using this
asymptotic equivalence, we analyze two different thin film limits for the full
three-dimensional model via the methods of $\Gamma$-convergence applied to the
reduced two-dimensional model. In the first regime, in which the film thickness
vanishes while all other parameters remain fixed, we recover the local
two-dimensional Ginzburg-Landau model. On the other hand, when the film
thickness vanishes while the sample's lateral dimensions diverge at the right
rate, we show that the system exhibits a transition from homogeneous to
spatially modulated global energy minimizers. We identify a sharp threshold for
this transition.
",0,1,1,0,0,0
20272,LOCATA challenge: speaker localization with a planar array,"  This document describes our submission to the 2018 LOCalization And TrAcking
(LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker
using the Global Coherence Field (GCF) computed from multiple microphone pairs
of a DICIT planar array. One of the main challenges when using such an array
with omnidirectional microphones is the front-back ambiguity, which is
particularly evident in Task 5. We address this challenge by post-processing
the peaks of the GCF and exploiting the attenuation introduced by the frame of
the array. Moreover, the intermittent nature of speech and the changing
orientation of the speaker make localization difficult. For Tasks 3 and 5, we
also employ a Particle Filter (PF) that favors the spatio-temporal continuity
of the localization results.
",1,0,0,0,0,0
20273,Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers,"  The number of component classifiers chosen for an ensemble greatly impacts
the prediction ability. In this paper, we use a geometric framework for a
priori determining the ensemble size, which is applicable to most of existing
batch and online ensemble classifiers. There are only a limited number of
studies on the ensemble size examining Majority Voting (MV) and Weighted
Majority Voting (WMV). Almost all of them are designed for batch-mode, hardly
addressing online environments. Big data dimensions and resource limitations,
in terms of time and memory, make determination of ensemble size crucial,
especially for online environments. For the MV aggregation rule, our framework
proves that the more strong components we add to the ensemble, the more
accurate predictions we can achieve. For the WMV aggregation rule, our
framework proves the existence of an ideal number of components, which is equal
to the number of class labels, with the premise that components are completely
independent of each other and strong enough. While giving the exact definition
for a strong and independent classifier in the context of an ensemble is a
challenging task, our proposed geometric framework provides a theoretical
explanation of diversity and its impact on the accuracy of predictions. We
conduct a series of experimental evaluations to show the practical value of our
theorems and existing challenges.
",1,0,0,1,0,0
20274,Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers,"  Low-rank modeling has many important applications in computer vision and
machine learning. While the matrix rank is often approximated by the convex
nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
better empirical performance. However, the resulting optimization problem is
much more challenging. Recent state-of-the-art requires an expensive full SVD
in each iteration. In this paper, we show that for many commonly-used nonconvex
low-rank regularizers, a cutoff can be derived to automatically threshold the
singular values obtained from the proximal operator. This allows such operator
being efficiently approximated by power method. Based on it, we develop a
proximal gradient algorithm (and its accelerated variant) with inexact proximal
splitting and prove that a convergence rate of O(1/T) where T is the number of
iterations is guaranteed. Furthermore, we show the proposed algorithm can be
well parallelized, which achieves nearly linear speedup w.r.t the number of
threads. Extensive experiments are performed on matrix completion and robust
principal component analysis, which shows a significant speedup over the
state-of-the-art. Moreover, the matrix solution obtained is more accurate and
has a lower rank than that of the nuclear norm regularizer.
",1,0,0,1,0,0
20275,Computational Eco-Systems for Handwritten Digits Recognition,"  Inspired by the importance of diversity in biological system, we built an
heterogeneous system that could achieve this goal. Our architecture could be
summarized in two basic steps. First, we generate a diverse set of
classification hypothesis using both Convolutional Neural Networks, currently
the state-of-the-art technique for this task, among with other traditional and
innovative machine learning techniques. Then, we optimally combine them through
Meta-Nets, a family of recently developed and performing ensemble methods.
",0,0,0,1,0,0
20276,Compressing networks with super nodes,"  Community detection is a commonly used technique for identifying groups in a
network based on similarities in connectivity patterns. To facilitate community
detection in large networks, we recast the network to be partitioned into a
smaller network of 'super nodes', each super node comprising one or more nodes
in the original network. To define the seeds of our super nodes, we apply the
'CoreHD' ranking from dismantling and decycling. We test our approach through
the analysis of two common methods for community detection: modularity
maximization with the Louvain algorithm and maximum likelihood optimization for
fitting a stochastic block model. Our results highlight that applying community
detection to the compressed network of super nodes is significantly faster
while successfully producing partitions that are more aligned with the local
network connectivity, more stable across multiple (stochastic) runs within and
between community detection algorithms, and overlap well with the results
obtained using the full network.
",1,1,0,0,0,0
20277,Mode specific electronic friction in dissociative chemisorption on metal surfaces: H$_2$ on Ag(111),"  Electronic friction and the ensuing nonadiabatic energy loss play an
important role in chemical reaction dynamics at metal surfaces. Using molecular
dynamics with electronic friction evaluated on-the-fly from Density Functional
Theory, we find strong mode dependence and a dominance of nonadiabatic energy
loss along the bond stretch coordinate for scattering and dissociative
chemisorption of H$_2$ on the Ag(111) surface. Exemplary trajectories with
varying initial conditions indicate that this mode-specificity translates into
modulated energy loss during a dissociative chemisorption event. Despite minor
nonadiabatic energy loss of about 5\%, the directionality of friction forces
induces dynamical steering that affects individual reaction outcomes,
specifically for low-incidence energies and vibrationally excited molecules.
Mode-specific friction induces enhanced loss of rovibrational rather than
translational energy and will be most visible in its effect on final energy
distributions in molecular scattering experiments.
",0,1,0,0,0,0
20278,The quantum auxiliary linear problem & quantum Darboux-Backlund transformations,"  We explore the notion of the quantum auxiliary linear problem and the
associated problem of quantum Backlund transformations (BT). In this context we
systematically construct the analogue of the classical formula that provides
the whole hierarchy of the time components of Lax pairs at the quantum level
for both closed and open integrable lattice models. The generic time evolution
operator formula is particularly interesting and novel at the quantum level
when dealing with systems with open boundary conditions. In the same frame we
show that the reflection K-matrix can also be viewed as a particular type of
BT, fixed at the boundaries of the system. The q-oscillator (q-boson) model, a
variant of the Ablowitz-Ladik model, is then employed as a paradigm to
illustrate the method. Particular emphasis is given to the time part of the
quantum BT as possible connections and applications to the problem of quantum
quenches as well as the time evolution of local quantum impurities are evident.
A discussion on the use of Bethe states as well as coherent states for the
study of the time evolution is also presented.
",0,1,0,0,0,0
20279,Simplified Minimal Gated Unit Variations for Recurrent Neural Networks,"  Recurrent neural networks with various types of hidden units have been used
to solve a diverse range of problems involving sequence data. Two of the most
recent proposals, gated recurrent units (GRU) and minimal gated units (MGU),
have shown comparable promising results on example public datasets. In this
paper, we introduce three model variants of the minimal gated unit (MGU) which
further simplify that design by reducing the number of parameters in the
forget-gate dynamic equation. These three model variants, referred to simply as
MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset
and from the Reuters Newswire Topics (RNT) dataset. The new models have shown
similar accuracy to the MGU model while using fewer parameters and thus
lowering training expense. One model variant, namely MGU2, performed better
than MGU on the datasets considered, and thus may be used as an alternate to
MGU or GRU in recurrent neural networks.
",1,0,0,1,0,0
20280,Parallel G-duplex and C-duplex DNA with Uninterrupted Spines of AgI-Mediated Base Pairs,"  Hydrogen bonding between nucleobases produces diverse DNA structural motifs,
including canonical duplexes, guanine (G) quadruplexes and cytosine (C)
i-motifs. Incorporating metal-mediated base pairs into nucleic acid structures
can introduce new functionalities and enhanced stabilities. Here we
demonstrate, using mass spectrometry (MS), ion mobility spectrometry (IMS) and
fluorescence resonance energy transfer (FRET), that parallel-stranded
structures consisting of up to 20 G-Ag(I)-G contiguous base pairs are formed
when natural DNA sequences are mixed with silver cations in aqueous solution.
FRET indicates that duplexes formed by poly(cytosine) strands with 20
contiguous C-Ag(I)-C base pairs are also parallel. Silver-mediated G-duplexes
form preferentially over G-quadruplexes, and the ability of Ag+ to convert
G-quadruplexes into silver-paired duplexes may provide a new route to
manipulating these biologically relevant structures. IMS indicates that
G-duplexes are linear and more rigid than B-DNA. DFT calculations were used to
propose structures compatible with the IMS experiments. Such inexpensive,
defect-free and soluble DNA-based nanowires open new directions in the design
of novel metal-mediated DNA nanotechnology.
",0,0,0,0,1,0
20281,Constraints on Vacuum Energy from Structure Formation and Nucleosynthesis,"  This paper derives an upper limit on the density $\rho_{\scriptstyle\Lambda}$
of dark energy based on the requirement that cosmological structure forms
before being frozen out by the eventual acceleration of the universe. By
allowing for variations in both the cosmological parameters and the strength of
gravity, the resulting constraint is a generalization of previous limits. The
specific parameters under consideration include the amplitude $Q$ of the
primordial density fluctuations, the Planck mass $M_{\rm pl}$, the
baryon-to-photon ratio $\eta$, and the density ratio $\Omega_M/\Omega_b$. In
addition to structure formation, we use considerations from stellar structure
and Big Bang Nucleosynthesis (BBN) to constrain these quantities. The resulting
upper limit on the dimensionless density of dark energy becomes
$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4<10^{-90}$, which is $\sim30$ orders of
magnitude larger than the value in our universe
$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4\sim10^{-120}$. This new limit is much
less restrictive than previous constraints because additional parameters are
allowed to vary. With these generalizations, a much wider range of universes
can develop cosmic structure and support observers. To constrain the
constituent parameters, new BBN calculations are carried out in the regime
where $\eta$ and $G=M_{\rm pl}^{-2}$ are much larger than in our universe. If
the BBN epoch were to process all of the protons into heavier elements, no
hydrogen would be left behind to make water, and the universe would not be
viable. However, our results show that some hydrogen is always left over, even
under conditions of extremely large $\eta$ and $G$, so that a wide range of
alternate universes are potentially habitable.
",0,1,0,0,0,0
20282,Dynamic Mobile Edge Caching with Location Differentiation,"  Mobile edge caching enables content delivery directly within the radio access
network, which effectively alleviates the backhaul burden and reduces
round-trip latency. To fully exploit the edge resources, the most popular
contents should be identified and cached. Observing that content popularity
varies greatly at different locations, to maximize local hit rate, this paper
proposes an online learning algorithm that dynamically predicts content hit
rate, and makes location-differentiated caching decisions. Specifically, a
linear model is used to estimate the future hit rate. Considering the
variations in user demand, a perturbation is added to the estimation to account
for uncertainty. The proposed learning algorithm requires no training phase,
and hence is adaptive to the time-varying content popularity profile.
Theoretical analysis indicates that the proposed algorithm asymptotically
approaches the optimal policy in the long term. Extensive simulations based on
real world traces show that, the proposed algorithm achieves higher hit rate
and better adaptiveness to content popularity fluctuation, compared with other
schemes.
",1,0,0,0,0,0
20283,Online Learning for Distribution-Free Prediction,"  We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented online with a runtime that scales linearly in the number of
samples; has a constant memory requirement; avoids local minima problems; and
prunes away redundant feature dimensions without relying on restrictive
assumptions on the data distribution. In conjunction with the split conformal
approach, it also produces distribution-free prediction confidence intervals in
a computationally efficient manner. The method is demonstrated on both real and
synthetic datasets.
",1,0,0,1,0,0
20284,"Thermal transitions, pseudogap behavior and BCS-BEC crossover in Fermi-Fermi mixtures","  We study the mass imbalanced Fermi-Fermi mixture within the framework of a
two-dimensional lattice fermion model. Based on the thermodynamic and species
dependent quasiparticle behavior we map out the finite temperature phase
diagram of this system and show that unlike the balanced Fermi superfluid there
are now two different pseudogap regimes as PG-I and PG-II. While within the
PG-I regime both the fermionic species are pseudogapped, PG-II corresponds to
the regime where pseudogap feature survives only in the light species. We
believe that the single particle spectral features that we discuss in this
paper are observable through the species resolved radio frequency spectroscopy
and momentum resolved photo emission spectroscopy measurements on systems such
as, 6$_{Li}$-40$_{K}$ mixture. We further investigate the interplay between the
population and mass imbalances and report that at a fixed population imbalance
the BCS-BEC crossover in a Fermi-Fermi mixture would require a critical
interaction (U$_{c}$), for the realization of the uniform superfluid state. The
effect of imbalance in mass on the exotic Fulde-Ferrell-Larkin-Ovchinnikov
(FFLO) superfluid phase has been probed in detail in terms of the thermodynamic
and quasiparticle behavior of this phase. It has been observed that in spite of
the s-wave symmetry of the pairing field a nodal superfluid gap is realized in
the LO regime. Our results on the various thermal scales and regimes are
expected to serve as benchmarks for the experimental observations on
6$_{Li}$-40$_{K}$ mixture.
",0,1,0,0,0,0
20285,Spectral Approximation for Ergodic CMV Operators with an Application to Quantum Walks,"  We establish concrete criteria for fully supported absolutely continuous
spectrum for ergodic CMV matrices and purely absolutely continuous spectrum for
limit-periodic CMV matrices. We proceed by proving several variational
estimates on the measure of the spectrum and the vanishing set of the Lyapunov
exponent for CMV matrices, which represent CMV analogues of results obtained
for Schrödinger operators due to Y.\ Last in the early 1990s. Having done so,
we combine those estimates with results from inverse spectral theory to obtain
purely absolutely continuous spectrum.
",0,0,1,0,0,0
20286,Quantitative Photoacoustic Imaging in the Acoustic Regime using SPIM,"  While in standard photoacoustic imaging the propagation of sound waves is
modeled by the standard wave equation, our approach is based on a generalized
wave equation with variable sound speed and material density, respectively. In
this paper we present an approach for photoacoustic imaging, which in addition
to recovering of the absorption density parameter, the imaging parameter of
standard photoacoustics, also allows to reconstruct the spatially varying sound
speed and density, respectively, of the medium. We provide analytical
reconstruction formulas for all three parameters based in a linearized model
based on single plane illumination microscopy (SPIM) techniques.
",0,0,1,0,0,0
20287,Integrated Deep and Shallow Networks for Salient Object Detection,"  Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifically, our method takes results of unsupervised saliency
(Robust Background Detection, RBD) and normalized color images as inputs, and
directly learns an end-to-end mapping between inputs and the corresponding
saliency maps. The color images are fed into a Fully Convolutional Neural
Networks (FCNN) adapted from semantic segmentation to exploit high-level
semantic cues for salient object detection. Then the results from deep FCNN and
RBD are concatenated to feed into a shallow network to map the concatenated
feature maps to saliency maps. Finally, to obtain a spatially consistent
saliency map with sharp object boundaries, we fuse superpixel level saliency
map at multi-scale. Extensive experimental results on 8 benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches with a margin.
",1,0,0,0,0,0
20288,Feedback Capacity over Networks,"  In this paper, we investigate the fundamental limitations of feedback
mechanism in dealing with uncertainties for network systems. The study of
maximum capability of feedback control was pioneered in Xie and Guo (2000) for
scalar systems with nonparametric nonlinear uncertainty. In a network setting,
nodes with unknown and nonlinear dynamics are interconnected through a directed
interaction graph. Nodes can design feedback controls based on all available
information, where the objective is to stabilize the network state. Using
information structure and decision pattern as criteria, we specify three
categories of network feedback laws, namely the
global-knowledge/global-decision, network-flow/local-decision, and
local-flow/local-decision feedback. We establish a series of network capacity
characterizations for these three fundamental types of network control laws.
First of all, we prove that for global-knowledge/global-decision and
network-flow/local-decision control where nodes know the information flow
across the entire network, there exists a critical number
$\big(3/2+\sqrt{2}\big)/\|A_{\mathrm{G}}\|_\infty$, where $3/2+\sqrt{2}$ is as
known as the Xie-Guo constant and $A_{\mathrm{G}}$ is the network adjacency
matrix, defining exactly how much uncertainty in the node dynamics can be
overcome by feedback. Interestingly enough, the same feedback capacity can be
achieved under max-consensus enhanced local flows where nodes only observe
information flows from neighbors as well as extreme (max and min) states in the
network. Next, for local-flow/local-decision control, we prove that there
exists a structure-determined value being a lower bound of the network feedback
capacity. These results reveal the important connection between network
structure and fundamental capabilities of in-network feedback control.
",1,0,0,0,0,0
20289,Flexural phonons in supported graphene: from pinning to localization,"  We identify graphene layer on a disordered substrate as a possible system
where Anderson localization of phonons can be observed. Generally, observation
of localization for scattering waves is not simple, because the Rayleigh
scattering is inversely proportional to a high power of wavelength. The
situation is radically different for the out of plane vibrations, so-called
flexural phonons, scattered by pinning centers induced by a substrate. In this
case, the scattering time for vanishing wave vector tends to a finite limit.
One may, therefore, expect that physics of the flexural phonons exhibits
features characteristic for electron localization in two dimensions, albeit
without complications caused by the electron-electron interactions. We confirm
this idea by calculating statistical properties of the Anderson localization of
flexural phonons for a model of elastic sheet in the presence of the pinning
centers. Finally, we discuss possible manifestations of the flexural phonons,
including the localized ones, in the electronic thermal conductance.
",0,1,0,0,0,0
20290,"Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack","  Many approaches for testing configurable software systems start from the same
assumption: it is impossible to test all configurations. This motivated the
definition of variability-aware abstractions and sampling techniques to cope
with large configuration spaces. Yet, there is no theoretical barrier that
prevents the exhaustive testing of all configurations by simply enumerating
them, if the effort required to do so remains acceptable. Not only this: we
believe there is lots to be learned by systematically and exhaustively testing
a configurable system. In this case study, we report on the first ever
endeavour to test all possible configurations of an industry-strength, open
source configurable software system, JHipster, a popular code generator for web
applications. We built a testing scaffold for the 26,000+ configurations of
JHipster using a cluster of 80 machines during 4 nights for a total of 4,376
hours (182 days) CPU time. We find that 35.70% configurations fail and we
identify the feature interactions that cause the errors. We show that sampling
strategies (like dissimilarity and 2-wise): (1) are more effective to find
faults than the 12 default configurations used in the JHipster continuous
integration; (2) can be too costly and exceed the available testing budget. We
cross this quantitative analysis with the qualitative assessment of JHipster's
lead developers.
",1,0,0,0,0,0
20291,Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells,"  We propose a reinforcement learning (RL) based closed loop power control
algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an
indoor environment served by small cells. The main contributions of our paper
are to 1) use RL to solve performance tuning problems in an indoor cellular
network for voice bearers and 2) show that our derived lower bound loss in
effective signal to interference plus noise ratio due to neighboring cell
failure is sufficient for VoLTE power control purposes in practical cellular
networks. In our simulation, the proposed RL-based power control algorithm
significantly improves both voice retainability and mean opinion score compared
to current industry standards. The improvement is due to maintaining an
effective downlink signal to interference plus noise ratio against adverse
network operational issues and faults.
",1,0,0,1,0,0
20292,Ancient shrinking spherical interfaces in the Allen-Cahn flow,"  We consider the parabolic Allen-Cahn equation in $\mathbb{R}^n$, $n\ge 2$,
$$u_t= \Delta u + (1-u^2)u \quad \hbox{ in } \mathbb{R}^n \times (-\infty,
0].$$ We construct an ancient radially symmetric solution $u(x,t)$ with any
given number $k$ of transition layers between $-1$ and $+1$. At main order they
consist of $k$ time-traveling copies of $w$ with spherical interfaces distant
$O(\log |t| )$ one to each other as $t\to -\infty$. These interfaces are
resemble at main order copies of the {\em shrinking sphere} ancient solution to
mean the flow by mean curvature of surfaces: $|x| = \sqrt{- 2(n-1)t}$. More
precisely, if $w(s)$ denotes the heteroclinic 1-dimensional solution of $w'' +
(1-w^2)w=0$ $w(\pm \infty)= \pm 1$ given by $w(s) = \tanh \left(\frac
s{\sqrt{2}} \right) $ we have $$ u(x,t) \approx \sum_{j=1}^k
(-1)^{j-1}w(|x|-\rho_j(t)) - \frac 12 (1+ (-1)^{k}) \quad \hbox{ as } t\to
-\infty $$ where
$$\rho_j(t)=\sqrt{-2(n-1)t}+\frac{1}{\sqrt{2}}\left(j-\frac{k+1}{2}\right)\log\left(\frac
{|t|}{\log |t| }\right)+ O(1),\quad j=1,\ldots ,k.$$
",0,0,1,0,0,0
20293,Measuring the Eccentricity of Items,"  The long-tail phenomenon tells us that there are many items in the tail.
However, not all tail items are the same. Each item acquires different kinds of
users. Some items are loved by the general public, while some items are
consumed by eccentric fans. In this paper, we propose a novel metric, item
eccentricity, to incorporate this difference between consumers of the items.
Eccentric items are defined as items that are consumed by eccentric users. We
used this metric to analyze two real-world datasets of music and movies and
observed the characteristics of items in terms of eccentricity. The results
showed that our defined eccentricity of an item does not change much over time,
and classified eccentric and noneccentric items present significantly distinct
characteristics. The proposed metric effectively separates the eccentric and
noneccentric items mixed in the tail, which could not be done with the previous
measures, which only consider the popularity of items.
",1,0,0,0,0,0
20294,Dataset: Rare Event Classification in Multivariate Time Series,"  A real-world dataset is provided from a pulp-and-paper manufacturing
industry. The dataset comes from a multivariate time series process. The data
contains a rare event of paper break that commonly occurs in the industry. The
data contains sensor readings at regular time-intervals (x's) and the event
label (y). The primary purpose of the data is thought to be building a
classification model for early prediction of the rare event. However, it can
also be used for multivariate time series data exploration and building other
supervised and unsupervised models.
",0,0,0,1,0,0
20295,Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues,"  An experimental setup for consecutive measurement of ion and x-ray absorption
in tissue or other materials is introduced. With this setup using a 3D-printed
sample container, the reference stopping-power ratio (SPR) of materials can be
measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine
tissue samples were prepared for measurement, comprising five samples each of
13 tissue types representing about 80% of the total body mass (three different
muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone).
Using a standard stoichiometric calibration for single-energy CT (SECT) as well
as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all
tissues and then compared to the measured reference. With the SECT approach,
the SPRs of all tissues were predicted with a mean error of (-0.84 $\pm$ 0.12)%
and a mean absolute error of (1.27 $\pm$ 0.12)%. In contrast, the DECT-based
SPR predictions were overall consistent with the measured reference with a mean
error of (-0.02 $\pm$ 0.15)% and a mean absolute error of (0.10 $\pm$ 0.15)%.
Thus, in this study, the potential of DECT to decrease range uncertainty could
be confirmed in biological tissue.
",0,1,0,0,0,0
20296,How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,"  In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi
Oreskes and Erik Conway describe the ""tobacco strategy"", which was used by the
tobacco industry to influence policy makers regarding the health risks of
tobacco products. The strategy involved two parts, consisting of (1) promoting
and sharing independent research supporting the industry's preferred position
and (2) funding additional research, but selectively publishing the results. We
introduce a model of the Tobacco Strategy, and use it to argue that both prongs
of the strategy can be extremely effective--even when policy makers rationally
update on all evidence available to them. As we elaborate, this model helps
illustrate the conditions under which the Tobacco Strategy is particularly
successful. In addition, we show how journalists engaged in ""fair"" reporting
can inadvertently mimic the effects of industry on public belief.
",1,0,0,0,0,0
20297,A complete and partial integrability technique of the Lorenz system,"  In this paper we deal with the well-known nonlinear Lorenz system that
describes the deterministic chaos phenomenon. We consider an interesting
problem with time-varying phenomena in quantum optics. Then we establish from
the motion equations the passage to the Lorenz system. Furthermore, we show
that the reduction to the third order non linear equation can be performed.
Therefore, the obtained differential equation can be analytically solved in
some special cases and transformed to Abel, Dufing, Painlevé and
generalized Emden-Fowler equations. So, a motivating technique that permitted a
complete and partial integrability of the Lorenz system is presented.
",0,1,0,0,0,0
20298,Comparision of the definitions of generalized solution of the Cauchy problem for quasi-linear equation,"  In preprint we consider and compare different definitions of generalized
solution of the Cauchy problem for 1d-scalar quasilinear equation (conservation
law). We start from the classical approaches goes back to I.M. Gelfand, O.A.
Oleinik, S.N. Kruzhkov and move to the modern finite-difference approximations
approaches belongs to A.A. Shananin and G.M. Henkin. We discuss the conditions
that provide definitions to be equivalent.
",0,0,1,0,0,0
20299,On The Complexity of Sparse Label Propagation,"  This paper investigates the computational complexity of sparse label
propagation which has been proposed recently for processing network structured
data. Sparse label propagation amounts to a convex optimization problem and
might be considered as an extension of basis pursuit from sparse vectors to
network structured datasets. Using a standard first-order oracle model, we
characterize the number of iterations for sparse label propagation to achieve a
prescribed accuracy. In particular, we derive an upper bound on the number of
iterations required to achieve a certain accuracy and show that this upper
bound is sharp for datasets having a chain structure (e.g., time series).
",0,0,0,1,0,0
20300,Detecting Qualia in Natural and Artificial Agents,"  The Hard Problem of consciousness has been dismissed as an illusion. By
showing that computers are capable of experiencing, we show that they are at
least rudimentarily conscious with potential to eventually reach
superconsciousness. The main contribution of the paper is a test for confirming
certain subjective experiences in a tested agent. We follow with analysis of
benefits and problems with conscious machines and implications of such
capability on future of computing, machine rights and artificial intelligence
safety.
",1,0,0,0,0,0
20301,The interplay between Steinberg algebras and partial skew rings,"  We study the interplay between Steinberg algebras and partial skew rings: For
a partial action of a group in a Hausdorff, locally compact, totally
disconnected topological space, we realize the associated partial skew group
ring as a Steinberg algebra (over the transformation groupoid attached to the
partial action). We then apply this realization to characterize diagonal
preserving isomorphisms of partial skew group rings, over commutative algebras,
in terms of continuous orbit equivalence of the associated partial actions.
Finally, we show that any Steinberg algebra, associated to a Hausdorff ample
groupoid, can be seen as a partial skew inverse semigroup ring.
",0,0,1,0,0,0
20302,Type-II Dirac Photons,"  The Dirac equation for relativistic electron waves is the parent model for
Weyl and Majorana fermions as well as topological insulators. Simulation of
Dirac physics in three-dimensional photonic crystals, though fundamentally
important for topological phenomena at optical frequencies, encounters the
challenge of synthesis of both Kramers double degeneracy and parity inversion.
Here we show how type-II Dirac points---exotic Dirac relativistic waves yet to
be discovered---are robustly realized through the nonsymmorphic screw symmetry.
The emergent type-II Dirac points carry nontrivial topology and are the mother
states of type-II Weyl points. The proposed all-dielectric architecture enables
robust cavity states at photonic-crystal---air interfaces and anomalous
refraction, with very low energy dissipation.
",0,1,0,0,0,0
20303,Improving Network Robustness against Adversarial Attacks with Compact Convolution,"  Though Convolutional Neural Networks (CNNs) have surpassed human-level
performance on tasks such as object classification and face verification, they
can easily be fooled by adversarial attacks. These attacks add a small
perturbation to the input image that causes the network to misclassify the
sample. In this paper, we focus on neutralizing adversarial attacks by compact
feature learning. In particular, we show that learning features in a closed and
bounded space improves the robustness of the network. We explore the effect of
L2-Softmax Loss, that enforces compactness in the learned features, thus
resulting in enhanced robustness to adversarial perturbations. Additionally, we
propose compact convolution, a novel method of convolution that when
incorporated in conventional CNNs improves their robustness. Compact
convolution ensures feature compactness at every layer such that they are
bounded and close to each other. Extensive experiments show that Compact
Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform
better than existing methods in defending adversarial attacks, without
incurring any additional training overhead compared to CNNs.
",1,0,0,1,0,0
20304,From sudden quench to adiabatic dynamics in the attractive Hubbard model,"  We study the crossover between the sudden quench limit and the adiabatic
dynamics of superconducting states in the attractive Hubbard model. We focus on
the dynamics induced by the change of the attractive interaction during a
finite ramp time which is varied in order to track the evolution of the
dynamical phase diagram from the sudden quench to the equilibrium limit. Two
different dynamical regimes are realized for quenches towards weak and strong
coupling interactions. At weak coupling the dynamics depends only on the energy
injected into the system, whereas a dynamics retaining memory of the initial
state takes place at strong coupling. We show that this is related to a sharp
transition between a weak and a strong coupling quench dynamical regime, which
defines the boundaries beyond which a dynamics independent from the initial
state is recovered. Comparing the dynamics in the superconducting and
non-superconducting phases we argue that this is due to the lack of an
adiabatic connection to the equilibrium ground state for non-equilibrium
superconducting states in the strong coupling quench regime.
",0,1,0,0,0,0
20305,Strongly exchange-coupled and surface-state-modulated magnetization dynamics in Bi2Se3/YIG heterostructures,"  We report strong interfacial exchange coupling in Bi2Se3/yttrium iron garnet
(YIG) bilayers manifested as large in-plane interfacial magnetic anisotropy
(IMA) and enhancement of damping probed by ferromagnetic resonance (FMR). The
IMA and spin mixing conductance reached a maximum when Bi2Se3 was around 6
quintuple-layer (QL) thick. The unconventional Bi2Se3 thickness dependence of
the IMA and spin mixing conductance are correlated with the evolution of
surface band structure of Bi2Se3, indicating that topological surface states
play an important role in the magnetization dynamics of YIG.
Temperature-dependent FMR of Bi2Se3/YIG revealed signatures of magnetic
proximity effect of $T_c$ as high as 180 K, and an effective field parallel to
the YIG magnetization direction at low temperature. Our study sheds light on
the effects of topological insulators on magnetization dynamics, essential for
development of TI-based spintronic devices.
",0,1,0,0,0,0
20306,Survivable Probability of SDN-enabled Cloud Networking with Random Physical Link Failure,"  Software-driven cloud networking is a new paradigm in orchestrating physical
resources (CPU, network bandwidth, energy, storage) allocated to network
functions, services, and applications, which is commonly modeled as a
cross-layer network. This model carries a physical network representing the
physical infrastructure, a logical network showing demands, and
logical-to-physical node/link mappings. In such networks, a single failure in
the physical network may trigger cascading failures in the logical network and
disable network services and connectivity. In this paper, we propose an
evaluation metric, survivable probability, to evaluate the reliability of such
networks under random physical link failure(s). We propose the concept of base
protecting spanning tree and prove the necessary and sufficient conditions for
its existence and relation to survivability. We then develop mathematical
programming formulations for reliable cross-layer network routing design with
the maximal reliable probability. Computation results demonstrate the viability
of our approach.
",1,0,0,0,0,0
20307,Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers,"  PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.
",1,0,0,1,0,0
20308,Engineering a Simplified 0-Bit Consistent Weighted Sampling,"  The Min-Hashing approach to sketching has become an important tool in data
analysis, information retrial, and classification. To apply it to real-valued
datasets, the ICWS algorithm has become a seminal approach that is widely used,
and provides state-of-the-art performance for this problem space. However, ICWS
suffers a computational burden as the sketch size K increases. We develop a new
Simplified approach to the ICWS algorithm, that enables us to obtain over 20x
speedups compared to the standard algorithm. The veracity of our approach is
demonstrated empirically on multiple datasets and scenarios, showing that our
new Simplified CWS obtains the same quality of results while being an order of
magnitude faster.
",0,0,0,1,0,0
20309,Quantifying macroeconomic expectations in stock markets using Google Trends,"  Among other macroeconomic indicators, the monthly release of U.S.
unemployment rate figures in the Employment Situation report by the U.S. Bureau
of Labour Statistics gets a lot of media attention and strongly affects the
stock markets. I investigate whether a profitable investment strategy can be
constructed by predicting the likely changes in U.S. unemployment before the
official news release using Google query volumes for related search terms. I
find that massive new data sources of human interaction with the Internet not
only improves U.S. unemployment rate predictability, but can also enhance
market timing of trading strategies when considered jointly with macroeconomic
data. My results illustrate the potential of combining extensive behavioural
data sets with economic data to anticipate investor expectations and stock
market moves.
",0,0,0,0,0,1
20310,Effects of Incomplete Ionization on Beta - Ga2O3 Power Devices: Unintentional Donor with Energy 110 meV,"  Understanding the origin of unintentional doping in Ga2O3 is key to
increasing breakdown voltages of Ga2O3 based power devices. Therefore,
transport and capacitance spectroscopy studies have been performed to better
understand the origin of unintentional doping in Ga2O3. Previously unobserved
unintentional donors in commercially available (-201) Ga2O3 substrates have
been electrically characterized via temperature dependent Hall effect
measurements up to 1000 K and found to have a donor energy of 110 meV. The
existence of the unintentional donor is confirmed by temperature dependent
admittance spectroscopy, with an activation energy of 131 meV determined via
that technique, in agreement with Hall effect measurements. With the
concentration of this donor determined to be in the mid to high 10^16 cm^-3
range, elimination of this donor from the drift layer of Ga2O3 power
electronics devices will be key to pushing the limits of device performance.
Indeed, analytical assessment of the specific on-resistance (Ronsp) and
breakdown voltage of Schottky diodes containing the 110 meV donor indicates
that incomplete ionization increases Ronsp and decreases breakdown voltage as
compared to Ga2O3 Schottky diodes containing only the shallow donor. The
reduced performance due to incomplete ionization occurs in addition to the
usual tradeoff between Ronsp and breakdown voltage. To achieve 10 kV operation
in Ga2O3 Schottky diode devices, analysis indicates that the concentration of
110 meV donors must be reduced below 5x10^14 cm^-3 to limit the increase in
Ronsp to one percent.
",0,1,0,0,0,0
20311,A single coordinate framework for optic flow and binocular disparity,"  Optic flow is two dimensional, but no special qualities are attached to one
or other of these dimensions. For binocular disparity, on the other hand, the
terms 'horizontal' and 'vertical' disparities are commonly used. This is odd,
since binocular disparity and optic flow describe essentially the same thing.
The difference is that, generally, people tend to fixate relatively close to
the direction of heading as they move, meaning that fixation is close to the
optic flow epipole, whereas, for binocular vision, fixation is close to the
head-centric midline, i.e. approximately 90 degrees from the binocular epipole.
For fixating animals, some separations of flow may lead to simple algorithms
for the judgement of surface structure and the control of action. We consider
the following canonical flow patterns that sum to produce overall flow: (i)
'towards' flow, the component of translational flow produced by approaching (or
retreating from) the fixated object, which produces pure radial flow on the
retina; (ii) 'sideways' flow, the remaining component of translational flow,
which is produced by translation of the optic centre orthogonal to the
cyclopean line of sight and (iii) 'vergence' flow, rotational flow produced by
a counter-rotation of the eye in order to maintain fixation. A general flow
pattern could also include (iv) 'cyclovergence' flow, produced by rotation of
one eye relative to the other about the line of sight. We consider some
practical advantages of dividing up flow in this way when an observer fixates
as they move. As in some previous treatments, we suggest that there are certain
tasks for which it is sensible to consider 'towards' flow as one component and
'sideways' + 'vergence' flow as another.
",0,0,0,0,1,0
20312,Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition,"  Neurons process information by transforming barrages of synaptic inputs into
spiking activity. Synaptic inhibition suppresses the output firing activity of
a neuron, and is commonly classified as having a subtractive or divisive effect
on a neuron's output firing activity. Subtractive inhibition can narrow the
range of inputs that evoke spiking activity by eliminating responses to
non-preferred inputs. Divisive inhibition is a form of gain control: it
modifies firing rates while preserving the range of inputs that evoke firing
activity. Since these two ""modes"" of inhibition have distinct impacts on neural
coding, it is important to understand the biophysical mechanisms that
distinguish these response profiles.
We use simulations and mathematical analysis of a neuron model to find the
specific conditions for which inhibitory inputs have subtractive or divisive
effects. We identify a novel role for the A-type Potassium current (IA). In our
model, this fast-activating, slowly- inactivating outward current acts as a
switch between subtractive and divisive inhibition. If IA is strong (large
maximal conductance) and fast (activates on a time-scale similar to spike
initiation), then inhibition has a subtractive effect on neural firing. In
contrast, if IA is weak or insufficiently fast-activating, then inhibition has
a divisive effect on neural firing. We explain these findings using dynamical
systems methods to define how a spike threshold condition depends on synaptic
inputs and IA.
Our findings suggest that neurons can ""self-regulate"" the gain control
effects of inhibition via combinations of synaptic plasticity and/or modulation
of the conductance and kinetics of A-type Potassium channels. This novel role
for IA would add flexibility to neurons and networks, and may relate to recent
observations of divisive inhibitory effects on neurons in the nucleus of the
solitary tract.
",0,0,0,0,1,0
20313,Cyclic Datatypes modulo Bisimulation based on Second-Order Algebraic Theories,"  Cyclic data structures, such as cyclic lists, in functional programming are
tricky to handle because of their cyclicity. This paper presents an
investigation of categorical, algebraic, and computational foundations of
cyclic datatypes. Our framework of cyclic datatypes is based on second-order
algebraic theories of Fiore et al., which give a uniform setting for syntax,
types, and computation rules for describing and reasoning about cyclic
datatypes. We extract the ""fold"" computation rules from the categorical
semantics based on iteration categories of Bloom and Esik. Thereby, the rules
are correct by construction. We prove strong normalisation using the General
Schema criterion for second-order computation rules. Rather than the fixed
point law, we particularly choose Bekic law for computation, which is a key to
obtaining strong normalisation. We also prove the property of ""Church-Rosser
modulo bisimulation"" for the computation rules. Combining these results, we
have a remarkable decidability result of the equational theory of cyclic data
and fold.
",1,0,0,0,0,0
20314,High-order harmonic generation from highly-excited states in acetylene,"  High-order harmonic generation (HHG) from aligned acetylene molecules
interacting with mid infra-red (IR), linearly polarized laser pulses is studied
theoretically using a mixed quantum-classical approach in which the electrons
are described using time-dependent density functional theory while the ions are
treated classically. We find that for molecules aligned perpendicular to the
laser polarization axis, HHG arises from the highest-occupied molecular orbital
(HOMO) while for molecules aligned along the laser polarization axis, HHG is
dominated by the HOMO-1. In the parallel orientation we observe a double
plateau with an inner plateau that is produced by ionization from and
recombination back to an autoionizing state. Two pieces of evidence support
this idea. Firstly, by choosing a suitably tuned vacuum ultraviolet pump pulse
that directly excites the autoionizing state we observe a dramatic enhancement
of all harmonics in the inner plateau. Secondly, in certain circumstances, the
position of the inner plateau cut-off does not agree with the classical
three-step model. We show that this discrepancy can be understood in terms of a
minimum in the dipole recombination matrix element from the continuum to the
autoionizing state. As far as we are aware, this represents the first
observation of harmonic enhancement over a wide range of frequencies arising
from autoionizing states in molecules.
",0,1,0,0,0,0
20315,Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes,"  Here we report on a set of programs developed at the ZMBH Bio-Imaging
Facility for tracking real-life images of cellular processes. These programs
perform 1) automated tracking; 2) quantitative and comparative track analyses
of different images in different groups; 3) different interactive visualization
schemes; and 4) interactive realistic simulation of different cellular
processes for validation and optimal problem-specific adjustment of image
acquisition parameters (tradeoff between speed, resolution, and quality with
feedback from the very final results). The collection of programs is primarily
developed for the common bio-image analysis software ImageJ (as a single Java
Plugin). Some programs are also available in other languages (C++ and
Javascript) and may be run simply with a web-browser; even on a low-end Tablet
or Smartphone. The programs are available at
this https URL
",1,1,0,0,0,0
20316,Vprop: Variational Inference using RMSprop,"  Many computationally-efficient methods for Bayesian deep learning rely on
continuous optimization algorithms, but the implementation of these methods
requires significant changes to existing code-bases. In this paper, we propose
Vprop, a method for Gaussian variational inference that can be implemented with
two minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces
the memory requirements of Black-Box Variational Inference by half. We derive
Vprop using the conjugate-computation variational inference method, and
establish its connections to Newton's method, natural-gradient methods, and
extended Kalman filters. Overall, this paper presents Vprop as a principled,
computationally-efficient, and easy-to-implement method for Bayesian deep
learning.
",1,0,0,1,0,0
20317,Hidden symmetries in $N$-layer dielectric stacks,"  The optical properties of a multilayer system of dielectric media with
arbitrary $N$ layers is investigated. Each layer is one of two dielectric
media, with thickness one-quarter the wavelength of light in that medium,
corresponding to a central frequency. Using the transfer matrix method, the
transmittance $T$ is calculated for all possible $2^N$ sequences for small $N$.
Unexpectedly, it is found that instead of $2^N$ different values of $T$ at the
central frequency ($T_0$), there are either $(N/2+1)$ or $(N+1)$ discrete
values of $T_0$ for even or odd $N$, respectively. We explain the high
degeneracy in the $T_0$ values by defining new symmetry operations that do not
change $T_0$. Analytical formulae were derived for the $T_0$ values and their
degeneracy as functions of $N$ and an integer parameter for each sequence we
call ""charge"". Additionally, the bandwidth of the transmission spectra at $f_0$
is investigated, revealing some asymptotic behavior at large $N$.
",0,1,0,0,0,0
20318,Solvability regions of affinely parameterized quadratic equations,"  Quadratic systems of equations appear in several applications. The results in
this paper are motivated by quadratic systems of equations that describe
equilibrium behavior of physical infrastructure networks like the power and gas
grids. The quadratic systems in infrastructure networks are parameterized- the
parameters can represent uncertainty (estimation error in resistance/inductance
of a power transmission line, for example)or controllable decision variables
(power outputs of generators,for example). It is then of interest to understand
conditions on the parameters under which the quadratic system is guaranteed to
have a solution within a specified set (for example, bounds on voltages and
flows in a power grid). Given nominal values of the parameters at which the
quadratic system has a solution and the Jacobian of the quadratic system at the
solution i snon-singular, we develop a general framework to construct convex
regions around the nominal value such that the system is guaranteed to have a
solution within a given distance of the nominal solution. We show that several
results from recen tliterature can be recovered as special cases of our
framework,and demonstrate our approach on several benchmark power systems.
",1,0,1,0,0,0
20319,Mid-price estimation for European corporate bonds: a particle filtering approach,"  In most illiquid markets, there is no obvious proxy for the market price of
an asset. The European corporate bond market is an archetypal example of such
an illiquid market where mid-prices can only be estimated with a statistical
model. In this OTC market, dealers / market makers only have access, indeed, to
partial information about the market. In real-time, they know the price
associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client
(D2C) markets, they know the result of the requests for quotes (RFQ) they
answered, and they have access to composite prices (e.g., Bloomberg CBBT). This
paper presents a Bayesian method for estimating the mid-price of corporate
bonds by using the real-time information available to a dealer. This method
relies on recent ideas coming from the particle filtering (PF) / sequential
Monte-Carlo (SMC) literature.
",0,0,0,0,0,1
20320,Matching RGB Images to CAD Models for Object Pose Estimation,"  We propose a novel method for 3D object pose estimation in RGB images, which
does not require pose annotations of objects in images in the training stage.
We tackle the pose estimation problem by learning how to establish
correspondences between RGB images and rendered depth images of CAD models.
During training, our approach only requires textureless CAD models and aligned
RGB-D frames of a subset of object instances, without explicitly requiring pose
annotations for the RGB images. We employ a deep quadruplet convolutional
neural network for joint learning of suitable keypoints and their associated
descriptors in pairs of rendered depth images which can be matched across
modalities with aligned RGB-D views. During testing, keypoints are extracted
from a query RGB image and matched to keypoints extracted from rendered depth
images, followed by establishing 2D-3D correspondences. The object's pose is
then estimated using the RANSAC and PnP algorithms. We conduct experiments on
the recently introduced Pix3D dataset and demonstrate the efficacy of our
proposed approach in object pose estimation as well as generalization to object
instances not seen during training.
",1,0,0,0,0,0
20321,A Model for Paired-Multinomial Data and Its Application to Analysis of Data on a Taxonomic Tree,"  In human microbiome studies, sequencing reads data are often summarized as
counts of bacterial taxa at various taxonomic levels specified by a taxonomic
tree. This paper considers the problem of analyzing two repeated measurements
of microbiome data from the same subjects. Such data are often collected to
assess the change of microbial composition after certain treatment, or the
difference in microbial compositions across body sites. Existing models for
such count data are limited in modeling the covariance structure of the counts
and in handling paired multinomial count data. A new probability distribution
is proposed for paired-multinomial count data, which allows flexible covariance
structure and can be used to model repeatedly measured multivariate count data.
Based on this distribution, a test statistic is developed for testing the
difference in compositions based on paired multinomial count data. The proposed
test can be applied to the count data observed on a taxonomic tree in order to
test difference in microbiome compositions and to identify the subtrees with
different subcompositions. Simulation results indicate that proposed test has
correct type 1 errors and increased power compared to some commonly used
methods. An analysis of an upper respiratory tract microbiome data set is used
to illustrate the proposed methods.
",0,0,0,1,0,0
20322,Adhesion-induced Discontinuous Transitions and Classifying Social Networks,"  Transition points mark qualitative changes in the macroscopic properties of
large complex systems. Explosive transitions, exhibiting properties of both
continuous and discontinuous phase transitions, have recently been uncovered in
network growth processes. Real networks not only grow but often also
restructure, yet common network restructuring processes, such as small world
rewiring, do not exhibit phase transitions. Here, we uncover a class of
intrinsically discontinuous transitions emerging in network restructuring
processes controlled by \emph{adhesion} -- the preference of a chosen link to
remain connected to its end node. Deriving a master equation for the temporal
network evolution and working out an analytic solution, we identify genuinely
discontinuous transitions in non-growing networks, separating qualitatively
distinct phases with monotonic and with peaked degree distributions.
Intriguingly, our analysis of heuristic data indicates a separation between the
same two forms of degree distributions distinguishing abstract from
face-to-face social networks.
",1,0,0,0,0,0
20323,Numerical simulation of polynomial-speed convergence phenomenon,"  We provide a hybrid method that captures the polynomial speed of convergence
and polynomial speed of mixing for Markov processes. The hybrid method that we
introduce is based on the coupling technique and renewal theory. We propose to
replace some estimates in classical results about the ergodicity of Markov
processes by numerical simulations when the corresponding analytical proof is
difficult. After that, all remaining conclusions can be derived from rigorous
analysis. Then we apply our results to two 1D microscopic heat conduction
models. The mixing rate of these two models are expected to be polynomial but
very difficult to prove. In both examples, our numerical results match the
expected polynomial mixing rate well.
",0,0,1,0,0,0
20324,Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates,"  Primarily motivated by the drug development process, several publications
have now presented methodology for the design of multi-arm multi-stage
experiments with normally distributed outcome variables of known variance.
Here, we extend these past considerations to allow the design of what we refer
to as an abcd multi-arm multi-stage experiment. We provide a proof of how
strong control of the a-generalised type-I familywise error-rate can be
ensured. We then describe how to attain the power to reject at least b out of c
false hypotheses, which is related to controlling the b-generalised type-II
familywise error-rate. Following this, we detail how a design can be optimised
for a scenario in which rejection of any d null hypotheses brings about
termination of the experiment. We achieve this by proposing a highly
computationally efficient approach for evaluating the performance of a
candidate design. Finally, using a real clinical trial as a motivating example,
we explore the effect of the design's control parameters on the statistical
operating characteristics.
",0,0,0,1,0,0
20325,Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit Models,"  In this paper we present the co-simulation of a PID class power converter
controller and an electrical circuit by means of the waveform relaxation
technique. The simulation of the controller model is characterized by a
fixed-time stepping scheme reflecting its digital implementation, whereas a
circuit simulation usually employs an adaptive time stepping scheme in order to
account for a wide range of time constants within the circuit model. In order
to maintain the characteristic of both models as well as to facilitate model
replacement, we treat them separately by means of input/output relations and
propose an application of a waveform relaxation algorithm. Furthermore, the
maximum and minimum number of iterations of the proposed algorithm are
mathematically analyzed. The concept of controller/circuit coupling is
illustrated by an example of the co-simulation of a PI power converter
controller and a model of the main dipole circuit of the Large Hadron Collider.
",1,1,0,0,0,0
20326,Some Investigations about the Properties of Maximum Likelihood Estimations Based on Lower Record Values for a Sub-Family of the Exponential Family,"  Here, in this paper it has been considered a sub family of exponential
family. Maximum likelihood estimations (MLE) for the parameter of this family,
probability density function, and cumulative density function based on a sample
and based on lower record values have been obtained. It has been considered
Mean Square Error (MSE) as a criterion for determining which is better in
different situations. Additionally, it has been proved some theories about the
relations between MLE based on lower record values and based on a random
sample. Also, some interesting asymptotically properties for these estimations
have been shown during some theories.
",0,0,1,1,0,0
20327,Some Insights on Synthesizing Optimal Linear Quadratic Controller Using Krotov's Sufficiency Conditions,"  This paper revisits the problem of optimal control law design for linear
systems using the global optimal control framework introduced by Vadim Krotov.
Krotov's approach is based on the idea of total decomposition of the original
optimal control problem (OCP) with respect to time, by an $ad$ $hoc$ choice of
the so-called Krotov's function or solving function, thereby providing
sufficient conditions for the existence of global solution based on another
optimization problem, which is completely equivalent to the original OCP. It is
well known that the solution of this equivalent optimization problem is
obtained using an iterative method. In this paper, we propose suitable Krotov's
functions for linear quadratic OCP and subsequently, show that by imposing
convexity condition on this equivalent optimization problem, there is no need
to compute an iterative solution. We also give some key insights into the
solution procedure of the linear quadratic OCP using the proposed methodology
in contrast to the celebrated Calculus of Variations (CoV) and
Hamilton-Jacobi-Bellman (HJB) equation based approach.
",1,0,0,0,0,0
20328,Schumann resonance transients and the search for gravitational waves,"  Schumann resonance transients which propagate around the globe can
potentially generate a correlated background in widely separated gravitational
wave detectors. We show that due to the distribution of lightning hotspots
around the globe these transients have characteristic time lags, and this
feature can be useful to further suppress such a background, especially in
searches of the stochastic gravitational-wave background. A brief review of the
corresponding literature on Schumann resonances and lightnings is also given.
",0,1,0,0,0,0
20329,Learning to Fly by Crashing,"  How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: this https URL
",1,0,0,0,0,0
20330,Estimating Graphlet Statistics via Lifting,"  Exploratory analysis over network data is often limited by our ability to
efficiently calculate graph statistics, which can provide a model-free
understanding of macroscopic properties of a network. This work introduces a
framework for estimating the graphlet count - the number of occurrences of a
small subgraph motif (e.g. a wedge or a triangle) in the network. For massive
graphs, where accessing the whole graph is not possible, the only viable
algorithms are those which act locally by making a limited number of vertex
neighborhood queries.
We introduce a Monte Carlo sampling technique for graphlet counts, called
lifting, which can simultaneously sample all graphlets of size up to $k$
vertices. We outline three variants of lifted graphlet counts: the ordered,
unordered, and shotgun estimators. We prove that our graphlet count updates are
unbiased for the true graphlet count, have low correlation between samples, and
have a controlled variance. We compare the experimental performance of lifted
graphlet counts to the state-of-the art graphlet sampling procedures: Waddling
and the pairwise subgraph random walk.
",0,0,0,1,0,0
20331,Calibration for the (Computationally-Identifiable) Masses,"  As algorithms increasingly inform and influence decisions made about
individuals, it becomes increasingly important to address concerns that these
algorithms might be discriminatory. The output of an algorithm can be
discriminatory for many reasons, most notably: (1) the data used to train the
algorithm might be biased (in various ways) to favor certain populations over
others; (2) the analysis of this training data might inadvertently or
maliciously introduce biases that are not borne out in the data. This work
focuses on the latter concern.
We develop and study multicalbration -- a new measure of algorithmic fairness
that aims to mitigate concerns about discrimination that is introduced in the
process of learning a predictor from data. Multicalibration guarantees accurate
(calibrated) predictions for every subpopulation that can be identified within
a specified class of computations. We think of the class as being quite rich;
in particular, it can contain many overlapping subgroups of a protected group.
We show that in many settings this strong notion of protection from
discrimination is both attainable and aligned with the goal of obtaining
accurate predictions. Along the way, we present new algorithms for learning a
multicalibrated predictor, study the computational complexity of this task, and
draw new connections to computational learning models such as agnostic
learning.
",1,0,0,1,0,0
20332,The distribution of old stars around the Milky Way's central black hole I: Star counts,"  (abridged) In this paper we revisit the problem of inferring the innermost
structure of the Milky Way's nuclear star cluster via star counts, to clarify
whether it displays a core or a cusp around the central black hole. Through
image stacking and improved PSF fitting we push the completeness limit about
one magnitude deeper than in previous, comparable work. Contrary to previous
work, we analyse the stellar density in well-defined magnitude ranges in order
to be able to constrain stellar masses and ages. The RC and brighter giant
stars display a core-like surface density profile within a projected radius
R<0.3 pc of the central black hole, in agreement with previous studies, but
show a cusp-like surface density distribution at larger R. The surface density
of the fainter stars can be described well by a single power-law at R<2 pc. The
cusp-like profile of the faint stars persists even if we take into account the
possible contamination of stars in this brightness range by young pre-main
sequence stars. The data are inconsistent with a core-profile for the faint
stars.Finally, we show that a 3D Nuker law provides a very good description of
the cluster structure. We conclude that the observed stellar density at the
Galactic Centre, as it can be inferred with current instruments, is consistent
with the existence of a stellar cusp around the Milky Way's central black hole,
Sgr A*. This cusp is well developed inside the influence radius of about 3 pc
of Sgr A* and can be described by a single three-dimensional power-law with an
exponent gamma=1.23+-0.05. The apparent lack of RC stars and brighter giants at
projected distances of R < 0.3 pc (R<8"") of the massive black hole may indicate
that some mechanism has altered their distribution or intrinsic luminosity.
",0,1,0,0,0,0
20333,On infinite order differential operators in fractional viscoelasticity,"  In this paper we discuss some general properties of viscoelastic models
defined in terms of constitutive equations involving infinitely many
derivatives (of integer and fractional order). In particular, we consider as a
working example the recently developed Bessel models of linear viscoelasticiy
that, for short times, behave like fractional Maxwell bodies of order $1/2$.
",0,1,1,0,0,0
20334,Gap structure of FeSe determined by field-angle-resolved specific heat measurements,"  Quasiparticle excitations in FeSe were studied by means of specific heat
($C$) measurements on a high-quality single crystal under rotating magnetic
fields. The field dependence of $C$ shows three-stage behavior with different
slopes, indicating the existence of three gaps ($\Delta_1$, $\Delta_2$, and
$\Delta_3$). In the low-temperature and low-field region, the azimuthal-angle
($\phi$) dependence of $C$ shows a four-fold symmetric oscillation with sign
change. On the other hand, the polar-angle ($\theta$) dependence manifests as
an anisotropy-inverted two-fold symmetry with unusual shoulder behavior.
Combining the angle-resolved results and the theoretical calculation, the
smaller gap $\Delta_1$ is proved to have two vertical-line nodes or gap minima
along the $k_z$ direction, and is determined to reside on the electron-type
$\varepsilon$ band. $\Delta_2$ is found to be related to the electron-type
$\delta$ band, and is isotropic in the $ab$-plane but largely anisotropic out
of the plane. $\Delta_3$ residing on the hole-type $\alpha$ band shows a small
out-of-plane anisotropy with a strong Pauli-paramagnetic effect.
",0,1,0,0,0,0
20335,Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets,"  This paper presents a model based on Deep Learning algorithms of LSTM and GRU
for facilitating an anomaly detection in Large Hadron Collider superconducting
magnets. We used high resolution data available in Post Mortem database to
train a set of models and chose the best possible set of their
hyper-parameters. Using Deep Learning approach allowed to examine a vast body
of data and extract the fragments which require further experts examination and
are regarded as anomalies. The presented method does not require tedious manual
threshold setting and operator attention at the stage of the system setup.
Instead, the automatic approach is proposed, which achieves according to our
experiments accuracy of 99%. This is reached for the largest dataset of 302 MB
and the following architecture of the network: single layer LSTM, 128 cells, 20
epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.
All the experiments were run on GPU Nvidia Tesla K80
",0,1,0,0,0,0
20336,Electro-Oxidation of Ni42 Steel: A highly Active Bifunctional Electrocatalyst,"  Janus type Water-Splitting Catalysts have attracted highest attention as a
tool of choice for solar to fuel conversion. AISI Ni 42 steel was upon harsh
anodization converted in a bifunctional electrocatalyst. Oxygen evolution
reaction- (OER) and hydrogen evolution reaction (HER) are highly efficiently
and steadfast catalyzed at pH 7, 13, 14, 14.6 (OER) respectively at pH 0, 1,
13, 14, 14.6 (HER). The current density taken from long-term OER measurements
in pH 7 buffer solution upon the electro activated steel at 491 mV
overpotential was around 4 times higher (4 mA/cm2) in comparison with recently
developed OER electrocatalysts. The very strong voltage-current behavior of the
catalyst shown in OER polarization experiments at both pH 7 and at pH 13 were
even superior to those known for IrO2-RuO2. No degradation of the catalyst was
detected even when conditions close to standard industrial operations were
applied to the catalyst. A stable Ni-, Fe- oxide based passivating layer
sufficiently protected the bare metal for further oxidation. Quantitative
charge to oxygen- (OER) and charge to hydrogen (HER) conversion was confirmed.
High resolution XPS spectra showed that most likely gamma-NiO(OH) and FeO(OH)
are the catalytic active OER and NiO is the catalytic active HER species.
",0,1,0,0,0,0
20337,Shape-constrained partial identification of a population mean under unknown probabilities of sample selection,"  A prevailing challenge in the biomedical and social sciences is to estimate a
population mean from a sample obtained with unknown selection probabilities.
Using a well-known ratio estimator, Aronow and Lee (2013) proposed a method for
partial identification of the mean by allowing the unknown selection
probabilities to vary arbitrarily between two fixed extreme values. In this
paper, we show how to leverage auxiliary shape constraints on the population
outcome distribution, such as symmetry or log-concavity, to obtain tighter
bounds on the population mean. We use this method to estimate the performance
of Aymara students---an ethnic minority in the north of Chile---in a national
educational standardized test. We implement this method in the new statistical
software package scbounds for R.
",0,0,1,1,0,0
20338,Warped metrics for location-scale models,"  This paper argues that a class of Riemannian metrics, called warped metrics,
plays a fundamental role in statistical problems involving location-scale
models. The paper reports three new results : i) the Rao-Fisher metric of any
location-scale model is a warped metric, provided that this model satisfies a
natural invariance condition, ii) the analytic expression of the sectional
curvature of this metric, iii) the exact analytic solution of the geodesic
equation of this metric. The paper applies these new results to several
examples of interest, where it shows that warped metrics turn location-scale
models into complete Riemannian manifolds of negative sectional curvature. This
is a very suitable situation for developing algorithms which solve problems of
classification and on-line estimation. Thus, by revealing the connection
between warped metrics and location-scale models, the present paper paves the
way to the introduction of new efficient statistical algorithms.
",0,0,1,1,0,0
20339,Detecting Changes in Time Series Data using Volatility Filters,"  This work develops techniques for the sequential detection and location
estimation of transient changes in the volatility (standard deviation) of time
series data. In particular, we introduce a class of change detection algorithms
based on the windowed volatility filter. The first method detects changes by
employing a convex combination of two such filters with differing window sizes,
such that the adaptively updated convex weight parameter is then used as an
indicator for the detection of instantaneous power changes. Moreover, the
proposed adaptive filtering based method is readily extended to the
multivariate case by using recent advances in distributed adaptive filters,
thereby using cooperation between the data channels for more effective
detection of change points. Furthermore, this work also develops a novel change
point location estimator based on the differenced output of the volatility
filter. Finally, the performance of the proposed methods were evaluated on both
synthetic and real world data.
",1,0,0,0,0,0
20340,A minimax and asymptotically optimal algorithm for stochastic bandits,"  We propose the kl-UCB ++ algorithm for regret minimization in stochastic
bandit models with exponential families of distributions. We prove that it is
simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
bound) and minimax optimal. This is the first algorithm proved to enjoy these
two properties at the same time. This work thus merges two different lines of
research with simple and clear proofs.
",0,0,1,1,0,0
20341,Eigenfunctions of Periodic Differential Operators Analytic in a Strip,"  Ordinary differential operators with periodic coefficients analytic in a
strip act on a Hardy-Hilbert space of analytic functions with inner product
defined by integration over a period on the boundary of the strip. Simple
examples show that eigenfunctions may form a complete set for a narrow strip,
but completeness may be lost for a wide strip. Completeness of the
eigenfunctions in the Hardy-Hilbert space is established for regular second
order operators with matrix-valued coefficients when the leading coefficient
satisfies a positive real part condition throughout the strip.
",0,0,1,0,0,0
20342,Infinite ergodic index of the ehrenfest wind-tree model,"  The set of all possible configurations of the Ehrenfest wind-tree model
endowed with the Hausdorff topology is a compact metric space. For a typical
configuration we show that the wind-tree dynamics has infinite ergodic index in
almost every direction. In particular some ergodic theorems can be applied to
show that if we start with a large number of initially parallel particles their
directions decorrelate as the dynamics evolve answering the question posed by
the Ehrenfests.
",0,0,1,0,0,0
20343,Arrays of strongly-coupled atoms in a one-dimensional waveguide,"  We study the cooperative optical coupling between regularly spaced atoms in a
one-dimensional waveguide using decompositions to subradiant and superradiant
collective excitation eigenmodes, direct numerical solutions, and analytical
transfer-matrix methods. We illustrate how the spectrum of transmitted light
through the waveguide including the emergence of narrow Fano resonances can be
understood by the resonance features of the eigenmodes. We describe a method
based on superradiant and subradiant modes to engineer the optical response of
the waveguide and to store light. The stopping of light is obtained by
transferring an atomic excitation to a subradiant collective mode with the zero
radiative resonance linewidth by controlling the level shift of an atom in the
waveguide. Moreover, we obtain an exact analytic solution for the transmitted
light through the waveguide for the case of a regular lattice of atoms and
provide a simple description how the light transmission may present large
resonance shifts when the lattice spacing is close, but not exactly equal, to
half of the wavelength of the light. Experimental imperfections such as
fluctuations of the positions of the atoms and loss of light from the waveguide
are easily quantified in the numerical simulations, which produce the natural
result that the optical response of the atomic array tends toward the response
of a gas with random atomic positions.
",0,1,0,0,0,0
20344,"MEG-Derived Functional Tractography, Results for Normal and Concussed Cohorts","  Measures of neuroelectric activity from each of 18 automatically identified
white matter tracts were extracted from resting MEG recordings from a
normative, n=588, and a chronic TBI, traumatic brain injury, n=63, cohort, 60
of whose TBIs were mild. Activity in the TBI cohort was significantly reduced
compared with the norms for ten of the tracts, p < 10-6 for each. Significantly
reduced activity (p < 10-3) was seen in more than one tract in seven mTBI
individuals and one member of the normative cohort.
",0,0,0,0,1,0
20345,Coincidence of magnetic and valence quantum critical points in CeRhIn5 under pressure,"  We present accurate electrical resistivity measurements along the two
principle crystallographic axes of the pressure-induced heavy-fermion
superconductor CeRhIn5 up to 5.63 GPa. For both directions, a valence crossover
line is identified in the p-T plane and the extrapolation of this line to zero
temperature coincides with the collapse of the magnetic ordering temperature.
Furthermore, it is found that the p-T phase diagram of CeRhIn5 in the valence
crossover region is very similar to that of CeCu2Si2. These results point to
the essential role of Ce-4f electron delocalization in both destroying magnetic
order and realizing superconductivity in CeRhIn5 under pressure.
",0,1,0,0,0,0
20346,Sequential Inverse Approximation of a Regularized Sample Covariance Matrix,"  One of the goals in scaling sequential machine learning methods pertains to
dealing with high-dimensional data spaces. A key related challenge is that many
methods heavily depend on obtaining the inverse covariance matrix of the data.
It is well known that covariance matrix estimation is problematic when the
number of observations is relatively small compared to the number of variables.
A common way to tackle this problem is through the use of a shrinkage estimator
that offers a compromise between the sample covariance matrix and a
well-conditioned matrix, with the aim of minimizing the mean-squared error. We
derived sequential update rules to approximate the inverse shrinkage estimator
of the covariance matrix. The approach paves the way for improved large-scale
machine learning methods that involve sequential updates.
",0,0,0,1,0,0
20347,"Sesqui-arrays, a generalisation of triple arrays","  A triple array is a rectangular array containing letters, each letter
occurring equally often with no repeats in rows or columns, such that the
number of letters common to two rows, two columns, or a row and a column are
(possibly different) non-zero constants. Deleting the condition on the letters
common to a row and a column gives a double array. We propose the term
\emph{sesqui-array} for such an array when only the condition on pairs of
columns is deleted. Thus all triple arrays are sesqui-arrays.
In this paper we give three constructions for sesqui-arrays. The first gives
$(n+1)\times n^2$ arrays on $n(n+1)$ letters for $n\geq 2$. (Such an array for
$n=2$ was found by Bagchi.) This construction uses Latin squares. The second
uses the \emph{Sylvester graph}, a subgraph of the Hoffman--Singleton graph, to
build a good block design for $36$ treatments in $42$ blocks of size~$6$, and
then uses this in a $7\times 36$ sesqui-array for $42$ letters.
We also give a construction for $K\times(K-1)(K-2)/2$ sesqui-arrays on
$K(K-1)/2$ letters. This construction uses biplanes. It starts with a block of
a biplane and produces an array which satisfies the requirements for a
sesqui-array except possibly that of having no repeated letters in a row or
column. We show that this condition holds if and only if the \emph{Hussain
chains} for the selected block contain no $4$-cycles. A sufficient condition
for the construction to give a triple array is that each Hussain chain is a
union of $3$-cycles; but this condition is not necessary, and we give a few
further examples.
We also discuss the question of which of these arrays provide good designs
for experiments.
",0,0,1,1,0,0
20348,Investigating how well contextual features are captured by bi-directional recurrent neural network models,"  Learning algorithms for natural language processing (NLP) tasks traditionally
rely on manually defined relevant contextual features. On the other hand,
neural network models using an only distributional representation of words have
been successfully applied for several NLP tasks. Such models learn features
automatically and avoid explicit feature engineering. Across several domains,
neural models become a natural choice specifically when limited characteristics
of data are known. However, this flexibility comes at the cost of
interpretability. In this paper, we define three different methods to
investigate ability of bi-directional recurrent neural networks (RNNs) in
capturing contextual features. In particular, we analyze RNNs for sequence
tagging tasks. We perform a comprehensive analysis on general as well as
biomedical domain datasets. Our experiments focus on important contextual words
as features, which can easily be extended to analyze various other feature
types. We also investigate positional effects of context words and show how the
developed methods can be used for error analysis.
",1,0,0,0,0,0
20349,A Hybridizable Discontinuous Galerkin solver for the Grad-Shafranov equation,"  In axisymmetric fusion reactors, the equilibrium magnetic configuration can
be expressed in terms of the solution to a semi-linear elliptic equation known
as the Grad-Shafranov equation, the solution of which determines the poloidal
component of the magnetic field. When the geometry of the confinement region is
known, the problem becomes an interior Dirichlet boundary value problem. We
propose a high order solver based on the Hybridizable Discontinuous Galerkin
method. The resulting algorithm (1) provides high order of convergence for the
flux function and its gradient, (2) incorporates a novel method for handling
piecewise smooth geometries by extension from polygonal meshes, (3) can handle
geometries with non-smooth boundaries and x-points, and (4) deals with the
semi-linearity through an accelerated two-grid fixed-point iteration. The
effectiveness of the algorithm is verified with computations for cases where
analytic solutions are known on configurations similar to those of actual
devices (ITER with single null and double null divertor, NSTX, ASDEX upgrade,
and Field Reversed Configurations).
",0,1,0,0,0,0
20350,Sketching Linear Classifiers over Data Streams,"  We introduce a new sub-linear space sketch---the Weight-Median Sketch---for
learning compressed linear classifiers over data streams while supporting the
efficient recovery of large-magnitude weights in the model. This enables
memory-limited execution of several statistical analyses over streams,
including online feature selection, streaming data explanation, relative
deltoid detection, and streaming estimation of pointwise mutual information.
Unlike related sketches that capture the most frequently-occurring features (or
items) in a data stream, the Weight-Median Sketch captures the features that
are most discriminative of one stream (or class) compared to another. The
Weight-Median Sketch adopts the core data structure used in the Count-Sketch,
but, instead of sketching counts, it captures sketched gradient updates to the
model parameters. We provide a theoretical analysis that establishes recovery
guarantees for batch and online learning, and demonstrate empirical
improvements in memory-accuracy trade-offs over alternative memory-budgeted
methods, including count-based sketches and feature hashing.
",1,0,0,1,0,0
20351,Tamed to compatible when b^(2+) = 1 and b^1 = 2,"  Weiyi Zhang noticed recently a gap in the proof of the main theorem of the
authors article ""Tamed to compatible: Symplectic forms via moduli space
integration"" [T] for the case when the symplectic 4-manifold in question has
first Betti number 2 (and necessarily self-dual second Betti number 1). This
note explains how to fill this gap.
",0,0,1,0,0,0
20352,The Mass Transference Principle: Ten Years On,"  In this article we discuss the Mass Transference Principle due to Beresnevich
and Velani and survey several generalisations and variants, both deterministic
and random. Using a Hausdorff measure analogue of the inhomogeneous
Khintchine-Groshev Theorem, proved recently via an extension of the Mass
Transference Principle to systems of linear forms, we give an alternative proof
of a general inhomogeneous Jarn\'{\i}k-Besicovitch Theorem which was originally
proved by Levesley. We additionally show that without monotonicity Levesley's
theorem no longer holds in general. Thereafter, we discuss recent advances by
Wang, Wu and Xu towards mass transference principles where one transitions from
$\limsup$ sets defined by balls to $\limsup$ sets defined by rectangles (rather
than from ""balls to balls"" as is the case in the original Mass Transference
Principle). Furthermore, we consider mass transference principles for
transitioning from rectangles to rectangles and extend known results using a
slicing technique. We end this article with a brief survey of random analogues
of the Mass Transference Principle.
",0,0,1,0,0,0
20353,Ground-state properties of Ca$_2$ from narrow line two-color photoassociation,"  By two-color photoassociation of $^{40}$Ca four weakly bound vibrational
levels in the Ca$_2$ \Xpot ground state potential were measured, using highly
spin-forbidden transitions to intermediate states of the coupled system
$^3\Pi_{u}$ and $^3\Sigma^+ _{u}$ near the ${^3P_1}$+${^1S_0}$ asymptote. From
the observed binding energies, including the least bound state, the long range
dispersion coefficients $\mathrm{C}_6, \mathrm{C}_8,\mathrm{C}_{10}$ and a
precise value for the s-wave scattering length of 308.5(50)~$a_0$ were derived.
From mass scaling we also calculated the corresponding scattering length for
other natural isotopes. From the Autler-Townes splitting of the spectra, the
molecular Rabi frequency has been determined as function of the laser intensity
for one bound-bound transition. The observed value for the Rabi-frequency is in
good agreement with calculated transition moments based on the derived
potentials, assuming a dipole moment being independent of internuclear
separation for the atomic pair model.
",0,1,0,0,0,0
20354,On the semisimplicity of the cyclotomic quiver Hecke algebra of type C,"  We provide criteria for the cyclotomic quiver Hecke algebras of type C to be
semisimple. In the semisimple case, we construct the irreducible modules.
",0,0,1,0,0,0
20355,"On the origin of the shallow and ""replica"" bands in FeSe monolayer superconductors","  We compare electronic structures of single FeSe layer films on SrTiO$_3$
substrate (FeSe/STO) and K$_x$Fe$_{2-y}$Se$_{2}$ superconductors obtained from
extensive LDA and LDA+DMFT calculations with the results of ARPES experiments.
It is demonstrated that correlation effects on Fe-3d states are sufficient in
principle to explain the formation of the shallow electron -- like bands at the
M(X)-point. However, in FeSe/STO these effects alone are apparently
insufficient for the simultaneous elimination of the hole -- like Fermi surface
around the $\Gamma$-point which is not observed in ARPES experiments. Detailed
comparison of ARPES detected and calculated quasiparticle bands shows
reasonable agreement between theory and experiment. Analysis of the bands with
respect to their origin and orbital composition shows, that for FeSe/STO system
the experimentally observed ""replica"" quasiparticle band at the M-point
(usually attributed to forward scattering interactions with optical phonons in
SrTiO$_3$ substrate) can be reasonably understood just as the LDA calculated
Fe-3d$_{xy}$ band, renormalized by electronic correlations. The only
manifestation of the substrate reduces to lifting the degeneracy between
Fe-3d$_{xz}$ and Fe-3d$_{yz}$ bands in the vicinity of M-point. For the case of
K$_x$Fe$_{2-y}$Se$_{2}$ most bands observed in ARPES can also be understood as
correlation renormalized Fe-3d LDA calculated bands, with overall semi --
quantitative agreement with LDA+DMFT calculations.
",0,1,0,0,0,0
20356,A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers,"  Regularization techniques are widely employed in optimization-based
approaches for solving ill-posed inverse problems in data analysis and
scientific computing. These methods are based on augmenting the objective with
a penalty function, which is specified based on prior domain-specific expertise
to induce a desired structure in the solution. We consider the problem of
learning suitable regularization functions from data in settings in which
precise domain knowledge is not directly available. Previous work under the
title of `dictionary learning' or `sparse coding' may be viewed as learning a
regularization function that can be computed via linear programming. We
describe generalizations of these methods to learn regularizers that can be
computed and optimized via semidefinite programming. Our framework for learning
such semidefinite regularizers is based on obtaining structured factorizations
of data matrices, and our algorithmic approach for computing these
factorizations combines recent techniques for rank minimization problems along
with an operator analog of Sinkhorn scaling. Under suitable conditions on the
input data, our algorithm provides a locally linearly convergent method for
identifying the correct regularizer that promotes the type of structure
contained in the data. Our analysis is based on the stability properties of
Operator Sinkhorn scaling and their relation to geometric aspects of
determinantal varieties (in particular tangent spaces with respect to these
varieties). The regularizers obtained using our framework can be employed
effectively in semidefinite programming relaxations for solving inverse
problems.
",1,0,1,1,0,0
20357,A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network,"  Network models are applied in numerous domains where data can be represented
as a system of interactions among pairs of actors. While both statistical and
mechanistic network models are increasingly capable of capturing various
dependencies amongst these actors, these dependencies imply the lack of
independence. This poses statistical challenges for analyzing such data,
especially when there is only a single observed network, and often leads to
intractable likelihoods regardless of the modeling paradigm, which limit the
application of existing statistical methods for networks. We explore a
subsampling bootstrap procedure to serve as the basis for goodness of fit and
model selection with a single observed network that circumvents the
intractability of such likelihoods. Our approach is based on flexible
resampling distributions formed from the single observed network, allowing for
finer and higher dimensional comparisons than simply point estimates of
quantities of interest. We include worked examples for model selection, with
simulation, and assessment of goodness of fit, with duplication-divergence
model fits for yeast (S.cerevisiae) protein-protein interaction data from the
literature. The proposed procedure produces a flexible resampling distribution
that can be based on any statistics of one's choosing and can be employed
regardless of choice of model.
",0,0,0,1,0,0
20358,Nonlinear Acceleration of Stochastic Algorithms,"  Extrapolation methods use the last few iterates of an optimization algorithm
to produce a better estimate of the optimum. They were shown to achieve optimal
convergence rates in a deterministic setting using simple gradient iterates.
Here, we study extrapolation methods in a stochastic setting, where the
iterates are produced by either a simple or an accelerated stochastic gradient
algorithm. We first derive convergence bounds for arbitrary, potentially biased
perturbations, then produce asymptotic bounds using the ratio between the
variance of the noise and the accuracy of the current point. Finally, we apply
this acceleration technique to stochastic algorithms such as SGD, SAGA, SVRG
and Katyusha in different settings, and show significant performance gains.
",0,0,1,0,0,0
20359,Motion of Massive Particles in Rindler Space and the Problem of Fall at the Centre,"  The motion of a massive particle in Rindler space has been studied and
obtained the geodesics of motion. The orbits in Rindler space are found to be
quite different from that of Schwarzschild case. The paths are not like the
Perihelion Precession type. Further we have set up the non-relativistic
Schrodinger equation for the particle in the quantum mechanical scenario in
presence of background constant gravitational field and investigated the
problem of fall of the particle at the center. This problem is also treated
classically. Unlike the conventional scenario, here the fall occurs at the
surface of a sphere of unit radius.
",0,1,0,0,0,0
20360,Measuring Sample Quality with Kernels,"  Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics
fail to detect these biases, researchers have developed computable Stein
discrepancy measures that provably determine the convergence of a sample to its
target distribution. This approach was recently combined with the theory of
reproducing kernels to define a closed-form kernel Stein discrepancy (KSD)
computable by summing kernel evaluations across pairs of sample points. We
develop a theory of weak convergence for KSDs based on Stein's method,
demonstrate that commonly used KSDs fail to detect non-convergence even for
Gaussian targets, and show that kernels with slowly decaying tails provably
determine convergence for a large class of target distributions. The resulting
convergence-determining KSDs are suitable for comparing biased, exact, and
deterministic sample sequences and simpler to compute and parallelize than
alternative Stein discrepancies. We use our tools to compare biased samplers,
select sampler hyperparameters, and improve upon existing KSD approaches to
one-sample hypothesis testing and sample quality improvement.
",1,0,0,1,0,0
20361,Diversity of preferences can increase collective welfare in sequential exploration problems,"  In search engines, online marketplaces and other human-computer interfaces
large collectives of individuals sequentially interact with numerous
alternatives of varying quality. In these contexts, trial and error
(exploration) is crucial for uncovering novel high-quality items or solutions,
but entails a high cost for individual users. Self-interested decision makers,
are often better off imitating the choices of individuals who have already
incurred the costs of exploration. Although imitation makes sense at the
individual level, it deprives the group of additional information that could
have been gleaned by individual explorers. In this paper we show that in such
problems, preference diversity can function as a welfare enhancing mechanism.
It leads to a consistent increase in the quality of the consumed alternatives
that outweighs the increased cost of search for the users.
",1,0,0,0,0,0
20362,Epidemic spread in interconnected directed networks,"  In the real world, many complex systems interact with other systems. In
addition, the intra- or inter-systems for the spread of information about
infectious diseases and the transmission of infectious diseases are often not
random, but with direction. Hence, in this paper, we build epidemic model based
on an interconnected directed network, which can be considered as the
generalization of undirected networks and bipartite networks. By using the
mean-field approach, we establish the Susceptible-Infectious-Susceptible model
on this network. We theoretically analyze the model, and obtain the basic
reproduction number, which is also the generalization of the critical number
corresponding to undirected or bipartite networks. And we prove the global
stability of disease-free and endemic equilibria via the basic reproduction
number as a forward bifurcation parameter. We also give a condition for
epidemic prevalence only on a single subnetwork. Furthermore, we carry out
numerical simulations, and find that the independence between each node's in-
and out-degrees greatly reduce the impact of the network's topological
structure on disease spread.
",0,1,0,0,0,0
20363,Controllability and maximum matchings of complex networks,"  Previously, the controllability problem of a linear time-invariant dynamical
system was mapped to the maximum matching (MM) problem on the bipartite
representation of the underlying directed graph, and the sizes of MMs on random
bipartite graphs were calculated analytically with the cavity method at zero
temperature limit. Here we present an alternative theory to estimate MM sizes
based on the core percolation theory and the perfect matching of cores. Our
theory is much more simplified and easily interpreted, and can estimate MM
sizes on random graphs with or without symmetry between out- and in-degree
distributions. Our result helps to illuminate the fundamental connection
between the controllability problem and the underlying structure of complex
systems.
",1,0,0,0,0,0
20364,"Dimers, crystals and quantum Kostka numbers","  We relate the counting of honeycomb dimer configurations on the cylinder to
the counting of certain vertices in Kirillov-Reshetikhin crystal graphs. We
show that these dimer configurations yield the quantum Kostka numbers of the
small quantum cohomology ring of the Grassmannian, i.e. the expansion
coefficients when multiplying a Schubert class repeatedly with different Chern
classes. This allows one to derive sum rules for Gromov-Witten invariants.
",0,0,1,0,0,0
20365,Generalized Task-Parameterized Skill Learning,"  Programming by demonstration has recently gained much attention due to its
user-friendly and natural way to transfer human skills to robots. In order to
facilitate the learning of multiple demonstrations and meanwhile generalize to
new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been
recently developed. This model has achieved reliable performance in areas such
as human-robot collaboration and dual-arm manipulation. However, the crucial
task frames and associated parameters in this learning framework are often set
by the human teacher, which renders three problems that have not been addressed
yet: (i) task frames are treated equally, without considering their individual
importance, (ii) task parameters are defined without taking into account
additional task constraints, such as robot joint limits and motion smoothness,
and (iii) a fixed number of task frames are pre-defined regardless of whether
some of them may be redundant or even irrelevant for the task at hand. In this
paper, we generalize the task-parameterized learning by addressing the
aforementioned problems. Moreover, we provide a novel learning perspective
which allows the robot to refine and adapt previously learned skills in a low
dimensional space. Several examples are studied in both simulated and real
robotic systems, showing the applicability of our approach.
",1,0,0,0,0,0
20366,Nonparametric Bayesian volatility learning under microstructure noise,"  Aiming at financial applications, we study the problem of learning the
volatility under market microstructure noise. Specifically, we consider noisy
discrete time observations from a stochastic differential equation and develop
a novel computational method to learn the diffusion coefficient of the
equation. We take a nonparametric Bayesian approach, where we model the
volatility function a priori as piecewise constant. Its prior is specified via
the inverse Gamma Markov chain. Sampling from the posterior is accomplished by
incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs
sampler. Good performance of the method is demonstrated on two representative
synthetic data examples. Finally, we apply the method on the EUR/USD exchange
rate dataset.
",0,0,0,0,0,1
20367,Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals,"  Supervised learning based methods for source localization, being data driven,
can be adapted to different acoustic conditions via training and have been
shown to be robust to adverse acoustic environments. In this paper, a
convolutional neural network (CNN) based supervised learning method for
estimating the direction-of-arrival (DOA) of multiple speakers is proposed.
Multi-speaker DOA estimation is formulated as a multi-class multi-label
classification problem, where the assignment of each DOA label to the input
feature is treated as a separate binary classification problem. The phase
component of the short-time Fourier transform (STFT) coefficients of the
received microphone signals are directly fed into the CNN, and the features for
DOA estimation are learnt during training. Utilizing the assumption of disjoint
speaker activity in the STFT domain, a novel method is proposed to train the
CNN with synthesized noise signals. Through experimental evaluation with both
simulated and measured acoustic impulse responses, the ability of the proposed
DOA estimation approach to adapt to unseen acoustic conditions and its
robustness to unseen noise type is demonstrated. Through additional empirical
investigation, it is also shown that with an array of M microphones our
proposed framework yields the best localization performance with M-1
convolution layers. The ability of the proposed method to accurately localize
speakers in a dynamic acoustic scenario with varying number of sources is also
shown.
",1,0,0,0,0,0
20368,Follow-up of eROSITA and Euclid Galaxy Clusters with XMM-Newton,"  A revolution in galaxy cluster science is only a few years away. The survey
machines eROSITA and Euclid will provide cluster samples of never-before-seen
statistical quality. XMM-Newton will be the key instrument to exploit these
rich datasets in terms of detailed follow-up of the cluster hot gas content,
systematically characterizing sub-samples as well as exotic new objects.
",0,1,0,0,0,0
20369,Effect of viscosity ratio on the self-sustained instabilities in planar immiscible jets,"  Previous studies have shown that intermediate surface tension has a
counterintuitive destabilizing effect on 2-phase planar jets. Here, the
transition process in confined 2D jets of two fluids with varying viscosity
ratio is investigated using DNS. Neutral curves for persistent oscillations are
found by recording the norm of the velocity residuals in DNS for over 1000
nondimensional time units, or until the signal has reached a constant level in
a logarithmic scale - either a converged steady state, or a ""statistically
steady"" oscillatory state. Oscillatory final states are found for all viscosity
ratios (0.1-10). For uniform viscosity (m=1), the first bifurcation is through
a surface tension-driven global instability. For low viscosity of the outer
fluid, there is a mode competition between a steady asymmetric Coanda-type
attachment mode and the surface tension-induced mode. At moderate surface
tension, the Coanda-type attachment dominates and eventually triggers
time-dependent convective bursts. At high surface tension, the surface
tension-dominated mode dominates. For high viscosity of the outer fluid,
persistent oscillations appear due to a strong convective instability. Finally,
the m=1 jet remains unstable far from the inlet when the shear profile is
nearly constant. Comparing this to a parallel Couette flow (without inflection
points), we show that in both flows, a hidden interfacial mode brought out by
surface tension becomes temporally and absolutely unstable in an intermediate
Weber and Reynolds regime. An energy analysis of the Couette setup shows that
surface tension, although dissipative, induces a velocity field near the
interface which extracts energy from the flow through a viscous mechanism. This
study highlights the rich dynamics of immiscible planar uniform-density jets,
where several self-sustained and convective mechanisms compete depending on the
exact parameters.
",0,1,0,0,0,0
20370,On the Effects of Batch and Weight Normalization in Generative Adversarial Networks,"  Generative adversarial networks (GANs) are highly effective unsupervised
learning frameworks that can generate very sharp data, even for data such as
images with complex, highly multimodal distributions. However GANs are known to
be very hard to train, suffering from problems such as mode collapse and
disturbing visual artifacts. Batch normalization (BN) techniques have been
introduced to address the training. Though BN accelerates the training in the
beginning, our experiments show that the use of BN can be unstable and
negatively impact the quality of the trained model. The evaluation of BN and
numerous other recent schemes for improving GAN training is hindered by the
lack of an effective objective quality measure for GAN models. To address these
issues, we first introduce a weight normalization (WN) approach for GAN
training that significantly improves the stability, efficiency and the quality
of the generated samples. To allow a methodical evaluation, we introduce
squared Euclidean reconstruction error on a test set as a new objective
measure, to assess training performance in terms of speed, stability, and
quality of generated samples. Our experiments with a standard DCGAN
architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
indicate that training using WN is generally superior to BN for GANs, achieving
10% lower mean squared loss for reconstruction and significantly better
qualitative results than BN. We further demonstrate the stability of WN on a
21-layer ResNet trained with the CelebA data set. The code for this paper is
available at this https URL
",1,0,0,1,0,0
20371,A noise-immune cavity-assisted non-destructive detection for an optical lattice clock in the quantum regime,"  We present and implement a non-destructive detection scheme for the
transition probability readout of an optical lattice clock. The scheme relies
on a differential heterodyne measurement of the dispersive properties of
lattice-trapped atoms enhanced by a high finesse cavity. By design, this scheme
offers a 1st order rejection of the technical noise sources, an enhanced
signal-to-noise ratio, and an homogeneous atom-cavity coupling. We
theoretically show that this scheme is optimal with respect to the photon shot
noise limit. We experimentally realize this detection scheme in an operational
strontium optical lattice clock. The resolution is on the order of a few atoms
with a photon scattering rate low enough to keep the atoms trapped after
detection. This scheme opens the door to various different interrogations
protocols, which reduce the frequency instability, including atom recycling,
zero-dead time clocks with a fast repetition rate, and sub quantum projection
noise frequency stability.
",0,1,0,0,0,0
20372,Remarks on planar edge-chromatic critical graphs,"  The only open case of Vizing's conjecture that every planar graph with
$\Delta\geq 6$ is a class 1 graph is $\Delta = 6$. We give a short proof of the
following statement: there is no 6-critical plane graph $G$, such that every
vertex of $G$ is incident to at most three 3-faces. A stronger statement
without restriction to critical graphs is stated in \cite{Wang_Xu_2013}.
However, the proof given there works only for critical graphs. Furthermore, we
show that every 5-critical plane graph has a 3-face which is adjacent to a
$k$-face $(k\in \{3,4\})$.
For $\Delta = 5$ our result gives insights into the structure of planar
$5$-critical graphs, and the result for $\Delta=6$ gives support for the truth
of Vizing's planar graph conjecture.
",0,0,1,0,0,0
20373,The LOFAR window on star-forming galaxies and AGN - curved radio SEDs and IR-radio correlation at $0 < z < 2.5$,"  We present a study of the low-frequency radio properties of star forming (SF)
galaxies and active galactic nuclei (AGN) up to redshift $z=2.5$. The new
spectral window probed by the Low Frequency Array (LOFAR) allows us to
reconstruct the radio continuum emission from 150 MHz to 1.4 GHz to an
unprecedented depth for a radio-selected sample of $1542$ galaxies in $\sim 7~
\rm{deg}^2$ of the LOFAR Boötes field. Using the extensive multi-wavelength
dataset available in Boötes and detailed modelling of the FIR to UV spectral
energy distribution (SED), we are able to separate the star-formation (N=758)
and the AGN (N=784) dominated populations. We study the shape of the radio SEDs
and their evolution across cosmic time and find significant differences in the
spectral curvature between the SF galaxy and AGN populations. While the radio
spectra of SF galaxies exhibit a weak but statistically significant flattening,
AGN SEDs show a clear trend to become steeper towards lower frequencies. No
evolution of the spectral curvature as a function of redshift is found for SF
galaxies or AGN. We investigate the redshift evolution of the infrared-radio
correlation (IRC) for SF galaxies and find that the ratio of total infrared to
1.4 GHz radio luminosities decreases with increasing redshift: $ q_{\rm 1.4GHz}
= (2.45 \pm 0.04) \times (1+z)^{-0.15 \pm 0.03} $. Similarly, $q_{\rm 150MHz}$
shows a redshift evolution following $ q_{\rm 150GHz} = (1.72 \pm 0.04) \times
(1+z)^{-0.22 \pm 0.05}$. Calibration of the 150 MHz radio luminosity as a star
formation rate tracer suggests that a single power-law extrapolation from
$q_{\rm 1.4GHz}$ is not an accurate approximation at all redshifts.
",0,1,0,0,0,0
20374,Interplay between the Inverse Scattering Method and Fokas's Unified Transform with an Application,"  It is known that the initial-boundary value problem for certain integrable
partial differential equations (PDEs) on the half-line with integrable boundary
conditions can be mapped to a special case of the Inverse Scattering Method
(ISM) on the full-line. This can also be established within the so-called
Unified Transform (UT) for initial-boundary value problems with linearizable
boundary conditions. In this paper, we show a converse to this statement within
the Ablowitz-Kaup-Newell-Segur (AKNS) scheme: the ISM on the full-line can be
mapped to an initial-boundary value problem with linearizable boundary
conditions. To achieve this, we need a matrix version of the UT that was
introduced by the author to study integrable PDEs on star-graphs. As an
application of the result, we show that the new, nonlocal reduction of the AKNS
scheme introduced by Ablowitz and Musslimani to obtain the nonlocal Nonlinear
Schrödinger (NLS) equation can be recast as an old, local reduction, thus
putting the nonlocal NLS and the NLS equations on equal footing from the point
of view of the reduction group theory of Mikhailov.
",0,1,1,0,0,0
20375,GNC of the SphereX Robot for Extreme Environment Exploration on Mars,"  Wheeled ground robots are limited from exploring extreme environments such as
caves, lava tubes and skylights. Small robots that can utilize unconventional
mobility through hopping, flying or rolling can overcome these limitations.
Mul-tiple robots operating as a team offer significant benefits over a single
large ro-bot, as they are not prone to single-point failure, enable distributed
command and control and enable execution of tasks in parallel. These robots can
complement large rovers and landers, helping to explore inaccessible sites,
obtaining samples and for planning future exploration missions. Our robots, the
SphereX, are 3-kg in mass, spherical and contain computers equivalent to
current smartphones. They contain an array of guidance, navigation and control
sensors and electronics. SphereX contains room for a 1-kg science payload,
including for sample return. Our work in this field has recognized the need for
miniaturized chemical mobility systems that provide power and propulsion. Our
research explored the use of miniature rockets, including solid rockets,
bi-propellants including RP1/hydrogen-peroxide and
polyurethane/ammonium-perchlorate. These propulsion options provide maximum
flight times of 10 minutes on Mars. Flying, especially hovering consumes
significant fuel; hence, we have been developing our robots to perform
ballistic hops that enable the robots to travel efficiently over long
distances. Techniques are being developed to enable mid-course correction
during a ballistic hop. Using multiple cameras, it is possible to reconstitute
an image scene from motion blur. Hence our approach is to enable photo mapping
as the robots travel on a ballistic hop. The same images would also be used for
navigation and path planning. Using our proposed design approach, we are
developing low-cost methods for surface exploration of planetary bodies using a
network of small robots.
",1,1,0,0,0,0
20376,Stable Signatures for Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence,"  When studying flocking/swarming behaviors in animals one is interested in
quantifying and comparing the dynamics of the clustering induced by the
coalescence and disbanding of animals in different groups. In a similar vein,
studying the dynamics of social networks leads to the problem of characterizing
groups/communities as they form and disperse throughout time.
Motivated by this, we study the problem of obtaining persistent homology
based summaries of time-dependent data. Given a finite dynamic graph (DG), we
first construct a zigzag persistence module arising from linearizing the
dynamic transitive graph naturally induced from the input DG. Based on standard
results, we then obtain a persistence diagram or barcode from this zigzag
persistence module. We prove that these barcodes are stable under perturbations
in the input DG under a suitable distance between DGs that we identify.
More precisely, our stability theorem can be interpreted as providing a lower
bound for the distance between DGs. Since it relies on barcodes, and their
bottleneck distance, this lower bound can be computed in polynomial time from
the DG inputs.
Since DGs can be given rise by applying the Rips functor (with a fixed
threshold) to dynamic metric spaces, we are also able to derive related stable
invariants for these richer class of dynamic objects.
Along the way, we propose a summarization of dynamic graphs that captures
their time-dependent clustering features which we call formigrams. These
set-valued functions generalize the notion of dendrogram, a prevalent tool for
hierarchical clustering. In order to elucidate the relationship between our
distance between two DGs and the bottleneck distance between their associated
barcodes, we exploit recent advances in the stability of zigzag persistence due
to Botnan and Lesnick, and to Bjerkevik.
",0,0,1,0,0,0
20377,A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space,"  We are developing a system for human-robot communication that enables people
to communicate with robots in a natural way and is focused on solving problems
in a shared space. Our strategy for developing this system is fundamentally
data-driven: we use data from multiple input sources and train key components
with various machine learning techniques. We developed a web application that
is collecting data on how two humans communicate to accomplish a task, as well
as a mobile laboratory that is instrumented to collect data on how two humans
communicate to accomplish a task in a physically shared space. The data from
these systems will be used to train and fine-tune the second stage of our
system, in which the robot will be simulated through software. A physical robot
will be used in the final stage of our project. We describe these instruments,
a test-suite and performance metrics designed to evaluate and automate the data
gathering process as well as evaluate an initial data set.
",1,0,0,0,0,0
20378,Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan,"  The recent turn towards quantitative text-as-data approaches in IR brought
new ways to study the discursive landscape of world politics. Here seen as
complementary to qualitative approaches, quantitative assessments have the
advantage of being able to order and make comprehensible vast amounts of text.
However, the validity of unsupervised methods applied to the types of text
available in large quantities needs to be established before they can speak to
other studies relying on text and discourse as data. In this paper, we
introduce a new text corpus of United Nations Security Council (UNSC) speeches
on Afghanistan between 2001 and 2017; we study this corpus through unsupervised
topic modeling (LDA) with the central aim to validate the topic categories that
the LDA identifies; and we discuss the added value, and complementarity, of
quantitative text-as-data approaches. We set-up two tests using mixed- method
approaches. Firstly, we evaluate the identified topics by assessing whether
they conform with previous qualitative work on the development of the situation
in Afghanistan. Secondly, we use network analysis to study the underlying
social structures of what we will call 'speaker-topic relations' to see whether
they correspondent to know divisions and coalitions in the UNSC. In both cases
we find that the unsupervised LDA indeed provides valid and valuable outputs.
In addition, the mixed-method approaches themselves reveal interesting patterns
deserving future qualitative research. Amongst these are the coalition and
dynamics around the 'women and human rights' topic as part of the UNSC debates
on Afghanistan.
",1,0,0,0,0,0
20379,Lattice Gas with Molecular Dynamics Collision Operator,"  We introduce a lattice gas implementation that is based on coarse-graining a
Molecular Dynamics (MD) simulation. Such a lattice gas is similar to standard
lattice gases, but its collision operator is informed by an underlying MD
simulation. This can be considered an optimal lattice gas implementation
because it allows for the representation of any system that can be simulated
with MD. We show here that equilibrium behavior of the popular lattice
Boltzmann algorithm is consistent with this optimal lattice gas. This
comparison allows us to make a more accurate identification of the expressions
for temperature and pressure in lattice Boltzmann simulations which turn out to
be related not only to the physical temperature and pressure but also to the
lattice discretization. We show that for any spatial discretization we need to
choose a particular temporal discretization to recover the lattice Boltzmann
equilibrium.
",0,1,0,0,0,0
20380,From dynamical systems with time-varying delay to circle maps and Koopmanism,"  In the present paper we investigate the influence of the retarded access by a
time-varying delay on the dynamics of delay systems. We show that there are two
universality classes of delays, which lead to fundamental differences in
dynamical quantities such as the Lyapunov spectrum. Therefore we introduce an
operator theoretic framework, where the solution operator of the delay system
is decomposed into the Koopman operator describing the delay access and an
operator similar to the solution operator known from systems with constant
delay. The Koopman operator corresponds to an iterated map, called access map,
which is defined by the iteration of the delayed argument of the delay
equation. The dynamics of this one-dimensional iterated map determines the
universality classes of the infinite-dimensional state dynamics governed by the
delay differential equation. In this way, we connect the theory of time-delay
systems with the theory of circle maps and the framework of the Koopman
operator. In the present paper we extend our previous work [Otto, Müller, and
Radons, Phys. Rev. Lett. 118, 044104 (2017)], by elaborating the mathematical
details and presenting further results also on the Lyapunov vectors.
",0,1,1,0,0,0
20381,Engineering Frequency-dependent Superfluidity in Bose-Fermi Mixtures,"  Unconventional superconductivity or superfluidity are among the most exciting
and fascinating quantum states in condensed matter physics. Usually these
states are characterized by non-trivial spatial symmetry of the pairing order
parameter, such as in $^{3}He$ and high-$T_{c}$ cuprates. Besides spatial
dependence the order parameter could have unconventional frequency dependence,
which is also allowed by Fermi-Dirac statistics. For instance, odd-frequency
pairing is an exciting paradigm when discussing exotic superfluidity or
superconductivity and is yet to be realized in the experiments. In this paper
we propose a symmetry-based method of controlling frequency dependence of the
pairing order parameter via manipulating the inversion symmetry of the system.
First, a toy model is introduced to illustrate that frequency dependence of the
order parameter can be adjusted by controlling the inversion symmetry of the
system. Second, taking advantage of the recent rapid developments of shaken
optical lattices in ultracold gases, we propose a Bose-Fermi mixture to realize
such frequency dependent superfluids. The key idea is introducing the
frequency-dependent attraction between Fermions mediated by Bogoliubov phonons
with asymmetric dispersion. Our proposal should pave an alternative way for
exploring frequency-dependent superconductors or superfluids with cold atoms.
",0,1,0,0,0,0
20382,In-Silico Proportional-Integral Moment Control of Stochastic Reaction Networks with Applications to Gene Expression (with Dimerization),"  The problem of controlling the mean and the variance of a species of interest
in a simple gene expression is addressed. It is shown that the protein mean
level can be globally and robustly tracked to any desired value using a simple
PI controller that satisfies certain sufficient conditions. Controlling both
the mean and variance however requires an additional control input, e.g. the
mRNA degradation rate, and local robust tracking of mean and variance is proved
to be achievable using multivariable PI control, provided that the reference
point satisfies necessary conditions imposed by the system. Even more
importantly, it is shown that there exist PI controllers that locally, robustly
and simultaneously stabilize all the equilibrium points inside the admissible
region. The results are then extended to the mean control of a gene expression
with protein dimerization. It is shown that the moment closure problem can be
circumvented without invoking any moment closure technique. Local stabilization
and convergence of the average dimer population to any desired reference value
is ensured using a pure integral control law. Explicit bounds on the controller
gain are provided and shown to be valid for any reference value. As a
byproduct, an explicit upper-bound of the variance of the monomer species,
acting on the system as unknown input due to the moment openness, is obtained.
The results are illustrated by simulation.
",1,0,0,0,1,0
20383,Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China,"  Big, fine-grained enterprise registration data that includes time and
location information enables us to quantitatively analyze, visualize, and
understand the patterns of industries at multiple scales across time and space.
However, data quality issues like incompleteness and ambiguity, hinder such
analysis and application. These issues become more challenging when the volume
of data is immense and constantly growing. High Performance Computing (HPC)
frameworks can tackle big data computational issues, but few studies have
systematically investigated imputation methods for enterprise registration data
in this type of computing environment. In this paper, we propose a big data
imputation workflow based on Apache Spark as well as a bare-metal computing
cluster, to impute enterprise registration data. We integrated external data
sources, employed Natural Language Processing (NLP), and compared several
machine-learning methods to address incompleteness and ambiguity problems found
in enterprise registration data. Experimental results illustrate the
feasibility, efficiency, and scalability of the proposed HPC-based imputation
framework, which also provides a reference for other big georeferenced text
data processing. Using these imputation results, we visualize and briefly
discuss the spatiotemporal distribution of industries in China, demonstrating
the potential applications of such data when quality issues are resolved.
",1,0,0,0,0,0
20384,Fast Simulation of Vehicles with Non-deformable Tracks,"  This paper presents a novel technique that allows for both computationally
fast and sufficiently plausible simulation of vehicles with non-deformable
tracks. The method is based on an effect we have called Contact Surface Motion.
A comparison with several other methods for simulation of tracked vehicle
dynamics is presented with the aim to evaluate methods that are available
off-the-shelf or with minimum effort in general-purpose robotics simulators.
The proposed method is implemented as a plugin for the open-source
physics-based simulator Gazebo using the Open Dynamics Engine.
",1,0,0,0,0,0
20385,Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory,"  Recurrent neural networks (RNNs) have been drawing much attention with great
success in many applications like speech recognition and neural machine
translation. Long short-term memory (LSTM) is one of the most popular RNN units
in deep learning applications. LSTM transforms the input and the previous
hidden states to the next states with the affine transformation, multiplication
operations and a nonlinear activation function, which makes a good data
representation for a given task. The affine transformation includes rotation
and reflection, which change the semantic or syntactic information of
dimensions in the hidden states. However, considering that a model interprets
the output sequence of LSTM over the whole input sequence, the dimensions of
the states need to keep the same type of semantic or syntactic information
regardless of the location in the sequence. In this paper, we propose a simple
variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension
of hidden states keeps persistent information across time, so that the space
keeps the same meaning over the whole sequence. In addition, to improve the
nonlinear transformation power, we add a feedforward layer in the PRU
structure. In the experiment, we evaluate our proposed methods with three
different tasks, and the results confirm that our methods have better
performance than the conventional LSTM.
",0,0,0,1,0,0
20386,Satellite Image-based Localization via Learned Embeddings,"  We propose a vision-based method that localizes a ground vehicle using
publicly available satellite imagery as the only prior knowledge of the
environment. Our approach takes as input a sequence of ground-level images
acquired by the vehicle as it navigates, and outputs an estimate of the
vehicle's pose relative to a georeferenced satellite image. We overcome the
significant viewpoint and appearance variations between the images through a
neural multi-view model that learns location-discriminative embeddings in which
ground-level images are matched with their corresponding satellite view of the
scene. We use this learned function as an observation model in a filtering
framework to maintain a distribution over the vehicle's pose. We evaluate our
method on different benchmark datasets and demonstrate its ability localize
ground-level images in environments novel relative to training, despite the
challenges of significant viewpoint and appearance variations.
",1,0,0,0,0,0
20387,Optimality of codes with respect to error probability in Gaussian noise,"  We consider geometrical optimization problems related to optimizing the error
probability in the presence of a Gaussian noise. One famous questions in the
field is the ""weak simplex conjecture"". We discuss possible approaches to it,
and state related conjectures about the Gaussian measure, in particular, the
conjecture about minimizing of the Gaussian measure of a simplex. We also
consider antipodal codes, apply the Šidák inequality and establish some
theoretical and some numerical results about their optimality.
",1,0,1,0,0,0
20388,Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs,"  Time delay neural networks (TDNNs) are an effective acoustic model for large
vocabulary speech recognition. The strength of the model can be attributed to
its ability to effectively model long temporal contexts. However, current TDNN
models are relatively shallow, which limits the modelling capability. This
paper proposes a method of increasing the network depth by deepening the kernel
used in the TDNN temporal convolutions. The best performing kernel consists of
three fully connected layers with a residual (ResNet) connection from the
output of the first to the output of the third. The addition of
spectro-temporal processing as the input to the TDNN in the form of a
convolutional neural network (CNN) and a newly designed Grid-RNN was
investigated. The Grid-RNN strongly outperforms a CNN if different sets of
parameters for different frequency bands are used and can be further enhanced
by using a bi-directional Grid-RNN. Experiments using the multi-genre broadcast
(MGB3) English data (275h) show that deep kernel TDNNs reduces the word error
rate (WER) by 6% relative and when combined with the frequency dependent
Grid-RNN gives a relative WER reduction of 9%.
",0,0,0,1,0,0
20389,A Structured Self-attentive Sentence Embedding,"  This paper proposes a new model for extracting an interpretable sentence
embedding by introducing self-attention. Instead of using a vector, we use a
2-D matrix to represent the embedding, with each row of the matrix attending on
a different part of the sentence. We also propose a self-attention mechanism
and a special regularization term for the model. As a side effect, the
embedding comes with an easy way of visualizing what specific parts of the
sentence are encoded into the embedding. We evaluate our model on 3 different
tasks: author profiling, sentiment classification, and textual entailment.
Results show that our model yields a significant performance gain compared to
other sentence embedding methods in all of the 3 tasks.
",1,0,0,0,0,0
20390,When Neurons Fail,"  We view a neural network as a distributed system of which neurons can fail
independently, and we evaluate its robustness in the absence of any (recovery)
learning phase. We give tight bounds on the number of neurons that can fail
without harming the result of a computation. To determine our bounds, we
leverage the fact that neural activation functions are Lipschitz-continuous.
Our bound is on a quantity, we call the \textit{Forward Error Propagation},
capturing how much error is propagated by a neural network when a given number
of components is failing, computing this quantity only requires looking at the
topology of the network, while experimentally assessing the robustness of a
network requires the costly experiment of looking at all the possible inputs
and testing all the possible configurations of the network corresponding to
different failure situations, facing a discouraging combinatorial explosion.
We distinguish the case of neurons that can fail and stop their activity
(crashed neurons) from the case of neurons that can fail by transmitting
arbitrary values (Byzantine neurons). Interestingly, as we show in the paper,
our bound can easily be extended to the case where synapses can fail.
We show how our bound can be leveraged to quantify the effect of memory cost
reduction on the accuracy of a neural network, to estimate the amount of
information any neuron needs from its preceding layer, enabling thereby a
boosting scheme that prevents neurons from waiting for unnecessary signals. We
finally discuss the trade-off between neural networks robustness and learning
cost.
",0,0,0,1,0,0
20391,Heavy Traffic Limit for a Tandem Queue with Identical Service Times,"  We consider a two-node tandem queueing network in which the upstream queue is
M/G/1 and each job reuses its upstream service requirement when moving to the
downstream queue. Both servers employ the first-in-first-out policy. We
investigate the amount of work in the second queue at certain embedded arrival
time points, namely when the upstream queue has just emptied. We focus on the
case of infinite-variance service times and obtain a heavy traffic process
limit for the embedded Markov chain.
",0,0,1,0,0,0
20392,Autonomous Sweet Pepper Harvesting for Protected Cropping Systems,"  In this letter, we present a new robotic harvester (Harvey) that can
autonomously harvest sweet pepper in protected cropping environments. Our
approach combines effective vision algorithms with a novel end-effector design
to enable successful harvesting of sweet peppers. Initial field trials in
protected cropping environments, with two cultivar, demonstrate the efficacy of
this approach achieving a 46% success rate for unmodified crop, and 58% for
modified crop. Furthermore, for the more favourable cultivar we were also able
to detach 90% of sweet peppers, indicating that improvements in the grasping
success rate would result in greatly improved harvesting performance.
",1,0,0,0,0,0
20393,AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: perpendicular flows of gas and dark matter,"  AMI observations towards CIZA J2242+5301, in comparison with observations of
weak gravitational lensing and X-ray emission from the literature, are used to
investigate the behaviour of non-baryonic dark matter (NBDM) and gas during the
merger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence
of high pressure gas elongated perpendicularly to the X-ray and weak-lensing
morphologies which, given the merger-axis constraints in the literature,
implies that high pressure gas is pushed out into a linear structure during
core passing. Simulations in the literature closely matching the inferred
merger scenario show the formation of gas density and temperature structures
perpendicular to the merger axis. These SZ observations are challenging for
modified gravity theories in which NBDM is not the dominant contributor to
galaxy-cluster gravity.
",0,1,0,0,0,0
20394,Development of a computer-aided design software for dental splint in orthognathic surgery,"  In the orthognathic surgery, dental splints are important and necessary to
help the surgeon reposition the maxilla or mandible. However, the traditional
methods of manual design of dental splints are difficult and time-consuming.
The research on computer-aided design software for dental splints is rarely
reported. Our purpose is to develop a novel special software named EasySplint
to design the dental splints conveniently and efficiently. The design can be
divided into two steps, which are the generation of initial splint base and the
Boolean operation between it and the maxilla-mandibular model. The initial
splint base is formed by ruled surfaces reconstructed using the manually picked
points. Then, a method to accomplish Boolean operation based on the distance
filed of two meshes is proposed. The interference elimination can be conducted
on the basis of marching cubes algorithm and Boolean operation. The accuracy of
the dental splint can be guaranteed since the original mesh is utilized to form
the result surface. Using EasySplint, the dental splints can be designed in
about 10 minutes and saved as a stereo lithography (STL) file for 3D printing
in clinical applications. Three phantom experiments were conducted and the
efficiency of our method was demonstrated.
",1,1,0,0,0,0
20395,"Energy-transport systems for optical lattices: derivation, analysis, simulation","  Energy-transport equations for the transport of fermions in optical lattices
are formally derived from a Boltzmann transport equation with a periodic
lattice potential in the diffusive limit. The limit model possesses a formal
gradient-flow structure like in the case of the energy-transport equations for
semiconductors. At the zeroth-order high temperature limit, the
energy-transport equations reduce to the whole-space logarithmic diffusion
equation which has some unphysical properties. Therefore, the first-order
expansion is derived and analyzed. The existence of weak solutions to the
time-discretized system for the particle and energy densities with periodic
boundary conditions is proved. The difficulties are the nonstandard degeneracy
and the quadratic gradient term. The main tool of the proof is a result on the
strong convergence of the gradients of the approximate solutions. Numerical
simulations in one space dimension show that the particle density converges to
a constant steady state if the initial energy density is sufficiently large,
otherwise the particle density converges to a nonconstant steady state.
",0,0,1,0,0,0
20396,"First principles study of structural, magnetic and electronic properties of CrAs","  We report ab initio density functional calculations of the structural and
magnetic properties, and the electronic structure of CrAs. To simulate the
observed pressure-driven experimental results, we perform our analysis for
different volumes of the unit cell, showing that the structural, magnetic and
electronic properties strongly depend on the size of the cell. We find that the
calculated quantities are in good agreement with the experimental data, and we
review our results in terms of the observed superconductivity.
",0,1,0,0,0,0
20397,Subspace Robust Wasserstein distances,"  Making sense of Wasserstein distances between discrete measures in
high-dimensional settings remains a challenge. Recent work has advocated a
two-step approach to improve robustness and facilitate the computation of
optimal transport, using for instance projections on random real lines, or a
preliminary quantization to reduce the number of points. We propose in this
work a new robust variant of the Wasserstein distance. This quantity captures
the maximal possible distance that can be realized between these two measures,
after they have been projected orthogonally on a lower k dimensional subspace.
We show that this distance inherits several favorably properties of OT, and
that computing it can be cast as a convex problem involving the top k
eigenvalues of the second order moment matrix of the displacements induced by a
transport plan. We provide algorithms to approximate the computation of this
saddle point using entropic regularization, and illustrate the interest of this
approach empirically.
",1,0,0,1,0,0
20398,Wind accretion onto compact objects,"  X-ray emission associated to accretion onto compact objects displays
important levels of photometric and spectroscopic time-variability. When the
accretor orbits a Supergiant star, it captures a fraction of the supersonic
radiatively-driven wind which forms shocks in its vicinity. The amplitude and
stability of this gravitational beaming of the flow conditions the mass
accretion rate responsible, in fine, for the X-ray luminosity of those
Supergiant X-ray Binaries. The capacity of this low angular momentum inflow to
form a disc-like structure susceptible to be the stage of well-known
instabilities remains at stake. Using state-of-the-art numerical setups, we
characterized the structure of a Bondi-Hoyle-Lyttleton flow onto a compact
object, from the shock down to the vicinity of the accretor, typically five
orders of magnitude smaller. The evolution of the mass accretion rate and of
the bow shock which forms around the accretor (transverse structure, opening
angle, stability, temperature profile) with the Mach number of the incoming
flow is described in detail. The robustness of those simulations based on the
High Performance Computing MPI-AMRVAC code is supported by the topology of the
inner sonic surface, in agreement with theoretical expectations. We developed a
synthetic model of mass transfer in Supergiant X-ray Binaries which couples the
launching of the wind accordingly to the stellar parameters, the orbital
evolution of the streamlines in a modified Roche potential and the accretion
process. We show that the shape of the permanent flow is entirely determined by
the mass ratio, the filling factor, the Eddington factor and the alpha-force
multiplier. Provided scales such as the orbital period are known, we can trace
back the observables to evaluate the mass accretion rates, the accretion
mechanism (stream or wind-dominated) and the shearing of the inflow.
",0,1,0,0,0,0
20399,Riemannian Stein Variational Gradient Descent for Bayesian Inference,"  We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian
inference method that generalizes Stein Variational Gradient Descent (SVGD) to
Riemann manifold. The benefits are two-folds: (i) for inference tasks in
Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information
geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the
unique advantages of SVGD to the Riemannian world. To appropriately transfer to
Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD,
which are required by the intrinsically different characteristics of general
Riemann manifolds from Euclidean spaces. We also discover Riemannian Stein's
Identity and Riemannian Kernelized Stein Discrepancy. Experimental results show
the advantages over SVGD of exploring distribution geometry and the advantages
of particle-efficiency, iteration-effectiveness and approximation flexibility
over other inference methods on Riemann manifolds.
",0,0,0,1,0,0
20400,Suspension-thermal noise in spring-antispring systems for future gravitational-wave detectors,"  Spring-antispring systems have been investigated as possible low-frequency
seismic isolation in high-precision optical experiments. These systems provide
the possibility to tune the fundamental resonance frequency to, in principle,
arbitrarily low values, and at the same time maintain a compact design of the
isolation system. It was argued though that thermal noise in spring-antispring
systems would not be as small as one may naively expect from lowering the
fundamental resonance frequency. In this paper, we present a detailed
calculation of the suspension thermal noise for a specific spring-antispring
system, namely the Roberts linkage. We find a concise expression of the
suspension thermal noise spectrum, which assumes a form very similar to the
well-known expression for a simple pendulum. It is found that while the Roberts
linkage can provide strong seismic isolation due to a very low fundamental
resonance frequency, its thermal noise is rather determined by the dimension of
the system. We argue that this is true for all horizontal mechanical isolation
systems with spring-antispring dynamics. This imposes strict requirements on
mechanical spring-antispring systems for the seismic isolation in potential
future low-frequency gravitational-wave detectors as we discuss for the four
main concepts: atom-interferometric, superconducting, torsion-bars, and
conventional laser interferometer.
",0,1,0,0,0,0
20401,Fooling Sets and the Spanning Tree Polytope,"  In the study of extensions of polytopes of combinatorial optimization
problems, a notorious open question is that for the size of the smallest
extended formulation of the Minimum Spanning Tree problem on a complete graph
with $n$ nodes. The best known lower bound is $\Omega(n^2)$, the best known
upper bound is $O(n^3)$.
In this note we show that the venerable fooling set method cannot be used to
improve the lower bound: every fooling set for the Spanning Tree polytope has
size $O(n^2)$.
",1,0,1,0,0,0
20402,From Azéma supermartingales of finite honest times to optional semimartingales of class-($Σ$),"  Given a finite honest time, we derive representations for the additive and
multiplicative decomposition of it's Azéma supermartingale in terms of
optional supermartingales and its running supremum. We then extend the notion
of semimartingales of class-$(\Sigma)$ to optional semimartingales with jumps
in its finite variation part, allowing one to establish formulas similar to the
Madan-Roynette-Yor option pricing formulas for larger class of processes.
Finally, we introduce the optional multiplicative systems associated with
positive submartingales and apply them to construct random times with given
Azéma supermartingale.
",0,0,0,0,0,1
20403,Single versus Double Blind Reviewing at WSDM 2017,"  In this paper we study the implications for conference program committees of
using single-blind reviewing, in which committee members are aware of the names
and affiliations of paper authors, versus double-blind reviewing, in which this
information is not visible to committee members. WSDM 2017, the 10th ACM
International ACM Conference on Web Search and Data Mining, performed a
controlled experiment in which each paper was reviewed by four committee
members. Two of these four reviewers were chosen from a pool of committee
members who had access to author information; the other two were chosen from a
disjoint pool who did not have access to this information. This information
asymmetry persisted through the process of bidding for papers, reviewing
papers, and entering scores. Reviewers in the single-blind condition typically
bid for 22% fewer papers, and preferentially bid for papers from top
institutions. Once papers were allocated to reviewers, single-blind reviewers
were significantly more likely than their double-blind counterparts to
recommend for acceptance papers from famous authors and top institutions. The
estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for
top universities and companies respectively, so the result is tangible. For
female authors, the associated odds multiplier of 0.78 is not statistically
significant in our study. However, a meta-analysis places this value in line
with that of other experiments, and in the context of this larger aggregate the
gender effect is also statistically significant.
",1,0,0,0,0,0
20404,Scale-variant Topological Information for Characterizing Complex Networks,"  Real-world networks are difficult to characterize because of the variation of
topological scales, the non-dyadic complex interactions, and the fluctuations.
Here, we propose a general framework to address these problems via a
methodology grounded on topology data analysis. By observing the diffusion
process in a network at a single specified timescale, we can map the network
nodes to a point cloud, which contains the topological information of the
network at a single scale. We then calculate the point clouds constructed over
variable timescales, which provide scale-variant topological information and
enable a deep understanding of the network structure and functionality.
Experiments on synthetic and real-world data demonstrate the effectiveness of
our framework in identifying network models, classifying real-world networks
and detecting transition points in time-evolving networks. Our work presents a
unified analysis that is potentially applicable to more complicated network
structures such as multilayer and multiplex networks.
",1,0,0,0,0,0
20405,Topological orbital superfluid with chiral d-wave order in a rotating optical lattice,"  Topological superfluid is an exotic state of quantum matter that possesses a
nodeless superfluid gap in the bulk and Andreev edge modes at the boundary of a
finite system. Here, we study a multi-orbital superfluid driven by attractive
s-wave interaction in a rotating optical lattice. Interestingly, we find that
the rotation induces the inter- orbital hybridization and drives the system
into topological orbital superfluid in accordance with intrinsically chiral
d-wave pairing characteristics. Thanks to the conservation of spin, the
topological orbital superfluid supports four rather than two chiral Andreev
edge modes at the boundary of the lattice. Moreover, we find that the intrinsic
harmonic confining potential forms a circular spatial barrier which accumulates
atoms and supports a mass current under injection of small angular momentum as
external driving force. This feature provides an experimentally detectable
phenomenon to verify the topological orbital superfluid with chiral d-wave
order in a rotating optical lattice.
",0,1,0,0,0,0
20406,Optimal client recommendation for market makers in illiquid financial products,"  The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.
",0,0,0,1,0,0
20407,Design of an Audio Interface for Patmos,"  This paper describes the design and implementation of an audio interface for
the Patmos processor, which runs on an Altera DE2-115 FPGA board. This board
has an audio codec included, the WM8731. The interface described in this work
allows to receive and send audio from and to the WM8731, and to synthesize,
store or manipulate audio signals writing C programs for Patmos. The audio
interface described in this paper is intended to be used with the Patmos
processor. Patmos is an open source RISC ISAs with a load-store architecture,
that is optimized for Real-Time Systems. Patmos is part of a project founded by
the European Union called T-CREST (Time-predictable Multi-Core Architecture for
Embedded Systems).[5] The structure of this project is integrated with the
Patmos project: new hardware modules have been added as IOs, which allow the
communication between the processor and the audio codec. These modules include
a clock generator for the audio chip, ADC and DAC modules for the audio
conversion from analog to digital and vice versa, and an I2C module which
allows setting configuration parameters on the audio codec. Moreover, a top
module has been created, which connects all the modules previously mentioned
between them, to Patmos and to the WM8731, using the external pins of the FPGA.
",1,0,0,0,0,0
20408,A Classifying Variational Autoencoder with Application to Polyphonic Music Generation,"  The variational autoencoder (VAE) is a popular probabilistic generative
model. However, one shortcoming of VAEs is that the latent variables cannot be
discrete, which makes it difficult to generate data from different modes of a
distribution. Here, we propose an extension of the VAE framework that
incorporates a classifier to infer the discrete class of the modeled data. To
model sequential data, we can combine our Classifying VAE with a recurrent
neural network such as an LSTM. We apply this model to algorithmic music
generation, where our model learns to generate musical sequences in different
keys. Most previous work in this area avoids modeling key by transposing data
into only one or two keys, as opposed to the 10+ different keys in the original
music. We show that our Classifying VAE and Classifying VAE+LSTM models
outperform the corresponding non-classifying models in generating musical
samples that stay in key. This benefit is especially apparent when trained on
untransposed music data in the original keys.
",1,0,0,1,0,0
20409,On Estimation of Isotonic Piecewise Constant Signals,"  Consider a sequence of real data points $X_1,\ldots, X_n$ with underlying
means $\theta^*_1,\dots,\theta^*_n$. This paper starts from studying the
setting that $\theta^*_i$ is both piecewise constant and monotone as a function
of the index $i$. For this, we establish the exact minimax rate of estimating
such monotone functions, and thus give a non-trivial answer to an open problem
in the shape-constrained analysis literature. The minimax rate involves an
interesting iterated logarithmic dependence on the dimension, a phenomenon that
is revealed through characterizing the interplay between the isotonic shape
constraint and model selection complexity. We then develop a penalized
least-squares procedure for estimating the vector
$\theta^*=(\theta^*_1,\dots,\theta^*_n)^T$. This estimator is shown to achieve
the derived minimax rate adaptively. For the proposed estimator, we further
allow the model to be misspecified and derive oracle inequalities with the
optimal rates, and show there exists a computationally efficient algorithm to
compute the exact solution.
",0,0,1,1,0,0
20410,Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams,"  The concept of stochastic configuration networks (SCNs) others a solid
framework for fast implementation of feedforward neural networks through
randomized learning. Unlike conventional randomized approaches, SCNs provide an
avenue to select appropriate scope of random parameters to ensure the universal
approximation property. In this paper, a deep version of stochastic
configuration networks, namely deep stacked stochastic configuration network
(DSSCN), is proposed for modeling non-stationary data streams. As an extension
of evolving stochastic connfiguration networks (eSCNs), this work contributes a
way to grow and shrink the structure of deep stochastic configuration networks
autonomously from data streams. The performance of DSSCN is evaluated by six
benchmark datasets. Simulation results, compared with prominent data stream
algorithms, show that the proposed method is capable of achieving comparable
accuracy and evolving compact and parsimonious deep stacked network
architecture.
",0,0,0,1,0,0
20411,Extracting Syntactic Patterns from Databases,"  Many database columns contain string or numerical data that conforms to a
pattern, such as phone numbers, dates, addresses, product identifiers, and
employee ids. These patterns are useful in a number of data processing
applications, including understanding what a specific field represents when
field names are ambiguous, identifying outlier values, and finding similar
fields across data sets. One way to express such patterns would be to learn
regular expressions for each field in the database. Unfortunately, exist- ing
techniques on regular expression learning are slow, taking hundreds of seconds
for columns of just a few thousand values. In contrast, we develop XSystem, an
efficient method to learn patterns over database columns in significantly less
time. We show that these patterns can not only be built quickly, but are
expressive enough to capture a number of key applications, including detecting
outliers, measuring column similarity, and assigning semantic labels to columns
(based on a library of regular expressions). We evaluate these applications
with datasets that range from chemical databases (based on a collaboration with
a pharmaceutical company), our university data warehouse, and open data from
MassData.gov.
",1,0,0,0,0,0
20412,Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification,"  We address the problem of multi-class classification in the case where the
number of classes is very large. We propose a double sampling strategy on top
of a multi-class to binary reduction strategy, which transforms the original
multi-class problem into a binary classification problem over pairs of
examples. The aim of the sampling strategy is to overcome the curse of
long-tailed class distributions exhibited in majority of large-scale
multi-class classification problems and to reduce the number of pairs of
examples in the expanded data. We show that this strategy does not alter the
consistency of the empirical risk minimization principle defined over the
double sample reduction. Experiments are carried out on DMOZ and Wikipedia
collections with 10,000 to 100,000 classes where we show the efficiency of the
proposed approach in terms of training and prediction time, memory consumption,
and predictive performance with respect to state-of-the-art approaches.
",1,0,0,1,0,0
20413,"Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents","  Travel time on a route varies substantially by time of day and from day to
day. It is critical to understand to what extent this variation is correlated
with various factors, such as weather, incidents, events or travel demand level
in the context of dynamic networks. This helps a better decision making for
infrastructure planning and real-time traffic operation. We propose a
data-driven approach to understand and predict highway travel time using
spatio-temporal features of those factors, all of which are acquired from
multiple data sources. The prediction model holistically selects the most
related features from a high-dimensional feature space by correlation analysis,
principle component analysis and LASSO. We test and compare the performance of
several regression models in predicting travel time 30 min in advance via two
case studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a
2.3-mile corridor of I-376E in Pittsburgh region. We found that some
bottlenecks scattered in the network can imply congestion on those corridors at
least 30 minutes in advance, including those on the alternative route to the
corridors of study. In addition, real-time travel time is statistically related
to incidents on some specific locations, morning/afternoon travel demand,
visibility, precipitation, wind speed/gust and the weather type. All those
spatio-temporal information together help improve prediction accuracy,
comparing to using only speed data. In both case studies, random forest shows
the most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%
respectively in afternoon peak hours for the entire year of 2014.
",0,0,0,1,0,0
20414,The power of sum-of-squares for detecting hidden structures,"  We study planted problems---finding hidden structures in random noisy
inputs---through the lens of the sum-of-squares semidefinite programming
hierarchy (SoS). This family of powerful semidefinite programs has recently
yielded many new algorithms for planted problems, often achieving the best
known polynomial-time guarantees in terms of accuracy of recovered solutions
and robustness to noise. One theme in recent work is the design of spectral
algorithms which match the guarantees of SoS algorithms for planted problems.
Classical spectral algorithms are often unable to accomplish this: the twist in
these new spectral algorithms is the use of spectral structure of matrices
whose entries are low-degree polynomials of the input variables. We prove that
for a wide class of planted problems, including refuting random constraint
satisfaction problems, tensor and sparse PCA, densest-k-subgraph, community
detection in stochastic block models, planted clique, and others, eigenvalues
of degree-d matrix polynomials are as powerful as SoS semidefinite programs of
roughly degree d. For such problems it is therefore always possible to match
the guarantees of SoS without solving a large semidefinite program. Using
related ideas on SoS algorithms and low-degree matrix polynomials (and inspired
by recent work on SoS and the planted clique problem by Barak et al.), we prove
new nearly-tight SoS lower bounds for the tensor and sparse principal component
analysis problems. Our lower bounds for sparse principal component analysis are
the first to suggest that going beyond existing algorithms for this problem may
require sub-exponential time.
",1,0,0,0,0,0
20415,An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis,"  Polynomial chaos expansions (PCE) have seen widespread use in the context of
uncertainty quantification. However, their application to structural
reliability problems has been hindered by the limited performance of PCE in the
tails of the model response and due to the lack of local metamodel error
estimates. We propose a new method to provide local metamodel error estimates
based on bootstrap resampling and sparse PCE. An initial experimental design is
iteratively updated based on the current estimation of the limit-state surface
in an active learning algorithm. The greedy algorithm uses the bootstrap-based
local error estimates for the polynomial chaos predictor to identify the best
candidate set of points to enrich the experimental design. We demonstrate the
effectiveness of this approach on a well-known analytical benchmark
representing a series system, on a truss structure and on a complex realistic
frame structure problem.
",0,0,0,1,0,0
20416,Experimental Tests of Spirituality,"  We currently harness technologies that could shed new light on old
philosophical questions, such as whether our mind entails anything beyond our
body or whether our moral values reflect universal truth.
",0,0,0,0,1,0
20417,Chern classes and Gromov--Witten theory of projective bundles,"  We prove that the Gromov--Witten theory (GWT) of a projective bundle can be
determined by the Chern classes and the GWT of the base. It completely answers
a question raised in a previous paper (arXiv:1607.00740). Its consequences
include that the GWT of the blow-up of X at a smooth subvariety Z is uniquely
determined by GWT of X, Z plus some topological data.
",0,0,1,0,0,0
20418,Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations,"  We introduce the concept of numerical Gaussian processes, which we define as
Gaussian processes with covariance functions resulting from temporal
discretization of time-dependent partial differential equations. Numerical
Gaussian processes, by construction, are designed to deal with cases where: (1)
all we observe are noisy data on black-box initial conditions, and (2) we are
interested in quantifying the uncertainty associated with such noisy data in
our solutions to time-dependent partial differential equations. Our method
circumvents the need for spatial discretization of the differential operators
by proper placement of Gaussian process priors. This is an attempt to construct
structured and data-efficient learning machines, which are explicitly informed
by the underlying physics that possibly generated the observed data. The
effectiveness of the proposed approach is demonstrated through several
benchmark problems involving linear and nonlinear time-dependent operators. In
all examples, we are able to recover accurate approximations of the latent
solutions, and consistently propagate uncertainty, even in cases involving very
long time integration.
",1,0,1,1,0,0
20419,Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery,"  The quality of experience (QoE) is known to be subjective and
context-dependent. Identifying and calculating the factors that affect QoE is
indeed a difficult task. Recently, a lot of effort has been devoted to estimate
the users QoE in order to improve video delivery. In the literature, most of
the QoE-driven optimization schemes that realize trade-offs among different
quality metrics have been addressed under the assumption of homogenous
populations. Nevertheless, people perceptions on a given video quality may not
be the same, which makes the QoE optimization harder. This paper aims at taking
a step further in order to address this limitation and meet users profiles. To
do so, we propose a closed-loop control framework based on the
users(subjective) feedbacks to learn the QoE function and optimize it at the
same time. Our simulation results show that our system converges to a steady
state, where the resulting QoE function noticeably improves the users
feedbacks.
",1,0,0,0,0,0
20420,Deep CNN based feature extractor for text-prompted speaker recognition,"  Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level feature extractor for all states and use cosine similarity
metric for scoring. The key feature of our network is the Max-Feature-Map
activation function, which acts as an embedded feature selector. By using
multitask learning scheme to train the high-level feature extractor we were
able to surpass the classic baseline systems in terms of quality and achieved
impressive results for such a novice approach, getting 2.85% EER on the RSR2015
evaluation set. Fusion of the proposed and the baseline systems improves this
result.
",0,0,0,1,0,0
20421,Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs,"  Consider a stochastic process being controlled across a communication
channel. The control signal that is transmitted across the control channel can
be replaced by a malicious attacker. The controller is allowed to implement any
arbitrary detection algorithm to detect if an attacker is present. This work
characterizes some fundamental limitations of when such an attack can be
detected, and quantifies the performance degradation that an attacker that
seeks to be undetected or stealthy can introduce.
",1,0,1,0,0,0
20422,Efficient Version-Space Reduction for Visual Tracking,"  Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.
",1,0,0,0,0,0
20423,Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts,"  Most of the JavaScript code deployed in the wild has been minified, a process
in which identifier names are replaced with short, arbitrary and meaningless
names. Minified code occupies less space, but also makes the code extremely
difficult to manually inspect and understand. This paper presents Context2Name,
a deep learningbased technique that partially reverses the effect of
minification by predicting natural identifier names for minified names. The
core idea is to predict from the usage context of a variable a name that
captures the meaning of the variable. The approach combines a lightweight,
token-based static analysis with an auto-encoder neural network that summarizes
usage contexts and a recurrent neural network that predict natural names for a
given usage context. We evaluate Context2Name with a large corpus of real-world
JavaScript code and show that it successfully predicts 47.5% of all minified
identifiers while taking only 2.9 milliseconds on average to predict a name. A
comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
approach performs comparably in terms of accuracy while improving in terms of
efficiency. Moreover, Context2Name complements the state-of-the-art by
predicting 5.3% additional identifiers that are missed by both existing tools.
",1,0,0,1,0,0
20424,Reconstructing fluid dynamics with micro-finite element,"  In the theory of the Navier-Stokes equations, the viscous fluid in
incompressible flow is modelled as a homogeneous and dense assemblage of
constituent ""fluid particles"" with viscous stress proportional to rate of
strain. The crucial concept of fluid flow is the velocity of the particle that
is accelerated by the pressure and viscous interaction around it. In this
paper, by virtue of the alternative constituent ""micro-finite element"", we
introduce a set of new intrinsic quantities, called the vortex fields, to
characterise the relative orientation between elements and the feature of
micro-eddies in the element, while the description of viscous interaction in
fluid returns to the initial intuition that the interlayer friction is
proportional to the slip strength. Such a framework enables us to reconstruct
the dynamics theory of viscous fluid, in which the flowing fluid can be
modelled as a finite covering of elements and consequently indicated by a
space-time differential manifold that admits complex topological evolution.
",0,1,0,0,0,0
20425,Towards Black-box Iterative Machine Teaching,"  In this paper, we make an important step towards the black-box machine
teaching by considering the cross-space machine teaching, where the teacher and
the learner use different feature representations and the teacher can not fully
observe the learner's model. In such scenario, we study how the teacher is
still able to teach the learner to achieve faster convergence rate than the
traditional passive learning. We propose an active teacher model that can
actively query the learner (i.e., make the learner take exams) for estimating
the learner's status and provably guide the learner to achieve faster
convergence. The sample complexities for both teaching and query are provided.
In the experiments, we compare the proposed active teacher with the omniscient
teacher and verify the effectiveness of the active teacher model.
",1,0,0,1,0,0
20426,On Generalization and Regularization in Deep Learning,"  Why do large neural network generalize so well on complex tasks such as image
classification or speech recognition? What exactly is the role regularization
for them? These are arguably among the most important open questions in machine
learning today. In a recent and thought provoking paper [C. Zhang et al.]
several authors performed a number of numerical experiments that hint at the
need for novel theoretical concepts to account for this phenomenon. The paper
stirred quit a lot of excitement among the machine learning community but at
the same time it created some confusion as discussions on OpenReview.net
testifies. The aim of this pedagogical paper is to make this debate accessible
to a wider audience of data scientists without advanced theoretical knowledge
in statistical learning. The focus here is on explicit mathematical definitions
and on a discussion of relevant concepts, not on proofs for which we provide
references.
",0,0,1,1,0,0
20427,Nonparametric Inference for Auto-Encoding Variational Bayes,"  We would like to learn latent representations that are low-dimensional and
highly interpretable. A model that has these characteristics is the Gaussian
Process Latent Variable Model. The benefits and negative of the GP-LVM are
complementary to the Variational Autoencoder, the former provides interpretable
low-dimensional latent representations while the latter is able to handle large
amounts of data and can use non-Gaussian likelihoods. Our inspiration for this
paper is to marry these two approaches and reap the benefits of both. In order
to do so we will introduce a novel approximate inference scheme inspired by the
GP-LVM and the VAE. We show experimentally that the approximation allows the
capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large
without losing a highly interpretable representation, allowing reconstruction
quality to be unlimited by Z at the same time as a low-dimensional space can be
used to perform ancestral sampling from as well as a means to reason about the
embedded data.
",1,0,0,1,0,0
20428,Imbalanced Malware Images Classification: a CNN based Approach,"  Deep convolutional neural networks (CNNs) can be applied to malware binary
detection through images classification. The performance, however, is degraded
due to the imbalance of malware families (classes). To mitigate this issue, we
propose a simple yet effective weighted softmax loss which can be employed as
the final layer of deep CNNs. The original softmax loss is weighted, and the
weight value can be determined according to class size. A scaling parameter is
also included in computing the weight. Proper selection of this parameter has
been studied and an empirical option is given. The weighted loss aims at
alleviating the impact of data imbalance in an end-to-end learning fashion. To
validate the efficacy, we deploy the proposed weighted loss in a pre-trained
deep CNN model and fine-tune it to achieve promising results on malware images
classification. Extensive experiments also indicate that the new loss function
can fit other typical CNNs with an improved classification performance.
",1,0,0,1,0,0
20429,Integrated optical force sensors using focusing photonic crystal arrays,"  Mechanical oscillators are at the heart of many sensor applications. Recently
several groups have developed oscillators that are probed optically, fabricated
from high-stress silicon nitride films. They exhibit outstanding force
sensitivities of a few aN/Hz$^{1/2}$ and can also be made highly reflective,
for efficient detection. The optical read-out usually requires complex
experimental setups, including positioning stages and bulky cavities, making
them impractical for real applications. In this paper we propose a novel way of
building fully integrated all-optical force sensors based on low-loss silicon
nitride mechanical resonators with a photonic crystal reflector. We can
circumvent previous limitations in stability and complexity by simulating a
suspended focusing photonic crystal, purely made of silicon nitride. Our design
allows for an all integrated sensor, built out of a single block that
integrates a full Fabry-Pérot cavity, without the need for assembly or
alignment. The presented simulations will allow for a radical simplification of
sensors based on high-Q silicon nitride membranes. Our results comprise, to the
best of our knowledge, the first simulations of a focusing mirror made from a
mechanically suspended flat membrane with subwavelength thickness. Cavity
lengths between a few hundred $\mu$m and mm should be directly realizable.
",0,1,0,0,0,0
20430,Comparison of Signaling and Media Approaches to Detect VoIP SPIT Attack,"  IP networks became the most dominant type of information networks nowadays.
It provides a number of services and makes it easy for users to be connected.
IP networks provide an efficient way with a large number of services compared
to other ways of voice communication. This leads to the migration to make voice
calls via IP networks. Despite the wide range of IP networks services,
availability, and its capabilities, there still a large number of security
threats that affect IP networks and for sure affecting other services based on
it and voice is one of them. This paper discusses reasons of migration from
making voice calls via IP networks and leaving legacy networks, requirements to
be available in IP networks to support voice transport, and concentrating on
SPIT attack and its detection methods. Experiments took place to compare the
different approaches used to detect spam over VoIP networks.
",1,0,0,0,0,0
20431,Coupling functions: Universal insights into dynamical interaction mechanisms,"  The dynamical systems found in Nature are rarely isolated. Instead they
interact and influence each other. The coupling functions that connect them
contain detailed information about the functional mechanisms underlying the
interactions and prescribe the physical rule specifying how an interaction
occurs. Here, we aim to present a coherent and comprehensive review
encompassing the rapid progress made recently in the analysis, understanding
and applications of coupling functions. The basic concepts and characteristics
of coupling functions are presented through demonstrative examples of different
domains, revealing the mechanisms and emphasizing their multivariate nature.
The theory of coupling functions is discussed through gradually increasing
complexity from strong and weak interactions to globally-coupled systems and
networks. A variety of methods that have been developed for the detection and
reconstruction of coupling functions from measured data is described. These
methods are based on different statistical techniques for dynamical inference.
Stemming from physics, such methods are being applied in diverse areas of
science and technology, including chemistry, biology, physiology, neuroscience,
social sciences, mechanics and secure communications. This breadth of
application illustrates the universality of coupling functions for studying the
interaction mechanisms of coupled dynamical systems.
",0,1,0,0,0,0
20432,Uncertainty quantification for radio interferometric imaging: II. MAP estimation,"  Uncertainty quantification is a critical missing component in radio
interferometric imaging that will only become increasingly important as the
big-data era of radio interferometry emerges. Statistical sampling approaches
to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
can in principle recover the full posterior distribution of the image, from
which uncertainties can then be quantified. However, for massive data sizes,
like those anticipated from the Square Kilometre Array (SKA), it will be
difficult if not impossible to apply any MCMC technique due to its inherent
computational cost. We formulate Bayesian inference problems with
sparsity-promoting priors (motivated by compressive sensing), for which we
recover maximum a posteriori (MAP) point estimators of radio interferometric
images by convex optimisation. Exploiting recent developments in the theory of
probability concentration, we quantify uncertainties by post-processing the
recovered MAP estimate. Three strategies to quantify uncertainties are
developed: (i) highest posterior density credible regions; (ii) local credible
intervals (cf. error bars) for individual pixels and superpixels; and (iii)
hypothesis testing of image structure. These forms of uncertainty
quantification provide rich information for analysing radio interferometric
observations in a statistically robust manner. Our MAP-based methods are
approximately $10^5$ times faster computationally than state-of-the-art MCMC
methods and, in addition, support highly distributed and parallelised
algorithmic structures. For the first time, our MAP-based techniques provide a
means of quantifying uncertainties for radio interferometric imaging for
realistic data volumes and practical use, and scale to the emerging big-data
era of radio astronomy.
",0,1,0,1,0,0
20433,Semialgebraic Invariant Synthesis for the Kannan-Lipton Orbit Problem,"  The \emph{Orbit Problem} consists of determining, given a linear
transformation $A$ on $\mathbb{Q}^d$, together with vectors $x$ and $y$,
whether the orbit of $x$ under repeated applications of $A$ can ever reach $y$.
This problem was famously shown to be decidable by Kannan and Lipton in the
1980s.
In this paper, we are concerned with the problem of synthesising suitable
\emph{invariants} $\mathcal{P} \subseteq \mathbb{R}^d$, \emph{i.e.}, sets that
are stable under $A$ and contain $x$ and not $y$, thereby providing compact and
versatile certificates of non-reachability. We show that whether a given
instance of the Orbit Problem admits a semialgebraic invariant is decidable,
and moreover in positive instances we provide an algorithm to synthesise
suitable invariants of polynomial size.
It is worth noting that the existence of \emph{semilinear} invariants, on the
other hand, is (to the best of our knowledge) not known to be decidable.
",1,0,1,0,0,0
20434,Design and characterization of the Large-Aperture Experiment to Detect the Dark Age (LEDA) radiometer systems,"  The Large-Aperture Experiment to Detect the Dark Age (LEDA) was designed to
detect the predicted O(100)mK sky-averaged absorption of the Cosmic Microwave
Background by Hydrogen in the neutral pre- and intergalactic medium just after
the cosmological Dark Age. The spectral signature would be associated with
emergence of a diffuse Ly$\alpha$ background from starlight during 'Cosmic
Dawn'. Recently, Bowman et al. (2018) have reported detection of this predicted
absorption feature, with an unexpectedly large amplitude of 530 mK, centered at
78 MHz. Verification of this result by an independent experiment, such as LEDA,
is pressing. In this paper, we detail design and characterization of the LEDA
radiometer systems, and a first-generation pipeline that instantiates a signal
path model. Sited at the Owens Valley Radio Observatory Long Wavelength Array,
LEDA systems include the station correlator, five well-separated redundant dual
polarization radiometers and backend electronics. The radiometers deliver a
30-85MHz band (16<z<34) and operate as part of the larger interferometric
array, for purposes ultimately of in situ calibration. Here, we report on the
LEDA system design, calibration approach, and progress in characterization as
of January 2016. The LEDA systems are currently being modified to improve
performance near 78 MHz in order to verify the purported absorption feature.
",0,1,0,0,0,0
20435,Automatic Bayesian Density Analysis,"  Making sense of a dataset in an automatic and unsupervised fashion is a
challenging problem in statistics and AI. Classical approaches for density
estimation are usually not flexible enough to deal with the uncertainty
inherent to real-world data: they are often restricted to fixed latent
interaction models and homogeneous likelihoods; they are sensitive to missing,
corrupt and anomalous data; moreover, their expressiveness generally comes at
the price of intractable inference. As a result, supervision from statisticians
is usually needed to find the right model for the data. However, as domain
experts do not necessarily have to be experts in statistics, we propose
Automatic Bayesian Density Analysis (ABDA) to make density estimation
accessible at large. ABDA automates the selection of adequate likelihood models
from arbitrarily rich dictionaries while modeling their interactions via a deep
latent structure adaptively learned from data as a sum-product network. ABDA
casts uncertainty estimation at these local and global levels into a joint
Bayesian inference problem, providing robust and yet tractable inference.
Extensive empirical evidence shows that ABDA is a suitable tool for automatic
exploratory analysis of heterogeneous tabular data, allowing for missing value
estimation, statistical data type and likelihood discovery, anomaly detection
and dependency structure mining, on top of providing accurate density
estimation.
",0,0,0,1,0,0
20436,MLCapsule: Guarded Offline Deployment of Machine Learning as a Service,"  With the widespread use of machine learning (ML) techniques, ML as a service
has become increasingly popular. In this setting, an ML model resides on a
server and users can query the model with their data via an API. However, if
the user's input is sensitive, sending it to the server is not an option.
Equally, the service provider does not want to share the model by sending it to
the client for protecting its intellectual property and pay-per-query business
model. In this paper, we propose MLCapsule, a guarded offline deployment of
machine learning as a service. MLCapsule executes the machine learning model
locally on the user's client and therefore the data never leaves the client.
Meanwhile, MLCapsule offers the service provider the same level of control and
security of its model as the commonly used server-side execution. In addition,
MLCapsule is applicable to offline applications that require local execution.
Beyond protecting against direct model access, we demonstrate that MLCapsule
allows for implementing defenses against advanced attacks on machine learning
models such as model stealing/reverse engineering and membership inference.
",0,0,0,1,0,0
20437,Brownian forgery of statistical dependences,"  The balance held by Brownian motion between temporal regularity and
randomness is embodied in a remarkable way by Levy's forgery of continuous
functions. Here we describe how this property can be extended to forge
arbitrary dependences between two statistical systems, and then establish a new
Brownian independence test based on fluctuating random paths. We also argue
that this result allows revisiting the theory of Brownian covariance from a
physical perspective and opens the possibility of engineering nonlinear
correlation measures from more general functional integrals.
",0,0,1,1,0,0
20438,Internal sizes in $μ$-abstract elementary classes,"  Working in the context of $\mu$-abstract elementary classes ($\mu$-AECs) -
or, equivalently, accessible categories with all morphisms monomorphisms - we
examine the two natural notions of size that occur, namely cardinality of
underlying sets and internal size. The latter, purely category-theoretic,
notion generalizes e.g. density character in complete metric spaces and
cardinality of orthogonal bases in Hilbert spaces. We consider the relationship
between these notions under mild set-theoretic hypotheses, including weakenings
of the singular cardinal hypothesis. We also establish preliminary results on
the existence and categoricity spectra of $\mu$-AECs, including specific
examples showing dramatic failures of the eventual categoricity conjecture
(with categoricity defined using cardinality) in $\mu$-AECs.
",0,0,1,0,0,0
20439,Seasonal forecasts of the summer 2016 Yangtze River basin rainfall,"  The Yangtze River has been subject to heavy flooding throughout history, and
in recent times severe floods such as those in 1998 have resulted in heavy loss
of life and livelihoods. Dams along the river help to manage flood waters, and
are important sources of electricity for the region. Being able to forecast
high-impact events at long lead times therefore has enormous potential benefit.
Recent improvements in seasonal forecasting mean that dynamical climate models
can start to be used directly for operational services. The teleconnection from
El Niño to Yangtze River basin rainfall meant that the strong El Niño in
winter 2015/2016 provided a valuable opportunity to test the application of a
dynamical forecast system.
This paper therefore presents a case study of a real time seasonal forecast
for the Yangtze River basin, building on previous work demonstrating the
retrospective skill of such a forecast. A simple forecasting methodology is
presented, in which the forecast probabilities are derived from the historical
relationship between hindcast and observations. Its performance for 2016 is
discussed. The heavy rainfall in the May-June-July period was correctly
forecast well in advance. August saw anomalously low rainfall, and the
forecasts for the June-July-August period correctly showed closer to average
levels. The forecasts contributed to the confidence of decision-makers across
the Yangtze River basin. Trials of climate services such as this help to
promote appropriate use of seasonal forecasts, and highlight areas for future
improvements.
",0,1,0,0,0,0
20440,Modeling Interference Via Symmetric Treatment Decomposition,"  Classical causal inference assumes a treatment meant for a given unit does
not have an effect on other units. When this ""no interference"" assumption is
violated, new types of spillover causal effects arise, and causal inference
becomes much more difficult. In addition, interference introduces a unique
complication where outcomes may transmit treatment influences to each other,
which is a relationship that has some features of a causal one, but is
symmetric. In settings where detailed temporal information on outcomes is not
available, addressing this complication using statistical inference methods
based on Directed Acyclic Graphs (DAGs) (Ogburn & VanderWeele, 2014) leads to
conceptual difficulties.
In this paper, we develop a new approach to decomposing the spillover effect
into direct (also known as the contagion effect) and indirect (also known as
the infectiousness effect) components that extends the DAG based treatment
decomposition approach to mediation found in (Robins & Richardson, 2010) to
causal chain graph models (Lauritzen & Richardson, 2002). We show that when
these components of the spillover effect are identified in these models, they
have an identifying functional, which we call the symmetric mediation formula,
that generalizes the mediation formula in DAGs (Pearl, 2011). We further show
that, unlike assumptions in classical mediation analysis, an assumption
permitting identification in our setting leads to restrictions on the observed
data law, making the assumption empirically falsifiable. Finally, we discuss
statistical inference for the components of the spillover effect in the special
case of two interacting outcomes, and discuss a maximum likelihood estimator,
and a doubly robust estimator.
",0,0,0,1,0,0
20441,A Bayesian algorithm for distributed network localization using distance and direction data,"  A reliable, accurate, and affordable positioning service is highly required
in wireless networks. In this paper, the novel Message Passing Hybrid
Localization (MPHL) algorithm is proposed to solve the problem of cooperative
distributed localization using distance and direction estimates. This hybrid
approach combines two sensing modalities to reduce the uncertainty in
localizing the network nodes. A statistical model is formulated for the
problem, and approximate minimum mean square error (MMSE) estimates of the node
locations are computed. The proposed MPHL is a distributed algorithm based on
belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
improves the identifiability of the localization problem and reduces its
sensitivity to the anchor node geometry, compared to distance-only or
direction-only localization techniques. For example, the unknown location of a
node can be found if it has only a single neighbor; and a whole network can be
localized using only a single anchor node. Numerical results are presented
showing that the average localization error is significantly reduced in almost
every simulation scenario, about 50% in most cases, compared to the competing
algorithms.
",1,0,0,1,0,0
20442,Bounds for multivariate residues and for the polynomials in the elimination theorem,"  We present several upper bounds for the height of global residues of rational
forms on an affine variety. As a consequence, we deduce upper bounds for the
height of the coefficients in the Bergman-Weil trace formula.
We also present upper bounds for the degree and the height of the polynomials
in the elimination theorem on an affine variety. This is an arithmetic analogue
of Jelonek's effective elimination theorem, that plays a crucial role in the
proof of our bounds for the height of global residues.
",0,0,1,0,0,0
20443,An adsorbed gas estimation model for shale gas reservoirs via statistical learning,"  Shale gas plays an important role in reducing pollution and adjusting the
structure of world energy. Gas content estimation is particularly significant
in shale gas resource evaluation. There exist various estimation methods, such
as first principle methods and empirical models. However, resource evaluation
presents many challenges, especially the insufficient accuracy of existing
models and the high cost resulting from time-consuming adsorption experiments.
In this research, a low-cost and high-accuracy model based on geological
parameters is constructed through statistical learning methods to estimate
adsorbed shale gas content
",0,1,0,1,0,0
20444,A Semantic Loss Function for Deep Learning with Symbolic Knowledge,"  This paper develops a novel methodology for using symbolic knowledge in deep
learning. From first principles, we derive a semantic loss function that
bridges between neural output vectors and logical constraints. This loss
function captures how close the neural network is to satisfying the constraints
on its output. An experimental evaluation shows that it effectively guides the
learner to achieve (near-)state-of-the-art results on semi-supervised
multi-class classification. Moreover, it significantly increases the ability of
the neural network to predict structured objects, such as rankings and paths.
These discrete concepts are tremendously difficult to learn, and benefit from a
tight integration of deep learning and symbolic reasoning methods.
",1,0,0,1,0,0
20445,Chaotic properties of a turbulent isotropic fluid,"  By tracking the divergence of two initially close trajectories in phase space
in an Eulerian approach to forced turbulence, the relation between the maximal
Lyapunov exponent $\lambda$, and the Reynolds number $Re$ is measured using
direct numerical simulations, performed on up to $2048^3$ collocation points.
The Lyapunov exponent is found to solely depend on the Reynolds number with
$\lambda \propto Re^{0.53}$ and that after a transient period the divergence of
trajectories grows at the same rate at all scales. Finally a linear divergence
is seen that is dependent on the energy forcing rate. Links are made with other
chaotic systems.
",0,1,0,0,0,0
20446,White light emission from silicon nanoparticles,"  As one of the most important semiconductors, silicon (Si) has been used to
fabricate electronic devices, waveguides, detectors, and solar cells etc.
However, its indirect bandgap hinders the use of Si for making good emitters1.
For integrated photonic circuits, Si-based emitters with sizes in the range of
100-300 nm are highly desirable. Here, we show that efficient white light
emission can be realized in spherical and cylindrical Si nanoparticles with
feature sizes of ~200 nm. The up-converted luminescence appears at the magnetic
and electric multipole resonances when the nanoparticles are resonantly excited
at their magnetic and electric dipole resonances by using femtosecond (fs)
laser pulses with ultralow low energy of ~40 pJ. The lifetime of the white
light is as short as ~52 ps, almost three orders of magnitude smaller than the
state-of-the-art results reported so far for Si (~10 ns). Our finding paves the
way for realizing efficient Si-based emitters compatible with current
semiconductor fabrication technology, which can be integrated to photonic
circuits.
",0,1,0,0,0,0
20447,The collapse of ecosystem engineer populations,"  Humans are the ultimate ecosystem engineers who have profoundly transformed
the world's landscapes in order to enhance their survival. Somewhat
paradoxically, however, sometimes the unforeseen effect of this ecosystem
engineering is the very collapse of the population it intended to protect. Here
we use a spatial version of a standard population dynamics model of ecosystem
engineers to study the colonization of unexplored virgin territories by a small
settlement of engineers. We find that during the expansion phase the population
density reaches values much higher than those the environment can support in
the equilibrium situation. When the colonization front reaches the boundary of
the available space, the population density plunges sharply and attains its
equilibrium value. The collapse takes place without warning and happens just
after the population reaches its peak number. We conclude that overpopulation
and the consequent collapse of an expanding population of ecosystem engineers
is a natural consequence of the nonlinear feedback between the population and
environment variables.
",0,1,0,0,0,0
20448,Highly Viscous Microjet Generator,"  This paper describes a simple yet novel system for generating a highly
viscous microjet. The jet is produced inside a wettable thin tube partially
submerged in a liquid. The gas-liquid interface inside the tube, which is
initially concave, is kept much deeper than that outside the tube. An impulsive
force applied at the bottom of a liquid container leads to significant
acceleration of the liquid inside the tube followed by flow-focusing due to the
concave interface. The jet generation process can be divided into two parts
that occur in different time scales, i.e. the Impact time (impact duration $\le
O(10^{-4})$ s) and Focusing time (focusing duration $\gg O(10^{-4})$ s). In
Impact time, the liquid accelerates suddenly due to the impact. In Focusing
time, the microjet emerges due to flow-focusing. In order to explain the sudden
acceleration inside the tube in Impact time, we develop a physical model based
on a pressure impulse approach. Numerical simulations confirm the proposed
model, indicating that the basic mechanism of the acceleration of the liquid
due to the impulsive force is elucidated. Remarkably, the viscous effect is
negligible in Impact time. In contrast, in Focusing time, the viscosity plays
an important role in the microjet generation. We experimentally and numerically
investigate the velocity of microjets with various viscosities. We find that
higher viscosities lead to reduction of the jet velocity, which can be
described by using Reynolds number (the ratio between the inertia force and the
viscous force). This novel device may be a starting point for next-generation
technologies, such as high-viscosity inkjet printers including bioprinters and
needle-free injection devices for minimally invasive medical treatments.
",0,1,0,0,0,0
20449,Section problems for configuration spaces of surfaces,"  In this paper we give a close-to-sharp answer to the basic questions: When is
there a continuous way to add a point to a configuration of $n$ ordered points
on a surface $S$ of finite type so that all the points are still distinct? When
this is possible, what are all the ways to do it? More precisely, let
PConf$_n(S)$ be the space of ordered $n$-tuples of distinct points in $S$. Let
$f_n(S): \text{PConf}_{n+1}(S) \to \text{PConf}_n(S)$ be the map given by
$f_n(x_0,x_1,\ldots,x_n):=(x_1,\ldots,x_n)$. We will classify all continuous
sections of $f_n$ by proving:
1. If $S=\mathbb{R}^2$ and $n>3$, any section of $f_{n}(S)$ is either ""adding
a point at infinity"" or ""adding a point near $x_k$"". (We define these two terms
in Section 2.1, whether we can define ""adding a point near $x_k$"" or ""adding a
point at infinity"" depends in a delicate way on properties of $S$.)
2. If $S=S^2$ a $2$-sphere and $n>4$, any section of $f_{n}(S)$ is ""adding a
point near $x_k$"", if $S=S^2$ and $n=2$, the bundle $f_n(S)$ does not have a
section. (We define this term in Section 3.2)
3. If $S=S_g$ a surface of genus $g>1$ and for $n>1$, the bundle $f_{n}(S)$
does not have a section.
",0,0,1,0,0,0
20450,Odd-triplet superconductivity in single-level quantum dots,"  We study the interplay of spin and charge coherence in a single-level quantum
dot. A tunnel coupling to a superconducting lead induces superconducting
correlations in the dot. With full spin symmetry retained, only even-singlet
superconducting correlations are generated. An applied magnetic field or
attached ferromagnetic leads partially or fully reduce the spin symmetry, and
odd-triplet superconducting correlations are generated as well. For
single-level quantum dots, no other superconducting correlations are possible.
We analyze, with the help of a diagrammatic real-time technique, the interplay
of spin symmetry and superconductivity and its signatures in electronic
transport, in particular current and shot noise.
",0,1,0,0,0,0
20451,From Neuronal Models to Neuronal Dynamics and Image Processing,"  This paper is an introduction to the membrane potential equation for neurons.
Its properties are described, as well as sample applications. Networks of these
equations can be used for modeling neuronal systems, which also process images
and video sequences, respectively. Specifically, (i) a dynamic retina is
proposed (based on a reaction-diffusion system), which predicts afterimages and
simple visual illusions, (ii) a system for texture segregation (texture
elements are understood as even-symmetric contrast features), and (iii) a
network for detecting object approaches (inspired by the locust visual system).
",0,0,0,0,1,0
20452,A compilation of LEGO Technic parts to support learning experiments on linkages,"  We present a compilation of LEGO Technic parts to provide easy-to-build
constructions of basic planar linkages. Some technical issues and their
possible solutions are discussed. To solve questions on fine details---like
deciding whether the motion is an exactly straight line or not---we refer to
the dynamic mathematics software tool GeoGebra.
",0,0,1,0,0,0
20453,On the Casas-Alvero conjecture,"  The conjecture is formulated in an affine structure and linked with
dimension=1 of the defined CA sets. Then some known results are proved in this
context. The short intended proof relies on a direct yet unclear statement
about homogeneous dependence of algebraic equations. This might not be a
complete proof or even one on the right track, but it may provoke more thoughts
in this respect as expected.
",0,0,1,0,0,0
20454,Effect of compressibility and aspect ratio on performance of long elastic seals,"  Recent experiments show no statistical impact of seal length on the
performance of long elastomeric seals in relatively smooth test fixtures.
Motivated by these results, we analytically and computationally investigate the
combined effects of seal length and compressibility on the maximum differential
pressure a seal can support. We present a Saint-Venant type analytic shear lag
solution for slightly compressible seals with large aspect ratios, which
compares well with nonlinear finite element simulations in regions far from the
ends of the seal. However, at the high- and low-pressure ends, where fracture
is observed experimentally, the analytic solution is in poor agreement with
detailed finite element calculations. Nevertheless, we show that the analytic
solution provides far-field stress measures that correlate, over a range of
aspect ratios and bulk moduli, the calculated energy release rates for the
growth of small cracks at the two ends of the seal. Thus a single finite
element simulation coupled with the analytic solution can be used to determine
tendencies for fracture at the two ends of the seal over a wide range of
geometry and compressibility. Finally, using a hypothetical critical energy
release rate, predictions for whether a crack on the high-pressure end will
begin to grow before or after a crack on the low-pressure end begins to grow
are made using the analytic solution and compared with finite element
simulations for finite deformation, hyperelastic seals.
",0,1,0,0,0,0
20455,Integral models of reductive groups and integral Mumford-Tate groups,"  Let $G$ be a reductive algebraic group over a $p$-adic field or number field
$K$, and let $V$ be a $K$-linear faithful representation of $G$. A lattice
$\Lambda$ in the vector space $V$ defines a model $\hat{G}_{\Lambda}$ of $G$
over $\mathscr{O}_K$. One may wonder to what extent $\Lambda$ is determined by
the group scheme $\hat{G}_{\Lambda}$. In this paper we prove that up to a
natural equivalence relation on the set of lattices there are only finitely
many $\Lambda$ corresponding to one model $\hat{G}_{\Lambda}$. Furthermore, we
relate this fact to moduli spaces of abelian varieties as follows: let
$\mathscr{A}_{g,n}$ be the moduli space of principally polarised abelian
varieties of dimension $g$ with level $n$ structure. We prove that there are at
most finitely many special subvarieties of $\mathscr{A}_{g,n}$ with a given
integral generic Mumford-Tate group.
",0,0,1,0,0,0
20456,A proof of Boca's Theorem,"  We give a general method of extending unital completely positive maps to
amalgamated free products of C*-algebras. As an application we give a dilation
theoretic proof of Boca's Theorem.
",0,0,1,0,0,0
20457,Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations,"  Convolution with Green's function of a differential operator appears in a lot
of applications e.g. Lippmann-Schwinger integral equation. Algorithms for
computing such are usually non-trivial and require non-uniform mesh. However,
recently Vico, Greengard and Ferrando developed method for computing
convolution with smooth functions with compact support with spectral accuracy,
requiring nothing more than Fast Fourier Transform (FFT). Their approach is
very suitable for the low-rank tensor implementation which we develop using
Quantized Tensor Train (QTT) decomposition.
",1,0,0,0,0,0
20458,"Mott insulators of hardcore bosons in 1D: many-body orders, entanglement, edge modes","  Many-body phenomena were always an integral part of physics comprising of
collective behaviors through self-organization, in systems consisting of many
components and degrees of freedom. We investigate the collective behaviors of
strongly interacting particles confined in one dimension. We show that
many-body orders with topological characteristics can be found at the Mott
insulator limit for hardcore bosons, at different fillings, without considering
the spin degree of freedom or long-range microscopic interactions. These orders
have unique properties like weak or strong quantum correlations (entanglement),
quantified by the entanglement entropy, edge excitations/modes and gapped
energy spectrum with highly degenerate ground state, bearing resemblance to
topologically ordered phases of matter.
",0,1,0,0,0,0
20459,Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects,"  When performing Bayesian data analysis using a general linear mixed model,
the resulting posterior density is almost always analytically intractable.
However, if proper conditionally conjugate priors are used, there is a simple
two-block Gibbs sampler that is geometrically ergodic in nearly all practical
settings, including situations where $p > n$ (Abrahamsen and Hobert, 2017).
Unfortunately, the (conditionally conjugate) multivariate normal prior on
$\beta$ does not perform well in the high-dimensional setting where $p \gg n$.
In this paper, we consider an alternative model in which the multivariate
normal prior is replaced by the normal-gamma shrinkage prior developed by
Griffin and Brown (2010). This change leads to a much more complex posterior
density, and we develop a simple MCMC algorithm for exploring it. This
algorithm, which has both deterministic and random scan components, is easier
to analyze than the more obvious three-step Gibbs sampler. Indeed, we prove
that the new algorithm is geometrically ergodic in most practical settings.
",0,0,1,1,0,0
20460,Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction,"  With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.
",1,0,1,0,0,0
20461,Horcrux: A Password Manager for Paranoids,"  Vulnerabilities in password managers are unremitting because current designs
provide large attack surfaces, both at the client and server. We describe and
evaluate Horcrux, a password manager that is designed holistically to minimize
and decentralize trust, while retaining the usability of a traditional password
manager. The prototype Horcrux client, implemented as a Firefox add-on, is
split into two components, with code that has access to the user's master's
password and any key material isolated into a small auditable component,
separate from the complexity of managing the user interface. Instead of
exposing actual credentials to the DOM, a dummy username and password are
autofilled by the untrusted component. The trusted component intercepts and
modifies POST requests before they are encrypted and sent over the network. To
avoid trusting a centralized store, stored credentials are secret-shared over
multiple servers. To provide domain and username privacy, while maintaining
resilience to off-line attacks on a compromised password store, we incorporate
cuckoo hashing in a way that ensures an attacker cannot determine if a guessed
master password is correct. Our approach only works for websites that do not
manipulate entered credentials in the browser client, so we conducted a
large-scale experiment that found the technique appears to be compatible with
over 98% of tested login forms.
",1,0,0,0,0,0
20462,Cross-validation,"  This text is a survey on cross-validation. We define all classical
cross-validation procedures, and we study their properties for two different
goals: estimating the risk of a given estimator, and selecting the best
estimator among a given family. For the risk estimation problem, we compute the
bias (which can also be corrected) and the variance of cross-validation
methods. For estimator selection, we first provide a first-order analysis
(based on expectations). Then, we explain how to take into account second-order
terms (from variance computations, and by taking into account the usefulness of
overpenalization). This allows, in the end, to provide some guidelines for
choosing the best cross-validation method for a given learning problem.
",0,0,1,1,0,0
20463,Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees,"  Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
requires the collection of calibration data for new subjects prior to online
use. Calibration time can be reduced or eliminated e.g.~by transfer of a
pre-trained classifier or unsupervised adaptive classification methods which
learn from scratch and adapt over time. While such heuristics work well in
practice, none of them can provide theoretical guarantees. Our objective is to
modify an event-related potential (ERP) paradigm to work in unison with the
machine learning decoder to achieve a reliable calibration-less decoding with a
guarantee to recover the true class means.
Method: We introduce learning from label proportions (LLP) to the BCI
community as a new unsupervised, and easy-to-implement classification approach
for ERP-based BCIs. The LLP estimates the mean target and non-target responses
based on known proportions of these two classes in different groups of the
data. We modified a visual ERP speller to meet the requirements of the LLP. For
evaluation, we ran simulations on artificially created data sets and conducted
an online BCI study with N=13 subjects performing a copy-spelling task.
Results: Theoretical considerations show that LLP is guaranteed to minimize
the loss function similarly to a corresponding supervised classifier. It
performed well in simulations and in the online application, where 84.5% of
characters were spelled correctly on average without prior calibration.
Significance: The continuously adapting LLP classifier is the first
unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
makes it an ideal solution to avoid a tedious calibration and to tackle
non-stationarities in the data. Additionally, LLP works on complementary
principles compared to existing unsupervised methods, allowing for their
further enhancement when combined with LLP.
",1,0,0,1,0,0
20464,Generalized Index Coding Problem and Discrete Polymatroids,"  The index coding problem has been generalized recently to accommodate
receivers which demand functions of messages and which possess functions of
messages. The connections between index coding and matroid theory have been
well studied in the recent past. Index coding solutions were first connected to
multi linear representation of matroids. For vector linear index codes discrete
polymatroids which can be viewed as a generalization of the matroids was used.
It was shown that a vector linear solution to an index coding problem exists if
and only if there exists a representable discrete polymatroid satisfying
certain conditions. In this work we explore the connections between generalized
index coding and discrete polymatroids. The conditions that needs to be
satisfied by a representable discrete polymatroid for a generalized index
coding problem to have a vector linear solution is established. From a discrete
polymatroid we construct an index coding problem with coded side information
and shows that if the index coding problem has a certain optimal length
solution then the discrete polymatroid satisfies certain properties. From a
matroid we construct a similar generalized index coding problem and shows that
the index coding problem has a binary scalar linear solution of optimal length
if and only if the matroid is binary representable.
",1,0,1,0,0,0
20465,Reconstruction via the intrinsic geometric structures of interior transmission eigenfunctions,"  We are concerned with the inverse scattering problem of extracting the
geometric structures of an unknown/inaccessible inhomogeneous medium by using
the corresponding acoustic far-field measurement. Using the intrinsic geometric
properties of the so-called interior transmission eigenfunctions, we develop a
novel inverse scattering scheme. The proposed method can efficiently capture
the cusp singularities of the support of the inhomogeneous medium. If further a
priori information is available on the support of the medium, say, it is a
convex polyhedron, then one can actually recover its shape. Both theoretical
analysis and numerical experiments are provided. Our reconstruction method is
new to the literature and opens up a new direction in the study of inverse
scattering problems.
",0,0,1,0,0,0
20466,Thermoelectric phase diagram of the SrTiO3-SrNbO3 solid solution system,"  Thermoelectric energy conversion - the exploitation of the Seebeck effect to
convert waste heat into electricity - has attracted an increasing amount of
research attention for energy harvesting technology. Niobium-doped strontium
titanate (SrTi1-xNbxO3) is one of the most promising thermoelectric material
candidates, particularly as it poses a much lesser environmental risk in
comparison to materials based on heavy metal elements. Two-dimensional electron
confinement, e.g. through the formation of superlattices or two-dimensional
electron gases, is recognized as an effective strategy to improve the
thermoelectric performance of SrTi1-xNbxO3. Although electron confinement is
closely related to the electronic structure, the fundamental electronic phase
behavior of the SrTi1-xNbxO3 solid solution system has yet to be
comprehensively investigated. Here, we present a thermoelectric phase diagram
for the SrTi1-xNbxO3 (0.05 =< x =< 1) solid solution system, which we derived
from the characterization of epitaxial films. We observed two thermoelectric
phase boundaries in the system, which originate from the step-like decrease in
carrier effective mass at x ~ 0.3, and from a local minimum in carrier
relaxation time at x ~ 0.5. The origins of these phase boundaries are
considered to be related to isovalent/heterovalent B-site substitution:
parabolic Ti 3d orbitals dominate electron conduction for compositions with x <
0.3, whereas the Nb 4d orbital dominates when x > 0.3. At x ~ 0.5, a tetragonal
distortion of the lattice, in which the B-site is composed of Ti4+ and Nb4+
ions, leads to the formation of tail-like impurity bands, which maximizes the
electron scattering. These results provide a foundation for further research
into improving the thermoelectric performance of SrTi1-xNbxO3.
",0,1,0,0,0,0
20467,"AMPA, NMDA and GABAA receptor mediated network burst dynamics in cortical cultures in vitro","  In this work we study the excitatory AMPA, and NMDA, and inhibitory GABAA
receptor mediated dynamical changes in neuronal networks of neonatal rat cortex
in vitro. Extracellular network-wide activity was recorded with 59 planar
electrodes simultaneously under different pharmacological conditions. We
analyzed the changes of overall network activity and network-wide burst
frequency between baseline and AMPA receptor (AMPA-R) or NMDA receptor (NMDA-R)
driven activity, as well as between the latter states and disinhibited
activity. Additionally, spatiotemporal structures of pharmacologically modified
bursts and recruitment of electrodes during the network bursts were studied.
Our results show that AMPA-R and NMDA-R receptors have clearly distinct roles
in network dynamics. AMPA-Rs are in greater charge to initiate network wide
bursts. Therefore NMDA-Rs maintain the already initiated activity. GABAA
receptors (GABAA-Rs) inhibit AMPA-R driven network activity more strongly than
NMDA-R driven activity during the bursts.
",0,0,0,0,1,0
20468,Coarse fundamental groups and box spaces,"  We use a coarse version of the fundamental group first introduced by Barcelo,
Kramer, Laubenbacher and Weaver to show that box spaces of finitely presented
groups detect the normal subgroups used to construct the box space, up to
isomorphism. As a consequence we have that two finitely presented groups admit
coarsely equivalent box spaces if and only if they are commensurable via normal
subgroups. We also provide an example of two filtrations $(N_i)$ and $(M_i)$ of
a free group $F$ such that $M_i>N_i$ for all $i$ with $[M_i:N_i]$ uniformly
bounded, but with $\Box_{(N_i)}F$ not coarsely equivalent to $\Box_{(M_i)}F$.
Finally, we give some applications of the main theorem for rank gradient and
the first $\ell^2$ Betti number, and show that the main theorem can be used to
construct infinitely many coarse equivalence classes of box spaces with various
properties.
",0,0,1,0,0,0
20469,Algebraic entropy of (integrable) lattice equations and their reductions,"  We study the growth of degrees in many autonomous and non-autonomous lattice
equations defined by quad rules with corner boundary values, some of which are
known to be integrable by other characterisations. Subject to an enabling
conjecture, we prove polynomial growth for a large class of equations which
includes the Adler-Bobenko-Suris equations and Viallet's $Q_V$ and its
non-autonomous generalization. Our technique is to determine the ambient degree
growth of the projective version of the lattice equations and to conjecture the
growth of their common factors at each lattice vertex, allowing the true degree
growth to be found. The resulting degrees satisfy a linear partial difference
equation which is universal, i.e. the same for all the integrable lattice
equations considered. When we take periodic reductions of these equations,
which includes staircase initial conditions, we obtain from this linear partial
difference equation an ordinary difference equation for degrees that implies
quadratic or linear degree growth. We also study growth of degree of several
non-integrable lattice equations. Exponential growth of degrees of these
equations, and their mapping reductions, is also proved subject to a
conjecture.
",0,1,0,0,0,0
20470,Measurement of mirror birefringence with laser heterodyne polarimetry,"  A laser heterodyne polarimeter (LHP) designed for the measurement of the
birefringence of dielectric super-mirrors is described and initial results are
reported. The LHP does not require an optical resonator and so promises
unprecedented accuracy in the measurement of the birefringence of individual
mirrors. The working principle of the LHP can be applied to the measurement of
vacuum birefringence and potentially ALPS (Any Light Particle Search).
",0,1,0,0,0,0
20471,Individual position diversity in dependence socioeconomic networks increases economic output,"  The availability of big data recorded from massively multiplayer online
role-playing games (MMORPGs) allows us to gain a deeper understanding of the
potential connection between individuals' network positions and their economic
outputs. We use a statistical filtering method to construct dependence networks
from weighted friendship networks of individuals. We investigate the 30
distinct motif positions in the 13 directed triadic motifs which represent
microscopic dependences among individuals. Based on the structural similarity
of motif positions, we further classify individuals into different groups. The
node position diversity of individuals is found to be positively correlated
with their economic outputs. We also find that the economic outputs of leaf
nodes are significantly lower than that of the other nodes in the same motif.
Our findings shed light on understanding the influence of network structure on
economic activities and outputs in socioeconomic system.
",1,1,0,0,0,0
20472,A formula for the nonsymmetric Opdam's hypergeometric function of type $A_2$,"  The aim of this paper is to give an explicit formula for the nonsymmetric
Heckman-Opdam's hypergeometric function of type $A_2$. This is obtained by
differentiating the corresponding symmetric hypergeometric function.
",0,0,1,0,0,0
20473,A new algorithm for irreducible decomposition of representations of finite groups,"  An algorithm for irreducible decomposition of representations of finite
groups over fields of characteristic zero is described. The algorithm uses the
fact that the decomposition induces a partition of the invariant inner product
into a complete set of mutually orthogonal projectors. By expressing the
projectors through the basis elements of the centralizer ring of the
representation, the problem is reduced to solving systems of quadratic
equations. The current implementation of the algorithm is able to split
representations of dimensions up to hundreds of thousands. Examples of
calculations are given.
",1,0,0,0,0,0
20474,Stability and Grothendieck,"  This note is a commentary on the model-theoretic interpretation of
Grothendieck's double limit characterization of weak relative compactness.
",0,0,1,0,0,0
20475,Visual Analogies between Atari Games for Studying Transfer Learning in RL,"  In this work, we ask the following question: Can visual analogies, learned in
an unsupervised way, be used in order to transfer knowledge between pairs of
games and even play one game using an agent trained for another game? We
attempt to answer this research question by creating visual analogies between a
pair of games: a source game and a target game. For example, given a video
frame in the target game, we map it to an analogous state in the source game
and then attempt to play using a trained policy learned for the source game. We
demonstrate convincing visual mapping between four pairs of games (eight
mappings), which are used to evaluate three transfer learning approaches.
",0,0,0,1,0,0
20476,Learning in the Repeated Secretary Problem,"  In the classical secretary problem, one attempts to find the maximum of an
unknown and unlearnable distribution through sequential search. In many
real-world searches, however, distributions are not entirely unknown and can be
learned through experience. To investigate learning in such a repeated
secretary problem we conduct a large-scale behavioral experiment in which
people search repeatedly from fixed distributions. In contrast to prior
investigations that find no evidence for learning in the classical scenario, in
the repeated setting we observe substantial learning resulting in near-optimal
stopping behavior. We conduct a Bayesian comparison of multiple behavioral
models which shows that participants' behavior is best described by a class of
threshold-based models that contains the theoretically optimal strategy.
Fitting such a threshold-based model to data reveals players' estimated
thresholds to be surprisingly close to the optimal thresholds after only a
small number of games.
",1,0,0,0,0,0
20477,Flexible Mixture Modeling on Constrained Spaces,"  This paper addresses challenges in flexibly modeling multimodal data that lie
on constrained spaces. Applications include climate or crime measurements in a
geographical area, or flow-cytometry experiments, where unsuitable recordings
are discarded. A simple approach to modeling such data is through the use of
mixture models, with each component following an appropriate truncated
distribution. Problems arise when the truncation involves complicated
constraints, leading to difficulties in specifying the component distributions,
and in evaluating their normalization constants. Bayesian inference over the
parameters of these models results in posterior distributions that are
doubly-intractable. We address this problem via an algorithm based on rejection
sampling and data augmentation. We view samples from a truncated distribution
as outcomes of a rejection sampling scheme, where proposals are made from a
simple mixture model, and are rejected if they violate the constraints. Our
scheme proceeds by imputing the rejected samples given mixture parameters, and
then resampling parameters given all samples. We study two modeling approaches:
mixtures of truncated components and truncated mixtures of components. In both
situations, we describe exact Markov chain Monte Carlo sampling algorithms, as
well as approximations that bound the number of rejected samples, achieving
computational efficiency and lower variance at the cost of asymptotic bias.
Overall, our methodology only requires practitioners to provide an indicator
function for the set of interest. We present results on simulated data and
apply our algorithm to two problems, one involving flow-cytometry data, and the
other, crime recorded in the city of Chicago.
",0,0,0,1,0,0
20478,Universal equilibrium scaling functions at short times after a quench,"  By analyzing spin-spin correlation functions at relatively short distances,
we show that equilibrium near-critical properties can be extracted at short
times after quenches into the vicinity of a quantum critical point. The time
scales after which equilibrium properties can be extracted are sufficiently
short so that the proposed scheme should be viable for quantum simulators of
spin models based on ultracold atoms or trapped ions. Our results, analytic as
well as numeric, are for one-dimensional spin models, either integrable or
nonintegrable, but we expect our conclusions to be valid in higher dimensions
as well.
",0,1,0,0,0,0
20479,First Results from CUORE: A Search for Lepton Number Violation via $0νββ$ Decay of $^{130}$Te,"  The CUORE experiment, a ton-scale cryogenic bolometer array, recently began
operation at the Laboratori Nazionali del Gran Sasso in Italy. The array
represents a significant advancement in this technology, and in this work we
apply it for the first time to a high-sensitivity search for a
lepton-number--violating process: $^{130}$Te neutrinoless double-beta decay.
Examining a total TeO$_2$ exposure of 86.3 kg$\cdot$yr, characterized by an
effective energy resolution of (7.7 $\pm$ 0.5) keV FWHM and a background in the
region of interest of (0.014 $\pm$ 0.002) counts/(keV$\cdot$kg$\cdot$yr), we
find no evidence for neutrinoless double-beta decay. The median statistical
sensitivity of this search is $7.0\times10^{24}$ yr. Including systematic
uncertainties, we place a lower limit on the decay half-life of
$T^{0\nu}_{1/2}$($^{130}$Te) > $1.3\times 10^{25}$ yr (90% C.L.). Combining
this result with those of two earlier experiments, Cuoricino and CUORE-0, we
find $T^{0\nu}_{1/2}$($^{130}$Te) > $1.5\times 10^{25}$ yr (90% C.L.), which is
the most stringent limit to date on this decay. Interpreting this result as a
limit on the effective Majorana neutrino mass, we find $m_{\beta\beta}<(110 -
520)$ meV, where the range reflects the nuclear matrix element estimates
employed.
",0,1,0,0,0,0
20480,Bernoulli-Carlitz and Cauchy-Carlitz numbers with Stirling-Carlitz numbers,"  Recently, the Cauchy-Carlitz number was defined as the counterpart of the
Bernoulli-Carlitz number. Both numbers can be expressed explicitly in terms of
so-called Stirling-Carlitz numbers. In this paper, we study the second analogue
of Stirling-Carlitz numbers and give some general formulae, including Bernoulli
and Cauchy numbers in formal power series with complex coefficients, and
Bernoulli-Carlitz and Cauchy-Carlitz numbers in function fields. We also give
some applications of Hasse-Teichmüller derivative to hypergeometric Bernoulli
and Cauchy numbers in terms of associated Stirling numbers.
",0,0,1,0,0,0
20481,Irreducible network backbones: unbiased graph filtering via maximum entropy,"  Networks provide an informative, yet non-redundant description of complex
systems only if links represent truly dyadic relationships that cannot be
directly traced back to node-specific properties such as size, importance, or
coordinates in some embedding space. In any real-world network, some links may
be reducible, and others irreducible, to such local properties. This dichotomy
persists despite the steady increase in data availability and resolution, which
actually determines an even stronger need for filtering techniques aimed at
discerning essential links from non-essential ones. Here we introduce a
rigorous method that, for any desired level of statistical significance,
outputs the network backbone that is irreducible to the local properties of
nodes, i.e. their degrees and strengths. Unlike previous approaches, our method
employs an exact maximum-entropy formulation guaranteeing that the filtered
network encodes only the links that cannot be inferred from local information.
Extensive empirical analysis confirms that this approach uncovers essential
backbones that are otherwise hidden amidst many redundant relationships and
inaccessible to other methods. For instance, we retrieve the hub-and-spoke
skeleton of the US airport network and many specialised patterns of
international trade. Being irreducible to local transportation and economic
constraints of supply and demand, these backbones single out genuinely
higher-order wiring principles.
",1,1,0,0,0,0
20482,Synkhronos: a Multi-GPU Theano Extension for Data Parallelism,"  We present Synkhronos, an extension to Theano for multi-GPU computations
leveraging data parallelism. Our framework provides automated execution and
synchronization across devices, allowing users to continue to write serial
programs without risk of race conditions. The NVIDIA Collective Communication
Library is used for high-bandwidth inter-GPU communication. Further
enhancements to the Theano function interface include input slicing (with
aggregation) and input indexing, which perform common data-parallel computation
patterns efficiently. One example use case is synchronous SGD, which has
recently been shown to scale well for a growing set of deep learning problems.
When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA
DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in
isolation. Yet Synkhronos remains general to any data-parallel computation
programmable in Theano. By implementing parallelism at the level of individual
Theano functions, our framework uniquely addresses a niche between manual
multi-device programming and prescribed multi-GPU training routines.
",1,0,0,0,0,0
20483,The PomXYZ Proteins Self-Organize on the Bacterial Nucleoid to Stimulate Cell Division,"  Cell division site positioning is precisely regulated to generate correctly
sized and shaped daughters. We uncover a novel strategy to position the FtsZ
cytokinetic ring at midcell in the social bacterium Myxococcus xanthus. PomX,
PomY and the nucleoid-binding ParA/MinD ATPase PomZ self-assemble forming a
large nucleoid-associated complex that localizes at the division site before
FtsZ to directly guide and stimulate division. PomXYZ localization is generated
through self-organized biased random motion on the nucleoid towards midcell and
constrained motion at midcell. Experiments and theory show that PomXYZ motion
is produced by diffusive PomZ fluxes on the nucleoid into the complex. Flux
differences scale with the intracellular asymmetry of the complex and are
converted into a local PomZ concentration gradient across the complex with
translocation towards the higher PomZ concentration. At midcell, fluxes
equalize resulting in constrained motion. Flux-based mechanisms may represent a
general paradigm for positioning of macromolecular structures in bacteria.
",0,0,0,0,1,0
20484,Quantitative Results on Diophantine Equations in Many Variables,"  We consider a system of polynomials $f_1,\ldots, f_R\in
\mathbb{Z}[x_1,\ldots, x_n]$ of the same degree with non-singular local zeros
and in many variables. Generalising the work of Birch (1962) we find
quantitative asymptotics (in terms of the maximum of the absolute value of the
coefficients of these polynomials) for the number of integer zeros of this
system within a growing box. Using a quantitative version of the
Nullstellensatz, we obtain a quantitative strong approximation result, i.e. an
upper bound on the smallest integer zero provided the system of polynomials is
non-singular.
",0,0,1,0,0,0
20485,Generalized Springer correspondence for symmetric spaces associated to orthogonal groups,"  Let $G = GL_N$ over an algebraically closed field of odd characteristic, and
$\theta$ an involutive automorphism on $G$ such that $H = (G^{\theta})^0$ is
isomorphic to $SO_N$. Then $G^{\iota\theta} = \{ g \in G \mid \theta(g) =
g^{-1} \}$ is regarded as a symmetric space $G/G^{\theta}$. Let
$G^{\iota\theta}_{uni}$ be the set of unipotent elements in $G^{\iota\theta}$.
$H$ acts on $G^{\iota\theta}_{uni}$ by the conjugation. As an analogue of the
generalized Springer correspondence in the case of reductive groups, we
establish in this paper the generalized Springer correspondence between
$H$-orbits in $G^{\iota\theta}_{uni}$ and irreducible representations of
various symmetric groups.
",0,0,1,0,0,0
20486,An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit,"  Our work focuses on the problem of predicting the transfer of pediatric
patients from the general ward of a hospital to the pediatric intensive care
unit. Using data collected over 5.5 years from the electronic health records of
two medical facilities, we develop classifiers based on adaptive boosting and
gradient tree boosting. We further combine these learned classifiers into an
ensemble model and compare its performance to a modified pediatric early
warning score (PEWS) baseline that relies on expert defined guidelines. To
gauge model generalizability, we perform an inter-facility evaluation where we
train our algorithm on data from one facility and perform evaluation on a
hidden test dataset from a separate facility. We show that improvements are
witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80
vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).
",1,0,0,1,0,0
20487,Effects of Network Structure on the Performance of a Modeled Traffic Network under Drivers' Bounded Rationality,"  We propose a minority route choice game to investigate the effect of the
network structure on traffic network performance under the assumption of
drivers' bounded rationality. We investigate ring-and-hub topologies to capture
the nature of traffic networks in cities, and employ a minority game-based
inductive learning process to model the characteristic behavior under the route
choice scenario. Through numerical experiments, we find that topological
changes in traffic networks induce a phase transition from an uncongested phase
to a congested phase. Understanding this phase transition is helpful in
planning new traffic networks.
",1,1,0,0,0,0
20488,Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law,"  Massive content about user's social, personal and professional life stored on
Online Social Networks (OSNs) has attracted not only the attention of
researchers and social analysts but also the cyber criminals. These cyber
criminals penetrate illegally into an OSN by establishing fake profiles or by
designing bots and exploit the vulnerabilities of an OSN to carry out illegal
activities. With the growth of technology cyber crimes have been increasing
manifold. Daily reports of the security and privacy threats in the OSNs demand
not only the intelligent automated detection systems that can identify and
alleviate fake profiles in real time but also the reinforcement of the security
and privacy laws to curtail the cyber crime. In this paper, we have studied
various categories of fake profiles like compromised profiles, cloned profiles
and online bots (spam-bots, social-bots, like-bots and influential-bots) on
different OSN sites along with existing cyber laws to mitigate their threats.
In order to design fake profile detection systems, we have highlighted
different category of fake profile features which are capable to distinguish
different kinds of fake entities from real ones. Another major challenges faced
by researchers while building the fake profile detection systems is the
unavailability of data specific to fake users. The paper addresses this
challenge by providing extremely obliging data collection techniques along with
some existing data sources. Furthermore, an attempt is made to present several
machine learning techniques employed to design different fake profile detection
systems.
",1,0,0,0,0,0
20489,Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis,"  Networked system often relies on distributed algorithms to achieve a global
computation goal with iterative local information exchanges between neighbor
nodes. To preserve data privacy, a node may add a random noise to its original
data for information exchange at each iteration. Nevertheless, a neighbor node
can estimate other's original data based on the information it received. The
estimation accuracy and data privacy can be measured in terms of $(\epsilon,
\delta)$-data-privacy, defined as the probability of $\epsilon$-accurate
estimation (the difference of an estimation and the original data is within
$\epsilon$) is no larger than $\delta$ (the disclosure probability). How to
optimize the estimation and analyze data privacy is a critical and open issue.
In this paper, a theoretical framework is developed to investigate how to
optimize the estimation of neighbor's original data using the local information
received, named optimal distributed estimation. Then, we study the disclosure
probability under the optimal estimation for data privacy analysis. We further
apply the developed framework to analyze the data privacy of the
privacy-preserving average consensus algorithm and identify the optimal noises
for the algorithm.
",1,0,0,0,0,0
20490,Geometric tuning of self-propulsion for Janus catalytic particles,"  Catalytic swimmers have attracted much attention as alternatives to
biological systems for examining collective microscopic dynamics and the
response to physico-chemical signals. Yet, understanding and predicting even
the most fundamental characteristics of their individual propulsion still
raises important challenges. While chemical asymmetry is widely recognized as
the cornerstone of catalytic propulsion, different experimental studies have
reported that particles with identical chemical properties may propel in
opposite directions. Here, we show that, beyond its chemical properties, the
detailed shape of a catalytic swimmer plays an essential role in determining
its direction of motion, demonstrating the compatibility of the classical
theoretical framework with experimental observations.
",0,1,0,0,0,0
20491,On the Bogolubov-de Gennes Equations,"  We consider the Bogolubov-de Gennes equations giving an equivalent
formulation of the BCS theory of superconductivity. We are interested in the
case when the magnetic field is present. We (a) discuss their general features,
(b) isolate key physical classes of solutions (normal, vortex and vortex
lattice states) and (c) prove existence of the normal, vortex and vortex
lattice states and stability/instability of the normal states for large/small
temperature or/and magnetic fields.
",0,0,1,0,0,0
20492,"High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking","  Penalized likelihood methods are widely used for high-dimensional regression.
Although many methods have been proposed and the associated theory is now
well-developed, the relative efficacy of different methods in finite-sample
settings, as encountered in practice, remains incompletely understood. There is
therefore a need for empirical investigations in this area that can offer
practical insight and guidance to users of these methods. In this paper we
present a large-scale comparison of penalized regression methods. We
distinguish between three related goals: prediction, variable selection and
variable ranking. Our results span more than 1,800 data-generating scenarios,
allowing us to systematically consider the influence of various factors (sample
size, dimensionality, sparsity, signal strength and multicollinearity). We
consider several widely-used methods (Lasso, Elastic Net, Ridge Regression,
SCAD, the Dantzig Selector as well as Stability Selection). We find
considerable variation in performance between methods, with results dependent
on details of the data-generating scenario and the specific goal. Our results
support a `no panacea' view, with no unambiguous winner across all scenarios,
even in this restricted setting where all data align well with the assumptions
underlying the methods. Lasso is well-behaved, performing competitively in many
scenarios, while SCAD is highly variable. Substantial benefits from a
Ridge-penalty are only seen in the most challenging scenarios with strong
multi-collinearity. The results are supported by semi-synthetic analyzes using
gene expression data from cancer samples. Our empirical results complement
existing theory and provide a resource to compare methods across a range of
scenarios and metrics.
",0,0,0,1,0,0
20493,Using Human Brain Activity to Guide Machine Learning,"  Machine learning is a field of computer science that builds algorithms that
learn. In many cases, machine learning algorithms are used to recreate a human
ability like adding a caption to a photo, driving a car, or playing a game.
While the human brain has long served as a source of inspiration for machine
learning, little effort has been made to directly use data collected from
working brains as a guide for machine learning algorithms. Here we demonstrate
a new paradigm of ""neurally-weighted"" machine learning, which takes fMRI
measurements of human brain activity from subjects viewing images, and infuses
these data into the training process of an object recognition learning
algorithm to make it more consistent with the human brain. After training,
these neurally-weighted classifiers are able to classify images without
requiring any additional neural data. We show that our neural-weighting
approach can lead to large performance gains when used with traditional machine
vision features, as well as to significant improvements with already
high-performing convolutional neural network features. The effectiveness of
this approach points to a path forward for a new class of hybrid machine
learning algorithms which take both inspiration and direct constraints from
neuronal data.
",1,0,0,0,0,0
20494,Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning,"  We use a deep learning model trained only on a patient's blood oxygenation
data (measurable with an inexpensive fingertip sensor) to predict impending
hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
with access to all the data recorded in a modern operating room. We also
provide a simple way to visualize the reason why a patient's risk is low or
high by assigning weight to the patient's past blood oxygen values. This work
has the potential to provide cutting-edge clinical decision support in
low-resource settings, where rates of surgical complication and death are
substantially greater than in high-resource areas.
",1,0,0,1,0,0
20495,Merging fragments of classical logic,"  We investigate the possibility of extending the non-functionally complete
logic of a collection of Boolean connectives by the addition of further Boolean
connectives that make the resulting set of connectives functionally complete.
More precisely, we will be interested in checking whether an axiomatization for
Classical Propositional Logic may be produced by merging Hilbert-style calculi
for two disjoint incomplete fragments of it. We will prove that the answer to
that problem is a negative one, unless one of the components includes only
top-like connectives.
",1,0,1,0,0,0
20496,Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series,"  We propose a new variational Bayes estimator for high-dimensional copulas
with discrete, or a combination of discrete and continuous, margins. The method
is based on a variational approximation to a tractable augmented posterior, and
is faster than previous likelihood-based approaches. We use it to estimate
drawable vine copulas for univariate and multivariate Markov ordinal and mixed
time series. These have dimension $rT$, where $T$ is the number of observations
and $r$ is the number of series, and are difficult to estimate using previous
methods. The vine pair-copulas are carefully selected to allow for
heteroskedasticity, which is a feature of most ordinal time series data. When
combined with flexible margins, the resulting time series models also allow for
other common features of ordinal data, such as zero inflation, multiple modes
and under- or over-dispersion. Using six example series, we illustrate both the
flexibility of the time series copula models, and the efficacy of the
variational Bayes estimator for copulas of up to 792 dimensions and 60
parameters. This far exceeds the size and complexity of copula models for
discrete data that can be estimated using previous methods.
",0,0,0,1,0,0
20497,COSMO: Contextualized Scene Modeling with Boltzmann Machines,"  Scene modeling is very crucial for robots that need to perceive, reason about
and manipulate the objects in their environments. In this paper, we adapt and
extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
there are many models on the subject, ours is the first to bring together
objects, relations, and affordances in a highly-capable generative model. For
this end, we introduce a hybrid version of BMs where relations and affordances
are introduced with shared, tri-way connections into the model. Moreover, we
contribute a dataset for relation estimation and modeling studies. We evaluate
our method in comparison with several baselines on object estimation,
out-of-context object detection, relation estimation, and affordance estimation
tasks. Moreover, to illustrate the generative capability of the model, we show
several example scenes that the model is able to generate.
",1,0,0,0,0,0
20498,Learning Large-Scale Topological Maps Using Sum-Product Networks,"  In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topological map extraction, we have found little previous work on the
problem of learning the topological map using a probabilistic model. Learning a
topological map means learning the structure of the large-scale space and
dependency between places, for example, how the evidence of a group of places
influence the attributes of other places. This is an important step towards
planning complex actions in the environment. In this thesis, we consider the
problem of using probabilistic deep learning model to learn the topological
map, which is essentially a sparse undirected graph where nodes represent
places annotated with their semantic attributes (e.g. place category). We
propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
due to their unique properties. We present two methods for learning topological
maps using SPNs: the place grid method and the template-based method. We
contribute an algorithm that builds SPNs for graphs using template models. Our
experiments evaluate the ability of our models to enable robots to infer
semantic attributes and detect maps with novel semantic attribute arrangements.
Our results demonstrate their understanding of the topological map structure
and spatial relations between places.
",1,0,0,0,0,0
20499,Entanglement scaling and spatial correlations of the transverse field Ising model with perturbations,"  We study numerically the entanglement entropy and spatial correlations of the
one dimensional transverse field Ising model with three different
perturbations. First, we focus on the out of equilibrium, steady state with an
energy current passing through the system. By employing a variety of
matrix-product state based methods, we confirm the phase diagram and compute
the entanglement entropy. Second, we consider a small perturbation that takes
the system away from integrability and calculate the correlations, the central
charge and the entanglement entropy. Third, we consider periodically weakened
bonds, exploring the phase diagram and entanglement properties first in the
situation when the weak and strong bonds alternate (period two-bonds) and then
the general situation of a period of n bonds. In the latter case we find a
critical weak bond that scales with the transverse field as $J'_c/J$ =
$(h/J)^n$, where $J$ is the strength of the strong bond, $J'$ of the weak bond
and $h$ the transverse field. We explicitly show that the energy current is not
a conserved quantity in this case.
",0,1,0,0,0,0
20500,Anyonic excitations of hardcore anyons,"  Strongly interacting many-body systems consisting of fermions or bosons can
host exotic quasiparticles with anyonic statistics. Here, we demonstrate that
many-body systems of anyons can also form anyonic quasi-particles. The charge
and statistics of the emergent anyons can be different from those of the
original anyons.
",0,1,0,0,0,0
20501,Chaotic behavior in Casimir oscillators: A case study for phase change materials,"  Casimir forces between material surfaces at close proximity of less than 200
nm can lead to increased chaotic behavior of actuating devices depending on the
strength of the Casimir interaction. We investigate these phenomena for phase
change materials in torsional oscillators, where the amorphous to crystalline
phase transitions lead to transitions between high and low Casimir force and
torque states respectively, without material compositions. For a conservative
system bifurcation curve and Poincare maps analysis show the absence of chaotic
behavior but with the crystalline phase (high force/torque state) favoring more
unstable behavior and stiction. However, for a non-conservative system chaotic
behavior can occur introducing significant risk for stiction, which is again
more pronounced for the crystalline phase. The latter illustrates the more
general scenario that stronger Casimir forces and torques increase the
possibility for chaotic behavior. The latter is making impossible to predict
whether stiction or stable actuation will occur on a long term basis, and it is
setting limitations in the design of micro/nano devices operating at short
range nanoscale separations.
",0,1,0,0,0,0
20502,Confluence in Probabilistic Rewriting,"  Driven by the interest of reasoning about probabilistic programming
languages, we set out to study a notion of unicity of normal forms for them. To
provide a tractable proof method for it, we define a property of distribution
confluence which is shown to imply the desired uniqueness (even for infinite
sequences of reduction) and further properties. We then carry over several
criteria from the classical case, such as Newman's lemma, to simplify proving
confluence in concrete languages. Using these criteria, we obtain simple proofs
of confluence for $\lambda_1$, an affine probabilistic $\lambda$-calculus, and
for Q$^*$, a quantum programming language for which a related property has
already been proven in the literature.
",1,0,0,0,0,0
20503,Structure preserving schemes for mean-field equations of collective behavior,"  In this paper we consider the development of numerical schemes for mean-field
equations describing the collective behavior of a large group of interacting
agents. The schemes are based on a generalization of the classical Chang-Cooper
approach and are capable to preserve the main structural properties of the
systems, namely nonnegativity of the solution, physical conservation laws,
entropy dissipation and stationary solutions. In particular, the methods here
derived are second order accurate in transient regimes whereas they can reach
arbitrary accuracy asymptotically for large times. Several examples are
reported to show the generality of the approach.
",0,1,0,0,0,0
20504,Localization optoacoustic tomography,"  Localization-based imaging has revolutionized fluorescence optical microscopy
and has also enabled unprecedented ultrasound images of microvascular
structures in deep tissues. Herein, we introduce a new concept of localization
optoacoustic tomography (LOAT) that employs rapid sequential acquisition of
three-dimensional optoacoustic images from flowing absorbing particles. We show
that the new method enables breaking through the spatial resolution barrier of
acoustic diffraction while further enhancing the visibility of structures under
limited-view tomographic conditions. Given the intrinsic sensitivity of
optoacoustics to multiple hemodynamic and oxygenation parameters, LOAT may
enable new level of performance in studying functional and anatomical
alterations of microcirculation.
",0,1,0,0,0,0
20505,Reduced-basis approach to many-body localization,"  Within the standard model of many-body localization, i.e., the disordered
chain of spinless fermions, we investigate how the interaction affects the
many-body states in the basis of noninteracting localized Anderson states. From
this starting point we follow the approach that uses a reduced basis of
many-body states. Together with an extrapolation to the full basis, it proves
to be efficient for the evaluation of the stiffnesses of local observables,
which remain finite within the non-ergodic regime and represent the hallmark of
the many-body localization (MBL). The method enables a larger span of system
sizes and, within the MBL regime, allows for a more careful analysis of the
size scaling of calculated quantities. On the other hand, the survival
stiffness as the representative of non--local quantities, reveals limitations
of the reduced-basis approach.
",0,1,0,0,0,0
20506,Electron-correlation study of Y III-Tc VII ions using a relativistic coupled-cluster theory,"  Spectroscopic properties, useful for plasma diagnostics and astrophysics, of
a few rubidium-like ions are studied here. We choose one of the simplest, but
correlationally challenging series where $d-$ and $f-$ orbitals are present in
the core and/or valence shells with $4d$ $^2D_{3/2}$ as the ground state. We
study different correlation characteristics of this series and make precise
calculations of electronic structure and rates of electromagnetic transitions.
Our calculated lifetimes and transition rates are compared with other available
experimental and theoretical values. Radiative rates of vacuum ultra-violet
electromagnetic transitions of the long lived Tc$^{6+}$ ion, useful in several
areas of physics and chemistry, are estimated. To the best of our knowledge,
there is no literature for most of these transitions.
",0,1,0,0,0,0
20507,Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection,"  This paper presents the submissions by the University of Zurich to the
SIGMORPHON 2017 shared task on morphological reinflection. The task is to
predict the inflected form given a lemma and a set of morpho-syntactic
features. We focus on neural network approaches that can tackle the task in a
limited-resource setting. As the transduction of the lemma into the inflected
form is dominated by copying over lemma characters, we propose two recurrent
neural network architectures with hard monotonic attention that are strong at
copying and, yet, substantially different in how they achieve this. The first
approach is an encoder-decoder model with a copy mechanism. The second approach
is a neural state-transition system over a set of explicit edit actions,
including a designated COPY action. We experiment with character alignment and
find that naive, greedy alignment consistently produces strong results for some
languages. Our best system combination is the overall winner of the SIGMORPHON
2017 Shared Task 1 without external resources. At a setting with 100 training
samples, both our approaches, as ensembles of models, outperform the next best
competitor.
",1,0,0,0,0,0
20508,Krylov methods for low-rank commuting generalized Sylvester equations,"  We consider generalizations of the Sylvester matrix equation, consisting of
the sum of a Sylvester operator and a linear operator $\Pi$ with a particular
structure. More precisely, the commutator of the matrix coefficients of the
operator $\Pi$ and the Sylvester operator coefficients are assumed to be
matrices with low rank. We show (under certain additional conditions) low-rank
approximability of this problem, i.e., the solution to this matrix equation can
be approximated with a low-rank matrix. Projection methods have successfully
been used to solve other matrix equations with low-rank approximability. We
propose a new projection method for this class of matrix equations. The choice
of subspace is a crucial ingredient for any projection method for matrix
equations. Our method is based on an adaption and extension of the extended
Krylov subspace method for Sylvester equations. A constructive choice of the
starting vector/block is derived from the low-rank commutators. We illustrate
the effectiveness of our method by solving large-scale matrix equations arising
from applications in control theory and the discretization of PDEs. The
advantages of our approach in comparison to other methods are also illustrated.
",0,0,1,0,0,0
20509,Atomic-scale origin of dynamic viscoelastic response and creep in disordered solids,"  Viscoelasticity has been described since the time of Maxwell as an
interpolation of purely viscous and purely elastic response, but its
microscopic atomic-level mechanism in solids has remained elusive. We studied
three model disordered solids: a random lattice, the bond-depleted fcc lattice,
and the fcc lattice with vacancies. Within the harmonic approximation for
central-force lattices, we applied sum-rules for viscoelastic response derived
on the basis of non-affine atomic motions. The latter motions are a direct
result of local structural disorder, and in particular, of the lack of
inversion-symmetry in disordered lattices. By defining a suitable quantitative
and general atomic-level measure of nonaffinity and inversion-symmetry, we show
that the viscoelastic responses of all three systems collapse onto a master
curve upon normalizing by the overall strength of inversion-symmetry breaking
in each system. Close to the isostatic point for central-force lattices,
power-law creep $G(t)\sim t^{-1/2}$ emerges as a consequence of the interplay
between soft vibrational modes and non-affine dynamics, and various analytical
scalings, supported by numerical calculations, are predicted by the theory.
",0,1,0,0,0,0
20510,On-sky closed loop correction of atmospheric dispersion for high-contrast coronagraphy and astrometry,"  Adaptive optic (AO) systems delivering high levels of wavefront correction
are now common at observatories. One of the main limitations to image quality
after wavefront correction comes from atmospheric refraction. An Atmospheric
dispersion compensator (ADC) is employed to correct for atmospheric refraction.
The correction is applied based on a look-up table consisting of dispersion
values as a function of telescope elevation angle. The look-up table based
correction of atmospheric dispersion results in imperfect compensation leading
to the presence of residual dispersion in the point-spread function (PSF) and
is insufficient when sub-milliarcsecond precision is required. The presence of
residual dispersion can limit the achievable contrast while employing
high-performance coronagraphs or can compromise high-precision astrometric
measurements. In this paper, we present the first on-sky closed-loop correction
of atmospheric dispersion by directly using science path images. The concept
behind the measurement of dispersion utilizes the chromatic scaling of focal
plane speckles. An adaptive speckle grid generated with a deformable mirror
(DM) that has a sufficiently large number of actuators is used to accurately
measure the residual dispersion and subsequently correct it by driving the ADC.
We have demonstrated with the Subaru Coronagraphic Extreme AO (SCExAO) system
on-sky closed-loop correction of residual dispersion to < 1 mas across H-band.
This work will aid in the direct detection of habitable exoplanets with
upcoming extremely large telescopes (ELTs) and also provide a diagnostic tool
to test the performance of instruments which require sub-milliarcsecond
correction.
",0,1,0,0,0,0
20511,Experimental observations and modelling of intrinsic rotation reversals in tokamaks,"  The progress made in understanding spontaneous toroidal rotation reversals in
tokamaks is reviewed and current ideas to solve this ten-year-old puzzle are
explored. The paper includes a summarial synthesis of the experimental
observations in AUG, C-Mod, KSTAR, MAST and TCV tokamaks, reasons why turbulent
momentum transport is thought to be responsible for the reversals, a review of
the theory of turbulent momentum transport and suggestions for future
investigations.
",0,1,0,0,0,0
20512,"Where, When, and How mmWave is Used in 5G and Beyon","  Wireless engineers and business planners commonly raise the question on
where, when, and how millimeter-wave (mmWave) will be used in 5G and beyond.
Since the next generation network is not just a new radio access standard, but
instead an integration of networks for vertical markets with diverse
applications, answers to the question depend on scenarios and use cases to be
deployed. This paper gives four 5G mmWave deployment examples and describes in
chronological order the scenarios and use cases of their probable deployment,
including expected system architectures and hardware prototypes. The paper
starts with 28 GHz outdoor backhauling for fixed wireless access and moving
hotspots, which will be demonstrated at the PyeongChang winter Olympic games in
2018. The second deployment example is a 60 GHz unlicensed indoor access system
at the Tokyo-Narita airport, which is combined with Mobile Edge Computing (MEC)
to enable ultra-high speed content download with low latency. The third example
is mmWave mesh network to be used as a micro Radio Access Network ({\mu}-RAN),
for cost-effective backhauling of small-cell Base Stations (BSs) in dense urban
scenarios. The last example is mmWave based Vehicular-to-Vehicular (V2V) and
Vehicular-to-Everything (V2X) communications system, which enables automated
driving by exchanging High Definition (HD) dynamic map information between cars
and Roadside Units (RSUs). For 5G and beyond, mmWave and MEC will play
important roles for a diverse set of applications that require both ultra-high
data rate and low latency communications.
",1,0,0,0,0,0
20513,"A simple script language for choreography of multiple, synchronizing non-anthropomorphic robots","  The scripting language described in this document is (in the first place)
intended to be used on robots developed by Anja M{\o}lle Lindelof and Henning
Christiansen as part of a research project about robots performing on stage.
The target robots are expected to appear as familiar domestic objects that
take their own life, so to speak, and perhaps perform together with human
players, creating at illusion of a communication between them. In the current
version, these robots' common behaviour is determined uniquely by a script
written in the language described here -- the only possible autonomy for the
robots is action to correct dynamically for inaccuracies that arise during a
performance.
The present work is preliminary and has not been compared to properly to
other research work in this area, and the testing is still limited. A first
implementation on small Lego Mindstorms based robots is under development by
Mads Saustrup Fox as part of his master thesis work.
",1,0,0,0,0,0
20514,Stability results for abstract evolution equations with intermittent time-delay feedback,"  We consider abstract evolution equations with on-off time delay feedback.
Without the time delay term, the model is described by an exponentially stable
semigroup. We show that, under appropriate conditions involving the delay term,
the system remains asymptotically stable. Under additional assumptions
exponential stability results are also obtained. Concrete examples illustrating
the abstract results are finally given.
",0,0,1,0,0,0
20515,Solar wind turbulent cascade from MHD to sub-ion scales: large-size 3D hybrid particle-in-cell simulations,"  Spectral properties of the turbulent cascade from fluid to kinetic scales in
collisionless plasmas are investigated by means of large-size three-dimensional
(3D) hybrid (fluid electrons, kinetic protons) particle-in-cell simulations.
Initially isotropic Alfvènic fluctuations rapidly develop a strongly
anisotropic turbulent cascade, mainly in the direction perpendicular to the
ambient magnetic field. The omnidirectional magnetic field spectrum shows a
double power-law behavior over almost two decades in wavenumber, with a
Kolmogorov-like index at large scales, a spectral break around ion scales, and
a steepening at sub-ion scales. Power laws are also observed in the spectra of
the ion bulk velocity, density, and electric field, both at magnetohydrodynamic
(MHD) and at kinetic scales. Despite the complex structure, the omnidirectional
spectra of all fields at ion and sub-ion scales are in remarkable quantitative
agreement with those of a two-dimensional (2D) simulation with similar physical
parameters. This provides a partial, a-posteriori validation of the 2D
approximation at kinetic scales. Conversely, at MHD scales, the spectra of the
density and of the velocity (and, consequently, of the electric field) exhibit
differences between the 2D and 3D cases. Although they can be partly ascribed
to the lower spatial resolution, the main reason is likely the larger
importance of compressible effects in a full geometry. Our findings are also in
remarkable quantitative agreement with solar wind observations.
",0,1,0,0,0,0
20516,Blind source separation of tensor-valued time series,"  The blind source separation model for multivariate time series generally
assumes that the observed series is a linear transformation of an unobserved
series with temporally uncorrelated or independent components. Given the
observations, the objective is to find a linear transformation that recovers
the latent series. Several methods for accomplishing this exist and three
particular ones are the classic SOBI and the recently proposed generalized FOBI
(gFOBI) and generalized JADE (gJADE), each based on the use of joint lagged
moments. In this paper we generalize the methodologies behind these algorithms
for tensor-valued time series. We assume that our data consists of a tensor
observed at each time point and that the observations are linear
transformations of latent tensors we wish to estimate. The tensorial
generalizations are shown to have particularly elegant forms and we show that
each of them is Fisher consistent and orthogonal equivariant. Comparing the new
methods with the original ones in various settings shows that the tensorial
extensions are superior to both their vector-valued counterparts and to two
existing tensorial dimension reduction methods for i.i.d. data. Finally,
applications to fMRI-data and video processing show that the methods are
capable of extracting relevant information from noisy high-dimensional data.
",0,0,1,1,0,0
20517,Mixed Cages,"  We introduce the notion of a $[z, r; g]$-mixed cage. A $[z, r; g]$-mixed cage
is a mixed graph $G$, $z$-regular by arcs, $r$-regular by edges, with girth $g$
and minimum order. In this paper we prove the existence of $[z, r ;g]$-mixed
cages and exhibit families of mixed cages for some specific values. We also
give lower and upper bounds for some choices of $z, r$ and $g$. In particular
we present the first results on $[z,r;g]$- mixed cages for $z=1$ and any $r\geq
1$ and $g\geq 3$, and for any $z\geq 1$, $r=1$ and $g=4$.
",0,0,1,0,0,0
20518,Single-beam dielectric-microsphere trapping with optical heterodyne detection,"  A technique to levitate and measure the three-dimensional position of
micrometer-sized dielectric spheres with heterodyne detection is presented. The
two radial degrees of freedom are measured by interfering light transmitted
through the microsphere with a reference wavefront, while the axial degree of
freedom is measured from the phase of the light reflected from the surface of
the microsphere. This method pairs the simplicity and accessibility of single
beam optical traps to a measurement of displacement that is intrinsically
calibrated by the wavelength of the trapping light and has exceptional immunity
to stray light. A theoretical shot noise limit of
$1.3\times10^{-13}\,\text{m}/\sqrt{\text{Hz}}$ for the radial degrees of
freedom, and $3.0\times10^{-15} \, \text{m}/\sqrt{\text{Hz}}$ for the axial
degree of freedom can be obtained in the system described. The measured
acceleration noise in the radial direction is $7.5\times10^{-5} \,
(\text{m/s}^2)/\sqrt{\text{Hz}}$.
",0,1,0,0,0,0
20519,An Empirical Analysis of Traceability in the Monero Blockchain,"  Monero is a privacy-centric cryptocurrency that allows users to obscure their
transactions by including chaff coins, called ""mixins,"" along with the actual
coins they spend. In this paper, we empirically evaluate two weaknesses in
Monero's mixin sampling strategy. First, about 62% of transaction inputs with
one or more mixins are vulnerable to ""chain-reaction"" analysis -- that is, the
real input can be deduced by elimination. Second, Monero mixins are sampled in
such a way that they can be easily distinguished from the real coins by their
age distribution; in short, the real input is usually the ""newest"" input. We
estimate that this heuristic can be used to guess the real input with 80%
accuracy over all transactions with 1 or more mixins. Next, we turn to the
Monero ecosystem and study the importance of mining pools and the former
anonymous marketplace AlphaBay on the transaction volume. We find that after
removing mining pool activity, there remains a large amount of potentially
privacy-sensitive transactions that are affected by these weaknesses. We
propose and evaluate two countermeasures that can improve the privacy of future
transactions.
",1,0,0,0,0,0
20520,Automatic Face Image Quality Prediction,"  Face image quality can be defined as a measure of the utility of a face image
to automatic face recognition. In this work, we propose (and compare) two
methods for automatic face image quality based on target face quality values
from (i) human assessments of face image quality (matcher-independent), and
(ii) quality values computed from similarity scores (matcher-dependent). A
support vector regression model trained on face features extracted using a deep
convolutional neural network (ConvNet) is used to predict the quality of a face
image. The proposed methods are evaluated on two unconstrained face image
databases, LFW and IJB-A, which both contain facial variations with multiple
quality factors. Evaluation of the proposed automatic face image quality
measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two
face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face
quality to select subsets of face images and video frames for matching
templates (i.e., multiple faces per subject) in the IJB-A protocol. To our
knowledge, this is the first work to utilize human assessments of face image
quality in designing a predictor of unconstrained face quality that is shown to
be effective in cross-database evaluation.
",1,0,0,0,0,0
20521,Stochastic Block Models are a Discrete Surface Tension,"  Networks, which represent agents and interactions between them, arise in
myriad applications throughout the sciences, engineering, and even the
humanities. To understand large-scale structure in a network, a common task is
to cluster a network's nodes into sets called ""communities"" such that there are
dense connections within communities but sparse connections between them. A
popular and statistically principled method to perform such clustering is to
use a family of generative models known as stochastic block models (SBMs). In
this paper, we show that maximum likelihood estimation in an SBM is a network
analog of a well-known continuum surface-tension problem that arises from an
application in metallurgy. To illustrate the utility of this bridge, we
implement network analogs of three surface-tension algorithms, with which we
successfully recover planted community structure in synthetic networks and
which yield fascinating insights on empirical networks from the field of
hyperspectral video segmentation.
",1,0,0,1,0,0
20522,Robust d-wave pairing symmetry in multi-orbital cobalt high temperature superconductors,"  The pairing symmetry of the newly proposed cobalt high temperature
(high-$T_c$) superconductors formed by vertex shared cation-anion tetrahedral
complexes is studied by the methods of mean field, random phase approximation
(RPA) and functional renormalization group (FRG) analysis. The results of all
these methods show that the $d_{x^2-y^2}$ pairing symmetry is robustly favored
near half filling. The RPA and FRG methods, which are valid in weak interaction
regions, predict that the superconducting state is also strongly orbital
selective, namely the $d_{x^2-y^2}$ orbital that has the largest density near
half filling among the three $t_{2g}$ orbitals dominates superconducting
pairing. These results suggest that the new materials, if synthesized, can
provide indisputable test to high-$T_c$ pairing mechanism and the validity of
different theoretical methods.
",0,1,0,0,0,0
20523,Thermodynamic properties of Ba$_2$CoSi$_2$O$_6$Cl$_2$ in strong magnetic field: Realization of flat-band physics in a highly frustrated quantum magnet,"  The search for flat-band solid-state realizations is a crucial issue to
verify or to challenge theoretical predictions for quantum many-body flat-band
systems. For frustrated quantum magnets flat bands lead to various
unconventional properties related to the existence of localized many-magnon
states. The recently synthesized magnetic compound Ba$_2$CoSi$_2$O$_6$Cl$_2$
seems to be an almost perfect candidate to observe these features in
experiments. We develop a theory for Ba$_2$CoSi$_2$O$_6$Cl$_2$ by adapting the
localized-magnon concept to this compound. We first show that our theory
describes the known experimental facts and then we propose new experimental
studies to detect a field-driven phase transition related to a
Wigner-crystal-like ordering of localized magnons at low temperatures.
",0,1,0,0,0,0
20524,Fisher Information and Natural Gradient Learning of Random Deep Networks,"  A deep neural network is a hierarchical nonlinear model transforming input
signals to output signals. Its input-output relation is considered to be
stochastic, being described for a given input by a parameterized conditional
probability distribution of outputs. The space of parameters consisting of
weights and biases is a Riemannian manifold, where the metric is defined by the
Fisher information matrix. The natural gradient method uses the steepest
descent direction in a Riemannian manifold, so it is effective in learning,
avoiding plateaus. It requires inversion of the Fisher information matrix,
however, which is practically impossible when the matrix has a huge number of
dimensions. Many methods for approximating the natural gradient have therefore
been introduced. The present paper uses statistical neurodynamical method to
reveal the properties of the Fisher information matrix in a net of random
connections under the mean field approximation. We prove that the Fisher
information matrix is unit-wise block diagonal supplemented by small order
terms of off-block-diagonal elements, which provides a justification for the
quasi-diagonal natural gradient method by Y. Ollivier. A unitwise
block-diagonal Fisher metrix reduces to the tensor product of the Fisher
information matrices of single units. We further prove that the Fisher
information matrix of a single unit has a simple reduced form, a sum of a
diagonal matrix and a rank 2 matrix of weight-bias correlations. We obtain the
inverse of Fisher information explicitly. We then have an explicit form of the
natural gradient, without relying on the numerical matrix inversion, which
drastically speeds up stochastic gradient learning.
",0,0,0,1,0,0
20525,Dynamic of plumes and scaling during the melting of a Phase Change Material heated from below,"  We identify and describe the main dynamic regimes occurring during the
melting of the PCM n-octadecane in horizontal layers of several sizes heated
from below. This configuration allows to cover a wide range of effective
Rayleigh numbers on the liquid PCM phase, up to $\sim 10^9$, without changing
any external parameter control. We identify four different regimes as time
evolves: (i) the conductive regime, (ii) linear regime, (iii) coarsening regime
and (iv) turbulent regime. The first two regimes appear at all domain sizes.
However the third and fourth regime require a minimum advance of the
solid/liquid interface to develop, and we observe them only for large enough
domains.
The transition to turbulence takes places after a secondary instability that
forces the coarsening of the thermal plumes. Each one of the melting regimes
creates a distinct solid/liquid front that characterizes the internal state of
the melting process. We observe that most of the magnitudes of the melting
process are ruled by power laws, although not all of them. Thus the number of
plumes, some regimes of the Rayleigh number as a function of time, the number
of plumes after the primary and secondary instability, the thermal and kinetic
boundary layers follow simple power laws. In particular, we find that the
Nusselt number scales with the Rayleigh number as $Nu \sim Ra^{0.29}$ in the
turbulent regime, consistent with theories and experiments on Rayleigh-Bénard
convection that show an exponent $2/7$.
",0,1,0,0,0,0
20526,Parallel mean curvature surfaces in four-dimensional homogeneous spaces,"  We survey different classification results for surfaces with parallel mean
curvature immersed into some Riemannian homogeneous four-manifolds, including
real and complex space forms, and product spaces. We provide a common framework
for this problem, with special attention to the existence of holomorphic
quadratic differentials on such surfaces. The case of spheres with parallel
mean curvature is also explained in detail, as well as the state-of-the-art
advances in the general problem.
",0,0,1,0,0,0
20527,"Recent implementations, applications, and extensions of the Locally Optimal Block Preconditioned Conjugate Gradient method (LOBPCG)","  Since introduction [A. Knyazev, Toward the optimal preconditioned
eigensolver: Locally optimal block preconditioned conjugate gradient method,
SISC (2001) DOI:10.1137/S1064827500366124] and efficient parallel
implementation [A. Knyazev et al., Block locally optimal preconditioned
eigenvalue xolvers (BLOPEX) in HYPRE and PETSc, SISC (2007)
DOI:10.1137/060661624], LOBPCG has been used is a wide range of applications in
mechanics, material sciences, and data sciences. We review its recent
implementations and applications, as well as extensions of the local optimality
idea beyond standard eigenvalue problems.
",1,0,0,1,0,0
20528,An evaluation of cosmological models from expansion and growth of structure measurements,"  We compare a large suite of theoretical cosmological models to observational
data from the cosmic microwave background, baryon acoustic oscillation
measurements of expansion, Type Ia SNe measurements of expansion, redshift
space distortion measurements of the growth of structure, and the local Hubble
constant. Our theoretical models include parametrizations of dark energy as
well as physical models of dark energy and modified gravity. We determine the
constraints on the model parameters, incorporating the redshift space
distortion data directly in the analysis. To determine whether models can be
ruled out, we evaluate the $p$ value (the probability under the model of
obtaining data as bad or worse than the observed data). In our comparison, we
find the well known tension of H$_0$ with the other data; no model resolves
this tension successfully. Among the models we consider, the large scale growth
of structure data does not affect the modified gravity models as a category
particularly differently than dark energy models; it matters for some modified
gravity models but not others, and the same is true for dark energy models. We
compute predicted observables for each model under current observational
constraints, and identify models for which future observational constraints
will be particularly informative.
",0,1,0,0,0,0
20529,The variety of $2$-dimensional algebras over an algebraically closed field,"  The work is devoted to the variety of $2$-dimensional algebras over an
algebraically closed field. Firstly, we classify such algebras modulo
isomorphism. Then we describe the degenerations and the closures of principal
algebra series in the variety under consideration. Finally, we apply our
results to obtain analogous descriptions for the subvarieties of flexible, and
bicommutative algebras. In particular, we describe rigid algebras and
irreducible components for these subvarieties.
",0,0,1,0,0,0
20530,A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM,"  We present a Bayesian object observation model for complete probabilistic
semantic SLAM. Recent studies on object detection and feature extraction have
become important for scene understanding and 3D mapping. However, 3D shape of
the object is too complex to formulate the probabilistic observation model;
therefore, performing the Bayesian inference of the object-oriented features as
well as their pose is less considered. Besides, when the robot equipped with an
RGB mono camera only observes the projected single view of an object, a
significant amount of the 3D shape information is abandoned. Due to these
limitations, semantic SLAM and viewpoint-independent loop closure using
volumetric 3D object shape is challenging. In order to enable the complete
formulation of probabilistic semantic SLAM, we approximate the observation
model of a 3D object with a tractable distribution. We also estimate the
variational likelihood from the 2D image of the object to exploit its observed
single view. In order to evaluate the proposed method, we perform pose and
feature estimation, and demonstrate that the automatic loop closure works
seamlessly without additional loop detector in various environments.
",1,0,0,0,0,0
20531,Generation of optical frequency combs via four-wave mixing processes for low- and medium-resolution astronomy,"  We investigate the generation of optical frequency combs through a cascade of
four-wave mixing processes in nonlinear fibres with optimised parameters. The
initial optical field consists of two continuous-wave lasers with frequency
separation larger than 40 GHz (312.7 pm at 1531 nm). It propagates through
three nonlinear fibres. The first fibre serves to pulse shape the initial
sinusoidal-square pulse, while a strong pulse compression down to sub-100 fs
takes place in the second fibre which is an amplifying erbium-doped fibre. The
last stage is a low-dispersion highly nonlinear fibre where the frequency comb
bandwidth is increased and the line intensity is equalised. We model this
system using the generalised nonlinear Schrödinger equation and investigate
it in terms of fibre lengths, fibre dispersion, laser frequency separation and
input powers with the aim to minimise the frequency comb noise. With the
support of the numerical results, a frequency comb is experimentally generated,
first in the near infra-red and then it is frequency-doubled into the visible
spectral range. Using a MUSE-type spectrograph, we evaluate the comb
performance for astronomical wavelength calibration in terms of equidistancy of
the comb lines and their stability.
",0,1,0,0,0,0
20532,Cross-referencing Social Media and Public Surveillance Camera Data for Disaster Response,"  Physical media (like surveillance cameras) and social media (like Instagram
and Twitter) may both be useful in attaining on-the-ground information during
an emergency or disaster situation. However, the intersection and reliability
of both surveillance cameras and social media during a natural disaster are not
fully understood. To address this gap, we tested whether social media is of
utility when physical surveillance cameras went off-line during Hurricane Irma
in 2017. Specifically, we collected and compared geo-tagged Instagram and
Twitter posts in the state of Florida during times and in areas where public
surveillance cameras went off-line. We report social media content and
frequency and content to determine the utility for emergency managers or first
responders during a natural disaster.
",1,0,0,0,0,0
20533,Nonlinear Dynamics of a Viscous Bubbly Fluid,"  A physical model of a three-dimensional flow of a viscous bubbly fluid in an
intermediate regime between bubble formation and breakage is presented. The
model is based on mechanics and thermodynamics of a single bubble coupled to
the dynamics of a viscous fluid as a whole, and takes into account multiple
physical effects, including gravity, viscosity, and surface tension.
Dimensionless versions of the resulting nonlinear model are obtained, and
values of dimensionless parameters are estimated for typical magma flows in
horizontal subaerial lava fields and vertical volcanic conduits.
Exact solutions of the resulting system of nonlinear equations corresponding
to equilibrium flows and traveling waves are analyzed in the one-dimensional
setting. Generalized Su-Gardner-type perturbation analysis is employed to study
approximate solutions of the model in the long-wave ansatz. Simplified
nonlinear partial differential equations (PDE) satisfied by the leading terms
of the perturbation solutions are systematically derived. It is shown that for
specific classes of perturbations, approximate solutions of the bubbly fluid
model arise from solutions of the classical diffusion, Burgers,
variable-coefficient Burgers, and Korteweg-de Vries equations.
",0,1,0,0,0,0
20534,ConvNet-Based Localization of Anatomical Structures in 3D Medical Images,"  Localization of anatomical structures is a prerequisite for many tasks in
medical image analysis. We propose a method for automatic localization of one
or more anatomical structures in 3D medical images through detection of their
presence in 2D image slices using a convolutional neural network (ConvNet).
A single ConvNet is trained to detect presence of the anatomical structure of
interest in axial, coronal, and sagittal slices extracted from a 3D image. To
allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling
is applied. After detection, 3D bounding boxes are created by combining the
output of the ConvNet in all slices.
In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100
abdomen CT scans were used. The heart, ascending aorta, aortic arch, and
descending aorta were localized in chest CT scans, the left cardiac ventricle
in cardiac CTA scans, and the liver in abdomen CT scans. Localization was
evaluated using the distances between automatically and manually defined
reference bounding box centroids and walls.
The best results were achieved in localization of structures with clearly
defined boundaries (e.g. aortic arch) and the worst when the structure boundary
was not clearly visible (e.g. liver). The method was more robust and accurate
in localization multiple structures.
",1,0,0,0,0,0
20535,Gradient estimates for heat kernels and harmonic functions,"  Let $(X,d,\mu)$ be a doubling metric measure space endowed with a Dirichlet
form $\E$ deriving from a ""carré du champ"". Assume that $(X,d,\mu,\E)$
supports a scale-invariant $L^2$-Poincaré inequality. In this article, we
study the following properties of harmonic functions, heat kernels and Riesz
transforms for $p\in (2,\infty]$:
(i) $(G_p)$: $L^p$-estimate for the gradient of the associated heat
semigroup;
(ii) $(RH_p)$: $L^p$-reverse Hölder inequality for the gradients of
harmonic functions;
(iii) $(R_p)$: $L^p$-boundedness of the Riesz transform ($p<\infty$);
(iv) $(GBE)$: a generalised Bakry-Émery condition.
We show that, for $p\in (2,\infty)$, (i), (ii) (iii) are equivalent, while
for $p=\infty$, (i), (ii), (iv) are equivalent.
Moreover, some of these equivalences still hold under weaker conditions than
the $L^2$-Poincaré inequality.
Our result gives a characterisation of Li-Yau's gradient estimate of heat
kernels for $p=\infty$, while for $p\in (2,\infty)$ it is a substantial
improvement as well as a generalisation of earlier results by
Auscher-Coulhon-Duong-Hofmann [7] and Auscher-Coulhon [6]. Applications to
isoperimetric inequalities and Sobolev inequalities are given. Our results
apply to Riemannian and sub-Riemannian manifolds as well as to non-smooth
spaces, and to degenerate elliptic/parabolic equations in these settings.
",0,0,1,0,0,0
20536,The Deep Underground Neutrino Experiment -- DUNE: the precision era of neutrino physics,"  The last decade was remarkable for neutrino physics. In particular, the
phenomenon of neutrino flavor oscillations has been firmly established by a
series of independent measurements. All parameters of the neutrino mixing are
now known and we have elements to plan a judicious exploration of new scenarios
that are opened by these recent advances. With precise measurements, we can
test the 3-neutrino paradigm, neutrino mass hierarchy and CP asymmetry in the
lepton sector. The future long-baseline experiments are considered to be a
fundamental tool to deepen our knowledge of electroweak interactions. The Deep
Underground Neutrino Experiment -- DUNE will detect a broad-band neutrino beam
from Fermilab in an underground massive Liquid Argon Time-Projection Chamber at
an L/E of about $10^3$ km / GeV to reach good sensitivity for CP-phase
measurements and the determination of the mass hierarchy. The dimensions and
the depth of the Far Detector also create an excellent opportunity to look for
rare signals like proton decay to study violation of baryonic number, as well
as supernova neutrino bursts, broadening the scope of the experiment to
astrophysics and associated impacts in cosmology. In this presentation, we will
discuss the physics motivations and the main experimental features of the DUNE
project required to reach its scientific goals.
",0,1,0,0,0,0
20537,Variations of $q$-Garnier system,"  We study several variants of q-Garnier system corresponding to various
directions of discrete time evolutions. We also investigate a relation between
the $q$-Garnier system and Suzuki's higher order $q$-Painlev/'e system by using
a duality of the $q$-KP system.
",0,1,1,0,0,0
20538,Quantum Quench dynamics in Non-local Luttinger Model: Rigorous Results,"  We investigate, in the Luttinger model with fixed box potential, the time
evolution of an inhomogeneous state prepared as a localized fermion added to
the noninteracting ground state. We proved that, if the state is evolved with
the interacting Hamiltonian, the averaged density has two peaks moving in
opposite directions, with a constant but renormalized velocity. We also proved
that a dynamical `Landau quasi-particle weight' appears in the oscillating part
of the averaged density, asymptotically vanishing with large time. The results
are proved with the Mattis-Lieb diagonalization method. A simpler proof with
the exact Bosonization formulas is also provided.
",0,0,1,0,0,0
20539,Dynamics of Relaxed Inflation,"  The cosmological relaxation of the electroweak scale has been proposed as a
mechanism to address the hierarchy problem of the Standard Model. A field, the
relaxion, rolls down its potential and, in doing so, scans the squared mass
parameter of the Higgs, relaxing it to a parametrically small value. In this
work, we promote the relaxion to an inflaton. We couple it to Abelian gauge
bosons, thereby introducing the necessary dissipation mechanism which slows
down the field in the last stages. We describe a novel reheating mechanism,
which relies on the gauge-boson production leading to strong electromagnetic
fields, and proceeds via the vacuum production of electron-positron pairs
through the Schwinger effect. We refer to this mechanism as Schwinger
reheating. We discuss the cosmological dynamics of the model and the
phenomenological constraints from CMB and other experiments. We find that a
cutoff close to the Planck scale may be achieved. In its minimal form, the
model does not generate sufficient curvature perturbations and additional
ingredients, such as a curvaton field, are needed.
",0,1,0,0,0,0
20540,Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling,"  In solving hard computational problems, semidefinite program (SDP)
relaxations often play an important role because they come with a guarantee of
optimality. Here, we focus on a popular semidefinite relaxation of K-means
clustering which yields the same solution as the non-convex original
formulation for well segregated datasets. We report an unexpected finding: when
data contains (greater than zero-dimensional) manifolds, the SDP solution
captures such geometrical structures. Unlike traditional manifold embedding
techniques, our approach does not rely on manually defining a kernel but rather
enforces locality via a nonnegativity constraint. We thus call our approach
NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive
understanding of its manifold learning capabilities, we develop a theoretical
analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally
optimal solution can be found by generic SDP solvers with polynomial time
complexity, they are too slow for modern datasets. To address this problem, we
analyze a non-convex heuristic and present a new, convex and yet efficient,
algorithm, based on the conditional gradient method. Our results render NOMAD a
versatile, understandable, and powerful tool for manifold learning.
",1,0,0,0,0,0
20541,Dispersive optical detection of magnetic Feshbach resonances in ultracold gases,"  Magnetically tunable Feshbach resonances in ultracold atomic systems are
chiefly identified and characterized through time consuming atom loss
spectroscopy. We describe an off-resonant dispersive optical probing technique
to rapidly locate Feshbach resonances and demonstrate the method by locating
four resonances of $^{87}$Rb, between the $|\rm{F} = 1, \rm{m_F}=1 \rangle$ and
$|\rm{F} = 2, \rm{m_F}=0 \rangle$ states. Despite the loss features being
$\lesssim0.1$ G wide, we require only 21 experimental runs to explore a
magnetic field range >18 G, where $1~\rm{G}=10^{-4}$ T. The resonances consist
of two known s-wave features in the vicinity of 9 G and 18 G and two previously
unobserved p-wave features near 5 G and 10 G. We further utilize the dispersive
approach to directly characterize the two-body loss dynamics for each Feshbach
resonance.
",0,1,0,0,0,0
20542,Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing,"  In Compressed Sensing, a real-valued sparse vector has to be recovered from
an underdetermined system of linear equations. In many applications, however,
the elements of the sparse vector are drawn from a finite set. Adapted
algorithms incorporating this additional knowledge are required for the
discrete-valued setup. In this paper, turbo-based algorithms for both cases are
elucidated and analyzed from a communications engineering perspective, leading
to a deeper understanding of the algorithm. In particular, we gain the
intriguing insight that the calculation of extrinsic values is equal to the
unbiasing of a biased estimate and present an improved algorithm.
",1,0,0,0,0,0
20543,Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances,"  Momentum methods such as Polyak's heavy ball (HB) method, Nesterov's
accelerated gradient (AG) as well as accelerated projected gradient (APG)
method have been commonly used in machine learning practice, but their
performance is quite sensitive to noise in the gradients. We study these
methods under a first-order stochastic oracle model where noisy estimates of
the gradients are available. For strongly convex problems, we show that the
distribution of the iterates of AG converges with the accelerated
$O(\sqrt{\kappa}\log(1/\varepsilon))$ linear rate to a ball of radius
$\varepsilon$ centered at a unique invariant distribution in the 1-Wasserstein
metric where $\kappa$ is the condition number as long as the noise variance is
smaller than an explicit upper bound we can provide. Our analysis also
certifies linear convergence rates as a function of the stepsize, momentum
parameter and the noise variance; recovering the accelerated rates in the
noiseless case and quantifying the level of noise that can be tolerated to
achieve a given performance. In the special case of strongly convex quadratic
objectives, we can show accelerated linear rates in the $p$-Wasserstein metric
for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through
a non-asymptotic analysis under some additional assumptions on the noise
structure. Our analysis for HB and AG also leads to improved non-asymptotic
convergence bounds in suboptimality for both deterministic and stochastic
settings which is of independent interest. To the best of our knowledge, these
are the first linear convergence results for stochastic momentum methods under
the stochastic oracle model. We also extend our results to the APG method and
weakly convex functions showing accelerated rates when the noise magnitude is
sufficiently small.
",1,0,0,1,0,0
20544,Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations,"  The recovery of approximately sparse or compressible coefficients in a
Polynomial Chaos Expansion is a common goal in modern parametric uncertainty
quantification (UQ). However, relatively little effort in UQ has been directed
toward theoretical and computational strategies for addressing the sparse
corruptions problem, where a small number of measurements are highly corrupted.
Such a situation has become pertinent today since modern computational
frameworks are sufficiently complex with many interdependent components that
may introduce hardware and software failures, some of which can be difficult to
detect and result in a highly polluted simulation result.
In this paper we present a novel compressive sampling-based theoretical
analysis for a regularized $\ell^1$ minimization algorithm that aims to recover
sparse expansion coefficients in the presence of measurement corruptions. Our
recovery results are uniform, and prescribe algorithmic regularization
parameters in terms of a user-defined a priori estimate on the ratio of
measurements that are believed to be corrupted. We also propose an iteratively
reweighted optimization algorithm that automatically refines the value of the
regularization parameter, and empirically produces superior results. Our
numerical results test our framework on several medium-to-high dimensional
examples of solutions to parameterized differential equations, and demonstrate
the effectiveness of our approach.
",0,0,1,0,0,0
20545,Learning to Remember Rare Events,"  Despite recent advances, memory-augmented deep neural networks are still
limited when it comes to life-long and one-shot learning, especially in
remembering rare events. We present a large-scale life-long memory module for
use in deep learning. The module exploits fast nearest-neighbor algorithms for
efficiency and thus scales to large memory sizes. Except for the
nearest-neighbor query, the module is fully differentiable and trained
end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
without the need to reset it during training.
Our memory module can be easily added to any part of a supervised neural
network. To show its versatility we add it to a number of networks, from simple
convolutional ones tested on image classification to deep sequence-to-sequence
and recurrent-convolutional models. In all cases, the enhanced network gains
the ability to remember and do life-long one-shot learning. Our module
remembers training examples shown many thousands of steps in the past and it
can successfully generalize from them. We set new state-of-the-art for one-shot
learning on the Omniglot dataset and demonstrate, for the first time, life-long
one-shot learning in recurrent neural networks on a large-scale machine
translation task.
",1,0,0,0,0,0
20546,Stacking-dependent electronic structure of trilayer graphene resolved by nanospot angle-resolved photoemission spectroscopy,"  The crystallographic stacking order in multilayer graphene plays an important
role in determining its electronic structure. In trilayer graphene,
rhombohedral stacking (ABC) is particularly intriguing, exhibiting a flat band
with an electric-field tunable band gap. Such electronic structure is distinct
from simple hexagonal stacking (AAA) or typical Bernal stacking (ABA), and is
promising for nanoscale electronics, optoelectronics applications. So far clean
experimental electronic spectra on the first two stackings are missing because
the samples are usually too small in size (um or nm scale) to be resolved by
conventional angle-resolved photoemission spectroscopy (ARPES). Here by using
ARPES with nanospot beam size (NanoARPES), we provide direct experimental
evidence for the coexistence of three different stackings of trilayer graphene
and reveal their distinctive electronic structures directly. By fitting the
experimental data, we provide important experimental band parameters for
describing the electronic structure of trilayer graphene with different
stackings.
",0,1,0,0,0,0
20547,Interaction energy between vortices of vector fields on Riemannian surfaces,"  We study a variational Ginzburg-Landau type model depending on a small
parameter $\epsilon>0$ for (tangent) vector fields on a $2$-dimensional
Riemannian surface. As $\epsilon\to 0$, the vector fields tend to be of unit
length and will have singular points of a (non-zero) index, called vortices.
Our main result determines the interaction energy between these vortices as a
$\Gamma$-limit (at the second order) as $\epsilon\to 0$.
",0,0,1,0,0,0
20548,Multimodal Nonlinear Microscope based on a Compact Fiber-format Laser Source,"  We present a multimodal non-linear optical (NLO) laser-scanning microscope,
based on a compact fiber-format excitation laser and integrating coherent
anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS) and
two-photon-excitation fluorescence (TPEF) on a single platform. We demonstrate
its capabilities in simultaneously acquiring CARS and SRS images of a blend of
6-{\mu}m poly(methyl methacrylate) beads and 3-{\mu}m polystyrene beads. We
then apply it to visualize cell walls and chloroplast of an unprocessed fresh
leaf of Elodea aquatic plant via SRS and TPEF modalities, respectively. The
presented NLO microscope, developed in house using off-the-shelf components,
offers full accessibility to the optical path and ensures its easy
re-configurability and flexibility.
",0,1,0,0,0,0
20549,The Diffuse Light of the Universe - On the microwave background before and after its discovery: open questions,"  In 1965, the discovery of a new type of uniform radiation, located between
radiowaves and infrared light, was accidental. Known today as Cosmic Microwave
background (CMB), this diffuse radiation is commonly interpreted as a fossil
light released in an early hot and dense universe and constitutes today the
main 'pilar' of the big bang cosmology. Considerable efforts have been devoted
to derive fundamental cosmological parameters from the characteristics of this
radiation that led to a surprising universe that is shaped by at least three
major unknown components: inflation, dark matter and dark energy. This is an
important weakness of the present consensus cosmological model that justifies
raising several questions on the CMB interpretation. Can we consider its
cosmological nature as undisputable? Do other possible interpretations exist in
the context of other cosmological theories or simply as a result of other
physical mechanisms that could account for it? In an effort to questioning the
validity of scientific hypotheses and the under-determination of theories
compared to observations, we examine here the difficulties that still exist on
the interpretation of this diffuse radiation and explore other proposed tracks
to explain its origin. We discuss previous historical concepts of diffuse
radiation before and after the CMB discovery and underline the limit of our
present understanding.
",0,1,0,0,0,0
20550,Classification of $5$-Dimensional Complex Nilpotent Leibniz Algebras,"  Leibniz algebras are certain generalization of Lie algebras. In this paper we
give the classification of $5-$dimensional complex non-Lie nilpotent Leibniz
algebras. We use the canonical forms for the congruence classes of matrices of
bilinear forms to classify the case $\dim(A^2)=3$ and $\dim(Leib(A))=1$ which
can be applied to higher dimensions. The remaining cases are classified via
direct method.
",0,0,1,0,0,0
20551,Haptics of Screwing and Unscrewing for its Application in Smart Factories for Disassembly,"  Reconstruction of skilled humans sensation and control system often leads to
a development of robust control for the robots. We are developing an unscrewing
robot for the automated disassembly which requires a comprehensive control
system, but unscrewing experiments with robots are often limited to several
conditions. On the contrary, humans typically have a broad range of screwing
experiences and sensations throughout their lives, and we conducted an
experiment to find these haptic patterns. Results show that people apply axial
force to the screws to avoid screwdriver slippage (cam-outs), which is one of
the key problems during screwing and unscrewing, and this axial force is
proportional to the torque which is required for screwing. We have found that
type of the screw head influences the amount of axial force applied. Using this
knowledge an unscrewing robot for the smart disassembly factory RecyBot is
developed, and experiments confirm the optimality of the strategy, used by
humans. Finally, a methodology for robust unscrewing algorithm design is
presented as a generalization of the findings. It can seriously speed up the
development of the screwing and unscrewing robots and tools.
",1,0,0,0,0,0
20552,$G$-invariant Szegö kernel asymptotics and CR reduction,"  Let $(X, T^{1,0}X)$ be a compact connected orientable CR manifold of
dimension $2n+1$ with non-degenerate Levi curvature. Assume that $X$ admits a
connected compact Lie group action $G$. Under certain natural assumptions about
the group action $G$, we show that the $G$-invariant Szegö kernel for $(0,q)$
forms is a complex Fourier integral operator, smoothing away $\mu^{-1}(0)$ and
there is a precise description of the singularity near $\mu^{-1}(0)$, where
$\mu$ denotes the CR moment map. We apply our result to the case when $X$
admits a transversal CR $S^1$ action and deduce an asymptotic expansion for the
$m$-th Fourier component of the $G$-invariant Szegö kernel for $(0,q)$ forms
as $m \to+\infty$. As an application, we show that if $m$ large enough,
quantization commutes with reduction.
",0,0,1,0,0,0
20553,Self-similar resistive circuits as fractal-like structures,"  In the present work we explore resistive circuits where the individual
resistors are arranged in fractal-like patterns. These circuits have some of
the characteristics typically found in geometric fractals, namely
self-similarity and scale invariance. Considering resistive circuits as graphs,
we propose a definition of self-similar circuits which mimics a self-similar
fractal. General properties of the resistive circuits generated by this
approach are investigated, and interesting examples are commented in detail.
Specifically, we consider self-similar resistive series, tree-like resistive
networks and Sierpinski's configurations with resistors.
",0,1,0,0,0,0
20554,DynaPhoPy: A code for extracting phonon quasiparticles from molecular dynamics simulations,"  We have developed a computational code, DynaPhoPy, that allow us to extract
the microscopic anharmonic phonon properties from molecular dynamics (MD)
simulations using the normal-mode-decomposition technique as presented by Sun
et al. [T. Sun, D. Zhang, R. Wentzcovitch, 2014]. Using this code we calculated
the quasiparticle phonon frequencies and linewidths of crystalline silicon at
different temperatures using both of first-principles and the Tersoff empirical
potential approaches. In this work we show the dependence of these properties
on the temperature using both approaches and compare them with reported
experimental data obtained by Raman spectroscopy [M. Balkanski, R. Wallis, E.
Haro, 1983 and R. Tsu, J. G. Hernandez, 1982].
",0,1,0,0,0,0
20555,Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models,"  The change detection problem is to determine if the Markov network structures
of two Markov random fields differ from one another given two sets of samples
drawn from the respective underlying distributions. We study the trade-off
between the sample sizes and the reliability of change detection, measured as a
minimax risk, for the important cases of the Ising models and the Gaussian
Markov random fields restricted to the models which have network structures
with $p$ nodes and degree at most $d$, and obtain information-theoretic lower
bounds for reliable change detection over these models. We show that for the
Ising model, $\Omega\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are
required from each dataset to detect even the sparsest possible changes, and
that for the Gaussian, $\Omega\left( \gamma^{-2} \log(p)\right)$ samples are
required from each dataset to detect change, where $\gamma$ is the smallest
ratio of off-diagonal to diagonal terms in the precision matrices of the
distributions. These bounds are compared to the corresponding results in
structure learning, and closely match them under mild conditions on the model
parameters. Thus, our change detection bounds inherit partial tightness from
the structure learning schemes in previous literature, demonstrating that in
certain parameter regimes, the naive structure learning based approach to
change detection is minimax optimal up to constant factors.
",1,0,1,0,0,0
20556,How Usable are Rust Cryptography APIs?,"  Context: Poor usability of cryptographic APIs is a severe source of
vulnerabilities. Aim: We wanted to find out what kind of cryptographic
libraries are present in Rust and how usable they are. Method: We explored
Rust's cryptographic libraries through a systematic search, conducted an
exploratory study on the major libraries and a controlled experiment on two of
these libraries with 28 student participants. Results: Only half of the major
libraries explicitly focus on usability and misuse resistance, which is
reflected in their current APIs. We found that participants were more
successful using rust-crypto which we considered less usable than ring before
the experiment. Conclusion: We discuss API design insights and make
recommendations for the design of crypto libraries in Rust regarding the detail
and structure of the documentation, higher-level APIs as wrappers for the
existing low-level libraries, and selected, good-quality example code to
improve the emerging cryptographic libraries of Rust.
",1,0,0,0,0,0
20557,Augmenting End-to-End Dialog Systems with Commonsense Knowledge,"  Building dialog agents that can converse naturally with humans is a
challenging yet intriguing problem of artificial intelligence. In open-domain
human-computer conversation, where the conversational agent is expected to
respond to human responses in an interesting and engaging way, commonsense
knowledge has to be integrated into the model effectively. In this paper, we
investigate the impact of providing commonsense knowledge about the concepts
covered in the dialog. Our model represents the first attempt to integrating a
large commonsense knowledge base into end-to-end conversational models. In the
retrieval-based scenario, we propose the Tri-LSTM model to jointly take into
account message and commonsense for selecting an appropriate response. Our
experiments suggest that the knowledge-augmented models are superior to their
knowledge-free counterparts in automatic evaluation.
",1,0,0,0,0,0
20558,A framework for Multi-A(rmed)/B(andit) testing with online FDR control,"  We propose an alternative framework to existing setups for controlling false
alarms when multiple A/B tests are run over time. This setup arises in many
practical applications, e.g. when pharmaceutical companies test new treatment
options against control pills for different diseases, or when internet
companies test their default webpages versus various alternatives over time.
Our framework proposes to replace a sequence of A/B tests by a sequence of
best-arm MAB instances, which can be continuously monitored by the data
scientist. When interleaving the MAB tests with an an online false discovery
rate (FDR) algorithm, we can obtain the best of both worlds: low sample
complexity and any time online FDR control. Our main contributions are: (i) to
propose reasonable definitions of a null hypothesis for MAB instances; (ii) to
demonstrate how one can derive an always-valid sequential p-value that allows
continuous monitoring of each MAB test; and (iii) to show that using rejection
thresholds of online-FDR algorithms as the confidence levels for the MAB
algorithms results in both sample-optimality, high power and low FDR at any
point in time. We run extensive simulations to verify our claims, and also
report results on real data collected from the New Yorker Cartoon Caption
contest.
",1,0,0,1,0,0
20559,Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization,"  This paper presents a statistical method of single-channel speech enhancement
that uses a variational autoencoder (VAE) as a prior distribution on clean
speech. A standard approach to speech enhancement is to train a deep neural
network (DNN) to take noisy speech as input and output clean speech. Although
this supervised approach requires a very large amount of pair data for
training, it is not robust against unknown environments. Another approach is to
use non-negative matrix factorization (NMF) based on basis spectra trained on
clean speech in advance and those adapted to noise on the fly. This
semi-supervised approach, however, causes considerable signal distortion in
enhanced speech due to the unrealistic assumption that speech spectrograms are
linear combinations of the basis spectra. Replacing the poor linear generative
model of clean speech in NMF with a VAE---a powerful nonlinear deep generative
model---trained on clean speech, we formulate a unified probabilistic
generative model of noisy speech. Given noisy speech as observed data, we can
sample clean speech from its posterior distribution. The proposed method
outperformed the conventional DNN-based method in unseen noisy environments.
",1,0,0,1,0,0
20560,BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning,"  Understanding the global optimality in deep learning (DL) has been attracting
more and more attention recently. Conventional DL solvers, however, have not
been developed intentionally to seek for such global optimality. In this paper
we propose a novel approximation algorithm, BPGrad, towards optimizing deep
models globally via branch and pruning. Our BPGrad algorithm is based on the
assumption of Lipschitz continuity in DL, and as a result it can adaptively
determine the step size for current gradient given the history of previous
updates, wherein theoretically no smaller steps can achieve the global
optimality. We prove that, by repeating such branch-and-pruning procedure, we
can locate the global optimality within finite iterations. Empirically an
efficient solver based on BPGrad for DL is proposed as well, and it outperforms
conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the
tasks of object recognition, detection, and segmentation.
",1,0,0,1,0,0
20561,Lagrangian Flow Network approach to an open flow model,"  Concepts and tools from network theory, the so-called Lagrangian Flow Network
framework, have been successfully used to obtain a coarse-grained description
of transport by closed fluid flows. Here we explore the application of this
methodology to open chaotic flows, and check it with numerical results for a
model open flow, namely a jet with a localized wave perturbation. We find that
network nodes with high values of out-degree and of finite-time entropy in the
forward-in-time direction identify the location of the chaotic saddle and its
stable manifold, whereas nodes with high in-degree and backwards finite-time
entropy highlight the location of the saddle and its unstable manifold. The
cyclic clustering coefficient, associated to the presence of periodic orbits,
takes non-vanishing values at the location of the saddle itself.
",0,1,0,0,0,0
20562,Some new gradient estimates for two nonlinear parabolic equations under Ricci flow,"  In this paper, by maximum principle and cutoff function, we investigate
gradient estimates for positive solutions to two nonlinear parabolic equations
under Ricci flow. The related Harnack inequalities are deduced. An result about
positive solutions on closed manifolds under Ricci flow is abtained. As
applications, gradient estimates and Harnack inequalities for positive
solutions to the heat equation under Ricci flow are derived. These results in
the paper can be regard as generalizing the gradient estimates of Li-Yau, J. Y.
Li, Hamilton and Li-Xu to the Ricci flow. Our results also improve the
estimates of S. P. Liu and J. Sun to the nonlinear parabolic equation under
Ricci flow.
",0,0,1,0,0,0
20563,On the pointwise iteration-complexity of a dynamic regularized ADMM with over-relaxation stepsize,"  In this paper, we extend the improved pointwise iteration-complexity result
of a dynamic regularized alternating direction method of multipliers (ADMM) for
a new stepsize domain. In this complexity analysis, the stepsize parameter can
even be chosen in the interval $(0,2)$ instead of interval
$(0,(1+\sqrt{5})/2)$. As usual, our analysis is established by interpreting
this ADMM variant as an instance of a hybrid proximal extragradient framework
applied to a specific monotone inclusion problem.
",0,0,1,0,0,0
20564,DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders,"  This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-imaging analysis were not developed to handle images with
highly complex anatomical structures, the results presented in this paper show
that functional representation extracted by CDAE can help learn features of
functional gene ontology categories for their classification in a highly
accurate manner. Using this CDAE representation, our method outperforms the
previous state-of-the-art classification rate, by improving the average AUC
from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
on input images that were downsampled significantly with respect to the
original ones to make it computationally feasible.
",1,0,0,1,0,0
20565,Nonlocal heat equations in the Heisenberg group,"  We study the following nonlocal diffusion equation in the Heisenberg group
$\mathbb{H}_n$, \[ u_t(z,s,t)=J\ast u(z,s,t)-u(z,s,t), \] where $\ast$ denote
convolution product and $J$ satisfies appropriated hypothesis. For the Cauchy
problem we obtain that the asymptotic behavior of the solutions is the same
form that the one for the heat equation in the Heisenberg group. To obtain this
result we use the spherical transform related to the pair
$(U(n),\mathbb{H}_n)$. Finally we prove that solutions of properly rescaled
nonlocal Dirichlet problem converge uniformly to the solution of the
corresponding Dirichlet problem for the classical heat equation in the
Heisenberg group.
",0,0,1,0,0,0
20566,Motion Planning Networks,"  Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods such as RRT*, A*, and D*, become ineffective as their
computational complexity increases exponentially with the dimensionality of the
motion planning problem. To address this issue, we present a neural
network-based novel planning algorithm which generates end-to-end
collision-free paths irrespective of the obstacles' geometry. The proposed
method, called MPNet (Motion Planning Network), comprises of a Contractive
Autoencoder which encodes the given workspaces directly from a point cloud
measurement, and a deep feedforward neural network which takes the workspace
encoding, start and goal configuration, and generates end-to-end feasible
motion trajectories for the robot to follow. We evaluate MPNet on multiple
planning problems such as planning of a point-mass robot, rigid-body, and 7 DOF
Baxter robot manipulators in various 2D and 3D environments. The results show
that MPNet is not only consistently computationally efficient in all 2D and 3D
environments but also show remarkable generalization to completely unseen
environments. The results also show that computation time of MPNet consistently
remains less than 1 second which is significantly lower than existing
state-of-the-art motion planning algorithms. Furthermore, through transfer
learning, the MPNet trained in one scenario (e.g., indoor living places) can
also quickly adapt to new scenarios (e.g., factory floors) with a little amount
of data.
",1,0,0,1,0,0
20567,Query Expansion Techniques for Information Retrieval: a Survey,"  With the ever increasing size of web, relevant information extraction on the
Internet with a query formed by a few keywords has become a big challenge. To
overcome this, query expansion (QE) plays a crucial role in improving the
Internet searches, where the user's initial query is reformulated to a new
query by adding new meaningful terms with similar significance. QE -- as part
of information retrieval (IR) -- has long attracted researchers' attention. It
has also become very influential in the field of personalized social document,
Question Answering over Linked Data (QALD), and, Text Retrieval Conference
(TREC) and REAL sets. This paper surveys QE techniques in IR from 1960 to 2017
with respect to core techniques, data sources used, weighting and ranking
methodologies, user participation and applications (of QE techniques) --
bringing out similarities and differences.
",1,0,0,0,0,0
20568,Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis,"  This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset.
",1,0,0,0,0,0
20569,Commutativity of integral quasi-arithmetic means on measure spaces,"  Let $(X, \mathscr{L}, \lambda)$ and $(Y, \mathscr{M}, \mu)$ be finite measure
spaces for which there exist $A \in \mathscr{L}$ and $B \in \mathscr{M}$ with
$0 < \lambda(A) < \lambda(X)$ and $0 < \mu(B) < \mu(Y)$, and let $I\subseteq
\mathbf{R}$ be a non-empty interval. We prove that, if $f$ and $g$ are
continuous bijections $I \to \mathbf{R}^+$, then the equation $$
f^{-1}\!\left(\int_X f\!\left(g^{-1}\!\left(\int_Y g \circ
h\;d\mu\right)\right)d \lambda\right)\! = g^{-1}\!\left(\int_Y
g\!\left(f^{-1}\!\left(\int_X f \circ h\;d\lambda\right)\right)d \mu\right)$$
is satisfied by every $\mathscr{L} \otimes \mathscr{M}$-measurable simple
function $h: X \times Y \to I$ if and only if $f=c g$ for some $c \in
\mathbf{R}^+$ (it is easy to see that the equation is well posed). An
analogous, but essentially different, result, with $f$ and $g$ replaced by
continuous injections $I \to \mathbf R$ and $\lambda(X)=\mu(Y)=1$, was recently
obtained in [Indag. Math. 27 (2016), 945-953].
",0,0,1,0,0,0
20570,Moderate Deviation for Random Elliptic PDEs with Small Noise,"  Partial differential equations with random inputs have become popular models
to characterize physical systems with uncertainty coming from, e.g., imprecise
measurement and intrinsic randomness. In this paper, we perform asymptotic rare
event analysis for such elliptic PDEs with random inputs. In particular, we
consider the asymptotic regime that the noise level converges to zero
suggesting that the system uncertainty is low, but does exists. We develop
sharp approximations of the probability of a large class of rare events.
",0,0,1,0,0,0
20571,Music Transformer,"  Music relies heavily on repetition to build structure and meaning.
Self-reference occurs on multiple timescales, from motifs to phrases to reusing
of entire sections of music, such as in pieces with ABA structure. The
Transformer (Vaswani et al., 2017), a sequence model based on self-attention,
has achieved compelling results in many generation tasks that require
maintaining long-range coherence. This suggests that self-attention might also
be well-suited to modeling music. In musical composition and performance,
however, relative timing is critically important. Existing approaches for
representing relative positional information in the Transformer modulate
attention based on pairwise distance (Shaw et al., 2018). This is impractical
for long sequences such as musical compositions since their memory complexity
for intermediate relative information is quadratic in the sequence length. We
propose an algorithm that reduces their intermediate memory requirement to
linear in the sequence length. This enables us to demonstrate that a
Transformer with our modified relative attention mechanism can generate
minute-long compositions (thousands of steps, four times the length modeled in
Oore et al., 2018) with compelling structure, generate continuations that
coherently elaborate on a given motif, and in a seq2seq setup generate
accompaniments conditioned on melodies. We evaluate the Transformer with our
relative attention mechanism on two datasets, JSB Chorales and
Piano-e-Competition, and obtain state-of-the-art results on the latter.
",1,0,0,1,0,0
20572,Residual-Based Detections and Unified Architecture for Massive MIMO Uplink,"  Massive multiple-input multiple-output (M-MIMO) technique brings better
energy efficiency and coverage but higher computational complexity than
small-scale MIMO. For linear detections such as minimum mean square error
(MMSE), prohibitive complexity lies in solving large-scale linear equations.
For a better trade-off between bit-error-rate (BER) performance and
computational complexity, iterative linear algorithms like conjugate gradient
(CG) have been applied and have shown their feasibility in recent years. In
this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO
detection, including minimal residual (MINRES) algorithm, generalized minimal
residual (GMRES) algorithm, and conjugate residual (CR) algorithm. RBD
algorithms focus on the minimization of residual norm per iteration, whereas
most existing algorithms focus on the approximation of exact signal. Numerical
results have shown that, for $64$-QAM $128\times 8$ MIMO, RBD algorithms are
only $0.13$ dB away from the exact matrix inversion method when BER$=10^{-4}$.
Stability of RBD algorithms has also been verified in various correlation
conditions. Complexity comparison has shown that, CR algorithm require $87\%$
less complexity than the traditional method for $128\times 60$ MIMO. The
unified hardware architecture is proposed with flexibility, which guarantees a
low-complexity implementation for a family of RBD M-MIMO detectors.
",1,0,0,0,0,0
20573,Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy,"  A split feasibility formulation for the inverse problem of
intensity-modulated radiation therapy (IMRT) treatment planning with
dose-volume constraints (DVCs) included in the planning algorithm is presented.
It involves a new type of sparsity constraint that enables the inclusion of a
percentage-violation constraint in the model problem and its handling by
continuous (as opposed to integer) methods. We propose an iterative algorithmic
framework for solving such a problem by applying the feasibility-seeking
CQ-algorithm of Byrne combined with the automatic relaxation method (ARM) that
uses cyclic projections. Detailed implementation instructions are furnished.
Functionality of the algorithm was demonstrated through the creation of an
intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and
also for a realistic base-of-skull chordoma treatment site. Monte Carlo
simulations of proton pencil beams of varying energy were conducted to obtain
dose distributions for the 2D test case. A research release of the Pinnacle3
proton treatment planning system was used to extract pencil beam doses for a
clinical base-of-skull chordoma case. In both cases the beamlet doses were
calculated to satisfy dose-volume constraints according to our new algorithm.
Examination of the dose-volume histograms following inverse planning with our
algorithm demonstrated that it performed as intended. The application of our
proposed algorithm to dose-volume constraint inverse planning was successfully
demonstrated. Comparison with optimized dose distributions from the research
release of the Pinnacle3 treatment planning system showed the algorithm could
achieve equivalent or superior results.
",0,1,1,0,0,0
20574,On 2-Verma modules for quantum $\mathfrak{sl}_2$,"  In this paper we study the superalgebra $A_n$, introduced by the authors in
previous work on categorification of Verma modules for quantum
$\mathfrak{sl}_2$. The superalgebra $A_n$ is akin to the nilHecke algebra, and
shares similar properties. In particular, we prove a uniqueness result about
2-Verma modules on $\Bbbk$-linear 2-categories.
",0,0,1,0,0,0
20575,On the Number of Single-Peaked Narcissistic or Single-Crossing Narcissistic Preference Profiles,"  We investigate preference profiles for a set $\mathcal{V}$ of voters, where
each voter $i$ has a preference order $\succ_i$ on a finite set $A$ of
alternatives (that is, a linear order on $A$) such that for each two
alternatives $a,b\in A$, voter $i$ prefers $a$ to $b$ if $a\succ_i b$. Such a
profile is narcissistic if each alternative $a$ is preferred the most by at
least one voter. It is single-peaked if there is a linear order
$\triangleright^{\text{sp}}$ on the alternatives such that each voter's
preferences on the alternatives along the order $\triangleright^{\text{sp}}$
are either strictly increasing, or strictly decreasing, or first strictly
increasing and then strictly decreasing. It is single-crossing if there is a
linear order $\triangleright^{\text{sc}}$ on the voters such that each pair of
alternatives divides the order $\triangleright^{\text{sc}}$ into at most two
suborders, where in each suborder, all voters have the same linear order on
this pair.
We show that for $n$ voters and $n$ alternatives,the number of single-peaked
narcissistic profiles is $\prod_{i=2}^{n-1} \binom{n-1}{i-1}$ while the number
of single-crossing narcissistic profiles is $2^{\binom{n-1}{2}}$.
",0,0,1,0,0,0
20576,Planet formation and disk-planet interactions,"  This review is based on lectures given at the 45th Saas-Fee Advanced Course
'From Protoplanetary Disks to Planet Formation' held in March 2015 in Les
Diablerets, Switzerland. Starting with an overview of the main characterictics
of the Solar System and extrasolar planets, we describe the planet formation
process in terms of the sequential accretion scenario. First the growth
processes of dust particles to planetesimals and subsequently to terrestrial
planets or planetary cores are presented. This is followed by the formation
process of the giant planets either by core accretion or gravitational
instability. Finally, the dynamical evolution of the orbital elements as driven
by disk-planet interaction and the overall evolution of multi-object systems is
presented.
",0,1,0,0,0,0
20577,Cherednik algebras and Calogero-Moser cells,"  Using the representation theory of Cherednik algebras at $t=0$ and a Galois
covering of the Calogero-Moser space, we define the notions of left, right and
two-sided Calogero-Moser cells for any finite complex reflection group. To each
Caloger-Moser two-sided cell is associated a Calogero-Moser family, while to
each Calogero-Moser left cell is associated a Calogero-Moser cellular
representation. We study properties of these objects and we conjecture that,
whenever the reflection group is real (i.e. is a Coxeter group), these notions
coincide with the one of Kazhdan-Lusztig left, right and two-sided cells,
Kazhdan-Lusztig families and Kazhdan-Lusztig cellular representations.
",0,0,1,0,0,0
20578,Khovanov complexes of rational tangles,"  We show that the Khovanov complex of a rational tangle has a very simple
representative whose backbone of non-zero morphisms forms a zig-zag.
Furthermore, this minimal complex can be computed quickly by an inductive
algorithm. (For example, we calculate $Kh(8_2)$ by hand.) We find that the
bigradings of the subobjects in these minimal complexes can be described by
matrix actions, which after a change of basis is the reduced Burau
representation of $B_3$.
",0,0,1,0,0,0
20579,Asymmetric Connectedness of Fears in the U.S. Financial Sector,"  We study how shocks to the forward-looking expectations of investors buying
call and put options transmit across the financial system. We introduce a new
contagion measure, called asymmetric fear connectedness (AFC), which captures
the information related to ""fear"" on the two sides of the options market and
can be used as a forward-looking systemic risk monitoring tool. The decomposed
connectedness measures provide timely predictive information for near-future
macroeconomic conditions and uncertainty indicators, and they contain
additional valuable information that is not included in the aggregate
connectedness measure. The role of a positive/negative ""fear""
transmitter/receiver emerges clearly when we focus more closely on
idiosyncratic events for financial institutions. We identify banks that are
predominantly positive/negative receivers of ""fear"", as well as banks that
positively/negatively transmit ""fear"" in the financial system.
",0,0,0,0,0,1
20580,Neural Network Based Speaker Classification and Verification Systems with Enhanced Features,"  This work presents a novel framework based on feed-forward neural network for
text-independent speaker classification and verification, two related systems
of speaker recognition. With optimized features and model training, it achieves
100% classification rate in classification and less than 6% Equal Error Rate
(ERR), using merely about 1 second and 5 seconds of data respectively. Features
with stricter Voice Active Detection (VAD) than the regular one for speech
recognition ensure extracting stronger voiced portion for speaker recognition,
speaker-level mean and variance normalization helps to eliminate the
discrepancy between samples from the same speaker. Both are proven to improve
the system performance. In building the neural network speaker classifier, the
network structure parameters are optimized with grid search and dynamically
reduced regularization parameters are used to avoid training terminated in
local minimum. It enables the training goes further with lower cost. In speaker
verification, performance is improved with prediction score normalization,
which rewards the speaker identity indices with distinct peaks and penalizes
the weak ones with high scores but more competitors, and speaker-specific
thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus
with 8K sampling rate is used here. First 200 male speakers are used to train
and test the classification performance. The testing files of them are used as
in-domain registered speakers, while data from the remaining 126 male speakers
are used as out-of-domain speakers, i.e. imposters in speaker verification.
",1,0,0,0,0,0
20581,Slimness of graphs,"  Slimness of a graph measures the local deviation of its metric from a tree
metric. In a graph $G=(V,E)$, a geodesic triangle $\bigtriangleup(x,y,z)$ with
$x, y, z\in V$ is the union $P(x,y) \cup P(x,z) \cup P(y,z)$ of three shortest
paths connecting these vertices. A geodesic triangle $\bigtriangleup(x,y,z)$ is
called $\delta$-slim if for any vertex $u\in V$ on any side $P(x,y)$ the
distance from $u$ to $P(x,z) \cup P(y,z)$ is at most $\delta$, i.e. each path
is contained in the union of the $\delta$-neighborhoods of two others. A graph
$G$ is called $\delta$-slim, if all geodesic triangles in $G$ are
$\delta$-slim. The smallest value $\delta$ for which $G$ is $\delta$-slim is
called the slimness of $G$. In this paper, using the layering partition
technique, we obtain sharp bounds on slimness of such families of graphs as (1)
graphs with cluster-diameter $\Delta(G)$ of a layering partition of $G$, (2)
graphs with tree-length $\lambda$, (3) graphs with tree-breadth $\rho$, (4)
$k$-chordal graphs, AT-free graphs and HHD-free graphs. Additionally, we show
that the slimness of every 4-chordal graph is at most 2 and characterize those
4-chordal graphs for which the slimness of every of its induced subgraph is at
most 1.
",1,0,0,0,0,0
20582,SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems,"  Despite their immense popularity, deep learning-based acoustic systems are
inherently vulnerable to adversarial attacks, wherein maliciously crafted
audios trigger target systems to misbehave. In this paper, we present
SirenAttack, a new class of attacks to generate adversarial audios. Compared
with existing attacks, SirenAttack highlights with a set of significant
features: (i) versatile -- it is able to deceive a range of end-to-end acoustic
systems under both white-box and black-box settings; (ii) effective -- it is
able to generate adversarial audios that can be recognized as specific phrases
by target acoustic systems; and (iii) stealthy -- it is able to generate
adversarial audios indistinguishable from their benign counterparts to human
perception. We empirically evaluate SirenAttack on a set of state-of-the-art
deep learning-based acoustic systems (including speech command recognition,
speaker recognition and sound event classification), with results showing the
versatility, effectiveness, and stealthiness of SirenAttack. For instance, it
achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18
model, while the generated adversarial audios are also misinterpreted by
multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing
Voice, and IBM Speech-to-Text. We further evaluate three potential defense
methods to mitigate such attacks, including adversarial training, audio
downsampling, and moving average filtering, which leads to promising directions
for further research.
",1,0,0,0,0,0
20583,k-server via multiscale entropic regularization,"  We present an $O((\log k)^2)$-competitive randomized algorithm for the
$k$-server problem on hierarchically separated trees (HSTs). This is the first
$o(k)$-competitive randomized algorithm for which the competitive ratio is
independent of the size of the underlying HST. Our algorithm is designed in the
framework of online mirror descent where the mirror map is a multiscale
entropy. When combined with Bartal's static HST embedding reduction, this leads
to an $O((\log k)^2 \log n)$-competitive algorithm on any $n$-point metric
space. We give a new dynamic HST embedding that yields an $O((\log k)^3 \log
\Delta)$-competitive algorithm on any metric space where the ratio of the
largest to smallest non-zero distance is at most $\Delta$.
",1,0,1,0,0,0
20584,Some Large Sample Results for the Method of Regularized Estimators,"  We present a general framework for studying regularized estimators; i.e.,
estimation problems wherein ""plug-in"" type estimators are either ill-defined or
ill-behaved. We derive primitive conditions that imply consistency and
asymptotic linear representation for regularized estimators, allowing for
slower than $\sqrt{n}$ estimators as well as infinite dimensional parameters.
We also provide data-driven methods for choosing tuning parameters that, under
some conditions, achieve the aforementioned results. We illustrate the scope of
our approach by studying a wide range of applications, revisiting known results
and deriving new ones.
",0,0,1,0,0,0
20585,Coregionalised Locomotion Envelopes - A Qualitative Approach,"  'Sharing of statistical strength' is a phrase often employed in machine
learning and signal processing. In sensor networks, for example, missing
signals from certain sensors may be predicted by exploiting their correlation
with observed signals acquired from other sensors. For humans, our hands move
synchronously with our legs, and we can exploit these implicit correlations for
predicting new poses and for generating new natural-looking walking sequences.
We can also go much further and exploit this form of transfer learning, to
develop new control schemas for robust control of rehabilitation robots. In
this short paper we introduce coregionalised locomotion envelopes - a method
for multi-dimensional manifold regression, on human locomotion variates. Herein
we render a qualitative description of this method.
",1,0,0,1,0,0
20586,Optimal Invariant Tests in an Instrumental Variables Regression With Heteroskedastic and Autocorrelated Errors,"  This paper uses model symmetries in the instrumental variable (IV) regression
to derive an invariant test for the causal structural parameter. Contrary to
popular belief, we show there exist model symmetries when equation errors are
heteroskedastic and autocorrelated (HAC). Our theory is consistent with
existing results for the homoskedastic model (Andrews, Moreira and Stock(2006}
and Chamberlain (2007}), but in general uses information on the structural
parameter beyond the Anderson-Rubin, score, and rank statistics. This suggests
that tests based only the Anderson-Rubin and score statistics discard
information on the causal parameter of interest. We apply our theory to
construct designs in which these tests indeed have power arbitrarily close to
size. Other tests, including other adaptations to the CLR test, do not suffer
the same deficiencies. Finally, we use the model symmetries to propose novel
weighted-average power tests for the HAC-IV model.
",0,0,1,1,0,0
20587,Longitudinal electric field: from Maxwell equation to non-locality in time and space,"  In this paper we use the classical electrodynamics to show that the Lorenz
gauge can be incompatible with some particular solutions of the d Alembert
equations for electromagnetic potentials. In its turn, the d Alembert equations
for the elec- tromagnetic potentials is the result of application of the Lorenz
gauge to general equations for the potentials. The last ones is the
straightforward consequence of Maxwell equations. Since the d Alembert
equations and the electromagnetic poten- tials are necessary for quantum
electrodynamics formulation, one should oblige to satisfy these equations also
in classical case. The solution of d Alembert equations, which modifies
longitudinal electric field is found. The requirement of this modifi- cation
follows from the necessity to satisfy the physical condition of impossibility
of instantaneous transferring of interaction in space.
",0,1,0,0,0,0
20588,Estimating linear functionals of a sparse family of Poisson means,"  Assume that we observe a sample of size n composed of p-dimensional signals,
each signal having independent entries drawn from a scaled Poisson distribution
with an unknown intensity. We are interested in estimating the sum of the n
unknown intensity vectors, under the assumption that most of them coincide with
a given 'background' signal. The number s of p-dimensional signals different
from the background signal plays the role of sparsity and the goal is to
leverage this sparsity assumption in order to improve the quality of estimation
as compared to the naive estimator that computes the sum of the observed
signals. We first introduce the group hard thresholding estimator and analyze
its mean squared error measured by the squared Euclidean norm. We establish a
nonasymptotic upper bound showing that the risk is at most of the order of
{\sigma}^2(sp + s^2sqrt(p)) log^3/2(np). We then establish lower bounds on the
minimax risk over a properly defined class of collections of s-sparse signals.
These lower bounds match with the upper bound, up to logarithmic terms, when
the dimension p is fixed or of larger order than s^2. In the case where the
dimension p increases but remains of smaller order than s^2, our results show a
gap between the lower and the upper bounds, which can be up to order sqrt(p).
",0,0,1,1,0,0
20589,Fabrication of porous microrings via laser printing and ion-beam post-etching,"  Pulsed-laser dry printing of noble-metal microrings with a tunable internal
porous structure, which can be revealed via an ion-beam etching post-procedure,
was demonstrated. Abundance and average size of the pores inside the microrings
were shown to be tuned in a wide range by varying incident pulse energy and a
nitrogen doping level controlled in the process of magnetron deposition of the
gold film in the appropriate gaseous environment. The fabricated porous
microrings were shown to provide many-fold near-field enhancement of incident
electromagnetic fields, which was confirmed by mapping of the characteristic
Raman band of a nanometer-thick covering layer of Rhodamine 6G dye molecules
and supporting finite-difference time-domain calculations. The proposed laser
printing/ion-beam etching approach is demonstrated to be a unique tool aimed at
designing and fabricating multifunctional plasmonic structures and metasurfaces
for spectroscopic bioidentification based on surface-enhanced infrared
absorption, Raman scattering and photoluminescence detection schemes.
",0,1,0,0,0,0
20590,Performance Measurements of Supercomputing and Cloud Storage Solutions,"  Increasing amounts of data from varied sources, particularly in the fields of
machine learning and graph analytics, are causing storage requirements to grow
rapidly. A variety of technologies exist for storing and sharing these data,
ranging from parallel file systems used by supercomputers to distributed block
storage systems found in clouds. Relatively few comparative measurements exist
to inform decisions about which storage systems are best suited for particular
tasks. This work provides these measurements for two of the most popular
storage technologies: Lustre and Amazon S3. Lustre is an open-source, high
performance, parallel file system used by many of the largest supercomputers in
the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web
Services offering, and offers a scalable, distributed option to store and
retrieve data from anywhere on the Internet. Parallel processing is essential
for achieving high performance on modern storage systems. The performance tests
used span the gamut of parallel I/O scenarios, ranging from single-client,
single-node Amazon S3 and Lustre performance to a large-scale, multi-client
test designed to demonstrate the capabilities of a modern storage appliance
under heavy load. These results show that, when parallel I/O is used correctly
(i.e., many simultaneous read or write processes), full network bandwidth
performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3
connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These
results demonstrate that S3 is well-suited to sharing vast quantities of data
over the Internet, while Lustre is well-suited to processing large quantities
of data locally.
",1,1,0,0,0,0
20591,Multi-Objective Event-triggered Consensus of Linear Multi-agent Systems,"  This paper proposes a distributed consensus algorithm for linear event-based
heterogeneous multi-agent systems (MAS). The proposed scheme is event-triggered
in the sense that an agent selectively transmits its information within its
local neighbourhood based on a directed network topology under the fulfillment
of certain conditions. Using the Lyapunov stability theorem, the system
constraints and event-triggering condition are expressed in terms of several
linear matrix inequalities (LMIs) to derive the consensus parameters. The
objective is to design the transmission threshold and minimum-norm
heterogeneous control gains which collectively ensure an exponential consensus
convergence rate for the closed-loop systems. The LMI computed control gains
are robust to uncertainty with some deviation from their nominal values
allowed. The practicability of the proposed event-based framework is further
studied by proving the Zeno behaviour exclusion. Numerical simulations quantify
the advantages of our event-triggered consensus approach in second-order,
linear and heterogeneous multi-agent systems.
",1,0,0,0,0,0
20592,Private and Secure Coordination of Match-Making for Heavy-Duty Vehicle Platooning,"  A secure and private framework for inter-agent communication and coordination
is developed. This allows an agent, in our case a fleet owner, to ask questions
or submit queries in an encrypted fashion using semi-homomorphic encryption.
The submitted query can be about the interest of the other fleet owners for
using a road at a specific time of the day, for instance, for the purpose of
collaborative vehicle platooning. The other agents can then provide appropriate
responses without knowing the content of the questions or the queries. Strong
privacy and security guarantees are provided for the agent who is submitting
the queries. It is also shown that the amount of the information that this
agent can extract from the other agent is bounded. In fact, with submitting one
query, a sophisticated agent can at most extract the answer to two queries.
This secure communication platform is used subsequently to develop a
distributed coordination mechanisms among fleet owners.
",1,0,1,0,0,0
20593,Kernel Robust Bias-Aware Prediction under Covariate Shift,"  Under covariate shift, training (source) data and testing (target) data
differ in input space distribution, but share the same conditional label
distribution. This poses a challenging machine learning task. Robust Bias-Aware
(RBA) prediction provides the conditional label distribution that is robust to
the worstcase logarithmic loss for the target distribution while matching
feature expectation constraints from the source distribution. However,
employing RBA with insufficient feature constraints may result in high
certainty predictions for much of the source data, while leaving too much
uncertainty for target data predictions. To overcome this issue, we extend the
representer theorem to the RBA setting, enabling minimization of regularized
expected target risk by a reweighted kernel expectation under the source
distribution. By applying kernel methods, we establish consistency guarantees
and demonstrate better performance of the RBA classifier than competing methods
on synthetically biased UCI datasets as well as datasets that have natural
covariate shift.
",1,0,0,1,0,0
20594,Degeneration in VAE: in the Light of Fisher Information Loss,"  While enormous progress has been made to Variational Autoencoder (VAE) in
recent years, similar to other deep networks, VAE with deep networks suffers
from the problem of degeneration, which seriously weakens the correlation
between the input and the corresponding latent codes, deviating from the goal
of the representation learning. To investigate how degeneration affects VAE
from a theoretical perspective, we illustrate the information transmission in
VAE and analyze the intermediate layers of the encoders/decoders. Specifically,
we propose a Fisher Information measure for the layer-wise analysis. With such
measure, we demonstrate that information loss is ineluctable in feed-forward
networks and causes the degeneration in VAE. We show that skip connections in
VAE enable the preservation of information without changing the model
architecture. We call this class of VAE equipped with skip connections as SCVAE
and perform a range of experiments to show its advantages in information
preservation and degeneration mitigation.
",0,0,0,1,0,0
20595,Room-temperature spin transport in n-Ge probed by four-terminal nonlocal measurements,"  We demonsrtate electrical spin injection and detection in $n$-type Ge
($n$-Ge) at room temperature using four-terminal nonlocal spin-valve and
Hanle-effect measurements in lateral spin-valve (LSV) devices with
Heusler-alloy Schottky tunnel contacts. The spin diffusion length
($\lambda$$_{\rm Ge}$) of the Ge layer used ($n \sim$ 1 $\times$ 10$^{19}$
cm$^{-3}$) at 296 K is estimated to be $\sim$ 0.44 $\pm$ 0.02 $\mu$m.
Room-temperature spin signals can be observed reproducibly at the low bias
voltage range ($\le$ 0.7 V) for LSVs with relatively low resistance-area
product ($RA$) values ($\le$ 1 k$\Omega$$\mu$m$^{2}$). This means that the
Schottky tunnel contacts used here are more suitable than ferromagnet/MgO
tunnel contacts ($RA \ge$ 100 k$\Omega$$\mu$m$^{2}$) for developing Ge
spintronic applications.
",0,1,0,0,0,0
20596,Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning,"  We present an algorithm for rapidly learning controllers for robotics
systems. The algorithm follows the model-based reinforcement learning paradigm,
and improves upon existing algorithms; namely Probabilistic learning in Control
(PILCO) and a sample-based version of PILCO with neural network dynamics
(Deep-PILCO). We propose training a neural network dynamics model using
variational dropout with truncated Log-Normal noise. This allows us to obtain a
dynamics model with calibrated uncertainty, which can be used to simulate
controller executions via rollouts. We also describe set of techniques,
inspired by viewing PILCO as a recurrent neural network model, that are crucial
to improve the convergence of the method. We test our method on a variety of
benchmark tasks, demonstrating data-efficiency that is competitive with PILCO,
while being able to optimize complex neural network controllers. Finally, we
assess the performance of the algorithm for learning motor controllers for a
six legged autonomous underwater vehicle. This demonstrates the potential of
the algorithm for scaling up the dimensionality and dataset sizes, in more
complex control tasks.
",1,0,0,0,0,0
20597,Stealthy Deception Attacks Against SCADA Systems,"  SCADA protocols for Industrial Control Systems (ICS) are vulnerable to
network attacks such as session hijacking. Hence, research focuses on network
anomaly detection based on meta--data (message sizes, timing, command
sequence), or on the state values of the physical process. In this work we
present a class of semantic network-based attacks against SCADA systems that
are undetectable by the above mentioned anomaly detection. After hijacking the
communication channels between the Human Machine Interface (HMI) and
Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a
fake view of the industrial process, deceiving the human operator into taking
manual actions. Our most advanced attack also manipulates the messages
generated by the operator's actions, reversing their semantic meaning while
causing the HMI to present a view that is consistent with the attempted human
actions. The attacks are totaly stealthy because the message sizes and timing,
the command sequences, and the data values of the ICS's state all remain
legitimate.
We implemented and tested several attack scenarios in the test lab of our
local electric company, against a real HMI and real PLCs, separated by a
commercial-grade firewall. We developed a real-time security assessment tool,
that can simultaneously manipulate the communication to multiple PLCs and cause
the HMI to display a coherent system--wide fake view. Our tool is configured
with message-manipulating rules written in an ICS Attack Markup Language (IAML)
we designed, which may be of independent interest. Our semantic attacks all
successfully fooled the operator and brought the system to states of blackout
and possible equipment damage.
",1,0,0,0,0,0
20598,Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type,"  Despite their significant functional roles, beta-band oscillations are least
understood. Synchronization in neuronal networks have attracted much attention
in recent years with the main focus on transition type. Whether one obtains
explosive transition or a continuous transition is an important feature of the
neuronal network which can depend on network structure as well as synaptic
types. In this study we consider the effect of synaptic interaction (electrical
and chemical) as well as structural connectivity on synchronization transition
in network models of Izhikevich neurons which spike regularly with beta
rhythms. We find a wide range of behavior including continuous transition,
explosive transition, as well as lack of global order. The stronger electrical
synapses are more conducive to synchronization and can even lead to explosive
synchronization. The key network element which determines the order of
transition is found to be the clustering coefficient and not the small world
effect, or the existence of hubs in a network. These results are in contrast to
previous results which use phase oscillator models such as the Kuramoto model.
Furthermore, we show that the patterns of synchronization changes when one goes
to the gamma band. We attribute such a change to the change in the refractory
period of Izhikevich neurons which changes significantly with frequency.
",0,0,0,0,1,0
20599,Renyi Differential Privacy,"  We propose a natural relaxation of differential privacy based on the Renyi
divergence. Closely related notions have appeared in several recent papers that
analyzed composition of differentially private mechanisms. We argue that the
useful analytical tool can be used as a privacy definition, compactly and
accurately representing guarantees on the tails of the privacy loss.
We demonstrate that the new definition shares many important properties with
the standard definition of differential privacy, while additionally allowing
tighter analysis of composite heterogeneous mechanisms.
",1,0,0,0,0,0
20600,Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure,"  In this paper, we propose a quality enhancement network for Versatile Video
Coding (VVC) compressed videos by jointly exploiting spatial details and
temporal structure (SDTS). The network consists of a temporal structure
prediction subnet and a spatial detail enhancement subnet. The former subnet is
used to estimate and compensate the temporal motion across frames, and the
spatial detail subnet is used to reduce the compression artifacts and enhance
the reconstruction quality of the VVC compressed video. Experimental results
demonstrate the effectiveness of our SDTS-based approach. It offers over
7.82$\%$ BD-rate saving on the common test video sequences and achieves the
state-of-the-art performance.
",1,0,0,0,0,0
20601,Discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. A discrete Lawson correspondence,"  The main result of this paper is a discrete Lawson correspondence between
discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. This is a
correspondence between two discrete isothermic surfaces. We show that this
correspondence is an isometry in the following sense: it preserves the metric
coefficients introduced previously by Bobenko and Suris for isothermic nets.
Exactly as in the smooth case, this is a correspondence between nets with the
same Lax matrices, and the immersion formulas also coincide with the smooth
case.
",0,1,1,0,0,0
20602,Network Model Selection Using Task-Focused Minimum Description Length,"  Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented. Users of downstream network data may not even be
aware of these choices or their impacts. We propose a task-focused network
model selection methodology which addresses several key challenges. Our
approach constructs network models from underlying data and uses minimum
description length (MDL) criteria for selection. Our methodology measures
efficiency, a general and comparable measure of the network's performance of a
local (i.e. node-level) predictive task of interest. Selection on efficiency
favors parsimonious (e.g. sparse) models to avoid overfitting and can be
applied across arbitrary tasks and representations. We show stability,
sensitivity, and significance testing in our methodology.
",1,0,0,0,0,0
20603,Concentration of curvature and Lipschitz invariants of holomorphic functions of two variables,"  By combining analytic and geometric viewpoints on the concentration of the
curvature of the Milnor fibre, we prove that Lipschitz homeomorphisms preserve
the zones of multi-scale curvature concentration as well as the gradient canyon
structure of holomorphic functions of two variables. This yields the first new
Lipschitz invariants after those discovered by Henry and Parusinski in 2003.
",0,0,1,0,0,0
20604,Beam tuning and bunch length measurement in the bunch compression operation at the cERL,"  Realization of a short bunch beam by manipulating the longitudinal phase
space distribution with a finite longitudinal dispersion following an off-crest
accelera- tion is a widely used technique. The technique was applied in a
compact test accelerator of an energy-recovery linac scheme for compressing the
bunch length at the return loop. A diagnostic system utilizing coherent
transition radiation was developed for the beam tuning and for estimating the
bunch length. By scanning the beam parameters, we experimentally found the best
condition for the bunch compression. The RMS bunch length of 250+-50 fs was
obtained at a bunch charge of 2 pC. This result confirmed the design and the
tuning pro- cedure of the bunch compression operation for the future
energy-recovery linac (ERL).
",0,1,0,0,0,0
20605,Long-Lived Ultracold Molecules with Electric and Magnetic Dipole Moments,"  We create fermionic dipolar $^{23}$Na$^6$Li molecules in their triplet ground
state from an ultracold mixture of $^{23}$Na and $^6$Li. Using
magneto-association across a narrow Feshbach resonance followed by a two-photon
STIRAP transfer to the triplet ground state, we produce $3\,{\times}\,10^4$
ground state molecules in a spin-polarized state. We observe a lifetime of
$4.6\,\text{s}$ in an isolated molecular sample, approaching the $p$-wave
universal rate limit. Electron spin resonance spectroscopy of the triplet state
was used to determine the hyperfine structure of this previously unobserved
molecular state.
",0,1,0,0,0,0
20606,Bilinear generalized Radon transforms in the plane,"  Let $\sigma$ be arc-length measure on $S^1\subset \mathbb R^2$ and $\Theta$
denote rotation by an angle $\theta \in (0, \pi]$. Define a model bilinear
generalized Radon transform, $$B_{\theta}(f,g)(x)=\int_{S^1} f(x-y)g(x-\Theta
y)\, d\sigma(y),$$ an analogue of the linear generalized Radon transforms of
Guillemin and Sternberg \cite{GS} and Phong and Stein (e.g.,
\cite{PhSt91,St93}). Operators such as $B_\theta$ are motivated by problems in
geometric measure theory and combinatorics. For $\theta<\pi$, we show that
$B_{\theta}: L^p({\Bbb R}^2) \times L^q({\Bbb R}^2) \to L^r({\Bbb R}^2)$ if
$\left(\frac{1}{p},\frac{1}{q},\frac{1}{r}\right)\in Q$, the polyhedron with
the vertices $(0,0,0)$, $(\frac{2}{3}, \frac{2}{3}, 1)$, $(0, \frac{2}{3},
\frac{1}{3})$, $(\frac{2}{3},0,\frac{1}{3})$, $(1,0,1)$, $(0,1,1)$ and
$(\frac{1}{2},\frac{1}{2},\frac{1}{2})$, except for $\left(
\frac{1}{2},\frac{1}{2},\frac{1}{2} \right)$, where we obtain a restricted
strong type estimate. For the degenerate case $\theta=\pi$, a more restrictive
set of exponents holds. In the scale of normed spaces, $p,q,r \ge 1$, the type
set $Q$ is sharp. Estimates for the same exponents are also proved for a class
of bilinear generalized Radon transforms in $\mathbb R^2$ of the form $$
B(f,g)(x)=\int \int \delta(\phi_1(x,y)-t_1)\delta(\phi_2(x,z)-t_2)
\delta(\phi_3(y,z)-t_3) f(y)g(z) \psi(y,z) \, dy\, dz, $$ where $\delta$
denotes the Dirac distribution, $t_1,t_2,t_3\in\mathbb R$, $\psi$ is a smooth
cut-off and the defining functions $\phi_j$ satisfy some natural geometric
assumptions.
",0,0,1,0,0,0
20607,Diffusion of new products with recovering consumers,"  We consider the diffusion of new products in the discrete Bass-SIR model, in
which consumers who adopt the product can later ""recover"" and stop influencing
their peers to adopt the product. To gain insight into the effect of the social
network structure on the diffusion, we focus on two extreme cases. In the
""most-connected"" configuration where all consumers are inter-connected
(complete network), averaging over all consumers leads to an aggregate model,
which combines the Bass model for diffusion of new products with the SIR model
for epidemics. In the ""least-connected"" configuration where consumers are
arranged on a circle and each consumer can only be influenced by his left
neighbor (one-sided 1D network), averaging over all consumers leads to a
different aggregate model which is linear, and can be solved explicitly. We
conjecture that for any other network, the diffusion is bounded from below and
from above by that on a one-sided 1D network and on a complete network,
respectively. When consumers are arranged on a circle and each consumer can be
influenced by his left and right neighbors (two-sided 1D network), the
diffusion is strictly faster than on a one-sided 1D network. This is different
from the case of non-recovering adopters, where the diffusion on one-sided and
on two-sided 1D networks is identical. We also propose a nonlinear model for
recoveries, and show that consumers' heterogeneity has a negligible effect on
the aggregate diffusion.
",1,1,0,0,0,0
20608,Answering Complex Questions Using Open Information Extraction,"  While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.
",1,0,0,0,0,0
20609,On Testing Quantum Programs,"  A quantum computer (QC) can solve many computational problems more
efficiently than a classic one. The field of QCs is growing: companies (such as
DWave, IBM, Google, and Microsoft) are building QC offerings. We position that
software engineers should look into defining a set of software engineering
practices that apply to QC's software. To start this process, we give examples
of challenges associated with testing such software and sketch potential
solutions to some of these challenges.
",1,0,0,0,0,0
20610,Degree weighted recurrence networks for the analysis of time series data,"  Recurrence networks are powerful tools used effectively in the nonlinear
analysis of time series data. The analysis in this context is done mostly with
unweighted and undirected complex networks constructed with specific criteria
from the time series. In this work, we propose a novel method to construct
""weighted recurrence network""(WRN) from a time series and show how it can
reveal useful information regarding the structure of a chaotic attractor, which
the usual unweighted recurrence network cannot provide. Especially, we find the
node strength distribution of the WRN, from every chaotic attractor follows a
power law (with exponential tail) with the index characteristic to the fractal
structure of the attractor. This leads to a new class among complex networks,
to which networks from all standard chaotic attractors are found to belong. In
addition, we present generalized definitions for clustering coefficient and
characteristic path length and show that these measures can effectively
discriminate chaotic dynamics from white noise and $1/f$ colored noise. Our
results indicate that the WRN and the associated measures can become
potentially important tools for the analysis of short and noisy time series
from the real world systems as they are clearly demarked from that of noisy or
stochastic systems.
",0,1,0,0,0,0
20611,Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age,"  As Ocean General Circulation Models (OGCMs) move into the petascale age,
where the output from global high-resolution model runs can be of the order of
hundreds of terabytes in size, tools to analyse the output of these models will
need to scale up too. Lagrangian Ocean Analysis, where virtual particles are
tracked through hydrodynamic fields, is an increasingly popular way to analyse
OGCM output, by mapping pathways and connectivity of biotic and abiotic
particulates. However, the current software stack of Lagrangian Ocean Analysis
codes is not dynamic enough to cope with the increasing complexity, scale and
need for customisation of use-cases. Furthermore, most community codes are
developed for stand-alone use, making it a nontrivial task to integrate virtual
particles at runtime of the OGCM. Here, we introduce the new Parcels code,
which was designed from the ground up to be sufficiently scalable to cope with
petascale computing. We highlight its API design that combines flexibility and
customisation with the ability to optimise for HPC workflows, following the
paradigm of domain-specific languages. Parcels is primarily written in Python,
utilising the wide range of tools available in the scientific Python ecosystem,
while generating low-level C-code and using Just-In-Time compilation for
performance-critical computation. We show a worked-out example of its API, and
validate the accuracy of the code against seven idealised test cases. This
version~0.9 of Parcels is focussed on laying out the API, with future work
concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.
",1,1,0,0,0,0
20612,TFLMS: Large Model Support in TensorFlow by Graph Rewriting,"  While accelerators such as GPUs have limited memory, deep neural networks are
becoming larger and will not fit with the memory limitation of accelerators for
training. We propose an approach to tackle this problem by rewriting the
computational graph of a neural network, in which swap-out and swap-in
operations are inserted to temporarily store intermediate results on CPU
memory. In particular, we first revise the concept of a computational graph by
defining a concrete semantics for variables in a graph. We then formally show
how to derive swap-out and swap-in operations from an existing graph and
present rules to optimize the graph. To realize our approach, we developed a
module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the
TensorFlow repository for contributing to the TensorFlow community. With TFLMS,
we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size,
respectively. In particular, we were able to train 3DUNet using images of size
of $192^3$ for image segmentation, which, without TFLMS, had been done only by
dividing the images to smaller images, which affects the accuracy.
",0,0,0,1,0,0
20613,Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties,"  Understanding the relationship between the structure of light-harvesting
systems and their excitation energy transfer properties is of fundamental
importance in many applications including the development of next generation
photovoltaics. Natural light harvesting in photosynthesis shows remarkable
excitation energy transfer properties, which suggests that pigment-protein
complexes could serve as blueprints for the design of nature inspired devices.
Mechanistic insights into energy transport dynamics can be gained by leveraging
numerically involved propagation schemes such as the hierarchical equations of
motion (HEOM). Solving these equations, however, is computationally costly due
to the adverse scaling with the number of pigments. Therefore virtual
high-throughput screening, which has become a powerful tool in material
discovery, is less readily applicable for the search of novel excitonic
devices. We propose the use of artificial neural networks to bypass the
computational limitations of established techniques for exploring the
structure-dynamics relation in excitonic systems. Once trained, our neural
networks reduce computational costs by several orders of magnitudes. Our
predicted transfer times and transfer efficiencies exhibit similar or even
higher accuracies than frequently used approximate methods such as secular
Redfield theory
",0,1,0,1,0,0
20614,Multi-Player Bandits: A Trekking Approach,"  We study stochastic multi-armed bandits with many players. The players do not
know the number of players, cannot communicate with each other and if multiple
players select a common arm they collide and none of them receive any reward.
We consider the static scenario, where the number of players remains fixed, and
the dynamic scenario, where the players enter and leave at any time. We provide
algorithms based on a novel `trekking approach' that guarantees constant regret
for the static case and sub-linear regret for the dynamic case with high
probability. The trekking approach eliminates the need to estimate the number
of players resulting in fewer collisions and improved regret performance
compared to the state-of-the-art algorithms. We also develop an epoch-less
algorithm that eliminates any requirement of time synchronization across the
players provided each player can detect the presence of other players on an
arm. We validate our theoretical guarantees using simulation based and real
test-bed based experiments.
",0,0,0,1,0,0
20615,High-resolution investigation of spinal cord and spine,"  High-resolution non-invasive 3D study of intact spine and spinal cord
morphology on the level of complex vascular and neuronal organization is a
crucial issue for the development of treatments for the injuries and
pathologies of central nervous system (CNS). X-ray phase contrast tomography
enables high quality 3D visualization in ex-vivo mouse model of both vascular
and neuronal network of the soft spinal cord tissue at the scale from
millimeters to hundreds of nanometers without any contrast agents and
sectioning. Until now, 3D high resolution visualization of spinal cord mostly
has been limited by imaging of organ extracted from vertebral column because
high absorbing boney tissue drastically reduces the morphological details of
soft tissue in image. However, the extremely destructive procedure of bones
removal leads to sample deterioration and, therefore, to the lack of
considerable part of information about the object. In this work we present the
data analysis procedure to get high resolution and high contrast 3D images of
intact mice spinal cord surrounded by vertebras, preserving all richness of
micro-details of the spinal cord inhabiting inside. Our results are the first
step forward to the difficult way toward the high- resolution investigation of
in-vivo model central nervous system.
",0,1,0,0,0,0
20616,Bohr--Rogosinski radius for analytic functions,"  There are a number of articles which deal with Bohr's phenomenon whereas only
a few papers appeared in the literature on Rogosinski's radii for analytic
functions defined on the unit disk $|z|<1$. In this article, we introduce and
investigate Bohr-Rogosinski's radii for analytic functions defined for $|z|<1$.
Also, we prove several different improved versions of the classical Bohr's
inequality. Finally, we also discuss the Bohr-Rogosinski's radius for a class
of subordinations. All the results are proved to be sharp.
",0,0,1,0,0,0
20617,Methods to locate Saddle Points in Complex Landscapes,"  We present a class of simple algorithms that allows to find the reaction path
in systems with a complex potential energy landscape. The approach does not
need any knowledge on the product state and does not require the calculation of
any second derivatives. The underlying idea is to use two nearby points in
configuration space to locate the path of slowest ascent. By introducing a weak
noise term, the algorithm is able to find even low-lying saddle points that are
not reachable by means of a slowest ascent path. Since the algorithm makes only
use of the value of the potential and its gradient, the computational effort to
find saddles is linear in the number of degrees of freedom, if the potential is
short-ranged. We test the performance of the algorithm for two potential energy
landscapes. For the Müller-Brown surface we find that the algorithm always
finds the correct saddle point. For the modified Müller-Brown surface, which
has a saddle point that is not reachable by means of a slowest ascent path, the
algorithm is still able to find this saddle point with high probability.
",0,1,0,0,0,0
20618,On the Complexity of Opinions and Online Discussions,"  In an increasingly polarized world, demagogues who reduce complexity down to
simple arguments based on emotion are gaining in popularity. Are opinions and
online discussions falling into demagoguery? In this work, we aim to provide
computational tools to investigate this question and, by doing so, explore the
nature and complexity of online discussions and their space of opinions,
uncovering where each participant lies.
More specifically, we present a modeling framework to construct latent
representations of opinions in online discussions which are consistent with
human judgements, as measured by online voting. If two opinions are close in
the resulting latent space of opinions, it is because humans think they are
similar. Our modeling framework is theoretically grounded and establishes a
surprising connection between opinions and voting models and the sign-rank of a
matrix. Moreover, it also provides a set of practical algorithms to both
estimate the dimension of the latent space of opinions and infer where opinions
expressed by the participants of an online discussion lie in this space.
Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,
and the Newsroom app suggest that unidimensional opinion models may often be
unable to accurately represent online discussions, provide insights into human
judgements and opinions, and show that our framework is able to circumvent
language nuances such as sarcasm or humor by relying on human judgements
instead of textual analysis.
",1,0,0,1,0,0
20619,Free energy distribution of the stationary O'Connell-Yor directed random polymer model,"  We study the semi-discrete directed polymer model introduced by O'Connell-Yor
in its stationary regime, based on our previous work on the stationary
$q$-totally asymmetric simple exclusion process ($q$-TASEP) using a two-sided
$q$-Whittaker process. We give a formula for the free energy distribution of
the polymer model in terms of Fredholm determinant and show that the universal
KPZ stationary distribution appears in the long time limit. We also consider
the limit to the stationary KPZ equation and discuss the connections with
previously found formulas.
",0,1,1,0,0,0
20620,Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13),"  In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor
of Alzheimer's Disease (AD) in the cognitive domain -- using data from each
patient's previous visits, and testing on future (held-out) data. We start by
learning a population-level model using multi-modal data from previously seen
patients using a base Gaussian Process (GP) regression. The personalized GP
(pGP) is formed by adapting the base GP sequentially over time to a new
(target) patient using domain adaptive GPs. We extend this personalized
approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
months. We compare this approach to a GP model trained only on past data of the
target patients (tGP), as well as to a new approach that combines pGP with tGP.
We find that the new approach, combining pGP with tGP, leads to large
improvements in accurately forecasting future ADAS-Cog13 scores.
",0,0,0,1,0,0
20621,Analysis and Control of a Non-Standard Hyperbolic PDE Traffic Flow Model,"  The paper provides results for a non-standard, hyperbolic, 1-D, nonlinear
traffic flow model on a bounded domain. The model consists of two first-order
PDEs with a dynamic boundary condition that involves the time derivative of the
velocity. The proposed model has features that are important from a
traffic-theoretic point of view: is completely anisotropic and information
travels forward exactly at the same speed as traffic. It is shown that, for all
physically meaningful initial conditions, the model admits a globally defined,
unique, classical solution that remains positive and bounded for all times.
Moreover, it is shown that global stabilization can be achieved for arbitrary
equilibria by means of an explicit boundary feedback law. The stabilizing
feedback law depends only on the inlet velocity and consequently, the
measurement requirements for the implementation of the proposed boundary
feedback law are minimal. The efficiency of the proposed boundary feedback law
is demonstrated by means of a numerical example.
",1,0,1,0,0,0
20622,Learning Robust Representations for Computer Vision,"  Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.
",1,0,0,1,0,0
20623,Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements,"  In this work, we explore the problems of detecting the number of narrow-band,
far-field targets and estimating their corresponding directions from single
snapshot measurements. The principles of sparse signal recovery (SSR) are used
for the single snapshot detection and estimation of multiple targets. In the
SSR framework, the DoA estimation problem is grid based and can be posed as the
lasso optimization problem. However, the SSR framework for DoA estimation gives
rise to the grid mismatch problem, when the unknown targets (sources) are not
matched with the estimation grid chosen for the construction of the array
steering matrix at the receiver. The block sparse recovery framework is known
to mitigate the grid mismatch problem by jointly estimating the targets and
their corresponding offsets from the estimation grid using the group lasso
estimator. The corresponding detection problem reduces to estimating the
optimal regularization parameter ($\tau$) of the lasso (in case of perfect
grid-matching) or group-lasso estimation problem for achieving the required
probability of correct detection ($P_c$). We propose asymptotic and finite
sample test statistics for detecting the number of sources with the required
$P_c$ at moderate to high signal to noise ratios. Once the number of sources
are detected, or equivalently the optimal $\hat{\tau}$ is estimated, the
corresponding estimation and grid matching of the DoAs can be performed by
solving the lasso or group-lasso problem at $\hat{\tau}$
",0,0,0,1,0,0
20624,Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels,"  The latent feature relational model (LFRM) is a generative model for
graph-structured data to learn a binary vector representation for each node in
the graph. The binary vector denotes the node's membership in one or more
communities. At its core, the LFRM miller2009nonparametric is an overlapping
stochastic blockmodel, which defines the link probability between any pair of
nodes as a bilinear function of their community membership vectors. Moreover,
using a nonparametric Bayesian prior (Indian Buffet Process) enables learning
the number of communities automatically from the data. However, despite its
appealing properties, inference in LFRM remains a challenge and is typically
done via MCMC methods. This can be slow and may take a long time to converge.
In this work, we develop a small-variance asymptotics based framework for the
non-parametric Bayesian LFRM. This leads to an objective function that retains
the nonparametric Bayesian flavor of LFRM, while enabling us to design
deterministic inference algorithms for this model, that are easy to implement
(using generic or specialized optimization routines) and are fast in practice.
Our results on several benchmark datasets demonstrate that our algorithm is
competitive to methods such as MCMC, while being much faster.
",0,0,0,1,0,0
20625,Towards a Science of Mind,"  The ancient mind/body problem continues to be one of deepest mysteries of
science and of the human spirit. Despite major advances in many fields, there
is still no plausible link between subjective experience (qualia) and its
realization in the body. This paper outlines some of the elements of a rigorous
science of mind (SoM) - key ideas include scientific realism of mind, agnostic
mysterianism, careful attention to language, and a focus on concrete
(touchstone) questions and results.
",1,0,0,0,1,0
20626,Resistance distance criterion for optimal slack bus selection,"  We investigate the dependence of transmission losses on the choice of a slack
bus in high voltage AC transmission networks. We formulate a transmission loss
minimization problem in terms of slack variables representing the additional
power injection that each generator provides to compensate the transmission
losses. We show analytically that for transmission lines having small,
homogeneous resistance over reactance ratios ${r/x\ll1}$, transmission losses
are generically minimal in the case of a unique \textit{slack bus} instead of a
distributed slack bus. For the unique slack bus scenario, to lowest order in
${r/x}$, transmission losses depend linearly on a resistance distance based
indicator measuring the separation of the slack bus candidate from the rest of
the network. We confirm these results numerically for several IEEE and Pegase
testcases, and show that our predictions qualitatively hold also in the case of
lines having inhomogeneous ${r/x}$ ratios, with optimal slack bus choices
reducing transmission losses by ${10}\%$ typically.
",1,1,0,0,0,0
20627,Interpolation in the Presence of Domain Inhomogeneity,"  Standard interpolation techniques are implicitly based on the assumption that
the signal lies on a homogeneous domain. In this letter, the proposed
interpolation method instead exploits prior information about domain
inhomogeneity, characterized by different, potentially overlapping, subdomains.
By introducing a domain-similarity metric for each sample, the interpolation
process is then based on a domain-informed consistency principle. We illustrate
and demonstrate the feasibility of domain-informed linear interpolation in 1D,
and also, on a real fMRI image in 2D. The results show the benefit of
incorporating domain knowledge so that, for example, sharp domain boundaries
can be recovered by the interpolation, if such information is available.
",0,0,1,0,0,0
20628,Lectures on the mean values of functionals -- An elementary introduction to infinite-dimensional probability,"  This is an elementary introduction to infinite-dimensional probability. In
the lectures, we compute the exact mean values of some functionals on C[0,1]
and L[0,1] by considering these functionals as infinite-dimensional random
variables. The results show that there exist the complete concentration of
measure phenomenon for these mean values since the variances are all zeroes.
",0,1,1,1,0,0
20629,Synthesis of Optimal Resilient Control Strategies,"  Repair mechanisms are important within resilient systems to maintain the
system in an operational state after an error occurred. Usually, constraints on
the repair mechanisms are imposed, e.g., concerning the time or resources
required (such as energy consumption or other kinds of costs). For systems
modeled by Markov decision processes (MDPs), we introduce the concept of
resilient schedulers, which represent control strategies guaranteeing that
these constraints are always met within some given probability. Assigning
rewards to the operational states of the system, we then aim towards resilient
schedulers which maximize the long-run average reward, i.e., the expected mean
payoff. We present a pseudo-polynomial algorithm that decides whether a
resilient scheduler exists and if so, yields an optimal resilient scheduler. We
show also that already the decision problem asking whether there exists a
resilient scheduler is PSPACE-hard.
",1,0,0,0,0,0
20630,Limits of the Kucera-Gacs coding method,"  Every real is computable from a Martin-Loef random real. This well known
result in algorithmic randomness was proved by Kucera and Gacs. In this survey
article we discuss various approaches to the problem of coding an arbitrary
real into a Martin-Loef random real,and also describe new results concerning
optimal methods of coding. We start with a simple presentation of the original
methods of Kucera and Gacs and then rigorously demonstrate their limitations in
terms of the size of the redundancy in the codes that they produce. Armed with
a deeper understanding of these methods, we then proceed to motivate and
illustrate aspects of the new coding method that was recently introduced by
Barmpalias and Lewis-Pye and which achieves optimal logarithmic redundancy, an
exponential improvement over the original redundancy bounds.
",0,0,1,0,0,0
20631,Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming,"  This paper proposes the use of subspace tracking algorithms for performing
MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation (PASTd)
and the orthogonal Oja (OOJA) algorithms to our framework and obtain two
channel estimation algorithms. The hybrid analog/digital nature of the
beamformer is also explicitly taken into account at the algorithm design stage.
Numerical results show that the proposed estimation algorithms are effective,
and that they perform better than two relevant competing alternatives available
in the open literature.
",1,0,0,0,0,0
20632,Thomas Precession for Dressed Particles,"  We consider a particle dressed with boundary gravitons in three-dimensional
Minkowski space. The existence of BMS transformations implies that the
particle's wavefunction picks up a Berry phase when subjected to changes of
reference frames that trace a closed path in the asymptotic symmetry group. We
evaluate this phase and show that, for BMS superrotations, it provides a
gravitational generalization of Thomas precession. In principle, such phases
are observable signatures of asymptotic symmetries.
",0,1,1,0,0,0
20633,Exponential random graphs behave like mixtures of stochastic block models,"  We study the behavior of exponential random graphs in both the sparse and the
dense regime. We show that exponential random graphs are approximate mixtures
of graphs with independent edges whose probability matrices are critical points
of an associated functional, thereby satisfying a certain matrix equation. In
the dense regime, every solution to this equation is close to a block matrix,
concluding that the exponential random graph behaves roughly like a mixture of
stochastic block models. We also show existence and uniqueness of solutions to
this equation for several families of exponential random graphs, including the
case where the subgraphs are counted with positive weights and the case where
all weights are small in absolute value. In particular, this generalizes some
of the results in a paper by Chatterjee and Diaconis from the dense regime to
the sparse regime and strengthens their bounds from the cut-metric to the
one-metric.
",1,0,1,1,0,0
20634,Certificate Enhanced Data-Flow Analysis,"  Proof-carrying-code was proposed as a solution to ensure a trust relationship
between two parties: a (heavyweight) analyzer and a (lightweight) checker. The
analyzer verifies the conformance of a given application to a specified
property and generates a certificate attesting the validity of the analysis
result. It suffices then for the checker just to test the consistency of the
proof instead of constructing it. We set out to study the applicability of this
technique in the context of data- flow analysis. In particular, we want to know
if there is a significant performance difference between the analyzer and the
checker. Therefore, we developed a tool, called DCert, implementing an
inter-procedural context and flow-sensitive data-flow analyzer and checker for
Android. Applying our tool to real-world large applications, we found out that
checking can be up to 8 times faster than verification. This important gain in
time suggests a potential for equipping applications on app stores with
certificates that can be checked on mobile devices which are limited in
computation and storage resources. We describe our implementation and report on
experimental results.
",1,0,0,0,0,0
20635,Small cells in a Poisson hyperplane tessellation,"  Until now, little was known about properties of small cells in a Poisson
hyperplane tessellation. The few existing results were either heuristic or
applying only to the two dimensional case and for very specific size
functionals and directional distributions. This paper fills this gap by
providing a systematic study of small cells in a Poisson hyperplane
tessellation of arbitrary dimension, arbitrary directional distribution
$\varphi$ and with respect to an arbitrary size functional $\Sigma$. More
precisely, we investigate the distribution of the typical cell $Z$, conditioned
on the event $\{\Sigma(Z)<a\}$, where $a\to0$ and $\Sigma$ is a size
functional, i.e. a functional on the set of convex bodies which is continuous,
not identically zero, homogeneous of degree $k>0$, and increasing with respect
to set inclusion. We focus on the number of facets and the shape of such small
cells. We show in various general settings that small cells tend to minimize
the number of facets and that they have a non degenerated limit shape
distribution which depends on the size $\Sigma$ and the directional
distribution. We also exhibit a class of directional distribution for which
cells with small inradius do not tend to minimize the number of facets.
",0,0,1,0,0,0
20636,Privacy-Aware Guessing Efficiency,"  We investigate the problem of guessing a discrete random variable $Y$ under a
privacy constraint dictated by another correlated discrete random variable $X$,
where both guessing efficiency and privacy are assessed in terms of the
probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum
probability of correctly guessing $Y$ given an auxiliary random variable $Z$,
where the maximization is taken over all $P_{Z|Y}$ ensuring that the
probability of correctly guessing $X$ given $Z$ does not exceed $\epsilon$. We
show that the map $\epsilon\mapsto h(P_{XY}, \epsilon)$ is strictly increasing,
concave, and piecewise linear, which allows us to derive a closed form
expression for $h(P_{XY}, \epsilon)$ when $X$ and $Y$ are connected via a
binary-input binary-output channel. For $(X^n, Y^n)$ being pairs of independent
and identically distributed binary random vectors, we similarly define
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ under the assumption that $Z^n$ is also
a binary vector. Then we obtain a closed form expression for
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ for sufficiently large, but nontrivial
values of $\epsilon$.
",1,0,1,1,0,0
20637,Positioning services of a travel agency in social networks,"  In this paper the methods of forming a travel company customer base by means
of social networks are observed. These methods are made to involve web-users of
the social networks (VK.com and Facebook) for positioning of the service of the
travel agency ""New Europe"" on the Internet. The methods of applying the
maintenance activities and interests of web-users are also used. So, the main
method of information exchanging in modern network society is on-line social
networks. The rapid development and improvement of such information and
communication technologies is a key factor in the positioning of the travel
agency brand in the global information space. The absence of time and space
restrictions and the speed of spreading of the information among an aim
audience of social networks create all the conditions for effective
popularization of the travel agency ""New Europe"" and its service in the
Internet.
",1,0,0,0,0,0
20638,Inverse Mapping for Rainfall-Runoff Models using History Matching Approach,"  In this paper, we consider two rainfall-runoff computer models. The first
model is Matlab-Simulink model which simulates runoff from windrow compost pad
(located at the Bioconversion Center in Athens, GA) over a period of time based
on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which
estimates surface runoff in the Middle Oconee River in Athens, GA. The input
parameter spaces of both models are sensitive and high dimensional, the model
output for every input combination is a time-series of runoff, and these two
computer models generate a wide spectrum of outputs including some that are far
from reality. In order to improve the prediction accuracy, in this paper we
propose to apply a history matching approach for calibrating these hydrological
models, which also gives better insights for improved management of these
systems.
",0,0,0,1,0,0
20639,Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE,"  Classification of imbalanced datasets is a challenging task for standard
algorithms. Although many methods exist to address this problem in different
ways, generating artificial data for the minority class is a more general
approach compared to algorithmic modifications. SMOTE algorithm and its
variations generate synthetic samples along a line segment that joins minority
class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
generalization of the SMOTE data generation mechanism. G-SMOTE generates
synthetic samples in a geometric region of the input space, around each
selected minority instance. While in the basic configuration this region is a
hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
a line segment, emulating, in the last case, the SMOTE mechanism. The
performance of G-SMOTE is compared against multiple standard oversampling
algorithms. We present empirical results that show a significant improvement in
the quality of the generated data when G-SMOTE is used as an oversampling
algorithm.
",1,0,0,0,0,0
20640,Liveness Verification and Synthesis: New Algorithms for Recursive Programs,"  We consider the problems of liveness verification and liveness synthesis for
recursive programs. The liveness verification problem (LVP) is to decide
whether a given omega-context-free language is contained in a given
omega-regular language. The liveness synthesis problem (LSP) is to compute a
strategy so that a given omega-context-free game, when played along the
strategy, is guaranteed to derive a word in a given omega-regular language. The
problems are known to be EXPTIME-complete and EXPTIME-complete, respectively.
Our contributions are new algorithms with optimal time complexity. For LVP, we
generalize recent lasso-finding algorithms (also known as Ramsey-based
algorithms) from finite to recursive programs. For LSP, we generalize a recent
summary-based algorithm from finite to infinite words. Lasso finding and
summaries have proven to be efficient in a number of implementations for the
finite state and finite word setting.
",1,0,0,0,0,0
20641,A semiparametric approach for bivariate extreme exceedances,"  Inference over tails is performed by applying only the results of extreme
value theory. Whilst such theory is well defined and flexible enough in the
univariate case, multivariate inferential methods often require the imposition
of arbitrary constraints not fully justifed by the underlying theory. In
contrast, our approach uses only the constraints imposed by theory. We build on
previous, theoretically justified work for marginal exceedances over a high,
unknown threshold, by combining it with flexible, semiparametric copulae
specifications to investigate extreme dependence. Whilst giving probabilistic
judgements about the extreme regime of all marginal variables, our approach
formally uses the full dataset and allows for a variety of patterns of
dependence, be them extremal or not. A new probabilistic criterion quantifying
the possibility that the data exhibits asymptotic independence is introduced
and its robustness empirically studied. Estimation of functions of interest in
extreme value analyses is performed via MCMC algorithms. Attention is also
devoted to the prediction of new extreme observations. Our approach is
evaluated through a series of simulations, applied to real data sets and
assessed against competing approaches. Evidence demonstrates that the bulk of
the data does not bias and improves the inferential process for the extremal
dependence.
",0,0,0,1,0,0
20642,Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network,"  The Soil Moisture Active Passive (SMAP) mission has delivered valuable
sensing of surface soil moisture since 2015. However, it has a short time span
and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
learning neural network, Long Short-Term Memory (LSTM), we created a system
that predicts SMAP level-3 soil moisture data with atmospheric forcing,
model-simulated moisture, and static physiographic attributes as inputs. The
system removes most of the bias with model simulations and improves predicted
moisture climatology, achieving small test root-mean-squared error (<0.035) and
high correlation coefficient >0.87 for over 75\% of Continental United States,
including the forested Southeast. As the first application of LSTM in
hydrology, we show the proposed network avoids overfitting and is robust for
both temporal and spatial extrapolation tests. LSTM generalizes well across
regions with distinct climates and physiography. With high fidelity to SMAP,
LSTM shows great potential for hindcasting, data assimilation, and weather
forecasting.
",0,0,0,1,0,0
20643,Note on Green Function Formalism and Topological Invariants,"  It has been discovered previously that the topological order parameter could
be identified from the topological data of the Green function, namely the
(generalized) TKNN invariant in general dimensions, for both non-interacting
and interacting systems. In this note, we show that this phenomena has a clear
geometric derivation. This proposal could be regarded as an alternative proof
for the identification of the corresponding topological invariant and
topological order parameter.
",0,0,1,0,0,0
20644,Unsupervised Document Embedding With CNNs,"  We propose a new model for unsupervised document embedding. Leading existing
approaches either require complex inference or use recurrent neural networks
(RNN) that are difficult to parallelize. We take a different route and develop
a convolutional neural network (CNN) embedding model. Our CNN architecture is
fully parallelizable resulting in over 10x speedup in inference time over RNN
models. Parallelizable architecture enables to train deeper models where each
successive layer has increasingly larger receptive field and models longer
range semantic structure within the document. We additionally propose a fully
unsupervised learning algorithm to train this model based on stochastic forward
prediction. Empirical results on two public benchmarks show that our approach
produces comparable to state-of-the-art accuracy at a fraction of computational
cost.
",1,0,0,1,0,0
20645,A metric model for the functional architecture of the visual cortex,"  The purpose of this work is to construct a model for the functional
architecture of the primary visual cortex (V1), based on a structure of metric
measure space induced by the underlying organization of receptive profiles
(RPs) of visual cells. In order to account for the horizontal connectivity of
V1 in such a context, a diffusion process compatible with the geometry of the
space is defined following the classical approach of K.-T. Sturm. The
construction of our distance function does neither require any group
parameterization of the family of RPs, nor involve any differential structure.
As such, it adapts to non-parameterized sets of RPs, possibly obtained through
numerical procedures; it also allows to model the lateral connectivity arising
from non-differential metrics such as the one induced on a pinwheel surface by
a family of filters of vanishing scale. On the other hand, when applied to the
classical framework of Gabor filters, this construction yields a distance
approximating the sub-Riemannian structure proposed as a model for V1 by G.
Citti and A. Sarti [J Math Imaging Vis 24: 307 (2006)], thus showing itself to
be consistent with existing cortex models.
",0,0,0,0,1,0
20646,Bayesian parameter identification in Cahn-Hilliard models for biological growth,"  We consider the inverse problem of parameter estimation in a diffuse
interface model for tumour growth. The model consists of a fourth-order
Cahn--Hilliard system and contains three phenomenological parameters: the
tumour proliferation rate, the nutrient consumption rate, and the chemotactic
sensitivity. We study the inverse problem within the Bayesian framework and
construct the likelihood and noise for two typical observation settings. One
setting involves an infinite-dimensional data space where we observe the full
tumour. In the second setting we observe only the tumour volume, hence the data
space is finite-dimensional. We show the well-posedness of the posterior
measure for both settings, building upon and improving the analytical results
in [C. Kahle and K.F. Lam, Appl. Math. Optim. (2018)]. A numerical example
involving synthetic data is presented in which the posterior measure is
numerically approximated by the Sequential Monte Carlo approach with tempering.
",0,0,0,1,0,0
20647,Transport Phase Diagram and Anderson Localization in Hyperuniform Disordered Photonic Materials,"  Hyperuniform disordered photonic materials (HDPM) are spatially correlated
dielectric structures with unconventional optical properties. They can be
transparent to long-wavelength radiation while at the same time have isotropic
band gaps in another frequency range. This phenomenon raises fundamental
questions concerning photon transport through disordered media. While optical
transparency is robust against recurrent multiple scattering, little is known
about other transport regimes like diffusive multiple scattering or Anderson
localization. Here we investigate band gaps, and we report Anderson
localization in two-dimensional stealthy HDPM using numerical simulations of
the density of states and optical transport statistics. To establish a unified
view, we propose a transport phase diagram. Our results show that, depending
only on the degree of correlation, a dielectric material can transition from
localization behavior to a bandgap crossing an intermediate regime dominated by
tunneling between weakly coupled states.
",0,1,0,0,0,0
20648,Nearly Maximally Predictive Features and Their Dimensions,"  Scientific explanation often requires inferring maximally predictive features
from a given data set. Unfortunately, the collection of minimal maximally
predictive features for most stochastic processes is uncountably infinite. In
such cases, one compromises and instead seeks nearly maximally predictive
features. Here, we derive upper-bounds on the rates at which the number and the
coding cost of nearly maximally predictive features scales with desired
predictive power. The rates are determined by the fractal dimensions of a
process' mixed-state distribution. These results, in turn, show how widely-used
finite-order Markov models can fail as predictors and that mixed-state
predictive features offer a substantial improvement.
",1,1,0,1,0,0
20649,A Survey Of Cross-lingual Word Embedding Models,"  Cross-lingual representations of words enable us to reason about word meaning
in multilingual contexts and are a key facilitator of cross-lingual transfer
when developing natural language processing models for low-resource languages.
In this survey, we provide a comprehensive typology of cross-lingual word
embedding models. We compare their data requirements and objective functions.
The recurring theme of the survey is that many of the models presented in the
literature optimize for the same objectives, and that seemingly different
models are often equivalent modulo optimization strategies, hyper-parameters,
and such. We also discuss the different ways cross-lingual word embeddings are
evaluated, as well as future challenges and research horizons.
",1,0,0,0,0,0
20650,Space-efficient classical and quantum algorithms for the shortest vector problem,"  A lattice is the integer span of some linearly independent vectors. Lattice
problems have many significant applications in coding theory and cryptographic
systems for their conjectured hardness. The Shortest Vector Problem (SVP),
which is to find the shortest non-zero vector in a lattice, is one of the
well-known problems that are believed to be hard to solve, even with a quantum
computer. In this paper we propose space-efficient classical and quantum
algorithms for solving SVP. Currently the best time-efficient algorithm for
solving SVP takes $2^{n+o(n)}$ time and $2^{n+o(n)}$ space. Our classical
algorithm takes $2^{2.05n+o(n)}$ time to solve SVP with only $2^{0.5n+o(n)}$
space. We then modify our classical algorithm to a quantum version, which can
solve SVP in time $2^{1.2553n+o(n)}$ with $2^{0.5n+o(n)}$ classical space and
only poly(n) qubits.
",1,0,0,0,0,0
20651,Low Power SI Class E Power Amplifier and RF Switch For Health Care,"  This research was to design a 2.4 GHz class E Power Amplifier (PA) for health
care, with 0.18um Semiconductor Manufacturing International Corporation CMOS
technology by using Cadence software. And also RF switch was designed at
cadence software with power Jazz 180nm SOI process. The ultimate goal for such
application is to reach high performance and low cost, and between high
performance and low power consumption design. This paper introduces the design
of a 2.4GHz class E power amplifier and RF switch design. PA consists of
cascade stage with negative capacitance. This power amplifier can transmit
16dBm output power to a 50{\Omega} load. The performance of the power amplifier
and switch meet the specification requirements of the desired.
",1,0,0,0,0,0
20652,Improved approximation algorithm for the Dense-3-Subhypergraph Problem,"  The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c
et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of
subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set
$W$ of $k$ elements from the universe, and maximize the number of sets, $S\in
{\cal S}$ so that $S\subseteq W$. The members in $U$ are called {\em vertices}
and the sets of ${\cal S}$ are called the {\em hyperedges}. This is the
simplest extension into hyperedges of the case of sets of size $2$ which is the
well known Dense $k$-subgraph problem.
The best known ratio for the Dense-$3$-Subhypergraph is $O(n^{0.69783..})$ by
Chlamt{á}c et al. We improve this ratio to $n^{0.61802..}$. More
importantly, we give a new algorithm that approximates Dense-$3$-Subhypergraph
within a ratio of $\tilde O(n/k)$, which improves the ratio of $O(n^2/k^2)$ of
Chlamt{á}c et al.
We prove that under the {\em log density conjecture} (see Bhaskara et al.
[STOC'10]) the ratio cannot be better than $\Omega(\sqrt{n})$ and demonstrate
some cases in which this optimum can be attained.
",1,0,0,0,0,0
20653,A Survey of Security Assessment Ontologies,"  A literature survey on ontologies concerning the Security Assessment domain
has been carried out to uncover initiatives that aim at formalizing concepts
from the Security Assessment field of research. A preliminary analysis and a
discussion on the selected works are presented. Our main contribution is an
updated literature review, describing key characteristics, results, research
issues, and application domains of the papers. We have also detected gaps in
the Security Assessment literature that could be the subject of further studies
in the field. This work is meant to be useful for security researchers who wish
to adopt a formal approach in their methods.
",1,0,0,0,0,0
20654,Distributed Online Learning of Event Definitions,"  Logic-based event recognition systems infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
that learns event definitions in the form of Event Calculus theories, in a
single pass over a data stream. In this work we present a version of OLED that
allows for distributed, online learning. We evaluate our approach on a
benchmark activity recognition dataset and show that we can significantly
reduce training times, exchanging minimal information between processing nodes.
",1,0,0,0,0,0
20655,Solving Graph Isomorphism Problem for a Special case,"  Graph isomorphism is an important computer science problem. The problem for
the general case is unknown to be in polynomial time. The base algorithm for
the general case works in quasi-polynomial time. The solutions in polynomial
time for some special type of classes are known. In this work, we have worked
with a special type of graphs. We have proposed a method to represent these
graphs and finding isomorphism between these graphs. The method uses a modified
version of the degree list of a graph and neighbourhood degree list. These
special type of graphs have a property that neighbourhood degree list of any
two immediate neighbours is different for every vertex.The representation
becomes invariant to the order in which the node was selected for giving the
representation making the isomorphism problem trivial for this case. The
algorithm works in $O(n^4)$ time, where n is the number of vertices present in
the graph. The proposed algorithm runs faster than quasi-polynomial time for
the graphs used in the study.
",1,0,0,0,0,0
20656,Selective inference for effect modification via the lasso,"  Effect modification occurs when the effect of the treatment on an outcome
varies according to the level of other covariates and often has important
implications in decision making. When there are tens or hundreds of covariates,
it becomes necessary to use the observed data to select a simpler model for
effect modification and then make valid statistical inference. We propose a two
stage procedure to solve this problem. First, we use Robinson's transformation
to decouple the nuisance parameters from the treatment effect of interest and
use machine learning algorithms to estimate the nuisance parameters. Next,
after plugging in the estimates of the nuisance parameters, we use the Lasso to
choose a low-complexity model for effect modification. Compared to a full model
consisting of all the covariates, the selected model is much more
interpretable. Compared to the univariate subgroup analyses, the selected model
greatly reduces the number of false discoveries. We show that the conditional
selective inference for the selected model is asymptotically valid given the
rate assumptions in classical semiparametric regression. Extensive simulation
studies are conducted to verify the asymptotic results and an epidemiological
application is used to demonstrate the method.
",0,0,1,1,0,0
20657,Is Climate Change Controversial? Modeling Controversy as Contention Within Populations,"  A growing body of research focuses on computationally detecting controversial
topics and understanding the stances people hold on them. Yet gaps remain in
our theoretical and practical understanding of how to define controversy, how
it manifests, and how to measure it. In this paper, we introduce a novel
measure we call ""contention"", defined with respect to a topic and a population.
We model contention from a mathematical standpoint. We validate our model by
examining a diverse set of sources: real-world polling data sets, actual voter
data, and Twitter coverage on several topics. In our publicly-released Twitter
data set of nearly 100M tweets, we examine several topics such as Brexit, the
2016 U.S. Elections, and ""The Dress"", and cross-reference them with other
sources. We demonstrate that the contention measure holds explanatory power for
a wide variety of observed phenomena, such as controversies over climate change
and other topics that are well within scientific consensus. Finally, we
re-examine the notion of controversy, and present a theoretical framework that
defines it in terms of population. We present preliminary evidence suggesting
that contention is one dimension of controversy, along with others, such as
""importance"". Our new contention measure, along with the hypothesized model of
controversy, suggest several avenues for future work in this emerging
interdisciplinary research area.
",1,1,0,0,0,0
20658,Defending Against Adversarial Attacks by Leveraging an Entire GAN,"  Recent work has shown that state-of-the-art models are highly vulnerable to
adversarial perturbations of the input. We propose cowboy, an approach to
detecting and defending against adversarial attacks by using both the
discriminator and generator of a GAN trained on the same dataset. We show that
the discriminator consistently scores the adversarial samples lower than the
real samples across multiple attacks and datasets. We provide empirical
evidence that adversarial samples lie outside of the data manifold learned by
the GAN. Based on this, we propose a cleaning method which uses both the
discriminator and generator of the GAN to project the samples back onto the
data manifold. This cleaning procedure is independent of the classifier and
type of attack and thus can be deployed in existing systems.
",0,0,0,1,0,0
20659,Spectral Graph Convolutions for Population-based Disease Prediction,"  Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.
",1,0,0,1,0,0
20660,An independence system as knot invariant,"  An independence system (with respect to the unknotting number) is defined for
a classical knot diagram. It is proved that the independence system is a knot
invariant for alternating knots. The exchange property for minimal unknotting
sets are also discussed. It is shown that there exists an infinite family of
knot diagrams whose corresponding independence systems are matroids. In
contrast, infinite families of knot diagrams exist whose independence systems
are not matroids.
",0,0,1,0,0,0
20661,A cavity-induced artificial gauge field in a Bose-Hubbard ladder,"  We consider theoretically ultracold interacting bosonic atoms confined to
quasi-one-dimensional ladder structures formed by optical lattices and coupled
to the field of an optical cavity. The atoms can collect a spatial phase
imprint during a cavity-assisted tunneling along a rung via Raman transitions
employing a cavity mode and a transverse running wave pump beam. By adiabatic
elimination of the cavity field we obtain an effective Hamiltonian for the
bosonic atoms, with a self-consistency condition. Using the numerical density
matrix renormalization group method, we obtain a rich steady state diagram of
self-organized steady states. Transitions between superfluid to Mott-insulating
states occur, on top of which we can have Meissner, vortex liquid, and vortex
lattice phases. Also a state that explicitly breaks the symmetry between the
two legs of the ladder, namely the biased-ladder phase is dynamically
stabilized.
",0,1,0,0,0,0
20662,A Review of Laser-Plasma Ion Acceleration,"  An overview of research on laser-plasma based acceleration of ions is given.
The experimental state of the art is summarized and recent progress is
discussed. The basic acceleration processes are briefly reviewed with an
outlook on hybrid mechanisms and novel concepts. Finally, we put focus on the
development of engineered targets for enhanced acceleration and of all-optical
methods for beam post-acceleration and control.
",0,1,0,0,0,0
20663,Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions,"  In this paper, we consider estimators for an additive functional of $\phi$,
which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d.
random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with
alphabet size $k$. We propose a minimax optimal estimator for the estimation
problem of the additive functional. We reveal that the minimax optimal rate is
characterized by the divergence speed of the fourth derivative of $\phi$ if the
divergence speed is high. As a result, we show there is no consistent estimator
if the divergence speed of the fourth derivative of $\phi$ is larger than
$p^{-4}$. Furthermore, if the divergence speed of the fourth derivative of
$\phi$ is $p^{4-\alpha}$ for $\alpha \in (0,1)$, the minimax optimal rate is
obtained within a universal multiplicative constant as $\frac{k^2}{(n\ln
n)^{2\alpha}} + \frac{k^{2-2\alpha}}{n}$.
",1,0,1,1,0,0
20664,Van der Waals Heterostructures Based on Allotropes of Phosphorene and MoSe2,"  The van der Waals heterostructures of allotropes of phosphorene (${\alpha}$-
and $\beta-P$) with MoSe2 (H-, T-, ZT- and SO-MoSe2) are investigated in the
framework of state-of-the-art density functional theory. The semiconducting
heterostructures, $\beta$-P /H-MoSe2 and ${\alpha}$-P / H-MoSe2, forms
anti-type structures with type I and type II band alignments, respectively,
whose bands are tunable with external electric field. ${\alpha}$-P / ZT-MoSe2
and ${\alpha}$-P / SO-MoSe2 form ohmic semiconductor-metal contacts while
Schottky barrier in $\beta$-P / T-MoSe2 can be reduced to zero by external
electric field to form ohmic contact which is useful to realize
high-performance devices. Simulated STM images of given heterostructures reveal
that ${\alpha}$-P can be used as a capping layer to differentiate between
various allotropes of underlying MoSe2. The dielectric response of considered
heterostructures is highly anisotropic in terms of lateral and vertical
polarization. The tunable electronic and dielectric response of van der Waals
phosphorene/MoSe2 heterostructure may find potentials applications in the
fabrication of optoelectronic devices.
",0,1,0,0,0,0
20665,On separated solutions of logistic population equation with harvesting,"  We provide a surprising answer to a question raised in S. Ahmad and A.C.
Lazer [2], and extend the results of that paper.
",0,0,1,0,0,0
20666,Nonreciprocal Electromagnetic Scattering from a Periodically Space-Time Modulated Slab and Application to a Quasisonic Isolator,"  Scattering of obliquely incident electromagnetic waves from periodically
space-time modulated slabs is investigated. It is shown that such structures
operate as nonreciprocal harmonic generators and spatial-frequency filters. For
oblique incidences, low-frequency harmonics are filtered out in the form of
surface waves, while high-frequency harmonics are transmitted as space waves.
In the quasisonic regime, where the velocity of the space-time modulation is
close to the velocity of the electromagnetic waves in the background medium,
the incident wave is strongly coupled to space-time harmonics in the forward
direction, while in the backward direction it exhibits low coupling to other
harmonics. This nonreciprocity is leveraged for the realization of an
electromagnetic isolator in the quasisonic regime and is experimentally
demonstrated at microwave frequencies.
",0,1,0,0,0,0
20667,Story Cloze Ending Selection Baselines and Data Examination,"  This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model the relation between the story and the provided endings. Our
experiments show that a model using representation features based on average
word embedding vectors over the given story words and the candidate ending
sentences words, joint with similarity features between the story and candidate
ending representations performed better than the neural models. Our best model
achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
",1,0,0,0,0,0
20668,Linear Quadratic Optimal Control Problems with Fixed Terminal States and Integral Quadratic Constraints,"  This paper is concerned with a linear quadratic (LQ, for short) optimal
control problem with fixed terminal states and integral quadratic constraints.
A Riccati equation with infinite terminal value is introduced, which is
uniquely solvable and whose solution can be approximated by the solution for a
suitable unconstrained LQ problem with penalized terminal state. Using results
from duality theory, the optimal control is explicitly derived by solving the
Riccati equation together with an optimal parameter selection problem. It turns
out that the optimal control is not only a feedback of the current state, but
also a feedback of the target (terminal state). Some examples are presented to
illustrate the theory developed.
",0,0,1,0,0,0
20669,A new sampling density condition for shift-invariant spaces,"  Let $X=\{x_i:i\in\mathbb{Z}\}$, $\dots<x_{i-1}<x_i<x_{i+1}<\dots$, be a
sampling set which is separated by a constant $\gamma>0$. Under certain
conditions on $\phi$, it is proved that if there exists a positive integer
$\nu$ such that
$$\delta_\nu:=\sup\limits_{i\in\mathbb{Z}}(x_{i+\nu}-x_i)<\dfrac{\nu}{2\pi}\left(\dfrac{c_{k}^2}{M_{2k}}\right)^{\frac{1}{4k}},$$
then every function belonging to a shift-invariant space $V(\phi)$ can be
reconstructed stably from its nonuniform sample values
$\{f^{(j)}(x_i):j=0,1,\dots, k-1, i\in\mathbb{Z}\}$, where $c_k$ is a
Wirtinger-Sobolev constant and $M_{2k}$ is a constant in Bernstein-type
inequality of $V(\phi)$. Further, when $k=1$, the maximum gap $\delta_\nu<\nu$
is sharp for certain shift-invariant spaces.
",0,0,1,0,0,0
20670,Experimental Design via Generalized Mean Objective Cost of Uncertainty,"  The mean objective cost of uncertainty (MOCU) quantifies the performance cost
of using an operator that is optimal across an uncertainty class of systems as
opposed to using an operator that is optimal for a particular system.
MOCU-based experimental design selects an experiment to maximally reduce MOCU,
thereby gaining the greatest reduction of uncertainty impacting the operational
objective. The original formulation applied to finding optimal system
operators, where optimality is with respect to a cost function, such as
mean-square error; and the prior distribution governing the uncertainty class
relates directly to the underlying physical system. Here we provide a
generalized MOCU and the corresponding experimental design. We then demonstrate
how this new formulation includes as special cases MOCU-based experimental
design methods developed for materials science and genomic networks when there
is experimental error. Most importantly, we show that the classical Knowledge
Gradient and Efficient Global Optimization experimental design procedures are
actually implementations of MOCU-based experimental design under their modeling
assumptions.
",0,0,0,1,0,0
20671,Parallelizing Over Artificial Neural Network Training Runs with Multigrid,"  Artificial neural networks are a popular and effective machine learning
technique. Great progress has been made parallelizing the expensive training
phase of an individual network, leading to highly specialized pieces of
hardware, many based on GPU-type architectures, and more concurrent algorithms
such as synthetic gradients. However, the training phase continues to be a
bottleneck, where the training data must be processed serially over thousands
of individual training runs. This work considers a multigrid reduction in time
(MGRIT) algorithm that is able to parallelize over the thousands of training
runs and converge to the exact same solution as traditional training would
provide. MGRIT was originally developed to provide parallelism for time
evolution problems that serially step through a finite number of time-steps.
This work recasts the training of a neural network similarly, treating neural
network training as an evolution equation that evolves the network weights from
one step to the next. Thus, this work concerns distributed computing approaches
for neural networks, but is distinct from other approaches which seek to
parallelize only over individual training runs. The work concludes with
supporting numerical results for two model problems.
",1,0,0,0,0,0
20672,Learning the Structure of Generative Models without Labeled Data,"  Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but selecting a structure automatically without any labeled data is a
distinct challenge. We propose a structure estimation method that maximizes the
$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
analysis shows that the amount of unlabeled data required to identify the true
structure scales sublinearly in the number of possible dependencies for a broad
class of models. Simulations show that our method is 100$\times$ faster than a
maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
We also show that our method provides an average of 1.5 F1 points of
improvement over existing, user-developed information extraction applications
on real-world data such as PubMed journal abstracts.
",1,0,0,1,0,0
20673,Robust Loss Functions under Label Noise for Deep Neural Networks,"  In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and on algorithms for estimating true labels from noisy labels. An
alternate approach would be to look for loss functions that are inherently
noise-tolerant. For binary classification there exist theoretical results on
loss functions that are robust to label noise. In this paper, we provide some
sufficient conditions on a loss function so that risk minimization under that
loss function would be inherently tolerant to label noise for multiclass
classification problems. These results generalize the existing results on
noise-tolerant loss functions for binary classification. We study some of the
widely used loss functions in deep networks and show that the loss function
based on mean absolute value of error is inherently robust to label noise. Thus
standard back propagation is enough to learn the true classifier even under
label noise. Through experiments, we illustrate the robustness of risk
minimization with such loss functions for learning neural networks.
",1,0,0,1,0,0
20674,A quality model for evaluating and choosing a stream processing framework architecture,"  Today, we have to deal with many data (Big data) and we need to make
decisions by choosing an architectural framework to analyze these data coming
from different area. Due to this, it become problematic when we want to process
these data, and even more, when it is continuous data. When you want to process
some data, you have to first receive it, store it, and then query it. This is
what we call Batch Processing. It works well when you process big amount of
data, but it finds its limits when you want to get fast (or real-time)
processing results, such as financial trades, sensors, user session activity,
etc. The solution to this problem is stream processing. Stream processing
approach consists of data arriving record by record and rather than storing it,
the processing should be done directly. Therefore, direct results are needed
with a latency that may vary in real-time.
In this paper, we propose an assessment quality model to evaluate and choose
stream processing frameworks. We describe briefly different architectural
frameworks such as Kafka, Spark Streaming and Flink that address the stream
processing. Using our quality model, we present a decision tree to support
engineers to choose a framework following the quality aspects. Finally, we
evaluate our model doing a case study to Twitter and Netflix streaming.
",1,0,0,0,0,0
20675,On the Hardness of Inventory Management with Censored Demand Data,"  We consider a repeated newsvendor problem where the inventory manager has no
prior information about the demand, and can access only censored/sales data. In
analogy to multi-armed bandit problems, the manager needs to simultaneously
""explore"" and ""exploit"" with her inventory decisions, in order to minimize the
cumulative cost. We make no probabilistic assumptions---importantly,
independence or time stationarity---regarding the mechanism that creates the
demand sequence. Our goal is to shed light on the hardness of the problem, and
to develop policies that perform well with respect to the regret criterion,
that is, the difference between the cumulative cost of a policy and that of the
best fixed action/static inventory decision in hindsight, uniformly over all
feasible demand sequences. We show that a simple randomized policy, termed the
Exponentially Weighted Forecaster, combined with a carefully designed cost
estimator, achieves optimal scaling of the expected regret (up to logarithmic
factors) with respect to all three key primitives: the number of time periods,
the number of inventory decisions available, and the demand support. Through
this result, we derive an important insight: the benefit from ""information
stalking"" as well as the cost of censoring are both negligible in this dynamic
learning problem, at least with respect to the regret criterion. Furthermore,
we modify the proposed policy in order to perform well in terms of the tracking
regret, that is, using as benchmark the best sequence of inventory decisions
that switches a limited number of times. Numerical experiments suggest that the
proposed approach outperforms existing ones (that are tailored to, or
facilitated by, time stationarity) on nonstationary demand models. Finally, we
extend the proposed approach and its analysis to a ""combinatorial"" version of
the repeated newsvendor problem.
",1,0,0,1,0,0
20676,Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study,"  Social networking sites such as Twitter have provided a great opportunity for
organizations such as public libraries to disseminate information for public
relations purposes. However, there is a need to analyze vast amounts of social
media data. This study presents a computational approach to explore the content
of tweets posted by nine public libraries in the northeastern United States of
America. In December 2017, this study extracted more than 19,000 tweets from
the Twitter accounts of seven state libraries and two urban public libraries.
Computational methods were applied to collect the tweets and discover
meaningful themes. This paper shows how the libraries have used Twitter to
represent their services and provides a starting point for different
organizations to evaluate the themes of their public tweets.
",1,0,0,1,0,0
20677,Weakly tripotent rings,"  We study the class of rings $R$ with the property that for $x\in R$ at least
one of the elements $x$ and $1+x$ are tripotent.
",0,0,1,0,0,0
20678,ML for Flood Forecasting at Scale,"  Effective riverine flood forecasting at scale is hindered by a multitude of
factors, most notably the need to rely on human calibration in current
methodology, the limited amount of data for a specific location, and the
computational difficulty of building continent/global level models that are
sufficiently accurate. Machine learning (ML) is primed to be useful in this
scenario: learned models often surpass human experts in complex
high-dimensional scenarios, and the framework of transfer or multitask learning
is an appealing solution for leveraging local signals to achieve improved
global performance. We propose to build on these strengths and develop ML
systems for timely and accurate riverine flood prediction.
",1,0,0,1,0,0
20679,Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis,"  We consider the frequency domain form of proper orthogonal decomposition
(POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is
derived from a space-time POD problem for statistically stationary flows and
leads to modes that each oscillate at a single frequency. This form of POD goes
back to the original work of Lumley (Stochastic tools in turbulence, Academic
Press, 1970), but has been overshadowed by a space-only form of POD since the
1990s. We clarify the relationship between these two forms of POD and show that
SPOD modes represent structures that evolve coherently in space and time while
space-only POD modes in general do not. We also establish a relationship
between SPOD and dynamic mode decomposition (DMD); we show that SPOD modes are
in fact optimally averaged DMD modes obtained from an ensemble DMD problem for
stationary flows. Accordingly, SPOD modes represent structures that are dynamic
in the same sense as DMD modes but also optimally account for the statistical
variability of turbulent flows. Finally, we establish a connection between SPOD
and resolvent analysis. The key observation is that the resolvent-mode
expansion coefficients must be regarded as statistical quantities to ensure
convergent approximations of the flow statistics. When the expansion
coefficients are uncorrelated, we show that SPOD and resolvent modes are
identical. Our theoretical results and the overall utility of SPOD are
demonstrated using two example problems: the complex Ginzburg-Landau equation
and a turbulent jet.
",0,1,0,0,0,0
20680,An optimal transportation approach for assessing almost stochastic order,"  When stochastic dominance $F\leq_{st}G$ does not hold, we can improve
agreement to stochastic order by suitably trimming both distributions. In this
work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic
order of these trimmed versions. Our characterization for that distance
naturally leads to consider a $\mathcal W_2$-based index of disagreement with
stochastic order, $\varepsilon_{\mathcal W_2}(F,G)$. We provide asymptotic
results allowing to test $H_0: \varepsilon_{\mathcal W_2}(F,G)\geq
\varepsilon_0$ vs $H_a: \varepsilon_{\mathcal W_2}(F,G)<\varepsilon_0$, that,
under rejection, would give statistical guarantee of almost stochastic
dominance. We include a simulation study showing a good performance of the
index under the normal model.
",0,0,0,1,0,0
20681,The Effect of Electron Lens as Landau Damping Device on Single Particle Dynamics in HL-LHC,"  An electron lens can serve as an effective mechanism for suppressing coherent
instabilities in high intensity storage rings through nonlinear amplitude
dependent betatron tune shift. However, the addition of a strong localized
nonlinear focusing element to the accelerator lattice may lead to undesired
effects in particle dynamics. We evaluate the effect of a Gaussian electron
lens on single particle motion in HL-LHC using numerical tracking simulations,
and compare the results to the case when an equal tune spread is generated by
conventional octupole magnets.
",0,1,0,0,0,0
20682,Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant,"  We search for sterile neutrinos in the holographic dark energy cosmology by
using the latest observational data. To perform the analysis, we employ the
current cosmological observations, including the cosmic microwave background
temperature power spectrum data from the Planck mission, the baryon acoustic
oscillation measurements, the type Ia supernova data, the redshift space
distortion measurements, the shear data of weak lensing observation, the Planck
lensing measurement, and the latest direct measurement of $H_0$ as well. We
show that, compared to the $\Lambda$CDM cosmology, the holographic dark energy
cosmology with sterile neutrinos can relieve the tension between the Planck
observation and the direct measurement of $H_0$ much better. Once we include
the $H_0$ measurement in the global fit, we find that the hint of the existence
of sterile neutrinos in the holographic dark energy cosmology can be given.
Under the constraint of the all-data combination, we obtain $N_{\rm eff}=
3.76\pm0.26$ and $m_{\nu,\rm sterile}^{\rm eff}< 0.215\,\rm eV$, indicating
that the detection of $\Delta N_{\rm eff}>0$ in the holographic dark energy
cosmology is at the $2.75\sigma$ level and the massless or very light sterile
neutrino is favored by the current observations.
",0,1,0,0,0,0
20683,Simulation of high temperature superconductors and experimental validation,"  In this work, we present a parallel, fully-distributed finite element
numerical framework to simulate the low-frequency electromagnetic response of
superconducting devices, which allows to efficiently exploit HPC platforms. We
select the so-called H-formulation, which uses the magnetic field as a state
variable. Nédélec elements (of arbitrary order) are required for an
accurate approximation of the H-formulation for modelling electromagnetic
fields along interfaces between regions with high contrast medium properties.
An h-adaptive mesh refinement technique customized for Nédélec elements
leads to a structured fine mesh in areas of interest whereas a smart coarsening
is obtained in other regions. The composition of a tailored, robust, parallel
nonlinear solver completes the exposition of the developed tools to tackle the
problem. First, a comparison against experimental data is performed to show the
availability of the finite element approximation to model the physical
phenomena. Then, a selected state-of-the-art 3D benchmark is reproduced,
focusing on the parallel performance of the algorithms.
",1,1,0,0,0,0
20684,Hardware Translation Coherence for Virtualized Systems,"  To improve system performance, modern operating systems (OSes) often
undertake activities that require modification of virtual-to-physical page
translation mappings. For example, the OS may migrate data between physical
frames to defragment memory and enable superpages. The OS may migrate pages of
data between heterogeneous memory devices. We refer to all such activities as
page remappings. Unfortunately, page remappings are expensive. We show that
translation coherence is a major culprit and that systems employing
virtualization are especially badly affected by their overheads. In response,
we propose hardware translation invalidation and coherence or HATRIC, a readily
implementable hardware mechanism to piggyback translation coherence atop
existing cache coherence protocols. We perform detailed studies using KVM-based
virtualization, showing that HATRIC achieves up to 30% performance and 10%
energy benefits, for per-CPU area overheads of 2%. We also quantify HATRIC's
benefits on systems running Xen and find up to 33% performance improvements.
",1,0,0,0,0,0
20685,MotifMark: Finding Regulatory Motifs in DNA Sequences,"  The interaction between proteins and DNA is a key driving force in a
significant number of biological processes such as transcriptional regulation,
repair, recombination, splicing, and DNA modification. The identification of
DNA-binding sites and the specificity of target proteins in binding to these
regions are two important steps in understanding the mechanisms of these
biological activities. A number of high-throughput technologies have recently
emerged that try to quantify the affinity between proteins and DNA motifs.
Despite their success, these technologies have their own limitations and fall
short in precise characterization of motifs, and as a result, require further
downstream analysis to extract useful and interpretable information from a
haystack of noisy and inaccurate data. Here we propose MotifMark, a new
algorithm based on graph theory and machine learning, that can find binding
sites on candidate probes and rank their specificity in regard to the
underlying transcription factor. We developed a pipeline to analyze
experimental data derived from compact universal protein binding microarrays
and benchmarked it against two of the most accurate motif search methods. Our
results indicate that MotifMark can be a viable alternative technique for
prediction of motif from protein binding microarrays and possibly other related
high-throughput techniques.
",1,0,0,0,0,0
20686,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"  This paper presents KeypointNet, an end-to-end geometric reasoning framework
to learn an optimal set of category-specific 3D keypoints, along with their
detectors. Given a single image, KeypointNet extracts 3D keypoints that are
optimized for a downstream task. We demonstrate this framework on 3D pose
estimation by proposing a differentiable objective that seeks the optimal set
of keypoints for recovering the relative pose between two views of an object.
Our model discovers geometrically and semantically consistent keypoints across
viewing angles and instances of an object category. Importantly, we find that
our end-to-end framework using no ground-truth keypoint annotations outperforms
a fully supervised baseline using the same neural network architecture on the
task of pose estimation. The discovered 3D keypoints on the car, chair, and
plane categories of ShapeNet are visualized at this http URL.
",0,0,0,1,0,0
20687,Multi-State Trajectory Approach to Non-Adiabatic Dynamics: General Formalism and the Active State Trajectory Approximation,"  A general theoretical framework is derived for the recently developed
multi-state trajectory (MST) approach from the time dependent Schrödinger
equation, resulting in equations of motion for coupled nuclear-electronic
dynamics equivalent to Hamilton dynamics or Heisenberg equation based on a new
multistate Meyer-Miller (MM) model. The derived MST formalism incorporates both
diabatic and adiabatic representations as limiting cases, and reduces to
Ehrenfest or Born-Oppenheimer dynamics in the mean field or the single state
limits, respectively. By quantizing nuclear dynamics to a particular active
state, the MST algorithm does not suffer from the instability caused by the
negative instant electronic population variables unlike the standard MM
dynamics. Furthermore the multistate representation for electron coupled
nuclear dynamics with each state associated with one individual trajectory
presumably captures single state dynamics better than the mean field
description. The coupled electronic-nuclear coherence is incorporated
consistently in the MST framework with no ad-hoc state switch and the
associated momentum adjustment or parameters for artificial decoherence, unlike
the original or modified surface hopping treatments. The implementation of the
MST approach to benchmark problems shows reasonably good agreement with exact
quantum calculations, and the results in both representations are similar in
accuracy. The active state trajectory (AST) approximation of the MST approach
provides a consistent interpretation to trajectory surface hopping, which
predicts the transition probabilities reasonably well for multiple nonadiabatic
transitions and conical intersection problems.
",0,1,0,0,0,0
20688,Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems,"  This paper presents a systematic approach for computing local solutions to
motion planning problems in non-convex environments using numerical optimal
control techniques. It extends the range of use of state-of-the-art numerical
optimal control tools to problem classes where these tools have previously not
been applicable. Today these problems are typically solved using motion
planners based on randomized or graph search. The general principle is to
define a homotopy that perturbs, or preferably relaxes, the original problem to
an easily solved problem. By combining a Sequential Quadratic Programming (SQP)
method with a homotopy approach that gradually transforms the problem from a
relaxed one to the original one, practically relevant locally optimal solutions
to the motion planning problem can be computed. The approach is demonstrated in
motion planning problems in challenging 2D and 3D environments, where the
presented method significantly outperforms a state-of-the-art open-source
optimizing sampled-based planner commonly used as benchmark.
",0,0,1,0,0,0
20689,High quality atomically thin PtSe2 films grown by molecular beam epitaxy,"  Atomically thin PtSe2 films have attracted extensive research interests for
potential applications in high-speed electronics, spintronics and
photodetectors. Obtaining high quality, single crystalline thin films with
large size is critical. Here we report the first successful layer-by-layer
growth of high quality PtSe2 films by molecular beam epitaxy. Atomically thin
films from 1 ML to 22 ML have been grown and characterized by low-energy
electron diffraction, Raman spectroscopy and X-ray photoemission spectroscopy.
Moreover, a systematic thickness dependent study of the electronic structure is
revealed by angle-resolved photoemission spectroscopy (ARPES), and helical spin
texture is revealed by spin-ARPES. Our work provides new opportunities for
growing large size single crystalline films for investigating the physical
properties and potential applications of PtSe2.
",0,1,0,0,0,0
20690,NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images,"  We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
reward function is designed keeping in mind the cost and weight constraints for
micro drone with minimum number of sensing modalities. Collection of small
number of expert data and knowledge based data aggregation is integrated into
the RL process to aid convergence. Experimentation is carried out on a Parrot
AR drone in different indoor arenas and the results are compared with other
baseline technologies. We demonstrate how the drone successfully avoids
obstacles and navigates across different arenas.
",1,0,0,1,0,0
20691,"Fast, Accurate and Fully Parallelizable Digital Image Correlation","  Digital image correlation (DIC) is a widely used optical metrology for
surface deformation measurements. DIC relies on nonlinear optimization method.
Thus an initial guess is quite important due to its influence on the converge
characteristics of the algorithm. In order to obtain a reliable, accurate
initial guess, a reliability-guided digital image correlation (RG-DIC) method,
which is able to intelligently obtain a reliable initial guess without using
time-consuming integer-pixel registration, was proposed. However, the RG-DIC
and its improved methods are path-dependent and cannot be fully parallelized.
Besides, it is highly possible that RG-DIC fails in the full-field analysis of
deformation without manual intervention if the deformation fields contain large
areas of discontinuous deformation. Feature-based initial guess is highly
robust while it is relatively time-consuming. Recently, path-independent
algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm,
was proposed to estimate the initial guess. Complete parallelizability is the
major advantage of the FFT-CC algorithm, while it is sensitive to small
deformation. Wu et al proposed an efficient integer-pixel search scheme, but
the parameters of this algorithm are set by the users empirically. In this
technical note, a fully parallelizable DIC method is proposed. Different from
RG-DIC method, the proposed method divides DIC algorithm into two parts:
full-field initial guess estimation and sub-pixel registration. The proposed
method has the following benefits: 1) providing a pre-knowledge of deformation
fields; 2) saving computational time; 3) reducing error propagation; 4)
integratability with well-established DIC algorithms; 5) fully
parallelizability.
",0,1,0,0,0,0
20692,Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations,"  The classical idea of evolutionarily stable strategy (ESS) modeling animal
behavior does not involve any spatial dependence. We considered a spatial
Hawk-Dove game played by animals in a patchy environment with wrap around
boundaries. We posit that each site contains the same number of individuals. An
evolution equation for analyzing the stability of the ESS is found as the mean
dynamics of the classical frequency dependent Moran process coupled via
migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
stability analysis of the model is performed and conditions to observe spatial
patterns are investigated. For the nearest neighbor interactions (including von
Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
destabilize the ESS of the game and observe pattern formation when the
dispersal rate is small enough. We numerically investigate the spatial patterns
arising from the replicator equations coupled via nearest neighbor payoff
calculation and dispersal.
",0,0,0,0,1,0
20693,Analysis of the measurements of anisotropic a.c. vortex resistivity in tilted magnetic fields,"  Measurements of the high-frequency complex resistivity in superconductors are
a tool often used to obtain the vortex parameters, such as the vortex
viscosity, the pinning constant and the depinning frequency. In anisotropic
superconductors, the extraction of these quantities from the measurements faces
new difficulties due to the tensor nature of the electromagnetic problem. The
problem is specifically intricate when the magnetic field is tilted with
respect to the crystallographic axes. Partial solutions exist in the
free-flux-flow (no pinning) and Campbell (pinning dominated) regimes. In this
paper we develop a full tensor model for the vortex motion complex resistivity,
including flux-flow, pinning, and creep. We give explicit expressions for the
tensors involved. We obtain that, despite the complexity of the physics, some
parameters remain scalar in nature. We show that under specific circumstances
the directly measured quantities do not reflect the true vortex parameters, and
we give procedures to derive the true vortex parameters from measurements taken
with arbitrary field orientations. Finally, we discuss the applicability of the
angular scaling properties to the measured and transformed vortex parameters
and we exploit these properties as a tool to unveil the existence of
directional pinning.
",0,1,0,0,0,0
20694,Deep Convolutional Neural Network to Detect J-UNIWARD,"  This paper presents an empirical study on applying convolutional neural
networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
steganographic method. Experiments guiding the architectural design of the CNNs
have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
size 512x512. Results have verified that both the pooling method and the depth
of the CNNs are critical for performance. Results have also proved that a
20-layer CNN, in general, outperforms the most sophisticated feature-based
methods, but its advantage gradually diminishes on hard-to-detect cases. To
show that the performance generalizes to large-scale databases and to different
cover sizes, one experiment has been conducted on the CLS-LOC dataset of
ImageNet containing more than one million covers cropped to unified size of
256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
proposed for large-scale JPEG steganalysis by 35%. Source code is available via
GitHub: this https URL
",1,0,0,0,0,0
20695,Observing Power-Law Dynamics of Position-Velocity Correlation in Anomalous Diffusion,"  In this letter we present a measurement of the phase-space density
distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion.
The PSDD is imaged using a direct tomographic method based on Raman velocity
selection. It reveals that the position-velocity correlation function
$C_{xv}(t)$ builds up on a timescale related to the initial conditions of the
ensemble and then decays asymptotically as a power-law. We show that the decay
follows a simple scaling theory involving the power-law asymptotic dynamics of
position and velocity. The generality of this scaling theory is confirmed using
Monte-Carlo simulations of two distinct models of anomalous diffusion.
",0,1,0,0,0,0
20696,"Modular curves, invariant theory and $E_8$","  The $E_8$ root lattice can be constructed from the modular curve $X(13)$ by
the invariant theory for the simple group $\text{PSL}(2, 13)$. This gives a
different construction of the $E_8$ root lattice. It also gives an explicit
construction of the modular curve $X(13)$.
",0,0,1,0,0,0
20697,Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs,"  We present convergence rate analysis for the approximate stochastic gradient
method, where individual gradient updates are corrupted by computation errors.
We develop stochastic quadratic constraints to formulate a small linear matrix
inequality (LMI) whose feasible set characterizes convergence properties of the
approximate stochastic gradient. Based on this LMI condition, we develop a
sequential minimization approach to analyze the intricate trade-offs that
couple stepsize selection, convergence rate, optimization accuracy, and
robustness to gradient inaccuracy. We also analytically solve this LMI
condition and obtain theoretical formulas that quantify the convergence
properties of the approximate stochastic gradient under various assumptions on
the loss functions.
",0,0,0,1,0,0
20698,Invariant holomorphic discs in some non-convex domains,"  We give a description of complex geodesics and we study the structure of
stationary discs in some non-convex domains for which complex geodesics are not
unique.
",0,0,1,0,0,0
20699,MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments,"  Recent advances in bioinformatics have made high-throughput microbiome data
widely available, and new statistical tools are required to maximize the
information gained from these data. For example, analysis of high-dimensional
microbiome data from designed experiments remains an open area in microbiome
research. Contemporary analyses work on metrics that summarize collective
properties of the microbiome, but such reductions preclude inference on the
fine-scale effects of environmental stimuli on individual microbial taxa. Other
approaches model the proportions or counts of individual taxa as response
variables in mixed models, but these methods fail to account for complex
correlation patterns among microbial communities. In this paper, we propose a
novel Bayesian mixed-effects model that exploits cross-taxa correlations within
the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers
global tests for treatment effects, local tests and estimation of treatment
effects on individual taxa, quantification of the relative contribution from
heterogeneous sources to microbiome variability, and identification of latent
ecological subcommunities in the microbiome. MIMIX is tailored to large
microbiome experiments using a combination of Bayesian factor analysis to
efficiently represent dependence between taxa and Bayesian variable selection
methods to achieve sparsity. We demonstrate the model using a simulation
experiment and on a 2x2 factorial experiment of the effects of nutrient
supplement and herbivore exclusion on the foliar fungal microbiome of
$\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global
Nutrient Network research initiative.
",0,0,0,1,0,0
20700,SpatEntropy: Spatial Entropy Measures in R,"  This article illustrates how to measure the heterogeneity of spatial data
presenting a finite number of categories via computation of spatial entropy.
The R package SpatEntropy contains functions for the computation of entropy and
spatial entropy measures. The extension to spatial entropy measures is a unique
feature of SpatEntropy. In addition to the traditional version of Shannon's
entropy, the package includes Batty's spatial entropy, O'Neill's entropy, Li
and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's
entropy, Parresol and Edwards' entropy and Altieri's entropy. The package is
able to work with both areal and point data. This paper is a general
description of SpatEntropy, as well as its necessary theoretical background,
and an introduction for new users.
",0,0,0,1,0,0
20701,Evidence for universality in the initial planetesimal mass function,"  Planetesimals may form from the gravitational collapse of dense particle
clumps initiated by the streaming instability. We use simulations of
aerodynamically coupled gas-particle mixtures to investigate whether the
properties of planetesimals formed in this way depend upon the sizes of the
particles that participate in the instability. Based on three high resolution
simulations that span a range of dimensionless stopping time $6 \times 10^{-3}
\leq \tau \leq 2$ no statistically significant differences in the initial
planetesimal mass function are found. The mass functions are fit by a
power-law, ${\rm d}N / {\rm d}M_p \propto M_p^{-p}$, with $p=1.5-1.7$ and
errors of $\Delta p \approx 0.1$. Comparing the particle density fields prior
to collapse, we find that the high wavenumber power spectra are similarly
indistinguishable, though the large-scale geometry of structures induced via
the streaming instability is significantly different between all three cases.
We interpret the results as evidence for a near-universal slope to the mass
function, arising from the small-scale structure of streaming-induced
turbulence.
",0,1,0,0,0,0
20702,On covering systems of integers,"  A covering system of the integers is a finite collection of modular residue
classes $\{a_m \bmod{m}\}_{m \in S}$ whose union is all integers. Given a
finite set $S$ of moduli, it is often difficult to tell whether there is a
choice of residues modulo elements of $S$ covering the integers. Hough has
shown that if the smallest modulus in $S$ is at least $10^{16}$, then there is
none. However, the question of whether there is a covering of the integers with
all odd moduli remains open. We consider multiplicative restrictions on the set
of moduli to generalize Hough's negative solution to the minimum modulus
problem. In particular, we find that every covering system of the integers has
a modulus divisible by a prime number less than or equal to $19$. Hough and
Nielsen have shown that every covering system has a modulus divisible by either
$2$ or $3$.
",0,0,1,0,0,0
20703,Asymptotically safe cosmology - a status report,"  Asymptotic Safety, based on a non-Gaussian fixed point of the gravitational
renormalization group flow, provides an elegant mechanism for completing the
gravitational force at sub-Planckian scales. At high energies the fixed point
controls the scaling of couplings such that unphysical divergences are absent
while the emergence of classical low-energy physics is linked to a crossover
between two renormalization group fixed points. These features make Asymptotic
Safety an attractive framework for cosmological model building. The resulting
scenarios may naturally give rise to a quantum gravity driven inflationary
phase in the very early universe and an almost scale-free fluctuation spectrum.
Moreover, effective descriptions arising from an renormalization group
improvement permit a direct comparison to cosmological observations as, e.g.
Planck data.
",0,1,0,0,0,0
20704,Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction,"  Future predictions on sequence data (e.g., videos or audios) require the
algorithms to capture non-Markovian and compositional properties of high-level
semantics. Context-free grammars are natural choices to capture such
properties, but traditional grammar parsers (e.g., Earley parser) only take
symbolic sentences as inputs. In this paper, we generalize the Earley parser to
parse sequence data which is neither segmented nor labeled. This generalized
Earley parser integrates a grammar parser with a classifier to find the optimal
segmentation and labels, and makes top-down future predictions. Experiments
show that our method significantly outperforms other approaches for future
human activity prediction.
",0,0,0,1,0,0
20705,COCrIP: Compliant OmniCrawler In-pipeline Robot,"  This paper presents a modular in-pipeline climbing robot with a novel
compliant foldable OmniCrawler mechanism. The circular cross-section of the
OmniCrawler module enables a holonomic motion to facilitate the alignment of
the robot in the direction of bends. Additionally, the crawler mechanism
provides a fair amount of traction, even on slippery surfaces. These advantages
of crawler modules have been further supplemented by incorporating active
compliance in the module itself which helps to negotiate sharp bends in small
diameter pipes. The robot has a series of 3 such compliant foldable modules
interconnected by the links via passive joints. For the desirable pipe diameter
and curvature of the bends, the spring stiffness value for each passive joint
is determined by formulating a constrained optimization problem using the
quasi-static model of the robot. Moreover, a minimum friction coefficient value
between the module-pipe surface which can be vertically climbed by the robot
without slipping is estimated. The numerical simulation results have further
been validated by experiments on real robot prototype.
",1,0,0,0,0,0
20706,An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics,"  This paper presents a learning-based approach for impromptu trajectory
tracking for non-minimum phase systems, i.e., systems with unstable inverse
dynamics. Inversion-based feedforward approaches are commonly used for
improving tracking performance; however, these approaches are not directly
applicable to non-minimum phase systems due to their inherent instability. In
order to resolve the instability issue, existing methods have assumed that the
system model is known and used pre-actuation or inverse approximation
techniques. In this work, we propose an approach for learning a stable,
approximate inverse of a non-minimum phase baseline system directly from its
input-output data. Through theoretical discussions, simulations, and
experiments on two different platforms, we show the stability of our proposed
approach and its effectiveness for high-accuracy, impromptu tracking. Our
approach also shows that including more information in the training, as is
commonly assumed to be useful, does not lead to better performance but may
trigger instability and impact the effectiveness of the overall approach.
",1,0,0,0,0,0
20707,Decay Estimates for 1-D Parabolic PDEs with Boundary Disturbances,"  In this work decay estimates are derived for the solutions of 1-D linear
parabolic PDEs with disturbances at both boundaries and distributed
disturbances. The decay estimates are given in the L2 and H1 norms of the
solution and discontinuous disturbances are allowed. Although an eigenfunction
expansion for the solution is exploited for the proof of the decay estimates,
the estimates do not require knowledge of the eigenvalues and the
eigenfunctions of the corresponding Sturm-Liouville operator. Examples show
that the obtained results can be applied for the stability analysis of
parabolic PDEs with nonlocal terms.
",1,0,1,0,0,0
20708,GEANT4 Simulation of Nuclear Interaction Induced Soft Errors in Digital Nanoscale Electronics: Interrelation Between Proton and Heavy Ion Impacts,"  A simple and self-consistent approach has been proposed for simulation of the
proton-induced soft error rate based on the heavy ion induced single event
upset cross-section data and vice versa. The approach relies on the GEANT4
assisted Monte Carlo simulation of the secondary particle LET spectra produced
by nuclear interactions. The method has been validated with the relevant
in-flight soft error rate data for space protons and heavy ions. An approximate
analytical relation is proposed and validated for a fast recalculation between
the two types of experimental data.
",0,1,0,0,0,0
20709,Analysis and Measurement of the Transfer Matrix of a 9-cell 1.3-GHz Superconducting Cavity,"  Superconducting linacs are capable of producing intense, stable, high-quality
electron beams that have found widespread applications in science and industry.
The 9-cell 1.3-GHz superconducting standing-wave accelerating RF cavity
originally developed for $e^+/e^-$ linear-collider applications [B. Aunes, {\em
et al.} Phys. Rev. ST Accel. Beams {\bf 3}, 092001 (2000)] has been broadly
employed in various superconducting-linac designs. In this paper we discuss the
transfer matrix of such a cavity and present its measurement performed at the
Fermilab Accelerator Science and Technology (FAST) facility. The experimental
results are found to be in agreement with analytical calculations and numerical
simulations.
",0,1,0,0,0,0
20710,Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores,"  This paper introduces the concept of size-aware sharding to improve tail
latencies for in-memory key-value stores, and describes its implementation in
the Minos key-value store. Tail latencies are crucial in distributed
applications with high fan-out ratios, because overall response time is
determined by the slowest response. Size-aware sharding distributes requests
for keys to cores according to the size of the item associated with the key. In
particular, requests for small and large items are sent to disjoint subsets of
cores. Size-aware sharding improves tail latencies by avoiding head-of-line
blocking, in which a request for a small item gets queued behind a request for
a large item. Alternative size-unaware approaches to sharding, such as
keyhash-based sharding, request dispatching and stealing do not avoid
head-of-line blocking, and therefore exhibit worse tail latencies. The
challenge in implementing size-aware sharding is to maintain high throughput by
avoiding the cost of software dispatching and by achieving load balancing
between different cores. Minos uses hardware dispatch for all requests for
small items, which form the very large majority of all requests. It achieves
load balancing by adapting the number of cores handling requests for small and
large items to their relative presence in the workload. We compare Minos to
three state-of-the-art designs of in-memory KV stores. Compared to its closest
competitor, Minos achieves a 99th percentile latency that is up to two orders
of magnitude lower. Put differently, for a given value for the 99th percentile
latency equal to 10 times the mean service time, Minos achieves a throughput
that is up to 7.4 times higher.
",1,0,0,0,0,0
20711,Abell 2744 may be a supercluster aligned along the sightline,"  To explain the unusual richness and compactness of the Abell 2744, we propose
a hypothesis that it may be a rich supercluster aligned along the sightline,
and present a supporting evidence obtained numerically from the MultiDark
Planck 2 simulations with a linear box size of $1\,h^{-1}$Gpc. Applying the
friends-of-friends (FoF) algorithm with a linkage length of $0.33$ to a sample
of the cluster-size halos from the simulations, we identify the superclusters
and investigate how many superclusters have filamentary branches that would
appear to be similar to the Abell 2744 if the filamentary axis is aligned with
the sightline. Generating randomly a unit vector as a sightline at the position
of the core member of each supercluster and projecting the positions of the
members onto the plane perpendicular to the direction of the sightline, we
measure two dimensional distances ($R_{2d}$) of the member halos from the core
for each supercluster. Defining a Abell 2744-like spuercluster as the one
having a filamentary branch composed of eight or more members with $R_{2d}\le
1\,$Mpc and masses comparable to those of the observed Abell 2744
substructures, we find one Abell 2744-like supercluster at $z=0.3$ and two at
$z=0$. Repeating the same analysis but with the data from the Big MultiDark
Planck simulations performed on a larger box of linear size of
$2.5\,h^{-1}$Mpc, we find that the number of the Abell 2744-like superclusters
at $z=0$ increases up to eighteen, among which three are found more massive
than $5\times 10^{15}\,M_{\odot}$.
",0,1,0,0,0,0
20712,Classification of Pressure Gradient of Human Common Carotid Artery and Ascending Aorta on the Basis of Age and Gender,"  The current work is done to see which artery has more chance of having
cardiovascular diseases by measuring value of pressure gradient in the common
carotid artery (CCA) and ascending aorta according to age and gender. Pressure
gradient is determined in the CCA and ascending aorta of presumed healthy
volunteers, having age between 10 and 60 years. A real 2D model of both aorta
and common carotid artery is constructed for different age groups using
computational fluid dynamics (CFD). Pressure gradient of both the arteries are
calculated and compared for different age groups and gender. It is found that
with increase in diameter of common carotid artery and ascending aorta with
advancing age pressure gradient decreases. The value of pressure gradient of
aorta is found less than common carotid artery in both cases of age and gender.
",0,1,0,0,0,0
20713,Second-order and local characteristics of network intensity functions,"  The last decade has witnessed an increase of interest in the spatial analysis
of structured point patterns over networks whose analysis is challenging
because of geometrical complexities and unique methodological problems. In this
context, it is essential to incorporate the network specificity into the
analysis as the locations of events are restricted to areas covered by line
segments. Relying on concepts originating from graph theory, we extend the
notions of first-order network intensity functions to second-order and local
network intensity functions. We consider two types of local indicators of
network association functions which can be understood as adaptations of the
primary ideas of local analysis on the plane. We develop the node-wise and
cross-hierarchical type of local functions. A real dataset on urban
disturbances is also presented.
",0,0,0,1,0,0
20714,Modeling of hysteresis loop and its applications in ferroelectric materials,"  In order to understand the physical hysteresis loops clearly, we constructed
a novel model, which is combined with the electric field, the temperature, and
the stress as one synthetically parameter. This model revealed the shape of
hysteresis loop was determined by few variables in ferroelectric materials: the
saturation of polarization, the coercive field, the electric susceptibility and
the equivalent field. Comparison with experimental results revealed the model
can retrace polarization versus electric field and temperature. As a
applications of this model, the calculate formula of energy storage efficiency,
the electrocaloric effect, and the P(E,T) function have also been included in
this article.
",0,1,0,0,0,0
20715,Online Factorization and Partition of Complex Networks From Random Walks,"  Finding the reduced-dimensional structure is critical to understanding
complex networks. Existing approaches such as spectral clustering are
applicable only when the full network is explicitly observed. In this paper, we
focus on the online factorization and partition of implicit large-scale
networks based on observations from an associated random walk. We formulate
this into a nonconvex stochastic factorization problem and propose an efficient
and scalable stochastic generalized Hebbian algorithm. The algorithm is able to
process dependent state-transition data dynamically generated by the underlying
network and learn a low-dimensional representation for each vertex. By applying
a diffusion approximation analysis, we show that the continuous-time limiting
process of the stochastic algorithm converges globally to the ""principal
components"" of the Markov chain and achieves a nearly optimal sample
complexity. Once given the learned low-dimensional representations, we further
apply clustering techniques to recover the network partition. We show that when
the associated Markov process is lumpable, one can recover the partition
exactly with high probability. We apply the proposed approach to model the
traffic flow of Manhattan as city-wide random walks. By using our algorithm to
analyze the taxi trip data, we discover a latent partition of the Manhattan
city that closely matches the traffic dynamics.
",1,0,1,1,0,0
20716,Powerful genome-wide design and robust statistical inference in two-sample summary-data Mendelian randomization,"  Two-sample summary-data Mendelian randomization (MR) has become a popular
research design to estimate the causal effect of risk exposures. With the
sample size of GWAS continuing to increase, it is now possible to utilize
genetic instruments that are only weakly associated with the exposure. To
maximize the statistical power of MR, we propose a genome-wide design where
more than a thousand genetic instruments are used. For the statistical
analysis, we use an empirical partially Bayes approach where instruments are
weighted according to their strength, thus weak instruments bring less
variation to the estimator. The estimator is highly efficient with many weak
genetic instruments and is robust to balanced and/or sparse pleiotropy. We
apply our method to estimate the causal effect of body mass index (BMI) and
major blood lipids on cardiovascular disease outcomes and obtain substantially
shorter confidence intervals. Some new and statistically significant findings
are: the estimated causal odds ratio of BMI on ischemic stroke is 1.19 (95% CI:
1.07--1.32, p-value < 0.001); the estimated causal odds ratio of high-density
lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95%
CI 0.73--0.84, p-value < 0.001). However, the estimated effect of HDL-C becomes
substantially smaller and statistically non-significant when we only use the
strong instruments. By employing a genome-wide design and robust statistical
methods, the statistical power of MR studies can be greatly improved. Our
empirical results suggest that, even though the relationship between HDL-C and
CAD appears to be highly heterogeneous, it may be too soon to completely
dismiss the HDL hypothesis.
",0,0,0,1,0,0
20717,A Benchmark on Reliability of Complex Discrete Systems: Emergency Power Supply of a Nuclear Power Plant,"  This paper contains two parts: the description of a real electrical system,
with many redundancies, reconfigurations and repairs, then the description of a
reliability model of this system, based on the BDMP (Boolean logic Driven
Markov Processes) formalism and partial results of a reliability and
availability calculation made from this model.
",1,0,0,0,0,0
20718,Riemannian Gaussian distributions on the space of positive-definite quaternion matrices,"  Recently, Riemannian Gaussian distributions were defined on spaces of
positive-definite real and complex matrices. The present paper extends this
definition to the space of positive-definite quaternion matrices. In order to
do so, it develops the Riemannian geometry of the space of positive-definite
quaternion matrices, which is shown to be a Riemannian symmetric space of
non-positive curvature. The paper gives original formulae for the Riemannian
metric of this space, its geodesics, and distance function. Then, it develops
the theory of Riemannian Gaussian distributions, including the exact expression
of their probability density, their sampling algorithm and statistical
inference.
",0,0,1,1,0,0
20719,Analisis of the power flow in Low Voltage DC grids,"  Power flow in a low voltage direct current grid (LVDC) is a non-linear
problem just as its counterpart ac. This paper demonstrates that, unlike in ac
grids, convergence and uniqueness of the solution can be guaranteed in this
type of grids. The result is not a linearization nor an approximation, but an
analysis of the set of non-linear algebraic equations, which is valid for any
LVDC grid regardless its size, topology or load condition. Computer simulation
corroborate the theoretical analysis.
",0,0,1,0,0,0
20720,Multi-scale bilinear restriction estimates for general phases,"  We prove (adjoint) bilinear restriction estimates for general phases at
different scales in the full non-endpoint mixed norm range, and give bounds
with a sharp and explicit dependence on the phases. These estimates have
applications to high-low frequency interactions for solutions to partial
differential equations, as well as to the linear restriction problem for
surfaces with degenerate curvature. As a consequence, we obtain new bilinear
restriction estimates for elliptic phases and wave/Klein-Gordon interactions in
the full bilinear range, and give a refined Strichartz inequality for the
Klein-Gordon equation. In addition, we extend these bilinear estimates to hold
in adapted function spaces by using a transference type principle which holds
for vector valued waves.
",0,0,1,0,0,0
20721,Program algebra for Turing-machine programs,"  This note presents an algebraic theory of instruction sequences with
instructions for Turing tapes as basic instructions, the behaviours produced by
the instruction sequences concerned under execution, and the interaction
between such behaviours and the Turing tapes provided by an execution
environment. This theory provides a setting for investigating issues relating
to computability and computational complexity that is more general than the
closely related Turing-machine models of computation. The theory is essentially
an instantiation of a parameterized algebraic theory which is the basis of a
line of research in which issues relating to a wide variety of subjects from
computer science have been rigorously investigated thinking in terms of
instruction sequences.
",1,0,0,0,0,0
20722,"CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions","  How can we find patterns and anomalies in a tensor, or multi-dimensional
array, in an efficient and directly interpretable way? How can we do this in an
online environment, where a new tensor arrives each time step? Finding patterns
and anomalies in a tensor is a crucial problem with many applications,
including building safety monitoring, patient health monitoring, cyber
security, terrorist detection, and fake user detection in social networks.
Standard PARAFAC and Tucker decomposition results are not directly
interpretable. Although a few sampling-based methods have previously been
proposed towards better interpretability, they need to be made faster, more
memory efficient, and more accurate.
In this paper, we propose CTD, a fast, accurate, and directly interpretable
tensor decomposition method based on sampling. CTD-S, the static version of
CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than
that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7
~ 12x more memory-efficient than the state-of-the-art method by removing
redundancy. CTD-D, the dynamic version of CTD, is the first interpretable
dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x
faster than already fast CTD-S by exploiting factors at previous time step and
by reordering operations. With CTD, we demonstrate how the results can be
effectively interpreted in the online distributed denial of service (DDoS)
attack detection.
",1,0,0,1,0,0
20723,Memory Augmented Control Networks,"  Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments.
",1,0,0,0,0,0
20724,Community Recovery in a Preferential Attachment Graph,"  A message passing algorithm is derived for recovering communities within a
graph generated by a variation of the Barabási-Albert preferential
attachment model. The estimator is assumed to know the arrival times, or order
of attachment, of the vertices. The derivation of the algorithm is based on
belief propagation under an independence assumption. Two precursors to the
message passing algorithm are analyzed: the first is a degree thresholding (DT)
algorithm and the second is an algorithm based on the arrival times of the
children (C) of a given vertex, where the children of a given vertex are the
vertices that attached to it. Comparison of the performance of the algorithms
shows it is beneficial to know the arrival times, not just the number, of the
children. The probability of correct classification of a vertex is
asymptotically determined by the fraction of vertices arriving before it. Two
extensions of Algorithm C are given: the first is based on joint likelihood of
the children of a fixed set of vertices; it can sometimes be used to seed the
message passing algorithm. The second is the message passing algorithm.
Simulation results are given.
",1,0,0,1,0,0
20725,The descriptive look at the size of subsets of groups,"  We explore the Borel complexity of some basic families of subsets of a
countable group (large, small, thin, sparse and other) defined by the size of
their elements. Applying the obtained results to the Stone-Čech
compactification $\beta G$ of $G$, we prove, in particular, that the closure of
the minimal ideal of $\beta G$ is of type $F_{\sigma\delta}$.
",0,0,1,0,0,0
20726,The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks,"  Capable of significantly reducing cell size and enhancing spatial reuse,
network densification is shown to be one of the most dominant approaches to
expand network capacity. Due to the scarcity of available spectrum resources,
nevertheless, the over-deployment of network infrastructures, e.g., cellular
base stations (BSs), would strengthen the inter-cell interference as well, thus
in turn deteriorating the system performance. On this account, we investigate
the performance of downlink cellular networks in terms of user coverage
probability (CP) and network spatial throughput (ST), aiming to shed light on
the limitation of network densification. Notably, it is shown that both CP and
ST would be degraded and even diminish to be zero when BS density is
sufficiently large, provided that practical antenna height difference (AHD)
between BSs and users is involved to characterize pathloss. Moreover, the
results also reveal that the increase of network ST is at the expense of the
degradation of CP. Therefore, to balance the tradeoff between user and network
performance, we further study the critical density, under which ST could be
maximized under the CP constraint. Through a special case study, it follows
that the critical density is inversely proportional to the square of AHD. The
results in this work could provide helpful guideline towards the application of
network densification in the next-generation wireless networks.
",1,0,0,0,0,0
20727,Spin-wave propagation in cubic anisotropic materials,"  The information carrier of modern technologies is the electron charge whose
transport inevitably generates Joule heating. Spin-waves, the collective
precessional motion of electron spins, do not involve moving charges and thus
avoid Joule heating. In this respect, magnonic devices in which the information
is carried by spin-waves attract interest for low-power computing. However
implementation of magnonic devices for practical use suffers from low spin-wave
signal and on/off ratio. Here we demonstrate that cubic anisotropic materials
can enhance spin-wave signals by improving spin-wave amplitude as well as group
velocity and attenuation length. Furthermore, cubic anisotropic material shows
an enhanced on/off ratio through a laterally localized edge mode, which closely
mimics the gate-controlled conducting channel in traditional field-effect
transistors. These attractive features of cubic anisotropic materials will
invigorate magnonics research towards wave-based functional devices.
",0,1,0,0,0,0
20728,Relaxing Exclusive Control in Boolean Games,"  In the typical framework for boolean games (BG) each player can change the
truth value of some propositional atoms, while attempting to make her goal
true. In standard BG goals are propositional formulas, whereas in iterated BG
goals are formulas of Linear Temporal Logic. Both notions of BG are
characterised by the fact that agents have exclusive control over their set of
atoms, meaning that no two agents can control the same atom. In the present
contribution we drop the exclusivity assumption and explore structures where an
atom can be controlled by multiple agents. We introduce Concurrent Game
Structures with Shared Propositional Control (CGS-SPC) and show that they ac-
count for several classes of repeated games, including iterated boolean games,
influence games, and aggregation games. Our main result shows that, as far as
verification is concerned, CGS-SPC can be reduced to concurrent game structures
with exclusive control. This result provides a polynomial reduction for the
model checking problem of specifications in Alternating-time Temporal Logic on
CGS-SPC.
",1,0,0,0,0,0
20729,Representing de Rham cohomology classes on an open Riemann surface by holomorphic forms,"  Let $X$ be a connected open Riemann surface. Let $Y$ be an Oka domain in the
smooth locus of an analytic subvariety of $\mathbb C^n$, $n\geq 1$, such that
the convex hull of $Y$ is all of $\mathbb C^n$. Let $\mathscr O_*(X, Y)$ be the
space of nondegenerate holomorphic maps $X\to Y$. Take a holomorphic $1$-form
$\theta$ on $X$, not identically zero, and let $\pi:\mathscr O_*(X,Y) \to
H^1(X,\mathbb C^n)$ send a map $g$ to the cohomology class of $g\theta$. Our
main theorem states that $\pi$ is a Serre fibration. This result subsumes the
1971 theorem of Kusunoki and Sainouchi that both the periods and the divisor of
a holomorphic form on $X$ can be prescribed arbitrarily. It also subsumes two
parametric h-principles in minimal surface theory proved by Forstneric and
Larusson in 2016.
",0,0,1,0,0,0
20730,Unidirectional control of optically induced spin waves,"  Unidirectional control of optically induced spin waves in a rare-earth iron
garnet crystal is demonstrated. We observed the interference of two spin-wave
packets with different initial phases generated by circularly polarized light
pulses. This interference results in unidirectional propagation if the
spin-wave sources are spaced apart at 1/4 of the wavelength of the spin waves
and the initial phase difference is set to pi/2. The propagating direction of
the spin wave is switched by the polarization helicity of the light pulses.
Moreover, in a numerical simulation, applying more than two spin-wave sources
with a suitable polarization and spot shape, arbitrary manipulation of the spin
wave by the phased array method was replicated.
",0,1,0,0,0,0
20731,Value Prediction Network,"  This paper proposes a novel deep reinforcement learning (RL) architecture,
called Value Prediction Network (VPN), which integrates model-free and
model-based RL methods into a single neural network. In contrast to typical
model-based RL methods, VPN learns a dynamics model whose abstract states are
trained to make option-conditional predictions of future values (discounted sum
of rewards) rather than of future observations. Our experimental results show
that VPN has several advantages over both model-free and model-based baselines
in a stochastic environment where careful planning is required but building an
accurate observation-prediction model is difficult. Furthermore, VPN
outperforms Deep Q-Network (DQN) on several Atari games even with
short-lookahead planning, demonstrating its potential as a new way of learning
a good state representation.
",1,0,0,0,0,0
20732,Scalable Generalized Linear Bandits: Online Computation and Hashing,"  Generalized Linear Bandits (GLBs), a natural extension of the stochastic
linear bandits, has been popular and successful in recent years. However,
existing GLBs scale poorly with the number of rounds and the number of arms,
limiting their utility in practice. This paper proposes new, scalable solutions
to the GLB problem in two respects. First, unlike existing GLBs, whose
per-time-step space and time complexity grow at least linearly with time $t$,
we propose a new algorithm that performs online computations to enjoy a
constant space and time complexity. At its heart is a novel Generalized Linear
extension of the Online-to-confidence-set Conversion (GLOC method) that takes
\emph{any} online learning algorithm and turns it into a GLB algorithm. As a
special case, we apply GLOC to the online Newton step algorithm, which results
in a low-regret GLB algorithm with much lower time and memory complexity than
prior work. Second, for the case where the number $N$ of arms is very large, we
propose new algorithms in which each next arm is selected via an inner product
search. Such methods can be implemented via hashing algorithms (i.e.,
""hash-amenable"") and result in a time complexity sublinear in $N$. While a
Thompson sampling extension of GLOC is hash-amenable, its regret bound for
$d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound
scales with $d$. Towards closing this gap, we propose a new hash-amenable
algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast
approximate hash-key computation (inner product) with a better accuracy than
the state-of-the-art, which can be of independent interest. We conclude the
paper with preliminary experimental results confirming the merits of our
methods.
",1,0,0,1,0,0
20733,Dome of magnetic order inside the nematic phase of sulfur-substituted FeSe under pressure,"  The pressure dependence of the structural, magnetic and superconducting
transitions and of the superconducting upper critical field were studied in
sulfur-substituted Fe(Se$_{1-x}$S$_{x}$). Resistance measurements were
performed on single crystals with three substitution levels ($x$=0.043, 0.096,
0.12) under hydrostatic pressures up to 1.8 GPa and in magnetic fields up to 9
T, and compared to data on pure FeSe. Our results illustrate the effects of
chemical and physical pressure on Fe(Se$_{1-x}$S$_{x}$). On increasing sulfur
content, magnetic order in the low-pressure range is strongly suppressed to a
small dome-like region in the phase diagrams. However, $T_s$ is much less
suppressed by sulfur substitution and $T_c$ of Fe(Se$_{1-x}$S$_{x}$) exhibits
similar non-monotonic pressure dependence with a local maximum and a local
minimum present in the low pressure range for all $x$. The local maximum in
$T_c$ coincides with the emergence of the magnetic order above $T_c$. At this
pressure the slope of the upper critical field decreases abruptly. The minimum
of $T_c$ correlates with a broad maximum of the upper critical field slope
normalized by $T_c$.
",0,1,0,0,0,0
20734,ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge,"  This paper describes Luminoso's participation in SemEval 2017 Task 2,
""Multilingual and Cross-lingual Semantic Word Similarity"", with a system based
on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
on general knowledge that relates the meanings of words and phrases. Our
submission to SemEval was an update of previous work that builds high-quality,
multilingual word embeddings from a combination of ConceptNet and
distributional semantics. Our system took first place in both subtasks. It
ranked first in 4 out of 5 of the separate languages, and also ranked first in
all 10 of the cross-lingual language pairs.
",1,0,0,0,0,0
20735,The symmetrized topological complexity of the circle,"  We determine the symmetrized topological complexity of the circle, using
primarily just general topology.
",0,0,1,0,0,0
20736,Intertwining operators among twisted modules associated to not-necessarily-commuting automorphisms,"  We introduce intertwining operators among twisted modules or twisted
intertwining operators associated to not-necessarily-commuting automorphisms of
a vertex operator algebra. Let $V$ be a vertex operator algebra and let
$g_{1}$, $g_{2}$ and $g_{3}$ be automorphisms of $V$. We prove that for
$g_{1}$-, $g_{2}$- and $g_{3}$-twisted $V$-modules $W_{1}$, $W_{2}$ and
$W_{3}$, respectively, such that the vertex operator map for $W_{3}$ is
injective, if there exists a twisted intertwining operator of type
${W_{3}\choose W_{1}W_{2}}$ such that the images of its component operators
span $W_{3}$, then $g_{3}=g_{1}g_{2}$. We also construct what we call the
skew-symmetry and contragredient isomorphisms between spaces of twisted
intertwining operators among twisted modules of suitable types. The proofs of
these results involve careful analysis of the analytic extensions corresponding
to the actions of the not-necessarily-commuting automorphisms of the vertex
operator algebra.
",0,0,1,0,0,0
20737,Enabling Visual Design Verification Analytics - From Prototype Visualizations to an Analytics Tool using the Unity Game Engine,"  The ever-increasing architectural complexity in contemporary ASIC projects
turns Design Verification (DV) into a highly advanced endeavor. Pressing needs
for short time-to-market has made automation a key solution in DV. However,
recurring execution of large regression suites inevitably leads to challenging
amounts of test results. Following the design science paradigm, we present an
action research study to introduce visual analytics in a commercial ASIC
project. We develop a cityscape visualization tool using the game engine Unity.
Initial evaluations are promising, suggesting that the tool offers a novel
approach to identify error-prone parts of the design, as well as coverage
holes.
",1,0,0,0,0,0
20738,Self-regulation promotes cooperation in social networks,"  Cooperative behavior in real social dilemmas is often perceived as a
phenomenon emerging from norms and punishment. To overcome this paradigm, we
highlight the interplay between the influence of social networks on
individuals, and the activation of spontaneous self-regulating mechanisms,
which may lead them to behave cooperatively, while interacting with others and
taking conflicting decisions over time. By extending Evolutionary game theory
over networks, we prove that cooperation partially or fully emerges whether
self-regulating mechanisms are sufficiently stronger than social pressure.
Interestingly, even few cooperative individuals act as catalyzing agents for
the cooperation of others, thus activating a recruiting mechanism, eventually
driving the whole population to cooperate.
",1,0,0,0,0,0
20739,Magnetic properties of nanoparticles compacts with controlled broadening of the particle size distribution,"  Binary random compacts with different proportions of small (volume V) and
large (volume 2V) bare maghemite nanoparticles (NPs) are used to investigate
the effect of controllably broadening the particle size distribution on the
magnetic properties of magnetic NP assemblies with strong dipolar interaction.
A series of eight random mixtures of highly uniform 9.0 and 11.5 nm diameter
maghemite particles prepared by thermal decomposition are studied. In spite of
severely broadened size distributions in the mixed samples, well defined
superspin glass transition temperatures are observed across the series, their
values increasing linearly with the weight fraction of large particles.
",0,1,0,0,0,0
20740,A Generative Model for Exploring Structure Regularities in Attributed Networks,"  Many real-world networks known as attributed networks contain two types of
information: topology information and node attributes. It is a challenging task
on how to use these two types of information to explore structural
regularities. In this paper, by characterizing potential relationship between
link communities and node attributes, a principled statistical model named
PSB_PG that generates link topology and node attributes is proposed. This model
for generating links is based on the stochastic blockmodels following a Poisson
distribution. Therefore, it is capable of detecting a wide range of network
structures including community structures, bipartite structures and other
mixture structures. The model for generating node attributes assumes that node
attributes are high dimensional and sparse and also follow a Poisson
distribution. This makes the model be uniform and the model parameters can be
directly estimated by expectation-maximization (EM) algorithm. Experimental
results on artificial networks and real networks containing various structures
have shown that the proposed model PSB_PG is not only competitive with the
state-of-the-art models, but also provides good semantic interpretation for
each community via the learned relationship between the community and its
related attributes.
",1,0,0,0,0,0
20741,On the maximal halfspace depth of permutation-invariant distributions on the simplex,"  We compute the maximal halfspace depth for a class of permutation-invariant
distributions on the probability simplex. The derivations are based on
stochastic ordering results that so far were only showed to be relevant for the
Behrens-Fisher problem.
",0,0,1,1,0,0
20742,Theoretical Evaluation of Li et al.'s Approach for Improving a Binary Watermark-Based Scheme in Remote Sensing Data Communications,"  This letter is about a principal weakness of the published article by Li et
al. in 2014. It seems that the mentioned work has a terrible conceptual mistake
while presenting its theoretical approach. In fact, the work has tried to
design a new attack and its effective solution for a basic watermarking
algorithm by Zhu et al. published in 2013, however in practice, we show the Li
et al.'s approach is not correct to obtain the aim. For disproof of the
incorrect approach, we only apply a numerical example as the counterexample of
the Li et al.'s approach.
",1,0,0,0,0,0
20743,Bioinformatics and Medicine in the Era of Deep Learning,"  Many of the current scientific advances in the life sciences have their
origin in the intensive use of data for knowledge discovery. In no area this is
so clear as in bioinformatics, led by technological breakthroughs in data
acquisition technologies. It has been argued that bioinformatics could quickly
become the field of research generating the largest data repositories, beating
other data-intensive areas such as high-energy physics or astroinformatics.
Over the last decade, deep learning has become a disruptive advance in machine
learning, giving new live to the long-standing connectionist paradigm in
artificial intelligence. Deep learning methods are ideally suited to
large-scale data and, therefore, they should be ideally suited to knowledge
discovery in bioinformatics and biomedicine at large. In this brief paper, we
review key aspects of the application of deep learning in bioinformatics and
medicine, drawing from the themes covered by the contributions to an ESANN 2018
special session devoted to this topic.
",0,0,0,1,1,0
20744,A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously,"  Capabilities of detecting temporal relations between two events can benefit
many applications. Most of existing temporal relation classifiers were trained
in a supervised manner. Instead, we explore the observation that regular event
pairs show a consistent temporal relation despite of their various contexts,
and these rich contexts can be used to train a contextual temporal relation
classifier, which can further recognize new temporal relation contexts and
identify new regular event pairs. We focus on detecting after and before
temporal relations and design a weakly supervised learning approach that
extracts thousands of regular event pairs and learns a contextual temporal
relation classifier simultaneously. Evaluation shows that the acquired regular
event pairs are of high quality and contain rich commonsense knowledge and
domain specific knowledge. In addition, the weakly supervised trained temporal
relation classifier achieves comparable performance with the state-of-the-art
supervised systems.
",1,0,0,0,0,0
20745,Privacy with Estimation Guarantees,"  We study the central problem in data privacy: how to share data with an
analyst while providing both privacy and utility guarantees to the user that
owns the data. In this setting, we present an estimation-theoretic analysis of
the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct
(in a mean-squared error sense) certain functions of the data (utility), while
other private functions should not be reconstructed with distortion below a
certain threshold (privacy). We demonstrate how $\chi^2$-information captures
the fundamental PUT in this case and provide bounds for the best PUT. We
propose a convex program to compute privacy-assuring mappings when the
functions to be disclosed and hidden are known a priori and the data
distribution is known. We derive lower bounds on the minimum mean-squared error
of estimating a target function from the disclosed data and evaluate the
robustness of our approach when an empirical distribution is used to compute
the privacy-assuring mappings instead of the true data distribution. We
illustrate the proposed approach through two numerical experiments.
",1,0,0,0,0,0
20746,A line of CFTs: from generalized free fields to SYK,"  We point out that there is a simple variant of the SYK model, which we call
cSYK, that is $SL(2,R)$ invariant for all values of the coupling. The
modification consists of replacing the UV part of the SYK action with a
quadratic bilocal term. The corresponding bulk dual is a non-gravitational
theory in a rigid AdS$_2$ background. At weak coupling cSYK is a generalized
free field theory; at strong coupling, it approaches the infrared of SYK. The
existence of this line of fixed points explains the previously found connection
between the three-point function of bilinears in these two theories at large
$q$.
",0,1,0,0,0,0
20747,Miura transformations for discrete Painlevé equations coming from the affine E$_8$ Weyl group,"  We derive integrable equations starting from autonomous mappings with a
general form inspired by the additive systems associated to the affine Weyl
group E$_8^{(1)}$. By deautonomisation we obtain two hitherto unknown systems,
one of which turns out to be a linearisable one, and we show that both these
systems arise from the deautonomisation of a non-QRT mapping. In order to
unambiguously prove the integrability of these nonautonomous systems, we
introduce a series of Miura transformations which allows us to prove that one
of these systems is indeed a discrete Painlevé equation, related to the
affine Weyl group E$_7^{(1)}$, and to cast it in canonical form. A similar
sequence of Miura transformations allows us to effectively linearise the second
system we obtain. An interesting off-shoot of our calculations is that the
series of Miura transformations, when applied at the autonomous limit, allows
one to transform a non-QRT invariant into a QRT one.
",0,1,1,0,0,0
20748,Virtual Molecular Dynamics,"  Molecular dynamics is based on solving Newton's equations for many-particle
systems that evolve along complex, highly fluctuating trajectories. The orbital
instability and short-time complexity of Newtonian orbits is in sharp contrast
to the more coherent behavior of collective modes such as density profiles. The
notion of virtual molecular dynamics is introduced here based on temporal
coarse-graining via Pade approximants and the Ito formula for stochastic
processes. It is demonstrated that this framework leads to significant
efficiency over traditional molecular dynamics and avoids the need to introduce
coarse-grained variables and phenomenological equations for their evolution. In
this framework, an all-atom trajectory is represented by a Markov chain of
virtual atomic states at a discrete sequence of timesteps, transitions between
which are determined by an integration of conventional molecular dynamics with
Pade approximants and a microstate energy annealing methodology. The latter is
achieved by a conventional and an MD NVE energy minimization schemes. This
multiscale framework is demonstrated for a pertussis toxin subunit undergoing a
structural transition, a T=1 capsid-like structure of HPV16 L1 protein, and two
coalescing argon droplets.
",0,1,0,0,0,0
20749,Regression with genuinely functional errors-in-covariates,"  Contamination of covariates by measurement error is a classical problem in
multivariate regression, where it is well known that failing to account for
this contamination can result in substantial bias in the parameter estimators.
The nature and degree of this effect on statistical inference is also
understood to crucially depend on the specific distributional properties of the
measurement error in question. When dealing with functional covariates,
measurement error has thus far been modelled as additive white noise over the
observation grid. Such a setting implicitly assumes that the error arises
purely at the discrete sampling stage, otherwise the model can only be viewed
in a weak (stochastic differential equation) sense, white noise not being a
second-order process. Departing from this simple distributional setting can
have serious consequences for inference, similar to the multivariate case, and
current methodology will break down. In this paper, we consider the case when
the additive measurement error is allowed to be a valid stochastic process. We
propose a novel estimator of the slope parameter in a functional linear model,
for scalar as well as functional responses, in the presence of this general
measurement error specification. The proposed estimator is inspired by the
multivariate regression calibration approach, but hinges on recent advances on
matrix completion methods for functional data in order to handle the nontrivial
(and unknown) error covariance structure. The asymptotic properties of the
proposed estimators are derived. We probe the performance of the proposed
estimator of slope using simulations and observe that it substantially improves
upon the spectral truncation estimator based on the erroneous observations,
i.e., ignoring measurement error. We also investigate the behaviour of the
estimators on a real dataset on hip and knee angle curves during a gait cycle.
",0,0,0,1,0,0
20750,The distance between a naive cumulative estimator and its least concave majorant,"  We consider the process $\widehat\Lambda_n-\Lambda_n$, where $\Lambda_n$ is a
cadlag step estimator for the primitive $\Lambda$ of a nonincreasing function
$\lambda$ on $[0,1]$, and $\widehat\Lambda_n$ is the least concave majorant of
$\Lambda_n$. We extend the results in Kulikov and Lopuhaä (2006, 2008) to the
general setting considered in Durot (2007). Under this setting we prove that a
suitably scaled version of $\widehat\Lambda_n-\Lambda_n$ converges in
distribution to the corresponding process for two-sided Brownian motion with
parabolic drift and we establish a central limit theorem for the $L_p$-distance
between $\widehat\Lambda_n$ and $\Lambda_n$.
",0,0,1,1,0,0
20751,Controllability of impulse controlled systems of heat equations coupled by constant matrices,"  This paper studies the approximate and null controllability for impulse
controlled systems of heat equations coupled by a pair (A,B) of constant
matrices. We present a necessary and sufficient condition for the approximate
controllability, which is exactly Kalman's controllability rank condition of
(A,B). We prove that when such a system is approximately controllable, the
approximate controllability over an interval [0,T] can be realized by adding
controls at arbitrary n different control instants
0<\tau_1<\tau_2<\cdots<\tau_n<T, provided that \tau_n-\tau_1<d_A, where
d_A=\min\{\pi/|Im \lambda| : \lambda\in \sigma(A)\}. We also show that in
general, such systems are not null controllable.
",0,0,1,0,0,0
20752,Nearly Optimal Constructions of PIR and Batch Codes,"  In this work we study two families of codes with availability, namely private
information retrieval (PIR) codes and batch codes. While the former requires
that every information symbol has $k$ mutually disjoint recovering sets, the
latter asks this property for every multiset request of $k$ information
symbols. The main problem under this paradigm is to minimize the number of
redundancy symbols. We denote this value by $r_P(n,k), r_B(n,k)$, for PIR,
batch codes, respectively, where $n$ is the number of information symbols.
Previous results showed that for any constant $k$, $r_P(n,k) =
\Theta(\sqrt{n})$ and $r_B(n,k)=O(\sqrt{n}\log(n)$. In this work we study the
asymptotic behavior of these codes for non-constant $k$ and specifically for
$k=\Theta(n^\epsilon)$. We also study the largest value of $k$ such that the
rate of the codes approaches 1, and show that for all $\epsilon<1$,
$r_P(n,n^\epsilon) = o(n)$, while for batch codes, this property holds for all
$\epsilon< 0.5$.
",1,0,0,0,0,0
20753,Submillimeter Array CO(2-1) Imaging of the NGC 6946 Giant Molecular Clouds,"  We present a CO(2-1) mosaic map of the spiral galaxy NGC 6946 by combining
data from the Submillimeter Array and the IRAM 30 m telescope. We identify 390
giant molecular clouds (GMCs) from the nucleus to 4.5 kpc in the disk. GMCs in
the inner 1 kpc are generally more luminous and turbulent, some of which have
luminosities >10^6 K km/s pc^2 and velocity dispersions >10 km/s. Large-scale
bar-driven dynamics likely regulate GMC properties in the nuclear region.
Similar to the Milky Way and other disk galaxies, GMC mass function of NGC 6946
has a shallower slope (index>-2) in the inner region, and a steeper slope
(index<-2) in the outer region. This difference in mass spectra may be
indicative of different cloud formation pathways: gravitational instabilities
might play a major role in the nuclear region, while cloud coalescence might be
dominant in the outer disk. Finally, the NGC 6946 clouds are similar to those
in M33 in terms of statistical properties, but they are generally less luminous
and turbulent than the M51 clouds.
",0,1,0,0,0,0
20754,Optical and Near-Infrared Spectra of sigma Orionis Isolated Planetary-mass Objects,"  We have obtained low-resolution optical (0.7-0.98 micron) and near-infrared
(1.11-1.34 micron and 0.8-2.5 micron) spectra of twelve isolated planetary-mass
candidates (J = 18.2-19.9 mag) of the 3-Myr sigma Orionis star cluster with a
view to determining the spectroscopic properties of very young, substellar
dwarfs and assembling a complete cluster mass function. We have classified our
targets by visual comparison with high- and low-gravity standards and by
measuring newly defined spectroscopic indices. We derived L0-L4.5 and M9-L2.5
using high- and low-gravity standards, respectively. Our targets reveal clear
signposts of youth, thus corroborating their cluster membership and planetary
masses (6-13 Mjup). These observations complete the sigma Orionis mass function
by spectroscopically confirming the planetary-mass domain to a confidence level
of $\sim$75 percent. The comparison of our spectra with BT-Settl solar
metallicity model atmospheres yields a temperature scale of 2350-1800 K and a
low surface gravity of log g ~ 4.0 [cm/s2], as would be expected for young
planetary-mass objects. We discuss the properties of the cluster least-massive
population as a function of spectral type. We have also obtained the first
optical spectrum of S Ori 70, a T dwarf in the direction of sigma Orionis. Our
data provide reference optical and near-infrared spectra of very young L dwarfs
and a mass function that may be used as templates for future studies of
low-mass substellar objects and exoplanets. The extrapolation of the sigma
Orionis mass function to the solar neighborhood may indicate that isolated
planetary-mass objects with temperatures of 200-300 K and masses in the
interval 6-13-Mjup may be as numerous as very low-mass stars.
",0,1,0,0,0,0
20755,The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight,"  The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,
aggressive indoor flight dataset collected using a custom-built quadrotor
platform for use in evaluation of agile perception.Inspired by the potential of
future high-speed fully-autonomous drone racing, the Blackbird dataset contains
over 10 hours of flight data from 168 flights over 17 flight trajectories and 5
environments at velocities up to $7.0ms^-1$. Each flight includes sensor data
from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz
IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion
capture ground truth. Camera images for each flight were photorealistically
rendered using FlightGoggles across a variety of environments to facilitate
easy experimentation of high performance perception algorithms. The dataset is
available for download at this http URL
",1,0,0,0,0,0
20756,An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification,"  Motivation: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging
technique that visualizes subcellular organization of single cells at
submolecular resolution and in near-native state. CECT captures large numbers
of macromolecular complexes of highly diverse structures and abundances.
However, the structural complexity and imaging limits complicate the systematic
de novo structural recovery and recognition of these macromolecular complexes.
Efficient and accurate reference-free subtomogram averaging and classification
represent the most critical tasks for such analysis. Existing subtomogram
alignment based methods are prone to the missing wedge effects and low
signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based
methods rely on integration operations, which are in principle computationally
infeasible for accurate calculation.
Results: Built on existing works, we propose an integrated method, Fast
Alignment Maximum Likelihood method (FAML), which uses fast subtomogram
alignment to sample sub-optimal rigid transformations. The transformations are
then used to approximate integrals for maximum-likelihood update of subtomogram
averages through expectation-maximization algorithm. Our tests on simulated and
experimental subtomograms showed that, compared to our previously developed
fast alignment method (FA), FAML is significantly more robust to noise and
missing wedge effects with moderate increases of computation cost.Besides, FAML
performs well with significantly fewer input subtomograms when the FA method
fails. Therefore, FAML can serve as a key component for improved construction
of initial structural models from macromolecules captured by CECT.
",0,0,0,1,1,0
20757,Local and global existence of solutions to a strongly damped wave equation of the $p$-Laplacian type,"  This article focuses on a quasilinear wave equation of $p$-Laplacian type: $$
u_{tt} - \Delta_p u - \Delta u_t=0$$ in a bounded domain
$\Omega\subset\mathbb{R}^3$ with a sufficiently smooth boundary
$\Gamma=\partial\Omega$ subject to a generalized Robin boundary condition
featuring boundary damping and a nonlinear source term. The operator
$\Delta_p$, $2 < p < 3$, denotes the classical $p$-Laplacian. The nonlinear
boundary term $f (u)$ is a source feedback that is allowed to have a
supercritical exponent, in the sense that the associated Nemytskii operator is
not locally Lipschitz from $W^{1,p}(\Omega)$ into $L^2(\Gamma)$. Under suitable
assumptions on the parameters we provide a rigorous proof of existence of a
local weak solution which can be extended globally in time provided the source
term satisfies an appropriate growth condition.
",0,0,1,0,0,0
20758,Quasi-flat representations of uniform groups and quantum groups,"  Given a discrete group $\Gamma=<g_1,\ldots,g_M>$ and a number $K\in\mathbb
N$, a unitary representation $\rho:\Gamma\to U_K$ is called quasi-flat when the
eigenvalues of each $\rho(g_i)\in U_K$ are uniformly distributed among the
$K$-th roots of unity. The quasi-flat representations of $\Gamma$ form
altogether a parametric matrix model $\pi:\Gamma\to C(X,U_K)$.
We compute here the universal model space $X$ for various classes of discrete
groups, notably with results in the case where $\Gamma$ is metabelian. We are
particularly interested in the case where $X$ is a union of compact homogeneous
spaces, and where the induced representation $\tilde{\pi}:C^*(\Gamma)\to
C(X,U_K)$ is stationary in the sense that it commutes with the Haar
functionals. We present several positive and negative results on this subject.
We also discuss similar questions for the discrete quantum groups, proving a
stationarity result for the discrete dual of the twisted orthogonal group
$O_2^{-1}$.
",0,0,1,0,0,0
20759,Nonparametric Preference Completion,"  We consider the task of collaborative preference completion: given a pool of
items, a pool of users and a partially observed item-user rating matrix, the
goal is to recover the \emph{personalized ranking} of each user over all of the
items. Our approach is nonparametric: we assume that each item $i$ and each
user $u$ have unobserved features $x_i$ and $y_u$, and that the associated
rating is given by $g_u(f(x_i,y_u))$ where $f$ is Lipschitz and $g_u$ is a
monotonic transformation that depends on the user. We propose a $k$-nearest
neighbors-like algorithm and prove that it is consistent. To the best of our
knowledge, this is the first consistency result for the collaborative
preference completion problem in a nonparametric setting. Finally, we
demonstrate the performance of our algorithm with experiments on the Netflix
and Movielens datasets.
",1,0,0,1,0,0
20760,Real-Time Reconstruction of Counting Process through Queues,"  For the emerging Internet of Things (IoT), one of the most critical problems
is the real-time reconstruction of signals from a set of aged measurements.
During the reconstruction, distortion occurs between the observed signal and
the reconstructed signal due to sampling and transmission. In this paper, we
focus on minimizing the average distortion defined as the 1-norm of the
difference of the two signals under the scenario that a Poisson counting
process is reconstructed in real-time on a remote monitor. Especially, we
consider the reconstruction under uniform sampling policy and two non-uniform
sampling policies, i.e., the threshold-based policy and the zero-wait policy.
For each of the policy, we derive the closed-form expression of the average
distortion by dividing the overall distortion area into polygons and analyzing
their structures. It turns out that the polygons are built up by sub-polygons
that account for distortions caused by sampling and transmission. The
closed-form expressions of the average distortion help us find the optimal
sampling parameters that achieve the minimum distortion. Simulation results are
provided to validate our conclusion.
",1,0,0,0,0,0
20761,Discrepancy-Based Algorithms for Non-Stationary Rested Bandits,"  We study the multi-armed bandit problem where the rewards are realizations of
general non-stationary stochastic processes, a setting that generalizes many
existing lines of work and analyses. In particular, we present a theoretical
analysis and derive regret guarantees for rested bandits in which the reward
distribution of each arm changes only when we pull that arm. Remarkably, our
regret bounds are logarithmic in the number of rounds under several natural
conditions. We introduce a new algorithm based on classical UCB ideas combined
with the notion of weighted discrepancy, a useful tool for measuring the
non-stationarity of a stochastic process. We show that the notion of
discrepancy can be used to design very general algorithms and a unified
framework for the analysis of multi-armed rested bandit problems with
non-stationary rewards. In particular, we show that we can recover the regret
guarantees of many specific instances of bandit problems with non-stationary
rewards that have been studied in the literature. We also provide experiments
demonstrating that our algorithms can enjoy a significant improvement in
practice compared to standard benchmarks.
",1,0,0,0,0,0
20762,Kondo lattice heavy fermion behavior in CeRh2Ga2,"  The physical properties of an intermetallic compound CeRh2Ga2 have been
investigated by magnetic susceptibility \chi(T), isothermal magnetization M(H),
heat capacity C_p(T), electrical resistivity \rho(T), thermal conductivity
\kappa(T) and thermopower S(T) measurements. CeRh2Ga2 is found to crystallize
with CaBe2Ge2-type primitive tetragonal structure (space group P4/nmm). No
evidence of long range magnetic order is seen down to 1.8 K. The \chi(T) data
show paramagnetic behavior with an effective moment \mu_eff ~ 2.5 \mu_B/Ce
indicating Ce^3+ valence state of Ce ions. The \rho(T) data exhibit Kondo
lattice behavior with a metallic ground state. The low-T C_p(T) data yield an
enhanced Sommerfeld coefficient \gamma = 130(2) mJ/mol K^2 characterizing
CeRh2Ga2 as a moderate heavy fermion system. The high-T C_p(T) and \rho(T) show
an anomaly near 255 K, reflecting a phase transition. The \kappa(T) suggests
phonon dominated thermal transport with considerably higher values of Lorenz
number L(T) compared to the theoretical Sommerfeld value L_0.
",0,1,0,0,0,0
20763,Khovanov homology and periodic links,"  Based on the results of the second author, we define an equivariant version
of Lee and Bar-Natan homology for periodic links and show that there exists an
equivariant spectral sequence from the equivariant Khovanov homology to
equivariant Lee homology. As a result we obtain new obstructions for a link to
be periodic. These obstructions generalize previous results of Przytycki and of
the second author.
",0,0,1,0,0,0
20764,A Note on Spectral Clustering and SVD of Graph Data,"  Spectral clustering and Singular Value Decomposition (SVD) are both widely
used technique for analyzing graph data. In this note, I will present their
connections using simple linear algebra, aiming to provide some in-depth
understanding for future research.
",1,0,0,0,0,0
20765,An explicit analysis of the entropic penalty in linear programming,"  Solving linear programs by using entropic penalization has recently attracted
new interest in the optimization community, since this strategy forms the basis
for the fastest-known algorithms for the optimal transport problem, with many
applications in modern large-scale machine learning. Crucial to these
applications has been an analysis of how quickly solutions to the penalized
program approach true optima to the original linear program. More than 20 years
ago, Cominetti and San Martín showed that this convergence is exponentially
fast; however, their proof is asymptotic and does not give any indication of
how accurately the entropic program approximates the original program for any
particular choice of the penalization parameter. We close this long-standing
gap in the literature regarding entropic penalization by giving a new proof of
the exponential convergence, valid for any linear program. Our proof is
non-asymptotic, yields explicit constants, and has the virtue of being
extremely simple. We provide matching lower bounds and show that the entropic
approach does not lead to a near-linear time approximation scheme for the
linear assignment problem.
",0,0,0,1,0,0
20766,Deconstructing the Tail at Scale Effect Across Network Protocols,"  Network latencies have become increasingly important for the performance of
web servers and cloud computing platforms. Identifying network-related tail
latencies and reasoning about their potential causes is especially important to
gauge application run-time in online data-intensive applications, where the
99th percentile latency of individual operations can significantly affect the
the overall latency of requests.
This paper deconstructs the ""tail at scale"" effect across TCP-IP, UDP-IP, and
RDMA network protocols. Prior scholarly works have analyzed tail latencies
caused by extrinsic network parameters like network congestion and flow
fairness. Contrary to existing literature, we identify surprising rare tails in
TCP-IP round-trip measurements that are as enormous as 110x higher than the
median latency. Our experimental design eliminates network congestion as a
tail-inducing factor. Moreover, we observe similar extreme tails in UDP-IP
packet exchanges, ruling out additional TCP-IP protocol operations as the root
cause of tail latency. However, we are unable to reproduce similar tail
latencies in RDMA packet exchanges, which leads us to conclude that the TCP/UDP
protocol stack within the operating system kernel is likely the primary source
of extreme latency tails.
",1,0,0,0,0,0
20767,Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,"  Accurate diagnosis of psychiatric disorders plays a critical role in
improving quality of life for patients and potentially supports the development
of new treatments. Many studies have been conducted on machine learning
techniques that seek brain imaging data for specific biomarkers of disorders.
These studies have encountered the following dilemma: An end-to-end
classification overfits to a small number of high-dimensional samples but
unsupervised feature-extraction has the risk of extracting a signal of no
interest. In addition, such studies often provided only diagnoses for patients
without presenting the reasons for these diagnoses. This study proposed a deep
neural generative model of resting-state functional magnetic resonance imaging
(fMRI) data. The proposed model is conditioned by the assumption of the
subject's state and estimates the posterior probability of the subject's state
given the imaging data, using Bayes' rule. This study applied the proposed
model to diagnose schizophrenia and bipolar disorders. Diagnosis accuracy was
improved by a large margin over competitive approaches, namely a support vector
machine, logistic regression, and multilayer perceptron with or without
unsupervised feature-extractors in addition to a Gaussian mixture model. The
proposed model visualizes brain regions largely related to the disorders, thus
motivating further biological investigation.
",1,0,0,1,0,0
20768,Deep Learning for Patient-Specific Kidney Graft Survival Analysis,"  An accurate model of patient-specific kidney graft survival distributions can
help to improve shared-decision making in the treatment and care of patients.
In this paper, we propose a deep learning method that directly models the
survival function instead of estimating the hazard function to predict survival
times for graft patients based on the principle of multi-task learning. By
learning to jointly predict the time of the event, and its rank in the cox
partial log likelihood framework, our deep learning approach outperforms, in
terms of survival time prediction quality and concordance index, other common
methods for survival analysis, including the Cox Proportional Hazards model and
a network trained on the cox partial log-likelihood.
",1,0,0,1,0,0
20769,When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks,"  We introduce a framework for the modeling of sequential data capturing
pathways of varying lengths observed in a network. Such data are important,
e.g., when studying click streams in information networks, travel patterns in
transportation systems, information cascades in social networks, biological
pathways or time-stamped social interactions. While it is common to apply graph
analytics and network analysis to such data, recent works have shown that
temporal correlations can invalidate the results of such methods. This raises a
fundamental question: when is a network abstraction of sequential data
justified? Addressing this open question, we propose a framework which combines
Markov chains of multiple, higher orders into a multi-layer graphical model
that captures temporal correlations in pathways at multiple length scales
simultaneously. We develop a model selection technique to infer the optimal
number of layers of such a model and show that it outperforms previously used
Markov order detection techniques. An application to eight real-world data sets
on pathways and temporal networks shows that it allows to infer graphical
models which capture both topological and temporal characteristics of such
data. Our work highlights fallacies of network abstractions and provides a
principled answer to the open question when they are justified. Generalizing
network representations to multi-order graphical models, it opens perspectives
for new data mining and knowledge discovery algorithms.
",1,1,0,0,0,0
20770,Outlier-robust moment-estimation via sum-of-squares,"  We develop efficient algorithms for estimating low-degree moments of unknown
distributions in the presence of adversarial outliers. The guarantees of our
algorithms improve in many cases significantly over the best previous ones,
obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al.
We also show that the guarantees of our algorithms match information-theoretic
lower-bounds for the class of distributions we consider. These improved
guarantees allow us to give improved algorithms for independent component
analysis and learning mixtures of Gaussians in the presence of outliers.
Our algorithms are based on a standard sum-of-squares relaxation of the
following conceptually-simple optimization problem: Among all distributions
whose moments are bounded in the same way as for the unknown distribution, find
the one that is closest in statistical distance to the empirical distribution
of the adversarially-corrupted sample.
",1,0,0,1,0,0
20771,Clustering High Dimensional Dynamic Data Streams,"  We present data streaming algorithms for the $k$-median problem in
high-dimensional dynamic geometric data streams, i.e. streams allowing both
insertions and deletions of points from a discrete Euclidean space $\{1, 2,
\ldots \Delta\}^d$. Our algorithms use $k \epsilon^{-2} poly(d \log \Delta)$
space/time and maintain with high probability a small weighted set of points (a
coreset) such that for every set of $k$ centers the cost of the coreset
$(1+\epsilon)$-approximates the cost of the streamed point set. We also provide
algorithms that guarantee only positive weights in the coreset with additional
logarithmic factors in the space and time complexities. We can use this
positively-weighted coreset to compute a $(1+\epsilon)$-approximation for the
$k$-median problem by any efficient offline $k$-median algorithm. All previous
algorithms for computing a $(1+\epsilon)$-approximation for the $k$-median
problem over dynamic data streams required space and time exponential in $d$.
Our algorithms can be generalized to metric spaces of bounded doubling
dimension.
",1,0,0,0,0,0
20772,Edge contact angle and modified Kelvin equation for condensation in open pores,"  We consider capillary condensation transitions occurring in open slits of
width $L$ and finite height $H$ immersed in a reservoir of vapour. In this case
the pressure at which condensation occurs is closer to saturation compared to
that occurring in an infinite slit ($H=\infty$) due to the presence of two
menisci which are pinned near the open ends. Using macroscopic arguments we
derive a modified Kelvin equation for the pressure, $p_{cc}(L;H)$, at which
condensation occurs and show that the two menisci are characterised by an edge
contact angle $\theta_e$ which is always larger than the equilibrium contact
angle $\theta$, only equal to it in the limit of macroscopic $H$. For walls
which are completely wet ($\theta=0$) the edge contact angle depends only on
the aspect ratio of the capillary and is well described by $\theta_e\approx
\sqrt{\pi L/2H}$ for large $H$. Similar results apply for condensation in
cylindrical pores of finite length. We have tested these predictions against
numerical results obtained using a microscopic density functional model where
the presence of an edge contact angle characterising the shape of the menisci
is clearly visible from the density profiles. Below the wetting temperature
$T_w$ we find very good agreement for slit pores of widths of just a few tens
of molecular diameters while above $T_w$ the modified Kelvin equation only
becomes accurate for much larger systems.
",0,1,0,0,0,0
20773,Thresholds for hanger slackening and cable shortening in the Melan equation for suspension bridges,"  The Melan equation for suspension bridges is derived by assuming small
displacements of the deck and inextensible hangers. We determine the thresholds
for the validity of the Melan equation when the hangers slacken, thereby
violating the inextensibility assumption. To this end, we preliminarily study
the possible shortening of the cables: it turns out that there is a striking
difference between even and odd vibrating modes since the former never shorten
the cables. These problems are studied both on beams and plates.
",0,1,1,0,0,0
20774,Bendable Cuboid Robot Path Planning with Collision Avoidance using Generalized $L_p$ Norms,"  Optimal path planning problems for rigid and deformable (bendable) cuboid
robots are considered by providing an analytic safety constraint using
generalized $L_p$ norms. For regular cuboid robots, level sets of weighted
$L_p$ norms generate implicit approximations of their surfaces. For bendable
cuboid robots a weighted $L_p$ norm in polar coordinates implicitly
approximates the surface boundary through a specified level set. Obstacle
volumes, in the environment to navigate within, are presumed to be
approximately described as sub-level sets of weighted $L_p$ norms. Using these
approximate surface models, the optimal safe path planning problem is
reformulated as a two stage optimization problem, where the safety constraint
depends on a point on the robot which is closest to the obstacle in the
obstacle's distance metric. A set of equality and inequality constraints are
derived to replace the closest point problem, which is then defines additional
analytic constraints on the original path planning problem. Combining all the
analytic constraints with logical AND operations leads to a general optimal
safe path planning problem. Numerically solving the problem involve conversion
to a nonlinear programing problem. Simulations for rigid and bendable cuboid
robot verify the proposed method.
",1,0,0,0,0,0
20775,"Quotients of triangulated categories and Equivalences of Buchweitz, Orlov and Amiot--Guo--Keller","  We give a sufficient condition for a Verdier quotient $\ct/\cs$ of a
triangulated category $\ct$ by a thick subcategory $\cs$ to be realized inside
of $\ct$ as an ideal quotient. As applications, we deduce three significant
results by Buchweitz, Orlov and Amiot--Guo--Keller.
",0,0,1,0,0,0
20776,Sorting sums of binary decision summands,"  A sum where each of the $N$ summands can be independently chosen from two
choices yields $2^N$ possible summation outcomes. There is an
$\mathcal{O}(K^2)$-algorithm that finds the $K$ smallest/largest of these sums
by evading the enumeration of all sums.
",1,0,0,0,0,0
20777,A Pursuit of Temporal Accuracy in General Activity Detection,"  Detecting activities in untrimmed videos is an important but challenging
task. The performance of existing methods remains unsatisfactory, e.g., they
often meet difficulties in locating the beginning and end of a long complex
action. In this paper, we propose a generic framework that can accurately
detect a wide variety of activities from untrimmed videos. Our first
contribution is a novel proposal scheme that can efficiently generate
candidates with accurate temporal boundaries. The other contribution is a
cascaded classification pipeline that explicitly distinguishes between
relevance and completeness of a candidate instance. On two challenging temporal
activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
significantly outperforms the existing state-of-the-art methods, demonstrating
superior accuracy and strong adaptivity in handling activities with various
temporal structures.
",1,0,0,0,0,0
20778,Adjusting systematic bias in high dimensional principal component scores,"  Principal component analysis continues to be a powerful tool in dimension
reduction of high dimensional data. We assume a variance-diverging model and
use the high-dimension, low-sample-size asymptotics to show that even though
the principal component directions are not consistent, the sample and
prediction principal component scores can be useful in revealing the population
structure. We further show that these scores are biased, and the bias is
asymptotically decomposed into rotation and scaling parts. We propose methods
of bias-adjustment that are shown to be consistent and work well in the finite
but high dimensional situations with small sample sizes. The potential
advantage of bias-adjustment is demonstrated in a classification setting.
",0,0,1,1,0,0
20779,Dynamical Exploration of Amplitude Bistability in Engineered Quantum Systems,"  Nonlinear systems, whose outputs are not directly proportional to their
inputs, are well known to exhibit many interesting and important phenomena
which have profoundly changed our technological landscape over the last 50
years. Recently the ability to engineer quantum metamaterials through
hybridisation has allowed to explore these nonlinear effects in systems with no
natural analogue. Here we investigate amplitude bistability, which is one of
the most fundamental nonlinear phenomena, in a hybrid system composed of a
superconducting resonator inductively coupled to an ensemble of
nitrogen-vacancy centres. One of the exciting properties of this spin system is
its extremely long spin life-time, more than ten orders of magnitude longer
than other relevant timescales of the hybrid system. This allows us to
dynamically explore this nonlinear regime of cavity quantum electrodynamics
(cQED) and demonstrate a critical slowing down of the cavity population on the
order of several tens of thousands of seconds - a timescale much longer than
observed so far for this effect. Our results provide the foundation for future
quantum technologies based on nonlinear phenomena.
",0,1,0,0,0,0
20780,Map Memorization and Forgetting in the IARA Autonomous Car,"  In this work, we present a novel strategy for correcting imperfections in
occupancy grid maps called map decay. The objective of map decay is to correct
invalid occupancy probabilities of map cells that are unobservable by sensors.
The strategy was inspired by an analogy between the memory architecture
believed to exist in the human brain and the maps maintained by an autonomous
vehicle. It consists in merging sensory information obtained during runtime
(online) with a priori data from a high-precision map constructed offline. In
map decay, cells observed by sensors are updated using traditional occupancy
grid mapping techniques and unobserved cells are adjusted so that their
occupancy probabilities tend to the values found in the offline map. This
strategy is grounded in the idea that the most precise information available
about an unobservable cell is the value found in the high-precision offline
map. Map decay was successfully tested and is still in use in the IARA
autonomous vehicle from Universidade Federal do Espírito Santo.
",1,0,0,0,0,0
20781,Effective Subgroup Separability of Finitely Generated Nilpotent Groups,"  This paper studies effective separability for subgroups of finitely generated
nilpotent groups and more broadly effective subgroup separability of finitely
generated nilpotent groups. We provide upper and lower bounds that are
polynomial with respect to the logarithm of the word length for infinite index
subgroups of nilpotent groups. In the case of normal subgroups, we provide an
exact computation generalizing work of the second author. We introduce a
function that quantifies subgroup separability, and we provide polynomial upper
and lower bounds. We finish by demonstrating that our results extend to
virtually nilpotent groups.
",0,0,1,0,0,0
20782,External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising,"  Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corrupted noise. Meanwhile, the noise in real-world noisy
images is very complex, which is hard to be described by simple distributions
such as Gaussian distribution, making real-world noisy image denoising a very
challenging problem. We propose to exploit the information in both external
data and the given noisy image, and develop an external prior guided internal
prior learning method for real-world noisy image denoising. We first learn
external priors from an independent set of clean natural images. With the aid
of learned external priors, we then learn internal priors from the given noisy
image to refine the prior model. The external and internal priors are
formulated as a set of orthogonal dictionaries to efficiently reconstruct the
desired image. Extensive experiments are performed on several real-world noisy
image datasets. The proposed method demonstrates highly competitive denoising
performance, outperforming state-of-the-art denoising methods including those
designed for real-world noisy images.
",1,0,0,0,0,0
20783,A Study of MAC Address Randomization in Mobile Devices and When it Fails,"  MAC address randomization is a privacy technique whereby mobile devices
rotate through random hardware addresses in order to prevent observers from
singling out their traffic or physical location from other nearby devices.
Adoption of this technology, however, has been sporadic and varied across
device manufacturers. In this paper, we present the first wide-scale study of
MAC address randomization in the wild, including a detailed breakdown of
different randomization techniques by operating system, manufacturer, and model
of device.
We then identify multiple flaws in these implementations which can be
exploited to defeat randomization as performed by existing devices. First, we
show that devices commonly make improper use of randomization by sending
wireless frames with the true, global address when they should be using a
randomized address. We move on to extend the passive identification techniques
of Vanhoef et al. to effectively defeat randomization in ~96% of Android
phones. Finally, we show a method that can be used to track 100% of devices
using randomization, regardless of manufacturer, by exploiting a previously
unknown flaw in the way existing wireless chipsets handle low-level control
frames.
",1,0,0,0,0,0
20784,Completion of the integrable coupling systems,"  In this paper, we proposed an procedure to construct the completion of the
integrable system by adding a perturbation to the generalized matrix problem,
which can be used to continuous integrable couplings, discrete integrable
couplings and super integrable couplings. As example, we construct the
completion of the Kaup-Newell (KN) integrable coupling, the
Wadati-Konno-Ichikawa (WKI) integrable couplingsis, vector
Ablowitz-Kaup-Newell-Segur (vAKNS) integrable couplings, the Volterra
integrable couplings, Dirac type integrable couplings and NLS-mKdV type
integrable couplings.
",0,1,0,0,0,0
20785,On the wildness of cambrian lattices,"  In this note, we investigate the representation type of the cambrian lattices
and some other related lattices. The result is expressed as a very simple
trichotomy. When the rank of the underlined Coxeter group is at most 2, the
lattices are of finite representation type. When the Coxeter group is a
reducible group of type A 3 1 , the lattices are of tame representation type.
In all the other cases they are of wild representation type.
",0,0,1,0,0,0
20786,Random Projections For Large-Scale Regression,"  Fitting linear regression models can be computationally very expensive in
large-scale data analysis tasks if the sample size and the number of variables
are very large. Random projections are extensively used as a dimension
reduction tool in machine learning and statistics. We discuss the applications
of random projections in linear regression problems, developed to decrease
computational costs, and give an overview of the theoretical guarantees of the
generalization error. It can be shown that the combination of random
projections with least squares regression leads to similar recovery as ridge
regression and principal component regression. We also discuss possible
improvements when averaging over multiple random projections, an approach that
lends itself easily to parallel implementation.
",0,0,1,1,0,0
20787,Properties of linear groups with restricted unipotent elements,"  We consider linear groups which do not contain unipotent elements of infinite
order, which includes all linear groups in positive characteristic, and show
that this class of groups has good properties which resemble those held by
groups of non positive curvature and which do not hold for arbitrary
characteristic zero linear groups. In particular if such a linear group is
finitely generated then centralisers virtually split and all finitely generated
abelian subgroups are undistorted. If further the group is virtually torsion
free (which always holds in characteristic zero) then we have a strong property
on small subgroups: any subgroup either contains a non abelian free group or is
finitely generated and virtually abelian, hence also undistorted. We present
applications, including that the mapping class group of a surface having genus
at least 3 has no faithful linear representation which is complex unitary or
over any field of positive characteristic.
",0,0,1,0,0,0
20788,Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator,"  The discrete Laplace operator is ubiquitous in spectral shape analysis, since
its eigenfunctions are provably optimal in representing smooth functions
defined on the surface of the shape. Indeed, subspaces defined by its
eigenfunctions have been utilized for shape compression, treating the
coordinates as smooth functions defined on the given surface. However, surfaces
of shapes in nature often contain geometric structures for which the general
smoothness assumption may fail to hold. At the other end, some explicit mesh
compression algorithms utilize the order by which vertices that represent the
surface are traversed, a property which has been ignored in spectral
approaches. Here, we incorporate the order of vertices into an operator that
defines a novel spectral domain. We propose a method for representing 3D meshes
using the spectral geometry of the Hamiltonian operator, integrated within a
sparse approximation framework. We adapt the concept of a potential function
from quantum physics and incorporate vertex ordering information into the
potential, yielding a novel data-dependent operator. The potential function
modifies the spectral geometry of the Laplacian to focus on regions with finer
details of the given surface. By sparsely encoding the geometry of the shape
using the proposed data-dependent basis, we improve compression performance
compared to previous results that use the standard Laplacian basis and spectral
graph wavelets.
",1,0,0,0,0,0
20789,Improved Convergence Rates for Distributed Resource Allocation,"  In this paper, we develop a class of decentralized algorithms for solving a
convex resource allocation problem in a network of $n$ agents, where the agent
objectives are decoupled while the resource constraints are coupled. The agents
communicate over a connected undirected graph, and they want to collaboratively
determine a solution to the overall network problem, while each agent only
communicates with its neighbors. We first study the connection between the
decentralized resource allocation problem and the decentralized consensus
optimization problem. Then, using a class of algorithms for solving consensus
optimization problems, we propose a novel class of decentralized schemes for
solving resource allocation problems in a distributed manner. Specifically, we
first propose an algorithm for solving the resource allocation problem with an
$o(1/k)$ convergence rate guarantee when the agents' objective functions are
generally convex (could be nondifferentiable) and per agent local convex
constraints are allowed; We then propose a gradient-based algorithm for solving
the resource allocation problem when per agent local constraints are absent and
show that such scheme can achieve geometric rate when the objective functions
are strongly convex and have Lipschitz continuous gradients. We have also
provided scalability/network dependency analysis. Based on these two
algorithms, we have further proposed a gradient projection-based algorithm
which can handle smooth objective and simple constraints more efficiently.
Numerical experiments demonstrates the viability and performance of all the
proposed algorithms.
",1,0,1,0,0,0
20790,Sensory Metrics of Neuromechanical Trust,"  Today digital sources supply an unprecedented component of human sensorimotor
data, the consumption of which is correlated with poorly understood maladies
such as Internet Addiction Disorder and Internet Gaming Disorder. This paper
offers a mathematical understanding of human sensorimotor processing as
multiscale, continuous-time vibratory interaction. We quantify human
informational needs using the signal processing metrics of entropy, noise,
dimensionality, continuity, latency, and bandwidth. Using these metrics, we
define the trust humans experience as a primitive statistical algorithm
processing finely grained sensorimotor data from neuromechanical interaction.
This definition of neuromechanical trust implies that artificial sensorimotor
inputs and interactions that attract low-level attention through frequent
discontinuities and enhanced coherence will decalibrate a brain's
representation of its world over the long term by violating the implicit
statistical contract for which self-calibration evolved. This approach allows
us to model addiction in general as the result of homeostatic regulation gone
awry in novel environments and digital dependency as a sub-case in which the
decalibration caused by digital sensorimotor data spurs yet more consumption of
them. We predict that institutions can use these sensorimotor metrics to
quantify media richness to improve employee well-being; that dyads and
family-size groups will bond and heal best through low-latency, high-resolution
multisensory interaction such as shared meals and reciprocated touch; and that
individuals can improve sensory and sociosensory resolution through deliberate
sensory reintegration practices. We conclude that we humans are the victims of
our own success, our hands so skilled they fill the world with captivating
things, our eyes so innocent they follow eagerly.
",0,1,0,0,0,0
20791,Quantum algorithms for training Gaussian Processes,"  Gaussian processes (GPs) are important models in supervised machine learning.
Training in Gaussian processes refers to selecting the covariance functions and
the associated parameters in order to improve the outcome of predictions, the
core of which amounts to evaluating the logarithm of the marginal likelihood
(LML) of a given model. LML gives a concrete measure of the quality of
prediction that a GP model is expected to achieve. The classical computation of
LML typically carries a polynomial time overhead with respect to the input
size. We propose a quantum algorithm that computes the logarithm of the
determinant of a Hermitian matrix, which runs in logarithmic time for sparse
matrices. This is applied in conjunction with a variant of the quantum linear
system algorithm that allows for logarithmic time computation of the form
$\mathbf{y}^TA^{-1}\mathbf{y}$, where $\mathbf{y}$ is a dense vector and $A$ is
the covariance matrix. We hence show that quantum computing can be used to
estimate the LML of a GP with exponentially improved efficiency under certain
conditions.
",0,0,0,1,0,0
20792,Detecting Oriented Text in Natural Images by Linking Segments,"  Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment is an oriented box covering a part of a word or text line; A link
connects two adjacent segments, indicating that they belong to the same word or
text line. Both elements are detected densely at multiple scales by an
end-to-end trained, fully-convolutional neural network. Final detections are
produced by combining segments connected by links. Compared with previous
methods, SegLink improves along the dimensions of accuracy, speed, and ease of
training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
Incidental (Challenge 4) benchmark, outperforming the previous best by a large
margin. It runs at over 20 FPS on 512x512 images. Moreover, without
modification, SegLink is able to detect long lines of non-Latin text, such as
Chinese.
",1,0,0,0,0,0
20793,Factorizable Module Algebras,"  The aim of this paper is to introduce and study a large class of
$\mathfrak{g}$-module algebras which we call factorizable by generalizing the
Gauss factorization of (square or rectangular) matrices. This class includes
coordinate algebras of corresponding reductive groups $G$, their parabolic
subgroups, basic affine spaces and many others. It turns out that tensor
products of factorizable algebras are also factorizable and it is easy to
create a factorizable algebra out of virtually any $\mathfrak{g}$-module
algebra. We also have quantum versions of all these constructions in the
category of $U_q(\mathfrak{g})$-module algebras. Quite surprisingly, our
quantum factorizable algebras are naturally acted on by the quantized
enveloping algebra $U_q(\mathfrak{g}^*)$ of the dual Lie bialgebra
$\mathfrak{g}^*$ of $\mathfrak{g}$.
",0,0,1,0,0,0
20794,Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors,"  Robot awareness of human actions is an essential research problem in robotics
with many important real-world applications, including human-robot
collaboration and teaming. Over the past few years, depth sensors have become a
standard device widely used by intelligent robots for 3D perception, which can
also offer human skeletal data in 3D space. Several methods based on skeletal
data were designed to enable robot awareness of human actions with satisfactory
accuracy. However, previous methods treated all body parts and features equally
important, without the capability to identify discriminative body parts and
features. In this paper, we propose a novel simultaneous Feature And Body-part
Learning (FABL) approach that simultaneously identifies discriminative body
parts and features, and efficiently integrates all available information
together to enable real-time robot awareness of human behaviors. We formulate
FABL as a regression-like optimization problem with structured
sparsity-inducing norms to model interrelationships of body parts and features.
We also develop an optimization algorithm to solve the formulated problem,
which possesses a theoretical guarantee to find the optimal solution. To
evaluate FABL, three experiments were performed using public benchmark
datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
robot in practical assistive living applications. Experimental results show
that our FABL approach obtains a high recognition accuracy with a processing
speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
to enable real-time robot awareness of human behaviors in practical robotics
applications.
",1,0,0,0,0,0
20795,Off-diagonal estimates of some Bergman-type operators on tube domains over symmetric cones,"  We obtain some necessary and sufficient conditions for the boundedness of a
family of positive operators defined on symmetric cones, we then deduce
off-diagonal boundedness of associated Bergman-type operators in tube domains
over symmetric cones.
",0,0,1,0,0,0
20796,Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation,"  Many methods for automated software test generation, including some that
explicitly use machine learning (and some that use ML more broadly conceived)
derive new tests from existing tests (often referred to as seeds). Often, the
seed tests from which new tests are derived are manually constructed, or at
least simpler than the tests that are produced as the final outputs of such
test generators. We propose annotation of generated tests with a provenance
(trail) showing how individual generated tests of interest (especially failing
tests) derive from seed tests, and how the population of generated tests
relates to the original seed tests. In some cases, post-processing of generated
tests can invalidate provenance information, in which case we also propose a
method for attempting to construct ""pseudo-provenance"" describing how the tests
could have been (partly) generated from seeds.
",1,0,0,1,0,0
20797,Learning Solving Procedure for Artificial Neural Network,"  It is expected that progress toward true artificial intelligence will be
achieved through the emergence of a system that integrates representation
learning and complex reasoning (LeCun et al. 2015). In response to this
prediction, research has been conducted on implementing the symbolic reasoning
of a von Neumann computer in an artificial neural network (Graves et al. 2016;
Graves et al. 2014; Reed et al. 2015). However, these studies have many
limitations in realizing neural-symbolic integration (Jaeger. 2016). Here, we
present a new learning paradigm: a learning solving procedure (LSP) that learns
the procedure for solving complex problems. This is not accomplished merely by
learning input-output data, but by learning algorithms through a solving
procedure that obtains the output as a sequence of tasks for a given input
problem. The LSP neural network system not only learns simple problems of
addition and multiplication, but also the algorithms of complicated problems,
such as complex arithmetic expression, sorting, and Hanoi Tower. To realize
this, the LSP neural network structure consists of a deep neural network and
long short-term memory, which are recursively combined. Through
experimentation, we demonstrate the efficiency and scalability of LSP and its
validity as a mechanism of complex reasoning.
",1,0,0,0,0,0
20798,On primordial black holes from an inflection point,"  Recently, it has been claimed that inflationary models with an inflection
point in the scalar potential can produce a large resonance in the power
spectrum of curvature perturbation. In this paper however we show that the
previous analyses are incorrect. The reason is twofold: firstly, the inflaton
is over-shot from a stage of standard inflation and so deviates from the
slow-roll attractor before reaching the inflection. Secondly, on the (or close
to) the inflection point, the ultra-slow-roll trajectory supersede the
slow-roll one and thus, the slow-roll approximations used in the literature
cannot be used. We then reconsider the model and provide a recipe for how to
produce nevertheless a large peak in the matter power spectrum via fine-tuning
of parameters.
",0,1,0,0,0,0
20799,Dropout as a Low-Rank Regularizer for Matrix Factorization,"  Regularization for matrix factorization (MF) and approximation problems has
been carried out in many different ways. Due to its popularity in deep
learning, dropout has been applied also for this class of problems. Despite its
solid empirical performance, the theoretical properties of dropout as a
regularizer remain quite elusive for this class of problems. In this paper, we
present a theoretical analysis of dropout for MF, where Bernoulli random
variables are used to drop columns of the factors. We demonstrate the
equivalence between dropout and a fully deterministic model for MF in which the
factors are regularized by the sum of the product of squared Euclidean norms of
the columns. Additionally, we inspect the case of a variable sized
factorization and we prove that dropout achieves the global minimum of a convex
approximation problem with (squared) nuclear norm regularization. As a result,
we conclude that dropout can be used as a low-rank regularizer with data
dependent singular-value thresholding.
",1,0,0,1,0,0
20800,Introduction to a Temporal Graph Benchmark,"  A temporal graph is a data structure, consisting of nodes and edges in which
the edges are associated with time labels. To analyze the temporal graph, the
first step is to find a proper graph dataset/benchmark. While many temporal
graph datasets exist online, none could be found that used the interval labels
in which each edge is associated with a starting and ending time. Therefore we
create a temporal graph data based on Wikipedia reference graph for temporal
analysis. This report aims to provide more details of this graph benchmark to
those who are interested in using it.
",1,1,0,0,0,0
20801,Orbifold equivalence: structure and new examples,"  Orbifold equivalence is a notion of symmetry that does not rely on group
actions. Among other applications, it leads to surprising connections between
hitherto unrelated singularities. While the concept can be defined in a very
general category-theoretic language, we focus on the most explicit setting in
terms of matrix factorisations, where orbifold equivalences arise from defects
with special properties. Examples are relatively difficult to construct, but we
uncover some structural features that distinguish orbifold equivalences -- most
notably a finite perturbation expansion. We use those properties to devise a
search algorithm, then present some new examples including Arnold
singularities.
",0,0,1,0,0,0
20802,Efficient Attention using a Fixed-Size Memory Representation,"  The standard content-based attention mechanism typically used in
sequence-to-sequence models is computationally expensive as it requires the
comparison of large encoder and decoder states at each time step. In this work,
we propose an alternative attention mechanism based on a fixed size memory
representation that is more efficient. Our technique predicts a compact set of
K attention contexts during encoding and lets the decoder compute an efficient
lookup that does not need to consult the memory. We show that our approach
performs on-par with the standard attention mechanism while yielding inference
speedups of 20% for real-world translation tasks and more for tasks with longer
sequences. By visualizing attention scores we demonstrate that our models learn
distinct, meaningful alignments.
",1,0,0,0,0,0
20803,Multiplicities of bifurcation sets of Pham singularities,"  The local multiplicities of the Maxwell sets in the spaces of versal
deformations of Pham holomorphic function singularities are calculated. A
similar calculation for some other bifurcation sets (generalized Stokes' sets)
defined by more complicated relations between the critical values is given.
Aplications to the complexity of algorithms enumerating topologically distinct
morsifications of complicated real function singularities are discussed.
",0,0,1,0,0,0
20804,Fast and scalable Gaussian process modeling with applications to astronomical time series,"  The growing field of large-scale time domain astronomy requires methods for
probabilistic data analysis that are computationally tractable, even with large
datasets. Gaussian Processes are a popular class of models used for this
purpose but, since the computational cost scales, in general, as the cube of
the number of data points, their application has been limited to small
datasets. In this paper, we present a novel method for Gaussian Process
modeling in one-dimension where the computational requirements scale linearly
with the size of the dataset. We demonstrate the method by applying it to
simulated and real astronomical time series datasets. These demonstrations are
examples of probabilistic inference of stellar rotation periods, asteroseismic
oscillation spectra, and transiting planet parameters. The method exploits
structure in the problem when the covariance function is expressed as a mixture
of complex exponentials, without requiring evenly spaced observations or
uniform noise. This form of covariance arises naturally when the process is a
mixture of stochastically-driven damped harmonic oscillators -- providing a
physical motivation for and interpretation of this choice -- but we also
demonstrate that it can be a useful effective model in some other cases. We
present a mathematical description of the method and compare it to existing
scalable Gaussian Process methods. The method is fast and interpretable, with a
range of potential applications within astronomical data analysis and beyond.
We provide well-tested and documented open-source implementations of this
method in C++, Python, and Julia.
",0,1,0,1,0,0
20805,Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs,"  Motivations Short-read accuracy is important for downstream analyses such as
genome assembly and hybrid long-read correction. Despite much work on
short-read correction, present-day correctors either do not scale well on large
data sets or consider reads as mere suites of k-mers, without taking into
account their full-length read information. Results We propose a new method to
correct short reads using de Bruijn graphs, and implement it as a tool called
Bcool. As a first st ep, Bcool constructs a compacted de Bruijn graph from the
reads. This graph is filtered on the basis of k-mer abundance then of unitig
abundance, thereby removing from most sequencing errors. The cleaned graph is
then used as a reference on which the reads are mapped to correct them. We show
that this approach yields more accurate reads than k-mer-spectrum correctors
while being scalable to human-size genomic datasets and beyond. Availability
and Implementation The implementation is open source and available at http:
//github.com/Malfoy/BCOOL under the Affero GPL license. Contact Antoine
Limasset antoine.limasset@gmail.com & Jean-François Flot jflot@ulb.ac.be &
Pierre Peterlongo pierre.peterlongo@inria.fr
",1,0,0,0,0,0
20806,Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning,"  Patient pain can be detected highly reliably from facial expressions using a
set of facial muscle-based action units (AUs) defined by the Facial Action
Coding System (FACS). A key characteristic of facial expression of pain is the
simultaneous occurrence of pain-related AU combinations, whose automated
detection would be highly beneficial for efficient and practical pain
monitoring. Existing general Automated Facial Expression Recognition (AFER)
systems prove inadequate when applied specifically for detecting pain as they
either focus on detecting individual pain-related AUs but not on combinations
or they seek to bypass AU detection by training a binary pain classifier
directly on pain intensity data but are limited by lack of enough labeled data
for satisfactory training. In this paper, we propose a new approach that mimics
the strategy of human coders of decoupling pain detection into two consecutive
tasks: one performed at the individual video-frame level and the other at
video-sequence level. Using state-of-the-art AFER tools to detect single AUs at
the frame level, we propose two novel data structures to encode AU combinations
from single AU scores. Two weakly supervised learning frameworks namely
multiple instance learning (MIL) and multiple clustered instance learning
(MCIL) are employed corresponding to each data structure to learn pain from
video sequences. Experimental results show an 87% pain recognition accuracy
with 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expression
dataset. Tests on long videos in a lung cancer patient video dataset
demonstrates the potential value of the proposed system for pain monitoring in
clinical settings.
",1,0,0,1,0,0
20807,A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects,"  Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over time. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectories in crowded scenes with frequent human interactions. In this work,
we consider the visibility status of a subject as a fluent variable, whose
change is mostly attributed to the subject's interaction with the surrounding,
e.g., crossing behind another object, entering a building, or getting into a
vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
causal-effect relations between an object's visibility fluent and its
activities, and develop a probabilistic graph model to jointly reason the
visibility fluent change (e.g., from visible to invisible) and track humans in
videos. We formulate this joint task as an iterative search of a feasible
causal graph structure that enables fast search algorithm, e.g., dynamic
programming method. We apply the proposed method on challenging video sequences
to evaluate its capabilities of estimating visibility fluent changes of
subjects and tracking subjects of interests over time. Results with comparisons
demonstrate that our method outperforms the alternative trackers and can
recover complete trajectories of humans in complicated scenarios with frequent
human interactions.
",1,0,0,0,0,0
20808,Exact Affine Counter Automata,"  We introduce an affine generalization of counter automata, and analyze their
ability as well as affine finite automata. Our contributions are as follows. We
show that there is a language that can be recognized by exact realtime affine
counter automata but by neither 1-way deterministic pushdown automata nor
realtime deterministic k-counter automata. We also show that a certain promise
problem, which is conjectured not to be solved by two-way quantum finite
automata in polynomial time, can be solved by Las Vegas affine finite automata.
Lastly, we show that how a counter helps for affine finite automata by showing
that the language MANYTWINS, which is conjectured not to be recognized by
affine, quantum or classical finite state models in polynomial time, can be
recognized by affine counter automata with one-sided bounded-error in realtime.
",1,0,0,0,0,0
20809,Preorder characterizations of lower separation axioms and their applications to foliations and flows,"  In this paper, we characterize several lower separation axioms $C_0, C_D$,
$C_R$, $C_N$, $\lambda$-space, nested, $S_{YS}$, $S_{YY}$, $S_{YS}$, and
$S_{\delta}$ using pre-order. To analyze topological properties of (resp.
dynamical systems) foliations, we introduce notions of topology (resp.
dynamical systems) for foliations. Then proper (resp. compact, minimal,
recurrent) foliations are characterized by separation axioms. Conversely, lower
separation axioms are interpreted into the condition for foliations and several
relations of them are described. Moreover, we introduce some notions for
topologies from dynamical systems and foliation theory.
",0,0,1,0,0,0
20810,Convergence of row sequences of simultaneous Padé-Faber approximants,"  We consider row sequences of vector valued Padé-Faber approximants
(simultaneous Padé-Faber approximants) and prove a Montessus de Ballore
type theorem.
",0,0,1,0,0,0
20811,A Deep Convolutional Neural Network for Background Subtraction,"  In this work, we present a novel background subtraction system that uses a
deep Convolutional Neural Network (CNN) to perform the segmentation. With this
approach, feature engineering and parameter tuning become unnecessary since the
network parameters can be learned from data by training a single CNN that can
handle various video scenes. Additionally, we propose a new approach to
estimate background model from video. For the training of the CNN, we employed
randomly 5 percent video frames and their ground truth segmentations taken from
the Change Detection challenge 2014(CDnet 2014). We also utilized
spatial-median filtering as the post-processing of the network outputs. Our
method is evaluated with different data-sets, and the network outperforms the
existing algorithms with respect to the average ranking over different
evaluation metrics. Furthermore, due to the network architecture, our CNN is
capable of real time processing.
",1,0,0,0,0,0
20812,A second main theorem for holomorphic curve intersecting hypersurfaces,"  In this paper, we establish a second main theorem for holomorphic curve
intersecting hypersurfaces in general position in projective space with level
of truncation. As an application, we reduce the number hypersurfaces in
uniqueness problem for holomorphic curve of authors before.
",0,0,1,0,0,0
20813,Partially hyperbolic diffeomorphisms with one-dimensional neutral center on 3-manifolds,"  We prove that for any partially hyperbolic diffeomorphism with one
dimensional neutral center on a 3-manifold, the center stable and center
unstable foliations are complete; moreover, each leaf of center stable and
center unstable foliations is a cylinder, a M$\ddot{o}$bius band or a plane.
Further properties of the Bonatti-Parwani-Potrie type of partially hyperbolic
diffeomorphisms are studied. Such examples are obtained by composing the time
$m$-map (for $m>0$ large) of a non-transitive Anosov flow $\phi_t$ on an
orientable 3-manifold with Dehn twists along some transverse tori, and the
examples are partially hyperbolic with one-dimensional neutral center. We prove
that the center foliation gives a topologically Anosov flow which is
topologically equivalent to $\phi_t$. We also prove that for the precise
example constructed by Bonatti-Parwani-Potrie, the center stable and center
unstable foliations are robustly complete.
",0,0,1,0,0,0
20814,Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network,"  Speech enhancement (SE) aims to reduce noise in speech signals. Most SE
techniques focus on addressing audio information only. In this work, inspired
by multimodal learning, which utilizes data from different modalities, and the
recent success of convolutional neural networks (CNNs) in SE, we propose an
audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual
streams into a unified network model. In the proposed AVDCNN SE model, audio
and visual data are first processed using individual CNNs, and then, fused into
a joint network to generate enhanced speech at the output layer. The AVDCNN
model is trained in an end-to-end manner, and parameters are jointly learned
through back-propagation. We evaluate enhanced speech using five objective
criteria. Results show that the AVDCNN yields notably better performance,
compared with an audio-only CNN-based SE model and two conventional SE
approaches, confirming the effectiveness of integrating visual information into
the SE process.
",1,0,0,1,0,0
20815,"Finding structure in the dark: coupled dark energy, weak lensing, and the mildly nonlinear regime","  We reexamine interactions between the dark sectors of cosmology, with a focus
on robust constraints that can be obtained using only mildly nonlinear scales.
While it is well known that couplings between dark matter and dark energy can
be constrained to the percent level when including the full range of scales
probed by future optical surveys, calibrating matter power spectrum emulators
to all possible choices of potentials and couplings requires many
computationally expensive n-body simulations. Here we show that lensing and
clustering of galaxies in combination with the Cosmic Microwave Background
(CMB) is capable of probing the dark sector coupling to the few percent level
for a given class of models, using only linear and quasi-linear Fourier modes.
These scales can, in principle, be described by semi-analytical techniques such
as the effective field theory of large-scale structure.
",0,1,0,0,0,0
20816,Semantic Autoencoder for Zero-Shot Learning,"  Existing zero-shot learning (ZSL) models typically learn a projection
function from a feature space to a semantic embedding space (e.g.~attribute
space). However, such a projection function is only concerned with predicting
the training seen class semantic representation (e.g.~attribute prediction) or
classification. When applied to test data, which in the context of ZSL contains
different (unseen) classes without training data, a ZSL model typically suffers
from the project domain shift problem. In this work, we present a novel
solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
encoder-decoder paradigm, an encoder aims to project a visual feature vector
into the semantic space as in the existing ZSL models. However, the decoder
exerts an additional constraint, that is, the projection/code must be able to
reconstruct the original visual feature. We show that with this additional
reconstruction constraint, the learned projection function from the seen
classes is able to generalise better to the new unseen classes. Importantly,
the encoder and decoder are linear and symmetric which enable us to develop an
extremely efficient learning algorithm. Extensive experiments on six benchmark
datasets demonstrate that the proposed SAE outperforms significantly the
existing ZSL models with the additional benefit of lower computational cost.
Furthermore, when the SAE is applied to supervised clustering problem, it also
beats the state-of-the-art.
",1,0,0,0,0,0
20817,An Information-Theoretic Optimality Principle for Deep Reinforcement Learning,"  We methodologically address the problem of Q-value overestimation in deep
reinforcement learning to handle high-dimensional state spaces efficiently. By
adapting concepts from information theory, we introduce an intrinsic penalty
signal encouraging reduced Q-value estimates. The resultant algorithm
encompasses a wide range of learning outcomes containing deep Q-networks as a
special case. Different learning outcomes can be demonstrated by tuning a
Lagrange multiplier accordingly. We furthermore propose a novel scheduling
scheme for this Lagrange multiplier to ensure efficient and robust learning. In
experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and
double deep Q-networks) in terms of both game-play performance and sample
complexity. These results remain valid under the recently proposed dueling
architecture.
",1,0,0,1,0,0
20818,Noise-synchronizability of opinion dynamics,"  With the analysis of noise-induced synchronization of opinion dynamics with
bounded confidence (BC), a natural and fundamental question is what opinion
structures can be synchronized by noise. In the traditional Hegselmann-Krause
(HK) model, each agent examines the opinion values of all the other ones and
then choose neighbors to update its own opinion according to the BC scheme. In
reality, people are more likely to interchange opinions with only some
individuals, resulting in a predetermined local discourse relationship as
introduced by the DeGroot model. In this paper, we consider an opinion dynamics
that combines the schemes of BC and local discourse topology and investigate
its synchronization induced by noise. The new model endows the heterogeneous HK
model with a time-varying discourse topology. With the proposed definition of
noise-synchronizability, it is shown that the compound noisy model is almost
surely noise-synchronizable if and only if the time-varying discourse graph is
uniformly jointly connected, taking the noise-induced synchronization of the
classical heterogeneous HK model as a special case. As a natural implication,
the result for the first time builds the equivalence between the connectivity
of discourse graph and the beneficial effect of noise for opinion consensus.
",1,0,0,0,0,0
20819,Conformer-selection by matter-wave interference,"  We establish that matter-wave interference at near-resonant ultraviolet
optical gratings can be used to spatially separate individual conformers of
complex molecules. Our calculations show that the conformational purity of the
prepared beam can be close to 100% and that all molecules remain in their
electronic ground state. The proposed technique is independent of the dipole
moment and the spin of the molecule and thus paves the way for
structure-sensitive experiments with hydrocarbons and biomolecules, such as
neurotransmitters and hormones, which evaded conformer-pure isolation so far
",0,1,0,0,0,0
20820,Building a Neural Machine Translation System Using Only Synthetic Parallel Data,"  Recent works have shown that synthetic parallel data automatically generated
by translation models can be effective for various neural machine translation
(NMT) issues. In this study, we build NMT systems using only synthetic parallel
data. As an efficient alternative to real parallel data, we also present a new
type of synthetic parallel corpus. The proposed pseudo parallel data are
distinct from previous works in that ground truth and synthetic examples are
mixed on both sides of sentence pairs. Experiments on Czech-German and
French-German translations demonstrate the efficacy of the proposed pseudo
parallel corpus, which shows not only enhanced results for bidirectional
translation tasks but also substantial improvement with the aid of a ground
truth real parallel corpus.
",1,0,0,0,0,0
20821,Learning to Predict Indoor Illumination from a Single Image,"  We propose an automatic method to infer high dynamic range illumination from
a single, limited field-of-view, low dynamic range photograph of an indoor
scene. In contrast to previous work that relies on specialized image capture,
user input, and/or simple scene models, we train an end-to-end deep neural
network that directly regresses a limited field-of-view photo to HDR
illumination, without strong assumptions on scene geometry, material
properties, or lighting. We show that this can be accomplished in a three step
process: 1) we train a robust lighting classifier to automatically annotate the
location of light sources in a large dataset of LDR environment maps, 2) we use
these annotations to train a deep neural network that predicts the location of
lights in a scene from a single limited field-of-view photo, and 3) we
fine-tune this network using a small dataset of HDR environment maps to predict
light intensities. This allows us to automatically recover high-quality HDR
illumination estimates that significantly outperform previous state-of-the-art
methods. Consequently, using our illumination estimates for applications like
3D object insertion, we can achieve results that are photo-realistic, which is
validated via a perceptual user study.
",1,0,0,1,0,0
20822,$H^\infty$-calculus for semigroup generators on BMO,"  We prove that the negative infinitesimal generator $L$ of a semigroup of
positive contractions on $L^\infty$ has a bounded $H^\infty(S_\eta^0)$-calculus
on BMO$(\sqrt L)$ for any angle $\eta>\pi/2$, provided the semigroup satisfies
Bakry-Emry's $\Gamma_2 $ criterion. Our arguments only rely on the properties
of the underlying semigroup and works well in the noncommutative setting. A key
ingredient of our argument is a quasi monotone property for the subordinated
semigroup $T_{t,\alpha}=e^{-tL^\alpha},0<\alpha<1$, that is proved in the first
half of the article.
",0,0,1,0,0,0
20823,A Bayesian Game without epsilon equilibria,"  We present a three player Bayesian game for which there is no epsilon
equilibria in Borel measurable strategies for small enough epsilon, however
there are non-measurable equilibria.
",1,0,1,0,0,0
20824,Towards Robust Interpretability with Self-Explaining Neural Networks,"  Most recent work on interpretability of complex machine learning models has
focused on estimating $\textit{a posteriori}$ explanations for previously
trained models around specific predictions. $\textit{Self-explaining}$ models
where interpretability plays a key role already during learning have received
much less attention. We propose three desiderata for explanations in general --
explicitness, faithfulness, and stability -- and show that existing methods do
not satisfy them. In response, we design self-explaining models in stages,
progressively generalizing linear classifiers to complex yet architecturally
explicit models. Faithfulness and stability are enforced via regularization
specifically tailored to such models. Experimental results across various
benchmark datasets show that our framework offers a promising direction for
reconciling model complexity and interpretability.
",0,0,0,1,0,0
20825,"Deep Learning Scaling is Predictable, Empirically","  Deep learning (DL) creates impactful advances following a virtuous recipe:
model architecture search, creating large training data sets, and scaling
computation. It is widely believed that growing training sets and models should
improve accuracy and result in better products. As DL application domains grow,
we would like a deeper understanding of the relationships between training set
size, computational scale, and model accuracy improvements to advance the
state-of-the-art.
This paper presents a large scale empirical characterization of
generalization error and model size growth as training sets grow. We introduce
a methodology for this measurement and test four machine learning domains:
machine translation, language modeling, image processing, and speech
recognition. Our empirical results show power-law generalization error scaling
across a breadth of factors, resulting in power-law exponents---the ""steepness""
of the learning curve---yet to be explained by theoretical work. Further, model
improvements only shift the error but do not appear to affect the power-law
exponent. We also show that model size scales sublinearly with data size. These
scaling relationships have significant implications on deep learning research,
practice, and systems. They can assist model debugging, setting accuracy
targets, and decisions about data set growth. They can also guide computing
system design and underscore the importance of continued computational scaling.
",1,0,0,1,0,0
20826,Coupled Self-Organized Hydrodynamics and Stokes models for suspensions of active particles,"  We derive macroscopic dynamics for self-propelled particles in a fluid. The
starting point is a coupled Vicsek-Stokes system. The Vicsek model describes
self-propelled agents interacting through alignment. It provides a
phenomenological description of hydrodynamic interactions between agents at
high density. Stokes equations describe a low Reynolds number fluid. These two
dynamics are coupled by the interaction between the agents and the fluid. The
fluid contributes to rotating the particles through Jeffery's equation.
Particle self-propulsion induces a force dipole on the fluid. After
coarse-graining we obtain a coupled Self-Organised Hydrodynamics (SOH)-Stokes
system. We perform a linear stability analysis for this system which shows that
both pullers and pushers have unstable modes. We conclude by providing
extensions of the Vicsek-Stokes model including short-distance repulsion,
finite particle inertia and finite Reynolds number fluid regime.
",0,1,1,0,0,0
20827,Tunable Spin-Orbit Torques in Cu-Ta Binary Alloy Heterostructures,"  The spin Hall effect (SHE) is found to be strong in heavy transition metals
(HM), such as Ta and W, in their amorphous and/or high resistivity form. In
this work, we show that by employing a Cu-Ta binary alloy as buffer layer in an
amorphous Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructure with
perpendicular magnetic anisotropy (PMA), the SHE-induced damping-like
spin-orbit torque (DL-SOT) efficiency $|\xi_{DL}|$ can be linearly tuned by
adjusting the buffer layer resistivity. Current-induced SOT switching can also
be achieved in these Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructures, and
we find the switching behavior better explained by a SOT-assisted domain wall
propagation picture. Through systematic studies on Cu$_{100-x}$Ta$_{x}$-based
samples with various compositions, we determine the lower bound of spin Hall
conductivity
$|\sigma_{SH}|\approx2.02\times10^{4}[\hbar/2e]\Omega^{-1}\cdot\operatorname{m}^{-1}$
in the Ta-rich regime. Based on the idea of resistivity tuning, we further
demonstrate that $|\xi_{DL}|$ can be enhanced from 0.087 for pure Ta to 0.152
by employing a resistive TaN buffer layer.
",0,1,0,0,0,0
20828,On Properties of Nests: Some Answers and Questions,"  By considering nests on a given space, we explore order-theoretical and
topological properties that are closely related to the structure of a nest. In
particular, we see how subbases given by two dual nests can be an indicator of
how close or far are the properties of the space from the structure of a
linearly ordered space. Having in mind that the term interlocking nest is a key
tool to a general solution of the orderability problem, we give a
characterization of interlocking nest via closed sets in the Alexandroff
topology and via lower sets, respectively. We also characterize bounded subsets
of a given set in terms of nests and, finally, we explore the possibility of
characterizing topological groups via properties of nests. All sections are
followed by a number of open questions, which may give new directions to the
orderability problem.
",0,0,1,0,0,0
20829,Differential Characters of Drinfeld Modules and de Rham Cohomology,"  We introduce differential characters of Drinfeld modules. These are
function-field analogues of Buium's p-adic differential characters of elliptic
curves and of Manin's differential characters of elliptic curves in
differential algebra, both of which have had notable Diophantine applications.
We determine the structure of the group of differential characters. This shows
the existence of a family of interesting differential modular functions on the
moduli of Drinfeld modules. It also leads to a canonical $F$-crystal equipped
with a map to the de Rham cohomology of the Drinfeld module. This $F$-crystal
is of a differential-algebraic nature, and the relation to the classical
cohomological realizations is presently not clear.
",0,0,1,0,0,0
20830,Strong submeasures and several applications,"  A strong submeasure on a compact metric space X is a sub-linear and bounded
operator on the space of continuous functions on X. A strong submeasure is
positive if it is non-decreasing. By Hahn-Banach theorem, a positive strong
submeasure is the supremum of a non-empty collection of measures whose masses
are uniformly bounded from above.
We give several applications of strong submeasures in various diverse topics,
thus illustrate the usefulness of this classical but largely overlooked notion.
The applications include:
- Pullback and pushforward of all measures by meromorphic selfmaps of compact
complex varieties.
- The existence of invariant positive strong submeasures for meromorphic maps
between compact complex varieties, a notion of entropy for such submeasures
(which coincide with the classical ones in good cases) and a version of the
Variation Principle.
- Intersection of every positive closed (1,1) currents on compact Kähler
manifolds. Explicit calculations are given for self-intersection of the current
of integration of some curves $C$ in a compact Kähler surface where the
self-intersection in cohomology is negative.
All of these points are new and have not been previously given in work by
other authors. In addition, we will apply the same ideas to entropy of
transcendental maps of $\mathbb{C}$ and $\mathbb{C}^2$.
",0,0,1,0,0,0
20831,Understanding Career Progression in Baseball Through Machine Learning,"  Professional baseball players are increasingly guaranteed expensive long-term
contracts, with over 70 deals signed in excess of \$90 million, mostly in the
last decade. These are substantial sums compared to a typical franchise
valuation of \$1-2 billion. Hence, the players to whom a team chooses to give
such a contract can have an enormous impact on both competitiveness and profit.
Despite this, most published approaches examining career progression in
baseball are fairly simplistic. We applied four machine learning algorithms to
the problem and soundly improved upon existing approaches, particularly for
batting data.
",1,0,0,1,0,0
20832,Testing the validity of the local and global GKLS master equations on an exactly solvable model,"  When deriving a master equation for a multipartite weakly-interacting open
quantum systems, dissipation is often addressed \textit{locally} on each
component, i.e. ignoring the coherent couplings, which are later added `by
hand'. Although simple, the resulting local master equation (LME) is known to
be thermodynamically inconsistent. Otherwise, one may always obtain a
consistent \textit{global} master equation (GME) by working on the energy basis
of the full interacting Hamiltonian. Here, we consider a two-node `quantum
wire' connected to two heat baths. The stationary solution of the LME and GME
are obtained and benchmarked against the exact result. Importantly, in our
model, the validity of the GME is constrained by the underlying secular
approximation. Whenever this breaks down (for resonant weakly-coupled nodes),
we observe that the LME, in spite of being thermodynamically flawed: (a)
predicts the correct steady state, (b) yields the exact asymptotic heat
currents, and (c) reliably reflects the correlations between the nodes. In
contrast, the GME fails at all three tasks. Nonetheless, as the inter-node
coupling grows, the LME breaks down whilst the GME becomes correct. Hence, the
global and local approach may be viewed as \textit{complementary} tools, best
suited to different parameter regimes.
",0,1,0,0,0,0
20833,Unsupervised Contact Learning for Humanoid Estimation and Control,"  This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.
",1,0,0,0,0,0
20834,Learning to Sequence Robot Behaviors for Visual Navigation,"  Recent literature in the robotics community has focused on learning robot
behaviors that abstract out lower-level details of robot control. To fully
leverage the efficacy of such behaviors, it is necessary to select and sequence
them to achieve a given task. In this paper, we present an approach to both
learn and sequence robot behaviors, applied to the problem of visual navigation
of mobile robots. We construct a layered representation of control policies
composed of low- level behaviors and a meta-level policy. The low-level
behaviors enable the robot to locomote in a particular environment while
avoiding obstacles, and the meta-level policy actively selects the low-level
behavior most appropriate for the current situation based purely on visual
feedback. We demonstrate the effectiveness of our method on three simulated
robot navigation tasks: a legged hexapod robot which must successfully traverse
varying terrain, a wheeled robot which must navigate a maze-like course while
avoiding obstacles, and finally a wheeled robot navigating in the presence of
dynamic obstacles. We show that by learning control policies in a layered
manner, we gain the ability to successfully traverse new compound environments
composed of distinct sub-environments, and outperform both the low-level
behaviors in their respective sub-environments, as well as a hand-crafted
selection of low-level policies on these compound environments.
",1,0,0,0,0,0
20835,"Evaluation complexity bounds for smooth constrained nonlinear optimisation using scaled KKT conditions, high-order models and the criticality measure $χ$","  Evaluation complexity for convexly constrained optimization is considered and
it is shown first that the complexity bound of $O(\epsilon^{-3/2})$ proved by
Cartis, Gould and Toint (IMAJNA 32(4) 2012, pp.1662-1695) for computing an
$\epsilon$-approximate first-order critical point can be obtained under
significantly weaker assumptions. Moreover, the result is generalized to the
case where high-order derivatives are used, resulting in a bound of
$O(\epsilon^{-(p+1)/p})$ evaluations whenever derivatives of order $p$ are
available. It is also shown that the bound of
$O(\epsilon_P^{-1/2}\epsilon_D^{-3/2})$ evaluations ($\epsilon_P$ and
$\epsilon_D$ being primal and dual accuracy thresholds) suggested by Cartis,
Gould and Toint (SINUM, 2015) for the general nonconvex case involving both
equality and inequality constraints can be generalized to a bound of
$O(\epsilon_P^{-1/p}\epsilon_D^{-(p+1)/p})$ evaluations under similarly
weakened assumptions. This paper is variant of a companion report (NTR-11-2015,
University of Namur, Belgium) which uses a different first-order criticality
measure to obtain the same complexity bounds.
",1,0,1,0,0,0
20836,One level density of low-lying zeros of quadratic and quartic Hecke $L$-functions,"  In this paper, we prove some one level density results for the low-lying
zeros of famliies of quadratic and quartic Hecke $L$-functions of the Gaussian
field. As corollaries, we deduce that, respectively, at least $94.27 \%$ and
$5\%$ of the members of the quadratic family and the quartic family do not
vanish at the central point.
",0,0,1,0,0,0
20837,An Online Convex Optimization Approach to Dynamic Network Resource Allocation,"  Existing approaches to online convex optimization (OCO) make sequential
one-slot-ahead decisions, which lead to (possibly adversarial) losses that
drive subsequent decision iterates. Their performance is evaluated by the
so-called regret that measures the difference of losses between the online
solution and the best yet fixed overall solution in hindsight. The present
paper deals with online convex optimization involving adversarial loss
functions and adversarial constraints, where the constraints are revealed after
making decisions, and can be tolerable to instantaneous violations but must be
satisfied in the long term. Performance of an online algorithm in this setting
is assessed by: i) the difference of its losses relative to the best dynamic
solution with one-slot-ahead information of the loss function and the
constraint (that is here termed dynamic regret); and, ii) the accumulated
amount of constraint violations (that is here termed dynamic fit). In this
context, a modified online saddle-point (MOSP) scheme is developed, and proved
to simultaneously yield sub-linear dynamic regret and fit, provided that the
accumulated variations of per-slot minimizers and constraints are sub-linearly
growing with time. MOSP is also applied to the dynamic network resource
allocation task, and it is compared with the well-known stochastic dual
gradient method. Under various scenarios, numerical experiments demonstrate the
performance gain of MOSP relative to the state-of-the-art.
",1,0,1,1,0,0
20838,Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity,"  Causal discovery from empirical data is a fundamental problem in many
scientific domains. Observational data allows for identifiability only up to
Markov equivalence class. In this paper we first propose a polynomial time
algorithm for learning the exact correctly-oriented structure of the transitive
reduction of any causal Bayesian networks with high probability, by using
interventional path queries. Each path query takes as input an origin node and
a target node, and answers whether there is a directed path from the origin to
the target. This is done by intervening the origin node and observing samples
from the target node. We theoretically show the logarithmic sample complexity
for the size of interventional data per path query, for continuous and discrete
networks. We further extend our work to learn the transitive edges using
logarithmic sample complexity (albeit in time exponential in the maximum number
of parents for discrete networks). This allows us to learn the full network. We
also provide an analysis of imperfect interventions.
",1,0,0,1,0,0
20839,The Fredholm alternative for the $p$-Laplacian in exterior domains,"  We investigate the Fredholm alternative for the $p$-Laplacian in an exterior
domain which is the complement of the closed unit ball in $\mathbb{R}^N$
($N\geq 2$). By employing techniques of Calculus of Variations we obtain the
multiplicity of solutions. The striking difference between our case and the
entire space case is also discussed.
",0,0,1,0,0,0
20840,Thermodynamic Stabilization of Precipitates through Interface Segregation: Chemical Effects,"  Precipitation hardening, which relies on a high density of intermetallic
precipitates, is a commonly utilized technique for strengthening structural
alloys. Structural alloys are commonly strengthened through a high density of
small size intermetallic precipitates. At high temperatures, however, the
precipitates coarsen to reduce the excess energy of the interface, resulting in
a significant reduction in the strengthening provided by the precipitates. In
certain ternary alloys, the secondary solute segregates to the interface and
results in the formation of a high density of nanosize precipitates that
provide enhanced strength and are resistant to coarsening. To understand the
chemical effects involved, and to identify such systems, we develop a
thermodynamic model using the framework of the regular nanocrystalline solution
model. For various global compositions, temperatures and thermodynamic
parameters, equilibrium configuration of Mg-Sn-Zn alloy is evaluated by
minimizing the Gibbs free energy function with respect to the region-specific
(bulk solid-solution, interface and precipitate) concentrations and sizes. The
results show that Mg$_2$Sn precipitates can be stabilized to nanoscale sizes
through Zn segregation to Mg/Mg$_2$Sn interface, and the precipitates can be
stabilized against coarsening at high-temperatures by providing a larger Zn
concentration in the system. Together with the inclusion of elastic strain
energy effects and the input of computationally informed interface
thermodynamic parameters in the future, the model is expected to provide a more
realistic prediction of segregation and precipitate stabilization in ternary
alloys of structural importance.
",0,1,0,0,0,0
20841,Wavelength Does Not Equal Pressure: Vertical Contribution Functions and their Implications for Mapping Hot Jupiters,"  Multi-band phase variations in principle allow us to infer the longitudinal
temperature distributions of planets as a function of height in their
atmospheres. For example, 3.6 micron emission originates from deeper layers of
the atmosphere than 4.5 micron due to greater water vapor absorption at the
longer wavelength. Since heat transport efficiency increases with pressure, we
expect thermal phase curves at 3.6 micron to exhibit smaller amplitudes and
greater phase offsets than at 4.5 micron; this trend is not observed. Of the
seven hot Jupiters with full-orbit phase curves at 3.6 and 4.5 micron, all have
greater phase amplitude at 3.6 micron than at 4.5 micron, while four of seven
exhibit a greater phase offset at 3.6 micron. We use a 3D
radiative-hydrodynamic model to calculate theoretical phase curves of HD
189733b, assuming thermo-chemical equilibrium. The model exhibits temperature,
pressure, and wavelength dependent opacity, primarily driven by carbon
chemistry: CO is energetically favored on the dayside, while CH4 is favored on
the cooler nightside. Infrared opacity therefore changes by orders of magnitude
between day and night, producing dramatic vertical shifts in the
wavelength-specific photospheres, which would complicate eclipse or phase
mapping with spectral data. The model predicts greater relative phase amplitude
and greater phase offset at 3.6 micron than at 4.5 micron, in agreement with
the data. Our model qualitatively explains the observed phase curves, but is in
tension with current thermo-chemical kinetics models that predict zonally
uniform atmospheric composition due to transport of CO from the hot regions of
the atmosphere.
",0,1,0,0,0,0
20842,Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction,"  We introduce a new algorithm, called CDER, for supervised machine learning
that merges the multi-scale geometric properties of Cover Trees with the
information-theoretic properties of entropy. CDER applies to a training set of
labeled pointclouds embedded in a common Euclidean space. If typical
pointclouds corresponding to distinct labels tend to differ at any scale in any
sub-region, CDER can identify these differences in (typically) linear time,
creating a set of distributional coordinates which act as a feature extraction
mechanism for supervised learning. We describe theoretical properties and
implementation details of CDER, and illustrate its benefits on several
synthetic examples.
",1,0,0,1,0,0
20843,Design Considerations for Proposed Fermilab Integrable RCS,"  Integrable optics is an innovation in particle accelerator design that
provides strong nonlinear focusing while avoiding parametric resonances. One
promising application of integrable optics is to overcome the traditional
limits on accelerator intensity imposed by betatron tune-spread and collective
instabilities. The efficacy of high-intensity integrable accelerators will be
undergo comprehensive testing over the next several years at the Fermilab
Integrable Optics Test Accelerator (IOTA) and the University of Maryland
Electron Ring (UMER). We propose an integrable Rapid-Cycling Synchrotron (iRCS)
as a replacement for the Fermilab Booster to achieve multi-MW beam power for
the Fermilab high-energy neutrino program. We provide a overview of the machine
parameters and discuss an approach to lattice optimization. Integrable optics
requires arcs with integer-pi phase advance followed by drifts with matched
beta functions. We provide an example integrable lattice with features of a
modern RCS - long dispersion-free drifts, low momentum compaction,
superperiodicity, chromaticity correction, separate-function magnets, and
bounded beta functions.
",0,1,0,0,0,0
20844,Consistent Estimation in General Sublinear Preferential Attachment Trees,"  We propose an empirical estimator of the preferential attachment function $f$
in the setting of general preferential attachment trees. Using a supercritical
continuous-time branching process framework, we prove the almost sure
consistency of the proposed estimator. We perform simulations to study the
empirical properties of our estimators.
",0,0,1,1,0,0
20845,Learned Watershed: End-to-End Learning of Seeded Segmentation,"  Learned boundary maps are known to outperform hand- crafted ones as a basis
for the watershed algorithm. We show, for the first time, how to train
watershed computation jointly with boundary map prediction. The estimator for
the merging priorities is cast as a neural network that is con- volutional
(over space) and recurrent (over iterations). The latter allows learning of
complex shape priors. The method gives the best known seeded segmentation
results on the CREMI segmentation challenge.
",1,0,0,0,0,0
20846,MAGIC Contributions to the 35th International Cosmic Ray Conference (ICRC2017),"  MAGIC (Major Atmospheric Gamma Imaging Cherenkov) is a system of two 17 m
diameter, F/1.03 Imaging Atmospheric Cherenkov Telescopes (IACT). They are
dedicated to the observation of gamma rays from galactic and extragalactic
sources in the very high energy range (VHE, 30 GeV to 100 TeV). This submission
contains links to the proceedings for the 35th International Cosmic Ray
Conference (ICRC2017), held in Bexco, Busan, Korea from the 12th to the 17th of
July, 2017.
",0,1,0,0,0,0
20847,Efficient Hidden Vector Encryptions and Its Applications,"  Predicate encryption is a new paradigm of public key encryption that enables
searches on encrypted data. Using the predicate encryption, we can search
keywords or attributes on encrypted data without decrypting the ciphertexts. In
predicate encryption, a ciphertext is associated with attributes and a token
corresponds to a predicate. The token that corresponds to a predicate $f$ can
decrypt the ciphertext associated with attributes $x$ if and only if $f(x)=1$.
Hidden vector encryption (HVE) is a special kind of predicate encryption. In
this thesis, we consider the efficiency, the generality, and the security of
HVE schemes. The results of this thesis are described as follows.
The first results of this thesis are efficient HVE schemes where the token
consists of just four group elements and the decryption only requires four
bilinear map computations, independent of the number of attributes in the
ciphertext. The construction uses composite order bilinear groups and is
selectively secure under the well-known assumptions. The second results are
efficient HVE schemes that are secure under any kind of pairing types. To
achieve our goals, we proposed a general framework that converts HVE schemes
from composite order bilinear groups to prime order bilinear groups. Using the
framework, we convert the previous HVE schemes from composite order bilinear
groups to prime order bilinear groups. The third results are fully secure HVE
schemes with short tokens. Previous HVE schemes were proven to be secure only
in the selective security model where the capabilities of the adversaries are
severely restricted. Using the dual system encryption techniques, we construct
fully secure HVE schemes with match revealing property in composite order
groups.
",1,0,0,0,0,0
20848,The Galaxy Clustering Crisis in Abundance Matching,"  Galaxy clustering on small scales is significantly under-predicted by
sub-halo abundance matching (SHAM) models that populate (sub-)haloes with
galaxies based on peak halo mass, $M_{\rm peak}$. SHAM models based on the peak
maximum circular velocity, $V_{\rm peak}$, have had much better success. The
primary reason $M_{\rm peak}$ based models fail is the relatively low abundance
of satellite galaxies produced in these models compared to those based on
$V_{\rm peak}$. Despite success in predicting clustering, a simple $V_{\rm
peak}$ based SHAM model results in predictions for galaxy growth that are at
odds with observations. We evaluate three possible remedies that could ""save""
mass-based SHAM: (1) SHAM models require a significant population of ""orphan""
galaxies as a result of artificial disruption/merging of sub-haloes in modern
high resolution dark matter simulations; (2) satellites must grow significantly
after their accretion; and (3) stellar mass is significantly affected by halo
assembly history. No solution is entirely satisfactory. However, regardless of
the particulars, we show that popular SHAM models based on $M_{\rm peak}$
cannot be complete physical models as presented. Either $V_{\rm peak}$ truly is
a better predictor of stellar mass at $z\sim 0$ and it remains to be seen how
the correlation between stellar mass and $V_{\rm peak}$ comes about, or SHAM
models are missing vital component(s) that significantly affect galaxy
clustering.
",0,1,0,0,0,0
20849,Supplying Dark Energy from Scalar Field Dark Matter,"  We consider the hypothesis that dark matter and dark energy consists of
ultra-light self-interacting scalar particles. It is found that the
Klein-Gordon equation with only two free parameters (mass and self-coupling) on
a Schwarzschild background, at the galactic length-scales has the solution
which corresponds to Bose-Einstein condensate, behaving as dark matter, while
the constant solution at supra-galactic scales can explain dark energy.
",0,1,0,0,0,0
20850,GHz-Band Integrated Magnetic Inductors,"  The demand on mobile electronics to continue to shrink in size while increase
in efficiency drives the demand on the internal passive components to do the
same. Power amplifiers require inductors with small form factors, high quality
factors, and high operating frequency in the single-digit GHz range. This work
explores the use of magnetic materials to satisfy the needs of power amplifier
inductor applications. This paper discusses the optimization choices regarding
material selection, device design, and fabrication methodology. The inductors
achieved here present the best performance to date for an integrated magnetic
core inductor at high frequencies with a 1 nH inductance and peak quality
factor of 4 at ~3 GHz. Such compact inductors show potential for efficiently
meeting the need of mobile electronics in the future.
",0,1,0,0,0,0
20851,Acylindrical actions on projection complexes,"  We simplify the construction of projection complexes due to
Bestvina-Bromberg-Fujiwara. To do so, we introduce a sharper version of the
Behrstock inequality, and show that it can always be enforced. Furthermore, we
use the new setup to prove acylindricity results for the action on the
projection complexes. We also treat quasi-trees of metric spaces associated to
projection complexes, and prove an acylindricity criterion in that context as
well.
",0,0,1,0,0,0
20852,Introducing Geometric Algebra to Geometric Computing Software Developers: A Computational Thinking Approach,"  Designing software systems for Geometric Computing applications can be a
challenging task. Software engineers typically use software abstractions to
hide and manage the high complexity of such systems. Without the presence of a
unifying algebraic system to describe geometric models, the use of software
abstractions alone can result in many design and maintenance problems.
Geometric Algebra (GA) can be a universal abstract algebraic language for
software engineering geometric computing applications. Few sources, however,
provide enough information about GA-based software implementations targeting
the software engineering community. In particular, successfully introducing GA
to software engineers requires quite different approaches from introducing GA
to mathematicians or physicists. This article provides a high-level
introduction to the abstract concepts and algebraic representations behind the
elegant GA mathematical structure. The article focuses on the conceptual and
representational abstraction levels behind GA mathematics with sufficient
references for more details. In addition, the article strongly recommends
applying the methods of Computational Thinking in both introducing GA to
software engineers, and in using GA as a mathematical language for developing
Geometric Computing software systems.
",1,0,0,0,0,0
20853,Recovering Nonuniform Planted Partitions via Iterated Projection,"  In the planted partition problem, the $n$ vertices of a random graph are
partitioned into $k$ ""clusters,"" and edges between vertices in the same cluster
and different clusters are included with constant probability $p$ and $q$,
respectively (where $0 \le q < p \le 1$). We give an efficient spectral
algorithm that recovers the clusters with high probability, provided that the
sizes of any two clusters are either very close or separated by $\geq
\Omega(\sqrt n)$. We also discuss a generalization of planted partition in
which the algorithm's input is not a random graph, but a random real symmetric
matrix with independent above-diagonal entries.
Our algorithm is an adaptation of a previous algorithm for the uniform case,
i.e., when all clusters are size $n / k \geq \Omega(\sqrt n)$. The original
algorithm recovers the clusters one by one via iterated projection: it
constructs the orthogonal projection operator onto the dominant $k$-dimensional
eigenspace of the random graph's adjacency matrix, uses it to recover one of
the clusters, then deletes it and recurses on the remaining vertices. We show
herein that a similar algorithm works in the nonuniform case.
",1,0,0,0,0,0
20854,Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory,"  Business Architecture (BA) plays a significant role in helping organizations
understand enterprise structures and processes, and align them with strategic
objectives. However, traditional BAs are represented in fixed structure with
static model elements and fail to dynamically capture business insights based
on internal and external data. To solve this problem, this paper introduces the
graph theory into BAs with aim of building extensible data-driven analytics and
automatically generating business insights. We use IBM's Component Business
Model (CBM) as an example to illustrate various ways in which graph theory can
be leveraged for data-driven analytics, including what and how business
insights can be obtained. Future directions for applying graph theory to
business architecture analytics are discussed.
",0,0,0,1,0,0
20855,Beyond Sparsity: Tree Regularization of Deep Models for Interpretability,"  The lack of interpretability remains a key barrier to the adoption of deep
models in many applications. In this work, we explicitly regularize deep models
so human users might step through the process behind their predictions in
little time. Specifically, we train deep time-series models so their
class-probability predictions have high accuracy while being closely modeled by
decision trees with few nodes. Using intuitive toy examples as well as medical
tasks for treating sepsis and HIV, we demonstrate that this new tree
regularization yields models that are easier for humans to simulate than
simpler L1 or L2 penalties without sacrificing predictive power.
",1,0,0,1,0,0
20856,Phase-type distributions in population genetics,"  Probability modelling for DNA sequence evolution is well established and
provides a rich framework for understanding genetic variation between samples
of individuals from one or more populations. We show that both classical and
more recent models for coalescence (with or without recombination) can be
described in terms of the so-called phase-type theory, where complicated and
tedious calculations are circumvented by the use of matrices. The application
of phase-type theory consists of describing the stochastic model as a Markov
model by appropriately setting up a state space and calculating the
corresponding intensity and reward matrices. Formulae of interest are then
expressed in terms of these aforementioned matrices. We illustrate this by a
few examples calculating the mean, variance and even higher order moments of
the site frequency spectrum in the multiple merger coalescent models, and by
analysing the mean and variance for the number of segregating sites for
multiple samples in the two-locus ancestral recombination graph. We believe
that phase-type theory has great potential as a tool for analysing probability
models in population genetics. The compact matrix notation is useful for
clarification of current models, in particular their formal manipulation
(calculation), but also for further development or extensions.
",0,0,0,1,1,0
20857,Robust Wald-type test in GLM with random design based on minimum density power divergence estimators,"  We consider the problem of robust inference under the important generalized
linear model (GLM) with stochastic covariates. We derive the properties of the
minimum density power divergence estimator of the parameters in GLM with random
design and used this estimator to propose a robust Wald-type test for testing
any general composite null hypothesis about the GLM. The asymptotic and
robustness properties of the proposed test are also examined for the GLM with
random design. Application of the proposed robust inference procedures to the
popular Poisson regression model for analyzing count data is discussed in
detail both theoretically and numerically with some interesting real data
examples.
",0,0,0,1,0,0
20858,Locally Smoothed Neural Networks,"  Convolutional Neural Networks (CNN) and the locally connected layer are
limited in capturing the importance and relations of different local receptive
fields, which are often crucial for tasks such as face verification, visual
question answering, and word sequence prediction. To tackle the issue, we
propose a novel locally smoothed neural network (LSNN) in this paper. The main
idea is to represent the weight matrix of the locally connected layer as the
product of the kernel and the smoother, where the kernel is shared over
different local receptive fields, and the smoother is for determining the
importance and relations of different local receptive fields. Specifically, a
multi-variate Gaussian function is utilized to generate the smoother, for
modeling the location relations among different local receptive fields.
Furthermore, the content information can also be leveraged by setting the mean
and precision of the Gaussian function according to the content. Experiments on
some variant of MNIST clearly show our advantages over CNN and locally
connected layer.
",1,0,0,1,0,0
20859,Asymptotic properties of a componentwise ARH(1) plug-in predictor,"  This paper presents new results on prediction of linear processes in function
spaces. The autoregressive Hilbertian process framework of order one (ARH(1)
process framework) is adopted. A componentwise estimator of the autocorrelation
operator is formulated, from the moment-based estimation of its diagonal
coefficients, with respect to the orthogonal eigenvectors of the
auto-covariance operator, which are assumed to be known. Mean-square
convergence to the theoretical autocorrelation operator, in the space of
Hilbert-Schmidt operators, is proved. Consistency then follows in that space.
For the associated ARH(1) plug-in predictor, mean absolute convergence to the
corresponding conditional expectation, in the considered Hilbert space, is
obtained. Hence, consistency in that space also holds. A simulation study is
undertaken to illustrate the finite-large sample behavior of the formulated
componentwise estimator and predictor. The performance of the presented
approach is compared with alternative approaches in the previous and current
ARH(1) framework literature, including the case of unknown eigenvectors.
",0,0,1,1,0,0
20860,The Effect of Population Control Policies on Societal Fragmentation,"  Population control policies are proposed and in some places employed as a
means towards curbing population growth. This paper is concerned with a
disturbing side-effect of such policies, namely, the potential risk of societal
fragmentation due to changes in the distribution of family sizes. This effect
is illustrated in some simple settings and demonstrated by simulation. In
addition, the dependence of societal fragmentation on family size distribution
is analyzed. In particular, it is shown that under the studied model, any
population control policy that disallows families of 3 or more children incurs
the possible risk of societal fragmentation.
",1,1,0,0,0,0
20861,Optimizing Beam Transport in Rapidly Compressing Beams on the Neutralized Drift Compression Experiment - II,"  The Neutralized Drift Compression Experiment-II (NDCX-II) is an induction
linac that generates intense pulses of 1.2 MeV helium ions for heating matter
to extreme conditions. Here, we present recent results on optimizing beam
transport. The NDCX-II beamline includes a 1-meter-long drift section
downstream of the last transport solenoid, which is filled with
charge-neutralizing plasma that enables rapid longitudinal compression of an
intense ion beam against space-charge forces. The transport section on NDCX-II
consists of 28 solenoids. Finding optimal field settings for a group of
solenoids requires knowledge of the envelope parameters of the beam. Imaging
the beam on scintillator gives the radius of the beam, but the envelope angle
dr/dz is not measured directly. We demonstrate how the parameters of the beam
envelope (r, dr/dz, and emittance) can be reconstructed from a series of images
taken at varying B-field strengths of a solenoid upstream of the scintillator.
We use this technique to evaluate emittance at several points in the NDCX-II
beamline and for optimizing the trajectory of the beam at the entry of the
plasma-filled drift section.
",0,1,0,0,0,0
20862,Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations,"  Many structured prediction problems (particularly in vision and language
domains) are ambiguous, with multiple outputs being correct for an input - e.g.
there are many ways of describing an image, multiple ways of translating a
sentence; however, exhaustively annotating the applicability of all possible
outputs is intractable due to exponentially large output spaces (e.g. all
English sentences). In practice, these problems are cast as multi-class
prediction, with the likelihood of only a sparse set of annotations being
maximized - unfortunately penalizing for placing beliefs on plausible but
unannotated outputs. We make and test the following hypothesis - for a given
input, the annotations of its neighbors may serve as an additional supervisory
signal. Specifically, we propose an objective that transfers supervision from
neighboring examples. We first study the properties of our developed method in
a controlled toy setup before reporting results on multi-label classification
and two image-grounded sequence modeling tasks - captioning and question
generation. We evaluate using standard task-specific metrics and measures of
output diversity, finding consistent improvements over standard maximum
likelihood training and other baselines.
",0,0,0,1,0,0
20863,Direct and indirect seismic inversion: interpretation of certain mathematical theorems,"  Quantitative methods are more familiar to most geophysicists with direct
inversion or indirect inversion. We will discuss seismic inversion in a high
level sense without getting into the actual algorithms. We will stay with
meta-equations and argue pros and cons based on certain mathematical theorems.
",0,1,0,0,0,0
20864,Kan's combinatorial spectra and their sheaves revisited,"  We define a right Cartan-Eilenberg structure on the category of Kan's
combinatorial spectra, and the category of sheaves of such spectra, assuming
some conditions. In both structures, we use the geometric concept of homotopy
equivalence as the strong equivalence. In the case of sheaves, we use local
equivalence as the weak equivalence. This paper is the first step in a
larger-scale program of investigating sheaves of spectra from a geometric
viewpoint.
",0,0,1,0,0,0
20865,Adiabatic Quantum Computing for Binary Clustering,"  Quantum computing for machine learning attracts increasing attention and
recent technological developments suggest that especially adiabatic quantum
computing may soon be of practical interest. In this paper, we therefore
consider this paradigm and discuss how to adopt it to the problem of binary
clustering. Numerical simulations demonstrate the feasibility of our approach
and illustrate how systems of qubits adiabatically evolve towards a solution.
",0,0,0,1,0,0
20866,Similarity Search Over Graphs Using Localized Spectral Analysis,"  This paper provides a new similarity detection algorithm. Given an input set
of multi-dimensional data points, where each data point is assumed to be
multi-dimensional, and an additional reference data point for similarity
finding, the algorithm uses kernel method that embeds the data points into a
low dimensional manifold. Unlike other kernel methods, which consider the
entire data for the embedding, our method selects a specific set of kernel
eigenvectors. The eigenvectors are chosen to separate between the data points
and the reference data point so that similar data points can be easily
identified as being distinct from most of the members in the dataset.
",1,0,0,0,0,0
20867,"Reminiscences of Julian Schwinger: Late Harvard, Early UCLA Years (1968-1981)","  These are reminiscences of my interactions with Julian Schwinger from 1968
through 1981 and beyond.
",0,1,0,0,0,0
20868,Site-resolved imaging of a bosonic Mott insulator using ytterbium atoms,"  We demonstrate site-resolved imaging of a strongly correlated quantum system
without relying on laser-cooling techniques during fluorescence imaging. We
observed the formation of Mott shells in the insulating regime and realized
thermometry on the atomic cloud. This work proves the feasibility of the
noncooled approach and opens the door to extending the detection technology to
new atomic species.
",0,1,0,0,0,0
20869,"A unified continuum and variational multiscale formulation for fluids, solids, and fluid-structure interaction","  We develop a unified continuum modeling framework for viscous fluids and
hyperelastic solids using the Gibbs free energy as the thermodynamic potential.
This framework naturally leads to a pressure primitive variable formulation for
the continuum body, which is well-behaved in both compressible and
incompressible regimes. Our derivation also provides a rational justification
of the isochoric-volumetric additive split of free energies in nonlinear
continuum mechanics. The variational multiscale analysis is performed for the
continuum model to construct a foundation for numerical discretization. We
first consider the continuum body instantiated as a hyperelastic material and
develop a variational multiscale formulation for the hyper-elastodynamic
problem. The generalized-alpha method is applied for temporal discretization. A
segregated algorithm for the nonlinear solver is designed and carefully
analyzed. Second, we apply the new formulation to construct a novel unified
formulation for fluid-solid coupled problems. The variational multiscale
formulation is utilized for spatial discretization in both fluid and solid
subdomains. The generalized-alpha method is applied for the whole continuum
body, and optimal high-frequency dissipation is achieved in both fluid and
solid subproblems. A new predictor multi-corrector algorithm is developed based
on the segregated algorithm to attain a good balance between robustness and
efficiency. The efficacy of the new formulations is examined in several
benchmark problems. The results indicate that the proposed modeling and
numerical methodologies constitute a promising technology for biomedical and
engineering applications, particularly those necessitating incompressible
models.
",0,1,0,0,0,0
20870,Time Assignment System and Its Performance aboard the Hitomi Satellite,"  Fast timing capability in X-ray observation of astrophysical objects is one
of the key properties for the ASTRO-H (Hitomi) mission. Absolute timing
accuracies of 350 micro second or 35 micro second are required to achieve
nominal scientific goals or to study fast variabilities of specific sources.
The satellite carries a GPS receiver to obtain accurate time information, which
is distributed from the central onboard computer through the large and complex
SpaceWire network. The details on the time system on the hardware and software
design are described. In the distribution of the time information, the
propagation delays and jitters affect the timing accuracy. Six other items
identified within the timing system will also contribute to absolute time
error. These error items have been measured and checked on ground to ensure the
time error budgets meet the mission requirements. The overall timing
performance in combination with hardware performance, software algorithm, and
the orbital determination accuracies, etc, under nominal conditions satisfies
the mission requirements of 35 micro second. This work demonstrates key points
for space-use instruments in hardware and software designs and calibration
measurements for fine timing accuracy on the order of microseconds for
mid-sized satellites using the SpaceWire (IEEE1355) network.
",0,1,0,0,0,0
20871,Common fixed points via $λ$-sequences in $G$-metric spaces,"  In this article, we use $\lambda$-sequences to derive common fixed points for
a family of self-mappings defined on a complete $G$-metric space. We imitate
some existing techniques in our proofs and show that the tools emlyed can be
used at a larger scale. These results generalise well known results in the
literature.
",0,0,1,0,0,0
20872,Bidirectional Nested Weighted Automata,"  Nested weighted automata (NWA) present a robust and convenient
automata-theoretic formalism for quantitative specifications. Previous works
have considered NWA that processed input words only in the forward direction.
It is natural to allow the automata to process input words backwards as well,
for example, to measure the maximal or average time between a response and the
preceding request. We therefore introduce and study bidirectional NWA that can
process input words in both directions. First, we show that bidirectional NWA
can express interesting quantitative properties that are not expressible by
forward-only NWA. Second, for the fundamental decision problems of emptiness
and universality, we establish decidability and complexity results for the new
framework which match the best-known results for the special case of
forward-only NWA. Thus, for NWA, the increased expressiveness of
bidirectionality is achieved at no additional computational complexity. This is
in stark contrast to the unweighted case, where bidirectional finite automata
are no more expressive but exponentially more succinct than their forward-only
counterparts.
",1,0,0,0,0,0
20873,Uniform Shapiro-Lopatinski conditions and boundary value problems on manifolds with bounded geometry,"  We study the regularity of the solutions of second order boundary value
problems on manifolds with boundary and bounded geometry. We first show that
the regularity property of a given boundary value problem $(P, C)$ is
equivalent to the uniform regularity of the natural family $(P_x, C_x)$ of
associated boundary value problems in local coordinates. We verify that this
property is satisfied for the Dirichlet boundary conditions and strongly
elliptic operators via a compactness argument. We then introduce a uniform
Shapiro-Lopatinski regularity condition, which is a modification of the
classical one, and we prove that it characterizes the boundary value problems
that satisfy the usual regularity property. We also show that the natural Robin
boundary conditions always satisfy the uniform Shapiro-Lopatinski regularity
condition, provided that our operator satisfies the strong Legendre condition.
This is achieved by proving that ""well-posedness implies regularity"" via a
modification of the classical ""Nirenberg trick"". When combining our regularity
results with the Poincaré inequality of (Ammann-Grosse-Nistor, preprint
2015), one obtains the usual well-posedness results for the classical boundary
value problems in the usual scale of Sobolev spaces, thus extending these
important, well-known theorems from smooth, bounded domains, to manifolds with
boundary and bounded geometry. As we show in several examples, these results do
not hold true anymore if one drops the bounded geometry assumption. We also
introduce a uniform Agmon condition and show that it is equivalent to the
coerciveness. Consequently, we prove a well-posedness result for parabolic
equations whose elliptic generator satisfies the uniform Agmon condition.
",0,0,1,0,0,0
20874,A Fourier-Chebyshev Spectral Method for Cavitation Computation in Nonlinear Elasticity,"  A Fourier-Chebyshev spectral method is proposed in this paper for solving the
cavitation problem in nonlinear elasticity. The interpolation error for the
cavitation solution is analyzed, the elastic energy error estimate for the
discrete cavitation solution is obtained, and the convergence of the method is
proved. An algorithm combined a gradient type method with a damped quasi-Newton
method is applied to solve the discretized nonlinear equilibrium equations.
Numerical experiments show that the Fourier-Chebyshev spectral method is
efficient and capable of producing accurate numerical cavitation solutions.
",0,0,1,0,0,0
20875,Learning Criticality in an Embodied Boltzmann Machine,"  Many biological and cognitive systems do not operate deep into one or other
regime of activity. Instead, they exploit critical surfaces poised at
transitions in their parameter space. The pervasiveness of criticality in
natural systems suggests that there may be general principles inducing this
behaviour. However, there is a lack of conceptual models explaining how
embodied agents propel themselves towards these critical points. In this paper,
we present a learning model driving an embodied Boltzmann Machine towards
critical behaviour by maximizing the heat capacity of the network. We test and
corroborate the model implementing an embodied agent in the mountain car
benchmark, controlled by a Boltzmann Machine that adjust its weights according
to the model. We find that the neural controller reaches a point of
criticality, which coincides with a transition point of the behaviour of the
agent between two regimes of behaviour, maximizing the synergistic information
between its sensors and the hidden and motor neurons. Finally, we discuss the
potential of our learning model to study the contribution of criticality to the
behaviour of embodied living systems in scenarios not necessarily constrained
by biological restrictions of the examples of criticality we find in nature.
",1,1,0,0,0,0
20876,A contemporary look at Hermann Hankel's 1861 pioneering work on Lagrangian fluid dynamics,"  The present paper is a companion to the paper by Villone and Rampf (2017),
titled ""Hermann Hankel's On the general theory of motion of fluids, an essay
including an English translation of the complete Preisschrift from 1861""
together with connected documents. Here we give a critical assessment of
Hankel's work, which covers many important aspects of fluid dynamics considered
from a Lagrangian-coordinates point of view: variational formulation in the
spirit of Hamilton for elastic (barotropic) fluids, transport (we would now say
Lie transport) of vorticity, the Lagrangian significance of Clebsch variables,
etc. Hankel's work is also put in the perspective of previous and future work.
Hence, the action spans about two centuries: from Lagrange's 1760-1761 Turin
paper on variational approaches to mechanics and fluid mechanics problems to
Arnold's 1966 founding paper on the geometrical/variational formulation of
incompressible flow. The 22-year old Hankel - who was to die 12 years later -
emerges as a highly innovative master of mathematical fluid dynamics, fully
deserving Riemann's assessment that his Preisschrift contains ""all manner of
good things.""
",0,1,1,0,0,0
20877,A Dynamic Edge Exchangeable Model for Sparse Temporal Networks,"  We propose a dynamic edge exchangeable network model that can capture sparse
connections observed in real temporal networks, in contrast to existing models
which are dense. The model achieved superior link prediction accuracy on
multiple data sets when compared to a dynamic variant of the blockmodel, and is
able to extract interpretable time-varying community structures from the data.
In addition to sparsity, the model accounts for the effect of social influence
on vertices' future behaviours. Compared to the dynamic blockmodels, our model
has a smaller latent space. The compact latent space requires a smaller number
of parameters to be estimated in variational inference and results in a
computationally friendly inference algorithm.
",0,0,0,1,0,0
20878,Current-mode Memristor Crossbars for Neuromemristive Systems,"  Motivated by advantages of current-mode design, this brief contribution
explores the implementation of weight matrices in neuromemristive systems via
current-mode memristor crossbar circuits. After deriving theoretical results
for the range and distribution of weights in the current-mode design, it is
shown that any weight matrix based on voltage-mode crossbars can be mapped to a
current-mode crossbar if the voltage-mode weights are carefully bounded. Then,
a modified gradient descent rule is derived for the current-mode design that
can be used to perform backpropagation training. Behavioral simulations on the
MNIST dataset indicate that both voltage and current-mode designs are able to
achieve similar accuracy and have similar defect tolerance. However, analysis
of trained weight distributions reveals that current-mode and voltage-mode
designs may use different feature representations.
",1,0,0,1,0,0
20879,Incomplete Gauss sums modulo primes,"  We obtain a new bound for incomplete Gauss sums modulo primes. Our argument
falls under the framework of Vinogradov's method which we use to reduce the
problem under consideration to bounding the number of solutions to two distinct
systems of congruences. The first is related to Vinogradov's mean value
theorem, although the second does not appear to have been considered before.
Our bound improves on current results in the range $N\ge
q^{2k^{-1/2}+O(k^{-3/2})}$.
",0,0,1,0,0,0
20880,FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI,"  Functional Magnetic Resonance Imaging is a noninvasive tool used to study
brain function. Detecting activation is challenged by many factors, and even
more so in low-signal scenarios that arise in the performance of high-level
cognitive tasks. We provide a fully automated and fast adaptive smoothing and
thresholding (FAST) algorithm that uses smoothing and extreme value theory on
correlated statistical parametric maps for thresholding. Performance on
experiments spanning a range of low-signal settings is very encouraging. The
methodology also performs well in a study to identify the cerebral regions that
perceive only-auditory-reliable and only-visual-reliable speech stimuli as well
as those that perceive one but not the other.
",0,0,1,1,0,0
20881,Improper multiferroicity and colossal dielectric constants in Bi$_{2}$CuO$_{4}$,"  The layered cuprate Bi$_{2}$CuO$_{4}$ is investigated using magnetic,
dielectric and pyroelectric measurements. This system is observed to be an
improper multiferroic, with a robust ferroelectric state being established near
the magnetic transition. Magnetic and dielectric measurements indicate the
presence of a region above the antiferromagnetic Neel temperature with
concomitant polar and magnetic short range order. Bi$_{2}$CuO$_{4}$ is also
seen to exhibit colossal dielectric constants at higher temperatures with
clearly distinguishable grain and grain boundary contributions, both of which
exhibit non-Debye relaxation.
",0,1,0,0,0,0
20882,Suppression of plasma echoes and Landau damping in Sobolev spaces by weak collisions in a Vlasov-Fokker-Planck equation,"  In this paper, we study Landau damping in the weakly collisional limit of a
Vlasov-Fokker-Planck equation with nonlinear collisions in the phase-space
$(x,v) \in \mathbb T_x^n \times \mathbb R^n_v$. The goal is four-fold: (A) to
understand how collisions suppress plasma echoes and enable Landau damping in
agreement with linearized theory in Sobolev spaces, (B) to understand how phase
mixing accelerates collisional relaxation, (C) to understand better how the
plasma returns to global equilibrium during Landau damping, and (D) to rule out
that collision-driven nonlinear instabilities dominate. We give an estimate for
the scaling law between Knudsen number and the maximal size of the perturbation
necessary for linear theory to be accurate in Sobolev regularity. We conjecture
this scaling to be sharp (up to logarithmic corrections) due to potential
nonlinear echoes in the collisionless model.
",0,0,1,0,0,0
20883,Deep Learning for Semantic Segmentation on Minimal Hardware,"  Deep learning has revolutionised many fields, but it is still challenging to
transfer its success to small mobile robots with minimal hardware.
Specifically, some work has been done to this effect in the RoboCup humanoid
football domain, but results that are performant and efficient and still
generally applicable outside of this domain are lacking. We propose an approach
conceptually different from those taken previously. It is based on semantic
segmentation and does achieve these desired properties. In detail, it is being
able to process full VGA images in real-time on a low-power mobile processor.
It can further handle multiple image dimensions without retraining, it does not
require specific domain knowledge for achieving a high frame rate and it is
applicable on a minimal mobile hardware.
",1,0,0,1,0,0
20884,"Overpartition $M2$-rank differences, class number relations, and vector-valued mock Eisenstein series","  We prove that the generating function of overpartition $M2$-rank differences
is, up to coefficient signs, a component of the vector-valued mock Eisenstein
series attached to a certain quadratic form. We use this to compute analogs of
the class number relations for $M2$-rank differences. As applications we split
the Kronecker-Hurwitz relation into its ""even"" and ""odd"" parts and calculate
sums over Hurwitz class numbers of the form $\sum_{r \in \mathbb{Z}} H(n -
2r^2)$.
",0,0,1,0,0,0
20885,Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph,"  We study the decentralized machine learning scenario where many users
collaborate to learn personalized models based on (i) their local datasets and
(ii) a similarity graph over the users' learning tasks. Our approach trains
nonlinear classifiers in a multi-task boosting manner without exchanging
personal data and with low communication costs. When background knowledge about
task similarities is not available, we propose to jointly learn the
personalized models and a sparse collaboration graph through an alternating
optimization procedure. We analyze the convergence rate, memory consumption and
communication complexity of our decentralized algorithms, and demonstrate the
benefits of our approach compared to competing techniques on synthetic and real
datasets.
",1,0,0,1,0,0
20886,On the well-posedness of SPDEs with singular drift in divergence form,"  We prove existence and uniqueness of strong solutions for a class of
second-order stochastic PDEs with multiplicative Wiener noise and drift of the
form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal
monotone graph in $\mathbb{R}^n \times \mathbb{R}^n$ obtained as the
subdifferential of a convex function satisfying very mild assumptions on its
behavior at infinity. The well-posedness result complements the corresponding
one in our recent work arXiv:1612.08260 where, under the additional assumption
that $\gamma$ is single-valued, a solution with better integrability and
regularity properties is constructed. The proof given here, however, is
self-contained.
",0,0,1,0,0,0
20887,Trace norm regularization and faster inference for embedded speech recognition RNNs,"  We propose and evaluate new techniques for compressing and speeding up dense
matrix multiplications as found in the fully connected and recurrent layers of
neural networks for embedded large vocabulary continuous speech recognition
(LVCSR). For compression, we introduce and study a trace norm regularization
technique for training low rank factored versions of matrix multiplications.
Compared to standard low rank training, we show that our method leads to good
accuracy versus number of parameter trade-offs and can be used to speed up
training of large models. For speedup, we enable faster inference on ARM
processors through new open sourced kernels optimized for small batch sizes,
resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond
LVCSR, we expect our techniques and kernels to be more generally applicable to
embedded neural networks with large fully connected or recurrent layers.
",1,0,0,1,0,0
20888,Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs,"  Sparse subspace clustering (SSC) is one of the current state-of-the-art
methods for partitioning data points into the union of subspaces, with strong
theoretical guarantees. However, it is not practical for large data sets as it
requires solving a LASSO problem for each data point, where the number of
variables in each LASSO problem is the number of data points. To improve the
scalability of SSC, we propose to select a few sets of anchor points using a
randomized hierarchical clustering method, and, for each set of anchor points,
solve the LASSO problems for each data point allowing only anchor points to
have a non-zero weight (this reduces drastically the number of variables). This
generates a multilayer graph where each layer corresponds to a different set of
anchor points. Using the Grassmann manifold of orthogonal matrices, the shared
connectivity among the layers is summarized within a single subspace. Finally,
we use $k$-means clustering within that subspace to cluster the data points,
similarly as done by spectral clustering in SSC. We show on both synthetic and
real-world data sets that the proposed method not only allows SSC to scale to
large-scale data sets, but that it is also much more robust as it performs
significantly better on noisy data and on data with close susbspaces and
outliers, while it is not prone to oversegmentation.
",0,0,0,1,0,0
20889,Deep Health Care Text Classification,"  Health related social media mining is a valuable apparatus for the early
recognition of the diverse antagonistic medicinal conditions. Mostly, the
existing methods are based on machine learning with knowledge-based learning.
This working note presents the Recurrent neural network (RNN) and Long
short-term memory (LSTM) based embedding for automatic health text
classification in the social media mining. For each task, two systems are built
and that classify the tweet at the tweet level. RNN and LSTM are used for
extracting features and non-linear activation function at the last layer
facilitates to distinguish the tweets of different categories. The experiments
are conducted on 2nd Social Media Mining for Health Applications Shared Task at
AMIA 2017. The experiment results are considerable; however the proposed method
is appropriate for the health text classification. This is primarily due to the
reason that, it doesn't rely on any feature engineering mechanisms.
",1,0,0,0,0,0
20890,"Study of cost functionals for ptychographic phase retrieval to improve the robustness against noise, and a proposal for another noise-robust ptychographic phase retrieval scheme","  Recently, efforts have been made to improve ptychography phase retrieval
algorithms so that they are more robust against noise. Often the algorithm is
adapted by changing the cost functional that needs to be minimized. In
particular, it has been suggested that the cost functional should be obtained
using a maximum-likelihood approach that takes the noise statistics into
account. Here, we consider the different choices of cost functional, and to how
they affect the reconstruction results. We find that seemingly the only
consistently reliable way to improve reconstruction results in the presence of
noise is to reduce the step size of the update function. In addition, a
noise-robust ptychographic reconstruction method has been proposed that relies
on adapting the intensity constraints
",1,1,0,0,0,0
20891,Predicting Native Language from Gaze,"  A fundamental question in language learning concerns the role of a speaker's
first language in second language acquisition. We present a novel methodology
for studying this question: analysis of eye-movement patterns in second
language reading of free-form text. Using this methodology, we demonstrate for
the first time that the native language of English learners can be predicted
from their gaze fixations when reading English. We provide analysis of
classifier uncertainty and learned features, which indicates that differences
in English reading are likely to be rooted in linguistic divergences across
native languages. The presented framework complements production studies and
offers new ground for advancing research on multilingualism.
",1,0,0,0,0,0
20892,A form of Schwarz's lemma and a bound for the Kobayashi metric on convex domains,"  We present a form of Schwarz's lemma for holomorphic maps between convex
domains $D_1$ and $D_2$. This result provides a lower bound on the distance
between the images of relatively compact subsets of $D_1$ and the boundary of
$D_2$. This is a natural improvement of an old estimate by Bernal-González
that takes into account the geometry of $\partial{D_1}$. We also provide a new
estimate for the Kobayashi metric on bounded convex domains.
",0,0,1,0,0,0
20893,The COM-negative binomial distribution: modeling overdispersion and ultrahigh zero-inflated count data,"  In this paper, we focus on the COM-type negative binomial distribution with
three parameters, which belongs to COM-type $(a,b,0)$ class distributions and
family of equilibrium distributions of arbitrary birth-death process. Besides,
we show abundant distributional properties such as overdispersion and
underdispersion, log-concavity, log-convexity (infinite divisibility), pseudo
compound Poisson, stochastic ordering and asymptotic approximation. Some
characterizations including sum of equicorrelated geometrically distributed
random variables, conditional distribution, limit distribution of COM-negative
hypergeometric distribution, and Stein's identity are given for theoretical
properties. COM-negative binomial distribution was applied to overdispersion
and ultrahigh zero-inflated data sets. With the aid of ratio regression, we
employ maximum likelihood method to estimate the parameters and the
goodness-of-fit are evaluated by the discrete Kolmogorov-Smirnov test.
",0,0,1,1,0,0
20894,Efficient Contextual Bandits in Non-stationary Worlds,"  Most contextual bandit algorithms minimize regret against the best fixed
policy, a questionable benchmark for non-stationary environments that are
ubiquitous in applications. In this work, we develop several efficient
contextual bandit algorithms for non-stationary environments by equipping
existing methods for i.i.d. problems with sophisticated statistical tests so as
to dynamically adapt to a change in distribution.
We analyze various standard notions of regret suited to non-stationary
environments for these algorithms, including interval regret, switching regret,
and dynamic regret. When competing with the best policy at each time, one of
our algorithms achieves regret $\mathcal{O}(\sqrt{ST})$ if there are $T$ rounds
with $S$ stationary periods, or more generally
$\mathcal{O}(\Delta^{1/3}T^{2/3})$ where $\Delta$ is some non-stationarity
measure. These results almost match the optimal guarantees achieved by an
inefficient baseline that is a variant of the classic Exp4 algorithm. The
dynamic regret result is also the first one for efficient and fully adversarial
contextual bandit.
Furthermore, while the results above require tuning a parameter based on the
unknown quantity $S$ or $\Delta$, we also develop a parameter free algorithm
achieving regret $\min\{S^{1/4}T^{3/4}, \Delta^{1/5}T^{4/5}\}$. This improves
and generalizes the best existing result $\Delta^{0.18}T^{0.82}$ by Karnin and
Anava (2016) which only holds for the two-armed bandit problem.
",1,0,0,1,0,0
20895,Search Engine Drives the Evolution of Social Networks,"  The search engine is tightly coupled with social networks and is primarily
designed for users to acquire interested information. Specifically, the search
engine assists the information dissemination for social networks, i.e.,
enabling users to access interested contents with keywords-searching and
promoting the process of contents-transferring from the source users directly
to potential interested users. Accompanying such processes, the social network
evolves as new links emerge between users with common interests. However, there
is no clear understanding of such a ""chicken-and-egg"" problem, namely, new
links encourage more social interactions, and vice versa. In this paper, we aim
to quantitatively characterize the social network evolution phenomenon driven
by a search engine. First, we propose a search network model for social network
evolution. Second, we adopt two performance metrics, namely, degree
distribution and network diameter. Theoretically, we prove that the degree
distribution follows an intensified power-law, and the network diameter
shrinks. Third, we quantitatively show that the search engine accelerates the
rumor propagation in social networks. Finally, based on four real-world data
sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical
findings. Furthermore, we find that the search engine dramatically increases
the speed of rumor propagation.
",1,1,0,0,0,0
20896,End-to-End Learning of Semantic Grasping,"  We consider the task of semantic robotic grasping, in which a robot picks up
an object of a user-specified class using only monocular images. Inspired by
the two-stream hypothesis of visual reasoning, we present a semantic grasping
framework that learns object detection, classification, and grasp planning in
an end-to-end fashion. A ""ventral stream"" recognizes object class while a
""dorsal stream"" simultaneously interprets the geometric relationships necessary
to execute successful grasps. We leverage the autonomous data collection
capabilities of robots to obtain a large self-supervised dataset for training
the dorsal stream, and use semi-supervised label propagation to train the
ventral stream with only a modest amount of human supervision. We
experimentally show that our approach improves upon grasping systems whose
components are not learned end-to-end, including a baseline method that uses
bounding box detection. Furthermore, we show that jointly training our model
with auxiliary data consisting of non-semantic grasping data, as well as
semantically labeled images without grasp actions, has the potential to
substantially improve semantic grasping performance.
",1,0,0,1,0,0
20897,Von Neumann Regular Cellular Automata,"  For any group $G$ and any set $A$, a cellular automaton (CA) is a
transformation of the configuration space $A^G$ defined via a finite memory set
and a local function. Let $\text{CA}(G;A)$ be the monoid of all CA over $A^G$.
In this paper, we investigate a generalisation of the inverse of a CA from the
semigroup-theoretic perspective. An element $\tau \in \text{CA}(G;A)$ is von
Neumann regular (or simply regular) if there exists $\sigma \in \text{CA}(G;A)$
such that $\tau \circ \sigma \circ \tau = \tau$ and $\sigma \circ \tau \circ
\sigma = \sigma$, where $\circ$ is the composition of functions. Such an
element $\sigma$ is called a generalised inverse of $\tau$. The monoid
$\text{CA}(G;A)$ itself is regular if all its elements are regular. We
establish that $\text{CA}(G;A)$ is regular if and only if $\vert G \vert = 1$
or $\vert A \vert = 1$, and we characterise all regular elements in
$\text{CA}(G;A)$ when $G$ and $A$ are both finite. Furthermore, we study
regular linear CA when $A= V$ is a vector space over a field $\mathbb{F}$; in
particular, we show that every regular linear CA is invertible when $G$ is
torsion-free elementary amenable (e.g. when $G=\mathbb{Z}^d, \ d \in
\mathbb{N}$) and $V=\mathbb{F}$, and that every linear CA is regular when $V$
is finite-dimensional and $G$ is locally finite with $\text{Char}(\mathbb{F})
\nmid o(g)$ for all $g \in G$.
",1,0,1,0,0,0
20898,A Distance Between Filtered Spaces Via Tripods,"  We present a simplified treatment of stability of filtrations on finite
spaces. Interestingly, we can lift the stability result for combinatorial
filtrations from [CSEM06] to the case when two filtrations live on different
spaces without directly invoking the concept of interleaving. We then prove
that this distance is intrinsic by constructing explicit geodesics between any
pair of filtered spaces. Finally we use this construction to obtain a
strengthening of the stability result.
",1,0,1,0,0,0
20899,Robust Shape Estimation for 3D Deformable Object Manipulation,"  Existing shape estimation methods for deformable object manipulation suffer
from the drawbacks of being off-line, model dependent, noise-sensitive or
occlusion-sensitive, and thus are not appropriate for manipulation tasks
requiring high precision. In this paper, we present a real-time shape
estimation approach for autonomous robotic manipulation of 3D deformable
objects. Our method fulfills all the requirements necessary for the
high-quality deformable object manipulation in terms of being real-time,
model-free and robust to noise and occlusion. These advantages are accomplished
using a joint tracking and reconstruction framework, in which we track the
object deformation by aligning a reference shape model with the stream input
from the RGB-D camera, and simultaneously upgrade the reference shape model
according to the newly captured RGB-D data. We have evaluated the quality and
robustness of our real-time shape estimation pipeline on a set of deformable
manipulation tasks implemented on physical robots. Videos are available at
this https URL
",1,0,0,0,0,0
20900,Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning,"  In recent years, reinforcement learning (RL) methods have been applied to
model gameplay with great success, achieving super-human performance in various
environments, such as Atari, Go, and Poker. However, those studies mostly focus
on winning the game and have largely ignored the rich and complex human
motivations, which are essential for understanding different players' diverse
behaviors. In this paper, we present a novel method called Multi-Motivation
Behavior Modeling (MMBM) that takes the multifaceted human motivations into
consideration and models the underlying value structure of the players using
inverse RL. Our approach does not require the access to the dynamic of the
system, making it feasible to model complex interactive environments such as
massively multiplayer online games. MMBM is tested on the World of Warcraft
Avatar History dataset, which recorded over 70,000 users' gameplay spanning
three years period. Our model reveals the significant difference of value
structures among different player groups. Using the results of motivation
modeling, we also predict and explain their diverse gameplay behaviors and
provide a quantitative assessment of how the redesign of the game environment
impacts players' behaviors.
",0,0,0,1,0,0
20901,Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization,"  In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate mentor, we can evolve a program that is on par
with top tournament-playing chess programs, outperforming a two-time World
Computer Chess Champion. This performance gain is achieved by evolving a
program with a smaller number of parameters in its evaluation function to mimic
the behavior of a superior mentor which uses a more extensive evaluation
function. In principle, our mentor-assisted approach could be used in a wide
range of problems for which appropriate mentors are available.
",1,0,0,1,0,0
20902,Universal Constraints on the Location of Extrema of Eigenfunctions of Non-Local Schrödinger Operators,"  We derive a lower bound on the location of global extrema of eigenfunctions
for a large class of non-local Schrödinger operators in convex domains under
Dirichlet exterior conditions, featuring the symbol of the kinetic term, the
strength of the potential, and the corresponding eigenvalue, and involving a
new universal constant. We show a number of probabilistic and spectral
geometric implications, and derive a Faber-Krahn type inequality for non-local
operators. Our study also extends to potentials with compact support, and we
establish bounds on the location of extrema relative to the boundary edge of
the support or level sets around minima of the potential.
",0,0,1,0,0,0
20903,InScript: Narrative texts annotated with script information,"  This paper presents the InScript corpus (Narrative Texts Instantiating Script
structure). InScript is a corpus of 1,000 stories centered around 10 different
scenarios. Verbs and noun phrases are annotated with event and participant
types, respectively. Additionally, the text is annotated with coreference
information. The corpus shows rich lexical variation and will serve as a unique
resource for the study of the role of script knowledge in natural language
processing.
",1,0,0,0,0,0
20904,Semimetallic and charge-ordered $α$-(BEDT-TTF)$_2$I$_3$: on the role of disorder in dc transport and dielectric properties,"  $\alpha$-(BEDT-TTF)$_2$I$_3$ is a prominent example of charge ordering among
organic conductors. In this work we explore the details of transport within the
charge-ordered as well as semimetallic phase at ambient pressure. In the
high-temperature semimetallic phase, the mobilities and concentrations of both
electrons and holes conspire in such a way to create an almost
temperature-independent conductivity as well as a low Hall effect. We explain
these phenomena as a consequence of a predominantly inter-pocket scattering
which equalizes mobilities of the two types of charge carriers. At low
temperatures, within the insulating charge-ordered phase two channels of
conduction can be discerned: a temperature-dependent activation which follows
the mean-field behavior, and a nearest-neighbor hopping contribution. Together
with negative magnetoresistance, the latter relies on the presence of disorder.
The charge-ordered phase also features a prominent dielectric peak which bears
a similarity to relaxor ferroelectrics. Its dispersion is determined by
free-electron screening and pushed by disorder well below the transition
temperature. The source of this disorder can be found in the anion layers which
randomly perturb BEDT-TTF molecules through hydrogen bonds.
",0,1,0,0,0,0
20905,Robust Photometric Stereo Using Learned Image and Gradient Dictionaries,"  Photometric stereo is a method for estimating the normal vectors of an object
from images of the object under varying lighting conditions. Motivated by
several recent works that extend photometric stereo to more general objects and
lighting conditions, we study a new robust approach to photometric stereo that
utilizes dictionary learning. Specifically, we propose and analyze two
approaches to adaptive dictionary regularization for the photometric stereo
problem. First, we propose an image preprocessing step that utilizes an
adaptive dictionary learning model to remove noise and other non-idealities
from the image dataset before estimating the normal vectors. We also propose an
alternative model where we directly apply the adaptive dictionary
regularization to the normal vectors themselves during estimation. We study the
practical performance of both methods through extensive simulations, which
demonstrate the state-of-the-art performance of both methods in the presence of
noise.
",0,0,0,1,0,0
20906,Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness,"  We present a micro aerial vehicle (MAV) system, built with inexpensive
off-the-shelf hardware, for autonomously following trails in unstructured,
outdoor environments such as forests. The system introduces a deep neural
network (DNN) called TrailNet for estimating the view orientation and lateral
offset of the MAV with respect to the trail center. The DNN-based controller
achieves stable flight without oscillations by avoiding overconfident behavior
through a loss function that includes both label smoothing and entropy reward.
In addition to the TrailNet DNN, the system also utilizes vision modules for
environmental awareness, including another DNN for object detection and a
visual odometry component for estimating depth for the purpose of low-level
obstacle detection. All vision systems run in real time on board the MAV via a
Jetson TX1. We provide details on the hardware and software used, as well as
implementation details. We present experiments showing the ability of our
system to navigate forest trails more robustly than previous techniques,
including autonomous flights of 1 km.
",1,0,0,0,0,0
20907,Origin of Charge Separation at Organic Photovoltaic Heterojunctions: A Mesoscale Quantum Mechanical View,"  The high efficiency of charge generation within organic photovoltaic blends
apparently contrasts with the strong ""classical"" attraction between newly
formed electron-hole pairs. Several factors have been identified as possible
facilitators of charge dissociation, such as quantum mechanical coherence and
delocalization, structural and energetic disorder, built-in electric fields,
nanoscale intermixing of the donor and acceptor components of the blends. Our
mesoscale quantum-chemical model allows an unbiased assessment of their
relative importance, through excited-state calculations on systems containing
thousands of donor and acceptor sites. The results on several model
heterojunctions confirm that the classical model severely overestimates the
binding energy of the electron-hole pairs, produced by vertical excitation from
the electronic ground state. Using physically sensible parameters for the
individual materials, we find that the quantum mechanical energy difference
between the lowest interfacial charge transfer states and the fully separated
electron and hole is of the order of the thermal energy.
",0,1,0,0,0,0
20908,Spectral Decimation for Families of Self-Similar Symmetric Laplacians on the Sierpinski Gasket,"  We construct a one-parameter family of Laplacians on the Sierpinski Gasket
that are symmetric and self-similar for the 9-map iterated function system
obtained by iterating the standard 3-map iterated function system. Our main
result is the fact that all these Laplacians satisfy a version of spectral
decimation that builds a precise catalog of eigenvalues and eigenfunctions for
any choice of the parameter. We give a number of applications of this spectral
decimation. We also prove analogous results for fractal Laplacians on the unit
Interval, and this yields an analogue of the classical Sturm-Liouville theory
for the eigenfunctions of these one-dimensional Laplacians.
",0,0,1,0,0,0
20909,Identification of multi-object dynamical systems: consistency and Fisher information,"  Learning the model parameters of a multi-object dynamical system from partial
and perturbed observations is a challenging task. Despite recent numerical
advancements in learning these parameters, theoretical guarantees are extremely
scarce. In this article, we study the identifiability of these parameters and
the consistency of the corresponding maximum likelihood estimate (MLE) under
assumptions on the different components of the underlying multi-object system.
In order to understand the impact of the various sources of observation noise
on the ability to learn the model parameters, we study the asymptotic variance
of the MLE through the associated Fisher information matrix. For example, we
show that specific aspects of the multi-target tracking (MTT) problem such as
detection failures and unknown data association lead to a loss of information
which is quantified in special cases of interest.
",0,0,1,1,0,0
20910,Elliptic curves maximal over extensions of finite base fields,"  Given an elliptic curve $E$ over a finite field $\mathbb{F}_q$ we study the
finite extensions $\mathbb{F}_{q^n}$ of $\mathbb{F}_q$ such that the number of
$\mathbb{F}_{q^n}$-rational points on $E$ attains the Hasse upper bound. We
obtain an upper bound on the degree $n$ for $E$ ordinary using an estimate for
linear forms in logarithms, which allows us to compute the pairs of isogeny
classes of such curves and degree $n$ for small $q$. Using a consequence of
Schmidt's Subspace Theorem, we improve the upper bound to $n\leq 11$ for
sufficiently large $q$. We also show that there are infinitely many isogeny
classes of ordinary elliptic curves with $n=3$.
",0,0,1,0,0,0
20911,CURE: Curvature Regularization For Missing Data Recovery,"  Missing data recovery is an important and yet challenging problem in imaging
and data science. Successful models often adopt certain carefully chosen
regularization. Recently, the low dimension manifold model (LDMM) was
introduced by S.Osher et al. and shown effective in image inpainting. They
observed that enforcing low dimensionality on image patch manifold serves as a
good image regularizer. In this paper, we observe that having only the low
dimension manifold regularization is not enough sometimes, and we need
smoothness as well. For that, we introduce a new regularization by combining
the low dimension manifold regularization with a higher order Curvature
Regularization, and we call this new regularization CURE for short. The key
step of solving CURE is to solve a biharmonic equation on a manifold. We
further introduce a weighted version of CURE, called WeCURE, in a similar
manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments
for image inpainting and semi-supervised learning show that the proposed CURE
and WeCURE significantly outperform LDMM and WNLL respectively.
",1,0,0,0,0,0
20912,Nonlinear parametric excitation effect induces stability transitions in swimming direction of flexible superparamagnetic microswimmers,"  Microscopic artificial swimmers have recently become highly attractive due to
their promising potential for biomedical applications. The pioneering work of
Dreyfus et al (2005) has demonstrated the motion of a microswimmer with an
undulating chain of superparamagnetic beads, which is actuated by an
oscillating external magnetic field. Interestingly, it has also been
theoretically predicted that the swimming direction of this swimmer will
undergo a $90^\circ$-transition when the magnetic field's oscillations
amplitude is increased above a critical value of $\sqrt{2}$. In this work, we
further investigate this transition both theoretically and experimentally by
using numerical simulations and presenting a novel flexible microswimmer with a
superparamagnetic head. We realize the $90^\circ$-transition in swimming
direction, prove that this effect depends on both frequency and amplitude of
the oscillating magnetic field, and demonstrate the existence of an optimal
amplitude, under which, maximal swimming speed can be achieved. By
asymptotically analyzing the dynamic motion of microswimmer with a minimal
two-link model, we reveal that the stability transitions representing the
changes in the swimming direction are induced by the effect of nonlinear
parametric excitation.
",0,1,0,0,0,0
20913,$L^p$ Mapping Properties for the Cauchy-Riemann Equations on Lipschitz Domains Admitting Subelliptic Estimates,"  We show that on bounded Lipschitz pseudoconvex domains that admit good weight
functions the $\overline{\partial}$-Neumann operators $N_q,
\overline{\partial}^* N_{q}$, and $\overline{\partial} N_{q}$ are bounded on
$L^p$ spaces for some values of $p$ greater than 2.
",0,0,1,0,0,0
20914,Fast dose optimization for rotating shield brachytherapy,"  Purpose: To provide a fast computational method, based on the proximal graph
solver (POGS) - a convex optimization solver using the alternating direction
method of multipliers (ADMM), for calculating an optimal treatment plan in
rotating shield brachytherapy (RSBT). RSBT treatment planning has more degrees
of freedom than conventional high-dose-rate brachytherapy (HDR-BT) due to the
addition of emission direction, and this necessitates a fast optimization
technique to enable clinical usage. // Methods: The multi-helix RSBT (H-RSBT)
delivery technique was considered with five representative cervical cancer
patients. Treatment plans were generated for all patients using the POGS method
and the previously considered commercial solver IBM CPLEX. The rectum, bladder,
sigmoid, high-risk clinical target volume (HR-CTV), and HR-CTV boundary were
the structures considered in our optimization problem, called the asymmetric
dose-volume optimization with smoothness control. Dose calculation resolution
was 1x1x3 mm^3 for all cases. The H-RSBT applicator has 6 helices, with 33.3 mm
of translation along the applicator per helical rotation and 1.7 mm spacing
between dwell positions, yielding 17.5 degree emission angle spacing per 5 mm
along the applicator.// Results: For each patient, HR-CTV D90, HR-CTV D100,
rectum D2cc, sigmoid D2cc, and bladder D2cc matched within 1% for CPLEX and
POGS. Also, we obtained similar EQD2 figures between CPLEX and POGS. POGS was
around 18 times faster than CPLEX. Over all patients, total optimization times
were 32.1-65.4 seconds for CPLEX and 2.1-3.9 seconds for POGS. // Conclusions:
POGS substantially reduced treatment plan optimization time around 18 times for
RSBT with similar HR-CTV D90, OAR D2cc values, and EQD2 figure relative to
CPLEX, which is significant progress toward clinical translation of RSBT. POGS
is also applicable to conventional HDR-BT.
",0,1,1,0,0,0
20915,3D Modeling of Electric Fields in the LUX Detector,"  This work details the development of a three-dimensional (3D) electric field
model for the LUX detector. The detector took data during two periods of
searching for weakly interacting massive particle (WIMP) searches. After the
first period completed, a time-varying non-uniform negative charge developed in
the polytetrafluoroethylene (PTFE) panels that define the radial boundary of
the detector's active volume. This caused electric field variations in the
detector in time, depth and azimuth, generating an electrostatic
radially-inward force on electrons on their way upward to the liquid surface.
To map this behavior, 3D electric field maps of the detector's active volume
were built on a monthly basis. This was done by fitting a model built in COMSOL
Multiphysics to the uniformly distributed calibration data that were collected
on a regular basis. The modeled average PTFE charge density increased over the
course of the exposure from -3.6 to $-5.5~\mu$C/m$^2$. From our studies, we
deduce that the electric field magnitude varied while the mean value of the
field of $\sim200$~V/cm remained constant throughout the exposure. As a result
of this work the varying electric fields and their impact on event
reconstruction and discrimination were successfully modeled.
",0,1,0,0,0,0
20916,Optimal Communication Strategies in Networked Cyber-Physical Systems with Adversarial Elements,"  This paper studies optimal communication and coordination strategies in
cyber-physical systems for both defender and attacker within a game-theoretic
framework. We model the communication network of a cyber-physical system as a
sensor network which involves one single Gaussian source observed by many
sensors, subject to additive independent Gaussian observation noises. The
sensors communicate with the estimator over a coherent Gaussian multiple access
channel. The aim of the receiver is to reconstruct the underlying source with
minimum mean squared error. The scenario of interest here is one where some of
the sensors are captured by the attacker and they act as the adversary
(jammer): they strive to maximize distortion. The receiver (estimator) knows
the captured sensors but still cannot simply ignore them due to the multiple
access channel, i.e., the outputs of all sensors are summed to generate the
estimator input. We show that the ability of transmitter sensors to secretly
agree on a random event, that is ""coordination"", plays a key role in the
analysis...
",1,0,0,0,0,0
20917,First- and Second-Order Models of Recursive Arithmetics,"  We study a quadruple of interrelated subexponential subsystems of arithmetic
WKL$_0^-$, RCA$^-_0$, I$\Delta_0$, and $\Delta$RA$_1$, which complement the
similarly related quadruple WKL$_0$, RCA$_0$, I$\Sigma_1$, and PRA studied by
Simpson, and the quadruple WKL$_0^\ast$, RCA$_0^\ast$, I$\Delta_0$(exp), and
EFA studied by Simpson and Smith. We then explore the space of subexponential
arithmetic theories between I$\Delta_0$ and I$\Delta_0$(exp). We introduce and
study first- and second-order theories of recursive arithmetic $A$RA$_1$ and
$A$RA$_2$ capable of characterizing various computational complexity classes
and based on function algebras $A$, studied by Clote and others.
",1,0,0,0,0,0
20918,Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks,"  Nowadays, modern earth observation programs produce huge volumes of satellite
images time series (SITS) that can be useful to monitor geographical areas
through time. How to efficiently analyze such kind of information is still an
open question in the remote sensing field. Recently, deep learning methods
proved suitable to deal with remote sensing data mainly for scene
classification (i.e. Convolutional Neural Networks - CNNs - on single images)
while only very few studies exist involving temporal deep learning approaches
(i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series.
In this letter we evaluate the ability of Recurrent Neural Networks, in
particular the Long-Short Term Memory (LSTM) model, to perform land cover
classification considering multi-temporal spatial data derived from a time
series of satellite images. We carried out experiments on two different
datasets considering both pixel-based and object-based classification. The
obtained results show that Recurrent Neural Networks are competitive compared
to state-of-the-art classifiers, and may outperform classical approaches in
presence of low represented and/or highly mixed classes. We also show that
using the alternative feature representation generated by LSTM can improve the
performances of standard classifiers.
",1,0,0,0,0,0
20919,Disentangling by Factorising,"  We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We
propose FactorVAE, a method that disentangles by encouraging the distribution
of representations to be factorial and hence independent across the dimensions.
We show that it improves upon $\beta$-VAE by providing a better trade-off
between disentanglement and reconstruction quality. Moreover, we highlight the
problems of a commonly used disentanglement metric and introduce a new metric
that does not suffer from them.
",0,0,0,1,0,0
20920,Symmetries and synchronization in multilayer random networks,"  In the light of the recently proposed scenario of asymmetry-induced
synchronization (AISync), in which dynamical uniformity and consensus in a
distributed system would demand certain asymmetries in the underlying network,
we investigate here the influence of some regularities in the interlayer
connection patterns on the synchronization properties of multilayer random
networks. More specifically, by considering a Stuart-Landau model of complex
oscillators with random frequencies, we report for multilayer networks a
dynamical behavior that could be also classified as a manifestation of AISync.
We show, namely, that the presence of certain symmetries in the interlayer
connection pattern tends to diminish the synchronization capability of the
whole network or, in other words, asymmetries in the interlayer connections
would enhance synchronization in such structured networks. Our results might
help the understanding not only of the AISync mechanism itself, but also its
possible role in the determination of the interlayer connection pattern of
multilayer and other structured networks with optimal synchronization
properties.
",0,1,0,0,0,0
20921,Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation,"  As part of autonomous car driving systems, semantic segmentation is an
essential component to obtain a full understanding of the car's environment.
One difficulty, that occurs while training neural networks for this purpose, is
class imbalance of training data. Consequently, a neural network trained on
unbalanced data in combination with maximum a-posteriori classification may
easily ignore classes that are rare in terms of their frequency in the dataset.
However, these classes are often of highest interest. We approach such
potential misclassifications by weighting the posterior class probabilities
with the prior class probabilities which in our case are the inverse
frequencies of the corresponding classes in the training dataset. More
precisely, we adopt a localized method by computing the priors pixel-wise such
that the impact can be analyzed at pixel level as well. In our experiments, we
train one network from scratch using a proprietary dataset containing 20,000
annotated frames of video sequences recorded from street scenes. The evaluation
on our test set shows an increase of average recall with regard to instances of
pedestrians and info signs by $25\%$ and $23.4\%$, respectively. In addition,
we significantly reduce the non-detection rate for instances of the same
classes by $61\%$ and $38\%$.
",1,0,0,1,0,0
20922,Muchnik degrees and cardinal characteristics,"  We provide a pair of dual results, each stating the coincidence of highness
properties from computability theory. We provide an analogous pair of dual
results on the coincidence of cardinal characteristics within ZFC.
A mass problem is a set of functions on $\omega$. For mass problems $\mathcal
C, \mathcal D$, one says that $\mathcal C$ is Muchnik reducible to $\mathcal D$
if each function in $\mathcal D$ computes a function in $\mathcal C$. In this
paper we view highness properties as mass problems, and compare them with
respect to Muchnik reducibility and its uniform strengthening, Medvedev
reducibility.
Let $\mathcal D(p)$ be the mass problem of infinite bit sequences $y$ (i.e.,
0,1 valued functions) such that for each computable bit sequence $x$, the
asymptotic lower density $\underline \rho$ of the agreement bit sequence $x
\leftrightarrow y$ is at most $p$ (this sequence takes the value 1 at a bit
position iff $x$ and $y$ agree).
We show that all members of this family of mass problems parameterized by a
real $p$ with $0 < p<1/2 $ have the same complexity in the sense of Muchnik
reducibility. This also yields a new version of Monin's affirmative answer to
the ""Gamma question"", whether $\Gamma(A)< 1/2$ implies $\Gamma(A)=0$ for each
Turing oracle $A$.
We also show, together with Joseph Miller, that for any order function~$g$
there exists a faster growing order function $h $ such that $\mathrm{IOE}(g) $
is strictly Muchnik below $\mathrm{IOE}(h)$.
We study cardinal characteristics analogous to the highness properties above.
For instance, $\mathfrak d (p)$ is the least size of a set $G$ of bit sequences
so that for each bit sequence $x$ there is a bit sequence $y$ in $G$ so that
$\underline \rho (x \leftrightarrow y) >p$. We prove within ZFC all the
coincidences of cardinal characteristics that are the analogs of the results
above.
",0,0,1,0,0,0
20923,Existence of solutions for a semirelativistic Hartree equation with unbounded potentials,"  We prove the existence of a solution to the semirelativistic Hartree equation
$$\sqrt{-\Delta+m^2}u+ V(x) u = A(x)\left( W * |u|^p \right) |u|^{p-2}u $$
under suitable growth assumption on the potential functions $V$ and $A$. In
particular, both can be unbounded from above.
",0,0,1,0,0,0
20924,Lattice Operations on Terms over Similar Signatures,"  Unification and generalization are operations on two terms computing
respectively their greatest lower bound and least upper bound when the terms
are quasi-ordered by subsumption up to variable renaming (i.e., $t_1\preceq
t_2$ iff $t_1 = t_2\sigma$ for some variable substitution $\sigma$). When term
signatures are such that distinct functor symbols may be related with a fuzzy
equivalence (called a similarity), these operations can be formally extended to
tolerate mismatches on functor names and/or arity or argument order. We
reformulate and extend previous work with a declarative approach defining
unification and generalization as sets of axioms and rules forming a complete
constraint-normalization proof system. These include the Reynolds-Plotkin
term-generalization procedures, Maria Sessa's ""weak"" unification with partially
fuzzy signatures and its corresponding generalization, as well as novel
extensions of such operations to fully fuzzy signatures (i.e., similar functors
with possibly different arities). One advantage of this approach is that it
requires no modification of the conventional data structures for terms and
substitutions. This and the fact that these declarative specifications are
efficiently executable conditional Horn-clauses offers great practical
potential for fuzzy information-handling applications.
",1,0,0,0,0,0
20925,Towards Physically Safe Reinforcement Learning under Supervision,"  This paper addresses the question of how a previously available control
policy $\pi_s$ can be used as a supervisor to more quickly and safely train a
new learned control policy $\pi_L$ for a robot. A weighted average of the
supervisor and learned policies is used during trials, with a heavier weight
initially on the supervisor, in order to allow safe and useful physical trials
while the learned policy is still ineffective. During the process, the weight
is adjusted to favor the learned policy. As weights are adjusted, the learned
network must compensate so as to give safe and reasonable outputs under the
different weights. A pioneer network is introduced that pre-learns a policy
that performs similarly to the current learned policy under the planned next
step for new weights; this pioneer network then replaces the currently learned
network in the next set of trials. Experiments in OpenAI Gym demonstrate the
effectiveness of the proposed method.
",1,0,0,1,0,0
20926,Complex Valued Risk Diversification,"  Risk diversification is one of the dominant concerns for portfolio managers.
Various portfolio constructions have been proposed to minimize the risk of the
portfolio under some constrains including expected returns. We propose a
portfolio construction method that incorporates the complex valued principal
component analysis into the risk diversification portfolio construction. The
proposed method is verified to outperform the conventional risk parity and risk
diversification portfolio constructions.
",0,0,0,0,0,1
20927,An Overview on Application of Machine Learning Techniques in Optical Networks,"  Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions.
",0,0,0,1,0,0
20928,Study of Minor Actinides Transmutation in PWR MOX fuel,"  The management of long-lived radionuclides in spent fuel is a key issue to
achieve the closed nuclear fuel cycle and the sustainable development of
nuclear energy. Partitioning-Transmutation is supposed to be an efficient
method to treat the long-lived radionuclides in spent fuel. Some Minor
Actinides (MAs) have very long half-lives among the radionuclides in the spent
fuel. Accordingly, the study of MAs transmutation is a significant work for the
post-processing of spent fuel.
In the present work, the transmutations in Pressurized Water Reactor (PWR)
mixed oxide (MOX) fuel are investigated through the Monte Carlo based code RMC.
Two kinds of MAs, $^{237}$Np and five MAs ($^{237}$Np, $^{241}$Am, $^{243}$Am,
$^{244}$Cm and $^{245}$Cm) are incorporated homogeneously into the MOX fuel
assembly. The transmutation of MAs is simulated with different initial MOX
concentrations.
The results indicate an overall nice efficiency of transmutation in both
initial MOX concentrations, especially for the two kinds of MAs primarily
generated in the UOX fuel, $^{237}$Np and $^{241}$Am. In addition, the
inclusion of $^{237}$Np in MOX has no large influence for other MAs, while the
transmutation efficiency of $^{237}$Np is excellent. The transmutation of MAs
in MOX fuel depletion is expected to be a new, efficient nuclear spent fuel
management method for the future nuclear power generation.
",0,1,0,0,0,0
20929,Undesired parking spaces and contractible pieces of the noncrossing partition link,"  There are two natural simplicial complexes associated to the noncrossing
partition lattice: the order complex of the full lattice and the order complex
of the lattice with its bounding elements removed. The latter is a complex that
we call the noncrossing partition link because it is the link of an edge in the
former. The first author and his coauthors conjectured that various collections
of simplices of the noncrossing partition link (determined by the undesired
parking spaces in the corresponding parking functions) form contractible
subcomplexes. In this article we prove their conjecture by combining the fact
that the star of a simplex in a flag complex is contractible with the second
author's theory of noncrossing hypertrees.
",0,0,1,0,0,0
20930,Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization,"  Due to their simplicity and excellent performance, parallel asynchronous
variants of stochastic gradient descent have become popular methods to solve a
wide range of large-scale optimization problems on multi-core architectures.
Yet, despite their practical success, support for nonsmooth objectives is still
lacking, making them unsuitable for many problems of interest in machine
learning, such as the Lasso, group Lasso or empirical risk minimization with
convex constraints.
In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse
method inspired by SAGA, a variance reduced incremental gradient algorithm. The
proposed method is easy to implement and significantly outperforms the state of
the art on several nonsmooth, large-scale problems. We prove that our method
achieves a theoretical linear speedup with respect to the sequential version
under assumptions on the sparsity of gradients and block-separability of the
proximal term. Empirical benchmarks on a multi-core architecture illustrate
practical speedups of up to 12x on a 20-core machine.
",1,0,1,1,0,0
20931,Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems,"  We consider the inverse problem of recovering an unknown functional parameter
$u$ in a separable Banach space, from a noisy observation $y$ of its image
through a known possibly non-linear ill-posed map ${\mathcal G}$. The data $y$
is finite-dimensional and the noise is Gaussian. We adopt a Bayesian approach
to the problem and consider Besov space priors (see Lassas et al. 2009), which
are well-known for their edge-preserving and sparsity-promoting properties and
have recently attracted wide attention especially in the medical imaging
community.
Our key result is to show that in this non-parametric setup the maximum a
posteriori (MAP) estimates are characterized by the minimizers of a generalized
Onsager--Machlup functional of the posterior. This is done independently for
the so-called weak and strong MAP estimates, which as we show coincide in our
context. In addition, we prove a form of weak consistency for the MAP
estimators in the infinitely informative data limit. Our results are remarkable
for two reasons: first, the prior distribution is non-Gaussian and does not
meet the smoothness conditions required in previous research on non-parametric
MAP estimates. Second, the result analytically justifies existing uses of the
MAP estimate in finite but high dimensional discretizations of Bayesian inverse
problems with the considered Besov priors.
",0,0,1,1,0,0
20932,Non-LTE line formation of Fe in late-type stars IV: Modelling of the solar centre-to-limb variation in 3D,"  Our ability to model the shapes and strengths of iron lines in the solar
spectrum is a critical test of the accuracy of the solar iron abundance, which
sets the absolute zero-point of all stellar metallicities. We use an extensive
463-level Fe atom with new photoionisation cross-sections for FeI as well as
quantum mechanical calculations of collisional excitation and charge transfer
with neutral hydrogen; the latter effectively remove a free parameter that has
hampered all previous line formation studies of Fe in non-local thermodynamic
equilibrium (NLTE). For the first time, we use realistic 3D NLTE calculations
of Fe for a quantitative comparison to solar observations. We confront our
theoretical line profiles with observations taken at different viewing angles
across the solar disk with the Swedish 1-m Solar Telescope. We find that 3D
modelling well reproduces the observed centre-to-limb behaviour of spectral
lines overall, but highlight aspects that may require further work, especially
cross-sections for inelastic collisions with electrons. Our inferred solar iron
abundance is log(eps(Fe))=7.48+-0.04.
",0,1,0,0,0,0
20933,"Transmission clusters in the HIV-1 epidemic among men who have sex with men in Montreal, Quebec, Canada","  Background. Several studies have used phylogenetics to investigate Human
Immunodeficiency Virus (HIV) transmission among Men who have Sex with Men
(MSMs) in Montreal, Quebec, Canada, revealing many transmission clusters. The
Quebec HIV genotyping program sequence database now includes viral sequences
from close to 4,000 HIV-positive individuals classified as MSMs. In this paper,
we investigate clustering in those data by comparing results from several
methods: the conventional Bayesian and maximum likelihood-bootstrap methods,
and two more recent algorithms, DM-PhyClus, a Bayesian algorithm that produces
a measure of uncertainty for proposed partitions, and the Gap Procedure, a fast
distance-based approach. We estimate cluster growth by focusing on recent cases
in the Primary HIV Infection (PHI) stage. Results. The analyses reveal
considerable overlap between cluster estimates obtained from conventional
methods. The Gap Procedure and DM-PhyClus rely on different cluster definitions
and as a result, suggest moderately different partitions. All estimates lead to
similar conclusions about cluster expansion: several large clusters have
experienced sizeable growth, and a few new transmission clusters are likely
emerging. Conclusions. The lack of a gold standard measure for clustering
quality makes picking a best estimate among those proposed difficult. Work
aiming to refine clustering criteria would be required to improve estimates.
Nevertheless, the results unanimously stress the role that clusters play in
promoting HIV incidence among MSMs.
",0,0,0,1,0,0
20934,Conformal blocks attached to twisted groups,"  The aim of this paper is to generalize the notion of conformal blocks to the
situation in which the Lie algebra they are attached to is not defined over a
field, but depends on covering data of curves. The result will be a sheaf of
conformal blocks on the Hurwitz stack parametrizing Galois coverings of curves.
Many features of the classical sheaves of conformal blocks are proved to hold
in this more general setting, in particular the fusion rules, the propagation
of vacua and the WZW connection.
",0,0,1,0,0,0
20935,Statistical study on propagation characteristics of Omega signals (VLF) in magnetosphere detected by the Akebono satellite,"  This paper shows a statistical analysis of 10.2 kHz Omega broadcasts of an
artificial signal broadcast from ground stations, propagated in the
plasmasphere, and detected using an automatic detection method we developed. We
study the propagation patterns of the Omega signals to understand the
propagation characteristics that are strongly affected by plasmaspheric
electron density and the ambient magnetic field. We show the unique propagation
patterns of the Omega 10.2 kHz signal when it was broadcast from two
high-middle-latitude stations. We use about eight years of data captured by the
Poynting flux analyzer subsystem on board the Akebono satellite from October
1989 to September 1997. We demonstrate that the signals broadcast from almost
the same latitude (in geomagnetic coordinates) propagated differently depending
on the geographic latitude. We also study propagation characteristics as a
function of local time, season, and solar activity. The Omega signal tended to
propagate farther on the nightside than on the dayside and was more widely
distributed during winter than during summer. When solar activity was at
maximum, the Omega signal propagated at a lower intensity level. In contrast,
when solar activity was at minimum, the Omega signal propagated at a higher
intensity and farther from the transmitter station.
",0,1,0,0,0,0
20936,"Thermally induced stresses in boulders on airless body surfaces, and implications for rock breakdown","  This work investigates the macroscopic thermomechanical behavior of lunar
boulders by modeling their response to diurnal thermal forcing. Our results
reveal a bimodal, spatiotemporally-complex stress response. During sunrise,
stresses occur in the boulders' interiors that are associated with large-scale
temperature gradients developed due to overnight cooling. During sunset,
stresses occur at the boulders' exteriors due to the cooling and contraction of
the surface. Both kinds of stresses are on the order of 10 MPa in 1 m boulders
and decrease for smaller diameters, suggesting that larger boulders break down
more quickly. Boulders <30 cm exhibit a weak response to thermal forcing,
suggesting a threshold below which crack propagation may not occur. Boulders of
any size buried by regolith are shielded from thermal breakdown. As boulders
increase in size (>1 m), stresses increase to several 10s of MPa as the
behavior of their surfaces approaches that of an infinite halfspace. As the
thermal wave loses contact with the boulder interior, stresses become limited
to the near-surface. This suggests that the survival time of a boulder is not
only controlled by the amplitude of induced stress, but also by its diameter as
compared to the diurnal skin depth. While stresses on the order of 10 MPa are
enough to drive crack propagation in terrestrial environments, crack
propagation rates in vacuum are not well constrained. We explore the
relationship between boulder size, stress, and the direction of crack
propagation, and discuss the implications for the relative breakdown rates and
estimated lifetimes of boulders on airless body surfaces.
",0,1,0,0,0,0
20937,Some observations about generalized quantifiers in logics of imperfect information,"  We analyze the definitions of generalized quantifiers of imperfect
information that have been proposed by F.Engström. We argue that these
definitions are just embeddings of the first-order generalized quantifiers into
team semantics, and fail to capture an adequate notion of team-theoretical
generalized quantifier, save for the special cases in which the quantifiers are
applied to flat formulas. We also criticize the meaningfulness of the
monotone/nonmonotone distinction in this context. We make some proposals for a
more adequate definition of generalized quantifiers of imperfect information.
",0,0,1,0,0,0
20938,Predicting Demographics of High-Resolution Geographies with Geotagged Tweets,"  In this paper, we consider the problem of predicting demographics of
geographic units given geotagged Tweets that are composed within these units.
Traditional survey methods that offer demographics estimates are usually
limited in terms of geographic resolution, geographic boundaries, and time
intervals. Thus, it would be highly useful to develop computational methods
that can complement traditional survey methods by offering demographics
estimates at finer geographic resolutions, with flexible geographic boundaries
(i.e. not confined to administrative boundaries), and at different time
intervals. While prior work has focused on predicting demographics and health
statistics at relatively coarse geographic resolutions such as the county-level
or state-level, we introduce an approach to predict demographics at finer
geographic resolutions such as the blockgroup-level. For the task of predicting
gender and race/ethnicity counts at the blockgroup-level, an approach adapted
from prior work to our problem achieves an average correlation of 0.389
(gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms
this prior approach with an average correlation of 0.671 (gender) and 0.692
(race).
",1,0,0,1,0,0
20939,Text Compression for Sentiment Analysis via Evolutionary Algorithms,"  Can textual data be compressed intelligently without losing accuracy in
evaluating sentiment? In this study, we propose a novel evolutionary
compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
which makes use of Parts-of-Speech tags to compress text in a way that
sacrifices minimal classification accuracy when used in conjunction with
sentiment analysis algorithms. An analysis of PARSEC with eight commercial and
non-commercial sentiment analysis algorithms on twelve English sentiment data
sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss
in sentiment classification accuracy for (20%, 50%, 75%) data compression with
PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other
sentiment analysis algorithms are more severely affected by compression. We
conclude that significant compression of text data is possible for sentiment
analysis depending on the accuracy demands of the specific application and the
specific sentiment analysis algorithm used.
",1,0,0,1,0,0
20940,Training large margin host-pathogen protein-protein interaction predictors,"  Detection of protein-protein interactions (PPIs) plays a vital role in
molecular biology. Particularly, infections are caused by the interactions of
host and pathogen proteins. It is important to identify host-pathogen
interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI prediction techniques have limitations in terms of
large scale application and budget. Hence, computational approaches are
developed to predict PPIs. This study aims to develop large margin machine
learning models to predict interspecies PPIs with a special interest in
host-pathogen protein interactions (HPIs). Especially, we focus on seeking
answers to three queries that arise while developing an HPI predictor. 1) How
should we select negative samples? 2) What should be the size of negative
samples as compared to the positive samples? 3) What type of margin violation
penalty should be used to train the predictor? We compare two available methods
for negative sampling. Moreover, we propose a new method of assigning weights
to each training example in weighted SVM depending on the distance of the
negative examples from the positive examples. We have also developed a web
server for our HPI predictor called HoPItor (Host Pathogen Interaction
predicTOR) that can predict interactions between human and viral proteins. This
webserver can be accessed at the URL:
this http URL.
",1,0,0,1,0,0
20941,A Useful Solution of the Coupon Collector's Problem,"  The Coupon Collector's Problem is one of the few mathematical problems that
make news headlines regularly. The reasons for this are on one hand the immense
popularity of soccer albums (called Paninimania) and on the other hand that no
solution is known that is able to take into account all effects such as
replacement (limited purchasing of missing stickers) or swapping. In previous
papers we have proven that the classical assumptions are not fulfilled in
practice. Therefore we define new assumptions that match reality. Based on
these assumptions we are able to derive formulae for the mean number of
stickers needed (and the associated standard deviation) that are able to take
into account all effects that occur in practical collecting. Thus collectors
can estimate the average cost of completion of an album and its standard
deviation just based on elementary calculations. From a practical point of view
we consider the Coupon Collector's problem as solved.
-----
Das Sammelbilderproblem ist eines der wenigen mathematischen Probleme, die
regelmä{\ss}ig in den Schlagzeilen der Nachrichten vorkommen. Dies liegt
einerseits an der gro{\ss}en Popularität von Fu{\ss}ball-Sammelbildern
(Paninimania genannt) und andererseits daran, dass es bisher keine Lösung
gibt, die alle relevanten Effekte wie Nachkaufen oder Tauschen
berücksichtigt. Wir haben bereits nachgewiesen, dass die klassischen Annahmen
nicht der Realität entsprechen. Deshalb stellen wir neue Annahmen auf, die
die Praxis besser abbilden. Darauf aufbauend können wir Formeln für die
mittlere Anzahl benötigter Bilder (sowie deren Standardabweichung) ableiten,
die alle in der Praxis relevanten Effekte berücksichtigen. Damit können
Sammler die mittleren Kosten eines Albums sowie deren Standardabweichung nur
mit Hilfe von elementaren Rechnungen bestimmen. Für praktische Zwecke ist das
Sammelbilderproblem damit gelöst.
",0,0,1,0,0,0
20942,Accretion driven turbulence in filaments I: Non-gravitational accretion,"  We study accretion driven turbulence for different inflow velocities in star
forming filaments using the code ramses. Filaments are rarely isolated objects
and their gravitational potential will lead to radially dominated accretion. In
the non-gravitational case, accretion by itself can already provoke
non-isotropic, radially dominated turbulent motions responsible for the complex
structure and non-thermal line widths observed in filaments. We find that there
is a direct linear relation between the absolute value of the total density
weighted velocity dispersion and the infall velocity. The turbulent velocity
dispersion in the filaments is independent of sound speed or any net flow along
the filament. We show that the density weighted velocity dispersion acts as an
additional pressure term supporting the filament in hydrostatic equilibrium.
Comparing to observations, we find that the projected non-thermal line width
variation is generally subsonic independent of inflow velocity.
",0,1,0,0,0,0
20943,Contextual Outlier Interpretation,"  Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers does not receive much attention. Interpretation is becoming
increasingly important to help people trust and evaluate the developed models
through providing intrinsic reasons why the certain outliers are chosen. It is
difficult, if not impossible, to simply apply feature selection for explaining
outliers due to the distinct characteristics of various detection models,
complicated structures of data in certain applications, and imbalanced
distribution of outliers and normal instances. In addition, the role of
contrastive contexts where outliers locate, as well as the relation between
outliers and contexts, are usually overlooked in interpretation. To tackle the
issues above, in this paper, we propose a novel Contextual Outlier
INterpretation (COIN) method to explain the abnormality of existing outliers
spotted by detectors. The interpretability for an outlier is achieved from
three aspects: outlierness score, attributes that contribute to the
abnormality, and contextual description of its neighborhoods. Experimental
results on various types of datasets demonstrate the flexibility and
effectiveness of the proposed framework compared with existing interpretation
approaches.
",1,0,0,1,0,0
20944,Game Theory for Secure Critical Interdependent Gas-Power-Water Infrastructure,"  A city's critical infrastructure such as gas, water, and power systems, are
largely interdependent since they share energy, computing, and communication
resources. This, in turn, makes it challenging to endow them with fool-proof
security solutions. In this paper, a unified model for interdependent
gas-power-water infrastructure is presented and the security of this model is
studied using a novel game-theoretic framework. In particular, a zero-sum
noncooperative game is formulated between a malicious attacker who seeks to
simultaneously alter the states of the gas-power-water critical infrastructure
to increase the power generation cost and a defender who allocates
communication resources over its attack detection filters in local areas to
monitor the infrastructure. At the mixed strategy Nash equilibrium of this
game, numerical results show that the expected power generation cost deviation
is 35\% lower than the one resulting from an equal allocation of resources over
the local filters. The results also show that, at equilibrium, the
interdependence of the power system on the natural gas and water systems can
motivate the attacker to target the states of the water and natural gas systems
to change the operational states of the power grid. Conversely, the defender
allocates a portion of its resources to the water and natural gas states of the
interdependent system to protect the grid from state deviations.
",1,0,0,0,0,0
20945,Implicit Entity Linking in Tweets,"  Over the years, Twitter has become one of the largest communication platforms
providing key data to various applications such as brand monitoring, trend
detection, among others. Entity linking is one of the major tasks in natural
language understanding from tweets and it associates entity mentions in text to
corresponding entries in knowledge bases in order to provide unambiguous
interpretation and additional con- text. State-of-the-art techniques have
focused on linking explicitly mentioned entities in tweets with reasonable
success. However, we argue that in addition to explicit mentions i.e. The movie
Gravity was more ex- pensive than the mars orbiter mission entities (movie
Gravity) can also be mentioned implicitly i.e. This new space movie is crazy.
you must watch it!. This paper introduces the problem of implicit entity
linking in tweets. We propose an approach that models the entities by
exploiting their factual and contextual knowledge. We demonstrate how to use
these models to perform implicit entity linking on a ground truth dataset with
397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1)
the importance of linking implicit entities and its value addition to the
standard entity linking task, and 2) the importance of exploiting contextual
knowledge associated with an entity for linking their implicit mentions. We
also make the ground truth dataset publicly available to foster the research in
this new research area.
",1,0,0,0,0,0
20946,Non-cocompact Group Actions and $π_1$-Semistability at Infinity,"  A finitely presented 1-ended group $G$ has {\it semistable fundamental group
at infinity} if $G$ acts geometrically on a simply connected and locally
compact ANR $Y$ having the property that any two proper rays in $Y$ are
properly homotopic. This property of $Y$ captures a notion of connectivity at
infinity stronger than ""1-ended"", and is in fact a feature of $G$, being
independent of choices. It is a fundamental property in the homotopical study
of finitely presented groups. While many important classes of groups have been
shown to have semistable fundamental group at infinity, the question of whether
every $G$ has this property has been a recognized open question for nearly
forty years. In this paper we attack the problem by considering a proper {\it
but non-cocompact} action of a group $J$ on such an $Y$. This $J$ would
typically be a subgroup of infinite index in the geometrically acting
over-group $G$; for example $J$ might be infinite cyclic or some other subgroup
whose semistability properties are known. We divide the semistability property
of $G$ into a $J$-part and a ""perpendicular to $J$"" part, and we analyze how
these two parts fit together. Among other things, this analysis leads to a
proof (in a companion paper) that a class of groups previously considered to be
likely counter examples do in fact have the semistability property.
",0,0,1,0,0,0
20947,Robust Distributed Control of DC Microgrids with Time-Varying Power Sharing,"  This paper addresses the problem of output voltage regulation for multiple
DC/DC converters connected to a microgrid, and prescribes a scheme for sharing
power among different sources. This architecture is structured in such a way
that it admits quantifiable analysis of the closed-loop performance of the
network of converters; the analysis simplifies to studying closed-loop
performance of an equivalent {\em single-converter} system. The proposed
architecture allows for the proportion in which the sources provide power to
vary with time; thus overcoming limitations of our previous designs.
Additionally, the proposed control framework is suitable to both centralized
and decentralized implementations, i.e., the same control architecture can be
employed for voltage regulation irrespective of the availability of common
load-current (or power) measurement, without the need to modify controller
parameters. The performance becomes quantifiably better with better
communication of the demanded load to all the controllers at all the converters
(in the centralized case); however guarantees viability when such communication
is absent. Case studies comprising of battery, PV and generic sources are
presented and demonstrate the enhanced performance of prescribed optimal
controllers for voltage regulation and power sharing.
",1,0,1,0,0,0
20948,Quantum Lower Bounds for Tripartite Versions of the Hidden Shift and the Set Equality Problems,"  In this paper, we study quantum query complexity of the following rather
natural tripartite generalisations (in the spirit of the 3-sum problem) of the
hidden shift and the set equality problems, which we call the 3-shift-sum and
the 3-matching-sum problems.
The 3-shift-sum problem is as follows: given a table of $3\times n$ elements,
is it possible to circularly shift its rows so that the sum of the elements in
each column becomes zero? It is promised that, if this is not the case, then no
3 elements in the table sum up to zero. The 3-matching-sum problem is defined
similarly, but it is allowed to arbitrarily permute elements within each row.
For these problems, we prove lower bounds of $\Omega(n^{1/3})$ and
$\Omega(\sqrt n)$, respectively. The second lower bound is tight.
The lower bounds are proven by a novel application of the dual learning graph
framework and by using representation-theoretic tools.
",1,0,0,0,0,0
20949,A Parallel Direct Cut Algorithm for High-Order Overset Methods with Application to a Spinning Golf Ball,"  Overset methods are commonly employed to enable the effective simulation of
problems involving complex geometries and moving objects such as rotorcraft.
This paper presents a novel overset domain connectivity algorithm based upon
the direct cut approach suitable for use with GPU-accelerated solvers on
high-order curved grids. In contrast to previous methods it is capable of
exploiting the highly data-parallel nature of modern accelerators. Further, the
approach is also substantially more efficient at handling the curved grids
which arise within the context of high-order methods. An implementation of this
new algorithm is presented and combined with a high-order fluid dynamics code.
The algorithm is validated against several benchmark problems, including flow
over a spinning golf ball at a Reynolds number of 150,000.
",0,1,0,0,0,0
20950,A data assimilation algorithm: the paradigm of the 3D Leray-alpha model of turbulence,"  In this paper we survey the various implementations of a new data
assimilation (downscaling) algorithm based on spatial coarse mesh measurements.
As a paradigm, we demonstrate the application of this algorithm to the 3D
Leray-$\alpha$ subgrid scale turbulence model. Most importantly, we use this
paradigm to show that it is not always necessary that one has to collect coarse
mesh measurements of all the state variables, that are involved in the
underlying evolutionary system, in order to recover the corresponding exact
reference solution. Specifically, we show that in the case of the 3D
Leray$-\alpha$ model of turbulence the solutions of the algorithm, constructed
using only coarse mesh observations of any two components of the
three-dimensional velocity field, and without any information of the third
component, converge, at an exponential rate in time, to the corresponding exact
reference solution of the 3D Leray$-\alpha$ model. This study serves as an
addendum to our recent work on abridged continuous data assimilation for the 2D
Navier-Stokes equations. Notably, similar results have also been recently
established for the 3D viscous Planetary Geostrophic circulation model in which
we show that coarse mesh measurements of the temperature alone are sufficient
for recovering, through our data assimilation algorithm, the full solution;
viz. the three components of velocity vector field and the temperature.
Consequently, this proves the Charney conjecture for the 3D Planetary
Geostrophic model; namely, that the history of the large spatial scales of
temperature is sufficient for determining all the other quantities (state
variables) of the model.
",0,1,1,0,0,0
20951,Playing a true Parrondo's game with a three state coin on a quantum walk,"  Playing a Parrondo's game with a qutrit is the subject of this paper. We show
that a true quantum Parrondo's game can be played with a 3 state coin(qutrit)
in a 1D quantum walk in contrast to the fact that playing a true Parrondo's
game with a 2 state coin(qubit) in 1D quantum walk fails in the asymptotic
limits.
",1,1,0,0,0,0
20952,Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data,"  We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstrate their superiority over baseline approaches. The
X-LSTM improves parameter efficiency by processing each modality separately and
allowing for information flow between them by way of recurrent
cross-connections. We present a general hyperparameter optimisation technique
for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
state-of-the-art cross-modal approach, using a comparable number of parameters.
Finally, we visualise the model's predictions, revealing implications about
latent variables in this task.
",1,0,0,1,0,0
20953,Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions,"  The key idea of variational auto-encoders (VAEs) resembles that of
traditional auto-encoder models in which spatial information is supposed to be
explicitly encoded in the latent space. However, the latent variables in VAEs
are vectors, which can be interpreted as multiple feature maps of size 1x1.
Such representations can only convey spatial information implicitly when
coupled with powerful decoders. In this work, we propose spatial VAEs that use
feature maps of larger size as latent variables to explicitly capture spatial
information. This is achieved by allowing the latent variables to be sampled
from matrix-variate normal (MVN) distributions whose parameters are computed
from the encoder network. To increase dependencies among locations on latent
feature maps and reduce the number of parameters, we further propose spatial
VAEs via low-rank MVN distributions. Experimental results show that the
proposed spatial VAEs outperform original VAEs in capturing rich structural and
spatial information.
",1,0,0,1,0,0
20954,Optimal Ramp Schemes and Related Combinatorial Objects,"  In 1996, Jackson and Martin proved that a strong ideal ramp scheme is
equivalent to an orthogonal array. However, there was no good characterization
of ideal ramp schemes that are not strong. Here we show the equivalence of
ideal ramp schemes to a new variant of orthogonal arrays that we term augmented
orthogonal arrays. We give some constructions for these new kinds of arrays,
and, as a consequence, we also provide parameter situations where ideal ramp
schemes exist but strong ideal ramp schemes do not exist.
",1,0,0,0,0,0
20955,Do Neural Nets Learn Statistical Laws behind Natural Language?,"  The performance of deep learning in natural language processing has been
spectacular, but the reasons for this success remain unclear because of the
inherent complexity of deep learning. This paper provides empirical evidence of
its effectiveness and of a limitation of neural networks for language
engineering. Precisely, we demonstrate that a neural language model based on
long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law,
two representative statistical properties underlying natural language. We
discuss the quality of reproducibility and the emergence of Zipf's law and
Heaps' law as training progresses. We also point out that the neural language
model has a limitation in reproducing long-range correlation, another
statistical property of natural language. This understanding could provide a
direction for improving the architectures of neural networks.
",1,0,0,0,0,0
20956,Super-speeds with Zero-RAM: Next Generation Large-Scale Optimization in Your Laptop!,"  This article presents the novel breakthrough general purpose algorithm for
large scale optimization problems. The novel algorithm is capable of achieving
breakthrough speeds for very large-scale optimization on general purpose
laptops and embedded systems. Application of the algorithm to the Griewank
function was possible in up to 1 billion decision variables in double precision
took only 64485 seconds (~18 hours) to solve, while consuming 7,630 MB (7.6 GB)
or RAM on a single threaded laptop CPU. It shows that the algorithm is
computationally and memory (space) linearly efficient, and can find the optimal
or near-optimal solution in a fraction of the time and memory that many
conventional algorithms require. It is envisaged that this will open up new
possibilities of real-time large-scale problems on personal laptops and
embedded systems.
",1,0,0,0,0,0
20957,Recoverable Energy of Dissipative Electromagnetic Systems,"  Ambiguities in the definition of stored energy within distributed or
radiating electromagnetic systems motivate the discussion of the well-defined
concept of recoverable energy. This concept is commonly overlooked by the
community and the purpose of this communication is to recall its existence and
to discuss its relationship to fractional bandwidth. Using a rational function
approximation of a system's input impedance, the recoverable energy of lumped
and radiating systems is calculated in closed form and is related to stored
energy and fractional bandwidth. Lumped circuits are also used to demonstrate
the relationship between recoverable energy and the energy stored within
equivalent circuits produced by the minimum phase-shift Darlington's synthesis
procedure.
",0,1,0,0,0,0
20958,Elliptic Hall algebra on $\mathbb{F}_1$,"  We construct Hall algebra of elliptic curve over $\mathbb{F}_1$ using the
theory of monoidal scheme due to Deitmar and the theory of Hall algebra for
monoidal representations due to Szczesny. The resulting algebra is shown to be
a specialization of elliptic Hall algebra studied by Burban and Schiffmann.
Thus our algebra is isomorphic to the skein algebra for torus by the recent
work of Morton and Samuelson.
",0,0,1,0,0,0
20959,Approximate Bayesian inference with queueing networks and coupled jump processes,"  Queueing networks are systems of theoretical interest that give rise to
complex families of stochastic processes, and find widespread use in the
performance evaluation of interconnected resources. Yet, despite their
importance within applications, and in comparison to their counterpart
stochastic models in genetics or mathematical biology, there exist few relevant
approaches for transient inference and uncertainty quantification tasks in
these systems. This is a consequence of strong computational impediments and
distinctive properties of the Markov jump processes induced by queueing
networks. In this paper, we offer a comprehensive overview of the inferential
challenge and its comparison to analogue tasks within related mathematical
domains. We then discuss a model augmentation over an approximating network
system, and present a flexible and scalable variational Bayesian framework,
which is targeted at general-form open and closed queueing systems, with varied
service disciplines and priorities. The inferential procedure is finally
validated in a couple of uncertainty quantification tasks for network service
rates.
",1,0,0,1,0,0
20960,Universal features of price formation in financial markets: perspectives from Deep Learning,"  Using a large-scale Deep Learning approach applied to a high-frequency
database containing billions of electronic market quotes and transactions for
US equities, we uncover nonparametric evidence for the existence of a universal
and stationary price formation mechanism relating the dynamics of supply and
demand for a stock, as revealed through the order book, to subsequent
variations in its market price. We assess the model by testing its
out-of-sample predictions for the direction of price moves given the history of
price and order flow, across a wide range of stocks and time periods. The
universal price formation model is shown to exhibit a remarkably stable
out-of-sample prediction accuracy across time, for a wide range of stocks from
different sectors. Interestingly, these results also hold for stocks which are
not part of the training sample, showing that the relations captured by the
model are universal and not asset-specific.
The universal model --- trained on data from all stocks --- outperforms, in
terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear
models trained on time series of any given stock, showing that the universal
nature of price formation weighs in favour of pooling together financial data
from various stocks, rather than designing asset- or sector-specific models as
commonly done. Standard data normalizations based on volatility, price level or
average spread, or partitioning the training data into sectors or categories
such as large/small tick stocks, do not improve training results. On the other
hand, inclusion of price and order flow history over many past observations is
shown to improve forecasting performance, showing evidence of path-dependence
in price dynamics.
",0,0,0,1,0,1
20961,A New Tracking Algorithm for Multiple Colloidal Particles Close to Contact,"  In this paper, we propose a new algorithm based on radial symmetry center
method to track colloidal particles close to contact, where the optical images
of the particles start to overlap in digital video microscopy. This overlapping
effect is important to observe the pair interaction potential in colloidal
studies and it appears as additional interaction in the measurement of the
interaction with conventional tracking analysis. The proposed algorithm in this
work is simple, fast and applicable for not only two particles but also three
and more particles without any modification. The algorithm uses gradient
vectors of the particle intensity distribution, which allows us to use a part
of the symmetric intensity distribution in the calculation of the actual
particle position. In this study, simulations are performed to see the
performance of the proposed algorithm for two and three particles, where the
simulation images are generated by using fitted curve to experimental particle
image for different sized particles. As a result, the algorithm yields the
maximum error smaller than 2 nm for 5.53 {\mu}m silica particles in contact
condition.
",0,1,0,0,0,0
20962,Critical Percolation Without Fine Tuning on the Surface of a Topological Superconductor,"  We present numerical evidence that most two-dimensional surface states of a
bulk topological superconductor (TSC) sit at an integer quantum Hall plateau
transition. We study TSC surface states in class CI with quenched disorder.
Low-energy (finite-energy) surface states were expected to be critically
delocalized (Anderson localized). We confirm the low-energy picture, but find
instead that finite-energy states are also delocalized, with universal
statistics that are independent of the TSC winding number, and consistent with
the spin quantum Hall plateau transition (percolation).
",0,1,0,0,0,0
20963,Low-luminosity stellar wind accretion onto neutron stars in HMXBs,"  Features and applications of quasi-spherical settling accretion onto rotating
magnetized neutron stars in high-mass X-ray binaries are discussed. The
settling accretion occurs in wind-fed HMXBs when the plasma cooling time is
longer than the free-fall time from the gravitational capture radius, which can
take place in low-luminosity HMXBs with $L_x\lesssim 4\times 10^{36}$ erg/s. We
briefly review the implications of the settling accretion, focusing on the SFXT
phenomenon, which can be related to instability of the quasi-spherical
convective shell above the neutron star magnetosphere due to magnetic
reconnection from fast temporarily magnetized winds from OB-supergiant. If a
young neutron star in a wind-fed HMXB is rapidly rotating, the propeller regime
in a quasi-spherical hot shell occurs. We show that X-ray spectral and temporal
properties of enigmatic $\gamma$ Cas Be-stars are consistent with failed
settling accretion regime onto a propelling neutron star. The subsequent
evolutionary stage of $\gamma$ Cas and its analogs should be the X Per-type
binaries comprising low-luminosity slowly rotating X-ray pulsars.
",0,1,0,0,0,0
20964,Faithful Inversion of Generative Models for Effective Amortized Inference,"  Inference amortization methods share information across multiple
posterior-inference problems, allowing each to be carried out more efficiently.
Generally, they require the inversion of the dependency structure in the
generative model, as the modeller must learn a mapping from observations to
distributions approximating the posterior. Previous approaches have involved
inverting the dependency structure in a heuristic way that fails to capture
these dependencies correctly, thereby limiting the achievable accuracy of the
resulting approximations. We introduce an algorithm for faithfully, and
minimally, inverting the graphical model structure of any generative model.
Such inverses have two crucial properties: (a) they do not encode any
independence assertions that are absent from the model and; (b) they are local
maxima for the number of true independencies encoded. We prove the correctness
of our approach and empirically show that the resulting minimally faithful
inverses lead to better inference amortization than existing heuristic
approaches.
",1,0,0,1,0,0
20965,A social Network Analysis of the Operations Research/Industrial Engineering Faculty Hiring Network,"  We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE)
faculty hiring network, consisting of 1,179 faculty origin and destination data
together with attribute data from 83 ORIE departments. A social network
analysis of faculty hires can reveal important patterns in an academic field,
such as the existence of a hierarchy or sociological aspects such as the
presence of communities of departments. We first statistically test for the
existence of a linear hierarchy in the network and for its steepness. We find a
near linear hierarchical order of the departments, proposing a new index for
hiring networks, which we contrast with other indicators of hierarchy,
including published rankings. A single index is not capable to capture the full
structure of a complex network, however, so we next fit a latent exponential
random graph model (ERGM) to the network, which is able to reproduce its main
observed characteristics: high incidence of self-hiring, skewed out-degree
distribution, low density and clustering. Finally, we use the latent variables
in the ERGM to simplify the network to one where faculty hires take place among
three groups of departments. We contrast our findings with those reported for
other related disciplines, Computer Science and Business.
",1,0,0,1,0,0
20966,One-sample aggregate data meta-analysis of medians,"  An aggregate data meta-analysis is a statistical method that pools the
summary statistics of several selected studies to estimate the outcome of
interest. When considering a continuous outcome, typically each study must
report the same measure of the outcome variable and its spread (e.g., the
sample mean and its standard error). However, some studies may instead report
the median along with various measures of spread. Recently, the task of
incorporating medians in meta-analysis has been achieved by estimating the
sample mean and its standard error from each study that reports a median in
order to meta-analyze the means. In this paper, we propose two alternative
approaches to meta-analyze data that instead rely on medians. We systematically
compare these approaches via simulation study to each other and to methods that
transform the study-specific medians and spread into sample means and their
standard errors. We demonstrate that the proposed median-based approaches
perform better than the transformation-based approaches, especially when
applied to skewed data and data with high inter-study variance. In addition,
when meta-analyzing data that consists of medians, we show that the
median-based approaches perform considerably better than or comparably to the
best-case scenario for a transformation approach: conducting a meta-analysis
using the actual sample mean and standard error of the mean of each study.
Finally, we illustrate these approaches in a meta-analysis of patient delay in
tuberculosis diagnosis.
",0,0,0,1,0,0
20967,QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts,"  Large inter-datacenter transfers are crucial for cloud service efficiency and
are increasingly used by organizations that have dedicated wide area networks
between datacenters. A recent work uses multicast forwarding trees to reduce
the bandwidth needs and improve completion times of point-to-multipoint
transfers. Using a single forwarding tree per transfer, however, leads to poor
performance because the slowest receiver dictates the completion time for all
receivers. Using multiple forwarding trees per transfer alleviates this
concern--the average receiver could finish early; however, if done naively,
bandwidth usage would also increase and it is apriori unclear how best to
partition receivers, how to construct the multiple trees and how to determine
the rate and schedule of flows on these trees. This paper presents QuickCast, a
first solution to these problems. Using simulations on real-world network
topologies, we see that QuickCast can speed up the average receiver's
completion time by as much as $10\times$ while only using $1.04\times$ more
bandwidth; further, the completion time for all receivers also improves by as
much as $1.6\times$ faster at high loads.
",1,0,0,0,0,0
20968,Contemporary machine learning: a guide for practitioners in the physical sciences,"  Machine learning is finding increasingly broad application in the physical
sciences. This most often involves building a model relationship between a
dependent, measurable output and an associated set of controllable, but
complicated, independent inputs. We present a tutorial on current techniques in
machine learning -- a jumping-off point for interested researchers to advance
their work. We focus on deep neural networks with an emphasis on demystifying
deep learning. We begin with background ideas in machine learning and some
example applications from current research in plasma physics. We discuss
supervised learning techniques for modeling complicated functions, beginning
with familiar regression schemes, then advancing to more sophisticated deep
learning methods. We also address unsupervised learning and techniques for
reducing the dimensionality of input spaces. Along the way, we describe methods
for practitioners to help ensure that their models generalize from their
training data to as-yet-unseen test data. We describe classes of tasks --
predicting scalars, handling images, fitting time-series -- and prepare the
reader to choose an appropriate technique. We finally point out some
limitations to modern machine learning and speculate on some ways that
practitioners from the physical sciences may be particularly suited to help.
",1,1,0,0,0,0
20969,Uniform diamond coatings on WC-Co hard alloy cutting inserts deposited by a microwave plasma CVD,"  Polycrystalline diamond coatings have been grown on cemented carbide
substrates with different aspect ratios by a microwave plasma CVD in
methane-hydrogen gas mixtures. To protect the edges of the substrates from
non-uniform heating due to the plasma edge effect, a special plateholder with
pockets for group growth has been used. The difference in heights of the
substrates and plateholder, and its influence on the diamond film mean grain
size, growth rate, phase composition and stress was investigated. The substrate
temperature range, within which uniform diamond films are produced with good
adhesion, is determined. The diamond-coated cutting inserts produced at
optimized process exhibited a reduction of cutting force and wear resistance by
a factor of two, and cutting efficiency increase by 4.3 times upon turning A390
Al-Si alloy as compared to performance of uncoated tools.
",0,1,0,0,0,0
20970,Analysing Soccer Games with Clustering and Conceptors,"  We present a new approach for identifying situations and behaviours, which we
call ""moves"", from soccer games in the 2D simulation league. Being able to
identify key situations and behaviours are useful capabilities for analysing
soccer matches, anticipating opponent behaviours to aid selection of
appropriate tactics, and also as a prerequisite for automatic learning of
behaviours and policies. To support a wide set of strategies, our goal is to
identify situations from data, in an unsupervised way without making use of
pre-defined soccer specific concepts such as ""pass"" or ""dribble"". The recurrent
neural networks we use in our approach act as a high-dimensional projection of
the recent history of a situation on the field. Similar situations, i.e., with
similar histories, are found by clustering of network states. The same networks
are also used to learn so-called conceptors, that are lower-dimensional
manifolds that describe trajectories through a high-dimensional state space
that enable situation-specific predictions from the same neural network. With
the proposed approach, we can segment games into sequences of situations that
are learnt in an unsupervised way, and learn conceptors that are useful for the
prediction of the near future of the respective situation.
",1,0,0,0,0,0
20971,On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates,"  The sum of Log-normal variates is encountered in many challenging
applications such as in performance analysis of wireless communication systems
and in financial engineering. Several approximation methods have been developed
in the literature, the accuracy of which is not ensured in the tail regions.
These regions are of primordial interest wherein small probability values have
to be evaluated with high precision. Variance reduction techniques are known to
yield accurate, yet efficient, estimates of small probability values. Most of
the existing approaches, however, have considered the problem of estimating the
right-tail of the sum of Log-normal random variables (RVS). In the present
work, we consider instead the estimation of the left-tail of the sum of
correlated Log-normal variates with Gaussian copula under a mild assumption on
the covariance matrix. We propose an estimator combining an existing
mean-shifting importance sampling approach with a control variate technique.
The main result is that the proposed estimator has an asymptotically vanishing
relative error which represents a major finding in the context of the left-tail
simulation of the sum of Log-normal RVs. Finally, we assess by various
simulation results the performances of the proposed estimator compared to
existing estimators.
",0,0,1,1,0,0
20972,Why optional stopping is a problem for Bayesians,"  Recently, optional stopping has been a subject of debate in the Bayesian
psychology community. Rouder (2014) argues that optional stopping is no problem
for Bayesians, and even recommends the use of optional stopping in practice, as
do Wagenmakers et al. (2012). This article addresses the question whether
optional stopping is problematic for Bayesian methods, and specifies under
which circumstances and in which sense it is and is not. By slightly varying
and extending Rouder's (2014) experiment, we illustrate that, as soon as the
parameters of interest are equipped with default or pragmatic priors - which
means, in most practical applications of Bayes Factor hypothesis testing -
resilience to optional stopping can break down. We distinguish between four
types of default priors, each having their own specific issues with optional
stopping, ranging from no-problem-at-all (Type 0 priors) to quite severe (Type
II and III priors).
",0,0,1,1,0,0