diff --git "a/test.csv" "b/test.csv" new file mode 100644--- /dev/null +++ "b/test.csv" @@ -0,0 +1,61238 @@ +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 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 $07$), 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