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2106.13589
|
H{\aa}vard Bakke Bjerkevik
|
H{\aa}vard Bakke Bjerkevik and Michael Lesnick
|
$\ell^p$-Distances on Multiparameter Persistence Modules
|
49 pages. Rewrote beginning of introduction; other minor changes
| null | null | null |
math.AT cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motivated both by theoretical and practical considerations in topological
data analysis, we generalize the $p$-Wasserstein distance on barcodes to
multiparameter persistence modules. For each $p\in [1,\infty]$, we in fact
introduce two such generalizations $d_{\mathcal I}^p$ and $d_{\mathcal M}^p$,
such that $d_{\mathcal I}^\infty$ equals the interleaving distance and
$d_{\mathcal M}^\infty$ equals the matching distance. We show that on 1- or
2-parameter persistence modules over prime fields, $d_{\mathcal I}^p$ is the
universal (i.e., largest) metric satisfying a natural stability property; this
extends a stability theorem of Skraba and Turner for the $p$-Wasserstein
distance on barcodes in the 1-parameter case, and is also a close analogue of a
universality property for the interleaving distance given by the second author.
We also show that $d_{\mathcal M}^p\leq d_{\mathcal I}^p$ for all $p\in
[1,\infty]$, extending an observation of Landi in the $p=\infty$ case. We
observe that on 2-parameter persistence modules, $d_{\mathcal M}^p$ can be
efficiently approximated. In a forthcoming companion paper, we apply some of
these results to study the stability of ($2$-parameter) multicover persistent
homology.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.71027 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.16112
|
Shaofeng Jiang
|
Vladimir Braverman and Shaofeng H.-C. Jiang and Robert Krauthgamer and
Xuan Wu
|
Coresets for Clustering with Missing Values
| null | null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
We provide the first coreset for clustering points in $\mathbb{R}^d$ that
have multiple missing values (coordinates). Previous coreset constructions only
allow one missing coordinate. The challenge in this setting is that objective
functions, like $k$-Means, are evaluated only on the set of available
(non-missing) coordinates, which varies across points. Recall that an
$\epsilon$-coreset of a large dataset is a small proxy, usually a reweighted
subset of points, that $(1+\epsilon)$-approximates the clustering objective for
every possible center set.
Our coresets for $k$-Means and $k$-Median clustering have size
$(jk)^{O(\min(j,k))} (\epsilon^{-1} d \log n)^2$, where $n$ is the number of
data points, $d$ is the dimension and $j$ is the maximum number of missing
coordinates for each data point. We further design an algorithm to construct
these coresets in near-linear time, and consequently improve a recent
quadratic-time PTAS for $k$-Means with missing values [Eiben et al., SODA 2021]
to near-linear time.
We validate our coreset construction, which is based on importance sampling
and is easy to implement, on various real data sets. Our coreset exhibits a
flexible tradeoff between coreset size and accuracy, and generally outperforms
the uniform-sampling baseline. Furthermore, it significantly speeds up a
Lloyd's-style heuristic for $k$-Means with missing values.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.16220
|
Jes\'us Carrete
|
Hadri\'an Montes-Campos and Jes\'us Carrete and Sebastian Bichelmaier
and Luis M. Varela and Georg K. H. Madsen
|
A Differentiable Neural-Network Force Field for Ionic Liquids
| null | null | null | null |
physics.comp-ph physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present NeuralIL, a model for the potential energy of an ionic liquid that
accurately reproduces first-principles results with orders-of-magnitude savings
in computational cost. Based on a multilayer perceptron and spherical Bessel
descriptors of the atomic environments, NeuralIL is implemented in such a way
as to be fully automatically differentiable. It can thus be trained on
ab-initio forces instead of just energies, to make the most out of the
available data, and can efficiently predict arbitrary derivatives of the
potential energy. Using ethylammonium nitrate as the test system, we obtain
out-of-sample accuracies better than 2 meV/atom (<0.05 kcal/mol) in the
energies and 70 meV/{\AA} in the forces. We show that encoding the element
specific density in the spherical Bessel descriptors is key to achieving this.
Harnessing the information provided by the forces drastically reduces the
amount of atomic configurations required to train a neural network force field
based on atom-centered descriptors. We choose the Swish-1 activation function
and discuss the role of this choice in keeping the neural network
differentiable. Furthermore, the possibility of training on small data sets
allows for an ensemble-learning approach to the detection of extrapolation.
Finally, we find that a separate treatment of long-range interactions is not
required to achieve a high-quality representation of the potential energy
surface of these dense ionic systems.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711243 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.02461
|
Yuchu Liu
|
Yuchu Liu, David Issa Mattos, Jan Bosch, Helena Holmstr\"om Olsson,
Jonn Lantz
|
Size matters? Or not: A/B testing with limited sample in automotive
embedded software
|
In proceedings of the 2021 47th Euromicro Conference on Software
Engineering and Advanced Applications (SEAA)
| null |
10.1109/SEAA53835.2021.00046
| null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
A/B testing is gaining attention in the automotive sector as a promising tool
to measure causal effects from software changes. Different from the web-facing
businesses, where A/B testing has been well-established, the automotive domain
often suffers from limited eligible users to participate in online experiments.
To address this shortcoming, we present a method for designing balanced control
and treatment groups so that sound conclusions can be drawn from experiments
with considerably small sample sizes. While the Balance Match Weighted method
has been used in other domains such as medicine, this is the first paper to
apply and evaluate it in the context of software development. Furthermore, we
describe the Balance Match Weighted method in detail and we conduct a case
study together with an automotive manufacturer to apply the group design method
in a fleet of vehicles. Finally, we present our case study in the automotive
software engineering domain, as well as a discussion on the benefits and
limitations of the A/B group design method.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710641 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.06930
|
Behrooz Yousefzadeh
|
Antonio Palermo, Behrooz Yousefzadeh, Chiara Daraio, Alessandro
Marzani
|
Rayleigh wave propagation in nonlinear metasurfaces
| null | null |
10.1016/j.jsv.2021.116599
| null |
physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the propagation of Rayleigh waves in a half-space coupled to a
nonlinear metasurface. The metasurface consists of an array of nonlinear
oscillators attached to the free surface of a homogeneous substrate. We
describe, analytically and numerically, the effects of nonlinear interaction
force and energy loss on the dispersion of Rayleigh waves. We develop
closed-form expressions to predict the dispersive characteristics of nonlinear
Rayleigh waves by adopting a leading-order effective medium description. In
particular, we demonstrate how hardening nonlinearity reduces and eventually
eliminates the linear filtering bandwidth of the metasurface. Softening
nonlinearity, in contrast, induces lower and broader spectral gaps for weak to
moderate strengths of nonlinearity, and narrows and eventually closes the gaps
at high strengths of nonlinearity. We also observe the emergence of a spatial
gap (in wavenumber) in the in-phase branch of the dispersion curves for
softening nonlinearity. Finally, we investigate the interplay between
nonlinearity and energy loss and discuss their combined effects on the
dispersive properties of the metasurface. Our analytical results, supported by
finite element simulations, demonstrate the mechanisms for achieving tunable
dispersion characteristics in nonlinear metasurfaces.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712445 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.06936
|
Jean Barbier Dr.
|
Jean Barbier, Wei-Kuo Chen, Dmitry Panchenko, and Manuel S\'aenz
|
Performance of Bayesian linear regression in a model with mismatch
| null | null | null | null |
math.PR cond-mat.dis-nn cs.IT cs.LG math-ph math.IT math.MP math.ST stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we analyze, for a model of linear regression with gaussian
covariates, the performance of a Bayesian estimator given by the mean of a
log-concave posterior distribution with gaussian prior, in the high-dimensional
limit where the number of samples and the covariates' dimension are large and
proportional. Although the high-dimensional analysis of Bayesian estimators has
been previously studied for Bayesian-optimal linear regression where the
correct posterior is used for inference, much less is known when there is a
mismatch. Here we consider a model in which the responses are corrupted by
gaussian noise and are known to be generated as linear combinations of the
covariates, but the distributions of the ground-truth regression coefficients
and of the noise are unknown. This regression task can be rephrased as a
statistical mechanics model known as the Gardner spin glass, an analogy which
we exploit. Using a leave-one-out approach we characterize the mean-square
error for the regression coefficients. We also derive the log-normalizing
constant of the posterior. Similar models have been studied by Shcherbina and
Tirozzi and by Talagrand, but our arguments are much more straightforward. An
interesting consequence of our analysis is that in the quadratic loss case, the
performance of the Bayesian estimator is independent of a global "temperature"
hyperparameter and matches the ridge estimator: sampling and optimizing are
equally good.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710785 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.07994
|
Yaqing Wang
|
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, Dejing Dou
|
Property-Aware Relation Networks for Few-Shot Molecular Property
Prediction
|
accepted as NeurIPS 2021 Spotlight
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular property prediction plays a fundamental role in drug discovery to
identify candidate molecules with target properties. However, molecular
property prediction is essentially a few-shot problem which makes it hard to
use regular machine learning models. In this paper, we propose Property-Aware
Relation networks (PAR) to handle this problem. In comparison to existing
works, we leverage the fact that both relevant substructures and relationships
among molecules change across different molecular properties. We first
introduce a property-aware embedding function to transform the generic
molecular embeddings to substructure-aware space relevant to the target
property. Further, we design an adaptive relation graph learning module to
jointly estimate molecular relation graph and refine molecular embeddings
w.r.t. the target property, such that the limited labels can be effectively
propagated among similar molecules. We adopt a meta-learning strategy where the
parameters are selectively updated within tasks in order to model generic and
property-aware knowledge separately. Extensive experiments on benchmark
molecular property prediction datasets show that PAR consistently outperforms
existing methods and can obtain property-aware molecular embeddings and model
molecular relation graph properly.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711469 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.10353
|
Pawel Tecmer Dr hab
|
Giorgio Visentin and Alexei A. Buchachenko and Pawe{\l} Tecmer
|
Reexamination of the ground state Born-Oppenheimer Yb$_2$ potential
|
1 figure, 18 pages (version of record)
|
Phys. Rev. A 104, 052807 (2021)
|
10.1103/PhysRevA.104.052807
| null |
physics.atom-ph physics.chem-ph physics.comp-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The precision of the photoassociation spectroscopy of Yb dimer in degenerate
gases is enough to improve the constraints on the new short-range gravity-like
forces if the theoretical knowledge of the Born-Oppenheimer interatomic
potential and non-Born-Oppenheimer interactions is refined [M. Borkowski et al.
Sci. Rep. A {\bf 9}, 14807 (2019)]. The ground-state interaction potential of
ytterbium dimer is investigated at the eXact 2-component core-correlated
CCSD(T) level of {\it ab initio} theory in the complete basis set limit with
extensive augmentation by diffuse functions. For the small basis set the
comparison is made with the four-component relativistic finite-nuclei CCSD(T)
calculations to identify the contraction of the dimer bond length as the main
unrecoverable consequence of the scalar-relativistic approximation. Empirical
constraint on the number of bound vibrational energy levels of the
$^{174}$Yb$_2$ dimer is accounted for by representing the global {\it ab
initio}-based Born-Oppenheimer potential with the model semianalytical function
containing the scale and shift parameters. The results support the previous
evaluation of the Yb dimer potentials from the photoassociation spectroscopy
data and provide an accurate and flexible reference for future refinement of
the constraints on the short-range gravity-like forces by ultracold atomic
spectroscopy.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.10702
|
Jacinto Ulloa
|
Jacinto Ulloa, Jef Wambacq, Roberto Alessi, Esteban Samaniego, Geert
Degrande, Stijn Fran\c{c}ois
|
A micromechanics-based variational phase-field model for fracture in
geomaterials with brittle-tensile and compressive-ductile behavior
|
Postprint version. Minor corrections
| null |
10.1016/j.jmps.2021.104684
| null |
cond-mat.mtrl-sci cs.NA math.NA
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a framework for modeling failure in quasi-brittle
geomaterials under different loading conditions. A micromechanics-based model
is proposed in which the field variables are linked to physical mechanisms at
the microcrack level: damage is related to the growth of microcracks, while
plasticity is related to the frictional sliding of closed microcracks.
Consequently, the hardening/softening functions and parameters entering the
free energy follow from the definition of a single degradation function and the
elastic material properties. The evolution of opening microcracks in tension
leads to brittle behavior and mode I fracture, while the evolution of closed
microcracks under frictional sliding in compression/shear leads to ductile
behavior and mode II fracture. Frictional sliding is endowed with a
non-associative law, a crucial aspect of the model that considers the effect of
dilation and allows for realistic material responses with non-vanishing
frictional energy dissipation. Despite the non-associative law, a variationally
consistent formulation is presented using notions of energy balance and
stability, following the energetic formulation for rate-independent systems.
The material response of the model is first described, followed by the
numerical implementation procedure and several benchmark finite element
simulations. The results highlight the ability of the model to describe
tensile, shear, and mixed-mode fracture, as well as responses with
brittle-to-ductile transition. A key result is that, by virtue of the
micromechanical arguments, realistic failure modes can be captured, without
resorting to the usual heuristic modifications considered in the phase-field
literature. The numerical results are thoroughly discussed with reference to
previous numerical studies, experimental evidence, and analytical fracture
criteria.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711794 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.12688
|
Michel Fliess
|
Michel Fliess, C\'edric Join, Kaouther Moussa, Seddik M. Djouadi,
Mohamed W. Alsager
|
Toward simple "in silico" experiments for drugs administration in some
cancer treatments
|
IFAC Symposium on Biological and Medical Systems - 11th BMS 2021 --
Ghent, Belgium, 19-22 September 2021
| null |
10.1016/j.ifacol.2021.10.263
| null |
eess.SY cs.SY math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
We present some "in silico" experiments to design combined chemo- and
immunotherapy treatment schedules. We introduce a new framework by combining
flatness-based control, which is a model-based setting, along with model-free
control. The flatness property of the used mathematical model yields
straightforward reference trajectories. They provide us with the nominal
open-loop control inputs. Closing the loop via model-free control allows to
deal with the uncertainties on the injected drug doses. Several numerical
simulations illustrating different case studies are displayed. We show in
particular that the considered health indicators are driven to the safe region,
even for critical initial conditions. Furthermore, in some specific cases there
is no need to inject chemotherapeutic agents.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709806 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.13355
|
Ashlesha Kumar Ms.
|
Ashlesha Kumar, Kuldip Singh Sangwan and Dhiraj
|
A Computer Vision-Based Approach for Driver Distraction Recognition
using Deep Learning and Genetic Algorithm Based Ensemble
|
12 pages, Presented in 20th International Conference on Artificial
Intelligence and Soft Computing (ICAISC 2021)
| null |
10.1007/978-3-030-87897-9_5
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
As the proportion of road accidents increases each year, driver distraction
continues to be an important risk component in road traffic injuries and
deaths. The distractions caused by the increasing use of mobile phones and
other wireless devices pose a potential risk to road safety. Our current study
aims to aid the already existing techniques in driver posture recognition by
improving the performance in the driver distraction classification problem. We
present an approach using a genetic algorithm-based ensemble of six independent
deep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, Vanilla
CNN, Modified DenseNet, and InceptionV3 + BiLSTM. We test it on two
comprehensive datasets, the AUC Distracted Driver Dataset, on which our
technique achieves an accuracy of 96.37%, surpassing the previously obtained
95.98%, and on the State Farm Driver Distraction Dataset, on which we attain an
accuracy of 99.75%. The 6-Model Ensemble gave an inference time of 0.024
seconds as measured on our machine with Ubuntu 20.04(64-bit) and GPU as GeForce
GTX 1080.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.01818
|
Naoki Sato
|
Naoki Sato, Zhisong Qu, David Pfefferl\'e, Robert L. Dewar
|
Quasisymmetric magnetic fields in asymmetric toroidal domains
|
19 pages, 5 figures
|
Physics of Plasmas 28, 112507 (2021)
|
10.1063/5.0065633
| null |
math.AP physics.plasm-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We explore the existence of quasisymmetric magnetic fields in asymmetric
toroidal domains. These vector fields can be identified with a class of
magnetohydrodynamic equilibria in the presence of pressure anisotropy. First,
using Clebsch potentials, we derive a system of two coupled nonlinear first
order partial differential equations expressing a family of quasisymmetric
magnetic fields in bounded domains. In regions where flux surfaces and surfaces
of constant field strength are not tangential, this system can be further
reduced to a single degenerate nonlinear second order partial differential
equation with externally assigned initial data. Then, we exhibit regular
quasisymmetric vector fields which correspond to local solutions of anisotropic
magnetohydrodynamics in asymmetric toroidal domains such that tangential
boundary conditions are fulfilled on a portion of the bounding surface. The
problems of boundary shape and locality are also discussed. We find that
symmetric magnetic fields can be fitted into asymmetric domains, and that the
mathematical difficulty encountered in the derivation of global quasisymmetric
magnetic fields lies in the topological obstruction toward global extension
affecting local solutions of the governing nonlinear first order partial
differential equations.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711224 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.02584
|
Jiho Song
|
Seong-Hwan Hyun, Jiho Song, Keunwoo Kim, Jong-Ho Lee, and Seong-Cheol
Kim
|
Adaptive Beam Design for V2I Communications using Vehicle Tracking with
Extended Kalman Filter
|
14 pages, 11 figures, accepted to IEEE Transactions on Vehicular
Technology
| null |
10.1109/TVT.2021.3127696
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vehicle-to-everything communication system is a strong candidate for
improving the driving experience and automotive safety by linking vehicles to
wireless networks. To take advantage of the full benefits of vehicle
connectivity, it is essential to ensure a stable network connection between
roadside unit (RSU) and fast-moving vehicles. Based on the extended Kalman
filter (EKF), we develop a vehicle tracking algorithm to enable reliable radio
connections. For the vehicle tracking algorithm, we focus on estimating the
rapid changes in the beam direction of a high-mobility vehicle while reducing
the feedback overhead. Furthermore, we design a beamforming codebook that
considers the road layout and RSU. By leveraging the proposed beamforming
codebook, vehicles on the road can expect a service quality similar to that of
conventional cellular services. Finally, a beamformer selection algorithm is
developed to secure sufficient gain for the system's link budget. Numerical
results verify that the EKF-based vehicle tracking algorithm and the proposed
beamforming structure are more suitable for vehicle-to-infrastructure networks
compared to existing schemes.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.707165 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.04650
|
Alper Demir
|
Alper Demir
|
Adaptive Time-Resolved Mass Spectrometry with Nanomechanical Resonant
Sensors
|
8 pages, 3 figures
| null |
10.1109/JSEN.2021.3127244
| null |
physics.app-ph cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nanomechanical resonant sensors that are based on detecting and tracking the
resonance frequency deviations due to events of interest are being advocated
for a variety of applications. All sensor schemes currently in use are subject
to a basic trade-off between accuracy and speed, while there is great interest
in improving both in order to enable unprecedented and widespread applications.
Based on a thorough understanding of the characteristics of current resonant
sensor architectures, we propose adaptive and flexible sensor schemes. Unlike
recently proposed time-resolved mechanical detection methods, the proposed
schemes do not require ensemble averaging of the resonator response for many
independent identical stimuli. Distinct one-time events can be detected in
real-time with high time resolution with an accuracy that then improves
considerably with elapsed time. While the proposed adaptive schemes also need
to abide by the fundamental speed versus accuracy trade-off, we show that there
is still "some room at the bottom" for improvement with sensor architecture
innovations. Pareto optimal performance that reaches a bound that is imposed by
the fundamental thermomechanical noise can be achieved.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709849 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.06453
|
Sheng Yue
|
Sheng Yue, Ju Ren, Jiang Xin, Deyu Zhang, Yaoxue Zhang, Weihua Zhuang
|
Efficient Federated Meta-Learning over Multi-Access Wireless Networks
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Federated meta-learning (FML) has emerged as a promising paradigm to cope
with the data limitation and heterogeneity challenges in today's edge learning
arena. However, its performance is often limited by slow convergence and
corresponding low communication efficiency. In addition, since the available
radio spectrum and IoT devices' energy capacity are usually insufficient, it is
crucial to control the resource allocation and energy consumption when
deploying FML in practical wireless networks. To overcome the challenges, in
this paper, we rigorously analyze each device's contribution to the global loss
reduction in each round and develop an FML algorithm (called NUFM) with a
non-uniform device selection scheme to accelerate the convergence. After that,
we formulate a resource allocation problem integrating NUFM in multi-access
wireless systems to jointly improve the convergence rate and minimize the
wall-clock time along with energy cost. By deconstructing the original problem
step by step, we devise a joint device selection and resource allocation
strategy to solve the problem with theoretical guarantees. Further, we show
that the computational complexity of NUFM can be reduced from $O(d^2)$ to
$O(d)$ (with the model dimension $d$) via combining two first-order
approximation techniques. Extensive simulation results demonstrate the
effectiveness and superiority of the proposed methods in comparison with
existing baselines.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.07192
|
Gregory Rosenthal
|
Gregory Rosenthal, Henry Yuen
|
Interactive Proofs for Synthesizing Quantum States and Unitaries
|
65 pages. Innovations in Theoretical Computer Science (ITCS) 2022
| null | null | null |
quant-ph cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Whereas quantum complexity theory has traditionally been concerned with
problems arising from classical complexity theory (such as computing boolean
functions), it also makes sense to study the complexity of inherently quantum
operations such as constructing quantum states or performing unitary
transformations. With this motivation, we define models of interactive proofs
for synthesizing quantum states and unitaries, where a polynomial-time quantum
verifier interacts with an untrusted quantum prover, and a verifier who accepts
also outputs an approximation of the target state (for the state synthesis
problem) or the result of the target unitary applied to the input state (for
the unitary synthesis problem); furthermore there should exist an "honest"
prover which the verifier accepts with probability 1.
Our main result is a "state synthesis" analogue of the inclusion
$\mathsf{PSPACE} \subseteq \mathsf{IP}$: any sequence of states computable by a
polynomial-space quantum algorithm (which may run for exponential time) admits
an interactive protocol of the form described above. Leveraging this state
synthesis protocol, we also give a unitary synthesis protocol for polynomial
space-computable unitaries that act nontrivially on only a
polynomial-dimensional subspace. We obtain analogous results in the setting
with multiple entangled provers as well.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.00229
|
Pengcheng Xia
|
Pengcheng Xia, Haoyu wang, Bingyu Gao, Weihang Su, Zhou Yu, Xiapu Luo,
Chao Zhang, Xusheng Xiao, Guoai Xu
|
Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap
Decentralized Exchange
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The prosperity of the cryptocurrency ecosystem drives the need for digital
asset trading platforms. Beyond centralized exchanges (CEXs), decentralized
exchanges (DEXs) are introduced to allow users to trade cryptocurrency without
transferring the custody of their digital assets to the middlemen, thus
eliminating the security and privacy issues of traditional CEX. Uniswap, as the
most prominent cryptocurrency DEX, is continuing to attract scammers, with
fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take
the first step to detect and characterize scam tokens on Uniswap. We first
collect all the transactions related to Uniswap V2 exchange and investigate the
landscape of cryptocurrency trading on Uniswap from different perspectives.
Then, we propose an accurate approach for flagging scam tokens on Uniswap based
on a guilt-by-association heuristic and a machine-learning powered technique.
We have identified over 10K scam tokens listed on Uniswap, which suggests that
roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam
tokens and liquidity pools are created specialized for the "rug pull" scams,
and some scam tokens have embedded tricks and backdoors in the smart contracts.
We further observe that thousands of collusion addresses help carry out the
scams in league with the scam token/pool creators. The scammers have gained a
profit of at least \$16 million from 39,762 potential victims. Our observations
in this paper suggest the urgency to identify and stop scams in the
decentralized finance ecosystem, and our approach can act as a whistleblower
that identifies scam tokens at their early stages.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.714609 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.00343
|
Isabel Segura-Bedmar
|
Isabel Segura-Bedmar, David Camino-Perdonas, Sara Guerrero-Aspizua
|
Exploring deep learning methods for recognizing rare diseases and their
clinical manifestations from texts
| null | null | null | null |
cs.CL cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Although rare diseases are characterized by low prevalence, approximately 300
million people are affected by a rare disease. The early and accurate diagnosis
of these conditions is a major challenge for general practitioners, who do not
have enough knowledge to identify them. In addition to this, rare diseases
usually show a wide variety of manifestations, which might make the diagnosis
even more difficult. A delayed diagnosis can negatively affect the patient's
life. Therefore, there is an urgent need to increase the scientific and medical
knowledge about rare diseases. Natural Language Processing (NLP) and Deep
Learning can help to extract relevant information about rare diseases to
facilitate their diagnosis and treatments. The paper explores the use of
several deep learning techniques such as Bidirectional Long Short Term Memory
(BiLSTM) networks or deep contextualized word representations based on
Bidirectional Encoder Representations from Transformers (BERT) to recognize
rare diseases and their clinical manifestations (signs and symptoms) in the
RareDis corpus. This corpus contains more than 5,000 rare diseases and almost
6,000 clinical manifestations. BioBERT, a domain-specific language
representation based on BERT and trained on biomedical corpora, obtains the
best results. In particular, this model obtains an F1-score of 85.2% for rare
diseases, outperforming all the other models.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710465 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.00771
|
David Viedma
|
David Viedma, Ver\`onica Ahufinger, Jordi Mompart
|
Supersymmetry-enhanced Stark-Chirped Rapid-Adiabatic-Passage in
multimode optical waveguides
|
9 pages, 10 figures
|
Opt. Express 29, 39200-39213 (2021)
|
10.1364/OE.442475
| null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a method to efficiently pump an excited mode of a multimode
optical waveguide starting from a fundamental-mode input by combining
Stark-Chirped Rapid Adiabatic Passage (SCRAP) and Supersymmetry (SUSY)
transformations. In a two-waveguide set, we implement SCRAP by modulating the
core refractive index of one waveguide, which is evanescently coupled to its
SUSY partner. SCRAP provides an efficient transfer of light intensity between
the modes of different waveguides, while SUSY allows to control which modes are
supported. Using both techniques allows to achieve fidelities above 99% for the
pumping of the excited mode of a two-mode waveguide. Additionally, we show that
SCRAP can be exploited to spatially separate superpositions of fundamental and
excited modes, and how SUSY can also improve the results for this application.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710848 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.01050
|
Amir Gholami
|
Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby,
Michael W. Mahoney
|
Characterizing possible failure modes in physics-informed neural
networks
|
22 pages
|
NeurIPS 2021
| null | null |
cs.LG cs.AI cs.NA math.NA physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work in scientific machine learning has developed so-called
physics-informed neural network (PINN) models. The typical approach is to
incorporate physical domain knowledge as soft constraints on an empirical loss
function and use existing machine learning methodologies to train the model. We
demonstrate that, while existing PINN methodologies can learn good models for
relatively trivial problems, they can easily fail to learn relevant physical
phenomena for even slightly more complex problems. In particular, we analyze
several distinct situations of widespread physical interest, including learning
differential equations with convection, reaction, and diffusion operators. We
provide evidence that the soft regularization in PINNs, which involves
PDE-based differential operators, can introduce a number of subtle problems,
including making the problem more ill-conditioned. Importantly, we show that
these possible failure modes are not due to the lack of expressivity in the NN
architecture, but that the PINN's setup makes the loss landscape very hard to
optimize. We then describe two promising solutions to address these failure
modes. The first approach is to use curriculum regularization, where the PINN's
loss term starts from a simple PDE regularization, and becomes progressively
more complex as the NN gets trained. The second approach is to pose the problem
as a sequence-to-sequence learning task, rather than learning to predict the
entire space-time at once. Extensive testing shows that we can achieve up to
1-2 orders of magnitude lower error with these methods as compared to regular
PINN training.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711825 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.07161
|
Arsenii Ashukha
|
Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia
Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka,
Kiwoong Park, Victor Lempitsky
|
Resolution-robust Large Mask Inpainting with Fourier Convolutions
|
Winter Conference on Applications of Computer Vision (WACV 2022)
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Modern image inpainting systems, despite the significant progress, often
struggle with large missing areas, complex geometric structures, and
high-resolution images. We find that one of the main reasons for that is the
lack of an effective receptive field in both the inpainting network and the
loss function. To alleviate this issue, we propose a new method called large
mask inpainting (LaMa). LaMa is based on i) a new inpainting network
architecture that uses fast Fourier convolutions (FFCs), which have the
image-wide receptive field; ii) a high receptive field perceptual loss; iii)
large training masks, which unlocks the potential of the first two components.
Our inpainting network improves the state-of-the-art across a range of datasets
and achieves excellent performance even in challenging scenarios, e.g.
completion of periodic structures. Our model generalizes surprisingly well to
resolutions that are higher than those seen at train time, and achieves this at
lower parameter&time costs than the competitive baselines. The code is
available at \url{https://github.com/saic-mdal/lama}.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712188 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.14379
|
Fangcen Liu
|
Fangcen Liu, Chenqiang Gao, Fang Chen, Deyu Meng, Wangmeng Zuo, Xinbo
Gao
|
Infrared Small-Dim Target Detection with Transformer under Complex
Backgrounds
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The infrared small-dim target detection is one of the key techniques in the
infrared search and tracking system. Since the local regions similar to
infrared small-dim targets spread over the whole background, exploring the
interaction information amongst image features in large-range dependencies to
mine the difference between the target and background is crucial for robust
detection. However, existing deep learning-based methods are limited by the
locality of convolutional neural networks, which impairs the ability to capture
large-range dependencies. Additionally, the small-dim appearance of the
infrared target makes the detection model highly possible to miss detection. To
this end, we propose a robust and general infrared small-dim target detection
method with the transformer. We adopt the self-attention mechanism of the
transformer to learn the interaction information of image features in a larger
range. Moreover, we design a feature enhancement module to learn discriminative
features of small-dim targets to avoid miss detection. After that, to avoid the
loss of the target information, we adopt a decoder with the U-Net-like skip
connection operation to contain more information of small-dim targets. Finally,
we get the detection result by a segmentation head. Extensive experiments on
two public datasets show the obvious superiority of the proposed method over
state-of-the-art methods and the proposed method has stronger cross-scene
generalization and anti-noise performance.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710842 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.00080
|
Raphael St-Gelais
|
Nikaya Snell, Chang Zhang, Gengyang Mu, Alexandre Bouchard, Raphael
St-Gelais
|
Heat Transport in Silicon Nitride Drum Resonators and its Influence on
Thermal Fluctuation-induced Frequency Noise
|
11 pages, 7 figures, v2: correction of spurious paragraph breaks from
v1. No content changes
| null | null | null |
physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Silicon nitride (SiN) drumhead resonators offer a promising platform for
thermal sensing due to their high mechanical quality factor and the high
temperature sensitivity of their resonance frequency. As such, gaining an
understanding of heat transport in SiN resonators as well as their sensing
noise limitations is of interest, both of which are goals of the present work.
We first present new experimental results on radiative heat transport in SiN
membrane, which we use for benchmarking two recently proposed theoretical
models. We measure the characteristic thermal response time of square SiN
membranes with a thickness of 90 $\pm$ 1.7 nm and side lengths from 1.5 to 12
mm. A clear transition between radiation and conduction dominated heat
transport is measured, in close correspondence with theory. In the second
portion of this work, we use our experimentally validated heat transport model
to provide a closed-form expression for thermal fluctuation-induced frequency
noise in SiN membrane resonators. We find that, for large area SiN membranes,
thermal fluctuations can be greater than thermomechanical contributions to
frequency noise. For the specific case of thermal radiation sensing
applications, we also derive the noise equivalent power resulting from thermal
fluctuation-induced frequency noise, and we show in which conditions it reduces
to the classical detectivity limit of thermal radiation sensors. Our work
therefore provides a path towards achieving thermal radiation sensors operating
at the never attained fundamental detectivity limit of bolometric sensing. We
also identify questions that remain when attempting to push the limits of
radiation sensing, in particular, the effect of thermal fluctuation noise in
closed-loop frequency tracking schemes remains to be clarified.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709837 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.06465
|
Detian Huang
|
Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, Qichao
Zhou
|
Breaking the Dilemma of Medical Image-to-image Translation
| null | null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that
dominate the field of medical image-to-image translation. However, neither
modes are ideal. The Pix2Pix mode has excellent performance. But it requires
paired and well pixel-wise aligned images, which may not always be achievable
due to respiratory motion or anatomy change between times that paired images
are acquired. The Cycle-consistency mode is less stringent with training data
and works well on unpaired or misaligned images. But its performance may not be
optimal. In order to break the dilemma of the existing modes, we propose a new
unsupervised mode called RegGAN for medical image-to-image translation. It is
based on the theory of "loss-correction". In RegGAN, the misaligned target
images are considered as noisy labels and the generator is trained with an
additional registration network to fit the misaligned noise distribution
adaptively. The goal is to search for the common optimal solution to both
image-to-image translation and registration tasks. We incorporated RegGAN into
a few state-of-the-art image-to-image translation methods and demonstrated that
RegGAN could be easily combined with these methods to improve their
performances. Such as a simple CycleGAN in our mode surpasses latest NICEGAN
even though using less network parameters. Based on our results, RegGAN
outperformed both Pix2Pix on aligned data and Cycle-consistency on misaligned
or unpaired data. RegGAN is insensitive to noises which makes it a better
choice for a wide range of scenarios, especially for medical image-to-image
translation tasks in which well pixel-wise aligned data are not available
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.713432 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.08949
|
Zhale Nowroozilarki
|
Zhale Nowroozilarki, Arash Pakbin, James Royalty, Donald K.K. Lee, and
Bobak J. Mortazavi
|
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted
Nonparametric Hazards
| null |
10.1109/BHI50953.2021.9508537
|
10.1109/BHI50953.2021.9508537
| null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Electronic Health Record (EHR) systems provide critical, rich and valuable
information at high frequency. One of the most exciting applications of EHR
data is in developing a real-time mortality warning system with tools from
survival analysis. However, most of the survival analysis methods used recently
are based on (semi)parametric models using static covariates. These models do
not take advantage of the information conveyed by the time-varying EHR data. In
this work, we present an application of a highly scalable survival analysis
method, BoXHED 2.0 to develop a real-time in-ICU mortality warning indicator
based on the MIMIC IV data set. Importantly, BoXHED can incorporate
time-dependent covariates in a fully nonparametric manner and is backed by
theory. Our in-ICU mortality model achieves an AUC-PRC of 0.41 and AUC-ROC of
0.83 out of sample, demonstrating the benefit of real-time monitoring.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.09949
|
Sterenn Guerrier
|
Sterenn Guerrier, Christian Dorize, Elie Awwad and J\'er\'emie
Renaudier
|
Towards Polarization-Insensitive Coherent Coded Phase OTDR
|
Conference paper (27th International Conference on Optical Fiber
Sensors, 2020), 4 pages, 6 figures
|
Optical Fiber Sensors Conference 2020 Special Edition
|
10.1364/OFS.2020.T3.20
|
ITD-19-59665D
|
eess.SP physics.ins-det physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
We explore the alternatives for interrogating a fiber sensor from the
polarization point of view, and demonstrate a better accuracy with dual
polarization probing for coherent phi-OTDR compared with single polarization
probing.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.10394
|
Lin Wang
|
Lin Wang and Kuk-Jin Yoon
|
Deep Learning for HDR Imaging: State-of-the-Art and Future Trends
|
IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI), main and suppl. material
| null | null | null |
eess.IV cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
High dynamic range (HDR) imaging is a technique that allows an extensive
dynamic range of exposures, which is important in image processing, computer
graphics, and computer vision. In recent years, there has been a significant
advancement in HDR imaging using deep learning (DL). This study conducts a
comprehensive and insightful survey and analysis of recent developments in deep
HDR imaging methodologies. We hierarchically and structurally group existing
deep HDR imaging methods into five categories based on (1) number/domain of
input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel
learning strategies, and (5) applications. Importantly, we provide a
constructive discussion on each category regarding its potential and
challenges. Moreover, we review some crucial aspects of deep HDR imaging, such
as datasets and evaluation metrics. Finally, we highlight some open problems
and point out future research directions.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.713581 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.11061
|
Luca Reggio
|
Luca Reggio
|
Polyadic Sets and Homomorphism Counting
|
40 pages. v3: Minor changes. Presentation improved
| null | null | null |
math.CT cs.LO math.RA
|
http://creativecommons.org/licenses/by/4.0/
|
A classical result due to Lovasz (1967) shows that the isomorphism type of a
graph is determined by homomorphism counts. That is, graphs G and H are
isomorphic whenever the number of homomorphisms from K to G is the same as the
number of homomorphisms from K to H for all graphs K. Variants of this result,
for various classes of finite structures, have been exploited in a wide range
of research fields, including graph theory and finite model theory.
We provide a categorical approach to homomorphism counting based on the
concept of polyadic (finite) set. The latter is a special case of the notion of
polyadic space introduced by Joyal (1971) and related, via duality, to Boolean
hyperdoctrines in categorical logic. We also obtain new homomorphism counting
results applicable to a number of infinite structures, such as finitely
branching trees and profinite algebras.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708023 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.12481
|
Qian Zhang
|
Kaibo Hu, Qian Zhang, Jiayu Han, Lixiu Wang, Zhimin Zhang
|
Spurious solutions for high order curl problems
|
23 pages, 7 figures
| null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate numerical solutions of high order curl problems with various
formulations and finite elements. We show that several classical conforming
finite elements lead to spurious solutions, while mixed formulations with
finite elements in complexes solve the problems correctly.
To explain the numerical results, we clarify the cohomological structures in
high order curl problems by relating the partial differential equations to the
Hodge-Laplacian boundary problems of the gradcurl-complexes.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710176 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.13541
|
Sanghyun Hong
|
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yi\u{g}itcan Kaya,
Tudor Dumitra\c{s}
|
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving
Adversarial Outcomes
|
Accepted to NeurIPS 2021 [Poster]
| null | null | null |
cs.LG cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantization is a popular technique that $transforms$ the parameter
representation of a neural network from floating-point numbers into
lower-precision ones ($e.g.$, 8-bit integers). It reduces the memory footprint
and the computational cost at inference, facilitating the deployment of
resource-hungry models. However, the parameter perturbations caused by this
transformation result in $behavioral$ $disparities$ between the model before
and after quantization. For example, a quantized model can misclassify some
test-time samples that are otherwise classified correctly. It is not known
whether such differences lead to a new security vulnerability. We hypothesize
that an adversary may control this disparity to introduce specific behaviors
that activate upon quantization. To study this hypothesis, we weaponize
quantization-aware training and propose a new training framework to implement
adversarial quantization outcomes. Following this framework, we present three
attacks we carry out with quantization: (i) an indiscriminate attack for
significant accuracy loss; (ii) a targeted attack against specific samples; and
(iii) a backdoor attack for controlling the model with an input trigger. We
further show that a single compromised model defeats multiple quantization
schemes, including robust quantization techniques. Moreover, in a federated
learning scenario, we demonstrate that a set of malicious participants who
conspire can inject our quantization-activated backdoor. Lastly, we discuss
potential counter-measures and show that only re-training consistently removes
the attack artifacts. Our code is available at
https://github.com/Secure-AI-Systems-Group/Qu-ANTI-zation
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708364 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.14124
|
Wang Chen
|
Wang Chen, Jian Chen, Weitian Wu, Xinmin Yang, Hui Li
|
A novel multiobjective evolutionary algorithm based on decomposition and
multi-reference points strategy
| null | null | null | null |
cs.NE math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many real-world optimization problems such as engineering design can be
eventually modeled as the corresponding multiobjective optimization problems
(MOPs) which must be solved to obtain approximate Pareto optimal fronts.
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been
regarded as a significantly promising approach for solving MOPs. Recent studies
have shown that MOEA/D with uniform weight vectors is well-suited to MOPs with
regular Pareto optimal fronts, but its performance in terms of diversity
usually deteriorates when solving MOPs with irregular Pareto optimal fronts. In
this way, the solution set obtained by the algorithm can not provide more
reasonable choices for decision makers. In order to efficiently overcome this
drawback, we propose an improved MOEA/D algorithm by virtue of the well-known
Pascoletti-Serafini scalarization method and a new strategy of multi-reference
points. Specifically, this strategy consists of the setting and adaptation of
reference points generated by the techniques of equidistant partition and
projection. For performance assessment, the proposed algorithm is compared with
existing four state-of-the-art multiobjective evolutionary algorithms on
benchmark test problems with various types of Pareto optimal fronts. According
to the experimental results, the proposed algorithm exhibits better diversity
performance than that of the other compared algorithms. Finally, our algorithm
is applied to two real-world MOPs in engineering optimization successfully.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.00110
|
Henning Lange
|
Henning Lange, J. Nathan Kutz
|
FC2T2: The Fast Continuous Convolutional Taylor Transform with
Applications in Vision and Graphics
| null | null | null | null |
cs.LG cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Series expansions have been a cornerstone of applied mathematics and
engineering for centuries. In this paper, we revisit the Taylor series
expansion from a modern Machine Learning perspective. Specifically, we
introduce the Fast Continuous Convolutional Taylor Transform (FC2T2), a variant
of the Fast Multipole Method (FMM), that allows for the efficient approximation
of low dimensional convolutional operators in continuous space. We build upon
the FMM which is an approximate algorithm that reduces the computational
complexity of N-body problems from O(NM) to O(N+M) and finds application in
e.g. particle simulations. As an intermediary step, the FMM produces a series
expansion for every cell on a grid and we introduce algorithms that act
directly upon this representation. These algorithms analytically but
approximately compute the quantities required for the forward and backward pass
of the backpropagation algorithm and can therefore be employed as (implicit)
layers in Neural Networks. Specifically, we introduce a root-implicit layer
that outputs surface normals and object distances as well as an
integral-implicit layer that outputs a rendering of a radiance field given a 3D
pose. In the context of Machine Learning, $N$ and $M$ can be understood as the
number of model parameters and model evaluations respectively which entails
that, for applications that require repeated function evaluations which are
prevalent in Computer Vision and Graphics, unlike regular Neural Networks, the
techniques introduce in this paper scale gracefully with parameters. For some
applications, this results in a 200x reduction in FLOPs compared to
state-of-the-art approaches at a reasonable or non-existent loss in accuracy.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.00579
|
Chinmaya Kumar Dehury Dr.
|
Chinmaya Kumar Dehury, Prasan Kumar Sahoo, Bharadwaj Veeravalli
|
RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud
Platforms
|
This is accepted in IEEE TCC. The preprint version will be uploaded
soon
| null |
10.1109/TCC.2021.3126677
| null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The applications that are deployed in the cloud to provide services to the
users encompass a large number of interconnected dependent cloud components.
Multiple identical components are scheduled to run concurrently in order to
handle unexpected failures and provide uninterrupted service to the end user,
which introduces resource overhead problem for the cloud service provider.
Furthermore such resource-intensive fault tolerant strategies bring extra
monetary overhead to the cloud service provider and eventually to the cloud
users. In order to address these issues, a novel fault tolerant strategy based
on the significance level of each component is developed. The communication
topology among the application components, their historical performance,
failure rate, failure impact on other components, dependencies among them,
etc., are used to rank those application components to further decide on the
importance of one component over others. Based on the rank, a Markov Decision
Process (MDP) model is presented to determine the number of replicas that
varies from one component to another. A rigorous performance evaluation is
carried out using some of the most common practically useful metrics such as,
recovery time upon a fault, average number of components needed, number of
parallel components successfully executed, etc., to quote a few, with similar
component ranking and fault tolerant strategies. Simulation results demonstrate
that the proposed algorithm reduces the required number of virtual and physical
machines by approximately 10% and 4.2%, respectively, compared to other similar
algorithms.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712595 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.02121
|
Jussi Leinonen
|
Jussi Leinonen
|
Spatiotemporal Weather Data Predictions with Shortcut
Recurrent-Convolutional Networks: A Solution for the Weather4cast challenge
|
6 pages, 5 figures. To be published in the proceedings of the 1st
workshop on Complex Data Challenges in Earth Observation (CDCEO) 2021.
Associated code can be found at
https://github.com/jleinonen/weather4cast-stage1
| null | null | null |
cs.LG physics.ao-ph
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents the neural network model that was used by the author in
the Weather4cast 2021 Challenge Stage 1, where the objective was to predict the
time evolution of satellite-based weather data images. The network is based on
an encoder-forecaster architecture making use of gated recurrent units (GRU),
residual blocks and a contracting/expanding architecture with shortcuts similar
to U-Net. A GRU variant utilizing residual blocks in place of convolutions is
also introduced. Example predictions and evaluation metrics for the model are
presented. These demonstrate that the model can retain sharp features of the
input for the first predictions, while the later predictions become more
blurred to reflect the increasing uncertainty.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711005 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03263
|
Chenghong Bian
|
Chenghong Bian, Mingyu Yang, Chin-Wei Hsu, Hun-Seok Kim
|
Deep Learning Based Near-Orthogonal Superposition Code for Short Message
Transmission
|
6 pages, 7 figures
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Massive machine type communication (mMTC) has attracted new coding schemes
optimized for reliable short message transmission. In this paper, a novel deep
learning based near-orthogonal superposition (NOS) coding scheme is proposed
for reliable transmission of short messages in the additive white Gaussian
noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional
modulation (HDM), the NOS encoder spreads the information bits to multiple
near-orthogonal high dimensional vectors to be combined (superimposed) into a
single vector for transmission. The NOS decoder first estimates the information
vectors and then performs a cyclic redundancy check (CRC)-assisted K-best
tree-search algorithm to further reduce the packet error rate. The proposed NOS
encoder and decoder are deep neural networks (DNNs) jointly trained as an auto
encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal
codewords. Simulation results show the proposed deep learning-based NOS scheme
outperforms HDM and Polar code with CRC-aided list decoding for short(32-bit)
message transmission.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709856 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03708
|
Jeffrey Liu
|
Rene Garcia Franceschini, Jeffrey Liu, Saurabh Amin
|
Damage Estimation and Localization from Sparse Aerial Imagery
|
Version presented at NeurIPS 2021 AI+HADR workshop
| null | null | null |
eess.IV cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Aerial images provide important situational awareness for responding to
natural disasters such as hurricanes. They are well-suited for providing
information for damage estimation and localization (DEL); i.e., characterizing
the type and spatial extent of damage following a disaster. Despite recent
advances in sensing and unmanned aerial systems technology, much of
post-disaster aerial imagery is still taken by handheld DSLR cameras from
small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU
information, and images are taken opportunistically post-event by operators. As
such, DEL from such imagery is still a highly manual and time-consuming
process. We propose an approach to both detect damage in aerial images and
localize it in world coordinates, with specific focus on detecting and
localizing flooding. The approach is based on using structure from motion to
relate image coordinates to world coordinates via a projective transformation,
using class activation mapping to detect the extent of damage in an image, and
applying the projective transformation to localize damage in world coordinates.
We evaluate the performance of our approach on post-event data from the 2016
Louisiana floods, and find that our approach achieves a precision of 88%. Given
this high precision using limited data, we argue that this approach is
currently viable for fast and effective DEL from handheld aerial imagery for
disaster response.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.71103 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04264
|
Chenglong Li
|
Chenglong Li, Tianhao Zhu, Lei Liu, Xiaonan Si, Zilin Fan, Sulan Zhai
|
Cross-Modal Object Tracking: Modality-Aware Representations and A
Unified Benchmark
|
In Submission
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In many visual systems, visual tracking often bases on RGB image sequences,
in which some targets are invalid in low-light conditions, and tracking
performance is thus affected significantly. Introducing other modalities such
as depth and infrared data is an effective way to handle imaging limitations of
individual sources, but multi-modal imaging platforms usually require elaborate
designs and cannot be applied in many real-world applications at present.
Near-infrared (NIR) imaging becomes an essential part of many surveillance
cameras, whose imaging is switchable between RGB and NIR based on the light
intensity. These two modalities are heterogeneous with very different visual
properties and thus bring big challenges for visual tracking. However, existing
works have not studied this challenging problem. In this work, we address the
cross-modal object tracking problem and contribute a new video dataset,
including 654 cross-modal image sequences with over 481K frames in total, and
the average video length is more than 735 frames. To promote the research and
development of cross-modal object tracking, we propose a new algorithm, which
learns the modality-aware target representation to mitigate the appearance gap
between RGB and NIR modalities in the tracking process. It is plug-and-play and
could thus be flexibly embedded into different tracking frameworks. Extensive
experiments on the dataset are conducted, and we demonstrate the effectiveness
of the proposed algorithm in two representative tracking frameworks against 17
state-of-the-art tracking methods. We will release the dataset for free
academic usage, dataset download link and code will be released soon.
| 2021-11-12T00:00:00 |
new_dataset
| true | 0.715026 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04427
|
Santiago Andr\'es Azcoitia
|
Santiago Andr\'es Azcoitia, Costas Iordanou, Nikolaos Laoutaris
|
What Is the Price of Data? A Measurement Study of Commercial Data
Marketplaces
|
13 pages, 13 figures, 7 tables
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A large number of Data Marketplaces (DMs) have appeared in the last few years
to help owners monetise their data, and data buyers fuel their marketing
process, train their ML models, and perform other data-driven decision
processes. In this paper, we present a first of its kind measurement study of
the growing DM ecosystem and shed light on several totally unknown facts about
it. For example, we show that the median price of live data products sold under
a subscription model is around US\$1,400 per month. For one-off purchases of
static data, the median price is around US\$2,200. We analyse the prices of
different categories of data and show that products about telecommunications,
manufacturing, automotive, and gaming command the highest prices. We also
develop classifiers for comparing prices across different DMs as well as a
regression analysis for revealing features that correlate with data product
prices.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04734
|
Hongyi Wang
|
Hongyi Wang, Shiao Xie, Lanfen Lin, Yutaro Iwamoto, Xian-Hua Han,
Yen-Wei Chen, Ruofeng Tong
|
Mixed Transformer U-Net For Medical Image Segmentation
| null | null | null | null |
eess.IV cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Though U-Net has achieved tremendous success in medical image segmentation
tasks, it lacks the ability to explicitly model long-range dependencies.
Therefore, Vision Transformers have emerged as alternative segmentation
structures recently, for their innate ability of capturing long-range
correlations through Self-Attention (SA). However, Transformers usually rely on
large-scale pre-training and have high computational complexity. Furthermore,
SA can only model self-affinities within a single sample, ignoring the
potential correlations of the overall dataset. To address these problems, we
propose a novel Transformer module named Mixed Transformer Module (MTM) for
simultaneous inter- and intra- affinities learning. MTM first calculates
self-affinities efficiently through our well-designed Local-Global
Gaussian-Weighted Self-Attention (LGG-SA). Then, it mines inter-connections
between data samples through External Attention (EA). By using MTM, we
construct a U-shaped model named Mixed Transformer U-Net (MT-UNet) for accurate
medical image segmentation. We test our method on two different public
datasets, and the experimental results show that the proposed method achieves
better performance over other state-of-the-art methods. The code is available
at: https://github.com/Dootmaan/MT-UNet.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04886
|
Tejas Sudharshan Mathai
|
Tarun Mattikalli, Tejas Sudharshan Mathai, and Ronald M. Summers
|
Universal Lesion Detection in CT Scans using Neural Network Ensembles
|
Accepted at SPIE 2022
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In clinical practice, radiologists are reliant on the lesion size when
distinguishing metastatic from non-metastatic lesions. A prerequisite for
lesion sizing is their detection, as it promotes the downstream assessment of
tumor spread. However, lesions vary in their size and appearance in CT scans,
and radiologists often miss small lesions during a busy clinical day. To
overcome these challenges, we propose the use of state-of-the-art detection
neural networks to flag suspicious lesions present in the NIH DeepLesion
dataset for sizing. Additionally, we incorporate a bounding box fusion
technique to minimize false positives (FP) and improve detection accuracy.
Finally, to resemble clinical usage, we constructed an ensemble of the best
detection models to localize lesions for sizing with a precision of 65.17% and
sensitivity of 91.67% at 4 FP per image. Our results improve upon or maintain
the performance of current state-of-the-art methods for lesion detection in
challenging CT scans.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04993
|
Kai Wang
|
Kai Wang, Xialei Liu, Andy Bagdanov, Luis Herranz, Shangling Jui,
Joost van de Weijer
|
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot
Image Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most meta-learning approaches assume the existence of a very large set of
labeled data available for episodic meta-learning of base knowledge. This
contrasts with the more realistic continual learning paradigm in which data
arrives incrementally in the form of tasks containing disjoint classes. In this
paper we consider this problem of Incremental Meta-Learning (IML) in which
classes are presented incrementally in discrete tasks. We propose an approach
to IML, which we call Episodic Replay Distillation (ERD), that mixes classes
from the current task with class exemplars from previous tasks when sampling
episodes for meta-learning. These episodes are then used for knowledge
distillation to minimize catastrophic forgetting. Experiments on four datasets
demonstrate that ERD surpasses the state-of-the-art. In particular, on the more
challenging one-shot, long task sequence incremental meta-learning scenarios,
we reduce the gap between IML and the joint-training upper bound from 3.5% /
10.1% / 13.4% with the current state-of-the-art to 2.6% / 2.9% / 5.0% with our
method on Tiered-ImageNet / Mini-ImageNet / CIFAR100, respectively.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710641 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05009
|
Bangwei She
|
M\'aria Luk\'a\v{c}ov\'a-Medvi\v{d}ov\'a, Bangwei She, Yuhuan Yuan
|
Error estimates of the Godunov method for the multidimensional
compressible Euler system
|
31 pages
| null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We derive a priori error of the Godunov method for the multidimensional Euler
system of gas dynamics. To this end we apply the relative energy principle and
estimate the distance between the numerical solution and the strong solution.
This yields also the estimates of the $L^2$-norm of errors in density, momentum
and entropy. Under the assumption that the numerical density and energy are
bounded, we obtain a convergence rate of $1/2$ for the relative energy in the
$L^1$-norm. Further, under the assumption -- the total variation of numerical
solution is bounded, we obtain the first order convergence rate for the
relative energy in the $L^1$-norm. Consequently, numerical solutions (density,
momentum and entropy) converge in the $L^2$-norm with the convergence rate of
$1/2$. The numerical results presented for Riemann problems are consistent with
our theoretical analysis.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05072
|
Gabriele D'Acunto
|
Gabriele D'Acunto, Paolo Bajardi, Francesco Bonchi, Gianmarco De
Francisci Morales
|
The Evolving Causal Structure of Equity Risk Factors
| null |
ACM International Conference on AI in Finance, 2021
|
10.1145/3490354.3494370
| null |
q-fin.ST cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, multi-factor strategies have gained increasing popularity in
the financial industry, as they allow investors to have a better understanding
of the risk drivers underlying their portfolios. Moreover, such strategies
promise to promote diversification and thus limit losses in times of financial
turmoil. However, recent studies have reported a significant level of
redundancy between these factors, which might enhance risk contagion among
multi-factor portfolios during financial crises. Therefore, it is of
fundamental importance to better understand the relationships among factors.
Empowered by recent advances in causal structure learning methods, this paper
presents a study of the causal structure of financial risk factors and its
evolution over time. In particular, the data we analyze covers 11 risk factors
concerning the US equity market, spanning a period of 29 years at daily
frequency.
Our results show a statistically significant sparsifying trend of the
underlying causal structure. However, this trend breaks down during periods of
financial stress, in which we can observe a densification of the causal network
driven by a growth of the out-degree of the market factor node. Finally, we
present a comparison with the analysis of factors cross-correlations, which
further confirms the importance of causal analysis for gaining deeper insights
in the dynamics of the factor system, particularly during economic downturns.
Our findings are especially significant from a risk-management perspective.
They link the evolution of the causal structure of equity risk factors with
market volatility and a worsening macroeconomic environment, and show that, in
times of financial crisis, exposure to different factors boils down to exposure
to the market risk factor.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.706994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05314
|
Titus Masese PhD
|
Kazuki Yoshii, Titus Masese, Minami Kato, Keigo Kubota, Hiroshi Senoh
and Masahiro Shikano
|
Sulfonylamide-Based Ionic Liquids for High-Voltage Potassium-Ion
Batteries with Honeycomb Layered Cathode Oxides
|
29 pages, 8 figures, 1 table, 1 cover art
| null |
10.1002/celc.201900689
| null |
physics.chem-ph cond-mat.mtrl-sci cond-mat.other
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The world is at the cusp of a new era where pivotal importance is being
attached to the development of sustainable and high-performance energy storage
systems. Potassium-ion batteries are deemed not only as cheap battery
candidates, but also as the penultimate high-voltage energy storage systems
within the monovalent-cation chemistries. However, their performance and
sustainability are undermined by the lack of suitable electrolytes for
high-voltage operation particularly due to the limited availability of cathode
materials. Here, the potential of ionic liquids based on potassium
bis(trifluoromethanesulfonyl)amide (KTFSA) as high-voltage electrolytes is
presented by assessing their physicochemical properties, along with the
electrochemical properties upon coupling with new high-voltage layered cathode
materials. These ionic liquids demonstrate a lower redox potential for
potassium dissolution / deposition (with a wide voltage tolerance of around
$6.0$ $\rm V$), placing them as feasible and safe electrolytes for high-voltage
potassium-ion battery configuration. This is proven by matching this
electrolyte with new high-voltage layered cathode compositions, demonstrating
stable electrochemical performance. The present findings of electrochemically
stable ionic liquids based on potassium bis(trifluoromethanesulfonyl)amide will
bolster further advancement of high-performance cathode materials, whose
performance at high-voltage regimes were apparently restricted by the paucity
of suitable and compatible electrolytes.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05424
|
Yao Lu
|
Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang,
Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov,
Sergey Levine
|
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at
Scale
| null | null | null | null |
cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Robotic skills can be learned via imitation learning (IL) using user-provided
demonstrations, or via reinforcement learning (RL) using large amountsof
autonomously collected experience.Both methods have complementarystrengths and
weaknesses: RL can reach a high level of performance, but requiresexploration,
which can be very time consuming and unsafe; IL does not requireexploration,
but only learns skills that are as good as the provided demonstrations.Can a
single method combine the strengths of both approaches? A number ofprior
methods have aimed to address this question, proposing a variety of tech-niques
that integrate elements of IL and RL. However, scaling up such methodsto
complex robotic skills that integrate diverse offline data and generalize
mean-ingfully to real-world scenarios still presents a major challenge. In this
paper, ouraim is to test the scalability of prior IL + RL algorithms and devise
a system basedon detailed empirical experimentation that combines existing
components in themost effective and scalable way. To that end, we present a
series of experimentsaimed at understanding the implications of each design
decision, so as to develop acombined approach that can utilize demonstrations
and heterogeneous prior datato attain the best performance on a range of
real-world and realistic simulatedrobotic problems. Our complete method, which
we call AW-Opt, combines ele-ments of advantage-weighted regression [1, 2] and
QT-Opt [3], providing a unifiedapproach for integrating demonstrations and
offline data for robotic manipulation.Please see https://awopt.github.io for
more details.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05537
|
Wataru Sasaki
|
Wataru Sasaki, Hiroshi Kawane, Satoko Miyahara, Kota Tsubouchi,
Tadashi Okoshi
|
Nation-wide Mood: Large-scale Estimation of People's Mood from Web
Search Query and Mobile Sensor Data
|
submitted to The Web Conference 2022. arXiv admin note: substantial
text overlap with arXiv:2011.00665
| null | null | null |
cs.CY cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to estimate the current affective statuses of web users has
considerable potential for the realization of user-centric services in the
society. However, in real-world web services, it is difficult to determine the
type of data to be used for such estimation, as well as collecting the ground
truths of such affective statuses. We propose a novel method of such estimation
based on the combined use of user web search queries and mobile sensor data.
The system was deployed in our product server stack, and a large-scale data
analysis with more than 11,000,000 users was conducted. Interestingly, our
proposed "Nation-wide Mood Score," which bundles the mood values of users
across the country, (1) shows the daily and weekly rhythm of people's moods,
(2) explains the ups and downs of people's moods in the COVID-19 pandemic,
which is inversely synchronized to the number of new COVID-19 cases, and (3)
detects the linkage with big news, which may affect many user's mood states
simultaneously, even in a fine-grained time resolution, such as the order of
hours.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.70069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05719
|
Xiaowen Cao
|
Xiaowen Cao and Guangxu Zhu and Jie Xu and Shuguang Cui
|
Transmission Power Control for Over-the-Air Federated Averaging at
Network Edge
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Over-the-air computation (AirComp) has emerged as a new analog power-domain
non-orthogonal multiple access (NOMA) technique for low-latency
model/gradient-updates aggregation in federated edge learning (FEEL). By
integrating communication and computation into a joint design, AirComp can
significantly enhance the communication efficiency, but at the cost of
aggregation errors caused by channel fading and noise. This paper studies a
particular type of FEEL with federated averaging (FedAvg) and AirComp-based
model-update aggregation, namely over-the-air FedAvg (Air-FedAvg). We
investigate the transmission power control to combat against the AirComp
aggregation errors for enhancing the training accuracy and accelerating the
training speed of Air-FedAvg. Towards this end, we first analyze the
convergence behavior (in terms of the optimality gap) of Air-FedAvg with
aggregation errors at different outer iterations. Then, to enhance the training
accuracy, we minimize the optimality gap by jointly optimizing the transmission
power control at edge devices and the denoising factors at edge server, subject
to a series of power constraints at individual edge devices. Furthermore, to
accelerate the training speed, we also minimize the training latency of
Air-FedAvg with a given targeted optimality gap, in which learning
hyper-parameters including the numbers of outer iterations and local training
epochs are jointly optimized with the power control. Finally, numerical results
show that the proposed transmission power control policy achieves significantly
faster convergence for Air-FedAvg, as compared with benchmark policies with
fixed power transmission or per-iteration mean squared error (MSE)
minimization. It is also shown that the Air-FedAvg achieves an
order-of-magnitude shorter training latency than the conventional FedAvg with
digital orthogonal multiple access (OMA-FedAvg).
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05796
|
Narges Ahani
|
Narges Ahani (1) and Andrew C. Trapp (1 and 2) ((1) Data Science
Program, Worcester Polytechnic Institute, Worcester, MA, (2) WPI Business
School, Worcester Polytechnic Institute, Worcester, MA)
|
Human-Centric Decision Support Tools: Insights from Real-World Design
and Implementation
| null | null | null | null |
cs.CY cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Decision support tools enable improved decision-making for challenging
decision problems by empowering stakeholders to process, analyze, visualize,
and otherwise make sense of a variety of key factors. Their intentional design
is a critical component of the value they create. All decision-support tools
share in common that there is a complex decision problem to be solved for which
decision-support is useful, and moreover, that appropriate analytics expertise
is available to produce solutions to the problem setting at hand. When
well-designed, decision support tools reduce friction and increase efficiency
in providing support for the decision-making process, thereby improving the
ability of decision-makers to make quality decisions. On the other hand, the
presence of overwhelming, superfluous, insufficient, or ill-fitting information
and software features can have an adverse effect on the decision-making process
and, consequently, outcomes. We advocate for an innovative, and perhaps
overlooked, approach to designing effective decision support tools: genuinely
listening to the project stakeholders, to ascertain and appreciate their real
needs and perspectives. By prioritizing stakeholder needs, a foundation of
mutual trust and understanding is established with the design team. We maintain
this trust is critical to eventual tool acceptance and adoption, and its
absence jeopardizes the future use of the tool, which would leave its
analytical insights for naught. We discuss examples across multiple contexts to
underscore our collective experience, highlight lessons learned, and present
recommended practices to improve the design and eventual adoption of decision
dupport tools.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709856 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05808
|
Lo\"ic Rakotoson
|
Lo\"ic Rakotoson, Charles Letaillieur, Sylvain Massip and Fr\'ejus
Laleye
|
BagBERT: BERT-based bagging-stacking for multi-topic classification
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes our submission on the COVID-19 literature annotation
task at Biocreative VII. We proposed an approach that exploits the knowledge of
the globally non-optimal weights, usually rejected, to build a rich
representation of each label. Our proposed approach consists of two stages: (1)
A bagging of various initializations of the training data that features weakly
trained weights, (2) A stacking of heterogeneous vocabulary models based on
BERT and RoBERTa Embeddings. The aggregation of these weak insights performs
better than a classical globally efficient model. The purpose is the
distillation of the richness of knowledge to a simpler and lighter model. Our
system obtains an Instance-based F1 of 92.96 and a Label-based micro-F1 of
91.35.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710791 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05811
|
Anders E. Kal{\o}r
|
Petar Popovski and Federico Chiariotti and Victor Croisfelt and Anders
E. Kal{\o}r and Israel Leyva-Mayorga and Letizia Marchegiani and Shashi Raj
Pandey and Beatriz Soret
|
Internet of Things (IoT) Connectivity in 6G: An Interplay of Time,
Space, Intelligence, and Value
|
Submitted for publication
| null | null | null |
cs.IT cs.NI math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Internet of Things (IoT) connectivity has a prominent presence in the 5G
wireless communication systems. As these systems are being deployed, there is a
surge of research efforts and visions towards 6G wireless systems. In order to
position the evolution of IoT within the 6G systems, this paper first takes a
critical view on the way IoT connectivity is supported within 5G. Following
that, the wireless IoT evolution is discussed through multiple dimensions:
time, space, intelligence, and value. We also conjecture that the focus will
broaden from IoT devices and their connections towards the emergence of complex
IoT environments, seen as building blocks of the overall IoT ecosystem.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709667 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05882
|
Mohamed Amgad
|
Lantian Zhang (1 and 2), Mohamed Amgad (2), Lee A.D. Cooper (2) ((1)
North Shore Country Day, Winnetka, IL, USA, (2) Department of Pathology,
Northwestern University, Chicago, IL, USA)
|
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully
Supervised Learning
|
7 pages, 4 figures, 4 tables
| null | null | null |
q-bio.QM cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Data labeling is often the most challenging task when developing
computational pathology models. Pathologist participation is necessary to
generate accurate labels, and the limitations on pathologist time and demand
for large, labeled datasets has led to research in areas including weakly
supervised learning using patient-level labels, machine assisted annotation and
active learning. In this paper we explore self-supervised learning to reduce
labeling burdens in computational pathology. We explore this in the context of
classification of breast cancer tissue using the Barlow Twins approach, and we
compare self-supervision with alternatives like pre-trained networks in
low-data scenarios. For the task explored in this paper, we find that ImageNet
pre-trained networks largely outperform the self-supervised representations
obtained using Barlow Twins.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711857 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05884
|
Martin Schmid
|
Martin Schmid
|
Search in Imperfect Information Games
|
doctoral thesis
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
From the very dawn of the field, search with value functions was a
fundamental concept of computer games research. Turing's chess algorithm from
1950 was able to think two moves ahead, and Shannon's work on chess from $1950$
includes an extensive section on evaluation functions to be used within a
search. Samuel's checkers program from 1959 already combines search and value
functions that are learned through self-play and bootstrapping. TD-Gammon
improves upon those ideas and uses neural networks to learn those complex value
functions -- only to be again used within search. The combination of
decision-time search and value functions has been present in the remarkable
milestones where computers bested their human counterparts in long standing
challenging games -- DeepBlue for Chess and AlphaGo for Go. Until recently,
this powerful framework of search aided with (learned) value functions has been
limited to perfect information games. As many interesting problems do not
provide the agent perfect information of the environment, this was an
unfortunate limitation. This thesis introduces the reader to sound search for
imperfect information games.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05885
|
Jianjun Hu
|
Nghia Nguyen, Steph-Yves Louis, Lai Wei, Kamal Choudhary, Ming Hu,
Jianjun Hu
|
Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph
Neural Networks
|
9 pages
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Lattice vibration frequencies are related to many important materials
properties such as thermal and electrical conductivity as well as
superconductivity. However, computational calculation of vibration frequencies
using density functional theory (DFT) methods is too computationally demanding
for a large number of samples in materials screening. Here we propose a deep
graph neural network-based algorithm for predicting crystal vibration
frequencies from crystal structures with high accuracy. Our algorithm addresses
the variable dimension of vibration frequency spectrum using the zero padding
scheme. Benchmark studies on two data sets with 15,000 and 35,552 samples show
that the aggregated $R^2$ scores of the prediction reaches 0.554 and 0.724
respectively. Our work demonstrates the capability of deep graph neural
networks to learn to predict phonon spectrum properties of crystal structures
in addition to phonon density of states (DOS) and electronic DOS in which the
output dimension is constant.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05890
|
Lev Evtodienko
|
Lev Evtodienko
|
Multimodal End-to-End Group Emotion Recognition using Cross-Modal
Attention
| null | null | null | null |
cs.CV cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Classifying group-level emotions is a challenging task due to complexity of
video, in which not only visual, but also audio information should be taken
into consideration. Existing works on multimodal emotion recognition are using
bulky approach, where pretrained neural networks are used as a feature
extractors and then extracted features are being fused. However, this approach
does not consider attributes of multimodal data and feature extractors cannot
be fine-tuned for specific task which can be disadvantageous for overall model
accuracy. To this end, our impact is twofold: (i) we train model end-to-end,
which allows early layers of neural network to be adapted with taking into
account later, fusion layers, of two modalities; (ii) all layers of our model
was fine-tuned for downstream task of emotion recognition, so there were no
need to train neural networks from scratch. Our model achieves best validation
accuracy of 60.37% which is approximately 8.5% higher, than VGAF dataset
baseline and is competitive with existing works, audio and video modalities.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709422 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05891
|
Carine Rognon
|
Carine Rognon, Loic Grossen, Stefano Mintchev, Jenifer Miehlbradt,
Silvestro Micera and Dario Floreano
|
A Portable and Passive Gravity Compensation Arm Support for Drone
Teleoperation
|
13 pages, 12 figures, 4 tables
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Gesture-based interfaces are often used to achieve a more natural and
intuitive teleoperation of robots. Yet, sometimes, gesture control requires
postures or movements that cause significant fatigue to the user. In a previous
user study, we demonstrated that na\"ive users can control a fixed-wing drone
with torso movements while their arms are spread out. However, this posture
induced significant arm fatigue. In this work, we present a passive arm support
that compensates the arm weight with a mean torque error smaller than 0.005
N/kg for more than 97% of the range of motion used by subjects to fly,
therefore reducing muscular fatigue in the shoulder of on average 58%. In
addition, this arm support is designed to fit users from the body dimension of
the 1st percentile female to the 99th percentile male. The performance analysis
of the arm support is described with a mechanical model and its implementation
is validated with both a mechanical characterization and a user study, which
measures the flight performance, the shoulder muscle activity and the user
acceptance.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708244 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05894
|
Seung Won Min
|
Seung Won Min, Kun Wu, Mert Hidayeto\u{g}lu, Jinjun Xiong, Xiang Song,
Wen-mei Hwu
|
Graph Neural Network Training with Data Tiering
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph Neural Networks (GNNs) have shown success in learning from
graph-structured data, with applications to fraud detection, recommendation,
and knowledge graph reasoning. However, training GNN efficiently is challenging
because: 1) GPU memory capacity is limited and can be insufficient for large
datasets, and 2) the graph-based data structure causes irregular data access
patterns. In this work, we provide a method to statistical analyze and identify
more frequently accessed data ahead of GNN training. Our data tiering method
not only utilizes the structure of input graph, but also an insight gained from
actual GNN training process to achieve a higher prediction result. With our
data tiering method, we additionally provide a new data placement and access
strategy to further minimize the CPU-GPU communication overhead. We also take
into account of multi-GPU GNN training as well and we demonstrate the
effectiveness of our strategy in a multi-GPU system. The evaluation results
show that our work reduces CPU-GPU traffic by 87-95% and improves the training
speed of GNN over the existing solutions by 1.6-2.1x on graphs with hundreds of
millions of nodes and billions of edges.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.71262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05895
|
Javier Andreu-Perez Dr
|
Javier Andreu-Perez, Humberto P\'erez-Espinosa, Eva Timonet, Mehrin
Kiani, Manuel I. Gir\'on-P\'erez, Alma B. Benitez-Trinidad, Delaram Jarchi,
Alejandro Rosales-P\'erez, Nick Gatzoulis, Orion F. Reyes-Galaviz, Alejandro
Torres-Garc\'ia, Carlos A. Reyes-Garc\'ia, Zulfiqar Ali, Francisco Rivas
|
A Generic Deep Learning Based Cough Analysis System from Clinically
Validated Samples for Point-of-Need Covid-19 Test and Severity Levels
| null |
IEEE Transactions on Services Computing (2021)
|
10.1109/TSC.2021.3061402
| null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We seek to evaluate the detection performance of a rapid primary screening
tool of Covid-19 solely based on the cough sound from 8,380 clinically
validated samples with laboratory molecular-test (2,339 Covid-19 positives and
6,041 Covid-19 negatives). Samples were clinically labeled according to the
results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle
threshold, and lymphocytes count from the patients. Our proposed generic method
is an algorithm based on Empirical Mode Decomposition (EMD) with subsequent
classification based on a tensor of audio features and a deep artificial neural
network classifier with convolutional layers called DeepCough'. Two different
versions of DeepCough based on the number of tensor dimensions, i.e.
DeepCough2D and DeepCough3D, have been investigated. These methods have been
deployed in a multi-platform proof-of-concept Web App CoughDetect to administer
this test anonymously. Covid-19 recognition results rates achieved a promising
AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and
specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three
severity levels. Our proposed web tool and underpinning algorithm for the
robust, fast, point-of-need identification of Covid-19 facilitates the rapid
detection of the infection. We believe that it has the potential to
significantly hamper the Covid-19 pandemic across the world.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05916
|
Tuanfeng Wang
|
Tuanfeng Y. Wang and Duygu Ceylan and Krishna Kumar Singh and Niloy J.
Mitra
|
Dance In the Wild: Monocular Human Animation with Neural Dynamic
Appearance Synthesis
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Synthesizing dynamic appearances of humans in motion plays a central role in
applications such as AR/VR and video editing. While many recent methods have
been proposed to tackle this problem, handling loose garments with complex
textures and high dynamic motion still remains challenging. In this paper, we
propose a video based appearance synthesis method that tackles such challenges
and demonstrates high quality results for in-the-wild videos that have not been
shown before. Specifically, we adopt a StyleGAN based architecture to the task
of person specific video based motion retargeting. We introduce a novel motion
signature that is used to modulate the generator weights to capture dynamic
appearance changes as well as regularizing the single frame based pose
estimates to improve temporal coherency. We evaluate our method on a set of
challenging videos and show that our approach achieves state-of-the art
performance both qualitatively and quantitatively.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05917
|
Javier Andreu-Perez Dr
|
Delaram Jarchi, Javier Andreu-Perez, Mehrin Kiani, Oldrich Vysata,
Jiri Kuchynka, Ales Prochazka, Saeid Sane
|
Recognition of Patient Groups with Sleep Related Disorders using
Bio-signal Processing and Deep Learning
|
Paper is offered by the publisher as Open Acess:
https://www.mdpi.com/1424-8220/20/9/2594
|
Sensors 20.9 (2020): 2594
|
10.3390/s20092594
| null |
cs.LG cs.AI cs.ET
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Accurately diagnosing sleep disorders is essential for clinical assessments
and treatments. Polysomnography (PSG) has long been used for detection of
various sleep disorders. In this research, electrocardiography (ECG) and
electromayography (EMG) have been used for recognition of breathing and
movement-related sleep disorders. Bio-signal processing has been performed by
extracting EMG features exploiting entropy and statistical moments, in addition
to developing an iterative pulse peak detection algorithm using synchrosqueezed
wavelet transform (SSWT) for reliable extraction of heart rate and
breathing-related features from ECG. A deep learning framework has been
designed to incorporate EMG and ECG features. The framework has been used to
classify four groups: healthy subjects, patients with obstructive sleep apnea
(OSA), patients with restless leg syndrome (RLS) and patients with both OSA and
RLS. The proposed deep learning framework produced a mean accuracy of 72% and
weighted F1 score of 0.57 across subjects for our formulated four-class
problem.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708824 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05923
|
Andreas Pavlogiannis
|
Adam Husted Kjelstr{\o}m, Andreas Pavlogiannis
|
The Decidability and Complexity of Interleaved Bidirected Dyck
Reachability
| null | null | null | null |
cs.PL cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
Dyck reachability is the standard formulation of a large domain of static
analyses, as it achieves the sweet spot between precision and efficiency, and
has thus been studied extensively. Interleaved Dyck reachability (denoted
$D_k\odot D_k$) uses two Dyck languages for increased precision (e.g., context
and field sensitivity) but is well-known to be undecidable. As many static
analyses yield a certain type of bidirected graphs, they give rise to
interleaved bidirected Dyck reachability problems. Although these problems have
seen numerous applications, their decidability and complexity has largely
remained open. In a recent work, Li et al. made the first steps in this
direction, showing that (i) $D_1\odot D_1$ reachability (i.e., when both Dyck
languages are over a single parenthesis and act as counters) is computable in
$O(n^7)$ time, while (ii) $D_k\odot D_k$ reachability is NP-hard.
In this work we address the decidability and complexity of all variants of
interleaved bidirected Dyck reachability. First, we show that $D_1\odot D_1$
reachability can be computed in $O(n^3\cdot \alpha(n))$ time, significantly
improving over the existing $O(n^7)$ bound. Second, we show that $D_k\odot D_1$
reachability (i.e., when one language acts as a counter) is decidable, in
contrast to the non-bidirected case where decidability is open. We further
consider $D_k\odot D_1$ reachability where the counter remains linearly
bounded. Our third result shows that this bounded variant can be solved in
$O(n^2\cdot \alpha(n))$ time, while our fourth result shows that the problem
has a (conditional) quadratic lower bound, and thus our upper bound is
essentially optimal. Fifth, we show that full $D_k\odot D_k$ reachability is
undecidable. This improves the recent NP-hardness lower-bound, and shows that
the problem is equivalent to the non-bidirected case.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.707771 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05934
|
Huanbo Sun
|
Huanbo Sun, Katherine J. Kuchenbecker, Georg Martius
|
A soft thumb-sized vision-based sensor with accurate all-round force
perception
|
1 table, 5 figures, 24 pages for the main manuscript. 5 tables, 12
figures, 27 pages for the supplementary material. 8 supplementary videos
| null | null | null |
cs.RO cs.CV cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-based haptic sensors have emerged as a promising approach to robotic
touch due to affordable high-resolution cameras and successful computer-vision
techniques. However, their physical design and the information they provide do
not yet meet the requirements of real applications. We present a robust, soft,
low-cost, vision-based, thumb-sized 3D haptic sensor named Insight: it
continually provides a directional force-distribution map over its entire
conical sensing surface. Constructed around an internal monocular camera, the
sensor has only a single layer of elastomer over-molded on a stiff frame to
guarantee sensitivity, robustness, and soft contact. Furthermore, Insight is
the first system to combine photometric stereo and structured light using a
collimator to detect the 3D deformation of its easily replaceable flexible
outer shell. The force information is inferred by a deep neural network that
maps images to the spatial distribution of 3D contact force (normal and shear).
Insight has an overall spatial resolution of 0.4 mm, force magnitude accuracy
around 0.03 N, and force direction accuracy around 5 degrees over a range of
0.03--2 N for numerous distinct contacts with varying contact area. The
presented hardware and software design concepts can be transferred to a wide
variety of robot parts.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.672117 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05936
|
Atefeh Sohrabizadeh
|
Atefeh Sohrabizadeh, Yuze Chi, Jason Cong
|
SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for
Graph Similarity Computation
|
12 pages
| null | null | null |
cs.LG cs.AR cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While there have been many studies on hardware acceleration for deep learning
on images, there has been a rather limited focus on accelerating deep learning
applications involving graphs. The unique characteristics of graphs, such as
the irregular memory access and dynamic parallelism, impose several challenges
when the algorithm is mapped to a CPU or GPU. To address these challenges while
exploiting all the available sparsity, we propose a flexible architecture
called SPA-GCN for accelerating Graph Convolutional Networks (GCN), the core
computation unit in deep learning algorithms on graphs. The architecture is
specialized for dealing with many small graphs since the graph size has a
significant impact on design considerations. In this context, we use SimGNN, a
neural-network-based graph matching algorithm, as a case study to demonstrate
the effectiveness of our architecture. The experimental results demonstrate
that SPA-GCN can deliver a high speedup compared to a multi-core CPU
implementation and a GPU implementation, showing the efficiency of our design.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710666 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05937
|
Krishanu Das Baksi
|
Krishanu Das Baksi
|
Recent Advances in Automated Question Answering In Biomedical Domain
| null | null | null | null |
cs.AI cs.CL cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The objective of automated Question Answering (QA) systems is to provide
answers to user queries in a time efficient manner. The answers are usually
found in either databases (or knowledge bases) or a collection of documents
commonly referred to as the corpus. In the past few decades there has been a
proliferation of acquisition of knowledge and consequently there has been an
exponential growth in new scientific articles in the field of biomedicine.
Therefore, it has become difficult to keep track of all the information in the
domain, even for domain experts. With the improvements in commercial search
engines, users can type in their queries and get a small set of documents most
relevant for answering their query, as well as relevant snippets from the
documents in some cases. However, it may be still tedious and time consuming to
manually look for the required information or answers. This has necessitated
the development of efficient QA systems which aim to find exact and precise
answers to user provided natural language questions in the domain of
biomedicine. In this paper, we introduce the basic methodologies used for
developing general domain QA systems, followed by a thorough investigation of
different aspects of biomedical QA systems, including benchmark datasets and
several proposed approaches, both using structured databases and collection of
texts. We also explore the limitations of current systems and explore potential
avenues for further advancement.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710409 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05939
|
Ha Le
|
Ha Le, Junming Wu, Louis Yu, Melissa Lynn
|
A study on Channel Popularity in Twitch
| null | null | null | null |
cs.SI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the past few decades, there has been an increasing need for Internet users
to host real time events online and to share their experiences with live,
interactive audiences. Online streaming services like Twitch have attracted
millions of users to stream and to spectate. There have been few studies about
the prediction of streamers' popularity on Twitch. In this paper, we look at
potential factors that can contribute to the popularity of streamers. Streamer
data was collected through consistent tracking using Twitch's API during a 4
weeks period. Each user's streaming information such as the number of current
viewers and followers, the genre of the stream etc., were collected. From the
results, we found that the frequency of streaming sessions, the types of
content and the length of the streams are major factors in determining how much
viewers and subscribers streamers can gain during sessions.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05940
|
Babak Hemmatian
|
Babak Hemmatian, Sheridan Feucht, Rachel Avram, Alexander Wey, Muskaan
Garg, Kate Spitalnic, Carsten Eickhoff, Ellie Pavlick, Bjorn Sandstede,
Steven Sloman
|
A Novel Corpus of Discourse Structure in Humans and Computers
|
In the 2nd Workshop on Computational Approaches to Discourse (CODI)
at EMNLP 2021 (extended abstract). 3 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel corpus of 445 human- and computer-generated documents,
comprising about 27,000 clauses, annotated for semantic clause types and
coherence relations that allow for nuanced comparison of artificial and natural
discourse modes. The corpus covers both formal and informal discourse, and
contains documents generated using fine-tuned GPT-2 (Zellers et al., 2019) and
GPT-3(Brown et al., 2020). We showcase the usefulness of this corpus for
detailed discourse analysis of text generation by providing preliminary
evidence that less numerous, shorter and more often incoherent clause relations
are associated with lower perceived quality of computer-generated narratives
and arguments.
| 2021-11-12T00:00:00 |
new_dataset
| true | 0.677634 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05941
|
Amur Ghose
|
Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark
Coates
|
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
|
Accepted and presented at ICCAD 2021
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Presently with technology node scaling, an accurate prediction model at early
design stages can significantly reduce the design cycle. Especially during
logic synthesis, predicting cell congestion due to improper logic combination
can reduce the burden of subsequent physical implementations. There have been
attempts using Graph Neural Network (GNN) techniques to tackle congestion
prediction during the logic synthesis stage. However, they require informative
cell features to achieve reasonable performance since the core idea of GNNs is
built on the message passing framework, which would be impractical at the early
logic synthesis stage. To address this limitation, we propose a framework that
can directly learn embeddings for the given netlist to enhance the quality of
our node features. Popular random-walk based embedding methods such as
Node2vec, LINE, and DeepWalk suffer from the issue of cross-graph alignment and
poor generalization to unseen netlist graphs, yielding inferior performance and
costing significant runtime. In our framework, we introduce a superior
alternative to obtain node embeddings that can generalize across netlist graphs
using matrix factorization methods. We propose an efficient mini-batch training
method at the sub-graph level that can guarantee parallel training and satisfy
the memory restriction for large-scale netlists. We present results utilizing
open-source EDA tools such as DREAMPLACE and OPENROAD frameworks on a variety
of openly available circuits. By combining the learned embedding on top of the
netlist with the GNNs, our method improves prediction performance, generalizes
to new circuit lines, and is efficient in training, potentially saving over $90
\%$ of runtime.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.70939 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05943
|
Favyen Bastani
|
Favyen Bastani, Songtao He, Sam Madden
|
Self-Supervised Multi-Object Tracking with Cross-Input Consistency
|
NeurIPS 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a self-supervised learning procedure for training a
robust multi-object tracking (MOT) model given only unlabeled video. While
several self-supervisory learning signals have been proposed in prior work on
single-object tracking, such as color propagation and cycle-consistency, these
signals cannot be directly applied for training RNN models, which are needed to
achieve accurate MOT: they yield degenerate models that, for instance, always
match new detections to tracks with the closest initial detections. We propose
a novel self-supervisory signal that we call cross-input consistency: we
construct two distinct inputs for the same sequence of video, by hiding
different information about the sequence in each input. We then compute tracks
in that sequence by applying an RNN model independently on each input, and
train the model to produce consistent tracks across the two inputs. We evaluate
our unsupervised method on MOT17 and KITTI -- remarkably, we find that, despite
training only on unlabeled video, our unsupervised approach outperforms four
supervised methods published in the last 1--2 years, including Tracktor++,
FAMNet, GSM, and mmMOT.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710848 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05950
|
Giuseppina Carannante
|
Giuseppina Carannante, Dimah Dera, Ghulam Rasool and Nidhal C.
Bouaynaya
|
Self-Compression in Bayesian Neural Networks
|
submitted to 2020 IEEE International Workshop on Machine Learning for
Signal Processing
| null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning models have achieved human-level performance on various
tasks. This success comes at a high cost of computation and storage overhead,
which makes machine learning algorithms difficult to deploy on edge devices.
Typically, one has to partially sacrifice accuracy in favor of an increased
performance quantified in terms of reduced memory usage and energy consumption.
Current methods compress the networks by reducing the precision of the
parameters or by eliminating redundant ones. In this paper, we propose a new
insight into network compression through the Bayesian framework. We show that
Bayesian neural networks automatically discover redundancy in model parameters,
thus enabling self-compression, which is linked to the propagation of
uncertainty through the layers of the network. Our experimental results show
that the network architecture can be successfully compressed by deleting
parameters identified by the network itself while retaining the same level of
accuracy.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05953
|
Giuseppina Carannante
|
Giuseppina Carannante, Dimah Dera, Ghulam Rasool, Nidhal C. Bouaynaya,
and Lyudmila Mihaylova
|
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
|
submitted to 2020 IEEE International Workshop on Machine Learning for
Signal Processing
| null | null | null |
cs.LG cs.AI cs.CV math.PR
|
http://creativecommons.org/licenses/by/4.0/
|
Learning in uncertain, noisy, or adversarial environments is a challenging
task for deep neural networks (DNNs). We propose a new theoretically grounded
and efficient approach for robust learning that builds upon Bayesian estimation
and Variational Inference. We formulate the problem of density propagation
through layers of a DNN and solve it using an Ensemble Density Propagation
(EnDP) scheme. The EnDP approach allows us to propagate moments of the
variational probability distribution across the layers of a Bayesian DNN,
enabling the estimation of the mean and covariance of the predictive
distribution at the output of the model. Our experiments using MNIST and
CIFAR-10 datasets show a significant improvement in the robustness of the
trained models to random noise and adversarial attacks.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709416 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05956
|
Rahul Vigneswaran K
|
Rahul Vigneswaran and Marc T. Law and Vineeth N. Balasubramanian and
Makarand Tapaswi
|
Feature Generation for Long-tail Classification
|
Accepted at ICVGIP'21. Code available at
https://github.com/rahulvigneswaran/TailCalibX
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The visual world naturally exhibits an imbalance in the number of object or
scene instances resulting in a \emph{long-tailed distribution}. This imbalance
poses significant challenges for classification models based on deep learning.
Oversampling instances of the tail classes attempts to solve this imbalance.
However, the limited visual diversity results in a network with poor
representation ability. A simple counter to this is decoupling the
representation and classifier networks and using oversampling only to train the
classifier. In this paper, instead of repeatedly re-sampling the same image
(and thereby features), we explore a direction that attempts to generate
meaningful features by estimating the tail category's distribution. Inspired by
ideas from recent work on few-shot learning, we create calibrated distributions
to sample additional features that are subsequently used to train the
classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset
with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the
efficacy of our approach and establish a new state-of-the-art. We also present
a qualitative analysis of generated features using t-SNE visualizations and
analyze the nearest neighbors used to calibrate the tail class distributions.
Our code is available at https://github.com/rahulvigneswaran/TailCalibX.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05961
|
Douglas Stinson
|
Navid Nasr Esfahani and Douglas Stinson
|
Rectangular, Range, and Restricted AONTs: Three Generalizations of
All-or-Nothing Transforms
| null | null | null | null |
math.CO cs.CR cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
All-or-nothing transforms (AONTs) were originally defined by Rivest as
bijections from $s$ input blocks to $s$ output blocks such that no information
can be obtained about any input block in the absence of any output block.
Numerous generalizations and extensions of all-or-nothing transforms have been
discussed in recent years, many of which are motivated by diverse applications
in cryptography, information security, secure distributed storage, etc. In
particular, $t$-AONTs, in which no information can be obtained about any $t$
input blocks in the absence of any $t$ output blocks, have received
considerable study.
In this paper, we study three generalizations of AONTs that are motivated by
applications due to Pham et al. and Oliveira et al. We term these
generalizations rectangular, range, and restricted AONTs. Briefly, in a
rectangular AONT, the number of outputs is greater than the number of inputs. A
range AONT satisfies the $t$-AONT property for a range of consecutive values of
$t$. Finally, in a restricted AONT, the unknown outputs are assumed to occur
within a specified set of "secure" output blocks. We study existence and
non-existence and provide examples and constructions for these generalizations.
We also demonstrate interesting connections with combinatorial structures such
as orthogonal arrays, split orthogonal arrays, MDS codes and difference
matrices.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712676 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05965
|
Merlyn Jaqueline Juarez Gutierrez
|
Merlyn Jaqueline Ju\'arez-Guti\'errez and W. Luis Moch\'an
|
C\'alculo de propiedades \'opticas de metamateriales
|
in Spanish
| null | null | null |
physics.optics cond-mat.mtrl-sci
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present an introduction to metamaterials, some of their optical
prop-erties, and examples of their uses. We develop an efficient theory for
thecalculation of the macroscopic permittivity of binary systems and
systemswith more components, in the non-retarded case and in the general case,
andwe present its implementation in a computational package and illustrate
itsuse. We discuss some applications regarding the design of optimized
nanos-tructured optical devices and we discuss the linear and non-linear
properties obtained.
Presentamos una introducci\'on a los metamateriales, algunas de sus
pro-piedades \'opticas y ejemplos de sus usos. Desarrollamos una teor\'ia
eficientepara el c\'alculo de su permitividad macrosc\'opica en sistemas
binarios o conm\'as componentes, en el caso no retardado y en el caso general,
y presenta-mos su implementaci\'on en un paquete computacional y su uso.
Finalizamosdiscutiendo algunas aplicaciones del mismo para el dise\~no de
dispositivos \'opti-cos nanoestructurados optimizados y discutimos las
propiedades lineales y nolineales obtenidas.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.705798 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05969
|
David Biagioni
|
David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rohit
Chintala, Jennifer King, Ahmed S. Zamzam
|
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in
Power Systems
| null | null | null | null |
cs.LG cs.AI cs.MA cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
We present the PowerGridworld software package to provide users with a
lightweight, modular, and customizable framework for creating
power-systems-focused, multi-agent Gym environments that readily integrate with
existing training frameworks for reinforcement learning (RL). Although many
frameworks exist for training multi-agent RL (MARL) policies, none can rapidly
prototype and develop the environments themselves, especially in the context of
heterogeneous (composite, multi-device) power systems where power flow
solutions are required to define grid-level variables and costs. PowerGridworld
is an open-source software package that helps to fill this gap. To highlight
PowerGridworld's key features, we present two case studies and demonstrate
learning MARL policies using both OpenAI's multi-agent deep deterministic
policy gradient (MADDPG) and RLLib's proximal policy optimization (PPO)
algorithms. In both cases, at least some subset of agents incorporates elements
of the power flow solution at each time step as part of their reward (negative
cost) structures.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05972
|
Can Karakus
|
Can Karakus, Rahul Huilgol, Fei Wu, Anirudh Subramanian, Cade Daniel,
Derya Cavdar, Teng Xu, Haohan Chen, Arash Rahnama, Luis Quintela
|
Amazon SageMaker Model Parallelism: A General and Flexible Framework for
Large Model Training
|
24 pages. Submitted for review
| null | null | null |
cs.LG cs.AI cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
With deep learning models rapidly growing in size, systems-level solutions
for large-model training are required. We present Amazon SageMaker model
parallelism, a software library that integrates with PyTorch, and enables easy
training of large models using model parallelism and other memory-saving
features. In contrast to existing solutions, the implementation of the
SageMaker library is much more generic and flexible, in that it can
automatically partition and run pipeline parallelism over arbitrary model
architectures with minimal code change, and also offers a general and
extensible framework for tensor parallelism, which supports a wider range of
use cases, and is modular enough to be easily applied to new training scripts.
The library also preserves the native PyTorch user experience to a much larger
degree, supporting module re-use and dynamic graphs, while giving the user full
control over the details of the training step. We evaluate performance over
GPT-3, RoBERTa, BERT, and neural collaborative filtering, and demonstrate
competitive performance over existing solutions.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05976
|
Mahmoud Fayed
|
Mahmoud S. Fayed
|
Classification of the Chess Endgame problem using Logistic Regression,
Decision Trees, and Neural Networks
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this study we worked on the classification of the Chess Endgame problem
using different algorithms like logistic regression, decision trees and neural
networks. Our experiments indicates that the Neural Networks provides the best
accuracy (85%) then the decision trees (79%). We did these experiments using
Microsoft Azure Machine Learning as a case-study on using Visual Programming in
classification. Our experiments demonstrates that this tool is powerful and
save a lot of time, also it could be improved with more features that increase
the usability and reduce the learning curve. We also developed an application
for dataset visualization using a new programming language called Ring, our
experiments demonstrates that this language have simple design like Python
while integrates RAD tools like Visual Basic which is good for GUI development
in the open-source world
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.704198 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05979
|
Isaac Cho
|
Abdullah-Al-Raihan Nayeem and Mohammed Elshambakey and Todd Dobbs and
Huikyo Lee and Daniel Crichton and Yimin Zhu and Chanachok Chokwitthaya and
William J. Tolone and Isaac Cho
|
A Visual Analytics Framework for Distributed Data Analysis Systems
| null | null | null | null |
cs.DC cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
This paper proposes a visual analytics framework that addresses the complex
user interactions required through a command-line interface to run analyses in
distributed data analysis systems. The visual analytics framework facilitates
the user to manage access to the distributed servers, incorporate data from the
source, run data-driven analysis, monitor the progress, and explore the result
using interactive visualizations. We provide a user interface embedded with
generalized functionalities and access protocols and integrate it with a
distributed analysis system. To demonstrate our proof of concept, we present
two use cases from the earth science and Sustainable Human Building Ecosystem
research domain.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709384 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05980
|
Jinsoo Choi
|
Jinsoo Choi, Jaesik Park, In So Kweon
|
Self-Supervised Real-time Video Stabilization
|
BMVC 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Videos are a popular media form, where online video streaming has recently
gathered much popularity. In this work, we propose a novel method of real-time
video stabilization - transforming a shaky video to a stabilized video as if it
were stabilized via gimbals in real-time. Our framework is trainable in a
self-supervised manner, which does not require data captured with special
hardware setups (i.e., two cameras on a stereo rig or additional motion
sensors). Our framework consists of a transformation estimator between given
frames for global stability adjustments, followed by scene parallax reduction
module via spatially smoothed optical flow for further stability. Then, a
margin inpainting module fills in the missing margin regions created during
stabilization to reduce the amount of post-cropping. These sequential steps
reduce distortion and margin cropping to a minimum while enhancing stability.
Hence, our approach outperforms state-of-the-art real-time video stabilization
methods as well as offline methods that require camera trajectory optimization.
Our method procedure takes approximately 24.3 ms yielding 41 fps regardless of
resolution (e.g., 480p or 1080p).
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709447 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05986
|
Irina Higgins
|
Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
|
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred
from Vision
| null | null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A recently proposed class of models attempts to learn latent dynamics from
high-dimensional observations, like images, using priors informed by
Hamiltonian mechanics. While these models have important potential applications
in areas like robotics or autonomous driving, there is currently no good way to
evaluate their performance: existing methods primarily rely on image
reconstruction quality, which does not always reflect the quality of the learnt
latent dynamics. In this work, we empirically highlight the problems with the
existing measures and develop a set of new measures, including a binary
indicator of whether the underlying Hamiltonian dynamics have been faithfully
captured, which we call Symplecticity Metric or SyMetric. Our measures take
advantage of the known properties of Hamiltonian dynamics and are more
discriminative of the model's ability to capture the underlying dynamics than
reconstruction error. Using SyMetric, we identify a set of architectural
choices that significantly improve the performance of a previously proposed
model for inferring latent dynamics from pixels, the Hamiltonian Generative
Network (HGN). Unlike the original HGN, the new HGN++ is able to discover an
interpretable phase space with physically meaningful latents on some datasets.
Furthermore, it is stable for significantly longer rollouts on a diverse range
of 13 datasets, producing rollouts of essentially infinite length both forward
and backwards in time with no degradation in quality on a subset of the
datasets.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.712782 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05989
|
Sachithra Lokuge
|
Sachithra Lokuge and Sophia Xiaoxia Duan
|
Towards Understanding Enablers of Digital Transformation in Small and
Medium-Sized Enterprises
| null | null | null | null |
cs.DL cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Even though, digital transformation has attracted much attention of both
academics and practitioners, a very limited number of studies have investigated
the digital transformation process in small and medium-sized enterprises (SMEs)
and the findings remain fragmented. Given the accessibility and availability of
digital technologies to launch digital transformation initiatives and the
importance of SMEs in the economy, a profound understanding of enablers of the
digital transformation process in SMEs is much needed. As such, to address
this, in this paper we conducted a comprehensive review of related literature
in information systems, management, and business disciplines, to identify key
enablers that facilitate the digital transformation process in SMEs.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.713257 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05990
|
Bo Wang
|
Bo Wang, Reza Mohajerpoor, Chen Cai, Inhi Kim, Hai L. Vu
|
Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and
Sparse-UNet
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide
high-resolution traffic states given the static and dynamic traffic information
obtained previously. The aim is to build a machine learning model for
predicting the normalized average traffic speed and flow of the subregions of
multiple large-scale cities using historical data points. The model is supposed
to be generic, in a way that it can be applied to new cities. By considering
spatiotemporal feature learning and modeling efficiency, we explore 3DResNet
and Sparse-UNet approaches for the tasks in this competition. The 3DResNet
based models use 3D convolution to learn the spatiotemporal features and apply
sequential convolutional layers to enhance the temporal relationship of the
outputs. The Sparse-UNet model uses sparse convolutions as the backbone for
spatiotemporal feature learning. Since the latter algorithm mainly focuses on
non-zero data points of the inputs, it dramatically reduces the computation
time, while maintaining a competitive accuracy. Our results show that both of
the proposed models achieve much better performance than the baseline
algorithms. The codes and pretrained models are available at
https://github.com/resuly/Traffic4Cast-2021.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710785 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05991
|
Sachithra Lokuge
|
Ali Alruthaya, Thanh-Thuy Nguyen and Sachithra Lokuge
|
The Application of Digital Technology and the Learning Characteristics
of Generation Z in Higher Education
| null | null | null | null |
cs.CY cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The Generation Z (Gen Z), or the digital natives have never experienced a
life without the internet. In addition, the advancement of digital technologies
such as social media, smart mobile technologies, cloud computing, and the
Internet-of-things has transformed how individuals perform their day-to-day
activities. Especially for Gen Z, the use of digital technology has become an
essential part of their daily routine, as a result, challenging the norm. As
such, Gen Z displays unique learning characteristics which are different from
previous generations. This change opens new avenues for exploring the impact of
digital technology on the learning characteristics of Gen Z and possible
applications to the higher education environment. By conducting a literature
review of 80 studies, this paper presents a comprehensive framework for
understanding the influence of digital technologies on the learning
characteristics of Gen Z in higher education.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.707436 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05993
|
Sachithra Lokuge
|
Ruwan Nagahawatta, Sachithra Lokuge, Matthew Warren and Scott Salzman
|
Cybersecurity Issues and Practices in a Cloud Context: A Comparison
Amongst Micro, Small and Medium Enterprises
| null | null | null | null |
cs.CR cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The advancement and the proliferation of information systems among
enterprises have given rise to understanding cybersecurity. Cybersecurity
practices provide a set of techniques and procedures to protect the systems,
networks, programs and data from attack, damage, or unauthorised access. Such
cybersecurity practices vary and are applied differently to different types of
enterprises. The purpose of this research is to compare the critical
cybersecurity threats and practices in the cloud context among micro, small,
and medium enterprises. By conducting a survey among 289 micro, small and
medium-sized enterprises in Australia, this study highlights the significant
differences in their cloud security practices. It also concludes that future
studies that focus on cybersecurity issues and practices in the context of
cloud computing should pay attention to these differences.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.708591 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05999
|
Kazutomo Yoshii
|
Kazutomo Yoshii
|
What Does the Post-Moore Era Mean for Research Software Engineering?
|
Research Software Engineers in HPC (RSE-HPC-2021)
https://us-rse.org/rse-hpc-2021/
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We are entering the post-Moore era where we no longer enjoy the free ride of
the performance growth from simply shrinking the transistor features. However,
this does not necessarily mean that we are entering a dark era of computing. On
the contrary, sustaining the performance growth of computing in the post-Moore
era itself is cutting-edge research. Concretely, heterogeneity and hardware
specialization are becoming promising approaches in hardware designs. However,
these are paradigm shifts in computer architecture. So what does the post-Moore
era mean for research software engineering? This position paper addresses such
a question by summarizing possible challenges and opportunities for research
software engineering in the post-Moore era. We then briefly discuss what is
missing and how we prepare to tackle such challenges and exploit opportunities
for the future of computing.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711425 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06002
|
Xiaochen Zou
|
Xiaochen Zou and Guoren Li and Weiteng Chen and Hang Zhang and Zhiyun
Qian
|
SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in
Linux kernel
|
17 pages, 9 figures; accepted to USENIX Security 2022
|
31st USENIX Security Symposium (USENIX Security 2022)
| null | null |
cs.CR cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Fuzzing has become one of the most effective bug finding approach for
software. In recent years, 24*7 continuous fuzzing platforms have emerged to
test critical pieces of software, e.g., Linux kernel. Though capable of
discovering many bugs and providing reproducers (e.g., proof-of-concepts), a
major problem is that they neglect a critical function that should have been
built-in, i.e., evaluation of a bug's security impact. It is well-known that
the lack of understanding of security impact can lead to delayed bug fixes as
well as patch propagation. In this paper, we develop SyzScope, a system that
can automatically uncover new "high-risk" impacts given a bug with seemingly
"low-risk" impacts. From analyzing over a thousand low-risk bugs on syzbot,
SyzScope successfully determined that 183 low-risk bugs (more than 15%) in fact
contain high-risk impacts, e.g., control flow hijack and arbitrary memory
write, some of which still do not have patches available yet.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.705969 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06003
|
Syed Raza Bashir Mr.
|
Syed Raza Bashir, Vojislav Misic
|
Detecting Fake Points of Interest from Location Data
|
Accepted in IEEE
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The pervasiveness of GPS-enabled mobile devices and the widespread use of
location-based services have resulted in the generation of massive amounts of
geo-tagged data. In recent times, the data analysis now has access to more
sources, including reviews, news, and images, which also raises questions about
the reliability of Point-of-Interest (POI) data sources. While previous
research attempted to detect fake POI data through various security mechanisms,
the current work attempts to capture the fake POI data in a much simpler way.
The proposed work is focused on supervised learning methods and their
capability to find hidden patterns in location-based data. The ground truth
labels are obtained through real-world data, and the fake data is generated
using an API, so we get a dataset with both the real and fake labels on the
location data. The objective is to predict the truth about a POI using the
Multi-Layer Perceptron (MLP) method. In the proposed work, MLP based on data
classification technique is used to classify location data accurately. The
proposed method is compared with traditional classification and robust and
recent deep neural methods. The results show that the proposed method is better
than the baseline methods.
| 2021-11-12T00:00:00 |
new_dataset
| true | 0.526343 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06011
|
Jeongwhan Choi
|
Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun
Lee, Noseong Park
|
Climate Modeling with Neural Diffusion Equations
|
Accepted by ICDM 2021
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Owing to the remarkable development of deep learning technology, there have
been a series of efforts to build deep learning-based climate models. Whereas
most of them utilize recurrent neural networks and/or graph neural networks, we
design a novel climate model based on the two concepts, the neural ordinary
differential equation (NODE) and the diffusion equation. Many physical
processes involving a Brownian motion of particles can be described by the
diffusion equation and as a result, it is widely used for modeling climate. On
the other hand, neural ordinary differential equations (NODEs) are to learn a
latent governing equation of ODE from data. In our presented method, we combine
them into a single framework and propose a concept, called neural diffusion
equation (NDE). Our NDE, equipped with the diffusion equation and one more
additional neural network to model inherent uncertainty, can learn an
appropriate latent governing equation that best describes a given climate
dataset. In our experiments with two real-world and one synthetic datasets and
eleven baselines, our method consistently outperforms existing baselines by
non-trivial margins.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06012
|
Denis McInerney
|
Denis Jered McInerney, Luyang Kong, Kristjan Arumae, Byron Wallace,
Parminder Bhatia
|
Kronecker Factorization for Preventing Catastrophic Forgetting in
Large-scale Medical Entity Linking
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-task learning is useful in NLP because it is often practically
desirable to have a single model that works across a range of tasks. In the
medical domain, sequential training on tasks may sometimes be the only way to
train models, either because access to the original (potentially sensitive)
data is no longer available, or simply owing to the computational costs
inherent to joint retraining. A major issue inherent to sequential learning,
however, is catastrophic forgetting, i.e., a substantial drop in accuracy on
prior tasks when a model is updated for a new task. Elastic Weight
Consolidation is a recently proposed method to address this issue, but scaling
this approach to the modern large models used in practice requires making
strong independence assumptions about model parameters, limiting its
effectiveness. In this work, we apply Kronecker Factorization--a recent
approach that relaxes independence assumptions--to prevent catastrophic
forgetting in convolutional and Transformer-based neural networks at scale. We
show the effectiveness of this technique on the important and illustrative task
of medical entity linking across three datasets, demonstrating the capability
of the technique to be used to make efficient updates to existing methods as
new medical data becomes available. On average, the proposed method reduces
catastrophic forgetting by 51% when using a BERT-based model, compared to a 27%
reduction using standard Elastic Weight Consolidation, while maintaining
spatial complexity proportional to the number of model parameters.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.71039 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06013
|
Tahiya Chowdhury
|
Tahiya Chowdhury
|
The Other Side of Black Screen: Rethinking Interaction in Synchronous
Remote Learning for Collaborative Programming
|
16 pages, 2 figures
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Collaborative learning environments such as programming labs are crucial for
learning experiential hands-on skills such as critical thinking and problem
solving, and peer discussion. In a traditional laboratory setting, many of
these skills can be practiced through natural interaction (verbal, facial) and
physical co-location. However, during and after a global pandemic, these
learning practices cannot be exercised safely in in-person settings any longer
and thus need to be re-imagined for a remote learning environment. As
discussions spur about effective remote learning practices, there is an urgency
for identifying the unique needs demanded by both students and instructors
under different learning environments. How can we design remote learning to
offer broadly accessible learning, by drawing in-person practices and combining
them with the power of remote learning solutions? In this case study, we
present observations of in-person and online versions of 2 introductory
programming courses offered before and during the COVID-19 pandemic. Our
observations reveal certain user needs and interaction practices under 5 themes
that are unique to students' prior experience with the curriculum and academic
level. We find that the current online video-conferencing platforms cannot
foster collaborative learning among peers, lacks learning ambiance and
spontaneous engagement between students and instructors. Based on our findings,
we propose design recommendations and intervention strategies to improve
current practices in synchronous remote learning that can facilitate a better
learning environment, particularly for introductory lab courses.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.705981 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06017
|
Yancey Liu
|
Yandong Liu, Chengzhong Xu, Hui Kong
|
Yaw-Guided Imitation Learning for Autonomous Driving in Urban
Environments
|
9 pages, 9 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing imitation learning methods suffer from low efficiency and
generalization ability when facing the road option problem in an urban
environment. In this paper, we propose a yaw-guided imitation learning method
to improve the road option performance in an end-to-end autonomous driving
paradigm in terms of the efficiency of exploiting training samples and
adaptability to changing environments. Specifically, the yaw information is
provided by the trajectory of the navigation map. Our end-to-end architecture,
Yaw-guided Imitation Learning with ResNet34 Attention (YILRatt), integrates the
ResNet34 backbone and attention mechanism to obtain an accurate perception. It
does not need high precision maps and realizes fully end-to-end autonomous
driving given the yaw information provided by a consumer-level GPS receiver. By
analyzing the attention heat maps, we can reveal some causal relationship
between decision-making and scene perception, where, in particular, failure
cases are caused by erroneous perception. We collect expert experience in the
Carla 0.9.11 simulator and improve the benchmark CoRL2017 and NoCrash.
Experimental results show that YILRatt has a 26.27% higher success rate than
the SOTA CILRS. The code, dataset, benchmark and experimental results can be
found at https://github.com/Yandong024/Yaw-guided-IL.git
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06022
|
Michael Leconte
|
M. Leconte and T. Kobayashi
|
Zonal profile corrugations and staircase formation: Role of the
transport crossphase
|
6 pages, 5 figures (low-quality figures)
|
Phys. Plasmas 28, 014503 (2021)
|
10.1063/5.0030018
| null |
physics.plasm-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recently, quasi-stationary structures called $E \times B$ staircases were
observed in gyrokinetic simulations, in all transport channels [Dif-Pradalier
et al. Phys. Rev. Lett. 114, 085004 (2015)]. We present a novel analytical
theory - supported by collisional drift-wave fluid simulations - for the
generation of density profile corrugations (staircase), independent of the
action of zonal flows: Turbulent fluctuations self-organize to generate
quasi-stationary radial modulations $\Delta\theta(r,t)$ of the transport
crossphase $\theta$ between density and electric potential fluctuations. The
radial modulations of the associated particle flux drive zonal corrugations of
the density profile, via a modulational instability. In turn, zonal density
corrugations regulate the turbulence via nonlinear damping of the fluctuations.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711212 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06023
|
Zhihang Dong
|
Qinze Yu, Zhihang Dong, Xingyu Fan, Licheng Zong and Yu Li
|
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest
for Annotating Antimicrobial Peptides
|
16 pages 8 figures
| null | null | null |
cs.LG cs.AI q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Identifying the targets of an antimicrobial peptide is a fundamental step in
studying the innate immune response and combating antibiotic resistance, and
more broadly, precision medicine and public health. There have been extensive
studies on the statistical and computational approaches to identify (i) whether
a peptide is an antimicrobial peptide (AMP) or a non-AMP and (ii) which targets
are these sequences effective to (Gram-positive, Gram-negative, etc.). Despite
the existing deep learning methods on this problem, most of them are unable to
handle the small AMP classes (anti-insect, anti-parasite, etc.). And more
importantly, some AMPs can have multiple targets, which the previous methods
fail to consider. In this study, we build a diverse and comprehensive
multi-label protein sequence database by collecting and cleaning amino acids
from various AMP databases. To generate efficient representations and features
for the small classes dataset, we take advantage of a protein language model
trained on 250 million protein sequences. Based on that, we develop an
end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate
AMP comprehensively. After identifying an AMP, it further predicts what targets
the AMP can effectively kill from eleven available classes. Extensive
experiments suggest that our framework outperforms state-of-the-art models in
both the binary classification task and the multi-label classification task,
especially on the minor classes.The model is robust against reduced features
and small perturbations and produces promising results. We believe HMD-AMP
contributes to both the future wet-lab investigations of the innate structural
properties of different antimicrobial peptides and build promising empirical
underpinnings for precise medicine with antibiotics.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.693071 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06025
|
William Arnold
|
William Arnold, Tarang Srivastava, Lucas Spangher, Utkarsha Agwan,
Costas Spanos
|
Adapting Surprise Minimizing Reinforcement Learning Techniques for
Transactive Control
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Optimizing prices for energy demand response requires a flexible controller
with ability to navigate complex environments. We propose a reinforcement
learning controller with surprise minimizing modifications in its architecture.
We suggest that surprise minimization can be used to improve learning speed,
taking advantage of predictability in peoples' energy usage. Our architecture
performs well in a simulation of energy demand response. We propose this
modification to improve functionality and save in a large scale experiment.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06026
|
Faisal Abu-Khzam
|
Faisal N. Abu-Khzam
|
A Note on the Maximum Number of Minimal Connected Dominating Sets in a
Graph
| null | null | null | null |
math.CO cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We prove constructively that the maximum possible number of minimal connected
dominating sets in a connected undirected graph of order $n$ is in
$\Omega(1.489^n)$. This improves the previously known lower bound of
$\Omega(1.4422^n)$ and reduces the gap between lower and upper bounds for
input-sensitive enumeration of minimal connected dominating sets in general
graphs as well as some special graph classes.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709868 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06029
|
Kevin Korb
|
Rodney T. O'Donnell, Kevin B. Korb and Lloyd Allison
|
Causal KL: Evaluating Causal Discovery
|
26 pages
| null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
The two most commonly used criteria for assessing causal model discovery with
artificial data are edit-distance and Kullback-Leibler divergence, measured
from the true model to the learned model. Both of these metrics maximally
reward the true model. However, we argue that they are both insufficiently
discriminating in judging the relative merits of false models. Edit distance,
for example, fails to distinguish between strong and weak probabilistic
dependencies. KL divergence, on the other hand, rewards equally all
statistically equivalent models, regardless of their different causal claims.
We propose an augmented KL divergence, which we call Causal KL (CKL), which
takes into account causal relationships which distinguish between
observationally equivalent models. Results are presented for three variants of
CKL, showing that Causal KL works well in practice.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.713276 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06031
|
Lanqing Guo
|
Lanqing Guo, Siyu Huang, Haosen Liu, Bihan Wen
|
FINO: Flow-based Joint Image and Noise Model
| null | null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
One of the fundamental challenges in image restoration is denoising, where
the objective is to estimate the clean image from its noisy measurements. To
tackle such an ill-posed inverse problem, the existing denoising approaches
generally focus on exploiting effective natural image priors. The utilization
and analysis of the noise model are often ignored, although the noise model can
provide complementary information to the denoising algorithms. In this paper,
we propose a novel Flow-based joint Image and NOise model (FINO) that
distinctly decouples the image and noise in the latent space and losslessly
reconstructs them via a series of invertible transformations. We further
present a variable swapping strategy to align structural information in images
and a noise correlation matrix to constrain the noise based on spatially
minimized correlation information. Experimental results demonstrate FINO's
capacity to remove both synthetic additive white Gaussian noise (AWGN) and real
noise. Furthermore, the generalization of FINO to the removal of spatially
variant noise and noise with inaccurate estimation surpasses that of the
popular and state-of-the-art methods by large margins.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06037
|
Shaojie Tang
|
Shaojie Tang
|
Constrained Stochastic Submodular Maximization with State-Dependent
Costs
| null | null | null | null |
cs.LG cs.AI math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we study the constrained stochastic submodular maximization
problem with state-dependent costs. The input of our problem is a set of items
whose states (i.e., the marginal contribution and the cost of an item) are
drawn from a known probability distribution. The only way to know the realized
state of an item is to select that item. We consider two constraints, i.e.,
\emph{inner} and \emph{outer} constraints. Recall that each item has a
state-dependent cost, and the inner constraint states that the total
\emph{realized} cost of all selected items must not exceed a give budget. Thus,
inner constraint is state-dependent. The outer constraint, one the other hand,
is state-independent. It can be represented as a downward-closed family of sets
of selected items regardless of their states. Our objective is to maximize the
objective function subject to both inner and outer constraints. Under the
assumption that larger cost indicates larger "utility", we present a constant
approximate solution to this problem.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709604 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06041
|
Anatoli Torokhti
|
Anatoli Torokhti
|
Processing of large sets of stochastic signals: filtering based on
piecewise interpolation technique
| null | null | null | null |
eess.SP cs.NA math.NA
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Suppose $K_{_Y}$ and $K_{_X}$ are large sets of observed and reference
signals, respectively, each containing $N$ signals. Is it possible to construct
a filter $F$ that requires a priori information only on few signals, $p\ll N$,
from $K_{_X}$ but performs better than the known filters based on a priori
information on every reference signal from $K_{_X}$? It is shown that the
positive answer is achievable under quite unrestrictive assumptions. The device
behind the proposed method is based on a special extension of the piecewise
linear interpolation technique to the case of random signal sets. The proposed
technique provides a single filter to process any signal from the arbitrarily
large signal set. The filter is determined in terms of pseudo-inverse matrices
so that it always exists.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.707771 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06044
|
Guillermo Federico Umbricht
|
Guillermo F. Umbricht, Diana Rubio, Claudio El Hasi
|
A Regularization Operator for the Source Approximation of a Transport
Equation
|
10 Pages, 2 Figures, 2 Tables
|
Mec\'anica Computacional Vol. 37, No. 50, pp. 1993-2002, 2019
| null | null |
math.NA cs.NA math.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Source identification problems have multiple applications in engineering such
as the identification of fissures in materials, determination of sources in
electromagnetic fields or geophysical applications, detection of contaminant
sources, among others. In this work we are concerned with the determination of
a time-dependent source in a transport equation from noisy data measured at a
fixed position. By means of Fourier techniques can be shown that the problem is
ill-posed in the sense that the solution exists but it does not vary
continuously with the data. A number of different techniques were developed by
other authors to approximate the solution. In this work, we consider a family
of parametric regularization operators to deal with the ill-posedness of the
problem. We proposed a manner to select the regularization parameter as a
function of noise level in data in order to obtain a regularized solution that
approximate the unknown source. We find a H\"older type bound for the error of
the approximated source when the unknown function is considered to be bounded
in a given norm. Numerical examples illustrate the convergence and stability of
the method.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709868 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06046
|
Chih-Pin Tan
|
Chih-Pin Tan, Chin-Jui Chang, Alvin W.Y. Su and Yi-Hsuan Yang
|
Music Score Expansion with Variable-Length Infilling
|
Going to published as a late-breaking demo paper at ISMIR 2021
| null | null | null |
cs.SD cs.AI eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we investigate using the variable-length infilling (VLI)
model, which is originally proposed to infill missing segments, to "prolong"
existing musical segments at musical boundaries. Specifically, as a case study,
we expand 20 musical segments from 12 bars to 16 bars, and examine the degree
to which the VLI model preserves musical boundaries in the expanded results
using a few objective metrics, including the Register Histogram Similarity we
newly propose. The results show that the VLI model has the potential to address
the expansion task.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06052
|
Soniya Yambem
|
Lekshmi A. Kurup, Cameron M. Cole, Joshua N. Arthur, Soniya D. Yambem
|
Graphene porous foams for capacitive pressure sensing
|
Main text - 8 figures, 11 pages, Supporting information - 7 figures,
7 pages
| null | null | null |
physics.app-ph cond-mat.mtrl-sci physics.ins-det
|
http://creativecommons.org/licenses/by/4.0/
|
Flexible pressure sensors are an attractive area of research due to their
potential applications in biomedical sensing and wearable devices. Among
flexible and wearable pressure sensors, capacitive pressure sensors show
significant advantages, owing to their potential low cost, ultra-low power
consumption, tolerance to temperature variations, high sensitivity, and low
hysteresis. In this work, we develop capacitive flexible pressure sensors using
graphene based conductive foams. In these soft and porous conductive foams,
graphene is present either as a coating of the pores in the foam, inside the
structure of the foam itself, or a combination of both. We demonstrate that
they are durable and sensitive at low pressure ranges (<10 kPa). Systematic
analysis of the different pressure sensors revealed that the porous foams with
graphene coated pores are the most sensitive (~ 0.137/kPa) in the pressure
range 0-6kPa. Additionally, we achieved very low limit of detection of 0.14 Pa,
which is one of the lowest values reported. Further, we demonstrated the
potential applications of our pressure sensors by showing detection of weak
physiological signals of the body. Our work is highly relevant for research in
flexible pressure sensors based on conductive foams as it shows the impact of
different ways of incorporating conductive material on performance of pressure
sensors.
| 2021-11-12T00:00:00 |
no_new_dataset
| false | 0.709982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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