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2111.07987
|
Vaclav Skala
|
Vaclav Skala, Pavel Lederbuch, Bohumir Sup
|
A Comparison of O(1) and Cyrus-Beck Line Clipping Algorithms in E2 and
E3
| null | null | null | null |
cs.CG cs.DS cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
A comparison of a new algorithm for line clipping in E2 and E3 by convex
polygon and/or polyhedron with O(1) processing complexity and Cyrus- Beck
algorithm is presented. The new algorithm in E2 is based on dual space
representation and space subdivision technique. The principle of algorithm in
E3 is based on the projection of polyhedron to three orthogonal E2 coordinate
systems. Algorithms have optimal complexities O(1) and demonstrates that
preprocessing can be used to speed up the line clipping significantly. Obvious
applications are for one polygon and/or polyhedron and many clipped lines.
Detailed theoretical estimations and experimental results are also presented.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711838 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07999
|
Youngwoon Lee
|
Youngwoon Lee and Joseph J. Lim and Anima Anandkumar and Yuke Zhu
|
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via
Terminal State Regularization
|
Published at the Conference on Robot Learning (CoRL) 2021
| null | null | null |
cs.LG cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Skill chaining is a promising approach for synthesizing complex behaviors by
sequentially combining previously learned skills. Yet, a naive composition of
skills fails when a policy encounters a starting state never seen during its
training. For successful skill chaining, prior approaches attempt to widen the
policy's starting state distribution. However, these approaches require larger
state distributions to be covered as more policies are sequenced, and thus are
limited to short skill sequences. In this paper, we propose to chain multiple
policies without excessively large initial state distributions by regularizing
the terminal state distributions in an adversarial learning framework. We
evaluate our approach on two complex long-horizon manipulation tasks of
furniture assembly. Our results have shown that our method establishes the
first model-free reinforcement learning algorithm to solve these tasks; whereas
prior skill chaining approaches fail. The code and videos are available at
https://clvrai.com/skill-chaining
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711193 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.08000
|
Emil Iftekhar
|
Viola Priesemann (1), Eberhard Bodenschatz (1), Sandra Ciesek (2), Eva
Grill (3), Emil N. Iftekhar (1), Christian Karagiannidis (4), Andr\'e Karch
(5), Mirjam Kretzschmar (6), Berit Lange (7), Sebastian A. M\"uller (8), Kai
Nagel (8), Armin Nassehi (9), Mathias W. Pletz (10), Barbara Prainsack (11),
Ulrike Protzer (12), Leif Erik Sander (13), Andreas Schuppert (14), Anita
Sch\"obel (15), Klaus \"Uberla (16), Carsten Watzl (17), Hajo Zeeb (18) ((1)
Max-Planck-Institut f\"ur Dynamik und Selbstorganisation, G\"ottingen, (2)
Universit\"atsklinikum Frankfurt, Goethe-Universit\"at, Frankfurt, (3)
Institut f\"ur Medizinische Informationsverarbeitung, Biometrie und
Epidemiologie, Ludwig-Maximilians-Universit\"at M\"unchen (LMU), M\"unchen,
(4) Lungenklinik K\"oln-Merheim, Universit\"at Witten/ Herdecke, (5)
Westf\"alische Wilhelms-Universit\"at M\"unster, M\"unster, (6) University
Medical Center Utrecht, Utrecht, Die Niederlande, (7) Epidemiologie,
Helmholtz-Zentrum f\"ur Infektionsforschung, Braunschweig, (8) Fachgebiet
Verkehrssystemplanung und Verkehrstelematik, Technische Universit\"at (TU)
Berlin, Berlin, (9) Institut f\"ur Soziologie,
Ludwig-Maximilians-Universit\"at M\"unchen (LMU), M\"unchen, (10) Institut
f\"ur Infektionsmedizin und Krankenhaushygiene, Universit\"atsklinikum Jena,
Jena, (11) Institut f\"ur Politikwissenschaft, Universit\"at Wien, Wien,
\"Osterreich, (12) Institut f\"ur Virologie, Technische Universit\"at
M\"unchen / Helmholtz Zentrum M\"unchen, M\"unchen, (13) Medizinische Klinik
mit Schwerpunkt Infektiologie und Pneumologie, Charit\'e -
Universit\"atsmedizin Berlin, Berlin, (14) RWTH Aachen /
Universit\"atsklinikum Aachen, Aachen, (15) Fraunhofer-Institut f\"ur Techno-
und Wirtschaftsmathematik (ITWM), Kaiserslautern und Fachbereich Mathematik,
TU Kaiserslautern, (16) Virologisches Institut, Universit\"atsklinikum
Erlangen, Erlangen, (17) Leibniz Institut f\"ur Arbeitsforschung (IfADo), TU
Dortmund, Dortmund, (18) Leibniz Institut f\"ur Pr\"aventionsforschung und
Epidemiologe-BIPS, Bremen)
|
Nachhaltige Strategien gegen die COVID-19-Pandemie in Deutschland im
Winter 2021/2022
|
in German, Extensive expert assessment on COVID-19 response policies
for the winter 2021/22
| null | null | null |
q-bio.OT physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this position paper, a large group of interdisciplinary experts outlines
response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022
in Germany. We review the current state of the COVID-19 pandemic, from
incidence and vaccination efficacy to hospital capacity. Building on this
situation assessment, we illustrate various possible scenarios for the winter,
and detail the mechanisms and effectiveness of the non-pharmaceutical
interventions, vaccination, and booster. With this assessment, we want to
provide orientation for decision makers about the progress and mitigation of
COVID-19.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1707.09608
|
Abhijit Roy
|
Abhijit Roy, Rakesh K. Singh, Maruthi M. Brundavanam
|
Controlled Modulation of Depolarization in Laser Speckle
| null |
Opt. Lett. 42, 4343-4346 (2017)
|
10.1364/OL.42.004343
| null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A new technique based on superposition of two speckle patterns is proposed
and demonstrated for controlled modulation of the spatial polarization
distribution of the resultant speckle. It is demonstrated both theoretically
and experimentally that controlled modulation of the spatial polarization
distribution of laser speckle can be achieved by proper choice of the
polarization states as well as the average spatial intensity of the constituent
speckles. It is also shown that the proposed technique is useful to generate
different speckle patterns with sinusoidal variation in their degree of
polarization, which can be tuned from zero to unity. This technique can find
applications in sensing, biomedical studies, and in determining the rotation of
the electric field vector after passing through a scattering medium.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1711.02124
|
Donald Stull
|
Neil Lutz and D. M. Stull
|
Projection Theorems Using Effective Dimension
| null | null | null | null |
cs.CC math.MG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we use the theory of computing to study fractal dimensions of
projections in Euclidean spaces. A fundamental result in fractal geometry is
Marstrand's projection theorem, which shows that for every analytic set E, for
almost every line L, the Hausdorff dimension of the orthogonal projection of E
onto L is maximal. We use Kolmogorov complexity to give two new results on the
Hausdorff and packing dimensions of orthogonal projections onto lines. The
first shows that the conclusion of Marstrand's theorem holds whenever the
Hausdorff and packing dimensions agree on the set E, even if E is not analytic.
Our second result gives a lower bound on the packing dimension of projections
of arbitrary sets. Finally, we give a new proof of Marstrand's theorem using
the theory of computing.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71123 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1712.05142
|
Linda Kleist
|
Michael G. Dobbins and Linda Kleist and Tillmann Miltzow and Pawe{\l}
Rz\k{a}\.zewski
|
Completeness for the Complexity Class $\forall \exists \mathbb{R}$ and
Area-Universality
|
36 pages, 17 figures
| null | null | null |
cs.CG cs.CC cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Exhibiting a deep connection between purely geometric problems and real
algebra, the complexity class $\exists \mathbb{R}$ plays a crucial role in the
study of geometric problems. Sometimes $\exists \mathbb{R}$ is referred to as
the 'real analog' of NP. While NP is a class of computational problems that
deals with existentially quantified boolean variables, $\exists \mathbb{R}$
deals with existentially quantified real variables.
In analogy to $\Pi_2^p$ and $\Sigma_2^p$ in the famous polynomial hierarchy,
we study the complexity classes $\forall \exists \mathbb{R}$ and $\exists
\forall \mathbb{R}$ with real variables. Our main interest is the
area-universality problem, where we are given a plane graph $G$, and ask if for
each assignment of areas to the inner faces of $G$, there exists a
straight-line drawing of $G$ realizing the assigned areas. We conjecture that
area-universality is $\forall \exists \mathbb{R}$-complete and support this
conjecture by proving $\exists \mathbb{R}$- and $\forall \exists
\mathbb{R}$-completeness of two variants of area-universality. To this end, we
introduce tools to prove $\forall \exists \mathbb{R}$-hardness and membership.
Finally, we present geometric problems as candidates for $\forall \exists
\mathbb{R}$-complete problems. These problems have connections to the concepts
of imprecision, robustness, and extendability.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707177 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1810.05555
|
Tommaso Petrucciani
|
Tommaso Petrucciani, Giuseppe Castagna, Davide Ancona, Elena Zucca
|
Semantic subtyping for non-strict languages
|
Extended version of a submission to the post-proceedings of TYPES'18
| null |
10.4230/LIPIcs.TYPES.2018.4
| null |
cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
Semantic subtyping is an approach to define subtyping relations for type
systems featuring union and intersection type connectives. It has been studied
only for strict languages, and it is unsound for non-strict semantics. In this
work, we study how to adapt this approach to non-strict languages: in
particular, we define a type system using semantic subtyping for a functional
language with a call-by-need semantics. We do so by introducing an explicit
representation for divergence in the types, so that the type system
distinguishes expressions that are results from those which are computations
that might diverge.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706235 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1811.09003
|
Fenglei Fan
|
Fenglei Fan, Dayang Wang, Hengtao Guo, Qikui Zhu, Pingkun Yan, Ge Wang
and Hengyong Yu
|
On a Sparse Shortcut Topology of Artificial Neural Networks
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In established network architectures, shortcut connections are often used to
take the outputs of earlier layers as additional inputs to later layers.
Despite the extraordinary effectiveness of shortcuts, there remain open
questions on the mechanism and characteristics. For example, why are shortcuts
powerful? Why do shortcuts generalize well? In this paper, we investigate the
expressivity and generalizability of a novel sparse shortcut topology. First,
we demonstrate that this topology can empower a one-neuron-wide deep network to
approximate any univariate continuous function. Then, we present a novel
width-bounded universal approximator in contrast to depth-bounded universal
approximators and extend the approximation result to a family of equally
competent networks. Furthermore, with generalization bound theory, we show that
the proposed shortcut topology enjoys excellent generalizability. Finally, we
corroborate our theoretical analyses by comparing the proposed topology with
popular architectures, including ResNet and DenseNet, on well-known benchmarks
and perform a saliency map analysis to interpret the proposed topology. Our
work helps enhance the understanding of the role of shortcuts and suggests
further opportunities to innovate neural architectures.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1902.01018
|
Changqing Ye
|
Changqing Ye (1), Junzhi Cui (2) ((1) LESC, Academy of Mathematics and
Systems Science, Chinese Academy of Sciences, Beijing, and School of
Mathematical Sciences, University of Chinese Academy of Sciences, (2) LESC,
Academy of Mathematics and Systems Science, Chinese Academy of Sciences,
Beijing)
|
A First-order Two-scale Analysis for Contact Problems with Small
Periodic Configurations
| null | null |
10.1137/19M1252326
| null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper is devoted to studying a type of contact problems modeled by
hemivariational inequalities with small periodic coefficients appearing in
PDEs, and the PDEs we considered are linear, second order and uniformly
elliptic. Under the assumptions, it is proved that the original problem can be
homogenized, and the solution weakly converges. We derive an
$O(\epsilon^{1/2})$ estimation which is pivotal in building the computational
framework. We also show that Robin problems--- a special case of contact
problems, it leads to an $O(\epsilon)$ estimation in $L^2$ norm. Our
computational framework is based on finite element methods, and the numerical
analysis is given, together with experiments to convince the estimation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709868 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1904.13351
|
Chiara Panosetti
|
Chiara Panosetti, Simon B. Anni\'es, Cristina Grosu, Stefan
Seidlmayer, Christoph Scheurer
|
DFTB modelling of lithium intercalated graphite with machine-learned
repulsive potential
|
Entirely revised parametrization followind bug fixes; results do not
substantially change. Added supplementary information
| null |
10.1021/acs.jpca.0c09388
| null |
physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lithium ion batteries have been a central part of consumer electronics for
decades. More recently, they have also become critical components in the
quickly arising technological fields of electric mobility and intermittent
renewable energy storage. However, many fundamental principles and mechanisms
are not yet understood to a sufficient extent to fully realize the potential of
the incorporated materials. The vast majority of concurrent lithium ion
batteries make use of graphite anodes. Their working principle is based on
intercalation---the embedding and ordering of (lithium-) ions in the
two-dimensional spaces between the graphene sheets. This important process---it
yields the upper bound to a battery's charging speed and plays a decisive role
for its longevity---is characterized by multiple phase transitions, ordered and
disordered domains, as well as non-equilibrium phenomena, and therefore quite
complex. In this work, we provide a simulation framework for the purpose of
better understanding lithium intercalated graphite and its behaviour during use
in a battery. In order to address the large systems sizes and long time scales
required to investigate said effects, we identify the highly efficient, but
semi-empirical Density Funtional Tight Binding (DFTB) as a suitable approach
and combine particle swarm optimization (PSO) with the machine learning (ML)
procedure Gaussian Process Regression (GPR) to obtain the necessary parameters.
Using the resulting parametrization, we are able to reproduce experimental
reference structures at a level of accuracy which is in no way inferior to much
more costly ab initio methods. We finally present structural properties and
diffusion barriers for some exemplary system states.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708213 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1907.05966
|
Gregory Puleo
|
Elliot Krop, Jessica McDonald, Gregory J. Puleo
|
Upper bounds for inverse domination in graphs
|
9 pages
|
Theory and Applications of Graphs, 8(2): Article 5, (2021)
|
10.20429/tag.2021.080205
| null |
math.CO cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In any graph $G$, the domination number $\gamma(G)$ is at most the
independence number $\alpha(G)$. The Inverse Domination Conjecture says that,
in any isolate-free $G$, there exists pair of vertex-disjoint dominating sets
$D, D'$ with $|D|=\gamma(G)$ and $|D'| \leq \alpha(G)$. Here we prove that this
statement is true if the upper bound $\alpha(G)$ is replaced by
$\frac{3}{2}\alpha(G) - 1$ (and $G$ is not a clique). We also prove that the
conjecture holds whenever $\gamma(G)\leq 5$ or $|V(G)|\leq 16$.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706197 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1908.05783
|
Laurent Risser
|
Laurent Risser, Alberto Gonzalez Sanz, Quentin Vincenot, Jean-Michel
Loubes
|
Tackling Algorithmic Bias in Neural-Network Classifiers using
Wasserstein-2 Regularization
| null | null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The increasingly common use of neural network classifiers in industrial and
social applications of image analysis has allowed impressive progress these
last years. Such methods are however sensitive to algorithmic bias, i.e. to an
under- or an over-representation of positive predictions or to higher
prediction errors in specific subgroups of images. We then introduce in this
paper a new method to temper the algorithmic bias in Neural-Network based
classifiers. Our method is Neural-Network architecture agnostic and scales well
to massive training sets of images. It indeed only overloads the loss function
with a Wasserstein-2 based regularization term for which we back-propagate the
impact of specific output predictions using a new model, based on the Gateaux
derivatives of the predictions distribution. This model is algorithmically
reasonable and makes it possible to use our regularized loss with standard
stochastic gradient-descent strategies. Its good behavior is assessed on the
reference Adult census, MNIST, CelebA datasets.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709177 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1911.04082
|
Behdad Chalaki
|
Behdad Chalaki and Andreas A. Malikopoulos
|
Time-Optimal Coordination for Connected and Automated Vehicles at
Adjacent Intersections
|
17 pages, 7 figures, 3 tables
|
IEEE Transactions on Intelligent Transportation Systems (2021)
1-16
|
10.1109/TITS.2021.3123479
| null |
math.OC cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we provide a hierarchical coordination framework for connected
and automated vehicles (CAVs) at two adjacent intersections. This framework
consists of an upper-level scheduling problem and a low-level optimal control
problem. By partitioning the area around two adjacent intersections into
different zones, we formulate a scheduling problem for each individual CAV
aimed at minimizing its total travel time. For each CAV, the solution of the
upper-level problem designates the arrival times at each zones on its path
which becomes the inputs of the low-level problem. The solution of the
low-level problem yields the optimal control input (acceleration/deceleration)
of each CAV to exit the intersections at the time specified in the upper-level
scheduling problem. We validate the performance of our proposed hierarchical
framework through extensive numerical simulations and comparison with
signalized intersections, centralized scheduling, and FIFO queuing policy.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
1912.00827
|
Ben Adlam
|
Ben Adlam, Jake Levinson, and Jeffrey Pennington
|
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep
Learning
| null | null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the distinguishing characteristics of modern deep learning systems is
that they typically employ neural network architectures that utilize enormous
numbers of parameters, often in the millions and sometimes even in the
billions. While this paradigm has inspired significant research on the
properties of large networks, relatively little work has been devoted to the
fact that these networks are often used to model large complex datasets, which
may themselves contain millions or even billions of constraints. In this work,
we focus on this high-dimensional regime in which both the dataset size and the
number of features tend to infinity. We analyze the performance of random
feature regression with features $F=f(WX+B)$ for a random weight matrix $W$ and
random bias vector $B$, obtaining exact formulae for the asymptotic training
and test errors for data generated by a linear teacher model. The role of the
bias can be understood as parameterizing a distribution over activation
functions, and our analysis directly generalizes to such distributions, even
those not expressible with a traditional additive bias. Intriguingly, we find
that a mixture of nonlinearities can improve both the training and test errors
over the best single nonlinearity, suggesting that mixtures of nonlinearities
might be useful for approximate kernel methods or neural network architecture
design.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70939 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2001.02411
|
Jakub Pek\'arek
|
Zden\v{e}k Dvo\v{r}\'ak, Jakub Pek\'arek
|
Induced odd cycle packing number, independent sets, and chromatic number
| null | null | null | null |
cs.DM cs.DS math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The induced odd cycle packing number $iocp(G)$ of a graph $G$ is the maximum
integer $k$ such that $G$ contains an induced subgraph consisting of $k$
pairwise vertex-disjoint odd cycles. Motivated by applications to geometric
graphs, Bonamy et al.~\cite{indoc} proved that graphs of bounded induced odd
cycle packing number, bounded VC dimension, and linear independence number
admit a randomized EPTAS for the independence number. We show that the
assumption of bounded VC dimension is not necessary, exhibiting a randomized
algorithm that for any integers $k\ge 0$ and $t\ge 1$ and any $n$-vertex graph
$G$ of induced odd cycle packing number at most $k$ returns in time
$O_{k,t}(n^{k+4})$ an independent set of $G$ whose size is at least
$\alpha(G)-n/t$ with high probability. In addition, we present
$\chi$-boundedness results for graphs with bounded odd cycle packing number,
and use them to design a QPTAS for the independence number only assuming
bounded induced odd cycle packing number.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707367 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2004.01917
|
Jichang Zhao
|
Xiaoling Tan, Jichang Zhao
|
The illiquidity network of stocks in China's market crash
| null | null | null | null |
q-fin.CP physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Chinese stock market experienced an abrupt crash in 2015, and over
one-third of its market value evaporated. Given its associations with fear and
the fine resolution with respect to frequency, the illiquidity of stocks may
offer a promising perspective for understanding and even signaling a market
crash. In this study, by connecting stocks with illiquidity comovements, an
illiquidity network is established to model the market. Compared to noncrash
days, on crash days, the market is more densely connected due to heavier but
more homogeneous illiquidity dependencies that facilitate abrupt collapses.
Critical stocks in the illiquidity network, particularly those in the finance
sector, are targeted for inspection because of their crucial roles in
accumulating and passing on illiquidity losses. The cascading failures of
stocks in market crashes are profiled as disseminating from small degrees to
high degrees that are usually located in the core of the illiquidity network
and then back to the periphery. By counting the days with random failures in
the previous five days, an early signal is implemented to successfully predict
more than half of the crash days, especially consecutive days in the early
phase. Additional evidence from both the Granger causality network and the
random network further testifies to the robustness of the signal. Our results
could help market practitioners such as regulators detect and prevent the risk
of crashes in advance.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712182 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2006.04204
|
Takumi Bessho
|
Takumi Bessho, Masatoshi Sato
|
Nielsen-Ninomiya Theorem with Bulk Topology: Duality in Floquet and
Non-Hermitian Systems
|
6+19 pages, 2+8 figures, 0+1 tables
|
Phys. Rev. Lett. 127, 196404 (2021)
|
10.1103/PhysRevLett.127.196404
| null |
cond-mat.mes-hall hep-lat math-ph math.MP physics.optics quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Nielsen-Ninomiya theorem is a fundamental theorem on the realization of
chiral fermions in static lattice systems in high-energy and condensed matter
physics. Here we extend the theorem in dynamical systems, which include the
original Nielsen-Ninomiya theorem in the static limit. In contrast to the
original theorem, which is a no-go theorem for bulk chiral fermions, the new
theorem permits them due to bulk topology intrinsic to dynamical systems. The
theorem is based on duality enabling a unified treatment of periodically driven
systems and non-Hermitian ones. We also present the extended theorem for
non-chiral gapless fermions protected by symmetry. Finally, as an application
of our theorem and duality, we predict a new type of chiral magnetic effect --
the non-Hermitian chiral magnetic skin effect.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712451 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2006.08170
|
Jin Zhang
|
Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie
Fan and Chongjie Zhang
|
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven
Exploration
| null |
In International Conference on Machine Learning (2021, pp.
12600-12610). PMLR
| null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks
and achieves fast adaptation to new tasks. Despite recent progress, efficient
exploration in meta-RL remains a key challenge in sparse-reward tasks, as it
requires quickly finding informative task-relevant experiences in both
meta-training and adaptation. To address this challenge, we explicitly model an
exploration policy learning problem for meta-RL, which is separated from
exploitation policy learning, and introduce a novel empowerment-driven
exploration objective, which aims to maximize information gain for task
identification. We derive a corresponding intrinsic reward and develop a new
off-policy meta-RL framework, which efficiently learns separate context-aware
exploration and exploitation policies by sharing the knowledge of task
inference. Experimental evaluation shows that our meta-RL method significantly
outperforms state-of-the-art baselines on various sparse-reward MuJoCo
locomotion tasks and more complex sparse-reward Meta-World tasks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708011 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2006.10138
|
Qi Qi
|
Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
|
An Online Method for A Class of Distributionally Robust Optimization
with Non-Convex Objectives
|
25 pages, 9 figures
| null | null | null |
cs.LG cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a practical online method for solving a class of
distributionally robust optimization (DRO) with non-convex objectives, which
has important applications in machine learning for improving the robustness of
neural networks. In the literature, most methods for solving DRO are based on
stochastic primal-dual methods. However, primal-dual methods for DRO suffer
from several drawbacks: (1) manipulating a high-dimensional dual variable
corresponding to the size of data is time expensive; (2) they are not friendly
to online learning where data is coming sequentially. To address these issues,
we consider a class of DRO with an KL divergence regularization on the dual
variables, transform the min-max problem into a compositional minimization
problem, and propose practical duality-free online stochastic methods without
requiring a large mini-batch size. We establish the state-of-the-art
complexities of the proposed methods with and without a Polyak-\L ojasiewicz
(PL) condition of the objective. Empirical studies on large-scale deep learning
tasks (i) demonstrate that our method can speed up the training by more than 2
times than baseline methods and save days of training time on a large-scale
dataset with $\sim$ 265K images, and (ii) verify the supreme performance of DRO
over Empirical Risk Minimization (ERM) on imbalanced datasets. Of independent
interest, the proposed method can be also used for solving a family of
stochastic compositional problems with state-of-the-art complexities.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709598 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2006.16651
|
R Inkulu
|
Debangshu Banerjee, R. Inkulu
|
Vertex Guarding for Dynamic Orthogonal Art Galleries
|
accepted to IJCGA
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We devise an algorithm for surveying a dynamic orthogonal polygonal domain by
placing one guard at each vertex in a subset of its vertices, i.e., whenever an
orthogonal polygonal domain {\cal P'} is modified to result in another
orthogonal polygonal domain {\cal P}, our algorithm updates the set of vertex
guards surveying {\cal P'} so that the updated guard set surveys {\cal P}. Our
algorithm modifies the guard placement in O(k \lg{(n+n')}) amortized time while
ensuring the updated orthogonal polygonal domain with h holes and n vertices is
guarded using at most \lfloor (n+2h)/4 \rfloor vertex guards. For the special
case of the initial orthogonal polygon being hole-free and each update
resulting in a hole-free orthogonal polygon, our guard update algorithm takes
O(k\lg{(n+n')}) worst-case time. Here, n' and n are the number of vertices of
the orthogonal polygon before and after the update, respectively; and, k is the
sum of |n - n'| and the number of updates to a few structures maintained by our
algorithm. Further, by giving a construction, we show it suffices for the
algorithm to consider only the case in which the parity of the number of reflex
vertices of both {\cal P'} and {\cal P} are equal.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709787 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2007.03859
|
Ryo Yoshinaka
|
Toshiki Saitoh, Ryo Yoshinaka, and Hans L. Bodlaender
|
Fixed-Treewidth-Efficient Algorithms for Edge-Deletion to Intersection
Graph Classes
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For a graph class $\mathcal{C}$, the $\mathcal{C}$-Edge-Deletion problem asks
for a given graph $G$ to delete the minimum number of edges from $G$ in order
to obtain a graph in $\mathcal{C}$. We study the $\mathcal{C}$-Edge-Deletion
problem for $\mathcal{C}$ the permutation graphs, interval graphs, and other
related graph classes. It follows from Courcelle's Theorem that these problems
are fixed parameter tractable when parameterized by treewidth. In this paper,
we present concrete FPT algorithms for these problems. By giving explicit
algorithms and analyzing these in detail, we obtain algorithms that are
significantly faster than the algorithms obtained by using Courcelle's theorem.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708855 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.00383
|
Aijun Hong
|
Aijun Hong, Yuxia Tang, and Junming Liu
|
High-throughput screening of quaternary compounds and new insight for
excellent thermoelectric performance
| null | null | null | null |
physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is well known that the high electric conductivity, large Seebeck
coefficient, and low thermal conductivity are preferred for enhancing
thermoelectric performance, but unfortunately, these properties are strongly
inter-correlated with no rational scenario for their efficient decoupling. This
big dilemma for thermoelectric research appeals for alternative strategic
solutions, while the high-throughput screening is one of them. In this work, we
start from total 3136 real electronic structures of the huge X2YZM4 quaternary
compound family and perform the high-throughput searching in terms of enhanced
thermoelectric properties. The comprehensive data-mining allows an evaluation
of the electronic and phonon characteristics of those promising thermoelectric
materials. More importantly, a new insight that the enhanced thermoelectric
performance benefits substantially from the coexisting quasi-Dirac and heavy
fermions plus strong optical-acoustic phonon hybridization, is proposed. This
work provides a clear guidance to theoretical screening and experimental
realization and thus towards development of performance-excellent
thermoelectric materials.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.01178
|
Nicolas Gonthier
|
Nicolas Gonthier and Sa\"id Ladjal and Yann Gousseau
|
Multiple instance learning on deep features for weakly supervised object
detection with extreme domain shifts
|
26 pages, 12 figures
|
Computer Vision and Image Understanding 2022
|
10.1016/j.cviu.2021.103299
|
103299
|
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Weakly supervised object detection (WSOD) using only image-level annotations
has attracted a growing attention over the past few years. Whereas such task is
typically addressed with a domain-specific solution focused on natural images,
we show that a simple multiple instance approach applied on pre-trained deep
features yields excellent performances on non-photographic datasets, possibly
including new classes. The approach does not include any fine-tuning or
cross-domain learning and is therefore efficient and possibly applicable to
arbitrary datasets and classes. We investigate several flavors of the proposed
approach, some including multi-layers perceptron and polyhedral classifiers.
Despite its simplicity, our method shows competitive results on a range of
publicly available datasets, including paintings (People-Art, IconArt),
watercolors, cliparts and comics and allows to quickly learn unseen visual
categories.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709239 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.02790
|
Evan Liu
|
Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
|
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning
without Sacrifices
|
International Conference on Machine Learning (ICML), 2021
| null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of meta-reinforcement learning (meta-RL) is to build agents that can
quickly learn new tasks by leveraging prior experience on related tasks.
Learning a new task often requires both exploring to gather task-relevant
information and exploiting this information to solve the task. In principle,
optimal exploration and exploitation can be learned end-to-end by simply
maximizing task performance. However, such meta-RL approaches struggle with
local optima due to a chicken-and-egg problem: learning to explore requires
good exploitation to gauge the exploration's utility, but learning to exploit
requires information gathered via exploration. Optimizing separate objectives
for exploration and exploitation can avoid this problem, but prior meta-RL
exploration objectives yield suboptimal policies that gather information
irrelevant to the task. We alleviate both concerns by constructing an
exploitation objective that automatically identifies task-relevant information
and an exploration objective to recover only this information. This avoids
local optima in end-to-end training, without sacrificing optimal exploration.
Empirically, DREAM substantially outperforms existing approaches on complex
meta-RL problems, such as sparse-reward 3D visual navigation. Videos of DREAM:
https://ezliu.github.io/dream/
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708421 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.04729
|
Lei Li
|
Lei Li and Veronika A. Zimmer and Julia A. Schnabel and Xiahai Zhuang
|
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification
of Left Atrium and Scars Incorporating Spatial and Shape Information
|
12 pages
| null | null | null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced
magnetic resonance imaging (LGE MRI) is an important task in clinical practice.
%, to guide ablation therapy and predict treatment results for atrial
fibrillation (AF) patients. The automatic segmentation is however still
challenging, due to the poor image quality, the various LA shapes, the thin
wall, and the surrounding enhanced regions. Previous methods normally solved
the two tasks independently and ignored the intrinsic spatial relationship
between LA and scars. In this work, we develop a new framework, namely
AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and
scar quantification are performed simultaneously in an end-to-end style. We
propose a mechanism of shape attention (SA) via an explicit surface projection,
to utilize the inherent correlation between LA and LA scars. In specific, the
SA scheme is embedded into a multi-task architecture to perform joint LA
segmentation and scar quantification. Besides, a spatial encoding (SE) loss is
introduced to incorporate continuous spatial information of the target, in
order to reduce noisy patches in the predicted segmentation. We evaluated the
proposed framework on 60 LGE MRIs from the MICCAI2018 LA challenge. Extensive
experiments on a public dataset demonstrated the effect of the proposed
AtrialJSQnet, which achieved competitive performance over the state-of-the-art.
The relatedness between LA segmentation and scar quantification was explicitly
explored and has shown significant performance improvements for both tasks. The
code and results will be released publicly once the manuscript is accepted for
publication via https://zmiclab.github.io/projects.html.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711387 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.00041
|
Hamed Rahimi
|
Hamed Rahimi, Yvan Picaud, Salvatore Costanzo, Giyyarpuram Madhusudan,
Olivier Boissier, kamal Deep Singh
|
Design and Simulation of a Hybrid Architecture for Edge Computing in 5G
and Beyond
|
Submitted to Special Issue on Smart Edge Computing and IoT of IEEE
Transaction on Computers
|
IEEE Transactions on Computers, Volume: 70 Issue: 8, 1213 - 1224,
17 March 2021
|
10.1109/TC.2021.3066579
|
21074202
|
cs.DC eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Edge Computing in 5G and Beyond is a promising solution for ultra-low latency
applications (e.g. Autonomous Vehicle, Augmented Reality, and Remote Surgery),
which have an extraordinarily low tolerance for the delay and require fast data
processing for a very high volume of data. The requirements of delay-sensitive
applications (e.g. Low latency, proximity, and Location/Context-awareness)
cannot be satisfied by Cloud Computing due to the high latency between User
Equipment and Cloud. Nevertheless, Edge Computing in 5G and beyond can promise
an ultra-high-speed caused by placing computation capabilities closer to
endpoint devices, whereas 5G encourages the speed rate that is 200 times faster
than 4G LTE-Advanced. This paper deeply investigates Edge Computing in 5G and
characterizes it based on the requirements of ultra-low latency applications.
As a contribution, we propose a hybrid architecture that takes advantage of
novel and sustainable technologies (e.g. D2D communication, Massive MIMO, SDN,
and NFV) and has major features such as scalability, reliability and ultra-low
latency support. The proposed architecture is evaluated based on an agent-based
simulation that demonstrates it can satisfy requirements and has the ability to
respond to high volume demands with low latency.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.00513
|
Allen Riddell
|
Allen Riddell and Troy J. Bassett
|
What Library Digitization Leaves Out: Predicting the Availability of
Digital Surrogates of English Novels
| null |
portal: Libraries and the Academy, 21(4), 885-900 (2021)
|
10.1353/pla.2021.0045
| null |
cs.DL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Library digitization has made more than a hundred thousand 19th-century
English-language books available to the public. Do the books which have been
digitized reflect the population of published books? An affirmative answer
would allow book and literary historians to use holdings of major digital
libraries as proxies for the population of published works, sparing them the
labor of collecting a representative sample. We address this question by taking
advantage of exhaustive bibliographies of novels published for the first time
in the British Isles in 1836 and 1838, identifying which of these novels have
at least one digital surrogate in the Internet Archive, HathiTrust, Google
Books, and the British Library. We find that digital surrogate availability is
not random. Certain kinds of novels, notably novels written by men and novels
published in multivolume format, have digital surrogates available at
distinctly higher rates than other kinds of novels. As the processes leading to
this outcome are unlikely to be isolated to the novel and the late 1830s, these
findings suggest that similar patterns will likely be observed during adjacent
decades and in other genres of publishing (e.g., non-fiction).
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.05404
|
Douglas Gon\c{c}alves
|
Douglas S. Goncalves and Carlile Lavor and Leo Liberti and Michael
Souza
|
A new algorithm for the $^K$DMDGP subclass of Distance Geometry Problems
|
This is a full version of the extended abstract accepted at CTW2020
| null |
10.1007/s00453-021-00835-6
| null |
math.CO cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The fundamental inverse problem in distance geometry is the one of finding
positions from inter-point distances. The Discretizable Molecular Distance
Geometry Problem (DMDGP) is a subclass of the Distance Geometry Problem (DGP)
whose search space can be discretized and represented by a binary tree, which
can be explored by a Branch-and-Prune (BP) algorithm. It turns out that this
combinatorial search space possesses many interesting symmetry properties that
were studied in the last decade. In this paper, we present a new algorithm for
this subclass of the DGP, which exploits DMDGP symmetries more effectively than
its predecessors. Computational results show that the speedup, with respect to
the classic BP algorithm, is considerable for sparse DMDGP instances related to
protein conformation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.02380
|
Jeremy Johnson
|
Zachary B. Zaccardi, Isaac C. Tangen, Gabriel A. Valdivia-Berroeta,
Charles B. Bahr, Karissa C. Kenney, Claire Rader, Matthew J. Lutz, Brittan P.
Hunter, David J. Michaelis, Jeremy A. Johnson
|
Enabling High-Power, Broadband THz Generation with 800-nm Pump
Wavelength
|
9 pages, 5 figures
| null |
10.1364/OE.437421
| null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The organic terahertz (THz) generation crystal BNA has recently gained
traction as a valuable source to produce broadband THz pulses. Even when pumped
with 800-nm light, thin BNA crystals can produce relatively high electric
fields with frequency components out to 5 THz. However, the THz output when
pumped with 800-nm light is limited by the damage threshold of the organic
crystal. Here we report that the damage threshold of BNA can be significantly
improved by physically bonding BNA to a high-thermal conductivity sapphire
window. When pumped with 800-nm light from an amplified Ti:sapphire laser
system, our bonded BNA (BNA-sapphire) generates 2.5 times higher electric field
strengths compared to bare BNA crystals. We characterize the average damage
threshold for bare BNA and BNA-sapphire, measure peak-to-peak electric field
strengths and THz waveforms, and determine the nonlinear transmission in BNA.
Pumping BNA-sapphire with 800-nm light results in peak-to-peak electric fields
exceeding 1 MV/cm, with strong broadband frequency components from 0.5-5 THz.
Our BNA-sapphire THz source is a promising alternative to tilted pulse front
LiNbO3 THz sources, which will enable many research groups without optical
parametric amplifiers to perform high-field, broadband THz spectroscopy.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710829 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.04615
|
Sean Ingimarson
|
Sean Ingimarson
|
An energy, momentum and angular momentum conserving scheme for a
regularization model of incompressible flow
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a new regularization model for incompressible fluid flow, which
is a regularization of the EMAC formulation of the Navier-Stokes equations
(NSE) that we call EMAC-Reg. The EMAC (energy, momentum, and angular momentum
conserving) formulation has proved to be a useful formulation because it
conserves energy, momentum and angular momentum even when the divergence
constraint is only weakly enforced. However it is still a NSE formulation and
so cannot resolve higher Reynolds number flows without very fine meshes. By
carefully introducing regularization into the EMAC formulation, we create a
model more suitable for coarser mesh computations but that still conserves the
same quantities as EMAC, i.e., energy, momentum, and angular momentum. We show
that EMAC-Reg, when semi-discretized with a finite element spatial
discretization is well-posed and optimally accurate. Numerical results are
provided that show EMAC-Reg is a robust coarse mesh model.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710051 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.06194
|
Naoki Wake
|
Naoki Wake, Machiko Sato, Kazuhiro Sasabuchi, Minako Nakamura,
Katsushi Ikeuchi
|
Labeling the Phrases of a Conversational Agent with a Unique
Personalized Vocabulary
|
8 pages, 3 figures. Submitted to and accepted by IEEE/SICE SII 2022.
Last updated November 12th, 2021
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mapping spoken text to gestures is an important research topic for robots
with conversation capabilities. According to studies on human co-speech
gestures, a reasonable solution for mapping is using a concept-based approach
in which a text is first mapped to a semantic cluster (i.e., a concept)
containing texts with similar meanings. Subsequently, each concept is mapped to
a predefined gesture. By using a concept-based approach, this paper discusses
the practical issue of obtaining concepts for a unique vocabulary personalized
for a conversational agent. Using Microsoft Rinna as an agent, we qualitatively
compare concepts obtained automatically through a natural language processing
(NLP) approach to those obtained manually through a sociological approach. We
then identify three limitations of the NLP approach: at the semantic level with
emojis and symbols; at the semantic level with slang, new words, and buzzwords;
and at the pragmatic level. We attribute these limitations to the personalized
vocabulary of Rinna. A follow-up experiment demonstrates that robot gestures
selected using a concept-based approach leave a better impression than randomly
selected gestures for the Rinna vocabulary, suggesting the usefulness of a
concept-based gesture generation system for personalized vocabularies. This
study provides insights into the development of gesture generation systems for
conversational agents with personalized vocabularies.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.07536
|
Remi Chou
|
Vidhi Rana, Remi A. Chou, and Hyuck Kwon
|
Secret Sharing from Correlated Gaussian Random Variables and Public
Communication
|
11 pages, two-column, 3 figures, accepted to IEEE Transactions on
Information Theory
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study an information-theoretic secret sharing problem,
where a dealer distributes shares of a secret among a set of participants under
the following constraints: (i) authorized sets of users can recover the secret
by pooling their shares, and (ii) non-authorized sets of colluding users cannot
learn any information about the secret. We assume that the dealer and
participants observe the realizations of correlated Gaussian random variables
and that the dealer can communicate with participants through a one-way,
authenticated, rate-limited, and public channel. Unlike traditional secret
sharing protocols, in our setting, no perfectly secure channel is needed
between the dealer and the participants. Our main result is a closed-form
characterization of the fundamental trade-off between secret rate and public
communication rate.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.09905
|
Ilya Valmianski
|
Ilya Valmianski, Nave Frost, Navdeep Sood, Yang Wang, Baodong Liu,
James J. Zhu, Sunil Karumuri, Ian M. Finn, and Daniel S. Zisook
|
SmartTriage: A system for personalized patient data capture,
documentation generation, and decision support
|
Accepted as a proceeding for ML4H 2021
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Symptom checkers have emerged as an important tool for collecting symptoms
and diagnosing patients, minimizing the involvement of clinical personnel. We
developed a machine-learning-backed system, SmartTriage, which goes beyond
conventional symptom checking through a tight bi-directional integration with
the electronic medical record (EMR). Conditioned on EMR-derived patient
history, our system identifies the patient's chief complaint from a free-text
entry and then asks a series of discrete questions to obtain relevant
symptomatology. The patient-specific data are used to predict detailed
ICD-10-CM codes as well as medication, laboratory, and imaging orders. Patient
responses and clinical decision support (CDS) predictions are then inserted
back into the EMR. To train the machine learning components of SmartTriage, we
employed novel data sets of over 25 million primary care encounters and 1
million patient free-text reason-for-visit entries. These data sets were used
to construct: (1) a long short-term memory (LSTM) based patient history
representation, (2) a fine-tuned transformer model for chief complaint
extraction, (3) a random forest model for question sequencing, and (4) a
feed-forward network for CDS predictions. In total, our system supports 337
patient chief complaints, which together make up $>90\%$ of all primary care
encounters at Kaiser Permanente.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.708994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.14784
|
Yuanhao Zhuo
|
Qingli Man, Yuanhao Zhuo
|
A Chinese Text Classification Method With Low Hardware Requirement Based
on Improved Model Concatenation
|
5 pages, 2 figures, 5 tables
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to improve the accuracy performance of Chinese text classification
models with low hardware requirements, an improved concatenation-based model is
designed in this paper, which is a concatenation of 5 different sub-models,
including TextCNN, LSTM, and Bi-LSTM. Compared with the existing ensemble
learning method, for a text classification mission, this model's accuracy is 2%
higher. Meanwhile, the hardware requirements of this model are much lower than
the BERT-based model.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712026 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.00983
|
Tobias Winkler
|
Tobias Winkler, Johannes Lehmann, Joost-Pieter Katoen
|
Out of Control: Reducing Probabilistic Models by Control-State
Elimination
|
full version including proofs, 33 pages
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
State-of-the-art probabilistic model checkers perform verification on
explicit-state Markov models defined in a high-level programming formalism like
the PRISM modeling language. Typically, the low-level models resulting from
such program-like specifications exhibit lots of structure such as repeating
subpatterns. Established techniques like probabilistic bisimulation
minimization are able to exploit these structures; however, they operate
directly on the explicit-state model. On the other hand, methods for reducing
structured state spaces by reasoning about the high-level program have not been
investigated that much. In this paper, we present a new, simple, and fully
automatic program-level technique to reduce the underlying Markov model. Our
approach aims at computing the summary behavior of adjacent locations in the
program's control-flow graph, thereby obtaining a program with fewer "control
states". This reduction is immediately reflected in the program's operational
semantics, enabling more efficient model checking. A key insight is that in
principle, each (combination of) program variable(s) with finite domain can
play the role of the program counter that defines the flow structure. Unlike
most other reduction techniques, our approach is property-directed and
naturally supports unspecified model parameters. Experiments demonstrate that
our simple method yields state-space reductions of up to 80% on practically
relevant benchmarks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.01522
|
Venkatraman Renganathan
|
Venkatraman Renganathan, Navid Hashemi, Justin Ruths, Tyler H. Summers
|
Higher-Order Moment-Based Anomaly Detection
|
arXiv admin note: text overlap with arXiv:1909.12506
| null |
10.1109/LCSYS.2021.3058269
| null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by/4.0/
|
The identification of anomalies is a critical component of operating complex,
and possibly large-scale and geo-graphically distributed cyber-physical
systems. While designing anomaly detectors, it is common to assume Gaussian
noise models to maintain tractability; however, this assumption can lead to the
actual false alarm rate being significantly higher than expected. Here we
design a distributionally robust threshold of detection using finite and fixed
higher-order moments of the detection measure data such that it guarantees the
actual false alarm rate to be upper bounded by the desired one. Further, we
bound the states reachable through the action of a stealthy attack and identify
the trade-off between this impact of attacks that cannot be detected and the
worst-case false alarm rate. Through numerical experiments, we illustrate how
knowledge of higher-order moments results in a tightened threshold, thereby
restricting an attacker's potential impact.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708244 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.08999
|
Vaneet Aggarwal
|
Kaushik Manchella, Marina Haliem, Vaneet Aggarwal, and Bharat Bhargava
|
PassGoodPool: Joint Passengers and Goods Fleet Management with
Reinforcement Learning aided Pricing, Matching, and Route Planning
|
18 pages, Accepted to IEEE Transactions on Intelligent Transportation
Systems, Special Issue on Modeling Dynamic Transportation Networks in the Age
of Connectivity, Autonomy and Data
| null | null | null |
cs.AI cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ubiquitous growth of mobility-on-demand services for passenger and goods
delivery has brought various challenges and opportunities within the realm of
transportation systems. As a result, intelligent transportation systems are
being developed to maximize operational profitability, user convenience, and
environmental sustainability. The growth of last mile deliveries alongside
ridesharing calls for an efficient and cohesive system that transports both
passengers and goods. Existing methods address this using static routing
methods considering neither the demands of requests nor the transfer of goods
between vehicles during route planning. In this paper, we present a dynamic and
demand aware fleet management framework for combined goods and passenger
transportation that is capable of (1) Involving both passengers and drivers in
the decision-making process by allowing drivers to negotiate to a mutually
suitable price, and passengers to accept/reject, (2) Matching of goods to
vehicles, and the multi-hop transfer of goods, (3) Dynamically generating
optimal routes for each vehicle considering demand along their paths, based on
the insertion cost which then determines the matching, (4) Dispatching idle
vehicles to areas of anticipated high passenger and goods demand using Deep
Reinforcement Learning (RL), (5) Allowing for distributed inference at each
vehicle while collectively optimizing fleet objectives. Our proposed model is
deployable independently within each vehicle as this minimizes computational
costs associated with the growth of distributed systems and democratizes
decision-making to each individual. Simulations on a variety of vehicle types,
goods, and passenger utility functions show the effectiveness of our approach
as compared to other methods that do not consider combined load transportation
or dynamic multi-hop route planning.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709466 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.10697
|
Mahdi Elhousni
|
Elhousni Mahdi, Zhang Ziming and Huang Xinming
|
Aerial Height Prediction and Refinement Neural Networks with Semantic
and Geometric Guidance
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning provides a powerful new approach to many computer vision tasks.
Height prediction from aerial images is one of those tasks that benefited
greatly from the deployment of deep learning which replaced old multi-view
geometry techniques. This letter proposes a two-stage approach, where first a
multi-task neural network is used to predict the height map resulting from a
single RGB aerial input image. We also include a second refinement step, where
a denoising autoencoder is used to produce higher quality height maps.
Experiments on two publicly available datasets show that our method is capable
of producing state-of-the-art results. Code is available at
https://github.com/melhousni/DSMNet.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.12250
|
Christian K\"ummerle
|
Christian K\"ummerle, Claudio Mayrink Verdun, Dominik St\"oger
|
Iteratively Reweighted Least Squares for Basis Pursuit with Global
Linear Convergence Rate
|
26 pages, 3 figures
|
NeurIPS 2021 (Spotlight)
| null | null |
math.OC cs.IT cs.LG cs.NA math.IT math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The recovery of sparse data is at the core of many applications in machine
learning and signal processing. While such problems can be tackled using
$\ell_1$-regularization as in the LASSO estimator and in the Basis Pursuit
approach, specialized algorithms are typically required to solve the
corresponding high-dimensional non-smooth optimization for large instances.
Iteratively Reweighted Least Squares (IRLS) is a widely used algorithm for this
purpose due its excellent numerical performance. However, while existing theory
is able to guarantee convergence of this algorithm to the minimizer, it does
not provide a global convergence rate. In this paper, we prove that a variant
of IRLS converges with a global linear rate to a sparse solution, i.e., with a
linear error decrease occurring immediately from any initialization, if the
measurements fulfill the usual null space property assumption. We support our
theory by numerical experiments showing that our linear rate captures the
correct dimension dependence. We anticipate that our theoretical findings will
lead to new insights for many other use cases of the IRLS algorithm, such as in
low-rank matrix recovery.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.14924
|
Peter Nejjar
|
Alexey Bufetov and Peter Nejjar
|
Cutoff profile of ASEP on a segment
|
22 pages, 3 Figures. V2: Corollary 1 has been given a proof. Several
smaller corrections following comments of referees
| null | null | null |
math.PR cs.DM math-ph math.MP
|
http://creativecommons.org/licenses/by/4.0/
|
This paper studies the mixing behavior of the Asymmetric Simple Exclusion
Process (ASEP) on a segment of length $N$. Our main result is that for particle
densities in $(0,1),$ the total-variation cutoff window of ASEP is $N^{1/3}$
and the cutoff profile is $1-F_{\mathrm{GUE}},$ where $F_{\mathrm{GUE}}$ is the
Tracy-Widom distribution function. This also gives a new proof of the cutoff
itself, shown earlier by Labb\'{e} and Lacoin. Our proof combines coupling
arguments, the result of Tracy-Widom about fluctuations of ASEP started from
the step initial condition, and exact algebraic identities coming from
interpreting the multi-species ASEP as a random walk on a Hecke algebra.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711249 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.06434
|
Isabella Furci
|
Matthias Bolten, Marco Donatelli, Paola Ferrari, Isabella Furci
|
A symbol based analysis for multigrid methods for Block-Circulant and
Block-Toeplitz Systems
| null | null | null | null |
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
In the literature, there exist several studies on symbol-based multigrid
methods for the solution of linear systems having structured coefficient
matrices. In particular, the convergence analysis for such methods has been
obtained in an elegant form in the case of Toeplitz matrices generated by a
scalar-valued function. In the block-Toeplitz setting, that is, in the case
where the matrix entries are small generic matrices instead of scalars, some
algorithms have already been proposed regarding specific applications and a
first rigorous convergence analysis has been performed in [7]. However, with
the existent symbol-based theoretical tools, it is still not possible to prove
the convergence of many multigrid methods known in the literature. This paper
aims to generalize the previous results giving more general sufficient
conditions on the symbol of the grid transfer operators.In particular, we treat
matrix-valued trigonometric polynomials which can be non-diagonalizable and
singular at all points and we express the new conditions in terms of the
eigenvectors associated with the ill-conditioned subspace. Moreover, we extend
the analysis to the V-cycle method proving a linear convergence rate under
stronger conditions, which resemble those given in the scalar case. In order to
validate our theoretical findings, we present a classical block structured
problem stemming from a FEM approximation of a second order differential
problem. We focus on two multigrid strategies that use the geometric and the
standard bisection grid transfer operators and we prove that both fall into the
category of projectors satisfying the proposed conditions. In addition, using a
tensor product argument, we provide a strategy to construct efficient V-cycle
procedures in the block multilevel setting.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708572 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.09985
|
Yuki Morita
|
Y. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata, P.
Schlatter
|
Applying Bayesian Optimization with Gaussian Process Regression to
Computational Fluid Dynamics Problems
| null | null |
10.1016/j.jcp.2021.110788
| null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
Bayesian optimization (BO) based on Gaussian process regression (GPR) is
applied to different CFD (computational fluid dynamics) problems which can be
of practical relevance. The problems are i) shape optimization in a lid-driven
cavity to minimize or maximize the energy dissipation, ii) shape optimization
of the wall of a channel flow in order to obtain a desired pressure-gradient
distribution along the edge of the turbulent boundary layer formed on the other
wall, and finally, iii) optimization of the controlling parameters of a
spoiler-ice model to attain the aerodynamic characteristics of the airfoil with
an actual surface ice. The diversity of the optimization problems, independence
of the optimization approach from any adjoint information, the ease of
employing different CFD solvers in the optimization loop, and more importantly,
the relatively small number of the required flow simulations reveal the
flexibility, efficiency, and versatility of the BO-GPR approach in CFD
applications. It is shown that to ensure finding the global optimum of the
design parameters of the size up to 8, less than 90 executions of the CFD
solvers are needed. Furthermore, it is observed that the number of flow
simulations does not significantly increase with the number of design
parameters. The associated computational cost of these simulations can be
affordable for many optimization cases with practical relevance.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.00539
|
Abhijit Roy
|
Abhijit Roy
|
Spatial statistics of superposition of two uncorrelated speckle patterns
with polarization diversity
| null |
Results in Optics 5, 100187 (2021)
|
10.1016/j.rio.2021.100187
| null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A detailed theoretical and experimental study on the effect of the
superposition of uncorrelated speckle patterns with polarization diversity on
the spatial statistics of the superposed speckle pattern is presented. It is
shown that depending on the mutual orientation of the polarization vectors of
the constituent speckle patterns, the maximum degree of coherence (DoC) and
degree of polarization (DoP) of the superposed speckle pattern changes between
a maximum and minimum value in a sinusoidal fashion. Moreover, the average
intensity ratio of the constituent speckle patterns is also found to be
affecting these variations. A study of the change in the visibility of the
two-point intensity correlation function also reveals a sinusoidal nature of
the variation and its dependence on the ratio of the average intensity, which
are found to be similar to the variations of the maximum DoC and DoP. A
detailed study on the changes in the normalized probability density function is
also performed for better understanding of the effect on the spatial
statistics.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709466 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.02504
|
Pierre Alquier
|
Dimitri Meunier and Pierre Alquier
|
Meta-strategy for Learning Tuning Parameters with Guarantees
| null |
Entropy, 2021, vol. 23, no. 10, 1257
|
10.3390/e23101257
| null |
stat.ML cs.LG math.ST stat.CO stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online learning methods, like the online gradient algorithm (OGA) and
exponentially weighted aggregation (EWA), often depend on tuning parameters
that are difficult to set in practice. We consider an online meta-learning
scenario, and we propose a meta-strategy to learn these parameters from past
tasks. Our strategy is based on the minimization of a regret bound. It allows
to learn the initialization and the step size in OGA with guarantees. It also
allows to learn the prior or the learning rate in EWA. We provide a regret
analysis of the strategy. It allows to identify settings where meta-learning
indeed improves on learning each task in isolation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711212 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.03752
|
Yusheng Su
|
Yusheng Su, Xu Han, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Peng Li,
Jie Zhou, Maosong Sun
|
CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of
Pre-trained Language Models
| null |
IEEE/ACM Transactions on Audio, Speech, and Language Processing
2021
|
10.1109/TASLP.2021.3105013
|
2329-9290
|
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fine-tuning pre-trained language models (PLMs) has demonstrated its
effectiveness on various downstream NLP tasks recently. However, in many
low-resource scenarios, the conventional fine-tuning strategies cannot
sufficiently capture the important semantic features for downstream tasks. To
address this issue, we introduce a novel framework (named "CSS-LM") to improve
the fine-tuning phase of PLMs via contrastive semi-supervised learning.
Specifically, given a specific task, we retrieve positive and negative
instances from large-scale unlabeled corpora according to their domain-level
and class-level semantic relatedness to the task. We then perform contrastive
semi-supervised learning on both the retrieved unlabeled and original labeled
instances to help PLMs capture crucial task-related semantic features. The
experimental results show that CSS-LM achieves better results than the
conventional fine-tuning strategy on a series of downstream tasks with few-shot
settings, and outperforms the latest supervised contrastive fine-tuning
strategies. Our datasets and source code will be available to provide more
details.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.702734 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.05367
|
Pierre Marchand
|
Pierre Marchand and Jeffrey Galkowski and Alastair Spence and Euan A.
Spence
|
Applying GMRES to the Helmholtz equation with strong trapping: how does
the number of iterations depend on the frequency?
| null | null | null | null |
math.NA cs.NA math.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider GMRES applied to discretisations of the high-frequency Helmholtz
equation with strong trapping; recall that in this situation the problem is
exponentially ill-conditioned through an increasing sequence of frequencies.
Under certain assumptions about the distribution of the eigenvalues, we prove
upper bounds on how the number of GMRES iterations grows with the frequency.
Our main focus is on boundary-integral-equation formulations of the exterior
Dirichlet and Neumann obstacle problems in 2- and 3-d; for these problems, we
investigate numerically the sharpness (in terms of dependence on frequency) of
both our bounds and various quantities entering our bounds. This paper is
therefore the first comprehensive study of the frequency-dependence of the
number of GMRES iterations for Helmholtz boundary-integral equations under
trapping.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711425 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.05414
|
Florian Kennel-Maushart
|
Florian Kennel-Maushart, Roi Poranne, Stelian Coros
|
Manipulability optimization for multi-arm teleoperation
|
Accepted for presentation at IEEE ICRA 2021, published in the
conference proceedings
|
2021 IEEE International Conference on Robotics and Automation
(ICRA), 2021, pp. 3956-3962
|
10.1109/ICRA48506.2021.9561105
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712676 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.06794
|
Yaofeng Desmond Zhong
|
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
|
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable
Contact Models
| null | null | null | null |
cs.RO cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The incorporation of appropriate inductive bias plays a critical role in
learning dynamics from data. A growing body of work has been exploring ways to
enforce energy conservation in the learned dynamics by encoding Lagrangian or
Hamiltonian dynamics into the neural network architecture. These existing
approaches are based on differential equations, which do not allow
discontinuity in the states and thereby limit the class of systems one can
learn. However, in reality, most physical systems, such as legged robots and
robotic manipulators, involve contacts and collisions, which introduce
discontinuities in the states. In this paper, we introduce a differentiable
contact model, which can capture contact mechanics: frictionless/frictional, as
well as elastic/inelastic. This model can also accommodate inequality
constraints, such as limits on the joint angles. The proposed contact model
extends the scope of Lagrangian and Hamiltonian neural networks by allowing
simultaneous learning of contact and system properties. We demonstrate this
framework on a series of challenging 2D and 3D physical systems with different
coefficients of restitution and friction. The learned dynamics can be used as a
differentiable physics simulator for downstream gradient-based optimization
tasks, such as planning and control.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711406 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.07632
|
Samuele Grillo
|
A. Samson Mogos and Samuele Grillo
|
Impact of EV Charging Stations in Power Grids in Italy and its
Mitigation Mechanisms
| null |
IEEE EEEIC/I&CPS Europe (2021). pp. 1-6
|
10.1109/EEEIC/ICPSEurope51590.2021.9584782
| null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Global warming leads the world to think of a different way of transportation:
avoiding internal combustion engines and electrifying the transportation
sector. With a high penetration of electric vehicle (EV) charging stations on
an existing power distribution network, the impact may be consistent. The loads
of the fast-charging stations would potentially result in increased peak load
demand, reduced reserve margins, voltage instability, and reliability problems.
The degrading performance of the power system due to the negative impact of the
EV charging stations can even lead to penalties to be paid by the distribution
system operator (DSO). This paper: i) investigates the impact of the \ac{ev}
charging station on the distribution network for what concerns voltage drop on
MV feeders and loading of transformers in primary substations, and ii) proposes
a mitigation mechanism. A realistic typical Italian grid has been used to
assess the impact of EV charging stations and to validate the mitigation
mechanism.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711481 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.08231
|
Linda Kleist
|
Moritz Buchem, Linda Kleist, Daniel Schmidt genannt Waldschmidt
|
Scheduling with Machine Conflicts
|
20 pages, 8 figures
| null | null | null |
cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the scheduling problem of makespan minimization while taking machine
conflicts into account. Machine conflicts arise in various settings, e.g.,
shared resources for pre- and post-processing of tasks or spatial restrictions.
In this context, each job has a blocking time before and after its processing
time, i.e., three parameters. We seek for conflict-free schedules in which the
blocking times of no two jobs intersect on conflicting machines. Given a set of
jobs, a set of machines, and a graph representing machine conflicts, the
problem SchedulingWithMachineConflicts (SMC), asks for a conflict-free schedule
of minimum makespan.
We show that, unless $\textrm{P}=\textrm{NP}$, SMC on $m$ machines does not
allow for a $\mathcal{O}(m^{1-\varepsilon})$-approximation algorithm for any
$\varepsilon>0$, even in the case of identical jobs and every choice of fixed
positive parameters, including the unit case. Complementary, we provide
approximation algorithms when a suitable collection of independent sets is
given. Finally, we present polynomial time algorithms to solve the problem for
the case of unit jobs on special graph classes. Most prominently, we solve it
for bipartite graphs by using structural insights for conflict graphs of star
forests.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709435 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.03314
|
Akhil Dixit
|
Akhil A. Dixit and Phokion G. Kolaitis
|
Consistent Answers of Aggregation Queries using SAT Solvers
|
18 pages, 10 figures, 7 tables
| null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The framework of database repairs and consistent answers to queries is a
principled approach to managing inconsistent databases. We describe the first
system able to compute the consistent answers of general aggregation queries
with the COUNT(A), COUNT(*), SUM(A), MIN(A), and MAX(A) operators, and with or
without grouping constructs. Our system uses reductions to optimization
versions of Boolean satisfiability (SAT) and then leverages powerful SAT
solvers. We carry out an extensive set of experiments on both synthetic and
real-world data that demonstrate the usefulness and scalability of this
approach.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.08137
|
Solvi Arnold
|
Solvi Arnold, Daisuke Tanaka, Kimitoshi Yamazaki
|
Cloth Manipulation Planning on Basis of Mesh Representations with
Incomplete Domain Knowledge and Voxel-to-Mesh Estimation
|
27 pages, 13 figures
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We consider the problem of open-goal planning for robotic cloth manipulation.
Core of our system is a neural network trained as a forward model of cloth
behaviour under manipulation, with planning performed through backpropagation.
We introduce a neural network-based routine for estimating mesh representations
from voxel input, and perform planning in mesh format internally. We address
the problem of planning with incomplete domain knowledge by means of an
explicit epistemic uncertainty signal. This signal is calculated from
prediction divergence between two instances of the forward model network and
used to avoid epistemic uncertainty during planning. Finally, we introduce
logic for handling restriction of grasp points to a discrete set of candidates,
in order to accommodate graspability constraints imposed by robotic hardware.
We evaluate the system's mesh estimation, prediction, and planning ability on
simulated cloth for sequences of one to three manipulations. Comparative
experiments confirm that planning on basis of estimated meshes improves
accuracy compared to voxel-based planning, and that epistemic uncertainty
avoidance improves performance under conditions of incomplete domain knowledge.
Planning time cost is a few seconds. We additionally present qualitative
results on robot hardware.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.10346
|
Stefano Savazzi
|
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, Mehdi Bennis
|
A Framework for Energy and Carbon Footprint Analysis of Distributed and
Federated Edge Learning
|
The work has been submitted to the IEEE for possible publication
| null |
10.1109/PIMRC50174.2021.9569307
| null |
cs.LG cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Recent advances in distributed learning raise environmental concerns due to
the large energy needed to train and move data to/from data centers. Novel
paradigms, such as federated learning (FL), are suitable for decentralized
model training across devices or silos that simultaneously act as both data
producers and learners. Unlike centralized learning (CL) techniques, relying on
big-data fusion and analytics located in energy hungry data centers, in FL
scenarios devices collaboratively train their models without sharing their
private data. This article breaks down and analyzes the main factors that
influence the environmental footprint of FL policies compared with classical
CL/Big-Data algorithms running in data centers. The proposed analytical
framework takes into account both learning and communication energy costs, as
well as the carbon equivalent emissions; in addition, it models both vanilla
and decentralized FL policies driven by consensus. The framework is evaluated
in an industrial setting assuming a real-world robotized workplace. Results
show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless
systems characterized by low bit/Joule efficiency (50 kbit/Joule or lower).
Consensus-driven FL does not require the parameter server and further reduces
emissions in mesh networks (200 kbit/Joule). On the other hand, all FL policies
are slower to converge when local data are unevenly distributed (often 2x
slower than CL). Energy footprint and learning loss can be traded off to
optimize efficiency.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710409 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.12719
|
Chaitanya Ryali
|
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
|
Characterizing and Improving the Robustness of Self-Supervised Learning
through Background Augmentations
|
Technical Report; Additional Results
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent progress in self-supervised learning has demonstrated promising
results in multiple visual tasks. An important ingredient in high-performing
self-supervised methods is the use of data augmentation by training models to
place different augmented views of the same image nearby in embedding space.
However, commonly used augmentation pipelines treat images holistically,
ignoring the semantic relevance of parts of an image-e.g. a subject vs. a
background-which can lead to the learning of spurious correlations. Our work
addresses this problem by investigating a class of simple, yet highly effective
"background augmentations", which encourage models to focus on
semantically-relevant content by discouraging them from focusing on image
backgrounds. Through a systematic investigation, we show that background
augmentations lead to substantial improvements in performance across a spectrum
of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety
of tasks, e.g. $\sim$+1-2% gains on ImageNet, enabling performance on par with
the supervised baseline. Further, we find the improvement in limited-labels
settings is even larger (up to 4.2%). Background augmentations also improve
robustness to a number of distribution shifts, including natural adversarial
examples, ImageNet-9, adversarial attacks, ImageNet-Renditions. We also make
progress in completely unsupervised saliency detection, in the process of
generating saliency masks used for background augmentations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.15388
|
Hany Abdulsamad
|
Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu and Jan Peters
|
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics
via Relative Entropy Trust-Regions
| null | null | null | null |
eess.SY cs.RO cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Trajectory optimization and model predictive control are essential techniques
underpinning advanced robotic applications, ranging from autonomous driving to
full-body humanoid control. State-of-the-art algorithms have focused on
data-driven approaches that infer the system dynamics online and incorporate
posterior uncertainty during planning and control. Despite their success, such
approaches are still susceptible to catastrophic errors that may arise due to
statistical learning biases, unmodeled disturbances, or even directed
adversarial attacks. In this paper, we tackle the problem of dynamics mismatch
and propose a distributionally robust optimal control formulation that
alternates between two relative entropy trust-region optimization problems. Our
method finds the worst-case maximum entropy Gaussian posterior over the
dynamics parameters and the corresponding robust policy. Furthermore, we show
that our approach admits a closed-form backward-pass for a certain class of
systems. Finally, we demonstrate the resulting robustness on linear and
nonlinear numerical examples.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.16804
|
Anton Ratnarajah Mr
|
Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha
|
TS-RIR: Translated synthetic room impulse responses for speech
augmentation
|
Accepted to IEEE ASRU 2021. Source code is available at
https://github.com/GAMMA-UMD/TS-RIR
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a method for improving the quality of synthetic room impulse
responses for far-field speech recognition. We bridge the gap between the
fidelity of synthetic room impulse responses (RIRs) and the real room impulse
responses using our novel, TS-RIRGAN architecture. Given a synthetic RIR in the
form of raw audio, we use TS-RIRGAN to translate it into a real RIR. We also
perform real-world sub-band room equalization on the translated synthetic RIR.
Our overall approach improves the quality of synthetic RIRs by compensating
low-frequency wave effects, similar to those in real RIRs. We evaluate the
performance of improved synthetic RIRs on a far-field speech dataset augmented
by convolving the LibriSpeech clean speech dataset [1] with RIRs and adding
background noise. We show that far-field speech augmented using our improved
synthetic RIRs reduces the word error rate by up to 19.9% in Kaldi far-field
automatic speech recognition benchmark [2].
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711606 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.01328
|
Niko S\"underhauf
|
Dimity Miller, Niko S\"underhauf, Michael Milford and Feras Dayoub
|
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
| null |
IEEE Robotics and Automation Letters (January 2022), Volume 7,
Issue 1, pages 215-222, ISSN 2377-3766
|
10.1109/LRA.2021.3123374
| null |
cs.CV cs.AI cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deployed into an open world, object detectors are prone to open-set errors,
false positive detections of object classes not present in the training
dataset. We propose GMM-Det, a real-time method for extracting epistemic
uncertainty from object detectors to identify and reject open-set errors.
GMM-Det trains the detector to produce a structured logit space that is
modelled with class-specific Gaussian Mixture Models. At test time, open-set
errors are identified by their low log-probability under all Gaussian Mixture
Models. We test two common detector architectures, Faster R-CNN and RetinaNet,
across three varied datasets spanning robotics and computer vision. Our results
show that GMM-Det consistently outperforms existing uncertainty techniques for
identifying and rejecting open-set detections, especially at the low-error-rate
operating point required for safety-critical applications. GMM-Det maintains
object detection performance, and introduces only minimal computational
overhead. We also introduce a methodology for converting existing object
detection datasets into specific open-set datasets to evaluate open-set
performance in object detection.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711782 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.04346
|
Jun Yang
|
Ziyong Chen and Jun Yang
|
Nucleus-electron correlation revising molecular bonding fingerprints
from the exact wavefunction factorization
| null | null |
10.1063/5.0056773
| null |
physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel theory and implementation for computing coupled electronic
and quantal nuclear subsystems on a single potential energy surface, moving
beyond the standard Born-Oppenheimer (BO) separation of nuclei and electrons.
We formulate an exact self-consistent nucleus-electron embedding potential from
the single product molecular wavefunction, and demonstrate that the fundamental
behavior of correlated nucleus-electron can be computed for mean-field
electrons that are responsive to a quantal anharmonic vibration of selected
nuclei in a discrete variable representation. Geometric gauge choices are
discussed and necessary for formulating energy invariant biorthogonal
electronic equations. Our method is further applied to characterize
vibrationally averaged molecular bonding properties of molecular energetics,
bond length, protonic and electron density. Moreover, post-Hartree-Fock
electron correlation can be conveniently computed on the basis of
nucleus-electron coupled molecular orbitals, as demonstrated to correlated
models of second-order M{\o}llet-Plesset perturbation and full configuration
interaction theories. Our approach not only accurately quantifies non-classical
nucleus-electron couplings for revising molecular bonding properties, but also
provides an alternative time-independent approach for deploying non-BO
molecular quantum chemistry.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70939 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.08341
|
Li Yang
|
Ruixiang Fei, Wenshen Song, Lauren Pusey-Nazzaro, Li Yang
|
PT-symmetry enabled spin circular photogalvanic effect in
antiferromagnetic insulators
|
18 pages, 4 figures
|
Physical Review Letters,127, 207402 (2021)
|
10.1103/PhysRevLett.127.207402
| null |
cond-mat.mtrl-sci cond-mat.mes-hall physics.optics
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The short timescale spin dynamics in antiferromagnets is an attractive
feature from the standpoint of ultrafast spintronics. Yet generating highly
polarized spin currents at room temperature remains a fundamental challenge for
antiferromagnets. We propose a spin circular photogalvanic effect (spin-CPGE),
in which circularly polarized light can produce a spin current without net
charge current at room temperature, through an "injection-current-like"
mechanism in parity-time(PT)-symmetric antiferromagnetic (AFM) insulators. We
demonstrate this effect by first-principles simulations of bilayer CrI3 and
room-temperature AFM hematite. Our calculations show that the spin-CPGE is
significant, and the magnitude of spin photo-current is comparable with the
widely observed charge photocurrent in ferroelectric materials. Interestingly,
this spin photocurrent is not sensitive to spin-orbit interactions, which were
regarded as fundamental mechanisms for generating spin current. Given the fast
response of light-matter interactions, large energy scale, and insensitivity to
spin-orbit interactions, our work gives hope to realizing a fast-dynamic and
temperature-robust pure spin current in a wide range of PT-symmetric AFM
materials, including weak-relativistic magnetic insulators and topological
axion insulators.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712664 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.08368
|
Ameni Trabelsi
|
Ameni Trabelsi, Ross J. Beveridge and Nathaniel Blanchard
|
Motion Prediction Performance Analysis for Autonomous Driving Systems
and the Effects of Tracking Noise
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous driving consists of a multitude of interacting modules, where each
module must contend with errors from the others. Typically, the motion
prediction module depends upon a robust tracking system to capture each agent's
past movement. In this work, we systematically explore the importance of the
tracking module for the motion prediction task and ultimately conclude that the
overall motion prediction performance is highly sensitive to the tracking
module's imperfections. We explicitly compare models that use tracking
information to models that do not across multiple scenarios and conditions. We
find that the tracking information plays an essential role and improves motion
prediction performance in noise-free conditions. However, in the presence of
tracking noise, it can potentially affect the overall performance if not
studied thoroughly. We thus argue practitioners should be mindful of noise when
developing and testing motion/tracking modules, or that they should consider
tracking free alternatives.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.09637
|
Paola Boito
|
Paola Boito, Roberto Grena
|
Quantum hub and authority centrality measures for directed networks
based on continuous-time quantum walks
| null |
Journal of Complex Networks, Volume 9, Issue 6, December 2021,
cnab038
|
10.1093/comnet/cnab038
| null |
math.NA cs.NA quant-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work we introduce, test and discuss three quantum methods for
computing hub and authority centrality scores in directed networks. The methods
are based on unitary, continuous-time quantum walks; the construction of a
suitable Hermitian Hamiltonian is achieved by performing a quantum walk on the
associated bipartite graph.
Two methods, called CQAu and CQAw, use the same evolution operator, inspired
by the classical HITS algorithm, but with different initial states; the
computation of hub and authority scores is performed simultaneously. The third
method, called CQG and inspired by classical PageRank, requires instead two
separate runs with different evolution operators, one for hub and one for
authority scores.
The methods are tested on several directed graphs with different sizes and
properties; a comparison with other well-established ranking algorithms is
provided. CQAw emerges as the most reliable of the three methods and yields
rankings that are largely compatible with results from HITS, although CQAu and
CQG also present interesting features and potential for applications.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.09801
|
Bishakh Chandra Ghosh
|
Bishakh Chandra Ghosh, Tanay Bhartia, Sourav Kanti Addya, Sandip
Chakraborty
|
Leveraging Public-Private Blockchain Interoperability for Closed
Consortium Interfacing
|
10 pages, 12 figures, accepted for publication in IEEE INFOCOM 2021
|
IEEE INFOCOM 2021
|
10.1109/INFOCOM42981.2021.9488683
| null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing adoption of private blockchain platforms, consortia
operating in various sectors such as trade, finance, logistics, etc., are
becoming common. Despite having the benefits of a completely decentralized
architecture which supports transparency and distributed control, existing
private blockchains limit the data, assets, and processes within its closed
boundary, which restricts secure and verifiable service provisioning to the
end-consumers. Thus, platforms such as e-commerce with multiple sellers or
cloud federation with a collection of cloud service providers cannot be
decentralized with the existing blockchain platforms. This paper proposes a
decentralized gateway architecture interfacing private blockchain with
end-users by leveraging the unique combination of public and private blockchain
platforms through interoperation. Through the use case of decentralized cloud
federations, we have demonstrated the viability of the solution. Our testbed
implementation with Ethereum and Hyperledger Fabric, with three service
providers, shows that such consortium can operate within an acceptable response
latency while scaling up to 64 parallel requests per second for cloud
infrastructure provisioning. Further analysis over the Mininet emulation
platform indicates that the platform can scale well with minimal impact over
the latency as the number of participating service providers increases.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.11856
|
Sangkha Borah
|
Sangkha Borah, Bijita Sarma, Michael Kewming, Gerard J. Milburn and
Jason Twamley
|
Measurement Based Feedback Quantum Control With Deep Reinforcement
Learning for Double-well Non-linear Potential
|
5 pages, 2 figures, journal article with supplementary materials
|
Phys. Rev. Lett., 127 (2021), 190403
|
10.1103/PhysRevLett.127.190403
| null |
quant-ph physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Closed loop quantum control uses measurement to control the dynamics of a
quantum system to achieve either a desired target state or target dynamics. In
the case when the quantum Hamiltonian is quadratic in ${x}$ and ${p}$, there
are known optimal control techniques to drive the dynamics towards particular
states e.g. the ground state. However, for nonlinear Hamiltonians such control
techniques often fail. We apply Deep Reinforcement Learning (DRL), where an
artificial neural agent explores and learns to control the quantum evolution of
a highly non-linear system (double well), driving the system towards the ground
state with high fidelity. We consider a DRL strategy which is particularly
motivated by experiment where the quantum system is continuously but weakly
measured. This measurement is then fed back to the neural agent and used for
training. We show that the DRL can effectively learn counter-intuitive
strategies to cool the system to a nearly-pure `cat' state which has a high
overlap fidelity with the true ground state.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709868 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.12709
|
Mohamed Afham
|
Mohamed Afham, Salman Khan, Muhammad Haris Khan, Muzammal Naseer,
Fahad Shahbaz Khan
|
Rich Semantics Improve Few-shot Learning
|
Accepted to 32nd British Machine Vision Conference (BMVC 2021)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human learning benefits from multi-modal inputs that often appear as rich
semantics (e.g., description of an object's attributes while learning about
it). This enables us to learn generalizable concepts from very limited visual
examples. However, current few-shot learning (FSL) methods use numerical class
labels to denote object classes which do not provide rich semantic meanings
about the learned concepts. In this work, we show that by using 'class-level'
language descriptions, that can be acquired with minimal annotation cost, we
can improve the FSL performance. Given a support set and queries, our main idea
is to create a bottleneck visual feature (hybrid prototype) which is then used
to generate language descriptions of the classes as an auxiliary task during
training. We develop a Transformer based forward and backward encoding
mechanism to relate visual and semantic tokens that can encode intricate
relationships between the two modalities. Forcing the prototypes to retain
semantic information about class description acts as a regularizer on the
visual features, improving their generalization to novel classes at inference.
Furthermore, this strategy imposes a human prior on the learned
representations, ensuring that the model is faithfully relating visual and
semantic concepts, thereby improving model interpretability. Our experiments on
four datasets and ablation studies show the benefit of effectively modeling
rich semantics for FSL.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709617 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.04677
|
Th\'ea De Seauve
|
Th\'ea de Seauve, Vincent Detalle, Alexandre Semerok, S\'ebastien Aze,
Olivier Grauby, Sophie Bosonnet, Kevin Ginestar, Jean-Marc Vallet
|
Continuous wave laser thermal restoration of oxidized lead-based
pigments in mural paintings
| null |
Appl. Phys. B 127, 162 (2021)
|
10.1007/s00340-021-07702-w
| null |
physics.chem-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Red lead and lead white are some of the most ancient and common pigments in
mural paintings. However, they tend to blacken with time due to their oxidation
to plattnerite (\b{eta}-PbO2). The possibility to induce the reconversion
reactions by CW laser heating is hereby discussed. A thermodynamic study by TGA
showed that direct cerussite or hydrocerussite formation from plattnerite are
not suitable reconversion routes, which was confirmed by laser irradiation
trials under CO2 and CO2/H2O fluxes. Minium (Pb3O4) and subsequent massicot
(\b{eta}-PbO) formation from plattnerite were achieved (confirmed by SEM-EDS,
XRD and micro-Raman) under Ar+, 810 nm diode and Nd:YAG lasers. The latter
appears to be the most suited for restauration purposes, given the broad minium
reconversion irradiance range. This is confirmed by successful trials on
macroscopic areas of naturally darkened red lead containing samples.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706836 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.04696
|
Zhichao Ruan
|
Junyi Huang, Yisheng Fang, and Zhichao Ruan
|
Antiferromagnetic spatial photonic Ising machine through optoelectronic
correlation computing
|
7 pages, 4 figures
|
Communications Physics, 4, 242 (2021)
|
10.1038/s42005-021-00741-x
| null |
cs.ET cond-mat.dis-nn physics.app-ph physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, spatial photonic Ising machines (SPIM) have been demonstrated to
compute the minima of Hamiltonians for large-scale spin systems. Here we
propose to implement an antiferromagnetic model through optoelectronic
correlation computing with SPIM. Also we exploit the gauge transformation which
enables encoding the spins and the interaction strengths in a single phase-only
spatial light modulator. With a simple setup, we experimentally show the ground
state search of an antiferromagnetic model with $40000$ spins in
number-partitioning problem. Thus such an optoelectronic computing exhibits
great programmability and scalability for the practical applications of
studying statistical systems and combinatorial optimization problems.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71022 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.04848
|
Fouad Trad
|
Salah El Falou, Fouad Trad
|
Forecast Analysis of the COVID-19 Incidence in Lebanon: Prediction of
Future Epidemiological Trends to Plan More Effective Control Programs
| null | null |
10.1109/ICABME53305.2021.9604861
| null |
cs.AI cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Ever since the COVID-19 pandemic started, all the governments have been
trying to limit its effects on their citizens and countries. This pandemic was
harsh on different levels for almost all populations worldwide and this is what
drove researchers and scientists to get involved and work on several kinds of
simulations to get a better insight into this virus and be able to stop it the
earliest possible. In this study, we simulate the spread of COVID-19 in Lebanon
using an Agent-Based Model where people are modeled as agents that have
specific characteristics and behaviors determined from statistical
distributions using Monte Carlo Algorithm. These agents can go into the world,
interact with each other, and thus, infect each other. This is how the virus
spreads. During the simulation, we can introduce different Non-Pharmaceutical
Interventions - or more commonly NPIs - that aim to limit the spread of the
virus (wearing a mask, closing locations, etc). Our Simulator was first
validated on concepts (e.g. Flattening the Curve and Second Wave scenario), and
then it was applied on the case of Lebanon. We studied the effect of opening
schools and universities on the pandemic situation in the country since the
Lebanese Ministry of Education is planning to do so progressively, starting
from 21 April 2021. Based on the results we obtained, we conclude that it would
be better to delay the school openings while the vaccination campaign is still
slow in the country.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710415 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.07518
|
Shunhua Jiang
|
Yi-Jun Chang, Ran Duan, Shunhua Jiang
|
Near-Optimal Time-Energy Trade-Offs for Deterministic Leader Election
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the energy complexity of the leader election problem in the
single-hop radio network model, where each device has a unique identifier in
$\{1, 2, \ldots, N\}$. Energy is a scarce resource for small battery-powered
devices. For such devices, most of the energy is often spent on communication,
not on computation. To approximate the actual energy cost, the energy
complexity of an algorithm is defined as the maximum over all devices of the
number of time slots where the device transmits or listens. Much progress has
been made in understanding the energy complexity of leader election in radio
networks, but very little is known about the trade-off between time and energy.
$\textbf{Time-energy trade-off:}$ For any $k \geq \log \log N$, we show that
a leader among at most $n$ devices can be elected deterministically in $O(k
\cdot n^{1+\epsilon}) + O(k \cdot N^{1/k})$ time and $O(k)$ energy if each
device can simultaneously transmit and listen, where $\epsilon > 0$ is any
small constant. This improves upon the previous $O(N)$-time $O(\log \log
N)$-energy algorithm by Chang et al. [STOC 2017]. We provide lower bounds to
show that the time-energy trade-off of our algorithm is near-optimal.
$\textbf{Dense instances:}$ For the dense instances where the number of
devices is $n = \Theta(N)$, we design a deterministic leader election algorithm
using only $O(1)$ energy. This improves upon the $O(\log^* N)$-energy algorithm
by Jurdzi\'{n}ski et al. [PODC 2002] and the $O(\alpha(N))$-energy algorithm by
Chang et al. [STOC 2017]. More specifically, we show that the optimal
deterministic energy complexity of leader election is
$\Theta\left(\max\left\{1, \log \frac{N}{n}\right\}\right)$ if the devices
cannot simultaneously transmit and listen, and it is $\Theta\left(\max\left\{1,
\log \log \frac{N}{n}\right\}\right)$ if they can.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70724 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.10319
|
Kunihiro Miyazaki
|
Kunihiro Miyazaki, Takayuki Uchiba, Kenji Tanaka, Kazutoshi Sasahara
|
Characterizing the Anti-Vaxxers' Reply Behavior on Social Media
|
ABCSS (WI-IAT'21 Companion). 11 pages, 2 figures
| null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although the online campaigns of anti-vaccine advocates, or anti-vaxxers,
severely threaten efforts for herd immunity, their reply behavior--the form of
directed messaging that can be sent beyond follow-follower
relationships--remains poorly understood. Here, we examined the characteristics
of anti-vaxxers' reply behavior on Twitter to attempt to comprehend their
characteristics of spreading their beliefs in terms of interaction frequency,
content, and targets. Among the results, anti-vaxxers more frequently conducted
reply behavior with other clusters, especially neutral accounts. Anti-vaxxers'
replies were significantly more toxic than those from neutral accounts and
pro-vaxxers, and their toxicity, in particular, was higher with regard to the
rollout of vaccines. Anti-vaxxers' replies were more persuasive than the others
in terms of the emotional aspect, rather than linguistical styles. The targets
of anti-vaxxers' replies tend to be accounts with larger numbers of followers
and posts, including accounts that relate to health care or represent
scientists, policy-makers, or media figures or outlets. We discussed how their
reply behaviors are effective in spreading their beliefs, as well as possible
countermeasures to restrain them. These findings should prove useful for
pro-vaxxers and platformers to promote trusted information while reducing the
effect of vaccine disinformation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711443 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.14250
|
Mikhail Usvyatsov
|
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim
Rakhuba, Andreas Krause, Konrad Schindler
|
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data
via Differentiable Cross-Approximation
| null |
Proc. International Conference on Computer Vision (ICCV) 2021
| null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We propose an end-to-end trainable framework that processes large-scale
visual data tensors by looking at a fraction of their entries only. Our method
combines a neural network encoder with a tensor train decomposition to learn a
low-rank latent encoding, coupled with cross-approximation (CA) to learn the
representation through a subset of the original samples. CA is an adaptive
sampling algorithm that is native to tensor decompositions and avoids working
with the full high-resolution data explicitly. Instead, it actively selects
local representative samples that we fetch out-of-core and on-demand. The
required number of samples grows only logarithmically with the size of the
input. Our implicit representation of the tensor in the network enables
processing large grids that could not be otherwise tractable in their
uncompressed form. The proposed approach is particularly useful for large-scale
multidimensional grid data (e.g., 3D tomography), and for tasks that require
context over a large receptive field (e.g., predicting the medical condition of
entire organs). The code is available at https://github.com/aelphy/c-pic.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.05275
|
Brendan Ross
|
Brendan Leigh Ross, Jesse C. Cresswell
|
Tractable Density Estimation on Learned Manifolds with Conformal
Embedding Flows
|
NeurIPS 2021 Camera-Ready. Code: https://github.com/layer6ai-labs/CEF
| null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Normalizing flows are generative models that provide tractable density
estimation via an invertible transformation from a simple base distribution to
a complex target distribution. However, this technique cannot directly model
data supported on an unknown low-dimensional manifold, a common occurrence in
real-world domains such as image data. Recent attempts to remedy this
limitation have introduced geometric complications that defeat a central
benefit of normalizing flows: exact density estimation. We recover this benefit
with Conformal Embedding Flows, a framework for designing flows that learn
manifolds with tractable densities. We argue that composing a standard flow
with a trainable conformal embedding is the most natural way to model
manifold-supported data. To this end, we present a series of conformal building
blocks and apply them in experiments with synthetic and real-world data to
demonstrate that flows can model manifold-supported distributions without
sacrificing tractable likelihoods.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711005 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.06149
|
Jun Yang
|
Qiujiang Liang, Jun Yang
|
Third-order many-body expansion of OSV-MP2 wavefunction for low-order
scaling analytical gradient computation
| null | null |
10.1021/acs.jctc.1c00581
| null |
physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a many-body expansion (MBE) formulation and implementation for
efficient computation of analytical energy gradients from OSV-MP2 theory based
on our earlier work (Zhou et al. J. Chem. Theory Comput. 2020, 16, 196-210).
The third-order MBE(3) expansion of OSV-MP2 wavefunction was developed to adopt
the orbital-specific clustering and long-range termination schemes, which
avoids term-by-term differentiations of the MBE energy bodies. We achieve
better efficiency by exploiting the algorithmic sparsity that allows to prune
out insignificant fitting integrals and OSV relaxations. With these
approximations, the present implementation is benchmarked on a range of
molecules that show an economic scaling in the linear and quadratic regimes for
computing MBE(3)-OSV-MP2 amplitude and gradient equations, respectively, and
yields normal accuracy comparable to the original OSV-MP2 results. The
MPI-3-based parallelism through shared memory one-sided communication is
further developed for improving parallel scalability and memory accessibility
by sorting the MBE(3) orbital clusters into independent tasks that are
distributed on multiple processes across many nodes, supporting both global and
local data locations in which selected MBE(3)-OSV-MP2 intermediates of
different sizes are distinguished and accordingly placed. The accuracy and
efficiency level of our MBE(3)-OSV-MP2 analytical gradient implementation is
finally illustrated in two applications: we show that the subtle coordination
structure differences of mechanically interlocked Cu-catenane complexes can be
distinguished when tuning ligand lengths; and the porphycene molecular dynamics
reveals the emergence of the vibrational signature arising from softened N-H
stretching associated with hydrogen transfer, using an MP2 level of electron
correlation and classical nuclei for the first time.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.06881
|
Adam Rumpf
|
Adam Rumpf (Florida Polytechnic University) and Hemanshu Kaul
(Illinois Institute of Technology)
|
A public transit network optimization model for equitable access to
social services
|
38 pages, 6 figures; replaced missing ancillary files; updated author
academic affiliations and replaced figures with colorblind-friendly versions.
In Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21),
October 5-9, 2021. ACM, New York, NY, USA
| null |
10.1145/3465416.3483288
| null |
math.OC cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We present a flexible public transit network design model which optimizes a
social access objective while guaranteeing that the system's costs and transit
times remain within a preset margin of their current levels. The purpose of the
model is to find a set of minor, immediate modifications to an existing bus
network that can give more communities access to the chosen services while
having a minimal impact on the current network's operator costs and user costs.
Design decisions consist of reallocation of existing resources in order to
adjust line frequencies and capacities. We present a hybrid tabu
search/simulated annealing algorithm for the solution of this
optimization-based model. As a case study we apply the model to the problem of
improving equity of access to primary health care facilities in the Chicago
metropolitan area. The results of the model suggest that it is possible to
achieve better primary care access equity through reassignment of existing
buses and implementation of express runs, while leaving overall service levels
relatively unaffected.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708364 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.08537
|
Kevin Tian
|
Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin
Tian
|
Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean
Estimation
|
64 pages, 1 figure. v2 improves results on bounded-covariance
clustering, polishes exposition
| null | null | null |
cs.DS cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the problem of list-decodable mean estimation, where an adversary
can corrupt a majority of the dataset. Specifically, we are given a set $T$ of
$n$ points in $\mathbb{R}^d$ and a parameter $0< \alpha <\frac 1 2$ such that
an $\alpha$-fraction of the points in $T$ are i.i.d. samples from a
well-behaved distribution $\mathcal{D}$ and the remaining $(1-\alpha)$-fraction
are arbitrary. The goal is to output a small list of vectors, at least one of
which is close to the mean of $\mathcal{D}$. We develop new algorithms for
list-decodable mean estimation, achieving nearly-optimal statistical
guarantees, with running time $O(n^{1 + \epsilon_0} d)$, for any fixed
$\epsilon_0 > 0$. All prior algorithms for this problem had additional
polynomial factors in $\frac 1 \alpha$. We leverage this result, together with
additional techniques, to obtain the first almost-linear time algorithms for
clustering mixtures of $k$ separated well-behaved distributions,
nearly-matching the statistical guarantees of spectral methods. Prior
clustering algorithms inherently relied on an application of $k$-PCA, thereby
incurring runtimes of $\Omega(n d k)$. This marks the first runtime improvement
for this basic statistical problem in nearly two decades.
The starting point of our approach is a novel and simpler near-linear time
robust mean estimation algorithm in the $\alpha \to 1$ regime, based on a
one-shot matrix multiplicative weights-inspired potential decrease. We
crucially leverage this new algorithmic framework in the context of the
iterative multi-filtering technique of Diakonikolas et al. '18, '20, providing
a method to simultaneously cluster and downsample points using one-dimensional
projections -- thus, bypassing the $k$-PCA subroutines required by prior
algorithms.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70724 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.09295
|
Andrea Rizzi
|
Andrea Rizzi, Paolo Carloni, Michele Parrinello
|
Targeted free energy perturbation revisited: Accurate free energies from
mapped reference potentials
|
Main Text: 7 pages, 2 figures, 18 equations. Supplemental Material: 4
pages, 26 equations
|
J. Phys. Chem. Lett. 12 (2021) 9449-9454
|
10.1021/acs.jpclett.1c02135
| null |
physics.comp-ph physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present an approach that extends the theory of targeted free energy
perturbation (TFEP) to calculate free energy differences and free energy
surfaces at an accurate quantum mechanical level of theory from a cheaper
reference potential. The convergence is accelerated by a mapping function that
increases the overlap between the target and the reference distributions.
Building on recent work, we show that this map can be learned with a
normalizing flow neural network, without requiring simulations with the
expensive target potential but only a small number of single-point
calculations, and, crucially, avoiding the systematic error that was found
previously. We validate the method by numerically evaluating the free energy
difference in a system with a double-well potential and by describing the free
energy landscape of a simple chemical reaction in the gas phase.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709384 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.12447
|
Roland Zimmermann
|
Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge,
Thomas S. A. Wallis, Wieland Brendel
|
How Well do Feature Visualizations Support Causal Understanding of CNN
Activations?
|
Presented at NeurIPS 2021. Shared first and last authorship. Project
website at
https://brendel-group.github.io/causal-understanding-via-visualizations/
| null | null | null |
cs.CV cs.AI cs.HC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A precise understanding of why units in an artificial network respond to
certain stimuli would constitute a big step towards explainable artificial
intelligence. One widely used approach towards this goal is to visualize unit
responses via activation maximization. These synthetic feature visualizations
are purported to provide humans with precise information about the image
features that cause a unit to be activated - an advantage over other
alternatives like strongly activating natural dataset samples. If humans indeed
gain causal insight from visualizations, this should enable them to predict the
effect of an intervention, such as how occluding a certain patch of the image
(say, a dog's head) changes a unit's activation. Here, we test this hypothesis
by asking humans to decide which of two square occlusions causes a larger
change to a unit's activation. Both a large-scale crowdsourced experiment and
measurements with experts show that on average the extremely activating feature
visualizations by Olah et al. (2017) indeed help humans on this task ($68 \pm
4$% accuracy; baseline performance without any visualizations is $60 \pm 3$%).
However, they do not provide any substantial advantage over other
visualizations (such as e.g. dataset samples), which yield similar performance
($66\pm3$% to $67 \pm3$% accuracy). Taken together, we propose an objective
psychophysical task to quantify the benefit of unit-level interpretability
methods for humans, and find no evidence that a widely-used feature
visualization method provides humans with better "causal understanding" of unit
activations than simple alternative visualizations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710785 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.03380
|
Rutav Shah
|
Rutav Shah, Vikash Kumar
|
RRL: Resnet as representation for Reinforcement Learning
|
Published at ICML 2021
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The ability to autonomously learn behaviors via direct interactions in
uninstrumented environments can lead to generalist robots capable of enhancing
productivity or providing care in unstructured settings like homes. Such
uninstrumented settings warrant operations only using the robot's
proprioceptive sensor such as onboard cameras, joint encoders, etc which can be
challenging for policy learning owing to the high dimensionality and partial
observability issues. We propose RRL: Resnet as representation for
Reinforcement Learning -- a straightforward yet effective approach that can
learn complex behaviors directly from proprioceptive inputs. RRL fuses features
extracted from pre-trained Resnet into the standard reinforcement learning
pipeline and delivers results comparable to learning directly from the state.
In a simulated dexterous manipulation benchmark, where the state of the art
methods fail to make significant progress, RRL delivers contact rich behaviors.
The appeal of RRL lies in its simplicity in bringing together progress from the
fields of Representation Learning, Imitation Learning, and Reinforcement
Learning. Its effectiveness in learning behaviors directly from visual inputs
with performance and sample efficiency matching learning directly from the
state, even in complex high dimensional domains, is far from obvious.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710396 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.04004
|
Yunzhu Li
|
Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, Antonio
Torralba
|
3D Neural Scene Representations for Visuomotor Control
|
Accepted to Conference on Robot Learning (CoRL 2021) as Oral
Presentation. The first two authors contributed equally. Project Page:
https://3d-representation-learning.github.io/nerf-dy/
| null | null | null |
cs.RO cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans have a strong intuitive understanding of the 3D environment around us.
The mental model of the physics in our brain applies to objects of different
materials and enables us to perform a wide range of manipulation tasks that are
far beyond the reach of current robots. In this work, we desire to learn models
for dynamic 3D scenes purely from 2D visual observations. Our model combines
Neural Radiance Fields (NeRF) and time contrastive learning with an
autoencoding framework, which learns viewpoint-invariant 3D-aware scene
representations. We show that a dynamics model, constructed over the learned
representation space, enables visuomotor control for challenging manipulation
tasks involving both rigid bodies and fluids, where the target is specified in
a viewpoint different from what the robot operates on. When coupled with an
auto-decoding framework, it can even support goal specification from camera
viewpoints that are outside the training distribution. We further demonstrate
the richness of the learned 3D dynamics model by performing future prediction
and novel view synthesis. Finally, we provide detailed ablation studies
regarding different system designs and qualitative analysis of the learned
representations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.04238
|
Michel Fliess
|
Michel Fliess, C\'edric Join, Dominique Sauter
|
Defense against DoS and load altering attacks via model-free control: A
proposal for a new cybersecurity setting
|
5th International Conference on Control and Fault-Tolerant Systems,
Saint-Rapha\"{e}l, France, September 29th - October 1st, 2021
| null |
10.1109/SysTol52990.2021.9595717
| null |
eess.SY cs.SY math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
Defense against cyberattacks is an emerging topic related to fault-tolerant
control. In order to avoid difficult mathematical modeling, model-free control
(MFC) is suggested as an alternative to classical control. For illustration
purpose a Load Frequency Control of multi-areas power network is considered. In
the simulations, load altering attacks and Denial of Service (DoS) in the
communication network are applied to the system. Our aim is to compare the
impact of cyberattacks on control loops closed via respectively a classical
controller in such situations and a model-free one. Computer experiments show
impressive results with MFC.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708168 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.07467
|
Tianyi Chen
|
Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang, Zhihui Zhu,
Luming Liang, Yixin Shi, Sheng Yi, Xiao Tu
|
Only Train Once: A One-Shot Neural Network Training And Pruning
Framework
|
Accepted by NeurIPS 2021
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Structured pruning is a commonly used technique in deploying deep neural
networks (DNNs) onto resource-constrained devices. However, the existing
pruning methods are usually heuristic, task-specified, and require an extra
fine-tuning procedure. To overcome these limitations, we propose a framework
that compresses DNNs into slimmer architectures with competitive performances
and significant FLOPs reductions by Only-Train-Once (OTO). OTO contains two
keys: (i) we partition the parameters of DNNs into zero-invariant groups,
enabling us to prune zero groups without affecting the output; and (ii) to
promote zero groups, we then formulate a structured-sparsity optimization
problem and propose a novel optimization algorithm, Half-Space Stochastic
Projected Gradient (HSPG), to solve it, which outperforms the standard proximal
methods on group sparsity exploration and maintains comparable convergence. To
demonstrate the effectiveness of OTO, we train and compress full models
simultaneously from scratch without fine-tuning for inference speedup and
parameter reduction, and achieve state-of-the-art results on VGG16 for CIFAR10,
ResNet50 for CIFAR10 and Bert for SQuAD and competitive result on ResNet50 for
ImageNet. The source code is available at
https://github.com/tianyic/only_train_once.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.11629
|
Ge-Peng Ji
|
Yi Zhang
|
ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic
Videos
|
22 pages, 17 figures, 7 tables (Project Page:
https://github.com/PanoAsh/ASOD60K) [new revision]
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Exploring to what humans pay attention in dynamic panoramic scenes is useful
for many fundamental applications, including augmented reality (AR) in retail,
AR-powered recruitment, and visual language navigation. With this goal in mind,
we propose PV-SOD, a new task that aims to segment salient objects from
panoramic videos. In contrast to existing fixation-/object-level saliency
detection tasks, we focus on audio-induced salient object detection (SOD),
where the salient objects are labeled with the guidance of audio-induced eye
movements. To support this task, we collect the first large-scale dataset,
named ASOD60K, which contains 4K-resolution video frames annotated with a
six-level hierarchy, thus distinguishing itself with richness, diversity and
quality. Specifically, each sequence is marked with both its super-/sub-class,
with objects of each sub-class being further annotated with human eye
fixations, bounding boxes, object-/instance-level masks, and associated
attributes (e.g., geometrical distortion). These coarse-to-fine annotations
enable detailed analysis for PV-SOD modelling, e.g., determining the major
challenges for existing SOD models, and predicting scanpaths to study the
long-term eye fixation behaviors of humans. We systematically benchmark 11
representative approaches on ASOD60K and derive several interesting findings.
We hope this study could serve as a good starting point for advancing SOD
research towards panoramic videos. The dataset and benchmark will be made
publicly available at https://github.com/PanoAsh/ASOD60K.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.14322
|
Enrico Camporeale
|
Enrico Camporeale, George J. Wilkie, Alexander Drozdov, Jacob Bortnik
|
Machine-learning based discovery of missing physical processes in
radiation belt modeling
|
9 pages, 8 figures, under review
| null | null | null |
physics.space-ph physics.plasm-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Real-time prediction of the dynamics of energetic electrons in Earth's
radiation belts incorporating incomplete observation data is important to
protect valuable artificial satellites and to understand their physical
processes. Traditionally, reduced models have employed a diffusion equation
based on the quasilinear approximation. Using a Physics-Informed Neural Network
(PINN) framework, we train and test a model based on Van Allen Probe data. We
present a recipe for gleaning physical insight from solving the ill-posed
inverse problem of inferring model coefficients from data using PINNs. With
this, it is discovered that the dynamics of "killer electrons" is described
more accurately instead by a drift-diffusion equation. A parameterization for
the diffusion and drift coefficients, which is both simpler and more accurate
than existing models, is presented.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710176 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.12173
|
Wenxuan Zou
|
Wenxuan Zou, Muyi Sun
|
CoCo DistillNet: a Cross-layer Correlation Distillation Network for
Pathological Gastric Cancer Segmentation
| null | null | null | null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, deep convolutional neural networks have made significant
advances in pathology image segmentation. However, pathology image segmentation
encounters with a dilemma in which the higher-performance networks generally
require more computational resources and storage. This phenomenon limits the
employment of high-accuracy networks in real scenes due to the inherent
high-resolution of pathological images. To tackle this problem, we propose CoCo
DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation
network for pathological gastric cancer segmentation. Knowledge distillation, a
general technique which aims at improving the performance of a compact network
through knowledge transfer from a cumbersome network. Concretely, our CoCo
DistillNet models the correlations of channel-mixed spatial similarity between
different layers and then transfers this knowledge from a pre-trained
cumbersome teacher network to a non-trained compact student network. In
addition, we also utilize the adversarial learning strategy to further prompt
the distilling procedure which is called Adversarial Distillation (AD).
Furthermore, to stabilize our training procedure, we make the use of the
unsupervised Paraphraser Module (PM) to boost the knowledge paraphrase in the
teacher network. As a result, extensive experiments conducted on the Gastric
Cancer Segmentation Dataset demonstrate the prominent ability of CoCo
DistillNet which achieves state-of-the-art performance.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.00383
|
Aleksandra Tucholska
|
Aleksandra M. Tucholska, Robert Moszynski
|
Molecular properties from the explicitly connected expressions of the
response functions within the coupled-cluster theory
|
35 pages, 1 figure
| null |
10.1016/bs.aiq.2021.05.009
| null |
physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We review the methods based on expectation value coupled cluster formalism -
a common framework for the derivation of properties: the ground-state average
value of an observable, cumulants of the second-order reduced density matrices,
polarization propagator, quadratic response function, and transition
probabilities.
We discuss the approximations and give examples of the most important
numerical results.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.00917
|
Eduardo Andreetta Fontana
|
Eduardo Andreetta Fontana and Fabio Petrillo
|
Mapping breakpoint types: an exploratory study
| null | null |
10.5281/zenodo.5663903
| null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Debugging is a relevant task for finding bugs during software development,
maintenance, and evolution. During debugging, developers use modern IDE
debuggers to analyze variables, step execution, and set breakpoints. Observing
IDE debuggers, we find several breakpoint types. However, what are the
breakpoint types? The goal of our study is to map the breakpoint types among
IDEs and academic literature. Thus, we mapped the gray literature on the
documentation of the nine main IDEs used by developers according to the three
public rankings. In addition, we performed a systematic mapping of academic
literature over 68 articles describing breakpoint types. Finally, we analyzed
the developers understanding of the main breakpoint types through a
questionnaire. We present three main contributions: (1) the mapping of
breakpoint types (IDEs and literature), (2) compiled definitions of breakpoint
types, (3) a breakpoint type taxonomy. Our contributions provide the first step
to organize breakpoint IDE taxonomy and lexicon, and support further debugging
research.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.02679
|
Tobias Dieselhorst
|
Tobias Dieselhorst, William Cook, Sebastiano Bernuzzi, David Radice
|
Machine Learning for Conservative-to-Primitive in Relativistic
Hydrodynamics
|
17 pages, 12 figures, 2 tables
|
Symmetry 2021, 13(11), 2157
|
10.3390/sym13112157
| null |
astro-ph.IM physics.comp-ph physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
The numerical solution of relativistic hydrodynamics equations in
conservative form requires root-finding algorithms that invert the
conservative-to-primitive variables map. These algorithms employ the equation
of state of the fluid and can be computationally demanding for applications
involving sophisticated microphysics models, such as those required to
calculate accurate gravitational wave signals in numerical relativity
simulations of binary neutron stars. This work explores the use of machine
learning methods to speed up the recovery of primitives in relativistic
hydrodynamics. Artificial neural networks are trained to replace either the
interpolations of a tabulated equation of state or directly the
conservative-to-primitive map. The application of these neural networks to
simple benchmark problems shows that both approaches improve over traditional
root finders with tabular equation-of-state and multi-dimensional
interpolations. In particular, the neural networks for the
conservative-to-primitive map accelerate the variable recovery by more than an
order of magnitude over standard methods while maintaining accuracy. Neural
networks are thus an interesting option to improve the speed and robustness of
relativistic hydrodynamics algorithms.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710867 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.02738
|
Shirley Anugrah Hayati
|
Shirley Anugrah Hayati, Dongyeop Kang, Lyle Ungar
|
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles
through Lexica
|
Accepted at EMNLP 2021 Main Conference, updated typos and Appendix
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
People convey their intention and attitude through linguistic styles of the
text that they write. In this study, we investigate lexicon usages across
styles throughout two lenses: human perception and machine word importance,
since words differ in the strength of the stylistic cues that they provide. To
collect labels of human perception, we curate a new dataset, Hummingbird, on
top of benchmarking style datasets. We have crowd workers highlight the
representative words in the text that makes them think the text has the
following styles: politeness, sentiment, offensiveness, and five emotion types.
We then compare these human word labels with word importance derived from a
popular fine-tuned style classifier like BERT. Our results show that the BERT
often finds content words not relevant to the target style as important words
used in style prediction, but humans do not perceive the same way even though
for some styles (e.g., positive sentiment and joy) human- and
machine-identified words share significant overlap for some styles.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.712432 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.04951
|
Samuele Grillo
|
Andrea Petriccioli (1) and Samuele Grillo (1) and David Comunello (2)
and Andrea Cacace (2) ((1) Politecnico di Milano, DEIB, Italy, (2) ABB S.p.A.
)
|
Development and Validation of a Scalable Fast Load Shedding Technique
for Industrial Power Systems
| null |
IEEE EEEIC/I&CPS Europe (2021). pp. 1-5
|
10.1109/EEEIC/ICPSEurope51590.2021.9584758
| null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The work aims to improve the existing fast load shedding algorithm for
industrial power system to increase performance, reliability, and scalability
for future expansions. The paper illustrates the development of a scalable
algorithm to compute the shedding matrix, and the test performed on a model of
the electric grid of an offshore platform. From this model it is possible to
study the impact on the transients of various parameters, such as spinning
reserve and delay time. Subsequently, the code is converted into Structured
Text and implemented on an ABB PLC. The scalability of the load shedding
algorithm is thus verified, confirming its performance with respect to the
computation of the shedding matrix and the usefulness of the dynamic
simulations during the design phase of the plant.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707771 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.08865
|
Jie Gu
|
Qinghui Sun, Jie Gu, Bei Yang, XiaoXiao Xu, Renjun Xu, Shangde Gao,
Hong Liu, Huan Xu
|
Interest-oriented Universal User Representation via Contrastive Learning
|
8 pages, during peer review
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
User representation is essential for providing high-quality commercial
services in industry. Universal user representation has received many interests
recently, with which we can be free from the cumbersome work of training a
specific model for each downstream application. In this paper, we attempt to
improve universal user representation from two points of views. First, a
contrastive self-supervised learning paradigm is presented to guide the
representation model training. It provides a unified framework that allows for
long-term or short-term interest representation learning in a data-driven
manner. Moreover, a novel multi-interest extraction module is presented. The
module introduces an interest dictionary to capture principal interests of the
given user, and then generate his/her interest-oriented representations via
behavior aggregation. Experimental results demonstrate the effectiveness and
applicability of the learned user representations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710672 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.01644
|
Suhwan Cho
|
Suhwan Cho, Heansung Lee, Minjung Kim, Sungjun Jang, Sangyoun Lee
|
Pixel-Level Bijective Matching for Video Object Segmentation
|
WACV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Semi-supervised video object segmentation (VOS) aims to track the designated
objects present in the initial frame of a video at the pixel level. To fully
exploit the appearance information of an object, pixel-level feature matching
is widely used in VOS. Conventional feature matching runs in a surjective
manner, i.e., only the best matches from the query frame to the reference frame
are considered. Each location in the query frame refers to the optimal location
in the reference frame regardless of how often each reference frame location is
referenced. This works well in most cases and is robust against rapid
appearance variations, but may cause critical errors when the query frame
contains background distractors that look similar to the target object. To
mitigate this concern, we introduce a bijective matching mechanism to find the
best matches from the query frame to the reference frame and vice versa. Before
finding the best matches for the query frame pixels, the optimal matches for
the reference frame pixels are first considered to prevent each reference frame
pixel from being overly referenced. As this mechanism operates in a strict
manner, i.e., pixels are connected if and only if they are the sure matches for
each other, it can effectively eliminate background distractors. In addition,
we propose a mask embedding module to improve the existing mask propagation
method. By embedding multiple historic masks with coordinate information, it
can effectively capture the position information of a target object.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.714634 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.02011
|
Zhirong Ye
|
Zhirong Ye, Xiangdong Wang, Hong Liu, Yueliang Qian, Rui Tao, Long
Yan, Kazushige Ouchi
|
Sound Event Detection Transformer: An Event-based End-to-End Model for
Sound Event Detection
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sound event detection (SED) has gained increasing attention with its wide
application in surveillance, video indexing, etc. Existing models in SED mainly
generate frame-level prediction, converting it into a sequence multi-label
classification problem. A critical issue with the frame-based model is that it
pursues the best frame-level prediction rather than the best event-level
prediction. Besides, it needs post-processing and cannot be trained in an
end-to-end way. This paper firstly presents the one-dimensional Detection
Transformer (1D-DETR), inspired by Detection Transformer for image object
detection. Furthermore, given the characteristics of SED, the audio query
branch and a one-to-many matching strategy for fine-tuning the model are added
to 1D-DETR to form Sound Event Detection Transformer (SEDT). To our knowledge,
SEDT is the first event-based and end-to-end SED model. Experiments are
conducted on the URBAN-SED dataset and the DCASE2019 Task4 dataset, and both
show that SEDT can achieve competitive performance.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712664 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.05635
|
Josef Bajada
|
Josef Bajada and Francesco Borg Bonello
|
Real-time EEG-based Emotion Recognition using Discrete Wavelet
Transforms on Full and Reduced Channel Signals
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Real-time EEG-based Emotion Recognition (EEG-ER) with consumer-grade EEG
devices involves classification of emotions using a reduced number of channels.
These devices typically provide only four or five channels, unlike the high
number of channels (32 or more) typically used in most current state-of-the-art
research. In this work we propose to use Discrete Wavelet Transforms (DWT) to
extract time-frequency domain features, and we use time-windows of a few
seconds to perform EEG-ER classification. This technique can be used in
real-time, as opposed to post-hoc on the full session data. We also apply
baseline removal preprocessing, developed in prior research, to our proposed
DWT Entropy and Energy features, which improves classification accuracy
significantly. We consider two different classifier architectures, a 3D
Convolutional Neural Network (3D CNN) and a Support Vector Machine (SVM). We
evaluate both models on subject-independent and subject dependent setups to
classify the Valence and Arousal dimensions of an individual's emotional state.
We test them on both the full 32-channel data provided by the DEAP dataset, and
also a reduced 5-channel extract of the same dataset. The SVM model performs
best on all the presented scenarios, achieving an accuracy of 95.32% on Valence
and 95.68% on Arousal for the full 32-channel subject-dependent case, beating
prior real-time EEG-ER subject-dependent benchmarks. On the subject-independent
case an accuracy of 80.70% on Valence and 81.41% on Arousal was also obtained.
Reducing the input data to 5 channels only degrades the accuracy by an average
of 3.54% across all scenarios, making this model appropriate for use with more
accessible low-end EEG devices.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.713862 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.06060
|
Ibrahim Tamim
|
Ibrahim Tamim, Anas Saci, Manar Jammal, Abdallah Shami
|
Downtime-Aware O-RAN VNF Deployment Strategy for Optimized Self-Healing
in the O-Cloud
|
6 pages, 4 figures, IEEE Global Communications Conference 2021
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the huge surge in the traffic of IoT devices and applications, mobile
networks require a new paradigm shift to handle such demand roll out. With the
5G economics, those networks should provide virtualized multi-vendor and
intelligent systems that can scale and efficiently optimize the investment of
the underlying infrastructure. Therefore, the market stakeholders have proposed
the Open Radio Access Network (O-RAN) as one of the solutions to improve the
network performance, agility, and time-to-market of new applications. O-RAN
harnesses the power of artificial intelligence, cloud computing, and new
network technologies (NFV and SDN) to allow operators to manage their
infrastructure in a cost-efficient manner. Therefore, it is necessary to
address the O-RAN performance and availability challenges autonomously while
maintaining the quality of service. In this work, we propose an optimized
deployment strategy for the virtualized O-RAN units in the O-Cloud to minimize
the network's outage while complying with the performance and operational
requirements. The model's evaluation provides an optimal deployment strategy
that maximizes the network's overall availability and adheres to the
O-RAN-specific requirements.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71086 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.06458
|
Chuang Ma
|
Chuang Ma, Dingchuan Xue, Shaoshuai Li, Zhengcheng Zhou, Yichao Zhu,
Xu Guo
|
Stiffness minimisation of graded microstructural configurations using
asymptotic analysis and machine learning
|
37 pages, 11 figures, 3 tables
| null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The article is aimed to address a mutually boosting use of asymptotic
analysis and machine learning, for fast stiffness design of configurations
infilled with smoothly-varying graded microstructures. The discussion is
conducted in the context of an improved asymptotic-homogenisation topology
optimisation (AHTO plus) framework. It is demonstrated that on one hand,
machine learning can be employed to represent the key but implicit
inter-relationships revealed from asymptotic analysis, and the evaluations of
the homogenised quantities, as well as the sensitivities of the design
variables, become quite efficient. On the other hand, the use of asymptotic
analysis identifies a computational routine for data acquisition, thus the
training data here are inexhaustible in theory. Key issues regarding
integration of the two methods, such as ensuring the positive definiteness of
the homogenised elasticity tensor represented with neural networks, are also
discussed. The accuracies and the efficiencies of the present scheme are
numerically demonstrated. For two-dimensional optimisation, it takes the
present algorithm roughly 300 seconds on a standard desktop computer, and this
qualifies the present scheme as one of the most efficient algorithms used for
the compliance optimisation of configurations infilled with complex
microstructures.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.07583
|
Cole Franks
|
Cole Franks, Rafael Oliveira, Akshay Ramachandran, Michael Walter
|
Near optimal sample complexity for matrix and tensor normal models via
geodesic convexity
|
Measured computation time on more instances
| null | null | null |
math.ST cs.LG quant-ph stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The matrix normal model, the family of Gaussian matrix-variate distributions
whose covariance matrix is the Kronecker product of two lower dimensional
factors, is frequently used to model matrix-variate data. The tensor normal
model generalizes this family to Kronecker products of three or more factors.
We study the estimation of the Kronecker factors of the covariance matrix in
the matrix and tensor models. We show nonasymptotic bounds for the error
achieved by the maximum likelihood estimator (MLE) in several natural metrics.
In contrast to existing bounds, our results do not rely on the factors being
well-conditioned or sparse. For the matrix normal model, all our bounds are
minimax optimal up to logarithmic factors, and for the tensor normal model our
bound for the largest factor and overall covariance matrix are minimax optimal
up to constant factors provided there are enough samples for any estimator to
obtain constant Frobenius error. In the same regimes as our sample complexity
bounds, we show that an iterative procedure to compute the MLE known as the
flip-flop algorithm converges linearly with high probability. Our main tool is
geodesic strong convexity in the geometry on positive-definite matrices induced
by the Fisher information metric. This strong convexity is determined by the
expansion of certain random quantum channels. We also provide numerical
evidence that combining the flip-flop algorithm with a simple shrinkage
estimator can improve performance in the undersampled regime.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.11013
|
Penghui Wang
|
Ziheng Xia, Ganggang Dong, Penghui Wang, Hongwei Liu
|
Spatial Location Constraint Prototype Loss for Open Set Recognition
|
9 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the challenges in pattern recognition is open set recognition.
Compared with closed set recognition, open set recognition needs to reduce not
only the empirical risk, but also the open space risk, and the reduction of
these two risks corresponds to classifying the known classes and identifying
the unknown classes respectively. How to reduce the open space risk is the key
of open set recognition. This paper explores the origin of the open space risk
by analyzing the distribution of known and unknown classes features. On this
basis, the spatial location constraint prototype loss function is proposed to
reduce the two risks simultaneously. Extensive experiments on multiple
benchmark datasets and many visualization results indicate that our methods is
superior to most existing approaches.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712595 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.01865
|
Dogan Can Cicek
|
Dogan C. Cicek, Enes Duran, Baturay Saglam, Furkan B. Mutlu, Suleyman
S. Kozat
|
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms
via Batch Prioritized Experience Replay
|
Accepted at The 33rd IEEE International Conference on Tools with
Artificial Intelligence (ICTAI 2021)
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The experience replay mechanism allows agents to use the experiences multiple
times. In prior works, the sampling probability of the transitions was adjusted
according to their importance. Reassigning sampling probabilities for every
transition in the replay buffer after each iteration is highly inefficient.
Therefore, experience replay prioritization algorithms recalculate the
significance of a transition when the corresponding transition is sampled to
gain computational efficiency. However, the importance level of the transitions
changes dynamically as the policy and the value function of the agent are
updated. In addition, experience replay stores the transitions are generated by
the previous policies of the agent that may significantly deviate from the most
recent policy of the agent. Higher deviation from the most recent policy of the
agent leads to more off-policy updates, which is detrimental for the agent. In
this paper, we develop a novel algorithm, Batch Prioritizing Experience Replay
via KL Divergence (KLPER), which prioritizes batch of transitions rather than
directly prioritizing each transition. Moreover, to reduce the off-policyness
of the updates, our algorithm selects one batch among a certain number of
batches and forces the agent to learn through the batch that is most likely
generated by the most recent policy of the agent. We combine our algorithm with
Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy
Gradient and evaluate it on various continuous control tasks. KLPER provides
promising improvements for deep deterministic continuous control algorithms in
terms of sample efficiency, final performance, and stability of the policy
during the training.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.02175
|
Diego Porres
|
Diego Porres
|
Discriminator Synthesis: On reusing the other half of Generative
Adversarial Networks
|
7 pages, 4 figures, NeurIPS Workshop on Machine Learning for
Creativity and Design 2021
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Generative Adversarial Networks have long since revolutionized the world of
computer vision and, tied to it, the world of art. Arduous efforts have gone
into fully utilizing and stabilizing training so that outputs of the Generator
network have the highest possible fidelity, but little has gone into using the
Discriminator after training is complete. In this work, we propose to use the
latter and show a way to use the features it has learned from the training
dataset to both alter an image and generate one from scratch. We name this
method Discriminator Dreaming, and the full code can be found at
https://github.com/PDillis/stylegan3-fun.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.02925
|
Kai Zhao
|
Xin Liang, Kai Zhao, Sheng Di, Sihuan Li, Robert Underwood, Ali M.
Gok, Jiannan Tian, Junjing Deng, Jon C. Calhoun, Dingwen Tao, Zizhong Chen,
Franck Cappello
|
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded
Lossy Compressors
|
13 pages
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Today's scientific simulations require a significant reduction of data volume
because of extremely large amounts of data they produce and the limited I/O
bandwidth and storage space. Error-bounded lossy compressor has been considered
one of the most effective solutions to the above problem. In practice, however,
the best-fit compression method often needs to be customized/optimized in
particular because of diverse characteristics in different datasets and various
user requirements on the compression quality and performance. In this paper, we
develop a novel modular, composable compression framework (namely SZ3), which
involves three significant contributions. (1) SZ3 features a modular
abstraction for the prediction-based compression framework such that the new
compression modules can be plugged in easily. (2) SZ3 supports multialgorithm
predictors and can automatically select the best-fit predictor for each data
block based on the designed error estimation criterion. (3) SZ3 allows users to
easily compose different compression pipelines on demand, such that both
compression quality and performance can be significantly improved for their
specific datasets and requirements. (4) In addition, we evaluate several lossy
compressors composed from SZ3 using the real-world datasets. Specifically, we
leverage SZ3 to improve the compression quality and performance for different
use-cases, including GAMESS quantum chemistry dataset and Advanced Photon
Source (APS) instrument dataset. Experiments show that our customized
compression pipelines lead to up to 20% improvement in compression ratios under
the same data distortion compared with the state-of-the-art approaches.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709849 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03418
|
Riccardo Grazzi
|
Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski,
Matthias Seeger, Cedric Archambeau
|
Meta-Forecasting by combining Global Deep Representations with Local
Adaptation
| null | null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While classical time series forecasting considers individual time series in
isolation, recent advances based on deep learning showed that jointly learning
from a large pool of related time series can boost the forecasting accuracy.
However, the accuracy of these methods suffers greatly when modeling
out-of-sample time series, significantly limiting their applicability compared
to classical forecasting methods. To bridge this gap, we adopt a meta-learning
view of the time series forecasting problem. We introduce a novel forecasting
method, called Meta Global-Local Auto-Regression (Meta-GLAR), that adapts to
each time series by learning in closed-form the mapping from the
representations produced by a recurrent neural network (RNN) to one-step-ahead
forecasts. Crucially, the parameters ofthe RNN are learned across multiple time
series by backpropagating through the closed-form adaptation mechanism. In our
extensive empirical evaluation we show that our method is competitive with the
state-of-the-art in out-of-sample forecasting accuracy reported in earlier
work.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709806 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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