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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