November 2022 arXiv papers — page 34

Saumitra Kulkarni, Snehal M. Shekatkar

Individual components of many real-world complex networks produce and exchange resources among themselves. However, because the resource production in such networks is almost always stochastic, fluctuations in the production are unavoidable. In this paper, we study the effect of fluctuations on the resource dependencies in complex networks. To this end, we c

Tianyu Wu, Yang Tang, Qiyu Sun, Luolin Xiong

In recent years, artificial intelligence has played an important role on accelerating the whole process of drug discovery. Various of molecular representation schemes of different modals (e.g. textual sequence or graph) are developed. By digitally encoding them, different chemical information can be learned through corresponding network structures. Molecular

Peilong Hong, Yi Liang, Zhigang Chen, Guoquan Zhang

Photonic analogs of the moir\'e superlattices mediated by interlayer electromagnetic coupling are expected to give rise to rich phenomena such as nontrivial flatband topology. Here, we propose and demonstrate a scheme to tune the flatbands in a bilayer moir\'e superlattice by employing the band offset. The band offset is changed by fixing the bands of one sl

Marcos V. Conde, Florin Vasluianu, Sabari Nathan, Radu Timofte

The new trend of full-screen devices implies positioning the camera behind the screen to bring a larger display-to-body ratio, enhance eye contact, and provide a notch-free viewing experience on smartphones, TV or tablets. On the other hand, the images captured by under-display cameras (UDCs) are degraded by the screen in front of them. Deep learning methods

M. Dheepika, Hassan Basari V. T., Titus K. Mathew

In this paper, we obtain the law of emergence with Tsallis entropy from the thermodynamic laws. We first derive the law of emergence from the equilibrium description of the unified first law and Clausius relation. However, it has been shown that considering Tsallis entropy as the horizon entropy, the Clausius relation $\delta Q=T dS$ does not hold due to non

Younghun Kim, Jeongsoo Kang, Younghun Kwon

Surface code is an error-correcting method that can be applied to the implementation of a usable quantum computer. At present, a promising candidate for a usable quantum computer is based on superconductor-specifically transmon. Because errors in transmon-based quantum computers appear biasedly as Z type errors, tailored surface and XZZX codes have been deve

Rick Groenendijk, Leo Dorst, Theo Gevers

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to comb

Cheng Lyu, Jiake Xie, Bo Xu, Cheng Lu

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that c

Thomas Dufils, Christoph Schran, Ji Chen, Andre K. Geim

Recent experiments have shown that the out-of-plane dielectric constant of water confined in nanoslits of graphite and hexagonal boron nitride (hBN) is vanishingly small. Despite extensive effort based mainly on classical force-field molecular dynamics (FFMD) approaches, the origin of this phenomenon is under debate. Here we used ab initio molecular dynamics

Aidyn Kassymov, Michael Ruzhansky, Durvudkhan Suragan

In this paper, we obtain a reverse version of the integral Hardy inequality on metric measure space with two negative exponents. Also, as for applications we show the reverse Hardy-Littlewood-Sobolev and the Stein-Weiss inequalities with two negative exponents on homogeneous Lie groups and with arbitrary quasi-norm, the result which appears to be new already

Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari-Trecate

Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures are a promising alternative, as they aim to adapt to recognizable patterns in the estimation error. In thi

ALICE Collaboration

The production of prompt $\mathrm {\Lambda_{c}^{+}}$ baryons has been measured at midrapidity in the transverse momentum interval $0<p_{\rm T}<1$ GeV/$c$ for the first time, in pp and p-Pb collisions at a centre-of-mass energy per nucleon-nucleon collision $\sqrt{s_\mathrm{NN}} = 5.02$ TeV. The measurement was performed in the decay channel ${\rm \Lambda_{c}

L. P. Csernai, I. N. Mishustin, L. M. Satarov, H. Stoecker

Recent validation experiments on laser irradiation of polymer foils with and without implanted golden nano-particles are discussed. First we analyze characteristics of craters, formed in the target after its interaction with laser beam. Preliminary experimental results show significant production of deuterons when both the energy of laser pulse and concentra

Georg C. Ganzenmüller, Puneeth Jakkula, Stefan Hiermaier

Background: The determination of yield stress curves for ductile metals from uniaxial material tests is complicated by the presence of tri-axial stress states due to necking. A need exists for a straightforward solution to this problem. Objective: This work presents a simple solution for this problem specific to axis-symmetric specimens. Equivalent uniaxial

Hongbo Li, Lingjie Duan

In congestion games, users make myopic routing decisions to jam each other, and the social planner with the full information designs mechanisms on information or payment side to regulate. However, it is difficult to obtain time-varying traffic conditions, and emerging crowdsourcing platforms (e.g., Waze and Google Maps) provide a convenient way for mobile us

Alessandro Ronca, Nadezda Alexandrovna Knorozova, Giuseppe De Giacomo

Every automaton can be decomposed into a cascade of basic prime automata. This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing a specific functionality. Any automaton can serve as a compo

De-Zhang Li, Xiao-Bao Yang

Efficient numerical algorithm for stochastic differential equation has been an important object in the research of statistical physics and mathematics for a long time. In this paper we study the highly accurate numerical algorithm of the overdamped Langevin equation. In particular, our interest is the behaviour of the numerical schemes for solving the overda

Danilo Marinho Fernandes, Jonatan Krolikowski, Zied Ben Houidi, Fuxing Chen

Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissions before to transmitting or receiving. Being able to accurately estimate interference resulting from a given state change (e.g., channel, bandwidth, power) would allow a better c

Yinpeng Wang

This paper focus on the theoretical analysis and simulation of electromagnetic wave transforms, which is widely encountered in teaching physics. When the electromagnetic wave is not consistent with the shape of the object, it is often necessary to introduce electromagnetic wave transforms to analyze the interaction between the two. In this paper, we discuss

Thomas M. Michelitsch, Federico Polito, Alejandro P. Riascos

In a recent work we introduced a semi-Markovian discrete-time generalization of the telegraph process. We referred this random walk to as squirrel random walk (SRW). The SRW is a discrete-time random walk on the one-dimensional infinite lattice where the step direction is reversed at arrival times of a discrete-time renewal process and remains unchanged at u

Yuma Ichikawa, Akira Nakagawa, Hiromoto Masayuki, Yuhei Umeda

Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods using a machine learning model. With latent generative models, SLMC methods realize efficient Monte Carlo updates with less autocorrelation. However, SLMC methods are difficult to directly apply to multimodal distributions for which training d

Nunzio A. Letizia, Nicola Novello, Andrea M. Tonello

Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the c

Chun-Ming Yip, Ming-Chung Chu, Shing-Chi Leung, Lap-Ming Lin

We show that a minimum-mass neutron star undergoes delayed explosion after mass removal from its surface. We couple the Newtonian hydrodynamics to a nuclear reaction network of $\sim4500$ isotopes to study the nucleosynthesis and neutrino emission during the explosion. An electron antineutrino burst with a peak luminosity of $\sim3\times10^{50}$ erg s$^{-1}$

Nicolas De Angelis, David Gascón, Sergio Gómez, Matthieu Heller

Large-area silicon photomultipliers (SiPMs) are desired in many applications where large surfaces have to be covered. For instance, a large area SiPM has been developed by Hamamatsu Photonics in collaboration with the University of Geneva, to equip gamma-ray cameras employed in imaging atmospheric Cherenkov telescopes. Being the sensor about 1 cm$^2$, a suit

Nikolay Kozyrev, Sergey Krivonos

In this paper we demonstrate that the different generalizations of the Schwarzians, supersymmetric or purely bosonic, can be easily constructed by using the nonlinear realizations technique.

Itai Lang, Dror Aiger, Forrester Cole, Shai Avidan

Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target point clouds and outputs the per-point tra

K. Sheshadri, D. Malterre, A. Fujimori, A. Chainani

The one-band and three-band Hubbard models which describe the electronic structure of cuprates indicate very different values of effective electronic parameters, such as the on-site Coulomb energy and the hybridization strength. In contrast, a comparison of electronic parameters of several cuprates with corresponding values from spectroscopy and scattering e

Michèle Levi, Zhewei Yin

We put forward a broader picture of the effective theory of a spinning particle within the EFT of spinning gravitating objects, through which we derive and establish the new precision frontier at the fifth PN (5PN) order. This frontier includes higher-spin sectors, quadratic and quartic in the spin, which both display novel physical features, due to the exte

Simon Krogmann, Pascal Lenzner, Alexander Skopalik

We study a non-cooperative two-sided facility location game in which facilities and clients behave strategically. This is in contrast to many other facility location games in which clients simply visit their closest facility. Facility agents select a location on a graph to open a facility to attract as much purchasing power as possible, while client agents c

ALICE Collaboration

The measurement of the production of deuterons, tritons and $^{3}\mathrm{He}$ and their antiparticles in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV is presented in this article. The measurements are carried out at midrapidity ($|y| < $ 0.5) as a function of collision centrality using the ALICE detector. The $p_{\rm T}$-integrated yields, the coa

David Ruiz

In 1971 J. Serrin proved that, given a smooth bounded domain $\Omega \subset \mathbb{R}^N$ and $u$ a positive solution of the problem: \begin{equation*} \begin{array}{ll} -\Delta u = f(u) &\mbox{in $\Omega$, } u =0 &\mbox{on $\partial\Omega$, } \partial_{\nu} u =\mbox{constant} &\mbox{on $\partial\Omega$, } \end{array} \end{equation*} then $\Omega$ is necess

Riccardo Polvara, Sergi Molina Mellado, Ibrahim Hroob, Grzegorz Cielniak

Long-term autonomy is one of the most demanded capabilities looked into a robot. The possibility to perform the same task over and over on a long temporal horizon, offering a high standard of reproducibility and robustness, is appealing. Long-term autonomy can play a crucial role in the adoption of robotics systems for precision agriculture, for example in a

Leander Stecker

We explore submersions introduced by reducible holonomy representations of connections with parallel skew torsion. A submersion theorem extending previous, less general, results is given. As our main application we show that parallel 3-$(\alpha,\delta)$-Sasaki manifolds admit 1-dimensional submersions onto nearly K\"ahler orbifolds. As a secondary applicatio

Maximilian Wötzel

A family $\mathcal{F}\subset 2^G$ of subsets of an abelian group $G$ is a Sidon system if the sumsets $A+B$ with $A,B\in \mathcal{F}$ are pairwise distinct. Cilleruelo, Serra and the author previously proved that the maximum size $F_k(n)$ of a Sidon system consisting of $k$-subsets of the first $n$ positive integers satisfies $C_k n^{k-1}\leq F_k(n) \leq \bi

Thomas Chaffey, Sebastian Banert, Pontus Giselsson, Richard Pates

It is shown that the behavior of an $m$-port circuit of maximal monotone elements can be expressed as a zero of the sum of a maximal monotone operator containing the circuit elements, and a structured skew-symmetric linear operator representing the interconnection structure, together with a linear output transformation. The Condat-V\~u algorithm solves inclu

Andreas Schaad, Dominik Binder

The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent re

Andrés M Rueda-Ramírez, Gregor J Gassner

In this paper, we show that diagonal-norm summation by parts (SBP) discretizations of general non-conservative systems of hyperbolic balance laws can be rewritten as a finite-volume-type formula, also known as flux-differencing formula, if the non-conservative terms can be written as the product of a local and a symmetric contribution. Furthermore, we show t

Tomasz Kobos, Grzegorz Lewicki

Let $X$ be a finite-dimensional normed space and let $Y \subseteq X$ be its proper linear subspace. The set of all minimal projections from $X$ to $Y$ is a convex subset of the space all linear operators from $X$ to $X$ and we can consider its affine dimension. We establish several results on the possible values of this dimension. We prove optimal upper boun

Yasufumi Araki, Jun'ichi Ieda

Topological electromagnetism owing to nontrivial momentum-space topology of electrons in insulators gives rise to diverse anomalous magnetoelectric responses. While conventional inductors and capacitors are based on classical electromagnetism described by Maxwell's equations, here we show that topological electromagnetism in combination with spin dynamics in

Alan Omar, Tim Vogel, Martin Hoffmann, Clara J. Saraceno

Multi-pass cell (MPC) based temporal pulse compressors have emerged in the last years as a powerful and versatile solution to the intrinsic issue of long pulses from Yb-based high-power ultrafast lasers. However, the spectral broadening of high-energy (typically more than 100 uJ) pulses has only been realized in complex setups, i.e., in large and costly, pre

Moritz Nottebaum, Stefan Roth, Simone Schaub-Meyer

Most deep learning methods for video frame interpolation consist of three main components: feature extraction, motion estimation, and image synthesis. Existing approaches are mainly distinguishable in terms of how these modules are designed. However, when interpolating high-resolution images, e.g. at 4K, the design choices for achieving high accuracy within

Xianzhang Chen, Guillaume Weick, Dietmar Weinmann, Rodolfo A. Jalabert

The conductance of graphene nanoribbons and nanoconstrictions under the effect of a scanning gate microscopy tip is systematically studied. Using a scattering approach for noninvasive probes, the first- and second-order conductance corrections caused by the tip potential disturbance are expressed explicitly in terms of the scattering states of the unperturbe

Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah Goodman

Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be

Mateus R. Pelicer, Débora P. Menezes, Mariana Dutra, Odilon Lourenço

It is well known that strongly correlated neutron-proton pairs, the short-range correlations (SRC), can modify many of the nuclear properties. In this work we have introduced, for the first time, short range correlations in the calculation of the nuclear pasta phase at zero temperature and checked how they affect its size and internal structure. We have used

Anatoly. V. Zasov, Natalia. A. Zaitseva

The content of gas in galaxies with an anomalously high relative mass of hydrogen $M_{HI}/M_*$ for a given mass of the stellar population $M_*$ (VHR-galaxies) is considered, using the available samples of such galaxies. It is shown that, within the optical diameter $D_{25}$, the mass of HI in VHR galaxies, as well as in galaxies with "normal" HI content, is

Ling Wu

Inspired by Hongjie Dong and Qi S. Zhang's article \cite{ZQ2}, we find that the analyticity in time for a smooth solution of the heat equation with exponential quadratic growth in the space variable can be extended to any complete noncompact Riemannian manifolds with Bakry-\'Emery Ricci curvature bounded below and the potential function being of at most quad

Fabio Cermelli, Matthieu Cord, Arthur Douillard

Continual learning for segmentation has recently seen increasing interest. However, all previous works focus on narrow semantic segmentation and disregard panoptic segmentation, an important task with real-world impacts. %a In this paper, we present the first continual learning model capable of operating on both semantic and panoptic segmentation. Inspired b

Yilong Yang, Pengwei Zhao

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nuc

Nils Hoyer, Francesca Pinna, Albrecht W. H. Kamlah, Francisco Nogueras-Lara

We combine archival HST and new JWST imaging data, covering the ultraviolet to mid-infrared regime, to morphologically analyze the nuclear star cluster (NSC) of NGC 628, a grand-design spiral galaxy. The cluster is located in a 200 pc x 400 pc cavity, lacking both dust and gas. We find roughly constant values for the effective radius (r_eff ~ 5 pc) and ellip

Muhammad Azeem Akbar, Arif Ali Khan, Sajjad Mahmood, Saima Rafi

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to th

Rasoul Nikbakht, Michail Dalgitsis, Sergio Barrachina-Muñoz, Sarang Kahvazadeh

MEC Sandbox is an excellent tool that simulates wireless networks and deploys ETSI Multi-access Edge Computing (MEC) APIs on top of the simulated wireless network. In this demo, we consume these APIs using a decision engine (DE) to scale a video-on-demand (VoD) application located on the network edge, assuming that the average number of users is a good proxy

Michail Christos Doukas, Stylianos Ploumpis, Stefanos Zafeiriou

We present Dynamic Neural Portraits, a novel approach to the problem of full-head reenactment. Our method generates photo-realistic video portraits by explicitly controlling head pose, facial expressions and eye gaze. Our proposed architecture is different from existing methods that rely on GAN-based image-to-image translation networks for transforming rende

Krystian Chachuła, Jakub Łyskawa, Bartłomiej Olber, Piotr Frątczak

The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as object detection. Dealing with errors in datasets is often limited to accepting that some fraction of examples are incorrect, estimating their confidence, and either assigning appropri

Artem Bohdan

Supernova remnants (SNRs) are believed to produce the majority of galactic cosmic rays (CRs). SNRs harbor non-relativistic collisionless shocks responsible for acceleration of CRs via diffusive shock acceleration (DSA), in which particles gain their energies via repeated interactions with the shock front. As the DSA theory involves pre-existing mildly energe

Hélion du Mas des Bourboux

Deep learning models have been developed for a variety of tasks and are deployed every day to work in real conditions. Some of these tasks are critical and models need to be trusted and safe, e.g. military communications or cancer diagnosis. These models are given real data, simulated data or combination of both and are trained to be highly predictive on the

Muhammad Azeem Akbar, Arif Ali Khan, Sajjad Mahmood, Saima Rafi

Quantum computing (QC) is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially tackle the limitations of classical computing. Over the last few years, major technology giants have invested in developing hardware and programming frameworks to develop quantum-specific applications. QC hardware t

Patrick Iff, Maciej Besta, Matheus Cavalcante, Tim Fischer

2.5D integration is an important technique to tackle the growing cost of manufacturing chips in advanced technology nodes. This poses the challenge of providing high-performance inter-chiplet interconnects (ICIs). As the number of chiplets grows to tens or hundreds, it becomes infeasible to hand-optimize their arrangement in a way that maximizes the ICI perf

Shreya Banerjee, Sayantani Bera, David F. Mota

The Lambda-Cold Dark Matter model explains cosmological observations most accurately till date. However, it is still plagued with various shortcomings at galactic scales. Models of dark matter such as superfluid dark matter, Bose-Einstein Condensate(BEC) dark matter and fuzzy dark matter have been proposed to overcome some of these drawbacks. In this work, w

Oleksandra Panova, Maciej Lewicki

NA61/SHINE is an experiment at the CERN Super Proton Synchrotron. The main goals of the experiment are the search for the critical point of strongly interacting matter and the study of the properties of the onset of deconfinement. To reach these goals, the two-dimensional scan in beam momentum ($13A-150A$ GeV/$c$) and system size ($p$+$p$, Be+Be, Ar+Sc, Xe+L

Winfried Hochstättler, Sophia Keip, Kolja Knauer

We generalize the (signed) Varchenko matrix of a hyperplane arrangement to complexes of oriented matroids and show that its determinant has a nice factorization. This extends previous results on hyperplane arrangements and oriented matroids.

ALICE Collaboration

The production of electrons from beauty-hadron decays was measured at midrapidity in proton-proton (pp) and central Pb-Pb collisions at center-of-mass energy per nucleon-nucleon pair $\sqrt{s_{\rm NN}}$ = 5.02 TeV, using the ALICE detector at the LHC. The cross section measured in pp collisions in the transverse momentum interval $2 < p_{\rm T} < 8$ GeV/$c$

Zhao Zhou, Xiangcheng Du, Yingbin Zheng, Cheng Jin

This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features are first extracted at different scales

Snehashish Chatterjee, Saurav Giri, Subham Majumdar, Prabir Dutta

The present work aims to address the electronic and magnetic properties of the intermetallic compound GdPd$_2$Bi through a comprehensive study of the structural, magnetic, electrical and thermal transport on a polycrystalline sample, followed by theoretical calculations. Our findings indicate that the magnetic ground state is antiferromagnetic in nature. Mag

Thomas Hoffmann, Douglas Brown

We present a novel method for determining gradients of parameterised quantum circuits (PQCs) in hybrid quantum-classical machine learning models by applying the multivariate version of the simultaneous perturbation stochastic approximation (SPSA) algorithm. The gradients of PQC layers can be calculated with an overhead of two evaluations per circuit per forw

Dennis Obster

Developing a theory for quantum gravity is one of the big open questions in theoretical high-energy physics. Recently, a tensor model approach has been considered that treats tensors as the generators of commutative non-associative algebras, which might be an appropriate interpretation of the canonical tensor model. In this approach, the non-associative alge

Patrick Iff, Maciej Besta, Matheus Cavalcante, Tim Fischer

Chips with hundreds to thousands of cores require scalable networks-on-chip (NoCs). Customization of the NoC topology is necessary to reach the diverse design goals of different chips. We introduce sparse Hamming graph, a novel NoC topology with an adjustable costperformance trade-off that is based on four NoC topology design principles we identified. To eff

Zhen Wang, Zheng Feng, Yanjun Li, Bowen Li

Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable bet

Dirk Siersma

In this paper we study the area function of polygons, where the vertices are sliding along curves. We give geometric criteria for the critical points and determine also the Hesse matrix at those points. This is the starting point for a Morse-theoretic approach, which includes the relation with the topology of the configuration spaces. Moreover the condition

Siyuan Li, Li Sun, Qingli Li

Pre-trained vision-language models like CLIP have recently shown superior performances on various downstream tasks, including image classification and segmentation. However, in fine-grained image re-identification (ReID), the labels are indexes, lacking concrete text descriptions. Therefore, it remains to be determined how such models could be applied to the

Arnaud Bougaham, Mohammed El Adoui, Isabelle Linden, Benoît Frénay

In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guar

Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang

The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming. To address this issue, we explore a new task, termed dataset expansion, aimed at expanding a ready-to-use small dataset by automatically creating new labeled samples. To this end, we pr

Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Haibing Jin

While federated learning has shown strong results in optimizing a machine learning model without direct access to the original data, its performance may be hindered by intermittent client availability which slows down the convergence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training under arbit

Qiran Zou, Yu Yang, Wing Yin Cheung, Chang Liu

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual conf

Yann Chaubet, Yannick Guedes Bonthonneau, Thibault Lefeuvre, Leo Tzou

We give an analytic description for the infinitesimal generator constructed by Applebaum-Estrade for L\'evy flights on a broad class of closed Riemannian manifolds including all negatively-curved manifolds, the flat torus and the sphere. Various properties of the associated semigroup and the asymptotics of the expected stopping time for L\'evy flight based r

Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky

As the scope of machine learning broadens, we observe a recurring theme of algorithmic monoculture: the same systems, or systems that share components (e.g. training data), are deployed by multiple decision-makers. While sharing offers clear advantages (e.g. amortizing costs), does it bear risks? We introduce and formalize one such risk, outcome homogenizati

A. C. Fabian, J. S. Sanders, G. J. Ferland, B. R. McNamara

We have recently uncovered Hidden Cooling Flows (HCFs) in the XMM RGS spectra of 3 clusters of galaxies, Centaurus, Perseus and A1835. Here we search for them in a wider sample of objects: the X-ray brightest group NGC5044; 4 moderate X-ray luminosity clusters Sersic 159, A262, A2052 and RXJ0821; and 3 high X-ray luminosity clusters RXJ1532, MACS 1931 and th

H. Garcilazo, A. Valcarce

JLab has recently found indications of the possible existence of a $\Sigma NN$ resonance at $(3.14 \pm 0.84) - i (2.28 \pm 1.2)$ MeV. In the past, using models that exploit symmetries between the two-baryon sector with and without strangeness, hyperon-nucleon interactions have been derived that reproduce the experimental data of the strangeness $-1$ sector.

Zhizheng Liu, Francesco Milano, Jonas Frey, Roland Siegwart

An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance. In this work, we study continual multi-scene adaptation for the task of semantic segment

Tianpeng Bao, Jiadong Chen, Wei Li, Xiang Wang

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recen

Johannes Henn, Rourou Ma, Kai Yan, Yang Zhang

We invent an automated method for computing the divergent part of Feynman integrals in dimensional regularization. Our method exploits simplifications from four-dimensional integration-by-parts identities. Leveraging algorithms from the literature, we show how to find simple differential equations for the divergent part of Feynman integrals. We illustrate th

Sahar Diskin, Ilay Hoshen, Michael Krivelevich, Maksim Zhukovskii

A classical vertex Ramsey result due to Ne\v{s}et\v{r}il and R\"odl states that given a finite family of graphs $\mathcal{F}$, a graph $A$ and a positive integer $r$, if every graph $B\in\mathcal{F}$ has a $2$-vertex-connected subgraph which is not a subgraph of $A$, then there exists an $\mathcal{F}$-free graph which is vertex $r$-Ramsey with respect to $A$

Iain Weaver, Ned Westwood, Travis Coan, Saffron O'Neill

Online communication about climate change is central to public discourse around this contested issue. Facebook is a dominant social media platform known to be a major source of information and online influence, yet discussion of climate change on the platform has remained largely unstudied due to difficulties in accessing data. This paper utilises Facebook's

Tomer Friedlander, Ron Shmelkin, Lior Wolf

A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the lat

Augustin Marignier, Thomas Kitching, Jason D. McEwen, Ana M. G. Ferreira

Uncertainty quantification is a crucial step of cosmological mass-mapping that is often ignored. Suggested methods are typically only approximate or make strong assumptions of Gaussianity of the shear field. Probabilistic sampling methods, such as Markov chain Monte Carlo (MCMC), draw samples form a probability distribution, allowing for full and flexible un

Rasoul Nikbakht, Sarang Kahvazadeh, Josep Mangues-Bafalluy

Edge caching can significantly improve the 5G networks' performance both in terms of delay and backhaul traffic. We use a reinforcement learning-based (RL-based) caching technique that can adapt to time-location-dependent popularity patterns for on-demand video contents. In a private 5G, we implement the proposed caching scheme as two virtual network functio

Hasan Arslan, Alnour Altoum, Mariam Zaarour

In this paper, we construct a mixed-base number system over the generalized symmetric group $G(m,1,n)$, which is a complex reflection group with a root system of type $B_n^{(m)}$. We also establish one-to-one correspondence between all positive integers in the set $\{1,\cdots,m^nn!\}$ and the elements of $G(m,1,n)$ by constructing the subexceedant function i

Philipp Hacker

As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They c

Satish Kumar, Subhra Sankar Dhar

In this article, we propose a one-sample test to check whether the support of the unknown distribution generating the data is homologically equivalent to the support of some specified distribution or not OR using the corresponding two-sample test, one can test whether the supports of two unknown distributions are homologically equivalent or not. In the cours

Adel Rahimi, Shaurya Jain

As the applications of Natural Language Processing (NLP) in sensitive areas like Political Profiling, Review of Essays in Education, etc. proliferate, there is a great need for increasing transparency in NLP models to build trust with stakeholders and identify biases. A lot of work in Explainable AI has aimed to devise explanation methods that give humans in

Ahmad Ibrahim, Hamed Nemati, Till Schlüter, Nils Ole Tippenhauer

The complexity of modern processor architectures has given rise to sophisticated interactions among their components. Such interactions may result in potential attack vectors in terms of side channels, possibly available to user-land exploits to leak secret data. Exploitation and countering of such side channels require a detailed understanding of the target

Takashi Sakurai, Shin Toriumi

We have investigated the probability distributions of sunspot area and magnetic flux by using the data from Royal Greenwich Observatory and USAF/NOAA. We have constructed a sample of 2995 regions with maximum-development areas $\ge$ 500 MSH (millionths of solar hemisphere), covering 146.7 years (1874--2020). The data were fitted by a power-law distribution a

Khaled Koutini, Shahed Masoudian, Florian Schmid, Hamid Eghbal-zadeh

The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale datasets are capable of capturing a diverse set of features, and learning a representation that can generalize onto unseen tasks and datasets that are from the same domain. Hence, these m

Wenxuan Zeng, Meng Li, Wenjie Xiong, Tong Tong

Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference. However, existing neural network architectures, including Vision Transformers (ViTs), are not designed or optimized for MPC and incur significant latency overhead. We observe Softmax accounts for the major l

Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath, Eduard A. Jorswieck

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity and poor scalability are proposed. In contrast, we propo

Isabelle Viole, Guillermo Valenzuela-Venegas, Marianne Zeyringer, Sabrina Sartori

A large portion of astronomy's carbon footprint stems from fossil fuels supplying the power demand of astronomical observatories. Here, we explore various isolated low-carbon power system setups for the newly planned Atacama Large Aperture Submillimeter Telescope, and compare them to a business-as-usual diesel power generated system. Technologies included in

Hanze Dong, Xi Wang, Yong Lin, Tong Zhang

Particle-based variational inference (VI) minimizes the KL divergence between model samples and the target posterior with gradient flow estimates. With the popularity of Stein variational gradient descent (SVGD), the focus of particle-based VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gra

Zi-Hang Zhou, Jin-Ping Zhu, Kai Wang

Various supernovae (SN), compact object coalescences, and tidal disruption events are widely believed to occur embedded in active galactic nuclei (AGN) accretion disks and generate detectable electromagnetic (EM) signals. We collectively refer to them as \emph{AGN disk transients}. The inelastic hadronuclear ($pp$) interactions between shock-accelerated cosm

Zhaohua Chen, Rui Ai, Mingwei Yang, Yuqi Pan

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random externa

Uriel Feige, Xin Huang

We consider the problem of allocating $m$ indivisible chores to $n$ agents with additive disvaluation (cost) functions. It is easy to show that there are picking sequences that give every agent (that uses the greedy picking strategy) a bundle of chores of disvalue at most twice her share value (maximin share, MMS, for agents of equal entitlement, and anypric

Tomohiro Harada, Kazunori Kohri, Misao Sasaki, Takahiro Terada

We study the effects of velocity dispersion on the formation of primordial black holes~(PBHs) in a matter-dominated era. The velocity dispersion is generated through the nonlinear growth of perturbations and has the potential to impede the gravitational collapse and thereby the formation of PBHs. To make discussions clear, we consider two distinct length sca