March 2023 arXiv papers — page 130

Giampiero Esposito

DeWitt's suggestion that the wave function of the universe should vanish at the classical big-bang singularity is here considered within the framework of one-loop quantum cosmology. For pure gravity at one loop about a flat four-dimensional background bounded by a 3-sphere, three choices of boundary conditions are considered: vanishing of the linearized magn

Isadora Cardoso-Pereira, Geraldo Gomes, Danilo Monteiro Ribeiro, Alberto de Souza

People with still face discrimination, which creates significant obstacles to accessing higher education, ultimately hindering their access to high-skilled occupations. In this study we present Catalisa, an eight-month training camp (developed by Zup Innovation) that hires and trains people with disabilities as software developers. We interviewed 12 Catalisa

Antonio Macaluso, Luca Clissa, Stefano Lodi, Claudio Sartori

Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance. However, the main limitation is the constraint to linear operations that hampers the representation of complex relationships in data. In this work, we propose an efficient implementation of quantum splines for non-li

Oguz Koc, Omur Ugur, A. Sevtap Kestel

Banks utilize credit scoring as an important indicator of financial strength and eligibility for credit. Scoring models aim to assign statistical odds or probabilities for predicting if there is a risk of nonpayment in relation to many other factors which may be involved in. This paper aims to illustrate the beneficial use of the eight machine learning (ML)

Hailu Bikila Yadeta

In this paper, we derive some new combinatorial inequalities by applying well known real analytic results like H\"{o}lder's inequality, Young's inequality, and Minkowiski's inequality to the recursively defined sequence $f_n$ of functions \begin{align*} f_0(x) & = \chi_{(-1/2, 1/2)} (x), \nonumber f_{n+1}(x) & = f_n(x+1/2)+ f_n(x-1/2), n \in \mathbb{N}\,\cup

Hazem Ibrahim, Rohail Asim, Matteo Varvello, Yasir Zaki

Location tags are designed to track personal belongings. Nevertheless, there has been anecdotal evidence that location tags are also misused to stalk people. Tracking is achieved locally, e.g., via Bluetooth with a paired phone, and remotely, by piggybacking on location-reporting devices which come into proximity of a tag. This paper studies the performance

L. E. Charón-García, J. Curole, L. Barrón-Palos, V. Gudkov

A self consistent description of angular correlations in neutron induced reactions is required for quantitative analysis of parity violating (PV) and time reversal invariance violating (TRIV) effects in neutron nucleus scattering. The 1.33 eV p-wave compound resonance in $^{117}$Sn is one of the few p-wave resonances where enough measurements have been perfo

Jürgen Knödlseder

We estimate the carbon footprint of astronomical research infrastructures, including space telescopes and probes and ground-based observatories. Our analysis suggests annual greenhouse gas emissions of $1.2\pm0.2$ MtCO$_2$e yr$^{-1}$ due to construction and operation of the world-fleet of astronomical observatories, corresponding to a carbon footprint of 36.

Gábor Bíró, Gergely Gábor Barnaföldi

The scaling properties of the final state charged hadron and mean jet multiplicity distributions, calculated by deep residual neural network architectures with different complexities are presented. The parton-level input of the neural networks are generated by the Hijing++ Monte Carlo event generator. Hadronization neural networks, trained with $\sqrt{s}=7$

Fallou Niakh

Risk-sharing is one way to pool risks without the need for a third party. To ensure the attractiveness of such a system, the rule should be accepted and understood by all participants. A desirable risk-sharing rule should fulfill actuarial fairness and Pareto optimality while being easy to compute. This paper establishes a one-to-one correspondence between a

Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado

Neural kernels have drastically increased performance on diverse and nonstandard data modalities but require significantly more compute, which previously limited their application to smaller datasets. In this work, we address this by massively parallelizing their computation across many GPUs. We combine this with a distributed, preconditioned conjugate gradi

Eleanor Hamilton, Edward Fauchon-Jones, Mark Hannam, Charlie Hoy

We present a public catalogue of numerical-relativity binary-black-hole simulations. The catalogue contains datasets from 80 distinct configurations of precessing binary-black-hole systems, with mass ratios up to $m_2/m_1 = 8$, dimensionless spin magnitudes on the larger black hole up to $|\vec{S}_2|/m_2^2 = 0.8$ (the small black hole is non-spinning), and a

Francisco-Jesús Castro-Jiménez, David Mond, Luis Narváez-Macarro

In this paper we study the comparison between the logarithmic and the meromorphic de Rham complexes along a divisor in a complex manifold. We focus on the case of free divisors, starting with the case of locally quasihomogeneous divisors, and we explain how D-module theory can be used for this comparison.

Kazi Injamamul Haque, Zerrin Yumak

This paper presents FaceXHuBERT, a text-less speech-driven 3D facial animation generation method that allows to capture personalized and subtle cues in speech (e.g. identity, emotion and hesitation). It is also very robust to background noise and can handle audio recorded in a variety of situations (e.g. multiple people speaking). Recent approaches employ en

Ciro Ciliberto

Given a smooth, irreducible, projective surface $S$, let $g(S)$ be the minimum geometric genus of an irreducible curve that moves in a linear system of positive dimension on $S$. We determine the value of this birational invariant for a general surface of degree $d$ in $\mathbb P^3$ and give a bound for $g(S)$ if $S$ is a general polarised K3 or abelian surf

Usha N. Bhosle, Indranil Biswas

We determine the Brauer group and Picard group of the moduli space $U^{' s}_{L,par}$ of stable parabolic vector bundles of rank $r$ with determinant $L$ on a real curve $Y$ of arithmetic genus $g \,\ge\, 2$ with at most nodes as singularities.

Gaia Grosso, Nicolò Lai, Marco Letizia, Jacopo Pazzini

We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference dataset, characterising the data behaviour under normal circumstances, via a likelihood-ratio hypothesis test. The model is based on a modern implementation of kernel methods, nonpa

Álvaro Porras, Line Roald, Juan Miguel Morales, Salvador Pineda

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low pro

Yongming Li, Xikui Ma, Xuchen Wang, Grigorii Ptitcyn

Realizing continuous sweeping of perfect anomalous reflection in a wide angular range has become a technical challenge. This challenge cannot be overcome by the conventional aperiodic reflectarrays and periodic metasurfaces or metagratings. In this paper, we investigate means to create scanning reflectarrays for the reflection of plane waves coming from any

Jiang Hu, Kangkang Deng, Jiayuan Wu, Quanzheng Li

This paper aims to develop a Newton-type method to solve a class of nonconvex composite programs. In particular, the nonsmooth part is possibly nonconvex. To tackle the nonconvexity, we develop a notion of strong prox-regularity which is related to the singleton property, Lipschitz continuity, and monotonicity of the associated proximal operator, and we veri

J. Baglio, F. Campanario, S. Glaus, M. Mühlleitner

After the discovery of the Higgs boson in 2012 at the CERN Large Hadron Collider (LHC), the study of its properties still leaves room for an extended Higgs sector with more than one Higgs boson. 2-Higgs Doublet Models (2HDMs) are well-motivated extensions of the Standard Model (SM) with five physical Higgs bosons: two CP-even states $h$ and $H$, one CP-odd s

Jaromir Savelka, Arav Agarwal, Christopher Bogart, Majd Sakr

We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments, often involving short snippets of code, from introductory and intermediate programming courses at the postsecondary level. This emerging technology stirs countless discussions of its potential uses (e.g., exercise genera

Anton Bernshteyn, Abhishek Dhawan

Vizing's theorem states that every graph $G$ of maximum degree $\Delta$ can be properly edge-colored using $\Delta + 1$ colors. The fastest currently known $(\Delta+1)$-edge-coloring algorithm for general graphs is due to Sinnamon and runs in time $O(m\sqrt{n})$, where $n :=|V(G)|$ and $m :=|E(G)|$. We investigate the case when $\Delta$ is constant, i.e., $\

Lorenzo Gotta, Sanjay Moudgalya, Leonardo Mazza

We consider a quantum lattice spin model featuring exact quasiparticle towers of eigenstates with low entanglement at finite size, known as quantum many-body scars (QMBS). We show that the states in the neighboring part of the energy spectrum can be superposed to construct entire families of low-entanglement states whose energy variance decreases asymptotica

Horaţiu Cheval, Laurenţiu Leuştean

In this note we apply a lemma due to Sabach and Shtern to compute linear rates of asymptotic regularity for Halpern-type nonlinear iterations studied in optimization and nonlinear analysis.

Feng Chen, Matthias Rempel, Yuhong Fan

Radiative magnetohydrodynamic simulation includes sufficiently realistic physics to allow for the synthesis of remote sensing observables that can be quantitatively compared with observations. We analyze the largest flare in a simulation of the emergence of large flare-productive active regions described by Chen et al. The flare releases $4.5\times10^{31}$ e

Matouš Vrba, Viktor Walter, Václav Pritzl, Michal Pliska

A new robust and accurate approach for the detection and localization of flying objects with the purpose of highly dynamic aerial interception and agile multi-robot interaction is presented in this paper. The approach is proposed for use on board of autonomous aerial vehicles equipped with a 3D LiDAR sensor. It relies on a novel 3D occupancy voxel mapping me

Andrii Chaika, Andrea Richaud, Alexander Yakimenko

Ghost vortices constitute an elusive class of topological excitations in quantum fluids since the relevant phase singularities fall within regions where the superfluid density is almost zero. Here we present a platform that allows for the controlled generation and observation of such vortices. Upon rotating an imbalanced mixture of two-component Bose-Einstei

Ilias Chalkidis

Following the hype around OpenAI's ChatGPT conversational agent, the last straw in the recent development of Large Language Models (LLMs) that demonstrate emergent unprecedented zero-shot capabilities, we audit the latest OpenAI's GPT-3.5 model, `gpt-3.5-turbo', the first available ChatGPT model, in the LexGLUE benchmark in a zero-shot fashion providing exam

Rémi Catellier, Samuel Vaiter, Damien Garreau

A fundamental issue in machine learning is the robustness of the model with respect to changes in the input. In natural language processing, models typically contain a first embedding layer, transforming a sequence of tokens into vector representations. While the robustness with respect to changes of continuous inputs is well-understood, the situation is les

Kiyanoush Nazari, Gabriele Gandolfi, Zeynab Talebpour, Vishnu Rajendran

This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is int

Zihao Zou, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implicitly by convolutional neural networks (CNNs) pre-trained for image reconstruction. In this work, we follow an alternative line of work based on learning explicit regularization fun

Amanda L. Baxter, Rafael F. Lang, Craig Zywicki, Stephanie M. Gardner

Course-based undergraduate research experiences (CUREs) increase students' access to research. This lesson plan describes an interdisciplinary CURE developed to be able to involve over 60 students per semester in original research using data from large particle physics experiments and telescopes, although the methods described can easily be adopted by other

Arindam Biswas, Jyoti Prakash Saha

We show that the cop number of the Cayley sum graph of a finite group $G$ with respect to a symmetric subset $S$ is at most twice its degree when the graph is connected, undirected. We also prove that a similar bound holds for the cop number of generalised Cayley graphs and twisted Cayley sum graphs under some conditions. These extend a result of Frankl to s

Minou Goetze, Christina Herdt, Ricarda Conrad, Stephan Stricker

Preliminary research indicated that an increasing number of young adults end up in debt collection. Yet, debt collection agencies (DCAs) are still lacking knowledge on how to approach these consumers. A large-scale mixed-methods survey of consumers in Germany (N = 996) was conducted to investigate preference shifts from traditional to digital payment, and co

Cheng Chen, Stephen H. Lubow, Rebecca G. Martin, C. J. Nixon

With $n$-body simulations we investigate the stability of tilted circumbinary planetary systems consisting of two nonzero mass planets. The planets are initially in circular orbits that are coplanar to each other, as would be expected if they form in a flat but tilted circumbinary gas disc and decouple from the disc within a time difference that is much less

Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad, Shiqi Wang

ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models have substantially pushed the boundary of code generation and achieved impressive performance. Despite their great power, the huge number of

T. Agarwal, C. Patra, A. Kataria, Rajeshwari R. Chaudhari

Two-dimensional (2D) superconductivity in artificial interfaces and atomic-thin layers has gained attention for its exotic quantum phenomena and practical applications. Although bulk van der Waals layered materials have been explored for 2D superconductivity, most of these compounds do not exhibit remarkable properties despite exhibiting 2D characteristics.

Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant, John Andrew Raine

In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state

Isobel M. Romero-Shaw, Ryosuke Hirai, Arash Bahramian, Reinhold Willcox

We conduct binary population synthesis to investigate the formation of wind-fed high-mass X-ray binaries containing black holes (BH-HMXBs). We evolve multiple populations of high-mass binary stars and consider BH-HMXB formation rates, masses, spins and separations. We find that systems similar to Cygnus X-1 likely form after stable Case A mass transfer (MT)

Manuel Schlierf

Very little is yet known regarding the Willmore flow of surfaces with Dirichlet boundary conditions. We consider surfaces with a rotational symmetry as initial data and prove a global existence and convergence result for solutions of the Willmore flow with initial data below an explicit, sharp energy threshold. Strikingly, this threshold depends on the presc

Aiqiang Zhang, Benda Xu, Jun Weng, Huiyou Chen

Jinping Neutrino Experiment plans to deploy a new type of 8-inch MCP-PMT with high photon detection efficiency for MeV-scale neutrino measurements. This work studies the performance of the MCP-PMTs, including the photon detection efficiency, the charge resolution of the single photoelectron, the transition time spread, single photoelectron response, rates of

S. Athira, Saulo L. L. Silva, Sushma Lakshmi, Sharath Kumar C

In this paper, we used a theoretical measure known as distance between the states, $\mathcal{E}(\rho_e)$, to determine the bipartite entanglement of a one dimensional magnetic dimer system. The calculation was compared with the well-known entanglement measure, concurrence, and found to be the same. $\mathcal{E}(\rho_e)$ was, then, expressed in terms of two t

Anchit Gupta, Wenhan Xiong, Yixin Nie, Ian Jones

Latent diffusion models for image generation have crossed a quality threshold which enabled them to achieve mass adoption. Recently, a series of works have made advancements towards replicating this success in the 3D domain, introducing techniques such as point cloud VAE, triplane representation, neural implicit surfaces and differentiable rendering based tr

Hongsuk Choi, Hyeongjin Nam, Taeryung Lee, Gyeongsik Moon

Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection. However, its effects on 3D human body pose and shape estimation (3DHPSE) are open to question, whose target is fixed to a unique class, the human, and has an inherent task gap with SSL. We

Milad Sefidgaran, Abdellatif Zaidi

In this paper, we establish novel data-dependent upper bounds on the generalization error through the lens of a "variable-size compressibility" framework that we introduce newly here. In this framework, the generalization error of an algorithm is linked to a variable-size 'compression rate' of its input data. This is shown to yield bounds that depend on the

Alex B. Grilo, Or Sattath, Quoc-Huy Vu

It is an important question to find constructions of quantum cryptographic protocols which rely on weaker computational assumptions than classical protocols. Recently, it has been shown that oblivious transfer and multi-party computation can be constructed from one-way functions, whereas this is impossible in the classical setting in a black-box way. In this

Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range view representation. In this work, we unveil several key factors in building powerful range view models. We observe that the "many-to-one" mapping, semantic incoherence, and shape

Friedrich W. Hehl

The Poincar\'e (inhomogeneous Lorentz) group underlies special relativity. In these lectures a consistent formalism is developed allowing an appropriate gauging of the Poincar\'e group. The physical laws are formulated in terms of points, orthonormal tetrad frames, and components of the matter fields with respect to these frames. The laws are postulated to b

Yuchen Xiao, Xiaosheng Zhuang

In this paper, we investigate in detail the structures of the variational characterization $A_{N,t}$ of the spherical $t$-design, its gradient $\nabla A_{N,t}$, and its Hessian $\mathcal{H}(A_{N,t})$ in terms of fast spherical harmonic transforms. Moreover, we propose solving the minimization problem of $A_{N,t}$ using the trust-region method to provide sphe

Christian Schnell

The purpose of this paper is to establish several new results about the Hodge theory of Lagrangian fibrations on (not necessarily compact) holomorphic symplectic manifolds. Let $M$ be a holomorphic symplectic manifold of dimension $2n$ that is K\"ahler but not necessarily compact, and let $\pi \colon M \to B$ be a Lagrangian fibration. We establish a relatio

Zhenghao Wu, Subhadeep Pal, Sinan Keten

Matrix-free nanocomposites made from polymer grafted nanoparticles (PGN) represent a paradigm shift in materials science because they greatly improve nanoparticle dispersion and offer greater tunability over rheological and mechanical properties in comparison to neat polymers. Utilizing the full potential of PGNs requires a deeper understanding of how polyme

J. S. Dowker

A discrete Funk--Hecke formula is set up using the analogy between ordinary and operator spherical harmonics. It is the fuzzy sphere analogue of the conventional theory. An example is related, in the classical limit, to the Rayleigh partial wave expansion.

Björn Liljegren-Sailer, Ion Victor Gosea

Balanced Singular Perturbation Approximation (SPA) is a model order reduction method for linear time-invariant systems that guarantees asymptotic stability and for which there exists an a priori error bound. In that respect, it is similar to Balanced Truncation (BT). However, the reduced models obtained by SPA generally introduce better approximation in the

Pedro Quaresma, João Marcos, Walther Neuper

The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to thi

J. P. Cassidy, J. Hofierka, B. Cunningham, C. M. Rawlins

Positron binding energies in halogenated hydrocarbons are calculated \emph{ab initio} using many-body theory. For chlorinated molecules, including planars for which the interaction is highly anisotropic, very good to excellent agreement with experiment and recent DFT-based model-potential calculations is found. Predictions for fluorinated and brominated mole

Jihao Ma, Ruihuan Fang, Chengyin Han, Xunda Jiang

To achieve Bose-Einstein condensation, one may implement evaporative cooling by dynamically regulating the power of laser beams forming the optical dipole trap. We propose and experimentally demonstrate a protocol of Bayesian optimization of Bose-Einstein condensation via the evaporative cooling model. Applying this protocol, pure Bose-Einstein condensate of

Cambridge Yang, Michael Littman, Michael Carbin

In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and computation. In recent years, researchers have introduced objectives and corresponding reinforcement-learning algor

Tianyi Lu, Yangfeng Su, Zhaojun Bai

In Part I of this paper, we introduced a two dimensional eigenvalue problem (2DEVP) of a matrix pair and investigated its fundamental theory such as existence, variational characterization and number of 2D-eigenvalues. In Part II, we proposed a Rayleigh quotient iteration (RQI)-like algorithm (2DRQI) for computing a 2D-eigentriplet of the 2DEVP near a prescr

Stefan Glock, David Munhá Correia, Benny Sudakov

Finding general conditions which ensure that a graph is Hamiltonian is a central topic in graph theory. An old and well known conjecture in the area states that any $d$-regular $n$-vertex graph $G$ whose second largest eigenvalue in absolute value $\lambda(G)$ is at most $\frac{d}{C}$, for some universal constant $C>0$, has a Hamilton cycle. In this paper, w

Jeffry L. Hirst, Carl Mummert

In this paper, methods of second order and higher order reverse mathematics are applied to versions of a theorem of Banach that extends the Schroeder-Bernstein theorem. Some additional results address statements in higher order arithmetic formalizing the uncountability of the power set of the natural numbers. In general, the formalizations of higher order pr

Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as we

Claudia de Rham, Sebastian Garcia-Saenz, Lavinia Heisenberg, Victor Pozsgay

It has recently been argued that half degrees of freedom could emerge in Lorentz and parity invariant field theories, using a non-linear Proca field theory dubbed Proca-Nuevo as a specific example. We provide two proofs, using the Lagrangian and Hamiltonian pictures, that the theory possesses a pair of second class constraints, leaving $D-1$ degrees of freed

Tong Jin, Donggyu Kim

We generalize Baker-Bowler's theory of matroids over tracts to orthogonal matroids, define orthogonal matroids with coefficients in tracts in terms of Wick functions, orthogonal signatures, circuit sets, and orthogonal vector sets, and establish basic properties on functoriality, duality, and minors. Our cryptomorphic definitions of orthogonal matroids over

Tianze Yang, Yuhong Cao, Guillaume Sartoretti

In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light intensity, or pollution levels) over a given domain, based on measurements taken along their trajectory. They must frequently replan their path to balance the exploration of new are

Georges Sadaka, Victor Kalt, Ionut Danaila, Frédéric Hecht

We present a finite element toolbox for the computation of Bogoliubov-de Gennes modes used to assess the linear stability of stationary solutions of the Gross-Pitaevskii (GP) equation. Applications concern one (single GP equation) or two-component (a system of coupled GP equations) Bose-Einstein condensates in one, two and three dimensions of space. An imple

Anna-Carolina Haensch, Sarah Ball, Markus Herklotz, Frauke Kreuter

Advanced large language models like ChatGPT have gained considerable attention recently, including among students. However, while the debate on ChatGPT in academia is making waves, more understanding is needed among lecturers and teachers on how students use and perceive ChatGPT. To address this gap, we analyzed the content on ChatGPT available on TikTok in

Rupert L. Frank, Konstantin Merz

We consider Hardy operators on the half-space, that is, ordinary and fractional Schr\"odinger operators with potentials given by the appropriate power of the distance to the boundary. We show that the scales of homogeneous Sobolev spaces generated by the Hardy operators and by the fractional Laplacian are comparable with each other when the coupling constant

Aidan J. Campbell, Valerio Vitale, Mauro Brotons-Gisbert, Hyeonjun Baek

The interplay of charge, spin, lattice, and orbital degrees of freedom leads to a wide range of emergent phenomena in strongly correlated systems. In heterobilayer transition metal dichalcogenide moir\'e systems, recent observations of Mott insulators and generalized Wigner crystals are well described by triangular lattice single-orbital Hubbard models based

Simon Ellinger

We study pathwise approximation of strong solutions of scalar stochastic differential equations (SDEs) at a single time in the presence of discontinuities of the drift coefficient. Recently, it has been shown by M\"uller-Gronbach and Yaroslavtseva (2022) that for all $p \in [1, \infty)$ a transformed Milstein-type scheme reaches an $L^p$-error rate of at lea

Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini

Telegram is one of the most popular instant messaging apps in today's digital age. In addition to providing a private messaging service, Telegram, with its channels, represents a valid medium for rapidly broadcasting content to a large audience (COVID-19 announcements), but, unfortunately, also for disseminating radical ideologies and coordinating attacks (C

Xintong Yang, Ze Ji, Jing Wu, Yu-kun Lai

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This paper provid

Paolo Acquistapace, Francesca Bucci

In this article we study the optimal control problem with quadratic functionals for a linear Volterra integro-differential equation in Hilbert spaces. With the finite history seen as an (additional) initial datum for the evolution, following the variational approach utilized in the study of the linear-quadratic problem for memoryless infinite dimensional sys

Tianyu Yu, Yangning Li, Jiaoyan Chen, Yinghui Li

Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relationship to achieve satisfactory performance. Some recent papers tackle this problem by few-shot learning with elaborately designed pipelines and pre-trained word vectors. However, t

Yuming Sun, Jian Kang, Chinmay Haridas, Nicholas R. Mayne

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality r

Bhavesh Shrimali, Oscar Lopez-Pamies

Shrimali and Lopez-Pamies (2023) have recently shown that the Griffith criticality condition that governs crack growth in viscoelastic elastomers can be reduced to a fundamental form that involves exclusively the intrinsic fracture energy $G_c$ of the elastomer and, in so doing, they have brought resolution to the complete description of the historically elu

Ondřej Wojewoda, Martin Hrtoň, Meena Dhankhar, Jakub Krčma

We study theoretically and experimentally the process of Brillouin light scattering on an array of silicon disks on a thin Permalloy layer. We show that phase-resolved Brillouin light scattering microscopy performed on an array of weakly interacting dielectric nanoresonators can detect nanoscale waves and measure their dispersion. In our experiment, we were

Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang, Di Hu

Audio-visual learning helps to comprehensively understand the world by fusing practical information from multiple modalities. However, recent studies show that the imbalanced optimization of uni-modal encoders in a joint-learning model is a bottleneck to enhancing the model's performance. We further find that the up-to-date imbalance-mitigating methods fail

Luis M. Briceño-Arias, Patrick L. Combettes, Francisco J. Silva

A perspective function is a construction which combines a base function defined on a given space with a nonlinear scaling function defined on another space and which yields a lower semicontinuous convex function on the product space. Since perspective functions are typically nonsmooth, their use in first-order algorithms necessitates the computation of their

Nicola Rizzo, Massimo Equi, Tuukka Norri, Veli Mäkinen

Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (Algorithmica, 2022) developed the Elastic Founder Graph ($\mathsf{EFG}$) representing an alignment of $m$ sequences of length $n$, drawn from alphabet $\Sigma$ plus the special gap character: the paths spell the original sequences or their recombination. By enfor

LuÍs Carvalho, Cristina Diogo, Sérgio Mendes, Helena Soares

We use the $\mathbb{R}$-linearity of $I\lambda-T$ to define $\sigma(T)$, the right spectrum of a right $\mathbb{H}$-linear operator $T$ in a right quaternionic Hilbert space. We show that $\sigma(T)$ coincides with the $S$-spectrum $\sigma_S(T)$.

Furkan Ozcelik, Rufin VanRullen

In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have ty

Mithun Goutham, Stephanie Stockar

The convex hull cheapest insertion heuristic is a well-known method that efficiently generates good solutions to the Traveling Salesperson Problem. However, this heuristic has not been adapted to account for precedence constraints that restrict the order in which locations can be visited. Such constraints result in the precedence constrained traveling salesp

Chiranjit Ray

Andrews and Newman introduced the minimal excludant or ``$mex$'' function for an integer partition $\pi$ of a positive integer $n$, $mex(\pi)$, as the smallest positive integer that is not a part of $\pi$. They defined $\sigma mex(n)$ to be the sum of $mex(\pi)$ taken over all partitions $\pi$ of $n$. We prove infinite families of congruence and multiplicati

Yuli Zou, Weijian Deng, Liang Zheng

Model calibration usually requires optimizing some parameters (e.g., temperature) w.r.t an objective function (e.g., negative log-likelihood). In this paper, we report a plain, important but often neglected fact that the objective function is influenced by calibration set difficulty, i.e., the ratio of the number of incorrectly classified samples to that of

Wenting Tan, Xiao Shi1, Cunchi Lv, Xiaofang Zhao

Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. However, its efficiency is limited due to 2 challenges. First, efficient elastic scheduling of multi-regional cloud resources is usually missing, affecting resource utilization and perf

Tao Chen, Ruirui Li, Jiafeng Fu, Daguang Jiang

Object detection on VHR remote sensing images plays a vital role in applications such as urban planning, land resource management, and rescue missions. The large-scale variation of the remote-sensing targets is one of the main challenges in VHR remote-sensing object detection. Existing methods improve the detection accuracy of high-resolution remote sensing

Jordan Awan, Zhanyu Wang

Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based "repro sample" approach to produce statistically valid confidence intervals and hypothesis tests, which builds on the work of Xie and

Idan Eldar, Nofar Carmeli, Benny Kimelfeld

We study the fine-grained complexity of conjunctive queries with grouping and aggregation. For common aggregate functions (e.g., min, max, count, sum), such a query can be phrased as an ordinary conjunctive query over a database annotated with a suitable commutative semiring. We investigate the ability to evaluate such queries by constructing in loglinear ti

Kazem Taghandiki, Elnaz Rezaei Ehsan

Today, the web has become a mandatory platform to express users' opinions, emotions and feelings about various events. Every person using his smartphone can give his opinion about the purchase of a product, the occurrence of an accident, the occurrence of a new disease, etc. in blogs and social networks such as (Twitter, WhatsApp, Telegram and Instagram) reg

Raphael Bennett-Tennenhaus, Daniel Labardini-Fragoso

Semilinear clannish algebras have been recently introduced by the first author and Crawley-Boevey as a generalization of Crawley-Boevey's clannish algebras. In the present paper, we associate semilinear clannish algebras to the (colored) triangulations of a surface with marked points and orbifold points, and exhibit a Morita equivalence between these algebra

Md. Istiak Hossain Shihab, Md. Rakibul Hasan, Mahfuzur Rahman Emon, Syed Mobassir Hossen

While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employ

Özlem Kap, Simon Hartmann, Harmen Hoek, Sissi de Beer

Polymer brush layers are responsive materials that swell in contact with good solvents and their vapors. We deposit drops of an almost completely wetting volatile oil onto an oleophilic polymer brush layer and follow the response of the system upon simultaneous exposure to both liquid and vapor. Interferometric imaging shows that a halo of partly swollen pol

Yucheng Xu, Li Nanbo, Arushi Goel, Zijian Guo

Videos depict the change of complex dynamical systems over time in the form of discrete image sequences. Generating controllable videos by learning the dynamical system is an important yet underexplored topic in the computer vision community. This paper presents a novel framework, TiV-ODE, to generate highly controllable videos from a static image and a text

Qi Chen, Ziyang Ma, Tao Liu, Xu Tan

Audio-driven talking face has attracted broad interest from academia and industry recently. However, data acquisition and labeling in audio-driven talking face are labor-intensive and costly. The lack of data resource results in poor synthesis effect. To alleviate this issue, we propose to use TTS (Text-To-Speech) for data augmentation to improve few-shot ab

Tiago Roxo, Joana C. Costa, Pedro R. M. Inácio, Hugo Proença

Current Active Speaker Detection (ASD) models achieve great results on AVA-ActiveSpeaker (AVA), using only sound and facial features. Although this approach is applicable in movie setups (AVA), it is not suited for less constrained conditions. To demonstrate this limitation, we propose a Wilder Active Speaker Detection (WASD) dataset, with increased difficul

Antoine Ayache, Julien Hamonier, Laurent Loosveldt

Wavelet-type random series representations of the well-known Fractional Brownian Motion (FBM) and many other related stochastic processes and fields have started to be introduced since more than two decades. Such representations provide natural frameworks for approximating almost surely and uniformly rough sample paths at different scales and for study of va

C. Mandriota, N. Menga, G. Carbone

In this study, we propose a theory of rough adhesive contact of viscoelastic materials in steady-state sliding. By exploiting a boundary formulation based on Green function approach, the unknown contact domain is calculated by enforcing the local energy balance at the contact edge, thus considering also the non-conservative work of internal stresses which is

Bikash Kanungo, Aaron D. Kaplan, Chandra Shahi, Vikram Gavini

Energy barriers, which control the rates of chemical reactions, are seriously underestimated by computationally-efficient semi-local approximations for the exchange-correlation energy. The accuracy of a semi-local density functional approximation is strongly boosted for reaction barrier heights by evaluating that approximation non-self-consistently on Hartre

Nastasia Makki, Nicolai Lang, Hans Peter Büchler

The role of quantum fluctuations in modifying the critical behavior of non-equilibrium phase transitions is a fundamental but unsolved question. In this study, we examine the absorbing state phase transition of a 1D chain of qubits undergoing a contact process that involves both coherent and classical dynamics. We adopt a discrete-time quantum model with sta