May 2024 arXiv papers — page 143

Nathan Kirk, Christiane Lemieux

Despite possessing the low-discrepancy property, the classical d dimensional Halton sequence is known to exhibit poorly distributed projections when d becomes even moderately large. This, in turn, often implies bad performance when implemented in quasi-Monte Carlo (QMC) methods in comparison to, for example, the Sobol' sequence. As an attempt to eradicate th

Piotr Śniady

The transition measure is a foundational concept introduced by Sergey Kerov to represent the shape of a Young diagram as a centered probability measure on the real line. Over a period of decades the transition measure turned out to be an invaluable tool for many problems of the asymptotic representation theory of the symmetric groups. Kerov also showed how t

Sidong Feng, Suyu Ma, Han Wang, David Kong

The importance of computational modeling of mobile user interfaces (UIs) is undeniable. However, these require a high-quality UI dataset. Existing datasets are often outdated, collected years ago, and are frequently noisy with mismatches in their visual representation. This presents challenges in modeling UI understanding in the wild. This paper introduces a

Alejandro Soto Rodríguez

The first measurements of the inclusive and normalised differential cross sections for the production of single top quarks in association with a W boson in proton-proton collisions at a centre-of-mass energy of 13.6 TeV are presented. The data used were recorded with the CMS detector at the LHC during 2022, and correspond to an integrated luminosity of 34.7

Michael Ackerman

This research paper discusses the advances made in the past decade in biomedicine and Large Language Models. To understand how the advances have been made hand-in-hand with one another, the paper also discusses the integration of Natural Language Processing techniques and tools into biomedicine. Finally, the goal of this paper is to expand on a survey conduc

Xia Su, Jon E. Froehlich, Eunyee Koh, Chang Xiao

Sound plays a crucial role in enhancing user experience and immersiveness in Augmented Reality (AR). However, current platforms lack support for AR sound authoring due to limited interaction types, challenges in collecting and specifying context information, and difficulty in acquiring matching sound assets. We present SonifyAR, an LLM-based AR sound authori

Lilit Avetisyan, X. Jessie Yang, Feng Zhou

Maintaining adequate situation awareness (SA) is crucial for the safe operation of conditionally automated vehicles (AVs), which requires drivers to regain control during takeover (TOR) events. This study developed a predictive model for real-time assessment of driver SA using multimodal data (e.g., galvanic skin response, heart rate and eye tracking data, a

Aubrey Condor, Zachary Pardos

We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model. Automatic grading may decrease the time burden of rating open-ended items for educators, but a lack of robust evaluation methods for these models can result in uncertainty of their quality. Current state-of-the-art ASAG models are configured to match hu

Bahareh Nouri, Jamshid Saeidian

This study aims on proposing a new structure for constructing Bernstein-like bases. The structure uses an auxiliary function and a shape parameter to construct a new family of bases from any family of blending functions. The new family of bases inherit almost all algebraic and geometric properties of the initial blending functions. The corresponding curves h

Per Helander, Alan G. Goodman, Craig D. Beidler, Michal Kuczyński

We draw attention to an interesting possibility in the design and operation of stellarator fusion reactors, which has hitherto been considered unrealistic under burning-plasma conditions. Thanks to recent advances in stellarator optimisation theory, it appears possible to create a positive (outward-pointing) radial electric field in the plasma core by carefu

Agustin G. Bonifacio, Nadia Guiñazu, Noelia Juarez, Pablo Neme

We study a one-to-one labor matching market. If a worker considers resigning from her current job to obtain a better one, how long does it take for this worker to actually get it? We present an algorithm that models this situation as a re-stabilization process involving a vacancy chain. Each step of the algorithm is a link of such a chain. We show that the l

Donglin Zhan, James Anderson

Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, tasks can vary both in their importance during different training stages and in whether they contain noisy labeled data or not, making a uniform approach suboptimal. To address these i

Ryan Milton, Sebouh J. Paul, Barak Schmookler, Miguel Arratia

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design's high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for e

Ellen Krusell, Yilin Wang, Hao Wu

We study the commutation relation for 2-radial SLE in the unit disc starting from two boundary points. We follow the framework introduced by Dub\'{e}dat. Under an additional requirement of the interchangeability of the two curves, we classify all locally commuting 2-radial SLE$_\kappa$ for $\kappa\in (0,8)$: it is either a two-sided radial SLE$_\kappa$ with

Dihia Lanasri

Nowadays, companies are racing towards Linked Open Data (LOD) to improve their added value, but they are ignoring their SPARQL query logs. If well curated, these logs can present an asset for decision makers. A naive and straightforward use of these logs is too risky because their provenance and quality are highly questionable. Users of these logs in a trust

David Allen, José J. La Luz, Stephen Majewicz, Marcos Zyman

In this paper we compute the Chermak-Delgado measure of the mod $p^{n}$ Heisenberg Group for any prime $p$. To achieve this we introduce the notion of the pseudocentralizer and prove various results about it.

Oren Bell, Ashwin Kumar, Chris Gill

Memory allocation is a fairly mature field of computer science. However, we challenge a prevailing assumption in the literature over the last 50 years which, if reconsidered, necessitates a fundamental reevaluation of many classical memory management algorithms. We pose a model where the allocation algorithm runs on host memory but allocates device memory an

Ramkarn Patne

Several studies have investigated the turbulent flow of elastoviscoplastic (EVP) fluids, which exhibit yield stress in addition to viscoelasticity. The instabilities that could be responsible for the transition to turbulence in the EVP fluid flows remain unknown. Thus, the present explores the linear stability of EVP plane Couette flow (PCF) by employing the

Daniel Gallimore, Jinfeng Liao

We study the charge and mass distributions within a nucleon and compute the associated squared radii based on a potential model approach. Different constituent quark configurations such as $\Delta$, $Y$, and quark-diquark are considered and compared, with model parameters calibrated by experimental measurements of the proton and neutron charge radius. The re

Edward Y. Chang

This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ethics. We introduce DIKE, an adversarial framework that enhances the LLMs' ability to internalize and reflect global human values, adapting to varied cultural contexts to promote transparency and trust among users. The

Andrew N. Jordan, John C. Howell, Achim Kempf, Shunxing Zhang

Previous work established fundamental bounds on subwavelength resolution for the radar range resolution problem, called superradar [Phys. Rev. Appl. 20, 064046 (2023)]. In this work, we identify the optimal waveforms for distinguishing the range resolution between two reflectors of identical strength. We discuss both the unnormalized optimal waveform as well

Dmitry Levko

Data-driven techniques developed in recent years for the discovery of equations describing complex physical phenomena open unique opportunities for plasma physics. These methods allow getting insights into the processes difficult for analytical description. Since gas discharges can be represented as complex electrical circuits consisting of impedances and ca

Immanuel Christopher Jebaraj, Oleksiy Agapitov, Vladimir Krasnoselskikh, Laura Vuorinen

Collisionless shock waves, ubiquitous in the universe, are crucial for particle acceleration in various astrophysical systems. Currently, the heliosphere is the only natural environment available for their in situ study. In this work, we showcase the collective acceleration of electrons and ions by one of the fastest in situ shocks ever recorded, observed by

Joel Rorseth, Parke Godfrey, Lukasz Golab, Divesh Srivastava

This paper demonstrates RAGE, an interactive tool for explaining Large Language Models (LLMs) augmented with retrieval capabilities; i.e., able to query external sources and pull relevant information into their input context. Our explanations are counterfactual in the sense that they identify parts of the input context that, when removed, change the answer t

Omid Zabeti

Suppose E and F are locally convex-solid vector lattices. Although we have a suitable vector lattice structure for the tensor product E and F (known as the Fremlin tensor product and denoted by E\otimesF), there is a lack of topological structure on E\otimes F, in general. In this note, we consider a topological attitude on E\otimes F that makes it into a lo

Ziqi Guo, Jack Felag, Jordan C. Rozum, Rion Brattig Correia

Social media data allows researchers to construct large digital cohorts to study the interplay between human behavior and medical treatment.Identifying the users most relevant to a specific health problem is, however, a challenge in that social media sites vary in the generality of their discourse. To filter relevant users on any social media, we have develo

Sándor Juhász, Zoltán Elekes, Virág Ilyés, Frank Neffke

Strong local clusters help firms compete on global markets. One explanation for this is that firms benefit from locating close to their suppliers and customers. However, the emergence of global supply chains shows that physical proximity is not necessarily a prerequisite to successfully manage customer-supplier relations anymore. This raises the question whe

M. Sajid, Rahul Sharma, Iman Beheshti, M. Tanveer

The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state-of-the-art review followed by a comprehensive evaluation o

Alexey S. Kotykhov, Konstantin Gubaev, Vadim Sotskov, Christian Tantardini

We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivatives of energies with respect to magnetic moments) as obtained spin-polarized density functional theory calcul

Dimitris Bertsimas, Cynthia Zeng

The escalating frequency and severity of natural disasters, exacerbated by climate change, underscore the critical role of insurance in facilitating recovery and promoting investments in risk reduction. This work introduces a novel Adaptive Robust Optimization (ARO) framework tailored for the calculation of catastrophe insurance premiums, with a case study a

Rixin Yu, Erdzan Hodzic, Karl-Johan Nogenmyr

Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDE). This paper explores the application of novel operator learning methodologies to unravel the intricate dynamics of f

Jinwei Lin

Front-following is more technically difficult to implement than the other two human following technologies, but front-following technology is more practical and can be applied in more areas to solve more practical problems. In this paper, we will analyze the detailed design of LRF groups, the structure and combination design of coordinate system of Robot Det

Halil Mutuk, Xian-Wei Kang

Motivated by the discovery of hidden-charm strange pentaquarks, we conduct a systematic study of the magnetic moments of the hidden-bottom strange pentaquarks in molecular picture. We calculate magnetic moments of hidden-bottom strange pentaquarks with strangeness-1 and 2. Magnetic moment gives valuable information about the inner structure and shape of the

Vivian Liu, Rubaiat Habib Kazi, Li-Yi Wei, Matthew Fisher

Creating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method perfo

I. D. Avdeev, M. O. Nestoklon

Using the atomistic tight-binding method in combination with symmetry analysis and extended effective mass theory we derive a phenomenological model for the fine structure of the ground electron and hole states in $[111]$-grown PbX, X=S,Se hexagonal and cylindrical nanowires. Projection of the tight-binding states to the basis of valley states enables the ex

Oem Trivedi, Maxim Khlopov

Holographic dark energy models have proven to be a very interesting way to study various aspects of late-time acceleration of the universe. In this work we extensively study HDE models with the Granda-Oliveros cutoff with an ansatz based approach. We consider the Tsallis, Barrow and PLEC HDE models in this regard and consdier simple power law, emergent unive

Ignace Aristide Minlend, Jing Wu

In this paper, we prove the existence of a family of non trivial compact subdomains $\O$ in the manifold $\mathcal{M}=\R^N\times \R/2\pi\Z, N\geq 2$ for which the overdetermined Neumann boundary value problem \begin{align}\label{Neumann1} \left \{ \begin{aligned} $-\D w&=\mu g(w) && \qquad \text{in $ \Omega$,}$ \frac{\partial w}{\partial\eta} &=0 &&\qquad \t

Robert Valente, Dilian Yang

In this paper, we initiate the study of higher rank Baumslag-Solitar semigroups and their related C*-algebras. We focus on two extreme, but interesting, classes - one is related to products of odometers and the other is related to Furstenberg's $\times p,, \times q$ conjecture. For the former class, whose C*-algebras are studied by H. Li and the second autho

Soonpil Kang, James F. Kelly, Anthony P. Austin, Francis X. Giraldo

This paper presents a multiscale modeling framework (MMF) to model moist atmospheric limited-area weather. The MMF resolves large-scale convection using a coarse grid while simultaneously resolving local features through numerous fine local grids and coupling them seamlessly. Both large- and small-scale processes are modeled using the compressible Navier-Sto

Zeng Wang, Lilas Alrahis, Likhitha Mankali, Johann Knechtel

Chip design is about to be revolutionized by the integration of large language, multimodal, and circuit models (collectively LxMs). While exploring this exciting frontier with tremendous potential, the community must also carefully consider the related security risks and the need for building trust into using LxMs for chip design. First, we review the recent

Masaki Kuribayashi, Kohei Uehara, Allan Wang, Daisuke Sato

Visual Language Navigation (VLN) powered robots have the potential to guide blind people by understanding route instructions provided by sighted passersby. This capability allows robots to operate in environments often unknown a prior. Existing VLN models are insufficient for the scenario of navigation guidance for blind people, as they need to understand ro

Ge Xu, Huajie Chen, Xingyu Gao

In this paper, we study numerical approximations of the ground states in finite temperature density functional theory. We formulate the problem with respect to the density matrices and justify the convergence of the finite dimensional approximations. Moreover, we provide an optimal a priori error estimate under some mild assumptions and present some numerica

J. E. Hirsch, F. Marsiglio

In Ref. [1] we surveyed the known hydride superconductors, and compared their resistive behavior to that of typical known superconductors, including conventional (e.g. NbN and MgB$_2$) and unconventional (e.g. YBCO) superconductors, and concluded that the behavior of the hydrides was indicative of nonstandard or no superconductivity. In the preceding comment

Alex Krotz, Roel Tempelaar

We present a mixed quantum-classical framework for the microscopic and non-Markovian modeling of exciton-phonon scattering in solid-state materials, and apply it to calculate the optical linewidths of monolayer MoS2. Within this framework, we combine reciprocal-space mixed quantum-classical dynamics with models for the quasiparticle band structure as well as

Athanasios P. Chrysologou, Nestor D. Chatzidiamantis, Alexandros-Apostolos A. Boulogeorgos, Zhiguo Ding

A fundamental objective of the forthcoming sixth-generation wireless networks is to concurrently serve a vast array of devices many of which, such as Internet-of-Things (IoT) sensors, are projected to have low power requirements or even operate in a battery-free manner. To achieve this goal, non-orthogonal multiple access (NOMA) and ambient backscatter commu

Piero Deidda, Nicola Segala, Mario Putti

We address the problem of computing the graph $p$-Laplacian eigenpairs for $p\in (2,\infty)$. We propose a reformulation of the graph $p$-Laplacian eigenvalue problem in terms of a constrained weighted Laplacian eigenvalue problem and discuss theoretical and computational advantages. We provide a correspondence between $p$-Laplacian eigenpairs and linear eig

Paco H. Talero Lopez

One of the most important active methodologies for physics learning developed in recent years is peer instruction. Its technique has allowed, among other things, to monitor students' conceptual learning. In this sense, \textcite{PhysRevSTPER.6.020105} has proposed a model that seeks to understand the dynamics of this methodology. However, her model is very p

Zhao Ren, Kevin Scheck, Qinhan Hou, Stefano van Gogh

Electromyography-to-Speech (ETS) conversion has demonstrated its potential for silent speech interfaces by generating audible speech from Electromyography (EMG) signals during silent articulations. ETS models usually consist of an EMG encoder which converts EMG signals to acoustic speech features, and a vocoder which then synthesises the speech signals. Due

Md Sadun Haq, Ali Saman Tosun, Turgay Korkmaz

Containerization has emerged as a revolutionary technology in the software development and deployment industry. Containers offer a portable and lightweight solution that allows for packaging applications and their dependencies systematically and efficiently. In addition, containers offer faster deployment and near-native performance with isolation and securi

Alberto Medina, Andres Villabon

The goal of this paper is to study the geometry of the connected unit component of the real general linear Lie group $4$ dimensional $G_0$ as a Lorentzian and flat affine manifold. As the group $G_0$ is naturally equipped with a bi-invariant Hessian metric $k^+$, relative to a bi-invariant flat affine structure $\nabla$, we examine both structures and the re

Kapil Saraswat, Prashanta Kumar Khandai, Deependra Singh Rawat, Venktesh Singh

The transverse momentum ($p_T$) spectra of charged hadrons in $p+p$, $p+Pb$ and $Pb+Pb$ collisions at $\sqrt {s_{NN}} = 5.02$ TeV are presented here within the rapidity range of $-2.5<y<2.0$. We study the medium effects, which is produced by heavy ion collisions, on the behaviour of charged hadrons, by using a phenomenological fit function. These effects are

Guangzeng Han, Jack Tsao, Xiaolei Huang

Lengthy documents pose a unique challenge to neural language models due to substantial memory consumption. While existing state-of-the-art (SOTA) models segment long texts into equal-length snippets (e.g., 128 tokens per snippet) or deploy sparse attention networks, these methods have new challenges of context fragmentation and generalizability due to senten

Daria Maksimova

We improve on Gonek-Montgomery's quantitative version of Kronecker's approximation theorem.

Catarina Brites, João Ascenso

In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a

V. L. Gorshenin, F. Ya. Khalili

Injecting a non-Gaussian (Fock or Shr\"odinger cat) quantum state into the dark port of a two-arm interferometer and a strong classical light into the bright one, it is possible, in principle, to detect a given phase shift unambiguously using the orthogonality between the original and displaced in the interferometer non-Gaussian states. The optical losses de

Po-Hsun Chu, Ching-Han Chen

Binary convolutional neural networks (BCNNs) provide a potential solution to reduce the memory requirements and computational costs associated with deep neural networks (DNNs). However, achieving a trade-off between performance and computational resources remains a significant challenge. Furthermore, the fully connected layer of BCNNs has evolved into a sign

Safouane Taoufik, Badr Missaoui

The Method of Successive Approximations (MSA) is a fixed-point iterative method used to solve stochastic optimal control problems. It is an indirect method based on the conditions derived from the Stochastic Maximum Principle (SMP), an extension of the Pontryagin Maximum Principle (PMP) to stochastic control problems. In this study, we investigate the contra

Qing Wu, Xu Guo, Lixuan Chen, Yanyan Liu

X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coefficients (LACs) of metals vary significantly with X-ray energy, causing a nonlinear beam hardening effect (BHE) in CT measurements. Reconstructing CT images from metal-corrupted me

Yunchuan Ma, Laiyun Qing, Guorong Li, Yuankai Qi

Despite the significant progress of fully-supervised video captioning, zero-shot methods remain much less explored. In this paper, we propose a novel zero-shot video captioning framework named Retrieval-Enhanced Test-Time Adaptation (RETTA), which takes advantage of existing pretrained large-scale vision and language models to directly generate captions with

Peter Tino, Robert Simon Fong, Roberto Fabio Leonarduzzi

This work proposes a time series prediction method based on the kernel view of linear reservoirs. In particular, the time series motifs of the reservoir kernel are used as representational basis on which general readouts are constructed. We provide a geometric interpretation of our approach shedding light on how our approach is related to the core reservoir

Ce Wang, Wanjie Sun

Remote sensing images captured by different platforms exhibit significant disparities in spatial resolution. Large scale factor super-resolution (SR) algorithms are vital for maximizing the utilization of low-resolution (LR) satellite data captured from orbit. However, existing methods confront challenges in recovering SR images with clear textures and corre

Zong Chen, Songyuan Fa, Yiqun Li

We present a novel framework for addressing the challenges of multi-Agent planning and formation control within intricate and dynamic environments. This framework transforms the Multi-Agent Path Finding (MAPF) problem into a Multi-Agent Trajectory Planning (MATP) problem. Unlike traditional MAPF solutions, our multilayer optimization scheme consists of a glo

Serene Shum, Nathan Wiebe

We provide a new paradigm for quantum simulation that is based on path integration that allows quantum speedups to be observed for problems that are more naturally expressed using the path integral formalism rather than the conventional sparse Hamiltonian formalism. We provide two novel quantum algorithms based on Hamiltonian versions of the path integral fo

Yao Liu, Ruoyu Wang, Yuanjiang Cao, Quan Z. Sheng

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatio-temporal dynamics

Xinyu Huang, Henrik Hellström, Carlo Fischione

This paper investigates over-the-air computation (AirComp) over multiple-access time-varying channels, where devices with high mobility transmit their sensing data to a fusion center (FC) for averaging. To combat the Doppler shift induced by time-varying channels, each device adopts orthogonal time frequency space (OTFS) modulation. Our objective is minimizi

Dimitrios Bousis, Leandros Perivolaropoulos

We investigate the redshift dependence of the Hubble tension by comparing the luminosity distances obtained using an up-to-date BAO dataset (including the latest DESI data) calibrated with the CMB-inferred sound horizon, and the Pantheon+ SnIa distances calibrated with Cepheids. Using a redshift tomography method, we find: 1) The BAO-inferred distances are d

Jiale Han, Xiaowu Dai

Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of futur

Joyce Lai, Peter Seiler

Online convex optimization (OCO) is a powerful tool for learning sequential data, making it ideal for high precision control applications where the disturbances are arbitrary and unknown in advance. However, the ability of OCO-based controllers to accurately learn the disturbance while maintaining closed-loop stability relies on having an accurate model of t

Lei Meng, Junjie Cao, Fei Li, Shenshen Yang

In the General Next-to-Minimal Supersymmetric Standard Model (GNMSSM), singlet particles may form a secluded sector of dark matter (DM), in which Singlino-like DM could achieve the observed relic abundance through various channels such as $\tilde{\chi}_1^0 \tilde{\chi}_1^0 \to h_s h_s, A_s A_s, h_s A_s$, where $h_s$ and $A_s$ represent singlet-dominated CP-e

Kamyar Zeinalipour, Yusuf Gökberk Keptiğ, Marco Maggini, Leonardo Rigutini

This paper introduces the first Turkish crossword puzzle generator designed to leverage the capabilities of large language models (LLMs) for educational purposes. In this work, we introduced two specially created datasets: one with over 180,000 unique answer-clue pairs for generating relevant clues from the given answer, and another with over 35,000 samples

Alex Wastnidge

Neural networks and deep learning are often deployed for the sake of the most comprehensive music generation with as little involvement as possible from the human musician. Implementations in aid of, or being a tool for, music practitioners are sparse. This paper proposes the integration of generative stacked autoencoder structures for rhythm generation, wit

Anik Mallik, Jiang Xie, Zhu Han

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is require

Soheli Mukherjee, Pierre Le Doussal, Naftali R. Smith

We investigate the statistics of the local time $\mathcal{T} = \int_0^T \delta(x(t)) dt$ that a run and tumble particle (RTP) $x(t)$ in one dimension spends at the origin, with or without an external drift. By relating the local time to the number of times the RTP crosses the origin, we find that the local time distribution $P(\mathcal{T})$ satisfies the lar

Volodymyr Fedynyak, Yaroslav Romanus, Oles Dobosevych, Igor Babin

In this paper, we show that transferring knowledge from other domains of video understanding combined with large-scale learning can improve robustness of Video Object Segmentation (VOS) under complex circumstances. Namely, we focus on integrating scene global motion knowledge to improve large-scale semi-supervised Video Object Segmentation. Prior works on VO

Shreyan Ganguly, Roshan Nayak, Rakshith Rao, Ujan Deb

Knowledge distillation, a widely used model compression technique, works on the basis of transferring knowledge from a cumbersome teacher model to a lightweight student model. The technique involves jointly optimizing the task specific and knowledge distillation losses with a weight assigned to them. Despite these weights playing a crucial role in the perfor

Wankang Zhai, Yuhan Wang

Understanding the dynamics of momentum and game fluctuation in tennis matches is cru-cial for predicting match outcomes and enhancing player performance. In this study, we present a comprehensive analysis of these factors using a dataset from the 2023 Wimbledon final. Ini-tially, we develop a sliding-window-based scoring model to assess player performance, a

Litong Zheng, Feng Hong, Weijie Xu, Wan Zheng

The Chinese numerical string corpus, serves as a valuable resource for speaker verification, particularly in financial transactions. Researches indicate that in short speech scenarios, text-dependent speaker verification (TD-SV) consistently outperforms text-independent speaker verification (TI-SV). However, TD-SV potentially includes the validation of text

Jinjie Yu, Nan Ding, Junhui Fan, Yunyong Tang

We present here the first systematic search of short timescale $\gamma$-ray flares from 29 high Galactic latitude BL Lac objects over 14 years of Fermi Large Area Telescope data. Using a combined Bayesian Blocks and HOP algorithm, we identified seven high-quality orbital timescale flare segments from three sources and quantified 24 short-timescale flare stru

Elio Quiroga Rodriguez

The search for extraterrestrial intelligence on exoplanets is a rich field of conceptual exploration. Author introduces two definitions that could help to narrow down the possibilities that an extraterrestrial civilization may or may not have initiated exploration of its own star system and beyond. It is concluded that in some cases certain extraterrestrial

Zhen Tan, Zongtan Zhou, Yangbing Ge, Zi Wang

The reliance on accurate camera poses is a significant barrier to the widespread deployment of Neural Radiance Fields (NeRF) models for 3D reconstruction and SLAM tasks. The existing method introduces monocular depth priors to jointly optimize the camera poses and NeRF, which fails to fully exploit the depth priors and neglects the impact of their inherent n

Volodymyr Fedynyak, Yaroslav Romanus, Bohdan Hlovatskyi, Bohdan Sydor

The recent works on Video Object Segmentation achieved remarkable results by matching dense semantic and instance-level features between the current and previous frames for long-time propagation. Nevertheless, global feature matching ignores scene motion context, failing to satisfy temporal consistency. Even though some methods introduce local matching branc

Shouxing Zhao, Min He

We revisit the dissociation of heavy quarkonia by thermal partons at the next-to-leading order (NLO, also known as inelastic parton scattering dissociation) in the Quark-Gluon Plasma (QGP). Utilizing the chromo-electric dipole coupling from QCD multipole expansion as an effective Hamiltonian, this has been conducted in the approach of second-order quantum me

Danilo Comminiello, Eleonora Grassucci, Danilo P. Mandic, Aurelio Uncini

Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their superior results when dealing with multidimensional signals in real-world 3D and 4D paradigms. This paper provides a foundational framework that serves as a roadmap for understanding wh

Long Peng, Yang Cao, Renjing Pei, Wenbo Li

Real-SR endeavors to produce high-resolution images with rich details while mitigating the impact of multiple degradation factors. Although existing methods have achieved impressive achievements in detail recovery, they still fall short when addressing regions with complex gradient arrangements due to the intensity-based linear weighting feature extraction m

Zexue Wu, Yifeng Gong, Aoqian Zhang

We utilized the Mamba model for time series data prediction tasks, and the experimental results indicate that our model performs well.

Yabo Wang, Bing Yang, Xiaofei Li

Extracting direct-path spatial feature is crucial for sound source localization in adverse acoustic environments. This paper proposes the IPDnet, a neural network that estimates direct-path inter-channel phase difference (DP-IPD) of sound sources from microphone array signals. The estimated DP-IPD can be easily translated to source location based on the know

Soner Aydin, Sinan Yildirim

Frequency estimation plays a critical role in many applications involving personal and private categorical data. Such data are often collected sequentially over time, making it valuable to estimate their distribution online while preserving privacy. We propose AdOBEst-LDP, a new algorithm for adaptive, online Bayesian estimation of categorical distributions

Pintu Debnath, Sourav Kanti Patra

In \cite{Fi} A. Fish proved that if $E_{1}$ and $E_{2}$ are two subsets of $\mathbb{Z}$ of positive upper Banach density, then there exists $k\in\mathbb{Z}\setminus{0}$ such that $k\cdot\mathbb{Z}\subset\left(E_{1}-E_{1}\right)\cdot\left(E_{2}-E_{2}\right)$. In \cite{G}, S. Goswami proved the same but a fundamental result on the set of prime numbers $\mathbb

Xiaoxiao Chi, Xuyun Zhang, Yan Wang, Lianyong Qi

Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference attacks (MIAs), leading to the leakage of users' membership privacy. However, existing MIAs relying on shadow training suffer a large performance drop when the attacker lacks knowledg

Constantin Schempp, Christian Friedrich

We propose a visual servoing method consisting of a detection network and a velocity trajectory planner. First, the detection network estimates the objects position and orientation in the image space. Furthermore, these are normalized and filtered. The direction and orientation is then the input to the trajectory planner, which considers the kinematic constr

Alexandru Aleman, Frej Dahlin

Given the reproducing kernel $k$ of the Hilbert space $\mathcal{H}_k$ we study spaces $\mathcal{H}_k(b)$ whose reproducing kernel has the form $k(1-bb^*)$, where $b$ is a row-contraction on $\mathcal{H}_k$. In terms of reproducing kernels this it the most far-reaching generalization of the classical de Branges-Rovnyaks spaces, as well as their very recent ge

Jinfu Zhu, Hongli Ding, Haokui Li, Qiaoye Ran

Many accelerators require considerable electronic systems for tests, verification, and operation. In Shenzhen Superconducting Soft X-ray Free Electron Laser (S3FEL), to meet the early tests and verification of various systems, save development expenses, and improve the reusability of hardware, firmware, and software systems, we have considered the needs of e

Kamilla Faizullina, Evgeni Burovski

We study a lattice model of a magnetic polymer where the XY spin variables are located on a self-avoiding walk (SAW) on a regular lattice in two and three dimensions. We consider the regime where both spins and conformations are dynamic, thus the XY model is defined on a dynamic lattice and conformations generate an annealed disorder. Using Monte Carlo simul

Xuelian Guo, Ivan Kaygorodov, Liming Tang

This is the second paper in our series of papers dedicated to the study of maps on the mirror Heisenberg-Virasoro algebra. The first paper is dedicated to the study of unary maps and the present paper is dedicated to the study of binary maps. Namely, we describe biderivations and left-symmetric algebra structures on the complex mirror Heisenberg-Virasoro alg

Phoebe Jing, Yijing Gao, Yuanhang Zhang, Xianlong Zeng

In the realm of predictive analytics, the nuanced domain knowledge of investigators often remains underutilized, confined largely to subjective interpretations and ad hoc decision-making. This paper explores the potential of Large Language Models (LLMs) to bridge this gap by systematically converting investigator-derived insights into quantifiable, actionabl

Özlem Tuğfe Demir, Lianet Méndez-Monsanto, Nicola Bastianello, Emma Fitzgerald

The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation inter

Zeyu Xiao, Zhiwei Xiong

Recent advancements in light field super-resolution (SR) have yielded impressive results. In practice, however, many existing methods are limited by assuming fixed degradation models, such as bicubic downsampling, which hinders their robustness in real-world scenarios with complex degradations. To address this limitation, we present LF-DEST, an effective bli

Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen

Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attention. In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute.

Ramit Debnath, Pengyu Zhang, Tianzhu Qin, R. Michael Alvarez

As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential deployment. Through a comprehensive data-driven investigation, this paper explores the types of news that captivate public interest in geoen

Feng Liu, Zhi Chen, Ruodu Wang, Shuming Wang

Problem definition: We consider a newsvendor problem with unknown demand distribution, where we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common characteristics and misspecification under which such characteristics might be misspecified. Methodology/results: The newsvendor hedges against ambiguity and mis