December 2024 arXiv papers — page 117

Robin Guillard, Martin Schecklmann, Jorge Simoes, Berthold Langguth

The influence of naps on tinnitus was systematically assessed by exploring the frequency, clinical and demographic characteristics of this phenomenon. 9,724 data from two different tinnitus databases (Tinnitus Hub: $n = 6115$; Tinnitus Research Initiative (TRI): $n = 3627$) were included. After separate analysis of the databases, these results were then comp

Yuchen Fang, Yuxuan Liang, Bo Hui, Zezhi Shao

Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic dispatching and path planning in city management and personal traveling. Spatio-temporal graph neural networks (STGNNs) stand out as the mainstream solution in this task. Nevertheless, the quadratic complexity of remarkable dynamic spatial modeling-based STGNN

F. C. Salgado, A. Kozan, D. Seipt, D. Hollatz

The pepper-pot method is a widely used technique, originally proposed for measuring the emittance of space-charge-dominated electron beams from radio-frequency photoinjectors. With recent advances in producing high-brightness electron beams via laser wakefield acceleration (LWFA), the method has also been applied to evaluate emittance in this new regime. Her

Ali Guven

Approximation properties of Ces\`{a}ro and Abel-Poisson means of hexagonal Fourier series are studied. The degree of approximation by these means of hexagonal Fourier series of functions, which are continuous and periodic with respect to the hexagon lattice, is estimated in terms of modulus of continuity of functions.

Matias Daneels, Jan Goedgebeur, Jarne Renders

An injective colouring of a graph is a colouring in which every two vertices sharing a common neighbour receive a different colour. Chen, Hahn, Raspaud and Wang conjectured that every planar graph of maximum degree $\Delta \ge 3$ admits an injective colouring with at most $\lfloor 3\Delta/2\rfloor$ colours. This was later disproved by Lu\v{z}ar and \v{S}krek

Haoran Zheng, Jieming Shi, Renchi Yang

Graph similarity computation (GSC) is to calculate the similarity between one pair of graphs, which is a fundamental problem with fruitful applications in the graph community. In GSC, graph edit distance (GED) and maximum common subgraph (MCS) are two important similarity metrics, both of which are NP-hard to compute. Instead of calculating the exact values,

Zhaoxuan Zhang, Le Zhang, Pengjie Zhang

This study employs a novel approach for reconstructing the thermal Sunyaev-Zeldovich (tSZ) effect power spectrum from Planck data using the Analytical Blind Separation (ABS) method. The ABS method improves the recovery of weak signals, by applying eigenmode exclusion for low signal-to-noise ratio regimes and introducing a shift parameter to stabilize calcula

Kai Zhang, Fujun Luan, Sai Bi, Jianming Zhang

Classifier-free guidance (CFG) is widely used in diffusion models but often introduces over-contrast and over-saturation artifacts at higher guidance strengths. We present EP-CFG (Energy-Preserving Classifier-Free Guidance), which addresses these issues by preserving the energy distribution of the conditional prediction during the guidance process. Our metho

Thomas Breuer, Kay Magaard, Robert A. Wilson

As part of the programme to re-compute the character tables of all the groups in the Atlas we re-compute the character table of $\mathbb M$, the Monster simple group. We operate under the uniqueness hypotheses of $\mathbb M$ and the existence of an ordinary faithful representation of degree $196883 = 47.59.71$ and determine the conjugacy classes and centrali

J. J. H. van Gemert, V. Breschi, D. R. Yntema, K. J. Keesman

Water distribution networks (WDNs) are critical systems for our society and detecting leakages is important for minimizing losses and water waste. This makes optimal sensor placement for leakage detection very relevant. Existing sensor placement methods rely on simulation-based scenarios, often lacking structure and generalizability, or depend on the knowled

Marlis Ontivero-Ortega, Luca Faes, Jesus M Cortes, Daniele Marinazzo

Leveraging the large body of work devoted in recent years to describe redundancy and synergy in multivariate interactions among random variables, we propose a novel approach to quantify cooperative effects in feature importance, one of the most used techniques for explainable artificial intelligence. In particular, we propose an adaptive version of a well-kn

Kaustav Chakraborty, Sumitava Kundu, Avisek Das

A sheared cube is made out of a cube by giving a shear to the body in one direction keeping one of the faces fixed. We investigate here the thermodynamic phase behavior of a family of such regular hard sheared cubes, each of the members of the family having a distinct angle made by the faces with the perpendicular on the fixed face. Hard particle Monte Carlo

Michael Wehrli, Alicia Durrer, Paul Friedrich, Volodimir Buchakchiyskiy

Purpose: Trochlear Dysplasia (TD) is a common malformation in adolescents, leading to anterior knee pain and instability. Surgical interventions such as trochleoplasty require precise planning to correct the trochlear groove. However, no standardized preoperative plan exists to guide surgeons in reshaping the femur. This study aims to generate patient-specif

Kristof Meding, Christoph Sorge

When does a digital image resemble reality? The relevance of this question increases as the generation of synthetic images -- so called deep fakes -- becomes increasingly popular. Deep fakes have gained much attention for a number of reasons -- among others, due to their potential to disrupt the political climate. In order to mitigate these threats, the EU A

Bohan Shen, Junjue Liao, Renjie He, Zekun Xu

The detonation behaviors during thermonuclear burning indicate a state of robust hot spot burning and are widely present in astronomical phenomena, such as supernovae. In this work, we propose an analytical model including alpha-particle deposition at the shock front, which significantly lowers the detonation threshold. The new temperature threshold is 13.4

Basudev Nag Chowdhury

The standard understanding of formal quantum theory is based upon the belief that the state of two interacting quantum systems can jointly evolve as, either an entangled state, e.g. in case of measurement or decoherence, or a separable state, e.g. in case of gate operations, through two different processes, i.e. process-1 and process-2, respectively, as sugg

Nan Sun, Han Fang, Yuxing Lu, Chengxin Zhao

DNN-based watermarking methods have rapidly advanced, with the ``Encoder-Noise Layer-Decoder'' (END) framework being the most widely used. To ensure end-to-end training, the noise layer in the framework must be differentiable. However, real-world distortions are often non-differentiable, leading to challenges in end-to-end training. Existing solutions only t

Jin Zhang, Kun Yang, Jianxin Yu, Huixia Fu

As a layered ferroelectric material, CuInP2S6 has garnered significant attention for its robust ferroelectric state and potential applications in memory devices. In this work, we demonstrate that with short laser pulses ultrafast reversible polarization switching within hundreds of femtoseconds can be achieved in ferroelectric CuInP$_2$S$_6$. Specifically, p

Bajiyo Baiju, Kavya Manohar, Leena G Pillai, Elizabeth Sherly

In this work, we present the development of a reverse transliteration model to convert romanized Malayalam to native script using an encoder-decoder framework built with attention-based bidirectional Long Short Term Memory (Bi-LSTM) architecture. To train the model, we have used curated and combined collection of 4.3 million transliteration pairs derived fro

Sergei P. Efimov

We consider Fock's fundamental theory of the hydrogen atom in momentum space which allows a realization of the previously predicted rotation group of a three-dimensional (3D) sphere in four-dimensional (4D) space. We then modify Fock's theory and abandon the momentum space description. To transform and simplify the theory, we use invariant tensor methods of

Zhencheng Li, Zhen Jiang, Yun Liu, Xi-Long Fan

Anisotropic stochastic gravitational wave background (SGWB) serves as a potential probe of the large-scale structure (LSS) of the universe. In this work, we explore the anisotropic SGWB from local ($z < \sim 0.085$) merging stellar mass compact binaries, specifically focusing on merging stellar binary black holes, merging neutron-star-black-hole binaries, an

Chiara Bigi, Cyriack Jego, Vincent Polewczyk, Alessandro De Vita

Systems with pronounced spin anisotropy play a pivotal role in advancing magnetization switching and spin-wave generation mechanisms, which are fundamental for spintronic technologies. Quasi-van der Waals ferromagnets, particularly Cr$_{1+\delta}$Te$_2$ compounds, represent seminal materials in this field, renowned for their delicate balance between frustrat

Jiacheng Liu, Yuanchun Li, Liangyan Li, Yi Sun

Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-concerning for end-users. In this paper, we propose to use natural language as the unified interface to process personal data and sense user context, which can effectively ease app de

Jiawei Li, Hongwei Yu, Jiansheng Chen, Xinlong Ding

Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate interference on the effectiveness of fusion results. To inv

Yi Zhong, Zhuoling Chen, Wenyi Zhang, Martin Haenggi

This paper introduces a new stochastic geometry-based model to analyze the Request-to-Send/Clear-to-Send (RTS/CTS) handshake mechanism in wireless local area networks (WLANs). We develop an advanced hard-core point process model, termed the dual-zone hard-core process (DZHCP), which extends traditional hard-core models to capture the spatial interactions and

Aditya Vavre, Ethan He, Dennis Liu, Zijie Yan

Scaling large language models (LLMs) significantly improves performance but comes with prohibitive computational costs. Mixture-of-Experts (MoE) models offer an efficient alternative, increasing capacity without a proportional rise in compute requirements. However, training MoE models from scratch poses challenges like overfitting and routing instability. We

Stefania Bellavia, Benedetta Morini, Mahsa Yousefi

We investigate stochastic gradient methods and stochastic counterparts of the Barzilai-Borwein steplengths and their application to finite-sum minimization problems. Our proposal is based on the Trust-Region-ish (TRish) framework introduced in [F. E. Curtis, K. Scheinberg, R. Shi, {\it A stochastic trust region algorithm based on careful step normalization},

Bin Yang, Yuming Qin, Alain Miranville, Ke Wang

In this paper, we consider the asymptotic behavior of weak solutions for nonclassical non-autonomous diffusion equations with a delay operator in time-dependent spaces when the nonlinear function $g$ satisfies subcritical exponent growth conditions, the delay operator $\varphi(t, u_t)$ contains some hereditary characteristics and the external force $k \in L_

Songyan Zhang, Wenhui Huang, Zihui Gao, Hao Chen

The emergence of general human knowledge and impressive logical reasoning capacity in rapidly progressed vision-language models (VLMs) have driven increasing interest in applying VLMs to high-level autonomous driving tasks, such as scene understanding and decision-making. However, an in-depth study on the relationship between knowledge proficiency, especiall

Kuan Zou, Aixin Sun, Yitong Ji, Hao Zhang

Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was

Yoshitaka Miyahara, Taiki Haga

We develop an unsupervised machine learning approach to classify disordered phases in a system of oppositely charged colloids. In this system, the interplay between Coulomb and van der Waals interactions leads to transitions in local structures, while the global structure remains disordered. Our method involves representing the local structures of the system

Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times, and difficulty selecting appropriate CPs. To address these challenges, we propose a novel end-to-end framework called Stable Matching EV Charging Assignment (SME

Xuemin Wang, Tianlong Gu, Xuguang Bao, Liang Chang

Graph-structured data is ubiquitous in today's connected world, driving extensive research in graph analysis. Graph Neural Networks (GNNs) have shown great success in this field, leading to growing interest in developing fair GNNs for critical applications. However, most existing fair GNNs focus on statistical fairness notions, which may be insufficient when

Qiao Sun, Jiexin Xie, Nanyang Ye, Qinying Gu

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we de

Xinhao Zhong, Bin Chen, Hao Fang, Xulin Gu

Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that has similar performance to that of the full real dataset. However, current dataset distillation methods often result in synthetic datasets that are excessively difficult for networ

Meng-Jia Chu, Jun Ren, Z. D. Wang

Subradiance shows promising applications in quantum information, yet its realization remains more challenging than superradiance due to the need to suppress various decay channels. This study introduces a state space within a single-excitation basis with perfect subradiance and genuine multipartite quantum entanglement resources for the all-to-all case. Util

Qiangchang Ju, Jiawei Wang, Junyan Zhang

We prove the low Mach number limit of non-isentropic ideal magnetohydrodynamic (MHD) equations with general initial data in the half-space whose boundary satisfies the perfectly conducting wall condition. By observing a special structure contributed by Lorentz force in vorticity analysis, we establish uniform estimates in suitable anisotropic Sobolev spaces

Jiawei Wang, Junyan Zhang

We prove the incompressible limit of non-isentropic inviscid elastodynamic equations with general initial data in 3D half-space. The deformation tensor is assumed to satisfy the neo-Hookean linear elasticity and degenerates in the normal direction on the solid wall. The uniform estimates in Mach number are established based on two important observations. Fir

Plácido A Souza Neto

Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an efficient approach. By projecting complex graphs into a lower-dimensional space, these techniques simplify the analysis and processing of lar

Riccardo Talami, Jonathan Wright, Bianca Howard

The complexity of performance-based building design stems from the evaluation of numerous candidate design options, driven by the plethora of variables, objectives, and constraints inherent in multi-disciplinary projects. This necessitates optimization approaches to support the identification of well performing designs while reducing the computational time o

Dipankar Maity, Arman Pourghorban

We consider a variant of the target defense problems where a group of defenders are tasked to simultaneously capture an intruder. The intruder's objective is to reach a target without being simultaneously captured by the defender team. Some of the defenders are sensing-limited and do not have any information regarding the intruder's position or velocity at a

Muhammad Yahya Ayyashy Mujahidan, Martin Clinton Tosima Manullang

Respiratory rate is a vital sign indicating various health conditions. Traditional contact-based measurement methods are often uncomfortable, and alternatives like respiratory belts and smartwatches have limitations in cost and operability. Therefore, a non-contact method based on Pixel Intensity Changes (PIC) with RGB camera images is proposed. Experiments

Leijo Jose, Anuradha Sharma

Let $\mathtt{R}$ be a finite commutative chain ring with the maximal ideal $\gamma\mathtt{R}$ of nilpotency index $e\geq 2,$ and let $\check{\mathtt{R}}=\mathtt{R}/\gamma^{s}\mathtt{R}$ for some positive integer $ s< e.$ In this paper, we study and characterize Galois $\mathtt{R}\check{\mathtt{R}}$-LCD codes of an arbitrary block-length. We show that each we

Dongyu Yao, Keling Yao, Junhong Zhou, Yinghao Zhang

The obesity phenomenon, known as the heavy issue, is a leading cause of preventable chronic diseases worldwide. Traditional calorie estimation tools often rely on specific data formats or complex pipelines, limiting their practicality in real-world scenarios. Recently, vision-language models (VLMs) have excelled in understanding real-world contexts and enabl

Chavhan Sujeet Yashavant, MitrajSinh Chavda, Saurabh Kumar, Amey Karkare

Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth millions of United States dollars, making them a lucrative target for attackers seeking to exploit vulnerabilities and steal funds. The Ethereum community has developed a rich set of tools to detect vulnerabilities in SCs, including reentrancy (RE) and unhandled exceptions (UX). A dat

Francesco Sylos Labini, Roberto Capuzzo-Dolcetta, Giordano De Marzo, Matteo Straccamore

We conducted an analysis of the velocity field of dwarf galaxies in the LITTLE THINGS sample, focusing on deriving 2D velocity maps that encompass both the transverse and radial velocity fields. Within the range of radial distances where velocity anisotropies are sufficiently small for the disc to be considered rotationally supported, and where the warped ge

X. Jiang, L. Meng, S. Li, D. Wu

Transfer learning (TL) has been widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) for reducing calibration efforts. However, backdoor attacks could be introduced through TL. In such attacks, an attacker embeds a backdoor with a specific pattern into the machine learning model. As a result, the model will misclassify a test samp

Rujiang Li, Xiangyu Kong, Wencai Wang, Yixi Wang

In nonlinear topological systems, edge solitons either originate from linear topological edge modes or emerge as nonlinearity-induced localized states without topological protection. While electric circuits (ECs) provide a platform for realizing various types of topological insulators, observation of edge solitons and transitions between them in EC lattices

Daniel Brunner

Photonic integration of thick holograms in waveguiding structures could be considered the chimera of photonics; multi-faceted and hard to tame. It is the fundamental, and hence indispensable, concept behind compact and monolithically integrated linear optical transformation1. The true relevance of this becomes apparent in the high-dimensional context of unco

Han-Gyeol Yun, Woong-Tae Kim, Jaehan Bae, Cheongho Han

We conduct three-dimensional hydrodynamic simulations to investigate the nonlinear outcomes and observability of vertical shear instability (VSI) in protoplanetary disks. Our models include both vertically isothermal and thermally stratified disks, with the latter representing realistic conditions featuring a hotter atmosphere above the midplane. We find tha

Aritra Bhattacharya

We explain the equality between the two sets of formulas for $q$-Whittaker functions and modified Hall-Littlewood functions obtained by Haglund, Haiman and Loehr - the Inv formula and Ayyer, Mandelshtam and Martin - the Quinv formula by use of weighted path symmetric functions introduced by Carlsson and Mellit.

Yifan Gao, Long Guo, Hong Liu

Cognitive impairment detection through spontaneous speech is a promising avenue for early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI), where timely intervention can significantly improve patient outcomes. The PROCESS Grand Challenge at ICASSP 2025 addresses these tasks by promoting innovative classification and regression method

Giorgi Butbaia, Jiadong Zang

Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In this work, we instead model the underlying vector fields using smooth neural fields. Owing to the fact that the activation functions in the neu

Ahmed Rashed

We investigate the decay process $J/\psi \rightarrow e^+ e^-\phi$, where the relatively clean electromagnetic (EM) transitions dominate at leading order at the tree level, while hadronic contributions arise only through hadronic loop transitions. The branching ratio of $J/\psi \rightarrow e^+ e^-\phi$ was estimated to be approximately $2.28 \times 10^{-8}$ k

Andy Yang, Lena Strobl, David Chiang, Dana Angluin

We study conditions under which transformers using soft attention can simulate hard attention, that is, effectively focus all attention on a subset of positions. First, we examine several subclasses of languages recognized by hard-attention transformers, which can be defined in variants of linear temporal logic. We demonstrate how soft-attention transformers

Han-Gyeol Yun, Woong-Tae Kim, Jaehan Bae, Cheongho Han

Vertical shear instability (VSI), driven by a vertical gradient of rotational angular velocity, is a promising source of turbulence in protoplanetary disks. We examine the semi-global stability of thermally stratified disks and find that the VSI consists of surface and body modes: surface modes are confined to regions of strong shear, while body modes extend

Leijo Jose, Anuradha Sharma

Let $\mathcal{R}_e=GR(p^e,r)[y]/\langle g(y),p^{e-1}y^t\rangle$ be a finite commutative chain ring, where $p$ is a prime number, $GR(p^e,r)$ is the Galois ring of characteristic $p^e$ and rank $r,$ $t$ and $k$ are positive integers satisfying $1\leq t\leq k$ when $e \geq 2,$ while $t=k$ when $e=1,$ and $g(y)=y^k+p(g_{k-1}y^{k-1}+\cdots+g_1y+g_0)\in GR(p^e,r)

Yanxu Mao, Peipei Liu, Tiehan Cui, Congying Liu

In recent years, text classification methods based on neural networks and pre-trained models have gained increasing attention and demonstrated excellent performance. However, these methods still have some limitations in practical applications: (1) They typically focus only on the matching similarity between sentences. However, there exists implicit high-valu

Jaehwan Jeong, Sumin In, Sieun Kim, Hannie Shin

The rising use of deepfakes in criminal activities presents a significant issue, inciting widespread controversy. While numerous studies have tackled this problem, most primarily focus on deepfake detection. These reactive solutions are insufficient as a fundamental approach for crimes where authenticity is disregarded. Existing proactive defenses also have

Yuxiao Wang, Wenpeng Neng, Zhenao Wei, Yu Lei

Human-object contact (HOT) is designed to accurately identify the areas where humans and objects come into contact. Current methods frequently fail to account for scenarios where objects are frequently blocking the view, resulting in inaccurate identification of contact areas. To tackle this problem, we suggest using a perspective interaction HOT detector ca

Zhuqiang Lu, Zhenfei Yin, Mengwei He, Zhihui Wang

Recently, Vision Large Language Models (VLLMs) integrated with vision encoders have shown promising performance in vision understanding. The key of VLLMs is to encode visual content into sequences of visual tokens, enabling VLLMs to simultaneously process both visual and textual content. However, understanding videos, especially long videos, remain a challen

A. Assouline, L. Pugliese, H. Chakraborti, Seunghun Lee

Flying qubits encode quantum information in propagating modes instead of stationary discrete states. Although photonic flying qubits are available, the weak interaction between photons limits the efficiency of conditional quantum gates. Conversely, electronic flying qubits can use Coulomb interactions, but the weaker quantum coherence in conventional semicon

J. A. Purcell, B. A. Brown, B. C. He, S. R. Stroberg

We present two developments which enhance the predictive power of empirical shell-model Hamiltonians for cases in which calibration data are sparse. A recent improvement in the ab initio derivation of effective Hamiltonians leads to a much better starting point for the optimization procedure. In addition, we introduce a protocol to avoid overfitting, enablin

Sehyeong Jo, Jungwon Seo

Chatbot-based customer support services have significantly advanced with the introduction of large language models (LLMs), enabling enhanced response quality and broader application across industries. However, while these advancements focus on reducing business costs and improving customer satisfaction, limited attention has been given to the experiences of

Vidya Sagar, Shikha Patel, Ashutosh Singh, Shayan Srinivasa Garani

We consider two-dimensional $(\lambda_1, \lambda_2)$-constacyclic codes over $\mathbb{F}_{q}$ of area $M N$, where $q$ is some power of prime $p$ with $\gcd(M,p)=1$ and $\gcd(N,p)=1$. With the help of common zero (CZ) set, we characterize 2-D constacyclic codes. Further, we provide an algorithm to construct an ideal basis of these codes by using their essent

Naiming Liu, Shashank Sonkar, Debshila Basu Mallick, Richard Baraniuk

This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive processes and concepts required for problem solving in a college-level introductory physics course. Our method leverages Large Language Models (LLMs) for automated labeling of physi

Joakim Schack Betzer, Jalil Boudjadar, Mirgita Frasheri, Prasad Talasila

As autonomous robots increasingly navigate complex and unpredictable environments, ensuring their reliable behavior under uncertainty becomes a critical challenge. This paper introduces a digital twin-based runtime verification for an autonomous mobile robot to mitigate the impact posed by uncertainty in the deployment environment. The safety and performance

Jingyi Zhou, Haoyu Zhang, Jiakang Yuan, Peng Ye

As a fundamental vision task, stereo matching has made remarkable progress. While recent iterative optimization-based methods have achieved promising performance, their feature extraction capabilities still have room for improvement. Inspired by the ability of vision foundation models (VFMs) to extract general representations, in this work, we propose AIO-St

Dongwon Kim

With no direct evidence for new physics at the TeV scale, deviations from the Standard Model (SM) can be explored systematically through Effective Field Theories (EFTs) such as the Standard Model EFT (SMEFT). SMEFT extends the SM by introducing higher-dimensional operators parametrized by Wilson coefficients, offering a framework to probe beyond the SM (BSM)

Yasamin Medghalchi, Moein Heidari, Clayton Allard, Leonid Sigal

Deep neural networks (DNNs) offer significant promise for improving breast cancer diagnosis in medical imaging. However, these models are highly susceptible to adversarial attacks--small, imperceptible changes that can mislead classifiers--raising critical concerns about their reliability and security. Traditional attacks rely on fixed-norm perturbations, mi

Shu-Yung Liu, Mei-Heng Yueh

Surface parameterization is a fundamental concept in fields such as differential geometry and computer graphics. It involves mapping a surface in three-dimensional space onto a two-dimensional parameter space. This process allows for the systematic representation and manipulation of surfaces of complicated shapes by simplifying them into a manageable planar

Pin-Yu Liao, Guan-Ming Su, Hsi-Yu Schive, Alexander Kunkel

Soliton cores at the center of fuzzy dark matter (FDM) halos provide a promising way to distinguish FDM from other dark matter models. However, the relation between solitons and their host halos remains contentious. Here, we rigorously examine this soliton-halo relation (SHR) using a rich set of cosmological simulations across various FDM particle masses, ha

Sosuke Yamao, Natsuki Miyahara, Yuki Harazono, Shun Takeuchi

With the increasing complexity of video data and the need for more efficient long-term temporal understanding, existing long-term video understanding methods often fail to accurately capture and analyze extended video sequences. These methods typically struggle to maintain performance over longer durations and to handle the intricate dependencies within the

Jing Bi, Yuting Wu, Weiwei Xing, Zhenjie Wei

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Advances in prompt engineering and fine-tuning techniques have further enhanced their ability to address complex reasoning challenges. However, these advanced capabilities are often exclusive to models exceeding 100 billion parameters. Although Chain-of-Though

Feng-Li Lin, Jhh-Jing Hong, Ching-Yu Huang

We consider the thermalization hypothesis of pure states in quantum Ising chain with $Z_2$ symmetry, XXZ chain with $U(1)$ symmetry, and XXX chain with $SU(2)$ symmetries. Two kinds of pure states are considered: the energy eigenstates and the typical states evolved unitarily from the random product states for a long enough period. We further group the typic

Xiwang Cao, Keqin Feng, Ying-Ying Tan

The study of quantum chromatic numbers of graphs is a hot research topic in recent years. However, the infinite family of graphs with known quantum chromatic numbers are rare, as far as we know, the only known such graphs (except for complete graphs, cycles, bipartite graphs and some trivial cases) are the Hadamard graphs $H_n$ with $2^n$ vertices and $n$ a

Jingwei Sun, Haifeng Wang, Hong Zhang, Xu Qian

This paper establishes a unified framework for the space-time convergence analysis of the energy-stable third-order accurate exponential time differencing Runge-Kutta schemes. By employing Fourier pseudo-spectral discretization in space and the inner product technique, we derive a rigorous Fourier eigenvalue analysis, which provides a detailed optimal conver

Xuanting Xie, Bingheng Li, Erlin Pan, Zhaochen Guo

Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the ``missing-half" node feature information, especially how these features can enhance clustering performance. This issue is further compounded by the challenges associated with high-dimensional features. Feature selection in graph clustering is part

Zhe Li, Yisheng He, Lei Zhong, Weichao Shen

Generating motion sequences conforming to a target style while adhering to the given content prompts requires accommodating both the content and style. In existing methods, the information usually only flows from style to content, which may cause conflict between the style and content, harming the integration. Differently, in this work we build a bidirection

Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fa

Junrui Xiao, Zhikai Li, Lianwei Yang, Yiduo Mei

Post-training quantization (PTQ) reduces excessive hardware cost by quantizing full-precision models into lower bit representations on a tiny calibration set, without retraining. Despite the remarkable progress made through recent efforts, traditional PTQ methods typically encounter failure in dynamic and ever-changing real-world scenarios, involving unpredi

Jiahuan He, Chao Kan, Wen Song

In this paper, our focus lies on the study of the second-order variational analysis of orthogonally invariant matrix functions. It is well-known that an orthogonally invariant matrix function is an extended-real-value function defined on ${\mathbb M}_{m,n}\,(n \leqslant m)$ of the form $f \circ \sigma$ for an absolutely symmetric function $f \colon \R^n \rig

Q. Hu

We report the measurements of $\Lambda$ hyperons' global and local spin polarization from second phase of the RHIC Beam Energy Scan (BES-II) in Au+Au collisions at $\sqrt{s_{NN}}$= 7.7--27 GeV. Global polarization measurements of $\bar{\Lambda}$ and $\Lambda$ show no significant differences, offering insights into the late-stage evolution of the magnetic fie

Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

Machine learning (ML) algorithms have become integral to decision making in various domains, including healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems pose significant ethical and social challenges. This study investigates the fairness of ML models applied to structured datasets in classificatio

Yating Yu, Congqi Cao, Yueran Zhang, Qinyi Lv

Zero-shot action recognition (ZSAR) requires collaborative multi-modal spatiotemporal understanding. However, finetuning CLIP directly for ZSAR yields suboptimal performance, given its inherent constraints in capturing essential temporal dynamics from both vision and text perspectives, especially when encountering novel actions with fine-grained spatiotempor

Simon Foucart

Predicting the value of a function $f$ at a new point given its values at old points is an ubiquitous scientific endeavor, somewhat less developed when $f$ produces multiple values that depend on one another, e.g. when it outputs likelihoods or concentrations. Considering the points as fixed (not random) entities and focusing on the worst-case, this article

Andrea Ercolino, Harim Jin, Norbert Langer, Luc Dessart

Stripped-envelope supernovae (SNe) are H-poor transients produced at the end of the life of massive stars that previously lost their H-rich envelope. Their progenitors are thought to be donor stars in mass-transferring binary systems, which were stripped of their H-rich envelopes some $10^6$yr before core collapse. A subset of the stripped-envelope SNe exhib

Hui Zhong, Hongliang Lu, Ting Gan, Yonghong Liu

Air pollutant exposure exhibits significant spatial and temporal variability, with localized hotspots, particularly in traffic microenvironments, posing health risks to commuters. Although widely used for air quality assessment, fixed-site monitoring stations are limited by sparse distribution, high costs, and maintenance needs, making them less effective in

Tao Liu, Ziyang Ma, Qi Chen, Feilong Chen

We present VQTalker, a Vector Quantization-based framework for multilingual talking head generation that addresses the challenges of lip synchronization and natural motion across diverse languages. Our approach is grounded in the phonetic principle that human speech comprises a finite set of distinct sound units (phonemes) and corresponding visual articulati

Victoria Sharmila Gomes, Amit Tribedi, Subhrajyoti Dey

Utilizing the matrix product formalism, we have studied the variation of entanglement and fidelity measures in the MP ground states of a generic anisotropic spin 2 chain with nearest neighbour interactions and shared symmetries. These MP states represent the exact Ground State solutions of the system and display distinctive characteristics determined by a ma

Meng-Hui Wu, Shubin Yu, Chun-Lei Tang

In this article, we study the existence and asymptotic properties of prescribed mass standing waves for the rotating dipolar Gross-Pitaevskii equation with a harmonic potential in the unstable regime. This equation arises as an effective model describing Bose-Einstein condensate of trapped dipolar quantum gases rotating at the speed $\Omega$. To be precise,

José Gilberto Barbosa de Medeiros Júnior, Andre Guarnier de Mitri, Diego Furtado Silva

This paper investigates the lack of research on activation functions for neural network models in time series tasks. It highlights the need to identify essential properties of these activations to improve their effectiveness in specific domains. To this end, the study comprehensively analyzes properties, such as bounded, monotonic, nonlinearity, and periodic

Li Chai, Donglin Wang

Lyric-to-melody generation is a highly challenging task in the field of AI music generation. Due to the difficulty of learning strict yet weak correlations between lyrics and melodies, previous methods have suffered from weak controllability, low-quality and poorly structured generation. To address these challenges, we propose CSL-L2M, a controllable song-le

Xiangxi Tian, Jie Shan

Generating continuous environmental models from sparsely sampled data is a critical challenge in spatial modeling, particularly for topography. Traditional spatial interpolation methods often struggle with handling sparse measurements. To address this, we propose a Transformer-based Generative Model for Spatial Interpolation (T-GMSI) using a vision transform

Minghui Liu, Tahseen Rabbani, Tony O'Halloran, Ananth Sankaralingam

Transformer-based large language models (LLMs) use the key-value (KV) cache to significantly accelerate inference by storing the key and value embeddings of past tokens. However, this cache consumes significant GPU memory. In this work, we introduce HashEvict, an algorithm that uses locality-sensitive hashing (LSH) to compress the KV cache. HashEvict quickly

Yikang Pan, Yi Zhu, Rand Xie, Yizhi Liu

Large Language Models (LLMs), while being increasingly dominant on a myriad of knowledge-intensive activities, have only had limited success understanding lengthy table-text mixtures, such as academic papers and financial reports. Recent advances of long-context LLMs have opened up new possibilities for this field. Nonetheless, we identify two roadblocks: (1

Ahmad Jafar Arifi, Parada T. P. Hutauruk, Kazuo Tsushima

We explore the modifications of hadron structure in a nuclear medium, focusing on the spacelike electromagnetic form factors (EMFFs) of light and heavy-light pseudoscalar mesons. By combining the light-front quark model (LFQM) with the quark-meson coupling (QMC) model, which reasonably reproduces EMFFs in free space and the saturation properties of nuclear m

Shuijiang Zhao

In this paper, we study the spherical maximal operator $ M_E $ over $ E\subset [1,2]$, restricted to radial functions. In higher dimensions $ d\geq 3$, we establish a complete range of $ L^p-$improving estimates for $ M_E $. In two dimensions, sharp results are also obtained for quasi-Assouad regular sets $E$. A notable feature is that the high-dimensional r

Yi Gu, Zhaorui Wang, Dongjun Ye, Renjing Xu

Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering. Extracting geometry from NeRF typically relies on the Marching Cubes algorithm, which uses a hand-crafted threshold to define the level set. However, this threshold-based approach requires laborious and scenario-specific tuning, limiting its practicality for real-world appli

Xinghong Fu, Masanori Hirano, Kentaro Imajo

Large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question: treating market prices as a time series, can large models be used to predict the market? In this paper, we answer this by evaluating the performance of the latest time series found