March 2025 arXiv papers — page 151

Iman Mohammed Attia

In the present paper, the author discusses the Generalized Odd Median Base Unit Rayleigh (GOMBUR) in relation to the Median Based Unit Rayleigh (MBUR) to evaluate the additive value of the new shape parameter on the estimation process as regards validity indices, goodness of fit statistics, estimated variances of the estimated parameters and their standard e

Hyeonho Jeong, Suhyeon Lee, Jong Chul Ye

We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos translation, leveraging publicly available image and video

Rushiraj Gadhvi, Soham Petkar, Priyansh Desai, Shreyas Ramachandran

Personalization is a critical yet often overlooked factor in boosting productivity and wellbeing in knowledge-intensive workplaces to better address individual preferences. Existing tools typically offer uniform guidance whether auto-generating email responses or prompting break reminders without accounting for individual behavioral patterns or stress trigge

Huaying Yuan, Zheng Liu, Minghao Qin, Hongjin Qian

Efficient long-video understanding~(LVU) remains a challenging task in computer vision. Current long-context vision-language models~(LVLMs) suffer from information loss due to compression and brute-force downsampling. While retrieval-augmented generation (RAG) methods mitigate this issue, their applicability is limited due to explicit query dependency. To ov

Bowen Tian, Zhengyang Xu, Mingqiang Wu, Songning Lai

With the pervasive integration of computer applications across industries, the presence of vulnerabilities within code bases poses significant risks. The diversity of software ecosystems coupled with the intricate nature of modern software engineering has led to a shift from manual code vulnerability identification towards the adoption of automated tools. Am

Junning Liang, Haowen Zheng, Yuying Zhang, Yongzhuo Gao

Turbojet-powered VTOL UAVs have garnered increased attention in heavy-load transport and emergency services, due to their superior power density and thrust-to-weight ratio compared to existing electronic propulsion systems. The main challenge with jet-powered UAVs lies in the complexity of thrust vectoring mechanical systems, which aim to mitigate the slow d

Zihua Chai, Zhaocong Wang, Xinghang Chen, Quanshen Shen

Rare-earth ions in bulk crystals are excellent solid-state quantum systems in quantum information science, owing to the exceptional optical and spin coherence properties. However, the weak fluorescence of single rare-earth ions present a significant challenge for scalability, necessitating the integration into micro-cavities. Thin films serve as a promising

Linli Yao, Haoning Wu, Kun Ouyang, Yuanxing Zhang

Despite recent advances in Video Large Language Models (VideoLLMs), effectively understanding long-form videos remains a significant challenge. Perceiving lengthy videos containing thousands of frames poses substantial computational burden. To mitigate this issue, this paper introduces Generative Frame Sampler (GenS), a plug-and-play module integrated with V

Anderson L de Araujo, Luc Deneire, Guillaume Urvoy-Keller, André L F de Almeida

Despite the rapid advancements in 5G technology, accurately assessing the energy consumption of its Radio Access Networks (RANs) remains a challenge due to the diverse range of applicable technologies and implementation solutions. Designing a versatile power model for estimating the 5G RANspecific power consumption requires extensive data collection and expe

Birger Moell

This study investigates the development and assessment of an artificial human designed as a conversational AI chatbot, focusing on its role as a clinical psychologist. The project involved creating a specialized chatbot using the Character.ai platform. The chatbot was designed to engage users in psychological discussions, providing advice and support with a

Songlin Yang, Tao Yang, Bo Hu

The deployment of sensors for air quality monitoring is constrained by high costs, leading to inadequate network coverage and data deficits in some areas. Utilizing existing observations, spatio-temporal kriging is a method for estimating air quality at unobserved locations during a specific period. Inductive spatio-temporal kriging with increment training s

Yubo Yang, Tao Yang, Xiaofeng Wu, Ziyu Guo

UAV swarms are widely used in emergency communications, area monitoring, and disaster relief. Coordinated by control centers, they are ideal for federated learning (FL) frameworks. However, current UAV-assisted FL methods primarily focus on single tasks, overlooking the need for multi-task training. In disaster relief scenarios, UAVs perform tasks such as cr

Haoyu Zhang, Qiaohui Chu, Meng Liu, Haoxiang Shi

AI personal assistants, deployed through robots or wearables, require embodied understanding to collaborate effectively with humans. However, current Multimodal Large Language Models (MLLMs) primarily focus on third-person (exocentric) vision, overlooking the unique challenges of first-person (egocentric) videos. Additionally, high acquisition costs limit da

Ba-Lei Tan, Chen Zhang, Qi-Yi Wu, Guo-Hao Dong

We investigate the ultrafast dynamics of the quasi-one-dimensional Kondo lattice CeCo$_2$Ga$_8$ using optical pump-probe spectroscopy. Time-resolved pump-probe reflectivity measurements reveal a strong anisotropy in the photoinduced response, which is a direct consequence of the material's unique electronic structure. The temperature dependence of the relaxa

Olivier Goux, Anthony Weaver, Selime Gürol, Oliver Guillet

Data assimilation involves estimating the state of a system by combining observations from various sources with a background estimate of the state. The weights given to the observations and background state depend on their specified error covariance matrices. Observation errors are often assumed to be uncorrelated even though this assumption is inaccurate fo

Mengting Liu, Di Li, J. R. Dawson, Joel M. Weisberg

We investigated HI absorption toward a single pulsar, PSR J1644$-$4559, and its variability over timescales from days to years, using Murriyang, CSIRO's Parkes Radio Telescope. Our 19 epochs of spectral observations, spanning 1.2 years with intervals as short as 1 day, provide the most comprehensive cadence coverage for monitoring HI absorption to date. We i

Lei qian, Zhichen Pan, Dongyue Jiang, Zichen Huang

The C$_2$H $N=1-0$ transition was used to investigate the possible line of sight sub-structures from the dense and optically thick in $^{13}$CO $J=1-0$ regions in the Ophiuchus star forming molecular cloud. With a 0.2 K or lower noise, multi-peak spectra were obtained and then used for identifying sub-structures. There are clues, e.g., the core velocity disp

Federico Spada

Loeb & Cloete (2025) intriguingly suggest that the near-Earth object 2005 VL$_1$ could be the lost Soviet probe Venera 2. Here I evaluate the plausibility of such a claim against the available data. I have re-determined the orbit of 2005 VL$_1$ (including a non-gravitational acceleration component) using the astrometric observations retrieved from the Minor

Soumil Kelkar, Prabal Saxena, Ravi Kopparapu, Joy Monteiro

A planet's spectrum is dynamic and only represents a time-dependent snapshot of its properties. Changing atmospheric conditions due to climate and weather patterns, particularly variation in cloud cover, can significantly affect the spectrum in ways that complicate the understanding of a planet's baseline atmospheric properties. Variable cloud cover and clou

Hangkai Qian, Bo Li, Qichen Wang

The rapid adoption of retrieval-augmented generation (RAG) systems has revolutionized large-scale content generation but has also highlighted the challenge of ensuring trustworthiness in retrieved information. This paper introduces ClaimTrust, a propagation-based trust scoring framework that dynamically evaluates the reliability of documents in a RAG system.

Cristina Parigini, Laura Pirovano, Roberto Armellin, Darren McKnight

With debris larger than 1 cm in size estimated to be over one million, precise cataloging efforts are essential to ensure space operations' safety. Compounding this challenge is the oversubscribed problem, where the sheer volume of space objects surpasses ground-based observatories' observational capacity. This results in sparse, brief observations and exten

Shaoyi Yang

Large language models (LLMs) have become vital tools for software development, but they often require verbose intermediate reasoning for complex code tasks, leading to high latency and costs. This research extends the Chain of Draft (CoD) method to software engineering, designing and evaluating multiple CoD variants tailored for code tasks. Through comprehen

David P. Hofmeyr

A novel formulation of the clustering problem is introduced in which the task is expressed as an estimation problem, where the object to be estimated is a function which maps a point to its distribution of cluster membership. Unlike existing approaches which implicitly estimate such a function, like Gaussian Mixture Models (GMMs), the proposed approach bypas

Mikhail Shkolnikov, Peter Petrov

The usual approach to tropical geometry is via degeneration of amoebas of algebraic subvarieties of an algebraic torus $(\mathbb{C}^*)^n$. An amoeba is logarithmic projection of the variety forgetting the angular part of coordinates, called the phase. Similar degeneration can be performed without ignoring the phase. The limit then is called phase tropical va

Sota Kawamura, Hirotada Honda, Shugo Nakamura, Takashi Sano

Automatic Video Object Segmentation (AVOS) refers to the task of autonomously segmenting target objects in video sequences without relying on human-provided annotations in the first frames. In AVOS, the use of motion information is crucial, with optical flow being a commonly employed method for capturing motion cues. However, the computation of optical flow

Zhehui Wu, Yong Chen, Naoto Yokoya, Wei He

Hyperspectral images (HSIs) often suffer from diverse and unknown degradations during imaging, leading to severe spectral and spatial distortions. Existing HSI restoration methods typically rely on specific degradation assumptions, limiting their effectiveness in complex scenarios. In this paper, we propose \textbf{MP-HSIR}, a novel multi-prompt framework th

Jiun Tian Hoe, Weipeng Hu, Wei Zhou, Chao Xie

This paper presents InteractEdit, a novel framework for reference-free Human-Object Interaction (HOI) editing that tackles the challenging task of transforming an existing interaction in an image into a new, desired interaction while preserving the identities of the subject and object. Unlike prior image editing tasks such as attribute manipulation, object r

Petia Guintchev, Joost J. Joosten, Sofia Santiago Fernández, Eric Sancho Adamson

We speak of a \textit{computational law} when that law is intended to be enforced by software through an automated decision-making process. As digital technologies evolve to offer more solutions for public administrations, we see an ever-increasing number of computational laws. Traditionally, law is written in natural language. Computational laws, however, s

Fengze Sun, Yanchuan Chang, Egemen Tanin, Shanika Karunasekera

The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which m

Qirui Sun, Yunyi Ni, Teli Yuan, Jingjing Zhang

This research presents Spiritus, an AI-assisted creation tool designed to streamline 2D character animation creation while enhancing creative flexibility. By integrating natural language processing and diffusion models, users can efficiently transform natural language descriptions into personalized 2D characters and animations. The system employs automated s

Rongjie Cui, Zelei Zhang, Qi Wei, Yu Zhang

For the nanoscale structures, disorder scattering plays a vital role in the carriers' transport, including electrons and high-frequency phonons. The capability for effectively treating the disorders, including both diagonal and off-diagonal disorders, is indispensable for quantum transport simulation of realistic device materials. In this work, we report a s

Li Xiao, Ming Zhu, Xiao-Hui Sun, Wolfgang Reich

We aim to study the polarization and magnetic field properties of the SNR HB 9 using new 21-cm continuum cube data from the Five-hundred-meter Aperture Spherical radio telescope (FAST). We computed the Faraday depth at 21 cm, and re-analyzed the rotation measures (RMs) of HB 9 using in addition Effelsberg 2695-MHz and Urumqi 4800-MHz polarization data. FAST

Haozhou Pang, Tianwei Ding, Lanshan He, Qi Gan

Dance serves as a profound and universal expression of human culture, conveying emotions and stories through movements synchronized with music. Although some current works have achieved satisfactory results in the task of single-person dance generation, the field of multi-person dance generation remains relatively novel. In this work, we present a group chor

Jin Li, Ziqiang He, Anwei Luo, Jian-Fang Hu

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffu

Yuanbo Nie, Eric C. Kerrigan

Direct collocation is a widely used method for solving dynamic optimization problems (DOPs), but its implementation simplicity and computational efficiency are limited for challenging problems like those involving singular arcs. In this paper, we introduce the direct transcription method of integrated residual regularized direct collocation (IRR-DC). This me

Sanjeev Saxena

There are several notions of duality between lines and points. In this note, it is shown that all these can be studied in a unified way. Most interesting properties are independent of specific choices. It is also shown that either dual mapping can be its own inverse or it can preserve relative order (but not both). Generalisation to higher dimensions is also

Yuechen Xie, Jie Song, Huiqiong Wang, Mingli Song

High-quality open-source text-to-image models have lowered the threshold for obtaining photorealistic images significantly, but also face potential risks of misuse. Specifically, suspects may use synthetic data generated by these generative models to train models for specific tasks without permission, when lacking real data resources especially. Protecting t

Minghui Ouyang

Given two subsets $A, B \subseteq \mathbb{F}_p$ and a binary relation $\mathcal{R} \subseteq A \times B$, the restricted sumset of $A, B$ with respect to $\mathcal{R}$ is defined as $A +_{\mathcal{R}} B = \{ a+b \colon (a,b) \notin \mathcal{R} \}$. When $\mathcal{R}$ is taken as the equality relation, determining the minimum value of $|A +_{\mathcal{R}} B|$

Taesun Yeom, Jaeho Lee

Weight space learning is an emerging paradigm in the deep learning community. The primary goal of weight space learning is to extract informative features from a set of parameters using specially designed neural networks, often referred to as \emph{metanetworks}. However, it remains unclear how these metanetworks learn solely from parameters. To address this

Jie Luo, Jeremy Kulcsar, Xueyin Chen, Giulio Giaconi

Quantum circuits embed data in a Hilbert space whose dimensionality grows exponentially with the number of qubits, allowing even shallow parameterised quantum circuits (PQCs) to represent highly-correlated probability distributions that are costly for classical networks to capture. Reinforcement-learning (RL) agents, which must reason over long-horizon, cont

L. V. Kardapoltsev, N. A. Melnikova

We present the NGAMMA Monte Carlo event generator for QED processes of $e^+e^-$ annihilation into a multiphoton final state, $e^+e^-\to N\gamma (N \ge 2)$. These processes are an important source of background in the study of $e^+e^-\to hadrons$ processes with a multiphoton final state, especially for experiments at low energy $e^+e^-$ colliders like SND, CM

Yaowu Fan, Jia Wan, Tao Han, Antoni B. Chan

Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restricting evaluation for a highly varying view and time in crowded

Yue Wang, Qizhou Wang, Feng Liu, Wei Huang

Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning

Yunjie Fang, Sheng Wu, Tao Yang, Xiaofeng Wu

Federated learning (FL) facilitates collaborative model training among multiple clients while preserving data privacy, often resulting in enhanced performance compared to models trained by individual clients. However, factors such as communication frequency and data distribution can contribute to feature drift, hindering the attainment of optimal training pe

Eyal Ackerman, Balázs Keszegh

We prove a quasi-linear upper bound on the size of $K_{t,t}$-free polygon visibility graphs. For visibility graphs of star-shaped and monotone polygons we show a linear bound. In the more general setting of $n$ points on a simple closed curve and visibility pseudo-segments, we provide an $O(n \log n)$ upper bound and an $\Omega(n\alpha(n))$ lower bound.

Maximilian Abstreiter, Sasu Tarkoma, Roberto Morabito

The rapid rise of Language Models (LMs) has expanded the capabilities of natural language processing, powering applications from text generation to complex decision-making. While state-of-the-art LMs often boast hundreds of billions of parameters and are primarily deployed in data centers, recent trends show a growing focus on compact models-typically under

Yonas Tefera, Quinten Van Baelen, Maarten Meire, Stijn Luca

This paper presents a constraint-guided deep learning framework for developing physically consistent health indicators in bearing prognostics and health management. Conventional data-driven methods often lack physical plausibility, while physics-based models are limited by incomplete system knowledge. To address this, we integrate domain knowledge into deep

H. Iqtaish, I. Louhichi, A. Yousef

In this paper, we provide a complete characterization of bounded Toeplitz operators $T_f$ on the harmonic Bergman space of the unit disk, where the symbol $f$ has a polar decomposition truncated above, that commute with $T_{z+\bar{g}}$, for a bounded analytic function $g$.

Mi-Ra Hwang, Eylee Jung, MuSeong Kim, DaeKil Park

The non-relativistic quantum mechanics with a generalized uncertainty principle (GUP) is examined in the Arthurs-Kelly system. The Feynman propagator for this system is exactly derived within the first order of the GUP parameter $\beta$. The application of it in the early universe stage is briefly discussed.

Tariq Aziz, Meng-Long Song, Liu Ye, Dong Wang

We develop a rigorous framework for quantifying quantum coherence in finite-dimensional systems by applying the Schur-Horn majorization theorem to relate eigenvalue distributions and diagonal entries of density matrices. Building on this foundation, we introduce a versatile suite of coherence measures, including the relative cross-entropy of coherence and it

Hiroki Matsumura, Shunsaku Kitagawa, Shiki Ogata, Riku Matsubayashi

To investigate the intrinsic magnetic properties of UTe$_2$, we performed $^{125}$Te-NMR measurements on the ultra-clean single-crystalline UTe$_2$ with superconducting transition temperature $T_{\rm c}$ = 2.1~K and compared the results with those of the $T_{\rm c}$ = 1.6~K sample. The broadening of the linewidth of the NMR spectrum in the $a$-axis magnetic

M. D. Filipović, Z. J. Smeaton, A. C. Bradley, D. Dobie

We present the results of an Australian Square Kilometre Array Pathfinder (ASKAP) 944 MHz and Very Large Array Sky Survey (VLASS) 3~GHz search for a radio-continuum counterpart of the recent ultra-high-energy (UHE) neutrino event, KM3-230213A. Using (ASKAP), we catalog 1052 radio sources within the 1.5$^\circ$ radius search area (68% certainty region) around

Huaning Liu, Gokce Dayanikli

In this paper, we propose a graphon game model to understand how rumor (such as fake news) propagates in large populations that are interacting on a network and how different policies affect the spread. We extend the SKIR model that is used to model rumor propagation and implement individual controls and weighted interactions with other agents to have contro

Chuyu Zhang, Xueyang Yu, Peiyan Gu, Xuming He

This paper addresses the problem of Rehearsal-Free Continual Category Discovery (RF-CCD), which focuses on continuously identifying novel class by leveraging knowledge from labeled data. Existing methods typically train from scratch, overlooking the potential of base models, and often resort to data storage to prevent forgetting. Moreover, because RF-CCD enc

Y. Akiba, H. Aso, J. T. Bertaux, D. Cacace

A new silicon-strip-type detector was developed for precise charged-particle tracking in the central rapidity region of heavy ion collisions. A new detector and collaboration at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory is sPHENIX, which is a major upgrade of the PHENIX detector. The intermediate tracker (INTT) is part of the adva

Syed Talal Ahmad, Haohui Lu, Sidong Liu, Annie Lau

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges, particularly in generating vaccine-related misinformation, which poses risks to public health. Despite research on human-authored misinformation, a notable gap remains in understanding how LLMs contribute to vac

Yuhuan You, Xihong Wu, Tianshu Qu

As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation. While Transformer and Diffusion models have advanced audio generation, a significant challenge persists in extracting precise s

Fan Lyu, Tianle Liu, Zhang Zhang, Fuyuan Hu

We introduce Test-Time Discovery (TTD) as a novel task that addresses class shifts during testing, requiring models to simultaneously identify emerging categories while preserving previously learned ones. A key challenge in TTD is distinguishing newly discovered classes from those already identified. To address this, we propose a training-free, hash-based me

Jiaying Fu, Xiruo Wang, Zhouyi Li, Kate Vi

While generative AI is advancing writing support tools, creative writing is often seen as the exclusive domain of skilled writers. This paper introduces "1001 Nights", a co-creative story-crafting game that transforms writing into a playful and rewarding activity. In this game, the AI agent takes on the role of a "moody" king with distinct storytelling prefe

Priyanshu Chaubey

Human communication has been profoundly changed by social media, which allows users to engage in previously unheard-of ways, such as text-based conversations, video chats, and live streaming. The digital landscape has started to change in recent years as a result of the introduction of Virtual Reality (VR) to these platforms. Instead of using conventional 2D

Ashish Tiwari, Mukul Singh, Ananya Singha, Arjun Radhakrishna

The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diver

Mohammad Tariqul Islam, Jason W. Fleischer

Uniform manifold approximation and projection (UMAP) is among the most popular neighbor embedding methods. The method samples pairs of point indices according to similarities in the high-dimensional space, and applies attractive and repulsive forces to their coordinates in the low-dimensional embedding. In this paper, we analyze the forces to reveal their ef

Fabrizio Tamburini

Following the Hilbert-P\'olya approach to the Riemann Hypothesis, we present an exact spectral realization of the nontrivial zeros of the Riemann zeta function $\zeta(z)$ with a Mellin-Barnes integral that explicitly contains it. This integral defines the spectrum of the real-valued energy eigenvalues $E_n$ of a Majorana particle in a $(1+1)$-dimensional Rin

Yuhao Sun, Shixin Zhang, Wenzhuang Li, Jie Zhao

With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to addr

Ao Cao, Fuyong Wang, Zhongxin Liu

This paper introduces an online data-driven learning scheme designed to address a novel problem in propensity formation and containment control for fully heterogeneous multi-agent systems. Unlike traditional approaches that rely on the eigenvalues of the Laplacian matrix, this problem considers the determination of follower positions based on propensity fact

Kaifeng Zou, Xiaoyi Feng, Peng Wang, Tao Huang

Generative models are widely used in visual content creation. However, current text-to-image models often face challenges in practical applications-such as textile pattern design and meme generation-due to the presence of unwanted elements that are difficult to separate with existing methods. Meanwhile, subject-reference generation has emerged as a key resea

Pei-Sze Tan, Sailaja Rajanala, Arghya Pal, Raphaël C. -W. Phan

Detecting concealed emotions within apparently normal expressions is crucial for identifying potential mental health issues and facilitating timely support and intervention. The task of spotting macro and micro-expressions involves predicting the emotional timeline within a video, accomplished by identifying the onset, apex, and offset frames of the displaye

Sehwan Kim, Rui Wang, Wenbin Lu

In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when dealing with high-dimensional predictors that are complexly interrelated. Many existing deep learning approaches for estimatin

Josnei Novacoski, Mark Spivakovsky

Consider a simple algebraic valued field extension $(L/K,v)$ and denote by $\mathcal O_L$ and $\mathcal O_K$ the corresponding valuation rings. The main goal of this paper is to present, under certain assumptions, a description of $\mathcal O_L$ in terms of generators and relations over $\mathcal O_K$. The main tool used here are complete sequences of key po

Dipayan Sengupta, Saumya Panda

Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requiring multi-drug regimens. Traditional evidence synthesis methods, such as component network meta-analysis (cNMA), often face parameter explosion and limited interpretability, especially when modeling interaction eff

Lijie Hu, Junchi Liao, Weimin Lyu, Shaopeng Fu

Backdoor attacks pose a serious threat to deep learning models by allowing adversaries to implant hidden behaviors that remain dormant on clean inputs but are maliciously triggered at inference. Existing backdoor attack methods typically rely on explicit triggers such as image patches or pixel perturbations, which makes them easier to detect and limits their

Zihao Chen, Hisashi Handa, Miho Ohsaki, Kimiaki Shirahama

Several backbone models pre-trained on general domain datasets can encode a sentence into a widely useful embedding. Such sentence embeddings can be further enhanced by domain adaptation that adapts a backbone model to a specific domain. However, domain adaptation for low-resource languages like Japanese is often difficult due to the scarcity of large-scale

Sifan Yu, Feng He, Anlan Xie, Luxi Zhao

With the growing demand for dynamic real-time applications, online admission control for time-critical event-triggered (ET) traffic in Time-Sensitive Networking (TSN) has become a critical challenge. The main issue lies in dynamically allocating bandwidth with real-time guarantees in response to traffic changes while also meeting the requirements for rapid r

Arqum Hashmi, M. Umar Farooq, Mizuki Tani, Kazuhiro Yabana

We theoretically study the ultrafast optical control of multiple valley states in two-dimensional (2D) tin sulfide (SnS) monolayers, a member of the layered group-IV monochalcogenides, which is a promising class of materials for overcoming current challenges in valleytronics. By combining time-dependent density functional theory with Maxwells equations, we s

Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu

Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data

Rui Yang, Lin Song, Yicheng Xiao, Runhui Huang

Recent advancements in large language models (LLMs) have significantly propelled the development of large multi-modal models (LMMs), highlighting the potential for general and intelligent assistants. However, most LMMs model visual and textual modalities separately, leading to recent efforts to develop native LMMs using a single transformer. Despite the prom

Hamed Jabbari Asl, Eiji Uchibe

This paper introduces a novel model-free and a partially model-free algorithm for inverse optimal control (IOC), also known as inverse reinforcement learning (IRL), aimed at estimating the cost function of continuous-time nonlinear deterministic systems. Using the input-state trajectories of an expert agent, the proposed algorithms separately utilize control

Zhaoling Chen, Xiangru Tang, Gangda Deng, Fang Wu

Code localization--identifying precisely where in a codebase changes need to be made--is a fundamental yet challenging task in software maintenance. Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code sections. The challenge lies in bridging natural language problem descriptions with the appropriate code elem

Jie Zeng

This paper studies the derivation and well-posedness of a class of high - order water wave equations, the fifth - order Benjamin - Bona - Mahony (BBM) equation. Low - order models have limitations in describing strong nonlinear and high - frequency dispersion effects. Thus, it is proposed to improve the modeling accuracy of water wave dynamics on long - time

Yefei He, Yuanyu He, Shaoxuan He, Feng Chen

Visual autoregressive models typically adhere to a raster-order ``next-token prediction" paradigm, which overlooks the spatial and temporal locality inherent in visual content. Specifically, visual tokens exhibit significantly stronger correlations with their spatially or temporally adjacent tokens compared to those that are distant. In this paper, we propos

Ye Luo

Recently, the study of circuits and cycles within the homology classes of graphs has attracted considerable research interest. However, the detection and counting of shorter circuits in homology classes, especially the shortest ones, remain underexplored. This paper aims to fill this gap by solving the problem of detecting and counting the shortest cycles in

Hee Jun Yang, Alexander Heinlein, Hyea Hyun Kim

Approximate solutions of partial differential equations (PDEs) obtained by neural networks are highly affected by hyper parameter settings. For instance, the model training strongly depends on loss function design, including the choice of weight factors for different terms in the loss function, and the sampling set related to numerical integration; other hyp

Ryan K. Krueger, Sharon Aviran, David H. Mathews, Jeffrey Zuber

The Nearest Neighbor model is the $\textit{de facto}$ thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains $\approx13,000$ underlying thermodynamic parameters, and fitting these to both experimental and structural data is computational

Xi Yan, Lang Cui, Luis C. Ho

We present the multi-frequency, multi-epoch Very Long Baseline Interferometry (VLBI) study of the two-sided jets in the low-luminosity active galactic nucleus NGC 3998, where physical properties of the jets on parsec scales remain poorly understood. Using Very Long Baseline Array data observed at 1.4, 1.7, 2.3, and 5 GHz, we detect symmetric twin jets aligne

Xi Kong, Yuke Zhang, Chenyu Ji, Shuangju Chang

This study presents a novel method using spin quantum sensors to explore temporal variations of fundamental constants, significantly expanding the frequency range and providing constraints on scalar dark matter.

Xiaowei Bi, Zheyuan Xu

While multi-modal learning has advanced significantly, current approaches often create inconsistencies in representation and reasoning of different modalities. We propose UMaT, a theoretically-grounded framework that unifies visual and auditory inputs as structured text for large language models, addressing semantic alignment, temporal synchronization, and e

Yuta Tanimura, Yuki Ishii, Kenta Takata, Takahiro Uemura

The concept of bound states in the continuum (BIC) has been advancing light confinement technology in leaky environments. In this letter, we propose and numerically demonstrate a slow light waveguide based on a BIC mode. We considered a waveguide with a polymer core loaded on a plane slab, which supports a leaky guided mode coupled to the radiation continuum

Liwen Lin, Nan Lib, Shuchen Zhao

Children with ADHD often struggle with executive function (EF) and motor skills, impacting their academics and social life. While medications are commonly used, they have side effects, leading to interest in non-drug treatments. Physical activity (PA) has shown promise in improving cognitive and motor skills in children with ADHD. This study examined the sho

Sicheng He, Zeyu Shangguan, Kuanning Wang, Yongchong Gu

Sequentially grasping multiple objects with multi-fingered hands is common in daily life, where humans can fully leverage the dexterity of their hands to enclose multiple objects. However, the diversity of object geometries and the complex contact interactions required for high-DOF hands to grasp one object while enclosing another make sequential multi-objec

Yu Peng, Guoqing Zhang, Huadong Pang

IoT-based devices and wearable sensors are now common in daily life, with smartwatches, smartphones, and other digital tools tracking physical activity and health data. This lifelogging process provides valuable insights into people's lives. This paper analyzes a publicly available lifelog dataset of 14 individuals to explore how exercise affects mood and, i

Steven Kelk, Simone Linz, Charles Semple

Agreement forests continue to play a central role in the comparison of phylogenetic trees since their introduction more than 25 years ago. More specifically, they are used to characterise several distances that are based on tree rearrangement operations and related quantifiers of dissimilarity between phylogenetic trees. In addition, the concept of agreement

Mingjun Sun, Chongjun Ouyang, Shaochuan Wu, Yuanwei Liu

The pinching-antenna system (PASS) introduces new degrees of freedom (DoFs) for physical layer security (PLS) through pinching beamforming. In this paper, a couple of scenarios for secure beamforming for PASS are studied. 1) For the case with a single legitimate user (Bob) and a single eavesdropper (Eve), a closed-form expression for the optimal baseband bea

Ryoma Saito

In this paper, we prove the solvability of the vortex equation on a holomorphic vector bundle over a compact Hermitian manifold using the continuity method, and show the Kobayashi-Hitchin correspondence for holomorphic pairs. This work extends Bradlow's Kobayashi-Hitchin correspondence over compact K\"{a}hler manifolds to compact non-K\"{a}hler manifolds.

Xiaolong Wang, Jing Feng, Gege Wang, Tong Li

Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density functions are only attainable in specific cases. To speed up the solving process of parameterized FPEs with several system parameters, we introduce a deep learning

Daoyuan Li, Zuyuan Yang, Shengli Xie

Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms

Atiq Ur Rehman, Muhammad Farooq

The sample covariance matrix becomes non-invertible in high-dimensional settings, making classical multivariate statistical methods inapplicable. Various regularization techniques address this issue by imposing a structured target matrix to improve stability and invertibility. While diagonal matrices are commonly used as targets due to their simplicity, more

Mooho Song, Hyeryung Son, Jay-Yoon Lee

Examining logical inconsistencies among multiple statements (such as collections of sentences or question-answer pairs) is a crucial challenge in machine learning, particularly for ensuring the safety and reliability of models. Traditional methods that rely on pairwise comparisons often fail to capture inconsistencies that only emerge when more than two stat

Yucheng Suo, Fan Ma, Kaixin Shen, Linchao Zhu

Visual instructions for long-horizon tasks are crucial as they intuitively clarify complex concepts and enhance retention across extended steps. Directly generating a series of images using text-to-image models without considering the context of previous steps results in inconsistent images, increasing cognitive load. Additionally, the generated images often

Xi Yan, Ru-Sen Lu

Low-luminosity Active Galactic Nuclei (LLAGN) represent a unique class of AGN in the local universe. Extensive studies of these objects are essential for a comprehensive understanding of jet physics, as past research has largely focused on more powerful radio sources. In this report, we present our recent VLBI studies of two prominent nearby LLAGN, NGC 4261

Yuanzhu Huang, Yang Zhou

We present a method to compute the symmetry-resolved entanglement entropy of spherical regions in higher-dimensional conformal field theories. By employing Casini-Huerta-Myers mapping, we transform the entanglement problem into thermodynamic calculations in hyperbolic space. This method is demonstrated through computations in both free field theories and hol