November 2025 arXiv papers — page 96

Chao Chen, Yulu Liu, Hongyan Lu, Zihao Wang

The discovery of two-dimensional magnetic materials has provided an ideal platform for exploring physical phenomena in the two-dimensional limit. However, intrinsic two-dimensional antiferromagnetic materials have been rarely reported, limiting systematic studies of their electronic properties. The discovery of novel intrinsic two-dimensional antiferromagnet

Jinhao Yang, Shaojiong Zhou, Zhibin Wang, Jiahua Xu

Long COVID "brain fog" is a common and debilitating subjective syndrome often associated with persistent cognitive impairment after COVID-19 infection. Here we identify a specific regional brain dysfunction that mediates this cognitive impairment and provide evidence that targeted neuromodulation improves this deficit. In 120 patients with long COVID brain f

Pengcheng Shi

The aorta, the body's largest artery, is prone to pathologies such as dissection, aneurysm, and atherosclerosis, which often require timely intervention. Minimally invasive repairs involving branch vessels necessitate detailed 3D anatomical analysis. Existing methods often overlook hierarchical anatomical relationships while struggling with severe class imba

Zhaoyu Liu, Kan Jiang, Murong Ma, Zhe Hou

Precise event spotting (PES) aims to recognize fine-grained events at exact moments and has become a key component of sports analytics. This task is particularly challenging due to rapid succession, motion blur, and subtle visual differences. Consequently, most existing methods rely on domain-specific, end-to-end training with large labeled datasets and ofte

Xiangyu Li, Chen Wang, Yumao Liu, Dengbo He

Most existing autonomous-driving datasets (e.g., KITTI, nuScenes, and the Waymo Perception Dataset), collected by human-driving mode or unidentified driving mode, can only serve as early training for the perception and prediction of autonomous vehicles (AVs). To evaluate the real behavioral safety of AVs controlled in the black box, we present the first end-

Xuan Zhao, Zhongyu Zhang, Yuge Huang, Yuxi Mi

Existing state-of-the-art image tokenization methods leverage diverse semantic features from pre-trained vision models for additional supervision, to expand the distribution of latent representations and thereby improve the quality of image reconstruction and generation. These methods employ a locally supervised approach for semantic supervision, which limit

Jingdong Zhang, Lingzhi Zhang, Qing Liu, Mang Tik Chiu

Digital images are often degraded by soft effects such as lens flare, haze, shadows, and reflections, which reduce aesthetics even though the underlying pixels remain partially visible. The prevailing works address these degradations in isolation, developing highly specialized, specialist models that lack scalability and fail to exploit the shared underlying

Zihuai Zhao, Yujuan Ding, Wenqi Fan, Qing Li

Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender systems have emerged as a prevalent paradigm for advancing personalized recommendations. Recently, retrieval-augmented generation (RAG) has drawn growing interest to facilitate the

Jiajun Hou, Chenyu Zhang, Rui Meng

Entity Linking (EL), the task of mapping textual entity mentions to their corresponding entries in knowledge bases, constitutes a fundamental component of natural language understanding. Recent advancements in Large Language Models (LLMs) have demonstrated remarkable potential for enhancing EL performance. Prior research has leveraged LLMs to improve entity

Dawei Shen

Let $A$ be a Nakayama algebra. Using Ringel's resolution quiver, we give a criterion to decide whether $A$ is minimal Auslander-Gorenstein. The criterion strongly relies on the parity of the selfinjective dimension of $A$.

Musab Alamoudi, Robert L. Paige, Vic Patrangenaru

Projective shape analysis provides a geometric framework for studying digital images acquired by pinhole digital cameras. In the classical projective shape (PS) method, landmark configurations are represented in $(\RP^2)^{k-4}$, where $k$ is the number of landmarks observed. This representation is invariant under the action of the full projective group on th

Yanshan Li, Ke Ma, Miaomiao Wei, Linhui Dai

Existing self-supervised contrastive learning methods for skeleton-based action recognition often process all skeleton regions uniformly, and adopt a first-in-first-out (FIFO) queue to store negative samples, which leads to motion information loss and non-optimal negative sample selection. To address these challenges, this paper proposes Dominance-Game Contr

Zhuo Li, Junjia Liu, Zhipeng Dong, Tao Teng

Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies still suffer from substantial performance degradation during downstream deployment. Although fine-tuning can mitigate this issue, its reliance on costly demonstration collection and intensive computation makes it i

Seungjae Lee, Aeryeong Seo

For a symmetric differential on the compact quotient $\Sigma = \mathbb{B}^n / \Gamma$ of the complex unit ball $\mathbb{B}^n \subset \mathbb{C}^n$ by a discrete subgroup $\Gamma \subset \mathrm{Aut}(\mathbb{B}^n)$, there exists a corresponding weighted $L^2$-holomorphic function on $(\mathbb{B}^n \times \mathbb{B}^n)/\Gamma$, where $\Gamma$ acts diagonally o

Seunghun Lee, Eran Nevo

We show that for $2\le d\le 4$, every finite geometric simplicial complex $\Delta$ in $\mathbb{R}^d$ with vertices on the moment curve can be extended to a triangulation $T$ of the cyclic polytope $C$ where $\Delta, T$ and $C$ all have the same vertex set. Further, for $d\ge 5$ we construct for every $n\ge d+3$ complexes $\Delta$ on $n$ vertices for which no

Yibo Meng, Xiaolan Ding, Lyumanshan Ye, Zhiming Liu

Over the past decade, specialized social networking applications have become a cornerstone of life for many gay men in China. This paper employs a longitudinal mixed-methods approach to investigate how Chinese men who have sex with men (MSM) have shifted their attitudes toward these platforms between approximately 2013 and 2023. Drawing on archival analysis

Frederic Le Pimpec, Cameron Baribeau, Tonia Batten, Grant Bilbrough

In the next 5 to 10 years, new fourth generation light sources will be coming online either from upgrades or as brand new facilities. The question regarding the competitiveness and the usability of a third generation light source is certainly in the mind of the scientific personnel, which includes both the beamline and the accelerator staff of any third Gen

Naveen Lamba, Sanju Tiwari, Manas Gaur

LLMs still struggle with hallucination, especially when confronted with symbolic triggers like modifiers, negation, numbers, exceptions, and named entities. Yet, we lack a clear understanding of where these symbolic hallucinations originate, making it crucial to systematically handle such triggers and localize the emergence of hallucination inside the model.

Denis Bodrov, Xinping Xu, Dmitrii Gavrilov, Pavel Pakhlov

We present a track-finding algorithm for the Belle II experiment that specifically targets so-called kinks: signatures of charged particles decaying or scattering in-flight in the detector material, resulting in a sudden and significant change of the particle's flight direction. Our benchmark studies of this Kink Finder show that the reconstruction efficienc

Juan Manuel Sánchez Cerritos

We prove the existence of planar $D_n$--equivariant choreographies in the $n$--body problem with homogeneous potential of degree $-\alpha$, $0<\alpha<2$. Each body follows the same closed path, rotated and time-shifted, forming a choreography whenever the winding number $W$ is coprime with $n$. Using Mawhin's coincidence degree, we establish collision-free p

Xinliang Zhang, Lei Zhu, Hangzhou He, Shuang Zeng

Multimodal Large Language Models (MLLMs) have demonstrated substantial value in unified text-image understanding and reasoning, primarily by converting images into sequences of patch-level tokens that align with their architectural paradigm. However, patch-level tokenization leads to a quadratic growth in image tokens, burdening MLLMs' understanding and reas

Junpeng Zhao, Lin Li, Kaixi Hu, Kaize Shi

Signed graphs model complex relationships through positive and negative edges, with widespread real-world applications. Given the sensitive nature of such data, selective removal mechanisms have become essential for privacy protection. While graph unlearning enables the removal of specific data influences from Graph Neural Networks (GNNs), existing methods a

Hao Lang, Fei Huang, Yongbin Li

Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-quality data for model alignment, some works on weak-to-strong generalization (W2SG) finetune a strong pretrained model with a weak supervisor so that it can generalize beyond weak sup

Hao Yang, Shao-Qin Guo, Zhi-Qing Zhang

The weak decays of the $D_{(s)}$ meson provide a pivotal platform to advance our understanding of the Standard Model (SM) and to explore New Physics (NP). In recent years, experiments have collected a significant amount of data on the $D_{(s)}$ meson decays, particularly from BESIII, which provides substantial support for theoretical research. In this work,

Yibo Meng, Rong Fu, Lyumanshan Ye, Zhiming Liu

This study explores the design of Intelligent User Interfaces (IUIs) to address the profound existential loneliness of terminally ill individuals. While Human-Computer Interaction (HCI) has made inroads in "Thanatechnology," current research often focuses on practical aspects like digital legacy management, overlooking the subjective, existential needs of th

Yuchen Zhang, Yang Liu, Hongwei Yu, Puxun Wu

The assumption that photons are massless is a foundational postulate of modern physics, yet it remains subject to experimental verification. Fast radio bursts (FRBs), with their cosmological distances and precisely measured dispersion, offer an excellent laboratory for testing this hypothesis. In this work, we propose an improved distribution function for th

Supawit Chockchowwat, Sumay Thakurdesai, Zhaoheng Li, Matthew Krafczyk

Ranging from batch scripts to computational notebooks, modern data science tools rely on massive and evolving object graphs that represent structured data, models, plots, and more. Persisting these objects is critical, not only to enhance system robustness against unexpected failures but also to support continuous, non-linear data exploration via versioning.

Xiaoquan Sun, Ruijian Zhang, Kang Pang, Bingchen Miao

Household tidying is an important application area, yet current benchmarks neither model user preferences nor support mobility, and they generalize poorly, making it hard to comprehensively assess integrated language-to-action capabilities. To address this, we propose RoboTidy, a unified benchmark for language-guided household tidying that supports Vision-La

Laura Musgrave, Arnab Bhattacharjee, Tapan Kumar Saha

This work presents a case study of optimal energy management of a large Heating Ventilation and Cooling (HVAC) system within a university campus in Australia using Reinforcement Learning (RL). The HVAC system supplies to nine university buildings with an annual average electricity consumption of $\sim2$ GWh. Updated chiller Coefficient of Performance (COP) c

Fatima Kazi

Large Language models (LLMs), such as ChatGPT, have gained popularity in recent years with the advancement of Natural Language Processing (NLP), with use cases spanning many disciplines and daily lives as well. LLMs inherit explicit and implicit biases from the datasets they were trained on; these biases can include social, ethical, cultural, religious, and

Arnab Bhattacharjee

This study examines the economic impact of post-hoc uncertainty discounting in predictive energy management, specifically in battery energy arbitrage. A 2.2 MWh, 1.1 MW Tesla battery, emulating operations at the University of Queensland's St. Lucia campus, is used as a test system. Traditionally, Model Predictive Control (MPC) frameworks rely on deterministi

Xun Lin, Shuai Wang, Yi Yu, Zitong Yu

Multimodal Face Anti-Spoofing (FAS) methods, which integrate multiple visual modalities, often suffer even more severe performance degradation than unimodal FAS when deployed in unseen domains. This is mainly due to two overlooked risks that affect cross-domain multimodal generalization. The first is the modal representation invariant risk, i.e., whether rep

Jesse L Everett

Spectrotemporal encoding of optical quantum information is emerging as a powerful tool in quantum information technology. Processing of spectrotemporal information has recently been demonstrated in multi-mode quantum memories, based on extensions to memory protocols. We simulate one such process, the fractional Fourier transform, in a system based on a dual

Jiajun Ma, Yongchao Zhang, Chao Zhang, Zhao Lv

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connect

Ibrahim Khalil Kabir, Muhammad Faizan Mysorewala

Socially aware navigation is a fast-evolving research area in robotics that enables robots to move within human environments while adhering to the implicit human social norms. The advent of Deep Reinforcement Learning (DRL) has accelerated the development of navigation policies that enable robots to incorporate these social conventions while effectively reac

E. Hiyama, T. Doi

With the advancement of first-principles calculations for baryon-baryon interactions, it becomes possible to obtain reliable hyperon-nucleon potentials by lattice QCD simulations with the HAL QCD method. High-precision few-body methods, such as the Gaussian Expansion Method (GEM), are applicable to solve quantum few-body systems up to four- and five-body sys

Jaime Bajo, Manuel de León, Asier López-Gordón

A variational formulation for non-equilibrium thermodynamics was developed by Gay-Balmaz and Yoshimura. In a recent article, the first two authors of the present paper introduced partially cosymplectic structures as a geometric framework for thermodynamic systems, recovering the evolution equations obtained variationally. In this paper, we develop a discrete

Fatima Kazi, Alex Young, Yash Inani, Setareh Rafatirad

Large Language Models (LLMs) inherit explicit and implicit biases from their training datasets. Identifying and mitigating biases in LLMs is crucial to ensure fair outputs, as they can perpetuate harmful stereotypes and misinformation. This study highlights the need to address biases in LLMs amid growing generative AI. We studied bias-specific benchmarks suc

Laura Dodds, Maisy Lam, Waleed Akbar, Yibo Cheng

We present Wave-Former, a novel method capable of high-accuracy 3D shape reconstruction for completely occluded, diverse, everyday objects. This capability can open new applications spanning robotics, augmented reality, and logistics. Our approach leverages millimeter-wave (mmWave) wireless signals, which can penetrate common occlusions and reflect off hidde

Rayane Mouhli, Thomas Pierron

In computational anatomy, the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework has become a central tool for modeling smooth, invertible transformations between shapes such as curves or landmarks. In this paper, we extend this framework by enriching diffeomorphic deformations with transformations induced by finite-dimensional Lie groups (e.g.

Luyang Li, Min Zhou, Zhiyang Liu, Chonglong Xie

Mass spectra and Mott transitions of neutral mesons $K_0,{\bar K}_0,\pi_0,\eta,\eta'$ at finite temperature and magnetic field are investigated in a three-flavor PNJL model. We focus on the effect of gluons, which is simulated by the Polyakov potential, and the inverse magnetic catalysis (IMC) effect, which is mimicked by using a magnetic field dependent par

Hao Wang, Linqing Zhao, Xiuwei Xu, Jiwen Lu

Recent trends in SLAM and visual navigation have embraced 3D Gaussians as the preferred scene representation, highlighting the importance of estimating camera poses from a single image using a pre-built Gaussian model. However, existing approaches typically rely on an iterative \textit{render-compare-refine} loop, where candidate views are first rendered usi

Yuhua Jiang, Shuang Cheng, Yan Ding, Feifei Gao

Vision-language-action (VLA) models have recently emerged as a powerful paradigm for building generalist robots. However, traditional VLA models that generate actions through flow matching (FM) typically rely on rigid and uniform time schedules, i.e., synchronous FM (SFM). Without action context awareness and asynchronous self-correction, SFM becomes unstabl

Chelsea-Xi Chen, Zhe Zhang, Aven-Le Zhou

Robotic arm choreography often reproduces trajectories while missing cultural semantics. This study examines whether symbolic posture transfer with joint space compatible notation can preserve semantic fidelity on a six-degree-of-freedom arm and remain portable across morphologies. We implement ROPERA, a three-stage pipeline for encoding culturally codified

Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov

We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media. The method processes large and diverse array datasets using sparse dictionary learning, clustering, and multidimensional scaling. Starting from random initializations, the algorithm reliably extracts the unknown medium properties necessary fo

Renjie Chen, Viet Anh Nguyen, Huifu Xu

We propose a distributionally robust formulation for simultaneously estimating the covariance matrix and the precision matrix of a random vector.The proposed model minimizes the worst-case weighted sum of the Frobenius loss of the covariance estimator and Stein's loss of the precision matrix estimator against all distributions from an ambiguity set centered

Junchi Zhang, Jianbing Lu, Meizi Ou

This paper is devoted to the study of $2$-designs with $\lambda\ge (r,\lambda)^2$ admitting a flag-transitive automorphism group $G$. The group $G$ has been shown to be point-primitive of either almost simple or affine type. In this paper, we classify the $2$-designs with $\lambda \geq (r,\lambda)^2>1$ admitting a flag-transitive almost simple automorphism g

Eitan Farchi, Kiran Nayak, Papia Ghosh Majumdar, Saritha Route

Large Language Models (LLMs) are transforming Quality Engineering (QE) by automating the generation of artefacts such as requirements, test cases, and Behavior Driven Development (BDD) scenarios. However, ensuring the quality of these outputs remains a challenge. This paper presents a systematic technique to baseline and evaluate QE artefacts using quantifia

Naoki Shimoda, Akihiro Yamamoto

In this research, we combine Transformer-based relation extraction with matching of knowledge graphs (KGs) and apply them to answering multiple-choice questions (MCQs) while maintaining the traceability of the output process. KGs are structured representations of factual knowledge consisting of entities and relations. Due to the high construction cost, they

Omkar Mahesh Kashyap, Padegal Amit, Madhav Kashyap, Ashwini M Joshi

Aspect-Based Sentiment Analysis (ABSA) predicts sentiment polarity for specific aspect terms, a task made difficult by conflicting sentiments across aspects and the sparse context of short texts. Prior graph-based approaches model only pairwise dependencies, forcing them to construct multiple graphs for different relational views. These introduce redundancy,

Grant Ruan, Marija D. Ilic, Le Xie

Data centers host a variety of essential services such as cloud computing and artificial intelligence. Electric grid operators, however, have limited knowledge of the reliability risks of data center interconnection due to their unique operational characteristics. An emerging concern is the sub-synchronous resonance (SSR) which refer to unexpected voltage/cu

Hajun Kim, Hyunsik Na, Daeseon Choi

As the use of large language models (LLMs) continues to expand, ensuring their safety and robustness has become a critical challenge. In particular, jailbreak attacks that bypass built-in safety mechanisms are increasingly recognized as a tangible threat across industries, driving the need for diverse templates to support red-teaming efforts and strengthen d

Junhao Gong, Shoujie Li, Kit-Wa Sou, Changqing Guo

Conventional suction cups lack sensing capabilities for contact-aware manipulation in unstructured environments. This paper presents FlexiCup, a multimodal suction cup with wireless electronics that integrate dual-zone vision-tactile sensing. The central zone dynamically switches between vision and tactile modalities via illumination control, while the perip

Hojoon Ki, Jongsuk Kim, Minchan Kwon, Junmo Kim

Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set of differentiable audio effects. This paper proposes FxSearcher, a novel gradient-free framework that discovers the optimal configuration of audio effects (FX) to transform a sour

Sushant Mehta

Synthetic data has become essential for training foundation models, yet benchmark contamination threatens evaluation integrity. Although existing detection methods identify token-level overlap, they fail to detect semantic-level contamination where synthetic data conceptually resemble benchmarks without lexical overlap. This gap is critical as foundation mod

Luyang Fang, Tao Wang, Ping Ma, Xiaoming Zhai

Automated scoring of written constructed responses typically relies on separate models per task, straining computational resources, storage, and maintenance in real-world education settings. We propose UniMoE-Guided, a knowledge-distilled multi-task Mixture-of-Experts (MoE) approach that transfers expertise from multiple task-specific large models (teachers)

Sushant Mehta

Current agentic AI benchmarks predominantly evaluate task completion accuracy, while overlooking critical enterprise requirements such as cost-efficiency, reliability, and operational stability. Through systematic analysis of 12 main benchmarks and empirical evaluation of state-of-the-art agents, we identify three fundamental limitations: (1) absence of cost

Promise Ekpo, Saesha Agarwal, Felix Grimm, Lekan Molu

Fair workload enforcement in heterogeneous multi-agent systems that pursue shared objectives remains challenging. Fixed fairness penalties often introduce inefficiencies, training instability, and conflicting agent incentives. Reward-shaping approaches in fair Multi-Agent Reinforcement Learning (MARL) typically incorporate fairness through heuristic penaltie

Sogen Ikegami, Kiyu Fukui, Shun Okumura, Yasuyuki Kato

We investigate how the spectral and topological properties of electron systems evolve on a lattice that interpolates between the honeycomb and its 1/6-depleted structures through the introduction of selective random defects. We find that in certain parameter regimes, the topological properties of the two lattice systems are smoothly connected, whereas in oth

Jessy Xinyi Han, Devavrat Shah

Estimating causal effects on time-to-event outcomes from observational data is particularly challenging due to censoring, limited sample sizes, and non-random treatment assignment. The need for answering such "when-if" questions--how the timing of an event would change under a specified intervention--commonly arises in real-world settings with heterogeneous

Kavya Bhand, Payal Khubchandani, Jyoti Khubchandani

With the ever-growing demand for cybersecurity, static key encryption mechanisms are increasingly vulnerable to adversarial attacks due to their deterministic and non-adaptive nature. Brute-force attacks, key compromise, and unauthorized access have become highly common cyber threats. This research presents a novel fuzzy logic-based cryptographic framework t

Yu Zhong, Zihao Zhang, Rui Zhang, Lingdong Huang

Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions. Recent research underscores the potential of harnessing large language models (LLMs) for VLN, given their commonsense knowledge and general reasoning capabilities. Despite their strengths, a substantial gap in task completion pe

Chun Chet Ng, Jia Yu Lim, Wei Zeng Low

With the rapid progress of large language models (LLMs), financial information retrieval has become a critical industrial application. Extracting task-relevant information from lengthy financial filings is essential for both operational and analytical decision-making. We present PRISM, a training-free framework that integrates refined system prompting, in-co

Xiang Luo, Chang Liu, Gang Xiong, Chen Yang

Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious traffic a critical necessity as well as the identification of known classes. Recent studies have advanced this goal with deep models, but they often rely on task-specific architectu

Meilin Yu

High-order nodal space-time flux reconstruction (STFR) methods have been developed to solve hyperbolic conservation laws on curvilinear moving grids. Unlike the method-of-lines approach for moving domain simulation, the grid velocity is implicitly embedded into the curvilinear geometric representation of space-time elements. Several key issues in moving doma

Yui Kuboike, Toshiki Sato, Hiromasa Suzuki, Kai Matsunaga

Shell burning and internal mixing in massive stars play an important role in setting the initial conditions for core-collapse supernova explosions. In the late stages of stellar evolution, intense shell burning can cause distinct convective regions to merge, fundamentally restructuring the stellar interior. Although such phenomena are difficult to observe di

Chandrasekhar Gokavarapu, D. Madhusudhana Rao

This paper develops the structural and spectral foundations of noncommutative and n-ary Gamma semirings, extending the commutative ternary framework established in earlier studies. We introduce left, right, and two-sided ideals in the noncommutative setting, derive quotient characterizations of prime and semiprime ideals, and construct corresponding Gamma-Ja

Sabiha Afroz, Redwan Ibne Seraj Khan, Hadeel Albahar, Jingoo Han

Training large language models (LLMs) in the cloud faces growing memory bottlenecks due to the limited capacity and high cost of GPUs. While GPU memory offloading to CPU and NVMe has made large-scale training more feasible, existing approaches suffer from high tensor migration latency and suboptimal device memory utilization, ultimately increasing training t

Lyndsay Roach, Qiong Li, Nanwei Wang, Xin Gao

We propose a covariate-dependent discrete graphical model for capturing dynamic networks among discrete random variables, allowing the dependence structure among vertices to vary with covariates. This discrete dynamic network encompasses the dynamic Ising model as a special case. We formulate a likelihood-based approach for parameter estimation and statistic

Chenzi Jin, Yanir A. Rubinstein, Yang Zhang

We survey various notions of symmetry for toric varieties. These notions range from algebraic geometric, complex geometric, representation theoretic, combinatorial, convex geometric, to geometric stability. The main theorem gives the relationship between these notions. While mostly folklore knowledge, this does not seem to be readily available in the literat

Hao Zhen, Yunxiang Yang, Jidong J. Yang

Pedestrian-vehicle incidents remain a critical urban safety challenge, with pedestrians accounting for over 20% of global traffic fatalities. Although existing video-based systems can detect when incidents occur, they provide little insight into how these events unfold across the distinct cognitive phases of pedestrian behavior. Recent vision-language models

Liuyi Jin, Amran Haroon, Radu Stoleru, Pasan Gunawardena

Timely and accurate pre-arrival video streaming and analytics are critical for emergency medical services (EMS) to deliver life-saving interventions. Yet, current-generation EMS infrastructure remains constrained by one-to-one video streaming and limited analytics capabilities, leaving dispatchers and EMTs to manually interpret overwhelming, often noisy or r

Md Mosharaf Hossan, Rifat Ara Tasnim, Farjana Z Eishita

Mysophobia, or the fear of germs, is a prevalent anxiety disorder that significantly impacts daily life. This study investigates the potential of a gamified virtual reality (VR) intervention to simulate contamination-related scenarios and assess their emotional and psychological effects. A VR game based sneeze simulation was developed to evaluate its influen

Ziyi Xu, Zhiqiang Xie, Swapnil Gandhi, Christos Kozyrakis

Tensor parallelism (TP) enables large language models (LLMs) to scale inference efficiently across multiple GPUs, but its tight coupling makes systems fragile: a single GPU failure can halt execution, trigger costly KVCache recomputation, and introduce long-term compute and memory imbalance. We present FailSafe, a fault-tolerant TP serving system that sustai

Zijuan Gao, Qing Guo, Chengxiang Zhang

We study the existence of multiple segregated solutions to the critical coupled Schr\"odinger system \[ \begin{cases} -\Delta u_{1} = K_1(| y|) | u_{1}|^{2^*-2}u_{1}+\beta | u_{2}|^{\frac{2^{*}}{2}}| u_{1}|^{\frac{2^{*}}{2}-2}u_{1}, & y\in \mathbb R^N,\\ -\Delta u_{2} = K_2(| y|) | u_{2}|^{2^*-2}u_{2}+\beta | u_{1}|^{\frac{2^{*}}{2}}| u_{2}|^{\frac{2^{*}}{2}

Ryogo Hara, Shimpei Endo, Shion Yamashika

We study the entanglement asymmetry for the space-inversion symmetry of free fermions on a two-dimensional honeycomb lattice with an on-site energy imbalance between the two sublattices. We show that the entanglement asymmetry of a local subsystem exhibits nonanalytic dependence on the energy imbalance, due to the presence of Dirac points in the Brillouin zo

Ziyao Zeng, Jingcheng Ni, Ruyi Liu, Alex Wong

Text-to-image diffusion models can generate diverse content with flexible prompts, which makes them well-suited for customization through fine-tuning with a small amount of user-provided data. However, controllable fine-tuning that prevents models from learning undesired concepts present in the fine-tuning data, and from entangling those concepts with user p

Truong Vo, Weiyi Wu, Kaize Ding

Automatic ICD coding from clinical text is a critical task in medical NLP but remains hindered by the extreme long-tail distribution of diagnostic codes. Thousands of rare and zero-shot ICD codes are severely underrepresented in datasets like MIMIC-III, leading to low macro-F1 scores. In this work, we propose a data-centric framework that generates high-qual

Srivathsan Sivakumar, Faisal Z. Qureshi

Vision Transformers (ViTs) have demonstrated remarkable performance across a range of computer vision tasks; however, their high computational, memory, and energy demands hinder deployment on resource-constrained platforms. In this paper, we propose \emph{Cascaded-ViT (CViT)}, a lightweight and compute-efficient vision transformer architecture featuring a no

Sithmini Ranasingha, Agasthi Haputhanthri, Hansa Marasinghe, Nima Wickramasinghe

Neonates are highly susceptible to seizures, often leading to short or long-term neurological impairments. However, clinical manifestations of neonatal seizures are subtle and often lead to misdiagnoses. This increases the risk of prolonged, untreated seizure activity and subsequent brain injury. Continuous video electroencephalogram (cEEG) monitoring is the

Zhenyu Li, Tianyi Shang

Visual Place Recognition (VPR) aims to match query images against a database using visual cues. State-of-the-art methods aggregate features from deep backbones to form global descriptors. Optimal transport-based aggregation methods reformulate feature-to-cluster assignment as a transport problem, but the standard Sinkhorn algorithm symmetrically treats sourc

Chandrasekhar Gokavarapu, D. Madhusudhana Rao

This paper develops a comprehensive geometric and homological framework for derived Gamma-geometry, extending the theory of commutative ternary Gamma-semirings established in our earlier works. Building upon the ideal-theoretic, computational, and categorical foundations of Papers A to D (Rao 2025A, Rao 2025B1, Rao 2025B2, Rao 2025C, Rao 2025D), the present

Zeyu Cheng, Tongfei Liu, Tao Lei, Xiang Hua

Depth information is crucial for autonomous driving and intelligent robot navigation. The simplicity and flexibility of self-supervised monocular depth estimation are conducive to its role in these fields. However, most existing monocular depth estimation models consume many computing resources. Although some methods have reduced the model's size and improve

Le Yu, Zhengyue Zhao, Yawen Zheng, Yunhao Liu

Reasoning-augmented Vision-Language Models (RVLMs) rely on safety alignment to prevent harmful behavior, yet their exposed chain-of-thought (CoT) traces introduce new attack surfaces. In this work, we find that the safety alignment of RVLMs can be easily broken through a novel attack method termed \textbf{Stealth Fine-Tuning}. Our method elicits harmful reas

Jin-Peng Zhang, Guan-Yu Wang, Wen-Chao Zhang, Bo Feng

We investigate the inclusive $J/\psi$ production in proton-proton (pp) collisions at center-of-mass energies $\sqrt{s} = 5.02$, 7, and 13 TeV using the PACIAE 4.0 model. This model extends PYTHIA 8.3 by incorporating partonic and hadronic rescatterings before and after hadronization, respectively. Compared to our earlier study [K.-F. Ye et al., Phys. Rev. C

Lehuai Xu, Zirui Lu, Haoran Yang, Yina Zhou

With the increasing demand for real-time Electrocardiogram (ECG) classification on edge devices, existing models face challenges of high computational cost and limited accuracy on imbalanced datasets.This paper presents Multi-task DFNet, a lightweight multi-task framework for ECG classification across the MIT-BIH Arrhythmia Database and the PTB Diagnostic EC

Arlindo Skënderaj

I consider an environment in which a decision maker faces uncertainty and privately holds information in the form of a signal about the true state of the world. The decision maker purchases additional information from a data broker before receiving the signal realization. I characterize the data broker's optimal selling mechanism, which involves screening ov

Wenfeng Wang, Jiacheng Liu, Xiaofeng Hou, Xinfeng Xia

The immense memory requirements of state-of-the-art Mixture-of-Experts (MoE) models present a significant challenge for inference, often exceeding the capacity of a single accelerator. While offloading experts to host memory is a common solution, it introduces a severe I/O bottleneck over the PCIe bus, as the data-dependent nature of expert selection places

Xinpeng Chen, Xiaofeng Han, Kaihao Zhang, Guochao Ren

Layout design is a crucial step in developing mobile app pages. However, crafting satisfactory designs is time-intensive for designers: they need to consider which controls and content to present on the page, and then repeatedly adjust their size, position, and style for better aesthetics and structure. Although many design software can now help to perform t

Yiqing Shen, Chenjia Li, Mathias Unberath

Text-driven video editing enables users to modify video content only using text queries. While existing methods can modify video content if explicit descriptions of editing targets with precise spatial locations and temporal boundaries are provided, these requirements become impractical when users attempt to conceptualize edits through implicit queries refer

Jingren Liu, Shuning Xu, Qirui Yang, Yun Wang

All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Executio

Adit Jain, Vikram Krishnamurthy, Yiming Zhang

In this paper, we model and analyze how a network of interacting LLMs performs collaborative question-answering (CQA) in order to estimate a ground truth given a distributed set of documents. This problem is interesting because LLMs often hallucinate when direct evidence to answer a question is lacking, and these effects become more pronounced in a network o

Weijia Fan, Qiufu Li, Jiajun Wen, Xiaoyang Peng

For long-tailed recognition (LTR) tasks, high intra-class compactness and inter-class separability in both head and tail classes, as well as balanced separability among all the classifier vectors, are preferred. The existing LTR methods based on cross-entropy (CE) loss not only struggle to learn features with desirable properties but also couple imbalanced c

Junchen Li, Rongzheng Wang, Yihong Huang, Qizhi Chen

Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop question answering due to their limited capacity to capture complex dependencies across documents. Recent studies employ graph-based RAG to capture document connections. However, these

Danush Shekar, Shirsendu Nanda, Zhenyu Ye, Ryan Heller

This paper presents the setup assembled to characterize and measure the spatial and timing resolutions of AC-coupled Low Gain Avalanche Diodes (AC-LGADs), using a 1060 nm laser source to deposit initial charges with a defined calibration methodology. The results were compared to those obtained with a 120 GeV proton beam. Despite the differences in the charge

Hien Phan-Thanh, Nguyen Nguyen-Duc, Thuy Le-Quang, Tobias C. Hinse

This project presents the development and implementation of a compact spectrometer, named SPECTRUMMATE, tailored for small telescopes. Small telescopes offer several advantages: they are cost-effective, occupy less space, and are simpler to set up than larger instruments. This makes them particularly suitable for amateur astronomers and educational instituti

Fan Zhang, Haoyuan Ren, Fei Ma, Qiang Yin

Cross-view object Geo-localization aims to precisely pinpoint the same object across large-scale satellite imagery based on drone images. Due to significant differences in viewpoint and scale, coupled with complex background interference, traditional multi-stage "retrieval-matching" pipelines are prone to cumulative errors. To address this, we present SMGeo,

Jiayang Xu, Xintong Chen, Yufeng Liu, Xiaoli Guo

Correlation coefficient is widely used in biomedical and biological literature, yet its frequent misuse and misinterpretation undermine the credibility and reproducibility of the scientific findings. We systematically reviewed 1326 records of correlation analyses across 310 articles published in Science, Nature, and Nature Neuroscience in 2022. Our analysis

Jae Youn Ahn, Hong Beng Lim, Mario V. Wüthrich

Integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) models are a popular framework for modeling serial dependence in count time-series. While convenient for modeling, prediction, and estimation, INGARCH models lack a clear theoretical justification for the evolution step. This limitation not only makes interpretation difficult and

Luke Piszkin, Dervis Can Vural

In this work, we integrate theoretical modeling, molecular simulation, and empirical analysis to identify and characterize evolutionary hysteresis. We first show how epistatic interactions create bistable fitness landscapes and structural hysteresis in a two-locus Wright-Fisher model, revealing two distinct hysteresis regimes under cyclic and noisy selection