November 2025 arXiv papers — page 62

Weijie Jiang, Armando Ordorica, Jaewon Yang, Olafur Gudmundsson

User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retention is revisitation, i.e., when users return to view previously saved content, a behavior often sparked by personalized recommendations and user satisfaction. However, modeling and

Jiaying Zhou, Qingchao Chen

Open-Vocabulary Object Detection (OVOD) aims to generalize object recognition to novel categories, while Weakly Supervised OVOD (WS-OVOD) extends this by combining box-level annotations with image-level labels. Despite recent progress, two critical challenges persist in this setting. First, existing semantic prototypes, even when enriched by LLMs, are static

MinGyu Jeon, SuWan Cho, JaeYoung Shu

Large Language Models (LLMs) augmented with Knowledge Graphs (KGs) have advanced complex question answering, yet they often remain susceptible to failure when their initial high-level reasoning plan is flawed. This limitation, analogous to cognitive functional fixedness, prevents agents from restructuring their approach, leading them to pursue unworkable sol

Guanghong Zuo

Deep learning has emerged as a powerful framework for analyzing biomolecular dynamics trajectories, enabling efficient representations that capture essential system dynamics and facilitate mechanistic studies. We propose a neural network architecture incorporating Fourier Transform analysis to process trajectory data, achieving dual objectives: eliminating h

Taihao Zhang, Zhendong Peng, Cunhua Pan, Hong Ren

In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The ef

Gavin Ramsay

AM CVn binaries are the most compact of accreting binaries having orbital periods in the range ~5-70 min. They consist of a white dwarf accreting hydrogen deficient material from a degenerate or semi-degenerate star and are predicted to be amongst the verification sources for future gravitational wave observatories such as LISA. Using the recent catalogue of

Siteng Ma, Honghui Du, Prateek Mathur, Brendan S. Kelly

Detecting changes in longitudinal medical imaging using deep learning requires a substantial amount of accurately labeled data. However, labeling these images is notably more costly and time-consuming than labeling other image types, as it requires labeling across various time points, where new lesions can be minor, and subtle changes are easily missed. Deep

Meng Ding, Mingxi Lei, Shaopeng Fu, Shaowei Wang

Differentially private Stochastic Gradient Descent (DP-SGD) has become integral to privacy-preserving machine learning, ensuring robust privacy guarantees in sensitive domains. Despite notable empirical advances leveraging features from non-private, pre-trained models to enhance DP-SGD training, a theoretical understanding of feature dynamics in private lear

Shengyuan Wang, Zhiheng Zheng, Yu Shang, Lixuan He

The automated generation of high-fidelity, city-scale 3D environments remains a formidable challenge with profound academic and industrial implications. However, existing methods struggle to achieve the necessary quality, fidelity, and scalability. To address this, we propose UrbanWorld2.0, a reality-aligned intelligent multimodal synthesis engine that creat

Dmitry Pasechnyuk-Vilensky, Martin Takáč

We introduce a geometric and operator-theoretic formalism viewing optimization algorithms as discrete connections on a space of update operators. Each iterative method is encoded by two coupled channels-drift and diffusion-whose algebraic curvature measures the deviation from ideal reversibility and determines the attainable order of accuracy. Flat connectio

Rajat Subhra Hazra, Nikolai Kriukov, Michel Mandjes, Moritz Otto

We prove a functional central limit theorem for subgraph counts in a dynamic version of the random connection model. To establish tightness, we develop a dynamic extension of the cumulant method.

Shunsuke Saita, Finn Bastian Molzahn, Clara Delahousse, Julien Husson

Many biological, culinary, and engineering processes lead to the co-encapsulation of several soft particles within a liquid interface. In these situations the particles are bound together by the capillary forces that deform them and influence their biological or rheological properties. Here we introduce an experimental approach to encapsulate a controlled nu

Jiaolong Kong, Xiaofei Xie, Yiheng Xiong, Yuekun Wang

Large language models (LLMs) have recently demonstrated strong potential for automated program repair (APR). However, existing LLM-based techniques primarily rely on coarse-grained external feedback (e.g.,test results) to guide iterative patch generation, while lacking fine-grained internal signals that reveal why a patch fails or which parts of the generate

Jörn Zimmerling, Vladimir Druskin, Sofia Davydycheva, Wardana Saputra

We develop a new, efficient, and accurate method to simulate frequency-domain borehole electromagnetic (EM) measurements acquired in the presence of three-dimensional (3D) variations of the anisotropic subsurface conductivity. The method is based on solving the quasi-static Maxwell equations with a goal-oriented finite-volume discretization via block-quadrat

Renzhi Su, Stephen J. Curran, Jeremy Darling, Minfeng Gu

We report a constraint on the cosmological variation of the proton g-factor, $g_p$. By comparing the measured redshifts between \mbox{H\,{\sc i}} 21 cm and OH 18 cm lines observed with the newly commissioned Five-hundred-meter Aperture Spherical radio Telescope (FAST) toward PKS 1413+135 at $z$ = 0.24671, we obtain $\Delta g_{p}/g_{p} = (-4.3\pm2.5)\times10^

Ali Taheri, Vahideh Vahidifar

This article presents new gradient estimates for positive solutions to the nonlinear porous medium equation (NPME) in the context of smooth metric measure spaces. The diffusion operator here is the f-Laplacian and the gradient estimates of interest are mainly of Hamilton-Souplet-Zhang types. These estimates are established using a variety of methods and tech

Guanghong Zuo

Collapse Lineage Tree (CLTree) is a software tool that annotates, roots, and evaluates phylogenetic trees by using lineages. A recursive algorithm was designed to annotate the branches by the common taxonomic lineage of its descendants in a rooted tree. For an unrooted tree, it determines the root that best conforms to the taxonomic system based on the afore

Jin Cui, Boran Zhao, Jiajun Xu, Jiaqi Guo

Coreset selection compresses large datasets into compact, representative subsets, reducing the energy and computational burden of training deep neural networks. Existing methods are either: (i) DNN-based, which are tied to model-specific parameters and introduce architectural bias; or (ii) DNN-free, which rely on heuristics lacking theoretical guarantees. Ne

Nikita P. Kalinin, Joel Daniel Andersson

We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via matrix factorizations, has been shown to improve accuracy, prior theoretical work focused primarily on the prefix-sum workload. That workload assumes a constant learning rate, wherea

Jiayu Wang, Haoyu Bian, Haoran Sun, Shaoning Zeng

Image deraining is crucial for vision applications but is challenged by the complex multi-scale physics of rain and its coupling with scenes. To address this challenge, a novel approach inspired by multi-stage image restoration is proposed, incorporating Point Spread Function (PSF) mechanisms to reveal the image degradation process while combining dynamic ph

Chungeng Tian, Fenghua He, Ning Hao

The inconsistency issue in the Visual-Inertial Navigation System (VINS) is a long-standing and fundamental challenge. While existing studies primarily attribute the inconsistency to observability mismatch, these analyses are often based on simplified theoretical formulations that consider only prediction and SLAM correction. Such formulations fail to cover t

Chaoyuan Bai, Pingzhi Fan, Zhengchun Zhou, Zilong Liu

This paper proposes a novel multi-carrier modulation framework for high-mobility communication scenarios. Our key idea lies in spreading data symbols across the delay-Doppler (DD) domain through orthogonal chirp-Zak transform (CZT). To enable efficient signal multiplexing, the proposed modulation scheme employs a transmitter signal that maintains orthogonali

Tianlu Zhang, Qiang Zhang, Guiguang Ding, Jungong Han

Tracking and segmentation play essential roles in video understanding, providing basic positional information and temporal association of objects within video sequences. Despite their shared objective, existing approaches often tackle these tasks using specialized architectures or modality-specific parameters, limiting their generalization and scalability. R

Mingyu Jeon, Jaeyoung Suh, Suwan Cho, Dohyeon Kim

With the rapid advancement of Large Language Models (LLMs), recent studies have drawn attention to their potential for handling not only simple question-answer tasks but also more complex conversational abilities and performing human-like behavioral imitations. In particular, there is considerable interest in how accurately LLMs can reproduce real human emot

Jiayi Luo, Qingyun Sun, Yuecen Wei, Haonan Yuan

Multi-domain graph pre-training has emerged as a pivotal technique in developing graph foundation models. While it greatly improves the generalization of graph neural networks, its privacy risks under membership inference attacks (MIAs), which aim to identify whether a specific instance was used in training (member), remain largely unexplored. However, effec

Haodong Chen, Xianfei Han, Qwen

Accurate organ and lesion segmentation is a critical prerequisite for computer-aided diagnosis. Convolutional Neural Networks (CNNs), constrained by their local receptive fields, often struggle to capture complex global anatomical structures. To tackle this challenge, this paper proposes a novel hybrid architecture, HyM-UNet, designed to synergize the local

Jinping Wang, Zhiqiang Gao, Dinggen Zhang, Zhiwu Xie

Current methods for editing pre-trained models face significant challenges, primarily high computational costs and limited scalability. Task arithmetic has recently emerged as a promising solution, using simple arithmetic operations-addition and negation-based on task vectors which are the differences between fine-tuned and pre-trained model weights, to effi

Lun Huang, You Xie, Hongyi Xu, Tianpei Gu

Diffusion Transformers have demonstrated remarkable capabilities in visual synthesis, yet they often struggle with high-level semantic reasoning and long-horizon planning. This limitation frequently leads to visual hallucinations and mis-alignments with user instructions, especially in scenarios involving complex scene understanding, human-object interaction

Vibin Abraham, Priyabrata Senapati, Himadri Pathak, Bo Peng

Accurately resolving many-body satellite features in molecular core-level spectra requires theoretical approaches that capture electron correlation both efficiently and systematically. The recently developed time-dependent double coupled-cluster (TD-dCC) ansatz achieves this by combining correlation effects from the N- and (N-1)-electron sectors, but its exa

Hongpeng Li, Cristian Carcamo, Hongxing Rui, Volker John

A dynamic linear thermo-poroelasticity model, containing inertial and relaxation terms with second-order time derivatives, is investigated in this paper. The mathematical and numerical analysis of this model is performed in the frequency domain. The variational formulation is analyzed within the framework of Fredholm's alternative and T-coercivity. Under app

Naoki Masuyama, Yuichiro Toda, Yusuke Nojima, Hisao Ishibuchi

Clustering in stationary and nonstationary settings, where data distributions remain static or evolve over time, requires models that can adapt to distributional shifts while preserving previously learned cluster structures. This paper proposes an Adaptive Resonance Theory (ART)-based topological clustering algorithm that autonomously adjusts its recalculati

Jiayi Luo, Qingyun Sun, Lingjuan Lyu, Ziwei Zhang

Graph Foundation Models (GFMs) are pre-trained on diverse source domains and adapted to unseen targets, enabling broad generalization for graph machine learning. Despite that GFMs have attracted considerable attention recently, their vulnerability to backdoor attacks remains largely underexplored. A compromised GFM can introduce backdoor behaviors into downs

Zeyu He

We identify a distinct motive for search, termed catalytic exploration, where agents rationally explore alternatives they expect to reject to resolve uncertainty about the status quo. By decomposing option value into switching and catalytic components, we show that high exploration rates can coexist with bounded switching probabilities. This mechanism genera

Anubhab Chowdhury, Erik G. Larsson

This paper presents a framework for target detection and downlink data transmission in a repeater-assisted bi-static integrated sensing and communication system. A repeater is an active scatterer that retransmits incoming signals with a complex gain almost instantaneously, thereby enhancing sensing performance by amplifying the echoes reflected by the target

Oluleke Babayomi, Dong-Seong Kim

Electric Vehicle (EV) charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation. Therefore, this paper addresses these challenges by proposing a nove

Adela Bara, Simona-Vasilica Oprea

Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current bibliometric tools. The objective is to enable researchers to perform fully dynamic, code-based scientometric analysis using natural language NL instructions, eliminating the need for specialized programming skil

Kuangxiangzi Liu, Dhiman Chakraborty, Alexander Liggesmeyer, Andreas Zeller

Safety- and security-critical systems have to be thoroughly tested against their specifications. The state of practice is to have _natural language_ specifications, from which test cases are derived manually - a process that is slow, error-prone, and difficult to scale. _Formal_ specifications, on the other hand, are well-suited for automated test generation

Arun Kumar, Qiang Wu, Tao Zhu, Sushant G. Ghosh

We investigate strong gravitational lensing by a charged loop quantum gravity (LQG) black hole obtained through the polymerisation scheme of Borges \textit{et al.} \cite{Borges:2023fog}. These effective geometries replace the Reissner--Nordstr\"om singularity with a symmetric transition surface and admit an extremal, cold remnant determined by the minimal ar

Lei Li, Anand N. Vidyashankar

We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted divergence, the DM approach recovers EM as a special case and yields robust alternatives through different divergence choices. We establish that the sample objective decreases monotonically along the iterates, lea

Hiroto Honda

Exemplar-free class-incremental learning (EFCIL) aims to retain old knowledge acquired in the previous task while learning new classes, without storing the previous images due to storage constraints or privacy concerns. In EFCIL, the plasticity-stability dilemma, learning new tasks versus catastrophic forgetting, is a significant challenge, primarily due to

Akira Kusaba, Tetsuji Kuboyama, Karol Kawka, Pawel Kempisty

In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical composition, and elemental substitution, which explores chemical compositions that yield desirable properties in a given crys

Jinsong Zhang, Minghe Li, Jiayi Tian, Jinming Lu

High-order tensor decomposition has been widely adopted to obtain compact deep neural networks for edge deployment. However, existing studies focus primarily on its algorithmic advantages such as accuracy and compression ratio-while overlooking the hardware deployment efficiency. Such hardware-unaware designs often obscure the potential latency and energy be

Mohamed Mabrok, Yalda Zafari

State-space models (SSMs), particularly Mamba, have become powerful architectures for sequence modeling, yet their internal dynamics remain poorly understood compared to attention-based models. We introduce and validate the Influence Score, a controllability-based metric derived from the discretized state-space parameters of Mamba and computed through a back

Seokkyu An, Taisuke Ozaki

We establish a rigorous density functional theory (DFT) framework for core-level X-ray absorption spectroscopy (XAS) by formulating a constrained search for core-excited states based on the Gunnarsson-Lundqvist theorem. Within this framework, the explicit-core Delta SCF scheme enables shift-free absolute edge alignment and a consistent treatment of L/M edges

Oluleke Babayomi, Dong-Seong Kim

Maintaining economic efficiency and operational reliability in microgrid energy management systems under cyberattack conditions remains challenging. Most approaches assume non-anomalous measurements, make predictions with unquantified uncertainties, and do not mitigate malicious attacks on renewable forecasts for energy management optimization. This paper pr

Hao Li, Yuhao Wang, Xiantao Hu, Wenning Hao

RGB-Thermal (RGBT) tracking aims to exploit visible and thermal infrared modalities for robust all-weather object tracking. However, existing RGBT trackers struggle to resolve modality discrepancies, which poses great challenges for robust feature representation. This limitation hinders effective cross-modal information propagation and fusion, which signific

Kohei Yamamoto, Hakuto Suzuki, Jun Miyawaki

A two-dimensional resonant inelastic x-ray scattering (2D-RIXS) microscopy system has been developed at the beamline BL02U of NanoTerasu. The instrument combines a Wolter type-I mirror for spatial imaging with a varied-line-spacing grating spectrometer, simultaneously achieving micrometer-scale spatial resolution and ultrahigh energy resolution in the soft x

Sergey K. Aityan, William Claster, Karthik Sai Emani, Sohni Rais

A growing number of AI-generated texts raise serious concerns. Most existing approaches to AI-generated text detection rely on fine-tuning large transformer models or building ensembles, which are computationally expensive and often provide limited generalization across domains. Existing lightweight alternatives achieved significantly lower accuracy on large

Yangyang Liu, Yuhao Wang, Pingping Zhang

Multi-modal object Re-IDentification (ReID) is devoted to retrieving specific objects through the exploitation of complementary multi-modal image information. Existing methods mainly concentrate on the fusion of multi-modal features, yet neglecting the background interference. Besides, current multi-modal fusion methods often focus on aligning modality pairs

Chenyang Yu, Xuehu Liu, Pingping Zhang, Huchuan Lu

Large-scale vision-language models (e.g., CLIP) have recently achieved remarkable performance in retrieval tasks, yet their potential for Video-based Visible-Infrared Person Re-Identification (VVI-ReID) remains largely unexplored. The primary challenges are narrowing the modality gap and leveraging spatiotemporal information in video sequences. To address th

Jun Kevin, Pujianto Yugopuspito

This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio all

Ziheng Jia, Linhan Cao, Jinliang Han, Zicheng Zhang

Developing a robust visual quality assessment (VQualA) large multi-modal model (LMM) requires achieving versatility, powerfulness, and transferability. However, existing VQualA LMMs typically focus on a single task and rely on full-parameter fine-tuning, which makes them prone to overfitting on specific modalities or task types, thereby limiting their genera

Hao Wang, Xiaobao Wei, Ying Li, Qingpo Wuwu

Constructing photorealistic and controllable robotic arm digital assets from real observations is fundamental to robotic applications. Current approaches naively bind static 3D Gaussians according to URDF links, forcing them to follow an URDF-rigged motion passively. However, the idealized URDF-rigged motion cannot accurately model the actual motion captured

Tushti Patel, V. S. Prasannaa

We extend the Harrow-Hassidim-Lloyd (HHL) algorithm, which is well-studied in the qubit framework, to its qutrit counterpart (which we call qutrit HHL, as opposed to qubit HHL, which is HHL using qubits), and develop a program for its implementation. We design Weyl-Heisenberg gadgets, the qutrit equivalents of Pauli gadgets, and come up with a practical impl

Yuhao Wu, Ke Yang, Franziska Roesner, Tadayoshi Kohno

As AI agents attempt to autonomously act on users' behalf, they raise transparency and control issues. We argue that permission-based access control is indispensable in providing meaningful control to the users, but conventional permission models are inadequate for the automated agentic execution paradigm. We therefore propose automated permission management

Yulong Shi, Jiapeng Li, Lin Qi

Growing demands for clinical data privacy and storage constraints have spurred advances in Source Free Unsupervised Domain Adaptation (SFUDA). SFUDA addresses the domain shift by adapting models from the source domain to the unseen target domain without accessing source data, even when target-domain labels are unavailable. However, SFUDA faces significant ch

P. A. Bannykh, O. M. Sotnikov, V. V. Mazurenko

Exploring sign structures of quantum wave functions attracts considerable attention due to the potential for advances in modeling complex phases of matter. This stimulates developing different optimization procedures for imitating and manipulating sign structures of quantum states. In this work, utilizing a brute force approach based on a set of single-qubit

Xueqiang Wang, Qi Su, Siping Li

We examined the asymmetric deformation in collisions and the transition conditions from oblique to normal collisions and non-collisions to address the problem of oblique collisions of rigid bodies in classical mechanics. A closed solution satisfying the fundamental equations and adhering to the energy conservation law without introducing new material paramet

Dat Thanh Nguyen, Nguyen Hung Lam, Anh Hoang-Thi Nguyen, Trong-Hop Do

With the rapid rise of short-form videos, TikTok has become one of the most influential platforms among children and teenagers, but also a source of harmful content that can affect their perception and behavior. Such content, often subtle or deceptive, challenges traditional moderation methods due to the massive volume and real-time nature of uploads. This p

Freek Holvoet, Christopher Blier-Wong, Katrien Antonio

Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriting precision and enhancing risk management in insurance. However, spatial data are often unstructured, high-dimensional, and difficult to integrate into predictive models. Embedding methods are needed to convert spatia

Min Woo Park, Sanghack Lee

Intelligent agents equipped with causal knowledge can optimize their action spaces to avoid unnecessary exploration. The structural causal bandit framework provides a graphical characterization for identifying actions that are unable to maximize rewards by leveraging prior knowledge of the underlying causal structure. While such knowledge enables an agent to

Liangyang Ouyang, Yifei Huang, Mingfang Zhang, Caixin Kang

Understanding social interaction in video requires reasoning over a dynamic interplay of verbal and non-verbal cues: who is speaking, to whom, and with what gaze or gestures. While Multimodal Large Language Models (MLLMs) are natural candidates, simply adding visual inputs yields surprisingly inconsistent gains on social tasks. Our quantitative analysis of c

Haojin Yang, Rui Hu, Zequn Sun, Rui Zhou

Diffusion Language Models (DLMs) have shown strong potential for text generation and are becoming a competitive alternative to autoregressive models. The denoising strategy plays an important role in determining the quality of their outputs. Mainstream denoising strategies include Standard Diffusion and BlockDiffusion. Standard Diffusion performs global deno

B. L. S. Prakasa Rao

We investigate the asymptotic properties of the minimum $L_1$-norm estimator of the drift parameter for fractional Ornstein-Uhlenbeck type process driven by a Hermite process.

Tomoya Fujita, Hikofumi Suzuki, Shinpei Ogata, Hiroaki Hashiura

In the network design phase, designers typically assess the validity of the network configuration on paper. However, the interactions between devices based on network protocols can be complex, making this assessment challenging. Meanwhile, testing with actual devices incurs significant costs and effort for procurement and preparation. Traditional methods, ho

Kosei Nakamura, Hikofumi Suzuki, Shinpei Ogata, Hiroaki Hashiura

When network engineers design a network, they need to verify the validity of their design in a test environment. Since testing on actual equipment is expensive and burdensome for engineers, we have proposed automatic verification methods using simulators and consistency verification methods for a network configuration model. Combining these methods with conv

Yining Yuan, J. Ben Tamo, Micky C. Nnamdi, Yifei Wang

Large language models (LLMs) show promise in automating clinical diagnosis, yet their non-transparent decision-making and limited alignment with diagnostic standards hinder trust and clinical adoption. We address this challenge by proposing a two-stage diagnostic framework that enhances transparency, trustworthiness, and reliability. First, we introduce Evid

Shuo Zhang, Fabrizio Gotti, Fengran Mo, Jian-Yun Nie

Hallucination in large language models (LLMs) is a fundamental challenge, particularly in open-domain question answering. Prior work attempts to detect hallucination with model-internal signals such as token-level entropy or generation consistency, while the connection between pretraining data exposure and hallucination is underexplored. Existing studies sho

Kaibin Wang, Mingbao Lin

Processing long videos with multimodal large language models (MLLMs) poses a significant computational challenge, as the model's self-attention mechanism scales quadratically with the number of video tokens, resulting in high computational demand and slow inference speed. Current solutions, such as rule-based sub-sampling, learned frame selector, or memory-b

Li He

After the universal property of the six functor formalism $\Shv(-;\Sp)$ on locally compact Hausdorff spaces given by Zhu, we show that the six functor formalism $\Shv(-;\Sp)$ on light condensed anima in the sense of Heyer-Mann is initial among all six functor formalisms $D$ satisfying some mild conditions, and then we present some applications.

Zhiyu Xu, Weilong Yan, Yufei Shi, Xin Meng

Recent advancements in multimodal large language models (MLLMs) and video agent systems have significantly improved general video understanding. However, when applied to scientific video understanding and educating, a domain that demands external professional knowledge integration and rigorous step-wise reasoning, existing approaches often struggle. To bridg

Xueheng Shi, Robert Lund

A single joinpoint changepoint model partitions a time series into two segments, joined at the changepoint time by constraining the estimated piecewise linear regression responses to be continuous. This manuscript derives the exact asymptotic distribution of the changepoint existence test statistic gauging whether or not a second segment is necessary. The id

Xiangyan Kong, Xuecheng Wu, Xiongwei Zhao, Xiaodong Li

V2X prediction can alleviate perception incompleteness caused by limited line of sight through fusing trajectory data from infrastructure and vehicles, which is crucial to traffic safety and efficiency. However, in dense traffic scenarios, frequent identity switching of targets hinders cross-view association and fusion. Meanwhile, multi-source information te

Ruogu Ding, Xin Ning, Ulf Schlichtmann, Weikang Qian

Prefix adders are widely used in compute-intensive applications for their high speed. However, designing optimized prefix adders is challenging due to strict design rules and an exponentially large design space. We introduce PrefixGPT, a generative pre-trained Transformer (GPT) that directly generates optimized prefix adders from scratch. Our approach repres

P. Maneesha, Dilip Sasmal, Rakhi Saha, Kiran Baraik

This work involves the local structural investigation of the samples using Extended X-ray Absorption Spectra (EXAFS) analysis to investigate structural changes due to the Ca and Mn-modified BaTiO3. TEM investigation of the crystal structure reveals the coexistence of the tetragonal and hexagonal phases of BaTiO3. Band gap modification and Urbach tail variati

Yuchen Ying, Yiyang Dai, Wenda Li, Wenjie Huang

Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively ma

Jianghao Wu, Yasmeen George, Jin Ye, Yicheng Wu

Large language models (LLMs) and multimodal LLMs (MLL-Ms) excel at chain-of-thought reasoning but face distribution shift at test-time and a lack of verifiable supervision. Recent test-time reinforcement learning (TTRL) methods derive label-free pseudo-rewards from self-consistency voting over sampled trajectories, yet they often collapse: the majority-vote

Kartik Garg, Shourya Mishra, Kartikeya Sinha, Ojaswi Pratap Singh

Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while preserving different behavior outside training. The phenomenon was first documented for Claude 3 Opus and later examined across additional large language models. In these setups, the word "traini

Wenzhang Du

Many deployed learning systems must update models on streaming data under memory constraints. The default strategy, sequential fine-tuning on each new phase, is architecture-agnostic but often suffers catastrophic forgetting when later phases correspond to different sub-populations or tasks. Replay with a finite buffer is a simple alternative, yet its behavi

Govind Rajendran, Kushagra Sharma, Vijayalakshmi Chetlapalli, Jatin Parekh

The need for meter level location accuracy is driving increased adoption of 802.11 mc/az Fine Time Measurement (FTM) based ranging in Wi-Fi networks. In this paper, we present a comparative study of the ranging accuracy of 802.11mc and 802.11az protocols. We examine by real world measurements the critical parameters that influence the accuracy of FTM {\it{vi

Laura Jeanty, Brian Shuve

Long-lived particles (LLPs) are particles that are stable or that live long enough for their decays to be experimentally distinguishable in time or position from their production point. We provide an overview of the phenomenology and experimental signatures of LLPs, focusing on LLPs at the Large Hadron Collider (LHC). We explain what determines a particle's

Sushant Kala

Let $A$ be an abelian variety defined over a number field $\mathbb{Q}$, and let $\hat{h}$ be the N\'eron-Tate height on $A(\overline{\mathbb{Q}})$ corresponding to a symmetric ample line bundle on $A$. In this article, we prove that the N\'eron-Tate height of totally $p$-adic points is bounded below by an absolute constant depending only on $A$ for all but f

Yan Xu, Yixing Wang, Stella X. Yu

Given just a few glimpses of a scene, can you imagine the movie playing out as the camera glides through it? That's the lens we take on \emph{sparse-input novel view synthesis}, not only as filling spatial gaps between widely spaced views, but also as \emph{completing a natural video} unfolding through space. We recast the task as \emph{test-time natural vid

Jaswanth Bodempudi, Batta Siva Sairam, Madepalli Haritha, Sandesh Rao Mattu

Carrier aggregation (CA) is a technique that allows mobile networks to combine multiple carriers to increase user data rate. On the uplink, for power constrained users, this translates to the need for an efficient resource allocation scheme, where each user distributes its available power among its assigned uplink carriers. Choosing a good set of carriers an

Yuan Qu, Zhipeng Zhang, Chaojun Xu, Qiao Wan

In recent years, remote sensing change detection has garnered significant attention due to its critical role in resource monitoring and disaster assessment. Change detection tasks exist with different output granularities such as BCD, SCD, and BDA. However, existing methods require substantial expert knowledge to design specialized decoders that compensate f

Hui Lu, Yi Yu, Shijian Lu, Deepu Rajan

Temporal Action Detection (TAD) aims to identify and localize actions by determining their starting and ending frames within untrimmed videos. Recent Structured State-Space Models such as Mamba have demonstrated potential in TAD due to their long-range modeling capability and linear computational complexity. On the other hand, structured state-space models o

Wen Jiang, Yachen Wang, Zeqi Wu, Xingbai Xu

This paper develops limit theorems for random variables with network dependence, without requiring the individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit theorems in network statistics and econometrics, which are based on weak dependence concepts such as strong mixing, near-epoch

Yingjie Ma, Xun Lin, Yong Xu, Weicheng Xie

Face anti-spoofing (FAS) has recently advanced in multimodal fusion, cross-domain generalization, and interpretability. With large language models and reinforcement learning (RL), strategy-based training offers new opportunities to jointly model these aspects. However, multimodal reasoning is more complex than unimodal reasoning, requiring accurate feature r

Xiangrui Xiong, Zhou Zhou, Guocai Nong, Junlin Deng

Emotion recognition plays a pivotal role in enhancing human-computer interaction, particularly in movie recommendation systems where understanding emotional content is essential. While multimodal approaches combining audio and video have demonstrated effectiveness, their reliance on high-performance graphical computing limits deployment on resource-constrain

Jeonghwan Kim, Wontaek Kim, Yidan Lu, Jin Cheng

Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for evaluating these capabilities in real-world settings, and in direct comparison to humans, remain scarce. Existing evaluations often rely on pre-collected human motion datasets or simul

Sayantan Ganguly, Shion Samadder Chaudhury

The concept of anamorphic encryption, first formally introduced by Persiano et al. in their influential 2022 paper titled ``Anamorphic Encryption: Private Communication Against a Dictator,'' enables embedding covert messages within ciphertexts. One of the key distinctions between a ciphertext embedding a covert message and an original ciphertext, compared to

Wenda Li, Tongya Zheng, Shunyu Liu, Yu Wang

Heterogeneous graphs are widely present in real-world complex networks, where the diversity of node and relation types leads to complex and rich semantics. Efforts for modeling complex relation semantics in heterogeneous graphs are restricted by the limitations of predefined semantic dependencies and the scarcity of supervised signals. The advanced pre-train

Robert Krahn, Josia Mädler, Christoph Seidl, Christof Fetzer

Modern software systems are executed on a runtime stack with layers (virtualization, storage, trusted execution, etc.) each incurring an execution and/or monetary cost, which may be mitigated by finding suitable parameter configurations. While specialized parameter tuners exist, they are tied to a particular domain or use case, fixed in type and number of op

Keith Moore

Foundation segmentation models such as SAM and SAM-2 perform well on natural images but struggle with brain MRIs where structures like the caudate and thalamus lack sharp boundaries and have low contrast. Rather than fine tune these models (for example MedSAM), we propose a compositional alternative where the foundation model output is treated as an addition

Fernando López-García, John Rodriguez

In this paper, we establish a condition on weighted graphs with finite measure that guarantees the validity of a global Poincar\'e inequality. This condition can be viewed as a discrete analogue of the criterion introduced by J. Boman in 1982 for Whitney cubes, which in turn characterizes the condition originally proposed by F. John in his seminal 1961 work.

Hamza Alshamy, Isaiah Woram, Advay Mishra, Zihan Xia

We present Animated Territorial Data Extractor (ATDE), a computer vision tool that extracts quantitative territorial data from animated historical map videos. ATDE employs HSV-based color segmentation, RGB channel filtering, and Direct-Neighbor Filtering to identify and count pixels representing territorial control. Combined with preprocessing for temporal a

Tamzid Hossain, Md. Fahimul Islam, Farida Chowdhury

Immersive technologies expand the potential for collaborative sense-making and visual analysis via head-worn displays (HWDs), offering customizable, high-resolution perspectives of a shared visualization space. In such an immersive environment, window/view management is crucial for collaborative sense-making tasks. However, the role of document types (graphs

Padegal Amit, Omkar Mahesh Kashyap, Namitha Rayasam, Nidhi Shekhar

Quantifying modality contributions in multimodal models remains a challenge, as existing approaches conflate the notion of contribution itself. Prior work relies on accuracy-based approaches, interpreting performance drops after removing a modality as indicative of its influence. However, such outcome-driven metrics fail to distinguish whether a modality is

Hiroshi Wakui, Tetsuya Yamada

The Cauchy problem for the attraction-repulsion chemotaxis system in the whole $n$-dimensional space has uncountable constant steady states. In the attraction chemotaxis system, each positive constant steady state is stable if it is in a certain region. On the other hand, in the repulsion chemotaxis system, every positive constant steady state is stable. Our

Yi Zhang, Jintao Wang, Zheng Shi, Xu Wang

Fluid antenna systems (FAS) allow dynamic reconfiguration to achieve superior diversity gains and reliability. To quantify the performance scaling of FAS with a large number of antenna ports, this paper leverages extreme value theory (EVT) to conduct an asymptotic analysis of the outage probability (OP) and ergodic capacity (EC). The analysis reveals that th