November 2025 arXiv papers — page 107

Yilang Hao, Zhibin Chen

Electric trucks are increasingly deployed to reduce the trucking sector's carbon footprint, but their limited range and charging needs create operational challenges on mid- to long-haul routes. Truck platooning can mitigate range anxiety through energy savings and, in turn, influence routing and charging decisions, yet most existing studies focus on a single

Mehrab Mustafy Rahman, Jayanth Mohan, Tiberiu Sosea, Cornelia Caragea

Semi-supervised learning (SSL) has demonstrated high performance in image classification tasks by effectively utilizing both labeled and unlabeled data. However, existing SSL methods often suffer from poor calibration, with models yielding overconfident predictions that misrepresent actual prediction likelihoods. Recently, neural networks trained with {\tt m

Crystal Su

We study how to impose domain-consistent structure on large language models (LLMs) used for scientific reasoning and early-stage drug discovery. We present MedRule-KG, a compact knowledge-graph scaffold paired with a lightweight verifier that steers generation toward mathematically and biomedically valid outputs. The system injects curated symbolic facts int

Daniel Cavadia

Precise and real-time detection of gastrointestinal polyps during endoscopic procedures is crucial for early diagnosis and prevention of colorectal cancer. This work presents EndoSight AI, a deep learning architecture developed and evaluated independently to enable accurate polyp localization and detailed boundary delineation. Leveraging the publicly availab

Pritam P. Karmokar, William J. Beksi

Event cameras, by virtue of their working principle, directly encode motion within a scene. Many learning-based and model-based methods exist that estimate event-based optical flow, however the temporally dense yet spatially sparse nature of events poses significant challenges. To address these issues, contrast maximization (CM) is a prominent model-based op

Daivik Patel, Shrenik Patel

Large language models (LLMs) deployed in user-facing applications require long-horizon consistency: the ability to remember prior interactions, respect user preferences, and ground reasoning in past events. However, contemporary memory systems often adopt complex architectures such as knowledge graphs, multi-stage retrieval pipelines, and OS-style schedulers

Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim

Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory-efficient single-knowledge models suffer from a suboptimal knowledge replacement practice that discards valuable personalization, while high-performance dual-knowledge models are

Dhiren Panda, Manas Kumar Mohapatra, Rukmani Mohanta

Quantum coherence plays a crucial role in the dynamics of neutral meson systems, aiding in the extraction of various Standard Model parameters. However, real physical systems always interact with their surroundings, which causes decoherence. In case of time dependent analysis of non-leptonic neutral $B$ meson decays, this decoherence can be modeled using a s

Joseph T. A. Peterson, Manoranjan Majji, John L. Junkins

Closed-Form Kepler solutions in projective coordinates are used to define a corresponding set of eight orbit elements and obtain their governing equations for arbitrarily-perturbed two-body dynamics. The elements and their dynamics are singularity-free in all cases besides rectilinear motion (when angular momentum vanishes). The classic J2-perturbed two-body

Chen Ma, Ningfei Wang, Junhao Zheng, Qing Guo

Traffic Sign Recognition (TSR) systems play a critical role in Autonomous Driving (AD) systems, enabling real-time detection of road signs, such as STOP and speed limit signs. While these systems are increasingly integrated into commercial vehicles, recent research has exposed their vulnerability to physical-world adversarial appearance attacks. In such atta

Onur Vural, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Multivariate time series classification is increasingly investigated in space weather research as a means to predict intense solar flare events, which can cause widespread disruptions across modern technological systems. Magnetic field measurements of solar active regions are converted into structured multivariate time series, enabling predictive modeling ac

Xinghong Pan, Jianfeng Zhao

In this paper, we establish the vanishing viscosity limit result of the 2D stationary Navier-Stokes equations outside a rotating disc. On the boundary of the disc, the fluid is subjected to a small perturbation of a non zero rotation of rigid body. While at the spacial infinity, the fluid stays at rest. Due to the Prandtl-Batchelor theory, the limiting Euler

Yibo Meng, Zhiming Liu, Xiaochen Qin

Type 2 diabetes patients in China face many significant challenges in patient-provider communication and self management In light of this, this work designed,implemented,and evaluated an AI-driven, personalized, multi-functional mobile app system named T2MD Health. The appintegrates real-time patient- provider conversation transcription,medical terminology i

Ziling Fan, Ruijia Liang, Yiwen Hu

Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are therefore crucial for preventing systemic instability and supporting informed investment decisions. Traditional deep learning models, such a

Zirui Chen, Zhipeng Xue, Jiayuan Zhou, Xing Hu

Exploits are commonly used to demonstrate the presence of library vulnerabilities and validate their impact across different versions. However, their direct application to alternative versions often fails due to breaking changes introduced during evolution. These failures stem from both changes in triggering conditions (e.g., API refactorings) and broken dyn

Bokang Fu, Jiahao Wang, Xiaojing Liu, Yuli Liu

In recent years, large language models (LLMs) have excelled in language understanding and generation, powering advanced dialogue and recommendation systems. However, a significant limitation persists: these systems often model user preferences statically, failing to capture the dynamic and sequential nature of interactive behaviors. The sequence of a user's

Jinwoo Park

This paper investigates locally linear regression for locally stationary time series and develops theoretical results for locally linear smoothing and transfer learning. Existing analyses have focused on local constant estimators and given samples, leaving the principles of transferring knowledge from auxiliary sources across heterogeneous time-varying domai

Hao Jiang, Long Zhang, Guoquan Wang, Sheng Yu

Local-life recommendation have witnessed rapid growth, providing users with convenient access to daily essentials. However, this domain faces two key challenges: (1) spatial constraints, driven by the requirements of the local-life scenario, where items are usually shown only to users within a limited geographic area, indirectly reducing their exposure proba

Zhongkui Liu, Junquan Qin, Xiaoyan Yang

Let (RmR), (SmS) and (TmT) be Noetherian local rings sharing the same residue eld k and prime characteristic p > 0. We establish some formulas relating the h-function and s-multiplicity of the ber product R T S in terms of the h-functions and s-multiplicities of R, T and S. Furthermore, we derive formulas that connect the h-function and s-multiplicity of the

Yujie Li, Zezhi Shao, Chengqing Yu, Yisong Fu

Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that fails to capture global distribution shift. Methods like RevIN and its variants attempt to decouple distribution and pattern but still struggle with missing values, noisy observati

Chenghui Ren

The twin prime conjecture asserts that there are infinitely many pairs of primes that differ by two. While recent advances have improved our understanding of bounded prime gaps, the conjecture remains unresolved. This paper refines the weighted sieve method to estimate a sum over twin prime pairs, where each term is of the form \((1/p)(log(x^{{\alpha}}/p))^k

Xiaoteng Zhou, Kazuya Haraguchi, Hanchun Yuan

Given a family of graphs $\mathcal{F}$, a graph $G$ is $\mathcal{F}$-saturated if it is $\mathcal{F}$-free but the addition of any missing edge creates a copy of some $F \in \mathcal{F}$. The study of the minimum number of edges in $\mathcal{F}$-saturated graphs is a central topic in extremal graph theory. Let $(p+1)K_2$ denote a matching of size $p+1$. Dete

Chunyong Hu, Qi Luo, Jianyun Xu, Song Wang

In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework

Ke Yu, Shigeru Ishikura, Yukari Usukura, Yuki Shigoku

Synthetic tabular data, which are widely used in domains such as healthcare, enterprise operations, and customer analytics, are increasingly evaluated to ensure that they preserve both privacy and utility. While existing evaluation practices typically focus on distributional similarity (e.g., the Kullback-Leibler divergence) or predictive performance (e.g.,

Wei Jiang, Jiahao Cui, Yizheng Wu, Zhan Peng

Reconstructing high dynamic range (HDR) images from low dynamic range (LDR) bursts plays an essential role in the computational photography. Impressive progress has been achieved by learning-based algorithms which require LDR-HDR image pairs. However, these pairs are hard to obtain, which motivates researchers to delve into the problem of annotation-efficien

Botong Zhao, Qijun Shi, Shujing Lyu, Yue Lu

Existing industrial anomaly detection methods mainly determine whether an anomaly is present. However, real-world applications also require discovering and classifying multiple anomaly types. Since industrial anomalies are semantically subtle and current methods do not sufficiently exploit image priors, direct clustering approaches often perform poorly. To a

Guoyan Wang, Yanyan Huang, Chunlin Chen, Lifeng Wang

Cross-platform strategy game automation remains a challenge due to diverse user interfaces and dynamic battlefield environments. Existing Vision--Language Models (VLMs) struggle with generalization across heterogeneous platforms and lack precision in interface understanding and action execution. We introduce Yanyun-3, a VLM-based agent that integrates Qwen2.

Minjie Wang, Jinguang Han, Weizhi Meng

In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the privacy security and model integrity. Although the existing scheme uses threshold cryptography to mitigate the inference attack, it can not guarantee the verifiability of the aggrega

Dianbing Xi, Guoyuan An, Jingsen Zhu, Zhijian Liu

We propose PFAvatar (Pose-Fusion Avatar), a new method that reconstructs high-quality 3D avatars from Outfit of the Day(OOTD) photos, which exhibit diverse poses, occlusions, and complex backgrounds. Our method consists of two stages: (1) fine-tuning a pose-aware diffusion model from few-shot OOTD examples and (2) distilling a 3D avatar represented by a neur

Zhi Kou, Xiang-Rong Sheng, Shuguang Han, Zhishan Zhao

In industrial recommendation systems, pre-ranking models based on deep neural networks (DNNs) commonly adopt a sequential execution framework: feature fetching and model forward computation are triggered only after receiving candidates from the upstream retrieval stage. This design introduces inherent bottlenecks, including redundant computations of identica

D. B. Karki

Transport properties of single- and two-site mesoscopic quantum Hall (QH) circuits at high transparencies can be described in terms of the lowest-order backscattering processes, enabling a mapping to the boundary sine-Gordon model. We show that this description breaks down in circuits with four or more sites, where higher-order backscattering processes becom

Feng Lv, Haoxuan Feng, Zilu Zhang, Chunlong Xia

With the rapid advancement of intelligent transportation systems, text-driven image generation and editing techniques have demonstrated significant potential in providing rich, controllable visual scene data for applications such as traffic monitoring and autonomous driving. However, several challenges remain, including insufficient semantic richness of gene

Zain Shabeeb, Daniel Saeedi, Darin Tsui, Vida Jamali

Cryo-electron microscopy (cryo-EM) enables the atomic-resolution visualization of biomolecules; however, modern direct detectors generate data volumes that far exceed the available storage and transfer bandwidth, thereby constraining practical throughput. We introduce cryoSENSE, the computational realization of a hardware-software co-designed framework for c

Changhun Oh, Seongryong Oh, Jinwoo Hwang, Yoonsung Kim

3D Gaussian Splatting (3DGS) rendering in real-time on resource-constrained devices is essential for delivering immersive augmented and virtual reality (AR/VR) experiences. However, existing solutions struggle to achieve high frame rates, especially for high-resolution rendering. Our analysis identifies the sorting stage in the 3DGS rendering pipeline as the

Z. -H. Peng, S. Benetti, Y. -Z. Cai, A. Pastorello

We present optical photometric and spectroscopic observations of the rapidly declining Type IIL supernova (SN) 2016iog. SN 2016iog reached its peak $\sim$ 14 days after explosion, with an absolute magnitude in the $V$ band of $-18.64 \pm 0.15$ mag, followed by a steep decline of $8.85 \pm 0.15$~mag~(100\,d)$^{-1}$ post-peak. Such a high decline rate makes SN

Haokun Li, Yazhou Zhang, Jizhi Ding, Qiuchi Li

Can multi-modal large language models (MLLMs) truly understand what they can see? Extending Searle's Chinese Room into the multi-modal domain, this paper proposes the Visual Room argument: MLLMs may describe every visual detail precisely yet fail to comprehend the underlying emotions and intentions, namely seeing is not understanding. Building on this, we in

Ruihan Ma, Yuqing Cheng, Mengtao Sun

A dual-mode asymmetric transmission (AT) nanodevice based on the asymmetric and orthogonal grating-film-grating (AO-GFG) structure is proposed and systematically investigated theoretically. The device supports two distinct localized surface plasmon resonance (LSPR) modes for forward transmission, corresponding to the polarization-dependent (M1) and the polar

Marco Martens, Liviana Palmisano

In two-dimensional unfoldings of homoclinic tangencies, the parameter space contains codimension one laminations whose leaves consist of maps with invariant non-hyperbolic Cantor sets. These Cantor sets are wild both in the sense of Hofbauer-Keller and in the sense of Newhouse, and they contain Collet-Eckmann points with dense orbits. Hence, wildness and non

Kyler Siegel

These notes are based on a five-part minicourse on stabilized symplectic embeddings given in Les Mar\'ecottes, Switzerland during a September 2025 workshop. Our main goal is to explain the recent resolution of the (restricted) stabilized ellipsoid embedding problem by D. McDuff and the author. Along the way we also introduce various other ideas which shed li

Hasnain Mehmood, Tashfeen Akhtar, Jesús Espinosa-Romero, Mauricio Maldonado-Domínguez

Light-atom chromophores can display properties often associated with heavy-atom compounds, such as intersystem crossing leading to phosphorescence and singlet oxygen generation, yet their use remains comparatively underexplored. Here, we report the synthesis of HM610, a derivative of the benzylidenehydrazinylthiazole light-atom chromophore backbone. Spin-orb

Yu Hou, Won-Yong Shin

Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributio

Huiqiang Sun, Liao Shen, Zhan Peng, Kun Wang

Cinematic storytelling is profoundly shaped by the artful manipulation of photographic elements such as depth of field and exposure. These effects are crucial in conveying mood and creating aesthetic appeal. However, controlling these effects in generative video models remains highly challenging, as most existing methods are restricted to camera motion contr

Desheng Hu, Joachim Baumann, Aleksandra Urman, Elsa Lichtenegger

Google Search increasingly surfaces AI-generated content through features like AI Overviews (AIO) and Featured Snippets (FS), which users frequently rely on despite having no control over their presentation. Through a systematic algorithm audit of 1,508 real baby care and pregnancy-related queries, we evaluate the quality and consistency of these information

Dexin Zuo, Ang Li, Wei Wang, Wenxian Yu

Object 6D pose estimation, a crucial task for robotics and augmented reality applications, becomes particularly challenging when dealing with novel objects whose 3D models are not readily available. To reduce dependency on 3D models, recent studies have explored one-reference-based pose estimation, which requires only a single reference view instead of a com

Faqian Chong, Tiancheng Zhang, Yulun Wu, Bingtao Gao

Integrated photonics is increasingly demanded in applications such as large-scale data centers, intelligent sensing, and next-generation wireless communications, where compact, multifunctional, and energy-efficient components are essential. Inverse-designed photonics, empowered by optimization and learning algorithms, have emerged as a powerful paradigm for

Ruishu Zhu, Sida Huang, Ziheng Jiao, Hongyuan Zhang

Multimodal Large Language Models (MLLMs) have played an increasingly important role in multimodal intelligence. However, the existing fine-tuning methods often ignore cross-modal heterogeneity, limiting their full potential. In this work, we propose a novel fine-tuning strategy by injecting beneficial random noise, which outperforms previous methods and even

Yafang Wang, Yangjie Tian, Xiaoyu Shen, Gaoyang Zhang

Power grid fault diagnosis is a critical process hindered by its reliance on manual, error-prone methods. Technicians must manually extract reasoning logic from dense regulations and attempt to combine it with tacit expert knowledge, which is inefficient, error-prone, and lacks maintainability as ragulations are updated and experience evolves. While Large La

Yubo Chen, Wendong Wang, Guoxu Yang

It is well known that solutions to the Patlak--Keller--Segel system on $\mathbb{R}^2$ blow up in finite time if the initial mass exceeds $8\pi$. In this paper, we investigate the mixing effect induced by a Couette flow $(Ay, 0)$ with a quantitatively determined amplitude $A$, which suppresses bacterial aggregation. For the Patlak--Keller--Segel system advect

Xiaoyan Xu, Xuding Zhu

It was conjectured by Steinberg in 1976 that planar graphs without cycles of length 4 or 5 are 3-colorable. This conjecture attracted a substantial amount of attention and was finally refuted by Cohen-Addad, Hebdige, Kr\'{a}l', Li and Salgado in 2017. Although Steinberg's conjecture is settled, coloring of this family of graphs, as well as some other familie

Yiming Zhao, Jiwei Tang, Shimin Di, Libin Zheng

Recommending event schedules is a key issue in Event-based Social Networks (EBSNs) in order to maintain user activity. An effective recommendation is required to maximize the user's preference, subjecting to both time and geographical constraints. Existing methods face an inherent trade-off among efficiency, effectiveness, and generalization, due to the NP-h

Yingting Zhou, Wenbo Cui, Weiheng Liu, Guixing Chen

Transferring the depth-based end-to-end policy trained in simulation to physical robots can yield an efficient and robust grasping policy, yet sensor artifacts in real depth maps like voids and noise establish a significant sim2real gap that critically impedes policy transfer. Training-time strategies like procedural noise injection or learned mappings suffe

Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha

As renewable energy sources become more prevalent, accurately modeling power grid dynamics is becoming increasingly more complex. Concurrently, data acquisition and realtime system state monitoring are becoming more available for control centers. This motivates shifting from \textit{model- and Lyapunov-based} feedback controller designs toward \textit{model-

Yong Li, Yujun Huang, Yi Chen, Hui Cheng

Differential-driven wheeled robots (DWR) represent the quintessential type of mobile robots and find extensive appli- cations across the robotic field. Most high-performance control approaches for DWR explicitly utilize the linear and angular velocities of the trajectory as control references. However, existing research on time-optimal path parameterization

Yaohua Zha, Xue Yuerong, Chunlin Fan, Yuansong Wang

Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their generalizability to other 3D understanding tasks. In contrast, self-supervised point cloud models aim for general-purpose representation learni

Junbo Zou, Haotian Xia, Zhen Ye, Shengjie Zhang

Sports video understanding requires perceiving high-speed dynamics, complex rules, and long temporal contexts. Yet, current Multimodal Large Language Models (MLLMs) remain narrowly focused on single sports, specific tasks, or training-free paradigms. We introduce DeepSport, the first end-to-end trained MLLM for multi-task, multi-sport video understanding. De

Huayi Zhu, Xiu Shu, Youqiang Xiong, Qiao Liu

Current multi-modal image fusion methods typically rely on task-specific models, leading to high training costs and limited scalability. While generative methods provide a unified modeling perspective, they often suffer from slow inference due to the complex sampling trajectories from noise to image. To address this, we formulate image fusion as a direct pro

Tao Zou, Na Xiao, Ruihong Weng, Yifan Guo

Electrocorticographic brain computer interfaces are powerful emergent technologies for advancing basic neuroscience research and targeted clinical interventions. However, existing devices require trade-offs between coverage area, electrode density, surgical invasiveness and complication risk: limitations that fail to meet the demands of next-generation BCI.

Wanying Yu, Huasheng Xie, Aohua Mao, Haojie Ma

This paper presents a robust numerical method for calculating the total absorption rate of electromagnetic waves in magnetized plasmas, capable of determining the absorption ratio among different plasma components. The method adopts Ronnmark's expressions for the plasma dispersion function, $Z({\zeta})$, and the dielectric tensor, K, to overcome the converge

Tania-Amanda Fredrick Eneye, Ashlesha Malla, Pawan Paudel

This study explores the relationship between LinkedIn profile characteristics and professional success, focusing on the indicators of promotions, follower count, and career progression rate. By leveraging a dataset of over 62,000 anonymized LinkedIn profiles, we developed predictive models using machine learning techniques to identify the most influential fa

Jieun Kim, Muqing Yu, Ahmed Omran, Jiangfeng Yang

Superconductivity at oxide interfaces has intrigued researchers for decades, yet the underlying pairing mechanism remains elusive. Here we demonstrate that proximity to a ferroelectric quantum critical point dramatically enhances interfacial superconductivity in KTaO3. By precisely tuning KTaO3 to its quantum critical composition through 0.8% niobium doping,

Bo Hu, Jose C. Principe

This paper studies the interpretability of neural network features from a Bayesian Gaussian view, where optimizing a cost is reaching a probabilistic bound; learning a model approximates a density that makes the bound tight and the cost optimal, often with a Gaussian mixture density. The two examples are Mixture Density Networks (MDNs) using the bound for th

Hui Wang, Fafa Zhang, Xiaoyu Zhang, Chaoxu Mu

In goal-oriented dialogue tasks, the main challenge is to steer the interaction towards a given goal within a limited number of turns. Existing approaches either rely on elaborate prompt engineering, whose effectiveness is heavily dependent on human experience, or integrate policy networks and pre-trained policy models, which are usually difficult to adapt t

Jose Pinedo Soto

The prediction of spacetime singularities, regions of infinite curvature where classical physics breaks down, is one of the most profound challenges in General Relativity (GR). In particular, black hole solutions such as the Schwarzschild and Kerr metrics feature central singularities that signal the incompleteness of the theory in high curvature regimes. Th

Yuesheng Xu, Hector Munoz-Avila

We present online learning of Hierarchical Task Network (HTN) methods in the context of integrated HTN planning and LLM-based chatbots. Methods indicate when and how to decompose tasks into subtasks. Our method learner is built on top of the ChatHTN planner. ChatHTN queries ChatGPT to generate a decomposition of a task into primitive tasks when no applicable

Nan Zhang, Ya Yan Lu

We study a class of bound states in the continuum (BICs) in all-dielectric periodic structures, near which resonant states approach ideal circularly polarized states (CPSs). We term these BICs {\em asymptotically circularly polarized BICs} ({\em acp}-BICs) and identify two types: single-angle and all-angle. Single-angle {\em acp}-BICs permit convergence to l

Hao Li, Zhenfeng Zhuang, Jingyu Lin, Yu Liu

Due to the diversity of brain anatomy and the scarcity of annotated data, supervised anomaly detection for brain MRI remains challenging, driving the development of unsupervised anomaly detection (UAD) approaches. Current UAD methods typically utilize artificially generated noise perturbations on healthy MRIs to train generative models for normal anatomy rec

Zhiqi Li, Yuchen Sun, Greg Turk, Bo Zhu

We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domains by providing a theoretical formulation for Functional Flow Matching and a practical implementation for efficient training and sampling. We also introduce an $x_1$-prediction var

David E. Kaplan, Surjeet Rajendran

The cosmological constant problem represents a profound conflict between quantum field theory and general relativity. Unimodular gravity offers a compelling starting point by de-gravitating the vacuum energy of the Standard Model, but this framework traditionally trades the problem of vacuum energy for a fine-tuning of initial conditions, which manifest as a

Jun Huo, Hongge Ru, Bo Yang, Xingjian Chen

Soft multi-axis force/torque sensors provide safe and precise force interaction. Capturing the complete degree-of-freedom of force is imperative for accurate force measurement with six-axis force/torque sensors. However, cross-axis coupling can lead to calibration issues and decreased accuracy. In this instance, developing a soft and accurate six-axis sensor

Junjie Wu, Yumeng Fu, Chen Gong, Guohong Fu

AI-generated content (AIGC) technology has emerged as a prevalent alternative to create multimodal misinformation on social media platforms, posing unprecedented threats to societal safety. However, standard prompting leverages multimodal large language models (MLLMs) to identify the emerging misinformation, which ignores the misinformation attribution. To t

Kaixuan Zhang, Minxian Li, Mingwu Ren, Jiankang Deng

High Dynamic Range (HDR) 3D reconstruction is pivotal for professional content creation in filmmaking and virtual production. Existing methods typically rely on multi-exposure Low Dynamic Range (LDR) supervision to constrain the learning process within vast brightness spaces, resulting in complex, dual-branch architectures. This work explores the feasibility

Yasuhiro Murakami, Tathagata Ghosh, Soichiro Morisaki

We present a method for efficiently searching long-duration gravitational wave signals from compact binary coalescences (CBCs). The approach exploits the smooth frequency-domain behavior of ratios between neighboring waveform templates. The matched-filter signal-to-noise ratio (SNR) time series of a data segment is first computed for a reference template, an

Kaixin Zhang, Ruiqing Yang, Yuan Zhang, Shan You

Visual Autoregressive (VAR) models enable efficient image generation via next-scale prediction but face escalating computational costs as sequence length grows. Existing static pruning methods degrade performance by permanently removing weights or tokens, disrupting pretrained dependencies. To address this, we propose ActVAR, a dynamic activation framework t

Liangshun Wu, Wen Chen, Shunqing Zhang, Yajun Wang

In post-disaster space-air-ground integrated networks (SAGINs), terrestrial infrastructure is often impaired, and unmanned aerial vehicles (UAVs) must rapidly restore connectivity for mission-critical ground terminals in cluttered non-line-of-sight (NLoS) urban environments. To enhance coverage, UAVs employ movable antennas (MAs), while reconfigurable intell

Takuma Kokusho, Yuki Katsurada, Yong-Hyun Lee, Bon-Chul Koo

Phosphorus (P) is one of the key ingredients for life, yet its origins in galaxies remain poorly understood. In order to investigate the production of P by supernovae, we performed near-infrared (IR) [P II] and [Fe II] line mapping of 26 Galactic supernova remnants (SNRs) with the Infrared Survey Facility and Kanata telescopes, using the narrow-band filters

Arth Sojitra, Omer San

Training neural operators to approximate mappings between infinite-dimensional function spaces often requires extensive datasets generated by either demanding experimental setups or computationally expensive numerical solvers. This dependence on solver-based data limits scalability and constrains exploration across physical systems. Here we introduce the Met

Kunihiko Taira

While experiencing atmospheric turbulence on a commercial flight can be uncomfortable, it rarely compromises the stability of the aircraft. The situation is quite different for small air vehicles that operate in urban canyons, around mountainous terrains, and in the wakes of marine vessels, where they could encounter highly unsteady atmospheric conditions wi

Amelia Samandari, Andreas Willig, Barry Wu, Philippa Martin

Deployment of Unmanned Aerial Vehicles (UAVs) in autonomous formations necessitates accurate and timely communication of safety information. A communication protocol that supports timely and successful transfer of safety information between UAVs is therefore needed. This paper presents Distributed Self-allocated Time slot Reuse (D-STR). Our D-STR protocol ad

Xiao-Qian Mu, Hao-Fan Wang, Shao-Ming Fei

The Schmidt number is an important kind of characterization of quantum entanglement. Quantum states with higher Schmidt numbers demonstrate significant advantages in various quantum information processing tasks. By deriving a class of k-positive linear maps based on symmetric measurements, we present new Schmidt-number witnesses of class (k + 1). By detailed

Bo-Wen Fan, Run-Qiu Yang

We investigate the problem of bulk metric reconstruction in holography by leveraging the inverse scattering framework applied to boundary two-point correlation functions. We generalize our previous work of scalar field and show that reconstruction can be achieved using a single operator rather than a pair. We also apply this method into reconstruction of sta

Haochen Jiang, Dongdong Liu, Xianping Wu, Xu Yang

Motivated by the randomized sketch to solve a variety of problems in scientific computation, we improve both the maximal weighted residual Kaczmarz method and the randomized block average Kaczmarz method using two new randomized sketch techniques. Besides, convergence analyses of the proposed methods are provided. Furthermore, we establish an upper bound for

Worawalan Chatlatanagulchai, Hao Li, Yutaro Kashiwa, Brittany Reid

Agentic coding tools receive goals written in natural language, break them down into specific tasks, and write or execute code with minimal human intervention. Central to this process are agent context files (e.g., AGENTS.md and CLAUDE.md) that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of

Rakesh Halder, Pranab Sardar

Given a tree of hyperbolic metric spaces $\pi:X\to T$ a la Bestvina--Feighn (\cite{BF}), and a hyperbolic subspace $Y$ of $X$ with an induced tree of hyperbolic spaces structure over a subtree $S\subset T$, we address the question as to when the Cannon--Thurston (CT) map exists for the inclusion $Y\to X$. In this paper, we find additional sufficient conditio

Taiyi Su, Jian Zhu, Yaxuan Li, Chong Ma

Embodied world models aim to predict and interact with the physical world through visual observations and actions. However, existing models struggle to accurately translate low-level actions (e.g., joint positions) into precise robotic movements in predicted frames, leading to inconsistencies with real-world physical interactions. To address these limitation

Cheongjae Jang, Jonghyun Won, Soyeon Jun, Chun Kee Chung

Leveraging the Wasserstein distance -- a summation of sample-wise transport distances in data space -- is advantageous in many applications for measuring support differences between two underlying density functions. However, when supports significantly overlap while densities exhibit substantial pointwise differences, it remains unclear whether and how this

Zihao Lin, Zhenshan Shi, Sasa Zhao, Hanwei Zhu

Assessing human creativity through visual outputs, such as drawings, plays a critical role in fields including psychology, education, and cognitive science. However, current assessment practices still rely heavily on expert-based subjective scoring, which is both labor-intensive and inherently subjective. In this paper, we propose a data-driven framework for

Ashish Kumar Perukari, Polina Khoroshevskaya

Operating large autonomous fleets demands fast, resilient allocation of scarce resources (such as energy and fuel, charger access and maintenance slots, time windows, and communication bandwidth) under uncertainty. We propose a side-information-aware approach for resource allocation at scale that combines distributional predictions with decentralized coordin

Junyi Ma, Wentao Bao, Jingyi Xu, Guanzhong Sun

Forecasting how human hands move in egocentric views is critical for applications like augmented reality and human-robot policy transfer. Recently, several hand trajectory prediction (HTP) methods have been developed to generate future possible hand waypoints, which still suffer from insufficient prediction targets, inherent modality gaps, entangled hand-hea

Yang Hou, Andrea Pizzi, Huike Jin, Johannes Knolle

Periodically driven many-body systems generally heat towards a featureless 'infinite-temperature' state. As an alternative to uniform heating in a clean system, here we establish a Floquet superheating regime, where fast heating nucleates at ''hot spots" generated by rare fluctuations in the local energy with respect to an appropriate effective Hamiltonian.

Heyang Ma, Qirui Mi, Qipeng Yang, Zijun Fan

Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose

Si Li

This article reviews the program on connecting Batalin-Vilkovisky (BV) quantization with index theories of algebraic type. We explain how the classical algebraic index theorem can be proved in terms of BV quantization of topological quantum mechanics. This is generalized to 2d chiral CFT in which we present an elliptic chiral analog of the algebraic index th

Chukwuebuka Fortunate Ijezue, Tania-Amanda Fredrick Eneye, Maaz Amjad

This paper presents a transformer-based approach for classifying hope expressions in text. We developed and compared three architectures (BERT, GPT-2, and DeBERTa) for both binary classification (Hope vs. Not Hope) and multiclass categorization (five hope-related categories). Our initial BERT implementation achieved 83.65% binary and 74.87% multiclass accura

Jacob Erickson

Filter bubbles and echo chambers have received global attention from scholars, media organizations, and the general public. Filter bubbles have primarily been regarded as intrinsically negative, and many studies have sought to minimize their influence. The detrimental influence of filter bubbles is well-studied. Filter bubbles may, for example, create inform

Taisuke Hosaka, Etsuo Segawa

We consider the Grover walk on a finite graph composed of two arbitrary simple graphs connected by one edge, referred to as a bridge. The parameter $\epsilon>0$ assigned at the bridge represents the strength of connectivity: if $\epsilon=0$, then the graph is completely separated. We show that for sufficiently small values of $\epsilon$, a phenomenon called

Ke Wang, Qiang Zhang, Dongxiao Zhao

In this paper, we first study the endomorphisms of free-abelian times surface groups and give a characterization of when they are injective and surjective. Then, we see that free-abelian times hyperbolic groups are Hopfian but not co-Hopfian. Moreover, we give a complete classification of fixed subgroups of endomorphisms in free-abelian times surface groups,

Trung Thanh Nguyen, Yasutomo Kawanishi, Vijay John, Takahiro Komamizu

The widespread use of multi-sensor systems has increased research in multi-view action recognition. While existing approaches in multi-view setups with fully overlapping sensors benefit from consistent view coverage, partially overlapping settings where actions are visible in only a subset of views remain underexplored. This challenge becomes more severe in

Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal, Zeeshan Memon

Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) reasoning degradation, (4) retrieval fragility, and (5) multimodal misalignment. While existing surveys describe these phenomena empirically, they lack a rigorous theoretical synthes

Ruiqi Yang, Tian Yun, Zihan Wang, Ellie Pavlick

Multimodal large language models (LLMs) have made rapid progress in visual understanding, yet their extension from images to videos often reduces to a naive concatenation of frame tokens. In this work, we investigate what video finetuning brings to multimodal LLMs. We propose Visual Chain-of-Thought (vCoT), an explicit reasoning process that generates transi

Chen Jia

Bootstrapping large language models (LLMs) through preference-based policy optimization offers a promising direction for aligning model behavior with human preferences without relying on extensive manual annotations. In this work, we propose a novel preference-based policy optimization (PbPO) framework that formulates the learning process as a min-max game b

Ahmad Memon, Abdallah Mohamed

Manual grading of programming assignments in introductory computer science courses can be time-consuming and prone to inconsistencies. While unit testing is commonly used for automatic evaluation, it typically follows a binary pass/fail model and does not give partial marks. Recent advances in large language models (LLMs) offer the potential for automated, s