November 2025 arXiv papers — page 105

Xiaoyu Cheng, Hui Jiang, Jun Chen, Lei Zhang

Typically, scaling up the size of a system does not change the shape of its energy spectrum, other than making it denser. Exceptions, however, occur in the new phenomenon of non-Hermitian skin criticality, where closely competing generalized Brillouin zone (GBZ) solutions for non-Hermitian state accumulation give rise to anomalously scaling complex spectra.

Chao Yang, Boqian Zhang, Jinghao Xu, Guang Jiang

Diffusion-based methods have shown great promise in single image super-resolution (SISR); however, existing approaches often produce blurred fine details due to insufficient guidance in the high-frequency domain. To address this issue, we propose a High-Frequency Guided Diffusion Network based on Wavelet Decomposition (HDW-SR), which replaces the conventiona

Zahra Tabatabaei, Jon Sporring

According to the World Health Organization, breast cancer claimed the lives of approximately 685,000 women in 2020. Early diagnosis and accurate clinical decision making are critical in reducing this global burden. In this study, we propose THIR, a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages topological data analysis specific

Tianai Huang, Jiayuan Chen, Lu Lu, Pengcheng Chen

Large language models (LLMs) have demonstrated exceptional capabilities in general domains, yet their application in highly specialized and culturally-rich fields like Traditional Chinese Medicine (TCM) requires rigorous and nuanced evaluation. Building upon prior foundational work such as TCM-3CEval, which highlighted systemic knowledge gaps and the importa

Haodong Wang, Tao Zhuo, Xiuwei Zhang, Hanlin Yin

Achieving pixel-level registration between SAR and optical images remains a challenging task due to their fundamentally different imaging mechanisms and visual characteristics. Although deep learning has achieved great success in many cross-modal tasks, its performance on SAR-Optical registration tasks is still unsatisfactory. Gradient-based information has

Leonidas Mindrinos, Nikolaos Pallikarakis, Nikolaos L Tsitsas

In this work, we consider the inverse electromagnetic scattering problem for a magneto-dielectric cylinder covering an impedance cylinder of arbitrary shape. We solve it by introducing a divide-and-conquer framework using specially designed 1D multi-channel, circular-padding Convolutional Neural Networks. The solution of the direct problem provides us with t

Zhaoxin Shen, Dan Wu

To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local similarities among users and integrates their data using the law of large numbers (LLN), thereby improving the utilization of user behavior dat

Pieter Braam, Jan ten Thije Boonkkamp, Martijn Anthonissen, Koondanibha Mitra

We present an inverse method to compute freeform optical surfaces that transform a light distribution, parameterized by two source planes, into two separate target distributions. The surfaces can be reflectors or lenses, and control both the spatial and directional source and target coordinates of light rays. From energy conservation we derive Jacobian equat

Yifan Wang, Yiyao Yu, Yang Xia, Yan Xu

Cyber-attacks jeopardize the safe operation of inverter-based resource-dominated microgrids (IBR-dominated microgrids). At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modeling assumptions that are untenable under single-point measurement constraints. This paper proposes a Fractional-Order Mem

W. Haro Moya, C. Moni Bidin, M. C. Parisi, D. Geisler

Globular clusters in the Galactic bulge are difficult to study due to high extinction and severe crowding. VVV-CL001 is an old, metal-poor, and fast cluster in the inner bulge, whose extreme properties make it a key probe of the early chemical and dynamical evolution of the Milky Way. We derive its fundamental parameters by combining spectroscopy, astrometry

TC Singh, Sougata Mukherjea

Graph Neural Networks (GNNs) excel in graph-based learning tasks, but their complex, non-linear operations often render them as opaque "black boxes". This opacity hinders user trust, complicates debugging, bias detection, and adoption in critical domains requiring explainability. This paper introduces InteractiveGNNExplainer, a visual analytics framework to

Zaara Zabeen Arpa, Sadnam Sakib Apurbo, Nazia Karim Khan Oishee, Ajwad Abrar

Automatic Speech Recognition (ASR) transcripts, especially in low-resource languages like Bangla, contain a critical ambiguity: word-word repetitions can be either Repetition Disfluency (unintentional ASR error/hesitation) or Morphological Reduplication (a deliberate grammatical construct). Standard disfluency correction fails by erroneously deleting valid l

Samuele Burattini, Alessandro Ricci, Simon Mayer, Danai Vachtsevanou

In this paper we introduce and discuss an approach for multi-agent-oriented visual programming. This aims at enabling individuals without programming experience but with knowledge in specific target domains to design and (re)configure autonomous software. We argue that, compared to procedural programming, it should be simpler for users to create programs whe

Yi Pan, Bo-Chao Liu

We investigate the $K^- p \to \gamma \Sigma$ reaction using an effective Lagrangian approach within an isobar model framework. The model includes contributions from $s$-channel hyperon and hyperon resonance, $t$-channel $K$ and $K^*$, $u$-channel proton and $\Delta(1232)$ exchanges, and a phenomenological contact term. Our analysis focuses on the roles of va

Zhi Li, Jiancheng Yu

This study investigates the accretion process and observational signatures of thin accretion disks around a Schwarzschild black hole (BH) embedded in a Dehnen-type dark matter (DM) halo. We examine the influence of the density {\rho}_{s} and radius r_{s} of the DM halo on key disk properties, including the energy flux, temperature distribution, and emission

Jonathan Bader, Julius Irion, Jannis Kappel, Joel Witzke

The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy efficiency, gaining process-level insights into energy consumption is essential. While node-level energy consumption data can

Gianluca Inguglia, Huw Haigh, Kristyna Vitulova, Ulyana Dupletsa

We present the implementation of an anomaly-detection algorithm based on a deep convolutional autoencoder for the search for gravitational waves (GWs) in time-frequency spectrograms. Our method targets short-duration ($\lesssim 2\,\text{s}$) GW signals, exemplified by mergers of compact objects forming or involving an intermediate-mass black hole (IMBH). Suc

I. V. Dzedolik, S. V. Tomilin

We consider theoretical models of the nanolaser and logic gates on carbon nanotubes (CNTs). In our work, it is shown at pumping the nanoresonator of the nanolaser on CNT by optical radiation using a quantum dot as nano light emitted diode (LED), the coherent flow of surface plasmon-polaritons arises when the generation threshold is exceeded. The coherent pla

Sourya Dipta Das, Shubham Kumar, Kuldeep Yadav

Grammar competency estimation is essential for assessing linguistic proficiency in both written and spoken language; however, the spoken modality presents additional challenges due to its spontaneous, unstructured, and disfluent nature. Developing accurate grammar scoring models further requires extensive expert annotation, making large-scale data creation i

Arijit Chakrabarty, Aman Tripathi, Vimod Kumar, Anurag Tripathi

In this study, we attempt to characterize the shape of three different types of grains commonly used in the iron and steel-making industry, namely pellet, sinter and iron ore lump. We choose particles over the entire size ranges used in industrial-scale blast furnace and consider two different size ranges of pellet particles and four different size ranges fo

Rifen Lin, Alex Jinpeng Wang, Jiawei Mo, Min Li

Multimodal pretraining has revolutionized visual understanding, but its impact on video-based person re-identification (ReID) remains underexplored. Existing approaches often rely on video-text pairs, yet suffer from two fundamental limitations: (1) lack of genuine multimodal pretraining, and (2) text poorly captures fine-grained temporal motion-an essential

J. B. Nation, Gianluca Paolini

In [6] we proved that the universal theory of infinite free lattices is (algorithmically) decidable, leaving open the problem of decidability of the full theory of an (infinite) free lattice. We solve this problem by proving that, for every cardinal $\kappa \geq 3$, the first-order theory of the free lattice $\mathbf{F}_\kappa$ is undecidable.

Jakob Beise, María Durán de las Heras, Segev BenZvi, Spencer Griswold

Neutrinos produced in core-collapse supernova offer a direct probe into the hydrodynamics and energy transport mechanisms during the collapse and play a pivotal role in the shock revival and success of the supernova explosion. Fast-time features of the neutrino luminosity and energy spectrum encode information about phenomena such as turbulence, convection,

Shaoyuan Chen, Zhixuan Chen, Dawei Yang, Zhihang Yuan

Large Language Models (LLMs) fine-tuning techniques not only improve the adaptability to diverse downstream tasks, but also mitigate adverse effects of model quantization. Despite this, conventional quantization suffers from its structural limitation that hinders flexibility during the fine-tuning and deployment stages. Practical on-device tasks demand diffe

Junyu Wu, Jie Liu, Tianrui Pan, Jie Tang

In recent years, significant progress has been made in the field of deep learning for music demixing. However, there has been limited attention on real-time, low-latency music demixing, which holds potential for various applications, such as hearing aids, audio stream remixing, and live performances. Additionally, a notable tendency has emerged towards the d

Cesar Portocarrero Rodriguez, Laura Vandeweyen, Yosuke Yamamoto

The American Society of Civil Engineers has graded Americas infrastructure condition as a C, with the road system receiving a dismal D. Roads are vital to regional economic viability, yet their management, maintenance, and repair processes remain inefficient, relying on outdated manual or laser-based inspection methods that are both costly and time-consuming

Jiacheng Cheng, Xu Zhang, Guanghui Qiu, Yifang Zhang

Federated Learning (FL) enables collaborative training across decentralized data, but faces key challenges of bidirectional communication overhead and client-side data heterogeneity. To address communication costs while embracing data heterogeneity, we propose pFed1BS, a novel personalized federated learning framework that achieves extreme communication comp

Gorka Abad, Marina Krček, Stefanos Koffas, Behrad Tajalli

Backdoor attacks pose a significant threat to deep learning models by implanting hidden vulnerabilities that can be activated by malicious inputs. While numerous defenses have been proposed to mitigate these attacks, the heterogeneous landscape of evaluation methodologies hinders fair comparison between defenses. This work presents a systematic (meta-)analys

Yi Li, Yuhui Li, Jianzhao Wu, Luxuan Zhang

Twin-wire laser directed energy deposition (TW-LDED) provides a promising route for alloying and fabrication of compositionally graded structures. However, inherent multiparameter coupling in twin-wire systems critically exacerbates both process instabilities and compositional inhomogeneity. This unresolved issue escalates into a fundamental technological bo

Yangfan Liu, Xiong Xiong, Yong Liao, Mingli Qin

The genotype-phenotype gap is a persistent barrier to complex trait genetic dissection, worsened by the explosive growth of genomic data (1.5 billion variants identified in the UK Biobank WGS study) alongside persistently scarce and subjective human-defined phenotypes. Digital phenotyping offers a potential solution, yet existing tools fail to balance scalab

Xingxing Hu, Yunfang Tang

The star chromatic index of a graph $G$, denoted by $\chi'_{st}(G) $, is the minimum number of colors needed to properly color the edges of $G$ such that no path or cycle of length four is bi-colored. Casselgren et al. and Hou et al. independently proved that the star chromatic index of a cubic Halin graph, except a special graph, is at most $6$. It remains

Cong Li

We study how background electromagnetic fields modify quasi-real photon emission at the EIC and EicC through an effective coupling correction, thereby altering the photon flux spectrum. The resulting change in lepton-pair production via photon-photon fusion, where one photon arises from the electron and the other from the nuclear Coulomb field-serves as a cl

Zhiteng Chao, Yonghao Wang, Xinyu Zhang, Jiaxin Zhou

Large language models (LLMs) hold promise for automating integrated circuit (IC) engineering using register transfer level (RTL) hardware description languages (HDLs) like Verilog. However, challenges remain in ensuring the quality of Verilog generation. Complex designs often fail in a single generation due to the lack of targeted decoupling strategies, and

Longhui Zheng, Qiming Xia, Xiaolu Chen, Zhaoliang Liu

3D object detection is critical for autonomous driving, yet it remains fundamentally challenging to simultaneously maximize computational efficiency and capture long-range spatial dependencies. We observed that Mamba-based models, with their linear state-space design, capture long-range dependencies at lower cost, offering a promising balance between efficie

Yanda Zhu, Yuanyang Zhu, Daoyi Dong, Caihua Chen

Task decomposition has shown promise in complex cooperative multi-agent reinforcement learning (MARL) tasks, which enables efficient hierarchical learning for long-horizon tasks in dynamic and uncertain environments. However, learning dynamic task decomposition from scratch generally requires a large number of training samples, especially exploring the large

Junjie Yang, Yuhao Yan, Gang Wu, Yuxuan Wang

As Vision-Language Models (VLMs) increasingly gain traction in medical applications, clinicians are progressively expecting AI systems not only to generate textual diagnoses but also to produce corresponding medical images that integrate seamlessly into authentic clinical workflows. Despite the growing interest, existing medical visual benchmarks present not

Ali Asadi, Krishnendu Chatterjee, David Lurie, Raimundo Saona

Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, where a target set must be eventually visited, and the more general parity objectives can model all omega-regular specifications. For such objectives, the computational analysis pro

Shudong Wang, Xinfei Wang, Chenhao Zhang, Shanchen Pang

Multi-task reinforcement learning (MTRL) seeks to learn a unified policy for diverse tasks, but often suffers from gradient conflicts across tasks. Existing masking-based methods attempt to mitigate such conflicts by assigning task-specific parameter masks. However, our empirical study shows that coarse-grained binary masks have the problem of over-suppressi

Chenyang Li, Wenbing Tang, Yihao Huang, Sinong Simon Zhan

Vision-and-Language Navigation (VLN) agents have made remarkable progress, but their robustness remains insufficiently studied. Existing adversarial evaluations often rely on perturbations that manifest as unusual textures rarely encountered in everyday indoor environments. Errors under such contrived conditions have limited practical relevance, as real-worl

Huiwen Wu, Shuo Zhang, Yi Liu, Hongbin Ye

Due to the emergency and homogenization of Artificial Intelligence (AI) technology development, transformer-based foundation models have revolutionized scientific applications, such as drug discovery, materials research, and astronomy. However, seismic data presents unique characteristics that require specialized processing techniques for pretraining foundat

Yan Yang, Mouxiao Bian, Peiling Li, Bingjian Wen

The rapid advancement of large language models (LLMs) has accelerated their integration into clinical decision support, particularly in prescription review. To enable systematic and fine-grained evaluation, we developed RxBench, a comprehensive benchmark that covers common prescription review categories and consolidates 14 frequent types of prescription erro

Anshul Kumar, Gagan Raj Gupta, Manish Rai, Apu Chakraborty

Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they hold the potential to transform network optimization, automate troubleshooting, enhance customer support, and ensure regulatory compliance. However, their deployment in telecom is hindered by domain-specific chal

Bernardo Cockburn, Alexandre Ern, Rekha Khot

We present improved $L^2$-error estimates on the time-integrated primal variable for the wave equation in its first-order formulation. The space discretization relies on a hybrid nonconforming method, such as the hybridizable discontinuous Galerkin, the hybrid high-order or the weak Galerkin methods. We consider both equal-order and mixed-order settings on s

Julien Marché, Seokbeom Yoon

We establish an explicit relation between the adjoint Reidemeister torsion of the two-bridge knot $K(p,q)$ at any parabolic representation and the Frobenius algebra governing the signatures of SU$_2$-TQFT vector spaces at the root $\zeta=\exp(i\pi q/p)$. As applications, (a) we prove that the inverse sum of torsions is constant (i.e., independent of $p$ and

Hongyang Wang

In this paper, we establish an optimal $χ$-binding function for $(P_2\cup P_4,\text{ diamond})$-free graphs. We prove that for any graph $G$ in this class, $χ(G)\le 4$ when $ω(G)=2$, $χ(G)\le 6$ when $ω(G)=3$, and $χ(G)=ω(G)$ when $ω(G)\ge 4$, where $χ(G)$ and $ω(G)$ denote the chromatic number and clique number of $G$, respectively. This result extends the

Zonghao Ying, Moyang Chen, Nizhang Li, Zhiqiang Wang

Jailbreak attacks can circumvent model safety guardrails and reveal critical blind spots. Prior attacks on text-to-video (T2V) models typically add adversarial perturbations to obviously unsafe prompts, which are often easy to detect and defend. In contrast, we show that benign-looking prompts containing rich, implicit cues can induce T2V models to generate

Nigar Alishzade, Gulchin Abdullayeva

This study presents a systematic comparative analysis of recurrent and attention-based neural architectures for isolated sign language recognition. We implement and evaluate two representative models-ConvLSTM and Vanilla Transformer-on the Azerbaijani Sign Language Dataset (AzSLD) and the Word-Level American Sign Language (WLASL) dataset. Our results demonst

Hao Long, Silin Zhou, Lisi Chen, Shuo Shang

Recent learning-based methods have reduced the computational complexity of traditional trajectory similarity computation, but state-of-the-art (SOTA) methods still fail to leverage the comprehensive spectrum of trajectory information for similarity modeling. To tackle this problem, we propose \textbf{RePo}, a novel method that jointly encodes \textbf{Re}gion

Changxi Chi, Yufei Huang, Jun Xia, Jiangbin Zheng

Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translational biomedical research. However, a major bottleneck in this task is the unpaired nature of single-cell data, as the same cell cannot be observed both before and after perturbation

Anna Shchiptsova, Michael Obersteiner

The addition of phosphorus, in the form of mineral fertilizer, becomes necessary in most agricultural soils in order to achieve consistent high yield levels of intensive farming and maintain soil fertility. Recent consolidation of phosphate fertilizer industry has transformed fragmented trade into a single integrated global network, where a small group of la

Harsh Abhinandan, Aditya Dhanraj, Aryan Katoch, R. Raja Singh

Unmanned Aerial Vehicles (UAVs) or drones have witnessed a spectacular surge in applications for military, commercial, and civilian purposes. However, their potential for flight is always limited by the finite power budget of their onboard power supplies. The limited flight time problem has led to intensive research into new sources of power and innovative c

Yuqi Zhang, Guanying Chen, Jiaxing Chen, Chuanyu Fu

Reconstructing 3D scenes and synthesizing novel views from sparse input views is a highly challenging task. Recent advances in video diffusion models have demonstrated strong temporal reasoning capabilities, making them a promising tool for enhancing reconstruction quality under sparse-view settings. However, existing approaches are primarily designed for mo

Trevor Exley, Anderson Brazil Nardin, Petr Trunin, Diana Cafiso

This work introduces the Monolithic Unit (MU), an actuator-lattice-sensor building block for soft robotics. The MU integrates pneumatic actuation, a compliant lattice envelope, and candidate sites for optical waveguide sensing into a single printed body. In order to study reproducibility and scalability, a parametric design framework establishes deterministi

Kanad Bhattacharya

In this article, we attempt to understand various aspects of turbulent flows in electron hydrodynamics. We analyze a rectangular channel geometry in the presence of an electric field and a Corbino geometry in the presence of a magnetic field. In the former geometry, we analyze the conductivity of the fluid as well as the frequency spectrum of perturbations a

Xuxin Yang, Xue Yuan, Donghan Feng, Siru Chen

Constructing clean and low-carbon rural integrated energy system (RIES) is a fundamental requirement for supporting China's rural modernization and new-type urbanization. Existing research on RIES decarbonization primarily focuses on the optimal low-carbon operation of system-level energy devices at the macro level, while the synergistic carbon-reduction eff

Quanjiang Guo, Sijie Wang, Jinchuan Zhang, Ben Zhang

Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs--such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present

Anchita Dey, Soutrik Bandyopadhyay, Shubhendu Bhasin

In practical applications, the efficacy of a control algorithm relies critically on the accurate knowledge of the parameters and states of the underlying system. However, obtaining these quantities in practice is often challenging. Adaptive observers address this issue by performing simultaneous state and parameter estimation using only input-output measurem

Hirokuni Miyamoto, Kenta Suzuki, Shigeharu Moriya, Makiko Matsuura

Seagrass meadows contribute to the conservation of marine ecosystems, reduction in global warming impacts and pathogen controls. However, the decline in seagrass habitats due to environmental loads has become an urgent global issue. One way to address this issue is to better understand healthy seagrass habitats. Here, we estimate the structural characteristi

Qipeng Song, Nan Yang, Ziqi Xu, Yue Li

Machine unlearning aims to eliminate the influence of specific data from trained models to ensure privacy compliance. However, most existing methods assume full access to the original training dataset, which is often impractical. We address a more realistic yet challenging setting: few-shot zero-glance, where only a small subset of the retained data is avail

Hanzhe Liang, Jie Zhou, Can Gao, Bingyang Guo

3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data. However, existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly. To address this problem, we propose a novel Rotation

Zhaocheng Yu, Kui Jiang, Junjun Jiang, Xianming Liu

Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining methods have made considerable progress, they often struggle with fidelity of semantic and spatial details. To address these limitations, we propose the Multi-Prior Hierarchical Mamba (M

Wenya Wei, Sipeng Yang, Qixian Zhou, Ruochen Liu

In cooperative video games, traditional AI companions are deployed to assist players, who control them using hotkeys or command wheels to issue predefined commands such as ``attack'', ``defend'', or ``retreat''. Despite their simplicity, these methods, which lack target specificity, limit players' ability to give complex tactical instructions and hinder imme

Rasmus F. Orsoe, Stephan Meighen-Berger, Jeffrey Lazar, Jorge Prado

Neutrino telescopes are large-scale detectors designed to observe Cherenkov radiation produced from neutrino interactions in water or ice. They exist to identify extraterrestrial neutrino sources and to probe fundamental questions pertaining to the elusive neutrino itself. A central challenge common across neutrino telescopes is to solve a series of inverse

Shuaibin Fan, Senming Zhong, Wenchao Yan, Minglong Xue

Image dehazing is an important task in the field of computer vision, aiming at restoring clear and detail-rich visual content from haze-affected images. However, when dealing with complex scenes, existing methods often struggle to strike a balance between fine-grained feature representation of inhomogeneous haze distribution and global consistency modeling.

Fabian Böhm, Nils Kohl, Harald Köstler, Ulrich Rüde

We propose a robust, adaptive coarse-grid correction scheme for matrix-free geometric multigrid targeting PDEs with strongly varying coefficients. The method combines uniform geometric coarsening of the underlying grid with heterogeneous coarse-grid operators: Galerkin coarse grid approximation is applied locally in regions with large coefficient gradients,

Zhichao He, Mouxiao Bian, Jianhong Zhu, Jiayuan Chen

The Consolidated Standards of Reporting Trials statement is the global benchmark for transparent and high-quality reporting of randomized controlled trials. Manual verification of CONSORT adherence is a laborious, time-intensive process that constitutes a significant bottleneck in peer review and evidence synthesis. This study aimed to systematically evaluat

Fengzhi Xu, Ziyuan Yang, Mengyu Sun, Joey Tianyi Zhou

Medical image enhancement is clinically valuable, but existing methods require large-scale datasets to learn complex pixel-level mappings. However, the substantial training and storage costs associated with these datasets hinder their practical deployment. While dataset distillation (DD) can alleviate these burdens, existing methods mainly target high-level

Seungjae Kim, SeungJoon Lee, MyeongAh Cho

Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency but inherently fail on non-linear motion patterns. Conversely, recent data-driven motion predictors capture complex non-linear dynamics but suffer from limited domain generalization

Reiner Thomä, Carsten Andrich, Michael Döbereiner, Reza Faramarzahangari

Integrated Sensing and Communications (ISAC) will become a service in future mobile communication networks. It enables the detection and recognition of passive objects and environments using radar-like sensing. The ultimate advantage is the reuse of the mobile network and radio access resources for scene illumination, sensing, data transportation, computatio

Yu Zhu, Dan Zeng, Shuiwang Li, Qijun Zhao

Recent research in Category-Agnostic Pose Estimation (CAPE) has adopted fixed textual keypoint description as semantic prior for two-stage pose matching frameworks. While this paradigm enhances robustness and flexibility by disentangling the dependency of support images, our critical analysis reveals two inherent limitations of static joint embedding: (1) po

Xuecheng Chen, Jingao Xu, Wenhua Ding, Haoyang Wang

As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Ro

Doanh C. Bui, Ba Hung Ngo, Hoai Luan Pham, Khang Nguyen

Lifelong learning on Whole Slide Images (WSIs) aims to train or fine-tune a unified model sequentially on cancer-related tasks, reducing the resources and effort required for data transfer and processing, especially given the gigabyte-scale size of WSIs. In this paper, we introduce MergeSlide, a simple yet effective framework that treats lifelong learning as

Haiyang Li, Yijie Shen

Detecting Pancharatnam-Berry geometric phases of light typically requires interferometry or diffraction through a specially truncated aperture. Here, we introduce a simpler method that allows direct and fully visual detection of geometric phases in structured light without using interferometers or beam truncation. Our approach takes advantage of the geometri

Johan Bijnens, Nils Hermansson-Truedsson, Antonio Rodríguez-Sánchez

We combine existing perturbative results to show that a precise analytic determination of the charm-quark contribution to the hadronic light-by-light (HLbL) part of the muon anomalous magnetic moment is possible. Working in the $\overline{\mathrm{MS}}$ scheme, we include the NLO $\mathcal{O}(\alpha_s)$ correction, which significantly reduces the residual ren

Guy Damari, Itzik Klein

Autonomous underwater vehicles rely on precise navigation systems that combine the inertial navigation system and the Doppler velocity log for successful missions in challenging environments where satellite navigation is unavailable. The effectiveness of this integration critically depends on accurate alignment between the sensor reference frames. Standard m

Chuyuan Li, Giuseppe Carenini

We introduce BeDiscovER (Benchmark of Discourse Understanding in the Era of Reasoning Language Models), an up-to-date, comprehensive suite for evaluating the discourse-level knowledge of modern LLMs. BeDiscovER compiles 5 publicly available discourse tasks across discourse lexicon, (multi-)sentential, and documental levels, with in total 52 individual datase

Hong-Bang Liu, Zu-Ke Feng, Huan-Bo Feng, Di-Fan Yi

The Low Energy Polarization Detector (LPD) is a key component of the next-generation large-scale Gamma-Ray Burst polarimeter, POLAR-2. It is designed for polarization observations of transient sources in the soft X-ray energy range with a wide field of view (FOV). To validate the key technologies required for wide-FOV X-ray polarization measurements, the Cos

Ruinan Li, Tian Shen, Zhonggen Su

The randomized midpoint Langevin Monte Carlo (RLMC), introduced by Shen and Lee (2019), is a variant of classical Unadjusted Langevin Algorithm. It was shown in the literature that the RLMC is an efficient algorithm for approximating high-dimensional probability distribution $\pi$. In this paper, we establish the exponential ergodicity of RLMC with constant

Hayato Shimabukuro

We show that persistence-based topology of the 21 cm forest encodes information about Cosmic Dawn that is complementary to traditional amplitude- or correlation-based statistics. Applying topological data analysis to simulated one-dimensional forest spectra over a grid of X-ray heating efficiencies $f_X$ and warm-dark-matter masses $m_{\rm WDM}$ (which set t

Yuhan Chen, Yuxuan Liu, Long Zhang, Pengzhi Gao

Multi-turn interaction remains challenging for online reinforcement learning. A common solution is trajectory-level optimization, which treats each trajectory as a single training sample. However, this approach can be inefficient and yield misleading learning signals: it applies uniform sampling across tasks regardless of difficulty, penalizes correct interm

Arun Kumar Pati, Vlatko Vedral, Erik Sjoqvist

The fundamental division of the total quantum evolution phase into geometric and dynamical components is a central problem in quantum physics. Here, we prove a remarkably simple and universal law demonstrating that this partitioning is governed, at every instant, solely by a single geometric quantity: the Bargmann angle (Bures angle). This result provides a

Gerry Toft

We provide a characterisation of when a single-element contraction of a transversal matroid is itself transversal. Using this characterisation, we define a new class of transversal matroids closed under minors, which we call path-circular matroids. Path-circular matroids generalise both of the well-known classes of bicircular matroids and multi-path matroids

A-Long Zhou, Ya-Wen Xiao, Nuo Xu, Li-Li Gao

We investigate a non-Hermitian quantum battery based on the Su-Schrieffer-Heeger (SSH) lattice, charged through a parity-time (PT)-symmetric protocol that alternates gain and loss between the two sublattices. The interplay between lattice topology and non-Hermiticity gives rise to both bulk and edge exceptional points (EPs), which govern the charging dynamic

SeokJoo Kwak, Jihoon Kim, Boyoun Kim, Jung Jae Yoon

Graphical User Interface (GUI) grounding - the task of mapping natural language instructions to screen coordinates - is essential for autonomous agents and accessibility technologies. Existing systems rely on monolithic models or one-shot pipelines that lack modularity and fail under visual clutter and ambiguous instructions. We introduce MEGA-GUI, a multi-s

Hocheol Lee, Bogeun Gwak

The innermost stable circular orbit (ISCO) offers a fundamental test of spacetime structure. However, its behavior in higher-dimensional black holes influenced by anisotropic energy-momentum tensors remains insufficiently explored. In this work, we investigate the upper bound of the ISCO in higher-dimensional, static, spherically symmetric, and asymptoticall

Zhuoran Duan, Yuhao Wei, Guoshun Nan, Zijun Wang

Large models (LMs), such as ChatGPT, have made a significant impact across diverse domains and hold great potential to facilitate the evolution of network intelligence. Wireless-native multi-modal large models (WMLMs) can sense and understand the physical world through multi-modal data, serving as a key enabler that integrates communication, sensing, and int

Tian Shen, Zhonggen Su

The task of sampling from a high-dimensional distribution $\pi$ on $\R^d$ is a fundamental algorithmic problem with applications throughout statistics, engineering, and the sciences. Consider the Langevin diffusion on $\R^d$ \begin{align*} \dif X_t=-\nabla U(X_t)dt+\sqrt{2}dB_t, \end{align*} under mild conditions, it admits $\pi(\dif x)\propto \exp(-U(x))\di

Akira Yasuhara, Yamato Kirii, Takumi Sannomiya

The interaction between free electrons and optical modes underlies a variety of quantum and nanoscale light-matter phenomena, yet the associated momentum exchange with the sample largely remained overlooked. Here, we experimentally demonstrate the momentum transfer from free electrons to planar samples during optical mode excitation using momentum-resolved e

Daisuke Hirota

Let \( A_i \) be a commutative \( C^{*} \)-algebra for \( i = 1, 2 \), and denote by \( A_i^{+} \) its positive cone, consisting of all positive elements of \( A_i \). In this paper, we investigate surjective, not necessarily continuous mappings \( T: A_1^{+} \to A_2^{+} \) that satisfy the norm equality \[ \| T(a + b) \| = \| T(a) + T(b) \| \quad (a, b \in

Kyunghyun Lee, Yong-Min Shin, Minwoo Shin, Jihun Kim

Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance imaging-guided biopsy demonstrates promising performance in detecting cancer lesions, its practical application is limited by prolonged procedure times and high costs. To overcome the

Yehonatan Elisha, Seffi Cohen, Oren Barkan, Noam Koenigstein

Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse user queries. This ambiguity hinders the effective evaluation and practical utility of explanation methods. We address this gap by introducing the Reference-Frame $\times$ Granularity

Steven Landers, Benjamin Marsh

We analyze maximal extractable value in multiple concurrent proposer blockchains, where multiple blocks become data available before their final execution order is determined. This concurrency breaks the single builder assumption of sequential chains and introduces new MEV channels, including same tick duplicate steals, proposer to proposer auctions, and tim

Jiacheng Tang, Mingyue Feng, Jiachao Liu, Yaonong Wang

Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within these architectures that allows ego status to be easily leverage

Liuyi Jin, Pasan Gunawardena, Amran Haroon, Runzhi Wang

Emergency Medical Technicians (EMTs) operate in high-pressure environments, making rapid, life-critical decisions under heavy cognitive and operational loads. We present EMSGlass, a smart-glasses system powered by EMSNet, the first multimodal multitask model for Emergency Medical Services (EMS), and EMSServe, a low-latency multimodal serving framework tailor

Li-Ming Cao, Long-Yue Li, Xia-Yuan Liu

We study the images of black holes by gluing two Schwarzschild spacetimes with a thin shell where the Israel junction conditions are satisfied. By studying the refraction law for null geodesics at the spherical shell, and taking account of the light travel time delay, the images are obtained by ray tracing a geometrically and optically thin accretion disk. F

Yu-Peng Ma, Ming-Jian Gao, Jun-Hong An

The discovery of topological phases has ushered in a new era of condensed matter physics and revealed a variety of natural and artificial materials. They obey the bulk-boundary correspondence (BBC), which guarantees the emergence of boundary states with nonzero topological invariants in the bulk. Widespread attention has been paid to extending topological ph

Mohammad Reza Shafie, Morteza Hajiabadi, Hamed Khosravi, Mobina Noori

Microbial Fuel Cells (MFCs) offer a promising pathway for sustainable energy generation by converting organic matter into electricity through microbial processes. A key factor influencing MFC performance is the anode structure, where design and material properties play a crucial role. Existing predictive models struggle to capture the complex geometric depen

Chengxin Jiang, Hui Shan Wang, Chen Chen, Lingxiu Chen

Zigzag edges of graphene have long been predicted to exhibit magnetic electronic state near the Fermi level, which can cause spin-related phenomena and offer unique potentials for graphene-based spintronics. However, the magnetic conduction channels along these edges have yet been reported experimentally. Here, we report the observation on signatures of magn

An Ying, Yi Ge, Liu Guoliang, Sun Rongwu

In the fields of agricultural machinery, construction equipment, and special-purpose vehicles, hydrostatic transmission (HST) drive systems have witnessed a significant increase in market penetration. With the rapid development of intelligent and environmentally friendly trends, higher requirements have been imposed on HST control systems regarding operation

Nannan Chen, Yulai Ma, Fan Yang

In 2022, Holmsen showed that any graph with at least \( c \binom{n}{r} \) \(r\)-cliques but no induced complete $r$-partite graph $K_{2,\ldots, 2}$ must contain a clique of order \(\Omega(c^{2^{r-1}} n)\). In this paper, we study graphs forbidding semi-induced substructures and show that every $n$-vertex graph $G$ containing at least $c\binom{n}{r}$ copies o