November 2025 arXiv papers — page 16

Prateek Chanda, Prayas Agrawal, Saral Sureka, Lokesh Reddy Polu

Traditional curriculum learning proceeds from easy to hard samples, yet defining a reliable notion of difficulty remains elusive. Prior work has used submodular functions to induce difficulty scores in curriculum learning. We reinterpret adaptive subset selection and formulate it as a multi-armed bandit problem, where each arm corresponds to a submodular fun

Kelaiti Xiao, Liang Yang, Dongyu Zhang, Paerhati Tulajiang

We study idiom-based visual puns--images that align an idiom's literal and figurative meanings--and present an iterative framework that coordinates a large language model (LLM), a text-to-image model (T2IM), and a multimodal LLM (MLLM) for automatic generation and evaluation. Given an idiom, the system iteratively (i) generates detailed visual prompts, (ii)

Na Li, Chuhao Wu, Hongyang Zhou, Huiran Yi

With growing awareness of long-term health and wellness, everyday body management has become a widespread practice. Social media platforms and health-related applications offer abundant information for those pursuing healthier lifestyles and more positive body images. While prior Human-Computer Interaction research has focused extensively on technology-media

A. Chernyshev, V. Saleev

We suggest improvements for double counting subtraction scheme, which is needed for the consistent treatment of real corrections in the high-energy limit, and apply it to the single photon production at the NLO${}^\star$ approximation of the high-energy factorization approach. The presented improvements allow us to avoid the oversubtraction problem.

Yongsen Cheng, Yuanhao Cai, Yulun Zhang

Burst denoising methods are crucial for enhancing images captured on handheld devices, but they often struggle with large motion or suffer from prohibitive computational costs. In this paper, we propose DenoiseGS, the first framework to leverage the efficiency of 3D Gaussian Splatting for burst denoising. Our approach addresses two key challenges when applyi

Junho Kim, Seongwon Lee

Generating a coherent 3D scene representation from multi-view images is a fundamental yet challenging task. Existing methods often struggle with multi-view fusion, leading to fragmented 3D representations and sub-optimal performance. To address this, we introduce VG3T, a novel multi-view feed-forward network that predicts a 3D semantic occupancy via a 3D Gau

Hongjun Wang, Yitong Jiang, Collin McCarthy, David Wehr

Efficient vision transformer remains a bottleneck for high-resolution images and long-video related real-world applications. Generalized Spatial Propagation Network (GSPN) addresses this by replacing quadratic self-attention with a line-scan propagation scheme, bringing the cost close to linear in the number of rows or columns, while retaining accuracy. Desp

Ethan Ji, Yuanzhou Chen, Arush Ramteke, Fang Sun

Partial differential equations (PDEs) are central to dynamical systems modeling, particularly in hydrodynamics, where traditional solvers often struggle with nonlinearity and computational cost. Lagrangian neural surrogates such as GNS and SEGNN have emerged as strong alternatives by learning from particle-based simulations. However, these models typically o

Jinzhe Tan, Karim Benyekhlef

Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet provides abundant legal information and services, navigating complex websites, understanding legal terminology, and filling out procedural forms continue to pose barriers to accessing

Kushagra Pandya, Heli Hathi, Het Buch, Ravikumar R N

This research work dives into an in-depth evaluation of the YOLOv8 (You Only Look Once) algorithm's efficiency in object detection, specially focusing on Barcode and QR code recognition. Utilizing the real-time detection abilities of YOLOv8, we performed a study aimed at enhancing its talent in swiftly and correctly figuring out objects. Through large traini

Minyoung Kim, Paul Hongsuck Seo

The rapid growth of Artificial Intelligence-Generated Content (AIGC) raises concerns about the authenticity of digital media. In this context, image self-recovery, reconstructing original content from its manipulated version, offers a practical solution for understanding the attacker's intent and restoring trustworthy data. However, existing methods often fa

Yuhao Xu, Xiaoda Wang, Jiaying Lu, Sirui Ding

Electrocardiogram (ECG) analysis plays a vital role in the early detection, monitoring, and management of various cardiovascular conditions. While existing models have achieved notable success in ECG interpretation, they fail to leverage the interrelated nature of various cardiac abnormalities. Conversely, developing a specific model capable of extracting al

Chuan Wang, Xi-le Zhao, Zhilong Han, Liang Li

Matrix operations (e.g., inversion and singular value decomposition (SVD)) are fundamental in science and engineering. In many emerging real-world applications (such as wireless communication and signal processing), these operations must be performed repeatedly over matrices with parameters varying continuously. However, conventional methods tackle each matr

Onur Salan, Burak Çırağ, Onur Sever, İbrahim Hökelek

Implications of the advancements in the area of artificial intelligence to the wireless communications is extremely significant, especially in terms of resource management. In this paper, a Retrieval-Augmented Generation (RAG)-empowered Large Language Model (ReLLM)-driven dynamic radio resource management framework for Open Radio Access Network (O-RAN) inspi

Jian Fang, Yifei Li, Yijun Lou, Jian Wang

The hexagonal structure is ubiquitous in nature. The propagation phenomena occurring in a media with a hexagonal structure remain to be explored. One way of exploring this question is to formulate lattice dynamical systems and analyze the propagation dynamics. In this paper, we propose a lattice differential equation model featuring a discrete diffusion oper

Zhilong Zhao, Yindi Liu

Text-to-image AI models systematically encode geopolitical bias through visual representation. Drawing on Said's Orientalism and framing theory, we introduce Visual Orientalism - the dual standard whereby AI depicts Western nations through political-modern symbols while portraying Eastern nations through cultural-traditional symbols. Analyzing 396 AI-generat

Zhewen Hou, Jiajin Sun, Subashree Venkatasubramanian, Peter Jin

Machine learning (ML) has shown significant promise in studying complex geophysical dynamical systems, including turbulence and climate processes. Such systems often display sensitive dependence on initial conditions, reflected in positive Lyapunov exponents, where even small perturbations in short-term forecasts can lead to large deviations in long-term out

Byoung-moo Ann, Sang-Jun Choi, Hee Chul Park, Sercan Deve

Recent studies find that even drives far below the superconducting gap frequency may cause drive-induced quasiparticle generation (QPG) across Josephson junctions (JJs) of superconducting qubits (SCQs), posing a serious concern for fault-tolerant superconducting quantum computing (FTSQC). Nonetheless, quantitative experimental estimation on QPG rates has rem

Sebastian Padó, Kerstin Thomas

Emotions are a fundamental aspect of artistic expression. Due to their abstract nature, there is a broad spectrum of emotion realization in artworks. These are subject to historical change and their analysis requires expertise in art history. In this article, we investigate which aspects of emotional expression can be detected by current (2025) vision langua

Jiaxin Liu, Xiangyu Yan, Liang Peng, Lei Yang

Ensuring safety remains a key challenge for autonomous vehicles (AVs), especially in rare and complex scenarios. One critical but understudied aspect is the \textbf{potential risk} situations, where the risk is \textbf{not yet observable} but can be inferred from subtle precursors, such as anomalous behaviors or commonsense violations. Recognizing these prec

Tau Shean Lim, Shuoning Zhang

This paper studies a class of mixed mean-field jump processes on an abstract state space $\Pi$, together with their associated $N$-particle systems. The dynamics consist of the superposition of an independent Markovian component and a bounded mean-field jump interaction; in particular, piecewise deterministic Markov processes (PDMPs) with mean-field interact

Nan An, Weian Li, Qi Qi, Liang Zhang

In contemporary e-commerce platforms, search result pages display two types of items: ad items and organic items. Ad items are determined through an advertising auction system, while organic items are selected by a recommendation system. These systems have distinct optimization objectives, creating the challenge of effectively merging these two components. R

Anupama Ghorai, Jitraj Saha

In this article, we study the passage of limits from discrete to continuous condensing aggregation equation which comprises of Oort-Hulst-Safronov (OHS) equation together with inverse aggregation process. We establish the relation between discrete and continuous condensing aggregation equations in its most generalized form, where kinetic-kernels with respect

Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov, Jiong Lou

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the network. Existing defenses face a fundamental dilemma: lightweight single-auditor methods are prone to single points of failure, while robust committee-based approaches incur prohibitive computational costs in multi-

Neil R. Pichay, Sergiy S. Vasylyev, Audrey M. Liddle, Alexei V. Filippenko

We present spectroscopic and photometric observations of supernova (SN) 2021ukt, a peculiar short-plateau object that was originally identified as a Type IIn SN and later underwent an unprecedented transition to a Type Ib (possibly Type IIb) SN. The early-time light curves of SN 2021ukt exhibit a ~25 day plateau. Such a short phase of hydrogen recombination

Matt Wicks

The title equations were originally solved by making use of certain results on hypergeometric functions. Aside from these results, the classifications of the solutions uses very elementary arithmetic. The goal of this is to show that these solutions hold in a weak fragment of arithmetic; one strong enough to express the notions of even and odd that has been

Haoxiang Zhang, Ruihao Yuan, Lihui Zhang, Yushi Luo

The industrial adoption of Artificial Intelligence for Engineering (AI4E) faces two fundamental bottlenecks: scarce high-quality data and the lack of interpretability in black-box models-particularly critical in safety-sensitive sectors like aerospace. We present an explainable, few-shot AI4E framework that is systematically informed by physics and expert kn

Hengyuan Liu, Xia Song, Yong Liu, Zheng Li

Software debugging is a critical and time-consuming aspect of software development, with fault localization being a fundamental step that significantly impacts debugging efficiency. Mutation-Based Fault Localization (MBFL) has gained prominence due to its robust theoretical foundations and fine-grained analysis capabilities. However, recent studies have iden

Zheng Chang, Siqi Zhang, Wenqiang Huang, Tian Tian

Cryogenic systems necessitate extensive data transmission between room-temperature and cryogenic environments, as well as within the cryogenic temperature domain. High-speed, low-power data transmission is pivotal to enabling the deployment of larger-scale cryogenic systems, including the scalable quantum computing systems and the high-performance cryogenic

Yuhao Xu, Jiaying Lu, Sirui Ding, Defu Cao

In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collect heart activities, is used to diagnose cardiac conditions. Analyzing the ECG typically requires domain expertise, which is a roadblock to applying artificial intelligence (AI) for

Kai-Yuan Guo, Yan-Wu Wang, Xiao-Kang Liu, Zhi-Wei Liu

This paper addresses the distributed Nash Equilibrium seeking problem for aggregative games, where legitimate players' decisions are affected by potential malicious players. To describe players' behavior, we introduce a novel heterogeneous trustworthiness probabilistic framework by employing stochastic trust observations. To mitigate the waste of communicati

Nan An, Weian Li, Qi Qi, Changyuan Yu

Accurate attribution for multiple platforms is critical for evaluating performance-based advertising. However, existing attribution methods rely heavily on the heuristic methods, e.g., Last-Click Mechanism (LCM) which always allocates the attribution to the platform with the latest report, lacking theoretical guarantees for attribution accuracy. In this work

Mohammad Farajzadeh-Tehrani, Mohan Swaminathan

Let $X$ be a smooth projective variety over $\mathbb{C}$ with a simple normal crossings divisor $D\subset X$. We compare the notions of stable log maps to $(X,D)$ in algebraic geometry and symplectic topology. In particular, we prove an equivalence between fine (basic) algebraic log maps and symplectic log maps, and we define the symplectic analogue of fine

Nachuan Xiao, Shiwei Wang, Tianyun Tang, Kim-Chuan Toh

In this paper, we consider the feasibility problem, which aims to find a feasible point for the constraint set $\{x \in \mathbb{R}^n: c(x) = 0\}$ over a possibly non-regular subset $\mathcal{X} \subset \mathbb{R}^n$. Under the constraint nondegeneracy condition, we propose a modified alternating projection method. In our proposed method, based on the concept

Yu Jiang, Vasyl Tyberkevych, Yizhong Huang, Zixin Yan

Magnonic frequency combs (MFCs) offer a promising route to compact, energy-efficient platforms for on-chip coherent microwave signal generation and processing. Conventional on-chip comb generation typically relies on nonlinear resonators supporting a series of equidistant, low-loss resonances driven by a strong monochromatic signal, resulting in fixed comb s

Kei Kimura

This paper investigates the reconfiguration variant of the Constraint Satisfaction Problem (CSP), referred to as the Reconfiguration CSP (RCSP). Given a CSP instance and two of its solutions, RCSP asks whether one solution can be transformed into the other via a sequence of intermediate solutions, each differing by the assignment of a single variable. RCSP h

Yue-Gang Li, Ze Zheng, Jun-jie Wang, Ming He

Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To

Toranosuke Kokai, Akira Suzuki, Takahiro Suzuki, Yuma Tamura

In the context of algorithm theory, various studies have been conducted on spanning trees with desirable properties. In this paper, we consider the \textsc{Minimum Cover Spanning Tree} problem (MCST for short). Given a graph $G$ and a positive integer $k$, the problem determines whether $G$ has a spanning tree with a vertex cover of size at most $k$. We reve

Yaqi Wang, Zhi Li, Chengyu Wu, Jun Liu

Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We orga

Congjia Chen, Shen Yan, Yufu Qu

Point cloud registration is a fundamental task in 3D vision. Most existing methods only use geometric information for registration. Recently proposed RGB-D registration methods primarily focus on feature fusion or improving feature learning, which limits their ability to exploit image information and hinders their practical applicability. In this paper, we p

Sepideh Houshiar, Atila Poro, Abbas Abedini, Eduardo Fernández Lajús

We present a comprehensive photometric light curve and orbital period analysis of the W UMa-type contact binary EM Tuc. The O-C analysis constructed from all available eclipse timings exhibits a clear upward parabolic trend, indicating a continuous increase in the orbital period at a rate of \(dP/dt = (1.401 \pm 0.042)\times10^{-7}\,\mathrm{d\,yr^{-1}}\). Th

YuEun Lee, Jung Uk Kim

Video moment retrieval (MR) and highlight detection (HD) with natural language queries aim to localize relevant moments and key highlights in a video clips. However, existing methods overlook the importance of individual words, treating the entire text query and video clips as a black-box, which hinders contextual understanding. In this paper, we propose a n

Anh Nguyen, Stefan Lee

To interact effectively with humans in the real world, it is important for agents to understand language that describes the dynamics of the environment--that is, how the environment behaves--rather than just task instructions specifying "what to do". Understanding this dynamics-descriptive language is important for human-agent interaction and agent behavior.

Kyu Ri Park, Jiyoung Park, Seong Tae Kim, Hong Joo Lee

Change captioning aims to describe changes between a pair of images. However, existing works rely on visual features alone, which often fail to capture subtle but meaningful changes because they lack the ability to represent explicitly structured information such as object relationships and compositional semantics. To alleviate this, we present CORTEX (COmpo

Xu Shi, Haohan Wang, Yashuai Cao, Hengyu Zhang

Channel knowledge map (CKM) emerges as a promising framework to acquire location-specific channel information without consuming wireless resources, creating new horizons for advanced wireless network design and optimization. Despite its potential, the practical application of CKM in beam training faces several challenges. On one hand, the user's precise loca

Karthik Ravishankar

We study Ahmad pairs in the $\Sigma^0_2$ enumeration degrees. $(A,B)$ is an Ahmad pair if $A \not \leq_e B$ and every $Z <_e A$ satisfies $Z \leq_e B$. We characterize the degrees that are the left halves of an Ahmad pair as those that are $\lowww$ and join irreducible. We then show that the right half has to be $\highh$ giving a natural separation between t

Shuolin Zhang, Zhaonan Luo, Zhaoyang Yin

In this paper, we study the well-posedness of Fractional Rough Burgers equation driven by space-time noise in $H^s(\mathbb T)$ space. For the higher dissipation $\gamma\in(\frac{4}{3},2]$, we establish local well-posedness. Global well-posedness is further obtained when $\gamma$ is restricted to the interval $(\frac{5}{3}, 2]$. For the lower dissipation $\ga

Cong Liu, Xin Deng, R. E. Cohen

Understanding the structure and dynamics of Earth's inner core is essential for constraining its composition, thermal evolution, and seismic properties. Silicon is a probable major component of Earth's core. Using first-principles molecular dynamics and thermodynamic modeling, we investigate the structural, elastic, and transport properties of Fe-Si alloys a

Yangsen Ye, Jue Nan, Dong Chen, Torsten V. Zache

Characterizing quantum phases-of-matter at finite-temperature is essential for understanding complex materials and large-scale thermodynamic phenomena. Here, we develop algorithmic protocols for simulating quantum thermodynamics on quantum hardware through quantum kernel function expansion (QKFE), producing the free energy as an analytic function of temperat

Weiran Li, Yeqiang Liu, Yijie Wei, Mina Han

Multimodal Prompt Learning (MPL) has emerged as a pivotal technique for adapting large-scale Visual Language Models (VLMs). However, current MPL methods are fundamentally limited by their optimization of a single, static point representation. This paradigm is inherently brittle, leads to overfitting on base classes, and generalizes poorly to novel or ambiguo

Weiran Li, Yeqiang Liu, Yijie Wei, Mina Han

Multi-object tracking (MOT) is a fundamental task in computer vision with critical applications in autonomous driving and robotics. Multimodal MOT that integrates visible light and thermal infrared information is particularly essential for robust autonomous driving systems. However, effectively fusing these heterogeneous modalities is challenging. Simple str

Yang-Shuo Hsiung, Pisin Chen

Accurate prediction of particle creation from accelerating mirrors is crucial for interpreting forthcoming analog Hawking radiation experiments such as AnaBHEL. However, realistic experimental setups render the associated Bogoliubov integrals analytically intractable. To address this challenge, we introduce the Inertial Replacement Method (IRM), a hybrid ana

Hyukjin Kwon, Kazuyuki Sekizawa

Neutron stars can be regarded as natural laboratories that enable us to investigate nuclear matter properties under extreme conditions that are otherwise impossible to access in terrestrial experiments. Astrophysical observations of neutron stars provide invaluable information on existing nuclear interaction models and equations of state (EoSs) at various de

Sebastián Espinel-Ríos

Biotechnology can benefit from dynamic control to improve production efficiency. In this context, optogenetics enables modulation of gene expression using light as an external input, allowing fine-tuning of protein levels to unlock dynamic metabolic control and regulation of cell growth. Optogenetic systems can be actuated by light intensity. However, relyin

Kailin Lyu, Jianwei He, Long Xiao, Jianing Zeng

In open-world scenarios, Generalized Category Discovery (GCD) requires identifying both known and novel categories within unlabeled data. However, existing methods often suffer from prototype confusion caused by shortcut learning, which undermines generalization and leads to forgetting of known classes. We propose ClearGCD, a framework designed to mitigate r

Kumar Tanmay, Kriti Aggarwal, Paul Pu Liang, Subhabrata Mukherjee

Large Reasoning Models (LRMs) achieve strong performance in mathematics, code generation, and task planning, but their reliance on long chains of verbose "thinking" tokens leads to high latency, redundancy, and incoherent reasoning paths. Inspired by the Language of Thought Hypothesis, which posits that human reasoning operates over a symbolic, compositional

Shreyas Jayant Grampurohit, Satish Mulleti, Ajit Rajwade

In parallel beam computed tomography (CT), an object is reconstructed from a series of projections taken at different angles. However, in some industrial and biomedical imaging applications, the projection geometry is unknown, completely or partially. In this paper, we present a technique for two-dimensional (2D) tomography in which both viewing angles and s

Fang Li

The deployment of Large Language Models (LLMs) on consumer edge devices is throttled by the "Memory Wall" -- the prohibitive bandwidth and energy cost of fetching gigabytes of model weights from DRAM for every token generated. Current architectures (GPUs, NPUs) treat model weights as mutable software data, incurring massive energy penalties to maintain gener

Gurusha Juneja, Deepak Nathani, William Yang Wang

Process Reward Models (PRMs) enhance reasoning ability of LLMs by providing step-level supervision. However, their widespread adoption is limited due to expensive manual step-level annotation and poor generalization of static training data to novel errors. We introduce Adversarially Trained PRMs (\texttt{APRM}), where a Generator ($G$) learns to produce reas

Saeid Rajabi, Chengmo Yang, Satwik Patnaik

Formal Verification (FV) relies on high-quality SystemVerilog Assertions (SVAs), but the manual writing process is slow and error-prone. Existing LLM-based approaches either generate assertions from scratch or ignore structural patterns in hardware designs and expert-crafted assertions. This paper presents STELLAR, the first framework that guides LLM-based S

Md. Muhtashim Shahrier, Nazmul Haque, Md Asif Raihan, Md. Hadiuzzaman

As cities around the world aim to improve walkability and safety, understanding the irregular and unpredictable nature of pedestrian behavior has become increasingly important. This study introduces a data-driven framework for modeling chaotic pedestrian movement using empirically observed trajectory data and supervised learning. Videos were recorded during

Moyu Liao

We develop a new framework for evaluating the total policy effect in regression discontinuity designs (RDD), incorporating both the direct effect of treatment on outcomes and the indirect effect arising from distortions in the running variable when treatment becomes available. Our identification strategy combines a conditional parallel trend assumption to re

Konstantin Stracke, Connor W. Edwards, Jack D. Evans

We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simulations. Accuracy is evaluated across 13 diverse materials (nine metal-organic frameworks and four inorganic compounds), computing bulk modulus, thermal expansion, and thermal decom

Jingheng Ye, Shen Wang, Jiaqi Chen, Hebin Wang

Large Language Models (LLMs) have transformed artificial intelligence, offering profound opportunities for educational applications. However, their ability to provide fine-grained educational feedback for K-12 English writing remains underexplored. In this paper, we challenge the error analysis and pedagogical skills of LLMs by introducing the problem of Fin

Foivos Chnaras, Noah Kupinsky

For an odd prime $p$, we say a polynomial $f\in \mathbb F_p[X]$ computes square roots if $f(a)^2=a$ for all nonzero, perfect squares $a\in \mathbb F_p$. When $p\equiv 3 \mod 4$, it is easy to see that $f(X)=X^{\frac{p+1}{4}}$ is the smallest such polynomial. For $p\equiv 1 \mod 4$, the situation is less clear. Tonelli-Shanks offers an algorithm for construct

Shashwat Jaiswal, Shrikara Arun, Anjaly Parayil, Ankur Mallick

Low-Rank Adaptation (LoRA) has become the de facto method for parameter-efficient fine-tuning of large language models (LLMs), enabling rapid adaptation to diverse domains. In production, LoRA-based models are served at scale, creating multi-tenant environments with hundreds of adapters sharing a base model. However, state-of-the-art serving systems co-batch

Mingyan Li, Ewald Weber, David E. J. Waddington, Shanshan Shan

Magnetic resonance imaging-guided linear accelerators (MRI-Linacs) are an emerging treatment technology that enable online soft-tissue visualisation and adaptive radiotherapy. The Australian 1.0 T MRI-Linac employs a fixed, inline beamline, with treatment angles achieved via patient couch rotation, and currently relies on a radiolucent whole-body radiofreque

Etsuko Itou, Kei Iida, Kotaro Murakami, Daiki Suenaga

We investigated the phase structure and the equation of state (EoS) for dense two-color QCD at low temperatures using the lattice Monte Carlo simulations. A rich phase structure below the pseudo-critical temperature $T_c$ as a function of quark chemical potential has been revealed. In a high-density regime, we can see a superfluid phase, where the diquark co

Wooyeon Kim, Andreas Wieser, Pengyu Yang

We prove a local-global principle for representations of binary by quaternary quadratic forms. One of the main ingredients is a recent measure rigidity result of Einsiedler and Lindenstrauss for diagonalizable actions on quotients of products of $\mathrm{SL}_2$'s. Based on this, it suffices to show that limits of the uniform measures on the associated rank o

Marc M Nasser, Frédéric Poitevin, Kevin M Dalton

Serial crystallography experiments routinely produce thousands of diffraction patterns from crystals in random orientations. To turn this stream of images into a usable dataset, each pattern must be indexed before integration and merging can proceed. In practice, diffraction patterns may contain only a small number of reliable peaks, be contaminated by backg

Hodaka Mori, Shunsuke Tonogai, Yu Miyazaki, Akihide Hayashi

Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and rarity of chemical events. Conventional reactive force fields such as ReaxFF require system-specific parametrization, while universal machine learning interatomic potential

Shisir Shahriar Arif, Md. Muhtashim Shahrier, Nazmul Haque, Md Asif Raihan

Pedestrian safety remains a pressing concern in congested urban intersections, particularly in low- and middle-income countries where traffic is multimodal, and infrastructure often lacks formal control. Demographic factors like age and gender significantly influence pedestrian vulnerability, yet real-time monitoring systems rarely capture this information.

Chenyue Yu, Jianyu Yu

This paper addresses the challenges of storage and communication costs for large-scale datasets in resource-constrained edge devices by proposing a novel dataset quantization approach to reduce intra-sample redundancy. Unlike traditional dataset pruning and distillation methods that focus on inter-sample redundancy, the proposed method compresses each image

Yuyuan Li, Junjie Fang, Fengyuan Yu, Xichun Sheng

Federated Recommender Systems (FedRecs) leverage federated learning to protect user privacy by retaining data locally. However, user embeddings in FedRecs often encode sensitive attribute information, rendering them vulnerable to attribute inference attacks. Attribute unlearning has emerged as a promising approach to mitigate this issue. In this paper, we fo

Lu Li, Hechao Liu, Hongbo Hua, Zenan Du

A spanning subgraph $F$ of a graph $G$ is defined as an even factor of $G$, if the degree $d_F(v)=2k, k\in\mathbb{N}^+$ for every vertex $v\in V(G)$. This note establishes a sufficient condition to ensure that a connected graph $G$ of even order with the minimum degree $\delta$ contains an even factor based on the signless Laplacian spectral radius.

L. Theerthagiri

We provide an operational reinterpretation of wave-particle complementarity in the low-gain Zou-Wang-Mandel (ZWM) induced-coherence interferometer. In the low gain limit, each photon pair is emitted by either one of two nonlinear crystals. Preparing nonorthogonal conditional idler states that encode which-crystal information. While previous studies inferred

Jungwoo Seo, David Keetae Park, Shinjae Yoo, Jiook Cha

Generating whole-brain 4D fMRI sequences conditioned on cognitive tasks remains challenging due to the high-dimensional, heterogeneous BOLD dynamics across subjects/acquisitions and the lack of neuroscience-grounded validation. We introduce the first diffusion transformer for voxelwise 4D fMRI conditional generation, combining 3D VQ-GAN latent compression wi

Yue Ma, Oksana A. Chkrebtii, Stephen R. Niezgoda

Boundary constraints in physical, environmental and engineering models restrict smooth states such as temperature to follow known physical laws at the edges of their spatio-temporal domains. Examples include fixed-state or fixed-derivative (insulated) boundary conditions, and constraints that relate the state and the derivatives, such as in models of heat tr

Yuanyuan Bao, Zhongtao Wu

We propose a definition of the rotation number for transverse graph diagrams, extending the classical notion of the rotation number for plane curves. Using this, we introduce a normalized multi-variable Alexander polynomial for framed, oriented transverse graphs without sinks or sources, embedded in the 3-sphere $S^3$. We prove that our invariant coincides w

Bharat Sharman, Elkafi Hassini

Numerous graph neural network (GNN)-based algorithms have been proposed to solve graph-based combinatorial optimization problems (COPs), but methods to explain their predictions remain largely undeveloped. We introduce ARM-Explainer, a post-hoc, model-level explainer based on association rule mining, and demonstrate it on the predictions of the hybrid geomet

Wonjeong Ryu, Seungjun Yu, Seokha Moon, Hojun Choi

End-to-End (E2E) planning has become a powerful paradigm for autonomous driving, yet current systems remain fundamentally uncertainty-blind. They assume perception outputs are fully reliable, even in ambiguous or poorly observed scenes, leaving the planner without an explicit measure of uncertainty. To address this limitation, we propose a camera-only E2E fr

Xiu-Qi Chen, Rui-Zhi Zhang, Gang-Qin Liu, Huijie Zheng

Nitrogen vacancy (NV) centers in diamond have precipitated profound advances in microwave detection, manifesting themselves both in spatial resolution and sensitivity. However, typical methods based on Rabi oscillations are subject to detunings due to thermal and magnetic fluctuations and/or gradients, which introduce systematic errors and render the measure

Xurong Chen, Yunyun Fan, Shuangshi Fang, Zhao-Qing Feng

The High-Intensity Heavy-Ion Accelerator Facility (HIAF), currently under construction in Huizhou, Guangdong Province, China, is projected to be completed by 2025. This facility will be capable of producing proton and heavy-ion beams with energies reaching several GeV, thereby offering a versatile platform for advanced fundamental physics research. Key scien

Fengyi Fang, Sicheng Yang, Wenming Yang

Co-speech gesture generation has significantly advanced human-computer interaction, yet speaker movements remain constrained due to the omission of text-driven non-spontaneous gestures (e.g., bowing while talking). Existing methods face two key challenges: 1) the semantic prior gap due to the lack of descriptive text annotations in gesture datasets, and 2) t

Jiacheng Li, Songhe Feng

Test-time adaptation (TTA) enables online model adaptation using only unlabeled test data, aiming to bridge the gap between source and target distributions. However, in multimodal scenarios, varying degrees of distribution shift across different modalities give rise to a complex coupling effect of unimodal shallow feature shift and cross-modal high-level sem

Ratun Rahman, Dinh C. Nguyen

Variational Quantum Algorithms (VQAs) are becoming the primary computational primitive for next-generation quantum computers, particularly those embedded as resource-constrained accelerators in the emerging Quantum Internet of Things (QIoT). However, under such device-constrained execution conditions, the scalability of learning is severely limited by barren

Sacchin Sundar, Atman Kikani, Aaliya Alam, Sumukh Shrote

Underwater visual localization remains challenging due to wavelength-dependent attenuation, poor texture, and non-Gaussian sensor noise. We introduce MARVO, a physics-aware, learning-integrated odometry framework that fuses underwater image formation modeling, differentiable matching, and reinforcement-learning optimization. At the front-end, we extend trans

Kaizheng Zhang, Zuolin Jin, Zhihang Cheng, Ming Zeng

Based on the provided LaTeX code, here is the metadata for the submission form: Title: TokCom-UEP: Semantic Importance-Matched Unequal Error Protection for Resilient Image Transmission Author(s): Kaizheng Zhang, Zuolin Jin, Zhihang Cheng, Ming Zeng, Li Qiao, Zesong Fei Abstract: Token communication (TokCom), an emerging semantic communication framework power

Jiaye Wu, Saeed Hadadan, Geng Lin, Peihan Tu

Inverse rendering of indoor scenes remains challenging due to the ambiguity between reflectance and lighting, exacerbated by inter-reflections among multiple objects. While natural illumination-based methods struggle to resolve this ambiguity, co-located light-camera setups offer better disentanglement as lighting can be easily calibrated via Structure-from-

Soong Kyum Lee

While Kronecker coefficients $g(\lambda,\mu,\nu)$ with bounded rows are polynomial-time computable via lattice-point methods, no explicit closed-form formulas have been obtained for genuinely three-row cases in the 87 years since Murnaghan's foundational work. This paper provides such formulas for the first time and identifies a universal structural boundary

Zhongming Feng, Qiling Gao, Zeping Sui, Yun Lin

This letter proposes a two-stage distributionally robust optimization (DRO) framework for secure deployment and beamforming in an aerial reconfigurable intelligent surface (A-RIS) assisted millimeter-wave system. To account for multi-timescale uncertainties arising from user mobility, imperfect channel state information (CSI), and hardware impairments, our a

Finn G. Vamosi, Nils D. Forkert

When people reason about cause and effect, they often consider many competing "what if" scenarios before deciding which explanation fits best. Analogously, advanced language models capable of causal inference can consider multiple interventions and counterfactuals to judge the validity of causal claims. Crucially, this type of reasoning is less like a single

Jiawen Wei, Lan Jiang, Pengbo Wei, Ziwen Ye

Time series data is ubiquitous, with forecasting applications spanning from finance to healthcare. Beyond popular deterministic methods, generative models are gaining attention due to advancements in areas like image synthesis and video generation, as well as their inherent ability to provide probabilistic predictions. However, existing generative approaches

Kevin P. Pala, Surkhab Kaur Virk, Dekrayat Almaalol, Isabella Danhoni

We present the NuclearConfectionery, a modular framework for simulating the full dynamical evolution of relativistic heavy-ion collisions. Its core hydrodynamic module, CCAKE 2.0, represents a major advance over previous SPH-based relativistic hydrodynamic codes. CCAKE 2.0 simultaneously evolves energy-momentum and multiple conserved charges (B, S, Q) with a

Keliang Liu, Zizhi Chen, Mingcheng Li, Jingqun Tang

Document understanding is a long standing practical task. Vision Language Models (VLMs) have gradually become a primary approach in this domain, demonstrating effective performance on single page tasks. However, their effectiveness diminishes when handling long documents. In such scenarios, clues are often scattered across multiple pages and modalities, and

Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu, Juan Pablo Muñoz

Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequence processing advantages. A comprehensive understanding of selective SSMs in runtime behavior, resource utilization patterns, and scaling characteristics still remains unexplored,

Martina Del Gaudio, Federico Ghimenti, Surya Ganguli

We analyze continuous Hopfield associative memories augmented by additional, rapid short-term associative synaptic plasticity. Through the cavity method, we determine the boundary between the retrieval and forgetting, or spin-glass phase, of the network as a function of the fraction of stored memories and the neuronal gain. We find that short-term synaptic p

Yuying Zhang, Na Fan, Haowen Zheng, Junning Liang

Uncrewed aerial vehicles (UAVs) performing tasks such as transportation and aerial photography are vulnerable to intentional projectile attacks from humans. Dodging such a sudden and fast projectile poses a significant challenge for UAVs, requiring ultra-low latency responses and agile maneuvers. Drawing inspiration from baseball, in which pitchers' body mov

Andrey O. Leonov, Takayuki Shigenaga

Topological particle-like excitations such as skyrmions and hopfions offer rich opportunities for spintronic and photonic applications. While skyrmions have been extensively studied, the stabilization mechanisms and phase behavior of three-dimensional hopfions remain largely unexplored. Here, we investigate the formation, stability, and interactions of hopfi

Mingxu Zhang, Dazhong Shen, Ying Sun

Advances in large language models (LLMs) are accelerating discovery in molecular science. However, adapting molecular information to the serialized, token-based processing of LLMs remains a key challenge. Compared to other representations, molecular graphs explicitly encode atomic connectivity and local topological environments, which are key determinants of