October 2025 arXiv papers — page 196

Niharika Mathur, Tamara Zubatiy, Agata Rozga, Jodi Forlizzi

Designing Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults' preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults' requirements and perceptions of AI-generated expl

Mojtaba Mojtahedi, Borja Sierra Miranda

In this paper, we study a new Kripke-style semantics for classical modal logic, named as provability models. We study provability models for the propositional modal logics K, K4, S4 GL, GLP and the interpretability logic ILM. Provability models combine features of Kripke models with the assignment of logics to individual worlds. Originally introduced in [Moj

Qinhao Zhou, Xiang Xiang, Kun He, John E. Hopcroft

In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. Prompts for LLMs generally comprise two components: the \textit{instruction}, which defines the task or objective, and the \textit{input}, which is tailored to the instruction type.

Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang

Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world de

Dimitar Pashov, Casey Eichstaedt, Swagata Acharya, Mark van Schilfgaarde

SrCuO2 has long been considered a near-archetypal realization of a quasi one dimensional (1D) system of interacting electrons with short-range interactions. Within this framework, experimental observations - interpreted through the lens of the 1D Hubbard model-suggest that electron and hole excitations decay into two types of (unphysical) collective bosonic

Akira Ito, Takayuki Miura, Yosuke Todo

Deep Neural Networks (DNNs) have attracted significant attention, and their internal models are now considered valuable intellectual assets. Extracting such a model via oracle access to a DNN is conceptually similar to extracting a secret key from a block cipher. Consequently, cryptanalytic techniques, particularly differential-like attacks, have been active

Jing-Zong Zhang, Shuang Guo, Li-Lin Zhu, Lingxiao Wang

A central challenge in high-energy nuclear physics is to extract informative features from the high-dimensional final-state data of heavy-ion collisions (HIC) in order to enable reliable downstream analyses. Traditional approaches often rely on selected observables, which may miss subtle but physically relevant structures in the data. To address this, we int

Niharika Mathur, Tamara Zubatiy, Agata Rozga, Elizabeth Mynatt

Designing Conversational AI systems to support older adults requires more than usability and reliability, it also necessitates robustness in handling conversational breakdowns. In this study, we investigate how older adults navigate and repair such breakdowns while interacting with a voice-based AI system deployed in their homes for medication management. Th

Simon Plouffe

A calculation was performed to verify Proth-Gilbraith's conjecture for all prime numbers up to 0$^{14}$. The previous calculation was performed by Andrew Odlyzko in 1993 up to 0$^{13}$. This involves calculating the differences between consecutive primes in absolute value and starting over. The conjecture states that all lines except the first begin with 1.

Saumya B

Brain tumor segmentation is crucial for diagnosis and treatment planning, yet challenges such as class imbalance and limited model generalization continue to hinder progress. This work presents a reproducible evaluation of U-Net segmentation performance on brain tumor MRI using focal loss and basic data augmentation strategies. Experiments were conducted on

Jie Luo, Yuxuan Jiang, Xin Jin, Mingyu Liu

Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under complex conditions such as occlusion. Light field and LiDAR modalities provide complementary visual and spatial cues that are beneficial for robust perception; however, their effective integration is hindered by limite

Pallavi Katre, Bimalendu Mahapatra, Manaswita Karmakar, Sarang Jagdish

We report a regime transition in the coalescence of concentrated polymeric droplets in a pendant-pendant configuration. While Newtonian droplet coalescence has been extensively studied with distinct identification of viscous and inertial regimes, the presence of polymers introduces additional regimes governed by elasticity and molecular relaxation effects. T

Tomohiro Hayase, Benoît Collins, Ryo Karakida

Self-attention layers have become fundamental building blocks of modern deep neural networks, yet their theoretical understanding remains limited, particularly from the perspective of random matrix theory. In this work, we provide a rigorous analysis of the singular value spectrum of the attention matrix and establish the first Gaussian equivalence result fo

Kang An, Chenhao Si, Ming Yan, Shiqian Ma

Physics-Informed Neural Networks (PINNs) provide a powerful and general framework for solving Partial Differential Equations (PDEs) by embedding physical laws into loss functions. However, training PINNs is notoriously difficult due to the need to balance multiple loss terms, such as PDE residuals and boundary conditions, which often have conflicting objecti

Daoyuan Zhou, Xuchuang Wang, Lin Yang, Yang Gao

We study the stochastic Multiplayer Multi-Armed Bandit (MMAB) problem, where multiple players select arms to maximize their cumulative rewards. Collisions occur when two or more players select the same arm, resulting in no reward, and are observed by the players involved. We consider a distributed setting without central coordination, where each player can o

M. C. Diamantini, C. A. Trugenberger, V. M. Vinokur

The Berezinskii-Kosterlitz-Thouless (BKT) transition is the prototype of a phase transition driven by the formation and interaction of topological defects in two-dimensional (2D) systems. In typical models these are vortices: above a transition temperature $T_{\rm BKT}$ vortices are free, below this transition temperature they get confined. In this work we e

Leonardo Clarke, Alice E. Shapley, Natalie Lam, Michael W. Topping

We analyze JWST spectroscopic and HST+JWST photometric observations of 659 star-forming galaxies at $1.4<z<9$ from DR3 of the JADES survey and the AURORA Cycle 1 program. We measure the star-forming main sequence (SFMS) for galaxies above $10^{8.5}\rm\ M_\odot$ where the sample is largely representative, estimating star-formation rates (SFRs) using the H$α$

Zhipeng Liu, Peibo Duan, Xuan Tang, Baixin Li

Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences between textual and temporal modalities. In this paper, we develop a novel Transformer architecture designed for time series data, aiming to maximize its representational capacity. We

Brian Godwin Lim, Dominic Dayta, Benedict Ryan Tiu, Renzo Roel Tan

The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model h

Bin Xia, Bohao Peng, Yuechen Zhang, Junjia Huang

Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical user needs. Instruction-based editing relies solely on language instructions, which often fail to capture specific editing details, making reference images necessary. Meanwhile, s

Tianze Zhang, Yixuan Ma, Jun Wang

We present an integral equation-based method for the numerical solution of two-point boundary value systems. Special care is devoted to the mathematical formulation, namely the choice of the background Green's function that leads to a well-conditioned integral equation. We then make use of a high-order Nystrom discretization and a fast direct solver on the c

Yisha Wu, Cen Mia Zhao, Yuanpei Cao, Xiaoqing Su

We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' context-switching effort and redundant review. Our approach combines a fine-tuned Mixtral-8x7B model for continuous note generation with a DeBERTa-based classifier to filter tr

Juan Miguel Navarro Carranza

Benchmark scores for Large Language Models (LLMs) can be inflated by memorization of test items or near duplicates. We present a simple, protocol that probes generalization by re-evaluating models on paraphrased versions of benchmark questions. Using Mistral-7B-Instruct and Qwen2.5-7B-Instruct, we measure the accuracy gap between original and paraphrased ite

Maite Fernández-Unzueta, James Melbourne, Gerardo Palafox-Castillo

We investigate a convexity properties for normalized log moment generating function continuing a recent investigation of Chen of convex images of Gaussians. We show that any variable satisfying a ``Ehrhard-like'' property for its distribution function has a strictly convex normalized log moment generating function, unless the variable is Gaussian, in which c

Xu Bai, Muhammed Tawfiqul Islam, Rajkumar Buyya, Adel N. Toosi

Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled service

Cen Mia Zhao, Tiantian Zhang, Hanchen Su, Yufeng Wayne Zhang

We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adop

Yongxin Zhu, Jiawei Chen, Yuanzhe Chen, Zhuo Chen

We introduce Heptapod, an image autoregressive model that adheres to the foundational principles of language modeling. Heptapod employs \textbf{causal attention}, \textbf{eliminates reliance on CFG}, and \textbf{eschews the trend of semantic tokenizers}. Our key innovation is \textit{next 2D distribution prediction}: a causal Transformer with reconstruction-

Ahsan J. Cheema, Sunil Puria

Hidden hearing loss, or cochlear neural degeneration (CND), disrupts suprathreshold auditory coding without affecting clinical thresholds, making it difficult to diagnose. We present an information-theoretic framework to evaluate speech stimuli that maximally reveal CND by quantifying mutual information (MI) loss between inner hair cell (IHC) receptor potent

Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian

High-quality instruction data is critical for LLM alignment, yet existing open-source datasets often lack efficiency, requiring hundreds of thousands of examples to approach proprietary performance. In this work, we find that beyond the widely recognized importance of prompt-response quality, prompt difficulty itself plays a critical role in driving alignmen

Yuxi Liu, Yunfeng Ma, Yi Tang, Min Liu

Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability. Due to the diverse shapes and sizes of surface defects, SDD faces two main challenges: intraclass difference and interclass similarity. Existing methods primarily utilize manually designed models, which require extensive trial and error and often

Tian-Wen Cao, Zi-Qi Chen, Zi-Jian Li, Cheng Cheng

We present the properties of the nuclear star cluster (NSC) in the low surface brightness galaxy AGC 223218. The disk of the galaxy can be modeled using two S$\acute{\rm e}$rsic components with distinct central positions: one representing the inner bright disk and the other corresponding to the extended outer disk. We estimate the stellar masses of the NSC a

Tian-Wen Cao, Pei-Bin Chen, Zi-Jian Li, Cheng Cheng

We present integral field spectroscopy of ionized gas components in AGC 111629, an edge-on low surface brightness galaxy (LSBG) with a stellar mass of 5.7$\times$10$^{8}$ M$_{\odot}$. AGC 111629 displays an irregular H$\alpha$ morphology and an arch-like structure in the extraplanar region, which is absent in continuous stellar image. The irregular H$\alpha$

Xu Duan, Dongmei Chen

Optimal transport (OT) and Schr{\"o}dinger bridge (SB) problems have emerged as powerful frameworks for transferring probability distributions with minimal cost. However, existing approaches typically focus on endpoint matching while neglecting critical path-dependent properties -- particularly collision avoidance in multiagent systems -- which limits their

Hans Th. J. Steiger, Marco Beretta, Manuel Böhles, Alberto Garfagnini

One promising approach for future neutrinoless double beta decay ($0\nu\beta\beta$) searches is the incorporation of candidate isotopes into liquid scintillator detectors. In this work, a sample of the high-performance 1,2,4-trimethylbenzene-based liquid scintillator used in the Borexino experiment was loaded with different concentrations of Te-diol compound

Yunzhong Xiao, Yangmin Li, Hewei Wang, Yunlong Tang

Agents utilizing tools powered by large language models (LLMs) or vision-language models (VLMs) have demonstrated remarkable progress in diverse tasks across text and visual modalities. Unlike traditional tools such as calculators, which give deterministic outputs, neural tools perform uncertainly across task scenarios. While different tools for a task may e

Qiuyang Mang, Runyuan He, Suyang Zhong, Xiaoxuan Liu

Since 2020, automated testing for Database Management Systems (DBMSs) has flourished, uncovering hundreds of bugs in widely-used systems. A cornerstone of these techniques is test oracle, which typically implements a mechanism to generate equivalent query pairs, thereby identifying bugs by checking the consistency between their results. However, while applyi

Penghao Yu, Haotian Jiang, Zeyu Bao, Ruoxi Yu

Transformer has become the dominant architecture for sequence modeling, yet a detailed understanding of how its structural parameters influence expressive power remains limited. In this work, we study the approximation properties of transformers, with particular emphasis on the role of the number of attention heads. Our analysis begins with the introduction

Hamidreza Montazeri Hedesh, Milad Siami

We present a risk-aware safety certification method for autonomous, learning enabled control systems. Focusing on two realistic risks, state/input delays and interval matrix uncertainty, we model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-independent certificates that guarantee local e

Weiguo Lu, Gangnan Yuan, Hong-kun Zhang, Shangyang Li

Neural networks in general, from MLPs and CNNs to attention-based Transformers, are constructed from layers of linear combinations followed by nonlinear operations such as ReLU, Sigmoid, or Softmax. Despite their strength, these conventional designs are often limited in introducing non-linearity by the choice of activation functions. In this work, we introdu

Jiaman He, Zikang Leng, Dana McKay, Damiano Spina

Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs --

Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir

This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted developm

Young-Pil Choi, Sihyun Song

We establish the global existence of weak solutions to a nonlinear kinetic Fokker--Planck equation with degenerate diffusion, under either inflow or partial absorption-reflection boundary conditions. The novelty of our approach lies in constructing solutions under solely the physical assumptions on the initial and boundary data, namely finite mass, kinetic e

Duncan Stothers, Sophia Xu, Carlie Reeves, Lia Gracey

Accurate estimation of the body surface area (BSA) involved by a rash, such as psoriasis, is critical for assessing rash severity, selecting an initial treatment regimen, and following clinical treatment response. Attempts at segmentation of inflammatory skin disease such as psoriasis perform markedly worse on darker skin tones, potentially impeding equitabl

Yan Yang, Zhendong Li, Jianwei Zhao, Qingqing Wu

Multi-static cooperative sensing emerges as a promising technology for advancing integrated sensing and communication (ISAC), enhancing sensing accuracy and range. In this paper, we develop a unified design framework for joint active and passive sensing (JAPS). In particular, we consider a JAPSbased cooperative multi-static ISAC system for coexisting downlin

Paulo Akira F. Enabe, Rodrigo Provasi

This paper presents a mass-lumped Virtual Element Method (VEM) with explicit Strong Stability-Preserving Runge--Kutta (SSP-RK) time integration for two-dimensional parabolic problems on general polygonal meshes. A diagonal mass matrix is constructed via row-sum operations combined with flooring to ensure uniform positivity. Stabilization terms vanish identic

Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen

Traditional reinforcement learning from human feedback (RLHF) for large language models (LLMs) relies on expensive human-annotated datasets, while Reinforcement Learning from AI Feedback (RLAIF) also incurs significant costs, requiring the collection of diverse prompts and corresponding responses, often necessitating external reward models or proprietary mod

José Frías, José Carlos Gómez-Larrañaga, José Luis León-Medina, Fabiola Manjarrez-Gutiérrez

We propose a way to derive polynomial invariants of closed, orientable $3$-manifolds from Heegaard diagrams via cellularly embedded graphs. Given a Heegaard diagram of an irreducible $3$-manifold $M$, we associate a Heegaard graph $G\subset S$ on the Heegaard surface and restrict to those arising from minimal-genus splittings with a minimal number of vertice

Jane Breen, Mark Kempton, Adam Knudson, Matthew Shumway

We propose two possible definitions for a version of Kemeny's constant of a graph based on non-backtracking random walks (in place of the usual simple random walk). We show that these two definitions coincide for edge-transitive graphs, and give a condition generalizing edge-transitive for which equality holds, and investigate by how much they can differ in

Frank Wu, Mengye Ren

The Forward-Forward (FF) Algorithm is a recently proposed learning procedure for neural networks that employs two forward passes instead of the traditional forward and backward passes used in backpropagation. However, FF remains largely confined to supervised settings, leaving a gap at domains where learning signals can be yielded more naturally such as RL.

Liteng Yang, Yuliang Liu, Jing Liu, Hongxuan Li

Recently, progress has been made in the theory of turbulence, which provides a framework on how a deterministic process changes to a stochastic one owing to the change in thermodynamic states. It is well known that, in the framework of Newtonian mechanics, motions are dissipative; however, when subjected to periodic motion, a system can produce nondissipativ

Mansi Sakarvadia, Kareem Hegazy, Amin Totounferoush, Kyle Chard

A core challenge in scientific machine learning, and scientific computing more generally, is modeling continuous phenomena which (in practice) are represented discretely. Machine-learned operators (MLOs) have been introduced as a means to achieve this modeling goal, as this class of architecture can perform inference at arbitrary resolution. In this work, we

Zhiyuan Wei, Xiaoxuan Yang, Jing Sun, Zijian Zhang

The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and substantial economic loss. Consequently, robust code vulnerability detection is essential for software security. While Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language proce

Leshu Li, Jiayin Qin, Jie Peng, Zishen Wan

3D Gaussian Splatting (3DGS) based Simultaneous Localization and Mapping (SLAM) systems can largely benefit from 3DGS's state-of-the-art rendering efficiency and accuracy, but have not yet been adopted in resource-constrained edge devices due to insufficient speed. Addressing this, we identify notable redundancies across the SLAM pipeline for acceleration. W

Kh. M. Shadimetov, R. S. Karimov

This work presents problems of constructing finite-difference formulas in the Hilbert space, i.e., setting problems of constructing finite-difference formulas using functional methods. The work presents a functional statement of the problem of optimizing finite-difference formulas in the space $W_{2}^{\left(m,m-1\right)} \left(0,1\right)$. Here, representati

Xudong Li, Meixia Lin, Kim-Chuan Toh

In this work, we study the affine-constrained $\ell_1$ regularizers, which frequently arise in statistical and machine learning problems across a variety of applications, including microbiome compositional data analysis and sparse subspace clustering. With the aim of developing scalable second-order methods for solving optimization problems involving such re

He-Da Wang, Bo Wang, Qun-Li Lei, Yu-Qiang Ma

Jamming transition is traditionally regarded as a geometric transition governed by static contact networks. Recently, dynamic phase transitions of athermal particles under periodic shearing provide a new lens on this problem, leading to a conjecture that jamming transition corresponds to an absorbing-state transition within the Manna (conserved directed perc

Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Min-Yen Kan

State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing with linear scaling, yet how contextual information flows across layers in these architectures remains understudied. We present the first unified, token- and layer-wise analysis of representation propagation in SSMs and TB

Shafeeq Muhammed, Geeta Patkare, Rohan Phatak

Total of twelve samples in the U-La-O system with the compositions U1-yLayO2+x (y=0.025, 0.05, 0.1, up to 0.3) were synthesized by gel combustion synthesis method followed by appropriate heat treatment in air atmosphere. Comprehensive experimental analysis using various techniques like X-ray diffraction, thermogravimetry and oxygen to uranium ratio (O/U) are

Zhihao Wen, Wenkang Wei, Yuan Fang, Xingtong Yu

Knowledge-based Visual Question Answering (KVQA) requires models to ground entities in images and reason over factual knowledge. Recent work has introduced its implicit-knowledge variant, IK-KVQA, where a multimodal large language model (MLLM) is the sole knowledge source and answers are produced without external retrieval. Existing IK-KVQA approaches, howev

Prakhar Srivastava, Farrin Marouf Sofian, Francesco Immorlano, Kushagra Pandey

Despite advances in test-time scaling and diffusion finetuning, guidance for Auto-Regressive Diffusion Models (ARDMs) remains underexplored. We introduce an amortized framework that augments a pretrained ARDM with an offline-trained controller. By previewing future rollouts, the controller learns stepwise corrections that anticipate observations under a term

Zhi-Ang Chen, Hao-Ran Zeng, Wen-Wei Wang, Han Zhang

The realization of ultra-stable lasers with $10^{-17}$-level frequency stability has enabled a wide range of researches on precision metrology and fundamental science, where cryogenic single-crystalline cavities constitute the heart of such ultra-stable lasers. For further improvements in stability, increasing the cavity length at few-kelvin temperatures pro

Yunpeng Gong, Sihan Lan, Can Yang, Kunpeng Xu

Symbolic regression aims to find interpretable analytical expressions by searching over mathematical formula spaces to capture underlying system behavior, particularly in scientific modeling governed by physical laws. However, traditional methods lack mechanisms for extracting structured physical priors from time series observations, making it difficult to c

Shiye Su, Yuhui Zhang, Linqi Zhou, Rajesh Ranganath

Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While flow matching offers a natural framework for this task, its use has thus far primarily focused on the noise-to-data setting, while its application in the general distribution-to-di

Kruthika Gangaraju, Tanmayi Inaparthy, Jiaqi Yang, Yihao Zheng

People living with dementia (PLWDs) face progressively declining abilities in medication management-from simple forgetfulness to complete task breakdown-yet most assistive technologies fail to adapt to these changing needs. This one-size-fits-all approach undermines autonomy, accelerates dependence, and increases caregiver burden. Occupational therapy princi

Yasaman Torabi, Shahram Shirani, James P. Reilly

Non-Negative Matrix Factorization (NMF) is an unsupervised learning method offering low-rank representations across various domains such as audio processing, biomedical signal analysis, and image recognition. The incorporation of $\alpha$-divergence in NMF formulations enhances flexibility in optimization, yet extending these methods to multi-layer architect

Qiming Guo, Bishal Khatri, Hua Zhang, Wenlu Wang

Underground water and wastewater pipelines are vital for city operations but plagued by anomalies like leaks and infiltrations, causing substantial water loss, environmental damage, and high repair costs. Conventional manual inspections lack efficiency, while dense sensor deployments are prohibitively expensive. In recent years, artificial intelligence has a

Pallavi Saraf, Thirupathi Sivarani, Carlos Allende Prieto, Shashikiran Ganesh

The $r$-process enrichment in the Galaxy still remains elusive with regard to its nucleosynthesis conditions and the astrophysical sites where it occurs. As part of ongoing efforts to pinpoint the origin of chemically peculiar $r$-process-enhanced (RPE) stars, we concentrate in this study on the kinematics of RPE stars to investigate possible variations in t

Jiachen Li, Bang Wu, Xiaoyu Xia, Xiaoning Liu

Spiking Neural Networks (SNNs) have gained increasing attention for their superior energy efficiency compared to Artificial Neural Networks (ANNs). However, their security aspects, particularly under backdoor attacks, have received limited attention. Existing defense methods developed for ANNs perform poorly or can be easily bypassed in SNNs due to their eve

Yong Liu, Di Fu, Yang Luo, Zirui Zhu

We introduce Post-Optimization Model Edit (POME), a new algorithm that enhances the performance of fine-tuned large language models using only their pretrained and fine-tuned checkpoints, without requiring extra data or further optimization. The core idea is to apply a muon-style projection to $\Delta W$, the difference between the fine-tuned and pretrained

Yutong Zhou

The notion of $n$-exangulated categories was introduced by Herschend-Liu-Nakaoka, which is a simultaneous generalization of $n$-exact categories in the sense of Jasso and $(n+2)$-angulated categories in the sense of Geiss-Kelier-Oppermann. Let $(\mathscr{C},\mathbb{E},\mathfrak{s})$ be an $n$-exangulated category with enough projectives $\mathcal{P}$ and $\m

Murat Yasar Baskin

This study introduces Mean Averaging Smoothed Product (MASP) Spectrum, which is a modified version of the Harmonic Product Spectrum, designed to enhance pitch estimation for many algorithm-wise deceptive frequency spectra that still lead clear pitches, for both harmonic and inharmonic cases. By introducing a global mean based smoothing for spectrum, the MASP

Hayato Aizawa, Shinji Takeda, Yusuke Yoshimura

We investigate the phase structure of a two-dimensional lattice CP(1) model with a $\theta$ term. In particular, we aim to identify a critical region expected to exist along a $\theta=\pi$ line. To explore the phase structure non-perturbatively and avoid the sign problem, we employ the tensor renormalization group method. We make two improvements compared to

Canyu Lei, Benjamin Lobo, Jianxin Xie

Continuous glucose monitoring (CGM) provides dense and dynamic glucose profiles that enable reliable estimation of Ambulatory Glucose Profile (AGP) metrics, such as Time in Range (TIR), Time Below Range (TBR), and Time Above Range (TAR). However, the high cost and limited accessibility of CGM restrict its widespread adoption, particularly in low- and middle-

Ni Ding, Farhad Farokhi, Tao Guo, Yinfei Xu

For $\tilde{f}(t) = \exp(\frac{\alpha-1}{\alpha}t)$, this paper shows that the Sibson mutual information is an $\alpha$-leakage averaged over the adversary's $\tilde{f}$-mean relative information gain (on the secret) at elementary event of channel output $Y$ as well as the joint occurrence of elementary channel input $X$ and output $Y$. This interpretation i

Jiasong Chen, Linchen Qian, Ruonan Gong, Christina Sun

Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stre

Shijie Qin, Kun Xu, Shijun Liao

A chaotic system is called ultra-chaos when its statistics have sensitivity dependence on initial condition and/or other small disturbances. In this paper, using two-dimensional turbulent Kolmogorov flow as an example, we illustrate that tiny variation of initial condition of Navier-Stokes equations can lead to huge differences not only in spatiotemporal tra

Tao Feng, Tingfa Xu, Haolin Qin, Tianhao Li

Visual object tracking in real-world scenarios presents numerous challenges including occlusion, interference from similar objects and complex backgrounds-all of which limit the effectiveness of RGB-based trackers. Multispectral imagery, which captures pixel-level spectral reflectance, enhances target discriminability. However, the availability of multispect

Huaihai Lyu, Chaofan Chen, Senwei Xie, Pengwei Wang

Existing Vision-Language-Action (VLA) models can be broadly categorized into diffusion-based and auto-regressive (AR) approaches: diffusion models capture continuous action distributions but rely on computationally heavy iterative denoising. In contrast, AR models enable efficient optimization and flexible sequence construction, making them better suited for

Subhashri Chatterjee, Katsumasa Yoshioka, Taro Wakamura, Vasili Perebeinos

Directional photocurrents in two-dimensional materials arise from broken crystal symmetry, offering pathways to high-speed, bias-free photodetection beyond conventional devices. Tungsten ditelluride (WTe$_2$), a type-II Weyl semimetal, exhibits robust symmetry-breaking-induced edge photocurrents from competing nonlinear optical and photothermoelectric mechan

Wangda Zhu, Guang Chen, Yumeng Zhu, Lei Cai

Mathematical modelling (MM) is a key competency for solving complex real-world problems, yet many students struggle with abstraction, representation, and iterative reasoning. Artificial intelligence (AI) has been proposed as a support for higher-order thinking, but its role in MM education is still underexplored. This study examines the relationships among s

Jilei Xu, Miao He, Cédric Cerna, Yongbo Huang

Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel water

Kiwamu Fujiki, Shota Takahashi, Akiko Takeda

We propose a variant of the approximate Bregman proximal gradient (ABPG) algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function. ABPG is known to converge globally to a stationary point even when the smooth part of the objective function does not have a globally Lipschitz continuous gradient, and its iterates can ofte

Malu Sudha, Renee M. Ludlam, Jeroen Homan, Dacheng Lin

We performed the first simultaneous NICER & NuSTAR spectral and timing study of the Sco-like Z source GX 17+2. The source traced the full Z track during four observations. We detect signatures of relativistic reflection in the broadband spectra and report results using a reflection framework. The disk is relatively close to the innermost stable circular orbi

Kui Li, Mingxiang Li, Juncheng Wei

We study the Lane-Emden conjecture, which asserts the non-existence of non-trivial, non-negative solutions to the Lane-Emden system \[ -\Delta u = v^p, \quad -\Delta v = u^q, \quad x \in \mathbb{R}^n\] in the subcritical regime. By employing an Obata-type integral inequality, Picone's identity, and exploiting the scaling invariance of the system, we prove th

Aditya Chakravarty

Climate change is intensifying wildfire risks globally, making reliable forecasting critical for adaptation strategies. While machine learning shows promise for wildfire prediction from Earth observation data, current approaches lack uncertainty quantification essential for risk-aware decision making. We present the first systematic analysis of spatial uncer

Jingran Xu, Yuanyuan Liu, Yuanbiao Yang, Zhuo-Xu Cui

Magnetic resonance imaging (MRI) is a vital clinical diagnostic tool, yet its application is limited by prolonged scan times. Accelerating MRI reconstruction addresses this issue by reconstructing high-fidelity MR images from undersampled k-space measurements. In recent years, deep learning-based methods have demonstrated remarkable progress. However, most m

Yazhi Niu, Jialin Li, Lupei Qin, Xin-Qi Li

We apply the combined technique of postselected amplification and photon-recycling to an optical setup of magnetic field precision measurement. We propose two recycling schemes and carry out analytic expressions for the amplified signal and measurement sensitivity. The results show significant improvement of performance over conventional measurement. The und

Ronnie Cheng

Extending classical algebro-geometric constructions to arbitrary matroids, we construct a $K$-class $T_M\in K(M)$ for every loopless matroid $M$. When $M$ is realizable by a linear subspace $L$, $T_M$ recovers the $K$-class of the tangent bundle of the wonderful compactification $W_L$. We derive two formulas for the total Chern class of $T_M$ (one combinator

Benjamin Nuernberger, Samuel-Hunter Berndt, Robert Tapella, Laura Mann

ProtoSpace is a custom JPL-built platform to help scientists and engineers visualize their CAD models collaboratively in augmented reality (AR) and on the web in 3D. In addition to this main use case, ProtoSpace has been used throughout the entire spacecraft mission lifecycle and beyond: ventilator design and assembly; providing AR-based instructions to astr

Weidi Luo, Qiming Zhang, Tianyu Lu, Xiaogeng Liu

Command-line interface (CLI) agents powered by large language models (LLMs) can interpret natural-language requests, plan multi-step tasks, execute shell commands, and modify files and system state. As these agents are increasingly used for operating-system (OS) workflows, it is important to evaluate whether they can be misused to carry out security-relevant

Uswat Yusuf, Genevieve Caumartin, Diego Elias Costa

Context plays an important role in the quality of code completion, as Large Language Models (LLMs) require sufficient and relevant information to assist developers in code generation tasks. However, composing a relevant context for code completion poses challenges in large repositories: First, the limited context length of LLMs makes it impractical to includ

Shuo Shao, Yiming Li, Hongwei Yao, Yifei Chen

The substantial investment required to develop Large Language Models (LLMs) makes them valuable intellectual property, raising significant concerns about copyright protection. LLM fingerprinting has emerged as a key technique to address this, which aims to verify a model's origin by extracting an intrinsic, unique signature (a "fingerprint") and comparing it

Jie Xiong, Xiang Fan, Jing Jing, Weishan Yang

We investigate thermal photon production in the quark-gluon plasma (QGP) under strong magnetic fields using a magnetohydrodynamic (MHD) framework. Adopting the Bjorken flow model with power-law decaying magnetic fields $\mathbf{B}(\tau) = \mathbf{B}_0 (\tau_0/\tau)^a$ (where $a$ controls the decay rate, $B_0 = \sqrt{\sigma} T_0^2$, and $\sigma$ characterizes

Aditya Chakravarty

Climate change is increasingly disrupting agricultural systems, making accurate crop yield forecasting essential for food security. While deep learning models have shown promise in yield prediction using satellite and weather data, their ability to generalize across geographic regions and years - critical for real-world deployment - remains largely untested.

Andi Gu, Stephen P. Jordan

Decoded Quantum Interferometry (DQI) defines a duality that pairs decoding problems with optimization problems. The original work on DQI considered Reed-Solomon decoding, whose dual optimization problem, called Optimal Polynomial Intersection (OPI), is a polynomial regression problem over a finite field. Here, we consider a class of algebraic geometry codes

Feiran Li, Jiacheng Li, Marcos V. Conde, Beril Besbinar

We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is built upon a newly established evaluation benchmark featuring challenging low-light noisy images captured in the wild using five different DSLR cameras. Participants are tasked with

Zhaochun Ren, Zhou Yang, Chenglong Ye, Haizhou Sun

Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent methods use In-Context Learning (ICL), enhancing the representation of queries in the reasoning process through semantically similar examples, while further improving emotion recogn

Shisheng Lin, Shaoqi Huang, Minhui Yang, Xin Chen

Recent research on excitonic insulator has progressed mainly based on narrow bandgap semiconductor or semimetal. Herein, we realize excitonic insulator based on two-dimensional (2D) wide band gap diamond with transition temperature as high as 220K. The resistance rises dramatically by more than three orders, which can be explained by the Bose-Einstein conden

Shijie Gu, Jian Wang, Yanqing Zou

We study a family of genus-one contractible open manifolds constructed by iterated Whitehead doubling from a nontrivial knot $K$ and an even half-twist $m$. For each such pair, we prove that the resulting contractible open $3$-manifold $W(K,m)$ does not embed as an open subset of any compact, locally connected and locally $1$-connected metric $3$-space. We a

Xiaorui Li, Hui Liu, Wei Wang

There are two long-standing conjectures in Hamiltonian dynamics concerning Reeb flows on the boundaries of star-shaped domains in $\mathbb{R}^{2n}$ ($n \geq 2$). One conjecture states that such a Reeb flow possesses either $n$ or infinitely many prime closed orbits; the other states that all the closed Reeb orbits are irrationally elliptic when the domain is