October 2025 arXiv papers — page 36

Yu Luo, Jiamin Jiang, Jingfei Feng, Lei Tao

Incident management (IM) is central to the reliability of large-scale microservice systems. Yet manual IM, where on-call engineers examine metrics, logs, and traces is labor-intensive and error-prone in the face of massive and heterogeneous observability data. Existing automated IM approaches often struggle to generalize across systems, provide limited inter

Lei Sun, Elvar Örn Jónsson, Aleksei Ivanov, Ji Chen

The excited electronic states involved in the optical cycle preparation of a pure spin state of the negatively charged NV-defect in diamond are calculated using the HSE06 hybrid density functional and variational optimization of the orbitals. This includes the energy of the excited triplet as well as the two lowest singlet states with respect to the ground t

Joran Leest, Ilias Gerostathopoulos, Patricia Lago, Claudia Raibulet

Production machine learning (ML) systems fail silently -- not with crashes, but through wrong decisions. While observability is recognized as critical for ML operations, there is a lack empirical evidence of what practitioners actually capture. This study presents empirical results on ML observability in practice through seven focus group sessions in several

Miao Zhang, Shenao Wang, Guilin Zheng, Yanjie Zhao

Mini-programs, an emerging mobile application paradigm within super-apps, offer a seamless and installation-free experience. However, the adoption of the web-view component has disrupted their isolation mechanisms, exposing new attack surfaces and vulnerabilities. In this paper, we introduce a novel vulnerability called Cross Mini-program Cookie Sharing (CMC

Stefano Aleotti, Davide Bianchi, Florian Bossmann, Riley Yizhou Chen

We introduce variable projected augmented Lagrangian (VPAL) methods for solving generalized nonlinear Lasso problems with improved speed and accuracy. By eliminating the nonsmooth variable via soft-thresholding, VPAL transforms the problem into a smooth reduced formulation. For linear models, we develop a preconditioned variant that mimics Newton-type update

Chanwoo Park, Suyoung Park, Yelim Ahn, Jongmin Kim

While traditional line-level filtering techniques, such as line-level deduplication and trailing-punctuation filters, are commonly used, these basic methods can sometimes discard valuable content, negatively affecting downstream performance. In this paper, we introduce two methods-pattern-aware line-level deduplication (PLD) and pattern-aware trailing punctu

Wenan Zou

Exploring the general analytical solutions to the Euler equations for ideal fluids holds significant theoretical and practical importance. The steady flows in two-dimensional spaces are considered whether there is an analytical solution in the form of finite polynomials defined in the local region. By employing the tensorial representation and the complex va

Ovi Sarkar, Md Shafiuzzaman, Md. Faysal Ahamed, Golam Mahmud

Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality, and the role of prompt precise detection is of paramount interest in improving patient outcomes. Conventional diagnostic methods such as colonoscopy and histological examination routinely exhibit subjectivity, are extremely time-consuming, and are susceptible to variation. Thro

Hojin Cheon, Hyeongseok Seo, Jihun Jeon, Wooju Lee

The rapid expansion of electric vehicles has intensified the need for accurate and efficient diagnosis of lithium-ion batteries. Parameter identification of electrochemical battery models is widely recognized as a powerful method for battery health assessment. However, conventional metaheuristic approaches suffer from high computational cost and slow converg

Yang Du, Zhuoran Lin, Kaiqiang Song, Biao Wang

Recent advances in text-to-video (T2V) generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. However, strategies for optimizing video captions specifically for T2V training remain underexplored. In this paper, we introduce VC4VG (Video Captioning for Video Gene

Minsuk Ji, Sanghyeok Lee, Namhyuk Ahn

Despite their impressive realism, modern text-to-image models still struggle with compositionality, often failing to render accurate object counts, attributes, and spatial relations. To address this challenge, we present a training-free framework that combines an object-centric approach with self-refinement to improve layout faithfulness while preserving aes

Tuvi Etzion, Yuli Tan, Junling Zhou

This paper considers two closely related concepts, mixed Steiner system and nonuniform group divisible design (GDD). The distinction between the two concepts is the minimum Hamming distance, which is required for mixed Steiner systems but not required for nonuniform group divisible $t$-designs. In other words, it means that every mixed Steiner system is a no

Javier de Lucas, Marcin Zając

A stochastic Lie system on a manifold $M$ is a stochastic differential equation whose dynamics is described by a linear combination with functions depending on $\mathbb{R}^\ell$-valued semi-martigales of vector fields on $M$ spanning a finite-dimensional Lie algebra. We analyse new examples of stochastic Lie systems and Hamiltonian stochastic Lie systems, an

Myra B. McGuinness, Joanne E. McKenzie, Andrew Forbes, Flora Hui

It is recommended that measures of between-study effect heterogeneity be reported when conducting individual-participant data meta-analyses (IPD-MA). Methods exist to quantify inconsistency between trials via I^2 (the percentage of variation in the treatment effect due to between-study heterogeneity) when conducting two-stage IPD-MA, and when conducting one-

Jiajian Xie, Hubery Yin, Chen Li, Zhou Zhao

Diffusion models have achieved remarkable generative quality but remain bottlenecked by costly iterative sampling. Recent training-free methods accelerate diffusion process by reusing model outputs. However, these methods ignore denoising trends and lack error control for model-specific tolerance, leading to trajectory deviations under multi-step reuse and e

Yuchao Dong, Harry Zheng

This paper studies the time-inconsistent MV optimal stopping problem via a game-theoretic approach to find equilibrium strategies. To overcome the mathematical intractability of direct equilibrium analysis, we propose a vanishing regularization method: first, we introduce an entropy-based regularization term to the MV objective, modeling mixed-strategy stopp

Vivek Kalyan, Martin Andrews

Large Language Model (LLM) agents can leverage multiple turns and tools to solve complex tasks, with prompt-based approaches achieving strong performance. This work demonstrates that Reinforcement Learning (RL) can push capabilities significantly further by learning from experience. Through experiments on a legal document search benchmark, we show that our R

Kiran Bacsa, Wei Liu, Xudong Jian, Huangbin Liang

This study investigates the behavior of Causal Convolutional Neural Networks (CNNs) with quasi-linear activation functions when applied to time-series data characterized by multimodal frequency content. We demonstrate that, once trained, such networks exhibit properties analogous to Finite Impulse Response (FIR) filters, particularly when the convolutional k

Yotam Sherf, Bar Efrati, Gabriel Rozman, Moshe Harel

We present the Global Water Classifier (GWC), a supervised, geospatially extensive Machine Learning (ML) classifier trained on Sen2Cor corrected Sentinel-2 surface reflectance data. Using nearly 100 globally distributed inland water bodies, GWC distinguishes water across Chlorophyll-a (Chla) levels from non-water spectra (clouds, sun glint, snow, ice, aquati

Nao Kominato, Toshiki Sato, Yusuke Sakai, Ryota Hayakawa

We present the decadal brightening of non-thermal emission flux in the northeastern filament of the young supernova remnant Cassiopeia A (Cas A), which highlights dynamic processes in the forward shock. This filament, characterized by the highest particle acceleration rate among Cas A's outer shells, offers an exceptional opportunity to investigate underlyin

Cristina Arhiliuc, Raf Guns, Tim C. E. Engels

This study investigates how different approaches to disciplinary classification represent the Social Sciences and Humanities (SSH) in the Flemish VABB-SHW database. We compare organizational classification (based on author affiliation), channel-based cognitive classification (based on publication venues), and text-based publication-level classification (usin

Y. Guo, D. Wu, J. Zhang

Mushroom instability (MI) is a shear instability considered responsible for generating and amplifying magnetic fields in relativistic jets. While astrophysical jets are usually magnetized, how MI acts in magnetized jets remains poorly understood. In this paper, we investigate the effect of a flow-aligned external magnetic field on MI, with both theoretical a

Ziyu Liu, Yijing Liu, Jianfei Yuan, Minzhi Yan

Graph-based RAG constructs a knowledge graph (KG) from text chunks to enhance retrieval in Large Language Model (LLM)-based question answering. It is especially beneficial in domains such as biomedicine, law, and political science, where effective retrieval often involves multi-hop reasoning over proprietary documents. However, these methods demand numerous

Yuxuan Chen, Shengquan Xiang

We study large deviations from the invariant measure for nonlinear Schr\"odinger equations with colored noises on determining modes. The proof is based on a new abstract criterion, inspired by [V. Jak\v{s}i\'{c} et al., Comm. Pure Appl. Math., 68 (2015), 2108-2143]. To address the difficulty caused by fixed squeezing rate, we introduce a bootstrap argument t

Haotian Zhou, Xiaole Wang, He Li, Zhuo Qi

Navigating to a designated goal using visual information is a fundamental capability for intelligent robots. To address the practical demands of multi-modal, open-vocabulary goal queries and multi-goal visual navigation, we propose LagMemo, a navigation system that leverages a language 3D Gaussian Splatting memory. During a one-time exploration, LagMemo cons

Zan Wang, Siyu Chen, Luya Mo, Xinfeng Gao

We present DogMo, a large-scale multi-view RGB-D video dataset capturing diverse canine movements for the task of motion recovery from images. DogMo comprises 1.2k motion sequences collected from 10 unique dogs, offering rich variation in both motion and breed. It addresses key limitations of existing dog motion datasets, including the lack of multi-view and

Fengming Yu, Haiwei Pan, Kejia Zhang, Jian Guan

Knowledge distillation (KD) is an effective model compression technique that transfers knowledge from a high-performance teacher to a lightweight student, reducing computational and storage costs while maintaining competitive accuracy. However, most existing KD methods are tailored for homogeneous models and perform poorly in heterogeneous settings, particul

Sandeep Vissapragada, Vikrant Sahu, Gagan Raj Gupta, Vandita Singh

For doctors to truly trust artificial intelligence, it can't be a black box. They need to understand its reasoning, almost as if they were consulting a colleague. We created HistoLens1 to be that transparent, collaborative partner. It allows a pathologist to simply ask a question in plain English about a tissue slide--just as they would ask a trainee. Our sy

Arnav Shukla, Harsh Sharma, Srikant Bharadwaj, Vinayak Abrol

Heterogeneous chiplet-based systems improve scaling by disag-gregating CPUs/GPUs and emerging technologies (HBM/DRAM).However this on-package disaggregation introduces a latency inNetwork-on-Interposer(NoI). We observe that in modern large-modelinference, parameters and activations routinely move backand forth from HBM/DRAM, injecting large, bursty flows int

Disha Sheshanarayana, Tanishka Magar

Reasoning methods such as chain-of-thought prompting and self-consistency have shown immense potential to improve the accuracy of large language models across various reasoning tasks. However such methods involve generation of lengthy reasoning chains, which substantially increases token consumption, computational cost, and latency. To address this inefficie

Mathias Dufresne-Piché, Siva Nadarajah

The energy stable flux reconstruction (ESFR) method provides an efficient and flexible framework to devise high-order linearly stable numerical schemes which can achieve high levels of accuracy on unstructured grids. While superconvergent properties of ESFR schemes have been observed in numerical experiments, no formal proof of this behavior has been reporte

Wenbin Ding, Jun Chen, Mingjia Chen, Fei Xie

The rapid advancement of Large Language Models (LLMs) has marked a significant breakthrough in Artificial Intelligence (AI), ushering in a new era of Human-centered Artificial Intelligence (HAI). HAI aims to better serve human welfare and needs, thereby placing higher demands on the intelligence level of robots, particularly in aspects such as natural langua

Chanuka Karavita, Zehua Lyu, Dharshana Kasthurirathna, Mahendra Piraveenan

Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socioeconomic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates both at the agent and network levels remains under explored. This paper introduces a dual-lea

Junhong Zou, Zhenxu Sun, Yueqing Wang, Wei Qiu

Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to expensive Computational Fluid Dynamics (CFD) simulations, their applicability is often constrained by data scarcity and poor generalization across different aerodynamic domains. T

Shufan Shen, Zhaobo Qi, Junshu Sun, Qingming Huang

The visual representation of a pre-trained model prioritizes the classifiability on downstream tasks, while the widespread applications for pre-trained visual models have posed new requirements for representation interpretability. However, it remains unclear whether the pre-trained representations can achieve high interpretability and classifiability simulta

T. M. Dunster

The asymptotic behavior of solutions to the second-order linear differential equation $d^{2}w/dz^{2}=\{u^{2}f(\alpha,z)+g(z)\}w$ is analyzed for a large real parameter $u$ and $\alpha\in[0,\alpha_{0}]$, where $\alpha_{0}>0$ is fixed. The independent variable $z$ ranges over a complex domain $Z$ (possibly unbounded) on which $f(\alpha,z)$ and $g(z)$ are analy

Kang Zhang, Trung X. Pham, Suyeon Lee, Axi Niu

We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike prior approaches that rely on classifier-based or classifier-free guidance, MGAudio enables the generative model to guide itself through a dedicated training objective designed for

Yihan Wang, Peiyu Liu, Runyu Chen, Jiaxing Pu

Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advance

Nam Tran, Khoa Nguyen, Dongxi Liu, Josef Pieprzyk

Traceable signatures (Kiayas et al., EUROCRYPT 2004) is an anonymous digital signature system that extends the tracing power of the opening authority in group signatures. There are many known constructions of traceable signatures, but all are based on number-theoretic/pairing assumptions. For such reason, they may not be secure in the presence of quantum com

S. McKay, S. R. Parnell, R. M. Dalgliesh, N. V. Lavrik

Beams of light or matter that carry well-defined states of orbital angular momentum (OAM) are promising probes of topological and textured condensed matter systems such as magnetic skyrmions. Using spin-echo small-angle neutron scattering (SESANS), we demonstrate the production of vortex neutron beams from forked phase gratings of various topological charges

Tianyu Zuo, Xueyan Tang, Bu Sung Lee, Jianfei Cai

This paper studies an online cost optimization problem for distributed storage and access. The goal is to dynamically create and delete copies of data objects over time at geo-distributed servers to serve access requests and minimize the total storage and network cost. We revisit a recent algorithm in the literature and show that it does not have a competiti

Chong-Xing Yue, Xin-Yang Li, Shuo Yang, Mei-Shu-Yu Wang

Axion-like particles (ALPs) are well-motivated extensions of the standard model (SM) that appear in numerous new physics scenarios. In this paper, we concentrate on searches for long-lived ALPs predicted by the photophobic scenario at the HL-LHC with the center-of-mass energy $\sqrt{s}=14$ TeV and the integrated luminosity $\mathcal{L}=$ $3$ ab$^{-1}$. We co

Md. Rezuwan Hassan, Azmol Hossain, Kanij Fatema, Rubayet Sabbir Faruque

The Bengali language, spoken extensively across South Asia and among diasporic communities, exhibits considerable dialectal diversity shaped by geography, culture, and history. Phonological and pronunciation-based classifications broadly identify five principal dialect groups: Eastern Bengali, Manbhumi, Rangpuri, Varendri, and Rarhi. Within Bangladesh, furth

Vedant Gupta, Haotian Fu, Calvin Luo, Yiding Jiang

We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy that selects the appropriate discrete skill and continuous parameters at each timestep. Using a combination of temporal variational inference and information-theoretic regularizati

Arnaud Vanhaecke

We compute the first cohomology group of the symmetric algebra of the universal \'etale $p$-adic local system on the tower of coverings of Drinfeld's $p$-adic half-plane. The result takes a factorized form, using the $p$-adic Langlands correspondence in families over Kisin rings. This work extends the corresponding results of Colmez, Dospinescu, and Niziol f

Agus Gunawan, Samuel Teodoro, Yun Chen, Soo Ye Kim

Recent advancements in diffusion-based text synthesis have demonstrated significant performance in inserting and editing text within images via inpainting. However, despite the potential of text inpainting methods, three key limitations hinder their applicability to broader Text Image Manipulation (TIM) tasks: (i) the inability to remove text, (ii) the lack

Volodymyr M. Gavrylkiv

In this paper, we present complete classifications, up to isomorphism, of all two-element dimonoids, all commutative three-element dimonoids, and all abelian three-element dimonoids. We show that, up to isomorphism, there exist exactly 8 two-element dimonoids, of which 3 are commutative. Among these, 4 are abelian, and the remaining nonabelian dimonoids form

Raphael Lefevere

We consider the mirrors model in $d$ dimensions on an infinite slab and with unit density. This is a deterministic dynamics in a random environment. We argue that the crossing probability of the slab goes like $\kappa/(\kappa+N)$ where $N$ is the width of the slab. We are able to compute $\kappa$ perturbatively by using a multiscale approach. The only small

Shuaijie Li, Zhou Wang, Xiangyi Cui, Li Zhao

Tritium from tritiated methane (CH$_3$T) calibration is a significant impurity that restricts the sensitivity of the PandaX-4T dark matter detection experiment in the low-energy region. The CH$_3$T removal is essential for PandaX-4T and other liquid xenon dark matter direct detection experiments, as CH$_3$T serves as a critical component for low-energy calib

Quentin Auster, Kateryna Shapovalenko, Chuang Ma, Demaio Sun

We explore whether neural networks can decode brain activity into speech by mapping EEG recordings to audio representations. Using EEG data recorded as subjects listened to natural speech, we train a model with a contrastive CLIP loss to align EEG-derived embeddings with embeddings from a pre-trained transformer-based speech model. Building on the state-of-t

Fuyu Dong, Changbom Park

We apply an extended Alcock-Paczy\'nski (AP) test to the Sloan Digital Sky Survey data to constrain the dark energy models with the Chevallier-Polarski-Linder (CPL) parametrization of the dark energy equation of state. The extended AP test method uses the full shape of redshift-space two-point correlation funcion(CF) as the standard shape in order to measure

Moongyu Jeon, Sangwoo Shin, Dongjae Jeon, Albert No

We present an information-theoretic framework for discrete diffusion models that yields principled estimators of log-likelihood using score-matching losses. Inspired by the I-MMSE identity for the Gaussian setup, we derive analogous results for the discrete setting. Specifically, we introduce the Information-Minimum Denoising Score Entropy (I-MDSE) relation,

Yingying Zhou, Chao Liu, Hao Tian, Xin Zhang

Addressing the spatial uncertainty and spectral blending challenges in CSST slitless spectroscopy, we present a deep learning-driven, end-to-end framework based on the You Only Look Once (YOLO) models. This approach directly detects, classifies, and analyzes spectral traces from raw 2D images, bypassing traditional, error-accumulating pipelines. YOLOv5 effec

S. M. Al-Marzoug, B. B. Baizakov, U. Al Khawaja, H. Bahlouli

We consider a single impurity atom trapped in a double well (DW) potential created by a dipolar two-soliton molecule in a quasi-one-dimensional geometry. By solving the eigenvalue problem for the impurity atom in the DW potential, we find that its ground and first excited states are well separated from higher excited states. This allows it to be approximated

Md. Shihab Uddin, Md Nazmus Shakib, Rahul Bhadani

The increasing adoption of electric vehicles (EVs) necessitates an understanding of their driving behavior to enhance traffic safety and develop smart driving systems. This study compares classical and machine learning models for EV car following behavior. Classical models include the Intelligent Driver Model (IDM), Optimum Velocity Model (OVM), Optimal Velo

Rameez A. Malik, Yang Yang

The design of an indigenous Lunar Navigation Satellite System (LNSS) is receiving growing attention due to the surge in planned lunar missions and the limited accessibility of Earth-based Global Navigation Satellite Systems (GNSS) in the cislunar environment. Several studies have explored LNSS architecture using geometric analysis in both near and distant lu

Zhaoqi Sun, Qingsong Wang

Meta-heuristic algorithms are widely used to tackle complex optimization problems, including nonlinear, multimodal, and high-dimensional tasks. However, many existing methods suffer from premature convergence, limited exploration, and performance degradation in large-scale search spaces. To overcome these limitations, this paper introduces a novel Virus Diff

Yanjun Ji, Susanna Kirchhoff, Frank K. Wilhelm

High-fidelity qubit readout is a fundamental requirement for practical quantum computing systems. In this work, we investigate methods to enhance the measurement fidelity of flux qubits via a quantum flux parametron-mediated readout scheme. Through theoretical modeling and numerical simulations, we analyze the impact of different measurement bases on fidelit

Sayantan Mukherjee, Pritam Ranjan, Joysankar Bhattacharya

With the surge of domestic tourism in India and the influence of social media on young tourists, this paper aims to address the research question on how "social return" - responses received on social media sharing - of recent trip details can influence decision-making for short-term future travels. The paper develops a multi-model framework to build a predic

Rui Xing, Preslav Nakov, Timothy Baldwin, Jey Han Lau

Fact-checking on major platforms, such as X, Meta, and TikTok, is shifting from expert-driven verification to a community-based setup, where users contribute explanatory notes to clarify why a post might be misleading. An important challenge here is determining whether an explanation is helpful for understanding real-world claims and the reasons why, which r

A. V. Kotikov, I. A. Zemlyakov

We present a brief overview of analytical QCD, focusing primarily on a less common form of the analytical coupling A_{\rm MA}(Q^2), which is particularly convenient for Q^2\sim\Lambda^2. This form has been extensively used in recent studies of the (polarized) Bjorken sum rule and the Gross-Llewellyn Smith sum rule.

Sheng-Lun Xie, Aming Chen, Yun-Wei Yu, Shao-Lin Xiong

In theory, burst activity of the magnetar can lead to the formation of fireballs trapped by the magnetic field and corotating with the star. However, the smoking-gun observational evidence of the fireball is elusive. We envisage that the fireball emission should occasionally be eclipsed by the magnetar, especially when the burst duration is comparable to the

Arman Zadgar, Somayeh Fallah, Farshid Mehrdoust, Juan E. Trinidad Segovia

The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration remains computationally intensive and sensitive to local minima due to the model's nonlinear structure and high-dimensional parameter space. This paper introduces a hybrid deep learning-based framework that enhances bot

Xinwei Wu, Heng Liu, Jiang Zhou, Xiaohu Zhao

Large Language Models (LLMs) have advanced machine translation but remain vulnerable to hallucinations. Unfortunately, existing MT benchmarks are not capable of exposing failures in multilingual LLMs. To disclose hallucination in multilingual LLMs, we introduce a diagnostic framework with a taxonomy that separates Instruction Detachment from Source Detachmen

Austin Shouli, Yulia Bobkova, Ajay Kumar Shrestha

This paper investigates how smart devices covertly capture private conversations and discusses in more in-depth the implications of this for youth privacy. Using a structured review guided by the PRISMA methodology, the analysis focuses on privacy concerns, data capture methods, data storage and sharing practices, and proposed technical mitigations. To struc

Jin-Hong Chen, Lixin Dai, Kan Cheuk Kwan, Tom Man Kwan

A tidal disruption event (TDE) occurs when a star passes within the tidal radius of a supermassive black hole (SMBH). In TDEs it is expected that the orbital angular momentum of the disrupted star is generally misaligned with the SMBH spin axis, which should result in a misaligned super-Eddington disk precessing around the SMBH spin axis due to the Lense-Thi

Jingyi Xie, Chuhao Wu, Ge Wang, Rui Yu

As generative AI becomes embedded in children's learning spaces, families face new challenges in guiding its use. Middle childhood (ages 7-13) is a critical stage where children seek autonomy even as parental influence remains strong. Using self-directed learning (SDL) as a lens, we examine how parents perceive and support children's developing AI literacy t

Sangmin Kim, Hajun Kim, Gijeong Kim, Min-Gyu Kim

To generate reliable motion for legged robots through trajectory optimization, it is crucial to simultaneously compute the robot's path and contact sequence, as well as accurately consider the dynamics in the problem formulation. In this paper, we present a phase-based trajectory optimization that ensures the feasibility of translational dynamics and fri

Tianyi Ding, Ronghao Zheng, Senlin Zhang, Meiqin Liu

This work addresses the collaborative multi-robot autonomous online exploration problem, particularly focusing on distributed exploration planning for dynamically balanced exploration area partition and task allocation among a team of mobile robots operating in obstacle-dense non-convex environments. We present a novel topological map structure that simultan

Jorge A. Arroyo

I evaluate San Juan, Puerto Rico's late-night alcohol sales ordinance using a multi-outcome synthetic control that pools economic and public-safety series. I show that a common-weight estimator clarifies mechanisms under low-rank outcome structure. I find economically meaningful reallocations in targeted sectors -- restaurants and bars, gasoline and convenie

Wahid Bhimji, Chris Harris, Vinicius Mikuni, Benjamin Nachman

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper

Shingo Nozaki, Masahiro N. Machida

We investigate how the external medium surrounding prestellar cores affects the star formation process by conducting three-dimensional resistive magnetohydrodynamic simulations. The initial cores follow critical Bonnor-Ebert profiles and are embedded in environments with different ambient densities. The simulations follow the evolution at least until the env

Abeer M. Albalahi, Shibsankar Das, Akbar Ali, Jayjit Barman

For a graph $G$ with edge set $E$, let $d(w)$ denote the degree of a vertex $w$ in $G$. The hyperbolic Sombor index of $G$ is defined by $$HSO(G)=\sum_{uv\in E}(\min\{d(u),d(v)\})^{-1}\sqrt{(d(u))^2+(d(v))^2}.$$ If $\min\{d(u),d(v)\}$ is replaced with $\max\{d(u),d(v)\}$ in the formula of $HSO(G)$, then the complementary diminished Sombor (CDSO) index is obt

Erin S. Grant, Joseph F. Olorunyomi, Sam C. Scholten, Islay O. Robertson

The ability to modulate the photoluminescence (PL) of nanomaterials via spin-related effects is vital for many emerging quantum technologies, with nanoscale quantum sensing and imaging being particular areas of focus. Carbon-based quantum dots (CQDs) are among the most common forms of luminescent nanomaterials, appealing due to their ease of synthesis, tunab

Kanghyun Choi, Hyeyoon Lee, SunJong Park, Dain Kwon

Low-bit floating-point (FP) formats, such as FP8, provide significant acceleration and memory savings in model training thanks to native hardware support on modern GPUs and NPUs. However, we analyze that FP8 quantization offers speedup primarily for large-dimensional matrix multiplications, while inherent quantization overheads diminish speedup when applied

Zihan Zhao, Kaushik Pendiyala, Masood Mortazavi, Ning Yan

Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor-asymmetry problem: the richest modality available during laboratory data collection is absent or impractical at deployment time due to cost, fragility, or interference with physical interaction. We introduce PULSE, a

Qirong Zhu, Ansheng Wang, Shinji Tanaka, Yasutoshi Makino

This paper presents a VR-based guide dog training system designed to assist novice trainers in understanding guide dog behavior and issuing appropriate training commands. Guide dogs play a vital role in supporting independent mobility for visually impaired individuals, yet the limited number of skilled trainers restricts their availability. Training is highl

Agnideep Aich, Ashit Baran Aich

Kernel Stein discrepancies (KSDs) are widely used for goodness-of-fit testing, but standard KSDs can be insensitive to higher-order dependence features such as tail dependence. We introduce the Copula-Stein Discrepancy (CSD), which defines a Stein operator directly on the copula density to target dependence geometry rather than the joint score. For Archimede

Rahul Bhagat, B. Mishra

We have performed the dynamical system analysis to obtain the critical point in which, the value of the geometric and dynamical parameters satisfy the late-time cosmic behavior of the Universe. At the outset, the modified Friedmann equations have been reformulated into a system of coupled differential equations to ensure that the minimal set of equations req

Ziyao Cui, Minxing Zhang, Jian Pei

Privacy concerns have become increasingly critical in modern AI and data science applications, where sensitive information is collected, analyzed, and shared across diverse domains such as healthcare, finance, and mobility. While prior research has focused on protecting privacy in a single data release, many real-world systems operate under sequential or con

Xiucheng Zhang, Yang Jiang, Hongwei Qing, Jiashuo Bai

Perceptual ambiguity and task conflict limit multitask robotic manipulation via imitation learning. We propose a framework combining a Language-Conditioned Visual Representation (LCVR) module and a Language-conditioned Mixture-ofExperts Density Policy (LMoE-DP). LCVR resolves perceptual ambiguities by grounding visual features with language instructions, ena

Jeffrey A. Barrett, Eddy Keming Chen

We introduce a framework uniting algorithmic randomness with exchangeable credences to address foundational questions in philosophy of probability and philosophy of science. To demonstrate its power, we show how one might use the framework to derive the Principal Principle -- the norm that rational credence should match known objective chance -- without circ

Jacob B. Roberts, Catherine R. Ji, Isaac Donnell, Thomas D. Young

Proteins are traditionally optimized through the costly construction and measurement of many mutants. Active Learning-assisted Directed Evolution (ALDE) alleviates that cost by predicting the best improvements and iteratively testing mutants to inform predictions. However, existing ALDE methods face a critical limitation: selecting the highest-predicted muta

Jongsuk Kim, Jaeyoung Lee, Gyojin Han, Dongjae Lee

Recent advancements in deep learning and the availability of high-quality real-world driving datasets have propelled end-to-end autonomous driving. Despite this progress, relying solely on real-world data limits the variety of driving scenarios for training. Synthetic scenario generation has emerged as a promising solution to enrich the diversity of training

In Gim, Zhiyao Ma, Seung-seob Lee, Lin Zhong

Emerging large language model (LLM) applications involve diverse reasoning strategies and agentic workflows, straining the capabilities of existing serving systems built on a monolithic token generation loop. This paper introduces Pie, a programmable LLM serving system designed for flexibility and efficiency. Pie decomposes the traditional generation loop in

Hao Jia, Penghao Zhao, Hao Wu, Yuan Gao

Accurate and long-term spatiotemporal prediction for complex physical systems remains a fundamental challenge in scientific computing. While deep learning models, as powerful parametric approximators, have shown remarkable success, they suffer from a critical limitation: the accumulation of errors during long-term autoregressive rollouts often leads to physi

C. Tyler Diggans

As a quantification of the main bottleneck to flow over a graph, the network property of conductance plays an important role in the process of synchronization of network-coupled dynamical systems. Diffusive coupling terms serve not only to exchange information between nodes within a networked system, but ultimately to dissipate the entropy of the collective

B. M. Rodriguez-Lara, H. Ghaemi-Dizicheh, S. Dehdashti, A. Hanke

Photonic systems with exceptional points, where eigenvalues and corresponding eigenstates coalesce, have attracted interest due to their topological features and enhanced sensitivity to external perturbations. Non-Hermitian mode-coupling matrices provide a tractable analytic framework to model gain, loss, and chirality across optical, electronic, and mechani

Tu Anh Hoang Nguyen, Dang Nguyen, Tri-Nhan Vo, Thuc Duy Le

The utility of tabular data for tasks ranging from model training to large-scale data analysis is often constrained by privacy concerns or regulatory hurdles. While existing data generation methods, particularly those based on Generative Adversarial Networks (GANs), have shown promise, they frequently struggle with capturing complex causal relationship, main

Tyrus Whitman, Andrew Particka, Christopher Diers, Ian Griffin

In this study, we present and validate the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dynamical systems governed by ordinary differential equations (ODEs). While traditional numerical methods a re effective for many ODEs, they often struggle to achieve convergence in p

Sreeja Singh, Tamal Ghosh

Purpose: Optimization challenges in science, engineering, and real-world applications often involve complex, high-dimensional, and multimodal search spaces. Traditional optimization methods frequently struggle with local optima entrapment, slow convergence, and inefficiency in large-scale environments. This study aims to address these limitations by proposin

Changjie Luo, Feng Fang, Wenchang Zhou, Peng Zhang

We observe a fast Penning ionization in a dilute gas of cold rubidium Rydberg atoms, in the presence of a static electric field of 50 V/cm, with the ionization rate coefficients for two specific states being measured, which are orders of magnitude higher than the theoretical predictions in field-free space. Our analysis based on a polarized two-atom model re

Hui Sun, Zheng Xie, Hao-Yuan He, Ming Li

Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \emph{domain shift}. Significant domain shifts hinder effective knowledge transfer, leading to \emph{negative transfer} and deteriorating model performance. Therefore, mitigating negative transfer is e

Fikret Ceyhan, Thomas Faulkner

We discuss upper bounds on the mutual information for disjoint spherical regions of the CFT vacuum. To prove our bounds, we utilize the modular nuclearity condition, which is in turn related to finiteness of the thermal partition function of the CFT. Our bounds are satisfied by the conjectured geometric duals of these correlation measures in AdS/CFT, where t

Jinhao Liang, Kai Tao, Jiangong You

We construct examples of discontinuity of Lyapunov exponent in the spaces of quasiperiodic $\mathrm{SL}(2,\mathbb R)$-cocycles for fixed irrational frequencies. Especially, we prove that the Gevrey space $G^2$ is the transition space of continuity for all strong Diophantine frequencies. We also construct examples of discontinuity for other frequencies in les

Silvia Tosi, Ester Marini, Felice Cusano, Andrea Rossi

The Large Binocular Telescope (LBT) is a world-leading astronomical observatory, where the italian partnership has played an important role in increasing the telescope's productivity, both through an optimized observing strategy and through peer-reviewed publications that are well recognized by the international astronomical community. This manuscript provid

Nikolaos Karalias, Akbar Rafiey, Yifei Xu, Zhishang Luo

Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a central challenge of SSL for CO: solving problems with discrete constraints. We design an end-to-end differentiable framework that enables us to solve discrete constrained optimizatio

Xingyu Zhu, Beier Zhu, Shuo Wang, Kesen Zhao

Vision-language models (VLMs) such as CLIP demonstrate strong generalization in zero-shot classification but remain highly vulnerable to adversarial perturbations. Existing methods primarily focus on adversarial fine-tuning or prompt optimization; they often overlook the gaps in CLIP's encoded features, which is shown as the text and image features lie far a

Dipal Shah, Jordon Wade, Timothy Haithcoat, Robert Myers

Effective soil health management is crucial for sustaining agriculture, adopting ecosystem resilience, and preserving water quality. However, Missouri's diverse landscapes limit the effectiveness of broad generalized management recommendations. The lack of resolution in existing soil grouping systems necessitates data driven, site specific insights to guide

Shufan Shen, Junshu Sun, Shuhui Wang, Qingming Huang

Parameter-efficient fine-tuning (PEFT) aims to adapt pre-trained vision models to downstream tasks. Among PEFT paradigms, sparse tuning achieves remarkable performance by adjusting only the weights most relevant to downstream tasks, rather than densely tuning the entire weight matrix. Current methods follow a two-stage paradigm. First, it locates task-releva