October 2025 arXiv papers — page 81

A. Khodzitskii

Rota-Baxter operators on the polynomial algebra have been actively studied since the work of S.H. Zheng, L. Guo, and M. Rosenkranz (2015). Monomial operators of an arbitrary weight (2016), as well as injective operators of weight zero on $F[x]$ (2021), have been described. The author described monomial Rota-Baxter operators of nonzero weight on $F[x, y]$ com

Haonan Wang, Ying-li Zhang, Teruaki Suyama

We propose a two-field model where the inflaton $\chi$ is non-minimally coupled to the instanton $\phi$. By choosing an appropriate coupling function, we realize the scenario where the difference of the values of potential between false vacuum (FV) and true vacuum (TV) is maximized during inflation. Most of the bubbles are created at this time. After inflati

Ahan Basu, Ratnangshu Das, Pushpak Jagtap

The paper focuses on designing a controller for unknown dynamical multi-agent systems to achieve temporal reach-avoid-stay tasks for each agent while preventing inter-agent collisions. The main objective is to generate a spatiotemporal tube (STT) for each agent and thereby devise a closed-form, approximation-free, and decentralized control strategy that ensu

Seongmin Kim, Alec Kirkley

Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP often yields inaccurate estimates of order parameters and their susceptibilities in finite systems, particularly in sparse networks with few loops. Here, we show for both percolat

Yongguan Ke, Zhenzhi Peng, Muhib Ullah, Chaohong Lee

Quantum emitters coupled to traveling photons in waveguides, known as waveguide quantum electrodynamics (WQED), offer a powerful platform for understanding light-matter interactions and underpinning emergent quantum technologies. While WQED has been extensively studied in one dimension, two-dimensional (2D) WQED remains largely unexplored, where novel photon

Muhammad Salman Al Farisi, Takuya Kawata, Yoshihiro Hasegawa, Mohammad Nizar Mohamed Zukri

Animal experiments play a vital role in drug discovery and development by providing essential data on a drug's efficacy, safety, and physiological effects before advancing to human clinical trials. In this study, we propose a stent-based flow sensor designed to measure airflow in the airways of laboratory animals. The stent was fabricated from biocompatible

Teo Susnjak, Timothy R. McIntosh, Tong Liu, Paul Watters

This study presents a design science blueprint for an orchestrated AI assistant and co-pilot in doctoral supervision that acts as a socio-technical mediator. Design requirements are derived from Stakeholder Theory and bounded by Academic Integrity. We consolidated recent evidence on supervision gaps and student wellbeing, then mapped issues to adjacent large

Youngsik Hwang, Dong-Young Lim

Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimization (SOO) problem, face a critical trade-off between unlearning efficacy and model fidelity. This leads to three primary challenges: the risk of over-forgetting, a lack of fine-gr

Yongji Wu, Xueshen Liu, Haizhong Zheng, Juncheng Gu

Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows involve interleaving rollout and training stages with fundamentally different resource requirements. Rollout typically dominates overall execution time, yet scales efficiently through multiple independent instances. In

Benjamin Horowitz, Carolina Cuesta-Lazaro, Omar Yehia

Constructing a general-purpose framework for mapping between dark matter simulations and observable hydrodynamical simulation outputs is a long-standing problem in modern astrophysics. In this work, we present a new approach utilizing stochastic interpolants to map between cheap fast particle mesh simulations and baryonic quantities in three dimensions, requ

Paul Agbaje, Arkajyoti Mitra, Afia Anjum, Pranali Khose

Knowledge distillation (KD) techniques have emerged as a powerful tool for transferring expertise from complex teacher models to lightweight student models, particularly beneficial for deploying high-performance models in resource-constrained devices. This approach has been successfully applied to graph neural networks (GNNs), harnessing their expressive cap

Kial D. Stewart, Thomas G. Schmaltz, Callum J. Shakespeare

Polar amplification describes the predicted reduction in the latitudinal surface temperature gradient, which will have physical implications for mid-latitude dynamics. The precise nature of these dynamical consequences remains unclear. Here we explore aspects of polar amplification by way of 24 distinct idealised laboratory experiments. The apparatus employe

Yingchen Zhang, Ruqing Zhang, Jiafeng Guo, Wenjun Peng

Designing document identifiers (docids) that carry rich semantic information while maintaining tractable search spaces is a important challenge in generative retrieval (GR). Popular codebook methods address this by building a hierarchical semantic tree and constraining generation to its child nodes, yet their numeric identifiers cannot leverage the large lan

Xiaoqing Lan, Biqiao Xin, Bingshu Wang, Han Zhang

Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this

Lei Xu, Ling Wang

We propose a hybrid quantum-classical eigensolver to address the computational challenges of simulating strongly correlated quantum many-body systems, where the exponential growth of the Hilbert space and extensive entanglement render classical methods intractable. Our approach combines real-space sampling of tensor-network-bridged quantum circuits with symm

Hao Liu, Haoran Wang

Let $D$ be the non-split quaternion algebra over $\Q_p$. We prove that a class of admissible unitary Banach space representations of $D^{\times}$ are topologically of finite length.

York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum

Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. First, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data. Second, they lack a principled method for aggregating these signals

Xiaozhi Li, Huijun Di, Jian Li, Feng Liu

3D object detection is essential for autonomous driving. As an emerging sensor, 4D imaging radar offers advantages as low cost, long-range detection, and accurate velocity measurement, making it highly suitable for object detection. However, its sparse point clouds and low resolution limit object geometric representation and hinder multi-modal fusion. In thi

Kial D. Stewart, Callum J. Shakespeare, Thomas G. Schmaltz

Laboratory experiments with rotating tanks remain the premier physical analogue for atmospheric dynamics. Often, the equipment involved is engineered to be sufficiently versatile and modular so as to be able to accommodate experiments that explore a wide range of atmospheric processes. The exercise of initially configuring the apparatus then involves running

Yuzhen Ding, Hongying Feng, Martin Bues, Mirek Fatyga

Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning. Materials and methods: Rece

Ernest Fokoué

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foun

Ludovic Tangpi, Nizar Touzi

We develop a probabilistic framework to approximate Nash equilibria in symmetric $N$-player games in the large population regime, via the analysis of associated mean field games (MFGs). The approximation is achieved through the analysis of a McKean-Vlasov type Langevin dynamics and their associated particle systems, with convergence to the MFG solution estab

In-Hwan Jin, Hyeongju Mun, Joonsoo Kim, Kugjin Yun

Recent advances in dynamic scene reconstruction have significantly benefited from 3D Gaussian Splatting, yet existing methods show inconsistent performance across diverse scenes, indicating no single approach effectively handles all dynamic challenges. To overcome these limitations, we propose Mixture of Experts for Dynamic Gaussian Splatting (MoE-GS), a uni

Yining Li, Ziwei Wan, Chongjia Sun, Kaijun Feng

As 6G wireless communication systems evolve toward intelligence, high reconfigurability, and space-air-ground integration \cite{liu2025toward, liu2024near}, the limitations of traditional fixed antenna (TFA) have become increasingly prominent. As a remedy, spatially movable antenna (SMA) and electromagnetically reconfigurable antenna (ERA) have respectively

Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong

Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, where group-relative methods, such as GRPO, are popular due to their critic-free and normalized advantage estimation. However, in real-world code-editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted adv

Hang Zheng, Hongshen Xu, Yongkai Lin, Shuai Fan

The proliferation of Large Language Models (LLMs) has created a diverse ecosystem of models with highly varying performance and costs, necessitating effective query routing to balance performance and expense. Current routing systems often rely on a centralized external router trained on a fixed set of LLMs, making them inflexible and prone to poor performanc

Yizhu Wang, Sizhe Chen, Raghad Alkhudair, Basel Alomair

When large language model (LLM) agents are increasingly deployed to automate tasks and interact with untrusted external data, prompt injection emerges as a significant security threat. By injecting malicious instructions into the data that LLMs access, an attacker can arbitrarily override the original user task and redirect the agent toward unintended, poten

Jake Freeman

Hastie et al. (2022) found that ridge regularization is essential in high dimensional linear regression $y=\beta^Tx + \epsilon$ with isotropic co-variates $x\in \mathbb{R}^d$ and $n$ samples at fixed $d/n$. However, Hastie et al. (2022) also notes that when the co-variates are anisotropic and $\beta$ is aligned with the top eigenvalues of population covarian

Yaoyao Qian, Yuanli Wang, Jinda Zhang, Yun Zong

Current evaluation of web agents largely reduces to binary success metrics or conformity to a single reference trajectory, ignoring the structural diversity present in benchmark datasets. We present WebGraphEval, a framework that abstracts trajectories from multiple agents into a unified, weighted action graph. This representation is directly compatible with

Iván Ojeda-Ruiz, Young Ju Lee, Malcolm Dickens, Leonardo Cambisaca

Recent research has focused on mitigating algorithmic bias in clustering by incorporating fairness constraints into algorithmic design. Notions such as disparate impact, community cohesion, and cost per population have been implemented to enforce equitable outcomes. Among these, group fairness (balance) ensures that each protected group is proportionally rep

Fanze Kong, Jiayi Sun, Shuangquan Xie

One of the most significant findings in the study of spatial Solow-Swan models is the emergence of economic agglomeration, in which economic activities concentrate in specific regions. Such agglomeration provides a fundamental mechanism driving the spatial patterns of urbanization, labor migration, productivity growth, and resource allocation. In this paper,

Yuntao Wu, Lynn Tao, Ing-Haw Cheng, Charles Martineau

News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140,000 pairs of Bloomberg English and Japanese news articles covering aro

Mengying Jiang

The analogy to heat diffusion has enhanced our understanding of information flow in graphs and inspired the development of Graph Neural Networks (GNNs). However, most diffusion-based GNNs emulate passive heat diffusion, which still suffers from over-smoothing and limits their ability to capture global graph information. Inspired by the heat death of the univ

Lingfeng Li, Hong Shen, Jinniu Hu, Ying Zhang

Discretizing the Dirac equation on a uniform grid with the central difference formula often generates spurious states. We propose a staggered-grid scheme in the framework of the finite-difference method that suppresses these spurious states without introducing Wilson terms or ad-hoc filtering. In this approach, the large and small components of the Dirac equ

Matteo Bortolon, Nuno Ferreira Duarte, Plinio Moreno, Fabio Poiesi

Achieving dexterous robotic grasping with multi-fingered hands remains a significant challenge. While existing methods rely on complete 3D scans to predict grasp poses, these approaches face limitations due to the difficulty of acquiring high-quality 3D data in real-world scenarios. In this paper, we introduce GRASPLAT, a novel grasping framework that levera

Xiaoxing Ren, Nicola Bastianello, Thomas Parisini, Andreas A. Malikopoulos

In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning framework in which each agent performs several local updates of its policy and value function, where the latter is approximated by a multi-layer neural network, before exchanging in

Longxiang Huang, Benjamin Duret, François-Xavier Demoulin

A numerical investigation of flat fan sprays is conducted via Direct Numerical Simulations (DNS). Diverging liquid sheets are generated using tailored initial velocity profiles, where the opening angle serves as an explicit control parameter. The analysis reveals two distinct regimes: at low Weber numbers, the sheet features thick, retracting rims moving at

Nofar Carmeli, Nikolaos Tziavelis

We investigate the fine-grained complexity of direct access to Conjunctive Query (CQ) answers according to their position, ordered by the minimum (or maximum) value between attributes. We further use the tools we develop to explore a wealth of related tasks. We consider the task of ranked enumeration under min/max orders, as well as tasks concerning CQs with

Qian Xiong, Bo Yang, Weisong Sun, Yiran Zhang

Automated code generation driven by Large Lan- guage Models (LLMs) has enhanced development efficiency, yet generating complex application-level software code remains challenging. Multi-agent frameworks show potential, but existing methods perform inadequately in large-scale application-level software code generation, failing to ensure reasonable orga- nizat

Shaouna Shoaib Lodhi, Shoaib Lodhi

The rapid integration of Artificial Intelligence (AI) into K-12 STEM education presents transformative opportunities alongside significant ethical challenges. While AI-powered tools such as Intelligent Tutoring Systems (ITS), automated assessments, and predictive analytics enhance personalized learning and operational efficiency, they also risk perpetuating

Kai Zeng, Zhanqian Wu, Kaixin Xiong, Xiaobao Wei

Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to generation quality and controllability. However, they often overlook the evaluation of downstream perception tasks, which are $\mathbf{really\ crucial}$ for the performance of auton

Jiabin Wang, Wancheng Zhang, Zhenhua Zhang, Rui Xiong

Antiferromagnetic materials with spin splitting have attracted considerable attention for their symmetry-enabled anisotropic spin textures that sustain a zero net magnetization, thereby facilitating efficient spin-current generation. In this work, the highly efficient generation of nonrelativistic spin currents is demonstrated to be facilitated by the distin

Takehiro Aoshima, Yusuke Shinohara, Byeongseon Park

Reward-based fine-tuning of video diffusion models is an effective approach to improve the quality of generated videos, as it can fine-tune models without requiring real-world video datasets. However, it can sometimes be limited to specific performances because conventional reward functions are mainly aimed at enhancing the quality across the whole generated

Md Ashad Alam, Md Amanullah

Diabetes mellitus is a chronic metabolic disorder that necessitates novel therapeutic innovations due to its gradual progression and the onset of various metabolic complications. Research indicates that Ficus religiosa is a conventional medicinal plant that generates bioactive phytochemicals with potential antidiabetic properties. The investigation employs e

Zongyuan Liu, Jiajie Li, Shengfeng Zhu

We investigate multi-physical topology optimization for microfluidic mixers employing the phase-field model. The optimization problem is formulated using a modified Ginzburg-Landau free energy functional. To eliminate fluid blockage in microfluidic mixers, we incorporate the coupled Navier-Stokes, convection-diffusion and Poisson-Boltzmann equations. An Alle

Xujin Chen, Xiaodong Hu, Changjun Wang, Qingjie Ye

Let $V$ be a finite set of $n$ elements, $f: 2^V \rightarrow \mathbb{R}_+$ be a nonnegative monotone supermodular function, and $k$ be a positive integer no greater than $n$. This paper addresses the problem of maximizing $f(S)$ over all subsets $S \subseteq V$ subject to the cardinality constraint $|S| = k$ or $|S|\le k$. Let $r$ be a constant integer. The

Ping Li

Let $M$ be a $2n$-dimensional closed symplectic manifold admitting a Hamiltonian circle action with isolated fixed points. We show that if $M$ contains an $S^1$-invariant symplectic hypersurface $D$ such that $M\setminus D$ is a homology cell, which is satisfied when $M\setminus D$ is contractible, then $M$ and $D$ are homotopy complex projective spaces with

Xincheng Liu

This study evaluates the pedagogical soundness and usability of AI-generated lesson plans across five leading large language models: ChatGPT (GPT-5), Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2, and Grok 4. Beyond model choice, three structured prompt frameworks were tested: TAG (Task, Audience, Goal), RACE (Role, Audience, Context, Execution), and CO

Troy Losey, Jin Zhang, S. -W. Tsai

In this work, we introduce a solid-state platform for building quantum simulators using implanted spin centers in solid-state materials. We build upon the proposal for an $S=1$ chain of spin centers coupled through the magnetic dipole-dipole interaction and subjected to an external magnetic field as a quantum simulator for critical floating phases. We introd

Yansong Du, Jingtong Yao, Yuting Zhou, Feiyu Jiao

In-camera light scattering is a typical form of non-systematic interference in indirect Time-of-Flight (iToF) cameras, primarily caused by multiple reflections and optical path variations within the camera body. This effect can significantly reduce the accuracy of background depth measurements. To address this issue, this paper proposes a calibration-based m

Salman Ahsanullah, Neema Rafizadeh, Hui Zhao

NbOI$_2$ has recently emerged as a new van der Waals material combining semiconducting behavior with intrinsic in plane ferroelectricity and pronounced transport and optical anisotropy. However, its photocarrier dynamics remain largely unexplored. Here we report transient absorption spectroscopy of NbOI$_2$ using femtosecond pump probe reflectance measuremen

Hongshu Lin, Wenston J. T. Zang

Let $b_{t,i}(n)$ denote the total number of $i$-hooks in $t$-regular partitions of $n$. Singh and Barman conjectured that $b_{t+1,2}(n) \geq b_{t,2}(n)$ holds for all $t\ge 3$ and $n\ge 0$. This conjecture was known to hold for $t=3$ due to work of Barman Mahanta and Singh. In this paper, we prove this conjecture.

Stefanos Lappas, Bae Jun Park

In this paper, we study the boundedness properties of the (dyadic) maximal bilinear operator associated with rough homogeneous kernels on $\mathbb{R}$. We establish sharp $L^{p_1}(\mathbb{R}) \times L^{p_2}(\mathbb{R}) \to L^{p}(\mathbb{R})$ estimates in the full quasi-Banach range of exponents $1 < p_1, p_2 < \infty$ and $1/2 < p < \infty$. Our approach ext

Fengyuan Sun, Hui Chen, Xinhao Xu, Dandan Zheng

While multi-modal large language models (MLLMs) have made significant progress in recent years, the issue of hallucinations remains a major challenge. To mitigate this phenomenon, existing solutions either introduce additional data for further training or incorporate external or internal information during inference. However, these approaches inevitably intr

Warisa Nusrat, Mostafijur Rahman, Ayatullah Faruk Mollah

Malaria, which primarily spreads with the bite of female anopheles mosquitos, often leads to death of people - specifically children in the age-group of 0-5 years. Clinical experts identify malaria by observing RBCs in blood smeared images with a microscope. Lack of adequate professional knowledge and skills, and most importantly manual involvement may cause

Kartikeya Aneja, Manasvi Srivastava, Subhayan Das, Nagender Aneja

This paper presents a question answering system that operates exclusively on a knowledge graph retrieval without relying on retrieval augmented generation (RAG) with large language models (LLMs). Instead, a small paraphraser model is used to paraphrase the entity relationship edges retrieved from querying the knowledge graph. The proposed pipeline is divided

Hanna Zbroszczyk

We report the recent results from the HADES experiment obtained in Au+Au and Ag+Ag collisions at center-of-mass energies per nucleon pair of 2.42 and 2.55 GeV, respectively. In particular, measurements of hadronic and dilepton observables are presented, and prospects for the future experimental program are outlined.

Guy C. David, Pekka Pankka, Jeremy T. Tyson

We survey the current status of the questions posed by Juha Heinonen and Stephen Semmes in `Thirty-three yes or no questions about mappings, measures, and metrics' (Conformal Geometry and Dynamics, 1997).

Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto

Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assumes that all tasks contribute gradients of similar magnitudes; when this assumption fails, optimization becomes biased toward large-gradient tasks. In this paper, however, we show th

Eleonora Caruso, Elisa Costantini, Nathalie Degenaar, Maria Diaz Trigo

Dipping neutron star low-mass X-ray binaries (NS LMXBs) are systems that exhibit periodic drops in their X-ray light curves. These are believed to be caused by material at the impact point of the gas stream onto the accretion disk, the bulge. Dipping systems are observed at high inclination and provide exceptional opportunities to address important open ques

Yuqiao Tan, Shizhu He, Kang Liu, Jun Zhao

Reasoning models have demonstrated exceptional performance in tasks such as mathematics and logical reasoning, primarily due to their ability to engage in step-by-step thinking during the reasoning process. However, this often leads to overthinking, resulting in unnecessary computational overhead. To address this issue, Mode Selection aims to automatically d

Yi Yin, Yuntao Shou, Zao Dai, Yun Peng

In recent years, multimodal medical data-based survival analysis has attracted much attention. However, real-world datasets often suffer from the problem of incomplete modality, where some patient modality information is missing due to acquisition limitations or system failures. Existing methods typically infer missing modalities directly from observed ones

William Kuszmaul, Jingxun Liang, Renfei Zhou

We show how to construct a dynamic ordered dictionary, supporting insert/delete/rank/select on a set of $n$ elements from a universe of size $U$, that achieves the optimal amortized expected time complexity of $O(1 + \log n / \log \log U)$, while achieving a nearly optimal space consumption of $\log \binom{U}{n} + n / 2^{(\log n)^{\Omega(1)}} + \text{polylog

Ishan Joshi

In this paper we present a method to derive Eulerian continued fractions arising from a sequence of integrals. As examples, through a new derivation, we reproduce classical continued fraction expansions for the natural logarithm, the Riemann zeta function $\zeta(s)$, and polylogarithms, while also obtaining several new identities. Finally, we apply the metho

Yichi Zhang, Zhuo Chen, Lingbing Guo, Wen Zhang

Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal large language models (MLLMs). Among the various forms of abstractive information, Multi-Modal Relational Knowledge (MMRK), which represents abstract relational structures between multi-modal entities using node-edge forma

Yuanming Zhang, Zeyan Song, Jing Lu, Fei Chen

Recent promising results in auditory attention decoding (AAD) using scalp electroencephalography (EEG) have motivated the exploration of cEEGrid, a flexible and portable ear-EEG system. While prior cEEGrid-based studies have confirmed the feasibility of AAD, they often neglect the dynamic nature of attentional states in real-world contexts. To address this g

Qing-Yu Lan, Zhan-He Wang, Jun-Qian Jiang, Yu-Tong Wang

The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually designed rules and/or handcrafted features. In this work, we directly use the news sentiment scores derived from large language models, together w

Nishanth Sridhar Nakshatri, Shamik Roy, Manoj Ghuhan Arivazhagan, Hanhan Zhou

LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon through benchmarks built on structured knowledge bases like Wikidata, but they focus on widely-covered, easily-memorized popular entities and lack the dynamic structure needed to fair

Jihwan Bang, Juntae Lee, Seunghan Yang, Sungha Choi

Multi-hop retrieval-augmented generation (RAG) is a promising strategy for complex reasoning, yet existing iterative prompting approaches remain inefficient. They often regenerate predictable token sequences at every step and rely on stochastic stopping, leading to excessive token usage and unstable termination. We propose TSSS (Think Straight, Stop Smart),

Mojtaba Moattari

In the last decade, due to high resolution cameras and accurate meta-phase analyzes, the accuracy of chromosome classification has improved substantially. However, current Karyotyping systems demand large number of high quality train data to have an adequately plausible Precision per each chromosome. Such provision of high quality train data with accurate de

Keaton Kraiger, Jingjing Li, Skanda Bharadwaj, Jesse Scott

We propose FootFormer, a cross-modality approach for jointly predicting human motion dynamics directly from visual input. On multiple datasets, FootFormer achieves statistically significantly better or equivalent estimates of foot pressure distributions, foot contact maps, and center of mass (CoM), as compared with existing methods that generate one or two o

Thomas Wang, Haowen Li

As large language models (LLMs) are increasingly integrated into real-world applications, ensuring their safety, robustness, and privacy compliance has become critical. We present OpenGuardrails, the first fully open-source platform that unifies large-model-based safety detection, manipulation defense, and deployable guardrail infrastructure. OpenGuardrails

Dingjie Fu, Dianxing Shi

With the proliferation of the internet and the rapid advancement of Artificial Intelligence, leading technology companies face an urgent annual demand for a considerable number of software and algorithm engineers. To efficiently and effectively identify high-potential candidates from thousands of applicants, these firms have established a multi-stage selecti

Hongjun Liu, Leyu Zhou, Zijianghao Yang, Chao Yao

For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost-deployment balance. However, such spatial sparsity of EEG limits spatial fidelity, hurting learning and introducing bias. EEG spatial super-resolution methods aim to recover high-density EEG signals from sparse measurements, yet is often hindered by distribu

Ruiyang Jin, Yuke Zhou, Yujie Tang, Jie Song

Zeroth-order optimization (ZO) has been a powerful framework for solving black-box problems, which estimates gradients using zeroth-order data to update variables iteratively. The practical applicability of ZO critically depends on the efficiency of single-step gradient estimation and the overall query complexities. However, existing constrained ZO algorithm

Ken Ono

A conjecture by Sun states that the partition function $p(n)$, for $n>1$, is never a perfect power. Recent work by Merca et al. proposes generalizations of perfect-power repulsion for $p(n)$. In this note, we prove these generalizations for the functions $p_B(n)$, which count the number of partitions of $n$ with the largest part $\leq B$. If $B\geq 4$ and $k

Fangyuan Sun, Ilyas Fatkhullin, Niao He

Stochastic Natural Gradient Variational Inference (NGVI) is a widely used method for approximating posterior distribution in probabilistic models. Despite its empirical success and foundational role in variational inference, its theoretical underpinnings remain limited, particularly in the case of non-conjugate likelihoods. While NGVI has been shown to be a

Rafael Brahm, Trifon Trifonov, Andrés Jordán, Thomas Henning

We report the discovery and characterization of three transiting giant planets in the TIC118798035 system. The three planets were identified as transiting candidates from data of the TESS mission, and confirmed with ground-based photometric transit observations along with radial velocity variations obtained with FEROS, HARPS and ESPRESSO. The three planets p

Amila Indika, Igor Molybog

Numerous knowledge workers utilize spreadsheets in business, accounting, and finance. However, a lack of systematic documentation methods for spreadsheets hinders automation, collaboration, and knowledge transfer, which risks the loss of crucial institutional knowledge. This paper introduces Spreadsheet Operations Documentation (SOD), an AI task that involve

Paimon Goulart, Jordan Steinhauser, Kylene Shuler, Edward Korzus

Integration of diverse data will be a pivotal step towards improving scientific explorations in many disciplines. This work establishes a vision-language model (VLM) that encodes videos with text input in order to classify various behaviors of a mouse existing in and engaging with their environment. Importantly, this model produces a behavioral vector over t

Sam McKeown

We consider the Busemann process in planar directed first passage percolation. We extend existing techniques to establish the existence of the process in our setting and determine its distribution in a number of integrable models. As examples of their utility, we show how these explicit distributions may be used to quantify the semi-infinite geodesics passin

Federico Di Gennaro, Khaled Eldowa, Nicolò Cesa-Bianchi

In contrast to the classic formulation of partial monitoring, linear partial monitoring can model infinite outcome spaces, while imposing a linear structure on both the losses and the observations. This setting can be viewed as a generalization of linear bandits where loss and feedback are decoupled in a flexible manner. In this work, we address a nonstochas

S. X. Li, Keren Li, J. B. You, Y. -H. Chen

We introduce a hybrid quantum-classical framework for efficiently implementing approximate unitary dilations of non-unitary operators with enhanced noise resilience. The method embeds a target non-unitary operator into a subblock of a unitary matrix generated by a parameterized quantum circuit with universal expressivity, while a classical optimizer adjusts

Lei Ni, Nolan Wallach

The purpose of this paper is to develop a Lie algebraic approach to obtain new proofs of important results of H.-C. Wang, Tits and Wolf-Wang-Ziller on compact complex homogeneous manifolds emphasizing only those that admit a transitive compact group of biholomorphic transformations. The method only uses some standard results in Lie theory. The new approach p

Peng Wang, Minghao Gu, Qiang Huang

Catastrophic forgetting is a well-documented challenge in model fine-tuning, particularly when the downstream domain has limited labeled data or differs substantially from the pre-training distribution. Existing parameter-efficient fine-tuning methods largely operate in the weight space by modifying or augmenting the parameters of the pre-trained model, whic

George Stefan, Eleanor Pullenayegum

Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at pre-determined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the

Leah LeJeune, Omar Saucedo, Lauren M. Childs, Navid Ghaffarzadegan

During the recent pandemic, a rise in COVID-19 cases was followed by a decline in influenza. In the absence of cross-immunity, a potential explanation for the observed pattern is behavioral: non-pharmaceutical interventions (NPIs) designed and promoted for one disease also reduce the spread of others. We study short-term and long-term dynamics of two pathoge

Huaizhe Liu, Jiaqi Wu, Zhizongkai Wang, Bin Cao

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under th

Reya Vir, Sarvesh Bhatnagar

As machine learning models are increasingly fine-tuned on synthetic data, there is a critical risk of subtle misalignments spreading through interconnected AI systems. This paper investigates subliminal corruption, which we define as undesirable traits are transmitted through semantically neutral data, bypassing standard safety checks. While this phenomenon

Seri Khoury, Manish Purohit, Aaron Schild, Joshua Wang

The main goal in distributed symmetry-breaking is to understand the locality of problems; i.e., the radius of the neighborhood that a node needs to explore in order to arrive at its part of a global solution. In this work, we study the locality of matching problems in the family of regular graphs, which is one of the main benchmarks for establishing lower bo

Yunzhe Wang, Soham Hans, Volkan Ustun

Human team tactics emerge from each player's individual perspective and their ability to anticipate, interpret, and adapt to teammates' intentions. While advances in video understanding have improved the modeling of team interactions in sports, most existing work relies on third-person broadcast views and overlooks the synchronous, egocentric nature of multi

E. A. Zaborowski, P. Taylor, K. Honscheid, A. Cuceu

The sound horizon scale $r_s$ is a key source of information for early-time $H_0$ measurements, and is therefore a common target of new physics proposed to solve the Hubble tension. We present a sub-2% measurement of the Hubble constant that is independent of this scale, using data from the first data release of the Dark Energy Spectroscopic Instrument (DESI

Wenbo Liu, Guiyuan Cao, Zian Liu, Hongyang Chen

Fibre Bragg gratings (FBGs) are widely used in optical sensing and communication systems. Femtosecond laser inscription (FLI) enables hydrogen-free, thermally stable, high-resolution, and complex structures of FBG fabrication, but its practical application is limited by manual operation, low throughput, and sensitivity to laser alignment. In this study, we p

Antonio Nakid Cordero

Martin's Conjecture states that every definable function on the Turing degrees is either constant or increasing, and that every increasing function is an iterate of the Turing jump. This classification has already been corroborated for the class of uniformly invariant functions and a long-standing conjecture by Steel is that every definable function on the T

Ehsan Roohi

This paper presents a comprehensive scientometric analysis of the long-term impact of the 1979 Iranian Revolution on the nation scientific development. Using Scopus-indexed data from 1960 to 2024, we benchmark Iran publication trajectory against a carefully selected peer group representing diverse development models, established scientific leaders, Netherlan

Nazmus Sakib, Simeon Wuthier, Amanul Islam, Xiaobo Zhou

Distributed peer-to-peer (P2P) networking delivers the new blocks and transactions and is critical for the cryptocurrency blockchain system operations. Having poor P2P connectivity reduces the financial rewards from the mining consensus protocol. Previous research defines beneficalness of each Bitcoin peer connection and estimates the beneficialness based on

Yoshiyuki Kabashima, Kazushi Mimura

The Hopfield associative memory model stores random patterns in synaptic couplings according to Hebb's rule and retrieves them through gradient descent on an energy function. This conventional setting, where neurons are assumed to have monotonic transfer functions, has been central to understanding associative memory. Morita (1993, Neural Netw. 6 115), howev

Sanskar Amgain, Daniel Lobo, Atri Chatterjee, Swarup Bhunia

The growing use of third-party hardware accelerators (e.g., FPGAs, ASICs) for deep neural networks (DNNs) introduces new security vulnerabilities. Conventional model-level backdoor attacks, which only poison a model's weights to misclassify inputs with a specific trigger, are often detectable because the entire attack logic is embedded within the model (i.e.

Cheng Huang, Nyima Tashi, Fan Gao, Yutong Liu

Tibetan, one of the major low-resource languages in Asia, presents unique linguistic and sociocultural characteristics that pose both challenges and opportunities for AI research. Despite increasing interest in developing AI systems for underrepresented languages, Tibetan has received limited attention due to a lack of accessible data resources, standardized

J. I. Katz

Eighteen years after their discovery, the astronomical sources and radiation mechanisms of fast radio bursts remain mysterious. Their radiation is as bright as that of pulsars, with brightness temperatures as high as $\sim 10^{36}$ K, implying coherent emission, but the plasma physics that forms the coherent charge bunches, with net charges of order a Coulom