October 2025 arXiv papers — page 55

Wei Ni, Yangfan Qiu, Yanyan Xiao

This paper proposes a projection-free primal-dual dynamics for the nonsmooth composite optimization problems with equality and inequality constraints. To deal with optimization constraints, this paper departs from the use of gradient projection method, but resorts to the idea of mirror descent to design a continuous-time smooth optimization dynamics which ad

Ruixiang Mao, Xiangnan Ma, Qing Yang, Ziming Zhu

The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism creates a smooth and monotonic mapping from acoustic features to target tokens, achieving performance on Mandarin competitive with other NAR approaches. However, without finer-grained guidance, its stability degrades

Erum Mushtaq, Zalan Fabian, Yavuz Faruk Bakman, Anil Ramakrishna

Uncertainty Estimation (UE) plays a central role in quantifying the reliability of model outputs and reducing unsafe generations via selective prediction. In this regard, most existing probability-based UE approaches rely on predefined functions, aggregating token probabilities into a single UE score using heuristics such as length-normalization. However, th

Peter Ebenfelt, Ming Xiao, Hang Xu

Let $M$ be a complete K\"ahler manifold, and let $(L, h) \to M$ be a positive line bundle inducing a K\"ahler metric $g$ on $M$. We study two Bergman kernels in this setting: the Bergman kernel of the disk bundle of the dual line bundle $(L^*, h^*)$, and the Bergman kernel of the line bundle $(L^k, h^k)$, $k\geq 1$, twisted by the canonical line bundle of $(

Shouvik Sadhukhan, C. S. Narayanamurthy

New experimental technique have been proposed to discuss the turbulence impact reduction using beam shaping technique. In first phase of experiments, turbulence Impacted Vortex beam shaping technique has been introduced for a propagating beam which is effected by Kolmogorov type lab based turbulence simulator using Pseudo Random Phase Plate (PRPP). The new t

Austin A. Barr, Brij S. Karmur, Anthony J. Winder, Eddie Guo

Machine learning in neurosurgery is limited by challenges in assembling large, high-quality imaging datasets. Synthetic data offers a scalable, privacy-preserving solution. We evaluated the feasibility of generating realistic lateral cervical spine radiographs using a denoising diffusion probabilistic model (DDPM) trained on 4,963 images from the Cervical Sp

Sayantan Maitra

We consider the random field defined by the layering numbers of the Brownian loop soup in a bounded simply connected domain in the complex plane. We call this the layering field and show that, after a suitable renormalization, it converges to the subcritical Gaussian multiplicative chaos. The main technique for our proof is the Wiener-It\^{o} chaos expansion

Jianeng Wang, Matias Mattamala, Christina Kassab, Nived Chebrolu

Self-balancing exoskeletons offer a promising mobility solution for individuals with lower-limb disabilities. For reliable long-term operation, these exoskeletons require a perception system that is effective in changing environments. In this work, we introduce LT-Exosense, a vision-centric, multi-session mapping system designed to support long-term (semi)-a

E. A. Cerda-Méndez, Y. G. Rubo, K. Biermann, A. Camacho-Guardian

We systematically study the linewidths of multilevel exciton-polariton modes as a function of the detuning in a planar hybrid microcavity (MC) with low quality factor (Q~300) operating in the linear optical response regime. Using optical reflectivity, we observe that, counterintuitively, the linewidths of the polariton modes undergo a pronounced spectral nar

Hannes Bajohr

Despite a potential plateau in ML advancement, the societal impact of large language models lies not in approaching superintelligence but in generating text surfaces indistinguishable from human writing. While Critical AI Studies provides essential material and socio-technical critique, it risks overlooking how LLMs phenomenologically reshape meaning-making.

Ashutosh Anshul, Mohammad Zia Ur Rehman, Sri Akash Kadali, Nagendra Kumar

Detecting automated accounts (bots) among genuine users on platforms like Twitter remains a challenging task due to the evolving behaviors and adaptive strategies of such accounts. While recent methods have achieved strong detection performance by combining text, metadata, and user relationship information within graph-based frameworks, many of these models

Shuhong Liu, Lin Gu, Ziteng Cui, Xuangeng Chu

Participating in efforts to endow generative AI with the 3D physical world perception, we propose I2-NeRF, a novel neural radiance field framework that enhances isometric and isotropic metric perception under media degradation. While existing NeRF models predominantly rely on object-centric sampling, I2-NeRF introduces a reverse-stratified upsampling strateg

Vladislav Virtonen

The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these h

Rahul Ranjan, Mahendra Kumar Gurve, Anuj, Nitin

Developing benchmark datasets for low-resource languages poses significant challenges, primarily due to the limited availability of native linguistic experts and the substantial time and cost involved in annotation. Given these challenges, Maithili is still underrepresented in natural language processing research. It is an Indo-Aryan language spoken by more

Wenliang Pei, Chonghao Deng

We consider the critical Choquard system with both linear and nonlinear couplings $-\Delta v_1 + \mu_1 v_1 = ( I_\omega * |v_1|^{2_\omega^*} ) |v_1|^{2_\omega^* -2} v_1 + \theta p( I_\omega * |v_2|^q)|v_1|^{p-2} v_1 + \varepsilon v_2, \quad in \,\, \mathbb{R}^N, -\Delta v_2 + \mu_2 v_2 = ( I_\omega * |v_2|^{2_\omega^*} ) |v_2|^{2_\omega^* -2} v_2 + \theta q(

Lorenzo Magnino, Kai Shao, Zida Wu, Jiacheng Shen

Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL) for MFGs, existing methods are typically limited to finite spaces or stationary models, hindering their applicability to real-world problems. This paper introduces a novel deep r

Bhisham Dev Verma, Rameshwar Pratap, Keegan Kang

We consider the problem of estimating the trace and diagonal entries of an N-order tensor (where $N \geq 2$) under the framework where the tensor can only be accessed through tensor-vector multiplication. The aim is to estimate the tensor's diagonal entries and trace by minimizing the number of tensor-vector queries. The seminal work of Hutchinson and its ex

Woong-Bae G. Zee, Jun-Sung Moon, Sanjaya Paudel, Suk-Jin Yoon

Enhanced star formation (SF) with star-forming neighboring galaxies bolsters hydrodynamical contributions during paired interactions. Although the relative spin orientation between interacting galaxies can influence this effect, it has not been comprehensively explored. In this study, using a curated sample of nearby (0.02 < z < 0.06) spiral-spiral pairs and

Hindy Drillick, Shalin Parekh

In arbitrary spatial dimension $d\ge 1$, we study a generalized model of random walks in a time-varying random environment (RWRE) defined by a stochastic flow of kernels. We consider the quenched probability distribution of the random walker under a scaling where the time is of order $N$ and the spatial window is of size $N^{1/2}$. This spatial window may no

Yunhong Tao, Wenbing Tao, Xiang Xiang

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. With the advent of deep learning, the LLIE technique has achieved significant breakthroughs. However, existing LLIE methods either ignore the important role of frequency domain information or fail to effective

Deping Guo, Weihan Zhang, Canbo Zong, Cong Wang

Luttinger compensated antiferromagnets (LcAFMs), combining spin polarization with vanishing net magnetization, offering distinct advantages for next-generation spintronic applications. Using first-principles calculations, we demonstrate that conventional antiferromagnetic CrCl2 double chains can be transformed into one-dimensional LcAFMs under an external el

Y. I. Izotov, D. Schaerer, G. Worseck, N. G. Guseva

We present observations of eleven compact star-forming galaxies in the redshift range z = 0.6145 - 1.0053, with the Cosmic Origins Spectrograph (COS) on board the Hubble Space Telescope (HST). We aim to spectroscopically measure for the first time the Lyman continuum (LyC) over a wider rest-frame wavelength range of ~ 600 - 900A compared to ~ 850 - 900A in p

A. Hosseini, Y. Estaremi

The continuity of conditional expectation on Orlicz spaces is investigated. Indeed, we provide some necessary and sufficient conditions on a sequence $\{\mathcal{A}_n\}_{n\in\mathbb{N}}$ of $\sigma$-subalgebras for $L^{\varphi}$-convergence of the related conditional expectations. Our results generalize similar results in $L^p$-spaces.

Erhan Bayraktar, Yuqiong Wang

We study a class of degenerate diffusion generators arising in sequential testing and quickest detection problems with partial information. The observation process is driven by $k$ independent Brownian motions, while the hidden state takes $n+1$ values, with $k<n$. After transforming to posterior likelihood coordinates, we analyze Hörmander&#39;s condition b

Mohammadsajad Alipour, Mohammad Mohammadi Amiri

Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging their local data. However, the decentralized nature of FL also introduces vulnerabilities, as malicious clients can compromise or manipulate the training process. In this work, we i

W. S. Oliveira, J. Pimentel de Lima, F. A. Pinheiro, R. R. dos Santos

We present a numerical study of the two-dimensional quantum percolation model, revealing that a critical region with multifractal eigenstates mediates the transition from localized to delocalized states. By analyzing the mean level ratio and participation entropy, we identify two distinct transitions: a Berezinskii-Kosterlitz-Thouless (BKT) transition at the

Xiao Meng, Kei Fong Lam

We study a system of reaction-diffusion equations posed on a bounded domain composed of subdomains separated by a connected network with a metric graph structure. The reaction-diffusion dynamics with anisotropic diffusion on the graph edges are coupled to well-mixed ODE dynamics occurring at the vertices by junction conditions, and to similar PDE dynamics oc

Can Cui, Nung Kwan Yip

Over a bounded strictly convex domain in $\mathbb{R}^n$ with smooth boundary, we establish a priori gradient estimate for an anisotropic mean curvature flow with prescribed contact angle and Neumann boundary conditions. The estimates require careful analysis of the degeneracy property of the anisotropic mean curvature operator. As a result, for both problems

Minquan Cheng, Yifei Huang, Youlong Wu, Jinyan Wang

Coded caching is a promising technique to create coded multicast opportunities for cache-aided networks. By splitting each file into $F$ equal packets (i.e., the subpacketization level $F$) and letting each user cache a set of packets, the transmission load can be significantly reduced via coded multicasting. It has been shown that a higher subpacketization

Tao Wu, Yan Li

\textbf{B}lue \textbf{L}arge \textbf{A}mplitude \textbf{P}ulsators (BLAPs) are a type of variable star that has been identified relatively recently. They are characterized by their large amplitude, high gravity, and long periods (2-75 minutes). Some BLAPs exhibit a rich helium abundance on their surfaces, while some of the others show rich hydrogen atmospher

Renrong Shao, Wei Zhang, Jun Wang

Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during adaptation. The complexity of scene information and lack of the source domain make SFDA a difficult task. Recent studies have shown promising results, but many approaches to domain

Yuhang Gao, Xiang Xiang, Sheng Zhong, Guoyou Wang

Vision-Language Models (VLMs) have shown significant progress in open-set challenges. However, the limited availability of 3D datasets hinders their effective application in 3D scene understanding. We propose LOC, a general language-guided framework adaptable to various occupancy networks, supporting both supervised and self-supervised learning paradigms. Fo

Hyunseung Lim, Sooyohn Nam, Sungmin Na, Ji Yong Cho

Patent examination remains an ongoing challenge in the NLP literature even after the advent of large language models (LLMs), as it requires an extensive yet nuanced human judgment on whether a submitted claim meets the statutory standards of novelty and non-obviousness against previously granted claims -- prior art -- in expert domains. Previous NLP studies

Guangan Jiang, Tianzi Zhang, Dong Li, Zhenjun Zhao

Realistic animatable human avatars from monocular videos are crucial for advancing human-robot interaction and enhancing immersive virtual experiences. While recent research on 3DGS-based human avatars has made progress, it still struggles with accurately representing detailed features of non-rigid objects (e.g., clothing deformations) and dynamic regions (e

Jinzhe Liu, Junshu Sun, Shufan Shen, Chenxue Yang

Lifelong knowledge editing enables continuous, precise updates to outdated knowledge in large language models (LLMs) without computationally expensive full retraining. However, existing methods often accumulate errors throughout the editing process, causing a gradual decline in both editing accuracy and generalization. To tackle this problem, we propose Neur

K. R. van Nispen

Universal algebraic geometry is generalised from solutions of equations in a single algebra to the study of $\varphi$- or $K$-spectra, akin to the prime spectrum of a ring. We explore their basic properties and constructions, give a correspondence between certain quantifier-free propositions and closed sets in the Zariski topology of a free algebra, and show

Can Cui, Nung Kwan Yip

In this paper, we study surfaces which evolve by anisotropic mean curvature flow with contact angle boundary condition over a strictly convex domain in $\mathbb{R}^2$. We establish a prior gradient estimate for smooth solutions to this boundary value problem. The same approach can also handle Dirichlet boundary condition in $\mathbb{R}^n$, $n\geq 2$. For bot

Yijin Ren, Haifeng Xu, Qi Deng

This paper introduces new parameter-free first-order methods for convex optimization problems in which the objective function exhibits H\"{o}lder smoothness. Inspired by the recently proposed distance-over-gradient (DOG) technique, we propose an accelerated distance-adaptive method which achieves optimal anytime convergence rates for H\"{o}lder smooth proble

Eduardo Fabricio Gomes Trindade, Felipe Silveira de Almeida, Gioliano de Oliveira Braga, Rafael Pimenta de Mattos Paixão

Wi-Fi Channel State Information (CSI) has been extensively studied for sensing activities. However, its practical application in user authentication still needs to be explored. This study presents a novel approach to biometric authentication using Wi-Fi Channel State Information (CSI) data for palm recognition. The research delves into utilizing a Raspberry

Xuying LI

We present a novel approach for controllable mathematical reasoning that leverages self-optimizing thought vectors with entropy minimization. Our method introduces learnable thought vectors that dynamically modulate the internal reasoning process of large language models. Using Gemma-2-9B on GSM8K, we achieve 90.1% accuracy with a controllability score of 0.

Zhiqin Zhang, Yining Ma, Zhiguang Cao, Hoong Chuin Lau

Neural combinatorial optimization (NCO) has achieved remarkable performance, yet its learned model representations and decision rationale remain a black box. This impedes both academic research and practical deployment, since researchers and stakeholders require deeper insights into NCO models. In this paper, we take the first critical step towards interpret

Mushal Zia, Faisal Suwayyid, Yuta Hozumi, JunJie Wee

An accurate prediction of protein-nucleic acid binding affinity is vital for deciphering genomic processes, yet existing approaches often struggle in reconciling high accuracy with interpretability and computational efficiency. In this study, we introduce commutative algebra prediction (CAP), which couples persistent Stanley-Reisner theory with advanced sequ

Matthias Jammot, Björn Braun, Paul Streli, Rafael Wampfler

Understanding affect is central to anticipating human behavior, yet current egocentric vision benchmarks largely ignore the person's emotional states that shape their decisions and actions. Existing tasks in egocentric perception focus on physical activities, hand-object interactions, and attention modeling - assuming neutral affect and uniform personality.

Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan

Deepfake technologies are often associated with deception, misinformation, and identity fraud, raising legitimate societal concerns. Yet such narratives may obscure a key insight: deepfakes embody sophisticated capabilities for sensory manipulation that can alter human perception, potentially enabling beneficial applications in domains such as healthcare and

Wenxuan Bao, Ruxi Deng, Jingrui He

Pretrained vision-language models such as CLIP achieve strong zero-shot generalization but remain vulnerable to distribution shifts caused by input corruptions. In this work, we investigate how corruptions affect CLIP's image embeddings and uncover a consistent phenomenon we term as embedding variance collapse, where both intra-class and inter-class variance

Shiji Zhou, Tianbai Yu, Zhi Zhang, Heng Chang

Machine unlearning (MU) aims to efficiently remove sensitive or harmful memory from a pre-trained model. The key challenge is to balance the potential tradeoff between unlearning efficacy and utility preservation, which involves forgetting undesirable information as defined while maintaining the model's original performance. One potential way to tackle this

Xixian Liu, Rui Jiao, Zhiyuan Liu, Yurou Liu

Coordinate denoising has emerged as a promising method for 3D molecular pretraining due to its theoretical connection to learning molecular force field. However, existing denoising methods rely on oversimplied molecular dynamics that assume atomic motions to be isotropic and homoscedastic. To address these limitations, we propose a novel denoising framework

Jonathan Luk, Sung-Jin Oh, Dongxiao Yu

We consider a class of scalar quasilinear wave equations in three spatial dimensions satisfying the weak null condition. For solutions arising from small, localized, smooth data, we give an asymptotic formula describing the global asymptotics towards the future. We prove that the late-time asymptotics is given by a continuous superposition of decay rates, in

Dane W. deQuilettes, Eden Price, Linh M. Pham, Arthur Kurlej

Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged nitrogen vacancy (NV$^-$) and silicon vacancy centers (SiV$^-$), are emerging as quantum platforms poised for transition

Lihuang Fang, Xiao Hu, Yuchen Zou, Hong Zhang

Deep stereo matching has advanced significantly on benchmark datasets through fine-tuning but falls short of the zero-shot generalization seen in foundation models in other vision tasks. We introduce CogStereo, a novel framework that addresses challenging regions, such as occlusions or weak textures, without relying on dataset-specific priors. CogStereo embe

Karim Elmaaroufi, Liheng Lai, Justin Svegliato, Yutong Bai

Vision Language Models (VLMs) achieve strong performance on many vision-language tasks but often struggle with spatial reasoning$\unicode{x2014}$a prerequisite for many applications. Empirically, we find that a dataset produced by a current training data generation pipeline has a 57.6% human validation rate. These rates stem from current limitations: single-

Jiahui Li, Xinyue Liang, Geng Sun, Hui Kang

Low-altitude wireless networks (LAWNs) represent a promising architecture that integrates unmanned aerial vehicles (UAVs) as aerial nodes to provide enhanced coverage, reliability, and throughput for diverse applications. However, these networks face significant security vulnerabilities from both known and potential unknown eavesdroppers, which may threaten

Calin Chindris, Min Hyeok Kang, Daniel Kline

Let $\mathscr{P}$ be a poset and $\mathcal{S}$ a sequence of $n$ finite substes of $\mathscr{P}$. The Jordan type of a $\mathscr{P}$-persistence module $M$ at $\mathcal{S}$, denoted by $\mathsf{J}_{\mathcal{S}}(M) \in \mathbb{N}^n$, is defined as the Jordan type of a nilpotent operator $\mathbf{T}_{M, \mathcal{S}}$, which is constructed from $M$ and $\mathca

Ling Team, Ang Li, Ben Liu, Binbin Hu

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical

Enrique Casanova, Melvin Arias

This article explores an algebraic-recursive approach to construct differential operators that commute with a central operator $\hat{H}$ in quantum mechanics. Starting from the Schr\"odinger equation for a free particle, the work derives first-order symmetry generators, such as translations, rotations, and boosts, and examines their algebraic basis encompass

Zitiantao Lin, Yongpeng Sang, Yang Ye

Robotic manipulators are increasingly used to assist individuals with mobility impairments in object retrieval. However, the predominant joystick-based control interfaces can be challenging due to high precision requirements and unintuitive reference frames. Recent advances in human-robot interaction have explored alternative modalities, yet many solutions s

Kosuke Namekata, Hiroyuki Maehara, Yuta Notsu, Satoshi Honda

We report results from a five-year (132-night) dedicated observational campaign targeting two nearby young solar-type stars, EK Draconis ($\sim$50-125 Myr age) and V889 Hercules ($\sim$30 Myr age), using the 3.8m Seimei Telescope and Transiting Exoplanet Survey Satellite. The aim is to observationally constrain statistical properties of flaring radiation/hea

Kosuke Namekata, Kevin France, Jongchul Chae, Vladimir S. Airapetian

Coronal mass ejections (CMEs) on the early Sun may have profoundly influenced the planetary atmospheres of early Solar System planets. Flaring young solar analogues serve as excellent proxies for probing the plasma environment of the young Sun, yet their CMEs remain poorly understood. Here we report the detection of multi-wavelength Doppler shifts in Far-Ult

Billy Dickson, Zoran Tiganj

Most approaches to long-context processing increase the complexity of the transformer's internal architecture by integrating mechanisms such as recurrence or auxiliary memory modules. In this work, we introduce an alternative approach that modifies the input representation itself, rather than the transformer architecture. Inspired by cognitive models of huma

Xinyue Liang, Hui Kang, Junwei Che, Jiahui Li

While low-altitude wireless networks (LAWNs) based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications, they face severe signal attenuation in dense environments due to obstructions. To address this critical issue, we consider introducing collaborative beamforming (CB) of UAVs and omnidirectional reconfi

Bailey Trang, Parham Saremi, Alan Q. Wang, Fangrui Huang

Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify

Di Wang, Xiaoyu Zhang, Guodong Li, Wenyang Zhang

We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneous coefficient matrices across individuals, we accommodate shared low-dimensional structure alongside individual-specific deviations. To this end, we introduce a tensor-structured

Yusong Wu, Mason Wang, Heidi Lei, Stephen Brade

Music generation models can produce high-fidelity coherent accompaniment given complete audio input, but are limited to editing and loop-based workflows. We study real-time audio-to-audio accompaniment: as a model hears an input audio stream (e.g., a singer singing), it has to also simultaneously generate in real-time a coherent accompanying stream (e.g., a

Fatima Al-Janahi, Min-Seung Ko, Hao Zhu

Modeling dynamical systems is crucial across the science and engineering fields for accurate prediction, control, and decision-making. Recently, machine learning (ML) approaches, particularly neural ordinary differential equations (NODEs), have emerged as a powerful tool for data-driven modeling of continuous-time dynamics. Nevertheless, standard NODEs requi

Michael Carrion, Melissa M. Fuentes, Zaphenath Joseph, Alexander Nappo

The classical Erd\H{o}s--Ko--Rado (EKR) theorem characterizes the maximum size of intersecting families of $r$-element subsets of an $n$-element set. We study EKR-type questions for independent $r$-sets in \emph{pendant} graph constructions, obtained by attaching to each base vertex a clique of prescribed size. Our contributions are threefold. We give an alt

Xingjian Tao, Yiwei Wang, Yujun Cai, Yihong Luo

While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant challenge, particularly as high-resolution inputs cause visual positional encodings (VPEs) to degrade. We demonstrate that these encoding failures do not result in random noise but instead trigger predictable, directional

Kayhan Behdin, Qingquan Song, Sriram Vasudevan, Jian Sheng

Large Language Models (LLMs) have demonstrated impressive quality when applied to predictive tasks such as relevance ranking and semantic search. However, deployment of such LLMs remains prohibitively expensive for industry applications with strict latency and throughput requirements. In this work, we present lessons and efficiency insights from developing a

Saif E. Nouma, Attila A. Yavuz

The Internet of Things (IoT) relies heavily on resource-limited devices to communicate critical (e.g., military data) information under low-energy adversarial environments and low-latency wireless channels. Authenticated Encryption (AE) guarantees confidentiality, authenticity, and integrity, making it a vital security service for IoT. However, current deplo

R. Scott Johnson

Traditional graph centrality measures effectively quantify node importance but fail to capture the structural uniqueness of multi-scale connectivity patterns -- critical for understanding network resilience and function. This paper introduces Hamming Graph Metrics (HGM), a framework that represents a graph by its exact-$k$ reachability tensor $\mathcal{B}G\i

Xuanming Zhang

Large Language Models (LLMs) exhibit a troubling duality, capable of both remarkable generalization and brittle, verbatim memorization of their training data. This unpredictability undermines their reliability in high-stakes applications. In this work, we propose a unified framework to understand, identify, and control these distinct reasoning modes. First,

You-Jin Kim

Augmented Reality (AR) technologies are redefining how we perceive and interact with the world by seamlessly integrating digital elements into our physical surroundings. These technologies offer personalized experiences and transform familiar spaces by layering new narratives onto the real world. Through increased levels of perceived agency and immersive env

Steven Dale Cutkosky

In this article we investigate the condition that the Proj of a Rees algebra of a graded family of ideals in a Noetherian local ring $R$ is Noetherian. In many cases, the Proj will be Noetherian even when the Rees algebra is not. For instance, the Proj of the Rees algebra of a graded filtration of ideals will alway be Noetherian if the analytic spread of the

Su Liu, Xin Hu, Shurong Wen, Chengyi Chen

Physiologically Based Pharmacokinetic (PBPK) modeling is a key tool in drug development for predicting drug concentration dynamics across organs. Traditional PBPK approaches rely on ordinary differential equations with simplifying assumptions that limit their ability to capture nonlinear and system-level physiological interactions. In this work, we investiga

Yankai Chen, Xinni Zhang, Yifei Zhang, Yangning Li

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurological impairments. However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcom

Thomas Bailie, S. Karthik Mukkavilli, Varvara Vetrova, Yun Sing Koh

Climate events arise from intricate, multivariate dynamics governed by global-scale drivers, profoundly impacting food, energy, and infrastructure. Yet, accurate weather prediction remains elusive due to physical processes unfolding across diverse spatio-temporal scales, which fixed-resolution methods cannot capture. Hierarchical Graph Neural Networks (HGNNs

Andrew A. Couperus, Todd J. Henry, Aman Kar, Wei-Chun Jao

We report on a study of 36 pairs of `twin' M dwarfs in wide binaries and assess how similarly the stars behave. Stars in each twin pair have BP, RP, $J$, $H$, and $K_s$ differing by $<$0.10 mag, mass estimates matching within $<$3%, and presumably the same age and composition. We utilize short- and long-term photometry, multi-epoch spectroscopy, and archival

Shaunak Mishra, Reza Samadi, Diane Bérard

PLATO is designed to detect Earth-sized exoplanets around solar-type stars and to measure their radii with accuracy better than \(2\%\) via the transit method. Charge transfer inefficiency (CTI), a by-product of radiation damage to CCDs, can jeopardise this accuracy and therefore must be corrected. We assessed and quantified the impact of CTI on transit-dept

Runze Li, Wenchao Yan, Longqing Yi

When a high-power, femtosecond, circularly polarized (CP) laser pulse is incident on a micrometer-scale aperture in a solid foil target, it drives surface plasma oscillation, generating high-order harmonic vortices in the diffracted light. However, this mechanism has so far only been studied theoretically under ideal conditions. In this work, we perform nume

Jeremy L. Marzuola, Jonathan C. Mattingly

A model Hamiltonian dynamical system has been derived to study frequency cascades in the cubic defocusing nonlinear Schr\"odinger equation on the torus. Here, we explore the framework for exploring a canonical ensemble formulation of the dynamics through classification of energy minimizers for fixed mass and characterizing the invariant measure in a neighbor

A. Padoan, J. Eising, I. Markovsky

The paper extends core results of behavioral systems theory from linear to affine time-invariant systems. We characterize the behavior of affine time-invariant systems via kernel, input-output, state-space, and finite-horizon data-driven representations, demonstrating a range of structural parallels with linear time-invariant systems. Building on these repre

Chengxuan Li, August George, Reece Neff, Doo Nam Kim

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying prote

Asaf Karagila

The Bristol model is an inner model of $L[c]$, where $c$ is a Cohen real, which is not constructible from a set. The idea was developed in 2011 in a workshop taking place in Bristol, but was only written in detail by the author in [8]. This paper is a guide for those who want to get a broader view of the construction. We try to provide more intuition that mi

Alina Ene, Ta Duy Nguyen, Adrian Vladu

In this paper, we study the problem $\min_{x\in \mathbb{R}^{d},Nx=v}\sum_{i=1}^{n}f((Ax-b)_{i})$ for a quasi-self-concordant function $f:\mathbb{R}\to\mathbb{R}$, where $A,N$ are $n\times d$ and $m\times d$ matrices, $b,v$ are vectors of length $n$ and $m$ with $n\ge d.$ We show an algorithm based on a trust-region method with an oracle that can be implement

Pau Juan-Bartroli, José Ignacio Rivero-Wildemauwe

Rejections of positive offers in the Ultimatum Game have been attributed to different motivations. We show that a model combining social preferences and moral concerns provides a unifying explanation for these rejections while accounting for additional evidence. Under the preferences considered, a positive degree of spite is a necessary and sufficient condit

Pavlos Ntais

Large language models (LLMs) remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety mechanisms, posing significant risks in cybersecurity applications. We introduce Jailbreak Mimicry, a systematic methodology for training compact attacker models to automatically generate narrative-based jailbreak prompt

Atij Mahesh

Large Language Models (LLMs) still produce gender-stereotyped language even in occupation-neutral contexts that reflect deep societal biases (Rudinger et al., 2018). To address this, prior work has proposed prompting, constrained decoding (Dathathri et al., 2020; Zhou et al., 2024), post-processing, and fine-tuning-based alignment (Rafailov et al., 2023; Rav

David Bruns-Smith, Zhongming Xie, Avi Feller

Estimators in statistics and machine learning must typically trade off between efficiency, having low variance for a fixed target, and distributional robustness, such as multiaccuracy, or having low bias over a range of possible targets. In this paper, we consider a simple estimator, ridge boosting: starting with any initial predictor, perform a single boost

Sam Hopkins

We explain how to define the Robinson-Schensted-Knuth (RSK) correspondence in terms of local transformations called "toggles." (This note, which is not intended for publication and which is based on presentations of Alex Postnikov, was written in 2014 and has been circulating since then. We are finally posting it to the arXiv for preservation purposes.)

Emma Von Hoene, Aanya Gupta, Hamdi Kavak, Amira Roess

This study introduces the Spatial Health and Population Estimator (SHAPE), a spatial microsimulation framework that applies hierarchical iterative proportional fitting (IPF) to estimate two health risk behaviors and eleven health outcomes across multiple spatial scales. SHAPE was evaluated using county-level direct estimates from the Behavioral Risk Factor S

Whee Yeon Cheong, Sang-Sung Lee, Chanwoo Song, Jeffrey Hodgson

Context. It has recently been suggested that angular diameter distances derived from comparing the variability timescales of blazars to angular size measurements with very long baseline interferometry (VLBI) may provide an alternative method to study the cosmological evolution of the Universe. Once the intrinsic brightness temperature ($T_{\rm int}$) is know

Donald M. Davis

The odd part of 2^e! as e approaches infinity leads to a 2-adic integer z. The bits of z were publicized in OEIS-A359349, where two conjectures were made, relevant to computing z. We prove both of those conjectures. A second 2-adic integer, the limit of ((2^e-1)!!-1)/2^e, plays a key role in one proof.

Kangan Li, Yashar Mehmani

We aim to solve the incompressible Navier-Stokes equations within the complex microstructure of a porous material. Discretizing the equations on a fine grid using a staggered (e.g., marker-and-cell, mixed FEM) scheme results in a nonlinear residual. Adopting the Newton method, a linear system must be solved at each iteration, which is large, ill-conditioned,

J. R. Pybus, D. Dutta, H. Gao, O. Hen

The nuclear EMC effect is the observation that quark distributions in bound nucleons experience significant modification at large $x$ relative to free nucleons. Despite decades of measurements verifying the presence of this effect in quarks across a wide range of nuclei, behavior of large-$x$ gluons in nuclei remains almost completely unknown. As the nuclear

Siyu Zhu, Anastasiya Karpovich, Albert Chen, Jessica Koscheka

We tackle the challenge of training reliable code-fixing agents in real repositories, where complex builds and shifting dependencies make evaluation unstable. We developed a verifiable pipeline with success defined as post-fix build validation and improved reproducibility across ~1K real issues by pinning dependencies and disabling automatic upgrades. Buildi

Rahul Ajit

Given a local ring $(R, \mathfrak{m})$ and an ideal $\mathfrak{a}$ of positive height, we give a way of computing multiplier module ${J}(\omega_{{T}}, t^{-\lambda})$ for the extended Rees algebra ${T} =R[\mathfrak{a} t, t^{-1}]$ for an ideal $\mathfrak{a}$ by proving a decomposition theorem for ${J}(\omega_{{T}}, t^{-\lambda})$, (also see the works of Budur,

Luca Caldera, Lara Cavinato, Francesca Ieva

The variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability across multicenter studies. We present a novel image-based harmonization framework for 3D T1-weighted brain MRI, which disentangles anatomical content from scanner- and site-specific variations. The model

Martha Barnard, Jared D. Huling, Julian Wolfson

Positivity violations, which occur when some subgroups either always or never receive a treatment of interest, pose significant challenges for causal effect estimation with observational data. Recent balancing weight methods have proved to be highly effective in confounding control, however their utility is diminished in the presence of positivity violations

Antonio Olivas-Martinez, Fei Gao, Holly Janes

Active-controlled trials with non-inferiority objectives are often used when effective interventions are available, but new options may offer advantages or meet public health needs. In these trials, participants are randomized to an experimental intervention or an active control. The traditional non-inferiority criterion requires that the new intervention pr

Luca Caldera, Giacomo Bottacini, Lara Cavinato

Generative modeling has emerged as a powerful paradigm for representation learning, but its direct applicability to challenging fields like medical imaging remains limited: mere generation, without task alignment, fails to provide a robust foundation for clinical use. We propose MAGIC-Flow, a conditional multiscale normalizing flow architecture that performs

Himadri S. Pandey, Kai Wang, Gian-Gabriel P. Garcia

Restless multi-armed bandits (RMABs) provide a scalable framework for sequential decision-making under uncertainty, but classical formulations assume binary actions and a single global budget. Real-world settings, such as healthcare, often involve multiple interventions with heterogeneous costs and constraints, where such assumptions break down. We introduce