October 2025 arXiv papers — page 51

Yu Xia, Zhiqiang Xu

Compressed sensing has demonstrated that a general signal $\boldsymbol{x} \in \mathbb{F}^n$ ($\mathbb{F}\in \{\mathbb{R},\mathbb{C}\}$) can be estimated from few linear measurements with an error {proportional to} the best $k$-term approximation error, a property known as instance optimality. In this paper, we investigate instance optimality in the context o

Sui He, Shenbin Qian

Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that hinder their communication of mental distress. In this study, we evaluate the effectiveness of AI-generated images in supporting self-expression of mental distress. To achieve this

Feng He, Guodong Tan, Qiankun Li, Jun Yu

Light field microscopy (LFM) has become an emerging tool in neuroscience for large-scale neural imaging in vivo, notable for its single-exposure volumetric imaging, broad field of view, and high temporal resolution. However, learning-based 3D reconstruction in XLFM remains underdeveloped due to two core challenges: the absence of standardized datasets and th

Tadisetty Sai Yashwanth

Floating-point non-associativity makes fundamental deep learning operations, such as matrix multiplication (matmul) on GPUs, inherently non-deterministic. Despite this, the statistical structure of the resulting numerical error remains poorly understood. A common working assumption is that these errors behave as independent and identically distributed (i.i.d

Moritz Schubotz, Jan Philip SoloveJ

The IMU-ICIAM working group's new report on Fraudulent Publishing in the Mathematical Sciences documents how gaming of bibliometrics, predatory outlets and paper-mill activity are eroding trust in research, mathematics included. This short EMS note brings that analysis home to Europe. We urge readers to recognise the warning signs of fraudulent publishing, t

Yigui Feng, Qinglin Wang, Yang Liu, Ke Liu

Accurately analyzing spontaneous, unconscious micro-expressions is crucial for revealing true human emotions, but this task remains challenging in wild scenarios, such as natural conversation. Existing research largely relies on datasets from controlled laboratory environments, and their performance degrades dramatically in the real world. To address this is

Yufeng Shen, Zhiyu Song, Fenglin Yu, Leopold Wuhan Zhou

In this paper, we investigate the homotopy type and combinatorial properties of total cut complexes of squared cycle graphs. The total cut complexes are a new type of graphical complexes introduced by Bayer et al.(2024) to extend Fr\"oberg's theorem. In Bayer et al.[Topology of cut complexes of graphs, SIAM J. on Discrete Math. 38(2): 1630--1675 (2024)], the

Jiang-Chuan Yu, Yan Cao, Zexin Hu, Lijing Shao

Ultralight dark matter (ULDM) model is a leading dark matter candidate that arises naturally in extensions of the Standard Model. In the Galactic Center, ULDM manifests as dense hydrogen-like boson clouds or self-gravitating soliton cores. We present the first study of the gravitational effects of these ULDM structures on pulsar orbits around Sgr A*, using p

Eduard Popescu, Adrian Groza, Andreea Cernat

The task here is to predict the toxicological activity of chemical compounds based on the Tox21 dataset, a benchmark in computational toxicology. After a domain-specific overview of chemical toxicity, we discuss current computational strategies, focusing on machine learning and deep learning. Several architectures are compared in terms of performance, robust

Mahiro Ukai, Shuhei Kurita, Nakamasa Inoue

Object state recognition aims to identify the specific condition of objects, such as their positional states (e.g., open or closed) and functional states (e.g., on or off). While recent Vision-Language Models (VLMs) are capable of performing a variety of multimodal tasks, it remains unclear how precisely they can identify object states. To alleviate this iss

Onur Akgün

The coordination of multiple autonomous agents in high-speed, competitive environments represents a significant engineering challenge. This paper presents CRUISE (Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing), a reinforcement learning framework designed to solve this challenge in the demanding domain of multi-drone racing. CRUISE over

Gazi Tanbhir, Md. Farhan Shahriyar, Abdullah Md Raihan Chy

Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often suffer from high computational costs, limitations in feature representation, and challenges in generalizing from

Hai-Jun Li

We introduce a mechanism for multi-component dark matter (DM) that originates from axion mixing and present some of its defining properties. In this context, multi-component DM implies that the cold DM is composed of the QCD axion and many ultra-light axion-like particles (ALPs). This framework can be realized in the type IIB string axiverse with hierarchica

Onur Akgün

This paper introduces SPIRAL (Self-Play Incremental Racing Algorithm for Learning), a novel approach for training autonomous drones in multi-agent racing competitions. SPIRAL distinctively employs a self-play mechanism to incrementally cultivate complex racing behaviors within a challenging, dynamic environment. Through this self-play core, drones continuous

Md. Farhan Shahriyar, Gazi Tanbhir, Abdullah Md Raihan Chy

Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches, as computational techniques are key to improving the performance of image classification tasks. This study presents a hybrid approach that uses ResNet-50 (Residual Network) for feature extraction and Quantum Support Vector Machines

Archer Moore, Heejung Shim, Jingge Zhu, Mingming Gong

Semi-supervised learning (SSL) aims to train a machine learning model using both labelled and unlabelled data. While the unlabelled data have been used in various ways to improve the prediction accuracy, the reason why unlabelled data could help is not fully understood. One interesting and promising direction is to understand SSL from a causal perspective. I

Md. Mehedi Hasan

Recent work has revealed MOLE, the first practical attack to compromise GPU Trusted Execution Environments (TEEs), by injecting malicious firmware into the embedded Microcontroller Unit (MCU) of Arm Mali GPUs. By exploiting the absence of cryptographic verification during initialization, adversaries with kernel privileges can bypass memory protections, exfil

Junsik Jung, Yoonki Cho, Woo Jae Kim, Lin Wang

Exposure-agnostic video frame interpolation (VFI) is a challenging task that aims to recover sharp, high-frame-rate videos from blurry, low-frame-rate inputs captured under unknown and dynamic exposure conditions. Event cameras are sensors with high temporal resolution, making them especially advantageous for this task. However, existing event-guided methods

Yanfei Wang, Dmitry V. Churbanov, Raul L. Argun, Alexander V. Gorbachev

We consider the optimization of a neural network previously developed by the authors for the joint inversion of 3D gravitational and magnetic fields in the context of mineral exploration. The distinctive feature of this neural network is that it solves ill-posed (ill-conditioned) inverse problems. The neural network implements a special two-level algorithm.

Patrick Erik Bradley

Kernel functions for Laplacian integral operators are constructed on $p$-adic analytic manifolds using charts and transition maps from an atlas with connected nerve complex. In the compact case, an operator of Vladimirov-Taibleson type parametrised by a real parameter $s$ is defined. Its kernel function uses a geodetic-like distance function on the nerve com

Jing-An Sun, Hang Fan, Junchao Gong, Ben Fei

Data assimilation (DA) plays a pivotal role in numerical weather prediction by systematically integrating sparse observations with model forecasts to estimate optimal atmospheric initial condition for forthcoming forecasts. Traditional Bayesian DA methods adopt a Gaussian background prior as a practical compromise for the curse of dimensionality in atmospher

Kaveri Banerjee, Sajal Saha

Blockchain systems rely on decentralized ledgers and strong security guarantees. A key requirement is non-repudiation, which prevents denial of transaction authorship and supports integrity of recorded data. This work surveys digital signature schemes used in blockchain platforms and analyzes how they deliver non-repudiation and contribute to overall system

Ibuki Maeda, Rentian Yao, Atsushi Nitanda

The Schr\"odinger bridge problem seeks the optimal stochastic process that connects two given probability distributions with minimal energy modification. While the Sinkhorn algorithm is widely used to solve the static optimal transport problem, a recent work (Pooladian and Niles-Weed, 2024) proposed the Sinkhorn bridge, which estimates Schr\"odinger bridges

Zhifeng Wang, Xinyue Zheng, Chunyan Zeng

As information technology advances, education is moving from one-size-fits-all instruction toward personalized learning. However, most methods handle modeling, item selection, and feedback in isolation rather than as a closed loop. This leads to coarse or opaque student models, assumption-bound adaptivity that ignores diagnostic posteriors, and generic, non-

Jianhua Xian, Sai Hung Cheung, Cheng Su

This work presents a novel surface decomposition method for the sensitivity analysis of first-passage dynamic reliability of linear systems subjected to Gaussian random excitations. The method decomposes the sensitivity of first-passage failure probability into a sum of surface integrals over the constrained component limit-state hypersurfaces. The evaluatio

Wang Liu, Cunhua Pan, Hong Ren, Wei Zhang

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads

Jinhan Chen, Jianchun Liu, Hongli Xu, Xianjun Gao

The growing memory footprint of the Key-Value (KV) cache poses a severe scalability bottleneck for long-context Large Language Model (LLM) inference. While KV cache eviction has emerged as an effective solution by discarding less critical tokens, existing token-, block-, and sentence-level compression methods struggle to balance semantic coherence and memory

Iman Mohamed Attia

This paper presents an in-depth exploration of the innovative Median-based unit Rayleigh (MBUR) distribution, previously introduced by the author. This new approach is specifically designed for conducting quantile regression analysis, enabling researchers to gain valuable insights into real-world data applications. The author effectively demonstrates the fea

Dongyi Liu, Jiangtong Li

Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing trigger generators are often simplistic in structure and overly reliant on specific features, confining them to a single graph learning paradigm, such as graph supervised learning, graph contrastive learning,

Maximilian Matyash, Avigdor Gal, Arik Senderovich

With recent technological advances, process logs, which were traditionally deterministic in nature, are being captured from non-deterministic sources, such as uncertain sensors or machine learning models (that predict activities using cameras). In the presence of stochastically-known logs, logs that contain probabilistic information, the need for stochastic

Benedict Diederich, Ingo Fuchs, Haoran Wang, Holger Bierhoff

Building the optical setup for investigating biological questions comes with challenges. A major such challenge is setting up and synchronizing the control of multiple hardware components such as stages, cameras and lasers. With UC2-ESP we present a compact electronics system powered by the ESP32 microcontroller, designed to provide general-purpose control f

G B Kevin Arjun, Suvrojit Mitra, Sanjay Ghosh

Image downscaling is one of the key operations in recent display technology and visualization tools. By this process, the dimension of an image is reduced, aiming to preserve structural integrity and visual fidelity. In this paper, we propose a new image downscaling method which is built on the core ideas of image filtering and edge detection. In particular,

Jinbo Niu

Stroke remains a leading cause of death and disability worldwide, yet effective prediction of stroke risk using large-scale population data remains challenging due to data imbalance and high-dimensional features. In this study, we develop and evaluate regularized logistic regression models for stroke prediction using data from the 2022 Behavioral Risk Factor

Ziyuan He, Yuxuan Wang, Jiaqi Li, Kexin Liang

Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remain fundamentally limited and underexplored. This gap is especially significant in many real-world long-context applications that were rarely benchmarked. In this paper, we introduce Lo

Bibhabasu Debnath, Sahana Ray, Sanjay Ghosh

Images acquired in low-light environments present significant obstacles for computer vision systems and human perception, especially for applications requiring accurate object recognition and scene analysis. Such images typically manifest multiple quality issues: amplified noise, inadequate scene illumination, contrast reduction, color distortion, and loss o

Xinhui Wu, Gianluca Colò, Kouichi Hagino, Pengwei Zhao

Incorporating nuclear shell effects within the framework of orbital-free density functional theory (DFT) has remained a longstanding challenge in nuclear physics. While the Hohenberg-Kohn theorem formally guarantees the existence of an orbital-free density functional that is capable of describing all many-body effects, including shell effects, practical atte

Rainer Mandel, Tobias Weth

The present paper is devoted to existence results for time-periodic solutions of generalized nonlinear wave equations in a closed Riemannian manifold M. Our main focus lies on the doubly degenerate setting where the associated generalized wave operator has an infinite dimensional kernel and the nonlinearity may vanish on open subsets of M. To deal with this

Yuyang Ding, Chi Zhang, Juntao Li, Haibin Lin

Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context, models explore reasoning trajectories and exploit rollouts with correct answers as positive signals for policy optimization. However, these rollouts might involve flawed patterns

Ilya Dumanski

We propose the notion of perverse coherent sheaves for symplectic singularities and study its properties. In particular, it gives a basis of simple objects in the Grothendieck group of Poisson sheaves. We show that perverse coherent bases for the nilpotent cone and for the affine Grassmannian arise as particular cases of our construction.

Pedro Chumpitaz-Flores, My Duong, Ying Mao, Kaixun Hua

Clustering on NISQ hardware is constrained by data loading and limited qubits. We present \textbf{qc-kmeans}, a hybrid compressive $k$-means that summarizes a dataset with a constant-size Fourier-feature sketch and selects centroids by solving small per-group QUBOs with shallow QAOA circuits. The QFF sketch estimator is unbiased with mean-squared error $O(\v

Heekang Song, Wan Choi

In this paper, we propose an optimally structured gradient coding scheme to mitigate the straggler problem in distributed learning. Conventional gradient coding methods often assume homogeneous straggler models or rely on excessive data replication, limiting performance in real-world heterogeneous systems. To address these limitations, we formulate an optimi

Ashwin Ramachandran, Vaibhav Raj, Indrayumna Roy, Soumen Chakrabarti

Graph retrieval based on subgraph isomorphism has several real-world applications such as scene graph retrieval, molecular fingerprint detection and circuit design. Roy et al. [35] proposed IsoNet, a late interaction model for subgraph matching, which first computes the node and edge embeddings of each graph independently of paired graph and then computes a

Zhan Cao, Zhong-Jun Yang, Jin-Lei Yang, Tai-Fu Feng

In this papar, we study the production and decay of a charged Higgs boson, a pseudoscalar Higgs boson, and a $Z'$ boson at the LHC within the flavor-dependent model (FDM), at the LHC. Considering the constraints from perturbative unitarity and experimental measurements (e.g., the flavor physics data, higgs signal strengths, electroweak precision observables)

Manideep Pendyala

Pulsar surveys generate millions of candidates per run, overwhelming manual inspection. This thesis builds a deep learning pipeline for radio pulsar candidate selection that fuses array-derived features with image diagnostics. From approximately 500 GB of Giant Metrewave Radio Telescope (GMRT) data, raw voltages are converted to filterbanks (SIGPROC), then d

Jotaro Yano

We formalize a cross-domain "ZK coprocessor bridge" that lets Solana programs request private execution on Aztec L2 (via Ethereum) using Wormhole Verifiable Action Approvals (VAAs) as authenticated transport. The system comprises: (i) a Solana program that posts messages to Wormhole Core with explicit finality; (ii) an EVM Portal that verifies VAAs, enforces

Hao Zheng, Zirui Pang, Ling li, Zhijie Deng

Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data privacy, making Machine Unlearning (MU), the selective removal of learned information, a critical necessity. However, existing MU benchmarks for MLLMs are limited by a lack of image diversity, potential inaccuracies, and insufficient evaluation scenarios, which fail to capture

Chen Chen, Majid Abdolshah, Violetta Shevchenko, Hongdong Li

Existing diffusion-based super-resolution approaches often exhibit semantic ambiguities due to inaccuracies and incompleteness in their text conditioning, coupled with the inherent tendency for cross-attention to divert towards irrelevant pixels. These limitations can lead to semantic misalignment and hallucinated details in the generated high-resolution out

Daniel Lacker, Kavita Ramanan, Ruoyu Wu

We study locally interacting processes in discrete time, often called probabilistic cellular automata, indexed by locally finite graphs. For infinite regular trees and certain generalized Galton-Watson trees, we show that the marginal evolution at a single vertex and its neighborhood can be characterized by an autonomous stochastic recursion referred to as t

Jin Kozaki, Julian Lang, Yasha Neiman

We study Higher-Spin Gravity in 4-dimensional (Anti-)de Sitter space, at leading order in the interactions (cubic vertices), in the AdS lightcone formalism developed by Metsaev. Using the vertices' chiral structure, we extend the formalism into a broader class of lightcone frames, which allows for lightcones of bulk points. This enables us to write the light

Noshitha Padma Pratyusha Juttu, Sahithi Singireddy, Sravani Gona, Sujal Timilsina

Large Language Models (LLMs) have transformed text understanding, yet their adaptation to specialized legal domains remains constrained by the cost of full fine-tuning. This study provides a systematic evaluation of fine tuning, parameter efficient adaptation (LoRA, QLoRA), and zero-shot prompting strategies for unfair clause detection in Terms of Service (T

Sungmin Kang, Sumi Yun, Jingun Hong, Shin Yoo

Fault Localization (FL) aims to identify root causes of program failures. FL typically targets failures observed from test executions, and as such, often involves dynamic analyses to improve accuracy, such as coverage profiling or mutation testing. However, for large industrial software, measuring coverage for every execution is prohibitively expensive, maki

Xiang Fei, Tina Tian, Howie Choset, Lu Li

Loop closure is critical in Simultaneous Localization and Mapping (SLAM) systems to reduce accumulative drift and ensure global mapping consistency. However, conventional methods struggle in perceptually aliased environments, such as narrow pipes, due to vector quantization, feature sparsity, and repetitive textures, while existing solutions often incur high

Yen-Hong Wong, Lai-Kuan Wong

Aesthetic-driven image cropping is crucial for applications like view recommendation and thumbnail generation, where visual appeal significantly impacts user engagement. A key factor in visual appeal is composition--the deliberate arrangement of elements within an image. Some methods have successfully incorporated compositional knowledge through evaluation-b

Morgan Himes, Samiksha Krishnamurthy, Andrew Lizarraga, Srinath Saikrishnan

Upcoming surveys will produce billions of galaxy images but comparatively few spectra, motivating models that learn cross-modal representations. We build a dataset of 134,533 galaxy images (HSC-PDR2) and spectra (DESI-DR1) and adapt a Multi-Modal Masked Autoencoder (MMAE) to embed both images and spectra in a shared representation. The MMAE is a transformer-

Yicong Jiang, Zheng Tracy Ke

Vertex hunting (VH) is the task of estimating a simplex from noisy data points and has many applications in areas such as network and text analysis. We introduce a new variant, semi-supervised vertex hunting (SSVH), in which partial information is available in the form of barycentric coordinates for some data points, known only up to an unknown transformatio

Robert de Mello Koch, Antal Jevicki, Garreth Kemp, Anik Rudra

Continuing the formulation of finite $N$ Hilbert spaces in emergent theories we study in this work $S_{N}$ symmetric collective models. For the case of $N$ bosons in $d$ dimensions, which map to matrix models with commuting matrices, we describe a complete algorithm and give a detailed case study reproducing the expected primaries and determining secondary i

Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu, Spring Berman

In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is

Zi-Miao Huang, Changqian Li, Zuowei Liu

The supernova (SN) core, characterized by its extreme temperature and density, serves as a unique laboratory for new-physics searches. Low-energy supernovae (LESNe) provide particularly powerful probes, as their low explosion energies place stringent limits on any additional energy deposition in the mantle by new particles. We present refined LESN constraint

V. Sanchez Padilla, Albert Espinal, Jose Cordova-Garcia, Lisa Schibelius

Low-power Internet of Things (IoT) technologies are becoming increasingly important in engineering education as a tool to help students connect theory to real applications. However, many institutions face barriers that slow down their adoption in courses and labs. This paper reviews recent studies to understand these barriers and organizes them into three gr

Yang Tian, Fan Liu, Jingyuan Zhang, Wei Bi

Large Multimodal Models (LMMs) have achieved remarkable progress in generating photorealistic and prompt-aligned images, but they often produce outputs that contradict verifiable knowledge, especially when prompts involve fine-grained attributes or time-sensitive events. Conventional retrieval-augmented approaches attempt to address this issue by introducing

Michael Ito, Danai Koutra, Jenna Wiens

Random walk neural networks (RWNNs) have emerged as a promising approach for graph representation learning, leveraging recent advances in sequence models to process random walks. However, under realistic sampling constraints, RWNNs often fail to capture global structure even in small graphs due to incomplete node and edge coverage, limiting their expressivit

Pedro Chumpitaz-Flores, My Duong, Cristobal Heredia, Kaixun Hua

Constrained clustering leverages limited domain knowledge to improve clustering performance and interpretability, but incorporating pairwise must-link and cannot-link constraints is an NP-hard challenge, making global optimization intractable. Existing mixed-integer optimization methods are confined to small-scale datasets, limiting their utility. We propose

Jinho Cha, Justin Yu, Junyeol Ryu, Eunchan Daniel Cha

This study introduces an inverse behavioral optimization framework that integrates QALY-based health outcomes, ROI-driven incentives, and adaptive behavioral learning to quantify how policy design shapes national healthcare performance. Building on the FOSSIL (Flexible Optimization via Sample-Sensitive Importance Learning) paradigm, the model embeds a regret

Yuxiang Huang, Wei Wu, Qingyuan Mei, Yiheng Lin

Quantum magnetometry represents a fundamental component of quantum metrology, where trapped-ion systems have achieved $\rm{pT}/\sqrt{\rm{Hz}}$ sensitivity in single-ion radio-frequency magnetic field measurements via dressed states based dynamical decoupling. Here we propose a scalable trapped-ion magnetometer utilizing the mixed dynamical decoupling method,

Jing-An Sun, Li Yan, Charles Gale, Sangyong Jeon

Heavy-ion collision physics has entered the high precision era, demanding theoretical models capable of generating huge statistics to compare with experimental data. However, traditional hybrid models, which combine hydrodynamics and hadronic transport, are computationally intensive, creating a significant bottleneck. In this work, we introduce DiffHIC, an e

Armel Koulong, Ali Pakniyat

This paper presents a constructive framework for synthesizing provably safe controllers for nonlinear multi-agent systems subject to bounded disturbances. The methodology applies to systems representable in Brunovsky canonical form, accommodating arbitrary-order dynamics in multi-dimensional spaces. The central contribution is a method of constraint tighteni

Junran Wu, Beng Chin Ooi, Ke Xu

Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links. Given the noisy nature of real-world connections, the robustness of SGNN has also emerged as a pivotal research area. Under the supervision of empirical properties, graph structure learning has shown its robustness on si

Seohong Park, Aditya Oberai, Pranav Atreya, Sergey Levine

In this work, we present Transitive Reinforcement Learning (TRL), a new value learning algorithm based on a divide-and-conquer paradigm. TRL is designed for offline goal-conditioned reinforcement learning (GCRL) problems, where the aim is to find a policy that can reach any state from any other state in the smallest number of steps. TRL converts a triangle i

Allen Hunt, Didier Sornette

We propose a unified framework linking silicate weathering feedbacks, ecohydrological optimality and vegetation-climate interactions to explain Earth's Global Average Temperature evolution over the Phanerozoic. The framework integrates two complementary processes: (i) solute-transport-limited weathering, which governs long-term carbon sequestration, and (ii)

Molla Basir Ahamed, Partha Pratim Roy, Sujoy Majumder

We investigate improved forms of the Bohr inequality, using the quantity $S_r/\pi$, for analytic selfmaps in class $\mathcal{B}$ of $\mathbb{D}$, where $S_r$ is the area measure of $\mathbb{D}_r$. We then generalize the inequality for harmonic mappings ($\mathcal{P}^0_{\mathcal{H}}(M)$ and $\mathcal{W}^0_{\mathcal{H}}(\alpha)$ of the form $f = h + \overline{

Shondell Williams, Karen Blackmore, Regina Berretta, Michelle Mansfield

Demand for technology focused STEM professionals will increase globally over the coming decade, with many countries finding it difficult to meet growing demand. Compounding this are difficulties in attracting and retaining female technology-focused professionals. Research seeking to address this gender imbalance and workforce shortage focuses on increasing p

Rui Jin, Chen Chen, Yin Liu, Hongfu Sun

Accurate diagnosis of Parkinson's disease (PD) from MRI remains challenging due to symptom variability and pathological heterogeneity. Most existing methods rely on conventional magnitude-based MRI modalities, such as T1-weighted images (T1w), which are less sensitive to PD pathology than Quantitative Susceptibility Mapping (QSM), a phase-based MRI technique

Wenchao Xie, Qingsong Wang, Chengcheng Yan, Zheng Peng

The fully-connected tensor network (FCTN) decomposition has gained prominence in the field of tensor completion owing to its powerful capacity to capture the low-rank characteristics of tensors. Nevertheless, the recovery of local details in the reconstructed tensor still leaves scope for enhancement. In this paper, we propose efficient tensor completion mod

Alperen Duru, Mohammad Mozaffari, Ticao Zhang, Mehrnaz Afshang

Future immersive XR applications will require energy-efficient, high data rate, and low-latency wireless communications in uplink and downlink. One of the key considerations for supporting such XR applications is intelligent and adaptive resource allocation with edge offloading. To address these demands, this paper proposes a reinforcement learning-based res

Vaibhav Jindal, Hejian Sang, Chun-Mao Lai, Yanning Chen

Diffusion language models (dLLMs) are an emerging alternative to autoregressive (AR) generators, but aligning them to human preferences is challenging because sequence log-likelihoods are intractable and pairwise preference data are costly to collect. We introduce ELBO-KTO, which combines an ELBO surrogate for diffusion log-likelihoods with a prospect-theore

Ciera McFarland, Antonio Alvarez, Sarah Taher, Nathaniel Hanson

Vine robots extend their tubular bodies by everting material from the tip, enabling navigation in complex environments with a minimalist soft body. Despite their promise for field applications, especially in the urban search and rescue domain, performance is constrained by the weight of attached sensors or tools, as well as other design and control choices.

Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai, Chandan K. Reddy

Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials discovery (LLEMA), a unified framework that couples the scientific knowledge embedded in large language models with chemistry-informed evolutionary rules and memory-based refinemen

Stephen Scully, Guangzhao Zhu

A 2022 result of Karpenko establishes a conjecture of Hoffmann-Totaro on the possible values of the first higher isotropy index of an arbitrary anisotropic quadratic form of given dimension over an arbitrary field. For nondegenerate forms, this essentially goes back to a 2003 article of the same author on quadratic forms over fields of characteristic not $2$

Tran Van Khanh, Do Xuan Cho, Hoang Phi Dung

In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventuall

Ren Yin, Takashi Ishida, Masashi Sugiyama

As artificial intelligence (AI) systems approach and surpass expert human performance across a broad range of tasks, obtaining high-quality human supervision for evaluation and training becomes increasingly challenging. Our focus is on tasks that require deep knowledge and skills of multiple domains, where this bottleneck is severe. Unfortunately, even the b

Shawn M. P. McSorley, Benjamin P. Dix-Matthews, Andrew M. Lance, David R. Gozzard

Measurements of optical range and time-of-flight are crucial for a variety of high-precision technologies. Competitive optical measurement techniques have been developed that balance precision with accuracy and system complexity. Here, we present a continuous-wave synthetic wavelength interferometry technique that employs digitally tunable electro-optic freq

Muhammad Irfan, Anum Nawaz, Ayse Kosal Bulbul, Riku Klen

Negative emotions are linked to the onset of neurodegenerative diseases and dementia, yet they are often difficult to detect through observation. Physiological signals from wearable devices offer a promising noninvasive method for continuous emotion monitoring. In this study, we propose a lightweight, resource-efficient machine learning approach for binary e

Gareth Hardwick, Haizhao Yang

Solving partial differential equations (PDEs) with highly oscillatory solutions on complex domains remains a challenging and important problem. High-frequency oscillations and intricate geometries often result in prohibitively expensive representations for traditional numerical methods and lead to difficult optimization landscapes for machine learning-based

Haoran Wang, Shengyuan Niu, Henry Moon, Ian Willebeek-LeMair

This paper derives a new class of vector-valued reproducing kernel Hilbert spaces (vRKHS) defined in terms of operator-valued kernels for the representation of functional uncertainty arising in nonparametric adaptive control methods. These are referred to as maneuver or trajectory vRKHS KM in the paper, and they are introduced to address the curse of dimensi

Michael Scott, Siyu Liang, Alicia Wassink, Gina-Anne Levow

This paper presents a systematic evaluation of racial bias in four major commercial automatic speech recognition (ASR) systems using the Pacific Northwest English (PNWE) corpus. We analyze transcription accuracy across speakers from four ethnic backgrounds (African American, Caucasian American, ChicanX, and Yakama) and examine how sociophonetic variation con

Shengyuan Niu, Ali Bouland, Haoran Wang, Filippos Fotiadis

This paper presents a novel approach to formulating the actor-critic method for optimal control by casting policy iteration in reproducing kernel Hilbert spaces (RKHSs -- also known as native spaces). By tailoring the reproducing kernel and RKHS to the dynamics of the nonlinear optimal control problem, we leverage recent advancements in characterizing error

Shantanu Sarkar, Piotr Nabrzyski, Saurabh Prasad, Jose Luis Contreras-Vidal

Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, but low signal-to-noise ratios (SNR) due to various artifacts often compromise its utility. Conventional artifact removal methods require manual intervention or risk suppressing critical neural features during filtering/reconstruction. Recent advances in gener

Alexander D. Gilbert

This paper analyses the finite element component of the error when using preintegration to approximate the cdf and pdf for uncertainty quantification (UQ) problems involving elliptic PDEs with random inputs. It is a follow up to Gilbert, Kuo, Srikumar, SIAM J. Numer. Anal. 63 (2025), pp. 1025-1054, which introduced a method of density estimation for a class

Siyu Liang, Nicolas Ballier, Gina-Anne Levow, Richard Wright

How much audio is needed to fully observe a multilingual ASR model's learned sub-token inventory across languages, and does data disparity in multilingual pre-training affect how these tokens are utilized during inference? We address this question by analyzing Whisper's decoding behavior during inference across 49 languages. By logging decoding candidate sub

Gramoz Goranci, Shaofeng H. -C. Jiang, Peter Kiss, Qihao Kong

Tree embedding has been a fundamental method in algorithm design with wide applications. We focus on the efficiency of building tree embedding in various computational settings under high-dimensional Euclidean $\mathbb{R}^d$. We devise a new tree embedding construction framework that operates on an arbitrary metric decomposition with bounded diameter, offeri

Yuanhe Tian, Junjie Liu, Xican Yang, Haishan Ye

Pruning provides a practical solution to reduce the resources required to run large language models (LLMs) to benefit from their effective capabilities as well as control their cost for training and inference. Research on LLM pruning often ranks the importance of LLM parameters using their magnitudes and calibration-data activations and removes (or masks) th

Zhifeng Wang, Yaowei Dong, Chunyan Zeng

Knowledge tracing (KT) supports personalized learning by modeling how students' knowledge states evolve over time. However, most KT models emphasize mastery of discrete knowledge components, limiting their ability to characterize broader literacy development. We reframe the task as Literacy Tracing (LT), which models the growth of higher-order cognitive abil

Quin Aicken Davies, C. Clare Worley

Magnesium (Mg) isotopic ratios offer valuable insights into stellar nucleosynthesis and Galactic chemical evolution, particularly in distinguishing contributions from supernovae and asymptotic giant branch (AGB) stars. These isotopes are accessible through MgH molecular features in cool stars, yet their measurement remains challenging across spectral types.

Farzad Mahfouzi, Mark D. Stiles, Paul M. Haney

We use a combination of density functional theory calculations and optical modeling to establish that the electric field-induced Kerr rotation in metallic thin films has contributions from both non-equilibrium orbital moment accumulation (arising from the orbital Edelstein effect) and a heretofore overlooked surface Pockels effect. The Kerr rotation associat

Siyu Liang, Zhaxi Zerong

Tonogenesis-the historical process by which segmental contrasts evolve into lexical tone-has traditionally been studied through comparative reconstruction and acoustic phonetics. We introduce a computational approach that quantifies the functional role of pitch at different stages of this sound change by measuring how pitch manipulation affects automatic spe

Qiushi Wang, Xingpeng Li

The rapid increase in renewable energy sources (RES) implementation in the power system creates more severe network congestion, which may reduce grid operation efficiency and cause renewable curtailment. Deterministic optimization for the unit commitment shows that battery energy storage system (BESS)-based Virtual Transmission Line (VTL), as an alternative

Qianhan Zeng, Miao Han, Ke Xu, Feifei Wang

We study anomaly detection in images under a fixed-camera environment and propose a \emph{doubly smoothed} (DS) density estimator that exploits spatial structure to improve estimation accuracy. The DS estimator applies kernel smoothing twice: first over the value domain to obtain location-wise classical nonparametric density (CD) estimates, and then over the

Min Chen, Bingzhi Zhang, Quntao Zhuang, Junyu Liu

Quantum imaginary time evolution (QITE) algorithm is one of the most promising variational quantum algorithms (VQAs), bridging the current era of Noisy Intermediate-Scale Quantum devices and the future of fully fault-tolerant quantum computing. Although practical demonstrations of QITE and its potential advantages over the general VQA trained with vanilla gr

Seonghoon Yu, Dongjun Nam, Dina Katabi, Jeany Son

Knowledge Distillation (KD) aims to train a lightweight student model by transferring knowledge from a large, high-capacity teacher. Recent studies have shown that leveraging diverse teacher perspectives can significantly improve distillation performance; however, achieving such diversity typically requires multiple teacher networks, leading to high computat

Pritish Chakraborty, Indradyumna Roy, Soumen Chakrabarti, Abir De

Retrieving graphs from a large corpus, that contain a subgraph isomorphic to a given query graph, is a core operation in many real-world applications. While recent multi-vector graph representations and scores based on set alignment and containment can provide accurate subgraph isomorphism tests, their use in retrieval remains limited by their need to score