November 2025 arXiv papers — page 59

Subhajeet Das, Pritam Paul, Rohit Bahadur, Sohan Das

Stroke majorly causes death and disability worldwide, and early recognition is one of the key elements of successful treatment of the same. It is common to diagnose strokes using CT scanning, which is fast and readily available, however, manual analysis may take time and may result in mistakes. In this work, a pre-trained Vision Transformer-based transfer le

Rajashree Bar, Daibik Barik, Adri Bhattacharya, Partha Sarathi Mandal

Network decontamination is a well-known problem, in which the aim of the mobile agents should be to decontaminate the network (i.e., both nodes and edges). This problem comes with an added constraint, i.e., of \emph{monotonicity}, in which whenever a node or an edge is decontaminated, it must not get recontaminated. Hence, the name comes \emph{monotone decon

Yuting Gao, Wang Lan, Hengyuan Zhao, Linjiang Huang

Multimodal Mixture-of-Experts (MoE) models offer a promising path toward scalable and efficient large vision-language systems. However, existing approaches rely on rigid routing strategies (typically activating a fixed number of experts per token) ignoring the inherent heterogeneity in semantic importance across modalities. This leads to suboptimal compute a

Joseph Oladokun

Large Language Model agents often retrieve context from knowledge bases that lack structural consistency with the agent's current reasoning state, leading to incoherent reasoning chains. We introduce Path-Constrained Retrieval (PCR), a retrieval method that combines structural graph constraints with semantic search to ensure retrieved information maintains l

Zihao Yao, Jiankai Zuo, Yaying Zhang

Time series data plays a pivotal role in a wide variety of fields but faces challenges related to privacy concerns. Recently, synthesizing data via diffusion models is viewed as a promising solution. However, existing methods still struggle to capture long-range temporal dependencies and complex channel interrelations. In this research, we aim to utilize the

S. V. Gabielkov, I. V. Zhyganiuk, A. D. Skorbun, V. G. Kudlai

The values of the signal-to-noise ratio are determined, at which the method of processing X-ray diffraction data reveals reflections with intensity less than the noise component of the background. The possibilities of the method are demonstrated on weak reflections of $\alpha$-quartz. The method of processing X-ray diffraction data makes it possible to incre

Kazuhiro Tanaka

Proton matrix elements of the QCD energy-momentum tensor (EMT) are expressed by the gravitational form factors. The forward values of the gravitational form factors allow for a decomposition of the proton mass into contributions from quarks and gluons, and further subdivisions into contributions from quark masses and from the QCD trace anomaly may be conside

James C. Hateley

This paper develops a chiral adelic operator framework in which the functional--equation symmetry of global $L$--functions is realized directly in the spectrum of a Dirac--type Hamiltonian. Working on the id\`ele class space, we place a real--place Floquet Hamiltonian into an off--diagonal chiral form to obtain a global adelic Dirac operator with an exact in

Jialiang Zhang, Shuo Li, Yilun Wang, Shuxian Liu

Single-crystal chemical vapor deposition (CVD) diamond is a promising material for radiation detectors operating in extreme environments, owing to its outstanding radiation hardness. As nuclear and high-energy physics applications demand particle detectors that withstand higher radiation fluences, understanding the damage thresholds and degradation mechanism

Sajjan Acharya, Rajendra Baskota

Styled handwriting generation aims to synthesize handwritten text that looks both realistic and aligned with a specific writer's style. While recent approaches involving GAN, transformer and diffusion-based models have made progress, they often struggle to capture the full spectrum of writer-specific attributes, particularly global stylistic patterns that sp

Mohammad Aqib, Mohd Hamza, Ying Hei Chui, Qipei Mei

Building codes contain critical information for ensuring safety, regulatory compliance, and informed decision-making in construction and engineering. Automated question answering systems over such codes enable quick and accurate access to specific regulatory clauses, improving efficiency and reducing errors. Retrieval-Augmented Generation (RAG) systems are e

Raja Kumar, Arka Sadhu, Ram Nevatia

Large Vision Language Models (LVLMs) possess extensive text knowledge but struggles to utilize this knowledge for fine-grained image recognition, often failing to differentiate between visually similar categories. Existing fine-tuning methods using Reinforcement Learning (RL) with exact-match reward signals are often brittle, encourage memorization of traini

Ilia Ponomarenko

The family of generalized Paley graphs of prime power order $q$ and degree $(q-1)/k$ is studied. It is shown that the automorphism group of a graph in this family is a subgroup of ${\mathrm{A\Gamma L}}(1,q)$ whenever $q$ is sufficiently large relative to $k$. Furthermore, under the same conditions, the Weisfeiler-Leman dimension of these graphs is proved to

Rui Ding, Rodrigo Pires Ferreira, Yuxin Chen, Junhong Chen

We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a locally deployable DR instance that integrates local retrieval-augmented generation with large langua

Ben Shaw, Adam Rustad, Sofia Pelagalli Maia, Jake S. Rhodes

Recent work has demonstrated the utility of Random Forest (RF) proximities for various supervised machine learning tasks, including outlier detection, missing data imputation, and visualization. However, the utility of the RF proximities depends upon the success of the RF model, which itself is not the ideal model in all contexts. RF proximities have recentl

Mohan Reddy

This investigation presents an empirical analysis of the incompatibility between human psychometric frameworks and Large Language Model evaluation. Through systematic assessment of nine frontier models including GPT-5, Claude Opus 4.1, and Gemini 3 Pro Preview using the Cattell-Horn-Carroll theory of intelligence, we identify a paradox that challenges the fo

Harsh Rathva, Pruthwik Mishra, Shrikant Malviya

The detection of hallucinations in multilingual scientific text generated by Large Language Models (LLMs) presents significant challenges for reliable AI systems. This paper describes our submission to the SHROOM-CAP 2025 shared task on scientific hallucination detection across 9 languages. Unlike most approaches that focus primarily on model architecture, w

Pouya Shiri, Amirali Baniasadi

Capsule Network (CapsNet) is among the promising classifiers and a possible successor of the classifiers built based on Convolutional Neural Network (CNN). CapsNet is more accurate than CNNs in detecting images with overlapping categories and those with applied affine transformations. In this work, we propose a deep variant of CapsNet consisting of several c

Ying-ying Sun, Da-jun Zhang, Frank Nijhoff

A direct linearisation scheme, based on an elliptic Cauchy kernel, is set up for the lattice CKP equation. This leads to an elliptic parametrisation of the lattice CKP equation, together with its Lax triplet, which allows us to perform appropriate continuum limits and construct elliptic solutions. By selecting appropriate integration measures and domains for

Steven Oh, Tai Inui, Magdeline Kuan, Jia-Yeu Lin

Robotic manipulation tasks are contact-rich, yet most imitation learning (IL) approaches rely primarily on vision, which struggles to capture stiffness, roughness, slip, and other fine interaction cues. Tactile signals can address this gap, but existing sensors often require expensive, delicate, or integration-heavy hardware. In this work, we introduce MicCh

Svitlana Volkova, Peter Bautista, Avinash Hiriyanna, Gabriel Ganberg

The exponential growth of scientific knowledge has created significant barriers to cross-disciplinary knowledge discovery, synthesis and research collaboration. In response to this challenge, we present BioSage, a novel compound AI architecture that integrates LLMs with RAG, orchestrated specialized agents and tools to enable discoveries across AI, data scie

Kiran Thorat, Hongwu Peng, Yuebo Luo, Xi Xie

Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gained popularity as a potential solution to improve verification efficiency. However, there lacks a joint framework that considers all chip design domain knowledge, graph theory, and

Iman Rahimi

This study presents Part II of an AI-enhanced Decision Support System (DSS), extending Rahimi (2025, Part I) by introducing a fully uncertainty-aware optimization framework for long-term open-pit mine planning. Geological uncertainty is modelled using a Variational Autoencoder (VAE) trained on 50,000 spatial grade samples, enabling the generation of probabil

Medeu Abishev, Daulet Berkimbayev

Postulating a minimal discrete quantum of action $S=\hbar$ and a simple rule for the growth of an oriented graph, we construct a strict hierarchy of temporal layers $C N$ with discrete periods $\tau_N=N\hbar/E$. Each layer is specified by its configuration space, symplectic structure, update rule, and emergent symmetry. At $C1$ the state is represented by a

Lei Wang, Zikun Ye, Jinglong Zhao

Driven by recent advances in artificial intelligence (AI), a growing literature has demonstrated the potential for using large language models (LLMs) as scalable surrogates to generate human-like responses in many business applications. Two common approaches to improve the performance of LLMs include: fine-tuning, which aligns LLMs more closely with human re

Mengchun Zhang, Kateryna Shapovalenko, Yucheng Shao, Eddie Guo

Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of large-scale datasets to model it effectively. Existing methods often rely on synthetic subject generation or simplistic data augmentation, but these strategies fail to scale or gen

Shuai Zhang, Jingsong Mu, Cancan Zhao, Leiqi Tian

Neural radiance field (NeRF) is a promising approach for reconstruction and new view synthesis. However, previous NeRF-based reconstruction methods overlook the critical role of acoustic impedance in ultrasound imaging. Localization methods face challenges related to local minima due to the selection of initial poses. In this study, we design a robotic ultra

Lourdes Beatriz Cajica-Maceda, Freddy Alejandro Chaurra-Gutiérrez, Julio César Pérez-Sansalvador, Jesús García-Díaz

The Graph Burning Problem (GBP) is a combinatorial optimization problem that has gained relevance as a tool for quantifying a graph's vulnerability to contagion. Although it is based on a very simple propagation model, its decision version is NP-complete, and its optimization version is NP-hard. Many of its theoretical properties across different graph famil

Wanzhe Xu, Yutong Dai, Yitao Yang, Martin Loza

Accurate multivariate time-series prediction of vital signs and laboratory results is crucial for early intervention and precision medicine in intensive care units (ICUs). However, vital signs are often noisy and exhibit rapid fluctuations, while laboratory tests suffer from missing values, measurement lags, and device-specific bias, making integrative forec

Xiaoyu Wang, Xiaotian Li, Zhixiang Zhou, Chen Li

This paper revisits alternating low-rank updates for federated fine-tuning and examines their behavior in decentralized federated learning (DFL). While alternating the LoRA matrices has been shown to stabilize aggregation in centralized FL, extending this mechanism to decentralized, peer-to-peer communication introduces new challenges due to phase-state mism

Jungho Lee, Minhyeok Lee, Sunghun Yang, Minseok Kang

3D reconstruction in large-scale scenes is a fundamental task in 3D perception, but the inherent trade-off between accuracy and computational efficiency remains a significant challenge. Existing methods either prioritize speed and produce low-quality results, or achieve high-quality reconstruction at the cost of slow inference times. In this paper, we propos

Nasrin Sadeghzadeh, Masoumeh Yaghoubi

This paper explores the generalized projective Riemann curvature in Finsler geometry, focusing on the properties of projectively equivalent Finsler metrics and the invariance of their curvature structures under projective transformations. We extend the existing frameworks of projective Riemann and Ricci curvatures by introducing new characterizations of quad

Rui Peng, Ziru Liu, Lingyuan Ye, Yuxing Lu

Accurately modeling the relationship between perturbations, transcriptional responses, and phenotypic changes is essential for building an AI Virtual Cell (AIVC). However, existing methods typically constrained to modeling direct associations, such as Perturbation $\rightarrow$ RNA or Perturbation $\rightarrow$ Morphology, overlook the crucial causal link fr

Runwei Guan, Rongsheng Hu, Shangshu Chen, Ningyuan Xiao

Current roadside perception systems mainly focus on instance-level perception, which fall short in enabling interaction via natural language and reasoning about traffic behaviors in context. To bridge this gap, we introduce RoadSceneVQA, a large-scale and richly annotated visual question answering (VQA) dataset specifically tailored for roadside scenarios. T

Randall Balestriero, Hugues Van Assel, Sami BuGhanem, Lucas Maes

Foundation models and self-supervised learning (SSL) have become central to modern AI, yet research in this area remains hindered by complex codebases, redundant re-implementations, and the heavy engineering burden of scaling experiments. We present stable-pretraining, a modular, extensible, and performance-optimized library built on top of PyTorch, Lightnin

G. R. Boroun

Entanglement entropy in Deep Inelastic Scattering (DIS) from the DIS structure functions has emerged as a novel tool for probing observable quantities. The method proposed by Kharzeev-Levin to determine entanglement entropy in DIS from parton distribution functions (PDFs) improves on the momentum-space approach proposed by Lappi et al.[Eur. Phys. J. C {\bf84

Tetiana Bas, Krystian Novak

Large language models (LLMs) require precise behavior control for safe and effective deployment across diverse applications. Activation steering offers a promising approach for LLMs' behavioral control. We focus on the question of how steering effectiveness varies across different behavior types and whether the nature of target behaviors can predict steering

Sanskriti Das, Smita Mathur, Bret D. Lehmer, Steven W. Allen

We present a comprehensive analysis of 475 ks (438 ks unpublished & 37 ks archival) XMM-Newton/EPIC-pn observation of a nearby, highly inclined, star-forming, luminous infrared galaxy NGC 3221 through spatial, temporal, and spectral information. We confirm the presence of a low-luminosity (presumably Compton-thick) AGN. The 0.4$-$12 keV luminosity and the ha

Jiahao Liang, Haoran Yang, Xiangyu Zhao, Zhiwen Yu

Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they learn spurious environmental correlations instead of stable causal relationships, leading to substantial performance degradation under distribution shifts. While recent advancements

Yara Bahram, Mélodie Desbos, Mohammadhadi Shateri, Eric Granger

Diffusion models (DMs) produce high-quality images, yet their sampling remains costly when adapted to new domains. Distilled DMs are faster but typically remain confined within their teacher's domain. Thus, fast and high-quality generation for novel domains relies on two-stage pipelines: Adapt-then-Distill or Distill-then-Adapt. However, both add design comp

Jiahao Liang, Haoran Yang, Xiangyu Zhao, Zhiwen Yu

The challenges associated with large-scale user-item interaction graphs have attracted increasing attention in graph-based recommendation systems, primarily due to computational inefficiencies and inadequate information propagation. Existing methods provide partial solutions but suffer from notable limitations: model-centric approaches, such as sampling and

Jianqiao Zheng, Cameron Gordon, Yiping Ji, Hemanth Saratchandran

Task-agnostic tabular foundation models such as TabPFN have achieved impressive performance on tabular learning tasks, yet the origins of their inductive biases remain poorly understood. In this work, we study TabPFN through the lens of signal reconstruction and provide the first frequency-based analysis of its in-context learning behavior. We show that TabP

Yeji Song, Jaehyun Lee, Mijin Koo, JunHoo Lee

Accurately preserving motion while editing a subject remains a core challenge in video editing tasks. Existing methods often face a trade-off between edit and motion fidelity, as they rely on motion representations that are either overfitted to the layout or only implicitly defined. To overcome this limitation, we revisit point-based motion representation. H

Qingsong He, Jing Nan, Jiayu Jiao, Liangjie Tang

Large Language Models can break through knowledge and timeliness limitations by invoking external tools within the Model Context Protocol framework to achieve automated execution of complex tasks. However, with the rapid growth of enterprise-scale MCP services, efficiently and accurately matching target functionalities among thousands of heterogeneous tools

Zilong Li, Lean Fang, Anupam Sharma, Ping He

While many existing machine learning studies have focused on augmenting Reynolds averaged Navier Stokes (RANS) turbulence models for steady or time averaged unsteady flows, this paper takes a first step toward extending such augmentation to time resolved unsteady flows. An unsteady field inversion and machine learning (FIML) method is developed, in which a t

Yochay Jerby

In 1973 Montgomery formulated the pair correlation conjecture, predicting that the local spacing statistics of the nontrivial zeros of the Riemann zeta function coincide with those of eigenvalues of large Hermitian matrices from the Gaussian Unitary Ensemble (GUE). The zeta function, however, is a fixed deterministic object, and the mechanism by which its ze

Edward Kim, Yuri Cho, Jose Eduardo E. Lima, Julie Muccini

Digital health interventions increasingly deliver home exercise programs via sensor-equipped devices such as smartphones, enabling remote monitoring of adherence and performance. However, current software is usually authored before clinical encounters as libraries of modules for broad impairment categories. At the point of care, clinicians can only choose fr

Tuan Pham, Alessandro Rinaldo, Purnamrita Sarkar

We develop a new framework for deriving time-uniform concentration bounds for the output of stochastic sequential algorithms satisfying certain recursive inequalities akin to those defining the almost-supermartingale processes introduced by \cite{robbins1971convergence}. Our approach is of wide applicability, and can be deployed in settings in which exponent

Richard J. Young

Large vision-language models (VLMs) are increasingly deployed for optical character recognition (OCR) in healthcare settings, raising critical concerns about protected health information (PHI) exposure during document processing. This work presents the first systematic evaluation of inference-time vision token masking as a privacy-preserving mechanism for me

Tianyang Han, Junhao Su, Junjie Hu, Peizhen Yang

Text-to-image (T2I) models today are capable of producing photorealistic, instruction-following images, yet they still frequently fail on prompts that require implicit world knowledge. Existing evaluation protocols either emphasize compositional alignment or rely on single-round VQA-based scoring, leaving critical dimensions such as knowledge grounding, mult

Zhongkai Chen, Yihao Sun, Chao Yan, Han Zhou

Autonomous aerial vehicles (AAVs) have played a pivotal role in coverage operations and search missions. Recent advances in large language models (LLMs) offer promising opportunities to augment AAV intelligence. These advances help address complex challenges like area coverage optimization, dynamic path planning, and adaptive decision-making. However, the ab

Ved Mohan, El Mehdi Er Raqabi, Pascal Van Hentenryck

Ensuring that the right resource is available at the right location and time remains a major challenge for organizations operating large-scale logistics networks. The challenge comes from uneven demand patterns and the resulting asymmetric flow of resources across the arcs, which create persistent imbalances at the network nodes. Resource substitution among

S M Mehedi Zaman, Saubhagya Joshi, Yiyi Wu

As millions of people use ChatGPT for tasks such as education, writing assistance, and health advice, concerns have grown about how personal prompts and data are stored and used. This study explores how Reddit users collectively negotiate and respond to these privacy concerns. Posts were collected from three major subreddits -- r/Chatgpt, r/privacy, and r/Op

Yusei Fukumoto

We isolate a model-theoretic "standard-cut" phenomenon for true Pi0_1 sentences: if a model M satisfies ZFC + not-phi, then omega^M is not the standard omega, and any internal "witness" to not-phi is computationally inaccessible by Tennenbaum's theorem. Such a witness exists only to maintain syntactic consistency and carries no standard observational semanti

Mohamed Afane, Juntao Chen

This study investigates blood lead level (BLL) rates and testing among children under six years of age across the 42 neighborhoods in New York City from 2005 to 2021. Despite a citywide general decline in BLL rates, disparities at the neighborhood level persist and are not addressed in the official reports, highlighting the need for this comprehensive analys

Ruijie Fan, Junyan Ye, Huan Chen, Zilong Huang

Existing satellite video tracking methods often struggle with generalization, requiring scenario-specific training to achieve satisfactory performance, and are prone to track loss in the presence of occlusion. To address these challenges, we propose SatSAM2, a zero-shot satellite video tracker built on SAM2, designed to adapt foundation models to the remote

Yuhang Bai, Kristóf Bérczi, Johanna K. Siemelink

In the Maximum-size Properly Colored Forest problem, we are given an edge-colored undirected graph and the goal is to find a properly colored forest with as many edges as possible. We study this problem within a broader framework by introducing the Maximum-size Degree Bounded Matroid Independent Set problem: given a matroid, a hypergraph on its ground set wi

Tao Shen, Xin Wan, Taicai Chen, Rui Zhang

Unified multimodal models aim to integrate understanding and generation within a single framework, yet bridging the gap between discrete semantic reasoning and high-fidelity visual synthesis remains challenging. We present MammothModa2 (Mammoth2), a unified autoregressive-diffusion (AR-Diffusion) framework designed to effectively couple autoregressive semant

Shijun Li, Yu Wang, Jin Wang, Ying Li

Large Language Models (LLMs) have shown significant potential for improving recommendation systems through their inherent reasoning capabilities and extensive knowledge base. Yet, existing studies predominantly address warm-start scenarios with abundant user-item interaction data, leaving the more challenging cold-start scenarios, where sparse interactions h

Yueqi Wang, Guang Lin

Parametric PDEs power modern simulation, design, and digital-twin systems, yet their many-query workloads still hinge on repeatedly solving large finite-element systems. Existing operator-learning approaches accelerate this process but often rely on opaque learned trunks, require extensive labeled data, or break down when boundary and source data vary indepe

Xiaochen Zheng, Alvaro Serra, Ilya Schneider Chernov, Maddalena Marchesi

Pharmaceutical research and development has accumulated vast and heterogeneous archives of data. Much of this knowledge stems from discontinued programs, and reusing these archives is invaluable for reverse translation. However, in practice, such reuse is often infeasible. In this work, we introduce DiscoVerse, a multi-agent co-scientist designed to support

Abdolazim Rezaei, Mehdi Sookhak

This study introduces PEFT-DML, a parameter-efficient deep metric learning framework for robust multi-modal 3D object detection in autonomous driving. Unlike conventional models that assume fixed sensor availability, PEFT-DML maps diverse modalities (LiDAR, radar, camera, IMU, GNSS) into a shared latent space, enabling reliable detection even under sensor dr

Mojtaba A. Farahani, Md Irfan Khan, Thorsten Wuest

The convergence of Agentic AI and MAS enables a new paradigm for intelligent decision making in SMS. Traditional MAS architectures emphasize distributed coordination and specialized autonomy, while recent advances in agentic AI driven by LLMs introduce higher order reasoning, planning, and tool orchestration capabilities. This paper presents a hybrid agentic

Tingting Su, Xinyue Zhang, Jingyi Zhao

This paper proposes an Acceleration and Load-Dependent Electric Vehicle Routing Problem (ALD-EVRP), to optimize the energy consumption (EC) while capturing the effects of changing traffic conditions between peak and off-peak periods. We generalize the time-dependent speed model by replacing step functions with piecewise linear functions. The EC of each vehic

Christopher N. Angstmann, Daniel S. Han, Bruce I. Henry, Boris Z. Huang

We extend the random walk framework to include compounded steps, providing first-passage time (FPT) properties for a new class of superdiffusive processes, which are governed by the space-fractional spectral Fokker-Planck equation. This first-passage process leads to novel FPT properties, different from L\'evy flights, that account for space dependent forces

Sina Mokhtarzadeh Azar, Emad Bahrami, Enrico Pallotta, Gianpiero Francesca

In this work, we investigate diffusion-based video prediction models, which forecast future video frames, for continuous video streams. In this context, the models observe continuously new training samples, and we aim to leverage this to improve their predictions. We thus propose an approach that continuously adapts a pre-trained diffusion model to a video s

Kent Quanrud, Navid Tajkhorshid

The textbook algorithm for real-weighted single-source shortest paths takes $O(mn)$ time on a graph with $m$ edges and $n$ vertices. A recent breakthrough algorithm by [Fin24] takes $\tilde{O}(mn^{8/9})$ randomized time. The running time was subsequently improved to $\tilde{O}(mn^{4/5})$ [HJQ25] and then $\tilde{O}(mn^{3/4}+m^{4/5}n)$ [HJQ26]. We build on th

David A. Brewster, Yichen Huang, Michael Mitzenmacher, Martin A. Nowak

We study evolutionary dynamics on graphs in which each step consists of one birth and one death, also known as the Moran processes. There are two types of individuals: residents with fitness $1$ and mutants with fitness $r$. Two standard update rules are used in the literature. In Birth-death (Bd), a vertex is chosen to reproduce proportional to fitness, and

Bill Celmaster, Steve Rubin

Warp drive spacetimes capable of superluminal transportation, were first introduced in 1994 by Miguel Alcubierre and then generalized by others. These spacetimes violated the Weak Energy Condition (WEC). Lentz proposed a new type of warp drive in 2020. It was claimed that this warp spacetime has non-negative energy density and can therefore be sourced by a c

Dongmei Huang, Qunying Liao, Sihem Mesnager, Gaohua Tang

Currently known secondary construction techniques for linear codes mainly include puncturing, shortening, and extending. In this paper, we propose a novel method for the secondary construction of linear codes based on their weight functions. Specifically, we develop a general framework that constructs new linear codes from the set of codewords in a given cod

Yinqi Zeng, Renjie Li

The discovery of next-generation photoinitiators for two-photon polymerization (TPP) is hindered by the absence of large, open datasets containing the quantum-chemical and photophysical properties required to model photodissociation and excited-state behavior. Existing molecular datasets typically provide only basic physicochemical descriptors and therefore

Mostafijur Rahman Akhond, Gias Uddin

Metamorphic Relations (MRs) serve as a foundational mechanism for generating semantically equivalent mutations. Software engineering has advanced significantly in recent years with the advent of Large Language Models (LLMs). However, the reliability of LLMs in software engineering is often compromised by ambiguities and inconsistencies due to improper user s

Wei Zhen Teoh

Jointly forecasting trajectories of multiple interacting agents is a core challenge in sports analytics and other domains involving complex group dynamics. Accurate prediction enables realistic simulation and strategic understanding of gameplay evolution. Most existing models are evaluated solely on per-agent accuracy metrics (minADE, minFDE), which assess e

Sajad Khodadadian, Mehrdad Moharrami

We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown transition dynamics. We first study a UCBVI-type (model-based) algorithm and characterize the tail distribution of the cumulative regret $R_K$ over $K$ episodes via explicit bounds on $P(R_K \ge x)$

Jun Seok Oh, Sávio Ribas, Kevin Zhao, Qinghai Zhong

Let $G$ be a finite group. A finite collection of elements from $G$, where the order is disregarded and repetitions are allowed, is said to be a product-one sequence if its elements can be ordered such that their product in $G$ equals the identity element of $G$. Then, the Gao's constant $\mathsf E (G)$ of $G$ is the smallest integer $\ell$ such that every s

Zhiling Zheng

Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry

Eashan Vytla, Bhavanishankar Kalavakolanu, Andrew Perrault, Matthew McCrink

Current control algorithms for aerial robots struggle with robustness in dynamic environments and adverse conditions. Model-based reinforcement learning (RL) has shown strong potential in handling these challenges while remaining sample-efficient. Additionally, Dreamer has demonstrated that online model-based RL can be achieved using a recurrent world model

Shixun Huang, Eitan Grinspun, Yue Chang

Model reduction is essential for real-time simulation of deformable objects. Linear techniques such as PCA provide structured and predictable behavior, but their limited expressiveness restricts accuracy under large or nonlinear deformations. Nonlinear model reduction with neural networks offers richer representations and higher compression; however, without

Matthew Nyaaba, Macharious Nabang, Patrick Kyeremeh, Ibrahim Nantomah

STEAM education in many parts of the Global South remains abstract and weakly connected to learners sociocultural realities. This study examines how human experts evaluate the capacity of Generative AI (GenAI) to contextualize STEAM instruction in these settings. Using a convergent mixed-methods design grounded in human-centered and culturally responsive ped

Swastik Bhattacharya, Sanjay Das, Anand Menon, Shamik Kundu

Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these parameters efficiently in traditional accelerators is limited by data-transmission bottlenecks, motivating Compute-in-Memory (CiM) architectures that integrate computation within or near memory to reduce data m

Mohamed Afane, Ying Wang, Juntao Chen

Public health agencies face critical challenges in identifying high-risk neighborhoods for childhood lead exposure with limited resources for outreach and intervention programs. To address this, we develop a Priority Score integrating untested children proportions, elevated blood lead prevalence, and public health coverage patterns to support optimized resou

Aya Samadzelkava

Alzheimer's disease (AD) emerges from a complex interplay of molecular, cellular, and network-level disturbances that are not easily captured by traditional reductionist frameworks. Conventional analyses of gene expression often rely on thresholded correlation networks or clustering-based module detection, approaches that may obscure nonlinear structure and

Sijie Zheng, Fandong Meng, Jie Zhou

Motivated by recent work involving the analysis of leveraging spatial correlations in sparsified mean estimation, we present a novel procedure for constructing covariance estimator. The proposed Random-knots (Random-knots-Spatial) and B-spline (Bspline-Spatial) estimators of the covariance function are computationally efficient. Asymptotic pointwise of the c

Quanling Zhao, Yanru Chen, Runyang Tian, Sumukh Pinge

Hyperdimensional Computing (HDC) encodes information and data into high-dimensional distributed vectors that can be manipulated using simple bitwise operations and similarity searches, offering parallelism, low-precision hardware friendliness, and strong robustness to noise. These properties are a natural fit for SQL database workloads dominated by predicate

Mingi Kang

Magnetic Resonance Imaging (MRI) acquisitions require extensive scan times, limiting patient throughput and increasing susceptibility to motion artifacts. Accelerated parallel MRI techniques reduce acquisition time by undersampling k-space data, but require robust reconstruction methods to recover high-quality images. Traditional approaches like SENSE requir

Dmitry Skryabin, Arne Kordts, Richard Zeltner, Ronald Holzwarth

The discovery of optical frequency combs (OFCs) has revolutionised science and technology by bridging electronics and photonics, driving major advances in precision measurements, atomic clocks, spectroscopy, telecommunications, and astronomy. However, current OFC systems still require further development to enable broader adoption in fields such as communica

Saeid Jamshidi, Amin Nikanjam, Negar Shahabi, Kawser Wazed Nafi

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System (IDS) framework that integrates lightweight machine learning (ML) based IDS models with pre

Tuncay Aktosun, Abdon E. Choque-Rivero, Vassilis G. Papanicolaou, Mehmet Unlu

The Jacobi system with matrix-valued coefficients and with the spectral parameter depending on a matrix-valued weight factor is considered on the full-line lattice. The scattering from the full-line lattice is expressed in terms of the scattering from the fragments of the whole lattice by developing a factorization formula for the corresponding transition ma

Sucai Niu, Junyi Zhu

The Riemann-Hilbert approach is extended to discuss the well-posedness of the nonlinear Schr\"odinger-Gerdjikov-Ivanon equation. The Lipschitz continuity of potential in $H^{2}(\mathbb{R})\cap H^{1,1}(\mathbb{R})$ to scattering data is obtained through direct scattering transform. Two Riemann-Hilbert problems are constructed, and two sets of the reflection c

Evangela E. Shread, Trevor J. Weiss, Jerome J. Fang, Cameron Law

The inner stellar mass surface density within 1 kpc, Sigma1, has emerged as a suitable proxy for bulge growth and galaxy quenching. However, the dependence of cold gas content on Sigma1 has not been thoroughly explored. In this paper, we examine the relationship between Sigma1, as well as the mass-relative parameter delSigma1, and the atomic (fHI) and molecu

Seyed Feyzelloh Ghavami Mirmahalle, Seyed Ehsan Nedaaee Oskoee, Maniya Maleki

This work aims at generating 1D interface profiles of granular deposition by a conditional generative adversarial network (cGAN). Our cGAN model employs a U-Net generator and a ResNet discriminator that, in competition with each other, produce granular interfaces. The network is trained on dynamic simulation data from the LAMMPS granular package. Different f

Shireen Chand, Faith Baca, Emilio Ferrara

Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful or unfair outputs. While various techniques aim to mitigate these biases, their effects are often evaluated only along the dimension of the bias being targeted. This work investigates the cross-category consequences of targeted bias mitigation. We st

Kaley Brauer, Aditya Prasad Dash, Meet J. Vyas, Ahmed Salim

Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images with ground-truth physical labels, but domain shifts in PSF, noise, backgrounds, selection, and label priors degrade trans

Alexander Moretó, Noelia Rizo, Gabriel A. L. Souza

Let $G$ be a finite group and let $p$ be a prime. In this paper, we prove a strengthened version of Brauer's height zero conjecture for the principal $p$-block of $G$ that takes the action of a certain group of Galois automorphisms into account. This answers a conjecture recently proposed by Malle, Moretó, Rizo and Schaeffer Fry. We then use this to obta

Furen Deng, Yidong Xu, Fengquan Wu, Yanping Cong

Ground-based radio astronomical observation at frequencies below 30 MHz is hampered by the Ionosphere and radio frequency interference (RFI). The Discovering Sky at the Longest wavelength (DSL) mission, also known as the Hongmeng mission, employs a linear array of satellites on a circular orbit around the Moon to make interferometric observations in this ban

Anweshika Pattanayak, Sandip Roy, Abhishek Chaudhuri

We present an exact, time-resolved theory for a two-dimensional chiral active Brownian particle (cABP) with translational inertia. Using a Laplace-transform moment hierarchy, we derive closed-form expressions for the mean velocity, velocity-orientation projections, velocity autocorrelation, mean-squared velocity, mean-squared displacement, and the fourth mom

Maksim A. Gavreev, Evgeniy O. Kiktenko, Aleksey K. Fedorov, Anastasiia S. Nikolaeva

Simulating entangled, many-body quantum systems is notoriously hard, especially in the case of high-dimensional nature of physical underlying objects. In this work, we propose an approach for simulating the Potts model based on the Suzuki-Trotter decomposition that we construct for qudit systems. Specifically, we introduce two qudit-native decomposition sche

Jorge Garcia-Cabeza, Javier Albert-Smet, Zoraida Frias, Luis Mendo

Low Earth Orbit (LEO) satellite mega-constellations have emerged as a viable access solution for broadband connectivity in underserved areas. In 2024, Starlink, in partnership with T-Mobile, began beta testing an SMS-only Supplemental Coverage from Space (SCS) service. This marks the first large-scale deployment of Direct Satellite-to-Device (DS2D) communica

Yuxin He, Yunhui Wu

Let $\mathcal{M}_g$ be the moduli space of hyperbolic surfaces of genus $g$ endowed with the Weil-Petersson metric. We view the regularized determinant $\log \det(Δ_{X})$ of Laplacian as a function on $\mathcal{M}_g$ and show that there exists a universal constant $E>0$ such that as $g\to \infty$, (1) the expected value of $\left|\frac{\log \det(Δ_{X})}{4π(g

H. Zhang, L. Zhang, G. Epiphaniou, C. Maple

Intrusion Detection Systems (IDS) play a vital role in defending modern cyber physical systems against increasingly sophisticated cyber threats. Deep Reinforcement Learning-based IDS, have shown promise due to their adaptive and generalization capabilities. However, recent studies reveal their vulnerability to adversarial attacks, including Universal Adversa