November 2025 arXiv papers — page 112

Zijun Wang, Anjali Omer, Jacob Chakareski, Nicholas Mastronarde

Next-generation wireless networks will rely on mmWave/sub-THz spectrum and extremely large antenna arrays (ELAAs). This will push their operation into the near field where far-field beam management degrades and beam training becomes more costly and must be done more frequently. Because ELAA training and data transmission consume energy and training trades of

Chiung-Yi Tseng, Danyang Zhang, Tianyang Wang, Hongying Luo

The rapid advancement of large language models(LLMs) has prompted significant interest in their potential applications in medical domains. This paper presents a comprehensive benchmark evaluation of 27 state-of-the-art LLMs on Chinese medical examination questions, encompassing seven medical specialties across two professional levels. We introduce a robust e

Yuhao Zhan, Yuqing Zhang, Jing Yuan, Qixiang Ma

Existing Grammatical Error Correction (GEC) systems suffer from limited reference diversity, leading to underestimated evaluation and restricted model generalization. To address this issue, we introduce the Judge of Edit-Level Validity (JELV), an automated framework to validate correction edits from grammaticality, faithfulness, and fluency. Using our propos

Xuehui Dong, Miyu Feng, Chen Shao, Bokai Lai

The evolution of programmable metasurfaces (PM) from passive beamforming to active information transmission marks a paradigm shift for next-generation wireless systems. However, this transition is hindered by fundamental limitations in conventional metasurface transmitter architectures, including restricted modulation orders, symbol-level spatial inconsisten

Atharva Mehta, Rajesh Kumar, Aman Singla, Kartik Bisht

The rapid adoption of generative AI tools has heightened concerns regarding academic integrity, as students increasingly engage in dishonest practices by copying or paraphrasing AI-generated content. Existing plagiarism detection systems, which rely primarily on text-intrinsic features, are ineffective at identifying AI-assisted or paraphrased submissions. O

Jiachen Qian, Yang Zheng

This letter studies the problem of online multi-step-ahead prediction for unknown linear stochastic systems. Using conditional distribution theory, we derive an optimal parameterization of the prediction policy as a linear function of future inputs, past inputs, and past outputs. Based on this characterization, we propose an online least-squares algorithm to

Manoj Ghising, Nirpat Subba, Mohammed Tobrej, Binay Rai

We present a comprehensive spectral-timing analysis of a BHXB Swift J1727.8$-$1613 during its 2023 outburst, using five pointed \textit{NuSTAR} observations sampling the luminous hard-intermediate state. Broadband 3-79 keV spectroscopy employs a physically motivated model combining a cool truncated disk (\texttt{diskbb}), relativistic reflection (\texttt{rel

Qingfeng Sun, Qizhi Zhang

We find some equidistribution results connected to restriction quantum unique ergodicity problem in this paper. We shows that \begin{align*} \frac{1}{|\mathcal{B}_k|}\sum_{f\in \mathcal{B}_k} \int_{R}y^{k}|f(z)|^{2}\psi(z) d\mu_{R}(z)\to \frac{3}{\pi}\int_{R}\psi(z) d\mu_{R}(z) \end{align*} where $R$ is some subset of $\mathbb{H}$, $\psi$ is a nice function

Kang He, Boyu Chen, Yuzhe Ding, Fei Li

Multimodal Sentiment Analysis (MSA) seeks to understand human emotions by integrating textual, acoustic, and visual signals. Although multimodal fusion is designed to leverage cross-modal complementarity, real-world scenarios often exhibit modality competition: dominant modalities tend to overshadow weaker ones, leading to suboptimal performance. In this pap

Chenglong Wang, Yifu Huo, Yang Gan, Yongyu Mu

Previous methods evaluate reward models by testing them on a fixed pairwise ranking test set, but they typically do not provide performance information on each preference dimension. In this work, we address the evaluation challenge of reward models by probing preference representations. To confirm the effectiveness of this evaluation method, we construct a M

Haoyan Xu, Ruizhi Qian, Zhengtao Yao, Ziyi Liu

Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed graphs (TAGs), in which node information is expressed in natural language, remain underexplored, largely due to the absence of standardized benchmark datasets. In this work, we int

Adham M. Alkhadrawi, Kyungsu Kim, Arif M. Rahman, Fahad Mushabbab G. Alotaibi

We developed an explainable deep learning framework integrating protein-protein interaction (PPI) network centrality metrics with node embeddings for cancer therapeutic target prioritization. A high-confidence PPI network was constructed from STRING database interactions, computing six centrality metrics: degree, strength, betweenness, closeness, eigenvector

Yuzhou Liu, Jiarui Liu, Wanfu Gao

Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent complexity of interrelations among features, views and labels. Attention mechanisms provide an effective way for analyzing these intricate relationships. They can compute importance w

Fangqiang Du, Sixuan Chong, Zixuan Huang, Rui Qin

Singular value decomposition (SVD) is widely used for dimensionality reduction and noise suppression, and it plays a pivotal role in numerous scientific and engineering applications. As the dimensions of the matrix grow rapidly, the computational cost increases significantly, posing a serious challenge to the efficiency of data analysis and signal processing

Changzeng Fu, Shiwen Zhao, Yunze Zhang, Zhongquan Jian

Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal temporal dependencies across diverse behavioral contexts. Therefo

Misao Fukuda

The purpose of this research is to explore whether it is possible to construct a design theory for subscription services for intangible goods from a time discounting perspective, based on quantum information theory, which is the foundational theory for quantum computers and similar technologies. To this end, we propose a mathematical model of subscription se

Marco Pollanen

For decades, forensic statisticians have debated whether searching large DNA databases undermines the evidential value of a match. Modern surveillance faces an exponentially harder problem: screening populations across thousands of attributes using threshold rules rather than exact matching. Intuition suggests that requiring many coincidental matches should

O. V. Kaptsov

This paper examines the three-dimensional stationary equations of a polytropic gas and employs symmetry methods to construct exact analytical solutions. In the Chaplygin gas case, the analysis yields a highly general solution family depending on three arbitrary functions, while the general adiabatic index formulation admits explicit solutions parameterized b

Gaurav Jain, Brandon Baker, Joe Yin, Chenwei Xie

Snowpark enables Data Engineering and AI/ML workloads to run directly within Snowflake by deploying a secure sandbox on virtual warehouse nodes. This Snowpark Execution Environment (SEE) allows users to execute arbitrary workloads in Python and other languages in a secure and performant manner. As adoption has grown, the diversity of workloads has introduced

Sukanya Kudva, Edward Dowling, Anil Aswani

Existing auction mechanisms are vulnerable to bidder collusion, which substantially degrades revenue and non-colluder welfare. To design truthful mechanisms resilient to collusion, we introduce a novel approach that leverages a machine learning classifier to predict (even imprecisely) which bidders are colluding. We first establish a Bulow-Klemperer-type res

Moreom Akter, Wayne A. Barkhouse, Sandanuwan P Kalawila Vithanage, Gihan L. Gamage

We investigated the influence of Active Galactic Nuclei (AGN) feedback on star formation in red spiral galaxies by analyzing a sample of 324 red and 273 blue face-on spirals selected from 115 low-redshift galaxy clusters. This multi-wavelength dataset combines optical emission line data from the Sloan Digital Sky Survey with X-ray fluxes from Chandra and XMM

Hyun Youk

Living systems self-organize in ways that conventional physical frameworks-based on forces, energies, and continuous fields-cannot fully capture. Processes like gene regulation and cellular decision-making involve rule-based logic and computational interactions. Here, I introduce the concept of non-equilibrium capacity (NEC) to denote the finite capacity of

Xiaoyu Lin, Aniket Ghorpade, Hansheng Zhu, Justin Qiu

With the rapid adoption of multimodal large language models (MLLMs) across diverse applications, there is a pressing need for task-centered, high-quality training data. A key limitation of current training datasets is their reliance on sparse annotations mined from the Internet or entered via manual typing that capture only a fraction of an image's visual co

Abdullah Al Bashit, Prakash Nepal, Lee Makowski

X-ray scattering measurements of in situ human brain tissue encode structural signatures of pathological cross-$\beta$ inclusions, yet systematic exploitation of these data for automated detection remains challenging due to substrate contamination, strong inter-feature correlations, and limited sample sizes. This work develops a three-stage classification fr

Linfeng Xia, Heng Yuan, Bo Wang, Wei Cai

In this paper, we present a fast boundary integral method accelerated by the fast multipole method (FMM) for acoustic wave scattering governed by the scalar Helmholtz equation in multi-layered two-dimensional media. Multiple scatterers are randomly distributed in the multi-layered medium with some scatterers possibly intersecting layer interfaces. The bounda

Aidan Wen, Norah A. Alzahrani, Jingzhi Jiang, Andrew Joe

We introduce SeedAIchemy, an automated LLM-driven corpus generation tool that makes it easier for developers to implement fuzzing effectively. SeedAIchemy consists of five modules which implement different approaches at collecting publicly available files from the internet. Four of the five modules use large language model (LLM) workflows to construct search

Shreya Sharma

We classify the possible images of the action of the group of automorphisms of a smooth Fano threefold on its Picard group. We also study the first group cohomology of the Picard group for families of smooth Fano threefolds.

Jiahe Qian, Yuhao Shen, Zhangtianyi Chen, Juexiao Zhou

Medical visual question answering could support clinical decision making, yet current systems often fail under domain shift and produce answers that are weakly grounded in image evidence. This reliability gap arises when models attend to spurious regions and when retraining or additional labels are impractical at deployment time. We address this setting with

Jing-Juan Qi, Zhen-Yang Wang, Zhen-Hua Zhang, Xin-Heng Guo

CP violation in hadronic multi-body decays has been extensively studied, and the experimental breakthrough in the heayy baryon sector was made recently. Partial-Wave CP Asymmetries (PWCPAs) in multi-body decays of heavy hadrons, which although provide us with more interference information, suffer from the normalization problem, as is pointed out in this pape

Shigefumi Hata, Renaud Lambiotte, Hiroya Nakao, Ryota Kobayashi

Social media platforms have transformed the dynamics of collective opinion formation, enabling rapid, large-scale interactions while simultaneously exposing online discourse to polarization and manipulation. Traditional models of opinion dynamics often predict convergence to a consensus, yet empirical evidence consistently highlights persistent polarization

Changchun Feng, Xinyu Qiu, Laifa Tao, Lin Chen

The quantum Wasserstein distance (W-distance) is a fundamental metric for quantifying the distinguishability of quantum operations, with critical applications in quantum error correction. However, computing the W-distance remains computationally challenging for multiqubit systems due to exponential scaling. We present a machine learning framework that effici

Tao Zou, Chengfeng Wu, Tianxi Liao, Junchen Ye

Dynamic graph learning plays a pivotal role in modeling evolving relationships over time, especially for temporal link prediction tasks in domains such as traffic systems, social networks, and recommendation platforms. While Transformer-based models have demonstrated strong performance by capturing long-range temporal dependencies, their reliance on self-att

Siqi Chen, Cheng Liu, Neil J. Balmforth, Sheldon Green

A mathematical model is derived for the dynamics of a cylinder, or wheel, rolling over a thin viscous film. The model combines the Reynolds lubrication equation for the fluid with an equation of motion for the wheel. Two asymptotic limits are studied in detail to interrogate the dynamics of levitation: an infinitely wide wheel and a relatively narrow one. In

Qin-Mei Li, Qi-Bin Sun, Sheng-Bang Qian, Si-Yuan Zhu

The origin of the Universe and its material content remains one of the most fundamental questions in science. Gamma-ray bursts (GRBs), with their extreme luminosities and high-redshift detectability, provide a unique window into the history of cosmic formation and chemical evolution. Consequently, the GRB formation rate (FR) has been employed to trace the st

Ting Ma, Xianjin Liu, Qiwen Bao, Bolun Zhang

Mechanically reconfigurable metasurfaces capable of translation, rotation, and permutation have attracted considerable attention for high-capacity optical information storage and full-color holographic displays, owing to their low-power and high functional scalability, despite the additional system-level complexity introduced by precision rotation stages. Th

Yujia Liu, Sophia Yu, Hongyue Jin, Jessica Wen

Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with

Kamal Paykan

This paper proposes a reinforcement learning--based framework for cryptocurrency portfolio management using the Soft Actor--Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio optimization methods often struggle to adapt to the highly volatile and nonlinear dynamics of cryptocurrency markets. To address this, we desig

ANK Zaman, Prosenjit Chatterjee, Rajat Sharma

A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utilizing extra hours daily without sufficient rest and breaks. Once a driver undergoes such a scenario, it occasionally triggers drowsiness duri

Ningji Wei

Covering and elimination inequalities are central to combinatorial optimization, yet their role has largely been studied in problem-specific settings or via no-good cuts. This paper introduces a unified perspective that treats these inequalities as primitives for set system approximation in binary integer programs (BIPs). We show that arbitrary set systems a

Xiaoshuai Hao, Yingbo Tang, Lingfeng Zhang, Yanbiao Ma

Robotic manipulation and navigation are fundamental capabilities of embodied intelligence, enabling effective robot interactions with the physical world. Achieving these capabilities requires a cohesive understanding of the environment, including object recognition to localize target objects, object affordances to identify potential interaction areas and spa

Bo Fu, Dandan Jiang

In this paper, we study transfer learning for high-dimensional factor-augmented sparse linear models, motivated by applications in economics and finance where strongly correlated predictors and latent factor structures pose major challenges for reliable estimation. Our framework simultaneously mitigates the impact of high correlation and removes the addition

Rui Xue

Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challenges due to the neighbor explosion problem when training on large-scale graphs. To mitigate this, a promising class of GNN training algorithms utilizes historical embeddings to redu

Taekyun Kim, Dae San Kim

This paper addresses the unnatural appearance of the two-variable degenerate Fubini polynomials in a recently derived Spivey-type recurrence relation for the fully degenerate Bell polynomials. To solve this, we introduce a new family of polynomial which we also call the fully degenerate Bell polynomials, along with their two-variable counterparts. Our main c

Xilai Li, Xiaosong Li, Weijun Jiang

Multi-modality image fusion enhances scene perception by combining complementary information. Unified models aim to share parameters across modalities for multi-modality image fusion, but large modality differences often cause gradient conflicts, limiting performance. Some methods introduce modality-specific encoders to enhance feature perception and improve

Zhuoyuan Wang, Xiyu Deng, Hikaru Hoshino, Yorie Nakahira

Achieving long-term safety in uncertain/extreme environments while accounting for human preferences remains a fundamental challenge for autonomous systems. Existing methods often trade off long-term guarantees for fast real-time control and cannot adapt to variability in human preferences or risk tolerance. To address these limitations, we propose a language

Qi Wang, Xiaoming Chen, Qiao Qi, Zhaolin Wang

Low earth orbit (LEO) satellite constellations are becoming a cornerstone of next-generation satellite networks, enabling worldwide high-precision navigation and high-quality remote sensing. This paper proposes a novel dual-function LEO satellite constellation frame structure that effectively integrating navigation and remote sensing. Then, the Cramer-Rao bo

Yihang Yao, Guangtao Zeng, Raina Wu, Yang Zhang

Reinforcement learning (RL) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs). While RL has demonstrated substantial performance gains, it still faces key challenges, including low sampling efficiency and a strong dependence on model initialization: some models achieve rapid improvements with minimal

Jingqi Xu, Jingxi Lu, Chenghao Li, Sreetama Sarkar

Vision-Language Models (VLMs) have achieved remarkable progress in multimodal reasoning and generation, yet their high computational demands remain a major challenge. Diffusion Vision-Language Models (DVLMs) are particularly attractive because they enable parallel token decoding, but the large number of visual tokens still significantly hinders their inferen

Xiaoxing Lian, Aidong Yang, Jun Zhu, Peng Wang

Large language models (LLMs) and vision language models (VLMs), such as DeepSeek R1,OpenAI o3, and Gemini 2.5 Pro, have demonstrated remarkable reasoning capabilities across logical inference, problem solving, and decision making. However, spatial reasoning:a fundamental component of human cognition that includes mental rotation, navigation, and spatial rela

Jie Pan, Huanhuan Wang, Lin Zou, Xiaoyu Wang

Interlayer sliding, together with twist angle, is a crucial parameter that defines the atomic registry and thus determines the properties of two-dimensional (2D) material homobilayers. Here, we theoretically demonstrate that controlled interlayer sliding in bilayer graphene induces Berry curvature reversals, leading to topological states confined within a on

Warda Usman, Yixin Zou, Daniel Zappala

Human-centered threat modeling (HCTM) is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective, little is known about how they prepare for and engage with HCT

Richard J. Long

Syer and Tremaine's made-to-measure method and Schwarzschild's orbit superposition method are well-known within the field of stellar dynamical modeling. This research is concerned with assessing and comparing the operational capabilities of the two methods and, in particular, the impact on observable reproduction, orbit classifications and computer elapsed t

Montserrat Vite

Let $n=\frac{r(r+1)}{2}$ or $n=r(r+1)$. We prove that the property of being extremal is preserved under residuality on the Hilbert scheme of $n$ points in the plane.

Jiaji Ma, Puja Trivedi, Danai Koutra

Text-attributed graphs (TAGs), which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) to jointly model semantics and structure, resulting in more general and expressive models that achieve state-of-the-art performance on TAG benchmarks. H

Nuolin Sun, Linyuan Wang, Haonan Wei, Lei Li

ResNet has achieved tremendous success in computer vision through its residual connection mechanism. ResNet can be viewed as a discretized form of ordinary differential equations (ODEs). From this perspective, the multiple residual blocks within a single ResNet stage essentially perform multi-step discrete iterations of the feature transformation for that st

Nihal Mehta

This paper presents a mathematical interpretation of self-attention by connecting it to distributional semantics principles. We show that self-attention emerges from projecting corpus-level co-occurrence statistics into sequence context. Starting from the co-occurrence matrix underlying GloVe embeddings, we demonstrate how the projection naturally captures c

Xiaoyu Huang, Blake Jackson, Kyu-Hwan Lee

There is a large class of problems in algebraic combinatorics which can be distilled into the same challenge: construct an explicit combinatorial bijection. Traditionally, researchers have solved challenges like these by visually inspecting the data for patterns, formulating conjectures, and then proving them. But what is to be done if patterns fail to emerg

Wenjie Li, Jinglei Shi, Jin Han, Heng Guo

Clean images are crucial for visual tasks such as small object detection, especially at high resolutions. However, real-world images are often degraded by adverse weather, and weather restoration methods may sacrifice high-frequency details critical for analyzing small objects. A natural solution is to apply super-resolution (SR) after weather removal to rec

Yuehui Ma, Miaomiao Zhang, Hongchi Wang, Xuepeng Chen

We use $^{12}$CO (J=1-0) MWISP data to study turbulence in a segment of the Local Arm. Velocity slices at different kinematic distances show similar spatial power spectra (SPSs) and structure functions (SFs), demonstrating that the entire region forms a single turbulent field with a cascade extending from $\sim 400$ pc to sub-parsec scales. The SPS slopes of

Yushen Liu, Yanfu Zhang, Xugui Zhou

Automated insulin delivery for Type 1 Diabetes must balance glucose control and safety under uncertain meals and physiological variability. While reinforcement learning (RL) enables adaptive personalization, existing approaches struggle to simultaneously guarantee safety, leaving a gap in achieving both personalized and risk-aware glucose control, such as ov

Xinrui Li, Qi Cai, Yuanxin Wu

Structure from Motion (SfM) is a critical task in computer vision, aiming to recover the 3D scene structure and camera motion from a sequence of 2D images. The recent pose-only imaging geometry decouples 3D coordinates from camera poses and demonstrates significantly better SfM performance through pose adjustment. Continuing the pose-only perspective, this p

Yuting Tan, Yi Huang, Zhuo Li

Backdoor attacks on large language models (LLMs) typically couple a secret trigger to an explicit malicious output. We show that this explicit association is unnecessary for common LLMs. We introduce a compliance-only backdoor: supervised fine-tuning on a mostly benign dataset in which a small subset of prompts is suffixed with an arbitrary single-word trigg

Daniel Halpern-Leistner, Andres Fernandez Herrero

We define a version of stable maps into the classifying stack $B\mathrm{GL}_N$, and develop a corresponding notion of $K$-theoretic Gromov-Witten invariants. In this setting, the evaluation morphisms are not of finite type; the definition of the $K$-theoretic invariants proceeds by constructing a stability stratification of the moduli stack. In the absence o

Pablo G. Barrientos, Lorenzo J. Díaz, Yuri Ki, Cristina Lizana

We consider diffeomorphisms $f$ with heterodimensional cycles of co-index two, associated with saddles $P$ and $Q$ having unstable indices $\ell$ and $\ell+2$, respectively. In a partially hyperbolic setting, where a two-dimensional center direction and strong invariant manifolds are defined, we introduce the class of \emph{non-escaping cycles}, where the st

Jing Guo, Cheng Wang, Dong Wang

A numerical framework is proposed and analyzed for computing the ground state of Bose--Einstein condensates. A gradient flow approach is developed, incorporating both a Lagrange multiplier to enforce the $L^2$ conservation and a free energy dissipation. An explicit approximation is applied to the chemical potential, combined with an exponential time differen

Xi Xiao, Zhuxuanzi Wang, Mingqiao Mo, Chen Liu

The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose \ours, a self-supervised fr

Mohammad Abdi, Francesca Meneghello, Francesco Restuccia

Mobile devices increasingly require the parallel execution of several computing tasks offloaded at the wireless edge. Existing communication systems only support parallel transmissions at the bit level, which fundamentally limits the number of tasks that can be concurrently processed. To address this bottleneck, this paper introduces the new concept of Seman

Dhanesh Ramachandram, Anne Loefler, Surain Roberts, Amol Verma

Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learning models have attained very good predictive performance but their limited transparency, being black-box models, hinders their integration into clinical practice. To address this g

Sebastian Degen, Lisa Henetmayr, Magdaléna Mišinová, Paweł Pielasa

Simplicial arrangements are a special class of hyperplane arrangements, having the property that every chamber is a simplicial cone. It is known that the simpliciality property is preserved under taking restrictions. In this article we focus on the class of reflection arrangements and investigate two different polynomial invariants associated to them and the

Nivedina A. Sarma, Alexandra Grigoropoulos, Mustafa Arslan, Erika E. Salzman

Hierarchical materials in the natural world are often made through the self-assembly of amphiphilic molecules. Achieving similar structural complexity in synthetic materials requires understanding how various molecular parameters affect assembly behavior. In recent years, inverse surfactants -- molecules with hydrophobic head groups and hydrophilic macromole

Di. Wang, Yongjin. Li

In this paper, we investigate the extremal structure of the unit ball in the most general classes of Orlicz--Lorentz spaces. the characterizations of extreme points, strongly extreme points, and exposed points are given for Orlicz--Lorentz function spaces $\Lambda_{\varphi,\omega}$ generated by an arbitrary Orlicz function $\varphi$ and a non--increasing wei

Hyunki Seong, Seongwoo Moon, Hojin Ahn, Jehun Kang

Exploring open-world situations in an end-to-end manner is a promising yet challenging task due to the need for strong generalization capabilities. In particular, end-to-end autonomous driving in unstructured outdoor environments often encounters conditions that were unfamiliar during training. In this work, we present Vision-Language Action Retrieval (VLA-R

Shail Desai, Aditya Pawar, Li Lin, Xin Wang

Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression. At the same time, the rapid rise of AI-generated media has introduced serious risks, including misinformation, identity misuse, and the erosion of public trust as synthetic conte

Angelika Schwarz, Anton Anders, Cole Brower, Harun Bayraktar

The rapid growth of artificial intelligence (AI) has made low-precision formats such as FP16, FP8, and, most recently, block-scaled FP4 the primary focus of modern GPUs, where Tensor Cores now deliver orders-of-magnitude higher throughput than traditional FP64 pipelines. This hardware shift has sparked a new line of algorithm research: using low-precision un

Mordecai Waegell, Kelvin J. McQueen

According to a common view, Everettian quantum mechanics (EQM) is a local theory because it avoids nonlocal action at a distance, and this is an important point in EQM's favor. Unlike collapse theories, EQM does not allow an action on one system to change the reduced density matrix (RDM) of a remote entangled system - a clear case of nonlocal action. However

Jiaxiong Hao, Yunqing Huang, Nianyu Yi

In mesh-based numerical simulations, the interpolation of mesh-defined functions across different meshes is a critical task, and achieving high-precision interpolation is of great significance for improving the computational efficiency and numerical stability of algorithms. This paper proposes neural network based function mapping model across meshes, wherei

Zexin Zhang

In this paper, we investigate the uniform regularity and asymptotic behavior of solutions to the following Lotka-Volterra type system of strong competition with Dirichlet boundary conditions: \begin{align*} \left\{ \begin{array}{ll} -\Delta u_{i,\beta} = f_{i,\beta}(x, u_{i,\beta}) - \beta u_{i,\beta}^{p_i} \sum_{\substack{j=1 \\ j \neq i}}^k a_{ij} u_{j,\be

Xu Yang, Gady Agam

We introduce MSLoRA, a backbone-agnostic, parameter-efficient adapter that reweights feature responses rather than re-tuning the underlying backbone. Existing low-rank adaptation methods are mostly confined to vision transformers (ViTs) and struggle to generalize across architectures. MSLoRA unifies adaptation for both convolutional neural networks (CNNs) an

Hsuan-Yi Liao, Mathieu Stiénon, Ping Xu

The main goal of this paper is to study the formal geometry of dg manifolds \`a la Fedosov. For any dg manifold $(\mathcal{M}, Q)$, we construct a Fedosov dg foliation (or dg Lie algebroid) $\mathcal{F}_Q \to \mathcal{N}_Q$. We establish homotopy contractions between their respective spaces of polyvector fields, differential forms, polydifferential operators

Pedro A. Pury

This work addresses a key challenge in inventory management by developing a stochastic model that describes the dynamic distribution of inventory stock over time without assuming a specific demand distribution. Our model provides a flexible and applicable solution for situations with limited historical data and short-term predictions, making it well-suited f

Jiacheng Wang, Hao Li, Xing Yao, Ahmad Toubasi

Quantitative magnetization transfer (qMT) imaging provides myelin-sensitive biomarkers, such as the pool size ratio (PSR), which is valuable for multiple sclerosis (MS) assessment. However, qMT requires specialized 20-30 minute scans. We propose DEMIST to synthesize PSR maps from standard T1w and FLAIR images using a 3D latent diffusion model with three comp

Saleh Almohaimeed, Saad Almohaimeed, Mousa Jari, Khaled A. Alobaid

Many AI detection models have been developed to counter the presence of articles created by artificial intelligence (AI). However, if a human-authored article is slightly polished by AI, a shift will occur in the borderline decision of these AI detection models, leading them to consider it as AI-generated article. This misclassification may result in falsely

Christina Büsing, Maurice Draeger, Corinna Mathwieser

We study the parameterized complexity of scheduling unit-time jobs on parallel, identical machines under generalized precedence constraints for minimization of the makespan and the sum of completion times. In our setting, each job is equipped with a Boolean formula (precedence constraint) over the set of jobs. A schedule satisfies a job's precedence constrai

Saleh Almohaimeed, May Alsofyani, Saad Almohaimeed, Mansour Al Ghanim

In recent years, the task of cross-domain, context-dependent text-to-SQL has received significant attention. Enables users with no prior knowledge of SQL to have a conversation with databases using natural language. However, most of the available datasets and research have been conducted in English, along with some work in Chinese. To this date, no effort ha

Prithila Angkan, Amin Jalali, Paul Hungler, Ali Etemad

We propose a new representation learning solution for the classification of cognitive load based on Electroencephalogram (EEG). Our method integrates both time and frequency domains by first passing the raw EEG signals through the convolutional encoder to obtain the time domain representations. Next, we measure the Power Spectral Density (PSD) for all five E

Nicolo' Pagan, Andreas Philippou, Giulia De Pasquale

Modern social networks rely on recommender systems that inadvertently amplify misinformation by prioritizing engagement over content veracity. We present a control framework that mitigates misinformation spread while maintaining user engagement by penalizing content characteristics commonly exploited by false information, specifically, extreme negative senti

Jacob L. Barnett, Ramy El-Ganainy

We introduce a general framework for realizing $\mathcal{PT}$-like phase transitions in non-Hermitian systems without imposing explicit parity--time ($\mathcal{PT}$) symmetry. The approach is based on constructing a Hamiltonian as the square root of a supersymmetric partner energy-shifted by a constant. This formulation naturally leads to bipartite dynamics

Chen Gu, Yingying Sun, Yifan She, Donghui Hu

Federated learning (FL) enables multiple clients to collaboratively train a shared global model while preserving the privacy of their local data. Within this paradigm, the intellectual property rights (IPR) of client models are critical assets that must be protected. In practice, the central server responsible for maintaining the global model may maliciously

Cervane Grimaud, Florian Aubin, Benoît Mours, Thierry Pradier

Since the first gravitational wave detection in 2015, ground-based interferometer sensitivities have significantly improved, requiring highly precise calibration to ensure accurate reconstruction of the h(t) strain signal. In this talk we will outline the Virgo interferometer calibration steps performed in preparation of the O4b run started in April 2024. We

Sanghyo Park, Seuk Lee, Keunyoung Lee, Minhyeok Kim

Quantum computers based on cold-atom arrays offer long-lived qubits with programmable connectivity, yet their progress toward fault-tolerant operation is limited by the relatively low fidelity of site-selective local control. We introduce an artificial-intelligence (AI) framework that overcomes this limitation. Trained on atom-laser dynamics, a deep neural n

Xiantao Fan, Meet Hemant Parikh, Yi Liu, Xin-Yang Liu

Wall-pressure fluctuations in turbulent boundary layers drive flow-induced noise, structural vibration, and hydroacoustic disturbances, especially in underwater and aerospace systems. Accurate prediction of their wavenumber-frequency spectra is critical for mitigation and design, yet empirical/analytical models rely on simplifying assumptions and miss the fu

Stefano Civelli, Pietro Bernardelle, Frank Mols, Gianluca Demartini

The spread of targeted advertising on social media platforms has revolutionized political marketing strategies. Monitoring these digital campaigns is essential for maintaining transparency and accountability in democratic processes. Leveraging Meta's Ad Library, we analyze political advertising on Facebook and Instagram during the 2022 Australian federal

Nicolas Angelini, Ursula Molter

Given a positive, non-increasing sequence $a$ with finite sum equal to $1$, we consider the family of all closed subsets of $[0,1]$ whose complementary open intervals have lengths given by a rearrangement of the sequence $a$. We study the full range of possible $θ$-intermediate dimensions of these sets and, under suitable assumptions on the sequence, we show

Heiko Gimperlein, Magnus Goffeng, Nikoletta Louca

We study the geometric significance of Leinster's notion of magnitude for a smooth manifold with boundary of arbitrary dimension, motivated by open questions for the unit disk in $\mathbb{R}^2$. For a large class of distance functions, including embedded submanifolds of Euclidean space and Riemannian manifolds satisfying a technical condition, we show th

Marco Scaringi, Marco Bianchetti

Risk allocation, the decomposition of a portfolio-wide risk measure into component contributions, is a fundamental problem in financial risk management due to the non-additive nature of risk measures, the layered organizational structures of financial institutions, and the range of possible allocation strategies characterized by different rationales and prop

Sanjar Atamuradov

Virtual reality (VR) teleoperation has emerged as a promising approach for controlling humanoid robots in complex manipulation tasks. However, traditional teleoperation systems rely on inverse kinematics (IK) solvers and hand-tuned PD controllers, which struggle to handle external forces, adapt to different users, and produce natural motions under dynamic co

Divake Kumar, Patrick Poggi, Sina Tayebati, Devashri Naik

Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce Uncertainty-Guided Inference-Time Selection, a lightweight inference time framework that disentangles aleatoric (data-driven) and epistemic (model-driven) uncertainty directly in de

Zahra Zamanzadeh Darban, Qizhou Wang, Charu C. Aggarwal, Geoffrey I. Webb

Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond the training distribution due to decision boundaries that lack a clear definition of normality. Existing approaches typically address this by regularising the representation space du

Jeyarajalingam Varsha, Menan Velayuthan, Sumirtha Karunakaran, Rasan Nivethiga

Large Language Models (LLMs) have shown strong generalization across tasks in high-resource languages; however, their linguistic competence in low-resource and morphologically rich languages such as Tamil remains largely unexplored. Existing multilingual benchmarks often rely on translated English datasets, failing to capture the linguistic and cultural nuan

Shabnam Sodagari, Tommy Long

Quantum machine learning has emerged as a promising approach for medical image analysis, particularly in settings where compact models and expressive feature representations are desired. This paper presents a hybrid classical--quantum diagnostic framework that integrates dataset-specific preprocessing, transfer learning, and quantum convolutional neural netw