November 2025 arXiv papers — page 86

Mi Tian, Kun Zhang, Fei Liu, Jinglong Li

Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, CAT enables personalized learner modeling and has attracted substantial attention. Despite this progress

Tomoki Nakao, Kazumi Kasaura, Tadashi Kozuno

In decentralized multi-agent navigation, agents that independently compute their controls without communicating goals or intentions can fall into symmetry-induced deadlocks because each agent must decide how to pass others. We study this problem under the assumption that each agent has access to the current observable states of other agents, including their

Ihab Tabbara, Eliya Badr, Hussein Sibai

Hamilton-Jacobi (HJ) reachability analysis is a fundamental tool for the safety verification and control synthesis of nonlinear control systems. Classical HJ reachability analysis methods compute value functions over grids which discretize the continuous state space. Such approaches do not account for discretization errors and thus do not guarantee that the

Mirzabek Alloqulov, Sanjar Shaymatov, Bobomurat Ahmedov, Tao Zhu

In this work, we study the gravitational waveforms from the periodic orbits of a massive particle around a dyonic ModMax black hole. We begin with a brief analysis of the spacetime and then examine how its parameters influence the dynamics of a massive neutral particle using the Lagrangian formalism. In particular, we compute the characteristics of marginall

Xu Liu

Most of researchers on testing a significance of coefficient $\ubeta$ in high-dimensional linear regression models consider the classical hypothesis testing problem $H_0^{c}: \ubeta=\uzero \mbox{ versus } H_1^{c}: \ubeta \neq \uzero$. We take a different perspective and study the testing problem with the null hypothesis of no relevant difference between $\ub

Julian M. Rosalie, Anton Hohenwarter

An ultrafine grained magnesium alloy has been produced through room temperature high-pressure torsion (HPT) of solutionised Mg-1.35 wt.%Mn. Dynamic precipitation of nanometer-scale Mn particles occurred during deformation. These particles populated the grain boundaries, acting as pinning sites which allowed the alloy to develop a grain size of 140 nm after 0

Guocong Shang

We consider the Cauchy problem of coupled 3-D wave and Klein-Gordon equations with a quadratic form of nonlinearity. We show global existence under several conditions, including large derivative data for wave equations and the null conditions.

Goksu Oruc

We consider a fractional Korteweg de Vries-Benjamin Bona Mahony (KdV-BBM) type equation including both fractional dispersive terms of fractional KdV and fractional BBM equations. We aim to enhance the existence time of solutions with small initial data $|| u_0||_{H^{N+\alpha/2}}= \epsilon$ from $\frac{1}{\epsilon}$ to $\frac{1}{\epsilon^2}$. The proof relies

Gisella Croce, Antoine Henrot

In a recent paper, C. Gambicchia and A. Pratelli proved a quantitative isoperimetric inequality involving the isoperimetric deficit $\delta(K)$ and the barycentric distance $\lambda_0(K)$ for sets $K\subset \mathbb{R}^N$ with given diameter $D$ and measure. In this work we are interested in the optimal sets for this inequality in the plane, i.e. sets that mi

Ali Haider Shah, Naveed R. Butt, Asif Ahmad, Muhammad Omer Bin Saeed

This study focuses on the solution of partial differential equations (PDEs) by using physics-informed neural networks (PINNs). The Newell-Whitehead-Segel (NWS) equation and the Allen-Cahn equation belong to fundamental PDEs used mostly in various scientific disciplines. Different methods, including analytical and numerical approaches, have been proposed for

Can Huang, Weiwen Wang, Chuanju Xu

This work proposes an efficient, linear, and fully decoupled pressure-correction scheme for the 2D stochastic Navier-Stokes equations with multiplicative noise and Dirichlet boundary condition. Leveraging the auxiliary variable approach, the scheme is fully explicit yet unconditionally stable. At each time step, it only requires solving Poisson-type equation

Weijun Gao, Rundong He, Jinyang Dong, Yongshun Gong

Out-of-Distribution (OOD) detection is a critical capability for ensuring the safe deployment of machine learning models in open-world environments, where unexpected or anomalous inputs can compromise model reliability and performance. Activation-based methods play a fundamental role in OOD detection by mitigating anomalous activations and enhancing the sepa

Bashar Abdallah, Martyna E. Wojciechowska, Gustavo Santos, Edmand Yu

Much of the existing ML research focuses on model performance metrics, leaving limited attention to the long-term sustainability and resource efficiency of ML applications. While high performance is essential, ensuring efficient resource management is equally critical for robust deployment. This study addresses this gap by systematically identifying code sme

Liu Chang, Wang Ran

Consider the linear stochastic fractional heat equation with vanishing initial condition: $$ \frac{\partial u (t,x)}{\partial t}=-(-\Delta)^{\frac{\alpha}2}u (t,x) + \dot{W}(t,x),\quad t> 0,\, x\in \mathbb R, $$ where $-(-\Delta)^{\frac{\alpha}{2}}$ denotes the fractional Laplacian with power $\alpha\in (1,2]$, and the driving noise $\dot W$ is a centered Ga

Hiep Hong Trinh, Marjan Sirjani, Federico Ciccozzi, Abu Naser Masud

Model-based development enables quicker prototyping, earlier experimentation and validation of design intents. For a multi-agent system with complex asynchronous interactions and concurrency, formal verification, model-checking in particular, offers an automated mechanism for verifying desired properties. Timed Rebeca is an actor-based modelling language sup

Sirui Chen, Jiaao Li, Zhouningxin Wang

The frustration index of a signed graph is defined as the minimum number of negative edges among all switching-equivalent signatures. This can be regarded as a generalization of the classical \textsc{Max-Cut} problem in graphs, as the \textsc{Max-Cut} problem is equivalent to determining the frustration index of signed graphs with all edges being negative si

Jiajun Liu, Yimin Zhu, Xiaorui Liu, Mingye Cao

This paper proposed a novel fully-actuated hexacopter. It features a dual-frame passive tilting structure and achieves independent control of translational motion and attitude with minimal actuators. Compared to previous fully-actuated UAVs, it liminates internal force cancellation, resulting in higher flight efficiency and endurance under equivalent payload

Junichi Haruna

The gauge field formalism, or operator-valued cochain formalism, has recently emerged as a powerful framework for describing quantum Calderbank-Shor-Steane (CSS) codes. In this work, we extend this framework to construct a broad class of logical gates for general CSS codes, including the S, Hadamard, T, and (multi)controlled-Z gates, under the condition wher

Wei Hong, Shihu Li, Wei Liu

In the classical work [FK], Fujita and Kato established the local existence of solutions to the 3D Navier-Stokes equations in the critical $\mathbb{H}^{1/2}$-space. In this paper, we are concerned with the global well-posedness of the stochastic forced 3D Navier-Stokes equations in the $\mathbb{H}^{1/2}$-space under general initial conditions, where the stoc

Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi

Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the performance of ML models critically depends on the quality, size, and diversity of the training dataset. In materials science, this dependence is particularly important for learnin

Yongchao He, Taihao Zhang, Cunhua Pan, Hong Ren

This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furth

Pierre-Antoine Guihéneuf

This article consists in applications of [arXiv:2511.14232] in the case of homemomorphisms of higher genus surfaces whose homological rotation set is big enough -- a class of dynamics that is open. We first prove a structure theorem for the rotation set of such homeomorphisms: it is a finite union of convex sets, we get an optimal bound for the number of suc

Kwang Hak Kim, Velimir Todorovski, Miroslav Krstić

For the unicycle system, we provide constructive methods for the design of feedback laws that have one or more of the following properties: being nonmodular and globally exponentially stabilizing, inverse optimal, robust to arbitrary decrease or increase of input coefficients, adaptive, prescribed/fixed-time stabilizing, and safe (ensuring the satisfaction o

Byoung-Hee Kwon

This study introduces a pioneering approach in brain-computer interface (BCI) technology, featuring our novel concept of complex visual imagery for non-invasive electroencephalography (EEG)-based communication. Complex visual imagery, as proposed in our work, involves the user engaging in the mental visualization of complex upper limb movements. This innovat

Apurva Patil, Alfredo Duarte, Fabrizio Bisetti, Takashi Tanaka

The paper addresses a continuous-time continuous-space chance-constrained stochastic optimal control (SOC) problem where the probability of failure to satisfy given state constraints is explicitly bounded. We leverage the notion of exit time from continuous-time stochastic calculus to formulate a chance-constrained SOC problem. Without any conservative appro

Shaouna Lodhi

This paper critically analyzes the National Science Foundation's Division of Equity for Excellence in STEM. While supporting its mission to broaden participation for underrepresented groups, the study finds current policies inadequate for dismantling systemic barriers. Using Critical Race Theory and Mills's Racial Contract, the analysis reveals how well-inte

Alexander Shlapunov, Pavel Vilkov

We investigate the problem on Runge pairs for Sobolev solutions of strongly uniformly parabolic systems in non-cylindrical domains of a special kind. We prove that if the coefficients of a parabolic operator are constant, then two domains with sufficiently smooth boundaries, no parts of which are parallel to the plane $t=0$, form a Runge pair if and only if

Arunima Arunima, Mark R. Krumholz, Michael J. Ireland, Chuhan Zhang

Efforts to dynamically trace stars back to the now-dissolved clusters in which they formed rely implicitly on the assumption that stellar orbital actions are conserved. While this holds in a static, axisymmetric potential, it is unknown how strongly the time-varying, non-axisymmetric structure of a real galactic disk drives action drift that inhibits cluster

Giuseppe Matera

Economic behavior is shaped not only by quantitative information but also by the narratives through which such information is communicated and interpreted (Shiller, 2017). I show that narratives extracted from earnings calls significantly improve the prediction of both realized earnings and analyst expectations. To uncover the underlying mechanisms, I introd

António Caetano, Simon N. Chandler-Wilde, David P. Hewett

We study fractal sets $Γ\subset \mathbb{R}^n$ with non-empty interior $Ω$, that are attractors of iterated function systems (IFSs) of contracting similarities satisfying the open set condition. Examples for $n=2$ are the closures of the Koch snowflake domain and the Gosper island domain. Our first result is that $Ω$ is thick in the sense of Triebel. A conseq

Xinli Tao, Xin Dong, Xuezhong Zhou

With the rapid expansion of unstructured clinical texts in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting medical information. However, traditional supervised models such as CRF and BioClinicalBERT suffer from high annotation costs. Although zero-shot NER based on large language models

Vladislav Pedashenko, Laida Kushnareva, Yana Khassan Nibal, Eduard Tulchinskii

Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoenc

Tao Yang, Zhoulu Yu, Xuekui Xi, Changjun Jiang

This paper presents the design and implementation of a magnetic signal scanning and imaging system based on the giant magnetoimpedance (GMI) effect. The system employs a pair of performance-matched GMI sensing elements configured as a differential probe structure. Through co-optimized low-noise electronic and probe design, the system effectively suppresses b

Ranfei Chen, Ming Chen, Kaifei Wang

Diffusion Large Language Models (dLLMs) are rapidly emerging alongside autoregressive models as a powerful paradigm for complex reasoning, with reinforcement learning increasingly used for downstream alignment. Existing trajectory-based RL methods uniformly allocate policy gradients across denoising steps, implicitly treating all steps as equally important.

Chaofeng Guan, Gaojun Luo, Lan Luo, Yangyang Fei

The inherent uncertainty of communication channels implies that any coding scheme has a non-zero probability of failing to correct errors, making retransmission mechanisms essential. To ensure message reliability and integrity, a dual-layer redundancy framework is typically employed: error correction codes mitigate noise-induced impairments at the physical l

Bui Duc Son, Trinh Van Chien, Dong In Kim

The integration of generative artificial intelligence (GenAI) into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-domain vulnerabilities that arise across integrated sensing and c

Lixiang Chen, Yongtang Shi, Liwen Zhang

Let $G$ be a graph of genus $g$ with boundary $\delta\Omega$. For $g=0$, Lin and Zhao [J. Lond. Math. Soc. 112 (2025), Paper No. e70238] proved an upper bound for the first (non-trivial) Steklov eigenvalue of $(G, \delta\Omega )$, and they posed the problem of determining a corresponding bound for graphs of genus $g>0$. In this paper, we prove an $O\left(\fr

Kishor Datta Gupta, Marufa Kamal, Md. Mahfuzur Rahman, Fahad Rahman

Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledg

Max A. Nelson, Elif Keles, Eminenur Sen Tasci, Merve Yazol

Pediatric pancreatitis is a progressive and debilitating inflammatory condition, including acute pancreatitis and chronic pancreatitis, that presents significant clinical diagnostic challenges. Machine learning-based methods also face diagnostic challenges due to limited sample availability and multimodal imaging complexity. To address these challenges, this

Zimo Ji, Xunguang Wang, Zongjie Li, Pingchuan Ma

Large Language Model (LLM)-based agents with function-calling capabilities are increasingly deployed, but remain vulnerable to Indirect Prompt Injection (IPI) attacks that hijack their tool calls. In response, numerous IPI-centric defense frameworks have emerged. However, these defenses are fragmented, lacking a unified taxonomy and comprehensive evaluation.

Yinsheng Wang, Tario G You, Léonard Boussioux, Shan Liu

This paper introduces SOLID (Synergizing Optimization and Large Language Models for Intelligent Decision-Making), a novel framework that integrates mathematical optimization with the contextual capabilities of large language models (LLMs). SOLID facilitates iterative collaboration between optimization and LLMs agents through dual prices and deviation penalti

Qing Wang, Chong-Wah Ngo, Ee-Peng Lim

This paper addresses the challenges of learning representations for recipes and food images in the cross-modal retrieval problem. As the relationship between a recipe and its cooked dish is cause-and-effect, treating a recipe as a text source describing the visual appearance of a dish for learning representation, as the existing approaches, will create bias

Tairan He, Zi Wang, Haoru Xue, Qingwei Ben

A key barrier to the real-world deployment of humanoid robots is the lack of autonomous loco-manipulation skills. We introduce VIRAL, a visual sim-to-real framework that learns humanoid loco-manipulation entirely in simulation and deploys it zero-shot to real hardware. VIRAL follows a teacher-student design: a privileged RL teacher, operating on full state,

Jiajun Zhan, Zeyuan Ma, Yue-Jiao Gong, Kay Chen Tan

Evolutionary multitasking (EMT) algorithms typically require tailored designs for knowledge transfer, in order to assure convergence and optimality in multitask optimization. In this paper, we explore designing a systematic and generalizable knowledge transfer policy through Reinforcement Learning. We first identify three major challenges: determining the ta

Henglin Pu, Xuefeng Wang, Lu Su, Husheng Li

Integrated sensing and communication (ISAC) promises high spectral and power efficiencies by sharing waveforms, spectrum, and hardware across sensing and data links. Yet commercial cellular networks struggle to deliver fine angular, range, and Doppler resolution due to limited aperture, bandwidth, and coherent observation time. In this paper, we propose a sp

Raghu Vamsi Chittersu, Yuvraj Singh Rathore, Pranav Adlinge, Kunal Swami

Reference-based object composition involves integrating foreground reference image with background scene to produce harmonious fused image. This task becomes particularly challenging in cross-domain scenarios, where models must balance preserving the reference object's identity while harmonizing them to match stylized environments. This under-explored proble

David Yallup

High-dimensional multimodal sampling problems from lattice field theory (LFT) have become important benchmarks for machine learning assisted sampling methods. We show that GPU-accelerated particle methods, Sequential Monte Carlo (SMC) and nested sampling, provide a strong classical baseline that matches or outperforms state-of-the-art neural samplers in samp

Jatin Kumar, Wu Zeng, Anshuman Pasupalak, Massimo Pica Ciamarra

We show that two-dimensional systems of deformable particles undergo a continuous liquid-hexatic transition upon compression or cooling, but no hexatic-solid transition-even at zero temperature and high density. Numerical simulations reveal that solid-like configurations do not possess a lower energy than hexatic ones, so that at low temperatures the hexatic

Jian Deng, Yuandong Wang, Yangfu Zhu, Tao Feng

Robotic manipulation systems are increasingly deployed across diverse domains. Yet existing multi-modal learning frameworks lack inherent guarantees of geometric consistency, struggling to handle spatial transformations such as rotations and translations. While recent works attempt to introduce equivariance through bespoke architectural modifications, these

Hiromichi Tagawa, Connar Rowan, János Takátsy, Lorenz Zwick

Ground-based gravitational wave (GW) detectors have discovered about 200 compact object mergers. The astrophysical origins of these events are highly debated, and it is possible that at least a fraction of them originate from dynamical environments. Among these, the disks of active galactic nuclei (AGN) are particularly interesting as promising environments,

Haodong Li, Jingqi Zhang, Xiao Cheng, Peihua Mai

The remarkable language ability of Large Language Models (LLMs) stems from extensive training on vast datasets, often including copyrighted material, which raises serious concerns about unauthorized use. While Membership Inference Attacks (MIAs) offer potential solutions for detecting such violations, existing approaches face critical limitations and challen

Zhiyi Duan, Zixing Shi, Hongyu Yuan, Qi Wang

Knowledge Tracing (KT) aims to mine students' evolving knowledge states and predict their future question-answering performance. Existing methods based on heterogeneous information networks (HINs) are prone to introducing noises due to manual or random selection of meta-paths and lack necessary quality assessment of meta-path instances. Conversely, recent la

Yuxuan Gu, Weimin Bai, Yifei Wang, Weijian Luo

Masked auto-regressive diffusion models (MAR) benefit from the expressive modeling ability of diffusion models and the flexibility of masked auto-regressive ordering. However, vanilla MAR suffers from slow inference due to its hierarchical inference mechanism: an outer AR unmasking loop and an inner diffusion denoising chain. Such decoupled structure not onl

Kaikwan Lau, Andrew S. Na, Justin W. L. Wan

This paper presents a novel framework to accelerate score-based diffusion models. It first converts the standard stable diffusion model into the Fokker-Planck formulation which results in solving large linear systems for each image. For training involving many images, it can lead to a high computational cost. The core innovation is a cross-matrix Krylov proj

Wasif Jalal, Md Nafiu Rahman, Atif Hasan Rahman, M. Sohel Rahman

Accurate brain age estimation from structural MRI is a valuable biomarker for studying aging and neurodegeneration. Traditional regression and CNN-based methods face limitations such as manual feature engineering, limited receptive fields, and overfitting on heterogeneous data. Pure transformer models, while effective, require large datasets and high computa

Evan Frenklak, Yamin Arefeen, Jonathan I Tamir

Magnetic Resonance Imaging (MRI) diagnoses and manages a wide range of diseases, yet long scan times drive high costs and limit accessibility. AI methods have demonstrated substantial potential for reducing scan times, but despite rapid progress, clinical translation of AI often fails. One particular class of failure modes, referred to as implicit data crime

Geon Choi, Hangyul Yoon, Hyunju Shin, Hyunki Park

The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on complex, expert-level text inputs, creating a barrier to practical use. To address these limitations, we introduce instruction-guided lesion segmentation (ILS), a medical-domain adaptation of referring i

Ricardo Alonso, Zongguang Li

This paper considers a system of Boltzmann equations modelling the mixture of monatomic and polyatomic gases in an $L^{2}-L^{\infty}$ perturbation theory around global modified Maxwellians accounting for the internal energy of the mixture in the whole space and the torus. We investigate the pointwise decay in velocity and internal energy of the linearized Bo

Jikai Chen, Zhi Zeng, Hao Ma, Jianping Cheng

Four ultra-low background germanium spectrometers, called GeTHU, have been installed at the first phase of China Jinping Underground Laboratory (CJPL-I), and served for material screening of dark matter and neutrino experiments. Recently, a new multi-detector spectrometer with five germanium detectors has been developed and installed at the second phase of C

Akram Shafie, Jun Tong, Jinhong Yuan, Taka Sakurai

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising paradigm for ensuring reliable communications in doubly-selective channels. This work investigates the spectra and orthogonality characteristics of analog (direct) and approximate digital implementations of ODDM systems. We first determine the time and f

Rishikant Chigrupaatii, Ponnada Sai Tulasi Kanishka, Lalit Chandra Routhu, Martin Patel Sama Supratheek Reddy

With nearly 1.5 billion people and more than 120 major languages, India represents one of the most diverse regions in the world. As multilingual Vision-Language Models (VLMs) gain prominence, robust evaluation methodologies are essential to drive progress toward equitable AI for low-resource languages. Current multilingual VLM evaluations suffer from four ma

Subhashis Hazarika, Leonard Lupin-Jimenez, Rohit Vuppala, Ashesh Chattopadhyay

Efficient and sustainable maritime transport increasingly depends on reliable forecasting and adaptive routing, yet operational adoption remains difficult due to forecast latencies and the need for human judgment in rapid decision-making under changing ocean conditions. We introduce SWR-Viz, an AI-assisted visual analytics framework that combines a physics-i

German Lanzavecchia, Anastasiia Sapunova, Alan Szalai, Shukun Weng

Plasmonic nanopores combine nanofluidic confinement with electromagnetic field enhancement, enabling optical interrogation of single molecules in sub-wavelength volumes. Yet, direct optical readout within these metallic geometries has remained challenging due to fluorescence quenching near the surface. Here, we implement DNA-PAINT as a molecular reporter of

Geng Chen, Faris A. El-Katri, Yanbo Hu, Yannan Shen

In this paper, we consider the singularity formation of smooth solutions for the compressible radially symmetric Euler equations. By applying the characteristic method and the invariant domain idea, we show that, for polytropic ideal gases with $\gamma\geq3$, the smooth solution develops a singularity in finite time for a class of initial supersonic inward w

Kyotaro Tokoro, Hiromu Taketsugu, Norimichi Ukita

This paper proposes a novel metric for Human Motion Prediction (HMP). Since a single past sequence can lead to multiple possible futures, a probabilistic HMP method predicts such multiple motions. While a single motion predicted by a deterministic method is evaluated only with the difference from its ground truth motion, multiple predicted motions should als

Shunsuke Hirai, Ibuki Terada, Michi-To Suzuki

Molybdenum disulfide (MoS$_2$), a layered van der Waals material, has attracted considerable attention as a promising alternative to graphene for applications in field-effect transistors and nanophotonic devices because of its sizable band gap, high carrier mobility, large on/off ratio, and strong photoluminescence efficiency. A particularly intriguing prope

Michael E. Beverland, Malcolm Carroll, Andrew W. Cross, Theodore J. Yoder

The ultimate goal of quantum error correction is to create logical qubits with very low error rates (e.g. 1e-12) and assemble them into large-scale quantum computers capable of performing many (e.g. billions) of logical gates on many (e.g. thousands) of logical qubits. However, it is necessarily difficult to directly assess the performance of such high-quali

Yuchen Li, Zongxia Liang, Xiang Yu

This paper studies the mean field game (MFG) and N-player game on relative performance portfolio management with two heterogeneous populations. In addition to the Brownian idiosyncratic and common noise, the first population invests in assets driven by idiosyncratic Poisson jump risk, while the second population invests in assets subject to Poisson common no

Le Tung Giang, Vu Hoang Viet, Nguyen Xuan Tung, Trinh Van Chien

The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet classical models rely on large multi-layer perceptrons (MLPs) that are parameter-heavy and memory-bound. We prop

Yi Xu, Zhigang Chen, Rui Wang, Yangfan Li

In industrial equipment monitoring, fault diagnosis is critical for ensuring system reliability and enabling predictive maintenance. However, the scarcity of fault data, due to the rarity of fault events and the high cost of data annotation, significantly hinders data-driven approaches. Existing time-series generation models, optimized for abundant normal da

Jun Hyeun Kang, Jung Eek Son, Tae In Ahn

The application of spectral-shifting films in greenhouses to shift green light to red light has shown variable growth responses across crop species. However, the yield enhancement of crops under altered light quality is related to the collective effects of the specific biophysical characteristics of each species. Considering only one attribute of a crop has

Andrew Gracyk

It has been discovered that latent-Euclidean variational autoencoders (VAEs) admit, in various capacities, Riemannian structure. We adapt these arguments but for complex VAEs with a complex latent stage. We show that complex VAEs reveal to some level K\"ahler geometric structure. Our methods will be tailored for decoder geometry. We derive the Fisher informa

Yibei Li, Yuexin Cao, Zhixin Liu, Lihua Xie

In this paper, the inverse reinforcement learning (IRL) problem is addressed to reconstruct the unknown cost function underlying an observed optimal policy in a model-free manner, whose online adaptation with completely off-policy system data still remains unclear in the literature. Without prior knowledge of the system model parameters, an adaptive and dire

Omar A. M. Abdelraouf

Single-photon emitters (SPEs) based on nitrogen-vacancy centers in nanodiamonds (neutral NV0 (wavelength 575 nm) and negative NV- (wavelength 637 nm)) represent promising platforms for quantum nanophotonics applications, yet their emission efficiencies remain constrained by weak light-matter interactions. Multi-layer metasurfaces (MLM) offer unprecedented de

Xin Gao, Shaohan Yu, Zerui Chen, Yueming Lyu

Large Reasoning Models (LRMs) have significantly improved problem-solving through explicit Chain-of-Thought (CoT) reasoning. However, this capability creates a Safety-Helpfulness Paradox: the reasoning process itself can be misused to justify harmful actions or conceal malicious intent behind lengthy intermediate steps. Most existing benchmarks only check th

Nguyen-Khang Le, Hiep Nguyen, Ngoc-Minh Nguyen, Son T. Luu

Automated web application testing is a critical component of modern software development, with frameworks like Selenium widely adopted for validating functionality through browser automation. Among the essential aspects of such testing is the ability to interact with and validate web forms, a task that requires syntactically correct, executable scripts with

Jing Cao, Kui Jiang, Shenyi Li, Xiaocheng Feng

Self-supervised depth estimation has gained significant attention in autonomous driving and robotics. However, existing methods exhibit substantial performance degradation under adverse weather conditions such as rain and fog, where reduced visibility critically impairs depth prediction. To address this issue, we propose a novel self-evolution contrastive le

DoYoung Kim, Jin-Seop Lee, Noo-ri Kim, SungJoon Lee

Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing significant challenges for lightweight architectures with depth-wise

Jie Guo, Quansen Jiu

In this paper, we study the generalized Proudman-Johnson equation posed on the torus. In the critical regime where the parameter $a$ is close to and slightly greater than 1, we establish finite time blow-up of smooth solutions to the inviscid case. Moreover, we show that the blow-up is asymptotically self-similar for a class of smooth initial data. In contra

Jingzhuo Zhou

The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent threats that leverage research backgrounds, academic collaborat

Songze Li, Mingyu Gao, Tonghua Su, Xu-Yao Zhang

Multimodal continual instruction tuning enables multimodal large language models to sequentially adapt to new tasks while building upon previously acquired knowledge. However, this continual learning paradigm faces the significant challenge of catastrophic forgetting, where learning new tasks leads to performance degradation on previous ones. In this paper,

Yang Wu, Rujing Yao, Tong Zhang, Yufei Shi

Large Language Models (LLMs) are increasingly integrated into intelligent tutoring systems to provide human-like and adaptive instruction. However, most existing approaches fail to capture how students' knowledge evolves dynamically across their proficiencies, conceptual gaps, and forgetting patterns. This challenge is particularly acute in mathematics tutor

Ahmed Aboulfotouh, Hatem Abou-Zeid

Wireless foundation models (WFMs) have recently demonstrated promising capabilities, jointly performing multiple wireless functions and adapting effectively to new environments. However, while current WFMs process only one modality, depending on the task and operating conditions, the most informative modality changes and no single modality is best for all ta

Dogyoon Song

In randomized experiments, regression adjustment can improve the precision of average treatment effect (ATE) estimation using covariates without requiring a correctly specified outcome model. Although well studied in low-dimensional settings, its behavior in high-dimensional regimes, where the number of covariates $p$ may exceed the number of observations $n

D. Shapoval, M. Dudka

We analyse the critical properties of a weakly diluted (random) Ising model with the long-range interaction decaying with distance $x$ as $\sim x^{-d-\sigma}$ in a $d$-dimensional space. It is known to belong to a new long-range random universality class for certain values of the decay parameter $\sigma$. Exploiting the field-theoretic renormalization group

Firdavs Nasriddinov, Rafal Kocielnik, Anima Anandkumar, Andrew J. Hung

High-quality intraoperative feedback from a surgical trainer is pivotal for improving trainee performance and long-term skill acquisition. Automating natural, trainer-style feedback promises timely, accessible, and consistent guidance at scale but requires models that understand clinically relevant representations. We present a structure-aware pipeline that

Victor T. Voronchev

The present paper completes a series of our works on non-thermal nuclear processes in big bang nucleosynthesis (BBN) started in JCAP05(2008)010 (Part I) and 05(2009)001 (Part II). The processes are triggered by non-Maxwellian particles naturally born in the main BBN reactions. Half of these reactions generate fast particles k^+ (= n,p,t,3He,alpha). The other

Yangkendi Deng, Chenjie Fan, Zehua Zhao

We prove the sharp $L^4$ Strichartz estimate without derivative loss for the hyperbolic Schr\"odinger equation on $\mathbb{R}\times\mathbb{T}$, \begin{equation} \|e^{it (\partial_{x_{1}}^2-\partial_{x_{2}}^2)} \phi\|_{L^4_{t,x_{1},x_{2}}([0,1]\times \mathbb{R} \times \mathbb{T})}\lesssim \|\phi\|_{L_{x_{1},x_{2}}^2(\mathbb{R} \times \mathbb{T})}, \end{equati

Nikolai Karol

We investigate string graphs through the lens of graph product structure theory, which describes complicated graphs as subgraphs of strong products of simpler building blocks. A graph $G$ is called a string graph if its vertices can be represented by a collection $\mathcal{C}$ of continuous curves (called a string representation of $G$) in a surface so that

Lilia Ferrario

Long-period radio transients (LPRTs) are highly polarised, coherent radio sources with periods of minutes to hours and bursts typically lasting 10 to 100 s. Here we consider the apparently isolated subclass of LPRTs and argue that electron cyclotron maser emission (ECME) explains their narrow duty cycles and polarisation properties. In particular, we show th

Chun-Jung Lin, Tat-Jun Chin, Sourav Garg, Feras Dayoub

Accurate, up-to-date High-Definition (HD) maps are critical for urban planning, infrastructure monitoring, and autonomous navigation. However, these maps quickly become outdated as environments evolve, creating a need for robust methods that not only detect changes but also incorporate them into updated 3D representations. While change detection techniques h

Meihua Zhou, Xinyu Tong, Jiarui Zhao, Min Cheng

High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and by the limited adaptability of large-scale, general-purpose models. To address these challenges, we introduce Dynamic Curriculum Learning for Spatiotemporal Encoding (DCL-SE), an end-to-end framework centered on data-driven spatio

Bhargabi Saha, Malay K. Nandy

In this study, we investigate the full nonlinear dynamics of warm inflation driven by the quartic inflaton potential, avoiding any simplifying approximations. The thermal backreaction is incorporated through a dissipation coefficient that depends linearly on the temperature, and the model parameters are chosen to remain consistent with Planck observational c

Diksha Rani Bansal, Bibekananda Maji, Pragya Singh

Recently, Choie and Kumar extensively studied the Herglotz-Zagier-Novikov function $\mathfrak{F}(z;u,v)$, defined as \begin{align*} \mathfrak{F}(z;u,v) = \int_{0}^{1} \frac{\log(1-ut^z)}{v^{-1}-t} dt, \quad \textrm{for} \quad \mathfrak{Re}(z)>0. \end{align*} They obtained two-term, three-term and six-term functional equations for $\mathfrak{F}(z;u,v)$ and al

Philip Drammeh

Large language models (LLMs) promise to accelerate incident response in production systems, yet single-agent approaches generate vague, unusable recommendations. We present MyAntFarm.ai, a reproducible containerized framework demonstrating that multi-agent orchestration fundamentally transforms LLM-based incident response quality. Through 348 controlled tria

Phoebe Grosser, Monica Gutierrez Galan, Isabelle Savill-Brown, Alexander K. Ratcliffe

Micromotion in radio-frequency ion traps is generally considered detrimental for quantum logic gates, and is typically minimized in state-of-the-art experiments. However, as a deterministic effect, it can be incorporated into quantum control frameworks aimed at designing high-fidelity quantum logic controls. In this work, we demonstrate that micromotion can

Y. Sun, M. Alzate Banguero, P. Salev, Ivan K. Schuller

The ramp-reversal memory (RRM) effect in metal-insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and non-volatile memory devices. Our previously introduced defect motion model successfully explained RRM in vanadium dioxide (VO$_2$),

Eugene Ndiaye

Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. A candidate output is included in the prediction set if its non-conformity score is not considered extreme relative to the scores observed on a set of calibration examples. However, this procedure is only straightforward when scores are scalar-valu

Seokcheon Lee

Hints of a dynamical dark-energy equation of state have appeared in several combined cosmological probes. However, such indications may instead arise from the intrinsic likelihood geometry of individual datasets, residual inter-probe tension, or restrictive priors. These factors can mimic evidence for dynamical dark energy. To clarify these issues, we perfor

Yuhan Liang, Huiping Han, Hetian Chen, Yujun Zhang

The unique features of ultrafast spin dynamics and the absence of macroscopic magnetization in antiferromagnetic (AFM) materials provide a distinct route towards high-speed magnetic storage devices with low energy consumption and high integration density. However, these advantages also introduce challenges in probing and controlling AFM order, thereby restri