October 2025 arXiv papers — page 53

Ritabrata Biswas, Satyajit Pal

Higher dimensional Gauss-Bonnet gravity can be particularized to a four dimensional case either using the Glavan, D. and Lin, C. type \cite{glavan2020einstein} limiting method or by the Hordenski type \cite{gurses2007gauss} metric compactification procedure. Depending on ADM mass and Gauss Bonnet coupling parameter $\alpha_{GB}$, a black hole solution is pre

Mark Deaconu, Kaveh Mousavand, Charles Paquette

We study infinite string modules that are bricks over some gentle algebras. In particular, we first give a complete classification of these modules over the double-Kronecker gentle algebra and prove that each family is in bijection with a family of Sturmian (binary) words. We then generalize some of our results to a larger family of gentle algebras.

Zirui Pang, Hao Zheng, Zhijie Deng, Ling Li

LLM unlearning has emerged as a promising approach, aiming to enable models to forget hazardous/undesired knowledge at low cost while preserving as much model utility as possible. Among existing techniques, the most straightforward method is performing Gradient Ascent (GA) w.r.t. the forget data, thereby forcing the model to unlearn the forget dataset. Howev

Dimitrios Loukrezis, Dimitris G. Giovanis

This work introduces a method to equip data-driven polynomial chaos expansion surrogate models with intervals that quantify the predictive uncertainty of the surrogate. To that end, jackknife-based conformal prediction is integrated into regression-based polynomial chaos expansions. The jackknife algorithm uses leave-one-out residuals to generate predictive

Shengyuan Niu, Haoran Wang, Heejip Moon, Andrea L'Afflitto

This paper combines vector-valued reproducing kernel Hilbert space (vRKHS) embedding with robust adaptive observation, yielding an algorithm that is both non-parametric and robust. The main contribution of this paper lies in the ability of the proposed system to estimate the state of a plan model whose matched uncertainties are elements of an infinite-dimens

Yupeng Xie, Zhiyang Zhang, Yifan Wu, Sirong Lu

Visualization, a domain-specific yet widely used form of imagery, is an effective way to turn complex datasets into intuitive insights, and its value depends on whether data are faithfully represented, clearly communicated, and aesthetically designed. However, evaluating visualization quality is challenging: unlike natural images, it requires simultaneous ju

Vincent Rivasseau

In this paper we construct scalar cumulants for stable random matrix models with single trace interactions of arbitrarily high even order by Weingarten calculus. We obtain explicit and convergent expansions for these scalar cumulants in the limit N tend to infinity.

Revanth Rameshkumar, Jimson Huang, Yunxin Sun, Fei Xia

Large language models (LLMs) have shown significant progress in reasoning tasks. However, recent studies show that transformers and LLMs fail catastrophically once reasoning problems exceed modest complexity. We revisit these findings through the lens of large reasoning models (LRMs) -- LLMs fine-tuned with incentives for step-by-step argumentation and self-

Seyed Ahmad Hosseini Miangoleh, Amin Jalal Aghdasian, Farzaneh Abdollahi

In this paper, we propose Bootstrapped Language-Image Pretraining-driven Fused State Representation in Proximal Policy Optimization (BLIP-FusePPO), a novel multimodal reinforcement learning (RL) framework for autonomous lane-keeping (LK), in which semantic embeddings generated by a vision-language model (VLM) are directly fused with geometric states, LiDAR o

Egor Kolodin, Daria Khomich, Nikita Savushkin, Anastasia Ianina

We introduce GigaEmbeddings, a novel framework for training high-performance Russian-focused text embeddings through hierarchical instruction tuning of the decoder-only LLM designed specifically for Russian language (GigaChat-3B). Our three-stage pipeline, comprising large-scale contrastive pre-training in web-scale corpora, fine-tuning with hard negatives,

Haowen Zhou, Pawel Wojcik, Guo-Zhu Zhu, Guanming Lao

Laser cooling of large, complex molecules is a long-standing goal, instrumental for enabling new quantum technology and precision measurements. A primary consideration for the feasibility of laser cooling, which determines the efficiency and technical requirements of the process, is the number of excited-state decay pathways leading to vibrational excitation

Jindong Yang, Han Fang, Weiming Zhang, Nenghai Yu

Diffusion models have advanced rapidly in recent years, producing high-fidelity images while raising concerns about intellectual property protection and the misuse of generative AI. Image watermarking for diffusion models, particularly Noise-as-Watermark (NaW) methods, encode watermark as specific standard Gaussian noise vector for image generation, embeddin

Ana Paulina Figueroa, Eduardo Rivera-Campo

We characterize all partitions of the complete twisted graph $T_{2n}$ into plane spanning trees. In the case of partitions of $T_{2n}$ into isomorphic plane spanning trees, we show that all trees in these partitions must be balanced double stars. As a consequence of our results, any complete topological graph with $n$ vertices contains a complete topological

Samir Damji, Simrut Kurry, Shazia'Ayn Babul, Joydeep Bhattacharya

Creativity is a core cognitive capacity underlying innovation and adaptive problem solving, yet how it is represented in the brain's intrinsic functional architecture is not fully understood. While resting-state fMRI studies have identified large-scale network correlates associated with differences in creativity, EEG provides the temporal resolution for exam

Santiago Torres

Most stars form in dense stellar environments, where frequent close encounters can strongly perturb and reshape the early architecture of planetary systems. The solar system, with its rich population of distant comets, provides a natural laboratory to study these processes. We perform detailed numerical simulations using the LonelyPlanets framework that comb

Jan Simson, Alessandro Fabris, Cosima Fröhner, Frauke Kreuter

As machine learning (ML) systems are increasingly adopted in high-stakes decision-making domains, ensuring fairness in their outputs has become a central challenge. At the core of fair ML research are the datasets used to investigate bias and develop mitigation strategies. Yet, much of the existing work relies on a narrow selection of datasets--often arbitra

Jiazheng Li, Andreas Damianou, J Rosser, José Luis Redondo García

Chain-of-thought (CoT) traces promise transparency for reasoning language models, but prior work shows they are not always faithful reflections of internal computation. This raises challenges for oversight: practitioners may misinterpret decorative reasoning as genuine. We introduce Concept Walk, a general framework for tracing how a model's internal stance

Tatsuru Kikuchi

This paper develops a dual-channel framework for analyzing technology diffusion that integrates spatial decay mechanisms from continuous functional analysis with network contagion dynamics from spectral graph theory. Building on our previous studies, which establish Navier-Stokes-based approaches to spatial treatment effects and financial network fragility,

Frédéric Bourgeois, Salammbo Connolly

Linearized Legendrian contact homology (LCH) and bilinearized LCH are important homological invariants for Legendrian submanifolds in contact geometry. For legendrian knots in $\mathbb{R}^3$, very little was previously known about the possibility of having torsion in these invariants when they are defined over integer coefficients. In this paper, we give pro

Yao Lu, Yuqi Li, Wenbin Xie, Shanqing Yu

Although large language models (LLMs) have achieved revolutionary breakthroughs in many fields, their large model size and high computational cost pose significant challenges for practical deployment on resource-constrained edge devices. To this end, layer pruning has been proposed to reduce the computational overhead by directly removing redundant layers. H

Changhao Zhang, Matthew J. Clarkson, Mobarak I. Hoque

3D reconstruction of endoscopic surgery scenes plays a vital role in enhancing scene perception, enabling AR visualization, and supporting context-aware decision-making in image-guided surgery. A critical yet challenging step in this process is the accurate estimation of the endoscope's intrinsic parameters. In real surgical settings, intrinsic calibration i

M. Ebrahimi, Y. Huang, A. Rashedi, J. P. Davis

Motivated by recent claims, we revisit how coherent perfect absorption (CPA) influences cavity and polaritonic linewidths. Using standard input output theory and measurements on single port bare microwave cavities and cavity magnon hybrids, we find that CPA drives the on resonance reflection to zero while the spectral width remains set by the total decay rat

J. I. Díaz, T. A. Shaposhnikova, A. V. Podolskiy

Given an optimal control problem on a heterogeneous body with a periodical structure of particles depending on a small parameter e, we study the asymptotic behavior, as e converges to zero, of the optimal control functional and the optimal state when the initial problem is of parabolic type, and when on the particles' boundary, we assume a dynamic condition

Fiaz Ahmad, Nisar Hussain, Amna Qasim, Momina Hafeez

Ironic identification is a challenging task in Natural Language Processing, particularly when dealing with languages that differ in syntax and cultural context. In this work, we aim to detect irony in Urdu by translating an English Ironic Corpus into the Urdu language. We evaluate ten state-of-the-art machine learning algorithms using GloVe and Word2Vec embe

J. Abuhlail, A. Alfaraj

We study separation axioms for $X$-top-lattices (i.e. lattices $L$ for which a given subset $X\subseteq L\backslash \{1\}$ admits a \emph{Zariski-like topology}). Such spaces are $T_{0}$ and usually far away from being $T_{2}.$% We give graphical characterizations for an $X$-top-lattice to be $T_{1},$ $% T_{\frac{1}{4}},$ $T_{\frac{1}{2}},$ $T_{\frac{3}{4}}$

B. Maheshwari, P. Van Isacker, P. M. Walker

We present a shell-model analysis of $^{93}$Mo to investigate the unusual behavior of its ${21/2}^+$ isomer -- a prominent candidate for nuclear excitation by electronic capture. This state is unique as its decay is dominated by a slow electric hexadecapole $E4$ transition, while the typically much faster electric quadrupole $E2$ decay path is energetically

Haotao Xie, Ruilin Chen, Yicheng Wu, Zhan Zhao

In this work, we address the challenge of multilingual category relevance judgment in e-commerce search, where traditional ensemble-based systems improve accuracy but at the cost of heavy training, inference, and maintenance complexity. To overcome this limitation, we propose a simplified yet effective framework that leverages prompt engineering with Chain-o

Saikat Sengupta, Koulik Khamaru, Suvrojit Ghosh, Tirthankar Dasgupta

We study the problem of estimating the average treatment effect (ATE) under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, we consider a setting in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential o

Ryan J. Thomas, Samuel Legge, John D. Close

We propose a method for using a single-axis atom interferometric gravity gradiometer to measure off-diagonal elements of the gravity gradient tensor. By tilting the gradiometer, the measured gradient becomes a linear combination of different components of the gravity gradient tensor, and through multiple measurements at different tilts the separate tensor co

William Barker, Le Xuan Dong, Vu Trong Luong, Nguyen Duong Toan

We study the existence of monotone traveling wave solutions in a class of nonclassical diffusion equations that include both standard diffusion and a higher-order mixed space-time dispersive term. The reaction term is nonlinear and subject to general structural conditions. By employing the method of upper and lower solutions, using less smooth super and subs

Alberto G. Rojo

The model of magnetic monopoles that was proposed by Paul Dirac in 1931 has long been a subject of theoretical interest in physics because of its potential to explain the quantization of electric charge. While much attention has been given to non-Dirac monopoles, Dirac's model, which involves an infinitely thin solenoid known as a Dirac string, presents subt

Aryan Ranjan

We present a systematic trading framework that forecasts short-horizon market risk, identifies its underlying drivers, and generates alpha using a hybrid machine learning ensemble built to trade on the resulting signal. The framework integrates neural networks with tree-based voting models to predict five-day drawdowns in the S&P 500 ETF, leveraging a cross-

Ertian Chen

Estimation and counterfactual analysis in dynamic structural models rely on assumptions about the dynamic process of latent variables, which may be misspecified. We propose a framework to quantify the sensitivity of scalar parameters of interest (e.g., welfare, elasticity) to such assumptions. We derive bounds on the scalar parameter by perturbing a referenc

Atsuhisa Ota

The thermal plasma induces a plasmon-like mass shift for gravitational perturbations, which can modify their dynamics near the horizon scale in the early radiation-dominated universe. However, there are several seemingly reasonable ways to introduce this mass shift, reflecting an ambiguity in how one specifies the initial plasma state on a perturbed FLRW bac

David Anton, Henning Wessels, Ulrich Römer, Alexander Henkes

Constitutive model discovery refers to the task of identifying an appropriate model structure, usually from a predefined model library, while simultaneously inferring its material parameters. The data used for model discovery are measured in mechanical tests and are thus inevitably affected by noise which, in turn, induces uncertainties. Previously proposed

Mohammad Aghajani Asl, Majid Asgari-Bidhendi, Behrooz Minaei-Bidgoli

While Retrieval-Augmented Generation (RAG) mitigates hallucination and knowledge staleness in Large Language Models (LLMs), existing frameworks often falter on complex, multi-hop queries that require synthesizing information from disparate sources. Current advanced RAG methods, employing iterative or adaptive strategies, lack a robust mechanism to systematic

Weijia Qian, Erjia Cui, Ashley Brooks-Russell, Julia Wrobel

Cannabis consumption impairs key driving skills and increases crash risk, yet few objective, validated tools exists to identify acute cannabis use or impairment in traffic safety settings. Pupil response to light has emerged as a promising biomarker of recent cannabis use, but its predictive utility remains underexplored. We propose two functional accelerate

Krishan Kumar, Ashutosh Sharma, Gauransh Dingwani, Nikhil Gupta

Second-order Newton-type algorithms that leverage the exact Hessian or its approximation are central to solve nonlinear optimization problems. However, their applications in solving large-scale nonconvex problems are hindered by three primary challenges: (1) the high computational cost associated with Hessian evaluations, (2) its inversion, and (3) ensuring

Avirup Chakraborty

The European Union Emissions Trading System (EU ETS), the world's first and largest cap-and-trade carbon market, is a cornerstone of EU climate policy. This study provides a comprehensive empirical analysis of the EU carbon market's efficiency, price dynamics, and structural network from 2010 to 2020. First, we identify significant price clustering and short

Zehao Lu

The Wasserstein distance, also known as the Earth mover distance or optimal transport distance, is a widely used measure of similarity between probability distributions. This paper presents an linear programming based implementation of the multi-dimensional Wasserstein distance function in Scipy, a powerful scientific computing package in Python. Building up

Changti Wu, Shijie Lian, Zihao Liu, Lei Zhang

Solid geometry problem solving demands spatial mathematical reasoning that integrates spatial intelligence and symbolic reasoning. However, most existing multimodal mathematical reasoning benchmarks focus primarily on 2D plane geometry, rely on static datasets prone to data contamination and memorization, and evaluate models solely by final answers, overlook

Enyi Wang, Zhen Deng, Chuanchuan Pan, Bingwei He

This paper presents a learning-based approach for accurately estimating the 3D shape of flexible continuum robots subjected to external loads. The proposed method introduces a spatiotemporal neural network architecture that fuses multi-modal inputs, including current and historical tendon displacement data and RGB images, to generate point clouds representin

Phillip Mueller, Talip Uenlue, Sebastian Schmidt, Marcel Kollovieh

Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Traditional 3D editing approaches are time-consuming and demand specialized skills, while current image-based generative methods lack accuracy in geometric conditioning. To address these

Bo Yue, Sheng Xu, Kui Jia, Guiliang Liu

Humanoid robots represent a central frontier in embodied intelligence, as their anthropomorphic form enables natural deployment in humans' workspace. Brain-body co-design for humanoids presents a promising approach to realizing this potential by jointly optimizing control policies and physical morphology. Within this context, fall recovery emerges as a criti

Ethan Mines, Bonnie Dorr

Social media enables data-driven analysis of public opinion on contested issues. Target-Stance Extraction (TSE) is the task of identifying the target discussed in a document and the document's stance towards that target. Many works classify stance towards a given target in a multilingual setting, but all prior work in TSE is English-only. This work introduce

Xiao Hu, Yuansheng Lian, Ke Zhang, Yunxuan Li

This study proposes an interpretable prediction framework with literature-informed fine-tuned (LIFT) LLMs for truck driving risk prediction. The framework integrates an LLM-driven Inference Core that predicts and explains truck driving risk, a Literature Processing Pipeline that filters and summarizes domain-specific literature into a literature knowledge ba

Mengyu Ye, Jun Suzuki, Tatsuro Inaba, Tatsuki Kuribayashi

Recent interpretability work on large language models (LLMs) has been increasingly dominated by a feature-discovery approach with the help of proxy modules. Then, the quality of features learned by, e.g., sparse auto-encoders (SAEs), is evaluated. This paradigm naturally raises a critical question: do such learned features have better properties than those a

Carlos Magno O. Pereira, Edilberto O. Silva

In this work, we investigate the influence of torsion, Aharonov-Bohm flux, and external magnetic fields on the linear and nonlinear optical properties of a confined quantum system. The confinement potential is not assumed a priori, but emerges as a radial effective potential, analogous to a quantum dot, geometrically induced by the torsion of the material. S

Mustafa Mert Özyılmaz

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a fundamental NP-hard optimization problem in logistics. Solving large-scale instances remains computationally challenging for exact solvers. This paper introduces a multilevel graph coarsening and refinement strategy that aggregates customers into meta-nodes based on a spatio-temporal dis

Daiki Tanabe, Yuki Inoue, Vivek Kumar, Miftahul Ma'arif

Detection of sub-Hz gravitational waves is of significant importance for astrophysics. It enables the observation of intermediate-mass black hole mergers, the issuance of early alerts for gravitational-wave events, and the exploration of the stochastic gravitational-wave background. The Cryogenic sub-Hz cROss torsion-bar detector with quantum NOn-demolition

Kyle A. Oman

SWIFTGalaxy is an open-source astrophysics module that extends SWIFTSimIO to analyses of particles belonging to individual galaxies simulated with SWIFT. It inherits from and extends the functionality of SWIFTSimIO's SWIFTDataset class. It understands the content of halo catalogues and therefore which particles belong to a galaxy or other group of particles,

Kumar Saurav, Ness B. Shroff, Yingbin Liang

We consider a node-monitor pair, where the node's state varies with time. The monitor needs to track the node's state at all times; however, there is a fixed cost for each state query. So the monitor may instead predict the state using time-series forecasting methods, including time-series foundation models (TSFMs), and query only when prediction uncertainty

Johnathon Taylor

We extend the theory of distributive series of monads of \cite{EC1} by extending the definition to include an $\bN$-indexed collection of monads. Under certain conditions, distributive series of monads will have a colimit in the category of pointed endofunctors. We define a \emph{completable} distributive series of monads to be a distributive series of monad

Yifei Wang, Han Wang, Kehao Zhuang, Keith Moffat

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid d

Guilherme Sadovski

We show that four-dimensional topological Yang-Mills theories, when suitably coupled to Higgs-like fields, admit representations in terms of massive gauge fields in a non-trivial neighborhood of the minima moduli. In the adjoint representation, BRST instability is present beyond tree-level, and closely resembles the Coleman-Weinberg mechanism. The fundamenta

Ali Javidani, Babak Nadjar Araabi, Mohammad Amin Sadeghi

This paper introduces a novel approach that integrates graph theory into self-supervised representation learning. Traditional methods focus on intra-instance variations generated by applying augmentations. However, they often overlook important inter-instance relationships. While our method retains the intra-instance property, it further captures inter-insta

Hyeongon Park, Kyuhyeong Kwag, Daniel K. Molzahn, Rahul K. Gupta

Energy communities (ECs) are emerging as a promising decentralized model for managing cooperative distributed energy resources (DERs). As these communities expand and their operations become increasingly integrated into the grid, ensuring fairness in allocating operating costs among participants becomes a challenge. In distribution networks, DER operations a

Yanqing Fu, Chenrun Wang, Chao Huang, Zhuping Wang

Multi-agent ad hoc systems are dynamic collaborative systems in which multiple autonomous agents must cooperate with both known and unknown teammates in open environments, without relying on pre-coordinated strategies. These systems operate under conditions of uncertainty and partial observability, where team composition, agent behaviors, and environmental f

Jing Wang, Jiajun Liang, Jie Liu, Henglin Liu

Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. Within these frameworks, the policy update relies on importance-ratio clipping to constrain overconfident positive and negative gradients. However, in practice, we observe a systematic

Tuan-Phong Ngo, Bao-Ngoc Duong, Tuan-Anh Hoang, Joshua Dwight

Software testing is a critical component in the software engineering field and is important for software engineering education. Thus, it is vital for academia to continuously improve and update educational methods to reflect the current state of the field. The International Software Testing Qualifications Board (ISTQB) certification framework is globally rec

Antal van den Bosch, Ainhoa Risco Patón, Teun Buijse, Peter Berck

We present memory-based language modeling as an efficient, eco-friendly alternative to deep neural network-based language modeling. It offers log-linearly scalable next-token prediction performance and strong memorization capabilities. Implementing fast approximations of k-nearest neighbor classification, memory-based language modeling leaves a relatively sm

Zhiyuan Hua, Qiji Mo, Zebin Yao, Lixiao Cui

Approximate Nearest Neighbor Search (ANNS) has become a fundamental component in many real-world applications. Among various ANNS algorithms, graph-based methods are state-of-the-art. However, ANNS often suffers from a significant drop in accuracy for certain queries, especially in Out-of-Distribution (OOD) scenarios. To address this issue, a recent approach

Ryusuke Ikeda, Yuto Yokota

The electro-magnetic responses of ordered vortex lattices in unconventional superconductors are studied in a high field approximation. In the cases with a vortex lattice formed within the lowest Landau level of the superconducting order parameter (OP) such as a conventional s-wave paired system with a single OP and a nonchiral spin triplet paired one with mu

Christian Imenkamp, Martin Kabierski, Hendrik Reiter, Matthias Weidlich

Streaming process mining deals with the real-time analysis of event streams. A common approach for it is to adopt windowing mechanisms that select event data from a stream for subsequent analysis. However, the size of these windows denotes a crucial parameter, as it influences the representativeness of the window content and, by extension, of the analysis re

Chen Zhiqiang, Le Gentil Cedric, Lin Fuling, Lu Minghao

This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditional LIO algorithms perform poorly when dynamic objects dominate the scenes, particularly in geometrically sparse environments. Current approaches to dynamic LIO face a fundamental ch

Hongyu Guo

Analogical reasoning, the transfer of relational structures across contexts (e.g., planet is to sun as electron is to nucleus), is fundamental to scientific discovery. Yet human insight is often constrained by domain expertise and surface-level biases, limiting access to deeper, structure-driven analogies both within and across disciplines. Large language mo

Xianwen Zhu, Yuchen Ge, Yaomin Zhao, Zuoli Xiao

Direct numerical simulations of a low-pressure turbine with roughness elements distributed over the blade surface have been performed. A series of fifteen cases with varying roughness heights and streamwise wavenumbers are introduced to present a systematic study of the effect of roughness on the various transition phenomena in the suction-side boundary laye

Tanvir Kaur, Ashish Saxena

A black hole is a harmful node in a graph that destroys any agent entering it, making its identification a critical task. In the \emph{Black Hole Search with Verification (BHSV)} problem, a team of agents operates on a graph $G$ with the objective that at least one agent survives and correctly identifies an edge incident to the black hole; if no black hole e

Anas A. Rahman, Daniel Munoz George, James A. Mingo

We elucidate a bijection between ribbon graphs on the real projective plane and non-crossing annular pairings that relate to the $1/N$ correction term of the GOE and LOE spectral moments. We also derive analogous objects for the $1/N^2$ correction terms of said moments and their equivalents for the GUE and LUE.

Qingjie Wu, Miao Cui, Guangchi Zhang, Beixiong Zheng

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can use different multiple access schemes to coordinate multi-user task offloading. However, it is still unknown which scheme is the most energy-efficient, especially when the offloading blocklength is finite. To answer this question, this paper minimizes and compares the MEC-related e

Jianfeng Lu

We consider quantitative convergence analysis for hypocoercive dynamics such as Langevin and Lindblad equations describing classical and quantum open systems. Our goal is to provide an overview of recent results of hypocoercivity estimates based on space-time Poincare inequality, providing a unified treatment for classical and quantum dynamics. Furthermore,

Ibrahim Qamar, Saber Mahmoud, Seif Megahed, Mohamed Khaled

Intelligent vehicles are one of the most important outcomes gained from the world tendency toward automation. Applications of IVs, whether in urban roads or robot tracks, do prioritize lane path detection. This paper proposes an FPGA-based Lane Detector Vehicle LDV architecture that relies on the Sobel algorithm for edge detection. Operating on 416 x 416 ima

Odin Zhang, Xujun Zhang, Haitao Lin, Cheng Tan

Biomolecular interactions underpin almost all biological processes, and their rational design is central to programming new biological functions. Generative AI models have emerged as powerful tools for molecular design, yet most remain specialized for individual molecular types and lack fine-grained control over interaction details. Here we present ODesign,

Ofer Aharony, Suman Kundu, Tal Sheaffer

In earlier work we proposed a string theory dual to two dimensional Yang-Mills theory at zero coupling (which can also be thought of as a $BF$ theory), given by a Polyakov-like generalization of Ho\v rava's topological rigid string theory, and we showed that it correctly reproduces (in the $1/N$ expansion) several partition functions of $SU(N)$ Yang-Mills th

Naveed Ahmed Azam, Seemab Hayat

Counting non-isomorphic tree-like multigraphs that include self-loops and multiple edges is an important problem in combinatorial enumeration, with applications in chemical graph theory, polymer science, and network modeling. Traditional counting techniques, such as Polya's theorem and branching algorithms, often face limitations due to symmetry handling and

Yujie Xiao, Gongzhen Tang, Wenhui Liu, Jun Li

Timely access to laboratory values is critical for clinical decision-making, yet current approaches rely on invasive venous sampling and are intrinsically delayed. Electrocardiography (ECG), as a non-invasive and widely available signal, offers a promising modality for rapid laboratory estimation. Recent progress in deep learning has enabled the extraction o

Chenyu Zhang, Tairen Zhang, Lanjun Wang, Ruidong Chen

Using risky text prompts, such as pornography and violent prompts, to test the safety of text-to-image (T2I) models is a critical task. However, existing risky prompt datasets are limited in three key areas: 1) limited risky categories, 2) coarse-grained annotation, and 3) low effectiveness. To address these limitations, we introduce T2I-RiskyPrompt, a compr

Matthias J. Ehrhardt, Davide Murari, Ferdia Sherry

The existence of instabilities, for example in the form of adversarial examples, has given rise to a highly active area of research concerning itself with understanding and enhancing the stability of neural networks. We focus on a popular branch within this area which draws on connections to continuous dynamical systems and optimal control, giving a bird's e

Alexander Felix, Rudolf Hoffmann, Marcus Henninger, Stephan ten Brink

The introduction of Integrated Sensing and Communications (ISAC) in cellular systems is not expected to result in a shift away from the popular choice of cost- and energy-efficient analog or hybrid beamforming structures. However, this comes at the cost of limiting the angular capabilities to a confined space per acquisitions. Thus, as a prerequisite for the

Latika Mishra, Vinod Kumar Dwivedi, Vishal Kumar Chakradhary, Akila G. Prabhudessai

We report magnetic, optical and oxidation states of oxygen deficient TiO$_{2-\delta}$ nanoparticles (NPs) synthesized by environment-friendly green route using Phyllanthus niruri (PN) whole plant extract instead of leaf extract. Rietveld refinement of room temperature XRD pattern confirms the formation of pure phase anatase TiO$_2$ crystals in a tetragonal s

Quoc Anh Nguyen, Bernard Cheng, Kelvin Soh

Automatic Lyrics Transcription (ALT) for Vietnamese music presents unique challenges due to its tonal complexity and dialectal variations, but remains largely unexplored due to the lack of a dedicated dataset. Therefore, we curated the first large-scale Vietnamese ALT dataset (VietLyrics), comprising 647 hours of songs with line-level aligned lyrics and meta

Jacob Adenbaum, Fil Babalievsky, William Jungerman

Why do wages grow faster in bigger cities? We use French administrative data to decompose the urban wage growth premium and find that the answer has surprisingly little to do with cities themselves. While we document substantially faster wage growth in larger cities, 80% of the premium disappears after controlling for the composition of firms and coworkers.

Mary E. An, Paul M. Griffin, Jonathan G. Stine, Balakrishnan S. Ramakrishna

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to develop and evaluate an electronic health record (EHR) based prediction model to support early detection of MAS

Yujie Nie, Jianzhang Ni, Yonglong Ye, Yuan-Ting Zhang

Accurate diagnosis of Alzheimer's disease (AD) is essential for enabling timely intervention and slowing disease progression. Multimodal diagnostic approaches offer considerable promise by integrating complementary information across behavioral and perceptual domains. Eye-tracking and facial features, in particular, are important indicators of cognitive func

Hossein Emami, Hadi Sabri

This study used the pairing gap to identify nuclei as candidates for critical point symmetry around Z=40 and A=100. Nuclei around A = 100 display complex shape evolution and configuration crossing patterns. We utilized the experimental and algebraic frameworks of the interacting boson model and the newly developed interacting boson-fermion model to study the

Petar Orlić

Let $N$ be a positive integer. For every $d\mid N$ such that $(d,N/d)=1$ there exists an Atkin-Lehner involution $w_d$ of the modular curve $X_0(N)$. The curve $X_0^*(N)$ is a quotient curve of $X_0(N)$ by $B(N)$, the group of all involutions $w_d$. In this paper we determine all quotient curves $X_0^*(N)$ whose $\mathbb C$-gonality is equal to $4$. We also

Thibeau Wouters, Anna Puecher, Peter T. H. Pang, Tim Dietrich

We study the gravitational-wave trigger GW231109_235456, a sub-threshold binary neutron star merger candidate observed in the first part of the fourth observing run of the LIGO-Virgo-KAGRA collaboration. Assuming the trigger is of astrophysical origin, we analyze it using state-of-the-art waveform models and investigate the robustness of the inferred source

Yan Jiang, Ruihong Qiu, Zi Huang

Homophily, the tendency of nodes from the same class to connect, is a fundamental property of real-world graphs, underpinning structural and semantic patterns in domains such as citation networks and social networks. Existing methods exploit homophily through designing homophily-aware GNN architectures or graph structure learning strategies, yet they primari

Aimin Li, Elif Uysal

In distributed sensor networks, sensors often observe a dynamic process within overlapping regions. Due to random delays, these correlated observations arrive at the fusion center asynchronously, raising a central question: How can one fuse asynchronous yet correlated information for accurate remote fusion estimation? This paper addresses this challenge by s

Laxmi pant, Syed Ali Reza, Md Khalilor Rahman, MD Saifur Rahman

The growing instability of both global and domestic economic environments has increased the risk of financial distress at the household level. However, traditional econometric models often rely on delayed and aggregated data, limiting their effectiveness. This study introduces a machine learning-based early warning system that utilizes real-time digital and

Hugo Roger Paz

In technical subjects characterized by high enrollment, such as Basic Hydraulics, the assessment of reports necessitates superior levels of objectivity, consistency, and formative feedback; goals often compromised by faculty workload. This study presents the implementation of a generative artificial intelligence (AI) assisted assessment system, supervised by

Andrei Baroian

We study clinical Named Entity Recognition (NER) on the CADEC corpus and compare three families of approaches: (i) BERT-style encoders (BERT Base, BioClinicalBERT, RoBERTa-large), (ii) GPT-4o used with few-shot in-context learning (ICL) under simple vs.\ complex prompts, and (iii) GPT-4o with supervised fine-tuning (SFT). All models are evaluated on standard

D. Pavicevic, M. Nishida, J. Song, M. Buzzi

The generation of tunable narrowband pulses is increasingly being pursued in terahertz science, for example to study the nonlinear response of individual modes of solids and molecules. Here, we extend the chirp-and-delay method to achieve collinear phase-matched difference-frequency generation in the organic crystal N-benzyl-2-methyl-4-nitroaniline (BNA-S),

Devon A. Kelly, Christiana Chamon

Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor corruption and cybersecurity risks in industrial control systems (ICS), particularly due to high-frequency noise and sophisticated cyber-physical threats. This proof-of-concept (PoC) study demonstrates the adapt

Tianhui Liu, Hetian Pang, Xin Zhang, Jie Feng

Urban socio-economic sensing plays a vital role in advancing global sustainable development goals. With the advent of Large Vision-Language Models (LVLMs), new opportunities have emerged to address this challenge by framing it as a multi-modal perception and reasoning task. However, recent studies show that LVLMs still struggle to make accurate and interpret

S. Molina-Prados, N. M. Bulgakova, A. V. Bulgakov, J. Lancis

Nanoparticle synthesis via pulsed laser ablation in liquids has gained prominence as a versatile and environmentally friendly approach for producing ligand-free colloids with controlled composition, size, and morphology. While pulsed laser ablation in liquids offers unparalleled advantages in terms of nanoparticle purity and material versatility, enhancing t

Masaru Nagisa, Yasuo Watatani

We introduced non-linear traces of the Choquet type and the Sugeno type on semi-finite factors M in [36] as a non-commutative analog of the Choquet integral and Sugeno integral for non-additive measures. We need a weighted dimension function on the projections of M, which is an analog of a monotone measure. In this paper, we study the law of large numbers fo

Hugo Roger Paz

The emergence of Generative Artificial Intelligence (GenAI) has reshaped higher education, presenting both opportunities and ethical-pedagogical challenges. This article presents an empirical case study on the complete cycle (design, initial failure, redesign, and re-evaluation) of an intervention using an AI Tutor (ChatGPT) in the "Hydrology and Hydraulic W

Yi-Fan Wang, Kai Li, Fei Liu, Xin Xu

Photometric and spectroscopic studies of four long-period low mass-ratio contact binaries, V0508 And, V0844 Aur, V0699 Cep, and NSVS 6259046, are performed. V0508 And, V0844 Aur, and V0699 Cep are found to be A-type low-mass-ratio medium-contact binaries, while NSVS 6259046 is found to be an A-type deep-contact binary. O - C analysis indicates no long-term v

Wei Xia

We proposed Static and Dynamic -- two zero-shot logits-layer debiasing methods. Dynamic reduces bias by up to 70% with minimal fluency loss. Logits intervention outperforms hidden-layer approaches. We show semantic-aware logits intervention is stable and effective for debiasing aligned LLMs.