December 2025 arXiv papers — page 15

Károly Bezdek, Zsolt Lángi

A problem posed by Erd\H{o}s in 1945 initiated the study of non-separable arrangements of convex bodies. A finite collection of convex bodies in Euclidean $d$-space is called a non-separable family (or NS-family) if every hyperplane intersecting their convex hull also intersects at least one member of the family. Recent work has focused on minimal coverings

Zhe Li, Cheng Chi, Yangyang Wei, Boan Zhu

Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion from audio and then retargeting it to robots relies on explicit motion reconstruction, leading to cascaded errors, high latency, and disjointed acoustic-actuation mapping. We propose

Zhe Li, Boan Zhu, Yangyang Wei, Shuanghao Bai

Humans learn locomotion through visual observation, interpreting visual content first before imitating actions. However, state-of-the-art humanoid locomotion systems rely on either curated motion capture trajectories or sparse text commands, leaving a critical gap between visual understanding and control. Text-to-motion methods suffer from semantic sparsity

Simone Minniti, Jens Visbech, Claes Eskilsson, Nicola Parolini

We present a spectral element solver for the steady incompressible Navier-Stokes equations subject to a free surface. Utilizing the kinematic behaviour of the free surface boundary, an iterative pseudo-time procedure is proposed to determine the a priori unknown free surface profile. The numerical model is implemented in the open-source finite element framew

Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li

Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet retrieval and URL-based page fetching, limiting access to the richer information available through real browsing. While full browser interaction could unlock deeper capabilities, its

Keda Tao, Wenjie Du, Bohan Yu, Weiqiang Wang

Omnimodal large language models have made significant strides in unifying audio and visual modalities; however, they often face challenges in fine-grained cross-modal understanding and have difficulty with multimodal alignment. To address these limitations, we introduce OmniAgent, to our best knowledge, the first fully active perception agent that dynamicall

Ahmad Abushattal, Mashhoor A. Al-Wardat, Elliott P. Horch, Nikolaos Georgakarakos

As its periastron passage occurred during the third quarter of 2020, system 24 Aqr is of particular significance. New visual solutions for the latest speckle interferometry observations collected by the Lowell Discovery Telescope (LTD) with its new QWSSI speckle camera are presented here. A variety of techniques were used to analyze the system, including ORB

Elena Litvinova

Some recent advancements of the nuclear many-body theory and selected results on nuclear giant and pygmy resonances are presented. The theory is compactly reviewed, with a special focus on the emergent scale of the quasiparticle-vibration coupling (qPVC), which carries the order parameter associated with the qPVC vertex, and an efficient treatment of the nuc

Konstantin Yakovlev, Nikita Puchkin

We present a theory for simultaneous approximation of the score function and its derivatives, enabling the handling of data distributions with low-dimensional structure and unbounded support. Our approximation error bounds match those in the literature while relying on assumptions that relax the usual bounded support requirement. Crucially, our bounds are fr

Nayan Sharma, Ajay Tripathi

We present an analytical model showing how the gauge-invariant loop phase in a three-level closed-loop atomic system imprints as bright-dark lobes in Laguerre Gaussian probe beam intensity patterns. In the weak probe limit, the output intensity in such systems include Beer-Lambert absorption, a scattering term and loop phase dependent interference term with

Siqi Shao, Arshia Ghasemi, Hamed Farahani, R. A. Serota

We argue that negative skew and positive mean of the distribution of stock returns are largely due to the broken symmetry of stochastic volatility governing gains and losses. Starting with stochastic differential equations for stock returns and for stochastic volatility we argue that the distribution of stock returns can be effectively split in two -- for ga

Ritesh Bhola, Kedar Damle

Recent work that analyzed the effect of vacancy disorder on a short-range resonating valence bond spin liquid state of kagome-lattice antiferromagnets argued that such spin liquids are stable to vacancy disorder. The argument relied crucially on a numerical study that identified the following property of the site-diluted kagome lattice: maximum-density dimer

Abhishek Basu, Deepak Gupta, Dina Demner-Fushman, Shweta Yadav

The quest for seeking health information has swamped the web with consumers health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address this challenge is to summarize the quest

Alberto Bemporad

NashOpt is an open-source Python library for computing and designing generalized Nash equilibria (GNEs) in noncooperative games with shared constraints and real-valued decision variables. The library exploits the joint Karush-Kuhn-Tucker (KKT) conditions of all players to handle both general nonlinear GNEs and linear-quadratic games, including their variatio

Xiaoyu Li, Peidong Li, Xian Wu, Long Shi

Spatio-temporal alignment is crucial for temporal modeling of end-to-end (E2E) perception in autonomous driving (AD), providing valuable structural and textural prior information. Existing methods typically rely on the attention mechanism to align objects across frames, simplifying the motion model with a unified explicit physical model (constant velocity, e

Julius Julius, Nika Sergeevna Sokolova

In this paper, we discuss the construction of a map between weak (gauge) and strong (string) coupling degrees of freedom for the supersymmetric Wilson line-defect in the planar N=4 Super-Yang-Mills. By analysing the Partition Functions at zero and infinite coupling, we propose a map from degrees of freedom capturing single- and singlet two-particle states at

Deniz Akdemir

We present TabMixNN, a flexible PyTorch-based deep learning framework that synthesizes classical mixed-effects modeling with modern neural network architectures for tabular data analysis. TabMixNN addresses the growing need for methods that can handle hierarchical data structures while supporting diverse outcome types including regression, classification, an

LearnLM Team, Eedi, :, Albert Wang

One-to-one tutoring is widely considered the gold standard for personalized education, yet it remains prohibitively expensive to scale. To evaluate whether generative AI might help expand access to this resource, we conducted an exploratory randomized controlled trial (RCT) with $N = 165$ students across five UK secondary schools. We integrated LearnLM -- a

Konstantin Stepanyantz

Some recent all-loop results on the renormalization of supersymmetric theories are summarized and reviewed. In particular, we discuss how it is possible to construct expressions which do not receive quantum corrections in all orders for certain ${\cal N}=1$ supersymmetric theories. For instance, in ${\cal N}=1$ SQED+SQCD there is a renormalization group inva

Iris Xu, Guangtao Zeng, Zexue He, Charles Jin

Large language models (LLMs) have shown strong reasoning and coding capabilities, yet they struggle to generalize to real-world software engineering (SWE) problems that are long-horizon and out of distribution. Existing systems often rely on a single agent to handle the entire workflow-interpreting issues, navigating large codebases, and implementing fixes-w

Sreekar Bheemavarapu

The recent discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ under ambient-pressure in strained thin films raises the question of how superconductivity can be optimized through strain. In this work, we investigate the strain-dependent electronic structure and superconducting transition temperature ($T_c$) of La$_3$Ni$_2$O$_7$ using density

Shu Pu, Boya Zeng, Kaichen Zhou, Mengyu Wang

Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding their memorization could help prevent training data leakage and improve the diversity of generated results. In this paper, we design an evaluation framework to quantify memorizati

Nithisha Suryadevara, Vivek Reddy Srigiri

Joint modeling of longitudinal and survival data has become increasingly important in medical research, particularly for understanding disease progression in chronic conditions where both repeated biomarker measurements and time-to-event outcomes are available. Traditional two-stage methods, which analyze longitudinal and survival components separately, ofte

Rameez Farooq Shah, Syed Rashid Ahmad

We establish universal scaling laws and quantify aging in three-dimensional uniformly heated hard sphere granular gases through large-scale event-driven molecular dynamics ($N=500{,}000$). We report three primary quantitative discoveries: (i) The characteristic energy decay time exhibits a universal inverse scaling $\tau_0 \propto \epsilon^{-1.03 \pm 0.02}$

Ankan Aich, Emma D. Ryan, Kris Moe, Isaac Schmale

Accurate Monocular Depth Estimation (MDE) is critical for autonomous robotic surgery. However, existing self-supervised methods often exhibit a severe "ex-vivo to in-vivo gap": they achieve high accuracy on public datasets but struggle in actual clinical deployments. This disparity arises because the severe specular reflections and fluid-filled deformations

Chien-Ting Tung, Chenming Hu

We present NeuroSPICE, a physics-informed neural network (PINN) framework for device and circuit simulation. Unlike conventional SPICE, which relies on time-discretized numerical solvers, NeuroSPICE leverages PINNs to solve circuit differential-algebraic equations (DAEs) by minimizing the residual of the equations through backpropagation. It models device an

Till Hoffmann, Jukka-Pekka Onnela

Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challenging because the likelihood is often intractable. We show that, for a broad class of growing network models, information about model parameters is localized in the network, i.e., the

Luxuan Yang, Fei Lu, Ting Gao, Wei Wei

We propose a nonparametric method to learn the L\'evy density from probability density data governed by a nonlocal Fokker-Planck equation. We recast the problem as identifying the kernel in a nonlocal integral operator from discrete data, which leads to an ill-posed inverse problem. To regularize it, we construct an adaptive reproducing kernel Hilbert space

Monica Guica

This is a combined review on the Kerr/CFT correspondence on the one hand and solvable irrelevant deformations of two-dimensional QFTs - specifically, the $T\bar T$ and $J\bar T$ deformations - on the other. These subjects are interconnected, since the microscopic description of general black holes can be linked to very special irrelevant deformations of two-

Jake Hartnell, Eugenio Battaglia

Current DAO governance praxis limits organizational expressivity and reduces complex organizational decisions to token-weighted voting due to on-chain computational limits. This paper proposes verifiable off-chain computation (leveraging Verifiable Services, TEEs, and ZK proofs) as a framework to transcend these constraints while maintaining cryptoeconomic s

Deniz Akdemir

Distribution shift is the defining challenge of real-world machine learning. The dominant paradigm--Unsupervised Domain Adaptation (UDA)--enforces feature invariance, aligning source and target representations via symmetric divergence minimization [Ganin et al., 2016]. We demonstrate that this approach is fundamentally flawed: when domains are unequally info

Christoph Willibald, Lugh Martensen, Thomas Eiband, Dongheui Lee

Lengthy setup processes that require robotics expertise remain a major barrier to deploying robots for tasks involving high product variability and small batch sizes. As a result, collaborative robots, despite their advanced sensing and control capabilities, are rarely used for surface finishing in small-scale craft and manufacturing settings. To address thi

Julian Demeio, Damián Gvirtz-Chen

We establish non-unirational versions of Hilbert Irreducibility for all Hilbert modular surfaces which are of K3 type. As an application we prove new instances of the regular Inverse Galois Problem for the simple groups $\operatorname{PSL}_2(\mathbb{F}_{p^2})$ subject to congruence conditions on $p$.

Andrés Santos, Mariano López de Haro

We propose a simple and accurate approach to estimate the random close packing (RCP) fraction of binary hard-disk mixtures. By introducing a parameter based on the mixture's reduced third virial coefficient -- which effectively captures three-body correlations and excluded-area constraints -- we show that the RCP fraction depends nearly linearly on this para

Chandler Moore, Michael Febbraro, Juan Manfredi, Allen Wood

Additive manufacturing offers efficient production of plastic scintillators with nontrivial geometries using vat polymerization, allowing fabrication of geometries which would be difficult or even impossible to produce using conventional subtractive manufacturing. This work presents a novel photocurable scintillator formula that includes coumarin 450 as a te

Sahan Sanjaya, Aruna Jayasena, Prabhat Mishra

Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks in cybersecurity, which rely on data-dependent power variations

Yuwen Li, Wei Zhang, Zelong Huang, Mason Yang

Enabling Large Language Models (LLMs) to reliably invoke external tools remains a critical bottleneck for autonomous agents. Existing approaches suffer from three fundamental challenges: expensive human annotation for high-quality trajectories, poor generalization to unseen tools, and quality ceilings inherent in single-model synthesis that perpetuate biases

Heba Osama, Omar Elebiary, Youssef Qassim, Mohamed Amgad

Web applications increasingly face evasive and polymorphic attack payloads, yet traditional web application firewalls (WAFs) based on static rule sets such as the OWASP Core Rule Set (CRS) often miss obfuscated or zero-day patterns without extensive manual tuning. This work introduces WAMM, an AI-driven multiclass web attack detection framework designed to r

Frank Tian-Fang Ye, Xiaozi Gao

China's marriage registrations have declined substantially, dropping from 13.47 million couples in 2013 to 6.1 million in 2024. This study examined sentiment and moral elements underlying 219,358 marriage-related posts from Weibo and Xiaohongshu using large language model (LLM)-assisted content analysis. Drawing on Shweder's Big Three moral ethics framework,

Chuyi Tuo, Ming-Rui Li, Hong Yao

The experimental discovery of fractional quantum anomalous Hall (FQAH) states in tunable moir\'e superlattices has sparked intense interest in exploring the interplay between topological order and symmetry breaking phases. In this paper, we present a comprehensive numerical study of this interplay through large-scale density matrix renormalization group (DMR

Jakub Szefer

This document presents a concise overview of the contemporary research directions in quantum computer cybersecurity. The aim of this document is not to be a survey, but rather a succinct summary of the major research directions in quantum computer cybersecurity at the end of the first half of the current decade. The document has been inspired by the presenta

Z. M. McIntyre, Ji Zou, Jelena Klinovaja, Daniel Loss

The emerging field of quantum magnonics seeks to understand and harness the quantum properties of magnons -- quantized collective spin excitations in magnets. Squeezed magnon states arise naturally as the equilibrium ground states of anisotropic ferromagnets and antiferromagnets, representing an important class of nonclassical magnon states. In this work, we

Kenshin Obi, Ryo Yoshinaka, Hiroshi Fujimoto, Takuya Azumi

In recent years, the complexity and scale of embedded systems, especially in the rapidly developing field of autonomous driving systems, have increased significantly. This has led to the adoption of software and hardware approaches such as Robot Operating System (ROS) 2 and multi-core processors. Traditional manual program parallelization faces challenges, i

Vignesh Manoharan, Vijaya Ramachandran

The distance sensitivity oracle (DSO) problem asks us to preprocess a given graph $G=(V,E)$ in order to answer queries of the form $d(x,y,e)$, which denotes the shortest path distance in $G$ from vertex $x$ to vertex $y$ when edge $e$ is removed. This is an important problem for network communication, and it has been extensively studied in the sequential set

Christopher Burger

Traditional Statistical Process Control (SPC) is essential for quality management but is limited by its reliance on often violated statistical assumptions, leading to unreliable monitoring in modern, complex manufacturing environments. This paper introduces a hybrid framework that enhances SPC by integrating the distribution free, model agnostic guarantees o

Sicheng Zhang, Benshuai Lyu

In this paper, a semi-analytical model based on the Wiener-Hopf technique is developed to predict the turbulent boundary layer trailing edge noise from serrated edges, aiming to account for the correct three-dimensional noise source and propagation effects. The scattered surface pressure over a semi-infinite flat plate is first obtained using the Green's fun

Hippolyte Dourdent, Kyrylo Simonov, Andreas Leitherer, Emanuel-Cristian Boghiu

Closed timelike curves (CTCs) challenge our conception of causality by allowing information to loop back into its own past. Any consistent description of such scenarios must avoid time-travel paradoxes while respecting the no-new-physics principle, which requires that the set of operations available within any local spacetime region remain unchanged, irrespe

B V Rajarama Bhat, Repana Devendra

It is established that both discrete and continuous semigroups of unital quantum channels are eventually mixed unitary. This result is novel even for the subclass of Schur maps and stands in sharp contrast to the resolution of the asymptotic quantum Birkhoff conjecture by Haagerup and Musat, who demonstrated that tensor powers of some unital quantum channels

Janani Annur Thiruvengadam, Kiran Mayee Nabigaru, Anusha Kovi

The early detection of pancreatic neoplasm is a major clinical dilemma, and it is predominantly so because tumors are likely to occur with minimal contrast margins and a large spread anatomy-wide variation amongst patients on a CT scan. These complexities require to be addressed with an effective and scalable system that can assist in enhancing the salience

Agostino Capponi, Chengpiao Huang, J. Antonio Sidaoui, Kaizheng Wang

Does more data improve return prediction? In non-stationary financial markets, longer training windows improve prediction of complex models but incorporate outdated economic regimes, whereas simpler models require less data and are less vulnerable to changes in economic conditions. We formally characterize this nonstationarity-complexity tradeoff, showing th

Ji Zou, Valerii K. Kozin, Daniel Loss, Jelena Klinovaja

We report an emergent ac Josephson-like effect arising without external bias, driven by the interplay between nonreciprocity and nonlinearity in coupled condensates. Using a minimal model of three mutually nonreciprocally coupled condensates, we uncover a rich landscape of dynamical phases governed by generalized Josephson equations. This goes beyond the Kur

Nguyen Truong Khai, Luong Duc Vinh

Improving the accuracy of fire detection using infrared night vision cameras remains a challenging task. Previous studies have reported strong performance with popular detection models. For example, YOLOv7 achieved an mAP50-95 of 0.51 using an input image size of 640 x 1280, RT-DETR reached an mAP50-95 of 0.65 with an image size of 640 x 640, and YOLOv9 obta

Nikolai Beving, Jonas Marxen, Steffen Mueller, Johannes Betz

Steer-by-Wire systems replace mechanical linkages, which provide benefits like weight reduction, design flexibility, and compatibility with autonomous driving. However, they are susceptible to high-frequency disturbances from unintentional driver torque, known as driver impedance, which can degrade steering performance. Existing approaches either rely on dir

Damiano Marsili, Aditya Mehta, Ryan Y. Lin, Georgia Gkioxari

Vision-language models (VLMs) excel at broad visual understanding but remain coarse-grained, exhibit visual biases, and miss subtle visual details. Existing training corpora reinforce this limitation by emphasizing general recognition ("Is it a cat or a dog?") over fine-grained perception. To address this, we introduce a new training corpus and task designed

Pablo Yanes-Thomas, Rocío Jáuregui-Renaud, Santiago F. Caballero-Benítez, Daniel Sahagún Sánchez

MulAtoLEG (Multi-Atom Liouville Equation Generator) is an open-source Mathematica package for generating Liouville superoperators and Liouville equations, specialized for multilevel atomic systems comprising an arbitrary number of atoms. This scheme is based on an extension to multilevel atomic systems, originally developed by Lehmberg [R. H. Lehmberg, Phys.

Karlo Lelas, Robert Pezer

We introduce a new approach to deriving approximate analytical solutions of a harmonic oscillator damped by purely nonlinear, or combinations of linear and nonlinear damping forces. Our approach is based on choosing a suitable trial solution, i.e. an ansatz, which is the product of the time-dependent amplitude and the oscillatory (trigonometric) function tha

Chanchal K. Barman, Bishal Das, Alessio Filippetti, Aftab Alam

Recent years have seen a proliferation in investigations on Altermagnetism due to its exciting prospects both from an applications perspective and theoretical standpoint. Traditionally, altermagnets are distinguished from collinear antiferromagnets using the central concept of halving subgroups within the spin space group formalism. In this work, we propose

Olga Kaczmarczyk, Konrad Cyprych, Dominika Benkowska-Biernacka, Rafał Kowalczyk

Nanoconfined liquid crystals (LCs) and their nanocomposites are driving the next generation of photonic applications. Consequently, deepening our understanding of mesophase stability, defect topology, and the dynamic response of LCs at the nanoscale requires the development of novel characterization approaches. This motivates us to perform in situ observatio

Christopher Burger, Karmece Talley, Christina Trotter

The rapid advancement of Large Language Models (LLMs) presents a significant challenge to academic integrity within computing education. As educators seek reliable detection methods, this paper evaluates the capacity of three prominent LLMs (GPT-4, Claude, and Gemini) to identify AI-generated text in computing-specific contexts. We test their performance und

Marcin Markiewicz, Łukasz Pawela, Zbigniew Puchała

Twirling, i.e. averaging over symmetry actions, is a standard tool for reducing quantum states and channels to a symmetry-invariant form. We study channel twirling from the perspective of the channel-state duality and provide a constructive Choi-level description of the averaging map induced by arbitrary input/output representations. Our main technical resul

Dat Le, Thomas Manhardt, Moritz Venator, Johannes Betz

The detection of rare and hazardous driving scenarios is a critical challenge for ensuring the safety and reliability of autonomous systems. This research explores an unsupervised learning framework for detecting rare and extreme driving scenarios using naturalistic driving data (NDD). We leverage the recently proposed Deep Isolation Forest (DIF), an anomaly

Subhash Chandra, Syed Abbas

In this article, we introduce the notion of the Riemann-Liouville fractional integral of set-valued mappings via integrable selections. We establish fundamental properties of this fractional integral, including convexity, boundedness, and continuity with respect to the Hausdorff metric. The investigation of preservation of regularity under fractional integra

Paolo Restuccia, Enrico Pedretti, Francesca Benini, Sophie Loehlé

Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological b

Guillaume Neuttiens, Jonas Sauer

A well-posedness and maximal regularity result for the time-periodic Cahn-Hilliard-Gurtin system in the half space is proved. For this purpose, we introduce a novel class of complementing boundary conditions, extending the classical Lopatinski\u{\i}-Shapiro conditions from elliptic and parabolic theory to time-periodic mixed-order systems with general bounda

Zhirui Tang, Julian Koellermeier, Emil Løvbak, Giovanni Samaey

In plasma edge simulations, the behavior of neutral particles is often described by a Boltzmann--BGK equation. Solving this kinetic equation and estimating the moments of its solution are essential tasks, typically carried out using Monte Carlo (MC) methods. However, for large-sized reactors, like ITER and DEMO, high collision rates lead to a substantial com

Troy A. Kervin

Membrane particles such as proteins and lipids organize into zones that perform unique functions. Here, I introduce a topological and category-theoretic framework to represent particle and zone intra-scale interactions and inter-scale coupling. This involves carefully demarcating between different presheaf- or sheaf-assigned data levels to preserve functoria

Alessandro Carotenuto, Antonio Del Dono, Réamonn Ó Buachalla, Junaid Razzaq

We give an unexpectedly simple presentation of the maximal prolongation of a first-order differential calculus in terms of the bimodule map of a torsion-free bimodule connection. We then show that in the quantum homogeneous space case this simplifies even further. More explicitly, we show that the bimodule map associated to a bimodule connection, for any rel

Yu-Xiang Lin, Cheng-Han Chiang, Hung-yi Lee

In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn conversation, they cannot maintain the required speaking styles after several turns of interaction; we refer to this as the style amnesia of SLMs. We focus on paralinguistic speaking styles, including emotion, a

Zhuang Li

arXiv admin note: This paper has been withdrawn by arXiv due to disputed and unverifiable authorship and affiliation

Jing Fu Hu, Hang Lu, Bao Yuan Sun

Dark matter admixed neutron stars (DANSs) serve as a specific astrophysical laboratory for probing the features of dark matter (DM) and have emerged as a promising candidate for interpreting recent astrophysical observations (e.g., by NICER and LIGO/Virgo). Accurately constraining the internal DM content of DANSs is therefore of critical importance. In this

Ethan Chern, Zhulin Hu, Bohao Tang, Jiadi Su

Real-time video generation via diffusion is essential for building general-purpose multimodal interactive AI systems. However, the simultaneous denoising of all video frames with bidirectional attention via an iterative process in diffusion models prevents real-time interaction. While existing distillation methods can make the model autoregressive and reduce

Kenshin Obi, Takumi Onozawa, Hiroshi Fujimoto, Takuya Azumi

In recent years, autonomous vehicles have attracted attention as one of the solutions to various social problems. However, autonomous driving software requires real-time performance as it considers a variety of functions and complex environments. Therefore, this paper proposes a parallelization method for autonomous driving software using the Model-Based Dev

Shaohan Yu, Lijun Li, Chenyang Si, Lu Sheng

The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challenges, we propose ProGuard, a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks without the need for model adjustments required by

Daiki Shiono, Shumpei Miyawaki, Ryota Tanaka, Jun Suzuki

Following the initial flourishing of large language models (LLMs), there has been a surge in proposed large vision-language models (LVLMs) that integrate LLMs with vision capabilities. However, it has been observed that LVLMs, after tuning to visual instruction using commonly used training datasets, often fail to exhibit the instruction-following ability tha

Fendler Julie, Guihenneuc Chantal, Ancelet Sophie

Bayesian profile regression mixture models (BPRM) allow to assess a health risk in a multi-exposed population. These mixture models cluster individuals according to their exposure profile and their health risk. However, their results, based on Monte-Carlo Markov Chain (MCMC) algorithms, turned out to be unstable in different application cases. We suppose two

Desire Guel, Justin Pegd-Windé Kouraogo, Kouka Kouakou Nakoulma

Deploying fifth-generation (5G) networks in emerging markets demands a balance between performance targets and constraints in budget, spectrum, and infrastructure. We use MATLAB simulations to quantify how radio and architectural levers - MIMO (beamforming, diversity, spatial multiplexing), carrier aggregation (CA), targeted spectrum refarming to New Radio (

Amy Ingold, Loong Yi Lee, Richard Suphapol Diteesawat, Ajmal Roshan

Emerging wearable robotics demand design approaches that address not only function, but also social meaning. In response, we present Sumbrella, a soft robotic garment developed as a speculative fashion probe. We first detail the design and fabrication of the Sumbrella, including sequenced origami-inspired bistable units, fabric pneumatic actuation chambers,

Kessia Nepomuceno, Fabio Petrillo

Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectiv

Zhaoming Kong, Xiaowei Yang, Jiahuan Zhang

The proliferation of imaging devices and countless image data generated every day impose an increasingly high demand on efficient and effective image denoising. In this paper, we establish a theoretical connection between principal component analysis (PCA) and the Haar transform under circulant representation, and present a computationally simple denoising a

Siyu Jiao, Yiheng Lin, Yujie Zhong, Qi She

Recent progress in Multimodal Large Language Models (MLLMs) demonstrates that Chain-of-Thought (CoT) reasoning enables systematic solutions to complex understanding tasks. However, its extension to generation tasks remains nascent and limited by scenario-specific mechanisms that hinder generalization and adaptation. In this work, we present ThinkGen, the fir

Liyuan Cui, Guanhao Feng, Yuefeng Han, Jiayan Li

We tackle the challenge of estimating grouping structures and factor loadings in asset pricing models, where traditional regressions struggle due to sparse data and high noise. Existing approaches, such as those using fused penalties and multi-task learning, often enforce coefficient homogeneity across cross-sectional units, reducing flexibility. Clustering

Hanzheng Li, Xi Fang, Yixuan Li, Chaozheng Huang

The integration of Multimodal Large Language Models (MLLMs) into chemistry promises to revolutionize scientific discovery, yet their ability to comprehend the dense, graphical language of reactions within authentic literature remains underexplored. Here, we introduce RxnBench, a multi-tiered benchmark designed to rigorously evaluate MLLMs on chemical reactio

Alessio Carmelo Alvaro, Ignacio Castelli, Cédric Lorcé, Andreas Metz

We investigate the twist-2 unpolarized generalized parton distributions (GPDs) of quarks for an on-shell gluon target in quantum chromodynamics. These GPDs parametrize the leading-twist matrix elements of the nonlocal light-like flavor-singlet vector current. We compute them at one-loop accuracy in perturbation theory using a quark mass and dimensional regul

Zhehao Huang, Baijiong Lin, Jingyuan Zhang, Jingying Wang

Multi-model routing has evolved from an engineering technique into essential infrastructure, yet existing work lacks a systematic, reproducible benchmark for evaluating vision-language models (VLMs). We present VL-RouterBench to assess the overall capability of VLM routing systems systematically. The benchmark is grounded in raw inference and scoring logs fr

Vinod Kumar Bhardwaj, Saibal Ray, Kazuharu Bamba, Akram Ali

We investigate a late-time cosmological model for a homogeneous and isotropic space-time in the Rastall theory. We explore the observational constraints on the Hubble parameter by using the latest cosmological datasets such as cosmic microwave background radiation (Planck), baryon acoustic oscillations (DESI) and Type Ia Supernovae (Union 3.0). As a result,

P. V. Buividovich, B. Hind

We propose a numerical spectral reconstruction workflow for high-temperature gauge theories that incorporates elements of semi-classical real-time evolution directly into standard lattice QCD simulations via high-temperature dimensional reduction, thus counteracting the deterioration of Euclidean-time correlators at high temperatures. With a moderate numeric

Takahiro Uemura, Yuto Moritake, Eiichi Kuramochi, Masaaki Ono

Photonic topological insulators (PTIs) offer robust platforms for light manipulation, but reconfigurable control of their topological properties without degrading performance remains a major challenge. While phase-change materials (PCMs) provide large refractive index modulation, widely used materials such as Ge2Sb2Te5 (GST) have been successfully deployed i

Davide Filippozzi, Arash Rahimi-Iman

The design of chiral metasurfaces with tailored optical properties remains a central challenge in nanophotonics due to the highly nonlinear relationship between geometry and chiroptical response. Machine-learning-assisted optimization pipelines have recently emerged as efficient tools to accelerate this process, yet their performance strongly depends on the

Toqeer Ali Syed, Mishal Ateeq Almutairi, Mahmoud Abdel Moaty

Powerful autonomous systems, which reason, plan, and converse using and between numerous tools and agents, are made possible by Large Language Models (LLMs), Vision-Language Models (VLMs), and new agentic AI systems, like LangChain and GraphChain. Nevertheless, this agentic environment increases the probability of the occurrence of multimodal prompt injectio

Xiaolin Zhang, Chengye Yu, Haoran Li, Sobhan Kazempour

Lunar-based gravitational-wave interferometry is a fascinating endeavor, and was proposed as a promising approach to bridge the observational gap between space-borne and ground-based detectors. In this work, we adopt the Fisher-matrix method to examine the angular-resolution performance of the newly proposed Crater Interferometry Gravitational-wave Observato

A. Sila Okcu, M. Etem Bas, Ozgur B. Akan

Early cancer detection relies on invasive tissue biopsies or liquid biopsies limited by biomarker dilution. In contrast, tumour-derived extracellular vesicles (EVs) carrying biomarkers like melanoma-associated antigen-A (MAGE-A) are highly concentrated in the peri-tumoral interstitial space, offering a promising near-field target. However, at micrometre scal

Armin Shoushtarizadeh, Michele Cerminara, Corinne Chureau, Leah Friedman

Quantitative imaging of subcellular processes in living embryos, stem-cell systems, and organoid models requires microscopy platforms that combine high spatial resolution, fast volumetric acquisition, long-term stability, and minimal phototoxicity. Single-objective light-sheet approaches based on oblique plane microscopy (OPM) are well suited for live imagin

G. Angloher, M. R. Bharadwaj, A. Böhmer, M. Cababie

Sodium iodide (NaI) is a widely used scintillator in direct dark matter searches. In particular, NaI-based cryogenic scintillating calorimeters have emerged as promising candidates, like in the COSINUS experiment, for testing the annually modulating signal reported by DAMA/LIBRA. In this study, we investigate defect formation within NaI crystals and its impa

Martin Sulzmann

Lock sets are commonly used for dynamic analysis of deadlocks. The standard per-thread lock set construction only considers locks acquired in the same thread, but is unaware of locks acquired in another thread. This leads to false positives and false negatives. The underlying issue is that the commonly used notion of a critical section on which the lock set

Chawit Sakkawattana, Chatchai Promsiri, Supakchai Ponglertsakul

We consider null geodesics in the background of spherically symmetric object in Einstein-Maxwell-Dilaton (EMD) theory with coupling function $f(\Phi)=e^{-2\lambda \Phi}$. The spherical solution is characteristically described by dilaton coupling $\lambda$, integrated dilaton flux $D$ and magnetic charge $P$. Then, we derive geodesic equations by using the Ha

Carlos Cardoso-Isidoro, Enrique J. Galvez

Linearly polarized projections are the tacit means for performing Clauser-Horne-Shimony-Holt (CHSH) Bell-inequality tests using polarization-entangled photon pairs. The inequality is valid for all states on the Poincar\'e-Bloch sphere, but few laboratory studies have investigated violations with the full sphere. In this article, we explore the experimental v

Pengcheng Zhang

This is an expository note on a mod $p$ congruence relating the truncated hypergeometric sums associated to $\big((\frac{1}{2},\frac{1}{6},\frac{5}{6}),(1,1)\big)$ to symmetric squares of elliptic curves.

Jonathan Lozano-Mayo

We derive a continuous family of virial identities for O($n$) symmetric configurations, parameterized by an exponent $\alpha$ that controls the radial weighting. The family provides a systematic decomposition of the global constraint into radially-resolved components, with special $\alpha$ values isolating specific mechanisms. For BPS configurations, where t

Sahil Kale, Antonio Luca Alfeo

Hallucinations, the generation of apparently convincing yet false statements, remain a major barrier to the safe deployment of LLMs. Building on the strong performance of self-detection methods, we examine the use of structured knowledge representations, namely knowledge graphs, to improve hallucination self-detection. Specifically, we propose a simple yet p

Zongsheng Cao, Yangfan He, Anran Liu, Jun Xie

Recent advances in diffusion models have notably enhanced text-to-image (T2I) generation quality, but they also raise the risk of generating unsafe content. Traditional safety methods like text blacklisting or harmful content classification have significant drawbacks: they can be easily circumvented or require extensive datasets and extra training. To overco