October 2025 arXiv papers — page 24

Pradeep M, Ritesh Pallod, Satyen Abrol, Muthu Raman

Generative AI is reshaping fashion by enabling virtual looks and avatars making it essential to find real products that best match AI-generated styles. We propose an end-to-end product search system that has been deployed in a real-world, internet scale which ensures that AI-generated looks presented to users are matched with the most visually and semantical

Jun-Long Zhao, Li Yu, Ming Yang, Chui-Ping Yang

Quantum metrology is a science about quantum measurements and it plays a key role in precision of quantum parameter estimation. Meanwhile, quantum coherence is an important quantum feature and quantum Fisher information (QFI) is an important indicator for precision of quantum parameter estimation. In this paper, we explore the relationship between QFI and qu

Alexey Petukhov

For an associative algebra $A$ with a simple module $M$ with trivial endomorphisms and trivial annihilator we verify the countable separation property (CSP), i.e. we prove that there exists a list of nonzero elements $a_1, a_2,\ldots$ of $A$ such that every two-sided ideal of $A$ contains at least one such $a_i$. Based on this result we verify the countable

Nathan O. Silvano, Emilio Hernández-García, Cristóbal López

We study the role of fluctuations in particle systems modeled by Dean-Kawasaki-type equations, which describe the evolution of particle densities in systems with Brownian motion. By comparing microscopic simulations, stochastic partial differential equations, and their deterministic counterparts, we analyze four models of increasing complexity. Our results i

Pavlo Kozub, Nataliia Yilmaz, Svitlana Kozub

The study demonstrates the capabilities of a vector-based approach for calculating stoichiometric coefficients in chemical equations, using black powder as an illustrative example. A method is proposed for selecting and constraining intermediate interactions between reactants, as well as for identifying final products. It is shown that even a small number of

Amir Shakouri, Henk J. van Waarde, Tren M. J. T. Baltussen, W. P. M. H. Heemels

In this work, we study data-driven stabilization of linear time-invariant systems using prior knowledge of system-theoretic properties, specifically stabilizability and controllability. To formalize this, we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the prior k

Stéphane Devismes, Yoann Dieudonné, Arnaud Labourel

A team of mobile agents, starting from distinct nodes of a network, have to meet at the same node and declare that they all met. Agents execute the same algorithm, which they start when activated by an adversary or by an agent entering their initial node. When activated, agents traverse edges of the network in synchronous rounds. Their perception and communi

Ellen de Oliveira, Guido Neulaender

A comma category, exemplified in algebraic geometry by coherent systems, combines two categories over a third through morphisms between their objects. We establish sufficient conditions for it to be abelian, compute its Grothendieck group, and give necessary and sufficient criteria for it to be noetherian and artinian. Finally, we define a stability conditio

Lucas Q. Galvão, Anna Beatriz M. de Souza, Marcelo A. Moret, Clebson Cruz

This work presents a comprehensive overview of variational quantum computing and their key role in advancing quantum simulation. This work explores the simulation of quantum systems and sets itself apart from approaches centered on classical data processing, by focusing on the critical role of quantum data in Variational Quantum Algorithms (VQA) and Quantum

Wentao Hu, Zhengce Zhang

We study interior $\varepsilon$-regularity and Type I blowup criteria for suitable weak solutions to the three-dimensional incompressible MHD equations. Our starting point is a direct iteration scheme for the classical Caffarelli--Kohn--Nirenberg scaled energy quantities $A,E,C$ and $D$, which yields $\varepsilon$-regularity criteria under smallness assumpti

Yohei Sawada, Masashi Minamide, Yuyue Yan, Kazumune Hashimoto

Although the chaotic nature of the atmosphere may enable efficient control of tropical cyclones (TCs) via small-scale perturbations, few studies have proposed data-driven optimization methods to identify such perturbations. Here, we apply the recently proposed Ensemble Kalman Control (EnKC) to a TC simulation. We show that EnKC finds small-scale perturbation

Mohamad Abou Ali, Fadi Dornaika

Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models -- a practice known as conceptual retrofitting. This survey cuts through this confusion by introducing a novel dual-paradigm framework that categorizes a

Dmitriy Bilyk, Nicolas Nagel, Ian Ruohoniemi

We study point configurations on the torus $\mathbb T^d$ that minimize interaction energies with tensor product structure which arise naturally in the context of discrepancy theory and quasi-Monte Carlo integration. Permutation sets on $\mathbb T^2$ and Latin hypercube sets in higher dimensions (i.e. sets whose projections onto coordinate axes are equispaced

Fei Wei, Daoyuan Chen, Ce Wang, Yilun Huang

Large Language Models (LLMs) excel as passive responders, but teaching them to be proactive, goal-oriented partners, a critical capability in high-stakes domains, remains a major challenge. Current paradigms either myopically optimize single-turn attributes or rely on brittle, high-cost user simulators, creating a persistent ``reality gap''. To bridge this g

Eshika Khandelwal, Junyu Xie, Tengda Han, Max Bain

Audio Descriptions (ADs) convey essential on-screen information, allowing visually impaired audiences to follow videos. To be effective, ADs must form a coherent sequence that helps listeners to visualise the unfolding scene, rather than describing isolated moments. However, most automatic methods generate each AD independently, often resulting in repetitive

Kristoffer Eggestad, Ida C. Skogvoll, Øystein Gullbrekken, Benjamin A. D. Williamson

Simulating finite temperature phase transitions from first-principles is computationally challenging. Recently, molecular dynamics (MD) simulations using machine-learned force fields (MLFFs) have opened a new avenue for finite-temperature calculations with near-first-principles accuracy. Here we use MLFFs, generated using on-the-fly training, to investigate

Erik Brücken, Peter Andersson, Mihaela Bezak, Peter Dendooven

Spent nuclear fuel imaging before disposal is of utmost importance before long term disposal in dedicated storage facilities. Passive Gamma Emission Tomography (PGET) is an approved method by the International Atomic Energy Agency. The present detection system is based on small CZT detectors behind a tungsten-based collimator consisting of a linear array of

Tan Mei, Kezheng Zuo, Wanlin Jiang

In this paper, we present different characterizations of tripotent orthogonal matrices (i.e., A^3 = A = A^* ) in terms of matrix equations, integer powers of AA^* and A^*A, average of A, A^*, and A^{\dagger}, rank of matrices, and trace of matrices. We study certain properties of this class of matrices.

Can Yaylali

We compute the rational motive of the stack of local $G$-shtukas, for a split reductive group $G$, representing compactly supported cohomology in terms of the motive of the stack of $G$-zips. This result makes explicit use of the truncated version of local $G$-shtukas established by Viehmann-Wedhorn and the theoretical background on the $*$- and $!$-adjuncti

Xia Zhao, Peibiao Zhao

The study of the dual curvature measures [Y. Huang, E. Lutwak, D. Yang \& G. Y. Zhang, Acta. Math. 216 (2016): 325-388], which connects the cone-volume measure and Aleksandrov's integral curvature, and has created a precedent for the theoretical research of the dual Brunn-Minkowski theory. Motivated by the foregoing groundbreaking works, the present paper in

Shakib Yazdani, Yasser Hamidullah, Cristina España-Bonet, Eleftherios Avramidis

Automatic evaluation metrics are crucial for advancing sign language translation (SLT). Current SLT evaluation metrics, such as BLEU and ROUGE, are only text-based, and it remains unclear to what extent text-based metrics can reliably capture the quality of SLT outputs. To address this gap, we investigate the limitations of text-based SLT evaluation metrics

Caihao Weng, Yuqing Guo, Bowen Zhao, Ying Wang

Terahertz (THz) band is considered a promising candidate to meet the high-throughput requirement for future sixth-generation (6G) wireless communications due to its ultrawide bandwidth. However, due to the high penetration loss at high-frequencies, blockage becomes a serious problem in THz communications, especially in near-field indoor communications with n

Ali Sanaei, Ali Rajabzadeh

Large language models (LLMs) are increasingly utilized by researchers across a wide range of domains, and qualitative social science is no exception; however, this adoption faces persistent challenges, including interpretive bias, low reliability, and weak auditability. We introduce a framework that situates LLM usage along two dimensions, interpretive depth

Rolando Manuel Gonzales Martinez

I formalize the ontology of apocalyptic events as synchronized morphogenetic manifolds within the framework of Thom's catastrophe theory. Local catastrophes (folds, cusps, umbilici) are extended to higher-order systemic collapses through the synchronization of multiple morphogenetic manifolds. The resulting construct is the Apocalypsis: a topological meta-si

Mauro Carlone, Wolfgang Mück

For three-dimensional Kerr-de Sitter space-time, we find the singular energy-momentum and spin tensor sources that generate the non-trivial geometry. The energy-momentum tensor is symmetric, conserved and compatible with a spinning massive point particle whose mass and angular velocity we determine. The calculation is based on the analysis of the holonomy fo

Thang-Long Nguyen-Ho, Minh-Khoi Pham, Hoang-Bao Le

This report details our methodology and results developed for the Multilingual E-commerce Search Competition. The problem aims to recognize relevance between user queries versus product items in a multilingual context and improve recommendation performance on e-commerce platforms. Utilizing Large Language Models (LLMs) and their capabilities in other tasks,

Auguste Poiroux, Antoine Bosselut, Viktor Kunčak

Despite impressive results on curated benchmarks, the practical impact of large language models (LLMs) on research-level neural theorem proving and proof autoformalization is still limited. We introduce RLMEval, an evaluation suite for these tasks, focusing on research-level mathematics from real-world Lean formalization projects. RLMEval targets the evaluat

Asutosh Hota, Jussi P. P. Jokinen

The rapid advancement of Large Language Models (LLMs) is positioning language at the core of human-computer interaction (HCI). We argue that advancing HCI requires attention to the linguistic foundations of interaction, particularly implicature (meaning conveyed beyond explicit statements through shared context) which is essential for human-AI (HAI) alignmen

Zhengrong Guo

The electronic properties of carbon nanotubes are governed by their chirality, specified by the integer indices (n,m). While chirality-controlled synthesis has achieved notable successes, theoretical understanding remains predominantly focused on post-nucleation growth. Two fundamental obstacles impede deeper insight: the absence of a clear description of nu

Sydney Paltra, Jonas Dehning, Viola Priesemann, Kai Nagel

The COVID-19 pandemic reshaped human mobility through policy interventions and voluntary behavioral changes. Mobility adaptions helped mitigate pandemic spread, however our knowledge which environmental, social, and demographic factors helped mobility reduction and pandemic mitigation is patchy. We introduce a Bayesian hierarchical model to quantify heteroge

Ali Asgari, Annibale Panichella, Pouria Derakhshanfar, Mitchell Olsthoorn

AI Agents have rapidly gained prominence in both research and industry as systems that extend large language models with planning, tool use, memory, and goal-directed action. Despite this progress, the development and maintenance of Agent systems present recurring engineering difficulties that are not yet well characterized in developer-facing evidence. To a

Chaz Cornwall, Jeremy P. Bos

Formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain. However, by reducing complex cost functions to formations, discrepancies arise between maintaining the shape and minimizing the original cost function. For example, a Diamond or Box formation shape is often used for protecting all me

Ertugrul Mutlu

This paper explores a deliberately over-engineered approach to the classical problem of parity detection -- determining whether a number is odd or even -- by combining wavelet-based feature extraction with unsupervised clustering. Instead of relying on modular arithmetic, integers are transformed into wavelet-domain representations, from which multi-scale st

Lewis Cockram, Yueteng Yu, Jorge Pardo, Xiaomeng Li

Passive fatigue during conditional automated driving can compromise driver readiness and safety. This paper presents findings from a test-track study with 40 participants in a real-world automated driving scenario. In this scenario, a Large Language Model (LLM) based conversational agent (CA) was designed to check in with drivers and re-engage them with thei

Nasrin Rahimi, A. Murat Tekalp

Diffusion models have emerged as powerful priors for single-image restoration, but their application to zero-shot video restoration suffers from temporal inconsistencies due to the stochastic nature of sampling and complexity of incorporating explicit temporal modeling. In this work, we address the challenge of improving temporal coherence in video restorati

Johannes U. Lange, Diana Blanco, Alexie Leauthaud, Angus Wright

The effective redshift distribution $n(z)$ of galaxies is a critical component in the study of weak gravitational lensing. Here, we introduce a new method for determining $n(z)$ for weak lensing surveys based on high-quality redshifts and neural network-based importance weights. Additionally, we present the first unified photometric redshift calibration of t

Timon J. Vreman, Melissa J. Goodwin, Ad Lagendijk, Willem L. Vos

Two-dimensional (2D) photonic crystals offer strong control over the propagation of light through their bands. Theoretical methods for computing the band structure in 2D are well-established and fast because 2D photonic crystals are homogeneous in the third dimension. Experimental verification is scarce, however, especially in the telecom range, because real

Petter Törnberg

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper traces how the U.S. social media landscape has shifted across platforms, demographics, and politics. Overall platform use has declined, with the youngest and oldest Americans increasingly abstaining from social media altogether. Facebook, YouTube

Jiaming Cheng, Wei Chen, Bo Ai

The advent of artificial intelligence (AI)-native wireless communication is fundamentally reshaping the design paradigm of next-generation (NextG) systems, where intelligent air interfaces are expected to operate adaptively and efficiently in highly dynamic environments. Conventional orthogonal frequency division multiplexing (OFDM) systems rely heavily on p

Tim Matthies, Levente Rózsa, Roland Wiesendanger, Elena Y. Vedmedenko

Magnetic skyrmions have been proposed as promising candidates for storing information due to their high stability and easy manipulation by spin-polarized currents. Here, we study how these properties are influenced by the interlayer Dzyaloshinskii--Moriya interaction (IL-DMI), which stabilizes twin-skyrmions in magnetic bilayers. We find that the spin config

Bodoo Batnaran, Andrew L. Goodwin, Michael A. Hayward, Volker L. Deringer

We present a revised atomistic picture of the order-disorder transition in the archetypal orbital-ordered perovskite material, LaMnO$_{3}$. Our study uses machine-learning-driven molecular-dynamics simulations which describe the temperature evolution of pair distribution functions in close agreement with experiment. We find the orbital-disordered phase in La

Shakib Yazdani, Yasser Hamidullah, Cristina España-Bonet, Josef van Genabith

Most existing sign language translation (SLT) datasets are limited in scale, lack multilingual coverage, and are costly to curate due to their reliance on expert annotation and controlled recording setup. Recently, Vision Language Models (VLMs) have demonstrated strong capabilities as evaluators and real-time assistants. Despite these advancements, their pot

In Gim, Lin Zhong

Current large language model (LLM) serving systems, primarily designed for text completion, are neither efficient nor adaptable for increasingly complex LLM applications due to their inflexible design. We propose a new LLM serving system architecture that serves programs instead of prompts to address this problem. These programs, called LLM Inference Program

Sana Hafeez, Ghulam E Mustafa Abro, Hifza Mustafa

The rapid deployment of unmanned aerial vehicle (UAV) corridors in sixth-generation (6G) networks requires safe, intelligence-driven integrated sensing and communications (ISAC). Reconfigurable intelligent surfaces (RIS) enhance spectrum efficiency, localisation accuracy, and situational awareness, while introducing new vulnerabilities. The rise of quantum c

William H. Allen

Strongly regular graphs are regular graphs with a constant number of common neighbours between adjacent vertices, and a constant number of common neighbours between non-adjacent vertices. These graphs have been of great interest over the last few decades and often give rise to interesting groups of automorphisms. In this paper we take a reverse approach, and

Vijay Devane, Mohd Nauman, Bhargav Patel, Aniket Mahendra Wakchoure

The rapid advancement of large language models(LLMs) has intensified the need for domain and culture specific evaluation. Existing benchmarks are largely Anglocentric and domain-agnostic, limiting their applicability to India-centric contexts. To address this gap, we introduce BhashaBench V1, the first domain-specific, multi-task, bilingual benchmark focusin

Fabian Mies

The empirical Orlicz norm based on a random sample is defined as a natural estimator of the Orlicz norm of a univariate probability distribution. A law of large numbers is derived under minimal assumptions. The latter extends readily to a linear and a nonparametric regression model. Secondly, sufficient conditions for a central limit theorem with a standard

Timon J. Vreman, Ad Lagendijk, Willem L. Vos

We investigate the spontaneous emission of light in three-dimensional (3D) photonic crystals through theoretical calculations and simulations. It is well known that spontaneous emission depends on the radiative local density of states (RLDOS). Photonic band-gap crystals radically modulate the RLDOS, thereby controlling spontaneous emission. We compare two di

Changjie Wang, Mariano Scazzariello, Anoud Alshnakat, Roberto Guanciale

Formal verification is increasingly recognized as a critical foundation for building reliable software systems. However, the need for specialized expertise to write precise specifications, navigate complex proof obligations, and learn annotations often makes verification an order of magnitude more expensive than implementation. While modern AI systems can re

Kei Ikemura, Yifei Dong, David Blanco-Mulero, Alberta Longhini

Robotic manipulation of deformable and fragile objects presents significant challenges, as excessive stress can lead to irreversible damage to the object. While existing solutions rely on accurate object models or specialized sensors and grippers, this adds complexity and often lacks generalization. To address this problem, we present a vision-based reinforc

Amar S. Pote, Ganesh S. Kadu

The power graph of a group $G$ is a graph with vertex set $G$, where two distinct vertices $a$ and $b$ are adjacent if one of $a$ and $b$ is a power of the other. Similarly, the enhanced power graph of $G$ is a graph with vertex set $G$, where two distinct vertices are adjacent if they belong to the same cyclic subgroup. In this paper we give a simple algori

Yuqian Chai, Chaochao Wang, Weilei Wang

Although AI drafting tools have gained prominence in patent writing, the systematic evaluation of AI-generated patent content quality represents a significant research gap. To address this gap, We propose to evaluate patents using regulatory compliance, technical coherence, and figure-reference consistency detection modules, and then generate improvement sug

Jiahao Lou, Shufeng Gong, Quan Yu, Hao Guo

On-disk graph-based indexes are favored for billion-scale Approximate Nearest Neighbor Search (ANNS) due to their high performance and cost-efficiency. However, existing systems typically rely on a coupled storage architecture that co-locates vectors and graph topology, which introduces substantial redundant I/O during index updates, thereby degrading usabil

Sirine Louati

We investigate the high-probability estimation of discrete distributions from an \iid sample under $\chi^2$-divergence loss. Although the minimax risk in expectation is well understood, its high-probability counterpart remains largely unexplored. We provide sharp upper and lower bounds for the classical Laplace estimator, showing that it achieves optimal per

Alexander V. Savin, Artem P. Klinov

Using a two-dimensional coarse-grained chain model, planar slits in multilayer graphite crystals are simulated. It is shown that when covering a linear cavity on the flat surface of a graphite crystal with a multilayer graphene sheet, an open (unfilled slit) can form only if the cavity width does not exceed a critical value L_o (for width L>L_o, only a close

Filippo de Feo, Giorgio Fabbri, Silvia Faggian, Giuseppe Freni

We study optimal and strategic extraction of a renewable resource that is distributed over a network, migrates mass-conservatively across nodes, and evolves under nonlinear (concave) growth. A subset of nodes hosts extractors while the remaining nodes serve as reserves. We analyze a centralized planner and a non-cooperative game with stationary Markov strate

Clara Montagnon

We compute asymptotic Quasi-Normal Mode (QNM) frequencies -- i.e. frequencies with a very large Imaginary part -- of a Loop Quantum Gravity inspired Black Hole. The deformations from the Schwarzschild Black Hole are encoded via two parameters: the minimal area gap $a_0$ and the polymeric deformation parameter $P$. In this study, we focus on the effect of the

Manishankar Ailiga, Shubhashis Mallik, Gaurav Narain

In this paper, we consider the Euclidean partition function of uncharged and charged $AdS_{d+1}$ black hole geometries in canonical and grand canonical ensemble for $d\geq3$. It is seen that the partition function can be reduced to a one-dimensional integral, which can be investigated using methods of Picard-Lefschetz. The saddles of the system correspond to

Joshua Vedral, Nathaniel Morgan, Dmitri Kuzmin, Jacob Moore

We introduce a Lagrangian nodal discontinuous Galerkin (DG) cell-centered hydrodynamics method for solving multi-dimensional hyperbolic systems. By incorporating an adaptation of Zalesak's flux-corrected transport algorithm, we combine a first-order positivity-preserving scheme with a higher-order target discretization. This results in a flux-corrected Lagra

Youan Su

Uniform interpolation property (UIP) is a strengthening of Craig interpolation property. It was first established by Pitts(1992) based on a pure proof-theoretic method. UIP in multi-modal $\mathbf{K_n}$, $\mathbf{KD_n}$ and $\mathbf{KT_n}$ logic have been established by semantic approaches, however, a proof-theoretic approach is still lacking. B\'ilkov\'a (2

Alea Schröder, Steffen Gracla, Carsten Bockelmann, Dirk Wübben

Satellite-based communications are expected to be a substantial future market in 6G networks. As satellite constellations grow denser and transmission resources remain limited, frequency reuse plays an increasingly important role in managing inter-user interference. In the multi-user downlink, precoding enables the reuse of frequencies across spatially separ

Stelios Stylianou

We consider a four-player game on the discrete hypercube $Q_n = \{0,1\}^n$, where each of the four players has chosen a single vertex of the hypercube. Such a position is called a profile. Imagine there is a voter at every vertex, and each voter gives their vote to whichever player is closest to them, in terms of Hamming distance. If multiple players are tie

Vladimir Dorodnitsyn, Roman Kozlov, Sergey Meleshko

We review studies on the application of Lie group methods to delay ordinary differential equations (DODEs). For first- and second-order DODEs with a single delay parameter that depends on independent and dependent variables, the group classifications are performed. Classes of invariant DODEs for each Lie subgroup are written out. The symmetries allow us to c

Syed Luqman Shah, Nurul Huda Mahmood, Italo Atzeni

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as antenna counts grow. In this paper, we address this challenge by developing a novel framework based on Gaussian process regression (GPR) that predicts full CSI from only a few observ

Meng Hua, Haotian Wu, Deniz Gündüz

This paper introduces AirCNN, a novel paradigm for implementing convolutional neural networks (CNNs) via over-the-air (OTA) analog computation. By leveraging multiple reconfigurable intelligent surfaces (RISs) and transceiver designs, we engineer the ambient wireless propagation environment to emulate the operations of a CNN layer. To comprehensively evaluat

Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn

A core challenge of Monte Carlo Tree Search (MCTS) is its sample efficiency, which can be improved by grouping state-action pairs and using their aggregate statistics instead of single-node statistics. On the Go Abstractions in Upper Confidence bounds applied to Trees (OGA-UCT) is the state-of-the-art MCTS abstraction algorithm for deterministic environments

Bill Psomas, George Retsinas, Nikos Efthymiadis, Panagiotis Filntisis

The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of high-quality training and evaluation data. We introduce a new evaluation dataset, i-CIR, which, unlike existing datasets, focuses on an instance-level class definition. The goal is

Kumar Manas, Mert Keser, Alois Knoll

This survey provides an analysis of current methodologies integrating legal and logical specifications into the perception, prediction, and planning modules of automated driving systems. We systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches, emphasizing their capability to ensure regulatory compl

Marco Zaopo

We construct an extension of the proper orthochronous Lorentz group that includes space-time transformations for observers moving with superluminal relative velocities in arbitrary direction. This extension is generated by a realization of the Klein four group depending on polar and azimuthal angles identifying a spatial direction and is obtained with matric

Doan Nam Long Vu, Rui Tan, Lena Moench, Svenja Jule Francke

Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced previous work to rely mainly on generic information. We present a comprehensive corpus of synthetic therapist-client conversations generated through LLMs. We construct our generation pipeline, SQPsych (Structured

Anshuman Borgohain, Kanak Saha

Recent observations with the James Webb Space Telescope (JWST) have begun to reveal a surprising morphological diversity in galaxies within the first billion years after the Big Bang, including indications of structural maturity previously thought to arise much later. These findings raise fundamental questions about when and how well-known structural compone

Wengang Yang

This paper investigates the well-posedness of five classes of boundary value problems for the two-dimensional steady incompressible Euler equations in an annular domain. Three of these boundary conditions can be effectively addressed using the Grad-Shafranov method, and the well-posedness of solutions in the $C^{1,\al}$ space is established via variational t

Dongyijie Primo Pan, Lan Luo, Yike Wang, Pan Hui

Metabolic disorders present a pressing global health challenge, with China carrying the world's largest burden. While continuous glucose monitoring (CGM) has transformed diabetes care, its potential for supporting sub-health populations -- such as individuals who are overweight, prediabetic, or anxious -- remains underexplored. At the same time, large langua

Ilyes Batatia, Chen Lin, Joseph Hart, Elliott Kasoar

Creating a single unified interatomic potential capable of attaining ab initio accuracy across all chemistry remains a long-standing challenge in computational chemistry and materials science. This work introduces a training protocol for foundation machine-learning interatomic potentials (MLIPs) that bridge molecular, surface, and materials chemistry through

Adrien Weihs, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

While many problems in machine learning focus on learning mappings between finite-dimensional spaces, scientific applications require approximating mappings between function spaces, i.e., operators. We study the problem of learning collections of operators and provide both theoretical and empirical advances. We distinguish between two regimes: (i) multiple o

Junichiro Niimi

Large language models (LLMs) have been increasingly applied to a wide range of tasks, from natural language understanding to code generation. While they have also been used to assist in bibliographic recommendation, the hallucination of non-existent papers remains a major issue. Building on prior studies, this study hypothesizes that an LLM's ability to corr

Charlotte Myin, Benoît Mahault

We show that even weak nonreciprocal alignment leads to large-scale structure formation in flocking mixtures. By combining numerical simulations of a binary Vicsek model and the analysis of coarse-grained continuum equations, we demonstrate that nonreciprocity destabilizes the ordered phase formed by mutually aligning or anti-aligning species in a large part

Talia Meir, Kale Weichman, Alexey Arefiev, John P. Palastro

Direct laser acceleration (DLA) offers a compact source of high-charge, energetic electrons for generating secondary radiation or neutrons. While DLA in high-density plasma optimizes the energy transfer from a laser pulse to electrons, it exacerbates nonlinear propagation effects, such as filamentation, that can disrupt the acceleration process. Here, we sho

Ali Recai Yekta, Nicolas Loza, Jens Gramm, Michael Peter Schneider

Increasing complexity and connectivity of modern vehicles have heightened their vulnerability to cyberattacks. This paper addresses security challenges associated with the Unified Diagnostic Services (UDS) protocol, a critical communication framework for vehicle diagnostics in the automotive industry. We present security monitoring strategies for the UDS pro

Shangkun Weng, Wengang Yang

The Lorentz force induced by the magnetic field in MHD flow introduces a fundamental difference from pure gas dynamics by facilitating the anisotropic propagation of small disturbances, thus the type of steady MHD equations depends on not only the Mach number but also the Alfv\'en number. In the super-Alfv\'enic case, we derive an admissible condition for th

Janik Pinter, Maximilian Beichter, Ralf Mikut, Frederik Zahn

Residential prosumers with PV-battery systems increasingly manage their electricity exchange with the power grid to minimize costs. This study investigates the performance of Model Predictive Control (MPC) and Rule-Based Control (RBC) under 15/30/60 minute averaging commonly used in research, when Net Billing and battery degradation are considered. We simula

M Yashwanth, Sharannya Ghosh, Aditay Tripathi, Anirban Chakraborty

Visual Prompt Tuning (VPT) of pre-trained Vision Transformers (ViTs) has proven highly effective as a parameter-efficient fine-tuning technique for adapting large models to downstream tasks with limited data. Its parameter efficiency makes it particularly suitable for Federated Learning (FL), where both communication and computation budgets are often constra

Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago, Manfred Claassen

We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple data sources as outputs to improve the accuracy of latent variable inference under a single probabilistic framework. Similarly specified exact Gaussian processes scale poorly with la

Julien Martinelli

Physics-Informed Machine Learning (PIML) has successfully integrated mechanistic understanding into machine learning, particularly in domains governed by well-known physical laws. This success has motivated efforts to apply PIML to biology, a field rich in dynamical systems but shaped by different constraints. Biological modeling, however, presents unique ch

Muhittin Mungan, Eric Clement, Damien Vandembroucq, Srikanth Sastry

Disordered systems subject to a fluctuating environment can self-organize into a complex history-dependent response, retaining a memory of the driving. In sheared amorphous solids, self-organization is established by the emergence of a persistent system of mechanical instabilities that can repeatedly be triggered by the driving, leading to a state of high me

Tomas Hrycej, Bernhard Bermeitinger, Massimo Pavone, Götz-Henrik Wiegand

The key task of machine learning is to minimize the loss function that measures the model fit to the training data. The numerical methods to do this efficiently depend on the properties of the loss function. The most decisive among these properties is the convexity or non-convexity of the loss function. The fact that the loss function can have, and frequentl

Sidhanta Sahu, Phalguna Krishna Das Vana, Anupama Chauhan, Poulami Ghosh

To elucidate low-frequency vibrational modes, we investigate a benzoxazolium--coumarin (BCO+) donor-pi-acceptor derivative using transmission terahertz time-domain spectroscopy (THz-TDS). The retrieved complex refractive index reveals distinct modes at 0.62, 0.85, 1.30, 1.81, and 2.07 THz. Gas-phase density functional theory (DFT) agrees well with these feat

Muhammad Bilal Awan, Abdul Razzaq, Abdul Shahid

This paper investigates the performance of Large Language Models (LLMs) as generative optimizers for solving constrained multi-objective regression tasks, specifically within the challenging domain of inverse design (property-to-structure mapping). This problem, critical to materials informatics, demands finding complex, feasible input vectors that lie on th

Luca Capone, Alessandro Bondielli, Alessandro Lenci

This work investigates whether small-scale LMs can benefit from instruction tuning. We compare conversational and question-answering instruction tuning datasets, applied either in a merged or sequential curriculum, using decoder-only models with 100M and 140M parameters. Evaluation spans both fine-tuning (SuperGLUE) and zero-shot (BLiMP, EWoK, WUGs, entity t

Georgios L. Stavrinides, Helen D. Karatza

With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity. Data-intensive applications may have different degrees of parallelism and must effectively exploit data locality. Further

Rupesh Sapkota, Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo

Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this ap

Mario Galici, Alessandro Montinaro

A complete classification of the flag-transitive point-imprimitive symmetric $2$-$(v,k,\lambda )$ designs with $v<100$ is provided. Apart from the known examples with $\lambda \leq 10$, the complementary design of $PG_{5}(2)$, and the $2$-design $\mathcal{S}^{-}(3)$ constructed by Kantor in \cite{Ka75}, we found two non isomorphic $2$-$(64,28,12)$ designs. T

Roger D. Jones, Achille Giacometti, Alan M. Jones

We derive a formulation of the First Law of nonequilibrium thermodynamics for biological information-processing systems by partitioning entropy in the Second Law into microscopic and mesoscopic components and by assuming that natural selection promotes optimal information processing and transmission. The resulting framework demonstrates how mesoscopic inform

Roberto Viola, Mikel Irazola, José Ramón Juárez, Minh Nguyen

This paper investigates the energy implications of remote rendering for Virtual Reality (VR) applications within a real 5G testbed. Remote rendering enables lightweight devices to access high-performance graphical content by offloading computationally intensive tasks to Cloud-native Network Functions (CNFs) running on remote servers. However, this approach r

Pannan I. Kyesmen, Eugen Klein, Brindhu Malani S, Rostyslav Lesyuk

The search for non-toxic lead-free halide perovskites that can compete with the lead-based counterparts has led to the emergence of double perovskites as potential candidates. Among many options, Cs2AgBiBr6 stands out as one of the most suitable eco-friendly materials for numerous optoelectronic applications. In this study, quasi-2D Cs2AgBiBr6 nanosheets (NS

F. Javier Sánchez-Vidal

This paper investigates a fundamental methodological flaw in the investment-cash flow sensitivity model of Fazzari, Hubbard, and Petersen (1988). The model comes from a full accounting identity in which some components are missing, generating what I term an Accounting Semi-Identity, that mechanically links investment and cash flow, and this could bias coeffi

Javier Marín

Loss Given Default (LGD) modeling faces a fundamental data quality constraint: 90% of available training data consists of proxy estimates based on pre-distress balance sheets rather than actual recovery outcomes from completed bankruptcy proceedings. We demonstrate that this mixture-contaminated training structure causes systematic failure of recursive parti

David Plonka, Branden Palacio, Debbie Perouli

We perform a passive measurement study investigating how a Protective DNS service might perform in a Research & Education Network serving hundreds of member institutions. Utilizing freely-available DNS blocklists consisting of domain names deemed to be threats, we test hundreds of millions of users' real DNS queries, observed over a week's time, to find whic

Rik Verbiest, Julian Koellermeier

Shallow free surface flows are often characterized by both subdomains that require high modeling complexity and subdomains that can be sufficiently accurately modeled with low modeling complexity. Moreover, these subdomains may change in time as the water flows through the domain. This motivates the need for space and time adaptivity in the simulation of sha

Yvann Gaudillot-Estrada

We study the representation theory of various convolution algebras attached to the $q$-deformation of $\mathrm{SL}(2,\mathbb{R})$ from an algebraic perspective and beyond the unitary case. We show that many aspects of the classical representation theory of real semisimple groups can be transposed to this context. In particular, we prove an analogue of the Ha