December 2025 arXiv papers — page 88

Hampus Linander, Conor Heins, Alexander Tschantz, Marco Perin

Equivariance is a powerful prior for learning physical dynamics, yet exact group equivariance can degrade performance if the symmetries are broken. We propose object-centric world models built with geometric algebra neural networks, providing a soft geometric inductive bias. Our models are evaluated using simulated environments of 2d rigid body dynamics with

Junming Fu, Jishen Zeng, Yi Jiang, Peiyu Zhuang

Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper introduces the \textbf{Attribute Misbinding Attack}, a novel method that poses a threat to identity-preserving mod

Adrián Detavernier, Jasper De Bock

We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combi

Omar Namnakani, Yasmeen Abdrabou, Jonathan Grizou, Mohamed Khamis

The potential of gaze for hands-free mobile interaction is increasingly evident. While each gaze input technique presents distinct advantages and limitations, a combination can amplify strengths and mitigate challenges. We report on the results of a user study (N=24), in which we compared the usability and performance of pairing three popular gaze input tech

Valentin Haberl, Piotr Szewczak, Lyubomyr Zdomskyy

We work in the realm of sets of reals. We prove that in the Miller model and in a model constructed by Goldstern-Judah-Shelah all universally meager sets have size at most $\omega_1$. Some relations between combinatorial covering properties in these models allow to obtain the same limitations for sizes of Rothberger spaces and Hurewicz spaces with no homeomo

Wei Du, Shubham Toshniwal, Branislav Kisacanin, Sadegh Mahdavi

High-quality mathematical reasoning supervision requires diverse reasoning styles, long-form traces, and effective tool integration, capabilities that existing datasets provide only in limited form. Leveraging the multi-mode generation ability of gpt-oss-120b, we introduce Nemotron-Math, a large-scale mathematical reasoning dataset containing 7.5M solution t

Seyed Abolfazl Ghasemzadeh, Alexandre Alahi, Christophe De Vleeschouwer

Estimating 3D human poses from 2D images remains challenging due to occlusions and projective ambiguity. Multi-view learning-based approaches mitigate these issues but often fail to generalize to real-world scenarios, as large-scale multi-view datasets with 3D ground truth are scarce and captured under constrained conditions. To overcome this limitation, rec

Mats Ehrnström, Mark D. Groves

The KP-I equation arises as a weakly nonlinear model equation for gravity-capillary waves with Bond number $\beta>1/3$, also called strong surface tension. This equation has recently been shown to have a family of nondegenerate, symmetric `fully localised' or `lump' solitary waves which decay to zero in all spatial directions. The full-dispersion KP-I equati

Eigil Fjeldgren Rischel

Building on work of Chen, we give a universal property of the Markov category BorelStoch of standard Borel spaces and Markov kernels between them. To do this, we introduce a new notion of *coinflip*, or unbiased binary choice, in a Markov category. These are unique if they exist, and automatically preserved by all Markov functors which preserve coproducts. W

Yu Shi, Hong-Peng Lu, Li-Yun Zhang, Tian-Hao Su

Coronal mass ejections (CMEs) are major drivers of stellar space weather and can strongly influence the habitability of exoplanets. However, compared to the frequent occurrence of white-light flares, confirmed stellar CMEs remain extremely rare. Whether such flares are commonly accompanied by CMEs is a key question for solar-stellar comparative studies. Usin

Luca Torresi, Pascal Friederich

Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autonomous experimentation toward human-directed goals without requiring any direct human intervention. SDLs are successfully used in materials science, chemistry, and beyond, to optimis

Jiawei Liu, Atsushi Kanoke, Hidenori Endo, Kuniyasu Niizuma

Cerebral hemodynamics is fundamentally regulated by the Circle of Willis (CoW), which redistributes flow through communicating arteries to stabilize perfusion under anatomical variations and vascular stenosis. In this study, we develop a multiscale circulation model by coupling a systemic hemodynamic framework with a cerebral arterial network reconstructed f

Shavbo Salehi, Pedro Enrique Iturria-Rivera, Medhat Elsayed, Majid Bavand

Semantic communication addresses the limitations of the Shannon paradigm by focusing on transmitting meaning rather than exact representations, thereby reducing unnecessary resource consumption. This is particularly beneficial for video, which dominates network traffic and demands high bandwidth and power, making semantic approaches ideal for conserving reso

N. Castro, M. Garcia, A. Herrero, A. A. C. Sander

Very metal-poor massive stars in the Local Group are our best proxies for the Universe's first stars, making them essential for modeling reionization and early galactic chemical evolution. Studying such stars in our Local Universe is key to extrapolating our knowledge to more distant regions, where individual massive stars cannot be resolved but are dynamica

Malach Obisa Amonga, Benard Osero, Edna Too

Wildlife object detection plays a vital role in biodiversity conservation, ecological monitoring, and habitat protection. However, this task is often challenged by environmental variability, visual similarities among species, and intra-class diversity. This study investigates the effectiveness of two individual deep learning architectures ResNet-101 and Ince

Jiesong Lian, Ruizhe Zhong, Zixiang Zhou, Xiaoyue Mi

Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodological hurdles. Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise. Concurrently, the architectural design of VLM-based RMs, par

Apurba Biswas, Thomas Guérin

Rare events refer to qualitatively unlikely events whose realization can nevertheless have important consequences. Typically, the prediction of the kinetics of these events relies on Arrhenius laws, with exponentially distributed waiting times, and no correlations between successive occurrences. However, this description breaks down in the presence of long-t

Alisha Vasan, Mary Anne Limbach, Andrew Vanderburg, Rachel Bowens-Rubin

The JWST MIRI detector exhibits a flux deficit persistence, but its timescales and impacts remain largely uncharacterized, particularly at the longest imaging wavelengths. In this study, we analyze full-field MIRI imager observations at 21 $\mu$m (F2100W) to quantify detector persistence following a saturation event by a bright (K = 5.65 mag) nearby (8.12 $\

Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur, Mark T. Mitchison

Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage quantum-enhanced framework that casts local and global graph-optimization problems as quantum searches over discrete trajectory spaces. The solver is designed to operate efficiently by fi

Mamadou Ciss, Abdourahmane Diatta, El Hadji Abdoulaye Thiam

Let $\Omega$ be a bounded domain of $\mathbb{R}^{N+1}$ ($N \geq 3$) with smooth boundary $\partial \Omega$ and $\Sigma$ be a closed submanifold contained on $\partial \Omega$ and containing $0$. We are interesting in the existence of positive $H^1(\Omega)$-solution of the following Hardy-Sobolev trace type equation \begin{equation*} \begin{cases} -\Delta u+u

Dinesh Kumar Murugan, Nithyanandan Kanagaraj

Ultrashort-pulse propagation in graded-index multimode fibers is a highly nonlinear phenomenon driven by several physical processes. Although conventional numerical solvers can reproduce this behavior with high fidelity, their computational cost limits real-time prediction, rapid parameter exploration, experimental feedback, and especially inverse retrieval

Daragh King, Vasileios Koutavas, Laura Kovacs

Loop invariant generation remains a critical bottleneck in automated program verification. Recent work has begun to explore the use of Large Language Models (LLMs) in this area, yet these approaches tend to lack a reliable and structured methodology, with little reference to existing program verification theory. This paper presents NeuroInv, a neurosymbolic

Nobumasa Ishida, Yoshihiko Hasegawa

Cloud-based quantum computers do not provide users with access to hardware-level information such as the underlying Hamiltonians, which obstructs the characterization of their physical properties. We propose a method to infer the energy scales of gate Hamiltonians in such black-box quantum processors using only user-accessible data, by exploiting quantum spe

Valeria Granata, Francois Liot, Xing Wang, Steen Lysgaard

Data sharing in large consortia, such as research collaborations or industry partnerships, requires addressing both organizational and technical challenges. A common platform is essential to promote collaboration, facilitate exchange of findings, and ensure secure access to sensitive data. Key technical challenges include creating a scalable architecture, a

Hassan Nasreddine

We investigate entropy minimization problems for quantum states subject to convex block-separable constraints. Our principal result is a quantitative stability theorem: under a natural confining (fixed-support) hypothesis, if a state has entropy within {\epsilon} of the minimum permitted by the constraint, then it must lie within O({\epsilon}^{1/2}) in trace

Casper Loman, Loriana Pascual, Marjan van den Akker, Roel van den Broek

In many real world scheduling problems, the processing times of tasks are subject to uncertainty. This makes it essential to design schedules that are robust and able to handle potential disruptions. Therefore, we investigate measures that give us information about the robustness of a schedule. Although many measures can be found in literature, there is no c

Andrew Cleary, Qi Wang, Tamer A. Zaki

Starting from limited measurements of a turbulent flow, data assimilation (DA) attempts to estimate all the spatio-temporal scales of motion. Success is dependent on whether the system is observable from the measurements, or how much of the initial turbulent field is encoded in the available measurements. Adjoint-variational DA minimises the discrepancy betw

Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis

As machine learning models are increasingly deployed in high-stakes settings, e.g. as decision support systems in various societal sectors or in critical infrastructure, designers and auditors are facing the need to ensure that models satisfy a wider variety of requirements (e.g. compliance with regulations, fairness, computational constraints) beyond perfor

Hua Yang, Alejandro Velasco, Thanh Le-Cong, Md Nazmul Haque

The success of large language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confidential code from companies. This raises concerns about intellectual property compliance and the potential unauthorized use of license-restricted code. While membership inference (MI) techniques have

Vladimir Müller, Yuri Tomilov

Replacing operators with continuous operator-valued functions, we prove time-dependent versions of well-known results on compressions and diagonals of bounded operators. The setting of smooth functions is also addressed. Our results have no analogues in the literature and rely on a new technique. The results are especially transparent for selfadjoint operato

Robert Heumüller, Frank Ortmeier

The use of large language models like ChatGPT in code review offers promising efficiency gains but also raises concerns about correctness and safety. Existing evaluation methods for code review generation either rely on automatic comparisons to a single ground truth, which fails to capture the variability of human perspectives, or on subjective assessments o

Hannah Smith, Abhirup Banerjee, Leto Luana Riebel, Ruben Doste

The electrocardiogram (ECG) is modulated by torso-heart anatomy, and this challenges patients' diagnosis and risk stratification. This study aims to quantify how torso-heart anatomical factors affect sex-differences in ECG biomarkers in acute and chronic myocardial infarction (MI). We exploit the perfect control of AI-augmented multiscale modelling and simul

Yingxiang Hu, Mohammad N. Ivaki

We solve the capillary $L_p$-Christoffel--Minkowski problem in the half-space for $1<p<k+1$ in the class of even hypersurfaces. A crucial ingredient is a non-collapsing estimate that yields lower bounds for both the height and the capillary support function. Our result extends the capillary Christoffel--Minkowski existence result of \cite{HIS25}.

Puyu Cui, Yufeng Lu, Rongwei Yang, Chao Zu

This paper investigates the spectral properties of Toeplitz operators on the Bergman space of unit disk. We present an integral representation of $ T^*_{z^m}$, which establishes a connection between the Bergman functions and the solutions of PDE theory. In fact, by leveraging the Poincar\'e theorem in difference equations and the solution forms of differenti

Clément Elliker, Jesse Read, Sonia Vanier, Albert Bifet

Reliable prediction of train delays is essential for enhancing the robustness and efficiency of railway transportation systems. In this work, we reframe delay forecasting as a stochastic simulation task, modeling state-transition dynamics through imitation learning. We introduce Drift-Corrected Imitation Learning (DCIL), a novel self-supervised algorithm tha

Tobias Hainer, Ethan Berger, Esmée Berger, Olof Hildeberg

Point defects introduce localized electronic states that critically affect carrier trapping, recombination, and transport in functional materials. The associated charge transition levels (CTLs) can depend on temperature, requiring accurate treatment of vibrational and electronic free-energy contributions. In this work, we use machine-learned interatomic pote

Jeongseok Kim, Kangjin Kim

Due to the restricted resources, efficient scheduling in vertiports has received much more attention in the field of Urban Air Mobility (UAM). For the scheduling problem, we utilize a Mixed Integer Linear Programming (MILP), which is often formulated in a resource-restricted project scheduling problem (RCPSP). In this paper, we show our approach to handle bo

János Barát, Andrea Freschi, Géza Tóth

A {\it vertex-ordered} graph is a graph equipped with a linear ordering of its vertices. A pair of independent edges in an ordered graph can exhibit one of the following three patterns: separated, nested or crossing. We say a pair of independent edges is non-separated if it is either crossing or nested. Non-nested and non-crossing pairs are defined analogous

Xiangrui Xu, Zhize Li, Yufei Han, Bin Wang

Data Reconstruction Attacks (DRA) pose a significant threat to Federated Learning (FL) systems by enabling adversaries to infer sensitive training data from local clients. Despite extensive research, the question of how to characterize and assess the risk of DRAs in FL systems remains unresolved due to the lack of a theoretically-grounded risk quantification

Giulia Di Nunno, Nicola Giordano, Barbara Martinucci, Olena Tymoshenko

We develop a stochastic human-rodent compartment model for Mpox transmission that combines diffusion noise with Hawkes self-exciting jumps in the human infection dynamics. Including Hawkes processes allows, for instance, to model the short but significant spikes in transmission happening after crowded events. For the coupled human-rodent system, we prove glo

Yi-Jia Mao, En-Rui Zhou, Yang Li, Pei-Lun He

The recently available high-intensity quantum light pulses provide novel tools for controlling light-matter interactions. However, the rigor of the theoretical frameworks currently used to describe the interaction of strong quantum light with atoms and molecules remains unverified. Here, we establish a rigorous benchmark by solving the fully quantized time-d

Arnab Sarkar, Allan S. Johnson

The advent of nonlinear X-ray processes like sum-frequency generation and four-wave mixing raises the possibility of non-linear X-ray imaging, combining the high-resolution and elemental specificity of X-ray imaging with the state selectivity and sensitivity of non-linear optical imaging. While scanning imaging methods may be feasible, for linear X-ray proce

J. L. Figueiredo, J. T. Mendonça, H. Terças

We develop a quantum kinetic theory of two-dimensional electron gases in which exchange is treated self-consistently at the Hartree-Fock level and enters as a nonlocal, momentum-dependent field in phase space. By starting from the Coulomb Hamiltonian, we derive a Hartree-Fock-Wigner equation for the electronic Wigner function and obtain a closed fluid model

Jean-Guillaume de Damas, Laura Grigori, Igor Simunec, Edouard Timsit

We present an overview of randomized orthogonalization techniques that construct a well-conditioned basis whose sketch is orthonormal. Randomized orthogonalization has recently emerged as a powerful paradigm for reducing the computational and communication cost of state-of-the-art orthogonalization procedures on parallel architectures, while preserving, and

Xiang-Xiang Sun, Hoai Le, Ulf-G. Meißner, Andreas Nogga

Modern advanced nuclear ab initio approaches with the similarity renormalization group (SRG) softened interactions miss high-momentum information, thus rendering them less suitable for characterizing nucleon-nucleon short-range physics. We introduce a novel framework to construct SRG-independent nuclear wave functions from No-Core Shell Model calculations. A

Carsten Ellwein, Jingxi Zhang, Andreas Wortmann, Antony Ayman Alfy Meckhael

In manufacturing, digital twins, realized as Asset Administration Shells (AAS), have emerged as a prevalent practice. These digital replicas, often utilized as structured repositories of asset-related data, facilitate interoperability across diverse systems. However, extant approaches treat the AAS as a static information model, lacking support for dynamic s

Addhyaya Sharma, Ezra Bader, Ravindra K. Yadav, Juan Carlos Obeso Jureidini

Polariton condensation is a potential system state for performing analog computations, given that it exhibits quantum behavior at macroscopic scales readily probed with low-cost optical methods. Current methods of fabricating devices in polariton microcavities largely involve patterning the devices via e-beam lithography before the cavity is completed, which

Gaston Nieuviarts

This proceeding presents a synthesis of recent results on the emergence of pseudo-Riemannian structures from twisted spectral triples within the almostcommutative framework. It provides a unified algebraic mechanism for addressing the Lorentzian signature problem, demonstrating how the almost-commutative structure underlying the noncommutative Standard Model

Margot Teunisse, Martin van Hecke

Models of interacting hysteretic elements, called hysterons, capture the sequential response and complex memory effects in a wide range of complex systems and can guide the design of intelligent metamaterials. However, even simple models with few hysterons feature a bewildering number and variety of behaviors. Here we study the hysteron model in two physical

Sinan Emre, Victor Barasuol, Matteo Villa, Claudio Semini

This paper presents a Load-Based Variable Transmission (LBVT) mechanism designed to enhance robotic actuation by dynamically adjusting the transmission ratio in response to external torque demands. Unlike existing variable transmission systems that require additional actuators for active control, the proposed LBVT mechanism leverages a pre-tensioned spring a

Ben Swierzy, Marc Ohm, Michael Meier

Reusable software components, typically distributed as packages, are a central paradigm of modern software development. The JavaScript ecosystem serves as a prime example, offering millions of packages with their use being promoted as idiomatic. However, download statistics on npm raise security concerns as they indicate a high popularity of vulnerable packa

Yueqianji Chen, Kevin Williams, John H. Doonan, Paolo Remagnino

Automated extraction of individual plant branches from time-series imagery is essential for high-throughput phenotyping, yet it remains computationally challenging due to non-rigid growth dynamics and severe identity fragmentation within entangled canopies. To overcome these stage-dependent ambiguities, we propose ST-DETrack, a spatiotemporal-fusion dual-dec

Stefano Bolognesi

We show that a generalized Polyakov mechanism can lead to confinement at weak coupling in $3+1$ dimensions when the theory is placed in a non-trivial, spatially varying magnetic field background. Depending on the magnitude of the field and the length scale of its spatial variation, the "dual" Schwinger mechanism for monopole-antimonopole pair creation may or

Neeraj Sarna, Yuanyuan Li, Michael von Gablenz

Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could result in legal liabilities and financial losses for both the AI user and the developer. The current works explores the

Akihiro Kubo, Paavo Parmas, Shin Ishii

Model-based reinforcement learning (MBRL) reduces the cost of real-environment sampling by generating synthetic trajectories (called rollouts) from a learned dynamics model. However, choosing the length of the rollouts poses two dilemmas: (1) Longer rollouts better preserve on-policy training but amplify model bias, indicating the need for an intermediate ho

Naoki Kitazawa

We investigate regions formed by cylinders of circles of fixed radii. We investigate graphs obtained by collapsing each level set of the functions represented by the natural projections of them to the $1$-dimensional line. Some specific trees obtained in simple ways from so-called balanced trees are shown to be realized as such graphs. Related studies on reg

Stefan Edelkamp

In multi-player card games such as Skat or Bridge, the early stages of the game, such as bidding, game selection, and initial card selection, are often more critical to the success of the play than refined middle- and end-game play. At the current limits of computation, such early decision-making resorts to using statistical information derived from a large

Masato Nagatsuka, Toru Kojo

We investigate the onset of hyperons in baryonic (diquark) matter in two-color QCD (QC$_2$D) by introducing heavy quark doublets that emulate strange quarks. An even number of flavors is required to avoid the sign problem in lattice Monte Carlo simulations. To explore QC$_2$D matter containing both light and heavy quarks, we construct a model in which quarks

Longchen Dai, Zixuan Shen, Zhiheng Zhou, Peipeng Yu

Face recognition systems store face templates for efficient matching. Once leaked, these templates pose a threat: inverting them can yield photorealistic surrogates that compromise privacy and enable impersonation. Although existing research has achieved relatively realistic face template inversion, the reconstructed facial images exhibit over-smoothed facia

Haolong Yan, Jia Wang, Xin Huang, Yeqing Shen

Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-g

Quanxi Zhou, Wencan Mao, Manabu Tsukada, John C. S. Lui

Model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL) evolve along distinct paths but converge in the design of Dyna-Q [1]. However, modern RL methods still struggle with effective transferability across tasks and scenarios. Motivated by this limitation, we propose a generalized algorithm, Feature Model-Based Enhanced Actor-C

Emma S. Simpson, Paul J. Northrop

Modelling block maxima using the generalised extreme value (GEV) distribution is a classical and widely used method for studying univariate extremes. It allows for theoretically motivated estimation of return levels, including extrapolation beyond the range of observed data. A frequently overlooked challenge in applying this methodology comes from handling d

Peng Yuan, Zulin Wang, Tao Luo, Yuanhan Ni

This paper proposes an anti-interference affine frequency division multiplexing (AFDM) system to ensure reliability and resource efficiency under malicious high-power interference originating from adversarial devices in high-mobility scenarios. Closed-form expressions of interferences in the discrete affine Fourier transform (DAFT) domain are derived by util

Gongli Xi, Ye Tian, Mengyu Yang, Zhenyu Zhao

The structure of topology underpins much of the research on performance and robustness, yet available topology data are typically scarce, necessitating the generation of synthetic graphs with desired properties for testing or release. Prior diffusion-based approaches either embed conditions into the diffusion model, requiring retraining for each attribute an

Ishrak Alhajj Hassan

We study the discrete dynamical system obtained by repeatedly applying the Pearson correlation operator to a real matrix. Each step centers every row, normalizes each centered row to unit Euclidean norm, and forms the Gram matrix of the resulting rows. This produces a nonlinear map that underlies the classical CONCOR and GAP procedures. Despite its simple fo

Yeonwoo Cha, Semin Kim, Jinhyeon Kwon, Seunghoon Hong

Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, existing flow-based approaches are challenged by their inefficiency, as they require large-scale datasets often with restrictive pairing constraints, incur high computational cost from modeling joint distribution,

Shilei Li, Dawei Shi, Hao Yu, Ling Shi

Robustness and adaptivity are two competing objectives in Kalman filters (KF). Robustness involves temporarily inflating prior estimates of noise covariances, while adaptivity updates prior beliefs by exploiting measurements. In practical applications, both process and measurement noise can be influenced by outliers, be time-varying, or both. In this work, w

Dominik Szpara, Szczepan Głodzik, Nicholas Sedlmayr

Out-of-time ordered correlators are a probe of how the information of an initial perturbation is effectively scrambled under unitary time evolution, widely used to study quantum chaos. They have also been used to demonstrate that information is trapped in the zero dimensional edge modes of topological insulators and superconductors, and does not become scram

Jade Brisson, Bruno Colbois, Alexandre Girouard, Katie Gittins

We obtain upper bounds for the Steklov eigenvalues of warped products $\Omega\times_h\Sigma$, where $\Omega$ is a compact Riemannian manifold with boundary and $\Sigma$ is a closed Riemannian manifold. These bounds involve the volume of $\Omega$ and of $\partial\Omega$ as well as the eigenvalues of the Laplace operator on the fiber $\Sigma$ and the $L^p$-nor

Marco Genovese, Ivano Ruo-Berchera

Quantum illumination represents one of the most interesting examples of quantum technologies. On the one hand, it can find significant applications; on the other hand, it is one of the few quantum protocols robust against noise and losses. Here we present a short summary of the history of this quantum protocol.

Ehab Alkhateeb, Ali Ghorbani, Arash Habibi Lashkari

Detecting packed executables is a critical step in malware analysis, as packing obscures the original code and complicates static inspection. This study evaluates both classical feature-based methods and deep learning approaches that transform binary executables into visual representations, specifically, grayscale byte plots, and employ convolutional neural

Erika Rácza, Milan Malý, Jan Jedelský, Viktor Józsa

Spray characterization often relies on empirical formulas, statistical distributions, and derived quantities. Deterministic spray behavior originates from physics-governed mechanisms of atomization, \emph{e.g.}, nozzle geometry, boundary conditions, and hydrodynamic instabilities. Due to the stochastic nature of the atomization process, which originates from

Murat Uzundag, Ingrid Pelisoli, Stephane Charpinet, Alejandro H. Corsico

White dwarfs, the final evolutionary stage of the vast majority of stars, serve as critical tools for cosmochronology, studies of planetary system evolution, and laboratories for non-standard physics, including exotic cooling channels and weakly interacting particles, as well as crystallization processes. Beyond surface properties accessible via spectroscopy

Zhenhan Yin, Xuanhan Wang, Jiahao Jiang, Kaiyuan Deng

While leveraging abundant human videos and simulated robot data poses a scalable solution to the scarcity of real-world robot data, the generalization capability of existing vision-language-action models (VLAs) remains limited by mismatches in camera views, visual appearance, and embodiment morphologies. To overcome this limitation, we propose MiVLA, a gener

Simon Gutwein, Arthur Longuefosse, Jun Seita, Sabine Taschner-Mandl

Multiplexed tissue imaging measures dozens of protein markers per cell, yet most deep learning models still apply early channel fusion, assuming shared structure across markers. We investigate whether preserving marker independence, combined with deliberately shallow architectures, provides a more suitable inductive bias for self-supervised representation le

Héctor Ariza, Carmen Fernández, Antonio Galbis

We study composition operators whose symbols are suitable perturbations of the identity and which act between different weighted modulation classes. We consider both modulation spaces formed by tempered distributions and those whose elements are ultradistributions defined in terms of a subadditive weight.

Blanca Lopez, Angela Diaz-Bricio, Javier Perez, Ivan Vidal

Quantum Key Distribution (QKD) offers information-theoretic security by leveraging quantum mechanics, yet the cost and complexity of dedicated hardware and fiber infrastructure have so far limited large-scale deployment and experimentation. In this paper, we introduce Quditto, an automated open-access emulation platform that combines high-fidelity quantum-ch

Zhigang Pu, Kun Xu

This study extends the Unified Gas-Kinetic Scheme (UGKS) and the Unified Gas-Kinetic Wave-Particle (UGKWP) method for electrostatic plasma modeling, ensuring the correct asymptotic limits with respect to both the Debye length and the mean free path. By coupling collision and transport processes within the numerical flux, the proposed approach effectively rem

Jianfei Ma, Wee Sun Lee

At the boundary between the known and the unknown, an agent inevitably confronts the dilemma of whether to explore or to exploit. Epistemic uncertainty reflects such boundaries, representing systematic uncertainty due to limited knowledge. In this paper, we propose a Bayesian reinforcement learning (RL) algorithm, $\texttt{EUBRL}$, which leverages epistemic

József Kovács, Szabolcs Mészáros, Beáta Harmati, Borbála Cseh

Context. In order to determine stellar luminosities and radii, it is necessary to know the total bolometric fluxes emitted by the stars, or equivalently the bolometric corrections (BCs) as accurately as possible. Aims. The aim of this paper is to present and describe a new database of synthetic stellar magnitudes and bolometric corrections for 752 filters fr

Emanuel Milman, Joe Neeman

We verify that an isoperimetric minimizing cluster on a simply connected homogeneous Riemannian manifold with at most one end always has connected boundary. In particular, the boundary of a single-bubble isoperimetric minimizer on such manifolds must be connected, and hence all isoperimetric sets and their complements must be connected. This is demonstrably

Kirill Chmel, Eunji Kim, John Marshall, Tiffany Fisher-Love

Political apathy and skepticism of traditional authorities are increasingly common, but social media creators (SMCs) capture the public's attention. Yet whether these seemingly-frivolous actors shape political attitudes and behaviors remains largely unknown. Our pre-registered field experiment encouraged Americans aged 18-45 to start following five progressi

Yiming Pan, Sotirios Fragkos, Dominique Descamps, Stéphane Petit

Valleytronics aims to control electrons in a valley-specific manner for quantum information manipulation. Due to their strong in-plane anisotropy, which enables polarization-controlled optical transitions to distinct nondegenerate valleys, group-IV monochalcogenides have been recently proposed as promising candidates for next-generation valleytronic material

Phillip Stephan, Florian Euchner, Stephan ten Brink

Channel charting creates a low-dimensional representation of the radio environment in a self-supervised manner using manifold learning. Preserving relative spatial distances in the latent space, channel charting is well suited to support user localization. While prior work on channel charting has mainly focused on two-dimensional scenarios, real-world enviro

Zanxiang He, Meng Li, Liyun Shi, Weiye Daia

Polycystic Ovary Syndrome (PCOS) constitutes a significant public health issue affecting 10% of reproductive-aged women, highlighting the critical importance of developing effective diagnostic tools. Previous machine learning and deep learning detection tools are constrained by their reliance on large-scale labeled data and an lack of interpretability. Altho

Lev Kharlashkin, Eiaki Morooka, Yehor Tereshchenko, Mika Hämäläinen

ORACLE turns daily news into week-over-week, decision-ready insights for one of the Finnish University of Applied Sciences. The platform crawls and versions news, applies University-specific relevance filtering, embeds content, classifies items into PESTEL dimensions and builds a concise Time-Dependent Recursive Summary Graph (TRSG): two clustering layers su

Liang Peng, Yixuan Ye, Cheng Liu, Hangjun Che

Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clu

Anna Beliakova, Marco De Renzi, Quentin Faes

We use unimodular ribbon categories to construct quantum invariants of ribbon surfaces in $4$-dimensional $2$-handlebodies up to $1$-isotopy. In the process, we recover invariants due to Bobtcheva-Messia, Broda-Petit, Gainutdinov-Geer-Patureau-Runkel (in collaboration with the second author), and Lee-Yetter. Our approach does not assume semisimplicity, and i

Ruibo Shang, Sidhartha Jandhyala, Yujia Wu, Kevin Hoffer-Hawlik

Spectroscopic photoacoustic (sPA) imaging can potentially estimate blood oxygenation saturation (sO2) in vivo noninvasively. However, quantitatively accurate results require accurate optical fluence estimates. Robust modeling in heterogeneous tissue, where light with different wavelengths can experience significantly different absorption and scattering, is d

Chiara Schindler

In a separable Hilbert space, we study the minimization problem of a convex smooth function with Lipschitz continuous gradient whose evaluations are corrupted by random noise. To this end, we associate a stochastic inertial system that incorporates Tikhonov regularization with the optimization problem. We establish existence and uniqueness of a solution traj

Gerson C. Duarte-Filho, Julian Siegl, John Schliemann, J. Carlos Egues

The study of spectrum statistics, such as the consecutive-gap ratio distribution, has revealed many interesting properties of many-body complex systems. Here we propose a two-parameter surmise expression for such distribution to describe the crossover between the Gaussian orthogonal ensemble (GOE) and Poisson statistics. This crossover is observed in the iso

Fraser Aidan Kelvin Sanders

We generalise the theories of cosymplectic, contact, and cocontact manifolds to the infinite-dimensional setting and calculate model examples of time-dependent and dissipative Hamiltonian systems.

Reinhard Moratz, Niklas Daute, James Ondieki, Markus Kattenbeck

This paper deals with improving the capabilities of Large Language Models (LLM) to provide route instructions for pedestrian wayfinders by means of qualitative spatial relations.

Paul Staat, Daniel Davidovich, Christof Paar

Physical isolation from external networks - an airgap - aims to minimize exposure to remote attacks. Yet capable adversaries still achieve code execution on air-gapped systems, and prior work has shown that they can then wirelessly exfiltrate data via unintended emissions. In this work, we demonstrate the reverse direction: malicious code on an embedded devi

Arnau Barrera Roy, Albert Clapés Sintes

Computer vision and video understanding have transformed sports analytics by enabling large-scale, automated analysis of game dynamics from broadcast footage. Despite significant advances in player and ball tracking, pose estimation, action localization, and automatic foul recognition, anticipating actions before they occur in sports videos has received comp

Gregor Donabauer, Samy Ateia, Udo Kruschwitz, Maximilian Burger

We present MedNuggetizer, https://mednugget-ai.de/; access is available upon request.}, a tool for query-driven extraction and clustering of information nuggets from medical documents to support clinicians in exploring underlying medical evidence. Backed by a large language model (LLM), \textit{MedNuggetizer} performs repeated extractions of information nugg

Yunjie Fan, Matteo Sesia

We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs given a single input. This method can be combined with any multi-target regression model and guarantees finite-sample coverage. It is computationally efficient and yields informative prediction intervals even

S. P. Roshchupkin, M. V. Shakhov

The characteristic features of the resonant trident process (Oleinik resonances) have been theoretically studied in a wide range of frequencies and intensities of a circularly polarized strong electromagnetic wave. The resonant trident process is defined by two characteristic quantum energies: the characteristic energy of the nonlinear Compton effect and the

Mohammad Mahmoudi Filabadi, Guillaume Crevecoeur, Tom Lefebvre

Designing controllers under uncertainty requires balancing the need to explore system dynamics with the requirement to maintain reliable control performance. Dual control addresses this challenge by selecting actions that both regulate the system and actively gather informative data. This paper investigates the use of the Active Inference framework, grounded

Isabela Santiago de Matos, Charles Dalang, Tessa Baker, Raul Abramo

We measure for the first time the Hubble constant ($H_0$) from the cross-correlation of galaxies and gravitational waves (GW), by applying the $\textit{Peak Sirens}$ method. This method consists of finding the peak of the 3D angular cross-spectrum $C_{\ell}(z,D_L)$ between the galaxy redshifts ($z$) and the GW luminosity distances ($D_L$). Using two GW event