December 2025 arXiv papers — page 68

Marcos Llorca, Juan Luis Vázquez

We study the regularity of the bounded self-similar solution to the one-phase Stefan problem with fractional diffusion posed on the whole line. In terms of the enthalpy $h(x,t)$, the evolution problem reads \[ \begin{cases} \partial_t h + (-\Delta)^s \Phi(h) = 0 & \text{in } \mathbb{R}^n \times (0,T),\\[2mm] h(\cdot,0) = h_0 & \text{in } \mathbb{R}^n , \end{

Shaoyan Zhai, Mohamed Abdel-Aty, Chenzhu Wang, Rodrigo Vena Garcia

The advancement of safety-critical research in driving behavior in ADAS-equipped vehicles require real-world datasets that not only include diverse traffic scenarios but also capture high-risk edge cases such as near-miss events and system failures. However, existing datasets are largely limited to either simulated environments or human-driven vehicle data,

Alexander Marsteller, Dominic Batzler, Beate Bornschein, Lutz Bornschein

The literature on experimentally verified material properties of tritium is sparse but information about this is crucial in fusion for pellet production (Magnetic Confined Fusion), target fueling (Inertial Confined Fusion), cryogenic distillation, as well as in astroparticle physics for neutrino experiments, and search for rare physics. To improve on this, t

Michael Hartz, Stefan Richter

Every function in the Dirichlet space on the unit disc has an inner/outer factorization. We study which inner functions occur in this way. For Blaschke products, this is the well known question of which subsets of the disc are zero sets for the Dirichlet space. We also consider singular inner factors, and in particular prove an analogue of the Shapiro-Shield

Paola Di Maio

This paper introduces a standardized model card framework specifically designed for digital and web forensics. Building upon established model card methodologies and recent work on abstract models for digital forensic analysis, this paper presents a web based framework that generates model cards specifically designed to represent knowledge in the forensic do

David Kolenatý, Jiří Čapek, Stanislav Haviar, Jiří Rezek

We develop highly corrosion-resistant and conductive Ti-Nb-O coatings for metallic components -- bipolar plates (BPPs) and porous transport layers (PTLs) -- in PEM water electrolyzers. Using reactive high-power impulse magnetron sputtering (HiPIMS), we deposit compact 200 nm bilayer coatings onto SS316L substrates, systematically tailoring their composition.

Joanna Sliwa, Frank Schneider, Philipp Hennig, Jose Miguel Hernandez-Lobato

Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), enable fast specialization of large pre-trained models to different downstream applications. However, this process often leads to catastrophic forgetting of the model's prior domain knowledge. We address this issue with LaLoRA, a weight-space regularization technique that applies a

Sudhanshu Naithani, Pramod Kumar, Ritesh Dubey, Franck Thetiot

A precise detection of palladium (Pd) ions is a critical challenge with significant socio-economic implications across various industrial and chemical sectors. Due to its widespread use and poor biodegradability, Pd2+ accumulates in environmental ecosystems, posing severe risks to both the environment and living organisms. Consequently, there is a strong dem

Cheng Peng, Zhuo Su, Liao Wang, Chen Guo

We present FlexAvatar, a flexible large reconstruction model for high-fidelity 3D head avatars with detailed dynamic deformation from single or sparse images, without requiring camera poses or expression labels. It leverages a transformer-based reconstruction model with structured head query tokens as canonical anchor to aggregate flexible input-number-agnos

Charles-Edouard Bréhier, Adrien Busnot Laurent, Arnaud Debussche, Gilles Vilmart

For a class of ergodic parabolic semilinear stochastic partial differential equations (SPDEs) with gradient structure, we introduce a preconditioning technique and design high-order integrators for the approximation of the invariant distribution. The preconditioning yields improved temporal regularity of the dynamics while preserving the invariant distributi

Younes Naceur, Jie Wang, Victor Magron, Antonio Acín

Semidefinite relaxations of polynomial optimization have become a central tool for addressing the non-convex optimization problems over non-commutative operators that are ubiquitous in quantum information theory and, more in general, quantum physics. Yet, as these global relaxation methods rely on floating-point methods, the bounds issued by the semidefinite

Niklas Sapountzoglou, Aleksandra Zimmermann

This contribution provides numerical experiments for a finite volume scheme for an approximation of the stochastic Allen-Cahn equation with homogeneous Neumann boundary conditions. The approximation is done by a Yosida approximation of the subdifferential operator. The problem is set on a polygonal bounded domain in two or three dimensions. The non-linear ch

Chanh Kieu, Thanh T. N. Nguyen, Duc-Trong Le, Duc Gia-Anh Hoang

A reliable tropical cyclone (TC) climatology is the key to assessing historical and future changes in TC activities. While global TC records have been systematically maintained since the early 1940s, substantial uncertainties remain for the pre-satellite era during which TC observations relied mostly on scattered aircraft reconnaissance and sporadic ship rep

Martin Rosso, Muhammad Asad Jahangir Jaffar, Alessandro Brighente, Mauro Conti

Software Bill of Materials (SBOM) provides new opportunities for automated vulnerability identification in software products. While the industry is adopting SBOM-based Vulnerability Scanning (SVS) to identify vulnerabilities, we increasingly observe inconsistencies and unexpected behavior, that result in false negatives and silent failures. In this work, we

István Miklós

We study the \emph{Bipartite Degree Realization} (BDR) problem: given a graphic degree sequence $D$, decide whether it admits a realization as a bipartite graph. While bipartite realizability for a fixed vertex partition can be decided in polynomial time via the Gale--Ryser theorem, the computational complexity of BDR without a prescribed partition remains u

Nils Wittek, Anton Melnikov, Bert Kaiser, André Zimmermann

Microelectromechanical systems (MEMS) speakers are compact, scalable alternatives to traditional voice coil speakers, promising improved sound quality through precise semiconductor manufacturing. This review provides an overview of the research landscape, including ultrasound pulse-based and thermoacoustic sound generation, classifying MEMS speakers by actua

Leonardo Grimaldi, Michele Lucente, Silvia Pascoli

Leptogenesis is one of the most popular mechanisms to account for the observed baryon asymmetry of the Universe. A generic feature of leptogenesis is a large separation of scales between the epoch of baryon asymmetry production (sphaleron freeze-out at temperature $T \sim 130$ GeV) and the one where it affects the big bang nucleosynthesis processes (BBN at $

Andreas Bluhm, Eric Evert, Igor Klep, Victor Magron

Building on the matrix cube problem, inclusions of free spectrahedra have been used successfully to obtain relaxations of hard spectrahedral inclusion problems. The quality of such a relaxation is quantified by the inclusion constant associated with each free spectrahedron. While optimal values of inclusion constants were known in certain highly symmetric ca

Mohammad Aqib, Hemangi Madhusudan Shah, Dhriti Sundar Patra

In this paper, we revisit the study of almost Ricci-Bourguignon solitons by clarifying their position in the broader context of Einstein-type metrics. Motivated by known rigidity results for compact almost Ricci solitons, we aim to identify conditions under which a compact almost RB-soliton is trivial or exhibits special geometric properties. We compare our

Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li

The crystal structure of high-pressure solid hydrogen remains a fundamental open problem. Although the research frontier has mostly shifted toward ultra-high pressure phases above 400 GPa, we show that even the broken symmetry phase observed around 130~GPa requires revisiting due to its intricate coupling of electronic and nuclear degrees of freedom. Here, w

Jou-Hua Lai, Mykhaylo Shkolnikov, H. Mete Soner

We give a new formulation of the relative arbitrage problem from stochastic portfolio theory that asks for a time horizon beyond which arbitrage relative to the market exists in all ``sufficiently volatile'' markets. In our formulation, ``sufficiently volatile'' is interpreted as a lower bound on an ordered eigenvalue of the instantaneous covariation matrix,

Bernardo Flores, Yang Ni, Yanxun Xu, Peter Müller

We introduce a flexible framework for modeling dependent feature allocations. Our approach addresses limitations in traditional nonparametric methods by directly modeling the logit-probability surface of the feature paintbox, enabling the explicit incorporation of covariates and complex but tractable dependence structures. The core of our model is a Gaussian

S M Ruhul Kabir Howlader, Xiao Chen, Yifei Xie, Lu Liu

Over the last few decades, machine learning (ML) and deep learning (DL) solutions have demonstrated their potential across many applications by leveraging large amounts of high-quality data. However, strict data-sharing regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) have p

Sarah Zampa

We study how the equivariant signature of strongly invertible knots changes when one of the Boyle-Chen equivariant unknotting moves is applied. It follows form our results that the absolute value of the equivariant signature introduced by Alfieri-Boyle gives a lower bound to three times the equivariant unknotting number.

Ibukunoluwa Bukola, Meagan Sundstrom, Justin Gambrell, Colin Green

Various active learning methods have been developed for introductory physics, and these methods are increasingly being adopted by instructors. However, instructors often do not implement these methods exactly as was originally intended by the developers, as they may face issues related to funding and institutional support for active learning and/or have diff

Vanessa Yanza, Sergio A. Dzib, Aina Palau, William J. Henney

We present high-angular-resolution ($\sim0\rlap{.}''1$) VLA Ku-band (12--18 GHz) observations of two explosive molecular outflows (EMOs), DR 21 and G5.89, in a search for runaway stars related to these explosive events. In DR 21, we identified 13 compact radio sources (CRS), 9 located in the DR 21 core and near the CO streamer ejection region. The radio prop

Alatz Alvarez-Ahedo, Mikel Garcia de Andoin, Mikel Sanz

Digital-analog quantum computing with two-level systems is a computational paradigm that combines an analog Hamiltonian with single-qubit gates to achieve universality. We extend this framework to $d$-level systems by conjugating an analog Hamiltonian block with single-qudit gates drawn from the Weyl-Heisenberg basis, which provides a natural set of operatio

Björn Garbrecht, Edward Wang

We perform the first joint analysis of baryogenesis from initial Higgs charges (also called Higgsogenesis) and EDMs in a model with two Higgs doublets, a complex scalar and a Majorana fermion. In our proposed scenario, baryogenesis happens in three steps: (1) The decay of the scalar produces an asymmetry between the two Higgs doublets which is partly transfe

Myungjun Kang, Wonjun Sung, Sonu Verma, Sangmo Cheon

Symmetry and topology are essential principles in topological physics. Recently, the idea of sub-symmetry-protected topology -- where some of the original symmetries are broken while a remaining subset, called sub-symmetries, continues to protect specific boundary states -- has been developed. Here, we extend sub-symmetry-protected topology to higher-order t

Grégoire Beaudoire, Cédric Bentz, Christophe Picouleau

A graph $G = (V, E)$ is called antimagic if there exists a bijective labelling $f : E \rightarrow \{1, 2, \ldots, |E|\}$ such that the vertex-sums of labels over edges incident to a given vertex are all distinct. In this paper, we extend the antimagicness results over graphs with a dominating clique. We also introduce an alternative to the usual definition o

E. Paul, G. J. Chaplain, J. Li, T. A. Starkey

We develop a theoretical framework for time-modulated acoustic metasurfaces comprising a line array of modulated cavities, and show that bound acoustic surface waves can undergo temporal diffraction from bound states localised at an interface into bulk waves. The dispersion relation is derived via an operator formalism that captures the spatio-temporal coupl

Vishesh Tripathi, Uday Allu, Biddwan Ahmed

Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, yet their deployment in enterprise environments reveals a critical limitation: inconsistent adherence to custom instructions. This study presents a comprehensive evaluation of 13 leading LLMs across instruction compliance, response accuracy, and

N. Marquardt, C. Duffy, C. Proust, S. Badoux

We report bulk ultrasound measurements up to 80 T and down to 0.5 K of the field re-entrant superconducting phase of the unconventional superconductor UTe$_2$. Clear bulk signatures of superconductivity are observed in the longitudinal elastic mode $c_{11}$ for fields applied at a tilt angle of $\theta_{b-c} =30^\circ$ from $b$-axis. We confirm an upper crit

Suvrajit Bhattacharjee, Olof Giselsson, Sergey Neshveyev

Given a semisimple compact Lie group $G$ and a nonzero dominant integral weight $\lambda$, the highest weight $G_q$-modules $V_{n\lambda}$ form a subproduct system of finite dimensional Hilbert spaces. Using a conjectural asymptotic behavior of Clebsch-Gordan coefficients we identify the corresponding Cuntz-Pimsner algebras with algebras of quantized functio

Pegah Golchian, Marvin N. Wright

In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model explanations may differ between imputation methods, while ignoring additional imputation uncertainty and its influence on variance and confidence intervals. We therefore compare the

Paul Mangold, Eloïse Berthier, Eric Moulines

We present a novel theoretical analysis of Federated SARSA (FedSARSA) with linear function approximation and local training. We establish convergence guarantees for FedSARSA in the presence of heterogeneity, both in local transitions and rewards, providing the first sample and communication complexity bounds in this setting. At the core of our analysis is a

Jin Lei

I develop a reduced basis emulator for continuum-discretized coupled-channel (CDCC) calculations that achieves speedups of $\approx 10^2$ while maintaining sub-percent accuracy. The emulator is constructed using the proper orthogonal decomposition (POD) method applied to snapshots of CDCC solutions computed at sampled points in the optical potential paramete

David Luce, Adrien Gans, Nicolas Vandewalle, Sébastien Kiesgen de Richter

We experimentally measure paticle acceleration within the bulk during the discharge of a granular silo. We highlight the existence of a deceleration wave emerging at the outlet level near the dead zone and propagates toward the top of the medium. The wave emission frequency is extracted from spatiotemporal diagrams of the Eulerian instantaneous acceleration

Sarvesh Mangla, Joseph J. Mohr, Kristof Rozgonyi, Suman Chatterjee

The MeerKAT Large Area Synoptic Survey (MeerKLASS) collaboration has acquired multiple passes of L-band (856-1712 MHz) scanning observations over a 268 deg$^2$ sky region. This scanning enables efficient, large-area sky surveys by continuously scanning the MeerKAT array back and forth at fixed elevation while recording data at 2 sec intervals, progressively

Shihan Kanungo

In this paper, we study the saturation function $\mathrm{Sat}(n,u)$ for sequences. Saturation for sequences was introduced by Anand, Geneson, Kaustav, and Tsai (2021), who proved that $\mathrm{Sat}(n,u)=O(n)$ for two-letter sequences $u$ and conjectured that this bound holds for all sequences. We present an algorithm that constructs a $u$-saturated sequence

Enrique Fernández-Martínez, Ana Luisa Foguel, Xabier Marcano, Daniel Naredo-Tuero

We study a dynamical realization of the low-scale Inverse Seesaw mechanism in which the approximate $B-L$ symmetry is gauged and spontaneously broken. Anomaly cancellation requires additional chiral fermions, one of which becomes a stable dark matter candidate after symmetry breaking, while another remains massless and contributes to dark radiation. Focusing

Abhinav Verma, Olga Solodovnikova, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen

Entanglement is central to quantum physics, yet detecting and exploiting it in non-Gaussian systems remains a major challenge. In continuous variable platforms, standard inseparability criteria based on Gaussian statistics-such as the Duan-Simon criterion-fail when quantum correlations are encoded in higher moments of the field quadratures. Here we introduce

Xiaoqi Han, Víctor Gutiérrez-Basulto, Ru Li, Xiaoli Li

Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all knowledge of a specific entity while preserving the model's overall capabilities. Existing approaches typically rely on full-model fine-tuning or prompt-based interventions, which ca

Ana Stankovic, Mohamed Khalil Ben-Larbi, Wolfgang H. Müller

This paper proposes a sampling-based motion planner that employs a dual quaternion representation to generate smooth, collision-free six-degree-of-freedom pose trajectories for satellite rendezvous and docking under keep-out zone constraints. The proposed planner integrates the dual quaternion algebra directly into an RRT* framework, thereby enabling natural

Riccardo Di Sipio

We explore Bayesian reasoning as a means to quantify uncertainty in neural networks for question answering. Starting with a multilayer perceptron on the Iris dataset, we show how posterior inference conveys confidence in predictions. We then extend this to language models, applying Bayesian inference first to a frozen head and finally to LoRA-adapted transfo

Carlo A. Trugenberger

We review an approach that uses binary relations as the fundamental constituents of the universe, utilizing them as building blocks for both space and matter. The model is defined by an ultraviolet continuous fixed point of a statistical model on random networks, governed by the combinatorial Ollivier-Ricci curvature, which acts as a network analogue of the

Yudhistira Arief Wibowo

Diffusion models have shown strong potential for solving inverse problems such as single-image super-resolution, where a high-resolution image is recovered from a low-resolution observation using a pretrained unconditional prior. Conditioning methods, including Diffusion Posterior Sampling (DPS) and Manifold Constrained Gradient (MCG), can substantially impr

Dietmar Ferger

We show that the maximizing point and the supremum of the standardized uniform empirical process converge in distribution. Here, the limit variable (Z, Y ) has independent components. Moreover, Z attains the values zero and one with equal probability one half and Y follows the Gumbel-distribution.

Alexandre Personnic, Mihai Bâce

Video-based gaze estimation methods aim to capture the inherently temporal dynamics of human eye gaze from multiple image frames. However, since models must capture both spatial and temporal relationships, performance is limited by the feature representations within a frame but also between multiple frames. We propose the Spatio-Temporal Gaze Network (ST-Gaz

Pietro Colangelo, Fulvia De Fazio, Riccardo Pinto

We study the Dalitz decays of heavy quarkonia, which result from the internal virtual photon conversion into an $\ell^+ \ell^-$ lepton pair. Heavy-quark symmetries allow us to establish systematic relations between transitions of different quarkonium states, and to precisely determine the branching fractions for several charmonium and bottomonium decay modes

Yuriy N. Bakhvalov

This paper presents a deep machine learning architecture, the "polyharmonic cascade" -- a sequence of packages of polyharmonic splines, where each layer is rigorously derived from the theory of random functions and the principles of indifference. This makes it possible to approximate nonlinear functions of arbitrary complexity while preserving global smoothn

Lorenzo Maniscalco, Luciano Mari

We study the existence problem for achronal hypersurfaces $M \hookrightarrow \overline{M}$ in a globally hyperbolic spacetime, whose mean curvature is a prescribed -- possibly singular -- source, and whose boundary is a given smooth spacelike submanifold. Since $M$ is allowed to go null somewhere, the mean curvature prescription is to be understood in the di

James Burridge

Is it possible to develop a `physics of language' which can explain the spatial, temporal and social patterns we see, and which can predict future change like we forecast the weather? Such a theory is likely to involve ideas from statistical physics. A substantial literature already applies these ideas to language. However, we lack a model which can match th

Yifei Cheng, Yujia Zhu, Baiyang Li, Xinhao Deng

Modern HTTPS mechanisms such as Encrypted Client Hello (ECH) and encrypted DNS improve privacy but remain vulnerable to website fingerprinting (WF) attacks, where adversaries infer visited sites from encrypted traffic patterns. Existing WF methods rely on supervised learning with site-specific labeled traces, which limits scalability and fails to handle prev

Christoph Hollweck, Andrea Gorgi, Nicolo Antonelli, Marcus Wagner

The concept of trimming, embedding, or immersing geometries into a computational background mesh has gained considerable attention in recent years, particularly in isogeometric analysis (IGA). In this approach, the physical domain is represented independently from the computational mesh, allowing the latter to be generated more easily compared with body-fitt

S. Agarwal, J. A. Aguilar, N. Alden, S. Ali

The Radio Neutrino Observatory in Greenland (RNO-G) is currently under construction with the aim to detect neutrinos with energies beyond $\sim 10\,\mathrm{PeV}$. A critical part of early detector commissioning is the study of detector characteristics and potential backgrounds, for which cosmic rays play a crucial role. In this article, we report that the nu

Antonios Antoniadis, Denise Graafsma, Ruben Hoeksma, Maria Vlasiou

We study the computational complexity of scheduling jobs on a single speed-scalable processor with the objective of capturing the trade-off between the (weighted) flow time and the energy consumption. This trade-off has been extensively explored in the literature through a number of problem formulations that differ in the specific job characteristics and the

Pengzi Miao, Sehong Park

Given a complete Riemannian metric of nonnegative scalar curvature on $Σ\times (-\infty, 0 ] $, where $Σ$ denotes a $2$-sphere, we exhibit conditions that imply the existence of a closed minimal surface homologous to the boundary.

Yao Feng, Chendong Xiang, Xinyi Mao, Hengkai Tan

Robotic arm manipulation in data-scarce settings is a highly challenging task due to the complex embodiment dynamics and diverse contexts. Recent video-based approaches have shown great promise in capturing and transferring the temporal and physical interactions by pre-training on Internet-scale video data. However, such methods are often not optimized for t

Ruichen Tan, Jiawei Xue, Kota Tsubouchi, Takahiro Yabe

Accurate prediction of the next point of interest (POI) within human mobility trajectories is essential for location-based services, as it enables more timely and personalized recommendations. In particular, with the rise of these approaches, studies have shown that users exhibit different POI choices in their familiar and unfamiliar areas, highlighting the

João Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Temporão

Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transaction

Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng

Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a

S. Catalan-Gomez, M. Ibanez, J. Rico, V. Braza

Gallium nanoparticles (Ga-NPs) exhibit promising plasmonic properties spanning ultraviolet to infrared spectral regions, making them suitable for diverse nanophotonic applications. However, the synthesis of uniform and ordered Ga-NP arrays remains challenging due to size heterogeneity arising from coarsening during physical deposition. Here, we systematicall

Ramaneswaran Selvakumar, Cindy Tseng, Eesung Kim, Vijendra Raj Apsingekar

While end-to-end (E2E) automatic speech recognition (ASR) models excel at general transcription, they struggle to recognize rare or unseen named entities (e.g., contact names, locations), which are critical for downstream applications like virtual assistants. In this paper, we propose a contextual biasing method for attention based encoder decoder (AED) mode

Evangelos Sariyanidi, Gokul Nair, Lisa Yankowitz, Casey J. Zampella

Computational measurement of human behavior from video has recently become feasible due to major advances in AI. These advances now enable granular and precise quantification of facial expression, head movement, body action, and other behavioral modalities and are increasingly used in psychology, psychiatry, neuroscience, and mental health research. However,

Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe

We present three variants of a lightweight, fully connected artificial neural network, suited for interactive estimation of three-dimensional, spatially resolved volumes of scattered radiation fields and a corresponding training pipeline for radiation protection dosimetry in medical radiation fields, such as those found in interventional radiology and cardio

Deon Janse van Rensburg, Rogier Venderbosch, Yuri van der Werf, Jesus del Pozo Mellado

We demonstrate the implementation of $\Lambda$-enhanced gray molasses cooling on the D$_2$ line of $^{85}$Rb atoms in an optical tweezer array. This technique yields lower atomic temperatures of 4.0(2) $\mu$K compared to red-detuned polarization gradient cooling, and consequently extends the $T_2^*$ coherence time of the hyperfine clock qubit by a factor of

S. V. Demidov, D. S. Gorbunov, A. L. Polonski

In recent article [PhysRevD.109.055041 (2024)] it has been argued that production of dark photons -- hypothetical massive vectors -- in nuclear reactors via mixing with the visible photons is considerably enhanced (by a factor of 10) due to Compton scattering of the latter. We revisit the production of dark photons in the reactor environment and find that al

Andreas Kirchner, Federica Capellino, Eduardo Grossi, Stefan Floerchinger

The fluid-dynamical modeling of a nuclear collision at high energy usually starts shortly after the collision. A major source of uncertainty comes from the detailed modeling of the initial state. While the collision itself likely involves far-from-equilibrium dynamics, it is not excluded that a fluid theory of second order can reasonably well describe its so

Zhongwei Zhang, Fuchen Long, Wei Li, Zhaofan Qiu

The In-context generation paradigm recently has demonstrated strong power in instructional image editing with both data efficiency and synthesis quality. Nevertheless, shaping such in-context learning for instruction-based video editing is not trivial. Without specifying editing regions, the results can suffer from the problem of inaccurate editing regions a

Michele Coti Zelati, Lucas Ertzbischoff, David Gerard-Varet

We carry out a comprehensive linear stability analysis of active Brownian particle systems around a constant homogeneous state. These scalar models, being important prototypes for the continuous description of active matter, are Fokker-Planck type equations in position-orientation and are known to exhibit motility-induced phase separation. We fully character

Anton Minnekhanov, Gleb Tikhonowski, Georgy Ermolaev, Konstantin V. Kravtsov

Natural van der Waals (vdW) crystals with hyperbolic dispersion challenge artificial metamaterials but remain confined to the mid-infrared. The emergence of MoOCl2, a quasi-one-dimensional metal with in-plane hyperbolicity, overcomes this spectral limit, shifting the focus to the practical visible range. Here, using angle-resolved polarized Raman spectroscop

Wisnu Uriawan, Muhammad Saifurridwani Ijazi, Nizzami Ramdhan Arraudy, Onixa Shafa Putri Wibowo

This research aims to analyze and compare the effectiveness of Islamic da'wahh on two popular social me dia platforms, namely Instagram and TikTok. The analysis is conducted based on four main aspects: media characteristics, da'wahh communication strategies, audience engagement effec tiveness, and user behavioral responses. The research employs a a mixed-met

Iuliia Zalesskaia, Hassan Asgharzadeh B., Zahra Eslami, Hossein Fathi

Structured light, optical fields engineered in their spatial, polarization, or phase degrees of freedom, has become a key resource across advanced communication, sensing, imaging, and quantum technologies. Optical fibers nowadays play an essential role in this landscape, providing stable and scalable platforms for guiding, and amplifying complex modes such a

Daniel Metternich, Michael Schneider, Giuseppe Mercurio, Torstein Hegstad

Ultrashort laser pulses provide the fastest known way to switch magnetic order. Such excitation commonly creates nanometer-scale domains, even after homogeneous illumination when the position of nucleated domains is not externally defined. However, the physics of domain localization during such ultrafast phase transitions remains unresolved. Here, we use sho

Aimé Matheron, Michal Šmíd, Matt Zepf, Felix Karbstein

Ninety years after their prediction, quantum vacuum nonlinearities in macroscopic electromagnetic fields still await a direct experimental verification in the laboratory. A particularly promising route towards their first measurement is the collision of counter-propagating laser beams in a pump-probe type experiment. Here, the key challenge is to separate th

Zhaolin Cai, Huiyu Duan, Zitong Xu, Fan Li

Human-object interaction (HOI) detection aims to localize human-object pairs and the interactions between them. Existing methods operate under a closed-world assumption, treating the task as a classification problem over a small, predefined verb set, which struggles to generalize to the long-tail of unseen or ambiguous interactions in the wild. While recent

Michel Frising, Daniel Balcells

Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. While this means that personality frameworks would be highly valuable tools to characterize and control LLMs' behavior, current approaches remain either costly (post-training) or brittle (prompt engineering). Probing and steering via linear di

Pavel Mnev, Konstantin Wernli

We give a short review of our construction of a higher-loop perturbative invariant of framed 3-manifolds, generalizing the perturbative Chern-Simons invariant of Witten-Axelrod-Singer, associated to an acyclic flat connection, to an invariant given by the integral of a certain "Chern-Simons volume form" over a smooth closed component of the moduli space of f

Rajarshi Roy, Anirban Majumdar, Ritam Raha, David Parker

Reward specification plays a central role in reinforcement learning (RL), guiding the agent's behavior. To express non-Markovian rewards, formalisms such as reward machines have been introduced to capture dependencies on histories. However, traditional reward machines lack the ability to model precise timing constraints, limiting their use in time-sensitive

Mingyu Su, Jian Guan, Yuxian Gu, Minlie Huang

Post-training methods, especially Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), play an important role in improving large language models' (LLMs) complex reasoning abilities. However, the dominant two-stage pipeline (SFT then RL) suffers from a key inconsistency: SFT enforces rigid imitation that suppresses exploration and induces forgetting,

Yuri Taglieri Sáo, Olivier Roustant, Geraldo de Freitas Maciel

Global sensitivity analysis (GSA) of functional-output models is usually performed by combining statistical techniques, such as basis expansions, metamodeling and sampling based estimation of sensitivity indices. By neglecting truncation error from basis expansion, two main sources of errors propagate to the final sensitivity indices: the metamodeling relate

Barsha Shawa, Md Abu Talhamainuddin Ansary

The article proposes a Caputo fractional conjugate gradient (CFCG) method for unconstrained optimization problems which is applicable to smooth as well as non-smooth problmes. The proposed method uses a non-adaptive version of the Caputo fractional derivative that provides integer-order derivatives information. A descent direction is obtained using the Caput

Luka Milićević, Žarko Ranđelović

In this paper, we prove power-saving bounds for the corelation of the M\"obius function with polynomial phases of degree $k$ in function fields $\mathbb{F}_p[t]$, when $p > k$. The proof relies on a new approximation result for phases of biased multilinear forms and the recently established strong bounds for the problem of finding bounded codimension varieti

Even He

High-dimensional covariance estimation is notoriously sensitive to outliers. While statistically optimal estimators exist for general heavy-tailed distributions, they often rely on computationally expensive techniques like semidefinite programming or iterative M-estimation ($O(d^3)$). In this work, we target the specific regime of \textbf{Sub-Weibull distrib

Menna Elgabry, Ali Hamdi

This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet's Jury Theorem (CJT). Unlike conventional ensembles that often rely on homogeneous architectures, our approach combines architecturally diverse small transformer-based large language models (sLLMs) - BERT, RoBERTa, DistilB

Liandong Hu, Jian Dang, Zaichen Zhang

Massive machine-type communications (mMTC) demand robust solutions to support extensive connectivity efficiently. Unsourced random access (URA) has emerged as a promising approach, delivering high spectral and energy efficiency. Among URA code structures, the random spreading (RS) category is a key enabler, providing strong anti-interference capabilities thr

Adrian Constantin, Zhiwu Lin, Hao Zhu

We classify within the quasi-geostrophic framework all types of traveling waves in zonal bands of the planetary atmosphere at cloud level according to their wave speeds. This classification pertains to waves of all amplitudes, going beyond the small-amplitude perturbative regime. It provides a structurally robust criterion for determining which traveling-wav

Tijn De Wringer, Caroline Dorn, Emily O. Garvin, Stefano Marelli

Characterizing the interior structure of exoplanets is an inverse problem often solved using Bayesian inference, but this approach is hampered by the high computational cost of planetary structure models. To overcome this barrier, we present a robust framework that accelerates inference by replacing the computationally expensive physics-based forward model w

Guanlin Li, Cui-Qun Chen, Haoliang Shi, Zhengtao Liu

The realization of ambient-pressure superconductivity in La$_3$Ni$_2$O$_7$ thin films raises a fundamental question: is the metallic ground state driven by lattice strain or interfacial charge reconstruction? Using fully self-consistent DFT+$U$ calculations on La$_3$Ni$_2$O$_7$/SrLaAlO$_4$ heterostructures, we identify that intrinsic hole doping via interfac

Narjes Ansari, Félix Aviat, Jérôme Hénin, Jean-Philip Piquemal

Relative Binding Free Energy (RBFE) calculations are a cornerstone of rational hit-to-lead and lead optimization in modern drug discovery. However, the high computational cost and limited reliability in tackling large or complex molecular transformations often prevent their routine, high-throughput use. Here we introduce dual-LAO, a novel, highly efficient m

Felipe Hernández, Daniel Ranard, C. Jess Riedel

In chaotic quantum systems, an initially localized quantum state can deviate strongly from the corresponding classical phase-space distribution after the Ehrenfest time $t_{\mathrm{E}} \sim \log(\hbar^{-1})$, even in the limit $\hbar \to 0$. Decoherence by the environment is often invoked to explain the persistence of the quantum-classical correspondence at

Amaury Micheli, Yuto Oshima, Tomo Takahashi

We investigate how interactions affect the quantum state of scalar perturbations during inflation and the quantum correlations they may exhibit. Focusing on the case of scalar perturbations in single-field inflation, we model interactions using a Lindblad equation with a non-unitary contribution quadratic in the scalar perturbations, and of parametrisable am

Di Wu, Feng Yang, Wenhui Zhao, Jinwen Yu

Multi-view 3D object detection is a fundamental task in autonomous driving perception, where achieving a balance between detection accuracy and computational efficiency remains crucial. Sparse query-based 3D detectors efficiently aggregate object-relevant features from multi-view images through a set of learnable queries, offering a concise and end-to-end de

Faisal Suwayyid, Guo-Wei Wei

We introduce a persistent commutative algebra for studying the algebraic and combinatorial evolution of edge ideals of graphs and hypergraphs under filtration. Building on the Persistent Stanley--Reisner Theory (PSRT), we develop the notion of persistent edge ideals and analyze their graded Betti numbers across the filtration of graphs or hypergraphs. To ena

Igor Baskov

Each commutative algebra $A$ gives rise to a representation $\mathcal{L}_A$, which we call the Loday functor of $A$, of the category $\Omega$ of finite sets and surjective maps. In this paper we present two (infinite-dimensional) non-isomorphic algebras over $\mathbb{C}$ with isomorphic Loday functors -- the algebras of continuous functions on the M\"obius s

Song Chen, Kai-Yuan Zhang, Xi Sun, Shu-Zheng Zhou

Recent progress in constructing supramolecular assemblies with hierarchical chirality offers new opportunities to investigate the chirality-induced spin selectivity (CISS) effect and its potential applications. In this work, we systematically examine the CISS effect in such multichiral systems by designing a class of multilayer helical architectures construc

Vinícius Francisco da Silva, Heitor Leite, Fernando Magno Quintão Pereira

L-systems are a mathematical formalism proposed by biologist Aristid Lindenmayer with the aim of simulating organic structures such as trees, snowflakes, flowers, and other branching phenomena. They are implemented as a formal language that defines how patterns can be iteratively rewritten. This paper describes how such a formalism can be used to create arti

K. Duplat, G. Varas, O. Ramos

We analyze regional earthquake energy statistics from the Southern California and Japan seismic catalogs and find scale-invariant energy distributions characterized by an exponent $\tau \simeq 1.67$. To quantify how closely scale-invariant dynamics with different exponent values resemble real earthquakes, we generate synthetic energy distributions over a wid

Hugo Candan, Karim Noui, David Langlois

We study slowly rotating black hole solutions within Degenerate Higher Order Scalar Tensor (DHOST) theories. Starting from a static, spherically symmetric metric solution of a DHOST theory, we employ the Hartle-Thorne ansatz to model a slowly rotating spacetime. We show that the differential equation governing the frame-dragging function $\omega$ (which is s