March 2026 arXiv papers — page 42

Łukasz Iwanek, Marcin Mierzejewski, Adam S. Sajna

The dynamics of interacting particles in orbital magnetic fields are notoriously difficult to study, as this physics is inherently connected to electronic correlations in two-dimensional systems, for which no straightforward theoretical methods are available. Here, we report on the diffusive relaxation dynamics of two-dimensional interacting fermionic system

Anne-Marie Aubert

This set of lecture notes is an expanded version of a mini-course the author gave in March of 2025 for the program ``Representation Theory \& Noncommutative Geometry" at the Institut Henri Poincar\'e, Paris. The goal is to provide a survey of the main properties of the theta correspondence over finite fields of odd characteristic, including its compatibility

Liwei Jiang, Ashwin Pananjady

We study the unconstrained minimization of a smooth and strongly convex population loss function under a stochastic oracle that introduces both additive and multiplicative noise; this is a canonical and widely-studied setting that arises across operations research, signal processing, and machine learning. We begin by showing that standard approaches such as

Keyu Han, Juncheng Qian, Shaomin Chen

As a unique probe for geophysical research, geoneutrinos can reveal the distribution of internal heat sources in the Earth by detecting electron antineutrinos produced by the radioactive decay of $^{238}$U, $^{232}$Th, and $^{40}$K. However, commercial nuclear power plants continuously produce the same type of electron antineutrinos, which constitute a prima

Andrea Cosso

In High Energy Physics, detailed calorimeter simulations and reconstructions are essential for accurate energy measurements and particle identification, but their high granularity makes them computationally expensive. Developing data-driven techniques capable of recovering fine-grained information from coarser readouts, a task known as calorimeter superresol

Magali Jay

We introduce a new dynamical system: the wind-tree tiling billiards. This system studies trajectories of a ray in Euclidean space which has a negative refractive index when encountering rectangular obstacles located at lattice points. We show that for almost every configuration of the system, trajectories with initial vertical direction are trapped in an inf

Yu-Chen Guo, Jin-Ming Wang, Ying-Xin Li

The teapot effect refers to the tendency of a poured liquid to cling to the lip of a container and run down the outside. It is a familiar but physically rich example of flow separation. We present a low-cost experiment for introductory physics laboratories that uses 3D-printed cups, a simple flow regulator, and basic surface treatments to explore this phenom

Yuxiang Gan, Jianyu Dai, Laxmi Sai Viswanadha, Congjie Wei

Controlling polytype selection in polymer-derived silicon carbide (SiC) remains challenging since stacking sequences are determined locally at the nucleation front. Here, we demonstrate an interface-driven strategy to bias SiC polytype evolution by introducing compositionally complex TiVCrMoC3 MXene nanosheets at the preceramic stage. Under spark plasma sint

I. A. Sarmiento-Alvarado, P. Wiederhold, T. Matos

In this work, we obtain exact solutions to the $(n+2)$-dimensional Einstein Field Equations with a non-zero cosmological constant for $n > 1$. These solutions depend on a set $\{ A_a, a=1,2,\ldots , m \}$ of pairwise commuting constant matrices in $\mathfrak{sl} ( n, \mathbb{R} )$ and on a constant matrix $g_0$ in $\mathcal{I} (\{ A_a, a=1,\ldots , m \})$, d

Audace A. V. Dossou-Olory

For a graph $G$ with vertex set $V$, let N($G$) denote the number of nonempty subsets of $V$ that induce a connected graph in $G$. In this paper, we focus on determining N($G$) for $G$ in the family $\mathbb{B}_n$ of $n$-vertex bicyclic graphs. We find in $\mathbb{B}_n$ the structures of those graphs that possess the smallest, the largest, as well as the sec

Tommaso Ronconi, Anna Bonaldi, Marta Spinelli, Ivano Baronchelli

Upcoming radio surveys will probe the sky with unprecedented depth and sky coverage, enabling a broad range of cosmological and astrophysical applications, as well as powerful synergies with experiments at other wavelengths. The preparation and scientific exploitation of these surveys require realistic mock catalogues that capture the complexity of the radio

Giovanni A. Ummarino, Alessio Zaccone

A central challenge in nanoscale superconductivity is to understand and exploit the combined action of quantum confinement and proximity effects in experimentally realistic metallic heterostructures. We theoretically investigate superconducting bilayer heterostructures in which these two effects coexist. Using a generalized Eliashberg framework that incorpor

Ondrej Dyck, Andrew R. Lupini, Albina Borisevich, Miaofang Chi

Four-dimensional scanning transmission electron microscopy (4D-STEM) generates multi-gigabyte datasets, creating a growing mismatch between acquisition rates and practical storage, transfer, and interactive visualization capabilities. We systematically benchmark 13 lossless compression implementations across 5 representative datasets (8~MiB to 8~GiB, 49.5--9

Giulio Pisaneschi, Pierpaolo Serio, Estelle Gerbier, Andrea Dan Ryals

This paper presents an experimental platform for studying intentional-state attribution toward a non-humanoid robot. The system combines a simulated robot, realistic task environments, and large language model-based explanatory layers that can express the same behavior in mentalistic, teleological, or mechanistic terms. By holding behavior constant while var

Christian Schulz, Jakob Ternes, Henning Woydt

In the NP-hard Group Closeness Centrality Maximization problem, the input is a graph $G = (V,E)$ and a positive integer $k$, and the task is to find a set $S \subseteq V$ of size $k$ that minimizes group farness $f(S) = \sum_{v \in V} \min_{s \in S}\text{dist}(v,s)$. The state-of-the-art exact algorithm iteratively solves ILPs of increasing size until the fi

Joseph Marshall

This paper studies how to estimate an individual's taste for forming a connection with another individual in a network. It compares the difficulty of estimation with and without the assumption that utility is transferable between individuals, and with and without the assumption that regressors are symmetric across individuals in the pair. I show that when pa

Denys I. Bondar, Ole Steuernagel

In this methods paper, we show how to tridia\-go\-nalize two families of bosonic multimode systems: optomechanical and Bose-Hubbard hamiltonians. Using tools from number theory, we devise a rendering of these systems in the form of exact $D \times D$ tridiagonal symmetric matrices with real-valued entries. Such matrices can subsequently be exactly diagonaliz

Mingmeng Geng, Yuhang Dong, Thierry Poibeau

Through an analysis of arXiv papers, we report several shifts in word usage that are likely driven by large language models (LLMs) but have not previously received sufficient attention, such as the increased frequency of "beyond" and "via" in titles and the decreased frequency of "the" and "of" in abstracts. Due to the similarities among different LLMs, expe

Koichi Takahashi

This paper introduces Conchordal, a bio-acoustic instrument for generative composition whose sonic agents are governed by artificial life dynamics within a psychoacoustic fitness landscape. The system is built on Direct Cognitive Coupling (DCC), a design principle requiring that generative dynamics operate directly within a landscape derived from psychoacous

Chengshuai Yang

Designing a computational imaging system -- selecting operators, setting parameters, validating consistency -- requires weeks of specialist effort per modality, creating an expertise bottleneck that excludes the broader scientific community from prototyping imaging instruments. We introduce spec.md, a structured specification format, and three autonomous age

Armand de Villeroché, Rem-Sophia Mouradi, Vincent Le Guen, Sibo Cheng

Air flow modeling at a local scale is essential for applications such as pollutant dispersion modeling or wind farm modeling. To circumvent costly Computational Fluid Dynamics (CFD) computations, deep learning surrogate models have recently emerged as promising alternatives. However, in the context of urban air flow, deep learning models struggle to adapt to

José A. Carrillo, Piotr Gwiazda, Jakub Skrzeczkowski

Given two continuity equations with density-dependent velocities, we provide a new formula for the Wasserstein distance between the solutions in terms of the difference of velocities evaluated at the same density. The formula is particularly attractive to deduce quantitative estimates and rates of convergence for singular limits. We illustrate it using sever

Liang Zhang, Yu Fu, Xinyi Jin

Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as assessors of learners' reasoning. However, it remains unclear whether stronger math problem-solving ability is associated with stronger step-level assessment performance. This study examines that relationship using the GSM8K and MATH subsets of PROCES

Joshua Lackman

We show that the hyperkahler geometry of $T^*\mathbb{CP}^{n-1}$ can be described algebraically by the affine scheme of rank-1 projections, and that this description simultaneously yields explicit $SU(n)$-equivariant isometric embeddings \[ T^*\mathbb{CP}^{n-1} \hookrightarrow \mathbb{R}^{(n^2+1)^2}, \] as well as a generalization of the hyperkahler geometry

Mohammed Basheikh, Rujiravee Kongdee, Hood Thabit, Bijan Parsia

This study explored healthcare professionals' perspectives on the management of Type 1 Diabetes Mellitus (T1DM) through a two-part questionnaire. The first part examined how clinicians prioritise and apply current clinical guidelines, including the relative importance assigned to different aspects of T1DM management. The second part investigated clinicians'

Mirzabek Alloqulov, Bobomurat Ahmedov, Chengxun Yuan

In this article, we study the repetitive Penrose process for the Kerr-Taub-NUT black hole (BH). First of all, we briefly review the spacetime of the Kerr-Taub-NUT BH, including horizon and ergosphere structures. The results indicate that the event horizon and ergosphere radii increase under the influence of the gravitomagnetic charge $l$. Subsequently, we fi

Amos Onn, Tzipy Marx, Liming Tao, Tamir Biezuner

Short tandem repeats (STRs) are low-entropy regions in the genome, consisting of a short (1-6 bp) unit that is consecutively repeated multiple times. They are known for high mutational instability, due to so-called stutter-mutations, in which the number of units in the run increases or descreases. In particular, STRs with repeat unit length of 1-2 bp are pro

Karan Rathore, Mohan Mallick

In this work, we investigate the existence of multiple positive solutions for a weakly coupled system of nonlinear elliptic equations governed by Pucci extremal operators. Specifically, we consider the system: \[ \begin{cases} -{M}_{\lambda_1,\Lambda_1}^+(D^2u_1) = \mu f_1(u_1, u_2, \dots, u_n), & \text{in } \Omega, \\ -{M}_{\lambda_2,\Lambda_2}^+(D^2u_2) =

Jan-Niklas Schäfer, Tillmann Carl, Kristin Kühl, Sonja Kiehren-Ehses

The rapid advancement of high-performance computing infrastructure and its extended application produce an increasing amount of waste heat. This heat constitutes an unsustainable loss of energy as well as requires cooling solutions that transcend conventional thermal management. Here, we demonstrate a novel mechanism that converts vertical waste heat supply

Jialiang Tang, Xi Chen, Zhi-Yuan Wei

Counterdiabatic (CD) driving enables efficient quantum state preparation, but it requires implementing highly nonlocal adiabatic gauge potentials (AGP) that are impractical to compute and realize in large many-body systems. We introduce a \textit{weighted nested-commutator} (WNC) ansatz to approximate AGP using local operators. The WNC ansatz generalizes the

Qurat Ul Ain, Mohamed Amine Chatti, Nasim Yazdian Varjani, Farah Kamal

Explanations are central to improving transparency, trust, and user satisfaction in recommender systems (RS), yet it remains unclear how different explanation formats (visual vs. textual) are suited to users with different personal characteristics (PCs). To this end, we report a within-subject user study (n=54) comparing visual and textual explanations and e

Nicolò Cenni, Marina Barbiroli, Vittorio Degli-Esposti, Enrico M. Vitucci

The evolution toward 6G communication systems is expected to rely on integrated three-dimensional network architectures where terrestrial infrastructures coexist with non-terrestrial stations such as satellites, enabling ubiquitous connectivity and service continuity. In this context, accurate channel models for satellite-to-ground propagation in urban envir

A. Taillard, P. Gratier, J. A. Noble, E. Dartois

As the (JWST) pursues its observing journey, several thousands of icy-grain spectra are expected to be measured and analysed. The inventory of ices in particular, via the observations of background sources, is accessible for hundreds of lines of sight (LOSs) per molecular-cloud region, opening the possibility to add strong constraints on the solid phase chem

Jendrik Gördes, Christian Janzen, Arne J. Vereijken, Tingwei Li

We report on the epitaxial growth of antiferromagnetic Mn2Au on a Nb(001) substrate capped with a pseudomorphic layer of gold. We observe a layer-by-layer growth by means of medium-energy electron diffraction and confirm stoichiometry and surface structure by Auger electron spectroscopy and low-energy electron diffraction. Evaporation of 15 ML of ferromagnet

Hojjat Mousavi, Stanisław Stupkiewicz, Aneta Ustrzycka

This study investigates the influence of crystallographic orientation on fracture behavior and the resulting mechanical anisotropy in a Fe55Ni19Cr26 alloy crystal containing radiation-induced defects, using molecular dynamics (MD) simulations. Crack propagation is analyzed in irradiated samples with three selected high-symmetry crystallographic orientations

Yong-Chao Wu, Tero Mäkinen, Mikko Alava, Amin Esfandiarpour

CrCoNi medium-entropy alloys exhibit exceptional mechanical properties arising from pronounced chemical complexity, including short-range order (SRO), and low stacking fault energy, posing challenges for large-scale atomistic simulations. While most models focus on equimolar compositions, deviations from equimolarity provide an effective route to tuning prop

Liping Yi, Zhiming Zhao, Qinghua Hu

Social learning highlights that learning agents improve not in isolation, but through interaction and structured knowledge exchange with others. When introduced into machine learning, this principle gives rise to social machine learning (SML), where multiple agents collaboratively learn by sharing abstracted knowledge. Federated learning (FL) provides a natu

Ünsal Öztürk, Hatef Otroshi Shahreza, Sébastien Marcel

Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems that determine whether two face images are of the same person. Unlike dedicated face recognition systems, MLLMs approach this task through visual prompting and rely on general visual and reasoning abilities. However, the demographic fairness of these models rema

Matías Bruna

Assuming the generalized Lindel\"of hypothesis for Dirichlet $L$-functions, we establish that the least prime $p\equiv a\pmod{q}$ satisfies $p\ll_{\varepsilon} q^{2+\varepsilon}$. This achieves a bound that nearly matches the classical estimate implied by the generalized Riemann hypothesis.

Parkhi Bhardwaj, Shubhrangshu Dasgupta

A structured-light-based approach for detecting magneto-optical rotation is presented, in which polarization rotation is mapped onto a directly observable spatial degree of freedom. A radially polarized Laguerre-Gaussian beam interacts with cold $^{87}\mathrm{Rb}$ atoms in the presence of a longitudinal magnetic field, where magnetically induced circular bir

Nicholas G. Polson, Daniel Zantedeschi

This paper develops an information-theoretic framework for algorithmic complexity under regular identifiable fibering. The central question is: when a decoder is given information about the fiber label in a fibered geometric set, how much can the residual description length be reduced, and when does this reduction fail to bring dimension below the ambient ra

Sugar Singh Meena, David Barral, Ankan Das Roy, Sunita Meena

Encoding continuous-variable quantum information in the optical domain has recently enabled the generation of large entangled states, yet robust implementation remains a challenge. Here, we present a straightforward protocol for generating multipartite entanglement based on spontaneous parametric down-conversion in a circular array of quadratic nonlinear wav

Marek Balcerzak, Michał Popławski

We extend results of Boros and Menzer on the alternative equation $f(x)f(y)=0$ for generalized polynomials $f$, and their theorems on the conditional inequality $f(x)f(y)\ge 0$ for generalized monomials $f$ of even degree. We use similar methods and ideas. We replace the largeness, of the respective Borel plane set $D$, in the measure or in the Baire categor

M. Pivetta, M. Blanco-Rey, S. Reynaud, R. Baltic

We report magnetic bistability in single Dy atoms on NaCl(100) thin films. Individual Dy atoms substituting Na at the surface of the NaCl layer are thermally stable up to at least 300 K, display $4f^{9}$ occupancy, out-of-plane easy magnetization axis, and long spin relaxation time $T_1$ of about 10 s at 2.5 K; thereby they are the first single atom magnet o

Zhenchen Zhu, Ge Hu, Weixiong Tan, Kai Gao

The widespread adoption of CT has notably increased the number of detected lung nodules. However, current deep learning methods for classifying benign and malignant nodules often fail to comprehensively integrate global and local features, and most of them have not been validated through clinical trials. To address this, we developed DeepFAN, a transformer-b

Robin Kaiser, Martin Klötzer, Ecaterina Sava-Huss

We establish criteria under which stochastic networks in a Markovian environment stabilize, thus confirming Conjecture 7.2 from Levine-Greco [GL23]. The networks evolve on finite connected graphs $G=(V,E)$, and their dynamics are encoded by $V \times V$ toppling matrices $M$, whose columns record the expected number of topplings when the environment is in st

Ruadhaí Dervan, Rémi Reboulet

We introduce a theory of uniform K-stability for big line bundles on smooth projective varieties. This extends the existing theory both for varieties with ample line bundles, and for varieties with big anticanonical class. Our main result gives a valuative characterisation of uniform K-stability, through finite collections of divisorial valuations. We furthe

Sayed Sadiqul Islam, Tony J. Puthenpurakal

Let $A$ be a Dedekind domain of characteristic zero such that for each height one prime ideal $\mathfrak{p}$ in $A$, the local ring $A_{\mathfrak{p}}$ has mixed characteristic with finite residue field. Suppose that $R=A[X_1,\ldots,X_n]$ is a standard $\mathbb{N}^n$-graded polynomial ring over $A$, i.e., $\operatorname{deg} A=\underline{0}\in \mathbb{N}^n$ a

Ipsita Mandal

We develop a systematic framework for determining the nature of exceptional points of $n^{\rm th}$ order (EP$_n$s) in non-Hermitian (NH) systems, represented by complex square matrices. By expressing symmetry-preserving perturbations in the Jordan-normal basis of the defective matrix at an EP$_n$, we show that the upper-$k$ Hessenberg structure of the pertur

San Vũ Ngoc

The method initiated by Wentzel, Kramers, and Brillouin to find approximate solutions to the Schr\"odinger equation lies at the origin of the spectacular development of microlocal and semiclassical analysis. When used naively, the approach appears to break down at caustics, but Maslov showed how a simple generalization could overcome this difficulty. In this

Dongxiao Hong, David A. W. Barton, Simon A. Neild

Parametric uncertainty in nonlinear dynamical systems can fundamentally alter bifurcation behaviour, leading to qualitative response changes. Predicting operating margins/envelopes under such uncertainties is critical but challenging: conventional uncertainty quantification (UQ) methods struggle to efficiently propagate uncertainties across bifurcation bound

Christos Charmousis, Pedro G. S. Fernandes, Mokhtar Hassaine

We study Generalized Proca theories inspired by the recent regularised Proca theory of four-dimensional Gauss-Bonnet gravity. By abandoning the rigid constraints typically imposed by specific regularization schemes, we treat the coefficients of the terms in the action as free parameters. This approach uncovers a broader solution space that admits static and

Kewei Zhu, Yanze Xin, Jinwei Hu, Xiaoyuan Cheng

Predicting high-dimensional dynamical systems with irregular time steps presents significant challenges for current data-driven algorithms. These irregularities arise from missing data, sparse observations, or adaptive computational techniques, reducing prediction accuracy. To address these limitations, we propose a novel method: a Physics-Spatiotemporal Mas

Sebastian Merk, Caroline Lasser

We develop structure-preserving time integration schemes for Gaussian wave packet dynamics associated with the magnetic Schr\"odinger equation. The variational Dirac--Frenkel formulation yields a finite-dimensional Hamiltonian system for the wave packet parameters, where the presence of a magnetic vector potential leads to a non-separable structure and a mod

Pradosh Keshav MV, NS Kavya, Kenath Arun

Phenomenological models of interacting dark energy (IDE) often treat the late-time activation history of the dark sector coupling as an independent function. We show that in conformally coupled scalar--tensor theories, this freedom is constrained by the local restoring structure of the symmetry-breaking potential. Within the adiabatic tracking regime, the co

Wei Wang, Yutian Shen

Reflecting intelligent surface (RIS) is a promising technology for 6G mobile communications. However, identifying the niche of RIS within the mobile networks is a challenging task. To mitigate the escalating system complexity of mobile networks, we propose the concept of Intelligent Reflection as a Service (IRaaS), and discuss its system architecture, enabli

Giuseppe Scola

We perform a cluster expansion in the canonical ensemble with periodic boundary conditions, introducing a new choice of polymer activities that differs from the standard ones. This choice leads to an improved bound for the convergence of the cluster expansion, which we compare with the known one. We also recover the irreducible Mayer coefficients for the the

K. H. P. Henadhira Arachchige, B. Perri, A. S. Brun

The properties and spatial distribution of large-scale structures of the solar corona determine the observed solar wind structure at 1 au. Coronal holes are a major source of fast solar wind, an important geo-effective component, and appear as large dark patches in extreme ultraviolet images. Solar observatories provide images of the solar corona at differen

Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali

Repairing faulty quantum circuits is challenging and requires automated solutions. We present QRep, an automated repair approach that iteratively identifies and repairs faults in a circuit. QRep uniformly applies patches across the circuit and assigns each gate a suspiciousness score, reflecting its likelihood of being faulty. It then narrows the search spac

Sergio Domingo-Zubiaga

We show that several families of asymptotically rigid mapping class groups arise as explicit quotients of the fundamental group of a graph of groups, with mapping class groups as vertex and edge stabilizers. Using this description, and building on the work of Labru\`ere and Paris, we compute explicit presentations for asymptotically rigid mapping class group

Chengshuai Yang

Large language models can generate scientific simulation code, but the generated code silently fails on most non-textbook problems. We show that classical mathematical validation -- well-posedness, convergence, and error certification -- can be fully automated by a Judge Agent, reducing the silent-failure rate from 42% to 1.5% across 134 test cases spanning

Simon Bohlen, Julian Holland, Rudolf Weber

The interaction of ultrafast lasers with plasmas has been studied for many years, primarily with respect to fundamental emission mechanisms. Only in recent years has ionizing radiation emerged as a safety concern in ultrafast laser-based material processing, where high pulse energies, repetition rates, and average powers, combined with continuous material su

Adrien Cances, Quentin Mérigot, Luca Nenna

We study a nonlinear multimarginal optimal transport problem arising in risk management, where the objective is to maximize a spectral risk measure of the pushforward of a coupling by a cost function. Although this problem is inherently nonlinear, it is known to have an equivalent linear reformulation as a multimarginal transport problem with an additional m

Yuyang Xiao, Yifei Zhou, Haoran Wang, Wenxuan Ou

The lack of sufficiently diverse data, coupled with limited data efficiency, remains a major bottleneck for generalist robotic models, yet systematic strategies for collecting and curating such data are not fully explored. Task diversity arises from implicit factors that are sparsely distributed across multiple dimensions and are difficult to define explicit

D Yang Eng

We present a sparse convex-relaxation framework for estimating effective local scaling exponents from short boundary-anchored velocity-increment profiles ($N\approx40$). The detector solves an $\ell_1$-regularized regression in a mixed M\"untz--Sz\'asz/Jacobi dictionary and is interpreted throughout as a finite-scale, directional roughness diagnostic rather

Giuseppe Buonaiuto, Antonio Marquez Romero, Brian Coyle, Annie E. Paine

We extend Quantum Computing Quantum Monte Carlo (QCQMC) beyond ground-state energy estimation by systematically constructing the quantum circuits used for state preparation. Replacing the original Variational Quantum Eigensolver (VQE) prescription with task-adapted unitaries, we show that QCQMC can address excited-state spectra via Variational Fast Forwardin

Warren Johnson, Charles Lee

Selecting the appropriate model at inference time -- the routing problem -- requires jointly optimizing output quality, cost, latency, and governance constraints. Existing approaches delegate this decision to LLM-based classifiers or preference-trained routers that are themselves costly and high-latency, reducing a multi-objective optimization to single-dime

Matteo Salis, Gabriele Sartor, Rosa Meo, Stefano Ferraris

Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challenging task. Theory-based models have been the cornerstone of scientific understanding. However, their computational demands, simplifying assumptions, and calibration requirements limit their use. In recent year

Chengfeng Zhao, Junbo Qi, Yulou Liu, Zhiyang Dou

Simulating physically realistic garment deformations is an essential task for virtual immersive experience, which is often achieved by physics simulation methods. However, these methods are typically time-consuming, computationally demanding, and require costly hardware, which is not suitable for real-time applications. Recent learning-based methods tried to

Gabriele Farné, Fabrizio Boncoraglio, Lenka Zdeborová

A key capability of modern neural networks is their capacity to simultaneously learn underlying rules and memorize specific facts or exceptions. Yet, theoretical understanding of this dual capability remains limited. We introduce the Rules-and-Facts (RAF) model, a minimal solvable setting that enables precise characterization of this phenomenon by bridging t

Steven D. Bass, Laura Baudis, Gianfranco Bertone, Oliver Buchmueller

Particle physics and gravitational waves provide complementary probes of the deep structure of the Universe. Gravitational waves from the mergers of neutron stars and black holes are sensitive to the structure of dense quark matter and to different dark matter scenarios. Measurements of stochastic gravitational waves backgrounds can teach us about possible f

Jinyoung Lee, Stefano Tomasin, Dong-Hyun Jung

The number of low Earth orbit (LEO) satellite constellations has grown rapidly in recent years, bringing a major change to global wireless communications. As LEO satellite links take on a growing role in critical services such as emergency communications, navigation, wide-area data collection, and military operations, keeping these links secure has become an

Thomas Weighill, Ling Zhou

We introduce the notion of birth and death cochains as generalized versions of birth and death simplices in persistent cohomology. We show that birth and death cochains (unlike birth and death simplices) are always unique for a given persistent cohomology class. We use birth and death cochains to define birth and death content as generalizations of birth and

Daniele Ravasio, Claudia Sbardi, Marcello Farina, Andrea Ballarino

This paper proposes a physics-informed learning framework for a class of recurrent neural networks tailored to large-scale and networked systems. The approach aims to learn control-oriented models that preserve the structural and stability properties of the plant. The learning algorithm is formulated as a convex optimisation problem, allowing the inclusion o

Sk Miraj Ahmed, Xi Yu, Yunqi Li, Yuewei Lin

Accurate biodiversity identification from large-scale field data is a foundational problem with direct impact on ecology, conservation, and environmental monitoring. In practice, the core task is taxonomic prediction - inferring order, family, genus, or species from imperfect inputs such as specimen images, DNA barcodes, or both. Existing multimodal methods

Martin Mark Zan, Stefan Schwarz

The deployment of reconfigurable intelligent surfaces (RISs) introduces new challenges for resource allocation in multi-cell wireless networks, particularly when user loads are uneven across base stations. In this work, we consider RISs as shared infrastructure that must be dynamically assigned among competing base stations, and we address this problem using

De-Xing Zhu, Li-Qiang Zhu, Xun Chen, De-Fu Hou

Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD critical endpoint (CEP). The training dataset consists of the entropy density, baryon number susceptibility, and baryon density. The metric warp factor $A(z)$ and the gauge kinetic f

Agnieszka Pregowska

Implicit neural representations (INRs) offer continuous coordinate-based encodings for atlas registration, cross-modality resampling, sparse-view completion, and compact sharing of neuroanatomical data. Yet reproducible evaluation is lacking for high-resolution larval zebrafish microscopy, where preserving neuropil boundaries and fine neuronal processes is c

Xintao Hu, Feng-Qi Cui

With the emergence of AI techniques for depression diagnosis, the conflict between high demand and limited supply for depression screening has been significantly alleviated. Among various modal data, audio-based depression diagnosis has received increasing attention from both academia and industry since audio is the most common carrier of emotion transmissio

Liyan Song, Qingchun Li, Yang Cao

This paper studies a fractional attraction-repulsion system with time-space dependent growth source and nonlinear productions: \begin{equation*} \left\{ \begin{aligned}\label{1.1} &u_t = -(-\Delta)^\alpha u - \chi_1 \nabla \cdot (u \nabla v_1) + \chi_2 \nabla \cdot (u \nabla v_2) + a(x,t)u - b(x,t)u^\gamma, &x \in \mathbb{R}^N, \, t > 0, \\ &0 = \Delta v_1 -

Kenneth M. Hughes, Jesse S. Schelfhout, Charu Mishra, Timothy Leese

A high-flux source of strontium atoms is required for cold atom quantum technology applications. We present a re-entrant oven design that avoids the need for any vacuum feed-throughs and has an inherent temperature gradient to guard against clogging of the nozzle. The nozzle is fabricated by micro-machining of fused silica using selective laser etching; this

Zihao Qi, Chen Feng, Fan Zhang, Xiaozhong Xu

In user-generated content (UGC) transcoding, source videos typically suffer various degradations due to prior compression, editing, or suboptimal capture conditions. Consequently, existing video compression paradigms that solely optimize for fidelity relative to the reference become suboptimal, as they force the codec to replicate the inherent artifacts of t

Xuran Hu, Zhitong Xiong, Zhongcheng Hong, Yifang Ban

Current Large Multimodal Models (LMMs) in Earth Observation typically neglect the critical "vertical" dimension, limiting their reasoning capabilities in complex remote sensing geometries and disaster scenarios where physical spatial structures often outweigh planar visual textures. To bridge this gap, we introduce a comprehensive evaluation framework dedica

Ankit Mishra, Kang Hao Cheong

Quantum repeater networks distribute entanglement over lossy links while many users share a limited pool of entangled pairs. Most existing routing schemes either always use a single best path or rely on global optimizations that are hard to run in real time. Here we propose and analyze a simple alternative: a stochastic multipath rule in which each entanglem

Yuqian Fu, Haohuan Huang, Kaiwen Jiang, Jiacai Liu

On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on student rollouts. The standard implementation, however, usually reduces distribution matching to a sampled-token log-ratio, which can make the learning signal fragile on long rollouts whose prefixes drift away from th

Neha K. Nair, Aaron D'Souza

Saccharomyces cerevisiae is increasingly recognised as a key source for single-cell protein (SCP) production, a rising solution to global protein-supply challenges. This study presents a computational framework combining the Yeast9 genome-scale metabolic model (GEM) with machine learning and optimisation to predict and enhance biomass flux for SCP yield. The

Martin Zeman, Vojtěch Trávníček, Antonín Černoch, Jan Soubusta

This paper explores an efficient method for entanglement quantification in two-qubit and qubit-qutrit quantum systems based upon the framework of collective measurements in conjunction with machine learning. We introduce an adaptive measurement procedure in which measurement settings are dynamically adjusted based on prior measurement outcomes aiming to opti

Zelei Zhang, Jianxiong Zhai, Yi Zhang, Jiawei Yan

We propose a many-body mechanism for a strong Josephson diode effect (JDE) in an interacting nanoscale SQUID formed by two parallel quantum dots coupled to superconducting leads. Unlike conventional diode behavior, where nonreciprocity originates from a skewed current-phase relation within a single, continuously evolving ground state, the JDE reported here i

Rohan Nain, Philip M. Dee, Kipton Barros, Steven Johnston

A promising application of machine learning is the creation of low-cost surrogate models to mitigate computational bottlenecks in quantum many-body simulations. Here, we explore whether a neural network (NN) can be trained in the low-data regime, with one to two orders of magnitude fewer training examples than previous works, as an efficient substitute for t

Lalith Kumar Bhaskar, Sung-Gyu Kang, Oliver R. Waszkiewicz, Finn Giuliani

Corrosion originates from atomistic reactions occurring at dynamic solid liquid interfaces however, direct experimental observation of these reactions has remained elusive due to the inability to preserve transient interfacial states during characterization. To refine corrosion models, advanced techniques capable of analyzing corrosion interfaces at the atom

Nikolo Rohrmoser, Ghazal Ghazaei, Michael Sommersperger, Nassir Navab

Purpose: The integration of multimodal imaging into operating rooms paves the way for comprehensive surgical scene understanding. In ophthalmic surgery, by now, two complementary imaging modalities are available: operating microscope (OPMI) imaging and real-time intraoperative optical coherence tomography (iOCT). This first work toward temporal OPMI and iOCT

Jiayan Wu, Ting Zhang, Ruze Zhou

In this paper, we establish local decay estimates for the bi-Laplacian Schr\"{o}dinger equation with time-dependent (in particular, quasi-periodic) potentials in spatial dimension $n\ge14$. Moreover, under stronger spectral regularity hypotheses, the same result can be extended to dimension $n\ge9$. Our approach, based on asymptotic completeness and the exis

Nicola Bortolotti, Catalina Curceanu, Antonino Marciano, Kristian Piscicchia

Quantum gravity may modify the fundamental symmetries that govern identical particles. In particular, noncommutative spacetime frameworks predict deformations of Bose and Fermi statistics. Here we develop a relativistic quantum field theory based on the most general oscillator algebra compatible with $\theta$-deformed Poincar\'e symmetry. This construction g

Mistral-AI, :, Alexander H. Liu, Alexis Tacnet

We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens. These tokens are encoded and decoded with Voxtral Codec, a s

Wojciech Aleksander Wołoszyn

We classify the propositional modal validities arising from the category of sets under its natural classes of morphisms. The resulting validities depend on the morphism class, the size of the world, and the permitted substitution instances. Our main technical tool is a modality-and-quantifier elimination theorem for the first-order modal language of equality

Jinyoung Lee, Hyeonsik Yeom

This paper investigates an uplink multi-user covert communication system with spatially distributed users. Unlike prior works that approximate channel statistics using averaged parameters and homogeneous assumptions, this study explicitly models each user's geometric position and corresponding user-to-Willie and user-to-Bob channel variances. This approach e

Ziqi Zhang, Chandan Mondal, Siqi Xu, Xingbo Zhao

The twist-3 transverse spin--dependent nucleon structure function $g_2$ arises in high-energy processes involving a transversely polarized nucleon. Its connection to quark--gluon correlations allows for an interpretation in terms of the average transverse color Lorentz force acting on unpolarized quarks inside a transversely polarized nucleon. In this work,

John A. D. Appleby, Subham Pal

This paper gives necessary and sufficient conditions for the convergence of the solution of a weakly damped second order linear differential equation that is subjected to outside forcing, for which solutions of the unforced equation are asymptotically stable. Conditions are also given which characterise when the solution and its derivative tend to zero. Fina

Alexander Tocher, Anastasia Fialkov, Simon May, Ralf S. Klessen

Fuzzy Dark Matter (FDM), particularly in the $10^{-22}$ eV mass regime is frequently used to characterize wave-like interference effects. It exhibits macroscopic wave properties, which drive distinct baryonic dynamics within collapsed haloes. Using the hydrodynamical code AREPO with the AxiREPO module and primordial chemistry, we simulate the assembly of hal

John A. D. Appleby, Subham Pal

This paper develops a characterisation of when solutions of forced second order linear differential equations converge to the zero solution of the asymptotically stable and unforced second order equation, or when the solution is bounded, but not convergent, or is unbounded. We see thereby that forcing terms can exhibit unbounded and high--frequency oscillati