November 2025 arXiv papers — page 74

Tianjun Li, Qi Sun, Rui Sun, Lina Wu

Generalizing three-family chiral fermion conditions to $I_{ac}=-(3+h)$ and $I_{ac'}=h$, with positive integer $h$, we extend the landscape of three-family ${\cal N}=1$ supersymmetric Pati-Salam models in a broader region. Differing from the former investigation with $I_{ac}=-3$ and $I_{ac'}=0$, we do not restrict that the $a$ stack of D6-branes must be paral

Akash Deep, Svetlozar T. Rachev, Frank J. Fabozzi

We develop an econometric framework integrating heavy-tailed Student's $t$ distributions with behavioral probability weighting while preserving infinite divisibility. Using 432{,}752 observations across 86 assets (2004--2024), we demonstrate Student's $t$ specifications outperform Gaussian models in 88.4\% of cases. Bounded probability-weighting transformati

Valery Alexeev

We construct a sequence of complete moduli spaces $$E_0 \subset E_1 \subset E_2 \subset \dots E_n \subset\dots,$$ each of which is isomorphic to a weighted projective space. These spaces parameterize certain $n$-dimensional Calabi-Yau varieties associated with the Sylvester sequence $2,3,7,43,\dots$. They generalize the moduli space of elliptic curves $\over

Kevin Zwart

In this paper, we give an expository presentation of the paper of Olivier Mathieu. The paper of Mathieu proves that a Lie group-theoretic conjecture implies the Jacobian Conjecture. To give Mathieu's proof, we first review the required literature on representation theory in an expository way. We continue to prove some results on the irreducible subrepresenta

Tirthankar Sengupta, Bishakh Chandra Ghosh, Sandip Chakraborty, Shamik Sural

Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross-blockchain transactions. When disruptions occur due

Maureen Krumtünger, Alissa Wilms, Paul K. Faehrmann, Jens Eisert

Quantum chemistry and optimization are two of the most prominent applications of quantum computers. Variational quantum algorithms have been proposed for solving problems in these domains. However, the design of the quantum circuit ansatz remains a challenge. Of particular interest is developing a method to generate circuits for any given instance of a probl

Asmita S. Thool, Sourodeep Roy, Prahalad Kanti Barman, Kartick Biswas

In this study, we design a reservoir computing (RC) network by exploiting short- and long-term memory dynamics in Au/Ti/MoS$_2$/Au memristive devices. The temporal dynamics is engineered by controlling the thickness of the Chemical Vapor Deposited (CVD) MoS$_2$ films. Devices with a monolayer (1L)-MoS$_2$ film exhibit volatile (short-term memory) switching d

Junpeng Jing, Weixun Luo, Ye Mao, Krystian Mikolajczyk

Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community has regarded efficient models as incapable of zero-shot ability due to their limited capacity. In this paper, we introduce Lite Any Stereo, a stereo depth estimation framework that achieves strong zero-shot genera

Manuel A. Buen-Abad, Zackaria Chacko, Ina Flood, Can Kilic

Models in which a subcomponent of dark matter interacts with dark radiation have been proposed as a solution to the Hubble tension. In this framework, the interacting subcomponent of dark matter is in thermal equilibrium with the dark radiation in the early universe, but decouples from it around the time of matter-radiation equality. We study this general cl

Ariel Arza, Yuanlin Gong, Jun Guo, Xiaofei Huang

Ultralight dark matter candidates, such as axions and dark photons, are leading dark matter candidates. They may couple feebly to photons, sourcing oscillating electromagnetic signals in the Earth's conducting cavity formed between the ground and the ionosphere, providing detectable magnetic field signatures at wavelengths above the Earth's size. We carry ou

Alexander V. Khudyakov

We extend the class of abelian groups for which a conjecture of Asai and Yoshida on the number of crossed homomorphisms holds. We also prove a general result which connects certain problems concerning divisibility in groups to the Asai-Yoshida conjecture. One of the consequences is that for finite groups F and G the number |Hom(F,G)| is divisible by gcd(|G|,

Perrine Chassat, Van Tuan Nguyen, Lucas Ducrot, Emilie Lanoy

Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data have been explored, a promising alternative is the generation of synthetic control arms using generative AI. A central chal

Han Su, Zhongyan Li, Wanquan Liu

Universal approximation serves as the foundation of neural network learning algorithms. However, some networks establish their universal approximation property by demonstrating that the iterative errors converge in probability measure rather than the more rigorous norm convergence, which makes the universal approximation property of randomized learning netwo

Yuanbo Guo, Jun Xia, Yiyu Shi

As deep learning (DL) techniques become integral to various applications, ensuring model fairness while maintaining high performance has become increasingly critical, particularly in sensitive fields such as medical diagnosis. Although a variety of bias-mitigation methods have been proposed, many rely on computationally expensive debiasing strategies or suff

Guanchen Wu, Yuzhang Xie, Huanwei Wu, Zhe He

Integrating novel medical concepts and relationships into existing ontologies can significantly enhance their coverage and utility for both biomedical research and clinical applications. Clinical notes, as unstructured documents rich with detailed patient observations, offer valuable context-specific insights and represent a promising yet underutilized sourc

Xiaoyue Chen, Yuling Shi, Kaiyuan Li, Huandong Wang

Visual Auto-Regressive (VAR) models significantly reduce inference steps through the "next-scale" prediction paradigm. However, progressive multi-scale generation incurs substantial memory overhead due to cumulative KV caching, limiting practical deployment. We observe a scale-depth asymmetric dependency in VAR: early scales exhibit extreme sensitivity to ne

Loïc Fernandez, Jean-Loïc Kneur

We discuss recent improvements of the cold and dense QCD pressure owing to an all-order resummation of the soft modes, or to the so-called renormalization group optimized perturbation theory (RGOPT). Both approaches show a significant improvement of the residual renormalization scale dependence with respect to the state-of-the-art results for the perturbativ

Zachary Ellis, Jared Joselowitz, Yash Deo, Yajie He

As Automatic Speech Recognition (ASR) is increasingly deployed in clinical dialogue, standard evaluations still rely heavily on Word Error Rate (WER). This paper challenges that standard, investigating whether WER or other common metrics correlate with the clinical impact of transcription errors. We establish a gold-standard benchmark by having expert clinic

Jiaheng Zhang, Daqiang Zhang

The integration of Large Language Models (LLMs) into explainable recommendation systems often leads to a performance-efficiency trade-off in end-to-end architectures, where joint optimization of ranking and explanation can result in suboptimal compromises. To resolve this, we propose Prism, a novel decoupled framework that rigorously separates the recommenda

Pierrick Bournez, Luca Savant Aira, Thibaud Ehret, Gabriele Facciolo

Recently, 3D Gaussian Splatting has been introduced as a compelling alternative to NeRF for Earth observation, offering competitive reconstruction quality with significantly reduced training times. In this work, we extend the Earth Observation Gaussian Splatting (EOGS) framework to propose EOGS++, a novel method tailored for satellite imagery that directly o

Jaime Álvarez Urueña, David Camacho, Javier Huertas Tato

The rapid advancement of generative artificial intelligence has enabled the creation of synthetic images that are increasingly indistinguishable from authentic content, posing significant challenges for digital media integrity. This problem is compounded by the accelerated release cycle of novel generative models, which renders traditional detection approach

Éloïse Benito-Rodriguez, Einar Urdshals, Jasmina Nasufi, Nicky Pochinkov

Understanding Large Language Models (LLMs) is key to ensure their safe and beneficial deployment. This task is complicated by the difficulty of interpretability of LLM structures, and the inability to have all their outputs human-evaluated. In this paper, we present the first step towards a predictive framework, where the genre of a text used to prompt an LL

L. Caussou, H. Moutaabbid, M. Bernard, F. Margaillan

We study interlayer excitons in MoSe2/WSe2 heterobilayers, by combining lateral force microscopy and micro-photoluminescence spectroscopy. This allows us to correlate the spatial profile of the moir\'e superlattice with the distribution of optically active states accessible to interlayer excitons. In heterostructures where a few degrees twist angle is impose

Mathieu Mourichoux

We identify the local scaling limit of the Uniform Infinite Planar Quadrangulation (UIPQ) and of critical Boltzmann quadrangulations, when one simultaneously rescales the distances and reroot them far away from the root of a distinguished geodesic. The limiting space is the bigeodesic Brownian plane, which appears as the local limit of the Brownian sphere ar

Yuqi Li, Kuiye Ding, Chuanguang Yang, Hao Wang

Time-series forecasting is fundamental across many domains, yet training accurate models often requires large-scale datasets and substantial computational resources. Dataset distillation offers a promising alternative by synthesizing compact datasets that preserve the learning behavior of full data. However, extending dataset distillation to time-series fore

Alexander Armbruster, Lars Rohwedder, Andreas Wiese

We study the general scheduling problem (GSP) which generalizes and unifies several well-studied preemptive single-machine scheduling problems, such as weighted flow time, weighted sum of completion time, and minimizing the total weight of tardy jobs. We are given a set of jobs with their processing times and release times and seek to compute a (possibly pre

Haytham Ziani

This paper presents an applied analysis of local and global methods, with a focus on the Horn-Schunck algorithm for optical flow computation. We explore the theoretical and practical aspects of local approaches, such as the Lucas-Kanade method, and global techniques such as Horn-Schunck. Additionally, we implement a multiresolution version of the Horn-Schunc

L. Reina, L. Silvestrini

The Standard Model of particle physics provides a rigorous framework within which processes mediated by electroweak interactions can be calculated with great accuracy. By comparing with high-precision experimental measurements of the same processes, deviations from Standard Model predictions can be identified as indirect signals of new physics. In particular

Nithin Salevemula, Shreyas Pai

We study the problem of computing a Maximal Independent Set (MIS) in distributed networks where each node is a rational agent whose payoff depends on whether it joins the MIS. Classical distributed algorithms assume that nodes follow the prescribed protocol, but this assumption fails when nodes are strategic and may deviate if doing so increases their expect

Fan Yang, Shigeyuki Odashima, Shoichi Masui, Ikuo Kusajima

We present a robust multi-camera gymnast tracking, which has been applied at international gymnastics championships for gymnastics judging. Despite considerable progress in multi-camera tracking algorithms, tracking gymnasts presents unique challenges: (i) due to space restrictions, only a limited number of cameras can be installed in the gymnastics stadium;

Andrea Bisterzo, Shigeru Sakaguchi

We investigate the overdetermined torsion problem $\begin{cases} -\Delta u = 1 & \text{in}\ \Omega\\ u=0 & \text{on}\ \partial \Omega\\ \frac{\partial u}{\partial \nu}=\text{const.} & \text{on}\ \partial \Omega, \end{cases}$ where $\Omega$ is a smooth Riemannian domain. Domains admitting a solution to this problem are called \textit{Serrin domains}, after th

Nicholas C. Henderson, Nicholas Hartman

Ordering the expected outcomes across a collection of clusters after performing a covariate adjustment commonly arises in many applied settings, such as healthcare provider evaluation. Regression parameters in such covariate adjustment models are frequently estimated by maximum likelihood or through other criteria that do not directly evaluate the quality of

Xuemei Gu, Carlos Ruiz-Gonzalez, Mario Krenn

Two-mode squeezing is central to entangled-photon generation and nonlinear interferometry, yet standard perturbative low-gain treatments and Gaussian formalisms can obscure the interference of photon-number amplitudes, especially in nonlinear interferometers and at high gain. Here we derive a closed-form Fock-basis expression for the action of the two-mode s

Özay Ezerceli, Mahmoud El Hussieni, Selva Taş, Reyhan Bayraktar

Neural information retrieval systems excel in high-resource languages but remain underexplored for morphologically rich, lower-resource languages such as Turkish. Dense bi-encoders currently dominate Turkish IR, yet late-interaction models -- which retain token-level representations for fine-grained matching -- have not been systematically evaluated. We intr

Deepak Bolleddu

Conflict resolution and consensus building represent critical challenges in multi-agent systems, negotiations, and collaborative decision-making processes. This paper introduces Dialogue Diplomats, a novel end-to-end multi-agent reinforcement learning (MARL) framework designed for automated conflict resolution and consensus building in complex, dynamic envir

Agung Budiyono, Michael Moody, Hadyan L. Prihadi, Rafika Rahmawati

Quantum asymmetry and coherence are genuinely quantum resources that are essential to realize quantum advantage in information technologies. However, all quantum processes are fundamentally constrained by quantum speed limits, which raises the question on the corresponding bounds on the rate of consumption of asymmetry and coherence. In the present work, we

Wen-Qi Duan, Ming-Xuan Zhao, Jia-Qi Wang, Xin-Biao Xu

On-chip Brillouin laser gyroscopes harnessing opto-acoustic interaction are an emerging approach to detect rotation, due to their small footprint, excellent stability and low power consumption. However, previous implementations rely solely on optical readout, leaving the simultaneously generated saser (sound amplification by stimulated emission) undetected d

Rahul Kumar, Vipul Baghel, Sudhanshu Singh, Bikash Kumar Badatya

Accurate analysis of combat sports using computer vision has gained traction in recent years, yet the development of robust datasets remains a major bottleneck due to the dynamic, unstructured nature of actions and variations in recording environments. In this work, we present a comprehensive, well-annotated video dataset tailored for punch detection and cla

Ming-Lun Lee, Fu-Shiang Yang, Cheng-Kuan Lin, Yan-Ann Chen

Federated learning (FL) enables clients to collaboratively train a shared model in a distributed manner, setting it apart from traditional deep learning paradigms. However, most existing FL research assumes consistent client participation, overlooking the practical scenario of dynamic participation (DPFL), where clients may intermittently join or leave durin

Athanasios Georgakopoulos, Marco Magliaro, Luciano Mari, Andreas Savas-Halilaj

It was conjectured by Eells that the only harmonic maps $f : S^3 \to S^2$ are Hopf fibrations composed with conformal maps of $S^2$. We support this conjecture by proving its validity under suitable conditions on the Hessian and the singular values of $f$. Among the results, we obtain a pinching theorem in the spirit of that of Simons, Lawson and Chern, do C

Fan Yang, Sosuke Yamao, Ikuo Kusajima, Atsunori Moteki

Using ceiling-mounted cameras (CMCs) for indoor visual capturing opens up a wide range of applications. However, registering CMCs to the target scene layout presents a challenging task. While manual registration with specialized tools is inefficient and costly, automatic registration with visual localization may yield poor results when visual ambiguity exist

Yuxiang Wan, Ryan Devera, Wenjie Zhang, Ju Sun

Foundation flow-matching (FM) models promise universal priors for solving inverse problems (IPs); yet today, they trail behind domain-specific and even untrained priors. \emph{How can we unlock their potential?} We introduce FMPlug, a plug-in framework that redefines how foundation FMs are used in IPs. FMPlug combines an instance-guided, time-dependent warm-

N. J. Miller, P. F. L. Maxted, A. Hahlin, D. Graczyk

The eclipsing binary LL Aqr is a bright V = 9.32, detached system consisting of two solar-type stars in an eccentric orbit (P = 20.2 d). The secondary component, LL Aqr B, was previously found to have physical and atmospheric parameters very similar to the Sun. Using high-precision photometry from TESS along with previously published orbital solutions, we ob

Xiaoshuai Hao, Lei Zhou, Zhijian Huang, Zhiwen Hou

We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embodied sets new records across 17 embodied AI benchmarks in Task Planning, Affordance Prediction and Spatial Understanding, while also excelling in 12 autonomous driving

Holger I. Meinhardt

Recently, Maggiorano et al. (2025) claimed that they have developed a strongly polynomial-time combinatorial algorithm for the nucleolus in convex games that is based on the reduced game approach and submodular function minimization method. Thereby, avoiding the ellipsoid method with its negative side effects in numerical computation completely. However, we

Gabriele Cora, Gabriele Fioravanti, Francesco Pagliarin, Stefano Vita

We study regularity properties for solutions to elliptic equations that are degenerate or singular along orthogonal hyperplanes. The degenerate ellipticity is carried out by a weight term which is the monomial product of different powers of the distance functions to each hyperplane; that is, given the space dimension $d\geq2$, the number of orthogonally cros

Vadim Alekseev, Stefan Drigalla

We prove that every sofic approximation of a property (T) group is approximately isomorphic to one having geometric property (T), and more generally, a box space of graphs which has boundary geometric property (T) is approximately isomorphic to one having geometric property (T). We also prove that a sequence of bounded degree graphs is approximately isomorph

Tran T. A. Nghia, Nghia V. Vo, Khoa V. H. Vu

We propose several new nonsmooth Newton methods for solving convex composite optimization problems with polyhedral regularizers, while avoiding the computation of complicated second-order information on these functions. Under the tilt-stability condition at the optimal solution, these methods achieve the quadratic convergence rates expected of Newton schemes

Subir Majumder, Paul Ben Ishai, Gilad Orr

Advancing the functional performance of bismuth ferrite (BiFeO$_3$) requires precise control over phase stability and microstructure, challenges often complicated by secondary phase formation within the Bi$_2$O$_3$-Fe$_2$O$_3$ system. In this work, we employ a phase-diagram-guided synthesis strategy to clarify the processing-structure-property relationships

Nicholas Pellegrino, David Szczecina, Paul Fieguth

Incorrectly labelled training data are frustratingly ubiquitous in both benchmark and specially curated datasets. Such mislabelling clearly adversely affects the performance and generalizability of models trained through supervised learning on the associated datasets. Frameworks for detecting label errors typically require well-trained / well-generalized mod

Yuenong Ling, Imran Hayat, Konrad Goc, Adrian Lozano-Duran

We present a general-purpose wall model for large-eddy simulation. The model builds on the building-block flow principle, leveraging essential physics from simple flows to train a generalizable model applicable across complex geometries and flow conditions. The model addresses key limitations of traditional equilibrium wall models (EQWM) and improves upon sh

Andrea Pelissetto, Davide Rossini, Ettore Vicari

We study the out-of-equilibrium spinodal-like dynamics of three-dimensional $q$-state Potts systems driven across their thermal first-order transition in the thermodynamic limit, by a relaxational (heat-bath) dynamics. During the evolution, the inverse temperature $\beta$ increases linearly with time, as $\delta\beta(t)\equiv \beta(t)- \beta_{\rm fo} \sim t/

Aleksandra Ćalović, Sergei Nayakshin, Sarah Casewell, Núria Miret-Roig

Over the past 25 years, observations have uncovered a large population of free-floating planets (FFPs), whose origins remain debated. Massive FFPs (several Jupiter masses or more) may form via gravitational collapse of molecular clouds, similar to stars. Lower-mass FFPs likely originate in planetary systems and are later ejected through dynamical interaction

Luis E. Rodríguez, Andreas Reisenegger, Denis González-Caniulef, Cristóbal Petrovich

Passively cooling neutron stars (NSs) should reach undetectably low surface temperatures $T_s<10^4$ K in less than $10^7$ yr. However, HST observations have revealed likely thermal UV emission from the Gyr-old millisecond pulsars PSR~J0437$-$4715 and PSR~J2124$-$3358, and from the $\sim10^{7-8}$ yr-old classical pulsars PSR~B0950$+$08 and PSR~J0108$-$1431, i

Juan Cortina, Alejo Cifuentes-Santos, Tarek Hassan, Fernando Frias

In recent years, imaging atmospheric Cherenkov telescopes (IACTs) have emerged as promising platforms for optical interferometry through the use of intensity interferometry. IACTs combine large segmented mirrors, photodetectors with nanosecond-scale time response capable of detecting signals from just a few photo-electrons, and array configurations with base

Luca Naponiello

Transit Timing Variations (TTVs) are a powerful tool for detecting unseen companions in systems with known transiting exoplanets and for characterizing their masses and orbital properties. Large-scale and homogeneous TTV analyses are a valuable method to complement the demographics of planetary systems and understand the role of dynamical interactions. We pr

Om Dev Singh, Anubha Jindal

The concept of a quasi-metric space arises by relaxing the requirement of the symmetry axiom in the definition of a metric. This small variation alters several structural properties possessed by a standard metric space. This article aims to investigate the notion of UC quasi-metric spaces in a systematic manner. A quasi-metric space (X, d) is called a UC spa

Christian Corda, Carlo Cafaro, Newshaw Bahreyni

A maximun acceleration analysis by Pati dating back to 1992 is here improved by replacing the traditional Heisenberg Uncertainty Principle (HUP) with the Generalized Uncertainty Principle (GUP), which predicts the existence of a minimum length in Nature. This new approach allows one to find a numerical value for the maximum acceleration existing in Nature fo

Carlos Boned Riera, David Romero Sanchez, Oriol Ramos Terrades

In recent years, increasingly large models have achieved outstanding performance across CV tasks. However, these models demand substantial computational resources and storage, and their growing complexity limits our understanding of how they make decisions. Most of these architectures rely on the attention mechanism within Transformer-based designs. Building

Diego Fonseca, Mauricio Junca

We propose a flexible scenario-based regularized Sample Average Approximation (SBR-SAA) framework for stochastic optimization. This work is motivated by challenges in standard Wasserstein Distributionally Robust Optimization (WDRO), where out-of-sample performance, particularly tail risk, is sensitive to the choice of the p-norm, and formulations can be comp

Zahra Basiri, Alessandro Tomasino, Gabriel Jülg, Andrea Lanfranchi

Tunable lasers are essential for optical communication, spectroscopy, and precision sensing, where flexible and fast control of the laser wavelength is needed. However, conventional tunable laser systems often rely on mechanical actuation, which limits their tuning speed, stability, and repeatability. Alternative tuning methods, such as adjusting the tempera

Rui Wang, Yuexi Du, John Lewin, R. Todd Constable

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in breast cancer screening, tumor assessment, and treatment planning and monitoring. The dynamic changes in contrast in different tissues help to highlight the tumor in post-contrast images. However, varying acquisition protocols and individual factors result in large vari

Aleix Bassolas, Piero Birello, Julian Vicens

Energy production and management face significant political, economic, and environmental challenges, yet the rise in information consumption through social media undermines the availability of reliable knowledge to the general public. This study examines the ideas discussed in the energy transition content on YouTube, assesses the most effective methods of c

Rintaro Masaoka

Quadratic band touching in fermionic systems defines a universality class distinct from that of linear Dirac points, yet its characterization as a quantum critical point remains incomplete. In this work, I show that a $(d+1)$-dimensional free-fermion model with quadratic band touching exhibits spatial conformal invariance, and that its equal-time ground-stat

Ullas Bhat, Chrysa Avdellidou, Marco Delbo, Thomas Dyer

Dark asteroids with featureless neutral to red spectra are of particular interest due to their ability to potentially harbour primitive, hydrated, and possibly organic-rich material. These asteroids belong to the spectroscopic C-complex, to the X-types with low geometric visible albedo values as well as to the T- and D-type end members of the Bus-DeMeo spect

Zongcai Tan, Lan Wei, Dandan Zhang

Precise pose estimation of optical microrobots is essential for enabling high-precision object tracking and autonomous biological studies. However, current methods rely heavily on large, high-quality microscope image datasets, which are difficult and costly to acquire due to the complexity of microrobot fabrication and the labour-intensive labelling. Digital

Anantha Kedar Sarma Inampudi, Anjali A R, Pranabendu Ganguly, Syamsundar De

Optical phased arrays (OPAs) are a promising technology for realizing fast and on-chip non-mechanical beam steering. In this work, we propose and analyze the performance of a non-uniformly spaced antenna arrangement based on the Fibonacci Spiral. A unique prime-number-based parameterization for antenna positioning and a tunable positional-control parameter (

Anakin Dey, Zeyu Guo

One important question in algebraic complexity is understanding the complexity of polynomial ideals (Grochow, Bulletin of EATCS 131, 2020). Andrews and Forbes (STOC 2022) studied the determinantal ideals $I^{\det}_{n,m,r}$ generated by the $r\times r$ minors of $n\times m$ matrices. Over fields of characteristic zero or of sufficiently large characteristic,

Alex J. Oh, Colby J. Stoddard, Craig Queenan, Seongshik Oh

Thermoelectric materials can convert thermal energy into electricity, making them promising candidates for harvesting waste heat, an increasingly important challenge in the energy-intensive modern world. The search for improved thermoelectric materials is therefore an active area of research in materials physics. Despite their fundamental and practical signi

Giulio Gualandi, Fabio Saretto, Daniele Pedroli, Giacomo Corrielli

Femtosecond Laser Micromachining (FLM) is a powerful technology for the fabrication of photonic devices. In this context, the integration of resonant elements within the platform represents a key advancement, enhancing both its versatility and its compatibility with a wide range of optical and fluidic components specifically enabled by this technique. Here,

Mario P. Maletzki

The purpose of this note is to show in an accessible and self-contained way the existence of an isometric algebra embedding from $H^\infty(\D)$ into $L^\infty(\T)$, without appealing to Fatou's classical theorem on non-tangential limits of analytic functions, and relying only on results from complex and functional analysis that are typically covered in a sta

Haruka Kogure

We study provability predicates $\mathrm{Pr}_T(x)$ satisfying the following condition $\mathbf{E}$ from a modal logical perspective: $\mathbf{E}:$ if $ T \vdash \varphi \leftrightarrow \psi$, then $T \vdash \mathrm{Pr}_T(\ulcorner \varphi \urcorner) \leftrightarrow \mathrm{Pr}_T(\ulcorner \psi \urcorner)$. For this purpose, we develop a new method of embeddi

Matias P. Gonzalez, Roberto A. Lineros

We generalize thermal WIMP (Weakly Interacting Massive Particle) freeze-out within Tsallis nonextensive statistics. Using Curado-Tsallis $q$-distributions $f_q(E;\mu,T)$ we compute $q$-deformed number and energy densities, pressure, entropy density and Hubble rate, $\{n_q,\rho_q,P_q,s_q,H_q\}$. The Boltzmann equation is generalized accordingly to obtain the

Yoshikazu Giga, Michał Łasica, Piotr Rybka

We consider the question of convergence of a sequence of gradient flows defined on different Hilbert spaces. In order to give meaning to this idea, we introduce a notion of connecting operators. This permits us to generalize the concept of Mosco convergence of functionals to our present setting, and state a desired convergence result for gradient flows, whic

Rongjie Liao, Junhao Qiu, Xin Chen, Xiaoping Li

Customized static operator design has enabled widespread application of Evolutionary Algorithms (EAs), but their search effectiveness often deteriorates as evolutionary progresses. Dynamic operator configuration approaches attempt to alleviate this issue, but they typically rely on predefined operator structures and localized parameter control, lacking susta

Eliyas Suleyman, Paul Henderson, Eksan Firkat, Nicolas Pugeault

Video prediction is a fundamental task for various downstream applications, including robotics and world modeling. Although general video prediction models have achieved remarkable performance in standard scenarios, occlusion is still an inherent challenge in video prediction. We hypothesize that providing explicit information about motion (via point-flow) a

Sayak Mukherjee, Samrat Chatterjee, Emilie Purvine, Ted Fujimoto

Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large language model (LLM)-based reward design approach to generate autonomous cyber defense policies in a deep reinforcement learning (DRL)-driven experimental simulation environment. Mult

Poushali Sengupta, Yan Zhang, Frank Eliassen, Sabita Maharjan

Explainable AI (XAI) is increasingly essential as modern models become more complex and high-stakes applications demand transparency, trust, and regulatory compliance. Existing global attribution methods often incur high computational costs, lack stability under correlated inputs, and fail to scale efficiently to large or heterogeneous datasets. We address t

A. S. Kolisnyk, M. V. Shytov, E. V. Petrenko, A. V. Terekhov

The work analyzes the effect of a magnetic field $B$ directed along the $c$ axis ($B \parallel c$) up to 9~T on the resistivity $\rho(T)$, fluctuation conductivity (FLC) $\sigma'(T)$ and pseudogap $\Delta^*(T)$ in thin films of YBa$_2$Cu$_3$O$_{7-\delta}$ with a critical temperature of the superconducting transition $T_c = 88.8$~K. In contrast to previous wo

Dharana Joshi, Tanay Nag

We explore the non-reciprocal intracell hopping mediated non-Hermitian topological phases of an extended Su-Schrieffer-Heeger model hosting second-nearest-neighbour hopping. We microscopically analyze the phase boundaries using the non-Bloch momentum while the off-critical (critical) phases are directly associated with the gapped (gapless) nature of the non-

Luca Sabatini

We survey the known group properties that a sequence of finite groups or group actions needs to satisfy to admit subsets of bounded cardinality producing expander Cayley or Schreier graphs. We prove that an infinite amenable group and solvable groups of bounded derived length do not produce expander Schreier graphs, generalizing with easier proofs results of

Yanjun Gao, Yizhu Zhang, Yulin Chen, Jingjing Zhao

The attoclock is a powerful tool for probing ultrafast electron dynamics with attosecond precision.Here, we demonstrate an all-optical terahertz (THz) attoclock that reconstructs photoionization dynamics by detecting the THz radiation emitted from Ar atoms ionized by two-color (800 nm/400 nm) laser fields. In this approach, the polarization direction of the

Muhammad Sa'ood Shah, Asad Jeewa

Scalarisation functions are widely employed in MORL algorithms to enable intelligent decision-making. However, these functions often struggle to approximate the Pareto front accurately, rendering them unideal in complex, uncertain environments. This study examines selected Multi-Objective Reinforcement Learning (MORL) algorithms across MORL environments with

Ali Murtaza Caunhye, Asad Jeewa

The field of Offline Reinforcement Learning (RL) aims to derive effective policies from pre-collected datasets without active environment interaction. While traditional offline RL algorithms like Conservative Q-Learning (CQL) and Implicit Q-Learning (IQL) have shown promise, they often face challenges in balancing exploration and exploitation, especially in

Qiao Wang, Ping-An Hu

Plastic inorganic semiconductors are promising candidates for high performance stable flexible electronics. Germanium based chalcogenide materials are well known for excellent semiconducting properties, specifically superior carrier mobility. However, these materials typically exhibit inherent brittleness, which in germanium tellurides originates from the in

Martín Zapata, Federico Finkel, Artemio González-López

We introduce a novel analytical approach for studying free-fermion (XX) chains with smoothly varying, site-dependent hoppings and magnetic fields. Building on a discrete WKB-like approximation applied directly to the recurrence relation for the single-particle eigenfunctions, we derive a closed-form expression for the local fermion density profile as a funct

Niko Lindvall, Mikko Heino, Mikko Valkama

Very wideband apertures are needed in positioning, sensing, spectrum monitoring, and modern spread spectrum, e.g., frequency hopping systems. Vivaldi antennas are one of the prominent choices for the aforementioned systems due to their natural wideband characteristics. Furthermore, tightly-coupled antenna arrays have been researched in the recent years to ex

Clemens Pollak, Kersten Diers, Santiago Estrada, David Kügler

The corpus callosum, the largest commissural structure in the human brain, is a central focus in research on aging and neurological diseases. It is also a critical target for interventions such as deep brain stimulation and serves as an important biomarker in clinical trials, including those investigating remyelination therapies. Despite extensive research o

Ghaura Mahabaduge

We study the existence of equilateral polygons in planar integer lattices. Maehara showed that it's sufficient to work with rectangular lattices $\Lambda(m) = L[(1,0),(0,\sqrt{m})]$ with $m \equiv 3 \pmod{4}$. Building on results of Maehara and of Iino and Sakiyama, we show that for every such $m$ there exists $N$ such that for all $n \geq N$, the lattice $\

Mateusz Chiliński, Julita Ołtusek, Wojciech Jaśkowski

Arctic-Extract is a state-of-the-art model designed for extracting structural data (question answering, entities and tables) from scanned or digital-born business documents. Despite its SoTA capabilities, the model is deployable on resource-constrained hardware, weighting only 6.6 GiB, making it suitable for deployment on devices with limited resources, such

Weixuan Wang, Alejandro I. Maass, Dragan Nešić, Ying Tan

This paper addresses the emulation-based observer design for networked control systems (NCS) with linear plants that operate at two time scales in the presence of measurement noise. The system is formulated as a hybrid singularly perturbed dynamical system, enabling the systematic use of singular perturbation techniques to derive explicit bounds on the maxim

Akshit Pramod Anchan, Ameiy Acharya, Leki Chom Thungon

This paper proposes an optimization of Quantum Key Distribution (QKD) Networks using Graph Neural Networks (GNN) framework. Today, the development of quantum computers threatens the security systems of classical cryptography. Moreover, as QKD networks are designed for protecting secret communication, they suffer from multiple operational difficulties: adapti

Andrew Gomes

We investigate the processing of idiomatic expressions in transformer-based language models using a novel set of techniques for circuit discovery and analysis. First discovering circuits via a modified path patching algorithm, we find that idiom processing exhibits distinct computational patterns. We identify and investigate ``Idiom Heads,'' attention heads

Joonas Ilmavirta, Pieti Kirkkopelto, Antti Kykkänen

The elastic properties of a material are encoded in a stiffness tensor field and the propagation of elastic waves is modeled by the elastic wave equation. We characterize analytic and algebraic properties a general anisotropic stiffness tensor field has to satisfy in order for Finsler-geometric methods to be applicable in studying inverse problems related to

Koki Otaki, Yudai Kazuno, Masao Mori

In the standard cold dark matter (CDM) model, sub-galactic structures hierarchically collide and merge to build up larger structures. Mergers and collisions between dwarf galaxies and dark matter subhaloes (DMSHs) play an important role in the evolution and formation of structures within a massive galaxy. We investigate the collision frequency between DMSHs

Robert Tang

We introduce a flexible, categorical framework for large-scale geometry that clarifies basic behaviour of the metric Rips filtration and streamlines some constructions in geometric group theory. The paper has two main parts. First, we develop the theory of the metric Rips filtration and its colimit in natural coarse categories: informally, we characterise wh

L. J. Milligan

The SABRE (Sodium iodide with Active Background REjection) experiment aims to detect an annual rate modulation from dark matter interactions in ultra-high purity NaI(Tl) crystals which will provide a model independent test of the signal observed by DAMA/LIBRA. SABRE will consist of two separate detectors in the Northern and Southern hemispheres. SABRE South

Dávid Hvizdoš, Roman Čurík, Chris H. Greene

A recently developed energy-dependent frame transformation theory that incorporates both ionization and dissociation channels of the H$_2$ molecule, is extended to treat the ungerade states that occur both in dissociative recombination and as the final state in ground state photoabsorption. The theoretical treatment includes the rotational degrees of freedom

Margaux Vrech, Jan Major, Dominique Delande, Marcel Filoche

Classical particles in random potentials typically experience a percolation phase transition, being trapped in clusters of mean size $\chi$ that diverges algebraically at a percolation threshold. In contrast, quantum transport in random potentials is controlled by the Anderson localization length, which shows no distinct feature at this classical critical po

Lucile Laulin, Bastien Mallein

We consider in this article an Elephant Random Walk evolving in the plane. Specifically, this is a reinforced stochastic process in which the $n$th step is given by a random rotation of one of the previous steps chosen uniformly at random. We obtain a central limit theorem for this process, which shows that the process follows a randomly rotated logarithmic