November 2025 arXiv papers — page 93

Artur O. Lopes

In this review paper, we describe the use of couplings in several different mathematical problems. We consider the total variation norm, maximal coupling, and the $\bar{d}$-distance. We present a detailed proof of a result recently proved: the dual of the Ruelle operator is a contraction with respect to $1$-Wasserstein distance. We also show exponential conv

Jintao Zhang, Mingyue Cheng, Zirui Liu, Xianquan Wang

Time series generation is critical for a wide range of applications, which greatly supports downstream analytical and decision-making tasks. However, the inherent temporal heterogeneous induced by localized perturbations present significant challenges for generating structurally consistent time series. While flow matching provides a promising paradigm by mod

Trung Hieu Giang, Cristinel Mardare

A plate is rigid if its admissible displacement fields inducing vanishing two-dimensional strain tensors must vanish. We prove that the nonlinear model of Kirchhoff-Love for such a plate has a solution for any applied forces and boundary conditions. Then we give sufficient conditions on the data ensuring the rigidity of the plate. Together, these results sub

Xavier Zambrana-Puyalto, Alexander Johan Olsen, Søren Raza

We present a reflectivity-based method for measuring the in-plane refractive index of transparent van der Waals (vdW) materials. The approach enables the characterization of as small as $3 \times 3$~{\textmu}m$^2$ exfoliated flakes on a non-transmissive substrate without assuming any specific spectral shape of the refractive index. Exfoliated flakes are most

Diego Armando Pérez-Rosero, Danna Valentina Salazar-Dubois, Juan Camilo Lugo-Rojas, Andrés Marino Álvarez-Meza

These notes provide a self-contained introduction to kernel methods and their geometric foundations in machine learning. Starting from the construction of Hilbert spaces, we develop the theory of positive definite kernels, reproducing kernel Hilbert spaces (RKHS), and Hilbert-Schmidt operators, emphasizing their role in statistical estimation and representat

Denis Kleyko, Christopher J. Kymn, E. Paxon Frady, Amy Loutfi

In reservoir computing, an input sequence is processed by a recurrent neural network, the reservoir, which transforms it into a spatial pattern that a shallow readout network can then exploit for tasks such as memorization and time-series prediction or classification. Echo state networks (ESN) are a model class in which the reservoir is a traditional artific

Antoine Besset, Joris Tillet, Julien Alexandre dit Sandretto

A framework is presented for the verification of Signal Temporal Logic (STL) specifications over continuous-time nonlinear systems under uncertainty. Based on reachability analysis, the proposed method addresses indeterminate satisfaction caused by over-approximated reachable sets or incomplete simulations. STL semantics is extended via Boolean interval arit

Alexander Strunk, Roland Assam

This paper introduces Tensor Gauge Flow Models, a new class of Generative Flow Models that generalize Gauge Flow Models and Higher Gauge Flow Models by incorporating higher-order Tensor Gauge Fields into the Flow Equation. This extension allows the model to encode richer geometric and gauge-theoretic structure in the data, leading to more expressive flow dyn

Aditi Agarwal, Anjali Jain, Nikita Saxena, Ishan Deshpande

Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder farms, where accurate delineation requires the use of high resolution (HR) imagery which are available only at low revisit frequencies (e.g., annually). To support more frequent (su

Ananda Chakraborty

In this paper, we study variants of weight enumerators of linear codes over $\mathbb{F}_q$. We generalize the concept of average complete joint weight enumerators of two linear codes over $\mathbb{F}_q$. We also give its MacWilliams type identities. Then we establish a monomial analogue of Yoshida's theorem for this average complete joint weight enumerators.

Kyle McKee

In a canonical Stokes flow geometry, the Hele-Shaw cell, we show that tunable circulations induced by Lorentz forces in a conducting fluid enable particle control. We reveal that energy-optimal control paths correspond to geodesics of an emergent Riemannian metric defined over the fluid domain, which are time-optimal under a maximum-power constraint. Subject

Soham Ghosh, Gaurav Mittal

Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this

Miao Shang, Xiaopeng Hong

This paper introduces Gaussian Spatial Transport (GST), a novel framework that leverages Gaussian splatting to facilitate transport from the probability measure in the image coordinate space to the annotation map. We propose a Gaussian splatting-based method to estimate pixel-annotation correspondence, which is then used to compute a transport plan derived f

Dalia Ali, Dora Zhao, Allison Koenecke, Orestis Papakyriakopoulos

Although large language models (LLMs) are increasingly trained using human feedback for safety and alignment with human values, alignment decisions often overlook human social diversity. This study examines how incorporating pluralistic values affects LLM behavior by systematically evaluating demographic variation and design parameters in the alignment pipel

Alberto Domínguez Corella, Marc Quincampoix, Vladimir Veliov

The paper presents new sufficient conditions for the property of strong bi-metric regularity of the optimality map associated with an optimal control problem which is affine with respect to the control variable ({\em affine problem}). The optimality map represents the system of first order optimality conditions (Pontryagin maximum principle), and its regular

Anshu, Tattwamasi Amrutam, Pradyut Karmakar

We introduce a notion of the Fej\'er property for topological \'etale groupoids. As a consequence, we show that when $\mathcal{G}$ is a principal \'etale second countable groupoid satisfying the Fej\'er property, every closed $C_0(\mathcal{G}^0)$-bimodule $M\subset C_r^*(\mathcal{G})$ is of the form $\overline{C_c(U)}^r$ for some open set $U$. Moreover, we g

Bayu Adhi Tama, Jianwu Wang, Vandana Janeja, Mostafa Cham

Accurate subglacial bed topography is essential for ice sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior and reconstructs bed from the observed surface. A DeepLabV3+ decoder over a standard encoder (e.g.,ResNet-50) is trained wit

Isobel Falconer, David Horowitz

Scottish mathematician Colin MacLaurin (1698-1746) is best known for his A Treatise of Fluxions (1742), An Account of Sir Isaac Newton's Philosophical Discoveries (1748), and the appellation for a type of power series. However, it is hardly known that in 1714 at the age of sixteen MacLaurin penned a short manuscript wherein he tried to apply Newtonian princi

Gianfranco Casnati, Daniele Faenzi, Federica Galluzzi

We study instanton and Ulrich bundles on hypersurfaces of the projective space, with a focus on special cubic fourfolds and generalized Pfaffians, notably defined by skew-symmetric endomorphisms of Steiner bundles. We prove that the acyclic extensions of instantons deform to Ulrich bundles and deduce that the existence of instantons of low rank and charge im

Mingchen Zhong, Xin Lu, Dong Li, Senyan Xu

Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiven

Hiroki Inazu, Shun-ichi Kimura, Koki Suetsugu

In this paper, we consider $\mathcal{L}\mathcal{R}$-ending partisan rulesets as a branch of combinatorial game theory. In these rulesets, the sets of options of both players are the same. However, there are two kinds of terminal positions. If the game ends in one kind of terminal position, then a player wins, and if the game ends in the other kind of termina

Heng Zhao, Ruoyu Wang, Tianhang Zheng, Qi Li

The trust-based nature of Border Gateway Protocol (BGP) makes it vulnerable to disruptions like prefix hijacking and misconfigurations, threatening routing stability. Traditional detection relies on manual inspection with limited scalability. Machine/Deep Learning (M/DL) approaches automate detection but suffer from suboptimal precision, limited generalizabi

Hadi Barati, Ali Nayerifar, Mehdi Fardmanesh

In this work, the effects of dopamine neurotransmitter within the Cortico-Striatal-Thalamo-Cortical (CSTC) loop have been investigated. Simulations confirmed dopamine facilitates movement via thalamic disinhibition. Analysis of its impact on the signal-to-noise ratio (SNR) revealed a complex, region-specific outcome: SNR increased in some regions (e.g., D2 S

Clément Dumas

Mechanistic interpretability research requires reliable tools for analyzing transformer internals across diverse architectures. Current approaches face a fundamental tradeoff: custom implementations like TransformerLens ensure consistent interfaces but require coding a manual adaptation for each architecture, introducing numerical mismatch with the original

Jiazhen Gu, Jinchi Jiang, Qian Yu

Let \{B_t^H,t\geq0\} be a d-dimensional fractional Brownian motion. We prove that the approximation of the first-order derivative of self-intersection local time, defined as \alpha_{\varepsilon,t}^{(1)}(0)=-\int_0^t\int_0^sp_\varepsilon^{(1)}(B_s^H-B_r^H)\d r\d s, where p_\varepsilon^{(1)}(x_1,\cdots,x_d):=\partial _{x_1}p(x_1,\cdots,x_d) and p_\varepsilon(x

Michael Gundall, Hans D. Schotten

Achieving precise time synchronization in wireless systems is essential for both industrial applications and 5G, where sub-microsecond accuracy is required. However, since the Industrial Internet of Things (IIoT) market is negligible compared to the consumer electronics market, the so-called IIoT enhancements have not yet been implemented in silicon. Moreove

Daniël Wilten, Gideon Maillette de Buy Wenniger, Arjen Hommersom, Paul Lucassen

Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, that fall in categories such as genres and authors. However, while carousels offer structure and greater ease of navigation, they alone do n

Alberto Domínguez Corella, Vladimir Veliov

This paper revisits the issue of H\"older Strong Metric sub-Regularity (HSMs-R) of the optimality system associated with ODE optimal control problems that are affine with respect to the control. The main contributions are as follows. First, the metric in the control space, introduced in this paper, differs from the ones used so far in the literature in that

Michelle Mattille, Alexandre Mesot, Miriam Weisskopf, Nicole Ochsenbein-Kölble

Flexible robots hold great promise for enhancing minimally invasive surgery (MIS) by providing superior dexterity, precise control, and safe tissue interaction. Yet, translating these advantages into endoscopic interventions within open cavities remains challenging. The lack of anatomical constraints and the inherent flexibility of such devices complicate th

Vilém Rožek

Shallow flows are governed by the Navier-Stokes equations. They are commonly modelled using the shallow water equations, a great simplification of the Navier-Stokes equations, which often yields inaccurate results. For that reason, a model called shallow water moment equations has been developed. It uses more equations and variables than the shallow water eq

Michael Gundall, Jan Herbst, Robin Müller, Hans D. Schotten

Wireless time synchronization of mobile devices is a key enabler for numerous Industry 4.0 applications, such as coordinated and synchronized tasks or the generation of high-precision timestamps for machine learning or artificial intelligence algorithms. Traditional wireline clock synchronization protocols, however, cannot achieve the performance in wireless

Fabian Stricker, David Bermbach, Christian Zirpins

Federated learning (FL) is a new paradigm for training machine learning (ML) models without sharing data. While applying FL in cross-silo scenarios, where organizations collaborate, it is necessary that the FL system is reliable; however, participants can fail due to various reasons (e.g., communication issues or misconfigurations). In order to provide a rel

Nicola Rares Franco, Lorenzo Tedesco

We introduce conditional push-forward neural networks (CPFN), a generative framework for conditional distribution estimation. Instead of directly modeling the conditional density $f_{Y|X}$, CPFN learns a stochastic map $\varphi=\varphi(x,u)$ such that $\varphi(x,U)$ and $Y|X=x$ follow approximately the same law, with $U$ a suitable random vector of pre-defin

Feike van Veen, Sofie Kölling, Stijn R. de Wit, Roel Metsch

We study the three-dimensional topological insulator (Bi$_{1-x}$Sb$_{x}$)$_{2}$Te$_{3}$ in its ultrathin limit i.e. when the thickness is of the same order as the surface state penetration depth. It is expected that in this limit a hybridization gap opens at the Dirac point, which gives rise to a quantum spin Hall (QSH) or insulating phase, depending on the

Peilun Song, Shuguang Yang, Xiujuan Geng, Zhenzhong Gan

Adult language learning varies greatly among individuals. Traditionally associated with frontotemporal language regions, this variability is increasingly seen as stemming from distributed brain networks. However, the role of these networks and their topological organization in explaining these differences remains unclear. We hypothesize that graph-theory-bas

Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera, Jens Behrmann

Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output, require invasive measurements, e.g., arterial pressure waveforms (APW). As a non-invasive alternative, photoplethysmography (PPG) measurements are routinely collected in hospital s

Zhijun Tu, Xuan Fang, Jorge García-Rojas, Robert Williams

In order to test the robustness and reliability of the new generation spectral-line identifier PyEMILI, as initially introduced in Paper I, in line identification and establish a reference/benchmark dataset for future spectroscopic studies, we run the code on the line lists of a selected sample of emission-line nebulae, including planetary nebulae (PNe), HII

Mauricio Guerrero-Montero, Michal Bosy, Christopher D. Cooper

The Poisson-Boltzmann equation is widely used to model molecular electrostatics; however, it is usually solved in linearised form because the sinh nonlinearity is challenging, limiting its applicability in highly charged systems such as nucleic acids. This work presents a solution method for the nonlinear Poisson-Boltzmann equation based on a coupled finite/

Mulei Ma, Xinyi Xu, Minrui Xu, Zihan Chen

LLMs are increasingly executed in edge where limited GPU memory and heterogeneous computation jointly constrain deployment which motivates model partitioning and request scheduling. In this setting, minimizing latency requires addressing the tight coupling between model placement and request scheduling across heterogeneous nodes, as suboptimal decisions in o

Zongwei Zhen, Biqing Zeng

This paper addresses the task of interactive, conversational text-to-image retrieval. Our DIR-TIR framework progressively refines the target image search through two specialized modules: the Dialog Refiner Module and the Image Refiner Module. The Dialog Refiner actively queries users to extract essential information and generate increasingly precise descript

Dhriti Ranjan Dolai, Naveen Kumar

We consider the existence of the integrated density of states (IDS) of the magnetic Schr\"{o}dinger operator with a random potential on the Hilbert space \( L^2(\mathbb{R}^d) \), as an analogue of the law of large numbers (LLN) for trace functionals. In this work, we establish an analogue of the central limit theorem (CLT), which describes the fluctuations o

Ilan Kurtser, Yoav Koral, Eldad Holdengreber, Shmuel E. Schacham

We present the design, fabrication, and measurement of a high-temperature superconductor (HTSC) Stepped Impedance Resonator (SIR) band-pass filter for S-band applications, and its incorporation into a cryogenic receiver cascade. The 11-pole filter, implemented in YBa2Cu3O(7-x) (YBCO) thin films on sapphire, exhibits an ultra-low insertion loss (IL) of -0.1~d

Hong Gao, Yiming Bao, Xuezhen Tu, Yutong Xu

Video understanding requires not only visual recognition but also complex reasoning. While Vision-Language Models (VLMs) demonstrate impressive capabilities, they typically process videos largely in a single-pass manner with limited support for evidence revisit and iterative refinement. While recently emerging agent-based methods enable long-horizon reasonin

Zhou Li, Xiang Zhang, Yizhou Zhao, Haiqiang Chen

This paper investigates the information-theoretic decentralized secure aggregation (DSA) problem under practical groupwise secret keys and collusion resilience. In DSA, $K$ users are interconnected through error-free broadcast channels. Each user holds a private input and aims to compute the sum of all other users' inputs, while satisfying the security const

Joachim Stöckler

Approximate duals of B-splines were first used by Chui et al. (2004) for the purpose of constructing tight wavelet frames on bounded intervals. They are splines with local support, whose inner product with a polynomial in the spline space provides the exact coefficient in the representation of the same polynomial in the B-spline basis. This implies that the

J. L. Rizos, L. M. Lara, J. L. Ortiz, J. M. Madiedo

We present a comprehensive morphological and spectrophotometric analysis of the lunar impact that occurred on September 11, 2013, based on pre- and post-event observations by the Lunar Reconnaissance Orbiter (LRO). The crater formed exhibits a rim-to-rim diameter of $35 \pm 0.7$ m, a depth of $4.9 \pm 0.4$ m, and an ejecta blanket extending over 2 km with an

Yusen Cai, Qing Lin, Bhargava Satya Nunna, Mengmi Zhang

Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of such staged "visual diets", we train self-supervised learning (SSL) models on object-centric videos under constraints that simulate infant vision: grayscale-to-color (C), blur-to-s

Jinru Ding, Lu Lu, Chao Ding, Mouxiao Bian

Recent advances in medical large language models (LLMs), multimodal models, and agents demand evaluation frameworks that reflect real clinical workflows and safety constraints. We present MedBench v4, a nationwide, cloud-based benchmarking infrastructure comprising over 700,000 expert-curated tasks spanning 24 primary and 91 secondary specialties, with dedic

Mahdi Etumi, Hazel M. Taylor, Marie Farrell

In the development of safety and mission-critical systems, including autonomous space robotic missions, complex behaviour is captured during the requirements elicitation phase. Requirements are typically expressed using natural language which is ambiguous and not amenable to formal verification methods that can provide robust guarantees of system behaviour.

Yi Ding, Xushuo Tang, Zhengyi Yang, Wenqian Zhang

Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still published primarily as long, heterogeneous PDF documents. This makes it difficult to systematically answer seemingly simple questions. Existing tools either rely on brittle rule-based e

Mehrnoush Hajnorouzi, Astrid Rakow, Martin Fränzle

The steadily increasing level of automation in human-centred systems demands rigorous design methods for analysing and controlling interactions between humans and automated components, especially in safety-critical applications. The variability of human behaviour poses particular challenges for formal verification and synthesis. We present a model-based fram

Reydel Arrieta, José Proença, Patrick Meumeu Yomsi

Hybrid systems are increasingly used in critical applications such as medical devices, infrastructure systems, and autonomous vehicles. Lince is an academic tool for specifying and simulating such systems using a C-like language with differential equations. This paper presents recent experiments that enhance Lince with mechanisms for executing multiple simul

Angelo Ferrando

Assuring the safety and trustworthiness of autonomous systems is particularly difficult when learning-enabled components and open environments are involved. Formal methods provide strong guarantees but depend on complete models and static assumptions. Runtime verification (RV) complements them by monitoring executions at run time and, in its predictive varia

Marlow Fawn, Matthias Scheutz

We propose a method for combining Harmonic Control Lyapunov-Barrier Functions (HCLBFs) derived from Signal Temporal Logic (STL) specifications with any given robot policy to turn an unsafe policy into a safe one with formal guarantees. The two components are combined via HCLBF-derived safety certificates, thus producing commands that preserve both safety and

Diana C. Benjumea, Marie Farrell, Louise A. Dennis

Deploying autonomous robots in safety-critical domains requires architectures that ensure operational effectiveness and safety compliance. In this paper, we contribute the Safe-ROS architecture for developing reliable and verifiable autonomous robots in such domains. It features two distinct subsystems: (1) an intelligent control system that is responsible f

Marcela Gonçalves dos Santos, Sylvain Hallé, Fábio Petrillo

Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introd

Richard Golnik, Thomas Gatter, Peter F. Stadler, Nicola Vassena

Computer algebra methods for analyzing reaction networks often rely on the assumption of mass-action kinetics, which transform the governing ODEs into polynomial systems amenable to techniques such as Gr\"obner basis computation and related algebraic tools. However, these methods face significant computational complexity, limiting their applicability to rela

Ishan Saxena, Bernd Westphal, Martin Fränzle

Automated Driving Functions (ADFs) need to comply with spatial properties of varied complexity while driving on public roads. Since such situations are safety-critical in nature, it is necessary to continuously check ADFs for compliance with their spatial properties. Due to their complexity, such spatial properties need to be formalized to enable their autom

Rahul Sharma, Aru Beri, Biswajit Paul, Andrea Sanna

After nearly a decade of quiescence, the transient Be/X-ray binary pulsar RX J0520.5-6932 underwent an outburst in 2024. We performed X-ray monitoring of the source with NICER and AstroSat near the peak of the event. Our primary objective is to investigate the energy and luminosity dependence of the pulsed emission, characterize the spin evolution, and study

Dominik Grundt, Ishan Saxena, Malte Petersen, Bernd Westphal

Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs. Unfortunately, explaining the system behaviour of AI-based driving functions is particularly challenging, as decision-making processe

Juri Zach, Peer Stelldinger

Reliable perception of the environment is a key enabler for autonomous systems, where calibration and localization tasks often rely on robust visual markers. We introduce the PuzzlePole, a new type of fiducial markers derived from the recently proposed PuzzleBoard calibration pattern. The PuzzlePole is a cylindrical marker, enabling reliable recognition and

Avijit Chowdhury, Gargi Sen, Sayan Chakrabarti, Santabrata Das

The interplay between supermassive black holes (SMBHs) and their surrounding environment is fundamental to understanding galactic evolution. This work investigates the influence of a cold dark matter (DM) halo on the dynamics of relativistic, low angular momentum, inviscid, and advective hot accretion flow onto a galactic SMBH. Modeling the spacetime geometr

Sergio Giardino

The Dirac delta function potential is considered within the real Hilbert space approach for complex wave functions, as well as quaternionic wave functions. As has been previously determined, the real Hilbert space approach enables the possibility of self-interacting physical systems. The self-interaction precludes confining states, and also imposes non-stati

Xin Yi, Yue Li, Dongsheng Shi, Linlin Wang

Large Language Models (LLMs) are increasingly integrated into educational applications. However, they remain vulnerable to jailbreak and fine-tuning attacks, which can compromise safety alignment and lead to harmful outputs. Existing studies mainly focus on general safety evaluations, with limited attention to the unique safety requirements of educational sc

Zhengchunmin Dai, Jiaxiong Tang, Peng Sun, Honglong Chen

In decentralized machine learning paradigms such as Split Federated Learning (SFL) and its variant U-shaped SFL, the server's capabilities are severely restricted. Although this enhances client-side privacy, it also leaves the server highly vulnerable to model theft by malicious clients. Ensuring intellectual property protection for such capability-limited s

Ali Hanif, Yuchen Zhang, Pinjun Zheng, Tareq Y. Al-Naffouri

Low Earth orbit (LEO) satellites are a crucial component of the future non-terrestrial networks (NTN) due to lower latency, robust signal strengths, shorter revisit times, and dense constellations. However, acquiring reliable channel state information (CSI) in LEO satellite communication remains challenging owing to severe signal attenuation over long propag

Young-Beom Woo

Integrating multiple personalized concepts into a single image has recently become a significant area of focus within Text-to-Image (T2I) generation. However, existing methods often underperform on complex multi-object scenes due to unintended alterations in both personalized and non-personalized regions. This not only fails to preserve the intended prompt s

Xiaowei Xu, Justin Sonneck, Hongxiao Wang, Roman Burkard

Autonomous migration is essential for the function of immune cells such as neutrophils and plays a pivotal role in diverse diseases. Recently, we introduced ComplexEye, a multi-lens array microscope comprising 16 independent aberration-corrected glass lenses arranged at the pitch of a 96-well plate, capable of capturing high-resolution movies of migrating ce

Till Kaeufer, Rens Waters, Danny Gasman, Milou Temmink

Our knowledge of the chemical composition of the gas in the inner disc of intermediate-mass young stars is limited, due to the lack of suitable instrumentation. The launch of JWST has provided a significant improvement in our ability to probe gas in these inner discs. We analyse the gas composition and emitting conditions of the disc around HD 35929, a young

Paolo Victor Redondo, Raphaël Huser, Hernando Ombao

Alterations in functional brain connectivity characterize neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). As a non-invasive and cost-effective technique, electroencephalography (EEG) is gaining increasing attention for its potential to identify reliable biomarkers for early detection and differential diagnosis

Haobin Li, Mouxing Yang, Xi Peng

Recently, the general-to-customized paradigm has emerged as the dominant approach for Cross-Modal Retrieval (CMR), which reconciles the distribution shift problem between the source domain and the target domain. However, existing general-to-customized CMR methods typically assume that the entire target-domain data is available, which is easily violated in re

Hung M. Bui, Richard R. Hall, Martin Subira Jorge

The twisted fourth moment of the Riemann zeta-function was established by Hughes and Young [J. Reine Angew. Math. 641 (2010), 203--236] and later improved by Bettin, Bui, Li and Radziwill [J. Eur. Math. Soc. (JEMS) 22 (2020), 3953--3980]. In applications one would often like to take the Dirichlet polynomial to mimic either $1/\zeta^r(s)$ (a mollifier) or $\z

Yu Mei, Xutong Wang, Ziyao Zhang, Yiming Fu

Emotion education is critical for children aged 3 to 6. However, existing technologies largely focus on children's direct interaction with AI, overlooking the central role of parents in guiding early emotional development at home. To address this gap, we conducted co-design sessions with five kindergarten teachers and five parents to identify key parental ch

Suparna Sarkar, Santanu K. Maiti

The occurrence of a finite mismatch between the up and down spin energy channels due to the application of an electric field, leading to the generation of a polarized spin current from an unpolarized beam in antiferromagnetic materials, has already been established. But, in this work, we report for the first time that even in the absence of any electric fiel

Ankit Gupta, P. S. Burada

The diffusive behavior of small entities is strongly influenced by the flow of the surrounding medium, which is ubiquitous in natural and artificial environments. In this study, we investigate the transport characteristics of the inertial active Brownian particles (ABPs) in a microfluidic channel under a Poiseuille flow. The interplay between the inertia of

Wei Liu, Jiahong Li, Yiwen Shao, Dong Yu

Speech-LLM models have demonstrated great performance in multi-modal and multi-task speech understanding. A typical speech-LLM paradigm is integrating speech modality with a large language model (LLM). While the Whisper encoder was frequently adopted in previous studies for speech input, it shows limitations regarding input format, model scale, and semantic

M. Barabashko, A. Jeżowski, A. Krivchikov

The low-temperature isochoric heat capacity of cryocrystals was scaled using the universal scaling function. This universality links the magnitude of the anomaly and the characteristic temperature of the hump $T_{\mathrm{max}}$ in heat capacity, which is related to the first van Hove singularity in the phonon spectrum. For atomic, molecular, and quantum cryo

Thomas Houweling

Directional-change Intrinsic Time analysis has long revealed scaling laws in market microstructure, but the origin of their stability remains elusive. This article presents evidence that Intrinsic Time can be modeled as a memoryless exponential hazard process. Empirically, the proportion of directional changes to total events stabilizes near $1 - 1/e = 0.632

A. Kokori, A. Tsiaras, G. Pantelidou, A. Jones

The ExoClock project is an open platform aiming to monitor exoplanets by integrating observations from space and ground based telescopes. This study presents an updated catalogue of 620 exoplanet ephemerides, integrating 30000 measurements from ground-based telescopes (the ExoClock network), literature, and space telescopes (Kepler, K2 and TESS). The updated

Bastien Vuillod, Pierre-Alain Moellic, Jean-Max Dutertre

Large models adaptation through Federated Learning (FL) addresses a wide range of use cases and is enabled by Parameter-Efficient Fine-Tuning techniques such as Low-Rank Adaptation (LoRA). However, this distributed learning paradigm faces several security threats, particularly to its integrity, such as backdoor attacks that aim to inject malicious behavior d

Dun Zhang, Ziyang Zeng, Yudong Zhou, Shuyang Lu

This technical report presents the training methodology and evaluation results of the open-source Jasper-Token-Compression-600M model, released in November 2025. Building on previous distillation-based recipes from the English Stella and Jasper models, we successfully extend this approach to a bilingual (English and Chinese) domain, further enhancing model p

Sedat Bin Vedat, Enes Kutay Yarkan, Meftun Akarsu, Recep Kaan Karaman

Enterprise ERP systems managing hundreds of thousands of employee records face critical data quality challenges when human resources departments perform decentralized manual entry across multiple languages. We present an end-to-end pipeline combining automated data cleaning with LLM-driven SQL query generation, deployed on a production system managing 240,00

Moyu Zhang, Yujun Jin, Yun Chen, Jinxin Hu

Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely on a simple binary classification objective. However, existing generative models typically confine the generative paradigm to the training phase, primarily for representation learni

Nicola Gambino, Richard Garner, Christina Vasilakopoulou

We provide a unified treatment of several commuting tensor products considered in the literature, including the tensor product of enriched categories and the Boardman-Vogt tensor product of operads and symmetric multicategories, subsuming work of Elmendorf and Mandell. We then show how a commuting tensor product extends to bimodules, generalising results of

Shuyi Geng, Tao Zhou, Yi Zhou

A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a fundamental dilemma. On one hand, projecting all domains into a single unified visual space leads to inter-domain interference and semantic distortion, as large shifts may vary wit

I-Ting Lee, Bao-Kai Wang, Liang-Chi Chen, Wen Sheng Lim

Processing-in-memory (PIM) reduces data movement by executing near memory, but our large-scale characterization on real PIM hardware shows that end-to-end performance is often limited by disjoint host and device address spaces that force explicit staging transfers. In contrast, CXL-PIM provides a unified address space and cache-coherent access at the cost of

Daniel Parejo, Andrés Santos

We derive an explicit analytic expression for the first quantum correction to the second virial coefficient of a $d$-dimensional fluid whose particles interact via the generalized Lennard-Jones $(2n,n)$ potential. By introducing an appropriate change of variable, the correction term is reduced to a single integral that can be evaluated in closed form in term

Saksham Kumar, D Sridhar Aditya, T Likhil Kumar, Thulasi Bikku

Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combin

Marcin Płodzień

Information scrambling, the process by which quantum information spreads and becomes effectively inaccessible, is central to modern quantum statistical physics and quantum chaos. These lecture notes provide an introduction to information scrambling from both static and dynamical perspectives. The spectral properties of reduced density matrices arising from H

Xiuxiu Qi, Yu Yang, Jiannong Cao, Luyao Bai

Language-conditioned manipulation facilitates human-robot interaction via behavioral cloning (BC), which learns control policies from human demonstrations and serves as a cornerstone of embodied AI. Overcoming compounding errors in sequential action decisions remains a central challenge to improving BC performance. Existing approaches mitigate compounding er

Adam Chapman, Kelly McKinnie

This is a survey of the existing literature, the state of the art, and a few minor new results and open questions regarding the essential dimension of central simple algebras and finite sequences of such algebras over fields whose characteristic divides the degree of the algebras under discussion. Upper and lower bounds as well as a few precise evaluations o

Joachim Tesch, Giorgio Becherini, Prerana Achar, Anastasios Yiannakidis

Inferring 3D human motion from video remains a challenging problem with many applications. While traditional methods estimate the human in image coordinates, many applications require human motion to be estimated in world coordinates. This is particularly challenging when there is both human and camera motion. Progress on this topic has been limited by the l

Svetlana Seliunina, Daniel Schleich, Sven Behnke

In our work, we extend the current state-of-the-art approach for autonomous multi-UAV exploration to consumer-level UAVs, such as the DJI Mini 3 Pro. We propose a pipeline that selects viewpoint pairs from which the depth can be estimated and plans the trajectory that satisfies motion constraints necessary for odometry estimation. For the multi-UAV explorati

Aleksandra Borówka, Ioannis Chrysikos

We provide a natural higher-dimensional generalization of the adapted connections with skew-torsion on almost Hermitian and almost contact metric manifolds presented in \cite{FrIv}. We prove that a metric $f$-manifold $(M^{2n+s}, ϕ, ξ_i, η_j, g)$ with commuting characteristic vector fields admits a metric connection $\nabla$ with skew-torsion $T$ preserving

Fabian Schmidt, Noushiq Mohammed Kayilan Abdul Nazar, Markus Enzweiler, Abhinav Valada

Large Language Models (LLMs) are increasingly used for decision-making and planning in autonomous driving, showing promising reasoning capabilities and potential to generalize across diverse traffic situations. However, current LLM-based driving agents lack explicit mechanisms to enforce traffic rules and often struggle to reliably detect small, safety-criti

Chin-Yun Yu, György Fazekas

We introduce a general formulation for automatic differentiation through direct form filters, yielding a closed-form backpropagation that includes initial condition gradients. The result is a single expression that can represent both the filter and its gradients computation while supporting parallelism. C++/CUDA implementations in PyTorch achieve at least 10

Danyang Sun, Fadi Dornaika, Nagore Barrena

Due to the high cost of annotation or the rarity of some diseases, medical image segmentation is often limited by data scarcity and the resulting overfitting problem. Self-supervised learning and semi-supervised learning can mitigate the data scarcity challenge to some extent. However, both of these paradigms are complex and require either hand-crafted prete

Daniel Stilck França, Ngoc Hoang Anh Mai

We study quantum algorithms for approximating Lasserre's hierarchy values for polynomial optimization. Let $f,g_1,\ldots,g_m$ be real polynomials in $n$ variables and $f^\star$ the infimum of $f$ over the semialgebraic set $S(g)=\{x: g_i(x)\ge 0\}$. Let $\lambda_k$ be the value of the order-$k$ Lasserre relaxation. Assume either (i) $f^\star=\lambda_k$ and t

Oriol Cabanas-Tirapu, Sergio Cobo-Lopez, Savannah E. Sanchez, Forest L. Rohwer

Understanding how systems evolve over time often requires discovering the differential equations that govern their behavior. Automatically learning these equations from experimental data is challenging when the data are noisy or limited, and existing approaches struggle, in particular, with the estimation of unobserved derivatives. Here, we introduce an inte

Richard D. Ball, Amedeo Chiefa, Roy Stegeman

We present a global determination of parton distribution functions (PDFs) that accounts for higher twist corrections in deep-inelastic scattering (DIS) and linear power corrections for single inclusive jet and dijet production data from the LHC. We determine these corrections and their associated correlated uncertainties using a methodology based on the theo