December 2025 arXiv papers — page 99

Jiayan Cui, Zhihan Yang, Naihan Li, Jiankun Tian

This work proposes GLM-TTS, a production-level TTS system designed for efficiency, controllability, and high-fidelity speech generation. GLM-TTS follows a two-stage architecture, consisting of a text-to-token autoregressive model and a token-to-waveform diffusion model. With only 100k hours of training data, GLM-TTS achieves state-of-the-art performance on m

Alexey S. Shigaev, Victor D. Lakhno

Simple mechanical models of DNA play an important role in studying the dynamics of its open states. The main requirement when developing a DNA model is the correct selection of its effective potentials and parameters based on experimental data. At the same time, various experiments allow us to "see" different types of DNA open states. Consideration of this f

Suhrid Gupta, Muhammed Tawfiqul Islam, Rajkumar Buyya

Edge computing decentralizes computing resources, allowing for novel applications in domains such as the Internet of Things (IoT) in healthcare and agriculture by reducing latency and improving performance. This decentralization is achieved through the implementation of microservice architectures, which require low latencies to meet stringent service level a

Esther Salmerón-Manzano, David Muñoz-Rodríguez, Alberto-Jesus Perea-Moreno, Quetzalcoatl Hernandez-Escobedo

The rapid growth of photovoltaic (PV) technology in recent years called for a comprehensive assessment of the global scientific landscape on fires associated with PV energy installations. This study examines the scientific literature indexed in Scopus from 1983 to 2023. It reveals a striking increase in output since 2011, with nearly one hundred publications

Georgios Bouchouras, Dimitrios Doumanas, Andreas Soularidis, Konstantinos Kotis

This paper explores the integration of Large Language Models (LLMs) in the engineering of a Parkinson's Disease (PD) monitoring and alerting ontology through four key methodologies: One Shot (OS) prompt techniques, Chain of Thought (CoT) prompts, X-HCOME, and SimX-HCOME+. The primary objective is to determine whether LLMs alone can create comprehensive ontol

Yunnuo Xu, Yumeng Zhang, Yijie Mao, Bruno Clerck

Low-Earth-orbit (LEO) satellite communication systems face challenges due to high satellite mobility, which hinders the reliable acquisition of instantaneous channel state information at the transmitter (CSIT) and subsequently degrades multi-user transmission performance. This paper investigates a downlink multi-user multi-antenna system, and tackles the abo

Samuel Cruz Alegría, Bindi Çapriqi, Shega Likaj, Ken Trotti

Modern machine learning, especially the training of deep neural networks, depends on solving large-scale, highly nonconvex optimization problems, whose objective function exhibit a rough landscape. Motivated by the success of parallel preconditioners in the context of Krylov methods for large scale linear systems, we introduce a novel nonlinearly preconditio

Arnott Kidner, Eckhard Steffen, Weiqiang Yu

An $r$-regular graph is an $r$-graph, if every odd set of vertices is connected to its complement by at least $r$ edges. We prove for $r \in \{4,5\}$, every projective planar $r$-graph with no Petersen-minor is $r$-edge colorable.

M. Baran Ökten

We compute the achromatic gravitational imprint that Kerr spacetime leaves on linear polarization at the photon ring. Recasting parallel transport in a null Frenet--Serret frame yields a single scalar evolution law for the electric-vector position angle. On the observer's screen, the Kerr-minus-Schwarzschild pattern on the direct critical curve is nonzer

Gabriela Wojak, Ernest Górka, Michał Ćwiąkała, Dariusz Baran

This paper analyzes the relationship between cybersecurity management, data protection, and corporate reputation in the context of digital transformation. The study examines how organizations implement strategies and tools to mitigate cyber risks, comply with regulatory requirements, and maintain stakeholder trust. A quantitative research design was applied

Alva Markelius, Fethiye Irmak Doğan, Julie Bailey, Guy Laban

Institutional and social barriers in higher education often prevent students with disabilities from effectively accessing support, including lengthy procedures, insufficient information, and high social-emotional demands. This study empirically explores how disabled students perceive robot-based support, comparing two interaction roles, one information based

Zhibing Li, Mengchen Zhang, Tong Wu, Jing Tan

We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a generator directly on 4D data, achieving high fidelity, temporal coherence, and structural consistency. At the core of our metho

Hao Zhao, Qianjun Zheng, Peng Yan

Generating magnon frequency combs (MFCs) with tunable spacing via a single-frequency driving is crucial for practical applications but it typically relies on complex spin textures like skyrmions or vortices. Here, we theoretically and numerically demonstrate MFC generation in geometrically curved ferromagnetic thin films using single-frequency microwave exci

Zeping Shi, Wenbin Wu, Guangyi Wang, Mykhaylo Ozerov

Dirac fermions, characterized by their linear dispersion and relativistic nature, have emerged as a prominent class of quasiparticles in condensed matter physics. While the Dirac equation, initially developed in the context of high-energy physics, provides a remarkable framework for describing the electronic properties of these materials, the inherent symmet

K. Fahrion, J. van de Sande, K. R. Akhil, M. A. Beasley

Galaxy evolution is driven by processes occurring across a wide range of scales, from star formation within giant molecular clouds (parsec scales) to outflows and secular evolution across entire galaxies (kpc scales), and the interplay between galaxies, their dark matter haloes, and large-scale structures (Mpc scales). Connecting the distribution of baryonic

Saba Arife Bozpolat

This study presents a generalized n--bit superdense coding protocol that enables the transmission of n classical bits of information using an entangled n--qubit quantum system and the transmission of $n-1$ qubits. The protocol involves creating a maximally entangled n--qubit state, encoding the classical message with Pauli--Z and Pauli--X gates, and then tra

Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga, Kostas Marias

In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across heterogeneous metrics, ad-hoc scripts, and incomplete reporting practices. To address this gap, we introduce

Marvin Kopka, Azeem Majeed, Gabriella Spinelli, Austen El-Osta

Artificial Intelligence tools such as large language models are increasingly used by the public to obtain health information and guidance. In health-related contexts, following or rejecting AI-generated advice can have direct clinical implications. Existing instruments like the Trust in Automated Systems Survey assess trustworthiness of generic technology, a

Panayiotis Smeros, Vincent Emonet, Ruijie Wang, Ana-Claudia Sima

The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of generating structured queries, such as SPARQL queries, from natural language. However, these new approaches mostly focus on response accuracy over a single source while ignoring other evaluation criteria, such as federated q

María Anguiano, Francisco J. Suárez-Grau

This theoretical study deals with asymptotic behavior of a coupling between a thin film of fluid and an adjacent thin porous medium. We assume that the size of the microstructure of the porous medium is given by a small parameter $0<\varepsilon\ll 1$, the thickness of the thin porous medium is defined by a parameter $0<h_\varepsilon\ll 1$, and the thickness

Yu Chen, Hongwei Lin

Persistence diagrams (PDs) provide a powerful tool for understanding the topology of the underlying shape of a point cloud. However, identifying which points in PDs encode genuine signals remains challenging. This challenge directly hinders the practical adoption of topological data analysis in many applications, where automated and reliable interpretation o

Xiaoqian Shen, Min-Hung Chen, Yu-Chiang Frank Wang, Mohamed Elhoseiny

Grounded video question answering (GVQA) aims to localize relevant temporal segments in videos and generate accurate answers to a given question; however, large video-language models (LVLMs) exhibit limited temporal awareness. Although existing approaches based on Group Relative Policy Optimization (GRPO) attempt to improve temporal grounding, they still str

Brian Buckley, Adrian O'Hagan, Marie Galligan

The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variationa

Zeping Shi, Wenbin Wu, Zhiwei Zhang, Yuhan Du

We report the design and implementation of a high-flux, high-efficiency magneto-infrared spectroscopy system optimized for broadband measurements in high magnetic fields. The setup integrates a Fourier transform infrared spectrometer, a 12 T cryogen-free superconducting magnet, precision-polished and gold-plated light tubes, custom-designed reflective focusi

Kento Asai, Seishi Enomoto, Takuya Hirose, Masato Yamanaka

We study cosmological constraints on the asymmetric mediator scenario, a variant of the scotogenic model that addresses the origins of neutrino masses, dark matter (DM), and the baryon asymmetry. An SU(2)$_L$ doublet scalar $\eta$ mediates between the visible and dark sectors, while a singlet scalar $\sigma$ serves as the DM candidate. We evaluate the DM rel

Izaskun Jimenez-Serra, Giuliana Cosentino, Francisco Montenegro-Montes, Laura Colzi

Contrary to popular belief, the interstellar medium (ISM) is not empty; it is filled with atoms, dust particles, and molecules. Some of these molecules may have been the very building blocks of life that, delivered to Earth via comets and meteorites, could have given rise to Life itself. A large-area single-dish telescope with superb sensitivity, field-of-vi

Zixin Tang, Yiming Chen, Quentin Rouxel, Dianxi Li

Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision, a coarse-to-fine teleoperation system with immersive situa

Valentin Promies, Jasper Nalbach, Erika Ábrahám, Paul Wagner

To check the satisfiability of (non-linear) real arithmetic formulas, modern satisfiability modulo theories (SMT) solving algorithms like NLSAT depend heavily on single cell construction, the task of generalizing a sample point to a connected subset (cell) of $\mathbb{R}^n$, that contains the sample and over which a given set of polynomials is sign-invariant

Yifei He, Haoting Zhen, Gyu-Boong Jo

Dipolar quantum gases, encompassing atoms and molecules with significant dipole moments, exhibit unique long-range and anisotropic dipole-dipole interactions (DDI), distinguishing them from systems dominated by short-range contact interactions. This review explores their behavior across dimensions, focusing on magnetic atoms in quasi-2D in comparison to 3D.

Shruti Tandon, Apoorva Singh, B. N. Goswami, R. I. Sujith

The formation and intensification of a tropical cyclone is a complex phenomenon involving several feedback interactions between momentum and energetics of the storm, and across multiple spatio-temporal scales. Background vorticity interactions in the turbulent atmosphere play a crucial role in the formation of cyclones. How these vorticity interactions lead

Shreedhar Govil, Didier Stricker, Jason Rambach

Predicting driver attention is a critical problem for developing explainable autonomous driving systems and understanding driver behavior in mixed human-autonomous vehicle traffic scenarios. Although significant progress has been made through large-scale driver attention datasets and deep learning architectures, existing works are constrained by narrow front

Bruno Maria René Gonzalez, Peter Gjøl Jensen, Stefan Schmid, Jiří Srba

Petri nets are a modeling formalism capable of describing complex distributed systems and there exists a large number of both academic and industrial tools that enable automatic verification of model properties. Typical questions include reachability analysis and model checking against logics like LTL and CTL. However, these logics fall short when describing

I. Bailleul

This set of five lectures provides an introduction to regularity structures and their use for the study of singular stochastic partial differential equations. Two appendices provide some additional informations that enter in the main text either as some technical results or as some results that deepen the context within which we set these lectures.

Nick Leenders, Thomas Quadt, Boris Cule, Roy Lindelauf

Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. Existing approaches rely on Gaussian Process (GP) surrogates, which provide strong performance but limited interpretability. This limits real-world usability in high-stakes domains, such as healthcare, where interpretab

Moritz Hartlieb, Matteo Verni

We study the topological Brauer group of generalized Kummer varieties. We prove that it vanishes when their dimension is divisible by 4, while for all other dimensions except dimension 10 we prove that it is at most 8-torsion.

Melih Solmaz, Klaus Eitel, Alfredo Davide Ferella, Felix Kratzmeier

In deep underground laboratories, environmental neutrons, which are produced at the cavern walls, introduce a source of background to rare event searches. The flux and spectrum of the ambient neutrons vary considerably with time and location. Precise knowledge of this background is necessary to devise shielding and veto mechanisms, thereby improving the sens

Sascha Dick, Christoph Thompson, Chih-Wei Wu, Pablo Delgado

In this paper, we present an initial study for extending Open Dataset of Audio Quality (ODAQ) towards the impact of stereo processing. Monaural artifacts from ODAQ were adapted in combinations with left-right (LR) and mid-side (MS) stereo processing, across stimuli including solo instruments, typical wide stereo mixes and and hard-panned mixes. Listening tes

Marcin Baranek, Paweł Przybyłowicz

In this paper, we introduce the SPINNs (stochastic physics-informed neural networks) in a systematic manner. This provides a mathematical framework for approximating the solution of stochastic differential equations (SDEs) driven by Levy noise using artificial neural networks.

Evgenii Vasinovich, Alexander Moskvin

A simple theoretical model is developed to describe spin reorientation (SR) transitions in rare-earth orthoferrites and orthochromites RFeO3 and RCrO3. Within a ``single-doublet'' approach, the free energy includes anisotropy contributions from the 3d-sublattice and the splitting of the lower doublet of 4f-ions. The model predicts various types of SR transit

Wentao Wan, Kaiyu Wu, Qingyang Ma, Nan Kang

Recently, Visual Programming (VP) based on large language models (LLMs) has rapidly developed and demonstrated significant potential in complex Visual Reasoning (VR) tasks. Previous works to enhance VP have primarily focused on improving the quality of LLM-generated visual programs. However, they have neglected to optimize the VP-invoked pre-trained models,

Huizheng Wang, Taiquan Wei, Zichuan Wang, Dingcheng Jiang

Large language models (LLMs) demand significant memory and computation resources. Wafer-scale chips (WSCs) provide high computation power and die-to-die (D2D) bandwidth but face a unique trade-off between on-chip memory and compute resources due to limited wafer area. Therefore, tensor parallelism strategies for wafer should leverage communication advantages

Long D. H. My, Shao-Hen Chiew, Jing Hao Chai, Hui Khoon Ng

Surface codes are a popular error-correction route to fault-tolerant quantum computation. The so-called exponential backlog problem that can arise when one has to do logical $T$-gates within the surface code demands real-time decoding of the syndrome information to diagnose the appropriate Pauli frame in which to do the gate. This in turn puts a minimum requ

Ayaka Okuya, Satoshi Okuzumi, Aki Takigawa, Hanako Enomoto

Polluted white dwarfs provide unique constraints on the elemental compositions of planetary bodies. The tidal disruption of accreting bodies is thought to form circumstellar dusty disks, whose emission spectra could offer additional insights into the mineral phases of the accreted solid material. Silicates are detected in the mid-infrared spectra of several

Xingjian Wu, Hanyin Cheng, Xiangfei Qiu, Zhengyu Li

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support both deterministic and probabilistic forecasting via generative probabilistic modeling, thus ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. Through adapting variants

Kelly J. Davis

Formal, automated theorem proving has long been viewed as a challenge to artificial intelligence. We introduce here a new approach to computer theorem proving, one that employs specialized language models for Lean4 proof generation combined with recursive decomposition of difficult theorems into simpler entailing propositions. These models are coordinated th

Nat Sothanaphan

Erdos, Herzog and Piranian asked whether, for $n$ points in the plane with fixed diameter (maximum distance between points), an arrangement of a regular $n$-gon maximizes their product of all pairs of distances. Recently, it was discovered that, for every even $n \geq 4$, a regular $n$-gon is not a maximizer. However, the discovered improvement turns out to

Beatrice Degasperi

Hrushovski proved the Lie model theorem in full generality with model theoretic methods. The theorem states that for every approximate group there exists a generalized definable locally compact model, which, simplifying, is a quasi-homomorphism from the group generated by the approximate subgroup to a locally compact group with some particular properties. Pi

Esmail Gumaan

The choice of attention mechanism in Transformer models involves a critical trade-off between modeling quality and inference efficiency. Multi-Head Attention (MHA) offers the best quality but suffers from large Key-Value (KV) cache memory requirements during inference. Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) reduce memory usage but ofte

Michael Hinz, Jonas M. Tölle, Lauri Viitasaari

We consider four prototypes of variational problems and prove the existence of fractal minimizers through the direct method in the calculus of variations. By design these minimizers are H\"older curves or H\"older parametrizations of hypersurfaces whose images generally have a non-integer Hausdorff dimension. Although their origin is deterministic, their reg

Mahiro Atsuta, Naoto Dainobu, Takenori Kataoka

The local equivariant Tamagawa number conjecture (local ETNC) for a motive predicts a precise relationship between the local arithmetic complex and the root numbers which appear in the (conjectural) functional equations of the $L$-functions. In this paper, we prove the local ETNC for the Tate motives under a certain unramified condition at $p$. Our result gi

Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan

We study the problem of multi-class classification under system-level constraints expressible as linear functionals over randomized classifiers. We propose a post-processing approach that adjusts a given base classifier to satisfy general constraints without retraining. Our method formulates the problem as a linearly constrained stochastic program over rando

Gideon Chiusole, Christian Kuehn

We study the deterministic skeleton of the renormalized stochastic Allen--Cahn equation in spatial dimension $2$. For all sufficiently small regularization parameters $\delta>0$, we construct monotone traveling wave front solutions connecting the renormalized equilibria, derive a small-$\delta$ asymptotic description of their profile and speed, and identify

Yiqing Zhou, Yu Lei, Shuzheng Si, Qingyan Sun

Managing extensive context remains a critical bottleneck for Large Language Models (LLMs), particularly in applications like long-document question answering and autonomous agents where lengthy inputs incur high computational costs and introduce noise. Existing compression techniques often disrupt local coherence through discrete token removal or rely on imp

Aaron Poole, Kostas Skenderis, Marika Taylor

We present a comprehensive discussion of gravitational charges and radiation in four-dimensional asymptotically locally de Sitter (AldS) spacetimes. Such spacetimes have compact spatial sections and possess spacelike past and future infinities, $\mathscr{I}^\pm$. We show that the variational problem is well-posed if one specifies a conformal class up to thre

Tadeu Freitas, Carlos Novo, Manuel E. Correia, Rolando Martins

The growing sophistication, frequency, and diversity of cyberattacks increasingly exceed the capacity of individual entities to fully understand and counter them. While existing solutions, such as Security Information and Event Management (SIEM) systems, Security Orchestration, Automation, and Response (SOAR) platforms, and Security Operation Center (SOC), p

Salvatore Romano, Marco Grassia, Giuseppe Mangioni

Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-world networks for various applications. Graph Generative Models (GGMs) have emerged as a promising solution to this problem, leveraging deep learning techniques to learn the underly

Erion Morina, Martin Holler

This paper addresses the problem of learning reaction-diffusion (RD) systems from data while ensuring physical consistency and well-posedness of the learned models. Building on a regularization-based framework for structured model learning, we focus on learning parameterized reaction terms and investigate how to incorporate key physical properties, such as m

Juan-José Guzmán-Landa, Juan-Manuel Torres-Moreno, Miguel Figueroa-Saavedra, Ligia Quintana-Torres

The aim of this article is to introduce two Context-Free Grammars (CFG) for Nawatl Corpora expansion. Nawatl is an Amerindian language (it is a National Language of Mexico) of the $\pi$-language type, i.e. a language with few digital resources. For this reason the corpora available for the learning of Large Language Models (LLMs) are virtually non-existent,

Francisco M. Montenegro-Montes, Patricia Sánchez-Blázquez, Tony Mroczkowski, Armando Gil de Paz

Observations in the visible and near infrared are transforming our view of the processes affecting galaxy evolution, much of which is dominated by interactions with the large scale environment. Yet a complete picture is missing, as no corresponding high resolution view of the warm/hot intracluster, circumgalactic, and intergalactic media exists over large ar

Estelle Zheng, Nathan Cerisara, Sébastien Warichet, Emmanuel Helbert

Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods such as QLoRA reduce the number of trainable parameters, yet still incur high memory usage induced by the backward pass in the full model. We revisit Ladder Side Tuning (LST), a rarely explored PEFT technique tha

Jimmie Kwok, Holger Caesar, Andras Palffy

Automotive radar has shown promising developments in environment perception due to its cost-effectiveness and robustness in adverse weather conditions. However, the limited availability of annotated radar data poses a significant challenge for advancing radar-based perception systems. To address this limitation, we propose a novel framework to generate 4D ra

Juze Zhang, Changan Chen, Xin Chen, Heng Yu

Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human behavior as a translation task co-speech gesture or text-to-motion that maps a fixed utterance to motion clips-without requiring agentic decision-making about when to move, what to do, or how to adapt across mult

Zhan Zhang

Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. We present a practical framework, \emph{Inverted Causality Focusing Algorithm} (ICFA), that treats search as a target-conditioned reweighting process. ICFA reuses an available proposal sampler a

Ruozhao Yang, Mingfei Cheng, Gelei Deng, Tianwei Zhang

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models (LLMs) offer promising opportunities for automation, existing applications rely on simplistic prompting with

Rawan Alyahya, Asrar Alruwayqi, Atheer Alqarni, Asma Alkhaldi

The presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM). Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies. In this study, we explore radiogenomics techniques, a promising approach in precision medicine, to identify genetic ma

Kevin Dijkstra, Deepesh Toshniwal

This paper introduces a Variational Multiscale Stabilization (VMS) formulation of the incompressible Navier--Stokes equations that utilizes the Finite Element Exterior Calculus (FEEC) framework. The FEEC framework preserves the geometric and topological structure of continuous spaces and PDEs in the discrete spaces and model, and helps build stable and conve

Divyansh Pareek, Sewoong Oh, Simon S. Du

The success of modern multimodal representation learning relies on internet-scale datasets. Due to the low quality of a large fraction of raw web data, data curation has become a critical step in the training pipeline. Filtering using a trained model (i.e., teacher-based filtering) has emerged as a successful solution, leveraging a pre-trained model to compu

Alexandr Malijevský, Martin Pospíšil, Miriam Magočiová, Jiří Janek

We investigate complete wetting and drying at sinusoidally corrugated solid walls, focusing on the effects of wall geometry and interaction range. Two distinct interaction models are considered: one incorporating only short-ranged (SR) forces (applied to drying), and another including long-ranged (LR) van der Waals interactions (applied to wetting). The SR m

Aneesha Fernando, Surangika Ranathunga, Kristin Stock, Raj Prasanna

Georeferencing text documents has typically relied on either gazetteer-based methods to assign geographic coordinates to place names, or on language modelling approaches that associate textual terms with geographic locations. However, many location descriptions specify positions relatively with spatial relationships, making geocoding based solely on place na

Xixian Wu, Yang Ou, Pengchao Tian, Zian Yang

Vision-language models (VLMs) have demonstrated significant potential in Visual Question Answering (VQA). However, the susceptibility of VLMs to hallucinations can lead to overconfident yet incorrect answers, severely undermining answer reliability. To address this, we propose Dual-Assessment for VLM Reliability (DAVR), a novel framework that integrates Self

Romeo Brunetti, Klaus Fredenhagen, Kasia Rejzner

In this review, we summarize the main ideas of perturbative algebraic quantum field theory, which is a rigorous framework combining some of the Haag-Kastler axioms with perturbative methods involving formal power series. It allows for the construction of interacting QFT models in four spacetime dimensions and works on arbitrary globally hyperbolic manifolds.

Harry Ballington

Citation graph visualisation is a useful tool for contextual awareness in academic research. Unfortunately, existing solutions can suffer from several drawbacks, such as a poor scaling, shallow network traversal, freemium gating, and slow build times. Oignon is a free, open-source tool for systematically exploring academic research. It uses a dual-path ranki

Pradeep Pillai

Over the last decade several attempts have been made to extend biodiversity studies in ways that would allow researchers to explore how biodiversity-ecosystem functioning relationships may change across different spatial and temporal scales. Unfortunately, the studies based on these attempts often overlooked the serious issues that can arise when quantifying

Yixin Tan, Fang Feng, Shengfeng Zhu

We develop mathematical models for shape design and topology optimization in structural contact problems involving friction between elastic and rigid bodies. The governing mechanical constraint is a nonlinear, non-smooth, and non-convex hemivariational inequality, which provides a more general and realistic description of frictional contact forces than stand

Tao Tang, Enhui Ma, xia zhou, Letian Wang

Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inefficient. Generative models have emerged as a promising solution by synthesizing realistic sensor data. However, existing approaches primarily focus on single-modality generation, l

Karin Erdmann, Adam Skowyrski

We classify tame symmetric algebras of period four which are closely related to the spherical algebras introduced in [7]. This note provides a classification in the special case which naturally appears, when dealing with biregular Gabriel quivers.

Francesco Pierri, Theo Araujo, Sanne Kruikemeier, Philipp Lorenz-Spreen

The Digital Services Act (DSA) introduced by the European Union in 2022 offers a landmark framework for platform transparency, with Article 40 enabling vetted researchers to access data from major online platforms. Yet significant legal, technical, and organizational barriers still hinder effective research on systemic online risks. This piece outlines the k

Xichen Ding, Jianzhe Gao, Cong Pan, Wenguan Wang

Aerial Vision-and-Language Navigation (AVLN) requires Unmanned Aerial Vehicle (UAV) agents to localize targets in large-scale urban environments based on linguistic instructions. While successful navigation demands both global environmental reasoning and local scene comprehension, existing UAV agents typically adopt mono-granularity frameworks that struggle

Jiarong Fan, Juhyun Park. Thi Phuong Thuy Vo, Nicolas Brunel

Conformal prediction (CP) offers a principled framework for uncertainty quantification, but it fails to guarantee coverage when faced with missing covariates. In addressing the heterogeneity induced by various missing patterns, Mask-Conditional Valid (MCV) Coverage has emerged as a more desirable property than Marginal Coverage. In this work, we adapt split

Cornelius Wolff, Daniel Gomm, Madelon Hulsebos

Advances in large language models have accelerated progress in text-to-SQL, methods for converting natural language queries into valid SQL queries. A key bottleneck for developing generalizable text-to-SQL models is the lack of large-scale datasets with sufficient schema and query complexity, domain coverage, and task diversity. We introduce SQaLe: a large-s

Marthe Ballon, Andres Algaba, Brecht Verbeken, Vincent Ginis

Recent advances in the finetuning of large language models (LLMs) have significantly improved their performance on established benchmarks, emphasizing the need for increasingly difficult, synthetic data. A key step in this data generation pipeline is a method for estimating problem difficulty. Current approaches, such as human calibration or performance-base

Weifeng Qiu

We propose one finite element method for both second order linear uniformly elliptic PDE in non-divergence form and the uniformly elliptic Hamilton-Jacobi-Bellman (HJB) equation. For both linear elliptic PDE in non-divergence form and the HJB equation, we prove the well-posedness of strong solution in $W^{2,p}(Ω)$ and optimal convergence in discrete $W^{2,p}

Yang Bai, Liudi Yang, George Eskandar, Fengyi Shen

Video diffusion models provide powerful real-world simulators for embodied AI but remain limited in controllability for robotic manipulation. Recent works on trajectory-conditioned video generation address this gap but often rely on 2D trajectories or single modality conditioning, which restricts their ability to produce controllable and consistent robotic d

Gengshou Cao, Tamir Shaqarin, Zhutao Jiang, Yutong Liu

Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investigate a square FAWG consisting of 10x10 individually controllable fans with 4 cm width and a maximum velocity of 17 m/s. The goal is to maximize the turbulence intensity in the test

Céline Duval, Eric Luçon

We study the asymptotic properties of the solutions of a nonlinear renewal equation. The main contribution of the present article is to provide stability and convergence results around equilibrium solutions, under some local subcritical condition. Quantitative rates of convergence to equilibrium are established. Instability results are given in both the crit

Mengxue Zhang, Qingrui Cai, Yinyin Chen, Hang Jin

Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold s

Thibaud Plantegenet, Mihai Arghir, Mohamed-Amine Hassini, Pascal Jolly

The present work presents the experimental analyses of a rigid (short) and a flexible (long) rotor subject to thermal unbalance effects. The rotors are supported by a ball bearing and by a cylindrical journal bearing. The differential heating generated in the journal bearing is responsible for the thermal unbalance. The results obtained with the short rotor

Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea, Veronika Eyring

Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability than their classical counterparts. Here, we present work on using a quantum neural net (QNN) to develop a parameterization of cloud cover for an Earth system model (ESM). ESMs are ne

Chunyi Li, Emanuele Macrì, Alexander Perry, Paolo Stellari

We prove that stability conditions on the derived category of a product of curves of positive genus are uniquely determined by their central charge and the phase of skyscraper sheaves. As an application, we construct stability conditions on Hilbert schemes of points on certain surfaces, including some K3 surfaces of Kummer type.

Yuxi Sun, Wei Gao, Hongzhan Lin, Jing Ma

Human behaviors are often guided or constrained by social norms, which are defined as shared, commonsense rules. For example, underlying an action ``\textit{report a witnessed crime}" are social norms that inform our conduct, such as ``\textit{It is expected to be brave to report crimes}''. Current AI systems that assess valence (i.e., support or oppose) of

Mayank Sewlia, Christos K. Verginis, Dimos V. Dimarogonas

We consider the problem of cooperative manipulation by a mobile multi-manipulator system operating in obstacle-cluttered and highly constrained environments under spatio-temporal task specifications. The task requires transporting a grasped object while respecting both continuous robot dynamics and discrete geometric constraints arising from obstacles and na

Hadi M. Daniali, Martin v. Mohrenschildt

In modal analysis, the prevalent use of Gaussian-based wavelets (such as Morlet and Gabor) for damping estimation is rarely questioned. In this study, we challenge this conventional approach by systematically exploring envelope-based damping estimators and proposing a data-driven framework that optimizes the shape and parameters of the envelope utilizing syn

Robson Christie, Jaewoo Joo, Greg Kaplanek, Vincent Vennin

We investigate how quantum decoherence influences the tunneling dynamics of quantum fields in cosmological spacetimes. Specifically, we study a scalar field in an asymmetric double-well potential during inflation, coupled to environmental degrees of freedom provided by a continuum of spectator fields. This setup enables a systematic derivation of both Markov

Pamela Klaassen, David Eden, Alessio Traficante, Henrik Beuther

(Sub-)millimeter spectral lines can be used not only to understand the chemical complexity and enrichment history of an observed portion of our Galaxy, but with spectrally resolved lines, they reveal the physical conditions, dynamics, and even the ionisation state and magnetic field strengths of the gas component of our Galaxy. They are prime tracers of mass

Timo Klein, Thomas Lang, Andrii Shkabrii, Alexander Sturm

The exponential volume growth of hyperbolic geometry can embed the hierarchical relationships between states in reinforcement learning (RL) with far less distortion than Euclidean space. However, hyperbolic deep RL faces severe optimization challenges, and formal analysis of why optimization fails is lacking. We identify key factors that determine the succes

Yunfang Cai, Yongyuan Xiang, Kaifan Ji

High spatial resolution observation of solar prominence is an important observation subject of the New Vacuum Solar Telescope (NVST). While the current level of observation and image reconstruction technologies for solar prominences are advanced, a significant challenge remains in achieving high-precision alignment among high-resolution prominence images obs

Fu Liu, Warut Thawinrak

We extend the notion of parking function polytopes and study their geometric and combinatorial structure, including normal fans, face posets, and $h$-polynomials, as well as their connections to other classes of polytopes. To capture their combinatorial features, we introduce generalizations of ordered set partitions, called binary partitions and skewed bina

P. Fauverge, P. Jean, K. Sokolovsky, C. C. Cheung

Context. Numerous classical novae have been observed to emit {\gamma}-rays (E > 100 MeV) detected by the Fermi-LAT. The prevailing hypothesis attributes this emission to the interaction of accelerated particles within shocks in the nova ejecta. However, the lack of non-thermal X-ray detection coincident with the {\gamma}-rays remains a challenge to this theo

Qi He

Large-scale AI data center portfolios procure identical SKUs across geographically heterogeneous campuses, yet finance and operations require a single system-level 'world price' per SKU for budgeting and planning. A common practice is deployment-weighted blending of campus prices, which preserves total cost but can trigger Simpson-type aggregation failures:

Cassandra Krause, Mattias P. Heinrich, Ron Keuth

Between $15\,\%$ and $45\,\%$ of children experience a fracture during their growth years, making accurate diagnosis essential. Fracture morphology, alongside location and fragment angle, is a key diagnostic feature. In this work, we propose a method to extract fracture morphology by assigning automatically global AO codes to corresponding fracture bounding