December 2025 arXiv papers — page 71

Paul J. Groot, Simone Scaringi, Nancy Elias-Rosa

Underluminous Thermonuclear Supernovae (uTSNe) are an emerging class of transient events that resemble classic Supernovae Type Ia, but peak at much lower luminosities. Suspected to be the deflagrations of white dwarfs, they directly link the final stages of low-mass binary star evolution to extragalactic studies that are critical for cosmology. The ability t

Yuling Zhang, Lei Kang, Yancong Zhang, Guohui Hu

Understanding how molecular machines transduce mechanical force into chemical signals is a central goal in chemistry. The mechanosensitive ion channel Piezo1 is an archetypal nanoscale mechanotransducer, but the molecular principles by which it decodes distinct mechanical stimuli remain elusive. Here, we combine large-scale molecular dynamics simulations wit

Joan Licata, Vera Vértesi

The Giroux Correspondence states that two open book decompositions supporting the same contact structure are related by a sequence of positive open book stabilisations and destabilisations. In this note we show that any two open book decompositions supporting isotopic contact structures admit a common positive stabilisation.

Jiajun Sun, Zhanrui Cai, Wei Zhong

Stability and reproducibility are essential considerations in various applications of statistical methods. False Discovery Rate (FDR) control methods are able to control false signals in scientific discoveries. However, many FDR control methods, such as Model-X knockoff and data-splitting approaches, yield unstable results due to the inherent randomness of t

Barbara Brandolini, Ida de Bonis, Vincenzo Ferone, Gianpaolo Piscitelli

We present a fractional counterpart of a generalized Kohler-Jobin inequality, showing that, among all bounded, open sets $\Omega\subset \mathbb{R}^N$ with Lipschitz boundary, having the same fractional torsional rigidity, the first Dirichlet eigenvalue $\lambda_1(\Omega)$ of the fractional Laplacian attains its minimum on balls. With the same arguments we al

Yonathan Bornfeld, Shai Avidan

Private Inference (PI) uses cryptographic primitives to perform privacy preserving machine learning. In this setting, the owner of the network runs inference on the data of the client without learning anything about the data and without revealing any information about the model. It has been observed that a major computational bottleneck of PI is the calculat

S. A. Mousavi, A. Moshfeghi, F. Davoodian, E. Salahinejad

Nanopores (NPs) grown by anodizing can be partially hidden beneath a relatively compact surface oxide layer, which limits the volumetric surface area of these nanostructures. In this work, nitinol (NiTi) alloy was anodized in an electrolyte containing ethylene glycol, water, and sodium chloride in static and stirred electrolyte stages with the aim of removin

Léo Butsanets, Charles Corbière, Julien Khlaut, Pierre Manceron

In this work, we introduce RadImageNet-VQA, a large-scale dataset designed to advance radiologic visual question answering (VQA) on CT and MRI exams. Existing medical VQA datasets are limited in scale, dominated by X-ray imaging or biomedical illustrations, and often prone to text-based shortcuts. RadImageNet-VQA is built from expert-curated annotations and

Zabir Al Nazi, GM Shahariar, Md. Abrar Hossain, Wei Peng

Theory of Mind (ToM) - the ability to attribute beliefs and intents to others - is fundamental for social intelligence, yet Vision-Language Model (VLM) evaluations remain largely Western-centric. In this work, we introduce CulturalToM-VQA, a benchmark of 5,095 visually situated ToM probes across diverse cultural contexts, rituals, and social norms. Construct

Uttam Tiwari, Pragya Arora, A K Sood, Sriram Ramaswamy

Spatial confinement can induce geometrical frustration in condensed phases, giving rise to topological defects that confer materials with new and exotic properties. Here, we experimentally uncover the remarkable effect of confinement-induced defect strings termed `grain boundary scars' on the behavior of dense two-dimensional assemblies of granular spinners,

Majid Hosseinzadeh, Erfan Salahinejad

Platinum (Pt) and ruthenium (Ru), both members of the platinum-group metals (PGMs), are renowned for their exceptional resistance to corrosion, oxidation, and high temperatures, making them promising candidates for advanced high-temperature applications. This study investigates the direct current (DC) electrodeposition of Pt, Ru, and a binary Pt-Ru alloy ont

E. Bagnaschi, M. Chakraborti, S. Heinemeyer, I. Saha

One of the main goals of the ongoing LHC program is the search for BSM physics, with EW SUSY partners still allowed with masses as low as a few hundred GeV. Over the last years, searches for the ``golden channel'', $pp \to \tilde\chi^0_2 \tilde\chi^\pm_1 \to \tilde\chi^0_1 Z^{(*)} \, \tilde\chi^0_1 W^{\pm (*)}$ show consistent excesses between CMS and ATLAS

Ezgi Dağtekin, Ercan Erkalkan

Artificial intelligence (AI) based learning assistants and chatbots are increasingly integrated into higher education. While these tools are often evaluated in terms of technical performance, their successful and ethical use also depends on psychological factors such as trust, perceived risk, technology anxiety, and students general attitudes toward AI. This

Guangneng Hu

Large language models (LLMs) have shown potential in recommendation systems (RecSys) by using them as either knowledge enhancer or zero-shot ranker. A key challenge lies in the large semantic gap between LLMs and RecSys where the former internalizes language world knowledge while the latter captures personalized world of behaviors. Unfortunately, the researc

Athanasios Beslikas, Alan Sola

We study membership of rational inner functions on the bidisk $\mathbb{D}^2$ in a scale of Dirichlet spaces considered by Bera, Chavan, and Ghara, and in higher-order variants of these spaces. We give a characterization for membership in terms of the geometric concept of contact order of a rational inner function at its singular points, and we further record

Sravani Gunnu, Shanmukha Guttula, Hima Patel

Large Language Models (LLMs) are increasingly applied to real-world code generation, where functional correctness alone is insufficient for reliable deployment, developers also expect adherence to explicit requirements for robustness, formatting, and security. Existing benchmarks primarily assess correctness through test-case execution, offering limited insi

Yan Liu, Zeyu Ren, Pingzhong Tang, Zihe Wang

Deterministic auctions are attractive in practice due to their transparency, simplicity, and ease of implementation, motivating a sharper understanding of when they can attain the same outcomes as randomized mechanisms. We study deterministic implementation in single-item auctions under two notions of outcomes: (revenue, welfare) pairs and interim allocation

Jiajun Wu, Jian Yang, Wei Zhang, Lin Jing

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive labeled (e.g., question-answering pairs) or unlabeled datasets (e.g., code snippets), which are often expensive and difficult to obtain at scale. To address this limitation, this pa

Majid Hosseinzadeh, Erfan Salahinejad

This study investigates the influence of varying Pt/Ru ratios on the oxidation mechanism of NiCoCrAlYTa coatings with electrodeposited, vacuum-annealed Ptsingle bondRu overlays. Weight change measurements, scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were used

N. Kabeya, Y. Tokumoto, K. Tomiyama, N. Kimura

We report the superconducting gap in the van der Waals layered quasicrystal Cu-doped Ta$_{1.6}$Te, using a fast relaxation technique that removes the large nuclear contribution of $^{181}$Ta. The initial-slope method enabled detection of the electronic specific heat down to 60~mK, revealing a fully gapped state with $\Delta(0)/k_{\rm B} = 1.43$~K. Both the g

Mattéo Clémot, Julie Digne, Julien Tierny

The Delaunay-Rips filtration is a lighter and faster alternative to the well-known Rips filtration for low-dimensional Euclidean point clouds. Despite these advantages, it has seldom been studied. In this paper, we aim to bridge this gap by providing a thorough theoretical and empirical analysis of this construction. From a theoretical perspective, we show h

Yu-Pin Hsu, Yi-Hsuan Tseng

This paper investigates an information update system in which a mobile device monitors a physical process and sends status updates to an access point (AP). A fundamental trade-off arises between the timeliness of the information maintained at the AP and the update cost incurred at the device. To address this trade-off, we propose an online algorithm that det

Hadrien Kurkjian

We present an exact solution of the quantum kinetic equation of a weakly interacting Fermi gas in the crossover from the degenerate Fermi-liquid regime to the classical Boltzmann gas. We construct families of orthogonal polynomials tailored to each angular momentum channel, enabling a fast and systematically improvable decomposition of the phase-space distri

Andreas Krebs, Arne Meier

Hemaspaandra~et~al.~[JCSS 2010] conjectured that satisfiability for multi-modal logic restricted to the connectives XOR and 1, over frame classes T, S4, and S5, is solvable in polynomial time. We refute this for S5 frames, by proving NP-hardness.

Sara Avesani, Leevan Ling, Francesco Marchetti, Tizian Wenzel

We extend sharp direct and inverse approximation statements for kernel-based methods for finitely smooth kernels, i.e. those whose native spaces are norm-equivalent to Sobolev spaces. In particular, our inverse results are now formulated for a broad class of approximation schemes beyond interpolation, extending existing theory. Building on these results, we

Peixuan Zhang, Shuchen Weng, Jiajun Tang, Si Li

Social media platforms enable users to express emotions by posting text with accompanying images. In this paper, we propose the Affective Image Filter (AIF) task, which aims to reflect visually-abstract emotions from text into visually-concrete images, thereby creating emotionally compelling results. We first introduce the AIF dataset and the formulation of

Gabriel Benedict, Matthew Butler, Naved Merchant, Eetu Salama-Laine

The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem-solving tasks as measures of general intelligence. Small Language Models (SLMs) -- defined here as models under 10B parameters -- typically score 3-4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to ca

Fatima-Zahrae Akhyar, Wei Zhang, Gabriel Stoltz, Christof Schütte

Given a probability distribution $\mu$ in $\mathbb{R}^d$ represented by data, we study in this paper the generative modeling of the corresponding conditional probability distributions on the level-sets of a collective variable $\xi:\mathbb{R}^d \rightarrow \mathbb{R}^k$, where $1 \le k<d$. We propose a general and efficient learning approach that can learn g

Zhengmian Hu

Can artificial intelligence discover, from raw experience and without human supervision, concepts that humans have discovered? One challenge is that human concepts themselves are fluid: conceptual boundaries can shift, split, and merge as inquiry progresses (e.g., Pluto is no longer considered a planet). To make progress, we need a definition of "concept" th

Ruiting Liang, Samuel Dyson, Rina Foygel Barber, Daniel E. Holz

We study the problem of coincidence detection in time series data, where we aim to determine whether the appearance of simultaneous or near-simultaneous events in two time series is indicative of some shared underlying signal or synchronicity, or might simply be due to random chance. This problem arises across many applications, such as astrophysics (e.g., d

Haomin Qi, Fengfei Yu, Chengbo Huang

We present GraphCue, a topology-grounded retrieval and agent-in-the-loop framework for automated SDN configuration. Each case is abstracted into a JSON graph and embedded using a lightweight three-layer GCN trained with contrastive learning. The nearest validated reference is injected into a structured prompt that constrains code generation, while a verifier

Deqing Liu, Yinfeng Gao, Deheng Qian, Qichao Zhang

Existing end-to-end autonomous driving methods typically rely on imitation learning (IL) but face a key challenge: the misalignment between open-loop training and closed-loop deployment. This misalignment often triggers driver-initiated takeovers and system disengagements during closed-loop execution. How to leverage those expert takeover data from disengage

Enrique Alvarez, Jesus Anero

The Kerr-Schild gauge is generalized to the case that the vector generating the deformation is not null. Contrary to naive expectations, this vector generates a finite expansion for the curvature tensor. We prove a theorem on the conditions for the deformed metric being Ricci flat, namely that the deformation vector must be irrotational (then geodesic) in th

Yidong Chai, Yi Liu, Mohammadreza Ebrahimi, Weifeng Li

Social media platforms are plagued by harmful content such as hate speech, misinformation, and extremist rhetoric. Machine learning (ML) models are widely adopted to detect such content; however, they remain highly vulnerable to adversarial attacks, wherein malicious users subtly modify text to evade detection. Enhancing adversarial robustness is therefore e

Nurdan Karapinar, Emre Isik, Natalie A. Krivova, Hakan V. Senavci

Sunspot groups often emerge in spatial-temporal clusters, known as nests or complexes of activity. Quantifying how frequently such nesting occurs is important for understanding the organisation and recurrence of solar magnetic fields. We introduce an automated approach based on kernel density estimation and DBSCAN clustering to identify nests in the longitud

Yuqing Niu, Jieke Shi, Ruidong Han, Ye Liu

Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, provide isolated regions of CPU and memory for secure computation and are increasingly used to protect sensitive data and code across diverse application domains. However, little is known about how developers actually use TEEs in practice. This paper presents the first large-scale em

Dmitry Solnyshkov, Rafal Mirek, Darius Urbonas, Etsuki Kobiyama

We study the formation of topological defects via the Kibble-Zurek mechanism in a polariton supersolid in a liquid crystal microcavity with tunable Rashba-Dresselhaus spin-orbit coupling. We predict analytically two different scalings in the slow- and fast-quench regimes, and confirm these predictions numerically. We also present experimental results for the

Alexander Zhuravlev, Dmitry Tatarnikov, Yury Kurenkov, Stanislav Glybovski1

Studying the nature of electromagnetic fields of dipole sources over a homogeneous flat ground or impedance surfaces has a long history. In general, at a long distance $r$ from the source, the near-surface field is mostly contributed by the geometrical optics term (describing the radiation pattern), a guided wave, and the higher-order reactive contribution r

Wanli Xie, Jiale Zhang, Ruiqing Cao

In multi-attribute decision-making problems where the attribute values are interval grey numbers, a simplified form based on kernels and the degree of greyness is presented. Combining fuzzy graph theory with the kernel and the degree of greyness of interval grey numbers, grey graphs and their corresponding operation rules are presented. This paper presents a

Scott Thomson, Michael Bewong, Arash Mahboubi, Tanveer Zia

Our systematisation of knowledge on Social Engineering Attacks (SEAs), identifies the human, organisational, and adversarial dimensions of cyber threats. It addresses the growing risks posed by SEAs, highly relevant in the context physical cyber places, such as travellers at airports and residents in smart cities, and synthesizes findings from peer reviewed

Ruihui Wang, Jie Jiang, Yukun Luo

Solar polar fields are essential for the solar cycle and the heliospheric magnetic field. Cycle 25 is now entering its declining phase, the critical period during which most of the cycle's polar fields are established. Therefore, reliable polar-field prediction is now especially important. Polar-field evolution is governed by the poleward transport of alread

Orhan Donmez, G. Mustafa, M. Yousaf, Faisal Javed

In this study, we investigate the accretion dynamics and test particle motion around a non-rotating, spherically symmetric Lee-Wick black hole (BH) to reveal how the model parameters affect orbital stability and the quasi-periodic oscillations (QPOs) observed in X-ray binary systems. The spacetime geometry, characterized by the BH mass and the coupling param

Luca Fardin, Chris Armstrong, Alberto Astolfo, Sebastian Ignacio Allen Binet

Investigating the structure of matter at the nanoscale non destructively is a key capability enabled by X-ray imaging. One of the most powerful nano-imaging methods is X-ray ptychography, a coherent diffraction imaging technique that has become the go-to method at synchrotron facilities for applications ranging from brain imaging to battery materials. Howeve

Tingxiao Zhou, Leying Zhang, Zhengyang Chen, Yanmin Qian

The potential of synthetic data in text-to-speech (TTS) model training has gained increasing attention, yet its rationality and effectiveness require systematic validation. In this study, we systematically investigate the feasibility of using purely synthetic data for TTS training and explore how various factors--including text richness, speaker diversity, n

Ludovic Alvado, Nicolas Arveuf, Edouard Bechetoille, Guillaume Blanchard

We present the architecture, design and first test results of SPIDER, the first prototype of a TSMC CMOS 65 nm ASIC designed for the time measurement path of LHCb Electromagnetic Calorimeter after LS4 Upgrade. The main requirements for the readout of this detector are a time resolution below 15 ps rms above 5 GeV, and a channel occupancy up to 30\% (12 Meven

Wisnu Uriawan, Muhammad Aditya Hafizh Zahran, Inayah Ayu Deswita, Muhammad Ahsani Taqwim

Learning Wudhu for young children requires engaging and interactive media to foster a deep understanding of the worship procedures. This study aims to develop a Wudhu learning application based on Augmented Reality (AR) as an interactive and fun educational medium. The development method used includes the stages of needs analysis, system design, implementati

Najmeh Ghaderi, Birgit Jacob

This paper deals with the problem of designing unknown input observers for a class of coupled semilinear wave partial differential equations (PDE) systems. A state observer is designed to estimate the uncertain coupled wave PDE systems. Then, the analysis of the asymptotic stability and $H_{\infty}$ performance for the observer design of coupled wave PDE sys

Ivan Kralj, Lodovico Giaretta, Gordan Ježić, Ivana Podnar Žarko

Spatio-Temporal Graph Neural Networks (ST-GNNs) are well-suited for processing high-frequency data streams from geographically distributed sensors in smart mobility systems. However, their deployment at the edge across distributed compute nodes (cloudlets) createssubstantial communication overhead due to repeated transmission of overlapping node features bet

Chenming Zhou, Jiaan Wang, Yu Li, Lei Li

The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we intr

Shishuai Wang, Florian Wiesinger, Noemi Sgambelluri, Carolin Pirkl

The 3D fast silent multi-parametric mapping sequence with zero echo time (MuPa-ZTE) is a novel quantitative MRI (qMRI) acquisition that enables nearly silent scanning by using a 3D phyllotaxis sampling scheme. MuPa-ZTE improves patient comfort and motion robustness, and generates quantitative maps of T1, T2, and proton density using the acquired weighted ima

Xijie Huang, Jinhan Li, Tianyue Wu, Xin Zhou

Modern autonomous navigation systems predominantly rely on lidar and depth cameras. However, a fundamental question remains: Can flying robots navigate in clutter using solely monocular RGB images? Given the prohibitive costs of real-world data collection, learning policies in simulation offers a promising path. Yet, deploying such policies directly in the p

Yuki Horii, Tomoaki Murata, Tsutomu Kobayashi

We explore the possibility of parity-violating, nonminimally coupled 2-form field theories that retain the same dynamical degrees of freedom as a massive 2-form and thus are ghost-free. Starting from the most general kinetic terms and dimension four couplings between the 2-form field and the curvature tensors, we find a two-parameter family of such theories.

Mohamed Chenene, Jeanne Rouhier, Jean Daniélou, Mihir Sarkar

Public debates surrounding infrastructure and energy projects involve complex networks of stakeholders, arguments, and evolving narratives. Understanding these dynamics is crucial for anticipating controversies and informing engagement strategies, yet existing tools in media intelligence largely rely on descriptive analytics with limited transparency. This p

Ghafran Khan, Patryk Mach

We extend a recently proposed Monte Carlo scheme for computing stationary solutions of the general-relativistic Vlasov equation to the Kerr spacetime. As an example, we focus on razor-thin configurations of a gas confined to the equatorial plane and extending to spatial infinity. We consider monoenergetic models as well as solutions corresponding to planar M

Burt Totaro

A natural problem of algebraic dynamics is to classify the complex projective varieties that admit an endomorphism of degree greater than 1. Joshi solved the problem for all canonical del Pezzo surfaces with Picard number 1 except one, a surface with a du Val singularity of type $E_8$. The method of Bott vanishing does not resolve this case. We show here tha

Haomin Qi, Chengbo Huang, Zihan Dai, Yunkai Gao

We present a governance-aware hybrid fine-tuning framework for multilingual, low-resource adaptation of large language models. The core algorithm combines gradient-aligned low-rank updates with structured orthogonal transformations through layer-wise mixing and introduces unitary constraints in selected sub-layers to stabilize deep optimization. In tandem wi

Cuixin Yang, Rongkang Dong, Kin-Man Lam, Yuhang Zhang

As augmented reality and virtual reality applications gain popularity, image processing for OmniDirectional Images (ODIs) has attracted increasing attention. OmniDirectional Image Super-Resolution (ODISR) is a promising technique for enhancing the visual quality of ODIs. Before performing super-resolution, ODIs are typically projected from a spherical surfac

Jikai Jin, Vasilis Syrgkanis

We establish a general statistical optimality theory for estimation problems where the target parameter is a linear functional of an unknown nuisance component that must be estimated from data. This formulation covers many causal and predictive parameters and has applications to numerous disciplines. We adopt the structure-agnostic framework introduced by \c

Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose

Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes w

M. Arca-Sedda, I. Dvorkin, G. Franciolini, M. C. Artale

Ground-based gravitational-wave (GW) observatories have transformed our view of compact-object mergers, yet their reach still limits a comprehensive reconstruction of the processes that generate these systems. Only next-generation observatories, with order-of-magnitude improvements in sensitivity and access to lower frequencies, will be capable of radically

Jason Aebischer

Kaon physics observables are highly sensitive to New Physics (NP) effects and form in combination with the Standard Model Effective Field Theory (SMEFT) a powerful tool to study physics that goes beyond the Standard Model paradigm. We review recent SMEFT analyses in the Kaon sector and point out novel directions that might be investigated in the future.

Shiwei Xue, Ping Zhu, Haolong Li

Toroidal Alfv\'{e}n eigenmodes (TAEs) and energetic particle modes (EPMs) can both be excited by energetic particles from auxiliary heating and fusion-born alpha particles in a tokamak. Using the hybrid kinetic-MHD model implemented in the NIMROD code, the excitation of these modes and their properties are investigated in an advanced tokamak configuration wi

Xiao Tang, Zhen Ma, Bin Li, Cong Li

The pervasive threat of jamming attacks, particularly from adaptive jammers capable of optimizing their strategies, poses a significant challenge to the security and reliability of wireless communications. This paper addresses this issue by investigating anti-jamming communications empowered by an active reconfigurable intelligent surface. The strategic inte

Arthur Aubret, Jochen Triesch

There is strong and diverse evidence for mental rotation (MR) abilities in adults. However, current evidence for MR in children rests on just a few behavioral paradigms adapted from the adult literature. Here, we leverage recent computational models of the development of children's object recognition abilities to re-assess the evidence for MR in children. Th

Andrea Pelissetto, Davide Rossini, Ettore Vicari

We investigate the quantum dynamics generated by quantum quenches (QQs) of the Hamiltonian parameters in many-body systems, focusing on protocols that cross first-order and continuous quantum transitions, both in finite-size systems and in the thermodynamic limit. As a paradigmatic example, we consider the quantum Ising chain in the presence of homogeneous t

Yu Hua, Yaru Fu, Yalin Liu, Zheng Shi

The Pinching Antenna System (PAS) has emerged as a promising technology to dynamically reconfigure wireless propagation environments in 6G networks. By activating radiating elements at arbitrary positions along a dielectric waveguide, PAS can establish strong line-of-sight (LoS) links with users, significantly enhancing channel gain and deployment flexibilit

Shihang Li, Zhiqiang Gong, Minming Ye, Yue Gao

Recent advances in neural portrait animation have demonstrated remarked potential for applications in virtual avatars, telepresence, and digital content creation. However, traditional explicit warping approaches often struggle with accurate motion transfer or recovering missing regions, while recent attention-based warping methods, though effective, frequent

Dmitry Chicherin, Yu Wu, Zihao Wu, Yongqun Xu

We calculate all three-loop, five-point, massless planar Feynman integral families in the dimensional regularization scheme. This is a new milestone in Feynman integral computations. The analysis covers four distinct families of Feynman integrals for this configuration, for all of which we derive the canonical differential equations. Our results also confirm

Tatiana Nizkaia, Thomas Solymosi, Paolo Malgaretti, Peter Wasserscheid

We derive a theoretical model to elucidate the inhibition of catalytic activity during the dehydrogenation of Liquid Organic Hydrogen Carriers (LOHC). Within our model, we account for the reversible nature of the hydrogenation-dehydrogenation reaction as well as the transport of both LOHC and produced hydrogen. Our analysis reveals that the main limiting fac

Jochen Szangolies

Research at the intersection of quantum gravity and quantum information theory has seen significant success in describing the emergence of spacetime and gravity from quantum states whose entanglement entropy approximately obeys an area law. In a different direction, the Kaluza-Klein proposal aims to recover gauge symmetries by means of dimensional reduction

Qi-An Su, Qi Song, Hongjing Li, Kaiwen Fu

High-resolution sensing plays a significant role in scientific research and industrial production, but the practical implementation is constrained by the physical mechanisms of the sensors. To address the critical limitation, we propose a high-resolution sensing approach based on quantum state discrimination. Distinct from conventional strategies, the propos

Sander Moonemans, Sebastiaan Ram, Frédérique Meeuwsen, Carlijn Lems

Vision-language models (VLMs) have the potential to become co-pilots for pathologists. However, most VLMs either focus on small regions of interest within whole-slide images, provide only static slide-level outputs, or rely on data that is not publicly available, limiting reproducibility. Furthermore, training data containing WSIs paired with detailed clinic

Chaeha Kim

We provide causal mechanistic validation that in-context learning (ICL) decomposes into two separable mechanisms: Task Schema (abstract task type recognition) and Binding (specific input-output associations). Through activation patching experiments across 9 models from 7 Transformer families plus Mamba (370M-13B parameters), we establish three key findings:

Marine Aulnette, Michael Le Bars, Patrice Le Gal

In the present study, we test the predictions of the {\omega}-Equation against laboratory experiments with direct measurements of the vertical velocity w. Our results are further completed through the use of theoretical models of oceanic vortices, with the aim of helping oceanographers in better quantifying regions of upwelling and downwelling in the ocean.

Jiyun Kong, Jun-Hyuk Kim, Jong-Seok Lee

Video frame prediction extrapolates future frames from previous frames, but suffers from prediction errors in dynamic scenes due to the lack of information about the next frame. Event cameras address this limitation by capturing per-pixel brightness changes asynchronously with high temporal resolution. Prior research on event-based video frame prediction has

Hidefumi Matsuda, Koichi Hattori, Koichi Murase

We propose physics-informed neural networks (PINNs) as a numerical solver for relativistic spin hydrodynamics and demonstrate that the total angular momentum, i.e., the sum of orbital and spin angular momentum, is accurately conserved throughout the fluid evolution by imposing the conservation law directly in the loss function as a training target. This enab

Jose Vargas Quiros, Bart Liefers, Karin van Garderen, Jeroen Vermeulen

Purpose: To provide a diverse, high-quality dataset of color fundus images (CFIs) with detailed artery-vein (A/V) segmentation annotations, supporting the development and evaluation of machine learning algorithms for vascular analysis in ophthalmology. Methods: CFIs were sampled from the longitudinal Rotterdam Study (RS), encompassing a wide range of ages, d

Momina Liaqat Ali, Muhammad Abid, Muhammad Saqlain, Jose M. Merigo

Although large language models (LLMs) have recently become effective tools for language-conditioned control in embodied systems, instability, slow convergence, and hallucinated actions continue to limit their direct application to continuous control. A modular neuro-symbolic control framework that clearly distinguishes between low-level motion execution and

Lu Wei, Yuta Nakashima, Noa Garcia

The widespread adoption of text-to-image (T2I) generation has raised concerns about privacy, bias, and copyright violations. Concept erasure techniques offer a promising solution by selectively removing undesired concepts from pre-trained models without requiring full retraining. However, these methods are often evaluated on a limited set of concepts, relyin

Yunkai Dang, Meiyi Zhu, Donghao Wang, Yizhuo Zhang

Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on low-resolution imagery, and some high-resolution benchmarks suffer from flawed reasoning-task designs. We show that text-only LLMs can perform competitively with multimodal vision-

Dario Lucente, Letizia Cerutti, Martina Brofiga, Alessandro Sarracino

Neuronal cultures exhibit a complex activity, bursts, or avalanches, characterized by the coexistence of scale invariance and synchronization, quite stable with the percentage of inhibitory neurons. While this bistable behavior has been already observed in the past, the characterization of the statistical properties of avalanche activity and their temporal o

Michael Merry, Pat Riddle, Jim Warren

Inherent explainability is the gold standard in Explainable Artificial Intelligence (XAI). However, there is not a consistent definition or test to demonstrate inherent explainability. Work to date either characterises explainability through metrics, or appeals to intuition - "we know it when we see it". We propose a globally applicable criterion for inheren

Sahibpreet Singh, Pawan Kumar

This chapter explores the complexities of sports governance, taxation, dispute resolution, and the impact of digital transformation within the sports sector. This study identifies a critical research gap regarding the integration of innovative technologies to enhance governance and talent identification in sports law. The objective is to evaluate how data-dr

Fanxian Pei, Run-Wu Zhang, Lei Li, Dan Li

Generating and controlling spin current in miniaturized magnetic quantum devices remains a central objective of spintronics, due to its potential to enable future energy-efficient information technologies. Among the existing magnetic phases, altermagnetism have recently emerged as a highly promising platform for spin current generation and control, going bey

Michael Megrelishvili

We study the topology of circularly ordered sets. While the algebraic notion is classical, the general topological theory has received comparatively little attention. In this work we provide a self-contained topological exposition and present several new directions and results. Our aim is to initiate a systematic study of generalized circularly ordered topol

SuBeen Lee, GilHan Park, WonJun Moon, Hyun Seok Seong

Despite the impressive zero-shot capabilities of Vision-Language Models (VLMs), they often struggle in downstream tasks with distribution shifts from the pre-training data. Few-Shot Adaptation (FSA-VLM) has emerged as a key solution, typically using Parameter-Efficient Fine-Tuning (PEFT) to adapt models with minimal data. However, these PEFT methods are cons

Qi Song, Honglin Li, Yingchen Yu, Haoyi Zhou

Recent releases such as o3 highlight human-like "thinking with images" reasoning that combines tool use with stepwise verification, yet most open-source approaches still rely on text-only chains, rigid visual schemas, or single-step pipelines, limiting flexibility, interpretability, and transferability on complex tasks. We introduce CodeDance, which explores

F. Marino, F. Bonaiti, P. Demol, S. Bacca

Coupled-cluster theory is a powerful tool for first-principles calculations of atomic nuclei, enabling accurate predictions of nuclear observables across the Segr\`e chart. While coupled-cluster computations are especially efficient at shell closures, extensions have been developed to tackle open-shell nuclei, by exploiting the equation-of-motion method or b

Asil Kaan Bozcuoglu, Ziyuan Liu

RoboEarth was a pioneering initiative in cloud robotics, establishing a foundational framework for robots to share and exchange knowledge about actions, objects, and environments through a standardized knowledge graph. Initially, this knowledge was predominantly hand-crafted by engineers using RDF triples within OWL Ontologies, with updates, such as changes

Daksh Jain, Aarya Jain, Ashutosh Desai, Avyakt Verma

Strategic decision-making in Pok\'emon battles presents a unique testbed for evaluating large language models. Pok\'emon battles demand reasoning about type matchups, statistical trade-offs, and risk assessment, skills that mirror human strategic thinking. This work examines whether Large Language Models (LLMs) can serve as competent battle agents, capable o

Wenhao Yang, Yu Xia, Jinlong Huang, Shiyin Lu

Recent advances in large Vision-Language Models (VLMs) have exhibited strong reasoning capabilities on complex visual tasks by thinking with images in their Chain-of-Thought (CoT), which is achieved by actively invoking tools to analyze visual inputs rather than merely perceiving them. However, existing models often struggle to reflect on and correct themsel

Edgar Ribeiro João, Manuel Parra-Royón, Julián Garrido

The unprecedented volume of data from the Square Kilometre Array (SKA) telescopes will require the implementation of robust and solid strategies for efficient data processing and management. In this context, the SKA Regional Centre Network (SRCNet) -- a collaborative global infrastructure comprising multiple regional centres distributed across various geogra

M. F. Fauzi, H. S. Ramadhan, A. Sulaksono

We examine the observational discrepancies of two widely used models describing anisotropic (dark) matter distributions around a black hole, focusing on their photon spheres, shadow radii, and lensing observables. The models considered are the vacuum and Einstein cluster dark matter models, characterized by negative and zero radial pressure, respectively. Th

Kyeongmin Yeo, Yunhong Min, Jaihoon Kim, Minhyuk Sung

We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our approach focuses on effectively leveraging the embedding space and diffusion priors of pretrained latent image generative models while learning a material latent space, MatLat, through targeted fine-tuning. Unlike

Ratbek Dzhumashev, Ainura Tursunalieva

Traditional instrumental variable (IV) methods often struggle with weak or invalid instruments and rely heavily on external data. We introduce a Synthetic Instrumental Variable (SIV) approach that constructs valid instruments using only existing data. Our method leverages a data-driven dual tendency (DT) condition to identify valid instruments without requir

Guangjun Shen, Jiangpeng Wang, Xuekang Zhang

In this paper, we establish the propagation of chaos and Euler-Maruyama method of DDSDE driven by multiplicative fractional Brownian motion with Hurst parameter $H\in (\frac{\sqrt{5}-1}{2},1)$. We have not only obtained an upper bound for the error of the Euler-Maruyama method but also verified the correctness of this result via systematic numerical simulati

Abdullah M. Zyarah, Dhireesha Kudithipudi

Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual l

Fanpu Cao, Yaofo Chen, Zeng You, Wei Luo

Diffusion Transformers (DiTs) have achieved state-of-the-art performance in generative modeling, yet their high computational cost hinders real-time deployment. While feature caching offers a promising training-free acceleration solution by exploiting temporal redundancy, existing methods suffer from two key limitations: (1) uniform caching intervals fail to

Ulrich Abel, Ana Maria Acu, Margareta Heilmann, Ioan Rasa

This paper presents a representation for the kernel of the composition of multivariate Bernstein-Durrmeyer operators for functions defined on the standard simplex in $\mathbb{R}^d$.

Wuyi Liu, Le Jin, Junxian Yang, Yuanchao Yu

Automated defect inspection of assembled Printed Circuit Board Assemblies (PCBA) is quite challenging due to the insufficient labeled data, micro-defects with just a few pixels in visually-complex and high-resolution images. To address these challenges, we present HiSIR-Net, a High resolution, Self-supervised Reconstruction framework for pixel-wise PCBA loca