October 2025 arXiv papers — page 142

Shaoyang Zhou, Yingshu Li, Yunyi Liu, Lingqiao Liu

Chest Xray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision language models are increasingly applied to automate Chest Xray radiology report generation (CXRRRG), aiming for clinically accurate descriptions while reducing manual effort. Conven

Cong Zhao, Xiaozhou Zou

High-harmonic generation (HHG) in solids provides a powerful platform to probe ultrafast electron dynamics and interband--intraband coupling. However, disentangling the complex many-body contributions in the HHG spectrum remains challenging. Here we introduce a machine-learning approach based on a Transformer encoder to analyze and reconstruct HHG signals co

Siddhartha Chakraborty, Asok K. Nanda

Recently, weighted cumulative residual Tsallis entropy has been introduced in the literature as a generalization of weighted cumulative residual entropy. We study some new properties of weighted cumulative residual Tsallis entropy measure. Next, we propose some non-parametric estimators of this measure. Asymptotic properties of these estimators are discussed

Shun Maeta

Catino, Mastrolia, Monticelli, and Rigoli have launched an ambitious program to study known geometric solitons from a unified perspective, which they term Einstein-type manifolds. This framework allows one to treat Ricci solitons, Yamabe solitons, and all of their generalizations simultaneously. Einstein-type manifolds are characterized by four constants $\a

Ayush Chaudhary

Traditional security scanners fail when facing new attack patterns they haven't seen before. They rely on fixed rules and predetermined signatures, making them blind to novel threats. We present a fundamentally different approach: instead of memorizing specific attack patterns, we learn what makes systems genuinely secure. Our key insight is simple yet power

Meshkat Dolat, Andrew David Wright, Soudabeh Bahrami Gharamaleki, Loukia-Pantzechroula Merkouri

This study presents a detailed, open-source kinetic modelling computational framework for CO2 capture and utilisation using a newly formulated dual-function material (DFM) comprising 15 wt% Ni, 1 wt% Ru, and 10 wt% CaO supported on spherical alumina. A finite difference reactor model was developed to simulate the cyclic adsorption, purge, and hydrogenation s

Calvin M. Hooper, Ian R. Hooper, Simon A. R. Horsley

Quasinormal modes characterise the transient response of static optical cavities. Here, we introduce the notion of a Floquet quasinormal mode to describe transient responses in photonic time crystals. Contrasting their static counterparts, exceptional points associated with symmetry transitions are an inherent feature, as modes spontaneously and non-perturba

Šimun Mandić, Ana Senkić, Nataša Vujičić

Organic - inorganic heterostructures (HS) combine the strong light absorption and exciton generation capabilities of organic molecules with the unique excitonic properties of layered transition metal dichalcogenides (TMDs), where the interfacial band alignment dictates the optical response. In this work, we investigate the influence of H2Pc molecules on CVD-

Valentin Seitz, Jordy Trilaksono, Marta Garcia-Gasulla

Ensuring good performance is a key aspect in the development of codes that target HPC machines. As these codes are under active development, the necessity to detect performance degradation early in the development process becomes apparent. In addition, having meaningful insight into application scaling behavior tightly coupled to the development workflow is

Dirk Lauinger, Deepjyoti Deka, Sungho Shin

Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation

Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu

Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH

Marina Godinho, Dave Murphy

The main result of this paper is that there is an additive equivalence between $\overline{\mathcal{C}}_n$, the Paquette-Yildirim completion of the discrete cluster categories of Dynkin type $A_{\infty}$, and the perfect derived category of a certain DG algebra. This additive equivalence preserves some of the triangulated structure: it commutes with the suspe

F. N. Antlitz, X. Wang, M. Hoelzl, G. T. A. Huijsmans

Accurate modeling of core instabilities in tokamak plasmas is essential to understand the underlying physical mechanisms and their impact on plasma confinement. The ideal stability of the internal kink mode and the m = 1 collisionless tearing mode are analyzed numerically both with gyrokinetic and MHD codes. We compare the different models implemented in the

Norman Do, Paul Norbury

We define q-analogues of Mirzakhani's recursion for Weil-Petersson volumes and the Stanford-Witten recursion for super Weil-Petersson volumes. Okuyama recently introduced a q-deformation of the Gaussian Hermitian matrix model which produces quasi-polynomials that recover the Weil-Petersson volumes via a rescaled q to 1 limit. The q-deformations of the Weil-P

Chengyang Dong, Nan Guo

Autonomous driving decision-making at unsignalized intersections is highly challenging due to complex dynamic interactions and high conflict risks. To achieve proactive safety control, this paper proposes a deep reinforcement learning (DRL) decision-making framework integrated with a biased attention mechanism. The framework is built upon the Soft Actor-Crit

Jonas Stapmanns, Catarina Dias, Luke Eilers, Tobias Kühn

Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average cost constraint, and the curvature of the cost function. We find that when the c

Yin Kang, Weiyi Yin, Xianzhe Li, Yixuan Liu

High-power multi-color terahertz (THz) radiation exhibits extraordinary scientific application prospects at various scientific frontiers, for its capacity to deliver THz excitation at multiple frequencies simultaneously. However, the generation of high-power multi-color THz radiation with tunable frequencies remains a challenge for existing techniques. Here,

Peng Chen, Deliang Wei, Jiale Yao, Fang Li

Missing entries in multi dimensional data pose significant challenges for downstream analysis across diverse real world applications. These data are naturally represented as tensors, and recent completion methods integrating global low rank priors with plug and play denoisers have demonstrated strong empirical performance. However, these approaches often rel

Luigi Ambrosio, Toni Ikonen, Danka Lučić, Enrico Pasqualetto

This is the second of two works concerning the Sobolev calculus on metric measure spaces and its applications. In this work, we focus on several approaches to vector calculus in the non-smooth setting of complete and separable metric spaces equipped with a boundedly-finite Borel measure. More precisely, we study different notions of (co)vector fields and der

Dingyi Zuo, Hongjie Zhang, Jie Ou, Chaosheng Feng

The polarization of opinions, information segregation, and cognitive biases on social media have attracted significant academic attention. In real-world networks, information often spans multiple interrelated topics, posing challenges for opinion evolution and highlighting the need for frameworks that simulate interactions among topics. Existing studies base

Jialong Zuo, Yongtai Deng, Lingdong Kong, Jingkang Yang

Recent studies have shown that agent-based systems leveraging large language models (LLMs) for key information retrieval and integration have emerged as a promising approach for long video understanding. However, these systems face two major challenges. First, they typically perform modeling and reasoning on individual frames, struggling to capture the tempo

Spencer Boone, Joan Pau Sanchez Cuartialles, Stéphanie Lizy-Destrez

Saturn's moon Enceladus is an exciting destination for future exploration missions due to the scientifically interesting geyser region located on its South pole. In this work, we compile the different types of science orbit configurations that have been proposed in the literature and present numerical methods to compute each of them in the Saturn-Enceladus c

Joao Zambujal-Oliveira, Andre Silva, Rui Vasconcelos

As awareness of health and environmental issues grows, the demand for organic food is rising worldwide, yet consumers still struggle to distinguish genuine organic products from conventional ones. This information asymmetry creates incentives for some producers to mislabel conventional goods as organic in order to charge higher prices, threatening market int

Shunnosuke Yoshimura, Kento Kawaharazuka, Kei Okada

Tactile sensors have a wide range of applications, from utilization in robotic grippers to human motion measurement. If tactile sensors could be fabricated and integrated more easily, their applicability would further expand. In this study, we propose a tactile sensor-M3D-skin-that can be easily fabricated with high versatility by leveraging the infill patte

Lisa Fischer, Laura Blázquez Martínez, Robin Chenivière, Johann Troles

Thermal phonons are a major source of decoherence in quantum mechanical systems. Operating in the quantum ground state is therefore often an experimental prerequisite. Additionally to passive cooling in a cryogenic environment, active laser cooling enables the reduction of phonons at specific acoustic frequencies. Brillouin cooling has been used to show effi

Andrea Musso, Diego Rybski, Dirk Helbing, Frank Neffke

The share of the world population living in cities with more than one million people rose from 11% in 1975 to 24% in 2025 (our estimates). Will this trend towards greater concentration in large cities continue or level off? We introduce two new city population datasets that use consistent city definitions across countries and over time. The first covers the

Alvaro Ortiz, Tomasa Rodrigo, Pablo Saborido

Geopolitical and geoeconomic shocks reprice sovereign credit risk through different transmission channels. Using a daily panel of 42 advanced and emerging economies over 2018--2025, we show that geopolitical shocks raise sovereign CDS spreads primarily through direct sovereign repricing, while the Global Financial Cycle (GFC) channel moves in the opposite di

Laurin Brunner, Tobias Wiener, Tiago Mendes-Santos, Reyhaneh Khasseh

Recent advances in quantum simulator experiments enable unprecedented access to quantum many-body states through snapshot measurements of individual many-body configurations. Here, we introduce an exact renormalization group (RG) transformation that can be directly applied to any such snapshot dataset. Our SnapshotRG operates in real space, but can also be d

Etienne Levecque, Aurélien Noirault, Tomáš Pevn{ý}, Jan Butora

Steganographic schemes dedicated to generated images modify the seed vector in the latent space to embed a message. Whereas most steganalysis methods attempt to detect the embedding in the image space, this paper proposes to perform steganalysis in the latent space by modeling the statistical distribution of the norm of the latent vector. Specifically, we an

Quentin Pognan, Kyohei Kawaguchi, Shinya Wanajo, Sho Fujibayashi

Nebular phase kilonovae (KNe) have significant infra-red (IR) emission thought to be mostly forbidden emission lines from rapid neutron capture (r-process) species in neutron star merger ejecta. Lanthanide elements in particular have complex, open f-shell atomic structures with many IR transitions. Using non-local thermodynamic equilibrium (NLTE) radiative t

Sarah Lumpp, Mathias Drton

Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gaussian distributions converge to a singular Gaussian limit. Algebraically, this is due to the fact that at singular covariance matrices, Schur complements are not continuous functio

Anders Jerkstrand, Quentin Pognan, Smaranika Banerjee, Nicholas Sterling

A central question regarding neutron star mergers is whether they are able to produce all the r-process elements, from first to third peak. The high abundances of first-peak elements (atomic number $Z \sim 31-40$) in the solar composition means they may dominate the ejecta mass in kilonovae. We here study theoretical infrared signatures of such light element

Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng

We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and

Huu Tien Nguyen, Ahmed Karam Eldaly

This paper introduces a novel framework for image quality transfer based on conditional flow matching (CFM). Unlike conventional generative models that rely on iterative sampling or adversarial objectives, CFM learns a continuous flow between a noise distribution and target data distributions through the direct regression of an optimal velocity field. We eva

Fabrizio Orlando, Deborah Volpe, Giacomo Orlandi, Mariagrazia Graziano

Combinatorial Optimization (CO) problems exhibit exponential complexity, making their resolution challenging. Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm to obtain approximate solutions to largescale CO problems written in the Ising form. It explores the solution space by emulating the adiabatic evolution of a network of Kerr-nonlin

Jan Miller

The Efficient Adaptive Transformer (EAT) framework unifies three adaptive efficiency techniques - progressive token pruning, sparse attention, and dynamic early exiting - into a single, reproducible architecture for input-adaptive inference. EAT provides an open-source benchmarking pipeline that automates data processing, timing, and ablation across GLUE tas

Muhammad Yasir, Tong Lining, Kazuharu Bamba

Exact black holes in the Einstein Euler-Heisenberg theory are explored with an exponential entropy framework by using the topological current $\Psi$-mapping theory. The topology classes are investigated through the canonical, mixed, and grand canonical ensembles. In particular, the magnetic charge is fixed for the canonical ensemble, whereas the magnetic pot

Zahra Mobini, Hien Quoc Ngo, Ardavan Rahimian, Anvar Tukmanov

We investigate a fronthaul-limited cell-free massive multiple-input multiple-output (CF-mMIMO) system and propose a hybrid centralized-distributed precoding strategy that dynamically adapts to varying fronthaul and spectral efficiency (SE) requirements. The proposed approach divides users into two groups: one served by centralized precoding and the other by

Masahiro Negishi, Hyunsoo Park, Kinga O. Mastej, Aron Walsh

To address pressing scientific challenges such as climate change, increasingly sophisticated generative models are being developed to efficiently sample the large chemical space of potential functional materials. The proliferation of these models has necessitated the establishment of rigorous evaluation metrics. While uniqueness (U), novelty (N), and stabili

Aobo Chen, Zhenyu Yu

In the context of metric measure spaces, we introduce an axiomatic formulation of mixed local and nonlocal $p$-energy forms. Within this framework, we use the Poincar\'{e} inequality, the cutoff Sobolev inequality, and mild assumptions on the jump measure to establish the weak and strong elliptic Harnack inequalities for such mixed forms. Our approach is bas

Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf

Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learn

Lizhang Chen, Jonathan Li, Kaizhao Liang, Baiyu Su

We introduce Cautious Weight Decay (CWD), a one-line, optimizer-agnostic modification that applies weight decay only to parameter coordinates whose signs align with the optimizer update. Unlike standard decoupled decay, which implicitly optimizes a regularized or constrained objective, CWD preserves the original loss and admits a bilevel interpretation: it i

Shengyin Sun, Chen Ma, Jiehao Chen

In recent years, graph neural networks (GNNs) have facilitated the development of graph data mining. However, training GNNs requires sufficient labeled task-specific data, which is expensive and sometimes unavailable. To be less dependent on labeled data, recent studies propose to pre-train GNNs in a self-supervised manner and then apply the pre-trained GNNs

André Torneiro, Diogo Monteiro, Paulo Novais, Pedro Rangel Henriques

Urban monitoring of public infrastructure (such as waste bins, road signs, vegetation, sidewalks, and construction sites) poses significant challenges due to the diversity of objects, environments, and contextual conditions involved. Current state-of-the-art approaches typically rely on a combination of IoT sensors and manual inspections, which are costly, d

Yuyao Ge, Lingrui Mei, Zenghao Duan, Tianhao Li

The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology termed "Vibe Coding" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension. Despite its transformative pot

Sören Henning, Adriano Vogel, Esteban Perez-Wohlfeil, Otmar Ertl

Benchmarks and performance experiments are frequently conducted in cloud environments. However, their results are often treated with caution, as the presumed high variability of performance in the cloud raises concerns about reproducibility and credibility. In a recent study, we empirically quantified the impact of this variability on benchmarking results by

Ye Tian, Yanqiu Yu, Liangliang Song, Zhiquan Liu

Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementa

Sungkyung Kang, JungHwan Park, Masaki Taniguchi

We develop a $\mathrm{Pin}(2) \times \mathbb{Z}_2$-equivariant refinement of the lattice homotopy type for computing equivariant Seiberg--Witten Floer homotopy types. As an application, we construct a relatively exotic diffeomorphism on a compact contractible 4-manifold that survives two stabilizations.

Kazuki Daikuhara, Tadayuki Kodama, Haruka Kusakabe, Charles C. Steidel

We investigate galaxy populations in the HS1700+64 protocluster at $z=2.30$, characterized by two prominent linear filaments traced by spatially extended Ly$\alpha$ blobs. We conducted a wide area mapping of emission line galaxies across the protocluster using the unique combination of three matched narrow-band filters, corresponding to Ly$\alpha$, H$\alpha$

Junhyuk So, Chiwoong Lee, Shinyoung Lee, Jungseul Ok

Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong suc

Ivan Poparić, Leandra Vranješ Markić, Jordi Boronat

Using density functional theory, we have theoretically studied the formation and the stability of vortices in quantum liquid droplets composed of a mixture of hyperfine states of potassium. Following the experimental setup that produced quantum droplets for the first time, we work with squeezed drops that are compressed in one direction. By squeezing the dro

Hailay Kidu Teklehaymanot, Wolfgang Nejdl

Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-scale cross-linguistic evaluation of tokenization efficiency in over 200 languages to systematically quantify computational inequities in large language models (LLMs). Using a stan

C. L. Pereira, F. Braga-Ribas, B. Sicardy, R. Leiva

The centaur (2060) Chiron has long been a candidate for hosting material in orbit, based on occultation and photometric and spectroscopic data. Here, we present a multichord stellar occultation observed on 2023 September 10 UT that reveals new and complex structures surrounding Chiron. High-cadence light curves show multiple secondary events that are best ex

Wenjing Bian, Axel Barroso-Laguna, Tommaso Cavallari, Victor Adrian Prisacariu

Scene coordinate regression (SCR) models have proven to be powerful implicit scene representations for 3D vision, enabling visual relocalization and structure-from-motion. SCR models are trained specifically for one scene. If training images imply insufficient multi-view constraints SCR models degenerate. We present a probabilistic reinterpretation of traini

Vaishali Dhanoa, Gabriela Molina León, Eve Hoggan, Eduard Gröller

Visualization dashboards are regularly used for data exploration and analysis, but their complex interactions and interlinked views often require time-consuming onboarding sessions from dashboard authors. Preparing these onboarding materials is labor-intensive and requires manual updates when dashboards change. Recent advances in multimodal interaction power

Tim J. Schoonbeek, Shao-Hsuan Hung, Dan Lehman, Hans Onvlee

Procedure step recognition (PSR) aims to identify all correctly completed steps and their sequential order in videos of procedural tasks. The existing state-of-the-art models rely solely on detecting assembly object states in individual video frames. By neglecting temporal features, model robustness and accuracy are limited, especially when objects are parti

Huifa Li, Feilong Tang, Haochen Xue, Yulong Li

Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In thi

Olga Ovcharenko, Sebastian Schelter

Ensuring data quality at scale remains a persistent challenge for large organizations. Despite recent advances, maintaining accurate and consistent data is still complex, especially when dealing with multiple data modalities. Traditional error detection and correction methods tend to focus on a single modality, typically a table, and often miss cross-modal e

Honglin Wen, Pierre Pinson

Wind power producers can benefit from forming coalitions to participate cooperatively in electricity markets. To support such collaboration, various profit allocation rules rooted in cooperative game theory have been proposed. However, existing approaches overlook the lack of coherence among producers regarding forecast information, which may lead to ambigui

Vibhoothi Vibhoothi, Julien Zouein, Shanker Shreejith, Jean-Baptiste Kempf

The widespread adoption of advanced video codecs such as AV1 is often hindered by their high decoding complexity, posing a challenge for battery-constrained devices. While encoders can be configured to produce bitstreams that are decoder-friendly, estimating the decoding complexity and energy overhead for a given video is non-trivial. In this study, we syste

Vibhoothi Vibhoothi, François Pitié, Anil Kokaram

In the last decade, video workflows in the cinema production ecosystem have presented new use cases for video streaming technology. These new workflows, e.g. in On-set Virtual Production, present the challenge of requiring precise quality control and energy efficiency. Existing approaches to transcoding often fall short of these requirements, either due to a

Marta Dell'Atti, Galina Filipuk

We study several polynomial Hamiltonian systems of PIV-type (including the mixed case quasi-PIV), and show that via the iterative process of polynomial regularisation, it is possible to identify the "minimal" Hamiltonian system. The selected Hamiltonian function is associated with the Newton polygon with minimal area and smallest highest total degree.

Klaus Linhard, Philipp Bulling

Undesired acoustic feedback is a known issue in communication systems, such as speech in-car communication, public address systems, or hearing aids. Without additional precautions, there is a high risk that the adaptive filter - intended to cancel the feedback path - also suppresses parts of the desired signal. One solution is to decorrelate the loudspeaker

Sharath M Shankaranarayana, Soumava Kumar Roy, Prasad Sudhakar, Chandan Aladahalli

Although deep neural networks have provided impressive gains in performance, these improvements often come at the cost of increased computational complexity and expense. In many cases, such as 3D volume or video classification tasks, not all slices or frames are necessary due to inherent redundancies. To address this issue, we propose a novel learnable subsa

Bogdan Butyrin, Eric Moulines, Alexey Naumov, Sergey Samsonov

In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA) algorithm with decreasing step size. We consider the normal approximation by the Gaussian distribution with covariance matrix predicted by the Polyak-Juditsky central limit theor

Zilong Cui, Ran Gu

Semidefinite programming (SDP) is a fundamental class of convex optimization problems with diverse applications in mathematics, engineering, machine learning, and related disciplines. This paper investigates the application of the polyhedral bundle method to standard SDPs. The basic idea of this method is to approximate semidefinite constraints using linear

Elias Kiritsis, Sergio Morales-Tejera, Christopher Rosen

Generic solutions are studied in Einstein-scalar gravity in an ansatz that can interpolate between de Sitter and Anti-de Sitter regimes. The scalar potential is arbitrary. All solutions are determined by their end-points in the scalar field space. All such end-points are classified. This provides a complete classification and characterization of the full spa

Michel Boileau, Teruaki Kitano, Yuta Nozaki

For a link $L$ in the $3$-sphere, the $\pi$-orbifold group $G^\mathrm{orb}(L)$ is defined as a quotient of the link group $G(L)$ of $L$. When there exists an epimorphism $G^\mathrm{orb}(L)\to G^\mathrm{orb}(L')$ fitting into a certain commutative diagram, we define a relation $L\succeq L'$ and explore the relationships between the two links. Specifically, we

Alejandro González del Pino, Manuel Jesús Castro Díaz, Jorge Macías Sánchez

The dynamics of large-scale geophysical fluids is primarily governed by the balance between the Coriolis force and the pressure gradient. This phenomenon, known as geostrophic equilibrium, is the basis for the geostrophic model, which has proven to be extremely useful for understanding and forecasting large-scale atmospheric and oceanic dynamics. In the pres

Alberto M. Ruiz, Diego López-Alcalá, Gonzalo Rivero-Carracedo, Andrei Shumilin

Two-dimensional van der Waals (vdW) magnetic materials are versatile platforms for tailoring electronic and magnetic properties, in which the insertion of chemical species into their interlayer gaps offers a powerful route to engineer magnetism. Here, we focus on the A-type antiferromagnetic semiconductor CrPS$_4$ (T$_N$ = 38 K) and investigate its electroni

Wenli Shi, Clemence Grislain, Olivier Sigaud, Mohamed Chetouani

Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods typically prioritize efficiency, they often fail to make the robot's intentions clear to humans. Meanwhile, existing approaches to legible motion usually produce only a single "mos

Stefano Riva, Carolina Introini, Josè Nathan Kutz, Antonio Cammi

Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to

Rui Li, Jia-Chen Gu, Po-Nien Kung, Heming Xia

The rapid advancement of large language models (LLMs) has inspired researchers to integrate them extensively into the academic workflow, potentially reshaping how research is practiced and reviewed. While previous studies highlight the potential of LLMs in supporting research and peer review, their dual roles in the academic workflow and the complex interpla

Zheyu Wu, Matteo Nerini, Bruno Clerckx

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Conventional RIS is constrained to a diagonal scattering matrix, which limits its flexibility. Recently, beyond-diagonal RIS (BD-RIS) has been proposed as a more general RIS architecture class that allows inter-element connections and shows great pot

Kevin Krings, Nino S. Bohn, Thomas Ludwig

Recent advancements in generative artificial intelligence (GenAI), particularly large language models, have introduced new possibilities for software development practices. In our paper we investigate the emerging Vibe Coding (VC) paradigm that emphasizes intuitive, affect-driven, and improvisational interactions between developers and AI systems. Building u

Jinzhou Lin, Jie Zhou, Wenhao Xu, Rongtao Xu

Semantic Scene Completion (SSC) aims to infer complete 3D geometry and semantics from monocular images, serving as a crucial capability for camera-based perception in autonomous driving. However, existing SSC methods relying on temporal stacking or depth projection often lack explicit motion reasoning and struggle with occlusions and noisy depth supervision.

Jiahao Chen, Yu Cao, Hong Du, Yuanze Li

The material ZrTe$_5$ exhibits distinct topological phases, including a Weyl semimetal phase, characterized by a chiral anomaly and in-plane Hall effect, and a three-dimensional quantum Hall phase. The relationship between these phases remains poorly understood. This work systematically explores their connection in ZrTe$_5$ through rotatable, pressure-depend

Weijie Ren, Haowen Liu, Guang-Ren Duan

This paper proposes a unidirectionally connected fully actuated system (UC-FAS) approach for the sub-stabilization and tracking control of 6-DOF quadrotors, tackling limitations both in state-space and FAS framework to some extent. The framework systematically converts underactuated quadrotor dynamics into a UC-FAS model, unifying the existing different FAS

Susana Furtado, Charles Johnson

For a given reciprocal matrix A, we give a union of matrix intervals in which any consistent matrix obtained from an efficient vector for A lies, and, conversely, any consistent matrix in this union comes from an efficient vector for A. The maximal sets of entries in the lower and upper bound matrices of each interval that are attainable by some consistent m

Yushu Zhao, Yubin Qin, Yang Wang, Xiaolong Yang

Mixture-of-Experts (MoE) models have recently demonstrated exceptional performance across a diverse range of applications. The principle of sparse activation in MoE models facilitates an offloading strategy, wherein active experts are maintained in GPU HBM, while inactive experts are stored in CPU DRAM. The efficacy of this approach, however, is fundamentall

Virginia Murru, Matt P. Wand

We develop a version of variational inference for Bayesian count response regression-type models that possesses attractive attributes such as convexity and closed form updates. The convex solution aspect entails numerically stable fitting algorithms, whilst the closed form aspect makes the methodology fast and easy to implement. The essence of the approach i

Michela Proietti, Roberto Capobianco, Mariya Toneva

Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. We introduce a fine-grained input attribution method to identify the specific words most important for brain-LLM alignment, and leverage it to study a contentious research question about brain-LLM align

Sepideh Masoudi, Mark Edward Michael Daly, Jannis Kiesel, Stefan Tai

As data mesh architectures gain traction in federated environments, organizations are increasingly building consumer-specific data-sharing pipelines using modular, cloud-native transformation services. Prior work has shown that structuring these pipelines with reusable transformation stages enhances both scalability and energy efficiency. However, integratin

Árpád Baricz, Pranav Kumar, Saminathan Ponnusamy

We derive two distinct asymptotic expansions for the zeros $j_{\nu,k}^{(n)}$ of the $n$-th derivative of Bessel function $J_\nu^{(n)}(x)$. The first is a McMahon-type expansion for the case when $k \to \infty$ with fixed $\nu$, for which we also establish an explicit error bound. The second addresses the case when $\nu \to \infty$ with fixed $k$ and it invol

Giorgio Frangi, Matej Bajec, Guri K. Buza, Alexander Soloviev

Many phenomenological and effective field-theoretical (EFT) applications of magnetohydrodynamics (MHD) in the presence of a background magnetic field employ a simplifying assumption whereby the electromagnetic and the energy-momentum fluctuations decouple. In studies of magnetic transport, for example in magnetic diffusion, the conservation of energy and mom

Susana Furtado, Charles Johnson

We consider incomplete pairwise comparison matrices and determine exactly when they have a consistent completion and, if not, when they have a nearly consistent completion. We use the maximum 3-cycle product as a measure of inconsistency and show that, when the graph of the specified entries is chordal, a completion in which this measure is not increased is

Ayush Khaitan, Vijay Ganesh

Large language models have recently demonstrated advanced capabilities in solving IMO and Putnam problems; yet their role in research mathematics has remained fairly limited. The key difficulty is verification: suggested proofs may look plausible, but cannot be trusted without rigorous checking. We present a framework, called LLM+CAS, and an associated tool,

Igor N. Karnaukhov

Within the framework of a model, that takes into account two-particle hybridization of conduction and localized electrons, the effective interaction between conduction electrons is calculated. It is shown that this interaction is attractive when the energy of the localized electron corresponding to the two-particle state lies in the conduction band above the

Georgios Panayiotou, Anand Mathew Muthukulam Simon, Matteo Magnani, Ece Calikus

In this paper, we propose MOUFLON, a fairness-aware, modularity-based community detection method that allows adjusting the importance of partition quality over fairness outcomes. MOUFLON uses a novel proportional balance fairness metric, providing consistent and comparable fairness scores across multi-group and imbalanced network settings. We evaluate our me

Jiazhe Ma, John S. Biggins, Fan Feng, Zhongqiang Yang

Cholesteric liquid crystal elastomers (CLCEs) change color under strain, offering attractive prospects for smart textiles, soft robotics, and photonic devices. However, the helical structure of CLCEs averages out the exceptional anisotropy and soft elasticity of their nematic parents, leaving little scope for also using the director orientation to program th

Na Xing, Jasmin Er, Ricardo M. Vidal, Sandhya Khadka

At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichlo

Said Boulite, Abdellatif Elgrou, Lahcen Maniar, Abdelaziz Rhandi

We investigate a backward anisotropic stochastic parabolic equation with general dynamic boundary conditions, where the drift involves both $\mathbb{L}^2$ and $\mathbb{H}^{-1}$ bulk--surface terms. We first establish the well-posedness of this equation. Subsequently, we derive a new Carleman estimate through a two-step approach. In the first step, using a we

Donghwan Rho, Sieun Seo, Hyewon Sung, Chohong Min

As users increasingly interact with large language models (LLMs) using private information, secure and encrypted communication becomes essential. Homomorphic encryption (HE) provides a principled solution by enabling computation directly on encrypted data. Although prior work has explored aspects of running LLMs under HE, the challenge of text generation, pa

Henry Irvine, Georg A. Gottwald

The two-population Kuramoto-Sakaguchi model for interacting populations of phase oscillators exhibits chimera states whereby one population is synchronised, and the other is desynchronised. Which of the two populations is synchronised depends on the initial conditions. We show that this deterministic model exhibits random switches of their chimera states, al

K. Thomson-Paressant, C. Neiner, J. Labadie-Bartz, R. -M. Ouazzani

Context. At this time, the list of known magnetic {\delta} Scuti stars is extremely limited, with only a handful of well-studied examples. Aims. We seek to expand this list, by retrieving targets from a variety of sources and demonstrating that they present simultaneously a surface magnetic field signature and {\delta} Scuti pulsations. Methods. We obtained

Nicky Mol, Luka Peternel, Alessandro Ianniello, Denis Zatyagov

The use of robots in industrial settings continues to grow, driven by the need to address complex societal challenges such as labor shortages, aging populations, and ever-increasing production demands. In this abstract, we advocate for (and demonstrate) a transdisciplinary approach when considering robotics in the workplace. Transdisciplinarity emphasizes th

Abu Alex Aravindnath, Yi-Ju Ho, Fabian Schmitt, Dongyun Chen

Weyl semimetals, with their unique electronic band structure, have drawn significant interest for their potential to explore quantum anomalies in condensed matter systems. In this study, we investigate the large positive magneto-thermal conductance associated with the gravitational anomaly -- one of the predicted anomalies -- for a Weyl semimetal based on a

Mohamed Abdalmoaty, Verena Häberle, Xiuqiang He, Florian Dörfler

We propose a non-parametric frequency-domain method to identify small-signal $dq$-asymmetric grid impedances, over a wide frequency band, using grid-connected converters. Existing identification methods are faced with significant trade-offs: e.g., passive approaches rely on ambient harmonics and rare grid events and thus can only provide estimates at a few f

Nina Drobac, Margaux Brégère, Joseph de Vilmarest, Olivier Wintenberger

Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discre

Teemu Pihkakoski, Aravind Plathanam Babu, Pauli Taipale, Petri Liimatta

Quantum computers are expected to offer advantages in solving optimization problems challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO p