October 2025 arXiv papers — page 61

João A. Silva, Hervé Paulino, João M. Lourenço

Parsley is a resilient group-based Distributed Hash Table that incorporates a preemptive peer relocation technique and a dynamic data sharding mechanism to enhance robustness and balance. In addition to the hard limits on group size, defined by minimum and maximum thresholds, Parsley introduces two soft limits that define a target interval for maintaining st

Sina Molavipour, Alireza M. Javid, Cassie Ye, Björn Löfdahl

Robust yield curve estimation is crucial in fixed-income markets for accurate instrument pricing, effective risk management, and informed trading strategies. Traditional approaches, including the bootstrapping method and parametric Nelson-Siegel models, often struggle with overfitting or instability issues, especially when underlying bonds are sparse, bond p

Lemin Liu, Fangchao Hu, Honghua Jiang, Yaru Chen

In complex orchard environments, the phenotypic heterogeneity of different apple leaf diseases, characterized by significant variation among lesions, poses a challenge to traditional multi-scale feature fusion methods. These methods only integrate multi-layer features extracted by convolutional neural networks (CNNs) and fail to adequately account for the re

Aymane El Firdoussi, El Mahdi Chayti, Mohamed El Amine Seddik, Martin Jaggi

Fine-tuning has proven to be highly effective in adapting pre-trained models to perform better on new desired tasks with minimal data samples. Among the most widely used approaches are reparameterization methods, which update a target module by augmenting its frozen weight matrix with an additional trainable weight matrix. The most prominent example is Low R

Serli Ahzari, Akmam Akmam

The increasing complexity of abstract concepts in physics education and the low level of students critical thinking skills demand innovative instructional strategies aligned with 21st century competencies. This study aims to analyze students critical thinking skills as the foundation for developing physics interactive multimedia using a generative learning m

Mai Ye, Wenshan Hong, Tom Lacmann, Mehdi Frachet

We investigate the strain response of underdoped HgBa$_2$CuO$_{4+\delta}$ (Hg1201), by synchrotron X-ray diffraction and corresponding simulations of thermal diffuse scattering. The compression in the crystallographic $a$ direction leads to relatively small expansion in the $b$ and $c$ directions, with Poisson ratios $\nu_{ba}$=0.16 and $\nu_{ca}$=0.11, resp

Hossein Amiri, Mohammad Hashemi, Andreas Züfle

Recently, Foursquare released a global dataset with more than 100 million points of interest (POIs), each representing a real-world business on its platform. However, many entries lack complete metadata such as addresses or categories, and some correspond to non-existent or fictional locations. In contrast, OpenStreetMap (OSM) offers a rich, user-contributed

Lufan Chang

Large Language Models (LLMs) often struggle with generating truly innovative ideas, typically defaulting to high-probability, familiar concepts within their training data's "gravity wells." While advanced search-based methods like Tree of Thoughts (ToT) attempt to mitigate this, they are fundamentally limited by their reliance on unprincipled, inconsistent s

Qiang Liu, Wuganjing Song, Zhenzhou Lin, Feifan Chen

The reasoning capabilities of Large Language Models (LLMs) are typically developed through the single-turn reinforcement learning, whereas real-world applications often involve multi-turn interactions with human feedback, leading to a potential mismatch between training and deployment conditions. In this work, we study whether multi-turn training with human

Liang Ma, Xin Yang, Mei Li, Pengfei Shan

Metal hydrides MHx with low hydrogen content are not expected to show high-Tc superconductivity owing to the low hydrogen-derived electronic density of states at Fermi level and the limited hydrogen contribution to electron-phonon coupling strength. In this work, we report on the successful synthesis of a novel bismuth dihydride superconductor, Cmcm-BiH$_2$,

Reed Naidoo, Matt De Vries, Olga Fourkioti, Vicky Bousgouni

Understanding how cells respond to external stimuli is a central challenge in biomedical research and drug development. Current computational frameworks for modelling cellular responses remain restricted to two-dimensional representations, limiting their capacity to capture the complexity of cell morphology under perturbation. This dimensional constraint pos

Timur Aslyamov, Massimiliano Esposito, Mathijs Janssen

Electrochemical devices often charge both through Faradaic reactions and electric double layer formation. Here, we study these coupled processes in a model system of a long electrolyte-filled pore subject to a small suddenly-applied potential, close to the equilibrium potential $\Psi^\text{eq}$ at which there is no net Faradaic charge transfer. Specifically,

Philip Boeken, Onno Zoeter, Joris M. Mooij

Performative predictions are forecasts which influence the outcomes they aim to predict, undermining the existence of correct forecasts and standard methods of elicitation and estimation. We show that conditioning forecasts on covariates that separate them from the outcome renders the target distribution forecast-invariant, guaranteeing well-posedness of the

Lev Sakhnovich

In the present article, we assume that the first approximation of the scattering operator is given and that it has the logarithmic divergence. This first approximation allows us to construct the so called deviation factor. Using the deviation factor, we regularize all terms of the scattering operator's approximations. The infrared and ultraviolet cases as we

Richard D. Canary

This paper is designed to attract people who work on real hyperbolic manifolds to consider thinking about discrete subgroups of higher rank Lie groups. To that end, we breezily discuss some applications of the ideas from the theory of Kleinian groups in the higher rank setting.

Yunbo Hou, Tianle Yang, Ruijie Li, Li He

Recent advances in correlation-based sequential recommendation systems have demonstrated substantial success. Specifically, the attention-based model outperforms other RNN-based and Markov chains-based models by capturing both short- and long-term dependencies more effectively. However, solely focusing on item co-occurrences overlooks the underlying motivati

Myeongho Jeon, Jan Sobotka, Suhwan Choi, Maria Brbić

As future superhuman models become increasingly complex, accurately supervising their behavior may exceed human capabilities. Recent works have demonstrated that in such scenarios, weak models can effectively supervise strong models, a phenomenon known as weak-to-strong generalization. However, we find that naive weak-to-strong generalization fails under dis

R. Mignon-Risse, P. Varniere, F. Casse

The electromagnetic detection of circumbinary disks around pre-merger binary black holes (BBHs) relies on theoretical predictions. These are generally obtained through expensive numerical simulations, but simple or fast toy models are lacking to unleash the potential of these theoretical advances for observational purposes. We aim to present a simple toy mod

Vikas Kanaujia, Vipul Arora

Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling methods, such as Markov Chain Monte Carlo (MCMC), often suffer from slow convergence, critical slowing down, poor mode mixing, and high autocorrelation. In contrast, likelihood-based and adversarial machine learning

Priyanshu Karmakar, Soumyabrata Chaudhuri, Shubhojit Mallick, Manish Gupta

Recent efforts like TripCraft and TravelPlanner have advanced the use of Large Language Models ( LLMs) for personalized, constraint aware travel itinerary generation. Yet, real travel often faces disruptions. To address this, we present TripTide, the first benchmark evaluating LLM's ability to revise itineraries under realistic disruptions. TripTide models k

Leonard Schulz, Karl-Heinz Glassmeier, Moritz Herberhold, Adam Mitchell

Large satellite constellations are one of the main reasons for an increasing amount of mass being brought into low Earth orbit in recent years. After end of life, the satellites, as well as rocket stages, reenter Earth's atmosphere. This space waste burns up and thus injects a substantial amount of its matter into the mesosphere and lower thermosphere. A fir

Sebastian Brandt, Fabian Kuhn, Zahra Parsaeian

Understanding the role of randomness when solving locally checkable labeling (LCL) problems in the LOCAL model has been one of the top priorities in the research on distributed graph algorithms in recent years. For LCL problems in bounded-degree graphs, it is known that randomness cannot help more than polynomially, except in one case: if the deterministic c

Gianluca Sperduti, Alejandro Moreo

Research in linguistics has shown that humans can read words with internally scrambled letters, a phenomenon recently dubbed typoglycemia. Some specific NLP models have recently been proposed that similarly demonstrate robustness to such distortions by ignoring the internal order of characters by design. This raises a fundamental question: how can models per

Hossein Nemati, Saskia Jacoba Elisabeth Suijkerbuijk, Joost de Graaf

Cell competition in multicellular organisms has been shown to play a critical role during the development of organisms, cancer progression, and in the establishment and maintenance of tissue homeostasis. Various mechanisms of cell competition have been identified, including active elimination via mechanical forces or induced apoptosis, as well as competition

Shufan Shen, Junshu Sun, Qingming Huang, Shuhui Wang

The alignment of vision-language representations endows current Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities. However, the interpretability of the alignment component remains uninvestigated due to the difficulty in mapping the semantics of multi-modal representations into a unified concept set. To address this problem, we prop

Antoine Boutet, Lucas Magnana

Pre-trained large language models (LLMs) are becoming useful for various tasks. To improve their performance on certain tasks, it is necessary to fine-tune them on specific data corpora (e.g., medical reports, business data). These specialized data corpora may contain sensitive data (e.g., personal or confidential data) that will be memorized by the model an

Kanghui He, Anil Alan, Shengling Shi, Ton van den Boom

Obtaining control barrier functions (CBFs) with large safe sets for complex nonlinear systems and constraints is a challenging task. Predictive CBFs address this issue by using an online finite-horizon optimal control problem that implicitly defines a large safe set. The optimal control problem, also known as the predictive safety filter (PSF), involves pred

Tim Titze, Sabri Koraltan, Timo Schmidt, Mailin Matthies

Ferrimagnetic Fe/Gd multilayers host maze-like stripe domains that transform into a disordered bubble/skyrmion lattice under out-of-plane magnetic fields at ambient temperature. Femtosecond magneto-optics distinguishes these textures via their distinct coherent breathing dynamics. Crucially, applying a brief in-plane ``set'' magnetic field to the stripe stat

Evgeny Feigin, Markus Reineke

We study a class of Grassmannians of sub-bimodules over the path algebras of quivers. Our quiver Grassmannians include Escobar's brick manifolds as well as Labelle's generalizations. We give an explicit construction of the varieties in question, provide examples and clarify connection with the quiver representation spaces. We also prove smoothness of our Gra

Jian Zu, Zhiguo Xu, Jingyue Hao

Identifying the types of orbits is an important topic in the study of chaotic dynamical systems. Beyond the well-known distinctly chaotic and regular motions, we focus on dynamics occurring in regions where regular and chaotic motions coexist and intertwine, which potentially indicating weakly chaotic orbits. This intermediate regime lies between strongly ch

Danilo de Oliveira, Tal Peer, Jonas Rochdi, Timo Gerkmann

Significant research efforts are currently being dedicated to non-intrusive quality and intelligibility assessment, especially given how it enables curation of large scale datasets of in-the-wild speech data. However, with the increasing capabilities of generative models to synthesize high quality speech, new types of artifacts become relevant, such as gener

Jing Song, Yi-Yao Li, Melahat Bayar, Eulogio Oset

We have made a thorough study of the $D^+ \to \pi^+ \eta \eta $ reaction, recently measured by the BESIII collaboration, which shows an abnormal strength at high invariant masses in the $\pi\eta$ mass distribution. We studied in detail the triangle mechanism and the $f_0(1370)$ excitation modes that have been suggested to explain this abnormal feature, and c

Haiyang Li, Liao Yu, Qiang Yu, Yunliang Zang

Biological circuits have evolved to incorporate multiple modules that perform similar functions. In the fly olfactory circuit, both lateral inhibition (LI) and neuronal spike frequency adaptation (SFA) are thought to enhance pattern separation for odor learning. However, it remains unclear whether these mechanisms play redundant or distinct roles in this pro

Xuan Tang, Jichu Li, Difan Zou

The rapid scaling of large language models (LLMs) has made low-precision training essential for reducing memory, improving efficiency, and enabling larger models and datasets. Existing convergence theories for adaptive optimizers, however, assume all components are exact and neglect hardware-aware quantization, leaving open the question of why low-precision

Daniel Han-Kwan, Frédéric Rousset

In this work, we study the semiclassical limit of cubic Nonlinear Schr\"odinger equations for mixed states. We justify the limit to a singular Vlasov equation (in which the force field is proportional to the gradient of the density), for data with finite Sobolev regularity whose velocity profiles satisfy a quantum Penrose stability condition. This latter con

Dhruv Sarkar, Nishant Pandey, Sayak Ray Chowdhury

Regret in stochastic multi-armed bandits traditionally measures the difference between the highest reward and either the arithmetic mean of accumulated rewards or the final reward. These conventional metrics often fail to address fairness among agents receiving rewards, particularly in settings where rewards are distributed across a population, such as patie

Lu Zhang, Jiazuo Yu, Haomiao Xiong, Ping Hu

Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs face significant challenges in precisely understanding and localizing visual details in high-resolution images -- particularly when dealing with extra-small objects embedded in clut

Ji Won Park, Kyunghyun Cho

Accurately estimating semantic aleatoric and epistemic uncertainties in large language models (LLMs) is particularly challenging in free-form question answering (QA), where obtaining stable estimates often requires many expensive generations. We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding, covers both

Paloma R. Casale, Jose E. Amaro

We investigate meson-exchange currents (MEC) in the one-particle emission transverse response of nuclear matter, incorporating short-range correlations via the Bethe-Goldstone equation with a realistic nucleon-nucleon interaction. The interference between one-body and two-body currents, strengthened by the high-momentum components of correlated pairs, produc

Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributionally robust optimization (DRO) framework. By lifting the dynamical system into a linear space, we achieve a finite-dimensional approximation of the Koopman operator. We explicitly ac

Bingchen Miao, Rong Wei, Zhiqi Ge, Xiaoquan sun

3D Gaussian Splatting (3DGS), a 3D representation method with photorealistic real-time rendering capabilities, is regarded as an effective tool for narrowing the sim-to-real gap. However, it lacks fine-grained semantics and physical executability for Visual-Language Navigation (VLN). To address this, we propose SAGE-3D (Semantically and Physically Aligned Ga

Yarik Menchaca Resendiz, Roman Klinger

Large language models (LLMs) have demonstrated high performance on tasks expressed in natural language, particularly in zero- or few-shot settings. These are typically framed as supervised (e.g., classification) or unsupervised (e.g., clustering) problems. However, limited work evaluates LLMs as agents in reinforcement learning (RL) tasks (e.g., playing game

Jingxuan Kang, Rose-Mary Jose, Oliver Brandt, Lutz Geelhaar

The top-down fabrication of nanowires based on patterning via metal dewetting is a cost-effective and scalable approach that is particularly suited for applications requiring large arrays of nanowires. Advantageously, the nanowire diameter can be tailored by the initial metal film thickness. However, we show here that metal dewetting inherently leads to a co

Hagit Attiya, Constantin Enea, Enrique Román-Calvo

The fundamental tension between availability and consistency shapes the design of distributed storage systems. Classical results capture extreme points of this trade-off: the CAP theorem shows that strong models like linearizability preclude availability under partitions, while weak models like causal consistency remain implementable without coordination. Th

Prakhar Ganesh, Hsiang Hsu, Golnoosh Farnadi

Multiplicity, the existence of equally good yet competing models, has received growing attention in recent years. While prior work has emphasized modelling choices, the critical role of data in shaping multiplicity has been largely overlooked. In this work, we first introduce a neighbouring datasets framework, arguing that much of data processing can be refr

Andrea Bontempelli, Matteo Busso, Leonardo Javier Malcotti, Fausto Giunchiglia

Any digital personal assistant, whether used to support task performance, answer questions, or manage work and daily life, including fitness schedules, requires high-quality annotations to function properly. However, user annotations, whether actively produced or inferred from context (e.g., data from smartphone sensors), are often subject to errors and nois

Sanghyun Ahn, Wonje Choi, Junyong Lee, Jinwoo Park

Recent advances in large language models (LLMs) have enabled the automatic generation of executable code for task planning and control in embodied agents such as robots, demonstrating the potential of LLM-based embodied intelligence. However, these LLM-based code-as-policies approaches often suffer from limited environmental grounding, particularly in dynami

Changyu Ren, Ziyi Wang

This paper is devoted to the interior $C^2$ estimates for a class of sum Hessian quotient equations. For $0\leq l<k<n$, we establish the interior estimates and the Pogorelov type estimates. In the case $k=n$, we obtain a weaker Pogorelov type estimate for $0\leq l<n-1$.

Tobias Fuchs, Nadja Klein

Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning (PLL) addresses this challenge by training classifiers when each instance is associated with a set of candidate labels, only one of which is correct. While early PLL methods appro

Shengkang Chen, Tong Wu, Zhiyong Chen, Feng Yang

Reliable image transmission over wireless channels is particularly challenging at extremely low transmission rates, where conventional compression and channel coding schemes fail to preserve adequate visual quality. To address this issue, we propose a generative communication framework based on diffusion models, which integrates joint source channel coding (

Aida Abiad, Harper Reijnders, Michael Tait

We use a graph-theoretic approach which yields improvements on the known Gilbert-Varshamov (GV) bound for sum-rank-metric codes for certain parameters. In particular, we show that asymptotically $\mathbb{F}_q^{\mathbf{n} \times \mathbf{m}}$ can be partitioned into sum-rank-metric codes whose average size is bigger than the GV bound by a logarithmic factor fo

Francis Liu, Natalie Packham, Artur Sepp

This paper presents an option pricing model that incorporates clustered jumps using a bivariate Hawkes process. The process captures both self- and cross-excitation of positive and negative jumps, enabling the model to generate return dynamics with asymmetric, time-varying skewness and to produce positive or negative implied volatility skews. This feature is

Sukanya Patra, Souhaib Ben Taieb

Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant performance degradation. Existing solutions require access to the training pipelines, data or prior knowledge of the proportions of anomalies in the data, limiting their real-world

Stéphanie Laure Egome Nana, Arnaud Leclerc, Lorenzo Ugo Ancarani

Complex Gaussian basis sets are optimized to accurately represent continuum radial wavefunctions over the whole space. First, attention is put on the technical ability of the optimization method to get more flexible series of Gaussian exponents, in order to improve the accuracy of the fitting approach. Second, an indirect fitting method is proposed, allowing

Maxime Grosso, Pierre Riedinger, Jamal Daafouz

We present a MATLAB package called the Pha-sorArray Toolbox that has been developed to make harmonic analysis and control methods both practical and user-friendly. The toolbox adopts an object-oriented architecture that enables intuitive manipulation of periodic matrices through overloaded operators for addition, multiplication, convolution, and automatic To

Siddharth Mehrotra, Jin Huang, Xuelong Fu, Roel Dobbe

Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness

Shahaf Bassan, Michal Moshkovitz, Guy Katz

Generalized Additive Models (GAMs) are commonly considered *interpretable* within the ML community, as their structure makes the relationship between inputs and outputs relatively understandable. Therefore, it may seem natural to hypothesize that obtaining meaningful explanations for GAMs could be performed efficiently and would not be computationally infeas

Alejandro Blanco Peces, Jaime Merino

The Hubbard model on the Kagome lattice is a widely used interacting model for describing the electronic properties of various transition metal-based Kagome materials. We find altermagnetism driven by Coulomb interaction in the Kagome Hubbard model at Dirac filling with no spin-orbit coupling nor explicit spatial symmetry breaking present. We show how this i

Christian Alber, Lukas Holbach

We propose a multiscale spectral generalized finite element method (MS-GFEM) for discontinuous Galerkin (DG) discretizations. The method builds local approximations on overlapping subdomains as the sum of a local source solution and a correction from an optimal spectral coarse space, which is obtained from a generalized eigenproblem. The global solution is t

Theo Martin, Laurent Mugnier, Matthieu Valla, Pierre Etienne Allain

Wind speed measurements using heterodyne lidars are limited in spatial resolution because of the current signal processing methods. This limit is equal to c $\tau$ ( c is the speed of light and $\tau$ is the laser pulse duration) corresponding to the length of the atmosphere contributing to the wind speed measurement at one distance. To go beyond this limit,

Chaitanya Swamy, Vera Traub, Laura Vargas Koch, Rico Zenklusen

A famous conjecture of Goemans on single-source unsplittable flows states that one can turn any fractional flow into an unsplittable one of no higher cost, while increasing the load on any arc by at most the maximum demand. Despite extensive work on the topic, only limited progress has been made. Recently, Morell and Skutella suggested an alternative conject

Xiyang Zhang, Chen Liang, Haoxuan Qiu, Hongzhi Wang

Data selection is one of the fundamental problems in neural network training, particularly for multi-layer perceptrons (MLPs) where identifying the most valuable training samples from massive, multi-source, and heterogeneous data sources under budget constraints poses significant challenges. Existing data selection methods, including coreset construction, da

Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan

Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harmful content generation. Existing methods often apply coarse-grained constraints over the entire reasoning trajectories, which can undermine reasoning capability while failing to address the root causes of unsafe

Jean Schneider

This chapter reviews the definition of exoplanets and of brown dwarfs. Emphasis is given to the separation of these two populations. A traditional view is to declare {\guillemotleft} planet {\guillemotright} objects with a mass < 13 M Jup and {\guillemotleft} brown dwarf {\guillemotright} objects with a mass > 13 M Jup . By analogy with Solar System planets,

Vincent Kagan, Edouard Strickler, Denis Villemonais

We consider a slow-fast stochastic process where the slow component is a jump process on a measurable index set whose transition rates depend on the position of the fast component. Between the jumps, the fast component evolves according to an ergodic dynamic in a state space determined by the index process. We prove that, when the ergodic dynamics are accele

Christian Gaß, Harold C. Steinacker

We recently described a cosmological quantum spacetime of vanishing spatial curvature, which can be considered as background for the IKKT matrix model, assuming that the resulting gauge theory couples weakly. Building on this example, we construct a large class of spatially flat cosmological quantum spacetimes. We also elaborate on various details of their a

Pavankumar Chandankar, Robin Burchard

We present a noise-aware, sensor-specific ensemble approach for robust human activity recognition on the 2nd WEAR Dataset Challenge. Our method leverages the PatchTST transformer architecture, training four independent models-one per inertial sensor location-on a tampered training set whose 1-second sliding windows are augmented to mimic the test-time noise.

Christian Salomonsen, Kristoffer K. Wickstrøm, Samuel Kuttner, Elisabeth Wetzer

Kinetic modeling enables \textit{in vivo} quantification of tracer uptake and glucose metabolism in [${}^{18}$F]Fluorodeoxyglucose ([${}^{18}$F]FDG) dynamic positron emission tomography (dPET) imaging of mice. However, kinetic modeling requires the accurate determination of the arterial input function (AIF) during imaging, which is time-consuming and invasiv

Christiaan M. Geldenhuys, Günther Tonitz, Thomas R. Niesler

While recent sound event detection (SED) systems can identify baleen whale calls in marine audio, challenges related to false positive and minority-class detection persist. We propose the boundary proposal network (BPN), which extends an existing lightweight SED system. The BPN is inspired by work in image object detection and aims to reduce the number of fa

Xin Liu, Zhihui Liu

This paper establishes the first-order convergence rate for the ergodic error of numerical approximations to a class of stochastic ODEs (SODEs) with superlinear coefficients and multiplicative noise. By leveraging the generator approach to the Stein method, we derive a general error representation formula for one-step numerical schemes. Under suitable dissip

Laura M. Wolf, Vincent Albert Wolff, Simon Steuernagel, Kolja Thormann

Collective perception is a key aspect for autonomous driving in smart cities as it aims to combine the local environment models of multiple intelligent vehicles in order to overcome sensor limitations. A crucial part of multi-sensor fusion is track-to-track association. Previous works often suffer from high computational complexity or are based on heuristics

Florian Gossard, François Bachoc, Jean Baccou, Thibaut Le Gouic

This work addresses the interpolation of probability measures within a spatial statistics framework. We develop a Kriging approach in the Wasserstein space, leveraging the quantile function representation of the one-dimensional Wasserstein distance. To mitigate the inaccuracies in semivariogram estimation that arise from sparse datasets, we combine this form

Qiyong Zhong, Jiajie Su, Yunshan Ma, Julian McAuley

Generative recommendation (GR) models tokenize each action into a few discrete tokens (called semantic IDs) and autoregressively generate the next tokens as predictions, showing advantages such as memory efficiency, scalability, and the potential to unify retrieval and ranking. Despite these benefits, existing tokenization methods are static and non-personal

Benjamin Camus, Julien Houssay, Corentin Le Barbu, Eric Monteux

This work aims to train Deep Learning models to perform Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. To circumvent the lack of real labelled measurements, we resort to synthetic data produced by SAR simulators. Simulation offers full control over the virtual environment, which enables us to generate large and diversified datas

Robin Schmöcker, Christoph Schnell, Alexander Dockhorn

The Upper Confidence Bounds For Trees (UCT) algorithm is not agnostic to the reward scale of the game it is applied to. For zero-sum games with the sparse rewards of $\{-1,0,1\}$ at the end of the game, this is not a problem, but many games often feature dense rewards with hand-picked reward scales, causing a node's Q-value to span different magnitudes acros

Eliabelle Mauduit, Eloïse Berthier, Andrea Simonetto

We study a time-varying Bayesian optimization problem with bandit feedback, where the reward function belongs to a Reproducing Kernel Hilbert Space (RKHS). We approach the problem via an upper-confidence bound Gaussian Process algorithm, which has been proven to yield no-regret in the stationary case. The time-varying case is more challenging and no-regret r

Naomi Desobry, Elnura Zhalieva, Souhaib Ben Taieb

Probabilistic models must be well calibrated to support reliable decision-making. While calibration in single-output regression is well studied, defining and achieving multivariate calibration in multi-output regression remains considerably more challenging. The existing literature on multivariate calibration primarily focuses on diagnostic tools based on pr

Lu Liu, Wuqi Zhang, Lili Wei, Hao Guan

Decentralized Finance (DeFi) smart contracts manage billions of dollars, making them a prime target for exploits. Price manipulation vulnerabilities, often via flash loans, are a devastating class of attacks causing significant financial losses. Existing detection methods are limited. Reactive approaches analyze attacks only after they occur, while proactive

Hyeongyu Kim, Geonhui Han, Dosik Hwang

In recent advancements in Test Time Adaptation (TTA), most existing methodologies focus on updating normalization layers to adapt to the test domain. However, the reliance on normalization-based adaptation presents key challenges. First, normalization layers such as Batch Normalization (BN) are highly sensitive to small batch sizes, leading to unstable and i

Christophe Chatelain

Two replicas of a 2D Ising model are coupled by frustrated spin-spin interactions. It is known that this inter-layer coupling is marginal and that the bulk critical behavior belongs to the Ashkin-Teller (AT) universality class, as the $J_1-J_2$ Ising model. In this work, the surface critical behavior is studied numerically by Tensor Renormalization-Group cal

Thomas Gamet

We study the ground state energy of a system of N fermions with two spin states in the large N limit. The particles are placed in an inhomogeneous trapping potential and interact via scaled interactions. We study a dilute limit where the range of the interaction potential is much smaller than the typical inter-particle distance. We show that the energy per p

Junshu Sun, Wanxing Chang, Chenxue Yang, Qingming Huang

Graph attention has demonstrated superior performance in graph learning tasks. However, learning from global interactions can be challenging due to the large number of nodes. In this paper, we discover a new phenomenon termed over-aggregating. Over-aggregating arises when a large volume of messages is aggregated into a single node with less discrimination, l

Yu. N. Chiang, M. O. Dzyuba

The relativistic Rashba contribution to the spin-Hall effect in external electric and magnetic fields in Al and Pt was investigated. Schemes of edge accumulation of spins are proposed that take into account the flip of spins when they do not coincide with the direction of the magnetic field. Based on the obtained experimental data on the spin-Hall effect, an

Mingzhe Xing, Chang Tian, Jianan Zhang, Lichen Pan

Modern large-scale networks introduce significant complexity in understanding network behaviors, increasing the risk of misconfiguration. Prior work proposed to understand network behaviors by mining network configurations, typically relying on domain-specific languages interfaced with formal models. While effective, they suffer from a steep learning curve a

Jiangshan He, Hui Xie, Yuqiang Yang, Chunli Jia

Transcranial photobiomodulation (tPBM) therapy is an emerging, non-invasive neuromodulation technique that has demonstrated considerable potential in the field of neuropsychiatric disorders. Several studies have found that pulsed wave (PW) tPBM therapy yields superior biomodulatory effects. However, its neural mechanisms are still unknown which poses a signi

Kaiyu Song, Hanjiang Lai, Yaqing Zhang, Chuangjian Cai

In this paper, we introduce Topology Sculptor, Shape Refiner (TSSR), a novel method for generating high-quality, artist-style 3D meshes based on Discrete Diffusion Models (DDMs). Our primary motivation for TSSR is to achieve highly accurate token prediction while enabling parallel generation, a significant advantage over sequential autoregressive methods. By

Kaichun Wang, Yanguang Chen, Ting Zhang, Mengyao Bao

LLM-based conversational systems have become a popular gateway for information access, yet most existing chatbots struggle to handle news-related trending queries effectively. To improve user experience, an effective trending query detection method is urgently needed to enable differentiated processing of such target traffic. However, current research on tre

Yongyun Chen, Qiusheng Gu, Junhui Fan, Dingrong Xiong

We investigate the relationship among the jet magnetic field, black hole spin, black hole mass, Eddington ratio, and optical variability timescales in jetted active galactic nuclei (AGNs). By fitting a damped random walk (DRW) model to the g-band light curves, we obtain the characteristic variability timescale ($\tau_{\rm DRW}$) for 41 jetted AGNs with preci

Andrea Bonfanti, Ismael Medina, Roman List, Björn Staeves

Recent advances in Scientific Machine Learning have shown that second-order methods can enhance the training of Physics-Informed Neural Networks (PINNs), making them a suitable alternative to traditional numerical methods for Partial Differential Equations (PDEs). However, second-order methods induce large memory requirements, making them scale poorly with t

Andrew T. Powis, Eduardo Ahedo, Alejandro Álvarez Laguna, Nicolas Barléon

Low-temperature plasmas are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance.

Uma Pisarović, Taichi Ochi, Iryna Samarska, Ludovico Silvestri

In recent years, light-sheet fluorescence microscopy (LSFM) has emerged as a powerful tool for visualizing and analyzing cancer tissue samples, including patient-derived specimens, organoids, biopsies, and murine models. In this work, we highlight the current applications of deep tissue LSFM in oncology and illustrate its use across a variety of human cancer

Quentin Bucquet, Bérengère Podvin, Caroline Braud, Emmanuel Guilmineau

We present an efficient neural-based approach to estimate the instantaneous flow field around an airfoil from limited surface pressure measurements. The model, denoted SNN-POD, relies on two independent shallow neural networks to predict the instantaneous flow over a wide range of angles of attack [10{\textdegree},20{\textdegree}]. At all angles the global m

Xin Du, Kumiko Tanaka-Ishii

Large language models (LLMs) have achieved remarkable progress in natural language generation, yet they continue to display puzzling behaviors -- such as repetition and incoherence -- even when exhibiting low perplexity. This highlights a key limitation of conventional evaluation metrics, which emphasize local prediction accuracy while overlooking long-range

Shivam Saini, Jürgen Peissig

We introduce HiFi-HARP, a large-scale dataset of 7th-order Higher-Order Ambisonic Room Impulse Responses (HOA-RIRs) consisting of more than 100,000 RIRs generated via a hybrid acoustic simulation in realistic indoor scenes. HiFi-HARP combines geometrically complex, furnished room models from the 3D-FRONT repository with a hybrid simulation pipeline: low-freq

Qi-Ming Ding, Jiawei Peng, Junxiang Huang, Yukun Zhang

Accurate ground-state calculations on noisy quantum computers are fundamentally limited by restricted ansatz expressivity and unavoidable hardware errors. We introduce a hybrid-quantum classical framework that simultaneously addresses these challenges. Our method systematically purifies noisy two electron reduced density matrices from quantum devices by enfo

Joel Valdivia Ortega, Lorenz Lamm, Franziska Eckardt, Benedikt Schworm

Vision Transformers (ViTs), such as DINOv2, achieve strong performance across domains but often repurpose low-informative patch tokens in ways that reduce the interpretability of attention and feature maps. This challenge is especially evident in medical imaging, where domain shifts can degrade both performance and transparency. In this paper, we introduce R

Victoria J. Hodge, Colin Paterson, Ibrahim Habli

The operational capabilities and application domains of AI-enabled autonomous systems have expanded significantly in recent years due to advances in robotics and machine learning (ML). Demonstrating the safety of autonomous systems rigorously is critical for their responsible adoption but it is challenging as it requires robust methodologies that can handle

Boaz Moav, Ryan Gabrys, Eitan Yaakobi

DNA-based storage offers unprecedented density and durability, but its scalability is fundamentally limited by the efficiency of parallel strand synthesis. Existing methods either allow unconstrained nucleotide additions to individual strands, such as enzymatic synthesis, or enforce identical additions across many strands, such as photolithographic synthesis

Francesco Martinuzzi

Recurrent neural networks (RNNs) are a cornerstone of sequence modeling across various scientific and industrial applications. Owing to their versatility, numerous RNN variants have been proposed over the past decade, aiming to improve the modeling of long-term dependencies and to address challenges such as vanishing and exploding gradients. However, no cent

Antonio M. Puertas, Rubén Gómez González

We investigate the diffusion of an intruder in a granular gas, with both components modeled as smooth hard spheres, both immersed in a low viscosity carrier fluid to form a particle-laden suspension. In this system, dissipative particle collisions coexist with the action of a solvent. The latter is modeled via a viscous drag force and a stochastic Langevin-l