May 2025 arXiv papers — page 33

Wenjun Lu, Haodong Chen, Anqi Yi, Guoxi Huang

Novel view synthesis is a fundamental task in 3D computer vision that aims to reconstruct photorealistic images from novel viewpoints given a set of posed images. However, reconstruction quality degrades sharply under sparse-view conditions due to insufficient geometric cues. Existing methods, including Neural Radiance Fields (NeRF) and more recent 3D Gaussi

Afroja Parvin, Giovanni Samaey, Julian Koellermeier

In this paper, we develop the Hyperbolic Shallow Water Exner Moment model with Erosion and Deposition (HSWEMED), extending the shallow water moment framework to capture coupled morphodynamics with erosion and deposition. HSWEMED introduces a suspended-sediment concentration equation, couples concentration-dependent mixture density with the momentum and highe

Luca Aceto, Antonis Achilleos, Aggeliki Chalki, Anna Ingolfsdottir

Characteristic formulae give a complete logical description of the behaviour of processes modulo some chosen notion of behavioural semantics. They allow one to reduce equivalence or preorder checking to model checking, and are exactly the formulae in the modal logics characterizing classic behavioural equivalences and preorders for which model checking can b

Mohammed Alghadeer, Shuxiang Cao, Simone D Fasciati, Michele Piscitelli

Superconducting quantum circuits are a key platform for advancing quantum information processing and simulation. Scaling efforts currently encounter challenges such as Josephson-junction fabrication yield, design frequency targeting, and crosstalk arising both from spurious microwave modes and intrinsic interactions between qubits. We demonstrate a scalable

Alexander Hagg, Adam Gaier, Dominik Wilde, Alexander Asteroth

Novel techniques in evolutionary optimization, simulation and machine learning allow for a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. Under the term of full domain analysis we understand the ability to efficiently determine the full space of solutions in a problem domain, and analyze the beh

Yanwei Cui, Zenglin Liu, Qin Liu, Junlin Xiong

The anomalous metal state (AMS), observed in failed superconductors, provides insights into superconductivity and quantum criticality, with studies revealing unconventional quantum phases like the Bose metal. Recently, layered transition metal dichalcogenide (TMD) superconductors approaching the two-dimensional limit have garnered significant attention for t

Joe Needham, Giles Edkins, Govind Pimpale, Henning Bartsch

If AI models can detect when they are being evaluated, the effectiveness of evaluations might be compromised. For example, models could have systematically different behavior during evaluations, leading to less reliable benchmarks for deployment and governance decisions. We investigate whether frontier language models can accurately classify transcripts base

Shohei Higashiyama, Masao Utiyama

Lexical normalization research has sought to tackle the challenge of processing informal expressions in user-generated text, yet the absence of comprehensive evaluations leaves it unclear which methods excel across multiple perspectives. Focusing on unsegmented languages, we make three key contributions: (1) creating a large-scale, multi-domain Japanese norm

Konstantin Kirchheim, Frank Ortmeier

Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models operating in open-world scenarios. Current OOD detectors mainly rely on statistical models to identify unusual patterns in the latent representations of a deep neural network. This work proposes to augment existing OOD detectors with probabilistic reasoning,

Igor V. Nikolaev

We study numerical examples of the abelian extensions of the real quadratic number fields based on the results in Acta Mathematica Vietnamica 48 (2023), 271-281 (arXiv:0804.0057)

Yongcan Yu, Yanbo Wang, Ran He, Jian Liang

While (multimodal) large language models (LLMs) have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defense methods are proposed to defend against jailbreak attacks, however, they are often tailored to specific types of jailbreak attacks, limiting their effectiveness against diverse

Adam B. Cahaya

The momentum of light in dielectric media has been a century-long controversy that continues to attract significant interest. In a linear dielectric medium with refractive index n, the momentum is predicted to be smaller by a factor of n according to Abraham, and larger by the same factor according to Minkowski. By studying the coupled dynamics of electromag

Thomas SJ Burger, Amir Shahhosseini, Rodolphe Sepulchre

This paper introduces a model of excitability that unifies the mechanism of an important neuronal property both in time and in space. As a starting point, we revisit both a key model of temporal excitability, proposed by Hodgkin and Huxley, and a key model of spatial excitability, proposed by Amari. We then propose a novel model that captures the temporal an

Darshan Raju, Mahinder Ramdin, Thijs J. H. Vlugt

Experimentally determining thermophysical properties for various compositions commonly found in CO2 transportation systems is extremely challenging. To overcome this challenge, we performed Monte Carlo (MC) and molecular dynamics (MD) simulations of CO2 rich mixtures to compute thermophysical properties such as densities, thermal expansion coefficients, isot

Ilan Bouquet, Jiang Cao, Mathieu Luisier

Using an in-house Schroedinger-Poisson (SP) solver, we investigate the creation of a single hole spin qubit inside a triple-gate triangular silicon fin field effect transistor (Si FinFET) quantum device similar to experimental structures. The gate induced formation of the required quantum dot (QD) is monitored based on the Luttinger-Kohn 6x6 kp method accoun

Jialin Yan, Yu Cheng, Zhaoxia Yin, Xinpeng Zhang

The rapid development of Artificial Intelligence Generated Content (AIGC) has made high-fidelity generated audio widely available across the Internet, driving the advancement of audio steganography. Benefiting from advances in deep learning, current audio steganography schemes are mainly based on encoder-decoder network architectures. While these methods gua

Irfan Lone

The Fokker_Planck equation can be derived in a consistent manner through a microscopic approach based on a unified scheme of classical and quantum mechanics. Here we shall derive it through a purely quantum mechanical approach based on the reversible Schrodinger dynamics. We also give a brief discussion of the path integral representation of the Fokker_Planc

Maximiliano Hormazábal Lagos, Álvaro Bueno Saez, Héctor Cerezo-Costas, Pedro Alonso Doval

In this paper we expose our approach to solve the \textit{SemEval 2025 Task 8: Question-Answering over Tabular Data} challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language i

David A. Gabrielyan, Dmitry A. Volkov, Tatyana V. Bogdanova, Kristina D. Samoylenko

Recent advances in magnon spintronics have ignited interest in the interactions between the spin and elastic subsystems of magnetic materials. These interactions suggest a dynamic connection between collective excitations of spins, quantized as magnons, and elastic waves generated by perturbations in the crystal lattice, quantized as phonons. Both magnons an

Md Aquib Molla, Sanchari Goswami, Parongama Sen

We study the fate of a forager who searches for food performing a random walk on lattices. The forager consumes the available food on the site it visits and leaves it depleted but can survive up to $S$ steps without food. We introduce the concept of intermittent rest in the dynamics which allows the forager to rest with probability $p$ upon consumption of fo

Qiang Jia, Zhian Jia

Symmetry topological field theory (SymTFT), or topological holography, posits a correspondence between symmetries in a $d$-dimensional theory and topological order in a $(d+1)$-dimensional theory. In this work, we extend this framework to subsystem symmetries and develop subsystem SymTFT as a systematic tool to characterize and classify subsystem symmetry-pr

Alexander P. Antonov, Annika Vonhusen, Artem Ryabov, Philipp Maass

In overdamped particle motion across periodic landscapes, solitary cluster waves can occur at high particle densities and lead to particle transport even in the absence of thermal noise. Here we show that for driven motion under a constant drag, the sum of all particle displacements per soliton equals one wavelength of the periodic potential. This unit displ

Kiyoon Jeong, Jaehyuk Heo, Junyeong Son, Pilsung Kang

Zero-shot anomaly detection (ZSAD) enables anomaly detection without normal samples from target categories, addressing scenarios where task-specific training data is unavailable. However, existing ZSAD methods either neglect adaptation of vision-language models to anomaly detection or implement only partial adaptation. This paper proposes Head-adaptive CLIP

Benjamin Serfling, Hannes Reichert, Lorenzo Bayerlein, Konrad Doll

In this study, we present a novel LiDAR-based semantic segmentation framework tailored for autonomous forklifts operating in complex outdoor environments. Central to our approach is the integration of a dual LiDAR system, which combines forward-facing and downward-angled LiDAR sensors to enable comprehensive scene understanding, specifically tailored for ind

Yinjie Zhao, Heng Zhao, Bihan Wen, Yew-Soon Ong

With the rapid development of vision tasks and the scaling on datasets and models, redundancy reduction in vision datasets has become a key area of research. To address this issue, dataset distillation (DD) has emerged as a promising approach to generating highly compact synthetic datasets with significantly less redundancy while preserving essential informa

Youssef Mroueh, Nicolas Dupuis, Brian Belgodere, Apoorva Nitsure

We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Optimization (PPO), which improves training stability, sampling efficiency, and memory usage. In addition, a recent analysis of GRPO suggests that estimating the advantage function wit

Yizhong Chen, Zhibin Wang

The pseudopotential model within the Lattice Boltzmann Method (LBM) framework has emerged as a prominent approach in computational fluid dynamics due to its dual strengths in physical intuitiveness and computational tractability. However, when modeling wettability phenomenon, existing solid-fluid interaction schemes exhibit persistent challenges in multi-com

Jieyu Chen, Sebastian Lerch, Melanie Schienle, Tomasz Serafin

The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-ti

Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov

Sparse Autoencoders (SAEs) decompose language-model activations into sparse, interpretable features, but standard encoders usually treat the latent dictionary as a flat set of independent coordinates, leaving hierarchy and feature interactions to emerge only implicitly. We propose KronSAE, a design that factorizes the latent space into heads and forms post-l

Xiangxiang Dai, Xiaowei Sun, Jinhang Zuo, Xutong Liu

Co-branding has become a vital strategy for businesses aiming to expand market reach within recommendation systems. However, identifying effective cross-industry partnerships remains challenging due to resource imbalances, uncertain brand willingness, and ever-changing market conditions. In this paper, we provide the first systematic study of this problem an

Yan-Long Fang, Jeffrey Galkowski

We study scattering by metamaterials with negative indices of refraction, which are known to support \emph{surface plasmons} -- long-lived states that are highly localized at the boundary of the cavity. This type of states has found uses in a variety of modern technologies. In this article, we study surface plasmons in the setting of non-trapping cavities; i

Magdalena Proszewska, Tomasz Danel, Dawid Rymarczyk

Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, current evaluation frameworks for Explainable AI (XAI) in this domain often rely on artificial datasets or simplified tasks, employing data-derived metrics that fail to capture the c

Yuan Tseng, Titouan Parcollet, Rogier van Dalen, Shucong Zhang

Recent work suggests that large language models (LLMs) can improve performance of speech tasks compared to existing systems. To support their claims, results on LibriSpeech and Common Voice are often quoted. However, this work finds that a substantial amount of the LibriSpeech and Common Voice evaluation sets appear in public LLM pretraining corpora. This ca

Mingzhuang Wang, Yvyang Li, Xiyang Zhang, Fei Tan

Coral reefs, crucial for sustaining marine biodiversity and ecological processes (e.g., nutrient cycling, habitat provision), face escalating threats, underscoring the need for efficient monitoring. Coral reef ecological monitoring faces dual challenges of low efficiency in manual analysis and insufficient segmentation accuracy in complex underwater scenario

Cheng Hua, Arthur J. Swersey, Wenqian Xing, Yi Zhang

This paper presents a new model for solving the optimal server location problem in a spatial hypercube queueing model. Unlike deterministic location models, our approach accounts for server availability, varying utilization levels, and dependencies across servers. We prove that the problem is NP-hard and establish lower and upper bounds, as well as asymptoti

Armin Gießler, Felix Strehle, Jochen Illerhaus, Sören Hohmann

In this paper, we propose a novel dynamic state-feedback controller for polytopic linear parameter-varying (LPV) systems with constant input matrix. The controller employs a projected gradient flow method to continuously improve its control law and, under established conditions, converges to the optimal feedback gain of the corresponding linear quadratic reg

Alessio Cargioli, Miguel Montesinos Ballester, Sonja Gantner, Emilio Gini

Ring quantum cascade lasers (QCLs) proved to be a versatile tool for generating tunable and stable frequency combs in the mid infrared range in the form of quantum walk combs. By homogeneously integrating a racetrack QCL with a passive waveguide, which lays on top of the active region plane and therefore can be designed to be fully independent from the laser

Nedko Savov, Naser Kazemi, Deheng Zhang, Danda Pani Paudel

World models have recently gained prominence for action-conditioned visual prediction in complex environments. However, relying on only a few recent observations causes them to lose long-term context. Consequently, within a few steps, the generated scenes drift from what was previously observed, undermining temporal coherence. This limitation, common in stat

Jiho Hong, Bangti Jin, Zhizhang Wu

The subdiffusion model that involves a Caputo fractional derivative in time is widely used to describe anomalously slow diffusion processes. In this work we aim at recovering the locations of small conductivity inclusions in the model from boundary measurement, and develop novel direct algorithms based on the asymptotic expansion of the boundary measurement

Yaron Halle, Ariel Felner, Sven Koenig, Oren Salzman

The bi-objective shortest-path (BOSP) problem seeks to find paths between start and target vertices of a graph while optimizing two conflicting objective functions. We consider the BOSP problem in the presence of correlated objectives. Such correlations often occur in real-world settings such as road networks, where optimizing two positively correlated objec

Bin Li, Diwei Liu, Zehong Hu, Jia Jia

Online resource allocation under budget constraints critically depends on proper modeling of user arrival dynamics. Classical approaches employ stochastic user arrival models to derive near-optimal solutions through fractional matching formulations of exposed users for downstream allocation tasks. However, this is no longer a reasonable assumption when the e

Jens Chluba, Geoffrey Vasil, Richard Battye

In this work, we develop a novel formalism to include the effect of electron density fluctuations at ultra small scales (well below the sound horizon at last scattering) on the observed anisotropies of the Cosmic Microwave Background (CMB). We treat the electron field as an independent stochastic variable and obtain the required ensemble-averaged photon Bolt

Xia Zhou, Mark Wallace, Daniel D. Harabor, Zhenliang Ma

Mass transit systems are experiencing increasing congestion in many cities. The schedule-based transit assignment problem (STAP) involves a joint choice model for departure times and routes, defining a space-time path in which passengers decide when to depart and which route to take. User equilibrium (UE) models for the STAP indicates the current congestion

Jusheng Zhang, Jinzhou Tang, Sidi Liu, Mingyan Li

Human motion generative modeling or synthesis aims to characterize complicated human motions of daily activities in diverse real-world environments. However, current research predominantly focuses on either low-level, short-period motions or high-level action planning, without taking into account the hierarchical goal-oriented nature of human activities. In

Yunsoo Kim, Yusuf Abdulle, Honghan Wu

Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many rel

Youze Xue, Dian Li, Gang Liu

With the rapid advancement of multi-modal large language models (MLLMs) in recent years, the foundational Contrastive Language-Image Pretraining (CLIP) framework has been successfully extended to MLLMs, enabling more powerful and universal multi-modal embeddings for a wide range of retrieval tasks. Despite these developments, the core contrastive learning pa

Darshan Raju, Mahinder Ramdin, Jean-Marc Simon, Peter Kruger

Computation of the excess entropy from the second-order density expansion of the entropy holds strictly for infinite systems in the limit of small densities. For the reliable and efficient computation of excess entropy, it is important to understand finite-size effects. Here, expressions to compute excess entropy and Kirkwood-Buff (KB) integrals by integrati

A. Ploshkin, V. Tytskiy, A. Pismenny, V. Baikalov

We present Yambda-5B, a large-scale open dataset sourced from the Yandex Music streaming platform. Yambda-5B contains 4.79 billion user-item interactions from 1 million users across 9.39 million tracks. The dataset includes two primary types of interactions: implicit feedback (listening events) and explicit feedback (likes, dislikes, unlikes and undislikes).

Fatma Kader Bingöl, Adam Chapman, Ahmed Laghribi

We study the essential dimension of the set of isometry classes of $m$-tuples $(\varphi_1,...,\varphi_m)$ of quadratic $n$-fold Pfister forms over a field $F$ such that the Witt class of $\varphi_1 \perp \ldots \perp \varphi_m$ lies in $I_q^{n+1}F$. We show that the essential dimension is equal to $n+1$, when $m=3$, and is either $4$ or $5$, when $n=\text{ch

Charlotte Pouw, Afra Alishahi, Willem Zuidema

We analyze the syntactic sensitivity of Text-to-Speech (TTS) systems using methods inspired by psycholinguistic research. Specifically, we focus on the generation of intonational phrase boundaries, which can often be predicted by identifying syntactic boundaries within a sentence. We find that TTS systems struggle to accurately generate intonational phrase b

Amon Lahr, Johannes Köhler, Anna Scampicchio, Melanie N. Zeilinger

Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical contexts. In this paper, we derive a tight, non-asymptotic uncertainty bound for kernel-based estimation, which can also handle correlated noise sequences. Its computation relies on a mil

Georgia M. Kapitsaki, Maria Papoutsoglou

Free and open source software has gained a lot of momentum in the industry and the research community. The latest advances in privacy legislation, including the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), have forced the community to pay special attention to users' data privacy. The main aim of this work is to

Ugo Bruzzo, Daniel Hernández Ruipérez

We review the notion of stable supermap from SUSY curves to a fixed target superscheme, and prove that when the target is (super)projective, stable supermaps are parameterized by a Deligne-Mumford superstack with superschematic and separated diagonal. We characterize the bosonic reduction of this moduli superstack and see that it has a surjective morphism on

Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introd

Stefan Bleeck

Traditional audiometry often fails to fully characterize the functional impact of hearing loss on speech understanding, particularly supra-threshold deficits and frequency-specific perception challenges in conditions like presbycusis. This paper presents the development and simulated evaluation of a novel Automatic Speech Recognition (ASR)-based frequency-sp

Zhisong Wang, Yiwen Ye, Ziyang Chen, Yong Xia

Weakly supervised semantic segmentation (WSSS) in medical imaging struggles with effectively using sparse annotations. One promising direction for WSSS leverages gaze annotations, captured via eye trackers that record regions of interest during diagnostic procedures. However, existing gaze-based methods, such as GazeMedSeg, do not fully exploit the rich info

Zixuan Li, Xueliang Zhang, Lei Miao, Zhipeng Yan

Audio-Visual Target Speaker Extraction (AVTSE) aims to isolate a target speaker's voice in a multi-speaker environment with visual cues as auxiliary. Most of the existing AVTSE methods encode visual and audio features simultaneously, resulting in extremely high computational complexity and making it impractical for real-time processing on edge devices. To ta

Haibin He, Jing Zhang, Maoyuan Ye, Juhua Liu

Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall short of the performance seen in ITS. This paper identifies a key limitation in current video text spotters: limited recognition capability, even after extensive end-to-end trainin

Carlos Mera Acosta

We identify a magnetoelectric correction that completes the theoretical description of spin splitting (SS) in magnetic systems. Derived from the Dirac equation, this term couples local magnetic moments to the scalar electric potential, providing a third fundamental mechanism, alongside Zeeman and spin-orbit coupling (SOC), that governs SS in ferromagnets, an

Jiabao Brad Wang, Amir Vaxman

We introduce a novel method for directional-field design on meshes, enabling users to specify singularities at any location on a mesh. Our method uses a piecewise power-linear representation for phase and scale, offering precise control over field topology. The resulting fields are smooth and accommodate any singularity index and field symmetry. With this re

Ye Cheng, Minghui Xu, Yue Zhang, Kun Li

Access control in the Internet of Things (IoT) is becoming increasingly complex, as policies must account for dynamic and contextual factors such as time, location, user behavior, and environmental conditions. However, existing platforms either offer only coarse-grained controls or rely on rigid rule matching, making them ill-suited for semantically rich or

Xuyang Zhang, Xi Zhang, Liang Chen, Hao Shi

Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field of deep learning, yet their practical deployment in resource-constrained vision models remains largely unexplored. To address this gap, we introduce the Adaptive Cross-Hadamard (ACH) module, a novel operator that

Yuan Feng, Ye-Fei Yuan, Shuang-Nan Zhang

Relativistic reflection features in the X-ray spectra of accreting black holes are considered to be generated by the illumination of the accretion disk by the hot corona. In this work, we present a numerical method for the emission line profile and the reflection spectrum produced by an off-axis X-ray source. The X-ray source is considered as a point source,

Seung Gyu Jeong, Seong Eun Kim

Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesi

Senne Berden, Ali İrfan Mahmutoğulları, Dimos Tsouros, Tias Guns

Mathematical optimization is a fundamental tool for decision-making in a wide range of applications. However, in many real-world scenarios, the parameters of the optimization problem are not known a priori and must be predicted from contextual features. This gives rise to predict-then-optimize problems, where a machine learning model predicts problem paramet

Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin, Jiang Mo

Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking m

Yunsoo Kim, Jinge Wu, Su-Hwan Kim, Pardeep Vasudev

Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in generating radiology reports from chest X-rays (CXR). However, these models still suffer from hallucinations and clinically significant errors, limiting their reliability in real-world applications. In this stud

Andreas Dvorak, Ismaele V. Masiello, Yuji Hasegawa, Hartmut Lemmel

In its original formulation, Heisenberg's uncertainty principle describes a trade-off relation between the error of a quantum measurement and the thereby induced disturbance on the measured object. However, this relation is not valid in general. An alternative universally valid relation was derived by Ozawa in 2003, defining error and disturbance in a genera

Denis Petrov, Pascal Ruffing, Sebastian Zillien, Steffen Wendzel

In recent years, malware with tunneling (or: covert channel) capabilities is on the rise. While malware research led to several methods and innovations, the detection and differentiation of malware solely based on its DNS tunneling features is still in its infancy. Moreover, no work so far has used the DNS tunneling traffic to gain knowledge over the current

Subhadeep Mukherjee, Shashank Shekhar Pandey, A. S. Majumdar

We consider the 21\,cm brightness temperature as a probe of the Hubble tension in the framework of an inhomogeneous cosmological model. Employing Buchert's averaging formalism to study the effect of inhomogeneities on the background evolution, we consider scaling laws for the backreaction and curvature consistent with structure formation simulations. We cali

Jiří Ajgl, Ondřej Straka

Point-mass filters solve Bayesian recursive relations by approximating probability density functions of a system state over grids of discrete points. The approach suffers from the curse of dimensionality. The exponential increase of the number of the grid points can be mitigated by application of low-rank approximations of multidimensional arrays. Tensor tra

Lucas Hallgren, Radoslaw Wojtak, Jens Hjorth, Charles L. Steinhardt

Precise cosmological constraints from type Ia supernovae require adequately accurate corrections for host-galaxy extinction. Modelling these corrections is challenged by the problem of disentangling supernova intrinsic colours from host-galaxy interstellar reddening. The latter is commonly modelled in a probabilistic way assuming an exponential distribution

Ondřej Benedikt, Michal Sojka, Přemysl Šůcha, Pavel Zaykov

Multi-Processor Systems-on-Chip (MPSoC) can deliver high performance needed in many industrial domains, including aerospace. However, their high power consumption, combined with avionics safety standards, brings new thermal management challenges. This paper investigates techniques for offline thermal-aware allocation of periodic tasks on heterogeneous MPSoCs

Márton Hajdu, Laura Kovács, Andrei Voronkov

Redundancy elimination is one of the crucial ingredients of efficient saturation-based proof search. We improve redundancy elimination by introducing a new notion of redundancy, based on partial clauses and redundancy formulas, which is more powerful than the standard notion: there are both clauses and inferences that are redundant when we use our notions an

Christoph Hunkenschröder, Martin Koutecký, Asaf Levin, Tung Anh Vu

We study the general integer programming (IP) problem of optimizing a separable convex function over the integer points of a polytope: $\min \{f(\mathbf{x}) \mid A\mathbf{x} = \mathbf{b}, \, \mathbf{l} \leq \mathbf{x} \leq \mathbf{u}, \, \mathbf{x} \in \mathbb{Z}^n\}$. The number of variables $n$ is a variable part of the input, and we consider the regime wh

The Tien Mai

Bounded continuous responses -- such as proportions -- arise frequently in diverse scientific fields including climatology, biostatistics, and finance. Beta regression is a widely adopted framework for modeling such data, due to the flexibility of the Beta distribution over the unit interval. While Bayesian extensions of Beta regression have shown promise, e

Nan Liu, Ran Wang

We address the long-time asymptotics of the solution to the Cauchy problem of ccSP (coupled complex short pulse) equation on the line for decaying initial data that can support solitons. The ccSP system describes ultra-short pulse propagation in optical fibers, which is a completely integrable system and posses a $4\times4$ matrix Wadati--Konno--Ichikawa typ

Naomi Kombol, Ivan Martinović, Siniša Šegvić

Semantic segmentation is one of the most fundamental tasks in image understanding with a long history of research, and subsequently a myriad of different approaches. Traditional methods strive to train models up from scratch, requiring vast amounts of computational resources and training data. In the advent of moving to open-vocabulary semantic segmentation,

Yosuke Oyama, Yusuke Majima, Eiji Ohta, Yasufumi Sakai

Neural network potentials (NNPs) are crucial for accelerating computational materials science by surrogating density functional theory (DFT) calculations. Improving their accuracy is possible through pre-training and fine-tuning, where an NNP model is first pre-trained on a large-scale dataset and then fine-tuned on a smaller target dataset. However, this ap

Akshay Mahajan, Awadhesh Narayan

Sliding ferroelectricity is emerging as a distinct and promising mechanism for realizing ferroelectricity in low-dimensional systems, offering new design principles beyond the conventional ferroelectric mechanism. Further, the coexistence of the out-of-plane polarization with in-plane conductivity induced by electrostatic charge doping makes these systems st

Enrique de Amo, David García-Fernández, Manuel Úbeda-Flores

In this paper we obtain advances for the concept of directional $\rho$-coefficients, originally defined for the trivariate case in [Nelsen, R.B., \'Ubeda-Flores, M. (2011). Directional dependence in multivariate distributions. Ann. Inst. Stat. Math 64, 677-685] by extending it to encompass arbitrary dimensions and directions in multivariate space. We provide

Patrick de Laverny, Roxanne Ligi, Aurélien Crida, Alejandra Recio-Blanco

Complete, accurate, and precise catalogues of exoplanet host star (EHS) properties are essential to deriving high-quality exoplanet parameters. This paper aims at homogeneously parameterising EHS and their exoplanets, using Gaia and GSP-spec data. For 2573 EHS, we computed their L*, R*, and M*, with no prior assumption from stellar evolution models. Their Ga

Diptarko Mukherjee, Ashadul Halder, Debasish Majumdar, Abhijit Bandyopadhyay

The decay of superheavy dark matter from the early universe may undergo decay via QCD cascades and electroweak cascade to produce neutrinos as one of the decay products. We consider the neutrino events in and around PeV region reported by IceCube collaboration are due to the decay of such heavy dark matter. The neutrino spectrum could be from the decay proce

Yuzhen Huang, Weihao Zeng, Xingshan Zeng, Qi Zhu

Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the rel

Hyeonbin Hwang, Byeongguk Jeon, Seungone Kim, Jiyeon Kim

Autoregressive language models (LMs) generate one token at a time, yet human reasoning operates over higher-level abstractions - sentences, propositions, and concepts. This contrast raises a central question- Can LMs likewise learn to reason over structured semantic units rather than raw token sequences? In this work, we investigate whether pretrained LMs ca

A. J. Dimoff, R. J. Stancliffe, C. J. Hansen, R. M. Seeburger

About half of the mass of all heavy elements with mass number A > 90 is formed through the slow neutron capture process (s-process), occurring in evolved asymptotic giant branch (AGB) stars with masses ~1-6 $\rm{M_{\odot}}$. The s-process can be studied by modeling the accretion of material from AGB stars onto binary barium (Ba), CH, and carbon-enhanced meta

Darshana Saravanan, Makarand Tapaswi, Vineet Gandhi

To understand a prompt, Vision-Language models (VLMs) must perceive the image, comprehend the text, and build associations within and across both modalities. For instance, given an 'image of a red toy car', the model should associate this image to phrases like 'car', 'red toy', 'red object', etc. Feng and Steinhardt propose the Binding ID mechanism in LLMs,

Xinyue Hu, Zhibin Duan, Bo Chen, Mingyuan Zhou

Although deep neural networks have demonstrated significant success due to their powerful expressiveness, most models struggle to meet practical requirements for uncertainty estimation. Concurrently, the entangled nature of deep neural networks leads to a multifaceted problem, where various localized explanation techniques reveal that multiple unrelated feat

Matthias Runge, Michael Woerner, Denys I. Bondar, Thomas Elsaesser

Electrons in polar liquids give rise to a polaron resonance at a terahertz (THz) frequency \nu_0 depending on electron concentration. The impact of this resonance on light propagation is studied in experiments, where a femtosecond pump pulse generates electrons via multiphoton ionization and a THz probe pulse propagated through the excited sample is detected

Kevin Wittkowski, Pier Giuseppe Ledda, Edoardo Carlo Giordano, François Gallaire

Flows enabled by phoretic mechanisms are of significant interest in several biological and biomedical processes, such as bacterial motion and targeted drug delivery. Here, we develop a homogenization-based macroscopic boundary condition which describes the effective flow across a diffusiophoretic microstructured membrane, where the interaction between the me

Sepideh Masoudi, Sebastian Werner, Pierluigi Plebani, Stefan Tai

Data-sharing pipelines involve a series of stages that apply policy-based data transformations to enable secure and effective data exchange among organizations. Although numerous tools and platforms exist to manage governance and enforcement in these pipelines, energy efficiency in data exchange has received limited attention. This paper introduces a novel m

Jingyi Cui, Hongwei Wen, Yisen Wang

Self-supervised contrastive learning has emerged as a powerful tool in machine learning and computer vision to learn meaningful representations from unlabeled data. Meanwhile, its empirical success has encouraged many theoretical studies to reveal the learning mechanisms. However, in the existing theoretical research, the role of data augmentation is still u

Guoan Xu, Wenfeng Huang, Wenjing Jia, Jiamao Li

Vision Transformer (ViT) has made significant advancements in computer vision, thanks to its token mixer's sophisticated ability to capture global dependencies between all tokens. However, the quadratic growth in computational demands as the number of tokens increases limits its practical efficiency. Although recent methods have combined the strengths of con

Can Xiao, Jianyi Cheng, Aaron Zhao

Vision Transformers (ViTs) leverage the transformer architecture to effectively capture global context, demonstrating strong performance in computer vision tasks. A major challenge in ViT hardware acceleration is that the model family contains complex arithmetic operations that are sensitive to model accuracy, such as the Softmax and LayerNorm operations, wh

Marco Parigi, Stefano Martina, Francesco Aldo Venturelli, Filippo Caruso

Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However, existing algorithms are not easily scalable due to the limitations of near-term quantum devices. Following our previous work on QDMs, here we propose and implement two physics-insp

Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li

Multi-agent systems based on large language models (LLMs) advance automatic task completion in various fields, where debate is a common cooperation form for agents to solve complicated problems with reasoning and cross-review to solidify answers. Assessing the individual contributions of agents within these debates is crucial for system refinement and outcom

Christian Stelzer-Landauer

In this paper we study the approximation of Dirac operators with $\delta$-shell potentials in the norm resolvent sense. In particular, we consider the approximation of Dirac operators with confining electrostatic and Lorentz scalar $\delta$-shell potentials, where the support of the $\delta$-shell potentials is impermeable to particles modelled by such Dirac

Andrzej Grzesik, Justyna Jaworska, Bartłomiej Kielak, Piotr Kuc

For integers $k, \ell \geq 3$, let $\mathrm{ex}(n, \overrightarrow{C_k}, \overrightarrow{C_\ell})$ denote the maximum number of directed cycles of length $k$ in any oriented graph on $n$ vertices which does not contain a directed cycle of length $\ell$. We establish the order of magnitude of $\mathrm{ex}(n, \overrightarrow{C_k}, \overrightarrow{C_\ell})$ for

V. A. Balakirev, I. N. Onishchenko

By solving Maxwell's equations the exact dispersion equation for electromagnetic waves propagating in a layered coaxial ferrite line is obtained. In particular the analytical consideration is carried out for a simpler case of complete filling of the coaxial line with ferrite (i.e. homogeneous ferrite line). The behavior of dispersion curves of TEM - electrom

Mohammad Samin Nur Chowdhury, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux

Residual strain, a tensor quantity, is a critical material property that impacts the overall performance of metal parts. Neutron Bragg edge strain tomography is a technique for imaging residual strain that works by making conventional hyperspectral computed tomography measurements, extracting the average projected strain at each detector pixel, and processin