March 2025 arXiv papers — page 58

Kai Xu, Fei He

The topological and geometrical features at the boundary zone of planar polygonal networks remain poorly understood. Based on observations and mathematical proofs, we propose that marginal cells in the thalli of Pyropia haitanensis, a two-dimensional (2D) biological polygonal network, have an average edge number of approximately five. We demonstrate that thi

Adita Sultana, Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi, Abdulla All Noman

The current paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022, incorporating the ARDL methodology. The outcomes observe that AI innovation, renewable energy usage, and the digital economy reduce CO2 emissions, while GDP expansi

Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang

Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent studies investigate VLM personalization to understand user-provided concepts. However, they mainly focus on single-concept personalization, neglecting the existence and interplay of multiple concepts, wh

Xiao Cao, Beibei Lin, Bo Wang, Zhiyong Huang

3D texture swapping allows for the customization of 3D object textures, enabling efficient and versatile visual transformations in 3D editing. While no dedicated method exists, adapted 2D editing and text-driven 3D editing approaches can serve this purpose. However, 2D editing requires frame-by-frame manipulation, causing inconsistencies across views, while

Markus J. Buehler

We report fundamental insights into how agentic graph reasoning systems spontaneously evolve toward a critical state that sustains continuous semantic discovery. By rigorously analyzing structural (Von Neumann graph entropy) and semantic (embedding) entropy, we identify a subtle yet robust regime in which semantic entropy persistently dominates over structur

André L. P. Considera, Simon Thalabard

We investigate the behavior of fluid trajectories in a multifractal extension of the Kraichnan model of turbulent advection. The model couples a one-dimensional, Gaussian, white-in-time random flow to a frozen-in-time Gaussian multiplicative chaos (GMC). The resulting velocity field features an interplay between the roughness exponent $\xi\in(0,2]$, controll

Antonio Matteri, Andrea Ferrara, Andrea Pallottini

The presence of nine candidate galaxies at $z=17$ and $z=25$ discovered by the James Webb Space Telescope in relatively small sky areas, if confirmed, is virtually impossible to reconcile with the predictions of the current galaxy formation model. We show here that the implied UV luminosity density can be produced by a population of primordial black holes (P

Ivan Chuprov, Denis Derkach, Dmitry Efremenko, Aleksei Kychkin

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for solving the inverse advection-diffusion problem to localize pollution sources. The study focuses on optimizing neural network architectures to accurately model pollutant dispersion dynamics under diverse conditions, including scenarios with weak and strong winds and multi

Alessandro Barone, Dalibor Djukanovic, Georg von Hippel, Jonna Koponen

The isoscalar axial-vector form factor of the nucleon plays a key role in understanding the electroweak interaction of nucleons. For the interpretation of the spin structure of the nucleon the non-singlet isoscalar axial charge is indispensable. Moreover, $G_A^{u+d-2s}(Q^2)$ together with the isovector and singlet isoscalar form factors are needed for the fl

Nataliya Goncharuk

We prove that the classification of real-analytic vector fields on the two-torus up to orbital topological equivalence does not admit a complete numerical invariant that is a Borel function. Moreover, smooth vector fields that are difficult to classify appear in generic smooth 7-parameter families. In dimension 2, this improves the recent result of Gorodetsk

James Allsopp, Jake Diprose, Brianna R. Heazlewood, Chase Zagorec-Marks

This paper reports on the use of a convolutional neural network (CNN) methodology to analyse fluorescence images of calcium-ion Coulomb crystals in the gas phase. A transfer-learning approach is adopted using the publicly available RESNET50 model. It is demonstrated that by training the neural network on around 500,000 simulated images, we are able to determ

Yuxiu Cheng, Kun Wang, Kai Yang

In this paper, we propose a class of high-order and energy-stable implicit-explicit relaxation Runge-Kutta (IMEX RRK) schemes for solving the phase-field gradient flow models. By incorporating the scalar auxiliary variable (SAV) method, the original equations are reformulated into equivalent forms, and the modified energy is introduced. Then, based on the re

Mingxuan Wei, Siyu Chen, Yu Wang, Xichen Hu

Vortex $\gamma$ photons carrying orbital angular momenta (OAM) hold great potential for various applications. However, their generation remains a great challenge. Here, we successfully generate sub-MeV vortex $\gamma$ photons via all-optical inverse Compton scattering of relativistic electrons colliding with a sub-relativistic Laguerre-Gaussian laser. In pri

Emanuele Ratti

There is an overwhelming abundance of works in AI Ethics. This growth is chaotic because of how sudden it is, its volume, and its multidisciplinary nature. This makes difficult to keep track of debates, and to systematically characterize goals, research questions, methods, and expertise required by AI ethicists. In this article, I show that the relation betw

Xuan Li, Yuting Peng, Xiaoxuan Sun, Yifei Duan

With the rapid development of e-commerce, e-commerce platforms are facing an increasing number of fraud threats. Effectively identifying and preventing these fraudulent activities has become a critical research problem. Traditional fraud detection methods typically rely on supervised learning, which requires large amounts of labeled data. However, such data

Meva Himmetoglu, Ilja Ciernik, Ender Konukoglu

Segmenting healthy tissue structures alongside lesions in brain Magnetic Resonance Images (MRI) remains a challenge for today's algorithms due to lesion-caused disruption of the anatomy and lack of jointly labeled training datasets, where both healthy tissues and lesions are labeled on the same images. In this paper, we propose a method that is robust to les

Arus Harutyunyan, Armen Sedrakian

In this work, we extend the formalism of second-order relativistic dissipative hydrodynamics, developed previously using Zubarev's non-equilibrium statistical operator formalism. By employing a second-order expansion of the statistical operator in terms of hydrodynamic gradients, we demonstrate that new second-order terms emerge due to the coupling of two-po

Pengyu Liu, Beth A. Biller, Matthew A. Kenworthy, Clémence Fontanive

Low-mass stellar and substellar companions are indispensable objects for verifying evolutionary models that transition from low-mass stars to planets. Their formation is likely also affected by the surrounding stellar environment. The Fornax-Horologium (FH) association is a recently classified young association in the solar neighbourhood with a dissolving op

Yuxuan Zhang, Jinkui Hao, Bo Zhou

Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient comfort. Recent advancements in deep learning, particularly diffusion models, have improved accelerated MRI reconstruction. However, existing diffusion models' training often relies on fully sampled data, models incur

Eduard Emelyanov

We study order-to-weak continuous operators from an ordered Banach space to a normed space. It is proved that under rather mild conditions every order-to-weak continuous operator is bounded.

Liam A. V. Nagle-Cocco, James M. A. Steele, Shiyu Deng, Xiaotian Zhang

NaNiO$_2$ is a Ni$^{3+}$-containing layered material consisting of alternating triangular networks of Ni and Na cations, separated by octahedrally-coordinated O anions. At ambient pressure, it features a collinear Jahn--Teller distortion below $T^\mathrm{JT}_\mathrm{onset}\approx480$ K, which disappears in a first-order transition on heating to $T^\mathrm{JT

Usmi Mukherjee, Mohammad Masudur Rahman

Duplicate bug reports make up 42% of all reports in bug tracking systems (e.g., Bugzilla), causing significant maintenance overhead. Hence, detecting and resolving duplicate bug reports is essential for effective issue management. Traditional techniques often focus on detecting textually similar duplicates. However, existing literature has shown that up to 2

Zhengxian Wu, Chuanrui Zhang, Hangrui Xu, Peng Jiao

Gait recognition is emerging as a promising and innovative area within the field of computer vision, widely applied to remote person identification. Although existing gait recognition methods have achieved substantial success in controlled laboratory datasets, their performance often declines significantly when transitioning to wild datasets.We argue that th

Kim V. Berghaus, Marco Drewes, Sebastian Zell

We show for the first time that warm inflation is feasible with Standard Model (SM) gauge interactions alone. Our model consists of a minimal extension of the SM by a single scalar inflaton field with an axion-like coupling to gluons and a monomial potential. The effects of light fermions, which were previously argued to render warm inflation with the SM imp

D. B. Horoshko, V. S. Shchesnovich

Optical downconversion is widely used for generating photon pairs, squeezed and entangled states of light, making it an indispensable tool in quantum optics and quantum information. In the regime where the pump is much stronger than the generated field, the standard parametric approximation treats the pump amplitude as a fixed parameter of the model. This ap

Enric Nart, Josnei Novacoski

The depth of a simple algebraic extension $(L/K,v)$ of valued fields is the minimal length of the Mac Lane-Vaqui\'e chains of the valuations on $K[x]$ determined by the choice of different generators of the extension. In a previous paper, we characterized the defectless unibranched extensions of depth one. In this paper, we analyze this problem for towers of

Daphne Lenders, Andrea Pugnana, Roberto Pellungrini, Toon Calders

Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the classifier's performance on the accepted data while ensuring a minimum number of predictions. In this setting, often fairness concerns arise when the abstention mechanism solely redu

Sara Fish, Julia Shephard, Minkai Li, Ran I. Shorrer

We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs. First, we develop benchmarks derived from key problems in economics -- procurement, scheduling, and pricing -- that test an LLM's ability to learn from the environment in context. Second, we develop the framework of litmus tests, evaluations that qua

Radek Zavorka, Tomas Mikulasek, Josef Vychodil, Jiri Blumenstein

This paper presents a comprehensive measurement campaign aimed at evaluating indoor-to-indoor radio channels in dynamic scenarios, with a particular focus on applications such as autonomous ground vehicles (AGV). These scenarios are characterized by the height of the antennas, addressing the unique challenges of near-ground communication. Our study involves

Erik Hormann, Renaud Lambiotte

In this paper, we introduce ergodic sets, subsets of nodes of the networks that are dynamically disjoint from the rest of the network (i.e. that can never be reached or left following to the network dynamics). We connect their definition to purely structural considerations of the network and study some of their basic properties. We study numerically the pres

Félix Benoist, Luca Peliti, Pablo Sartori

Content-addressable memory, i.e. stored information that can be retrieved from content-based cues, is key to computation. Besides natural and artificial neural networks, physical learning systems have recently been shown to have remarkable ability in this domain. While classical neural network models encode memories as energy minima, biochemical systems have

Chenyi Li, Shengyang Xu, Chumin Sun, Li Zhou

Optimality conditions are central to analysis of optimization problems, characterizing necessary criteria for local minima. Formalizing the optimality conditions within the type-theory-based proof assistant Lean4 provides a precise, robust, and reusable framework essential for rigorous verification in optimization theory. In this paper, we introduce a formal

James D. Brownridge, Matthieu Zinet, Paul Sotta, Francois Ganachaud

Water exhibits many unique properties compared to other liquids, with some of these explained and others remaining enigmatic. Among them, it was proposed and extensively debated that hot water would freeze faster than cold water. Numerous studies have demonstrated the difficulty of successfully elucidating this effect, making explanations surrounding this ph

Łanucha Bartosz, Michalska Małgorzata, Nowak Maria

We obtain orthogonal decompositions for de Branges-Rovnyak spaces $\H\left( \tfrac {I^n(1+I)}{2}\right)$ and $\H\left( \tfrac {I(1+I^2)}{2}\right)$, where $I$ is an inner function. We also discuss the problem of cyclicity for these spaces.

Luis Avilés, Oscar Fuentealba, Diego Hidalgo, Pablo Rodríguez

The asymptotic structure of three-dimensional Carroll gravity with negative cosmological constant is studied. We formulate a consistent set of boundary conditions preserved by an infinite-dimensional extension of the AdS$_3$ Carroll algebra, which turns out to be isomorphic to a precise generalized BMS$_3$ algebra. This is described by four independent funct

Jeonghyeon Kim, Sangheum Hwang

Prior research on out-of-distribution detection (OoDD) has primarily focused on single-modality models. Recently, with the advent of large-scale pretrained vision-language models such as CLIP, OoDD methods utilizing such multi-modal representations through zero-shot and prompt learning strategies have emerged. However, these methods typically involve either

Chak Lam Shek, Amrit Singh Bedi, Anjon Basak, Ellen Novoseller

In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textbf{C}ritic (Loc-FACMAC). Existing state-of-the-art algorithms, such as FACMAC, rely on global reward information, which may not accurately reflect the quality of individual robots'

Anna Cima, Armengol Gasull, Víctor Mañosa, Francesc Mañosas

We study two natural families of methods of order $n\ge 2$ that are useful for solving numerically one variable equations $f(x)=0.$ The first family consists on the methods that depend on $x,f(x)$ and its successive derivatives up to $f^{(n-1)}(x)$ and the second family comprises methods that depend on $x,g(x)$ until $g^{\circ n}(x),$ where $g^{\circ m}(x)=g

Jacopo de Berardinis, Lorenzo Porcaro, Albert Meroño-Peñuela, Angelo Cangelosi

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creati

Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes

Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an untrusted environment. However, LLM agents are vulnerable to prompt injection attacks when handling untrusted data. In this paper we propose CaMeL, a robust defense that creates a protective system layer around the LLM, securing it even when underlying models are

Shrikant Malviya, Neelanjan Bhowmik, Stamos Katsigiannis

The aim of this work is to explore the potential of pre-trained vision-language models, e.g. Vision Transformers (ViT), enhanced with advanced data augmentation strategies for the detection of AI-generated images. Our approach leverages a fine-tuned ViT model trained on the Defactify-4.0 dataset, which includes images generated by state-of-the-art models suc

Atlas of Variant Effects Alliance, :, Jeffrey D. Calhoun, Moez Dawood

With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evid

Augusto B. Corrêa, André G. Pereira, Jendrik Seipp

In recent years, large language models (LLMs) have shown remarkable capabilities in various artificial intelligence problems. However, they fail to plan reliably, even when prompted with a detailed definition of the planning task. Attempts to improve their planning capabilities, such as chain-of-thought prompting, fine-tuning, and explicit "reasoning" still

Yang Liu, Hongjin Wang, Zepu Wang, Xiaoguang Zhu

Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the rarity and diversity of anomalies, existing methods only use easily collected regular events to model the inherent normality of normal spatial

Chak Lam Shek, Pratap Tokekar

Large Language Models (LLMs) have shown remarkable promise in reasoning and decision-making, yet their integration with Reinforcement Learning (RL) for complex robotic tasks remains underexplored. In this paper, we propose an LLM-guided hierarchical RL framework, termed LDSC, that leverages LLM-driven subgoal selection and option reuse to enhance sample effi

Tan-Khiem Huynh, Malcolm Egan, Giovanni Neglia, Jean-Marie Gorce

Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assumption that clients have access to pre-collected data sets, with limited investigation into scenarios where clients continuously collect data. In many real-world applications, partic

Tahiya Chowdhury

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS

Chenyi Li, Zichen Wang, Yifan Bai, Yunxi Duan

Block-structured problems are central to advances in numerical optimization and machine learning. This paper provides the formalization of convergence analysis for two pivotal algorithms in such settings: the block coordinate descent (BCD) method and the alternating direction method of multipliers (ADMM). Utilizing the type-theory-based proof assistant Lean4

Ananya Ipsita, Ramesh Kaki, Ziyi Liu, Mayank Patel

To address the shortage of a skilled workforce in the U.S. manufacturing industry, immersive Virtual Reality (VR)-based training solutions hold promising potential. To effectively utilize VR to meet workforce demands, it is important to understand the role of VR in manufacturing education. Therefore, we conduct a scoping review in the field. As a first step,

Li Zhaoping

Our brain recognizes only a tiny fraction of sensory input, due to an information processing bottleneck. This blinds us to most visual inputs. Since we are blind to this blindness, only a recent framework highlights this bottleneck by formulating vision as mainly looking and seeing. Looking selects a tiny fraction of visual information for progression throug

Duowang Zhu, Xiaohu Huang, Haiyan Huang, Hao Zhou

In this paper, we present Change3D, a framework that reconceptualizes the change detection and captioning tasks through video modeling. Recent methods have achieved remarkable success by regarding each pair of bi-temporal images as separate frames. They employ a shared-weight image encoder to extract spatial features and then use a change extractor to captur

Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang

Data are crucial in various computer-related fields, including music information retrieval (MIR), an interdisciplinary area bridging computer science and music. This paper introduces CCMusic, an open and diverse database comprising multiple datasets specifically designed for tasks related to Chinese music, highlighting our focus on this culturally rich domai

Andrew D. McRae

We study the nonconvex optimization landscapes of synchronization problems on spheres. First, we present new results for the statistical problem of synchronization over the two-element group $\mathbf{Z}_2$. We consider the nonconvex least-squares problem with $\mathbf{Z}_2 = \{\pm 1\}$ relaxed to the unit sphere in $\mathbf{R}^r$ for $r \geq 2$; for several

Yunsong Ning, Yi Yuan, Tao Yu, Hongyu Chen

As the multidisciplinary applications of cosmic-ray muons expand to large-scale and wide-area scenarios, the construction of cosmic-ray muon detector arrays has become a key solution to overcome the hardware limitations of individual detector. For muography, the array-based detector design enables fast-scanning of large target objects, allowing for rapid ide

Vivek Vekariya, Mojdeh Golagha, Andrea Stocco, Alexander Pretschner

High-quality test datasets are crucial for assessing the reliability of Deep Neural Networks (DNNs). Mutation testing evaluates test dataset quality based on their ability to uncover injected faults in DNNs as measured by mutation score (MS). At the same time, its high computational cost motivates researchers to seek alternative test adequacy criteria. We pr

Anirban Ghosh, Aniruddha Chandra, Tomas Mikulasek, Ales Prokes

Fifth generation (5G) new radio is now offering sidelink capability, which allows direct vehicle-to-vehicle (V2V) communication. Millimeter wave (mmWave) enables low-latency mission-critical V2V communications, such as forward crash warning, between two vehicles crossing on a road without dividers. In this article, we present a measurement-based path loss (P

Roberta Baggio, Killian Pujol, Florian Pantillon, Dominique Lambert

This study presents a hybrid neural network model for short-term (1-6 hours ahead) surface wind speed forecasting, combining Numerical Weather Prediction (NWP) with observational data from ground weather stations. It relies on the MeteoNet dataset, which includes data from global (ARPEGE) and regional (AROME) NWP models of the French weather service and mete

Zaira Romeo, Alberto Testolin

Affective reactions have deep biological foundations, however in humans the development of emotion concepts is also shaped by language and higher-order cognition. A recent breakthrough in AI has been the creation of multimodal language models that exhibit impressive intellectual capabilities, but their responses to affective stimuli have not been investigate

Patrick Dondl, Oliver Suchan

This work presents a framework for modeling three-dimensional scaffold-mediated bone regeneration and the associated optimization problem. By incorporating microstructure into the model through periodic homogenization, we capture the effects of microscale fluctuations on the bone growth process. Numerical results and optimized scaffold designs that explicitl

Yulong Zheng, Zicheng Jiang, Shengfeng He, Yandu Sun

Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from spars

Maidoun Mortada, Olivier Togni

Given a sequence $S=(s_1,s_2,\ldots,s_p)$, $p\geq 2$, of non-decreasing integers, an $S$-packing coloring of a graph $G$ is a partition of its vertex set into $p$ disjoint sets $V_1,\ldots, V_p$ such that any two distinct vertices of $V_i$ are at a distance greater than $s_i$, $1\le i\le p$. In this paper, we study the $S$-packing coloring problem on graphs

Umberto Laghi, Simone Manoni, Emanuele Parisi, Andrea Bartolini

In this work, we present the design and evaluation of a Processor Tracing System compliant with the RISC-V Efficient Trace specification for Instruction Branch Tracing. We integrate our system into the host domain of a state-of-the-art edge architecture based on CVA6. The proposed Tracing System introduces a total overhead of 9.2% in terms of resource utiliz

Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang

Large Language Models (LLMs), such as the GPT series, have driven significant industrial applications, leading to economic and societal transformations. However, a comprehensive understanding of their real-world applications remains limited. To address this, we introduce REALM, a dataset of over 94,000 LLM use cases collected from Reddit and news articles. R

Rupali Chandar, Ashley T. Barnes, David A. Thilker, Miranda Caputo

The PHANGS project is assembling a comprehensive, multi-wavelength dataset of nearby (~5-20 Mpc), massive star-forming galaxies to enable multi-phase, multi-scale investigations into the processes that drive star formation and galaxy evolution. To date, large survey programs have provided molecular gas (CO) cubes with ALMA, optical IFU spectroscopy with VLT/

Alessandro Casa, Davide Ferrari

A fundamental challenge in approximating an unknown density using finite Gaussian mixture models is selecting the number of mixture components, also known as order. Traditional approaches choose a single best model using information criteria. However, often models with different orders yield similar fits, leading to substantial model selection uncertainty an

Boris Y. Rubinstein

A vector partition problem asks for a number of nonnegative integer solutions to a system of several linear Diophantine equations with integer nonnegative coefficients. J.J. Sylvester put forward an idea of reduction of vector partition to a sum of scalar partitions. In the simplest case of two equations with positive coefficients A. Cayley performed a reduc

V. A. Karmanov, Zhimin Zhu, Ziqi Zhang, Kaiyu Fu

The relativistic light-front (LF) wave function of $^3$He is determined by the three-body LF equation for the Faddeev components in the momentum space. As an interaction, we take the one-meson exchange kernels, without the potential approximation. Within the explicitly covariant formulation of LF dynamics, we calculate the full relativistic $^3$He LF wave fu

Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos, Manuel Dahmen

Reinforcement learning (RL) can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task by optimizing the dynamic model or parameters in the policy's objective function or constraints, such as state bounds. However, the sample efficiency of RL is crucial, and to improve it, w

Valentina Capalbo, Damiana Battaglini, Marialaura Petroni, Giancarlo Ruocco

Structured illumination microscopy (SIM) can achieve a $2\times$ resolution enhancement beyond the classical diffraction limit by employing illumination translations with respect to the object. This method has also been successfully implemented in a ``blind'' configuration, i.e., with unknown illumination patterns, allowing for more relaxed constraints on th

Zifa Chen

UAV has been widely used in various fields. However, most of the existing object detectors used in drones are not end-to-end and require the design of various complex components and careful fine-tuning. Most of the existing end-to-end object detectors are designed for natural scenes. It is not ideal to apply them directly to UAV images. In order to solve the

Wenxi Chen, Raymond A. Yeh, Shaoshuai Mou, Yan Gu

Out-of-distribution (OOD) detection is the task of identifying inputs that deviate from the training data distribution. This capability is essential for safely deploying deep computer vision models in open-world environments. In this work, we propose a post-hoc method, Perturbation-Rectified OOD detection (PRO), based on the insight that prediction confidenc

Linwei Chen, Lin Gu, Liang Li, Chenggang Yan

While Dynamic Convolution (DY-Conv) has shown promising performance by enabling adaptive weight selection through multiple parallel weights combined with an attention mechanism, the frequency response of these weights tends to exhibit high similarity, resulting in high parameter costs but limited adaptability. In this work, we introduce Frequency Dynamic Con

Quentin Guigue, Marc Besse, Raphael Voituriez, Alexis M. Prevost

Animal morphogenesis involves complex tissue deformation processes, which require tight control over tissue rheology. Yet, it remains insufficiently understood how tissue rheology results from the interplay between cellular packing and cellular forces, such as cortical tension, cell pressure, and cell-cell adhesion. Here, we follow a biomimetic approach to s

Nitisha Singh, Sahaj K. Jha, Aniruddha Chandra, Radek Zavorka

In this letter, we examine the effect of misalignment angle on cluster-based power delay profile (PDP) modeling for a 60 GHz millimeter-wave uplink. The analysis uses real-world data, where fixed uplink scenarios are realized by placing the transmitter at ground level and the receiver at the building level. Both outdoor-to-indoor (O2I) and outdoor-to-outdoor

Iman Kazemian, Murat Yildirim, Paritosh Ramanan

Operations and maintenance (O&M) scheduling is a critical problem in leased manufacturing systems, with significant implications for operational efficiency, cost optimization, and machine reliability. Solving this problem involves navigating complex trade-offs between machine-level degradation risks, production throughput, and maintenance team logistics acro

Ata Deniz Aydin

The problem of quantization of measures looks for best approximations of probability measures on a metric space by discrete measures supported on $N$ points, where the error of approximation is measured with respect to the Wasserstein distance. Zador's theorem states that, for measures on $\mathbb{R}^d$ or $d$-dimensional Riemannian manifolds satisfying appr

Yan Jia, Harriet Evans, Zoe Porter, Simon Graham

In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary

R. Datta, C. Clauser, N. Ferraro, C. Liu

Runaway electrons (REs) generated during disruption events in tokamaks can carry mega-Ampere level currents, potentially causing damage to plasma-facing components. Understanding RE evolution during disruption events is important for evaluating strategies to mitigate RE damage. Using two-dimensional toroidally symmetric magnetohydrodynamic (MHD) simulations

Antonio Capolupo, Simone Monda, Gabriele Pisacane, Raoul Serao

We show that experiments designed to capture low energy neutrinos, like PTOLEMY are sensitive to the specific neutrino model. In particular, they might show a signature of the condensed flavor vacuum, featured in the flavor Fock space model, and allow to test the hypothesis according to which a possible dark matter component is determined by neutrino mixing.

Thomas Mannel, Daniel Moreno, Alexei A. Pivovarov

We compute next-to-leading order QCD corrections to the $1/m_b^2$ terms of the Heavy Quark Expansion for the contribution to inclusive nonleptonic bottom hadron decays induced by the charged-current operators mediating the transitions $b \to c \bar c q$ ($q = s,d$). Their contributions to lifetimes are Cabibbo favoured and have large Wilson coefficents, but

Sankarshan Sahu

We show how to compute the probability distributions of the order parameter of the $O(n)$ model at two-loop order of perturbation theory generalizing the methods developed for computing the same in case of the Ising model \cite{Sahu:2025bkp}. We show that even for the $O(n)$ model, there exists not one but a family of these probability distribution functions

Karl Svozil

Infinity is central to deriving macroscopic irreversibility from reversible microscopic laws across mathematics, theoretical computer science and physics. In analysis, infinite processes - such as Dedekind cuts and Cauchy sequences - construct real numbers as equivalence classes of rational approximations, bridging discrete rationals to the continuous real l

Dayou Du, Shijie Cao, Jianyi Cheng, Luo Mai

The growth of long-context Large Language Models (LLMs) significantly increases memory and bandwidth pressure during autoregressive decoding due to the expanding Key-Value (KV) cache. While accuracy-preserving KV-cache quantization (e.g., 4-bit or 2-bit) reduces memory footprint, existing systems decode inefficiently by relying solely on CUDA cores, underuti

Alberto Hijano, Henri Lyyra, Juha T. Muhonen, Tero T. Heikkilä

We investigate the use of driven qubits coupled to a harmonic oscillator to implement a $\sqrt{i\mathrm{SWAP}}$-gate. By dressing the qubits through an external driving field, the qubits and the harmonic oscillator can be selectively coupled, allowing for the measurement of individual qubit states, as well as leading to effective qubit-qubit interactions. We

Sanhita Das, Nivedita Kumari

Autonomic interfacial self-healing in elastomers enables their reprocessing and recycling, making them valuable for applications such as ballistic resistance, surface coatings, adhesives, and biomedical materials. This article prescribes a geometry-based damage-healing theory for autonomic healing in elastomers, built on a framework where damage induces an i

Nikolay Bobev, Guillermo Mera Álvarez, Hynek Paul

We use holography to study correlation functions of local operators in maximally supersymmetric Yang-Mills theories arising on the world-volume of D$p$-branes in the large-$N$ and strong-coupling limit. The relevant supergravity backgrounds obtained from the near-horizon limit of the D$p$-branes enjoy a scaling similarity, which leads to an auxiliary AdS spa

Alan Dao, Dinh Bach Vu, Bui Quang Huy

This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of language models for robotic manipulation in 3D Cartesian space. AlphaSpace employs a hierarchical semantics-based tokenization strategy that encodes spatial information at both coarse and fine-grained levels. Our approach represents objects with thei

Paulo Martins, Altigran da Silva, Johny Moreira, Edleno de Moura

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without requiring schema knowledge or query-language proficiency. Although numerous R-KwS methods have been proposed, most still focus on queries referring only to attribute values or primarily address performance enhancements,

Konstantin Pakulev, Alexander Vakhitov, Gonzalo Ferrer

Local features are essential to many modern downstream applications. Therefore, it is of interest to determine the properties of local features that contribute to the downstream performance for a better design of feature detectors and descriptors. In our work, we propose a new theoretical model for scoring feature points (keypoints) in the context of the two

Joel Lefever, Aleksander Labuda, Roger Proksch

A new method is introduced for calibrating lateral force as measured by an atomic force microscope (AFM), making use of both an interferometric detector and an optical beam detector on the same instrument. The method may be implemented automatically and performed with minimal user input. The microscope has the capability to measure the probe tip height in si

Adilet Yerkin, Pakizar Shamoi

Group Decision-Making (GDM) plays a crucial role in various real-life scenarios where individuals express their opinions in natural language rather than structured numerical values. Traditional GDM approaches often overlook the subjectivity and ambiguity present in human discussions, making it challenging to achieve a fair and consensus-driven decision. This

Henri Lyyra, Cliona Shakespeare, Simeoni Ahopelto, Teemu Loippo

Silicon is the foundation of current information technology, and a promising platform for future quantum information technology as silicon-based qubits exhibit some of the longest coherence times in solid-state. At the same time, silicon is the underlying material for advanced photonics activity, and photonics structures in silicon can be used to define opto

Denis Bernard, Tony Jin, Stefano Scopa, Shiyi Wei

We consider the boundary driven Quantum Symmetric Simple Inclusion Process (QSSIP) which describes a one-dimensional system of bosonic particles with stochastic nearest-neighbor hopping, modeled as a Brownian motion, with gain/loss processes at the endpoints of the chain driving the system out-of-equilibrium. Although the averaged QSSIP dynamics differs from

Nooshin Bahador

Mechanistic interpretability improves the safety, reliability, and robustness of large AI models. This study examined individual attention heads in vision transformers (ViTs) fine tuned on distorted 2D spectrogram images containing non relevant content (axis labels, titles, color bars). By introducing extraneous features, the study analyzed how transformer c

Robin Ollive, Stephane Louise

As in classical reversible computing, Quantum Arithmetic is typically seen as a set of tools that process binary data encoded into a quantum register to set the value of another quantum register. This article presents another approach to explain the Quantum Arithmetic in quantum computing. Here, Quantum Arithmetic is addressed with a matrix processing point

Nick McKenna, Xinnuo Xu, Jack Williams, Nick Wilson

A key consideration when training an LLM is whether the target language is more or less resourced, for example English compared to Welsh, or Python compared to Excel. Typical training data for programming languages consists of real program demonstrations coupled with explanatory human-written comments. In this work we present a novel approach to the creation

Wenchao Xie, Jiawei Xu, Zheng Peng, Qingsong Wang

The canonical polyadic (CP) decomposition is one of the most widely used tensor decomposition techniques. The conventional CP decomposition algorithm combines alternating least squares (ALS) with the normal equation. However, the normal equation is susceptible to numerical ill-conditioning, which can adversely affect the decomposition results. To mitigate th

Yunus Can Gültekin, Péter Scheepers, Yuncheng Yuan, Federico Corradi

We investigate the design of two neural network (NN) architectures recently proposed as decoders for forward error correction: the so-called single-label NN (SLNN) and multi-label NN (MLNN) decoders. These decoders have been reported to achieve near-optimal codeword- and bit-wise performance, respectively. Results in the literature show near-optimality for a

Rirong Yuan

This paper investigates the structure of fully nonlinear equations and their applications to geometric problems. We solve some fully nonlinear version of the Loewner-Nirenberg and Yamabe problems. Notably, we introduce Morse theory techniques to construct admissible metrics under a weak condition on the underlying metric, which can be further relaxed in a br

Michael O'Riordan, Ciarán M. Gilligan-Lee

Interference bias is a major impediment to identifying causal effects in real-world settings. For example, vaccination reduces the transmission of a virus in a population such that everyone benefits -- even those who are not treated. This is a source of bias that must be accounted for if one wants to learn the true effect of a vaccine on an individual's immu