March 2025 arXiv papers — page 50

Gaetano Calogero, Ioannis Deretzis, Giuseppe Fisicaro, Damiano Ricciarelli

The full design of relevant systems for quantum applications, ranging from quantum simulation to sensing, is presented using a combination of atomistic methods. A prototypical system features a two-dimensional ordered distribution of spins interacting with out-of-plane spin drivers/probes. It could be realized in wide-bandgap semiconductors through open-volu

Peng Jiang, Shijun Liao, Ling Liu, Bin Xie

This study employs high-fidelity numerical simulations to investigate the influence of appendages on the turbulent flow dynamics and far-field acoustic radiation of the SUBOFF submarine model at a Reynolds number of Re = 1.2*10^7. Utilizing a third-order numerical scheme combined with wall-modeled large eddy simulation (WMLES) and the Ffowcs Williams-Hawking

Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh

Neural Radiance Fields (NeRF) have shown impressive performances in the rendering of 3D scenes from arbitrary viewpoints. While RGB images are widely preferred for training volume rendering models, the interest in other radiance modalities is also growing. However, the capability of the underlying implicit neural models to learn and transfer information acro

Henry D. Potter, George F. R. Ellis, Kevin J. Mitchell

Free will discourse is primarily centred around the thesis of determinism. Much of the literature takes determinism as its starting premise, assuming it true for the sake of discussion, and then proceeds to present arguments for why, if determinism is true, free will would be either possible or impossible. This is reflected in the theoretical terrain of the

Linda Cook, Eun Jung Kim, Tomáš Masařík

Distributed networks are prone to errors so verifying their output is critical. Hence, we develop LOCAL certification protocols for graph properties in which nodes are given certificates that allow them to check whether their network as a whole satisfies some fixed property while only communicating with their local network. Most known LOCAL certification pro

Saurav Sharma, Didier Mutter, Nicolas Padoy

While vision-language models like CLIP have advanced zero-shot surgical phase recognition, they struggle with fine-grained surgical activities, especially action triplets. This limitation arises because current CLIP formulations rely on global image features, which overlook the fine-grained semantics and contextual details crucial for complex tasks like zero

Ravinder Kumar, Tufan Roy, Masafumi Shirai, Sachin Gupta

We investigate structural, magnetic and transport properties of CoRuTiSn equiatomic quaternary Heusler alloy. CoRuTiSn was synthesized by arc-melt technique. The room temperature powder XRD pattern was analyzed, and it was found that CoRuTiSn has a tetragonal crystal structure. Magnetic measurements show non-zero but small hysteresis indicating CoRuTiSn as a

Fredy Alejandro Mendoza López, Jefferson Rodriguez, Fabio Martínez

The absence of effective communication the deaf population represents the main social gap in this community. Furthermore, the sign language, main deaf communication tool, is unlettered, i.e., there is no formal written representation. In consequence, main challenge today is the automatic translation among spatiotemporal sign representation and natural text l

Nicolas Couture, Frédéric Bouchard, Alicia Sit, Guillaume Thekkadath

The manipulation of visible and near-infrared light at the single-photon level plays a key role in quantum communication systems where information is encoded into photonic degrees of freedom. In practical implementations, it is important to achieve this manipulation with high speeds, low loss, and low noise. In this work, we propose the use of terahertz~(THz

Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb, Ivan I. Kyrchei

Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivity increase, standard GNN training methods face significant computational and memory challenges, limiting their scalability and efficiency. In this paper, we present a novel framew

Mesfin Taye

Due to the persistence of latently infected CD4$^+$ T cells, achieving a functional cure for HIV-1 remains a significant challenge since the viruses are able to evade immune clearance, which in turn enables post-treatment viral rebound. Because traditional deterministic models assume a constant reactivation rate, they fail to capture the stochastic nature of

Timofey Kozhukhov, Benjamin Loewe, Kristian Thijssen, Tyler N. Shendruk

Colloidal inclusions in nematic fluids induce topological defects that govern their dynamics. These defects create well-understood rheological behavior in passive nematics, but the interplay between colloid-associated defects and spontaneously generated activity-induced defects introduces new dynamical regimes in active nematic turbulence. Using mesoscale si

Gianluca Occhetta, Luis E. Solá Conde

In this paper we study the Chow quotient ${\mathcal C}X$ of a convex variety $X$ of Picard number one by the action of a one dimensional torus having no non-trivial finite isotropy. Examples of these actions can be found in the rational homogeneous framework. We prove that the subvariety of ${\mathcal C}X$ parametrizing reducible torus-invariant cycles is a

Rupak Bose, Chinedu Innocent Nwoye, Aditya Bhat, Nicolas Padoy

The acquisition of annotated datasets with paired images and segmentation masks is a critical challenge in domains such as medical imaging, remote sensing, and computer vision. Manual annotation demands significant resources, faces ethical constraints, and depends heavily on domain expertise. Existing generative models often target single-modality outputs, e

Kabir Chakravarti, Soham Acharya, Sumanta Chakraborty, Sudipta Sarkar

The growing catalogue of gravitational wave events enables a statistical analysis of compact binary mergers, typically quantified by the merger rate density. This quantity can be influenced by ambient factors, following which, in this work we have investigated the impact of dark matter environment on the merger statistics. We construct a baseline astrophysic

Debanuj Chatterjee, Louis Etien, Simon Boivinet, Hervé Rigneault

High-resolution ultrasound based imaging techniques like photoacoustic (PA) imaging that require fast detection of acoustic waves, are often coupled with an opto-mechanical sensor like a Fabry-Perot cavity (FPC) for enhanced sensitivity at high frequency. Due to the inherent inhomogeneity of the FPC thickness, the resonance of the cavity can exhibit a spatia

Jan Kohút, Martin Dočekal, Michal Hradiš, Marek Vaško

Manual digitization of bibliographic metadata is time consuming and labor intensive, especially for historical and real-world archives with highly variable formatting across documents. Despite advances in machine learning, the absence of dedicated datasets for metadata extraction hinders automation. To address this gap, we introduce BiblioPage, a dataset of

Jan-Magnus Christmann, Laura Anna Maria D'Angelo, Herbert De Gersem, Sven Pfeiffer

This paper develops a homogenized harmonic balance finite element method (HomHBFEM) to predict the dynamic behavior of magnets with fast excitation cycles, including eddy current and skin effects. A homogenization technique for laminated yokes avoids resolving the individual laminates and the skin depth in the finite element (FE) mesh. Instead, the yoke is r

Ninghui Feng, Songning Lai, Xin Zhou, Jiayu Yang

In real-world time series forecasting, uncertainty and lack of reliable evaluation pose significant challenges. Notably, forecasting errors often arise from underfitting in-distribution data and failing to handle out-of-distribution inputs. To enhance model reliability, we introduce a dual rejection mechanism combining ambiguity and novelty rejection. Ambigu

Midas Nouwens, Janus Bager Kristensen, Kristjan Maalt, Rolf Bagge

Online tracking remains problematic, with compliance and ethical issues persisting despite regulatory efforts. Consent interfaces, the visible manifestation of this industry, have seen significant attention over the years. We present robust automated methods to study the presence, design, and third-party suppliers of consent interfaces at scale and the web s

Mehdi Moshtaghi, Siavash H. Khajavi, Joni Pajarinen

We introduce RGB-Th-Bench, the first benchmark designed to evaluate the ability of Vision-Language Models (VLMs) to comprehend RGB-Thermal image pairs. While VLMs have demonstrated remarkable progress in visual reasoning and multimodal understanding, their evaluation has been predominantly limited to RGB-based benchmarks, leaving a critical gap in assessing

Yabin Wang, Zhiwu Huang, Xiaopeng Hong

This paper identifies OpenSDI, a challenge for spotting diffusion-generated images in open-world settings. In response to this challenge, we define a new benchmark, the OpenSDI dataset (OpenSDID), which stands out from existing datasets due to its diverse use of large vision-language models that simulate open-world diffusion-based manipulations. Another outs

Shin-ichi Ohta

In proper, geodesic Gromov hyperbolic spaces, we investigate discrete-time gradient flows via the proximal point algorithm for unbounded Lipschitz convex functions. Assuming that the target convex function has negative asymptotic slope along some ray (thus unbounded below), we first prove the uniqueness of such a negative direction in the boundary at infinit

Samuel Rey, Ernesto Curbelo, Luca Martino, Fernando Llorente

This work addresses the problem of graph learning from data following a Gaussian Graphical Model (GGM) with a time-varying mean. Graphical Lasso (GL), the standard method for estimating sparse precision matrices, assumes that the observed data follows a zero-mean Gaussian distribution. However, this assumption is often violated in real-world scenarios where

Maryam Bala, Amina Imam Abubakar, Abdulhamid Abubakar, Abdulkadir Shehu Bichi

This paper presents our findings of the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes, MU-SHROOM, which focuses on identifying hallucinations and related overgeneration errors in large language models (LLMs). The shared task involves detecting specific text spans that constitute hallucinations in the outputs genera

Yuanyuan Zhang, Sijie Xiong, Rui Yang, EngGee Lim

Radar-based wellness monitoring is becoming an effective measurement to provide accurate vital signs in a contactless manner, but data scarcity retards the related research on deep-learning-based methods. Data augmentation is commonly used to enrich the dataset by modifying the existing data, but most augmentation techniques can only couple with classificati

Dariusz Zawisza

We consider a finite-time stochastic drift control problem with the assumption that the control is bounded and the system is controlled until the state process leaves the half-line. Assuming general conditions, it is proved that the resulting parabolic Hamilton-Jacobi-Bellman equation has a classical solution. In fact, we consider an even more general family

Niccolo Avogaro, Thomas Frick, Mattia Rigotti, Andrea Bartezzaghi

Large Vision-Language Models (VLMs) are increasingly being regarded as foundation models that can be instructed to solve diverse tasks by prompting, without task-specific training. We examine the seemingly obvious question: how to effectively prompt VLMs for semantic segmentation. To that end, we systematically evaluate the segmentation performance of severa

Soham Acharya, Shuvayu Roy, Sudipta Sarkar

Recent studies on extremal black holes within effective field theories (EFT) of gravity have revealed an intriguing phenomenon: tidal forces near the horizon experience significant enhancement due to EFT corrections, potentially leading to a breakdown of the EFT framework. In this work, we investigate this effect in a two-black-hole Majumdar-Papapetrou space

Kenta Suzuki

Let $G$ be a reductive group with Borel $B$ and Weyl group $W$. Then $B$-double cosets in $G$ are indexed by the Weyl group, say $O(w)$ for $w\in W$. Then we prove the minimal $B$-double coset in the convolution $O(w_1)*O(w_2)$ is $O(w_1w_2)$, which gives a geometric characterization of multiplication in $W$. This defines the abstract Weyl group $\mathbf W$

Francesco Gentile, Andrei Rotaru, Erik Tonni

We study the entanglement Hamiltonian of two disjoint blocks in the harmonic chain on the line and in its ground state. In the regime of large mass, the non vanishing terms are only the on-site and the nearest-neighbour ones. Analytic expressions are obtained for their profiles, which are written in terms of piecewise linear functions that can be discontinuo

Bo-Yu Chen, Tian-Sheuan Chang

This paper introduces the first low-power hardware accelerator for Spiking Transformers, an emerging alternative to traditional artificial neural networks. By modifying the base Spikformer model to use IAND instead of residual addition, the model exclusively utilizes spike computation. The hardware employs a fully parallel tick-batching dataflow and a time-s

Ibrahim Said Ahmad, Shiran Dudy, Tadesse Destaw Belay, Idris Abdulmumin

Understanding how emotions are expressed across languages is vital for building culturally-aware and inclusive NLP systems. However, emotion expression in African languages is understudied, limiting the development of effective emotion detection tools in these languages. In this work, we present a cross-linguistic analysis of emotion expression in 15 African

Kosuke Mizuno

This paper studies the relation among the number of spanning trees of intermediate graphs in a Galois cover, building on results for $(\mathbb{Z}/2\mathbb{Z})^m$-covers previously established by Hammer, Mattman, Sands, and Valli\`{e}res. We generalize their results to arbitrary finite Galois covers. Using the Ihara zeta function and the Artin--Ihara $L$-func

Tseng-Jen Li, Tian-Sheuan Chang

Transformer-based models have become the \textit{de facto} backbone across many fields, such as computer vision and natural language processing. However, as these models scale in size, external memory access (EMA) for weight and activations becomes a critical bottleneck due to its significantly higher energy consumption compared to internal computations. Whi

Kai-Chieh Hsu, Tian-Sheuan Chang

Sparse deep learning has reduced computation significantly, but its irregular non-zero data distribution complicates the data flow and hinders data reuse, increasing on-chip SRAM access and thus power consumption of the chip. This paper addresses the aforementioned issues by maximizing data reuse to reduce SRAM access by two approaches. First, we propose Eff

Xabier Yurrebaso, Fernando Ibañez, Ángel Longueira-Romero

Smart grids have undergone a profound digitization process, integrating new data-driven control and supervision techniques, resulting in modern digital substations (DS). Attackers are more focused on attacking the supply chain of the DS, as they a comprise a multivendor environment. In this research work, we present the Substation Bill of Materials (Subs-BOM

Yutong Liu, Mehrad Ansari, Robert Black, Jason Hattrick-Simpers

Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes for solid-state materials remains a significant challenge in inorganic chemistry. An interpretable machine learning model is therefore essen

Pragya Singh, Ritvik Budhiraja, Pankaj Jalote, Mohan Kumar

Physiological signals hold immense potential for ubiquitous emotion monitoring, presenting numerous applications in emotion recognition. However, harnessing this potential is hindered by significant challenges, particularly in the collection of annotations that align with physiological changes since the process hinges heavily on human participants. In this w

Maor Carmi, Michal Roth, Rivka Bekenstein

Subwavelength atomic lattices have emerged as a promising platform for quantum applications, leveraging collective superradiant and subradiant effects to enhance light-matter interactions. Integrating atomic lattices into nanostructures is at the front of effort toward any application with atomic lattices, but is still a challenging theoretical task, as the

Ozan Unal, Steven Marty, Dengxin Dai

Burst image super-resolution (BISR) reconstructs a high-resolution keyframe by aggregating complementary sub-pixel evidence from a short burst of low-resolution frames. Existing methods often process all burst frames with heavy backbones or maintain deep cross-frame interaction throughout the network, leading to redundant computation on non-key frames and li

Han Zhao, Haotian Wang, Yiping Peng, Sitong Zhao

The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces for general reasoning tasks, composed of high-quality and challenging reasoning problems. These problems are collected from a multitude of open-source datasets, subjected to semantic deduplication and meticulous cleaning to eliminate test set contamination. All responses within the da

Jörg Endrullis, Dominik Grzelak, Tobias Heindel, Jens Kosiol

This volume contains the post-proceedings of the Fourteenth and the Fifteenth International Workshops on Graph Computation Models (GCM 2023 and 2024). The workshops took place in Leicester, UK on 18th July 2023 and Enschede, the Netherlands on 9th July 2024, in each case as part of STAF (Software Technologies: Applications and Foundations). Graphs are common

Filip Krizek

High multiplicity final states of small collision systems, such as proton-proton or proton-nucleus, exhibit some signatures which resemble features associated with quark-gluon plasma (QGP) formation in heavy-ion collisions, e.g., collective phenomena or enhancement in produced strangeness. At the same time, there is no experimental evidence for QGP-induced j

Elena Gribelyuk, Honghao Lin, David P. Woodruff, Huacheng Yu

We introduce a novel technique for ``lifting'' dimension lower bounds for linear sketches in the real-valued setting to dimension lower bounds for linear sketches with polynomially-bounded integer entries when the input is a polynomially-bounded integer vector. Using this technique, we obtain the first optimal sketching lower bounds for discrete inputs in a

Ivano Basile, Pouya Golmohammadi

It is widely believed that global symmetries must be broken in Quantum Gravity. This includes higher-form symmetries, which are commonplace in supergravity coupled to vector multiplets. Recently, a quantitative criterion for the breaking of (higher-form) symmetries in effective field theories of gravity has been proposed. We studied this criterion in the con

Tobias Brixner, Stefan Mueller, Andreas Müller, Sebastian von Mammen

Real-time optics and spectroscopy simulations ideally provide results at update rates of 120 Hz or more without any noticeable delay between changing input parameters and the calculated results. Such calculations require models of sufficient speed yet adequate level of detail in the physical approximations to contain the essential features of the simulated p

Mays Al-Azzawi, Dung Doan, Tuomo Sipola, Jari Hautamäki

The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. These AI attacks can automate the process of penetrating a target or collecting sensitive data. The new methods can also accelerate the execution of the attacks. This review article examines the use of AI technologies in cybersecuri

Xiangting Meng, Jiaqi Yang, Mingshu Chen, Chenxin Yan

In the realm of object pose estimation, scenarios involving both dynamic objects and moving cameras are prevalent. However, the scarcity of corresponding real-world datasets significantly hinders the development and evaluation of robust pose estimation models. This is largely attributed to the inherent challenges in accurately annotating object poses in dyna

Bart S. van Lith

All squigonometric functions admit derivatives that can be expressed as polynomials of the squine and cosquine. We introduce a general framework that allows us to determine these polynomials recursively. We also provide an explicit formula for all coefficients of these polynomials. This also allows us to provide an explicit expression for the MacLaurin serie

Prabakaran Rajamanickam, Adam D. Weiss

The phenomenon of Taylor or shear-induced dispersion of a non-passive scalar field in a pulsatile pipe flow is investigated, accounting for the scalar field's influence on fluid density and transport coefficients. By employing multiple scale analysis, an effective one-dimensional, unsteady mixing problem for the scalar field is obtained, which includes the d

Hongcheng Gao, Jiashu Qu, Jingyi Tang, Baolong Bi

The hallucination of large multimodal models (LMMs), providing responses that appear correct but are actually incorrect, limits their reliability and applicability. This paper aims to study the hallucination problem of LMMs in video modality, which is dynamic and more challenging compared to static modalities like images and text. From this motivation, we fi

Yiming Chen, Yao Li, Ming Yao

We decompose the indicator function of each $(a, b)$-Catalan matroid polytope as a weighted sum of indicator function of matroid polytopes that correspond to direct sums of uniform matroids. Catalan matroids lie in the interior of the convex hull of direct sums of uniform matroids in the polytope of all matroids introduced by Ferroni and Fink. Moreover, we d

M. Rizki Oktavian, Anirudh Tunga, Amandeep Bakshi, Michael J. Mueterthies

The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward

Yeasir Rayhan, Walid G. Aref

The Next Token Prediction paradigm (NTP, for short) lies at the forefront of modern large foundational models that are pre-trained on diverse and large datasets. These models generalize effectively, and have proven to be very successful in Natural Language Processing (NLP). Inspired by the generalization capabilities of Large Language Models (LLMs), we inves

Mario Padilla Rodriguez, Mohamed Nafea

Cardiovascular disease remains one of the leading causes of mortality worldwide, underscoring the need for accurate as well as interpretable diagnostic machine learning tools. In this work, we investigate heart disease classification using electrocardiogram (ECG) data from two widely-utilized datasets: The MIT-BIH Arrhythmia and the PTB-XL datasets. We adapt

Yunhao Tang, Sid Wang, Lovish Madaan, Rémi Munos

We propose to scale RL to unverifiable data with a novel algorithm JEPO (Jensen's Evidence lower bound Policy Optimization). While most prior efforts on scaling RL for LLMs focus on verifiable data where ground truth answers are typically short-form and can be matched easily; we investigate the case where such assumptions are less valid (e.g., when answers a

Junhan Lyu, Tianle Zhai, Zicheng Peng, Xuhang Huang

This paper examines the impact of increasing minimum wages, focusing primarily on their effect on employment. Our research involved analyzing the statistics of panel data, testing fixed effects and stationary, conducting linear regression, and integrating the linear regression model with nonlinear model analysis. The results indicate that fluctuations in the

Misao Sasaki, Vicharit Yingcharoenrat, Ying-li Zhang

Recently, it was shown that in the absence of gravity there exist non-$O(4)$-symmetric instanton solutions with finite action beyond Coleman's instantons. In this paper, focusing on the false-vacuum decay in a single scalar field in flat Euclidean space, we provide a general discussion on $O(4)$-symmetric instantons that are singular at the true-vacuum bubbl

Preben Buchhave, Mengjia Ren, Clara Marika Velte

The fact that physical conservation laws can be derived from symmetry properties of space and time, as shown by Emily N\"other, has been utilized in predicting the development of the round turbulent jet from the jet exit to the far field. In particular, the developing region has been described using an analytical approach in combination with using a numerica

Pu Liu, Chaoxi Cui, Lei Li, Runze Li

Dynamic control of topological properties in materials is central to modern condensed matter physics, and Floquet engineering, utilizing periodic light fields, provides a promising avenue. Here, we use Floquet theory to theoretically study the topological response of a Z2 nodal line semimetal (NLSM) when driven by circularly polarized light (CPL). We demonst

Milan Groshev, Lanfranco Zanzi, Carmen Delgado, Xi Li

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world

Yunhao Tang, Taco Cohen, David W. Zhang, Michal Valko

We introduce a novel reinforcement learning algorithm (AGRO, for Any-Generation Reward Optimization) for fine-tuning large-language models. AGRO leverages the concept of generation consistency, which states that the optimal policy satisfies the notion of consistency across any possible generation of the model. We derive algorithms that find optimal solutions

Max W. Y. Lam, Yijin Xing, Weiya You, Jingcheng Wu

Autoregressive (AR) models have demonstrated impressive capabilities in generating high-fidelity music. However, the conventional next-token prediction paradigm in AR models does not align with the human creative process in music composition, potentially compromising the musicality of generated samples. To overcome this limitation, we introduce MusiCoT, a no

Maarten V. de Hoop, Matti Lassas, Jinpeng Lu, Lauri Oksanen

We study the inverse problem of determining a Signorini obstacle from boundary measurements for the isotropic elasticity system. We prove that the obstacle can be uniquely determined by a single measurement of displacement and normal stress for the Signorini problem on an open subset of the boundary up to a natural obstruction. In addition to considering the

Jérémy Thibault, Joseph Lenormand, Catalin Hritcu

Researchers aim to build secure compilation chains enforcing that if there is no attack a source context can mount against a source program then there is also no attack an adversarial target context can mount against the compiled program. Proving that these compilation chains are secure is, however, challenging, and involves a non-trivial back-translation st

Sándor R. Kovács, Tibor Norbert Szegedi, Ákos Tóth, Attila Németh

The neutron-rich isotopes with 30 $\leq$ Z $\leq$ 45 are thought to be synthesised in neutrino-driven winds after the collapse of a massive star. This nucleosynthesis scenario, called the weak r-process, is studied using nuclear reaction network calculations. The accuracy of the nucleosynthesis simulations is strongly influenced by the reliability of the nuc

Edward Gu, Ho Chit Siu, Melanie Platt, Isabelle Hurley

In this work, we present two novel contributions toward improving research in human-machine teaming (HMT): 1) a Minecraft testbed to accelerate testing and deployment of collaborative AI agents and 2) a tool to allow users to revisit and analyze behaviors within an HMT episode to facilitate shared mental model development. Our browser-based Minecraft testbed

Deepti Madurai Muthu, Priyanka S, Lalitha Rani N, P. G. Kubendran Amos

Reliable quantification of Ki-67, a key proliferation marker in breast cancer, is essential for molecular subtyping and informed treatment planning. Conventional approaches, including visual estimation and manual counting, suffer from interobserver variability and limited reproducibility. This study introduces an AI-assisted method using the YOLOv8 object de

Ge Gao, Siyue Teng, Tianhao Peng, Fan Zhang

While video compression based on implicit neural representations (INRs) has recently demonstrated great potential, existing INR-based video codecs still cannot achieve state-of-the-art (SOTA) performance compared to their conventional or autoencoder-based counterparts given the same coding configuration. In this context, we propose a Generative Implicit Vide

Seoyoung Kim, Chi Hoi Yip, Semin Yoo

We investigate $f$-Diophantine sets over finite fields via new explicit constructions of families of quasi-random hypergraphs from multivariate polynomials. In particular, our construction not only offers a systematic method for constructing quasi-random hypergraphs but also provides a unified framework for studying various hypergraphs arising from multivari

Yuyao Ge, Shenghua Liu, Yiwei Wang, Lingrui Mei

Recent advances in Large Language Models (LLMs) have introduced Reasoning Large Language Models (RLLMs), which employ extended thinking processes with reflection and self-correction capabilities, demonstrating the effectiveness of test-time scaling. RLLMs exhibit innate Chain-of-Thought (CoT) reasoning capability obtained from training, leading to a natural

Takeshi Kakizaki, Masanori Nakamura, Fukutaro Hamaoka, Shuto Yamamoto

Data center networks (DCNs) require a low-cost, low-power optical transceiver to handle increased traffic from generative artificial intelligence, video streaming services, and more. Improving the required signal-to-noise ratio (RSNR) by digital signal processing such as forward error correction (FEC) mitigates the requirements for electrical and optical com

David E Edmunds, Jan Lang

In this review paper we study non-compact operators and embeddings between function spaces, highlighting interesting phenomena and the significance of Bernstein numbers. In particular, we demonstrate that for non-compact maps the usual $s$-numbers (e.g., approximation, Kolmogorov, and entropy numbers) fail to reveal finer structural properties, and one must

Dimitrios Stamatios Bouras, Yihan Dai, Tairan Wang, Yingfei Xiong

While software requirements are often expressed in natural language, verifying the correctness of a program against such requirements is a hard and underexplored problem. Large language models (LLMs) are promising candidates for addressing this challenge, however our experience shows that they are ineffective in this task, often failing to detect even straig

Giovanni Franco Gabriel Marraffini, Andrés Cotton, Noe Fabian Hsueh, Axel Fridman

The question of how to make decisions that maximise the well-being of all persons is very relevant to design language models that are beneficial to humanity and free from harm. We introduce the Greatest Good Benchmark to evaluate the moral judgments of LLMs using utilitarian dilemmas. Our analysis across 15 diverse LLMs reveals consistently encoded moral pre

Shun Maeta

Thanks to the ambitious project initiated by Catino, Mastrolia, Monticelli and Rigoli, which aims to provide a unified viewpoint for various geometric solitons, many classes, including Ricci solitons, Yamabe solitons, $k$-Yamabe solitons, quasi-Yamabe solitons, and conformal solitons, can now be studied under a unified framework known as Einstein-type manifo

Mariia Pospelova, Yana Safonova

Motivation: Revealing structural variations across sequences of closely related individuals or species is crucial for understanding their diversification mechanisms and roles. Results: We developed PatchWorkPlot, a tool for visualization of pairwise alignments of multiple annotated sequences as dot plots combined into a single matrix. Availability and implem

Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos

In this work, we investigate the merits of explicitly optimizing for inference time algorithmic performance during model training. We show how optimizing for inference time performance can improve overall model efficacy. We consider generic inference time objectives with $k$ samples, with a focus on pass@$k$ and majority voting as two main applications. With

Ziji Guo, Haonan Tong, Zhilong Zhang, Danpu Liu

Recent advances in integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) have enabled their widespread deployment in critical applications such as emergency management. This paper investigates the challenge of efficient multitask multimodal data communication in UAV-assisted ISAC systems, in the considered system model, hyperspectral (H

Weida Liao, Eric Lauga

Recent microfluidic experiments have explored the precise positioning of micron-sized particles in liquid environments via laser-induced thermoviscous flow. From micro-robotics to biology at the subcellular scale, this versatile technique has found a broad range of applications. Through the interplay between thermal expansion and thermal viscosity changes, t

Mesfin Taye

In this work, we examine the impact of time-varying temperature and force on the thermodynamic features of active Brownian motor that moves with velocity against the force as well as passive Brownian motor. By deriving analytical expressions In this work, we examine the impact of time-varying temperature and force on the thermodynamic features of active Brow

Xinxing Cheng, Tianyang Zhang, Wenqi Lu, Qingjie Meng

Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimati

Weifei Jin, Junjie Su, Hejia Wang, Yulin Ye

With the widespread application of automatic speech recognition (ASR) systems, their vulnerability to adversarial attacks has been extensively studied. However, most existing adversarial examples are generated on specific individual models, resulting in a lack of transferability. In real-world scenarios, attackers often cannot access detailed information abo

Levin Seidt, Thomas Weber, Albert A. Seredin, Thomas Possmayer

Second-order nonlinear optical processes are fundamental to photonics, spectroscopy, and information technologies, with material platforms playing a pivotal role in advancing these applications. Here, we demonstrate the exceptional nonlinear optical properties of the van der Waals crystal 3R-MoS$_2$, a rhombohedral polymorph exhibiting high second-order opti

Shaolei Zhang, Jinyan Liu, Tianyi Qian, Xuesong Li

Convolutional neural networks (CNNs) and transformers are widely employed in constructing UNet architectures for medical image segmentation tasks. However, CNNs struggle to model long-range dependencies, while transformers suffer from quadratic computational complexity. Recently, Mamba, a type of State Space Models, has gained attention for its exceptional a

Mia Siemon, Ivan Nikolov, Thomas B. Moeslund, Kamal Nasrollahi

In Pose-based Video Anomaly Detection prior art is rooted on the assumption that abnormal events can be mostly regarded as a result of uncommon human behavior. Opposed to utilizing skeleton representations of humans, however, we investigate the potential of learning recurrent motion patterns of normal human behavior using 2D contours. Keeping all advantages

V. Kostiuk, A. Marchuk, A. Gusev, I. Chugunov

In theoretical investigations, various mechanisms have been put forward to explain the emergence of spiral patterns in galaxies. One of the few ways to find out the nature of spirals in a particular galaxy is to consider the so-called corotation radius, or corotation resonance. A distinctly defined corotation resonance is likely to indicate the existence of

Hanlin Wu, Xufeng Duan, Zhenguang Cai

Voice-based AI development faces unique challenges in processing both linguistic and paralinguistic information. This study compares how large audio-language models (LALMs) and humans integrate speaker characteristics during speech comprehension, asking whether LALMs process speaker-contextualized language in ways that parallel human cognitive mechanisms. We

Hoang Vu, Jennifer Haase, Henrik Leopold, Jan Mendling

Process automation is a crucial strategy for improving business processes, but little attention has been paid to the effects that automation has once it is operational. This paper addresses this research problem by reviewing the literature on human-automation interaction. Although many of the studies in this field have been conducted in different domains, th

Songtao Sun, Jingyi Li, Yuanfei Dong, Haoguang Liu

This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine learning, and natural language processing technologies, achieving functionalities such as task allocation, progress monitoring, and information sharing. The agents within the syste

Luke Grazette, Ross Hunter, Ella Noomen, Nicole Skidmore

The LHCb experiment at CERN has undergone a comprehensive upgrade, including a complete re-design of the trigger system into a hybrid-architecture, software-only system that delivers ten times more interesting signals per unit time than its predecessor. This increased efficiency - as well as the growing diversity of signals physicists want to analyse - makes

Youngkyoon Jang, Eduardo Pérez-Pellitero

We propose Covisibility Map-based Gaussian Splatting (CoMapGS), designed to recover underrepresented sparse regions in sparse novel view synthesis. CoMapGS addresses both high- and low-uncertainty regions by constructing covisibility maps, enhancing initial point clouds, and applying uncertainty-aware weighted supervision using a proximity classifier. Our co

Naftali R. Smith

We study the full distribution $P_{N}\left(A\right)$ of sums $A = \sum_{i=1}^N$ where $x_1, \dots, x_N$ are $N \gg 1$ independent and identically distributed random variables each sampled from a given distribution $p(x)$ with a subexponential $x \to \infty$ tail. We consider two particular cases: (I) the one-sided stretched exponential distribution $p(x) \pr

Jiaxi Zhao, Mo Zhou, Xinzhe Zuo, Wuchen Li

Mean field control (MFC) problems have vast applications in artificial intelligence, engineering, and economics, while solving MFC problems accurately and efficiently in high-dimensional spaces remains challenging. This work introduces variational conditional normalizing flow (VCNF), a neural network-based variational algorithm for solving general MFC proble

J. M. Benkő, A. Bódi, E. Plachy, L. Molnár

It has long been known that if the durations of the consecutive cycles of a pulsating star vary randomly, the O-C diagram could show quasi-periodic/irregular variations, even though the actual average period is constant. It is hypothesised that the period variation observed in many RR Lyrae stars, which are much faster and stronger than may be explained by a

João Olívia, Rui Dilão

We develop a minimal whole-heart model that describes cardiac electrical conduction and simulate a basic three-lead electrocardiogram (ECG). We compare our 3-lead ECG model with clinical data from a Norwegian athlete database. The results demonstrate a strong correlation with the ECGs recorded for these athletes. We simulate various pathologies of the heart'

Sean Gloumeau

Deep unsupervised anomaly detection has seen improvements in a supervised binary classification paradigm in which auxiliary external data is included in the training set as anomalous data in a process referred to as outlier exposure, which opens the possibility of exploring the efficacy of post-hoc calibration for anomaly detection and localization. Post-hoc

Dhananjaya Jayasundara, Sudarshan Rajagopalan, Yasiru Ranasinghe, Trac D. Tran

Implicit Neural Representations (INRs) are increasingly recognized as a versatile data modality for representing discretized signals, offering benefits such as infinite query resolution and reduced storage requirements. Existing signal compression approaches for INRs typically employ one of two strategies: 1. direct quantization with entropy coding of the tr

Daniel G. P. Petrini, Hae Yong Kim

Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intelligence have enabled increasingly sophisticated computer-aided diagnostic methods, evolving from patch-based classifiers to whole-image approaches and then to multi-view architectures that jointly analyze complementary pr