March 2025 arXiv papers — page 169

Xichen Tan, Yuanjing Luo, Yunfan Ye, Fang Liu

From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understanding benchmarks are relatively short, which makes them inadequate for effectively evaluating the long-sequence modeling capabilities of MLLMs. This highlights the urgent need for a comprehensive and integrated long

Runxi Wang, Ziheng Wang, Ting Lin, Jacob M. Raby

The rapid advancement of three-dimensional integrated circuits (3DICs) has heightened the need for early-phase design space exploration (DSE) to minimize design iterations and unexpected challenges. Emphasizing the pre-register-transfer level (Pre-RTL) design phase is crucial for reducing trial-and-error costs. However, 3DIC design introduces additional comp

Jingyu Chen, Kunrui Cao, Panagiotis D. Diamantoulakis, Lu Lv

This paper introduces the concept of wireless-powered zero-energy reconfigurable intelligent surface (zeRIS), and investigates a wireless-powered zeRIS aided communication system in terms of security, reliability and energy efficiency. In particular, we propose three new wireless-powered zeRIS modes: 1) in mode-I, N reconfigurable reflecting elements are adj

Gwenaël Ferrando, Shota Komatsu, Gabriel Lefundes, Didina Serban

We propose an integrability approach for planar three-point functions at finite coupling in $\mathcal{N}=2$ superconformal field theories obtained as $\mathbb{Z}_K$ orbifolds of $\mathcal{N}=4$ super Yang-Mills (SYM). Generalizing the hexagon formalism for $\mathcal{N}=4$ SYM, we reproduce the structure constants of Coulomb branch operators, previously obtai

Theodor Panayotov, Ivo Emanuilov

As distributed artificial intelligence (AI) and multi-agent architectures grow increasingly complex, the need for adaptive, context-aware routing becomes paramount. This paper introduces an enhanced, adaptive routing algorithm tailored for AI multi-agent networks, integrating priority-based cost functions and dynamic learning mechanisms. Building on an exten

Thomas Boucher, John Whittle, Evangelos B. Mazomenos

We demonstrate that quantum vision transformers (QViTs), vision transformers (ViTs) with self-attention (SA) mechanisms replaced by quantum self-attention (QSA) mechanisms, can match state-of-the-art (SOTA) biomedical image classifiers while using 99.99% fewer parameters. QSAs are produced by replacing linear SA layers with parameterised quantum neural netwo

Fanwen Wang, Ke Wen, Yaqing Luo, Yinzhe Wu

Quantitative MR (qMR) can provide numerical values representing the physical and chemical properties of the tissues. To collect a series of frames under varying settings, retrospective motion correction is essential to align the corresponding anatomical points or features. Under the assumption that the misalignment makes the discrepancy between the correspon

Carles Broto, Ran Levi, Bob Oliver

We correct here two errors in our earlier paper "An algebraic model for finite loop spaces" [arXiv:1212.2033]

Sikander Azam, Qaiser Rafiq, Eman Ramadan Elsharkawy, Muhammad Tahir Khan

This study uses the density functional theory (DFT) approach with GGA-PBE to assess the effect of substituting alkali metals in Rb$_{2}$CaH and Cs-doped Rb$_{2}$CaH$_{4}$ on their hydrogen storage potential. To address the challenges associated with predicting accurate electronic properties in materials containing heavier elements such as cesium, spin-orbit

Jackson Barr, Bingxuan Liu

In this article, we evaluate the performance of a data-driven background estimate method based on Gaussian Process Regression (GPR). A realistic background spectrum from a search conducted by CMS is considered, where a large sub-region below the trigger threshold is included. It is found that the $L_2$ regularisation can serve as a set of hyperparameters and

T. An, Y. Zhang, S. Frey, W. A. Baan

Compact symmetric objects (CSOs) trace the earliest phases of radio-galaxy growth; however, robust classification is difficult when radio cores are weak or invisible. We aim to develop and test a Gaia+VLBI approach that utilizes the high-precision optical astrometry of Gaia together with the high-resolution imaging of VLBI to reliably locate the central engi

Antoine Ayache, Andriy Olenko, Nemini Samarakoon

Multifractional processes extend the concept of fractional Brownian motion by replacing the constant Hurst parameter with a time-varying Hurst function. This extension allows for modulation of the roughness of sample paths over time. The paper introduces a new class of multifractional processes, the Gaussian Haar-based multifractional processes (GHBMP), whic

Erhard Aichinger, Simon Grünbacher

The complexity of solving equations over finite groups has been an active area of research over the last two decades, starting with Goldmann and Russell, \emph{The complexity of solving equations over finite groups} from 1999. One important case of a group with unknown complexity is the symmetric group $S_4.$ In 2023, Idziak, Kawa{\l}ek, and Krzaczkowski pub

Megala Anandan, Mária Lukáčová-Medvid'ová, S. V. Raghurama Rao

In this paper we study structure-preserving numerical methods for low Mach number barotropic Euler equations. Besides their asymptotic preserving properties that are crucial in order to obtain uniformly consistent and stable approximations of the Euler equations in their singular limit as the Mach number approaches zero, our aim is also to preserve discrete

Sabrina Ballhausen, Anne-Kathrin Ruß, Wolfgang Lieb, Anna Horn

Post-COVID Syndrome (PCS), encompassing the multifaceted sequelae of COVID-19, can be severity-graded using a score comprising 12 different long-term symptom complexes. Acute COVID-19 severity and individual resilience were previously identified as key predictors of this score. This study validated these predictors and examined their relationship to PCS symp

Yani Huang, Richong Zhang, Zhijie Nie, Junfan Chen

Fact-checking plays a crucial role in combating misinformation. Existing methods using large language models (LLMs) for claim decomposition face two key limitations: (1) insufficient decomposition, introducing unnecessary complexity to the verification process, and (2) ambiguity of mentions, leading to incorrect verification results. To address these challen

Carlo Bellavita, Marco M. Peloso

In this paper we consider Toeplitz operators with anti-analytic symbols on $H^1(\mathbb{C}^+)$. It is well known that there are no bounded Toeplitz operators $T_{\overline{\Theta}}\colon H^1(\mathbb{C}^+) \to H^1(\mathbb{C}^+)$, where $\Theta \in H^\infty(\mathbb{C}^+)$. We consider the subspace $H^1_{\Theta}=\left\lbrace f \in H^1(\mathbb{C}^+)\colon \int_{

Tero Mäkinen, Lumi Tuokkola, Joonas Lahikainen, Ivan V. Lomakin

The growth of cracks combines materials science, fracture mechanics, and statistical physics. The importance of fluctuations in the crack velocity is fundamental since it signals that the crack overcomes local barriers such as tough spots by avalanches. In ductile materials the omnipresent plasticity close to the crack tip influences the growth by history ef

Xin Wang, Stephanie Tulk Jesso, Sadamori Kojaku, David M Neyens

Trust plays a fundamental role in shaping the willingness of users to engage and collaborate with artificial intelligence (AI) systems. Yet, measuring user trust remains challenging due to its complex and dynamic nature. While traditional survey methods provide trust levels for long conversations, they fail to capture its dynamic evolution during ongoing int

Chuqi Wang, Chao Yu, Xin Xu, Yuman Gao

With the real need of field exploration in large-scale and extreme outdoor environments, cooperative exploration tasks have garnered increasing attention. This paper presents a comprehensive review of multi-robot cooperative exploration systems. First, we review the evolution of robotic exploration and introduce a modular research framework tailored for mult

Dennis Fremstad, Hans A. Winther

In order to constrain ultra light dark matter models with current and near future weak lensing surveys we need the predictions for the non-linear dark matter power-spectrum. This is commonly extracted from numerical simulations or from using semi-analytical methods. For ultra light dark matter models such numerical simulations are often very expensive due to

Won-Sang You, Tae-Gwan Ha, Seo-Young Lee, Kyung-Joong Kim

Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without human data. Most studies on zero-shot human-AI coordination have focused on enhancing the ego-agent's coordination ability in a given environment without considering the issue of generalization to unseen environments. Real-world applications of zero-shot human-A

Cristian Perez Jensen, Seyedmorteza Sadat

While classifier-free guidance (CFG) is essential for conditional diffusion models, it doubles the number of neural function evaluations (NFEs) per inference step. To mitigate this inefficiency, we introduce adapter guidance distillation (AGD), a novel approach that simulates CFG in a single forward pass. AGD leverages lightweight adapters to approximate CFG

Luca Padovani, Gianluigi Zavattaro

We study a theory of asynchronous session types ensuring that well-typed processes terminate under a suitable fairness assumption. Fair termination entails starvation freedom and orphan message freedom namely that all messages, including those that are produced early taking advantage of asynchrony, are eventually consumed. The theory is based on a novel fair

Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In this paper, we propose a distributed hierarchical federated learning (HFL) framework within the NTN architecture, leveraging a high altitude platform station (HAPS) constellation as

Gulizar Gunay, Engin Mermut

A module $M$ is {called} stable if it has no nonzero projective direct summand. For a ring $ R $, we study conditions under which $R$-modules from certain classes decompose as a direct sum of a projective submodule and a stable submodule. Over {an arbitrary} ring, modules of finite uniform dimension or finite hollow dimension can be decomposed as a direct su

Mikhail M. Ivanov

Effective field theory (EFT)-based full-shape analysis with simulation-based priors (SBPs) is a novel approach to galaxy clustering data analysis, which significantly boosts the constraining power by efficiently incorporating field-level simulation information from small scales. So far, SBPs have been mostly extracted from a large set of mock catalogs genera

Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Seid Muhie Yimam

We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and are spoken across various continents. The data instances are multi-labeled with six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. P

Junhao Guo, Hongxin Kong, Lang Feng

Routing is a crucial step in the VLSI design flow. With the advancement of manufacturing technologies, more constraints have emerged in design rules, particularly regarding obstacles during routing, leading to increased routing complexity. Unfortunately, many global routers struggle to efficiently generate obstacle-free solutions due to the lack of scalable

Hervé Henry

The propagation of a 3D crack in an heterogeneous material is studied using a phase field model. It is shown that in the case of randomly distributed inclusions of soft material in a matrix, the nature of the distribution has little effect on the effective elastic properties. On the opposite it affects significantly crack propagation. The less uniform distri

Fu Rong, Meng Lan, Qian Zhang, Lefei Zhang

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (SAM2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and

Lorenzo Iorio

The current LAGEOS-LARES 2 experiment aims to accurately measure the general relativistic Lense-Thirring effect in the gravitomagnetic field of the spinning Earth generated by the latter's angular momentum $\boldsymbol{J}$. The key quantity to a priori analytically assess the overall systematic uncertainty is the ratio $\mathcal{R}^{J_2}$ of the sum of the c

Haolin He, Ce Zhu, Le Zhang, Yipeng Liu

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estima

Varun Gupta

In this work, we will continue our analysis of some general probe M5 brane solutions from our previous work in $AdS_7 \times S^4$ spacetime (appeared in arxiv:2109.08551). These are codimension-2 in $AdS_7$ and preserve at least 2 supercharges when the worldvolume 3-form flux field strength is zero. We will turn on the field strength and find that the embedd

Rajesh Kumar Gupta, Meenu

In this article, we investigate the thermal properties of non-relativistic many-body systems at finite temperature and chemical potential. We compute the one-point function of various operators constructed out of the basic fields in ideal bosonic and fermionic many-body systems. The one-point function is non-zero only for operators with zero particle numbers

Imke van Heerden

In early 2025, Augmented Intelligence - Christie's first AI art auction - drew criticism for showcasing a controversial genre. Amid wider legal uncertainty, artists voiced concerns over data mining practices, notably with respect to copyright. The backlash could be viewed as a microcosm of AI's contested position in the creative economy. Touching on the auct

Alex Giacomini, Andronikos Paliathanasis, Alexey Toporensky

We investigate the evolution of anisotropies in Einstein-Gauss-Bonnet theory with a scalar field coupled to the Gauss-Bonnet term. Specifically, we examine the simplest scenario in which the scalar field lacks a kinetic term, and its kinetic contribution arises from an integration by parts of the Gauss-Bonnet scalar. We consider four- and five-dimensional an

Baiyu Chen, Wilson Wongso, Zechen Li, Yonchanok Khaokaew

The goal of creating intelligent, human-centered wearable systems for continuous activity understanding faces a fundamental trade-off: Egocentric video-based models capture rich semantic information and have demonstrated strong performance in human activity recognition (HAR), but their high power consumption, privacy concerns, and dependence on lighting limi

Shan He, Ruiyang Zhang

Accurate prediction of seismic responses and quantification of structural damage are critical in civil engineering. Traditional approaches such as finite element analysis could lack computational efficiency, especially for complex structural systems under extreme hazards. Recently, artificial intelligence has provided an alternative to efficiently model high

Isaac David, Arthur Gervais

In the age of powerful AI-generated text, automatic detectors have emerged to identify machine-written content. This poses a threat to author privacy and freedom, as text authored with AI assistance may be unfairly flagged. We propose AuthorMist, a novel reinforcement learning-based system to transform AI-generated text into human-like writing. AuthorMist le

Miaomiao Wei, Huatang Tan

Nondegenerate triple-photon parametric downconversion (NTPD) is a potential source for unconditional tripartite non-Gaussian entangled states of continuous variables. Recent experiment has demonstrated strong third-order correlations among bright photon triplets via microwave NTPD in a superconducting cavity [Phys. Rev. X 10, 011011 (2020)]. Previous theoret

Vishal Parmar, Vivek Baruah Thapa, Monika Sinha, Ignazio Bombaci

This study presents the first comprehensive Bayesian inference of neutron star matter, incorporating $\Delta$-resonances alongside hyperons and nucleons within a density-dependent relativistic hadron (DDRH) framework. Using constraints from nuclear saturation properties, chiral effective field theory ($\chi$EFT), NICER radius measurements, and tidal deformab

Dariusz Wójcik, Wiesław M. Macek

We apply statistical analysis to search for processes responsible for turbulence in physical systems. In our previous studies, we have shown that solar wind turbulence in the inertial range of large magnetohydrodynamic scales exhibits Markov properties. We have recently extended this approach on much smaller kinetic scales. Here we are testing for the Markov

Babagnidé François Koladjo, Ricardo Anderson Donte, Epiphane Sodjinou

In this paper, we investigate right-truncated count data models incorporating cavariates into the parameters. A regression method is proposed to model right-truncated count data exibiting high heterogeneity. The study encompasses the formulation of the proposed model, parameter estimation using an Expectation-Maximisation (EM) algorithm, and the properties o

Zhangyu Lai, Yilin Lu, Xinyang Li, Jianghang Lin

While existing anomaly synthesis methods have made remarkable progress, achieving both realism and diversity in synthesis remains a major obstacle. To address this, we propose AnomalyPainter, a zero-shot framework that breaks the diversity-realism trade-off dilemma through synergizing Vision Language Large Model (VLLM), Latent Diffusion Model (LDM), and our

Yubo Peng, Luping Xiang, Kun Yang, Kezhi Wang

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, preserving the accuracy and relevance of transmitted data while significantly reduc

Florent Ouabo Kamkumo, Ibrahim Mbouandi Njiasse, Ralf Wunderlich

Mathematical models of epidemics often use compartmental models dividing the population into several compartments. Based on a microscopic setting describing the temporal evolution of the subpopulation sizes in the compartments by stochastic counting processes one can derive macroscopic models for large populations describing the average behavior by associate

Kaspar Rosager Ludvigsen

Because digital devices and systems are widely used in all aspects of society, the risk of adversaries creating cyberattacks on a similar level remains high. As such, regulation of these aspects must follow, which is the domain of cybersecurity. Because this topic is worldwide, different jurisdictions should take inspiration from successful techniques elsewh

Feng Huang, Shuyuan Zheng, Zhaobing Qiu, Huanxian Liu

Infrared small target detection is currently a hot and challenging task in computer vision. Existing methods usually focus on mining visual features of targets, which struggles to cope with complex and diverse detection scenarios. The main reason is that infrared small targets have limited image information on their own, thus relying only on visual features

Xinyu Nan, Meng He, Zifan Chen, Bin Dong

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal ti

Aoi Wakuda

On a connected surface $N$ with negative Euler characteristic, the free homotopy class of a loop obtained by smoothing an intersection of two closed geodesics may wind around a puncture. Chas and Kabiraj showed that this phenomenon does not occur when the surface $N$ is orientable. In this paper, we prove that it occurs when $N$ is non-orientable and both ge

Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas

We propose a distributed control strategy to allow the control of a multi-agent system requiring k-hop interactions based on the design of distributed state and input observers. In particular, we design for each agent a finite time convergent state and input observer that exploits only the communication with the 1-hop neighbors to reconstruct the information

Siyuan Feng, Dengfeng Yan, Jin Liu, Haotong Han

Compared to conventional wheeled transportation systems designed for flat surfaces, soft robots exhibit exceptional adaptability to various terrains, enabling stable movement in complex environments. However, due to the risk of collision with obstacles and barriers, most soft robots rely on sensors for navigation in unstructured environments with uncertain b

Reginald Christian Bernardo, Stephen Appleby, Francis Bernardeau, Christophe Pichon

Rayleigh-L\'evy flights are simplified cosmological tools which capture certain essential statistical properties of the cosmic density field, including hierarchical structures in higher-order correlations, making them a valuable reference for studying the highly non-linear regime of structure formation. Unlike standard Markovian processes, they exhibit long-

Gangyang Li, Xiuwei Shang, Shaoyin Cheng, Junqi Zhang

Type recovery is a crucial step in binary code analysis, holding significant importance for reverse engineering and various security applications. Existing works typically simply target type identifiers within binary code and achieve type recovery by analyzing variable characteristics within functions. However, we find that the types in real-world binary pro

Fareed Qararyah, Mohammad Ali Maleki, Pedro Trancoso

Convolutional Neural Networks (CNNs) serve various applications with diverse performance and resource requirements. Model-aware CNN accelerators best address these diverse requirements. These accelerators usually combine multiple dedicated Compute Engines (CEs). The flexibility of Field-Programmable Gate Arrays (FPGAs) enables the design of such multiple Com

J. Alacoria, C. Saffe, A. Collado, A. Alejo

Our goal is to find new candidate lambda Boo stars that belong to binary systems. A detailed abundance determination of some candidates could confirm their true lambda Boo nature, while the composition of eventual late-type companions could be used as a proxy for the initial composition of the lambda Boo stars. Results. We obtained a group of 19 newly identi

Stephanie M. Wu, Abrania Marrero, Matthew R. Williams, Terrance D. Savitsky

There is limited understanding of how dietary behaviors cluster together and influence cardiometabolic health at a population level in Puerto Rico. Data availability is scarce, particularly outside of urban areas, and is often limited to non-probability sample (NPS) data where sample inclusion mechanisms are unknown. In order to generalize results to the bro

Miss. Elizabeth Olisa Monica Marinescu

Lithium-sulfur (Li-S) batteries offer a promising alternative to current lithium-ion (Li-ion) batteries, with a high theoretical energy density, improved safety and high abundance, low cost of materials. For Li-S to reach commercial application, it is essential to understand how the behaviour scales between cell formats; new material development is predomina

Philipp del Hougne

We present the "Virtual VNA 3.0" technique for estimating the scattering matrix of a \textit{non-reciprocal}, linear, passive, time-invariant device under test (DUT) with $N$ monomodal ports using a single measurement setup involving a vector network analyzer (VNA) with only $N_\mathrm{A}<N$ ports -- thus eliminating the need for any reconnections. We partit

Samuele Sandrini, Marco Faroni, Nicola Pedrocchi

Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and s

Junyeong Park, Seogyeong Jeong, Seyoung Song, Yohan Lee

Content moderation is a global challenge, yet major tech platforms prioritize high-resource languages, leaving low-resource languages with scarce native moderators. Since effective moderation depends on understanding contextual cues, this imbalance increases the risk of improper moderation due to non-native moderators' limited cultural understanding. Through

Fayez Abu-Ajamieh, Amine Ahriche, Nobuchika Okada

We investigate the experimental bounds on the Flavor-Violating (FV) couplings of the $Z$ boson to the charged leptons. In addition to the direct LHC searches for FV $Z$ decays to leptons, we investigate indirect bounds from flavor-conserving $Z$ decays to leptons at 1-loop, bounds from LEP searches, Electroweak Precision Observables (EWPO), $\ell_{i}\to\ell_

Haowen Bai, Jiangshe Zhang, Zixiang Zhao, Lilun Deng

Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image illumination from scene reflectance, provides a natural framework to ensure consistent scene representation and effective information fusion across varied exposure levels. However, t

Haicheng Liao, Hanlin Kong, Bonan Wang, Chengyue Wang

Accurate motion forecasting is crucial for safe autonomous driving (AD). This study proposes CoT-Drive, a novel approach that enhances motion forecasting by leveraging large language models (LLMs) and a chain-of-thought (CoT) prompting method. We introduce a teacher-student knowledge distillation strategy to effectively transfer LLMs' advanced scene understa

Santiago González-Gaitán, Claudia P. Gutiérrez, Gonçalo Martins, Tomás E. Müller-Bravo

The interstellar medium (ISM) has a number of tracers such as the Na I D 5890, 5896 AA absorption lines that are evident in the spectra of galaxies but also in those of individual astrophysical sources such as stars, novae or quasars. Here, we investigate narrow absorption features in the spectra of nearby supernovae (SNe) and compare them to local (< 0.5 kp

Chenglu Pan, Xiaogang Xu, Ganggui Ding, Yunke Zhang

Restoring low-resolution text images presents a significant challenge, as it requires maintaining both the fidelity and stylistic realism of the text in restored images. Existing text image restoration methods often fall short in hard situations, as the traditional super-resolution models cannot guarantee clarity, while diffusion-based methods fail to mainta

Ge Zheng, Alexandra Brintrup

In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the

Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo, Paolo Gamba

Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet

Chuanhao Wen, Zhiyong Hou, Zhiyuan Shang, Huan Yang

By measuring scanning tunneling spectroscopy on some large Bi islands deposited on FeTe$_{0.55}$Se$_{0.45}$ superconductors, we observe clear evidence of topological in-gap edge states with double peaks at about $\pm 1.0$ meV on the spectra measured near the perimeter of the islands. The edge states spread towards the inner side of the islands over a width o

Leah Wrenn Berman, Signe Lundqvist, Bernd Schulze, Brigitte Servatius

Let $P$ be a set of points and $L$ a set of lines in the (extended) Euclidean plane, and $I \subseteq P\times L$, where $i =(p,l) \in I$ means that point $p$ and line $l$ are incident. The incidences can be interpreted as quadratic constraints on the homogeneous coordinates of the points and lines. We study the space of incidence preserving motions of the gi

Ben Jourdan, Gregory Schwartzman, Peter Macgregor, He Sun

Coresets have become an invaluable tool for solving $k$-means and kernel $k$-means clustering problems on large datasets with small numbers of clusters. On the other hand, spectral clustering works well on sparse graphs and has recently been extended to scale efficiently to large numbers of clusters. We exploit the connection between kernel $k$-means and the

Luisa Consiglieri

The present paper deals with the study of the fluence rate over both healthy and tumor tissues in the presence of focal laser ablation (FLA). We propose new analytical solutions for the coupled partial differential equations (PDE) system, which includes the transport equation modeling the light penetration into biological tissue, the bioheat equation modelin

Anna Kononova

This is the first part of our work which is devoted to the uniqueness sets for spaces of entire functions. In this part we consider a set $\Lambda$ with angular density with respect to the order $\rho>0,$ satisfying the Lindel\"of condition. We find the value of the critical zero set type for $\Lambda$ in geometrical terms. We give a necessary and sufficient

Miaomiao Wei, Huatang Tan

In this paper, we present a scheme for remotely generating large-size motional Schr\"{o}dinger cat states in cavity optomechanical (OM) systems with non-Gaussian quantum steering of continuous variables. We consider that the output field from the OM cavity undergoes three typical kinds of multiphoton operations: multiphoton subtraction, multiphoton addition,

Guillermo Currás-Lorenzo, Álvaro Navarrete, Javier Núñez-Bon, Margarida Pereira

Numerical security proofs offer a versatile approach for evaluating the secret-key generation rate of quantum key distribution (QKD) protocols. However, existing methods typically require perfect source characterization, which is unrealistic in practice due to the presence of inevitable encoding imperfections and side channels. In this paper, we introduce a

Xingcheng Chen, Matteo Biagiola, Vincenzo Riccio, Marcelo d'Amorim

Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random perturbations to input semantic concepts until a failure is found or a timeout is reached. However, such randomness may hinder them from efficiently achieving their goal. This pap

Christian Pötzsche, Robert Skiba

The concept of parity due to Fitzpatrick, Pejsachowicz and Rabier is a central tool in the abstract bifurcation theory of nonlinear Fredholm operators. In this paper, we relate the parity to the Evans function, which is widely used in the stability analysis for traveling wave solutions to evolutionary PDEs. As application we obtain a flexible and general con

G. J. Milburn

We argue that special and general theories of relativity implicitly assume spacetime events correspond to quantum measurement outcomes. This leads to a change in how one should view the equivalence of spacetime and gravity. We describe a Bell test using time-like measurements that indicates a non classical causal structure that does not violate no-signaling.

Yariv Aizenbud, Barak Sober

A common observation in data-driven applications is that high-dimensional data have a low intrinsic dimension, at least locally. In this work, we consider the problem of point estimation for manifold-valued data. Namely, given a finite set of noisy samples of $\mathcal{M}$, a $d$ dimensional submanifold of $\mathbb{R}^D$, and a point $r$ near the manifold we

Jerzy Marcinkowski, Piotr Ostropolski-Nalewaja

Query Containment Problem (QCP) is a fundamental decision problem in query processing and optimization. While QCP has for a long time been completely understood for the case of set semantics, decidability of QCP for conjunctive queries under multi-set semantics ($QCP_{\text{CQ}}^{\text{bag}}$) remains one of the most intriguing open problems in database theo

Rubén López-Coto, David Green, Javier Méndez-Gallego, Emma de Oña Wilhelmi

Context: Several new phenomena have been surrounding the area of study of the repeating thermonuclear explosions called novae. For example, recurrent novae have been proven to be efficient cosmic ray hadronic accelerators thanks to the recent observations of RS Ophiuchi by different gamma-ray instruments. Novae have also demonstrated to have the ability to c

Zixuan Wang, Chi-Keung Tang, Yu-Wing Tai

Current audio generation conditioned by text or video focuses on aligning audio with text/video modalities. Despite excellent alignment results, these multimodal frameworks still cannot be directly applied to compelling movie storytelling involving multiple scenes, where "on-screen" sounds require temporally-aligned audio generation, while "off-screen" sound

Sangwoo Park, Seanie Lee, Byungjoo Kim, Sung Ju Hwang

Federated Learning (FL) is a widely used framework for training models in a decentralized manner, ensuring that the central server does not have direct access to data from local clients. However, this approach may still fail to fully preserve data privacy, as models from local clients are exposed to the central server during the aggregation process. This iss

Jianhong Ye, Siyuan Zhang, Yan Lin

Information systems generate a large volume of event log data during business operations, much of which consists of low-value and redundant information. When performance predictions are made directly from these logs, the accuracy of the predictions can be compromised. Researchers have explored methods to simplify and compress these data while preserving thei

Peipei Liu, Jian Sun, Rongkang Sun, Li Chen

Binary decompilation plays a vital role in various cybersecurity and software engineering tasks. Recently, end-to-end decompilation methods powered by large language models (LLMs) have attracted increasing attention for their ability to generate highly readable source code with minimal human intervention. However, existing LLM-based methods still struggle wi

Jimin Sohn, David R. Mortensen

Existing approaches to zero-shot Named Entity Recognition (NER) for low-resource languages have primarily relied on machine translation, whereas more recent methods have shifted focus to phonemic representation. Building upon this, we investigate how reducing the phonemic representation gap in IPA transcription between languages with similar phonetic charact

Kyungsik Nam, Won-Ki Seo

We estimate the temperature sensitivity of residential electricity demand during extreme temperature events using the distribution-to-scalar regression model. Rather than relying on simple averages or individual quantile statistics of raw temperature data, we construct distributional summaries, such as probability density, hazard rate, and quantile functions

Alessandro Alocco, Andrea Celotto, Luca Fasolo, Bernardo Galvano

This paper aims to quantify the linewidth of two-mode correlations in Traveling Wave Parametric Amplifiers (TWPAs). Artifacts induced by data acquisition and processing, such as windowing effects and acquisition time, are examined to understand their influence on the linewidth estimation of these correlations. The findings underscore the significance of acqu

Savannah Garmon, Gonzalo Ordonez, Kenichi Noba

A striking feature of cavity quantum electrodynamics is the existence of atom-photon bound states, which typically form when the coupling between the atom and its environment are strong enough that after de-excitation the atom can ``grab'' an emitted photon and re-absorb it, resulting in a virtual cloud surrounding the atom. Here we will demonstrate the exis

Jacob Swindell, Madeleine Darbyshire, Marija Popovic, Riccardo Polvara

Accurate agricultural weed mapping using UAVs is crucial for precision farming applications. Traditional methods rely on orthomosaic stitching from rigid flight paths, which is computationally intensive and time-consuming. Gaussian Process (GP)-based mapping offers continuous modelling of the underlying variable (i.e. weed distribution) but requires discreti

Ruochen Pi, Lianlei Shan

Collecting and annotating medical images is a time-consuming and resource-intensive task. However, generating synthetic data through models such as Diffusion offers a cost-effective alternative. This paper introduces a new method for the automatic generation of accurate semantic masks from synthetic lung X-ray images based on a stable diffusion model trained

Denitsa Saynova, Kajsa Hansson, Bastiaan Bruinsma, Annika Fredén

In this study, we investigate whether LLMs can be used to indicate if a study in the behavioural social sciences is replicable. Using a dataset of 14 previously replicated studies (9 successful, 5 unsuccessful), we evaluate the ability of both open-source (Llama 3 8B, Qwen 2 7B, Mistral 7B) and proprietary (GPT-4o) instruction-tuned LLMs to discriminate betw

Satyabrata Jana, Lawqueen Kanesh, Madhumita Kundu, Daniel Lokshtanov

In the Subset Feedback Arc Set in Tournaments, Subset-FAST problem we are given as input a tournament $T$ with a vertex set $V(T)$ and an arc set $A(T)$, along with a terminal set $S \subseteq V(T)$, and an integer $ k$. The objective is to determine whether there exists a set $ F \subseteq A(T) $ with $|F| \leq k$ such that the resulting graph $T-F $ contai

Xiaojian Yang, Kun Xu

This paper presents a novel methodology for the direct numerical modeling and simulation of turbulent flows. The kinetic model equation is firstly extended to turbulent flow with the account of coupled evolution of kinetic, thermal, and turbulent energy. Based on the kinetic model, a unified framework for the laminar and turbulent flow is constructed through

Nirmalya Jana, Atasi Chakraborty, Anamitra Mukherjee, Amit Agarwal

Metallic antiferromagnets are essential for efficient spintronic applications due to their fast switching and high mobility, yet room-temperature metallic antiferromagnets are rare. Here, we investigate YbMn$_2$Ge$_2$, a room temperature antiferromagnet, and establish it as an exfoliable layered metal with altermagnetic surface states. Using multi-orbital Hu

P. Leto, L. M. Oskinova, T. Shenar, G. A. Wade

Recently, a powerful magnetic field was discovered in the hot helium star classified as a quasi-Wolf-Rayet star of ~2Msun, member of the HD45166 system. Upon its explosion as a core-collapse supernova, it is expected to produce a strongly magnetic neutron star, a magnetar. Among the key parameters governing the pre-supernova evolution is the amount of mass l

Mona Sheikh Zeinoddin, Mobarak I. Hoque, Zafer Tandogdu, Greg Shaw

Accurate depth and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular depth estimation of endoscopic scenes via self-supervised learning (SSL), no prior work has explored their use for pose estimation. These methods rely on low r

Pawel Trajdos, Marek Kurzynski

Modern upper limb bioprostheses are typically controlled by sEMG signals using a pattern recognition scheme in the control process. Unfortunately, the sEMG signal is very susceptible to contamination that deteriorates the quality of the control system and reduces the usefulness of the prosthesis in the patient's everyday life. In the paper, the authors propo

Zihao He, Liu Liu, Dongchen Han, Kai Gao

Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring net