March 2025 arXiv papers — page 88

Janko Gravner, Alexander Holroyd, Sangchul Lee, David Sivakoff

In the polluted modified bootstrap percolation model, sites in the square lattice are independently initially occupied with probability $p$ or closed with probability $q$. A site becomes occupied at a subsequent step if it is not closed and has at least one occupied nearest neighbor in each of the two coordinates. We study the final density of occupied sites

Guangcai Mao, Shu Yang, Xiaofei Wang

The heterogeneous treatment effect plays a crucial role in precision medicine.There is evidence that real-world data, even subject to biases, can be employed as supplementary evidence for randomized clinical trials to improve the statistical efficiency of the heterogeneous treatment effect estimation. In this paper, for survival data with right censoring, we

Yujie Jiang, Haiquan Li, Yiting Liu, Haoran Li

Active systems of self-rotating elements inherently exhibit chirality, making them of fundamental interest due to parity violation. Using large-scale hydrodynamic simulations, we investigate the gelation of adhesive spinners confined to quasi-2D monolayers at low Reynolds numbers. Unlike the coarsening dynamics of passive colloids, spinner gelation follows a

Oskar Novak, Narayanan Rengaswamy

Quantum sensing holds great promise for high-precision magnetic field measurements. However, its performance is significantly limited by noise. The investigation of active quantum error correction to address this noise led to the Hamiltonian-Not-in-Lindblad-Span (HNLS) condition. This states that Heisenberg scaling is achievable if and only if the signal Ham

Sarosij Bose, Arindam Dutta, Sayak Nag, Junge Zhang

Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem. Consequently, when the scene is rendered from novel camera views, existing single image to 3D reconstruction methods render incoherent and blurry views. This problem is exacerbated when the unseen regions are far away fr

John Murzaku, Zifan Liu, Vaishnavi Muppala, Md Mehrab Tanjim

Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolving ambiguities in real-world, enterprise-level interactions, where context and domain-specific knowledge play a crucial role. In this demonstration, we introduce ECLAIR (Enhanced CL

Kosuke Nomura

The neutrinoless $\beta\beta$ ($0\nu\beta\beta$) decay nuclear matrix elements (NMEs) are calculated in the interacting boson model (IBM), which is based on the nuclear energy density functional (EDF) theory. The Hamiltonian of the IBM that gives rise to the energies and wave functions of the ground and excited states of $0\nu\beta\beta$ decay emitting isoto

Hyung S. Choi

We present a conceptual overview of a new substructure model of elementary particles, motivated in part by the Gell-Mann-Nishijima formula. In this approach, Weyl spinors endowed with proto-charges generate all known elementary fermions via their conjugate pairs, while bosons arise naturally as composites. The model predicts a new class of massive neutral bo

John Murzaku, Zifan Liu, Md Mehrab Tanjim, Vaishnavi Muppala

We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR generates clarification questions for ambiguous user queries and resolves ambiguity based on the user's response.We introduce a generalized architecture capable of integrating ambigu

H. Wibowo, B. C. Backes, J. Dobaczewski, R. P. de Groote

Within the nuclear DFT framework, employing the Skyrme UNEDF1 functional and incorporating pairing correlations, we determined the spectroscopic electric quadrupole and magnetic dipole moments of the $\nu11/2^{-}$ and $\pi7/2^{+}$ configurations in heavy, deformed, open-shell odd nuclei with $50\leq Z \leq 64$. The notions of self-consistent shape and spin p

Heming Zhang, Wenyu Li, Di Huang, Yinjie Tang

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that plays a crucial role in information extraction, question answering, and knowledge-based systems. Traditional deep learning-based NER models often struggle with domain-specific generalization and suffer from data sparsity issues. In this work, we introduce Knowledge

John Lopez Santander, Kenneth D. T-R McLaughlin, Victor H. Moll

In this paper we study a family of non-classical Jacobi polynomials with varying parameters of the form $\alpha_n=n+1/2$ and $\beta_n=-n-1/2$. We obtain global asymptotics for these polynomials, and use this to establish results on the location their zeros. The analysis is based on the Riemann Hilbert formulation of Jacobi polynomials derived from the non-he

David E. J. van Wijk, Ersin Das, Anil Alan, Samuel Coogan

Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurat

Gabriele Cassese, João P. G. Ramos

By applying new functional analysis tools in the framework of Fourier interpolation formulas, such as sc-Fredholm operators and Schauder frames, we are able to improve and refine several properties of these aforementioned formulas on the real line. As two examples of our main contributions, we highlight: (i) that we may upgrade perturbed interpolation bases

Sung-Soo Byun, Peter J. Forrester, Arno B. J. Kuijlaars, Sampad Lahiry

We consider asymptotics of planar orthogonal polynomials $P_{n,N}$ (where $\mathrm{deg}P_{n,N}=n$) with respect to the weight $$\frac{|z-w|^{2NQ_1}}{(1+|z|^2)^{N(1+Q_0+Q_1)+1}}, \quad(Q_0,Q_1 > 0)$$ in the whole complex plane. With $n, N\rightarrow\infty$ and $N-n$ fixed, we obtain the strong asymptotics of the polynomials, asymptotics for the weighted $L^2$

Yuqing Zhang, Qi Han, Ligeng Wang, Kai Cheng

Most existing graph-based semi-supervised hyperspectral image classification methods rely on superpixel partitioning techniques. However, they suffer from misclassification of certain pixels due to inaccuracies in superpixel boundaries, \ie, the initial inaccuracies in superpixel partitioning limit overall classification performance. In this paper, we propos

Mohammad A Razzaque, Shafiuzzaman K Khadem, Sandipan Patra, Glory Okwata

This paper presents a systematic review of recent advancements in V2G cybersecurity, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for detailed searches across three journal databases and included only peer-reviewed studies published between 2020 and 2024 (June). We identified and reviewed 133 V2G cyberse

Azim Akhtarshenas, Afshin Dini, Navid Ayoobi

Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training pr

Aman Agarwal, Daniel M. Siegel, Brian D. Metzger, Chris Nagele

The collapse of rotating massive (~$10 M_\odot$) stars resulting in hyperaccreting black holes (BHs; "collapsars") is a leading model for the central engines of long-duration gamma-ray bursts (GRBs) and a promising source of rapid neutron capture ("r-process") elements. R-process nucleosynthesis in disk outflows requires the accretion flow to self-neutronize

Yuan Lian, Cecilia Leung, Claire Newman, Leslie Tamppari

We analyze simulation results from the TitanWRF global circulation model to understand the mechanisms that maintain the equatorial superrotation in Titan's stratosphere. We find that the eddies associated with wave activities can transport angular momentum upgradient to zonal flow, leading to acceleration of the equatorial superrotation. The dominant wave mo

Mohamed Mahmoud Chems-Eddin

The main aim of this paper is to investigate Greenberg's conjecture for real biquadratic fields. More precisely, we propose the following problem: What are real biquadratic number fields $k$ such that ${\rm rank}(A(k_\infty)) = {\rm rank}(A(k_1))$?, where $A(k_\infty)$ is the $2$-Iwasawa module of $k$ and $A(k_1)$ is the $2$-class group of $k_1$ the first la

Joseph Emmanuel DL Dayo, Michel Onasis S. Ogbinar, Prospero C. Naval

The objective of this study is to design and implement a reinforcement learning (RL) environment using D\&D 5E combat scenarios to challenge smaller RL agents through interaction with a robust adversarial agent controlled by advanced Large Language Models (LLMs) like GPT-4o and LLaMA 3 8B. This research employs Deep Q-Networks (DQN) for the smaller agents, c

Serge. A. Krasnokutski, Lisa Ganner, Milan Ončák, Florian Foitzik

The diffuse interstellar bands (DIBs) have remained a mystery in astronomy since their discovery over a century ago. The only currently known carrier is C$_{60}^+$ responsible for five DIBs, while more than 550 are yet to be interpreted. The spectra of short carbon chain cations C$_n^+$, which are considered one of the most promising classes of species for t

Linji Wang, Tong Xu, Yuanjie Lu, Xuesu Xiao

Robotics Reinforcement Learning (RL) often relies on carefully engineered auxiliary rewards to supplement sparse primary learning objectives to compensate for the lack of large-scale, real-world, trial-and-error data. While these auxiliary rewards accelerate learning, they require significant engineering effort, may introduce human biases, and cannot adapt t

Oscar L. Cruz-González, Valérie Deplano, Badih Ghattas

Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundary conditions, and the computationally demanding and time-consuming nature of Computational Fluid Dynamics (CFD) simulations, it is crucial to explore new data assimilation algorith

Daniel Lee, Francisco Pernice, Amit Rajaraman, Ilias Zadik

Given an arbitrary subgraph $H=H_n$ and $p=p_n \in (0,1)$, the planted subgraph model is defined as follows. A statistician observes the union a random copy $H^*$ of $H$, together with random noise in the form of an instance of an Erdos-Renyi graph $G(n,p)$. Their goal is to recover the planted $H^*$ from the observed graph. Our focus in this work is to unde

Sin-Yu Huang, Renjie Liao, Vincent W. S. Wong

Multi-task semantic communication (SC) can reduce the computational resources in wireless systems since retraining is not required when switching between tasks. However, existing approaches typically rely on task-specific embeddings to identify the intended task, necessitating retraining the entire model when given a new task. Consequently, this drives the n

M. Teklishyn, L. M. Collazo Sánchez, U. Frankenfeld, J. M. Heuser

Silicon strip sensors have long been a reliable technology for particle detection. Here, we push the limits of silicon tracking detectors by targeting an unprecedentedly low material budget of 2%-7% $X_0$ in an 8-layer 4 m$^2$ detector designed for high-occupancy environments ($\leq$ 10 MHz/cm$^2$). To achieve this, we employ Double-Sided Double Metal (DSDM)

Matteo Cinelli, Stefano Cresci, Walter Quattrociocchi, Maurizio Tesconi

We explore the effects of coordinated users (i.e., users characterized by an unexpected, suspicious, or exceptional similarity) in information spreading on Twitter by quantifying the efficacy of their tactics in deceiving feed algorithms to maximize information outreach. In particular, we investigate the behavior of coordinated accounts within a large set of

Xin An, Francesco Giglio, Giulio Landolfi

We analyze thermodynamic models for fluid systems in equilibrium based on a virial expansion of the internal energy in terms of the volume density. We prove that the models, formulated for finite-size systems with $N$ particles, are exactly solvable to any expansion order, as expectation values of physical observables (e.g., volume density) are determined fr

Mathilde Aguiar, Pierre Zweigenbaum, Nona Naderi

Recruiting patients to participate in clinical trials can be challenging and time-consuming. Usually, participation in a clinical trial is initiated by a healthcare professional and proposed to the patient. Promoting clinical trials directly to patients via online recruitment might help to reach them more efficiently. In this study, we address the case where

Mariana Pereira de Melo, Leon Alexander Valencia, Wei-Liang Qian

It is well-known that the fundamental diagram in a realistic traffic system is featured by capacity drop. From a mesoscopic approach, we demonstrate that such a phenomenon is linked to the unique properties of stochastic noise, which, when considered a specific perturbation, may counterintuitively enhance the stability of the originally deterministic system.

Emanuele Caputo, Jesse Koivu, Danka Lučić, Tapio Rajala

This paper studies the relations between extendability of different classes of Sobolev $W^{1,1}$ and $BV$ functions from closed sets in general metric measure spaces. Under the assumption that the metric measure space satisfies a weak $(1,1)$-Poincar\'e inequality and measure doubling, we prove further properties for the extension sets. In the case of the Eu

Ryota Takamido, Jun Ota

This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing t

Leonardo Carofiglio, Giacomo Cherubini, Alessandro Gambini

We provide numerical evidence towards three conjectures on harmonic numbers by Eswarathasan--Levine and Boyd. Let $J_p$ denote the set of integers $n\geq 1$ such that the harmonic number $H_n$ is divisible by a prime $p$. The conjectures state that: $(i)$ $J_p$ is always finite and of the order $O(p^2(\log\log p)^{2+\epsilon})$; $(ii)$ the set of primes for

Sergey Dyachenko, Dmitry E. Pelinovsky

We address Euler's equations for irrotational gravity waves in an infinitely deep fluid rewritten in conformal variables. Stokes waves are traveling waves with the smooth periodic profile. In agreement with the previous numerical results, we give a rigorous proof that the zero eigenvalue bifurcation in the linearized equations of motion for co-periodic pertu

Weiwen Hu, Niccolò Parodi, Marcus Zepp, Ingo Feldmann

Open-vocabulary segmentation, powered by large visual-language models like CLIP, has expanded 2D segmentation capabilities beyond fixed classes predefined by the dataset, enabling zero-shot understanding across diverse scenes. Extending these capabilities to 3D segmentation introduces challenges, as CLIP's image-based embeddings often lack the geometric deta

Yitong Yang, Muhammad Naeem, Marly Van Assen, Jerome Yerly

Purpose: Ferumoxytal-enhanced 5D free-running whole heart CMR provides image quality comparable to CTA, but requires hours-long reconstruction time, preventing clinical usage. This study developed a variable projection augmented Lagrangian (VPAL) method for 5D motion-resolved image reconstruction and compared it with alternating direction method of multiplie

Ethan T. Custodio, Sulimon Sattari, Kevin A. Mitchell

When placed in parallel magnetic and electric fields, the electron trajectories of a classical hydrogen atom are chaotic. The classical escape rate of such a system can be computed with classical trajectory Monte Carlo techniques, but these computations require enormous numbers of trajectories, provide little understanding of the dynamical mechanisms involve

Sam Narimani, Solveig Roth Hoff, Kathinka Dahli Kurz, Kjell-Inge Gjesdal

Purpose: Segmentation of the breast lesion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an essential step to accurately diagnose and plan treatment and monitor progress. This study aims to highlight the impact of breast region segmentation (BRS) on deep learning-based breast lesion segmentation (BLS) in breast DCE-MRI. Methods Using t

Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza, Sergio Aguilera

The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety in LLM-driven plans remains a critical challenge, as these models often prioritize task completion over risk mitigation. This paper introduces SAFER (Safety-Aware Framework for Exe

Níckolas de Aguiar Alves, Bruno Arderucio Costa

The concept of mass is central to any theory of gravity. Nevertheless, defining mass in general relativity is a difficult task, and even when it can be accomplished, we still need to investigate whether the typical properties of mass in Newtonian gravity are still true in Einsteinian gravity. In this essay, we discuss "the measure of a mass" in relativity by

Jonathan D. Schultz, Kelsey A. Parker, Bashir Sbaiti, David N. Beratan

Two-dimensional electronic spectroscopy (2DES) has enabled significant discoveries in both biological and synthetic energy-transducing systems. Although deriving chemical information from 2DES is a complex task, machine learning (ML) offers exciting opportunities to translate complicated spectroscopic data into physical insight. Recent studies have found tha

Grégoire Sergeant-Perthuis, Toby St Clere Smithe, Léo Boitel

Undirected graphical models are a widely used class of probabilistic models in machine learning that capture prior knowledge or putative pairwise interactions between variables. Those interactions are encoded in a graph for pairwise interactions; however, generalizations such as factor graphs account for higher-degree interactions using hypergraphs. Inferenc

Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

The performance of sequential Monte Carlo (SMC) samplers heavily depends on the tuning of the Markov kernels used in the path proposal. For SMC samplers with unadjusted Markov kernels, standard tuning objectives, such as the Metropolis-Hastings acceptance rate or the expected-squared jump distance, are no longer applicable. While stochastic gradient-based en

Elizabeth Mieczkowski, Ruaridh Mon-Williams, Neil Bramley, Christopher G. Lucas

When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends on task parallelizability: the potential for multiple agents to execute task components concurrently. Drawing inspiration from Amdahl's Law in distributed systems, we present a clos

Bryanne McDonough, Olivia Curtis, Tereasa Brainerd

Scaling relationships, both integrated and spatially resolved, arise due to the physical processes that govern galaxy evolution and are frequently measured in both observed and simulated data. However, the accuracy and comparability of these measurements are hindered by various differences between studies such as spatial resolution, sample selection criteria

Shanghua Zheng, Yizhen Li, Liushuting Yang, Li Guo

This paper first develops a bialgebra theory for a noncommutative Novikov algebra, called a noncommutative Novikov bialgebra, which is further characterized by matched pairs and Manin triples of noncommutative Novikov algebras. The classical Yang-Baxter type equation, $\mathcal{O}$-operators, and noncommutative pre-Novikov algebras are introduced to study no

S. Kovacevic, W. Ali, T. K. Mandal, E. Martínez-Pañeda

The individual contributions of pH and chloride concentration to the corrosion kinetics of bioabsorbable magnesium (Mg) alloys remain unresolved despite their significant roles as driving factors in Mg corrosion. This study demonstrates and quantifies hitherto unknown separate effects of pH and chloride content on the corrosion of Mg alloys pertinent to biom

Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona

How do two deep neural networks differ in how they arrive at a decision? Measuring the similarity of deep networks has been a long-standing open question. Most existing methods provide a single number to measure the similarity of two networks at a given layer, but give no insight into what makes them similar or dissimilar. We introduce an interpretable repre

James I. Kwon, Anthony J. Brady, Victor V. Albert

We explore absolutely maximal entanglement (AME) and k-uniformity in continuous-variable (CV) quantum systems, and show that-unlike in qudit systems-such entanglement is readily realizable in both Gaussian and non-Gaussian quantum states of multiple modes. We demonstrate that Gaussian CV cluster states are generically AME, rederiving the results of [Phys. Re

Panagiota Moraiti, Efstathios Karypidis

This paper outlines our approach to the 5th CLVision challenge at CVPR, which addresses the Class-Incremental with Repetition (CIR) scenario. In contrast to traditional class incremental learning, this novel setting introduces unique challenges and research opportunities, particularly through the integration of unlabeled data into the training process. In th

Arturo De Marinis, Davide Murari, Elena Celledoni, Nicola Guglielmi

We study the approximation properties of neural ordinary differential equations (neural ODEs) in the space of continuous functions. Since a neural ODE requires input and output dimensions to be the same, while input and output dimensions of a continuous function are generally different, we need to embed an input into the latent space of the neural ODE, and t

Yogendra Limbu, Hari Paudyal, Eudes Gomes da Silva, Denis R. Candido

We report an \textit{ab initio} investigation of functionalized and 3$d$-electrons doped Cr$_2$C MXenes. Upon functionalization, the Cr$_2$C becomes chemically, dynamically, and mechanically stable, and it exhibits magnetic semiconducting behavior. Cr$_2$CF$_2$ stands out as a wide band gap semiconductor, possessing super exchange interaction mediated by F a

Ralph Sabbagh, Olga Movilla Miangolarra, Tryphon T. Georgiou

We study the control problem of regulating the purity of a quantum harmonic oscillator in a Gaussian state via weak measurements. Specifically, we assume time-invariant Hamiltonian dynamics and that control is exerted via the back-action induced from monitoring the oscillator's position and momentum observables; the manipulation of the detector measurement s

Meng Song

Supervised learning (SL) and reinforcement learning (RL) are both widely used to train general-purpose agents for complex tasks, yet their generalization capabilities and underlying mechanisms are not yet fully understood. In this paper, we provide a direct comparison between SL and RL in terms of zero-shot generalization. Using the Habitat visual navigation

I. Filikhin, R. Ya. Kezerashvili, B. Vlahovic

We explore the possible existence of light $\phi$-mesic nuclei using HAL QCD $\phi N$ interactions for the $^2S_{1/2}$ and $^4S_{3/2}$ channels. Particularly, using the Faddeev formalism in configuration space, the $\phi NN$ system, and $^{9}_{\phi}$Be and $^{6}_{\phi\phi}$He nuclei within the framework of the three-body cluster model, are investigated. The

Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng

Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities caused by high-dose exposure to critical structures remain a significant clinical challenge, underscoring the need for accurate prediction of clinical outcomes-encompassing both tumor c

J. P. Palastro, K. G. Miller, M. R. Edwards, A. L. Elliott

Space-time structured laser pulses feature an intensity peak that can travel at an arbitrary velocity while maintaining a near-constant profile. These pulses can propagate in uniform media, where their frequencies are correlated with continuous transverse wavevectors, or in structured media, such as a waveguide, where their frequencies are correlated with di

Michael Hahn, Xiangrong Fu, Stefan J. Hofmeister, Yifan Huang

We investigate the properties and relationship between Doppler-velocity fluctuations and intensity fluctuations in the off-limb quiet Sun corona. These are expected to reflect the properties of Alfvenic and compressive waves, respectively. The data come from the Coronal Multichannel Polarimeter (COMP). These data were studied using spectral methods to estima

Khaled Jawhar, Evangelos Kranakis

We study a search problem on capturing a moving target on an infinite real line. Two autonomous mobile robots (which can move with a maximum speed of 1) are initially placed at the origin, while an oblivious moving target is initially placed at a distance $d$ away from the origin. The robots can move along the line in any direction, but the target is oblivio

Hassan Oubba

In 1990 Kantor introduced the conservative algebra $\mathcal{W}(n)$ of all algebras (i.e. bilinear maps) on the $n$-dimensional vector space. In case $n >1$ the algebra $\mathcal{W}(n)$ does not belong to well known classes of algebras (such as associative, Lie, Jordan, Leibniz algebras). We describe $\frac{1}{2}$derivations, local (resp. $2$-local) $\frac{1

Jiaqi Liu, Jichao Zhang, Paolo Rota, Nicu Sebe

The Latent Diffusion Model (LDM) has demonstrated strong capabilities in high-resolution image generation and has been widely employed for Pose-Guided Person Image Synthesis (PGPIS), yielding promising results. However, the compression process of LDM often results in the deterioration of details, particularly in sensitive areas such as facial features and cl

Hiroki Hanai, Takuya Kiyokawa, Weiwei Wan, Kensuke Harada

Robotic packaging using wrapping paper poses significant challenges due to the material's complex deformation properties. The packaging process itself involves multiple steps, primarily categorized as folding the paper or creating creases. Small deviations in the robot's arm trajectory or force vector can lead to tearing or wrinkling of the paper, exacerbate

Manon Revel, Smitha Milli, Tyler Lu, Jamelle Watson-Daniels

Online comment sections, such as those on news sites or social media, have the potential to foster informal public deliberation, However, this potential is often undermined by the frequency of toxic or low-quality exchanges that occur in these settings. To combat this, platforms increasingly leverage algorithmic ranking to facilitate higher-quality discussio

Isaac Alpizar-Chacon, Hieke Keuning

Various studies have studied the impact of Generative AI on Computing Education. However, they have focused on the implications for novice programmers. In this experience report, we analyze the use of GenAI as a support tool for learning, creativity, and productivity in a web development course for undergraduate students with extensive programming experience

Yanis Benidir, Nicolas Gonthier, Clement Mallet

Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. This remains poorly addressed so far: methods either require large volumes of annotated data (semantic case), or are limited to restricted datasets (binary set-ups). Most approaches do not exhibit the versatility required for temporal and spatial

Blair Attard-Frost

This article re-imagines the governance of artificial intelligence (AI) through a transfeminist lens, focusing on challenges of power, participation, and injustice, and on opportunities for advancing equity, community-based resistance, and transformative change. AI governance is a field of research and practice seeking to maximize benefits and minimize harms

Fausto German, Brian Keith, Chris North

Traditional information retrieval is primarily concerned with finding relevant information from large datasets without imposing a structure within the retrieved pieces of data. However, structuring information in the form of narratives--ordered sets of documents that form coherent storylines--allows us to identify, interpret, and share insights about the con

Vladislav Borisov, Vladimir Yurovsky, Alexandra Peryatinskaya, Ilya Kudryashov

At present, there is no consensus on whether the spectral break in the cosmic-ray flux of all elements around 4 PeV is a general characteristic of the Milky Way or is determined by a combination of factors that significantly affect the energy position of the knee. We argue that considering the anisotropic propagation of cosmic rays within a realistically mod

Rahul Sundar, Didier Lucor, Sunetra Sarkar

For a data-driven and physics combined modelling of unsteady flow systems with moving immersed boundaries, Sundar {\it et al.} introduced an immersed boundary-aware (IBA) framework, combining Physics-Informed Neural Networks (PINNs) and the immersed boundary method (IBM). This approach was beneficial because it avoided case-specific transformations to a body

Ahmed M. Elmisery, Mirela Sertovic, Andrew Zayin, Paul Watson

The financial sector faces escalating cyber threats amplified by artificial intelligence (AI) and the advent of quantum computing. AI is being weaponized for sophisticated attacks like deepfakes and AI-driven malware, while quantum computing threatens to render current encryption methods obsolete. This report analyzes these threats, relevant frameworks, and

Qing Gao, Hailong Yuan

Through cross-matching the ASASSN photometric light curves and LAMOST spectroscopic observations, we serendipitously captured a rare major outburst event from the Be star EPIC 202060631, lasting over 1000 days. Fortuitously, the LAMOST ow-resolution spectra densely covered the flux rising stage with 20 epochs, while an additional 7 low-resolution and 11 medi

Cédric Vincent, Taehyoung Kim, Henri Meeß

Semantic segmentation from RGB cameras is essential to the perception of autonomous flying vehicles. The stability of predictions through the captured videos is paramount to their reliability and, by extension, to the trustworthiness of the agents. In this paper, we propose a lightweight video semantic segmentation approach-suited to onboard real-time infere

Robert Husák, Jan Kofroň, Filip Zavoral

While program comprehension tools often use static program analysis techniques to obtain useful information, they usually work only with sufficiently scalable techniques with limited precision. A possible improvement of this approach is to let the developer interactively reduce the scope of the code being analyzed and then apply a more precise analysis techn

Jeffrey D Shulkin, Abhipol Vibhatasilpin, Vedant Adhana

Pervasive sensing is transforming health and activity monitoring by enabling continuous and automated data collection through advanced sensing modalities. While extensive research has been conducted on human subjects, its application in livestock remains underexplored. In large-scale agriculture, real-time monitoring of biological signals and behavioral patt

Zhengrong Xie

A semi-Lagrangian discontinuous finite element scheme based on the characteristic Galerkin method (CSLDG) is investigated, which directly discretizes an integral invariant model derived from the coupling of the transport equation and its adjoint equation. First, the existence and stability of CSLDG are proven, along with the uniqueness of the numerical solut

William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling, Carl Lindström

Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance on a large array of tasks when applied at scale. Similarly, autonomous driving generates vast amounts of spatiotemporal data, alluding to the possibility of harnessing scale to lea

Arindam Dutta, Meng Zheng, Zhongpai Gao, Benjamin Planche

Reconstructing clothed humans from a single image is a fundamental task in computer vision with wide-ranging applications. Although existing monocular clothed human reconstruction solutions have shown promising results, they often rely on the assumption that the human subject is in an occlusion-free environment. Thus, when encountering in-the-wild occluded i

Eric De Giuli

Stochastic systems have a control-theoretic interpretation in which noise plays the role of control. In the weak-noise limit, relevant at low temperatures or in large populations, this leads to a precise mathematical mapping: the most probable trajectory between two states minimizes an action functional and corresponds to an optimal control strategy. In Lang

Anwesha Bhattacharyya, Ye Yu, Hanyu Yang, Rahul Singh

The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new model risks, primarily hallucinations and toxicity. As highly

Abdulaziz Al Mannai

Qatar's football sector has undergone a substantial technological transformation with the implementation of Electronic Performance and Tracking Systems (EPTS). This study examines the impact of cultural and technological factors on EPTS adoption, using Hofstede's Cultural Dimensions Theory and the Technology Acceptance Model (TAM) as theoretical frameworks.

Yuming Gu, Phong Tran, Yujian Zheng, Hongyi Xu

Generating high-quality 360-degree views of human heads from single-view images is essential for enabling accessible immersive telepresence applications and scalable personalized content creation. While cutting-edge methods for full head generation are limited to modeling realistic human heads, the latest diffusion-based approaches for style-omniscient head

Kyle Vedder

Scene flow estimation is the task of describing 3D motion between temporally successive observations. This thesis aims to build the foundation for building scene flow estimators with two important properties: they are scalable, i.e. they improve with access to more data and computation, and they are flexible, i.e. they work out-of-the-box in a variety of dom

Kim Morel, Louis-Philippe Coulombe, Jason F. Rowe, David Lafrenière

Secondary eclipse observations of exoplanets at near-infrared wavelengths enable the detection of thermal emission and reflected stellar light, providing insights into the thermal structure and aerosol composition of their atmospheres. These properties are intertwined, as aerosols influence the energy budget of the planet. WASP-80 b is a warm gas giant with

Hisashi Johno, Yuki Johno, Akitomo Amakawa, Junichi Sato

Purpose: Retrieval-augmented generation (RAG) is a technology to enhance the functionality and reliability of large language models (LLMs) by retrieving relevant information from reliable external knowledge (REK). RAG has gained interest in radiology, and we previously reported the utility of NotebookLM, an LLM with RAG (RAG-LLM), for lung cancer staging. Ho

Claudia Cecchetto, Mufti Mahmud, Vincenzo Sorrenti, Marta Maschietto

High-resolution recordings of extracellular potentials are fundamental for the study of neuronal networks, the basis of neuronal coding and transmission. Here we present an innovative method for an in vivo electrical imaging of Local Field Potentials using novel implantable neural interfaces with a high-density array of 256 recording sites and a spatial reso

Joris Raeymaekers

We clarify the relation between the classical double copy and the double copy for amplitudes in the setting of selfdual gauge and gravity theories. To this end we construct explicit all-order perturbative solutions in these theories and show that they are related by a version of color-kinematics duality. This relation can be expressed in a double-copy form a

Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, Juan A. Rodriguez

Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enhance computer workflows. While existing research focuses on online settings, desktop environments, critical for many professional and everyday tasks, remain underexplored due to data collection challenges and licensing i

Naveen Kumar, Ambesh Dixit, Vivek Vijay

Entropy has emerged as a dynamic, interdisciplinary, and widely accepted quantitative measure of uncertainty across different disciplines. A unified understanding of entropy measures, supported by a detailed review of their theoretical foundations and practical applications, is crucial to advance research across disciplines. This review article provides moti

Yuejia Zhai, Marco de Cesare, Carsten van de Bruck, Eleonora Di Valentino

We explore an interacting dark sector model in trace-free Einstein gravity where dark energy has a constant equation of state, $w=-1$, and the energy-momentum transfer potential is proportional to the cold dark matter density. Compared to the standard $\Lambda$CDM model, this scenario introduces a single additional dimensionless parameter, $\epsilon$, which

Àngel García-Blàzquez, Gurleen Kaur, Sugandha Maheshwary

A finite group G is said to be a cut group if all central units in the integral group ring ZG are trivial. In this article, we extend the notion of cut groups, by introducing extended cut groups. We study the properties of extended cut groups analogous to those known for cut groups and also characterise some substantial classes of groups having the property

Luis Lomelí-Núñez, A. Cortesi, A. V. Smith Castelli, M. L. Buzzo

We present the largest sample ($\sim$13,000 candidates, $\sim$3000 of wich are bona-fide candidates) of globular cluster (GCs) candidates reported in the Fornax Cluster so far. The survey is centered on the NGC 1399 galaxy, extending out to 5 virial radii (\rv) of the cluster. We carried out a photometric study using images observed in the 12-bands system of

Philip T. Gressman

Abstract H\"{o}lder-Brascamp-Lieb inequalities have become a ubiquitous tool in Fourier analysis in recent years, due in large part to a theorem of Bennett, Carbery, Christ, and Tao (2008,2010) characterizing finiteness of the H\"{o}lder-Brascamp-Lieb constant. Here we provide a new characterization of a substantially different nature involving directed grap

Zefeng Lin, Yi Xiao, Zhiqiang Mo, Qifan Zhang

Automatically adapting novels into screenplays is important for the TV, film, or opera industries to promote products with low costs. The strong performances of large language models (LLMs) in long-text generation call us to propose a LLM based framework Reader-Rewriter (R$^2$) for this task. However, there are two fundamental challenges here. First, the LLM

Christian Killer, Alessandro De Carli, Pascal Brun, Amadeo Victor Charlé

Centralized trust is ubiquitous in today's interconnected world, from computational resources to data storage and its underlying infrastructure. The monopolization of cloud computing resembles a feudalistic system, causing a loss of privacy and data ownership. Cloud Computing and the Internet in general face widely recognized challenges, such as (1) the cent

Miguel Ureña Pliego, Rubén Martínez Marín, Nianfang Shi, Takeru Shibayama

This study explores the integration of machine learning into urban aerial image analysis, with a focus on identifying infrastructure surfaces for cars and pedestrians and analyzing historical trends. It emphasizes the transition from convolutional architectures to transformer-based pre-trained models, underscoring their potential in global geospatial analysi

Olivier Mousis, Aaron Werlen, Tom Benest Couzinou, Antoine Schneeberger

Deuterium, a heavy isotope of hydrogen, is a key tracer of the formation of the Solar System. Recent JWST observations have expanded the dataset of D/H ratios in methane on the KBOs Eris and Makemake, providing new insights into their origins. This study examines the elevated D/H ratios in methane on these KBOs in the context of protosolar nebula dynamics an

Sabeen Ahmed, Nathan Parker, Margaret Park, Daniel Jeong

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However

Yu-Shao Jacky Chen, Mike P. C. Taverne, Kevin Chung-Che Huang, Ying-Lung Daniel Ho

One-dimensional (1D) polymer-based photonic crystals (PhCs) in the $1.55~\mu{}m$ wavelength range can be easily created using a two-photon direct laser writing system. To achieve shorter period structures, we report the use of thermal shrinkage of two-photon polymerized structures, at elevated temperatures, to eliminate unpolymerised material, leading to the