November 2024 arXiv papers — page 7

Angelika Romanou, Negar Foroutan, Anna Sotnikova, Zeming Chen

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (\ie, multilingual LLMs) is bottlenecked by the lack of high-quality ev

Chenguang Xiao, Shuo Wang

Data Heterogeneity is a major challenge of Federated Learning performance. Recently, momentum based optimization techniques have beed proved to be effective in mitigating the heterogeneity issue. Along with the model updates, the momentum updates are transmitted to the server side and aggregated. Therefore, the local training initialized with a global moment

Geerten Koers, Botond Szabo, Aad van der Vaart

We consider the recovery of an unknown function $f$ from a noisy observation of the solution $u_f$ to a partial differential equation that can be written in the form $\mathcal{L} u_f=c(f,u_f)$, for a differential operator $\mathcal{L}$ that is rich enough to recover $f$ from $\mathcal{L} u_f$. Examples include the time-independent Schr\"odinger equation $\De

James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić

The Vera C. Rubin Observatory is due to commence the 10-year Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the

Basit G. Sheikh, Rakesh Kumar, Susheel Kumar S

This paper presents a conjugate flow-thermal analysis of a rocket nozzle protected by a charring ablative thermal protection system (TPS). The study employs a coupled approach, integrating a CFD solver with an in-house transient material response code through the exchange of boundary conditions at the fluid-solid interface. The nozzle incorporates an AVCOAT

Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth

We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve consensus on predictions and improve accuracy. This approach imposes calibration conditions on each party, which are computationally and statistically tractable relaxations of Bayesi

Bethany McDonagh, Jin-Song von Storch, Emanuela Clementi, Nadia Pinardi

The generation and propagation sites of internal tides in the Mediterranean Sea are mapped through a comprehensive high-resolution numerical study. Two ocean general circulation models were used for this: NEMO v3.6, and ICON-O, both hydrostatic ocean models based on primitive equations with Boussinesq approximation, where NEMO is a regional Mediterranean Sea

Xin Lu, Yuhao Wang, Zhiheng Zhang

Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-world settings where interference between units is present. Network interference, in particular, has garnered significant attention. Structural models, like the linear-in-means mod

Alhun Aydin, Joonas Keski-Rahkonen, Anton M. Graf, Shaobing Yuan

The quantum acoustic framework has recently emerged as a non-perturbative, coherent approach to electron-lattice interactions, uncovering rich physics often obscured by perturbative methods with incoherent scattering events. Here, we model the strongly coupled dynamics of electrons and acoustic lattice vibrations within this framework, representing lattice v

Giovanni Lodone

We try to apply a known equivalence, for RH about Riemann Z function, to Dirichlet L functions with primitive characters. The aim is to give a small contribution to the proof of the generalized version of Riemann Hypothesis (RH).

Armin Saghafian, Amirmohammad Izadi, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah

Grounding the instruction in the environment is a key step in solving language-guided goal-reaching reinforcement learning problems. In automated reinforcement learning, a key concern is to enhance the model's ability to generalize across various tasks and environments. In goal-reaching scenarios, the agent must comprehend the different parts of the instruct

Yiming Wu, Wei Ji, Kecheng Zheng, Zicheng Wang

Recently, human motion analysis has experienced great improvement due to inspiring generative models such as the denoising diffusion model and large language model. While the existing approaches mainly focus on generating motions with textual descriptions and overlook the reciprocal task. In this paper, we present~\textbf{MoTe}, a unified multi-modal model t

Madhav Mohan, Julius de Hond, Servaas Kokkelmans

A clever choice and design of gate sets can reduce the depth of a quantum circuit, and can improve the quality of the solution one obtains from a quantum algorithm. This is especially important for near-term quantum computers that suffer from various sources of error that propagate with the circuit depth. Parametrized gates in particular have found use in bo

Priti Prasanna Mondal, Fouzul Atik

Consider two simple graphs, G1 and G2, with their respective vertex sets V(G1) and V(G2). The Kronecker product forms a new graph with a vertex set V(G1) X V(G2). In this new graph, two vertices, (x, y) and (u, v), are adjacent if and only if xu is an edge in G1 and yv is an edge in G2. While the adjacency spectrum of this product is known, the distance spec

Vladimír Zobač, Mikael Kuisma, Ask Hjorth Larsen, Tuomas Rossi

Density functional theory with linear combination of atomic orbitals (LCAO) basis sets is useful for studying large atomic systems, especially when it comes to computationally highly demanding time-dependent dynamics. We have implemented the Ehrenfest molecular dynamics (ED) method with the approximate approach of Tomfohr and Sankey within the projector augm

Benjamin Delarue, Guendalina Palmirotta

We prove that the Patterson-Sullivan and Wigner distributions on the unit sphere bundle of a convex-cocompact hyperbolic surface are asymptotically identical. This generalizes results in the compact case by Anantharaman-Zelditch and Hansen-Hilgert-Schröder.

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

An amplitude analysis of $B^-\rightarrow D^- D^0 K_\mathrm{S}^0$ decays is performed using proton-proton collision data, corresponding to an integrated luminosity of $9\,\text{fb}^{-1}$, collected with the LHCb detector at center-of-mass energies of 7, 8, and 13$\mathrm{\,Te\kern -0.1em V}$. A resonant structure of spin-parity $0^+$ is observed in the $D^0 K

Alexa Tyberg, Yunhao Fan, Gia-Wei Chern

We present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure

Xuan Ma, Zepeng Bao, Ming Zhong, Yuanyuan Zhu

In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Existing data-driven deep learning methods primarily focus on the spatial or temporal dependency between regions yet neglecting regions' fundamental functional difference. Though knowledge-driven physical methods hav

Yusuf Kasim, Tomaž Prosen

Dynamical systems can display a plethora of ergodic and ergodicity breaking behaviors, ranging from simple periodicity to ergodicity and chaos. Here we report an unusual type of non-ergodic behavior in a many-body discrete-time dynamical system, specifically a multi-periodic response with multi-fractal distribution of equilibrium spectral weights at all rati

Nathan Berkovits

A new manifestly spacetime-supersymmetric prescription for superstring amplitude computations is given using the pure spinor formalism which does not contain subtleties from poles in the pure spinor ghosts. This super-Poincare covariant prescription is related by a U(5)-covariant field redefinition to the Ramond-Neveu-Schwarz amplitude prescription where the

Maksim Lednev, Diego Fernández de la Pradilla, Frieder Lindel, Esteban Moreno

While experimental measurements of photon correlations have become routine in laboratories, theoretical access to these quantities for the light generated in complex nanophotonic devices remains a major challenge. Current methods are limited to specific simplified cases and lack generality. Here we present a novel method that provides access to photon statis

Konrad Wróbel

We introduce the space of infinite volume ends of a locally compact second countable (lcsc) space that admits a Radon measure. In certain cases, this coincides with the classical space of ends. Consider a discrete subgroup $\Gamma$ of a unimodular lcsc group $G$ that is not coamenable. Assume that $G$ has property (T) and the associated homogeneous space $G/

Ghaisud Din, Muqaddar Abbas, Pei Zhang

In this study, we investigate the dynamics of system composed of a single cavity consisting of an optical parametric amplifier (OPA) and a YIG sphere influenced by a bias magnetic field. This bias field leads to magnetostrictive effects on magnon modes that induces phonons. We investigate the position fluctuation spectrum and the output field spectrum, findi

Guofeng Mei, Wei Lin, Luigi Riz, Yujiao Wu

Enabling Large Language Models (LLMs) to understand the 3D physical world is an emerging yet challenging research direction. Current strategies for processing point clouds typically downsample the scene or divide it into smaller parts for separate analysis. However, both approaches risk losing key local details or global contextual information. In this paper

Yihan Chen, Jialin He, Allan Lo, Cong Luo

In 1975 Bollob\'{a}s, Erd\H{o}s, and Szemer\'{e}di asked what minimum degree guarantees an octahedral subgraph $K_3(2)$ in any tripartite graph $G$ with $n$ vertices in each vertex class. We show that $\delta(G)\geq n+2n^{\frac{5}{6}}$ suffices thus improving the bound $n+(1+o(1))n^{\frac{11}{12}}$ of Bhalkikar and Zhao obtained by following their approach.

Tiantian Geng, Jinrui Zhang, Qingni Wang, Teng Wang

Despite impressive advancements in video understanding, most efforts remain limited to coarse-grained or visual-only video tasks. However, real-world videos encompass omni-modal information (vision, audio, and speech) with a series of events forming a cohesive storyline. The lack of multi-modal video data with fine-grained event annotations and the high cost

Haorui He, Yuchen Song, Yuancheng Wang, Haoyang Li

The effectiveness of one-shot voice conversion (VC) decreases in real-world scenarios where reference speeches, which are often sourced from the internet, contain various disturbances like background noise. To address this issue, we introduce Noro, a noise-robust one-shot VC system. Noro features innovative components tailored for VC using noisy reference sp

Jeheon Woo, Seonghwan Kim, Jun Hyeong Kim, Woo Youn Kim

We introduce a framework for molecular structure optimization using denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Euclidean space, our method leverages a Riemannian metric that better aligns with molecular energy change, enabling more robust modeling of potential energy surfaces. By incorporatin

Aparna Sundar, Tony Russell-Rose, Udo Kruschwitz, Karen Machleit

As artificial intelligence (AI) becomes increasingly embedded in daily life, designing intuitive, trustworthy, and emotionally resonant AI-human interfaces has emerged as a critical challenge. This editorial introduces a Special Issue that explores the psychology of AI experience design, focusing on how interfaces can foster seamless collaboration between hu

Tzu-Yang Chou

Let $X$ be a surface with an ADE-singularity and let $\widetilde{X}$ be its crepant resolution. In this paper, we show that there exists a Bridgeland stability condition $\sigma_X$ on ${\rm D}^b(X)$ and a weak stability condition $\sigma_{\widetilde{X}}$ on the derived category of the desingularisation ${\rm D}^b(\widetilde{X})$, such that pushforward of $\s

Javier del Riego

This contribution presents recent cross section measurements for top quark pair production (tt) and single top quark production in association with a W boson (tW) in proton-proton collisions at the CMS experiment at LHC. For tt production, data from 2017 at a center-of-mass (CM) energy of 5.02 TeV with an integrated luminosity of 302 pb^-1 were analyzed, wit

Lida Shahbandari, Elahe Moradi, Mohammad Manthouri

Accurate prediction of stock market trends is crucial for informed investment decisions and effective portfolio management, ultimately leading to enhanced wealth creation and risk mitigation. This study proposes a novel approach for predicting stock prices in the stock market by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM

Zishuo Li, Anh Tung Nguyen, André M. H. Teixeira, Yilin Mo

This paper addresses the secure state estimation problem for continuous linear time-invariant systems with non-periodic and asynchronous sampled measurements, where the sensors need to transmit not only measurements but also sampling time-stamps to the fusion center. This measurement and communication setup is well-suited for operating large-scale control sy

Sida Sun, Shigeo Maruyama, Yan Li

The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFF), which can study the interface dynamics under near ab-initio accuracy, provides a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-

Sahabeh Saadati, Mohammad Manthouri

Accurate prediction of price behavior in the foreign exchange market is crucial. This paper proposes a novel approach that leverages technical indicators and deep neural networks. The proposed architecture consists of a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and attention mechanism. Initially, trend and oscillation technical in

Soichiro Kuwahara, Leo Tsukada

Stochastic background gravitational waves have not yet been detected by ground-based laser interferometric detectors, but recent improvements in detector sensitivity have raised considerable expectations for their eventual detection. Previous studies have introduced methods for exploring anisotropic background gravitational waves using Bayesian statistics. T

Idriss Boutaayamoua, Fouad Et-tahri, Lahcen Maniar

This paper deals with the insensitizing controllability property of the quasilinear parabolic equation with dynamic boundary conditions. This problem can be reformulated as a null controllability problem for a cascade quasilinear system with dynamic boundary conditions. To this end, we approach the problem by first dealing with null controllability in the fr

Can Ozkan, Dave Singelee

ICS environments are vital to the operation of critical infrastructure such as power grids, water treatment facilities, and manufacturing plants. However, these systems are vulnerable to cyber attacks due to their reliance on interconnected devices and networks, which could lead to catastrophic failures. Therefore, securing these systems from cyber threats b

Buu-Van Nguyen, Johan Romate, Cornelis Vuik

We propose an electrolyser model for steady-state load flow analysis of multi-carrier energy networks, where the electrolyser is capable of producing hydrogen gas and heat. We show that there are boundary conditions that lead to a well-posed problem. We derive these conditions for two cases, namely with a fixed and non-fixed ratio between gas and heat output

Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng

Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. However, not all pre-trained features are robust and those methods are largely indifferent to which ones to preserve. We propose dual risk minimization (DRM), which combines empiric

Yihao Wang, Marcus Klasson, Matias Turkulainen, Shuzhe Wang

Gaussian splatting enables fast novel view synthesis in static 3D environments. However, reconstructing real-world environments remains challenging as distractors or occluders break the multi-view consistency assumption required for accurate 3D reconstruction. Most existing methods rely on external semantic information from pre-trained models, introducing ad

Tomoaki Okayama, Kosei Arakawa, Ryo Kamigaki, Eita Yabumoto

The single exponential (SE) and double exponential (DE) formulas are widely recognized as efficient quadrature formulas for evaluating integrals with endpoint singularity. For integrals exhibiting algebraic singularity, explicit error bounds in a computable form have been provided, enabling computations with guaranteed accuracy. Such explicit error bounds ha

Ravinithesh Annapureddy, Alessandro Fornaroli, Daniel Gatica-Perez

This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies off

Matthew Chignoli, Jean-Jacques Slotine, Patrick M. Wensing, Sangbae Kim

Designs incorporating kinematic loops are becoming increasingly prevalent in the robotics community. Despite the existence of dynamics algorithms to deal with the effects of such loops, many modern simulators rely on dynamics libraries that require robots to be represented as kinematic trees. This requirement is reflected in the de facto standard format for

T. A. Sargsian, P. A. Mantashyan, D. B. Hayrapetyan

The study investigates and compares the impact of intense, non-diffractive, non-resonant structured laser beams with various intensity profiles on the properties of InAs/GaAs cylindrical quantum dot. The comparative study demon-strates that the different structured beams, having unique symmetries and hence properties, have significant effects on the confinem

Violeta Borges Marques, Arne Mertens

The framework of templicial vector spaces was put forth in arXiv:2302.02484v2 as a suitable generalization of simplicial sets in order to develop a theory of enriched quasi-categories, called quasi-categories in vector spaces. We construct a lift of Faonte's $A_{\infty}$-nerve arXiv:1312.2127v2 which lands in templicial vector spaces. Further, we show that w

RaviKanth Kaja

In an era marked by the widespread sharing of digital content, the need for a robust content-integrity verification goes beyond the confines of individual social media platforms. While verified profiles (such as blue ticks on platforms like Instagram and X) have become synonymous with credibility, the content they share often traverses a complex network of i

CMS Collaboration, TOTEM Collaboration

The TOTEM Roman pot detectors are used to reconstruct the transverse momentum of scattered protons and to estimate the transverse location of the primary interaction. This paper presents new methods of track reconstruction, measurements of strip-level detection efficiencies, cross-checks of the LHC beam optics, and detector alignment techniques, along with t

Arnaud Maret

A surface group representation into a Lie group is called totally elliptic if every simple closed curve on the surface is mapped to an elliptic element of the target group. In this note, we characterize all totally elliptic surface group representations into $\mathrm{PSL}_2\mathbb{R}$ and $\mathrm{PSL}_2\mathbb{C}$ by showing that they are either representat

Ahmad Rahimi, Alexandre Alahi

Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles. However, current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements. Addressing these limitations, this study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Div

Anaïs Berkes

Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, optimising system efficiency is crucial for reducing future energy use and mitigating climate change. Existing control strategies lack genera

Finn Michler, Argha Ghosh

We introduce multi-split continuous functions between topological spaces, a weaker form of continuity that generalizes split continuity while being stable under compositions. We will define the associated star multifunction and pre-multi-split multifunctions. Moreover, we will prove that multi-split continuity naturally emerges as the continuity property of

Fouad Trad, Ali Chehab

The effectiveness of Large Language Models (LLMs) significantly relies on the quality of the prompts they receive. However, even when processing identical prompts, LLMs can yield varying outcomes due to differences in their training processes. To leverage the collective intelligence of multiple LLMs and enhance their performance, this study investigates thre

Petar Veličković, Alex Vitvitskyi, Larisa Markeeva, Borja Ibarz

Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. Here we instead focus on combinatorial

Alizée Amsler Moulanier, Olivier Mousis, Alexis Bouquet, Christopher R. Glein

The presence of a hydrosphere on Europa raises questions about its habitability, and studies of its volatile inventory can provide insight into its formation process. Different scenarios suggest that Europa's volatiles could be derived from cometary ices or devolatilized building blocks. The study of post-accretion processes, in particular the "open ocean" p

Heloisa Oss Boll, Ali Amirahmadi, Amira Soliman, Stefan Byttner

Objective: In modern healthcare, accurately predicting diseases is a crucial matter. This study introduces a novel approach using graph neural networks (GNNs) and a Graph Transformer (GT) to predict the incidence of heart failure (HF) on a patient similarity graph at the next hospital visit. Materials and Methods: We used electronic health records (EHR) from

K. Maucó, C. F. Manara, A. Bayo, J. Hernández

Disk winds play a crucial role in the evolution of protoplanetary disks. Typical conditions for star and planet formation are in regions with intermediate or strong UV radiation fields produced by massive stars. The $\sigma$-Orionis cluster is the ideal site to study disk winds under these conditions; its outer parts can be used to study disk evolution, whil

Marica Minucci, Rodrigo Panosso Macedo

This work provides a spacetime interpretation of the confluent Heun functions within black hole perturbation theory (BHPT) and explores their relationship to the hyperboloidal framework. In BHPT, the confluent Heun functions are solutions to the radial Teukolsky equation, but they are traditionally studied without an explicit reference to the underlying spac

Xin Huang

We give a new proof, by using the terminology and notation in the textbook \cite{Lin18b}, to a result, due to Puig, stating that a stable equivalence of Morita type between two block algebras of finite groups induced by a bimodule with an endopermutation source preserves K\"ulshammer--Puig classes.

X. Chen, Z. Ding, E. Paillas, S. Nadathur

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ~2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the ``standard'' reconstruction framework, in whic

Dae-Won Kim, Eduardo Ros, Matthias Kadler, Thomas P. Krichbaum

We present a long-term strong correlation between millimeter (mm) radio and $\gamma$-ray emission in the flat-spectrum radio quasar (FSRQ) PKS 1424-418. The mm$-\gamma$-ray connection in blazars is generally thought to originate from the relativistic jet close to the central engine. We confirm a unique long-lasting mm$-\gamma$-ray correlation of PKS 1424-418

Diana-Elena Mirciu, Elena Resmerita

In this work we derive higher order error estimates for inverse problems distorted by non-additive noise, in terms of Bregman distances. The results are obtained by means of a novel source condition, inspired by the dual problem. Specifically, we focus on variational regularization having the Kullback-Leibler divergence as data-fidelity, and a convex penalty

Zijie Tao, Yunchi Zheng

Weprovide an upper bound for generalized Littlewood-Richardson coefficients $c^w_{uv}$, where $u$ is a two-row Young diagram corresponding to a Grassmannian permutation. We end with a conjecture on the upper bounds for all such structure constants.

Gergely Bérczi, Adam Zsolt Wagner

We apply a generative AI pattern-recognition technique called PatternBoost to study bootstrap percolation on hypercubes. With this, we slightly improve the best existing upper bound for the size of percolating subsets of the hypercube.

Reyhaneh Hashempour, Barbara Plank, Aline Villavicencio, Renato Cordeiro de Amorim

This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users' tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. L

Severin Bochem, Eduardo Gonzalez-Sanchez, Yves Bicker, Gabriele Fadini

Reinforcement learning often requires extensive training data. Simulation-to-real transfer offers a promising approach to address this challenge in robotics. While differentiable simulators offer improved sample efficiency through exact gradients, they can be unstable in contact-rich environments and may lead to poor generalization. This paper introduces a n

Fabien Poirier

This thesis is part of a CIFRE agreement between the company Othello and the LIASD laboratory. The objective is to develop an artificial intelligence system that can detect real-time dangers in a video stream. To achieve this, a novel approach combining temporal and spatial analysis has been proposed. Several avenues have been explored to improve anomaly det

Gustavo de Veciana, Sonia Fahmy, George Kesidis, Voicu Popescu

Virtual Reality (VR) applications have a number of unique characteristics that set them apart from traditional video streaming. These characteristics have major implications on the design of VR rendering, adaptation, prefetching, caching, and transport mechanisms. This paper contrasts VR to video streaming, stored 2D video streaming in particular, and discus

Xiyue Zhang, Zifan Wang, Yulong Gao, Licio Romao

In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In this work, we propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximat

Felix Pultar, Moritz Thuerlemann, Igor Gordiy, Eva Doloszeski

We present design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM) simulations. Substitution of a computationally expensive QM Hamiltonian by a NNP with the same accuracy largely reduces the comput

Liming Nie, Hao Liu, Jing Sun, Kabir Sulaiman Said

There is an increasing global emphasis on information accessibility, with numerous researchers actively developing automated tools to detect and repair accessibility issues, thereby ensuring that individuals with diverse abilities can independently access software products and services. However, current research still encounters significant challenges in two

Syed Mohammed Mostaque Billah, Ateya Ahmed Subarna, Sudipta Nandi Sarna, Ahmad Shawkat Wasit

Around seven million individuals in India, Bangladesh, Bhutan, and Nepal speak Santali, positioning it as nearly the third most commonly used Austroasiatic language. Despite its prominence among the Austroasiatic language family's Munda subfamily, Santali lacks global recognition. Currently, no translation models exist for the Santali language. Our paper aim

Gerard Pascual, Jordi Boronat, Kris Van Houcke

A two-dimensional spin-up ideal Fermi gas interacting attractively with a spin-down impurity in the continuum undergoes, at zero temperature, a first-order phase transition from a polaron to a dimeron state. Here we study a similar system on a square lattice, by considering the attractive 2D Fermi-Hubbard model with a single spin-down and a finite filling fr

Zacharie Ales, Cristian Duran-Matelunaa, Sourour Elloumi

The p-center problem consists in selecting p facilities from a set of possible sites and allocating a set of clients to them in such a way that the maximum distance between a client and the facility to which it is allocated is minimized. This paper proposes a new scalable exact solution algorithm based on client clustering and an iterative distance rounding

Valentina Fontirroig, Facundo A. Gomez, Marcelo Jaque Arancibia, Arianna Dolfi

Lopsided galaxies are late-type galaxies with a non-axisymmetric disk due to an uneven distribution of their stellar mass. Despite being a relatively common perturbation, several questions regarding its origin and the information that can be extracted from them about the evolutionary history of late-type galaxies. The advent of several large multi-band photo

Michael Tschannen, André Susano Pinto, Alexander Kolesnikov

Removing modeling constraints and unifying architectures across domains has been a key driver of the recent progress in training large multimodal models. However, most of these models still rely on many separately trained components such as modality-specific encoders and decoders. In this work, we further streamline joint generative modeling of images and te

Nandan Maiti, Manon Seppecher, Ludovic Leclercq

Accurately estimating traffic variables across unequipped portions of a network remains a significant challenge due to the limited coverage of sensor-equipped links, such as loop detectors and probe vehicles. A common approach is to apply uniform scaling, treating unequipped links as equivalent to equipped ones. This study introduces a novel framework to imp

P. Doležal, T. Biesner, Y. Li, R. Mathew Roy

Studying the magnetic ground states of frustrated antiferromagnets provides unique insight into the stability of quantum spin liquids, even if the anticipated state is not realized towards T = 0. Particularly relevant are structural modifications setting in at temperatures where the magnetic correlations come into play. Here we explore the lattice dynamics o

Mengbang Zou, Weisi Guo

Modeling the evolution of system with time-series data is a challenging and critical task in a wide range of fields, especially when the time-series data is regularly sampled and partially observable. Some methods have been proposed to estimate the hidden dynamics between intervals like Neural ODE or Exponential decay dynamic function and combine with RNN to

Tomás Hüttebräucker, Simone Fiorellino, Mohamed Sana, Paolo Di Lorenzo

In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables dif

David Dukić, Marin Petričević, Sven Ćurković, Jan Šnajder

TakeLab Retriever is an AI-driven search engine designed to discover, collect, and semantically analyze news articles from Croatian news outlets. It offers a unique perspective on the history and current landscape of Croatian online news media, making it an essential tool for researchers seeking to uncover trends, patterns, and correlations that general-purp

Gasser Elazab, Torben Gräber, Michael Unterreiner, Olaf Hellwich

Self-supervised monocular depth estimation (MDE) has gained popularity for obtaining depth predictions directly from videos. However, these methods often produce scale invariant results, unless additional training signals are provided. Addressing this challenge, we introduce a novel self-supervised metric-scaled MDE model that requires only monocular video d

Gianmaria Ventura, Michele Rossi, Jacopo Pegoraro

Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems, as it enables applications such as target classification, human activity recognition, and gait analysis. In practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices

Zhile Li

We consider solutions to the Navier-Stokes equations on $\mathbb{R}^2$ close to the Poiseuille flow with viscosity $0< \nu < 1$. For the linearized problem, we prove that when the $x$-frequency satisfy $|k| \ge \nu^{-\frac{1}{3}}$, the perturbation decays on a time-scale proportional to $\nu^{-\frac{1}{2}}|k|^{-\frac{1}{2}}$. Since it decays faster than the

Navid Salami Pargoo, Mahshid Ghasemi, Shuren Xia, Mehmet Kerem Turkcan

As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications-focusing on challenges like

Michal Lewandowski, Hamid Eghbalzadeh, Bernhard A. Moser

Many natural phenomena are characterized by self-similarity, for example the symmetry of human faces, or a repetitive motif of a song. Studying of such symmetries will allow us to gain deeper insights into the underlying mechanisms of complex systems. Recognizing the importance of understanding these patterns, we propose a geometrically inspired framework to

Hang Wang, Yanru Wang, Jianguo Zhang, Dapeng Zhou

We introduce the notion of dynamic asymptotic dimension growth for actions of discrete groups on compact spaces, and more generally for locally compact \'etale groupoids. Using the work of Bartels, L\"uck, and Reich, we bridge asymptotic dimension growth for countable discrete groups with our notion for their group actions, thereby providing numerous concret

Yosuke Asami, Toshiaki Hishida

Consider a generalized Oseen evolution operator in 3D exterior domains, that is generated by a non-autonomous linearized system arising from time-dependent rigid motions. This was found by Hansel and Rhandi, and then the theory was developed by the second author, however, desired regularity properties such as estimate of the temporal derivative as well as th

Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi, Lokesh Mishra

Retrieval Augmented Generation (RAG) systems are a widespread application of Large Language Models (LLMs) in the industry. While many tools exist empowering developers to build their own systems, measuring their performance locally, with datasets reflective of the system's use cases, is a technological challenge. Solutions to this problem range from non-spec

Victor E. Ambrus, Sören Schlichting, Clemens Werthmann

Exploiting the first measurements of the same ion species in OO collisons at RHIC and LHC, we propose an observable to distinguish whether collective behavior builds up through a hydrodynamic expansion of a strongly interacting QGP or few final state re-scatterings. Our procedure allows to disentangle the effects of the initial state geometry and the dynamic

Victor E. Ambrus, Sören Schlichting, Clemens Werthmann

High-energy nuclear collisions exhibit collective flow, which emerges as a dynamical response of the Quark-Gluon Plasma (QGP) to the initial state geometry of the collision. Collective flow in heavy-ion collisions is usually described within multi-stage evolution models, which employ a viscous relativistic hydrodynamic description of the space-time evolution

Sumitava Kundu, Kaustav Chakraborty, Avisek Das

Hard convex polyhedra, idealized models for anisotropic colloids and nanoparticles, are known to form variety of orientational phases despite the regular arrangement of particles in the crystalline assemblies. Based on the orientational behavior of the constituents particles, such phases could be categorized into freely rotating plastic crystals (PC), discre

Viktoria Vomhoff, Hyunseok Daniel Jang, Matteo Varvello, Stefan Geißler

Traditional cellular service was designed for global connectivity, but business and logistical constraints led to its fragmentation, with deployments limited to individual countries and regions. Initiatives like Mobile Virtual Network Operators (MVNOs), Mobile Network Aggregators (MNAs), and regulations like ''roam-like-at-home'' have partially restored glob

G. Gordillo-Núñez, A. Suzuki

In this note we investigate, as a natural continuation of [K. Castillo, Constr. Approx., 55 (2022) 605-627], the behaviour of the zeros of discrete paraorthogonal polynomials on the unit circle with respect to a real parameter.

Sumita Majhi, Pinaki Mitra

In 5G networks, non-orthogonal multiple access (NOMA) provides a number of benefits by providing uneven power distribution to multiple users at once. On the other hand, effective power allocation, successful successive interference cancellation (SIC), and user fairness all depend on precise channel state information (CSI). Because of dynamic channels, imperf

Bibo He, Jiachuan Yang, Fei Meng, Jialiang Yu

Two-color lasers with high coherence are paramount in precision measurement, accurate light-matter interaction, and low-noise photonic microwave generation. However, conventional two-color lasers often suffer from low coherence, particularly when these two colors face large frequency spacings. Here, harnessing the Pound-Drever-Hall technique, we synchronize

Andre O. Falcao

Mutual Information (MI) is a powerful statistical measure that quantifies shared information between random variables, particularly valuable in high-dimensional data analysis across fields like genomics, natural language processing, and network science. However, computing MI becomes computationally prohibitive for large datasets where it is typically require

Chaoxiong Ma, Yan Liang

Given that distributed systems face adversarial behaviors such as eavesdropping and cyberattacks, how to ensure the evidence fusion result is credible becomes a must-be-addressed topic. Different from traditional research that assumes nodes are cooperative, we focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying a

Maryam Azizi, Francisco A. Delesma, Matteo Giantomassi, Davis Zavickis

The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained from a Density Functional Theory (DFT) calculation are used as a starting point to build the Green's function G and the screened Coulomb interaction W, yielding th