March 2024 arXiv papers — page 83

Louis E. Devers, Perrine Bonavita, Christian Jost

Termites form complex dynamical trail networks from simple individual rules when exploring their environment. To help identify those simple rules, we reconstructed trail networks from time-lapse images of roaming termites. We quantified the trails' frequentations over time and compared them to the ones obtained by a null model. Arena borders were preferred i

Tejas Santanam, Helmut Simonis

This paper provides an overview of the state of teaching for Constraint Programming, based on a survey of the community for the 2023 Workshop on Teaching Constraint Programming at the CP 2023 conference in Toronto. The paper presents the results of the survey, as well as lists of books, video courses and other tutorial materials for teaching Constraint Progr

Cancan Wang, Ming Su, Gang Wang, Qingpo Zhang

Polynomial multiplication is a fundamental problem in symbolic computation. There are efficient methods for the multiplication of two univariate polynomials. However, there is rarely efficiently nontrivial method for the multiplication of two multivariate polynomials. Therefore, we consider a new multiplication mechanism that involves a) reversibly reducing

Manuel Marschall, Ines Fortmeier, Manuel Stavridis, Finn Hughes

The tilted-wave interferometer is a promising technique for the development of a reference measurement system for the highly accurate form measurement of aspheres and freeform surfaces. The technique combines interferometric measurements, acquired with a special setup, and sophisticated mathematical evaluation procedures. To determine the form of the surface

Alban Moor, Davide La Vecchia, Elvezio Ronchetti

We introduce innovative inference procedures for analyzing time series data. Our methodology enables density approximation and composite hypothesis testing based on Whittle's estimator, a widely applied M-estimator in the frequency domain. Its core feature involves the (general Legendre transform of the) cumulant generating function of the Whittle likelihood

Amin Bahmanian, Songling Shan

Motivated by generalizations of de Bruijn cycles to various combinatorial structures (Chung, Diaconis, and Graham), we study various Euler tours in set systems. Let $\mathcal{G}$ be a hypergraph whose corank and rank are $c\geq 3$ and $k$, respetively. The minimum $t$-degree of $\mathcal{G}$ is the fewest number of edges containing every $t$-subset of vertic

Shen Zheng, Anurag Ghosh, Srinivasa G. Narasimhan

Driving is challenging in conditions like night, rain, and snow. Lack of good labeled datasets has hampered progress in scene understanding under such conditions. Unsupervised Domain Adaptation (UDA) using large labeled clear-day datasets is a promising research direction in such cases. However, many UDA methods are trained with dominant scene backgrounds (e

Filip Ilic, He Zhao, Thomas Pock, Richard P. Wildes

Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions, but also contextual regions important for action recognition.

Mohamed Gharbi, Maria Di Mascolo, Christine Verdier

For decades, hospital services have been faced with the challenge of ensuring quality care for patients despite the pressures on staff due to workload overload. Nursing staff are particularly affected by this reality, with a patient/nursing staff ratio so imbalanced that it directly affects the quality of care and the well-being of nurses. This article exami

Gregor Kemper

In 1993, just about a century after the epoch of Classical Invariant Theory and almost 30 years after Mumford's seminal book on Geometric Invariant Theory, Bernd Sturmfels approached the subject from a new, algorithmic perspective in his book on Algorithms in Invariant Theory. This article aims to highlight some of the developments that followed the book. In

Dawid Krasiński, Taras Radul

We demonstrate that the idempotent barycenter map, associated with a t-norm $\ast$, is open if and only if the map of max-$\ast$ convex combination is open. As a corollary, we deduce that the idempotent barycenter map is open for spaces of idempotent measures associated with any t-norm $\ast$. Nevertheless, we illustrate that the characteristics of the idemp

Yingxin Lai, Guoqing Yang Yifan He, Zhiming Luo, Shaozi Li

With diverse presentation forgery methods emerging continually, detecting the authenticity of images has drawn growing attention. Although existing methods have achieved impressive accuracy in training dataset detection, they still perform poorly in the unseen domain and suffer from forgery of irrelevant information such as background and identity, affecting

Shanchuan Lin, Xiao Yang

We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion mod

Jordan C Rozum, Luis M Rocha

Minimum spanning trees and forests are powerful sparsification techniques that remove cycles from weighted graphs to minimize total edge weight while preserving node connectivity. They have applications in computer science, network science, and graph theory. Despite their utility and ubiquity, they have several limitations, including that they are only defin

Shan-Liang Zhang, Hongxi Xing

Within the framework of leading power factorization formalism of nonrelativistic quantum chromodynamics, we calculate the jet fragmentation function for $J/\psi$ production in proton-proton (pp) collisions ranging from $\sqrt{s}=500$ GeV to $13$ TeV. The reasonable agreements between theory and experimental data indicate that $J/\psi$ production within a jet

Samuel Antonio Silva do Rosario, Carlos Aldemir Farias da Silva

The growing interaction between traditional knowledge and formal sciences emerges as a relevant field of research. In this perspective, the aim of this article is to investigate the intersections between Physics and traditional knowledge in the ceramics practices of Vila Cuera, in Bragan\c{c}a, state of Para. Using an ethnographic approach, complemented by q

Federico Alessi, Alessandro Tundo, Marco Mobilio, Oliviero Riganelli

Modern distributed systems are highly dynamic and scalable, requiring monitoring solutions that can adapt to rapid changes. Monitoring systems that rely on external probes can only achieve adaptation through expensive operations such as deployment, undeployment, and reconfiguration. This poster paper introduces ReProbes, a class of adaptive monitoring probes

Haoyuan Li, Chang Xu, Wen Yang, Huai Yu

Cross-View Geo-Localization (CVGL) involves determining the geographical location of a query image by matching it with a corresponding GPS-tagged reference image. Current state-of-the-art methods predominantly rely on training models with labeled paired images, incurring substantial annotation costs and training burdens. In this study, we investigate the ada

Taekoo Oh, Naoto Nagaosa

Spin-orbit coupling stands as a pivotal determinant in the realm of condensed matter physics. In recent, its profound influence on spin dynamics opens up a captivating arena with promising applications. Notably, the topological insulator-ferromagnet heterostructure has been recognized for inducing spin dynamics through applied current, driven by spin-orbit t

Stanislav Musikhin, Valerian Nemchinsky, Yevgeny Raitses

A direct current (DC) arc discharge is a widely used method for large-scale production of metal nanoparticles, core-shell particles, and carbon nanotubes. Here, we explore the growth of iron nanoparticles in a modified DC arc discharge. Iron particles are produced by the evaporation of an anode, made from low-carbon steel. Methane admixture into argon gas se

R. Altmann, M. Deiml

We propose a novel time stepping method for linear poroelasticity by extending a recent iterative decoupling approach to the second-order case. This results in a two-step scheme with an inner iteration and a relaxation step. We prove second-order convergence for a prescribed number of inner iteration steps, only depending on the coupling strength of the elas

Fan Chen

Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, addressing Quality of Service (QoS) degradation caused by renewable energy supply and hardware recycling remains challenging: (1) prior accelera

Youjun Deng, Hongyu Liu, Liyan Zhu

We investigate the electromagnetic field concentration between two nearly-touching inclusions that possess high-contrast electric permittivities in the quasi-static regime. By using layer potential techniques and asymptotic analysis in the low-frequency regime, we derive low-frequency expansions that provide integral representations for the solutions of the

Rodrigo L. S. Silva, Clemens Verhoosel, Erik Quaeghebeur

We consider the problem of estimating a temperature-dependent thermal conductivity model (curve) from temperature measurements. We apply a Bayesian estimation approach that takes into account measurement errors and limited prior information of system properties. The approach intertwines system simulation and Markov chain Monte Carlo (MCMC) sampling. We inves

Yubin Zheng, Peng Tang, Tianjie Ju, Weidong Qiu

Medical image segmentation plays a vital role in clinic disease diagnosis and medical image analysis. However, labeling medical images for segmentation task is tough due to the indispensable domain expertise of radiologists. Furthermore, considering the privacy and sensitivity of medical images, it is impractical to build a centralized segmentation dataset f

Julius Düker, Alexander Rieber

We investigate how to efficiently set up work groups to boost group productivity, individual satisfaction, and learning. Therefore, we conduct a natural field experiment in a compulsory undergraduate course and study differences between self-selected and randomly assigned groups. We find that self-selected groups perform significantly worse on group assignme

Anjun Hu, Jindong Gu, Francesco Pinto, Konstantinos Kamnitsas

Foundation models pre-trained on web-scale vision-language data, such as CLIP, are widely used as cornerstones of powerful machine learning systems. While pre-training offers clear advantages for downstream learning, it also endows downstream models with shared adversarial vulnerabilities that can be easily identified through the open-sourced foundation mode

A. Alaakol, S. S. Agaev, K. Azizi, H. Sundu

Masses and current couplings of the charmonium and bottomonium hybrids $ \overline{c}gc$ and $\overline{b}gb$ with spin-parities $J^{\mathrm{PC} }=0^{++},\ 0^{+-},\ 0^{-+},\ 0^{--}$ and $1^{++},\ 1^{+-},\ 1^{-+},\ 1^{--}$ are calculated using QCD two-point sum rule method. Computations are performed by taking into account gluon condensates up to dimension 12

Cambyse Rouzé, Daniel Stilck França, Álvaro M. Alhambra

The preparation of thermal states of matter is a crucial task in quantum simulation. In this work, we prove that a recently introduced, efficiently implementable dissipative evolution thermalizes to the Gibbs state in time scaling polynomially with system size at high enough temperatures for any Hamiltonian that satisfies a Lieb-Robinson bound, such as local

Jinying Xiao, Ping Li, Zhe Tang, Jie Nie

Pruning before training enables the deployment of neural networks on smart devices. By retaining weights conducive to generalization, pruned networks can be accommodated on resource-constrained smart devices. It is commonly held that the distance on weight norms between the initialized and the fully-trained networks correlates with generalization performance

S. O Adebusola, P. A Owolawi, J. S Ojo, P. S Maswikaneng

Optical Communication in Free Space (FSO) bids more radio bandwidth, operates under a gratis license, and has a lower startup cost as compared to Radio Frequency (RF). Nonetheless, its vulnerability to variations in atmospheric meteorological circumstances is a concern. Ultimately, the purpose of this study is to use Principal Component Analysis (PCA) with A

Simon-Christian Klein

The approach presented in the second installment of this series is extended to multidimensional systems of conservation laws that are approximated via a Discontinuous Galerkin method on unstructured (triangular) grids. Special attention is paid to predicting the entropy dissipation from boundaries. The resulting schemes are free of tunable viscosity paramete

Jinying Xiao, Ping Li, Jie Nie, Zhe Tang

Large-scale Transformer models (TM) have demonstrated outstanding performance across various tasks. However, their considerable parameter size restricts their applicability, particularly on mobile devices. Due to the dynamic and intricate nature of gradients on TM compared to Convolutional Neural Networks, commonly used pruning methods tend to retain weights

Elena Ryumina, Maxim Markitantov, Dmitry Ryumin, Heysem Kaya

This paper presents the results of the SUN team for the Compound Expressions Recognition Challenge of the 6th ABAW Competition. We propose a novel audio-visual method for compound expression recognition. Our method relies on emotion recognition models that fuse modalities at the emotion probability level, while decisions regarding the prediction of compound

Runwei Guan, Liye Jia, Fengyufan Yang, Shanliang Yao

The perception of waterways based on human intent is significant for autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in water environments. Inspired by visual grounding, we introduce WaterVG, the first visual grounding dataset designed for USV-based waterway perception based on human prompts. WaterVG encompasses prompts describing mu

Eric Hannus, Tran Nguyen Le, David Blanco-Mulero, Ville Kyrki

Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot's physical limits. These

Matthijs Meijers, Denis Ruchnewitz, Jan Eberhardt, Malancha Karmakar

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set o

Antonello Calabro, Marco Castellano, Jorge A. Zavala, Laura Pentericci

GHZ2/GLASS-z12 has been recently observed by JWST with both NIRSpec and MIRI spectrographs, making it the most distant galaxy ($z_{spec}=12.34$) with complete spectroscopic coverage from rest-frame UV to optical. It is identified as a strong CIV$_{1549}$ emitter with many detected emission lines (NIV], HeII, OIII], NIII], CIII], [OII], [NeIII], [OIII], and H

Matteo Bortolon, Theodore Tsesmelis, Stuart James, Fabio Poiesi

We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically designed to operate in real-time and eliminates the need for an initial pose guess that is proximate to the sought solution. IFFNeRF utilizes the Metropolis-Hasting algorithm to sa

Daniel Daigle, Gene Freudenburg

Let k^[6] denote a polynomial ring in 6 variables over an algebraically closed field k of characteristic zero and consider the action of SL2(k) on k^[6] induced by the irreducible representation of SL2 of degree 5 (the binary quintic representation). We consider the ring Q = (k^[6])^SL2 of invariant polynomials and show that Aut_k(Q) = u(k), the unit group o

Francesco Azzollini, A. Gordon Emslie, Daniel L. Clarkson, Nicolina Chrysaphi

Radio signals propagating via the solar corona and solar wind are significantly affected by compressive waves, impacting properties of solar bursts as well as sources viewed through the turbulent solar atmosphere. While static fluctuations scatter radio waves elastically, moving, turbulent or oscillating density irregularities act to broaden the frequency of

Manuel Del Piano, Stefan Hohenegger, Francesco Sannino

In a recent work [arXiv:2307.13489 [gr-qc]], we have described spherically symmetric and static quantum black holes as deformations of the classical Schwarzschild metric that depend on the physical distance to the horizon. We have developed a framework that allows to compute the latter in a self-consistent fashion from the deformed geometry, in the vicinity

Seongjae Min, Junseok Yang, Sangjun Lim, Junyong Lee

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise

Maksym Taranukhin, Sahithya Ravi, Gabor Lukacs, Evangelos Milios

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collectio

Shimeng Huang, Jonas Peters, Niklas Pfister

Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean, variance, or the linear regression coefficient. In this work, we consider settings in which a response variable $Y$ and a set of covariates $X=(X^1,\ldots,X^{d+1})$ are observed o

Mingrui Yu, Boyuan Liang, Xiang Zhang, Xinghao Zhu

Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill that humans use to dexterously manipulate DLOs, which requires continuously changing the grasp point by in-hand sliding while holding the DLO to prevent it from falling. Achieving su

Dojun Park, Jiwoo Lee, Hyeyun Jeong, Seohyun Park

Benchmarks play a significant role in the current evaluation of Large Language Models (LLMs), yet they often overlook the models' abilities to capture the nuances of human language, primarily focusing on evaluating embedded knowledge and technical skills. To address this gap, our study evaluates how well LLMs understand context-dependent expressions from a p

J. C. Birget

We define various monoid versions of the R. Thompson group $V$, and prove connections with monoids of acyclic digital circuits. We show that the monoid $M_{2,1}$ (based on partial functions) is not embeddable into Thompson's monoid ${\sf tot}M_{2,1}$, but that ${\sf tot}M_{2,1}$ has a submonoid that maps homomorphically onto $M_{2,1}$. This leads to an effic

Jian He, Jing He

In this article, we prove that if $(\mathcal A ,\mathcal B,\mathcal C)$ is a recollement of extriangulated categories, then $n$-cotorsion pairs in $\mathcal A$ and $\mathcal C$ can induce $n$-cotorsion pairs in $\mathcal B$. Conversely, this holds true under natural assumptions. Besides, we give mild conditions on a pseudo cluster tilting subcategory on the

Kaiji Sekimoto, Muneki Yasuda

Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs) are often used for semi-supervised anomaly detection, where they are trained using only normal data points. In GBRBM-based anomaly detection, normal and anomalous data are classified based on a score that is identical to an energy function of the marginal GBRBM. However, the classification threshold i

Jakub Res, Ivan Homoliak, Martin Perešíni, Aleš Smrčka

AI assistants for coding are on the rise. However one of the reasons developers and companies avoid harnessing their full potential is the questionable security of the generated code. This paper first reviews the current state-of-the-art and identifies areas for improvement on this issue. Then, we propose a systematic approach based on prompt-altering method

Boren Li, Hang Li, Hangxin Liu

Animatronic robots hold the promise of enabling natural human-robot interaction through lifelike facial expressions. However, generating realistic, speech-synchronized robot expressions poses significant challenges due to the complexities of facial biomechanics and the need for responsive motion synthesis. This paper introduces a novel, skinning-centric appr

Sven Zschocke

The time delay of a light signal which propagates in the gravitational field of an isolated body is considered. The body can be of arbitrary but time-independent shape and inner structure and can be in uniform rotational motion, while the center of mass of the body is assumed to be at rest. The gravitational field is given in the post-Newtonian scheme and in

Ragousandirane Radjasandirane, Alexandre G. de Brevern

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting t

Haoqian Wu, Minda Zhao, Zhipeng Hu, Lincheng Li

ost recent popular Role-Playing Games (RPGs) allow players to create in-game characters with hundreds of adjustable parameters, including bone positions and various makeup options. Although text-driven auto-customization systems have been developed to simplify the complex process of adjusting these intricate character parameters, they are limited by their si

Dojun Park, Sebastian Padó

Almost all frameworks for the manual or automatic evaluation of machine translation characterize the quality of an MT output with a single number. An exception is the Multidimensional Quality Metrics (MQM) framework which offers a fine-grained ontology of quality dimensions for scoring (such as style, fluency, accuracy, and terminology). Previous studies hav

Samuel S. Gomez, Lorenzo Rovigatti

We numerically investigate the dynamics and linear rheology of disordered systems made of patchy particles, focussing on the role of valence, temperature and bonding mechanism. We demonstrate that the dynamics is enslaved to bonding, giving rise to an activated behaviour at low temperature. By independently computing the diffusion constant and the viscosity

Anna Kozak, Dominik Kędzierski, Jakub Piwko, Malwina Wojewoda

In many applications, model ensembling proves to be better than a single predictive model. Hence, it is the most common post-processing technique in Automated Machine Learning (AutoML). The most popular frameworks use ensembles at the expense of reducing the interpretability of the final models. In our work, we propose cattleia - an application that decipher

Jasper van der Kolk, Marián Boguñá, M. Ángeles Serrano

The Renormalization Group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their topology is based on the location of nodes in a hidden metric space, is one of the foundational approaches. However, the current methods assume that the geometric coupling is strong,

Philip K. Schwartz

We give a full classification of general affine connections on Galilei manifolds in terms of independently specifiable tensor fields. This generalises the well-known case of (torsional) Galilei connections, i.e. connections compatible with the metric structure of the Galilei manifold. Similarly to the well-known pseudo-Riemannian case, the additional freedom

Antoine Detaille, Katarzyna Mazowiecka

In this note, we study non-uniqueness for minimizing harmonic maps from $B^3$ to $\mathbb{S}^2$. We show that every boundary map can be modified to a boundary map that admits multiple minimizers of the Dirichlet energy by a small $W^{1,p}$-change for $p<2$. This strengthens a remark by the second-named author and Strzelecki. The main novel ingredient is a ho

Jules Flin, Sandro Franceschi

We study a Brownian motion with drift in a wedge of angle $\beta$ which is obliquely reflected on each edge along angles $\varepsilon$ and $\delta$. We assume that the classical parameter $\alpha=\frac{\delta+\varepsilon - \pi}{\beta}$ is greater than $1$ and we focus on transient cases where the process can either be absorbed at the vertex or escape to infi

Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao

Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently becoming critical for further enhancing the accuracy and optimizing storage efficiencies to align with the deployment demands. This research area, particularly in the context of D

Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski, Sofía Calero

The ability to efficiently predict adsorption properties of zeolites can be of large benefit in accelerating the design process of novel materials. The existing configuration space for these materials is wide, while existing molecular simulation methods are computationally expensive. In this work, we propose a model which is 4 to 5 orders of magnitude faster

Bürki Audrey, Vasishth Shravan

With the pandemic, many experimental psychologists and linguists have started to collect data over the internet (hereafter on-line data). The feasibility of such experiments and the sample sizes required to achieve sufficient statistical power in future experiments have to be assessed. This in turn requires information on effect sizes and variability. In a s

Pengzhi Li, Qiang Nie, Ying Chen, Xi Jiang

Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regenerating the entire image; 2) guidance solely by a reference image or text descriptions; and 3) time-consuming fine-tuning, which limits their practical application. In response, we

Alexander I. Bufetov, Juan Marshall-Maldonado, Boris Solomyak

The paper investigates H\"older and log-H\"older regularity of spectral measures for weakly mixing substitutions and the related question of quantitative weak mixing. It is assumed that the substitution is primitive, aperiodic, and its substitution matrix is irreducible over the rationals. In the case when there are no eigenvalues of the substitution matrix

S. V. Mironov, A. S. Mel'nikov, A. I. Buzdin

We suggest a theoretical description of the photogalvanic phenomena arising in superconducting condensates in the field of electromagnetic wave. The ac Hall effect and photon drag are shown to originate from the second-order nonlinear response of superconducting carriers caused by the suppression of their concentration due to the combined influence of the el

Sule Tekkesinoglu, Azra Habibovic, Lars Kunze

Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with the v

Snežana Č. Živković-Zlatanović

The aim of this paper is to introduce and study left and right versions of the class of generalized Drazin-Riesz invertible operators, as well as left and right versions of the class of generalized Drazin-meromorphic invertible operators.

Madhu B. M., Rashmi

Nanocomposites reinforced with hybrid fillers of multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were developed, aimed at improving electrical and morphological properties of the hybrid nanocomposites while reducing the cost of the final product. GNPs and MWCNTs nanofillers have shown improved electrical and morphological properties

Mikkel Bennedsen, Kim Christensen, Peter Christensen

We develop a framework for composite likelihood estimation of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that have been proposed to describe the random log-spot variance of financial asset returns. A simulatio

Daniel Fesalbon, Arvin De La Cruz, Marvin Mallari, Nelson Rodelas

Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained languag

Antonio Agresti, Max Sauerbrey

We consider strictly positive solutions to a class of fourth-order conservative quasilinear SPDEs on the $d$-dimensional torus modeled after the stochastic thin-film equation. We prove local Lipschitz estimates in Bessel potential spaces under minimal assumptions on the parameters and corresponding stochastic maximal $L^p$-regularity estimates for thin-film

José Antonio Carrillo, Shuchen Guo, Pierre-Emmanuel Jabin

We derive a class of space homogeneous Landau-like equations from stochastic interacting particles. Through the use of relative entropy, we obtain quantitative bounds on the distance between the solution of the N-particle Liouville equation and the tensorised solution of the limiting Landau-like equation.

Govind Vashishtha, Sumika Chauhan, Surinder Kumar, Rajesh Kumar

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hy

Zezhong Xu, Yincen Qu, Wen Zhang, Lei Liang

Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim to identify user interests based on historical interactions and recommend suitable items. However, existing works overlook two key challenges: (1) an interest corresponds to a poten

Fintan M. Bolton

Grover's algorithm, orginally conceived as a means of searching an unordered database, can also be used to extract solutions from the result sets generated by quantum computations. The Grover algorithm exploits the concept of an oracle function, which abstracts the process of matching a search item (returning 1 for a match and 0 otherwise), where searching f

Hanzhi Wang, Zhewei Wei, Ji-Rong Wen, Mingji Yang

We revisit the classic local graph exploration algorithm ApproxContributions proposed by Andersen, Borgs, Chayes, Hopcroft, Mirrokni, and Teng (WAW '07, Internet Math. '08) for computing an $\epsilon$-approximation of the PageRank contribution vector for a target node $t$ on a graph with $n$ nodes and $m$ edges. We give a worst-case complexity bound of Appro

Shige Peng, Shuzhen Yang, Wenqing Zhang

The integration and innovation of finance and technology have gradually transformed the financial system into a complex one. Analyses of the causesd of abnormal fluctuations in the financial market to extract early warning indicators revealed that most early warning systems are qualitative and causal. However, these models cannot be used to forecast the risk

Zezhong Xu, Peng Ye, Lei Liang, Huajun Chen

Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of KGs. Existing research has predominantly focused on addressing the issue of missing edges in KGs, thereby neglecting another aspect of incompleteness: the emergence of new entities

D. Yu. Akimov, I. S. Aleksandrov, F. B. Ata Kurbonova, V. A. Belov

RED-100 is a two-phase Xe detector designed and built for the study of coherent elastic neutrino-nucleus scattering CEvNS of reactor antineutrinos. A comprehensive calibration was performed in order to obtain important parameters of the detector during its exposition at the Kalinin Nuclear Power Plant (Tver, Russia). This paper describes the analysis of cali

Nibras Abo Alzahab, Lorenzo Scalise, Marco Baldi

In the quest for optimal EEG-based biometric authentication, this study investigates the pivotal balance for accurate identification without sacrificing performance or adding unnecessary computational complexity. Through a methodical exploration of segment durations, and employing a variety of sophisticated machine learning models, the research seeks to pinp

Prakash Pandey, Sudhir K. Pandey

Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principle approach.

Shen Li, Mouad Fattouhi, Tianxun Huang, Chen Lv

The antisymmetric interlayer exchange interaction, i.e., interlayer Dzyaloshinskii-Moriya interaction (IL-DMI) has attracted significant interest since this long-range chiral spin interaction provides a new dimension for controlling spin textures and dynamics. However, the role of IL-DMI in the field induced and spin-orbit torque (SOT) induced switching of s

Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong

In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. Most existing methods manually build specific CL Strategies (CLS) by human heuristics for certain datasets and tasks. However, manually developing CLS usually requires excessive prior knowledge about the data, and massive experiments to deter

Rupert L. Frank, Thomas Hoffmann-Ostenhof, Ari Laptev, Jan Philip Solovej

Given $0<s<\frac d2$ with $s\leq 1$, we are interested in the large $N$-behavior of the optimal constant $\kappa_N$ in the Hardy inequality $\sum_{n=1}^N (-\Delta_n)^s \geq \kappa_N \sum_{n<m} |X_n-X_m|^{-2s}$, when restricted to antisymmetric functions. We show that $N^{1-\frac{2s}d}\kappa_N$ has a positive, finite limit given by a certain variational probl

Zhe Xu, Xiaoli Yan, Liheng Yang, Zhike Xue

Solar filament eruptions are often characterized by stepwise evolution due to the involvement of multiple mechanisms, such as magnetohydrodynamic instabilities and magnetic reconnection. In this article, we investigated a confined filament eruption with a distinct two-stage evolution by using the imaging and spectroscopic observations from the Interface Regi

Casey Cartwright

The anti-de-Sitter/conformal field theory (AdS/CFT) correspondence is used to provide an estimate of the radius of convergence of the linearized gradient expansion of the hydrodynamic description of $\mathcal{N}=4$ supersymmetric Yang-Mills (SYM) theory minimally coupled to $U(1)$ gauge theory subjected to strong magnetic fields. The results of this work dem

Jie-Ru Hu, Massimo Boninsegni

The low-temperature properties of one and two layers of parahydrogen adsorbed on graphite are investigated theoretically through Quantum Monte Carlo simulations. We adopt a microscopic model that explicitly includes the corrugation of the substrate. We study the phase diagram of a monolayer up to second layer promotion, and the possible occurrence of superfl

Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao

Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these models generate? This work aims to provide an accessible entry point to understanding popular sample-based stat

Patrick Bourg, Benjamin Leather, Marc Casals, Adam Pound

Post-adiabatic models of extreme- and intermediate-mass-ratio inspirals will require calculations of second-order gravitational self-force effects in the spacetime of a spinning, Kerr black hole. We take a step toward such calculations by implementing the recently formulated Teukolsky puncture scheme with Green-Hollands-Zimmerman metric reconstruction [CQG 3

Sifeddine Benahmed, Soulaimane Berkane

This paper addresses the problem of simultaneous estimation of the position, linear velocity and orientation of a rigid body using single bearing measurements. We introduce a Riccati observer-based estimator that fuses measurements from a 3-axis accelerometer, a 3-axis gyroscope, a single body-frame vector observation (e.g., magnetometer), and a single beari

Jonathan J Carter, Pascal Birckigt, Oliver Gerberding, Sina M. Koehlenbeck

Compact, high-precision inertial sensors are needed to isolate many modern physics experiments from disturbances caused by seismic motion. We present a novel inertial sensor whose mechanical oscillator fits on a standard one-inch diameter optic. The oscillators achieve a Quality factor of over 600,000 and a resonance frequency of 50\,Hz, giving them a suspen

Chen Hu, Shirui Lyu, Eojin Rho, Daekyum Kim

Controlling hand exoskeletons to assist individuals with grasping tasks poses a challenge due to the difficulty in understanding user intentions. We propose that most daily grasping tasks during activities of daily living (ADL) can be deduced by analyzing object geometries (simple and complex) from 3D point clouds. The study introduces PointGrasp, a real-tim

Jiarui Wang, Thushara Abhayapala, Jihui Aimee Zhang, Prasanga Samarasinghe

Acoustic velocity vectors are important for human's localization of sound at low frequencies. This paper proposes a sound field reproduction algorithm, which matches the acoustic velocity vectors in a circular listening area. In previous work, acoustic velocity vectors are matched either at sweet spots or on the boundary of the listening area. Methods based

Tom Reichert, Jan Steinheimer, Volodymyr Vovchenko, Christoph Herold

It is shown that the hadronic phase in ultra-relativistic heavy ion collisions can be used to understand the properties of the $f_0(980)$ resonance. In particular it is shown that the centrality dependence of the $f_0(980)/\pi$ and $f_0(980)/\phi$ ratios depends strongly on the $f_0(980)\rightarrow \overline{K}+K$ branching ratio and whether the $f_0(980)$ i

Cho-Ho Chu

We characterise the positive cone of a real C*-algebra geometrically. Given an open cone $\Omega$ in a real Banach space $V$, with closure $\overline \Omega$, we show that $\Omega$ is the interior of the positive cone of a unital real C*-algebra if and only if it is a Finsler symmetric cone with an orientable extension, which is equivalent to the condition t

Andreas Florath

In the realm of formal theorem proving, the Coq proof assistant stands out for its rigorous approach to verifying mathematical assertions and software correctness. Despite the advances in artificial intelligence and machine learning, the specialized nature of Coq syntax and semantics poses unique challenges for Large Language Models (LLMs). Addressing this g

Michal Marvan

We consider general integrable curve nets in Euclidean space as a particular integrable geometry invariant with respect to rigid motions and net-preserving reparameterisations. For the purpose of their description, we first give an overview of the most important second-order invariants and relations among them. As a particular integrable example, we reinterp