December 2023 arXiv papers — page 88

Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer, Maniraman Periyasamy

Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here, we introduce a scheme for joint cutting of two-qubit rotation gates based on a virtual gate-teleportation protocol. By that, we significantly lower the previous upper bounds on the

Antonio Rafael Ramos-Rodriguez, Jose Aurelio Medina-Garrido, Jose Ruiz-Navarro

Purpose: Understanding the formation of entrepreneurial intentions is critical, given that it is the first step in the entrepreneurial process. Although entrepreneurial intention has been extensively studied, little attention has been paid on the intended timing of future entrepreneurial projects. This paper analyses entrepreneurial intentions among final-ye

G. Papadopoulos

We investigate the superalgebra of derivations generated by the fundamental forms on manifolds with reduced structure group. In particular, we point out a relation between the algebra of derivations of heterotic geometries that admit Killing spinors and the commutator algebra of holonomy symmetries in sigma models. We use this to propose a Lie bracket on the

Donatella Iacono, Elena Martinengo

We analyse infinitesimal deformations of morphisms of locally free sheaves on a smooth projective variety $X$ over an algebraically closed field of characteristic zero. In particular, we describe a differential graded Lie algebra controlling the deformation problem. As an application, we study infinitesimal deformations of pairs given by a locally free sheaf

Leon Mlodzian, Zhigang Sun, Hendrik Berkemeyer, Sebastian Monka

Trajectory prediction in traffic scenes involves accurately forecasting the behaviour of surrounding vehicles. To achieve this objective it is crucial to consider contextual information, including the driving path of vehicles, road topology, lane dividers, and traffic rules. Although studies demonstrated the potential of leveraging heterogeneous context for

H. Alharazin

In the framework of chiral effective field theory of delta resonances, nucleons and pions interacting with background gravitational field we calculate the gravitational form factors of the nucleon up to fourth order in the small scale expansion and obtain the long-range behavior of the corresponding contributions to the energy, spin, pressure and shear force

Amitis Shidani, Sattar Vakili

We consider regret minimization in a general collaborative multi-agent multi-armed bandit model, in which each agent faces a finite set of arms and may communicate with other agents through a central controller. The optimal arm for each agent in this model is the arm with the largest expected mixed reward, where the mixed reward of each arm is a weighted ave

Xiaoming Wang, Zhiguo Gong

Robot calligraphy is an emerging exploration of artificial intelligence in the fields of art and education. Traditional calligraphy generation researches mainly focus on methods such as tool-based image processing, generative models, and style transfer. Unlike the English alphabet, the number of Chinese characters is tens of thousands, which leads to difficu

Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan

Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI

Stephen Coughlan, Marco Franciosi, Rita Pardini, Sönke Rollenske

We introduce the notion of generalised Gorenstein spin structure on a curve and we give an explicit description of the associated section ring for curves of genus two with ample canonical bundle, obtaining five different formats.

Jesús Lovón-Melgarejo, Jose G. Moreno, Romaric Besançon, Olivier Ferret

Since Pretrained Language Models (PLMs) are the cornerstone of the most recent Information Retrieval (IR) models, the way they encode semantic knowledge is particularly important. However, little attention has been given to studying the PLMs' capability to capture hierarchical semantic knowledge. Traditionally, evaluating such knowledge encoded in PLMs relie

Jiawei Zhao, Kejiang Chen, Xiaojian Yuan, Yuang Qi

The rapid development of large language models (LLMs) has yielded impressive success in various downstream tasks. However, the vast potential and remarkable capabilities of LLMs also raise new security and privacy concerns if they are exploited for nefarious purposes due to their open-endedness. For example, LLMs may be used to plagiarize or imitate writing,

Akira Kofuji, Robert Peters

Nonlinear responses in crystalline solids are attracting a great deal of attention because of exciting phenomena, such as the bulk photovoltaic effect in noncentrosymmetric crystals and the third harmonic generation related to Higgs modes in superconductors, and their potential applicability to electronic devices. Recently, nonlinear responses have also been

Habib Ammari, Silvio Barandun, Bryn Davies, Erik Orvehed Hiltunen

This paper studies wave localisation in chains of finitely many resonators. There is an extensive theory predicting the existence of localised modes induced by defects in infinitely periodic systems. This work extends these principles to finite-sized systems. We consider finite systems of subwavelength resonators arranged in dimers that have a geometric defe

Jiahe Lan, Jie Wang, Baochen Yan, Zheng Yan

Speech recognition systems driven by DNNs have revolutionized human-computer interaction through voice interfaces, which significantly facilitate our daily lives. However, the growing popularity of these systems also raises special concerns on their security, particularly regarding backdoor attacks. A backdoor attack inserts one or more hidden backdoors into

Cinzia Bisi, Davide Cordella

Following ideas by Beardon, Minda and Baribeau, Rivard, Wegert in the context of the complex Schwarz-Pick Lemma, we use iterated hyperbolic difference quotients to prove a quaternionic multipoint Schwarz-Pick Lemma, in the context of the theory of slice regular functions. As applications, we obtain quaternionic Dieudonn\'e and Goluzin estimates. Finally, an

Alessandro Ilic Mezza, Riccardo Giampiccolo, Alberto Bernardini, Augusto Sarti

In the past, the field of drum source separation faced significant challenges due to limited data availability, hindering the adoption of cutting-edge deep learning methods that have found success in other related audio applications. In this manuscript, we introduce StemGMD, a large-scale audio dataset of isolated single-instrument drum stems. Each audio cli

Lena Verscht, Benjamin Kaminski

We aim at a holistic perspective on program logics, including Hoare and incorrectness logics. To this end, we study different classes of properties arising from the generalization of the aforementioned logics. We compare our results with the properties expressible in the language of Kleene algebra with top and tests.

E. Plachy

Nearly continuous, densely sampled, space-based photometry allows us to recover the finest details in the light variations of stars. The number of such light curves have been increasing rapidly in the last few years thanks to the extended mission of the Kepler space telescope and the launch of the TESS mission. This new era brings us new perspectives in RR L

Eric Wagner, Martin Serror, Klaus Wehrle, Martin Henze

Aggregation of message authentication codes (MACs) is a proven and efficient method to preserve valuable bandwidth in resource-constrained environments: Instead of appending a long authentication tag to each message, the integrity protection of multiple messages is aggregated into a single tag. However, while such aggregation saves bandwidth, a single lost m

Xiyuan Liu, Qingqing Wu, Rui Wang, Jun Wu

With the application of high-frequency communication and extremely large MIMO (XL-MIMO), the near-field effect has become increasingly apparent. The near-field beam design now requires consideration not only of the angle of arrival (AoA) information but also the curvature of arrival (CoA) information. However, due to their mutual coupling, orthogonally decom

Leonid Legashev, Alexander Shukhman, Vadim Badikov

Modern machine learning techniques in the natural language processing domain can be used to automatically generate scripts for goal-oriented dialogue systems. The current article presents a general framework for studying the automatic generation of scripts for goal-oriented dialogue systems. A method for preprocessing dialog data sets in JSON format is descr

T. Nordlander, P. Gruyters, O. Richard, A. J. Korn

Variations in chemical abundances with evolutionary phase have been identified among stars in globular and open clusters with a wide range of metallicities. In the metal-poor clusters, these variations compare well with predictions from stellar structure and evolution models considering the internal diffusive motions of atoms and ions, collectively known as

Akash Kumar, Artem Litvinenko, Nilamani Behera, Ahmad A. Awad

This chapter reviews the state of the art in mutually synchronized spin-torque and spin Hall nano-oscillator (STNO and SHNO) arrays. After briefly introducing the underlying physics, we discuss different nano-oscillator implementations and their functional properties with respect to frequency range, output power, phase noise, and modulation rates. We then in

Shan Tai Chan, Yuan Yuan

We study local holomorphic maps between K\"ahler manifolds preserving $(p,p)$-forms. In this direction, we prove that any such local holomorphic map $F$ is a holomorphic isometry up to a scalar constant provided that $p$ is strictly less than the complex dimension of the domain of $F$. We then study local holomorphic maps between finite dimensional complex s

Umberto Natale, Michael Moser

We present a comprehensive analysis of the implications of artificial latency in the Proposer-Builder Separation framework on the Ethereum network. Focusing on the MEV-Boost auction system, we analyze how strategic latency manipulation affects Maximum Extractable Value yields and network integrity. Our findings reveal both increased profitability for node op

Yuhan Li, Hongyu Liu, Catharine W. K. Lo

In this paper, we consider the inverse problem of determining the coefficients of interaction terms within some Lotka-Volterra models, with support from boundary observation of its non-negative solutions. In the physical background, the solutions to the predator-prey model stand for the population densities for predator and prey and are non-negative, which i

Xin Guo, Jiangwei Lao, Bo Dang, Yingying Zhang

Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless, these works primarily focus on a single modality without temporal and geo-context modeling, hampering their capabilities for diverse tasks. In this study, we present SkySense, a generic billion-scale model, pre-trained

Ira W. Herbst, Jesper Møller, Anne Marie Svane

Let $X=\sum_{k=1}^\infty X_k \beta^{-k}$ be the base-$\beta$ expansion of a continuous random variable $X$ on the unit interval where $\beta$ is the golden ratio. We study the asymptotic distribution and convergence rate of the scaled remainder $\sum_{k=n+1}^\infty X_k \beta^{n-k}$ when $n$ tends to infinity.

Xiaohui Zhang, Jiangyan Yi, Chenglong Wang, Chuyuan Zhang

The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms. Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack type

Eric Wagner, David Heye, Martin Serror, Ike Kunze

Industrial control systems increasingly rely on middlebox functionality such as intrusion detection or in-network processing. However, traditional end-to-end security protocols interfere with the necessary access to in-flight data. While recent work on middlebox-aware end-to-end security protocols for the traditional Internet promises to address the dilemma

Yuxing Zhong, Akio K. Inoue, Yuma Sugahara, Kana Morokuma-Matsui

Radio-loud active galactic nuclei (RLAGNs) are a unique AGN population and were thought to be preferentially associated with supermassive black holes (SMBHs) at low accretion rates. They could impact the host galaxy evolution by expelling cold gas through the jet-mode feedback. In this work, we studied CO(6-5) line emission in a high-redshift radio galaxy, M

Nikolaos Ioannis Bountos, Arthur Ouaknine, Ioannis Papoutsis, David Rolnick

Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change. Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data

C. V. Nithish Kumar, L. M. Robledo, I. Vidana

We study the structure of single $\Lambda$-hypernuclei using the Hartree--Fock--Bogoliubov method. Finite range Gogny-type forces are used to describe the nucleon-nucleon and $\Lambda$-nucleon interactions. Three different $\Lambda$-nucleon Gogny forces are built. The unknown parameters of these forces are obtained by fitting the experimental binding energie

Martin Liebetrau, Yvonne Dorenkamp, Oliver Bünermann, Jörg Behler

Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields -- from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the $\

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan, Nikolaos Melissinos

In the Multiagent Path Finding problem (MAPF for short), we focus on efficiently finding non-colliding paths for a set of $k$ agents on a given graph $G$, where each agent seeks a path from its source vertex to a target. An important measure of the quality of the solution is the length of the proposed schedule $\ell$, that is, the length of a longest path (i

Geoffrey Frost, Emily Morris, Joshua Jansen van Vüren, Thomas Niesler

Annotating a multilingual code-switched corpus is a painstaking process requiring specialist linguistic expertise. This is partly due to the large number of language combinations that may appear within and across utterances, which might require several annotators with different linguistic expertise to consider an utterance sequentially. This is time-consumin

Carolina Arata

This contribution discusses the measurements of direct photons in pp and Pb--Pb collisions from the LHC Run 2, as recorded by the ALICE experiment. Specifically, we focus on the isolated photons results obtained at $\sqrt{s_{\rm NN}}$ = 5.02 TeV. The isolated photons $\gamma^{\rm iso}$ spectra in pp and Pb--Pb collisions are presented together with the nucle

Yuqing Wang, Guoding Liu, Zhenhuan Liu, Yifan Tang

Accurate and robust estimation of quantum process properties is crucial for quantum information processing and quantum many-body physics. Combining classical shadow tomography and randomized benchmarking, Helsen et al. introduced a method to estimate the linear properties of quantum processes. In this work, we focus on the estimation protocols of nonlinear p

Shuntaro Aoki, Toshifumi Noumi, Fumiya Sano, Masahide Yamaguchi

Massive fields can imprint unique oscillatory features on primordial correlation functions or inflationary correlators, which is dubbed the cosmological collider signal. In this work, we analytically investigate the effects of a time-dependent mass of a scalar field on inflationary correlators, extending previous numerical studies and implementing techniques

Jiajun Zhang, Yuxiang Zhang, Hongwen Zhang, Xiao Zhou

Accurately modeling detailed interactions between human/hand and object is an appealing yet challenging task. Current multi-view capture systems are only capable of reconstructing multiple subjects into a single, unified mesh, which fails to model the states of each instance individually during interactions. To address this, previous methods use template-bas

Chaitra Chooda Chalavadi, V. Venkatesha, N. S. Kavya, S. V. Divya Rashmi

This manuscript investigates wormhole solutions within the framework of extended symmetric teleparallel gravity, incorporating non-commutative geometry, and conformal symmetries. To achieve this, we examine the linear wormhole model with anisotropic fluid under Gaussian and Lorentzian distributions. The primary objective is to derive wormhole solutions while

Yao Zhao, Haipeng Zhang, Shiwei Lyu, Ruiying Jiang

Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe outcomes of a recipient in treatment (e.g., receiving a certain promotion) and control (e.g., without promotion) groups simultaneously (i.e., counter-factual), uplift models are main

Linda Albanese, Andrea Alessandrelli, Alessia Annibale, Adriano Barra

Statistical mechanics of spin glasses is one of the main strands toward a comprehension of information processing by neural networks and learning machines. Tackling this approach, at the fairly standard replica symmetric level of description, recently Hebbian attractor networks with multi-node interactions (often called Dense Associative Memories) have been

Motoaki Hirayama, Takuya Nomoto, Ryotaro Arita

The chiral Majorana fermion is an exotic particle that is its own antiparticle. It can arise in a one-dimensional edge of topological materials, and especially that in a topological superconductor can be exploited in non-Abelian quantum computation. While the chiral Majorana mode (CMM) remains elusive, a promising situation is realized when superconductivity

Mahesh Datta Sai Ponnuru, Likhitha Amasala, Tanu Sree Bhimavarapu, Guna Chaitanya Garikipati

As the number and complexity of malware attacks continue to increase, there is an urgent need for effective malware detection systems. While deep learning models are effective at detecting malware, they are vulnerable to adversarial attacks. Attacks like this can create malicious files that are resistant to detection, creating a significant cybersecurity ris

Yuanhao Mao, Jipeng Xu, Shiyu Guan, Hongteng Ji

He-Ne lasers have been one of the most widely employed optoelectronic elements, playing irreplaceable roles in various applications, including optical detections, spectroscopy, interferometry, laser processing, and so on. For broad applications that require single-mode operations, the gain linewidth needs to be constrained, which conventionally can be obtain

Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim, Gaël Varoquaux

There are increasingly efficient data processing pipelines that work on vectors of numbers, for instance most machine learning models, or vector databases for fast similarity search. These require converting the data to numbers. While this conversion is easy for simple numerical and categorical entries, databases are strife with text entries, such as names o

A. Godichon-Baggioni, D. Nguyen, M-N Tran

This paper introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our approach is the avoidance of analytically computing the Fisher information matrix and its explicit inversion. Instead, we introduce an iterative procedure for gen

S. J. Zhao, L. Tao, P. P. Li, R. Soria

The black hole X-ray binary source 4U 1543--47 experienced a super-Eddington outburst in 2021, reaching a peak flux of up to $\sim1.96\times10^{-7}\rm erg\ \rm cm^{-2}\ \rm s^{-1}$ ($\sim 8.2$ Crab) in the 2--10\,keV band. Soon after the outburst began, it rapidly transitioned into the soft state. Our goal is to understand how the accretion disk structure de

Michael Baake, Anton Gorodetski, Jan Mazáč

The aim of this note is to show the existence of a large family of Cantorvals arising in the projection description of primitive two-letter substitutions. This provides a common and naturally occurring class of Cantorvals.

Björn Engelmann, Timo Breuer, Jana Isabelle Friese, Philipp Schaer

Simulating user interactions enables a more user-oriented evaluation of information retrieval (IR) systems. While user simulations are cost-efficient and reproducible, many approaches often lack fidelity regarding real user behavior. Most notably, current user models neglect the user's context, which is the primary driver of perceived relevance and the inter

Renxiang Guan, Zihao Li, Xianju Li, Chang Tang

Hyperspectral image (HSI) clustering is gaining considerable attention owing to recent methods that overcome the inefficiency and misleading results from the absence of supervised information. Contrastive learning methods excel at existing pixel level and super pixel level HSI clustering tasks. The pixel-level contrastive learning method can effectively impr

Jean-Marie Kai

This paper presents a flight control design for compound Vertical Takeoff and Landing (VTOL) vehicles. With their multitude of degrees of controllability as well as the significant variations in their flight characteristics, VTOL vehicles present challenges when it comes to designing their flight control system, especially for the transition phase where the

Yuming Chen, Yifang Xu, Yiran Yao, Sihui Wang

The mystery behind the bird nest's construction is not well understood. Our study focuses on the stability of a self-supporting nest-like structure. Firstly, we derived a stable/unstable phase boundary for the structure at the fixed coefficient of friction with varying geometrical parameters through force analysis. Structures with a lower height and greater

Luca Beber, Edoardo Lamon, Luigi Palopoli, Luca Fambri

Medical applications of robots are increasingly popular to objectivise and speed up the execution of several types of diagnostic and therapeutic interventions. Particularly important is a class of diagnostic activities that require physical contact between the robotic tool and the human body, such as palpation examinations and ultrasound scans. The practical

Qin Guo, Tianwei Lin

Recently, diffusion-based methods, like InstructPix2Pix (IP2P), have achieved effective instruction-based image editing, requiring only natural language instructions from the user. However, these methods often inadvertently alter unintended areas and struggle with multi-instruction editing, resulting in compromised outcomes. To address these issues, we intro

Chenyang Yu, Xuehu Liu, Yingquan Wang, Pingping Zhang

Large-scale language-image pre-trained models (e.g., CLIP) have shown superior performances on many cross-modal retrieval tasks. However, the problem of transferring the knowledge learned from such models to video-based person re-identification (ReID) has barely been explored. In addition, there is a lack of decent text descriptions in current ReID benchmark

Ritvik Aryan Kalra, Pratham Gupta, Ben Varghese, Nimmi Rangaswamy

We study bias and discrimination in the context of Bumble, an online dating platform in India. Drawing on research in AI fairness and inclusion studies we analyze algorithmic bias and their propensity to reproduce bias. We conducted an experiment to identify and address the presence of bias in the matching algorithms Bumble pushes to its users in the form of

Young Joo Han, Ha-Jin Yu

Modeling and synthesizing real sRGB noise is crucial for various low-level vision tasks, such as building datasets for training image denoising systems. The distribution of real sRGB noise is highly complex and affected by a multitude of factors, making its accurate modeling extremely challenging. Therefore, recent studies have proposed methods that employ d

Xiaoxu Xu, Yitian Yuan, Qiudan Zhang, Wenhui Wu

Learning to ground natural language queries to target objects or regions in 3D point clouds is quite essential for 3D scene understanding. Nevertheless, existing 3D visual grounding approaches require a substantial number of bounding box annotations for text queries, which is time-consuming and labor-intensive to obtain. In this paper, we propose 3D-VLA, a w

Kirill M. Semenov-Tian-Shansky, Paweł Sznajder

We revisit the application of the Froissart-Gribov (FG) projections in the analysis of amplitudes for the Deeply Virtual Compton Scattering (DVCS), providing essential information on generalized parton distributions (GPDs). The pivotal role of these projections in a systematic description of a hadron's response to the string-like QCD probes characterised by

Sergio Kazatzidis, Siamak Mehrkanoon

Self-supervised learning addresses the challenge encountered by many supervised methods, i.e. the requirement of large amounts of annotated data. This challenge is particularly pronounced in fields such as the electroencephalography (EEG) research domain. Self-supervised learning operates instead by utilizing pseudo-labels, which are generated by pretext tas

Alexey L. Smirnov

In this study, we consider the time-symmetric initial data problem for GR minimally coupled with a phantom scalar field and a Maxwell field. The main focus is on initial data sets describing two interacting mouths of the same traversable wormhole. These data sets are similar in many respects to the Misner initial data with two black holes.

Chenxi Bao, Di Zhou, Min Sheng, Yan Shi

Since the next-generation satellite network consisting of various service function domains, such as communication, observation, navigation, etc., is moving towards large-scale, using single-domain resources is difficult to provide satisfied and timely service guarantees for the rapidly increasing mission demands of each domain. Breaking the barriers of indep

Yang Xiang, Jingguang Tian, Xinhui Hu, Xinkang Xu

Generally, the performance of deep neural networks (DNNs) heavily depends on the quality of data representation learning. Our preliminary work has emphasized the significance of deep representation learning (DRL) in the context of speech enhancement (SE) applications. Specifically, our initial SE algorithm employed a gated recurrent unit variational autoenco

Maitreyi Jayaseelan, Joseph D. Murphree, Justin T. Schultz, Janne Ruostekoski

Atom optics demonstrates optical phenomena with coherent matter waves, providing a foundational connection between light and matter. Significant advances in optics have followed the realisation of structured light fields hosting complex singularities and topologically non-trivial characteristics. However, analogous studies are still in their infancy in the f

Yige Chen, Teng Hu, Yizhe Tang, Siyuan Chen

With the help of Score Distillation Sampling (SDS) and the rapid development of neural 3D representations, some methods have been proposed to perform 3D editing such as adding additional geometries, or overwriting textures. However, generalized 3D non-rigid editing task, which requires changing both the structure (posture or composition) and appearance (text

Marko Erceg, Sandeep Kumar Soni

The theory of abstract Friedrichs operators was introduced some fifteen years ago with the aim of providing a more comprehensive framework for the study of positive symmetric systems of first-order partial differential equations, nowadays better known as (classical) Friedrichs systems. Since then, the theory has not only been frequently applied in numerical

Pinhong Long, Huili Han, Halit Orhan, Huo Tang

Our objective is to usher and investigate the subclass$\widetilde{\mathcal{S^{*}_{\sum}}}^{\eta}_{q}(\mu,\lambda;\phi)$ of the function class $\sum$ of analytic and bi-univalent functions related with the symmetric $q$-derivative operator and the generalized Bernardi integral operator. On the one hand, without the generalized Bernardi integral operator we es

Veljko Askovic, Emmanuel Trélat, Hasnaa Zidani

Considering a general nonlinear dissipative finite dimensional optimal control problem in fixed time horizon T , we establish a two-term asymptotic expansion of the value function as $T\rightarrow+\infty$. The dominating term is T times the optimal value obtained from the optimal static problem within the classical turnpike theory. The second term, of order

K. Jiang, T. W. Huang, R. Li, C. T. Zhou

Branched flow is a universal phenomenon in which treebranch-like filaments form through traveling waves or particle flows in irregular mediums. Branched flow of high-current relativistic electron beams (REBs) has been recently discovered [Phys. Rev. Lett. \textbf{130}, 185001 (2023)]. It exhibits unique features, including remarkably high beam density at pre

Emeric Bouin, Jérôme Coville, Xi Zhang

This paper focuses on propagation phenomena in reaction-diffusion equations with a weaklymonostable nonlinearity. The reaction term can be seen as an intermediate between the classicallogistic one (or Fisher-KPP) and the standard weak Allee effect one. We investigate the effect ofthe decay rate of the initial data on the propagation rate. When the right tail

Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from

Yuhao Wang, Xuehu Liu, Pingping Zhang, Hu Lu

Multi-spectral object Re-identification (ReID) aims to retrieve specific objects by leveraging complementary information from different image spectra. It delivers great advantages over traditional single-spectral ReID in complex visual environment. However, the significant distribution gap among different image spectra poses great challenges for effective mu

Rachel Kim, Veniamin Veselovsky, Ashton Anderson

Societal change is often driven by shifts in public opinion. As citizens evolve in their norms, beliefs, and values, public policies change too. While traditional opinion polling and surveys can outline the broad strokes of whether public opinion on a particular topic is changing, they usually cannot capture the full multi-dimensional richness and diversity

Heli Järvenpää, Patricia Lago, Justus Bogner, Grace Lewis

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associated with designing environmentally friendly ML-enabled systems. While Green AI research, i.e., research that tries to minimize the energy footprint of AI, is receiving increasing att

Guo-Ye Yang, George Kiyohiro Nakayama, Zi-Kai Xiao, Tai-Jiang Mu

Great progress has been made in learning-based object detection methods in the last decade. Two-stage detectors often have higher detection accuracy than one-stage detectors, due to the use of region of interest (RoI) feature extractors which extract transformation-invariant RoI features for different RoI proposals, making refinement of bounding boxes and pr

Senmao Li, Taihang Hu, Joost van de Weijer, Fahad Shahbaz Khan

One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation methods. However, these methods require considerable computational resources. In this paper, we take another approach to diffusion model acceleration. We conduct a comprehensive study o

Élisabeth Gassiat, Sylvain Le Corff

In this paper, we consider variational autoencoders (VAE) for general state space models. We consider a backward factorization of the variational distributions to analyze the excess risk associated with VAE. Such backward factorizations were recently proposed to perform online variational learning and to obtain upper bounds on the variational estimation erro

Harris Papadopoulos, Haris Haralambous

This paper proposes an extension to conventional regression Neural Networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without a

Benjamin Melinand

We perform the so-called rigid lid limit on different shallow water models such as the abcd Bousssinesq systems or the Green-Naghdi equations. To do so we consider an appropriate nondimensionalization of these models where two small parameters are involved: the shallowness parameter $\mu$ and a parameter $\epsilon$ which can be interpreted as a Froude number

Rahul Biswas, SuryaNarayana Sripada, Somabha Mukherjee

Inferring causation from time series data is of scientific interest in different disciplines, particularly in neural connectomics. While different approaches exist in the literature with parametric modeling assumptions, we focus on a non-parametric model for time series satisfying a Markovian structural causal model with stationary distribution and without c

June-Woo Kim, Sangmin Bae, Won-Yang Cho, Byungjo Lee

Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a signif

Youhua Li, Hanwen Du, Yongxin Ni, Pengpeng Zhao

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold-start issue. In this paper, we unleash the boundarie

Xin Jin, Jonathan Larson, Weiwei Yang, Zhiqiang Lin

Binary code summarization, while invaluable for understanding code semantics, is challenging due to its labor-intensive nature. This study delves into the potential of large language models (LLMs) for binary code comprehension. To this end, we present BinSum, a comprehensive benchmark and dataset of over 557K binary functions and introduce a novel method for

Rémi Zamansky, Florian Le Roy de Bonneville, Frederic Risso

We performed numerical simulations of a homogeneous swarm of bubbles rising at large Reynolds number, Re = 760 with volume fractions ranging from 1% to 10%. We consider a simplified model in which the interfaces are not resolved, but which allows us to simulate flows with a large number of bubbles and to emphasize the interactions between bubble wakes. The l

Anusha A. S., Pradeep Kumar G., A. G. Ramakrishnan

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain during consciously controlled breathing at 2 cycles per minute (cpm), and breath-hold have been investigated and reported h

Bowen Tao, Lan Li, Xin-Chun Li, De-Chuan Zhan

Due to the advantages of leveraging unlabeled data and learning meaningful representations, semi-supervised learning and contrastive learning have been progressively combined to achieve better performances in popular applications with few labeled data and abundant unlabeled data. One common manner is assigning pseudo-labels to unlabeled samples and selecting

Baran Hashemi, Claudius Krause

In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle physics detectors are low-level objects (such as energy depositions or tracks) encoding the physics of collisions (the final state particles of hard scattering interactions). The com

Xilu Zhu

We consider general semilinear, multispeed Klein-Gordon systems in space dimension two with some non-degeneracy conditions. We prove that with small initial data such solutions are always global and scatter to a linear solution. This result partly extends the previous result obtained by Deng, who completely proved the 3D quasilinear case. To prove our result

Shizhan Liu, Zhengkai Jiang, Yuxi Li, Jinlong Peng

Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation. However, existing works usually ignore the correlation between selected samples and its local context in feature space, which leads to inferior usage of annotation budgets. In this work, we revisit the th

I. Potravnov, T. Ryabchikova, N. Piskunov, Y. Pakhomov

We present the results of the spectroscopic study of a chemically peculiar star HD 152564. Using medium-resolution ($R=37000$) observations obtained with the HRS spectrograph mounted on the South African Large Telescope we determined atmospheric parameters T$_{eff}$=11950$\pm200$ K and log g=3.6$\pm0.2$ dex. Abundance analysis revealed mild deficiency of the

H. Katsurayama, R. Wada, K. Moriyama, K. Tomita

To elucidate the role of the Hall effect in magnetohydrodynamic (MHD) aerobraking in rarefied flows,we measured the radial distributions of electron temperature and density in front of a magnetized model in a rarefied argon arcjet wind tunnel using the laser Thomson scattering method. We also developed a water-cooled magnetized model to prevent thermal demag

Xiaozhou Li

We investigate two efficient time discretizations for the post-processing technique of discontinuous Galerkin (DG) methods to solve hyperbolic conservation laws. The post-processing technique, which is applied at the final time of the DG method, can enhance the accuracy of the original DG solution (spatial superconvergence). One main difficulty of the post-p

Weicong Qin, Zelin Cao, Weijie Yu, Zihua Si

Legal document retrieval and judgment prediction are crucial tasks in intelligent legal systems. In practice, determining whether two documents share the same judgments is essential for establishing their relevance in legal retrieval. However, existing legal retrieval studies either ignore the vital role of judgment prediction or rely on implicit training ob

Jie Chen, Mouyuan Sun, Zhi-Xiang Zhang

Continuum reverberation mapping with high-cadence, long-term UV/optical monitoring of Active Galactic Nuclei (AGNs) enables us to resolve the AGN central engine sizes on different timescales. The frequency-resolved time lags of NGC 5548 (the target for the AGN STORM I campaign) are inconsistent with the X-ray reprocessing of the classical Shakura $\&$ Sunyae

Shuanghao Bai, Wanqi Zhou, Zhirong Luan, Donglin Wang

Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, s

Eunbin Seo, Gwanjun Shin, Eunho Lee

This paper proposes a specialized autonomous driving system that takes into account the unique constraints and characteristics of automotive systems, aiming for innovative advancements in autonomous driving technology. The proposed system systematically analyzes the intricate data flow in autonomous driving and provides functionality to dynamically adjust va

Mourad Sini, Haibing Wang, Qingyun Yao

We analyze the propagation of an incident electromagnetic wave in a purely-time modulated medium. Precisely, we assume that the permeability is unchanged while the permittivity has a multiple-step profile in time and uniformly constant in space. For this, we use time-dispersive Lorenz's model with time-dependent plasma frequency with highly concentrated valu