March 2024 arXiv papers — page 173

Jiaxin Li, Guangjun Zhu, Shiya Duan

This paper gives exact formulas for the regularity of edge ideals of edge-weighted integrally closed trees. In addition, we provide some linear upper bounds on the regularity of powers of such ideals.

Zi-Ting Chou, Sheng-Yu Huang, I-Jieh Liu, Yu-Chiang Frank Wang

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view images and the associated semantic maps ca

Eva Giboulot, Teddy Furon

Watermarking is a technical means to dissuade malfeasant usage of Large Language Models. This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM. Its new design leaves the LLM untouched (no modification of the weights, logits, temperature, or sa

Johannes Hirth, Tom Hanika

Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic mode

Mohammad Ali Labbaf Khaniki, Mohammad Manthouri

This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extract significant features from raw cryptoc

ZhenZhou Wang

The visual sensing system is one of the most important parts of the welding robots to realize intelligent and autonomous welding. The active visual sensing methods have been widely adopted in robotic welding because of their higher accuracies compared to the passive visual sensing methods. In this paper, we give a comprehensive review of the active visual se

Zhong Jiandong, Han Fei, Du Zongliang, Guo Xu

Peridynamics (PD), as a nonlocal theory, is well-suited for solving problems with discontinuities, such as cracks. However, the nonlocal effect of peridynamics makes it computationally expensive for dynamic fracture problems in large-scale engineering applications. As an alternative, this study proposes a multi-time-step (MTS) coupling model of PD and classi

Guadalupe Garcia Arellano, Joao Carlos de Aquino Carvalho, Hippolyte Mouhanna, Esther Butery

Miniaturizing and integrating atomic vapor cells is widely investigated for the purposes of fundamental measurements and technological applications such as quantum sensing. Extending such platforms to the realm of molecular physics is a fascinating prospect that paves the way for compact frequency metrology as well as for exploring light-matter interactions

Alon Nishry, Oren Yakir

The two-dimensional one-component plasma (OCP) is a model of electrically charged particles which are embedded in a uniform background of the opposite charge, and interact through a logarithmic potential. More than 30 years ago, Jancovici, Lebowitz and Manificat discovered an asymptotic law for probabilities of large charge fluctuations in the OCP. We prove

Maarten Vlaswinkel, Frank Willems

Cylinder pressure-based control is a key enabler for advanced pre-mixed combustion concepts. Besides guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion measures (e.g., Gross Ind

Jerónimo Cortez, Guillermo A. Mena Marugán, Alvaro Torres-Caballeros, José M. Velhinho

We address the issue of inequivalent Fock representations in Quantum Field Theory in a curved homogenous and anisotropic background, namely Kantowski-Sachs spacetime. A family of unitarily equivalent Fock representations that are invariant under the spatial isometries and implement a unitary dynamics can be achieved by means of a field redefinition that cons

Li Wang, Lei Sang, Quangui Zhang, Qiang Wu

Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item represen

Donglin Xia, Xiao Wang, Nian Liu, Chuan Shi

Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure information. However, it is widely acknowledged that the test graph structure may differ from the training graph structure, resulting in a structure shift. In this paper, we experime

Romain Holzmann, Volker Koch, Anar Rustamov, Joachim Stroth

We generalize and extend the recently proposed method to account for contributions of system size (or volume/participant) fluctuations to the experimentally measured moments of particle multiplicity distributions. We find that in the general case there are additional biases which are not directly accessible to experiment. These biases are, however, parametri

W. Benedikt Schmal

With the widespread dissemination of the internet, academia envisioned free availability and rapid dissemination of new knowledge. However, most researchers continued publishing in established journals instead of switching to fully open-access alternatives. That preserved the market power of the large commercial publishing houses owning thousands of journals

Nicholas Mitchell, David Chapman, Christopher McDevitt, Martin Read

A reduced kinetic method (RKM) with a first-principle collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigate non-local ion heat transport in multi-species plasmas. The RKM successfully reproduces local results for multi-species ion systems and the important features expected to arise du

Yoshia Abe, Tatsuya Daikoku, Yasuo Kuniyoshi

Recently, it has been recognized that large language models demonstrate high performance on various intellectual tasks. However, few studies have investigated alignment with humans in behaviors that involve sensibility, such as aesthetic evaluation. This study investigates the performance of GPT-4 with Vision, a state-of-the-art language model that can handl

Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Sediola Ruko

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public repositories. These models are often used directly or integrated in product pipelines with no particular pr

Tanveer Khan, Mindaugas Budzys, Khoa Nguyen, Antonis Michalas

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial computing power and are only as powerful as the data utilized. Due to high computational cost of ML methods, data scientists frequently use Machine Learning-as-a-Service (MLaaS) to out

Igor V. Kolokolov, Vladimir V. Lebedev, Mariya M. Tumakova

We examine fluctuations of vorticity inside the coherent vortex, appearing as a consequence of the inverse energy cascade in two-dimensional turbulence. Temporal and spacial correlations can be characterized by the pair correlation function. The interaction between the fluctuations leads to non-zero value of the third moment of vorticity. We examine the pair

Alessandro Pegoraro, Carlotta Segna, Kavita Kumari, Ahmad-Reza Sadeghi

Deep Learning (DL) models have become crucial in digital transformation, thus raising concerns about their intellectual property rights. Different watermarking techniques have been developed to protect Deep Neural Networks (DNNs) from IP infringement, creating a competitive field for DNN watermarking and removal methods. The predominant watermarking schemes

Masahiro Kato, Akihiro Oga, Wataru Komatsubara, Ryo Inokuchi

This study designs an adaptive experiment for efficiently estimating average treatment effects (ATEs). In each round of our adaptive experiment, an experimenter sequentially samples an experimental unit, assigns a treatment, and observes the corresponding outcome immediately. At the end of the experiment, the experimenter estimates an ATE using the gathered

Atsuto Seko, Atsushi Togo

Estimating force constants for crystal structures is crucial for calculating various phonon-related properties. However, this task becomes particularly challenging when dealing with a large number of atoms or when third- and higher-order force constants are required. In this study, we propose an efficient approach that involves constructing a complete orthon

Sourav Chowdhury, Apratim Ghosal, Suparna Roychowhury, Indranath Chaudhuri

Cardiovascular diseases are the leading cause of death and disability in the world and thus their detection is extremely important as early as possible so that it can be prognosed and managed appropriately. Hence, electrophysiological models dealing with cardiac conduction are critically important in the field of interdisciplinary sciences. The primary aim o

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

The $\Lambda^0_b \to D^+ D^- \Lambda$ decay is observed for the first time using proton-proton collision data collected by the LHCb experiment at a center-of-mass energy of $13 \mathrm{TeV}$, corresponding to an integrated luminosity of $5.3 \mathrm{fb}^{-1}$. Using the $B^0 \to D^+ D^- K_{\mathrm{S}}^0$ decay as a reference channel, the product of the relat

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, Yuusuke Nakano

The Vehicle Routing Problem (VRP) is a widely studied combinatorial optimization problem and has been applied to various practical problems. While the explainability for VRP is significant for improving the reliability and interactivity in practical VRP applications, it remains unexplored. In this paper, we propose RouteExplainer, a post-hoc explanation fram

Aranya Bhattacharya, Pingal Pratyush Nath, Himanshu Sahu

Recently, the propagation of information through quantum many-body systems, developed to study quantum chaos, have found many application from black holes to disordered spin systems. Among other quantitative tools, Krylov complexity has been explored as a diagnostic tool for information scrambling in quantum many-body systems. We introduce a universal limit

Nobel J. William, Ali Krayani, Lucio Marcenaro, Carlo Regazzoni

The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functionality at the Base station (BS). In the learning phase, multi-modal data signals contrived by the vehicles' integrated and sensing module are imbued into data-driven Generalized D

Ali Krayani, Khalid Khan, Lucio Marcenaro, Mario Marchese

This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the wor

Séamus Lankford, Haithem Afli, Andy Way

adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is built upon the widely-adopted OpenNMT ecosystem, and is particularly useful for new entrants to the field, as it simplifies the setup of the development environment and creation o

Sergio Rubio-Martín, María Teresa García-Ordás, Martín Bayón-Gutiérrez, Natalia Prieto-Fernández

Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision

Nikolay Pogodaev, Francesco Rossi

We consider a control system driven by a nonlocal continuity equation. Admissible controls are Lipschitz vector fields acting inside a fixed open set. We demonstrate that small perturbations of the initial measure, traced along Wasserstein geodesics, may be neutralized by admissible controls. More specifically, initial perturbations of order $\varepsilon$ ca

Yaozu Wu, Jiale Yuan, Chuanyu Zhang, Zitian Zhu

Quantum speed limits (QSLs) impose fundamental constraints on the evolution speed of quantum systems. Traditionally, the Mandelstam-Tamm (MT) and Margolus-Levitin (ML) bounds have been widely employed, relying on the standard deviation and mean of energy distribution to define the QSLs. However, these universal bounds only offer loose restrictions on the qua

Li Wang, Min Xu, Quangui Zhang, Yunxiao Shi

Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this problem is crucial, and existing methods often rely on techniques such as weight adjustment or leveraging unbiased data to eliminate this bias. However, we argue that not all biase

Yuanqing Gu, Yan Chen

Deployable polyhedrons can transform between Platonic and Archimedean polyhedrons to meet the demands of various engineering applications. However, the existing design solutions are often with multiple degrees of freedom and complicated mechanism links and joints, which greatly limited their potential in practice. Combining the fundamentals of solid geometry

Jin Li, Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of

Séamus Lankford, Haithem Afli, Órla Ní Loinsigh, Andy Way

Machine Translation is a mature technology for many high-resource language pairs. However in the context of low-resource languages, there is a paucity of parallel data datasets available for developing translation models. Furthermore, the development of datasets for low-resource languages often focuses on simply creating the largest possible dataset for gene

Kouichi Hirotani, Hsien Shang, Ruben Krasnopolsky, Kenichi Nishikawa

By general relativistic magnetohydrodynamic simulations, it is suggested that the rotational energy of a rapidly rotating black hole (BH) is preferentially extracted along the magnetic field lines threading the event horizon in the middle and lower latitudes. Applying this angle-dependent Poynting flux to the jet downstream, we demonstrate that the jets exhi

Shuhao Zhang, Jan Swevers

This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manag

Johann S. Brauchart, Peter J. Grabner

We extend the notion of hyperuniformity to the projective spaces $\mathbb{RP}^{d-1}$, $\mathbb{CP}^{d-1}$, $\mathbb{HP}^{d-1}$, and $\mathbb{OP}^2$. We show that hyperuniformity implies uniform distribution and present examples of deterministic point sets as well as point processes which exhibit hyperuniform behaviour.

Maciej Buze, Jean Feydy, Steven M. Roper, Karo Sedighiani

The microstructure of metals and foams can be effectively modelled with anisotropic power diagrams (APDs), which provide control over the shape of individual grains. One major obstacle to the wider adoption of APDs is the computational cost that is associated with their generation. We propose a novel approach to generate APDs with prescribed statistical prop

Wei Liu, Aleksi A. M. Leino, Arun Persaud, Qing Ji

Exposure of matter to high energy, heavy ions induces defects along the trajectories of the ions through electronic and nuclear energy loss processes. Defects, including color centers, can recombine or form along latent damage tracks in many materials, such as insulators and semiconductors. Latent tracks in diamond were only recently observed. Here, we repor

Raphael Baena, Lucas Drumetz, Vincent Gripon

In classification, it is usual to observe that models trained on a given set of classes can generalize to previously unseen ones, suggesting the ability to learn beyond the initial task. This ability is often leveraged in the context of transfer learning where a pretrained model can be used to process new classes, with or without fine tuning. Surprisingly, t

Wen Teng, Shuangjian Guo

The purpose of the present paper is to investigate cohomologies of Reynolds Lie-Yamaguti algebras of any weight and provide some applications. First, we introduce the notion of Reynolds Lie-Yamaguti algebras and give some new examples. Moreover, cohomologies of Reynolds operators and Reynolds Lie-Yamaguti algebras with coefficients in a suitable representati

Séverine Biard, Jujie Wu

We prove that a plurisubharmonic function on a domain in the complex Euclidean space is a locally VMO (Vanishing Mean Oscillation) function if and only if its Lelong number at each point vanishes. We also give a global version of this result when the boundary of the domain satisfies the \textit{interior sphere condition}. An example emphasizes the importance

Niels A. Wouda, Ward Romeijnders, Evrim Ursavas

Problem definition: We study efficient exact solution approaches to solve chance-constrained multicommodity network design problems under demand uncertainty, an important class of network design problems. The chance constraint requires us to construct a network that meets future commodity demand sufficiently often, which makes the problem challenging to solv

Y. Huang, S. Y. Xia, Y. F. Li, X. L. Tu

Proton-$^{133}$Cs elastic scattering at low momentum transfer is performed using an in-ring reaction technique at the Cooler Storage Ring at the Heavy Ion Research Facility in Lanzhou. Recoil protons from the elastic collisions between the internal H$_2$-gas target and the circulating $^{133}$Cs ions at 199.4 MeV/u are detected by a silicon-strip detector. T

Philipp Straubinger, Tommaso Fulcini, Gordon Fraser, Marco Torchiano

This paper investigates the integration and assessment of IntelliGame, a gamification plugin initially designed for Java development, within the realm of JavaScript unit testing. We aim to verify the generalizability of IntelliGame to JavaScript development and to provide valuable insights into the experiment's design. For this, we first customize IntelliGam

Eric Engel, Alexander Wegerich, Andreas Raabgrund, M. Alexander Schneider

In a recent publication [2D Materials, 8, 045033 (2021), arXiv:2103.11403], it was reported that the growth of a monolayer PdTe$_2$ in ultra-high vacuum could be achieved by deposition of tellurium on a palladium (111) crystal surface and subsequent thermal annealing. By means of low-energy electron diffraction intensity (LEED-IV) structural analysis, we sho

Youngjae Yoo, Chung-Yeon Lee, Byoung-Tak Zhang

Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms' slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot's movement in a changing environment. To solv

Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang

In this paper, we investigate the empirical counterpart of Group Distributionally Robust Optimization (GDRO), which aims to minimize the maximal empirical risk across $m$ distinct groups. We formulate empirical GDRO as a $\textit{two-level}$ finite-sum convex-concave minimax optimization problem and develop an algorithm called ALEG to benefit from its specia

Peng Dai, Yang Zhang, Tao Liu, Zhen Fan

It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper, we propose HMD-Poser, the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular, it can support a variety of input scenari

Gennadiy Averkov, Benjamin Peters, Sebastian Sager

We present a novel, general, and unifying point of view on sparse approaches to polynomial optimization. Solving polynomial optimization problems to global optimality is a ubiquitous challenge in many areas of science and engineering. Historically, different approaches on how to solve nonconvex polynomial optimization problems based on convex relaxations hav

Alexey Rosaev

The orbital dynamics of a very young asteroid pair (5026) Martes and 2005 WW113 is studied. We detect strong resonant perturbations of the larger member of the pair (5026) Martes by the 3:11 mean motion resonance with the Earth. The second asteroid of the pair (2005 WW113) has orbited far from the resonance and is not perturbed. We provide a new estimation o

Yuhong Sun, Zhangyue Yin, Qipeng Guo, Jiawen Wu

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To s

Philipp Straubinger, Gordon Fraser

Interruptions and context switches resulting from meetings, urgent tasks, emails, and queries from colleagues contribute to productivity losses in developers' daily routines. This is particularly challenging for tasks like software testing, which are already perceived as less enjoyable, prompting developers to seek distractions. To mitigate this, applying ga

Kathakali Biswas, Parongama Sen

We have studied a walk in a one-dimensional virtual space corresponding to an extended version of the three-state BChS model of opinion formation, originally proposed in Physica A {\bf 391}, 3257 (2012), in which the agents are located on a two dimensional lattice. The opinions are designated by the values $\pm1$ and zero. Here we also consider switches betw

Jan Kotlarz

This study investigates the application of Nash equilibrium strategies in optimizing forest harvesting decisions, focusing on multiple management objectives in forestry. Through simulation-based analysis, the research explores the evolution of various indicators during the game: 1) the mass of CO2 sequestration, 2) forest stands biodiversity, 3) the harveste

Ion Mitxelena, Mario Piris

In the past decade, natural orbital functional (NOF) approximations have emerged as prominent tools for characterizing electron correlation. Despite their effectiveness, these approaches, which rely on natural orbitals and their associated occupation numbers, often require hybridization with other methods to fully account for all correlation effects. Recentl

Helene Reichel, Aurelio Garcia-Valenzuela, Jose Andres Espino-Roman, Jorge Gil-Rostra

Early results on the plasma deposition of dielectric thin films on acoustic wave (AW) activated substrates revealed a densification pattern arisen from the focusing of plasma ions and their impact on specific areas of the piezoelectric substrate. Herein, we extend this methodology to tailor the plasma deposition of metals onto AW-activated LiNbO3 piezoelectr

Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist

Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a deep reinforcement learning (DRL) algorithm that achieves population-dependent Nash equilibrium without the need for averaging or sampling from history, inspired by Munchausen RL and

Tim Selig, Thomas März, Martin Storath, Andreas Weinmann

Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data. Popular approaches for LDCT image reconstruction are two-stage methods, typically consisting of the filtered backprojection (FBP) algorithm followed by a neural network for LDCT image enhancement. Two-stage methods are attractive for their simplicity

Rasita Vinay, Giovanni Spitale, Nikola Biller-Andorno, Federico Germani

This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation.

Shanshan Du, Hao Pan

Suppose that $k\geq 2$ and $A$ is a non-empty subset of a finite abelian group $G$ with $|G|>1$. Then the cardinality of the restricted sumset $$ k^\wedge A:=\{a_1+\cdots+a_k:\,a_1,\ldots,a_k\in A,\ a_i\neq a_j\text{ for }i\neq j\} $$ is at least $$ \min\{p(G), k|A|-k^2+1\}, $$ where $p(G)$ denotes the least prime divisor of $|G|$.

Mitsuru Tanaka, Yasuhiro Yamaguchi, Masayasu Harada

We analyze the mass and width of the doubly heavy tetraquark $T_{ccs}$ composed of a heavy diquark and a light-quark cloud with strangeness with assuming that a color antitriplet heavy diquark is a dominant component of the doubly charmed tetraquarks $T_{cc}$ and $T_{ccs}$. We construct an effective Lagrangian for masses of heavy hadrons based on the superfl

Chongsi Xie, Yi-Jian Du

One-loop integrands in Cachazo-He-Yuan (CHY) formula, which is based on the forward limit of tree-level amplitudes, involves linear propagators that are different from quadratic ones in traditional Feynman diagrams. In this paper, we provide a general approach to converting linear propagators in one-loop CHY formula into quadratic propagators, by refined gra

Zhiwei Zhu, Jiaqun Wei

Let $\mathscr{C}$ be an extriangulated category with enough projectives and injectives. We give a new definition of tilting subcategories of $\mathscr{C}$ and prove it coincides with the definition given in [19]. As applications, we introduce the notions of support $\tau$-tilting subcategories and $\tau$-cotorsion pairs of $\mathscr{C}$. We build a bijection

Benedikt Fesl, Michael Baur, Florian Strasser, Michael Joham

This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models. Contrary to related works utilizing generative priors, a lightweight convolutional neural network (CNN) with positional embedding of the signal-to-noise ratio (SNR) information is designed by learning the channel distribution in the

Bart M. N. Smets

These are the lecture notes that accompanied the course of the same name that I taught at the Eindhoven University of Technology from 2021 to 2023. The course is intended as an introduction to neural networks for mathematics students at the graduate level and aims to make mathematics students interested in further researching neural networks. It consists of

Hao Xue, Tianye Tang, Ali Payani, Flora D. Salim

With the advancement of large language models, language-based forecasting has recently emerged as an innovative approach for predicting human mobility patterns. The core idea is to use prompts to transform the raw mobility data given as numerical values into natural language sentences so that the language models can be leveraged to generate the description f

Jean-Michel Billiot, Eric Fontenas

In this article, we establish necessary and sufficient conditions for a polynomial of degree $n$ to have exactly $n$ real roots. A complete study of polynomials of degree five is carried out. The results are compared with those obtained using Sturm sequences.

Colin Cros, Pierre-Olivier Amblard, Christophe Prieur, Jean-François Da Rocha

This paper introduces a new conservative fusion method to exploit the correlated components within the estimation errors. Fusion is the process of combining multiple estimates of a given state to produce a new estimate with a smaller MSE. To perform the optimal linear fusion, the (centralized) covariance associated with the errors of all estimates is require

Zhongkai Hao, Chang Su, Songming Liu, Julius Berner

Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings. However, it is largely in its infancy due to the inherent complexity and diversity, such as long trajectories, multiple scales and varying dimensions of partial differential equations (PDEs) data. In this paper, we present a new

He Li, Ruihua Han, Zirui Zhao, Wei Xu

Virtual reality (VR) is a promising data engine for autonomous driving (AD). However, data fidelity in this paradigm is often degraded by VR inconsistency, for which the existing VR approaches become ineffective, as they ignore the inter-dependency between low-level VR synchronizer designs (i.e., data collector) and high-level VR synthesizer designs (i.e., d

Elie Odin, François Bachoc, Agnès Lagnoux

This work explores the dimension reduction problem for Bayesian nonparametric regression and density estimation. More precisely, we are interested in estimating a functional parameter $f$ over the unit ball in $\mathbb{R}^d$, which depends only on a $d^*$-dimensional subspace of $\mathbb{R}^d$, with $d^* < d$. It is well-known that rescaled Gaussian process

Thomas Pinetz, Erich Kobler, Robert Haase, Julian A. Luetkens

Recently, deep learning (DL)-based methods have been proposed for the computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects while preserving diagnostic value. Currently, the two main challenges for these approaches are the accurate prediction of contrast enhancement and the synthesis of realistic images. In this

Jorge Álvarez, Juan Carlos Armenteros, Camilo Torrón, Miguel Ortega-Martín

Radio advertising remains an integral part of modern marketing strategies, with its appeal and potential for targeted reach undeniably effective. However, the dynamic nature of radio airtime and the rising trend of multiple radio spots necessitates an efficient system for monitoring advertisement broadcasts. This study investigates a novel automated radio ad

K. V. Harsha, Jithin Ravi, Tobias Koch

Consider a binary statistical hypothesis testing problem, where $n$ independent and identically distributed random variables $Z^n$ are either distributed according to the null hypothesis $P$ or the alternative hypothesis $Q$, and only $P$ is known. A well-known test that is suitable for this case is the so-called Hoeffding test, which accepts $P$ if the Kull

Hangyu Wang, Jianghao Lin, Bo Chen, Yang Yang

The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction

Minyang Hu, Hong Chang, Zong Guo, Bingpeng Ma

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL by delving into two key questions: (1) How to quantify the relationship between \emph{training} and \emph{novel} tasks? (2) How does the relationship affect the \emph{adaptation d

Fabien Defrance, Andrew D. Beyer, Shibo Shu, Jack Sayers

Two-level systems (TLS) are an important, if not dominant, source of loss and noise for superconducting resonators such as those used in kinetic inductance detectors and some quantum information science platforms. They are similarly important for loss in photolithographically fabricated superconducting mm-wave/THz transmission lines. For both lumped-element

Nannan Ma, P. Z. Zhao, Jiangbin Gong

In a conventional circuit for quantum machine learning, the quantum gates used to encode the input parameters and the variational parameters are constructed with a fixed order. The resulting output function, which can be expressed in the form of a restricted Fourier series, has limited flexibility in the distributions of its Fourier coefficients. This indica

Quan Liu, Hongzi Zhu, Zhenxi Wang, Yunsong Zhou

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EY

Davide Manfredo, Vanessa Dörlich, Joachim Linn, Martin Arnold

This contribution aims at introducing first steps to develop hysteresis operator type inelastic constitutive laws for Cosserat rods for the simulation of cables composed of complex interior components. Motivated by the basic elements of Cosserat rod theory, we develop a specific approach to constitutive modelling adapted for this application. Afterwards, we

Yuan Li, Haowei Wu, Yi Yang

We study the $\textit{average-case deterministic query complexity}$ of boolean functions under a $\textit{uniform input distribution}$, denoted by $\mathrm{D}_\mathrm{ave}(f)$, the minimum average depth of zero-error decision trees that compute a boolean function $f$. This measure has found several applications across diverse fields, yet its understanding is

Filip Alamaa, Felix Ryde, Christoffer Lundman

The debate regarding the emission mechanism in gamma-ray bursts has been long-standing. Here, we study the spectral signatures of photospheric emission, accounting for subphotospheric dissipation by a radiation-mediated shock. The shocks are modeled using the Kompaneets RMS approximation (KRA). We find that the resulting observed spectra are soft, broad, and

Zihao Gao, Long Ma, Zhe Liu, Jun Huang

With the resistive-pulse technique (RPT), nanopores serve as the nanofluidic sensors of various analytes for their many physical and chemical properties. Here, we focus on the size measurement and its theoretical prediction for sub-200 nm nanoparticles with RPT. Through systematical investigation of the current blockade of nanoparticles across cylindrical na

Xuying Xiong, Xinyu Zhang, Weidong Jiang, Tianpeng Liu

Mainstream DNN-based SAR-ATR methods still face issues such as easy overfitting of a few training data, high computational overhead, and poor interpretability of the black-box model. Integrating physical knowledge into DNNs to improve performance and achieve a higher level of physical interpretability becomes the key to solving the above problems. This paper

Young-Min Go, Seong-Hyun Yu, Hyeong-Yeong Park, Minji Lee

Brain-computer interface (BCI) technology facilitates communication between the human brain and computers, primarily utilizing electroencephalography (EEG) signals to discern human intentions. Although EEG-based BCI systems have been developed for paralysis individuals, ongoing studies explore systems for speech imagery and motor imagery (MI). This study int

Natarajan Meghanathan

Exploratory factor analysis (EFA) is useful to identify the number and mapping of the hidden factors that could dominantly represent the features in the dataset. Principal component analysis (PCA) is the first step as part of the two-step procedure to conduct EFA, with the number of dominant principal components being the number of hidden factors and the ent

A. J. E. M. Janssen, B. Zwart

We develop explicit bounds for the tail of the distribution for the all-time supremum of a random walk with negative drift, where the increments have a truncated heavy-tailed distribution. As an application, we consider a ruin problem in the presence of re-insurance.

Minoru Kanega, Masahiro Sato

We theoretically investigate high-harmonic generation (HHG) in honeycomb-lattice graphene models when subjected to a DC electric field. By integrating the quantum master equation with the Boltzmann equation, we develop a numerical method to compute laser-driven dynamics in many-electron lattice systems under DC electric current. The method enables us to trea

Tirza Biron, Moshe Barboy, Eran Ben-Artzy, Alona Golubchik

Non-verbal signals in speech are encoded by prosody and carry information that ranges from conversation action to attitude and emotion. Despite its importance, the principles that govern prosodic structure are not yet adequately understood. This paper offers an analytical schema and a technological proof-of-concept for the categorization of prosodic signals

Carinne Cherf, Yuval Pinter

Neural machine translation (NMT) has progressed rapidly in the past few years, promising improvements and quality translations for different languages. Evaluation of this task is crucial to determine the quality of the translation. Overall, insufficient emphasis is placed on the actual sense of the translation in traditional methods. We propose a bidirection

Davide Manfredo, Mohammad Ali Saadat, Vanessa Dörlich, Joachim Linn

The simulation of inelastic effects in flexible slender technical devices has become of increasing interest in the past years. Different approaches have been considered depending on the effects relevant for the specific application. Recently, a mixed stress strain driven computational homogenisation has been proposed to model the dissipative nonlinear bendin

Jonathan David Evans

This is a series of three lectures I gave at the Korea Institute of Advanced Study in June 2019 at a workshop about "Algebraic and Symplectic Aspects of Degenerations of Complex Surfaces". I focus on the symplectic aspects, in particular on the case of cyclic quotient surface singularities. These notes have been available on a public Git repository since 201

Laura Pietanesi, Magdalena Marganska, Thomas Mayer, Michael Barth

Ferromagnetic resonance is used to reveal features of the buried electronic band structure at interfaces between ferromagnetic metals and topological insulators. By monitoring the evolution of magnetic damping, the application of this method to a hybrid structure consisting of a ferromagnetic layer and a 3D topological insulator reveals a clear fingerprint o

Tsz Ho Chan, Wenyi Xiao, Junhua Huang, Huiling Zhen

Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employed for these problems and neural network is tried as assistance to solvers. However, as SAT problems in the LEC context are distinctive due to their predominantly unsatisfiability n

Chao-Wei Huang, Chen-An Li, Tsu-Yuan Hsu, Chen-Yu Hsu

Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense retrievers typically require a substantial amount of paired data, which poses even greater challenges in multilingual scenarios. This paper introduces UMR, an Unsupervised Multilingua

Pedram Mirchi, Christophe Adessi, Samy Merabia, Ali Rajabpour

Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from ab-initio molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The