April 2024 arXiv papers — page 139

Feng Liang, Zhen Zhang, Haifeng Lu, Victor C. M. Leung

With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of algorithms and infrastructures, and heter

Anxin Yang, Zhijuan Du, Tao Sun

Substitute relationships are fundamental to people's daily lives across various domains. This study aims to comprehend and predict substitute relationships among products in diverse fields, extensively analyzing the application of machine learning algorithms, natural language processing, and other technologies. By comparing model methodologies across differe

Killian Bouzoud, Jacopo Ghiglieri

Hot axions, thermally produced in the Early Universe, would contribute to dark radiation and are thus subject to present and future constraints from $N_{\rm eff}$. In this paper we quantify the contribution to $N_{\rm eff}$ and its uncertainty in models with axion-gluon couplings from thermal dynamics above the QCD transition. In more detail, we determine th

Shoei Takahashi, Hikaru Manabe, Ryohei Miyadera

In this study, we study a Josephus problem algorithm. Let $n,k$ be positive integers and $g_k(n) = \left\lfloor \frac{n}{k-1} \right\rfloor +1$, where $ \left\lfloor \ \ \right\rfloor$ is a floor function. Suppose that there exists $p$ such that $g_{k}^{p-1}(0) < n(k-1) \leq g_{k}^{p}(0)$, where $g_{k}^p$ is the $p$-th functional power of $g_k$. Then, the la

Antonino Bella, Gianluca Bonifazi, Luca Lista, Dario Menasce

Excess mortality is defined as an increase in the number of deaths above what is expected based on historical trends, hereafter called baseline. In a previous paper, we introduced a statistical method that allows an unbiased and robust determination of the baseline to be used for the computation of excesses. A good determination of the baseline allows us to

Ralph Brinks

The aim of this article is relating the chemical master equation (CME) to the illness-death model for chronic diseases. We show that a recently developed differential equation for the prevalence directly follows from the CME. As an application, we use the theory of the CME in a simulation study about diabetes in Germany from a previous publication. We find a

Bas Janssens, Milan Niestijl

Motivated by asymptotic symmetry groups in general relativity, we consider projective unitary representations $\overline{\rho}$ of the Lie group $\mathrm{Diff}_c(M)$ of compactly supported diffeomorphisms of a smooth manifold $M$ that satisfy a so-called generalized positive energy condition. In particular, this captures representations that are in a suitabl

Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder

In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis. ADC has been introduced for automatic 3D point primitive management, controlling densification and pruning, however, with certain limitations in the

Kaiming Bian, Shitao Zhang, Fei Meng, Wen Zhang

Many supervised learning tasks have intrinsic symmetries, such as translational and rotational symmetry in image classifications. These symmetries can be exploited to enhance performance. We formulate the symmetry constraints into a concise mathematical form. We design two ways to adopt the constraints into the cost function, thereby shaping the cost landsca

Zi Long, Zhenhao Tang, Xianghua Fu, Jian Chen

Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30k. In these kinds of datasets, the

Jan Soukup

Consider a bicolored point set $P$ in general position in the plane consisting of $n$ blue and $n$ red points. We show that if a subset of the red points forms the vertices of a convex polygon separating the blue points, lying inside the polygon, from the remaining red points, lying outside the polygon, then the points of $P$ can be connected by non-crossing

Alessandro Benfenati, Alessio Marta

Neural networks are playing a crucial role in everyday life, with the most modern generative models able to achieve impressive results. Nonetheless, their functioning is still not very clear, and several strategies have been adopted to study how and why these model reach their outputs. A common approach is to consider the data in an Euclidean settings: recen

Le Cai, Sam Ferguson, Gengfa Fang, Hani Alshamrani

Existing research on music recommendation systems primarily focuses on recommending similar music, thereby often neglecting diverse and distinctive musical recordings. Musical outliers can provide valuable insights due to the inherent diversity of music itself. In this paper, we explore music outliers, investigating their potential usefulness for music disco

S. Normo, D. E. Morosan, E. K. J. Kilpua, J. Pomoell

Fast coronal mass ejections (CMEs) can drive shock waves capable of accelerating electrons to high energies. These shock-accelerated electrons act as sources of electromagnetic radiation, often in the form of solar radio bursts. Recent findings suggest that radio imaging of solar radio bursts can provide a means to estimate the lateral expansion of CMEs and

Blnd Yaseen, Hossein Hassani

Kurdish libraries have many historical publications that were printed back in the early days when printing devices were brought to Kurdistan. Having a good Optical Character Recognition (OCR) to help process these publications and contribute to the Kurdish languages resources which is crucial as Kurdish is considered a low-resource language. Current OCR syst

Máté Lencsés, Alessio Miscioscia, Giuseppe Mussardo, Gábor Takács

We revisit and extend Fisher's argument for a Ginzburg-Landau description of multicritical Yang-Lee models in terms of a single boson Lagrangian with potential $\varphi^2 (i \varphi)^n$. We explicitly study the cases of $n=1,2$ by a Truncated Hamiltonian Approach based on the free massive boson perturbed by $\boldsymbol P \boldsymbol T$ symmetric deformation

Sabrina Campano, Tahar Nabil, Meryl Bothua

This article presents a review of quantum computing research works for Natural Language Processing (NLP). Their goal is to improve the performance of current models, and to provide a better representation of several linguistic phenomena, such as ambiguity and long range dependencies. Several families of approaches are presented, including symbolic diagrammat

Wilhelm Kroschinsky, Domingos H. U. Marchetti, Manfred Salmhofer

We revisit the problem of controlling Polchinski's equation by the solution of an associate Hamilton-Jacobi equation which determines a norm majorant for the fermionic effective action. This method, referred to as the majorant method, was first introduced by D. Brydges and J. Wright in 1988, but its original formulation contains a gap which has never been ad

L. Bigwood, A. Amon, A. Schneider, J. Salcido

Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year

Marko Milivojević, Marcin Kurpas, Maedeh Rassekh, Dominik Legut

Effective control of interlayer interactions is a key element in modifying the properties of van der Waals heterostructures and the next step toward their practical applications. Focusing on the phosphorene-WSe$_2$ heterostructure, we demonstrate, using first-principles calculations, proximity-induced amplification of the spin-orbit coupling in phosphorene b

Hikaru Kawai, Kiyoharu Kawana, Kin-ya Oda, Kei Yagyu

In models with non-minimal Higgs sectors, enforcing (near) Higgs alignment, necessary to prevent significant deviations in the Higgs boson coupling from the standard model prediction, causes a serious fine-tuning problem. We demonstrate that the Higgs alignment is naturally deduced from the multicritical point principle (MPP) in the general two Higgs doublet

Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada

Self-supervised learning (SSL) using masked prediction has made great strides in general-purpose audio representation. This study proposes Masked Modeling Duo (M2D), an improved masked prediction SSL, which learns by predicting representations of masked input signals that serve as training signals. Unlike conventional methods, M2D obtains a training signal b

Mahnoor Naseer, Sundas Tariq, Naveed Riaz, Naveed Ahmed

Lightweight cryptography was primarily inspired by the design criteria of symmetric cryptography. It plays a vital role in ensuring the security, privacy, and reliability of microelectronic devices without compromising the overall functionality and efficiency. However, the increasingly platform specific design requirements prompted the development of a stand

Solenne Gaucher, Gilles Blanchard, Frédéric Chazal

The contamination detection problem aims to determine whether a set of observations has been contaminated, i.e. whether it contains points drawn from a distribution different from the reference distribution. Here, we consider a supervised problem, where labeled samples drawn from both the reference distribution and the contamination distribution are availabl

M. L. Kerr, G. De Rosi, K. V. Kheruntsyan

We present a comprehensive review on the state-of-the-art of the approximate analytic approaches describing the finite-temperature thermodynamic quantities of the Lieb-Liniger model of the one-dimensional (1D) Bose gas with contact repulsive interactions. This paradigmatic model of quantum many-body-theory plays an important role in many areas of physics --

Xingyi Yang, Xinchao Wang

The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per se presents a critical hurdle to efficiency. In this paper, we introduce Hash3D, a universal acceleration for 3D generation without model training. Central to Hash3D is the insight that feat

Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes

In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. However, challenges arise from biases that can be hidden not only in the node attributes but also in the connections between entities. Therefore, ensuring fairness in graph neural netw

Jun Wang, Chun-Cheng Chang, Jiafei Duan, Dieter Fox

The increasing affordability of robot hardware is accelerating the integration of robots into everyday activities. However, training a robot to automate a task requires expensive trajectory data where a trained human annotator moves a physical robot to train it. Consequently, only those with access to robots produce demonstrations to train robots. In this wo

Jonas Frede, Volker Kaibel, Maximilian Merkert

With every family of finitely many subsets of a finite-dimensional vector space over the Galois-field with two elements we associate a cyclic transversal polytope. It turns out that those polytopes generalize several well-known polytopes that are relevant in combinatorial optimization, among them cut polytopes as well as stable set and matching polytopes. We

Mark Goh

The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm designed for Combinatorial Optimization Problem (COP). We show that if a local algorithm is limited in performance at logarithmic depth for a spin glass type COP with an underlying Erd\"os--R\'enyi hypergraph, then a random regular hypergraph is similarly limited in performance as w

Ya Bai, Yang Jiang, Wenyang Zheng, Jiayin Chen

We have observed the Berry phase effect associated with interband coherence in topological surface states (TSSs) using two-color high-harmonic spectroscopy. This Berry phase accumulates along the evolution path of strong field-driven election-hole quasiparticles in electronic bands with strong spin-orbit coupling. By introducing a secondary weak field, we pe

D Boivin, R Jean-Marie-Désirée, A Najah, S Cuynet

In this work, coupled optical and electrical discharge measurements have been implemented to investigate the plasma state of a reactive HiPIMS TiO$_2$ deposition process running at a fixed duty cycle of 2% and at a repetition rate of 1 kHz. Investigations focus on both the effect of the erosion target and substrate-holder temperature in an Ar/O$_2$ gas mixtu

Teemu Hakkarainen, Joonas Hilska, Arttu Hietalahti, Sanna Ranta

Deterministic light sources capable of generating quantum states on-demand at wavelengths compatible with fiber optics and atmospheric transmission windows are essential for practical applications in quantum communication, distributed photonic quantum computing, and quantum metrology. Currently, the technology providing semiconductor quantum emitters with th

Toshihiro Kamiya

Although the context length limitation of large language models (LLMs) has been mitigated, it still hinders their application to software development tasks. This study proposes a method incorporating execution traces into RAG for inquiries about source code. Small-scale experiments confirm a tendency for the method to contribute to improving LLM response qua

P. J. Costello, G. G. Plunk

Upper bounds on the growth of instabilities in gyrokinetic systems have recently been derived by considering the optimal perturbations that maximise the growth of a chosen energy norm. This technique has previously been applied to two-species gyrokinetic systems with fully kinetic ions and electrons. However, in tokamaks and stellarators, the expectation fro

Ching-Kai Lin, Di-Chun Wei, Yun-Chien Cheng

This study presents a computer-aided diagnosis (CAD) system to assist early detection of lung metastases during endobronchial ultrasound (EBUS) procedures, significantly reducing follow-up time and enabling timely treatment. Due to limited cytology images and morphological similarities among cells, classifying lung metastases is challenging, and existing res

Yiwei Guo, Chenrun Wang, Yifan Yang, Hankun Wang

Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice synthesis (SVS). In this paper, we describe the systems developed by the SJTU X-LANCE group for the TTS (acoustic + vocoder), SVS, and ASR tracks in the Interspeech 2024 Speech Proce

Xiuqi Deng, Lu Xu, Xiyao Li, Jinkai Yu

Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can mitigate these issues, but is still a suboptimal solution due to the discrepancies between training tasks and model parameters. End-to-end training presents a promising solution fo

Qin Wang, Guangsheng Yu, Yilin Sai, H. M. N. Dilum Bandara

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount, AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrigh

Wei Jiang, Wei Wang

We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs into a discrete latent space spanned by learned visual codebooks, SVR-based compression transmits integer codeword indices, which is efficient and cross-platform robust. However, hi

Junbo Qiao, Wei Li, Haizhen Xie, Hanting Chen

Transformer is leading a trend in the field of image processing. Despite the great success that existing lightweight image processing transformers have achieved, they are tailored to FLOPs or parameters reduction, rather than practical inference acceleration. In this paper, we present a latency-aware image processing transformer, termed LIPT. We devise the l

Di Jin, Jacob Klein

The outstanding lubrication of articular cartilage in the major synovial joints such as hips and knees, essential for the joint well-being, has been attributed to boundary layers of lipids at the outer cartilage surfaces, which have very low friction mediated by the hydration lubrication mechanism at their highly hydrated exposed headgroups. However, the rol

Mathilde Noual

Hardin introduced the notorious concept of "tragedy of the commons". Worrying about the consequences of human overpopulation on the planet, he discussed "hard problems": problems with no technical solutions, that can only be addressed by way of an evolving morality. Hardin's tragedy of the commons predicts that the hard problem of human population growth dir

Christos N. Efrem, Ioannis Krikidis

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at both the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capa

Jian Zhu, Xin Zou, Yu Cui, Zhangmin Huang

Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and infere

Guram Bezhanishvili, James Madden, M. Andrew Moshier, Marcus Tressl

We investigate whether the set of subfit elements of a distributive semilattice is an ideal. This question was raised by the second author at the BLAST conference in 2022. We show that in general it has a negative solution, however if the semilattice is a lattice, then the solution is positive. This is somewhat unexpected since, as we show, a semilattice is

Robin Feldmann, Max Mörchen, Jakub Lang, Michał Lesiuk

In this work, we investigate the possibility of improving multireference-driven coupled cluster (CC) approaches with an algorithm that iteratively combines complete active space (CAS) calculations with tailored CC and externally corrected CC. This is accomplished by establishing a feedback loop between the CC and CAS parts of a calculation through similarity

Soheil Behnezhad, Alma Ghafari

We study the fully dynamic maximum matching problem. In this problem, the goal is to efficiently maintain an approximate maximum matching of a graph that is subject to edge insertions and deletions. Our focus is on algorithms that maintain the edges of a $(1-\epsilon)$-approximate maximum matching for an arbitrarily small constant $\epsilon > 0$. Until recen

Jesper Jansson, Christos Levcopoulos, Andrzej Lingas

We study the problem of computing the Voronoi diagram of a set of $n^2$ points with $O(\log n)$-bit coordinates in the Euclidean plane in a substantially sublinear in $n$ number of rounds in the congested clique model with $n$ nodes. Recently, Jansson et al. have shown that if the points are uniformly at random distributed in a unit square then their Voronoi

Arup Chattopadhyay, Supratim Jana

In the classical Hardy space $H^2(\mathbb{D})$, it is well-known that the kernel of the Hankel operator is invariant under the action of shift operator S and sometimes nearly invariant under the action of backward shift operator $S^{*}$. It appears in this paper that kernels of finite rank perturbations of Hankel operators are almost shift invariant as well

Andrea C. Burgess, Nicholas J. Cavenagh, Peter Danziger, David A. Pike

A $\delta$-colouring of the point set of a block design is said to be {\em weak} if no block is monochromatic. The {\em chromatic number} $\chi(S)$ of a block design $S$ is the smallest integer $\delta$ such that $S$ has a weak $\delta$-colouring. It has previously been shown that any Steiner triple system has chromatic number at least $3$ and that for each

Zhengqing Gao, Xu-Yao Zhang, Cheng-Lin Liu

Test-time adaptation (TTA) aims at adapting a model pre-trained on the labeled source domain to the unlabeled target domain. Existing methods usually focus on improving TTA performance under covariate shifts, while neglecting semantic shifts. In this paper, we delve into a realistic open-set TTA setting where the target domain may contain samples from unknow

Bohan Zhang, Anastasios Panagiotelis, Han Li

Forecast reconciliation has attracted significant research interest in recent years, with most studies taking the hierarchy of time series as given. We extend existing work that uses time series clustering to construct hierarchies, with the goal of improving forecast accuracy, in three ways. First, we investigate multiple approaches to clustering, including

Yiming Li

We investigate the properties of the BCZ map. Based on our findings, we define the moduli space associated with its excursions. Subsequently, we utilize the framework we build to establish a discretized analog of the Riemann hypothesis (RH) that holds in a stronger sense from a dynamical perspective. The analog is founded upon a reformulation of the RH, spec

Baoxing Jiang, Yujie Wan, Shenggen Ju

Few-Shot Aspect-Based Sentiment Analysis (FSABSA) is an indispensable and highly challenging task in natural language processing. However, methods based on Pre-trained Language Models (PLMs) struggle to accommodate multiple sub-tasks, and methods based on Generative Pre-trained Transformers (GPTs) perform poorly. To address the above issues, the paper design

J. K. Langley

Some results are proved concerning asymptotic and deficient values in connection with the second order linear differential equation $y'' + Ay = 0$, in which the coefficient $A$ is entire.

Atsushi Inoue

What does it mean to study PDE(=Partial Differential Equation)? How and what to do "to claim proudly that I'm studying a certain PDE"? Newton mechanic uses mainly ODE(=Ordinary Differential Equation) and describes nicely movements of Sun, Moon and Earth etc. Now, so-called quantum phenomenum is described by, say Schr\"odinger equation, PDE which explains bot

Juan Zhang, Yiyi Luo

This paper introduces a preconditioned method designed to comprehensively address the saddle point system with the aim of improving convergence efficiency. In the preprocessor construction phase, a technical approach for solving the approximate inverse matrix of sparse matrices is presented. The effectiveness of the proposed method is demonstrated through nu

Soham Sen, Sunandan Gangopadhyay

We consider a Bose-Einstein condensate interacting with a gravitational wave for the case when the gravitational fluctuations are quantized in order to incorporate quantum gravity effects into the theory. We observe that the solution of the time-dependent part of the pseudo-Goldstone boson has infusions from the noise induced by gravitons and the correspondi

Wei Zi, Siyi Wang, Hyunji Kim, Xiaoming Sun

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with a

Joscha Prochno, Mathias Sonnleitner, Jan Vybíral

The sequence of entropy numbers quantifies the degree of compactness of a linear operator acting between quasi-Banach spaces. We determine the asymptotic behavior of entropy numbers in the case of natural embeddings between finite-dimensional Lorentz spaces $\ell_{p,q}^n$ in all regimes; our results are sharp up to constants. This generalizes classical resul

Yupei Zhang, Li Pan, Qiushi Yang, Tan Li

Medical multi-modal pre-training has revealed promise in computer-aided diagnosis by leveraging large-scale unlabeled datasets. However, existing methods based on masked autoencoders mainly rely on data-level reconstruction tasks, but lack high-level semantic information. Furthermore, two significant heterogeneity challenges hinder the transfer of pre-traine

Kai Wang, Simon J. U. White, Alexander Szameit, Andrey A. Sukhorukov

Studying quantum correlations in the presence of loss is of critical importance for the physical modeling of real quantum systems. Here, we demonstrate the control of spatial correlations between entangled photons in a photonic chip, designed and modeled using the singular value decomposition approach. We show that engineered loss, using an auxiliary wavegui

Ying Li, Zhidi Lin, Kai Li, Michael Minyi Zhang

Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral approach to robust beamforming is the stochastic weighted minimum mean square error method (WMMSE). However, this method necessitates statistical channel information, which is typi

Sen Wang, Tianxiong Wang, Shulun Zhao, Zhen Feng

This article introduces an energy and spectral efficient multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission scheme designed for the future sixth generation (6G) wireless communication networks. The approach involves connecting each receiving radio frequency (RF) chain with multiple antenna elements and conducti

Chu-Dan Qiu, Yuan-De Jin, Jun-Xiang Zhang, Gang-Qin Liu

Repetitive Ramsey interferometry measurements (RIMs) are often used to measure qubit coherence, assuming that the environment remains unaffected after each measurement and the outcomes of all measurements are independent and identically distributed (i.i.d.). While this assumption is valid for a classical environment, it may not hold for a quantum environment

Wei Zi, Junhong Nie, Xiaoming Sun

In quantum computation, optimizing depth and number of ancillary qubits in quantum circuits is crucial due to constraints imposed by current quantum devices. This paper presents an innovative approach to implementing arbitrary symmetric Boolean functions using poly-logarithmic depth quantum circuits with logarithmic number of ancillary qubits. Symmetric func

Balázs Rácsai, Dávid Ferenc, Ádám Margócsy, Edit Mátyus

Drachmann's regularization approach is implemented for floating explicitly correlated Gaussians (fECGs) and molecular systems. Earlier applications of drachmannized relativistic corrections for molecular systems were hindered due to the unknown analytic matrix elements of $1/r_{ix}1/r_{jy}$-type operators with fECGs. In the present work, one of the $1/r$ fac

Tianchen Deng, Nailin Wang, Chongdi Wang, Shenghai Yuan

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: \textit{(a) inaccurate depth input.} Accurate depth input is impossible to get in real-world large-scale scenes. \textit{(b) inaccurate

Shohei Saga, David Alonso

We investigate the three-dimensional clustering of sources emitting electromagnetic pulses traveling through cold electron plasma, whose radial distance is inferred from their dispersion measure. As a distance indicator, dispersion measure is systematically affected by inhomogeneities in the electron density along the line of sight and special and general re

Afzal Ahmad, Linfeng Du, Zhiyao Xie, Wei Zhang

One of the primary challenges impeding the progress of Neural Architecture Search (NAS) is its extensive reliance on exorbitant computational resources. NAS benchmarks aim to simulate runs of NAS experiments at zero cost, remediating the need for extensive compute. However, existing NAS benchmarks use synthetic datasets and model proxies that make simplified

Marcel Niedermeier, Marc Nairn, Christian Flindt, Jose L. Lado

Quantum algorithms provide a potential strategy for solving computational problems that are intractable by classical means. Computing the topological invariants of topological matter is one central problem in research on quantum materials, and a variety of numerical approaches for this purpose have been developed. However, the complexity of quantum many-body

Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread applications in iterative algorithms. Over the past decades, research on SpMV optimization has made remarkable strides, giving rise to various optimization contributions. However, the comprehensive and systematic literature survey that introduces, analyzes, discusses, an

S. J. Wang, A. Kanellakopoulos, X. F. Yang, S. W. Bai

Collinear laser spectroscopy measurements were performed on $^{68-74}$Ge isotopes ($Z = 32$) at ISOLDE-CERN, by probing the $4s^2 4p^2 \, ^3\!P_1 \rightarrow 4s^2 4p 5s \, ^3\!P_1^o$ atomic transition (269~nm) of germanium. Nuclear charge radii are determined via the measured isotope shifts, revealing a larger local variation than the neighboring isotopic ch

Boris Kunyavskii, Ievgen Makedonskyi, Andriy Regeta

The length of an element $z$ of a Lie algebra $L$ is defined as the smallest number $s$ needed to represent $z$ as a sum of $s$ brackets. The bracket width of $L$ is defined as supremum of the lengths of its elements. Given a finite-dimensional simple Lie algebra $\mathfrak g$ over an algebraically closed field $k$ of characteristic zero, we study the bracke

Chanho Kim, Li Fuxin

Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However, physics happens in 3D space, where geometric information potentially plays an important role in modeling physical phenomena. In this work, we propose a novel U-net architecture based on continuo

Limeng Zhang, M. Ali Babar

Faced with the challenges of big data, modern cloud database management systems are designed to efficiently store, organize, and retrieve data, supporting optimal performance, scalability, and reliability for complex data processing and analysis. However, achieving good performance in modern databases is non-trivial as they are notorious for having dozens of

Sandra Ruiz-Gomez, Claas Abert, Pamela Morales-Fernández, Claudia Fernandez-Gonzalez

Topological defects, or singularities, play a key role in the statics and dynamics of complex systems. In magnetism, Bloch point singularities represent point defects that mediate the nucleation of textures such as skyrmions and hopfions. However, while the textures are typically stabilised in chiral magnets, the influence of chirality on the Bloch point sin

Changan Niu, Ting Zhang, Chuanyi Li, Bin Luo

Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code generated by LLMs, we propose to further evaluate its efficiency. More efficient code can lead to higher performance and execution efficiency of programs and software completed by LLM-a

Hsien-Kuei Hwang, Satoshi Kuriki

Based on $m$-fold integrated empirical measures, we study three new classes of goodness-of-fits tests, generalizing Anderson-Darling, Cram\'er-von Mises, and Watson statistics, respectively, and examine the corresponding limiting stochastic processes. The limiting null distributions of the statistics all lead to explicitly solvable cases with closed-form exp

Can Liu, Jiacheng Yu, Yuhan Guo, Jiayi Zhuang

We propose an approach to manipulate existing interactive visualizations to answer users' natural language queries. We analyze the natural language tasks and propose a design space of a hierarchical task structure, which allows for a systematic decomposition of complex queries. We introduce a four-level visualization manipulation space to facilitate in-situ

Matteo Gaibotti, Sofia G. Mogilevskaya, Andrea Piccolroaz, Davide Bigoni

An elastic disk is coated with an elastic rod, uniformly prestressed with a tensile or compressive axial force. The prestress state is assumed to be induced by three different models of external radial load or by 'shrink-fit' forcing the coating onto the disk. The prestressed coating/disk system, when loaded with an additional and arbitrary incremental exter

Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding

Yuhao Luo, Kehua Chen, Meixin Zhu

As a vital component in autonomous driving, accurate trajectory prediction effectively prevents traffic accidents and improves driving efficiency. To capture complex spatial-temporal dynamics and social interactions, recent studies developed models based on advanced deep-learning methods. On the other hand, recent studies have explored the use of deep genera

Yuantong Zhang, Hanyou Zheng, Daiqin Yang, Zhenzhong Chen

This paper addresses the task of space-time video super-resolution (ST-VSR). Existing methods generally suffer from inaccurate motion estimation and motion compensation (MEMC) problems for large motions. Inspired by recent progress in physics-informed neural networks, we model the challenges of MEMC in ST-VSR as a mapping between two continuous function spac

Alessandro Berti

pm4py is a process mining library for Python implementing several process mining (PM) artifacts and algorithms. It also offers methods to integrate PM with large language models (LLMs). This paper examines how the current paradigms of PM on LLM are implemented in pm4py, identifying challenges such as privacy, hallucinations, and the context window limit.

Juan Zhang, Wenlu Xun

This paper presents an effective low-rank generalized alternating direction implicit iteration (R-GADI) method for solving large-scale sparse and stable Lyapunov matrix equations and continuous-time algebraic Riccati matrix equations. The method is based on generalized alternating direction implicit iteration (GADI), which exploits the low-rank property of m

Pan Mu, Zhiying Du, Jinyuan Liu, Cong Bai

In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion. Nonetheless, prevailing approaches often involve directly feeding over-exposed and under-exposed images into the network, which leads to the under-utilization of inherent information present in the source images. Additionally, unsupervised techn

H. Q. Ye, Y. N. Zhang, T. Le, H. Q. Yuan

Much of the rich physics of correlated systems is manifested in the diverse range of intertwined ordered phases and other quantum states that are associated with different electronic and structural degrees of freedom. Here we find that PrCuSb$_2$ exhibits such phenomena, which at ambient pressure exhibits a fragile antiferromagnetic order, where cooling in a

Kaled M. Alshmrany, Mohannad Aldughaim, Chenfeng Wei, Tom Sweet

We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outper

Juan Zhang, Xiao Luo

In this paper, we focus on using optimization methods to solve matrix equations by transforming the problem of solving the Sylvester matrix equation or continuous algebraic Riccati equation into an optimization problem. Initially, we use a constrained convex optimization method (CCOM) to solve the Sylvester matrix equation with $\ell_{2,1}$-norm, where we pr

Zong-Wei Hong, Yu-Chen Lin

The domain of computer vision has experienced significant advancements in facial-landmark detection, becoming increasingly essential across various applications such as augmented reality, facial recognition, and emotion analysis. Unlike object detection or semantic segmentation, which focus on identifying objects and outlining boundaries, faciallandmark dete

Guangle Du, David S. Dean, Bing Miao, Rudolf Podgornik

It has been established for a long time that the long range van der Waals or thermal Casimir interaction between two semi-infinite dielectrics separated by a distance $H$ is screened by an intervening electrolyte. Here we show how this interaction is modified when an electric field of strength $E$ is applied parallel to the dielectric boundaries, leading to

Weijen Chen, Yang Yang, Kao-Hua Liu, Yun Suen Pai

To enhance the dining experience, prior studies in Human-Computer Interaction (HCI) and gastrophysics have demonstrated that modifying the static shape of solid foods can amplify taste perception. However, the exploration of dynamic shape-changing mechanisms in liquid foods remains largely untapped. In the present study, we employ cymatics, a scientific disc

Atsushi Ito

In this note, we study Seshadri constants and Gromov widths of toric surfaces via lattice widths of their moment polygons. We give the sharp lower bound of the ratio between the Gromov width of a symplectic toric $4$-fold and the lattice width of the moment polygon, which answers to a question raised by Codenotti, Hall and Hofscheier.

Zander W. Blasingame, Chen Liu

Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, none of

Khaled Humadi, Gunes Karabulut Kurt, Halim Yanikomeroglu

Distributed massive multiple-input multiple output (mMIMO) system for low earth orbit (LEO) satellite networks is introduced as a promising technique to provide broadband connectivity. Nevertheless, several challenges persist in implementing distributed mMIMO systems for LEO satellite networks. These challenges include providing scalable massive access imple

Yixuan Zhang, Dongyan Huo, Yudong Chen, Qiaomin Xie

Motivated by Q-learning, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize. We focus on two important classes of dynamics: 1) nonsmooth contractive SA with additive noise, and 2) synchronous and asynchronous Q-learning, which features both additive and multiplicative noise. For both dynamics, we establish weak convergence of

Zhida Zhang, Jie Cao, Wenkui Yang, Qihang Fan

The transformer networks are extensively utilized in face forgery detection due to their scalability across large datasets.Despite their success, transformers face challenges in balancing the capture of global context, which is crucial for unveiling forgery clues, with computational complexity.To mitigate this issue, we introduce Band-Attention modulated Ret

Seokweon Jung, DongHwa Shin, Hyeon Jeon, Jinwook Seo

Dynamic networks represent the complex and evolving interrelationships between real-world entities. Given the scale and variability of these networks, finding an optimal slicing interval is essential for meaningful analysis. Nonuniform timeslicing, which adapts to density changes within the network, is drawing attention as a solution to this problem. In this