July 2023 arXiv papers — page 10

Yuanhao Chen

Tone is a crucial component of the prosody of Shanghainese, a Wu Chinese variety spoken primarily in urban Shanghai. Tone sandhi, which applies to all multi-syllabic words in Shanghainese, then, is key to natural-sounding speech. Unfortunately, recent work on Shanghainese TTS (text-to-speech) such as Apple's VoiceOver has shown poor performance with tone san

Mohammadhasan Goharian, Vahid Goharian, Hamidreza Bolhasani

Gastrointestinal mucosal changes can cause cancers after some years and early diagnosing them can be very useful to prevent cancers and early treatment. In this article, 8 classes of mucosal changes and anatomical landmarks including Polyps, Ulcerative Colitis, Esophagitis, Normal Z-Line, Normal Pylorus, Normal Cecum, Dyed Lifted Polyps, and Dyed Lifted Marg

Huyuan Chen, Laurent Véron

Let $\lnlap$ be the logarithmic Laplacian operator with Fourier symbol $2\ln |\zeta|$, we study the expression of the diffusion kernel which is associated to the equation $$\partial_tu+ \lnlap u=0 \ \ {\rm in}\ \, (0,\tfrac N2) \times \R^N,\quad\quad u(0,\cdot)=0\ \ {\rm in}\ \, \R^N\setminus \{0\}.$$ We apply our results to give a classification of the solu

Chenxi Huang, Chaoyang Jiang, Zhenghua Chen

Trustworthy federated learning aims to achieve optimal performance while ensuring clients' privacy. Existing privacy-preserving federated learning approaches are mostly tailored for image data, lacking applications for time series data, which have many important applications, like machine health monitoring, human activity recognition, etc. Furthermore, prote

Sayan Tripathi, Jhilam Jana, Jaydeb Bhaumik

The reliability of memory devices is affected by radiation induced soft errors. Multiple cell upsets (MCUs) caused by radiation corrupt data stored in multiple cells within memories. Error correction codes (ECCs) are typically used to mitigate the effects of MCUs. Single error correction-double error detection (SEC-DED) codes are not the right choice against

Jafar Khodagholizadeh, Rohoollah Mohammadi, S. M. S. Movahed

Inspired by a dipole asymmetric template for the CMB temperature map in the primordial scalar fluctuations observed by Planck at a large scale, we examine the contribution of a similar template for power asymmetry in modifying the linear polarization pattern of CMB. Replacing un-modulated temperature fluctuation with dipolar modulated one in time evolution e

Maximilian R. Bailey, C. Miguel Barriuso Gutiérrez, José Martín-Roca, Vincent Niggel

The underlying mechanisms and physics of catalytic Janus microswimmers is highly complex, requiring details of the associated phoretic fields and the physiochemical properties of catalyst, particle, boundaries, and the fuel used. Therefore, developing a minimal (and more general) model capable of capturing the overall dynamics of these autonomous particles i

Behrooz Rasuli

Public libraries play a crucial role in disseminating knowledge to society. However, most of their users do not have the specialized knowledge to understand the new research findings. Providing plain language summaries (PLSs) in public libraries is a way to make the new research findings more accessible and understandable for the public. This article propose

Petro Kolosov

In this manuscript, we provide and discuss another approach to get derivative of odd-power such that is based on an identity in partial derivatives in terms of polynomial function $f_y$ defined as \[ f_{y} (x, z) = \sum_{k=1}^{z} \sum_{r=0}^{y} \mathbf{A}_{y,r} k^r (x-k)^r \] where $x, z\in \mathbb{R}$, $y$ is fixed constant $y \in \mathbb{N}$ and $\mathbf{A

Zhen Lei, Jie Liu, Zhaojie Yang

In this paper, we consider Klein-Gordon equations with cubic nonlinearity in three spatial dimensions, which are Hamiltonian perturbations of the linear one with potential. It is assumed that the corresponding Klein-Gordon operator $B = \sqrt{-\Delta + V(x) + m^2} $ admits an arbitrary number of possibly degenerate eigenvalues in $(0, m)$, and hence the unpe

Karlijn Kruiswijk, Gwenhaël de Wasseige

Astrophysical transient events like Gamma Ray Bursts (GRBs) have always been promising candidates for multi-messenger astronomy, with electromagnetic and gravitational wave signals having already been observed in GRBs such as GRB 170817A. The neutrino signatures of these bursts have been long-awaited as well, with many models predicting different spectra. Mo

Juyoung Yun

In this research, we address critical concerns related to the numerical instability observed in 16-bit computations of machine learning models. Such instability, particularly when employing popular optimization algorithms like Adam, often leads to unstable training of deep neural networks. This not only disrupts the learning process but also poses significan

Tianhao Peng, Yu Liang, Wenjun Wu, Jian Ren

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how su

Pieter van Goor, Robert Mahony, Manuel Schaller, Karl Worthmann

Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman framework. In eDMD a compression of the lifted system dynamics on the space spanned by finitely many observables is computed, in which the original space is embedded as a low-dimen

Michael Groom, Ben Thornber

The effects of initial conditions on the evolution of the Richtmyer-Meshkov instability (RMI) at early to intermediate times are analysed, using numerical simulations of an idealised version of recent shock tube experiments performed at the University of Arizona. The experimental results are bracketed by performing both implicit large-eddy simulations of the

Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for model training. In addition, data-efficient reinforcement learning requires the construction of strong inductive biases, which are ignored in the current MARL approaches. Inspired b

Luca Guglielmo, Leonardo Mariani, Giovanni Denaro

Estimating software testability can crucially assist software managers to optimize test budgets and software quality. In this paper, we propose a new approach that radically differs from the traditional approach of pursuing testability measurements based on software metrics, e.g., the size of the code or the complexity of the designs. Our approach exploits a

Mustafa Shukor, Corentin Dancette, Alexandre Rame, Matthieu Cord

Large Language Models (LLMs) have made the ambitious quest for generalist agents significantly far from being a fantasy. A key hurdle for building such general models is the diversity and heterogeneity of tasks and modalities. A promising solution is unification, allowing the support of a myriad of tasks and modalities within one unified framework. While few

Jinbo Wu, Xiaobo Gao, Xing Liu, Zhengyang Shen

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x 512) renderings can lead to the production of high-quality 3D models using latent diffusion priors. To enable rendering

Chiara Balestra, Carlo Maj, Emmanuel Müller, Andreas Mayr

The biological roles of gene sets are used to group them into collections. These collections are often characterized by being high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation and study of their content. Bioinformatics looked for solutions to reduce their dimension or increase their intepretabilit

M. Boström, M. Rizwan Khan, H. R. Gopidi, I. Brevik

The Casimir-Lifshitz interaction, a long-range force that arises between solids and molecules due to quantum fluctuations in electromagnetic fields, has been widely studied in solid-state physics. The degree of polarization in this interaction is influenced by the dielectric properties of the materials involved, which in turn are determined by factors such a

SujayKumar Reddy M, Gopakumar G

PM-Gati-Shakti Initiative, integration of ministries, including railways, ports, waterways, logistic infrastructure, mass transport, airports, and roads. Aimed at enhancing connectivity and bolstering the competitiveness of Indian businesses, the initiative focuses on six pivotal pillars known as "Connectivity for Productivity": comprehensiveness, prioritiza

Anis Allagui, Georgii Paradezhenko, Anastasiia Pervishko, Dmitry Yudin

The Debye-Falkenhagen differential equation is commonly used as a mean-field macroscopic model for describing electrochemical ionic drift and diffusion in dilute binary electrolytes when subjected to a suddenly applied potential smaller than the thermal voltage. However, the ionic transport in most electrochemical systems, such as electrochemical capacitors,

Keyu Pan, Yawen Zeng

The field of large language models (LLMs) has made significant progress, and their knowledge storage capacity is approaching that of human beings. Furthermore, advanced techniques, such as prompt learning and reinforcement learning, are being employed to address ethical concerns and hallucination problems associated with LLMs, bringing them closer to alignin

Li He

Since wearable technology was first conceptualized in 1960, it has become a crucial aspect of our daily life. People's daily lives have been made better and their health and well-being have improved because of its convenience, real-time, and efficiency, among other things. Haptic feedback and haptic-based cues are crucial tools to improve interaction when tr

MirSaleh Bahavarnia, Ahmad F. Taha

This paper introduces a method for efficiently updating a nominal stabilizing static output feedback (SOF) controller in perturbed linear systems. As operating points and state-space matrices change in dynamic systems, accommodating updates to the SOF controller are necessary. Traditional methods address such changes by re-solving for the updated SOF gain, w

Isabelle Tingzon, Nuala Margaret Cowan, Pierre Chrzanowski

Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the a

Huayuan Ye, Chenhui Li, Yang Li, Changbo Wang

We present InvVis, a new approach for invertible visualization, which is reconstructing or further modifying a visualization from an image. InvVis allows the embedding of a significant amount of data, such as chart data, chart information, source code, etc., into visualization images. The encoded image is perceptually indistinguishable from the original one.

Sabri Najafi, Shaban Ghalandarzadeh, Arezou Ranjbar Nejad Esfahani, Fateme Olia

In this paper, we introduce and investigate some properties of $\phi$-$\delta$-$S$-primary submodules, which is a generalization of the $\phi$-$\delta$-primary submodules and prime submodules in general. We extend a number of main results about $\phi$-$\delta$-primary submodule and prime submodule into the general framework. Moreover, we show that there is a

Cordula Reisch, Adrián Navas-Montilla, Ilhan Özgen-Xian

Understanding the dynamics of wildfire is crucial for developing management and intervention strategies. Mathematical and computational models can be used to improve our understanding of wildfire processes and dynamics. This paper presents a systematic study of a widely used advection-diffusion-reaction wildfire model with non-linear coupling. The importance

Xinze Li, Josep Pou, Jiaxin Dong, Fanfan Lin

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficie

Luman Ju, Kai Xu, Engui Fan

With $\bar{\partial}$-generalization of the Deift-Zhou steepest descent method, we investigate the long-time asymptotics of the solution to the Cauchy problem for the Hunter-Saxton (HS) equation \begin{eqnarray} &&u_{txx}-2\omega u_x+2u_xu_{xx}+uu_{xxx}=0,\quad x\in \mathbb{R},\ t>0,\nonumber\\ &&u(x,0)=u_0(x), \nonumber \end{eqnarray} where $u_0\in H^{3,4}(

Sang-Hoon Lee, Ha-Yeong Choi, Hyung-Seok Oh, Seong-Whan Lee

Despite rapid progress in the voice style transfer (VST) field, recent zero-shot VST systems still lack the ability to transfer the voice style of a novel speaker. In this paper, we present HierVST, a hierarchical adaptive end-to-end zero-shot VST model. Without any text transcripts, we only use the speech dataset to train the model by utilizing hierarchical

X. Li, X. Zhang, F. Lin, F. Blaabjerg

Parameter design is significant in ensuring a satisfactory holistic performance of power converters. Generally, circuit parameter design for power converters consists of two processes: analysis and deduction process and optimization process. The existing approaches for parameter design consist of two types: traditional approach and computer-aided optimizatio

Kazuma Nagao, Ippei Danshita, Seiji Yunoki

We develop a discrete truncated Wigner method to analyze the real-time evolution of dissipative SU(${\cal N}$) spin systems coupled with a Markovian environment. This semiclassical approach is not only numerically efficient but also particularly capable of accurately capturing local loss processes due to its local linearity in the dynamical equations. We app

Khoa D. Vo, Len T. Bui

The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior knowledge of the degradation process that caused the image to be low-resolution. The State of the Art (SOTA) model Real-ESRGAN has advanced perceptual loss and produced visually compelling outcomes using more complex degradation models to simulate

Piet Groeneboom, Geurt Jongbloed

In the recent paper [5], a Bayesian approach for constructing confidence intervals in monotone regression problems is proposed, based on credible intervals. We view this method from a frequentist point of view, and show that it corresponds to a percentile bootstrap method of which we give two versions. It is shown that a (non-percentile) smoothed bootstrap m

Jacob Bendsen, Peter Emil Carstensen, Asbjørn Thode Reenberg, Tobias K. S. Ritschel

We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including the brain, heart and lungs, liver, gut, and kidney, as well as muscle and adipose tissue. The model equations are formulated using stoichiometry and Michaelis-Menten kinetics to d

Yuguo Su, Wangjun Lu, Hai-Long Shi

Quantum metrology is recognized for its capability to offer high-precision estimation by utilizing quantum resources, such as quantum entanglement. Here, we propose a generalized Tavis-Cummings model by introducing the $XY$ spin interaction to explore the impact of the many-body effect on estimation precision, quantified by the quantum Fisher information (QF

Valentino Barbieri, Javier González-Colsa, Diana Matias, Aroa Duro-Castano

Polymersomes, vesicles self-assembled from amphiphilic polymers, are promising nanocarriers for the targeted intracellular delivery of therapeutics. Integrating inorganic light-absorbing materials with plasmonic properties, such as gold, into their membrane by in situ synthesis is a stepping stone to enable their use for photothermal therapy. Yet, it still n

Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev

Estimating the ratio of two probability densities from finitely many observations of the densities is a central problem in machine learning and statistics with applications in two-sample testing, divergence estimation, generative modeling, covariate shift adaptation, conditional density estimation, and novelty detection. In this work, we analyze a large clas

S. Nirmala Jenifer, Dibakar Ghosh, Paulsamy Muruganandam

We present a formula for determining synchronizability in large, randomized and weighted simplicial complexes. This formula leverages eigenratios and costs to assess complete synchronizability under diverse network topologies and intensity distributions. We systematically vary coupling strengths (pairwise and three-body), degree and intensity distributions t

Kaede Shintani, Hamada Rizk, Hirozumi Yamaguchi

With the increasing number of IoT devices, there is a growing demand for energy-free sensors. Human activity recognition holds immense value in numerous daily healthcare applications. However, the majority of current sensing modalities consume energy, thus limiting their sustainable adoption. In this paper, we present a novel activity recognition system that

Kemal Gültekin

The main aim of this study is to reveal curved space and particle physics effects on the formation of Bose-Einstein condensate (BEC) scalar fields in cosmology and around a black hole. Cosmological scalar fields for dark energy and dark matter may be considered as a result of Bose-Einstein condensation. In this regard, our main attention will be devoted to B

Yusheng Wang, Yonghoon Ji, Chujie Wu, Hiroshi Tsuchiya

2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging. There are demands to estimate 3D information per image for 3D mapping and robot navigation during fly-through missions. Recent learning-based methods have demonst

Sally Dong, Thomas Rothvoss

We show that any bounded integral function $f : A \times B \mapsto \{0,1, \dots, \Delta\}$ with rank $r$ has deterministic communication complexity $\Delta^{O(\Delta)} \cdot \sqrt{r} \cdot \log r$, where the rank of $f$ is defined to be the rank of the $A \times B$ matrix whose entries are the function values. As a corollary, we show that any $n$-dimensional

Jeffrey Kuan, Sunčica Čanić, Boris Muha

We investigate weak solutions to a fluid-structure interaction (FSI) problem between the flow of an incompressible, viscous fluid modeled by the Navier-Stokes equations, and a poroviscoelastic medium modeled by the Biot equations. These systems are coupled nonlinearly across an interface with mass and elastic energy, modeled by a reticular plate equation, wh

Rajashekhar V S, Dhaya C, Dinakar Raj C K, Dharshan P

In this work we present a technique to select the best robot for accomplishing a task assuming that the map of the environment is known in advance. To do so, capabilities of the robots are listed and the environments where they can be used are mapped. There are five robots that included for doing the tasks. They are the robotic lizard, half-humanoid, robotic

Yury A. Budkov, Petr E. Brandyshev

Based on the variational field theory framework, we extend our previous mean-field formalism, taking into account the electrostatic correlations of the ions. We employ a general covariant approach and derive a total stress tensor that considers the electrostatic correlations of ions. This is accomplished through an additional term that depends on the autocor

H. Rehman, G. Abbas, Tao Zhu, G. Mustafa

This paper deals with astrophysical accretion onto the magnetically charged Euler-Heisenberg black holes with scalar hair. We examine the accretion process of a variety of perfect fluids, including polytropic and isothermal fluids of the ultra-stiff, ultra-relativistic, and sub-relativistic forms, when fluid is accreting in the vicinity of the black hole. By

Junzhe Liu, Jin Lei, Zhongzhou Ren

We examine the validity of surface approximation for breakup reactions using a fully quantum-mechanical model proposed by Ichimura, Austern, and Vincent (IAV). Analogous to the semi-classical picture, we introduce radial cut-offs to scattering waves in the IAV framework, which we refer to as IAV-cut. Systematic calculations are conducted for nonelastic break

Yongming Luo

The present paper is inspired by a previous work \cite{Luo_Waveguide_MassCritical} of the author, where the large data scattering problem for the focusing cubic nonlinear Schr\"odinger equation (NLS) on $\mathbb{R}^2\times\mathbb{T}$ was studied. Nevertheless, the results from \cite{Luo_Waveguide_MassCritical} are by no means sharp, as we could not even prov

Elvis Han Cui, Bingbin Li, Yanan Li, Weng Kee Wong

Data augmentation for deep learning benefits model training, image transformation, medical imaging analysis and many other fields. Many existing methods generate new samples from a parametric distribution, like the Gaussian, with little attention to generate samples along the data manifold in either the input or feature space. In this paper, we verify that t

Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator in the distributional setting. To fully understand how the approximation errors of the Q-function affect the whole training process, we do some error analysis and theoretically sho

Chenyi Zhuang, Pan Gao, Aljosa Smolic

GAN inversion aims at inverting given images into corresponding latent codes for Generative Adversarial Networks (GANs), especially StyleGAN where exists a disentangled latent space that allows attribute-based image manipulation at latent level. As most inversion methods build upon Convolutional Neural Networks (CNNs), we transfer a hierarchical vision Trans

Wen-Long Sang, Tao Wang, Yu-Dong Zhang, Feng Feng

In this study, we compute the electromagnetic and hadronic decay widths of the S-wave fully heavy tetraquark $T_{4Q}$ ($Q=c$ or $b$) at lowest order in $\alpha_s$ and $v$, in the framework of nonrelativistic QCD. The short-distance coefficients are determined through the standard procedure of matching. The nonperturbative long-distance matrix elements are re

Xun Yuan, Yang Yang, Asif Iqbal, Prosanta Gope

Energy theft, characterized by manipulating energy consumption readings to reduce payments, poses a dual threat-causing financial losses for grid operators and undermining the performance of smart grids. Effective Energy Theft Detection (ETD) methods become crucial in mitigating these risks by identifying such fraudulent activities in their early stages. How

Johanne Hizanidis, Vassilios Kovanis

The optical power spectrum is the prime observable to dissect, understand and design the long-time behavior of arrays of optically coupled semiconductor lasers. A long-standing issue has been identified within the literature on injection locking: how the thickness of linewidth and the lineshape waveform envelope correlates with the deterministic evolution of

Jinyu Pan, Zhiyuan Huang, Yifei Chen, Fei Yu

We studied the underlying mechanism of broadband dispersive-wave emission within a resonance band of gas-filled anti-resonant hollow-core fiber. Both theoretical and experimental results unveiled that the high-order soliton, launched into the hollow-core fiber, experienced two stages of pulse compression, resulting in a multi-peak structure of the dispersive

P. M. Biesheuvel, H. Fan, M. Elimelech

We extend classical Flory-Rehner theory for the expansion and compression of porous materials such as cross-linked polymer networks. The theory includes volume exclusion, affinity with the solvent, and finite stretching of the polymer chains. We also modify this equilibrium theory -- that applies to equal expansion of a material in all directions -- to the s

Jianghong Li, Ying Lei, Songbai Yao, Jingtian Yu

In this study, a numerical investigation based on the Eulerian-Lagrangian model is conducted to explore a rotating detonation engine (RDE) fueled by liquid ethanol. The focus is on examining the characteristic phenomena of the two-phase rotating detonation wave (RDW) caused by droplet evaporation and varying inlet conditions. To enhance the evaporation of li

Pan Gao, Haoyue Tian, Jie Qin

Video frame interpolation has been actively studied with the development of convolutional neural networks. However, due to the intrinsic limitations of kernel weight sharing in convolution, the interpolated frame generated by it may lose details. In contrast, the attention mechanism in Transformer can better distinguish the contribution of each pixel, and it

Minh Hieu Phan, Zhibin Liao, Johan W. Verjans, Minh-Son To

Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have

Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli, Amirhossein Kazerouni

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to

Rua-Huan Tsaih, Yu-Hang Chien, Shih-Yi Chien

Studies on artificial neural networks rarely address both vanishing gradients and overfitting issues. In this study, we follow the pupil learning procedure, which has the features of interpreting, picking, understanding, cramming, and organizing, to derive the pupil learning mechanism (PLM) by which to modify the network structure and weights of 2-layer neur

Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu

Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel ($3\times3$ or more). However, the heavy computational footprint of such models prevents their deployment in real-time, resource-constrained environments. Conversely, $1\times1$ convolutions bring substantial computational

Chen Zhang

In modern dialogue systems, the use of Large Language Models (LLMs) has grown exponentially due to their capacity to generate diverse, relevant, and creative responses. Despite their strengths, striking a balance between the LLMs' creativity and their faithfulness to external knowledge remains a key challenge. This paper presents an innovative user-controlla

Jingxue Feng, Liangliang Wang

The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. A Beta-Dirichlet switching state-space transmission model is proposed to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As ti

Alexander Mielke, Riccarda Rossi, Artur Stephan

We consider generalized gradient systems in Banach spaces whose evolutions are generated by the interplay between an energy functional and a dissipation potential. We focus on the case in which the dual dissipation potential is given by a sum of two functionals and show that solutions of the associated gradient-flow evolution equation with combined dissipati

Joel Q. L. Chang

We provide a simple reformulation of the $\epsilon$-$\delta$ limit definition introduced in undergraduate calculus courses that enhances its pedagogical value for conceptual understanding and computational skill.

Yiming Pan, Ruoyu Yin

We present the construction of a reciprocal electromagnetic field by extending the Berry curvatures into four-dimensional (4D) energy-momentum space. The resulting governing equations, termed Berry-Maxwell equations, are derived, by incorporating Lorentz invariance to constrain the parameter space of energy-momentum. Notably, these Berry-Maxwell equations ex

Samra Kanwal, Chun Yun Kee, L. K. Ang

In this study, analytical models for space charge limited current (SCLC) transport in a porous (or disordered) trap-limited dielectric are derived for both planar and cylindrical configuration. By considering the porous solid as a fractional object characterized by a parameter a less than 1, we formulate its fractional capacitance and determine the SCLC tran

Daniele Paolo Anderle, Aiqiang Guo, Felix Hekhorn, Yutie Liang

The Electron-Ion Collider in China (EicC) has been proposed to study the inner structure of matter and fundamental laws of strong interactions. In this paper, we will present a conceptual design of the tracking system based on the state-of-art silicon detector and Micro-Pattern Gaseous Detector at the EicC and demonstrate that it will enable us to reconstruc

Qingwen Wang, Ying Jiang, Shiguang Feng, Lvzhou Li

Spatial search is an important problem in quantum computation, which aims to find a marked vertex on a graph. We propose a novel approach for designing deterministic quantum search algorithms on a variety of graphs via alternating quantum walks. Our approach is universal because it does not require an instance-specific analysis for different graphs. We highl

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be a short Artin local ring (i.e., $\mathfrak{m}^3 = 0$ and $\mathfrak{m}^2 \neq 0$). Assume $A$ is not a hypersurface ring. We show there exists $c_A \geq 2$ such that if $M$ is any finitely generated module with bounded betti-numbers then $c_A $ divides $\ell(M)$, the length of $M$. If $A$ is not a complete intersection then there ex

Henry W. Lin, Douglas Stanford

The double-scaled limit of the Sachdev-Ye-Kitaev (SYK) model takes the number of fermions and their interaction number to infinity in a coordinated way. In this limit, two entangled copies of the SYK model have a bulk description of sorts known as the "chord Hilbert space." We analyze a symmetry algebra acting on this Hilbert space, generated by the two Hami

Jiepeng Fang, Yixin Lan, Jie Xiao

We consider the localization $\mathcal{Q}_{\mathbf{V},\mathbf{W}}/\mathcal{N}_{\mathbf{V}}$ of Lusztig's sheaves for framed quivers, and define functors $E^{(n)}_{i},F^{(n)}_{i},K^{\pm}_{i},n\in \mathbb{N},i \in I$ between the localizations. With these functors, the Grothendieck group of localizations realizes the irreducible integrable highest weight module

Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui, Kosuke Ito

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring and simulation of quantum many-body systems. In the last decade, research and development of QCs have rapidly advanced. Now hundreds of physical qubits are at our disposal, and on

Robert C. Dalang, Fei Pu

We study the hitting probabilities of the solution to a system of $d$ stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive $d-6$-dimensional capacity, the solution visits this set almost surely.

Jean-Luc Lupien, Iman Shames, Antoine Lesage-Landry

An important challenge in the online convex optimization (OCO) setting is to incorporate generalized inequalities and time-varying constraints. The inclusion of constraints in OCO widens the applicability of such algorithms to dynamic and safety-critical settings such as the online optimal power flow (OPF) problem. In this work, we propose the first projecti

Chengyuan Zhang, Rui Chen, Jiacheng Zhu, Wenshuo Wang

Following a leading vehicle is a daily but challenging task because it requires adapting to various traffic conditions and the leading vehicle's behaviors. However, the question `Does the following vehicle always actively react to the leading vehicle?' remains open. To seek the answer, we propose a novel metric to quantify the interaction intensity within th

Pengjie Shi, Shizhe Feng, Zhiping Xu

A high-fidelity neural network-based force field, NN-F$^{3}$, is developed to cover the strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F$^{3}$ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance canno

Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge

Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessm

Faizuddin Ahmed

In this research work, our primary focus revolves around the examination of a specific category of traversable wormholes known as topologically charged generalized Schwarzschild-Simpson-Visser-type wormhole, $ds^2=-\Big(1-\frac{2\,M}{\sqrt{x^2+b^2}}\Big)\,dt^2+\Big(1-\frac{2\,M}{\sqrt{x^2+b^2}}\Big)^{-1}\,\Big(\frac{dx^2}{\alpha^2}\Big)+(x^2+a^2)\,(d\theta^2

Weiwei Zhu, Heng Xu, Dejiang Zhou, Lin Lin

The megajansky radio burst, FRB 20200428, and other bright radio bursts detected from the Galactic source SGR J1935+2154 suggest that magnetars can make fast radio bursts (FRBs), but the emission site and mechanism of FRB-like bursts are still unidentified. Here we report the emergence of a radio pulsar phase of the magnetar five months after FRB 20200428. 7

Ghadeer Almusaddar, Hoda Naghibijouybari

In heterogeneous SoCs, accelerators like integrated GPUs (iGPUs) are integrated on the same chip as CPUs, sharing the memory subsystem. In such systems, the massive memory requests from throughput-oriented accelerators significantly interfere with CPU memory requests. In addition to the large performance impact, this interference provides an attacker with a

Arkajit Ghosh, Wenqian Wu, Bibhu Prasad Sahu, Jian Wang

Nano-scale eutectics, such as rapid solidified Al-Si, exhibit enhanced yield strength and strain hardening but plasticity is limited by cracking of the hard phase (Si). Mechanisms that may suppress cracking and enable plastic co-deformation of soft and hard phases are key to maximizing plasticity in these high-strength microstructures. Using a combination of

Yang Lou, Qun Song, Qian Xu, Rui Tan

Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception. However, many existing fusion schemes do not consider the quality of each fusion input and may suffer from adverse conditions on one or more sensors. While predictive uncertainty has been applied to characterize single-modal object detection performa

Qingping Zhou, Jiayu Qian, Junqi Tang, Jinglai Li

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many linear inverse problems, nonlinear problems tend to impair the performance of the method. Inspired by momentum a

Changjie Chen

We provide a new approach to studying the moduli space of curves via Morse theory and hyperbolic geometry, by introducing a family of Morse functions on the moduli space $\overline{\mathcal{M}}_{g,n}$ of stable curves of genus $g$ with $n$ marked point, from the Teichm\"uller theoretic perspective. They are weighted exponential averages of the lengths of all

Jiaqi Liu, Peng Hang, Xiao qi, Jianqiang Wang

Autonomous driving technology is poised to transform transportation systems. However, achieving safe and accurate multi-task decision-making in complex scenarios, such as unsignalized intersections, remains a challenge for autonomous vehicles. This paper presents a novel approach to this issue with the development of a Multi-Task Decision-Making Generative P

Cen Liu, Guangxu Zhu, Fan Liu, Yuanwei Liu

The millimeter wave (mmWave) radar sensing-aided communications in vehicular mobile communication systems is investigated. To alleviate the beam training overhead under high mobility scenarios, a successive pose estimation and beam tracking (SPEBT) scheme is proposed to facilitate mmWave communications with the assistance of mmWave radar sensing. The propose

Zhijie Xia, Kyzyl Monteiro, Kevin Van, Ryo Suzuki

We introduce RealityCanvas, a mobile AR sketching tool that can easily augment real-world physical motion with responsive hand-drawn animation. Recent research in AR sketching tools has enabled users to not only embed static drawings into the real world but also dynamically animate them with physical motion. However, existing tools often lack the flexibility

Yu Yan, Hongzhi Wang, Jian Geng, Jian Ma

The knobs of modern database management systems have significant impact on the performance of the systems. With the development of cloud databases, an estimation service for knobs is urgently needed to improve the performance of database. Unfortunately, few attentions have been paid to estimate the performance of certain knob configurations. To fill this gap

Keiichi Ihara, Mehrad Faridan, Ayumi Ichikawa, Ikkaku Kawaguchi

This paper introduces HoloBots, a mixed reality remote collaboration system that augments holographic telepresence with synchronized mobile robots. Beyond existing mixed reality telepresence, HoloBots lets remote users not only be visually and spatially present, but also physically engage with local users and their environment. HoloBots allows the users to t

Andrea Elizabeth Biju, Sneha Srikanth, Krishna Manoj, Samadhan A. Pawar

The framework of mutually coupled oscillators on a network has served as a convenient tool for investigating the impact of various parameters on the dynamics of real-world systems. Compared to large networks of oscillators, minimal networks are more susceptible to changes in coupling parameters, the number of oscillators, and network topologies. In this stud

Shu-Feng Tsao, Helen Chen, Samantha Meyer, Zahid A. Butt

Existing communications and behavioral theories have been adopted to address health misinformation. Although various theories and models have been used to investigate the COVID-19 pandemic, there is no framework specially designed for social listening or misinformation studies using social media data and natural language processing techniques. This study aim

Neil Chulpongsatorn, Mille Skovhus Lunding, Nishan Soni, Ryo Suzuki

We introduce Augmented Math, a machine learning-based approach to authoring AR explorable explanations by augmenting static math textbooks without programming. To augment a static document, our system first extracts mathematical formulas and figures from a given document using optical character recognition (OCR) and computer vision. By binding and manipulati

Mohamed Belfkir, Talal Ahmed Chowdhury, Salah Nasri

We investigate the prospects of probing the doubly-charged scalars of the minimal Left-Right Symmetric model (MLRSM) at a muon collider. We assess its capability by studying the production of doubly-charged scalars and their subsequent decay into four charged lepton final states containing the same-charge lepton pairs. We find that the channels with same-cha

Yan D. R. Machado, Gleice C. M. Germano, Edison Pecoraro, Antonio Mario L. M. Costa

In this work, we compared the effect of Quartzite nanoparticles (QtzNP - crystalline SiO$_2$) and amorphous silica nanoparticles (ASNP) as scatters in a random laser (RL), correlating the laser efficiency to the morphology and refractive index (RI) of the scatterers. The RL was based on a laser media containing suspensions of QtzNPs or ASNPs in Rhodamine 6G