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40th International Symposium on Lattice Field Theory, LATTICE 2023

作者： Bennett, Ed Boyle, Peter Del Debbio, Luigi Forzano, Niccolò Hong, Deog Ki Lee, Jong-Wan Lenz, Julian Lin, C.-J. David Lucini, Biagio Lupo, Alessandro Piai, Maurizio Vadacchino, Davide Swansea Academy of Advanced Computing Swansea University Bay Campus Fabian Way Wales SwanseaSA1 8EN United Kingdom School of Physics and Astronomy University of Edinburgh EdinburghEH9 3FD United Kingdom Physics Department Brookhaven National Laboratory UptonNY11973 United States Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SwanseaSA2 8PP United Kingdom Department of Physics Pusan National University Busan46241 Korea Republic of Institute for Extreme Physics Pusan National University Busan46241 Korea Republic of Daejeon34126 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Centre for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Bay Campus Fabian Way Wales SwanseaSA1 8EN United Kingdom Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

Four-dimensional gauge theories based on symplectic Lie groups provide elegant realisations of the microscopic origin of several new physics models. Numerical studies pursued on the lattice provide quantitative information necessary for phenomenological applications. To this purpose, we implemented Sp(2N) gauge theories using Monte Carlo techniques within Grid, a performant framework designed for the numerical study of quantum field theories on the lattice. We show the first results obtained using this library, focusing on the case-study provided by the Sp(4) theory coupled to Nas = 4 Wilson-Dirac fermions transforming in the 2-index antisymmetric representation. In particular, we discuss preliminary tests of the algorithm and we test some of its main functionalities. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Lattice theory

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Layer-splitting methods for time-dependent Schrödinger equations of incommensurate systems

arXiv

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arXiv 2021年

作者： Wang, Ting Chen, Huajie Zhou, Aihui Zhou, Yuzhi School of Mathematical Sciences Beijing Normal University Beijing100875 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100094 China

This work considers numerical methods for the time-dependent Schrödinger equation of incommensurate systems. By using a plane wave method for spatial discretization, the incommensurate problem is lifted to a higher dimension that results in semidiscrete differential equations with extremely demanding computational cost. We propose several fully discrete time stepping schemes based on the idea of "layer-splitting", which decompose the semidiscrete problem into sub-problems that each corresponds to one of the periodic layers. Then these schemes handle only some periodic systems in the original lower dimension at each time step, which reduces the computational cost significantly and is natural to involve stochastic methods and parallel computing. Both theoretical analysis and numerical experiments are provided to support the reliability and efficiency of the algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Numerical methods

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Blockchain based electronic educational document management with role-based access control using machine learning model

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Scientific Reports 2025年第1期15卷 1-19页

作者： Chinnasamy, P. Subashini, B. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Pandey, Binay Kumar Pandey, Digvijay Lelisho, Mesfin Esayas Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India Department of Technical Education (Government of Uttar Pradesh) Uttar Pradesh Kanpur India Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%. © The Author(s) 2025.

关键词： Blockchain Electronic educational document management Remora swarm optimization Role-based access Temporal neural network

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A Robust Remote Client Password Authentication Technique for Smart Cities E-Governance Applications

A Robust Remote Client Password Authentication Technique for...

引用

Communications, computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Mohammad S. Obaidat Chandrashekhar Meshram Deepak Kartikey Kuei-Fang Hsiao Agbotiname Lucky Imoize Sharad Kumar Barve King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University Kattankulathur TN India School of Engineering The Amity University Noida UP India Department of Post Graduation Studies and Research in Mathematics Jayawanti Haksar Government Post Graduation College College of Barkatullah University Betul M.P. India Department of Mathematics S.S.P. Govt. College Waraseoni Balaghat M.P. India Computer Science Department University of Texas-Permian Basin Odessa TX USA Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Mathematics Pandit S. N. Shukla University Shahdol Shahdol Madhya Pradesh India

Smart cities are fast proliferating the global space in recent times. In order to achieve the envisioned next-generation cities, the smart city paradigm aspires to develop a completely integrated network-based infrastructure. With the development of innovative smart cities, ICT-based e-governance has made it possible for services to be delivered intelligently and interactively via online platforms. However, the communication between government agencies and other stakeholders in the smart city ecosystem is through open channels, which poses serious security concerns. In order to address this problem, this work proposes a robust remote client password authentication technique for e-governance applications in smart cities. The proposed authentication technique is lightweight, resistant to several attacks, and achieves low computational overhead.

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A multimodal framework for extraction and fusion of satellite images and public health data

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Scientific Data 2024年第1期11卷 634页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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Disconnection-mediated migration of interfaces in microstructures: I. Continuum model

arXiv

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arXiv 2021年

作者： Han, Jian Srolovitz, David J. Salvalaglio, Marco Department of Materials Science and Engineering City University of Hong Kong Hong Kong Hong Kong Department of Mechanical Engineering University of Hong Kong Pokfulam Road Hong Kong Hong Kong Hong Kong Institute for Advanced Study City University of Hong Kong Hong Kong Hong Kong Institute of Scientific Computing TU Dresden Dresden01062 Germany Dresden Center for Computational Materials Science TU Dresden Dresden01062 Germany

A long-standing goal of materials science is to understand, predict and control the evolution of microstructures in crystalline materials. Most microstructure evolution is controlled by interface motion;hence, the establishment of rigorous interface equations of motion is a universal goal of materials science. We present a new model for the motion of arbitrarily curved interfaces that respects the underlying crystallography of the two phases/domains meeting at the interface and is consistent with microscopic mechanisms of interface motion;i.e., disconnection migration (line defects in the interface with step and dislocation character). We derive the equation of motion for interface migration under the influence of a wide range of driving forces. In Part II of this paper [Salvalaglio, Han and Srolovitz, 2021], we implement the interface model and the equation of motion proposed in this paper in a diffuse interface simulation approach for complex morphology and microstructure evolution. Copyright © 2021, The Authors. All rights reserved.

关键词： Equations of motion

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Disconnection-mediated migration of interfaces in microstructures: II. Diffuse interface simulations

arXiv

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arXiv 2021年

作者： Salvalaglio, Marco Srolovitz, David J. Han, Jian Institute of Scientific Computing TU Dresden Dresden01062 Germany Dresden Center for Computational Materials Science TU Dresden Dresden01062 Germany Hong Kong Institute for Advanced Study City University of Hong Kong Hong Kong Hong Kong Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong Hong Kong Department of Materials Science and Engineering City University of Hong Kong Hong Kong Hong Kong

The motion of interfaces is an essential feature of microstructure evolution in crystalline materials. While atomic-scale descriptions provide mechanistic clarity, continuum descriptions are important for understanding microstructural evolution and upon which microscopic features it depends. We develop a microstructure evolution simulation approach that is linked to the underlying microscopic mechanisms of interface migration. We extend the continuum approach describing the disconnection-mediated motion of interfaces introduced in Part I [Han, Srolovitz and Salvalaglio, 2021] to a diffuse interface, phase-field model suitable for large-scale microstructure evolution. A broad range of numerical simulations showcases the capability of the method and the influence of microscopic interface migration mechanisms on microstructure evolution. These include, in particular, the effects of stress and its coupling to interface migration which arises from disconnections, showing how this leads to important differences from classical microstructure evolution represented by mean curvature flow. Copyright © 2021, The Authors. All rights reserved.

关键词： Numerical methods

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Neural-network quantum states for ultra-cold Fermi gases

arXiv

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arXiv 2023年

作者： Kim, Jane Fore, Bryce Carleo, Giuseppe Nys, Jannes Pescia, Gabriel Gandolfi, Stefano Hjorth-Jensen, Morten Lovato, Alessandro Department of Physics and Astronomy Facility for Rare Isotope Beams Michigan State University East LansingMI48824 United States Physics Division Argonne National Laboratory ArgonneIL60439 United States LausanneCH-1015 Switzerland Center for Quantum Science and Engineering École Polytechnique Fédérale de Lausanne [EPFL LausanneCH-1015 Switzerland Theory Division T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics Center for Computing in Science Education University of Oslo OsloN-0316 Norway Computational Science Division Argonne National Laboratory ArgonneIL60439 United States INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento38123 Italy

Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to a bosonic superfluid BEC state, which can be probed experimentally with high precision. However, the theoretical description of these properties is challenging due to the onset of strong pairing correlations and the non-perturbative nature of the interaction among the constituent particles. This work introduces a novel Pfaffian-Jastrow neural-network quantum state that includes backflow transformation based on message-passing architecture to efficiently encode pairing, and other quantum mechanical correlations. Our approach offers substantial improvements over comparable ansätze constructed within the Slater-Jastrow framework and outperforms state-of-the-art diffusion Monte Carlo methods, as indicated by our lower ground-state energies. We observe the emergence of strong pairing correlations through the opposite-spin pair distribution functions. Moreover, we demonstrate that transfer learning stabilizes and accelerates the training of the neural-network wave function, enabling the exploration of the BCS-BEC crossover region near unitarity. Our findings suggest that neural-network quantum states provide a promising strategy for studying ultra-cold Fermi gases. Copyright © 2023, The Authors. All rights reserved.

关键词： Wave functions

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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

arXiv

引用

arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review Research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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