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arXiv 2022年

作者： Kumar, Prabhat Nonaka, Andrew Jambunathan, Revathi Pahwa, Girish Salahuddin, Sayeef Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA94720 United States Berkeley Device Modeling Center University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

We present a massively parallel, 3D phase-field simulation framework for modeling ferroelectric materials based scalable logic devices. This code package, FerroX, self-consistently solves the time-dependent Ginzburg Landau (TDGL) equation for ferroelectric polarization, Poisson’s equation for electric potential, and charge equation for carrier densities in semiconductor regions. The algorithm is implemented using the AMReX software framework 1, which provides effective scalability on manycore and GPU-based supercomputing architectures. We demonstrate the performance of the algorithm with excellent scaling results on NERSC multicore and GPU systems, with a significant (15x) speedup on the GPU using a node-by-node comparison. We further demonstrate the applicability of the code in simulations of ferroelectric domain-wall induced negative capacitance (NC) effect in Metal-Ferroelectric-Insulator-Metal (MFIM) and Metal-Ferroelectric-Insulator-Semiconductor-Metal (MFISM) devices. The charge (Q) v.s. voltage (V) responses for these 3D structures clearly indicates stabilized negative capacitance with multidomain formation, which is corroborated by amplification of the voltage at the interface between the ferroelectric and dielectric layers. © 2022, CC BY.

关键词： Electric potential

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Smart Home Technology Network Construction Method and Device Interaction organization Concept 2

Smart Home Technology Network Construction Method and Device...

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2nd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2020

作者： Dudnik, Andriy Kuzmych, Lyudmyla Trush, Olexander Domkiv, Tetyana Leshchenko, Olga Vyshnivskyi, Viktor Kyiv National Taras Shevchenko University Department of Network and Internet Technologies Kyiv Ukraine National Aviation University Department of Computerized Electrotechnical Systems and Technologies Kyiv Ukraine Kyiv National Taras Shevchenko University Department of Network and Internet Technologies Kyiv Ukraine Interregional Academy of Personnel Monitoring Department of Computational Mathematics and Computer Modeling Kyiv Ukraine State University of Telecommunications Department of Computer Science Kyiv Ukraine

ISBN: (纸本)9781728190822

The main tasks confirming the scientific novelty and practical value of this paper are the development, improvement and research of methods and technologies for constructing the Smart Home system. In this work, an intelligent home network monitoring system is developed, which is divided into subsystems for controlling electrical and electro-mechanical devices. A method has been developed for measuring the distance between wireless sensors in a smart home system, in order to determine the point of occurrence of an emergency situation. An algorithm for 'bypassing' wireless sensor devices in a smart home system has been developed and studied, which allows it to function in areas of lack of direct visibility. A concept has been developed for placing sensors and ensuring the stable functioning of systems, which allows for timely response to emergency situations. © 2020 IEEE.

关键词： Internet of things

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Automated Object Recognition System based on Convolutional Autoencoder

Automated Object Recognition System based on Convolutional A...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Pylyp Prystavka Olha Cholyshkina Serge Dolgikh Denys Karpenko Department of Applied Mathematics National Aviation University Kyiv Ukraine Department of Computational Mathematics and Computer Modeling Interregional Academy of Personnel Management Kyiv Ukraine Department of Network Engineering Solana Networks Ottawa Canada

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

This paper describes the model, implementation and the experimental verification of an aerial image processing and recognition technology based on artificial neural networks, specifically, convolutional autoencoders and classifying perceptrons. An originally developed model that includes autoencoder preprocessing for compression and extraction of informative features was applied to the task of pattern recognition, namely, locating and identifying the objects of certain higher-level classes of interest in the images produced by aerial photography. Classification efficiency of the method was measured and compared with other common methods of classification, the advantages and shortcomings of the proposed approach analyzed and potential applications in real-time remote object recognition systems, as well as in automating the generation of training data for image recognition systems discussed.

关键词： Training Object recognition Feature extraction Image recognition Buildings Image segmentation Data visualization

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An extended triangulation to the Marching Cubes 33 algorithm

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Journal of the Brazilian computer Society 2019年第1期25卷 1页

作者： Custodio, Lis Pesco, Sinesio Silva, Claudio Computational Modeling Department Polytechnic Institute at the Rio de Janeiro State University Rio de Janeiro Brazil Department of Mathematics Pontificial Catholic University of Rio de Janeiro Rio de Janeiro Brazil Computer Science and Engineering Department New York University New York City United States

The Marching Cubes algorithm is arguably the most popular isosurface extraction algorithm. Since its inception, two problems have lingered, namely, triangle quality and topology correctness. Although there is an extensive literature to solve them, topology correctness is achieved in detriment of triangle quality and vice versa. In this paper, we present an extended version of the Marching Cubes 33 algorithm (a variation of the Marching Cubes algorithm which guarantees topological correctness), called Extended Marching Cubes 33. In the proposed algorithm, the grid vertex are labeled with "+," " −," and "=," according to the relationship between its scalar field value and the isovalue. The inclusion of the "=" grid vertex label naturally avoids degenerate triangles. As an application of our method, we use the proposed triangulation to improve the quality of the triangles in the generated mesh while preserving its topology as much as possible. © 2019, The Author(s).

关键词： Triangulation

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review Applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

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Numerical modeling of active microcavities with piercing holes using the Muller boundary integral equations and the Galerkin method

Numerical modeling of active microcavities with piercing hol...

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International Seminar Day on Diffraction

作者： Alina O. Oktyabrskaya Alexander O. Spiridonov Evgenii M. Karchevskii Institute of Computational Mathematics and Information Technologies Kazan Federal University Russia Laboratory of Computational Technologies and Computer Modeling Kazan Federal University Russia Department of Applied Mathematics Kazan Federal University Russia

ISBN: (数字)9781728158372

ISBN: (纸本)9781728158389

The current paper proposes a Galerkin method for calculating spectral characteristics of microcavity lasers with piercing holes. If boundaries of the cavity and holes are circles, then entries of the matrix have explicit expressions computed in the present work. Using the proposed algorithm, we calculate directivities, spectra, and thresholds of laser modes. Numerical experiments demonstrate that, by varying the location of the hole, we can increase the directivity, while the threshold gain stays low.

关键词： Eigenvalues and eigenfunctions Microcavities Mathematical model Method of moments Diffraction Integral equations Lasers

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Foundational Competencies and Responsibilities of a Research Software Engineer

arXiv

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： computer aided software engineering

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Design of turbomachinaries using numerical simulation results

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Journal of Physics: Conference Series 2020年第1期1611卷

作者： E Gurina Department of computational mathematics and computer modeling Tomsk State University Tomsk 40 Lenin str. 634050 Russia

computer simulation is the most modern method in the field of evaluating the parameters of a developed product in engineering problems. The essence of this method is to replace the real physical process that occurs during the operation of turbomachinery, with its mathematical model, and solve the problem with the help of modern computers. An approach with the replacement of a long (from several months to a year) and expensive development cycle by a numerical experiment (from several days to a month) will allow us to detect and eliminate the main flaws at the stage of product design, without realizing intermediate prototypes. Distributions of the main gas-dynamic parameters of the process studied (airflow velocity and pressure, mass flow rate) were obtained.

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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