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arXiv 2024年

作者： Schnake, Stefan Kendrick, Coleman Endeve, Eirik Stoyanov, Miroslav Hahn, Steven Hauck, Cory D. Green, David L. Snyder, Phil Canik, John Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Fusion Energy Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Department of Mathematics University of Tennessee KnoxvilleTN37996 United States Type One Energy Group Oak RidgeTN37830 United States Commonwealth Scientific and Industrial Research Organisation Mayfield WestNSW2304 Australia

Sparse-grid methods have recently gained interest in reducing the computational cost of solving high-dimensional kinetic equations. In this paper, we construct adaptive and hybrid sparse-grid methods for the Vlasov–Poisson–Lenard–Bernstein (VPLB) model. This model has applications to plasma physics and is simulated in two reduced geometries: a 0x3v space homogeneous geometry and a 1x3v slab geometry. We use the discontinuous Galerkin (DG) method as a base discretization due to its high-order accuracy and ability to preserve important structural properties of partial differential equations. We utilize a multiwavelet basis expansion to determine the sparse-grid basis and the adaptive mesh criteria. We analyze the proposed sparse-grid methods on a suite of three test problems by computing the savings afforded by sparse-grids in comparison to standard solutions of the DG method. The results are obtained using the adaptive sparse-grid discretization library ASGarD. Copyright © 2024, The Authors. All rights reserved.

关键词： Integral equations

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Fitness dependent optimizer with neural networks for COVID-19 patients

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computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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Group Spam Identification in IOT Home Automation Using Tracking Time Metadata

Group Spam Identification in IOT Home Automation Using Track...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Achilov Docent Saurabh Dhanik Pardeep Kumar Jindal Saif O. Husain Diwakar Bhardwaj Varsha Mittal Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan School of Computing Graphic Era Hill University Bhimtal India Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

With the stark increase due to smarter homes using online of Things, or IoT devices exchanging info far more frequently than we used to — all wirelessly. IoT devices are affected by several issues like possible cyberattacks, the unpredictability of network connectivity and data leakage since there are many types of IoT devices being used today. OBJECTIVES: To improve the privacy of data provided from web of Things (IoT) smart home devices by using machine training and computational modeling to detect anomalies. At its core the experiment contribution is how reliable Web of Things (WoT), i.e. IoT technologies transferring values from or to home things, reasons are taking different features such as average sq rate error origin and feature value in account of evaluations about internet-connected device efficacy. It is a metric that seems so important when we consider our dependency on every attached Internet of Things (IoT) device, in the network's architecture. The proposed method is applied and validated using a dataset of smart homes along with real temperature information collected for public access. Using Time series evidence collected by iot devices, the suggested method works extremely well as it can detect any anomaly or spam. This technique helps with the general health and safety of your entire structure by creating a solid base for evaluating the acceptability of Sea-of-things (SoT) gadgets in just a smart home network..

关键词： Training Data privacy Time series analysis Smart homes Smart meters Telecommunication computing Internet of Things Reliability Object recognition Tuning

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Ten simple rules for measuring the impact of workshops

arXiv

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arXiv 2018年

作者： Sufi, Shoaib Duckles, Beth Simera, Iveta Nurmikko-Fuller, Terhi Bellis, Louisa Miah, Wadud Wilde, Adriana Nenadic, Aleksandra Silva, Raniere de Beyer, Jennifer A. Struthers, Caroline Emsley, Iain Philippe, Olivier Balzano, Melissa Coelho, Sara Ford, Heather Jones, Catherine Higgins, Vanessa School of Computer Science University of Manchester Manchester United Kingdom Portland State University PortlandOR United States UK EQUATOR Centre Centre for Statistics in Medicine NDORMS University of Oxford Oxford United Kingdom College of Arts and Social Sciences Australian National University Canberra Australia Cancer Research UK Cambridge Centre University of Cambridge Cambridge United Kingdom Numerical Algorithms Group Oxford United Kingdom School of Computer Science University of St Andrews St Andrews United Kingdom Oxford e-Research Centre University of Oxford Oxford United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom ELIXIR Hub Wellcome Genome Campus Cambridge United Kingdom EGI Foundation Amsterdam Netherlands School of Media and Communication University of Leeds Leeds United Kingdom Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory Didcot United Kingdom School of Social Sciences University of Manchester Manchester United Kingdom

Workshops are used to explore a specific topic, transfer knowledge, solve identified problems or create something new. In funded research projects and other research endeavours, workshops are the mechanism to gather the wider project, community or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops. Copyright © 2018, The Authors. All rights reserved.

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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Foundational Data science Training for Health Equity Researchers at Minority Serving Institutions: A SoRDS Event

Foundational Data Science Training for Health Equity Researc...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Robert Quick Marcela Alfaro Córdoba Stephen Diggs Raphael Cobe Louise Bezuidenhout Hugh Shannahan Bianca Peterson Pervasive Technology Institute Indiana University Bloomington IN Dept of Statistics University of California Santa Cruz Santa Cruz USA California Digital Library University of California Curation Center (UC3) Oakland CA Center for Scientific Computing São Paulo State University São Paulo Brazil Data Archiving and Networked Services The Hague Netherlands Department of Computer Science Royal Holloway University of London Egham England Fathom Data Potchefstroom South Africa

Contemporary research, particularly when addressing the most significant transdisciplinary research challenges, cannot effectively be done without a range of skills relating to data management, data analysis, and cyberinfrastructure (CI). These data and CI skills are common to all disciplines that conduct data-centric research. Research Data science acts as a vital component of the scientific process. In a grassroots attempt to address this gap, the CODATA-RDA Schools of Research Data science (SoRDS) was founded in 2016 to provide instruction on foundational data science and open research concepts to early career researchers in low and middle-income countries. This partnership between international collaborators has since 2016 provided this training to over 1000 early career researchers in 24 events in 10 countries worldwide. The most recent event was held at Georgia Institute of Technology and focused on health equity and included researchers from minority-serving institutions in the southeast United States. This paper covers the background of the SoRDS project along with organization and curriculum details. It also covers the transition of the events from a non-domain-centric curriculum to spotlighting biological and social health equity data and what we learned to make future health-related events more engaging and valuable to the attendees. It also looks toward future events that will serve international students studying health informatics and other data-centric disciplines.

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

arXiv

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arXiv 2024年

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Mathematics Florida State University TallahasseeFL32306 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Scientific Computing Florida State University TallahasseeFL32306 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weather. While these models show considerable potential, they are not ready yet for operational use in weather forecasting or climate prediction. This is due to the lack of a data assimilation method as part of their workflow to enable the assimilation of incoming Earth system observations in real time. This limitation affects their effectiveness in predicting complex atmospheric phenomena such as tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, namely, the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. The ViT surrogate can represent either physics-based models or AI-based foundation models. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasigeostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. Copyright © 2024, The Authors. All rights reserved.

关键词： Supercomputers

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GPU algorithms for efficient exascale discretizations

arXiv

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arXiv 2021年

作者： Abdelfattah, Ahmad Barra, Valeria Beams, Natalie Bleile, Ryan Brown, Jed Camier, Jean-Sylvain Carson, Robert Chalmers, Noel Dobrev, Veselin Dudouit, Yohann Fischer, Paul Karakus, Ali Kerkemeier, Stefan Kolev, Tzanio Lan, Yu-Hsiang Merzari, Elia Min, Misun Phillips, Malachi Rathnayake, Thilina Rieben, Robert Stitt, Thomas Tomboulides, Ananias Tomov, Stanimire Tomov, Vladimir Vargas, Arturo Warburton, Tim Weiss, Kenneth Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Mathematics and Computer Science Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Innovative Computing Laboratory University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Colorado BoulderCO80309 United States Department of Mathematics Virginia Tech BlacksburgVA24061 United States AMD Research Advanced Micro Devices Inc. AustinTX78735 United States Weapons and Complex Integration Lawrence Livermore National Laboratory LivermoreCA94550 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Nuclear Engineering Penn State PA16802 United States Department of Mechanical Engineering Aristotle University of Thessaloniki 54124 Greece Mechanical Engineering Department Middle East Technical University Ankara06800 Turkey

In this paper we describe the research and development activities in the Center for Efficient Exascale Dis-cretization within the US Exascale computing Project, targeting state-of-the-art high-order finite-element algorithms for high-order applications on GPU-accelerated platforms. We discuss the GPU developments in several components of the CEED software stack, including the libCEED, MAGMA, MFEM, libParanumal, and Nek projects. We report performance and capability improvements in several CEED-enabled applications on both NVIDIA and AMD GPU systems. © 2021, CC BY-NC-ND.

关键词： Finite element method

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Publisher's Note: “Towards an efficient multiscale modeling of low-dimensional reactive systems: Study of numerical closure procedures” [J. Chem. Phys. 137, 204115 (2012)]

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The Journal of Chemical Physics 2013年第4期138卷

作者： Giacomo Mazzi Yannick De Decker Giovanni Samaey 1Scientific Computing Department of Computer Science KU Leuven Leuven Belgium 2Interdisciplinary Center for Nonlinear Phenomena and Complex Systems (CENOLI) Université Libre de Bruxelles Bruxelles Belgium

This article was originally published online on 27 November 2012 with an error in Eqs. (2) and (3). The term k2 above the arrows in each equation should have be

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2192卷 V页

作者： Yonezawa, Akinori Matsuoka, Satoshi University of Tokyo Department of Computer Science 7-3-1 Hongo Bunkyo-kuTokyo Japan Tokyo Institute of Technology Global Scientific Information and Computing Center Department of Mathematical and Computing Sciences 2-12-1 Oo-okayama Meguro-kuTokyo Japan

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