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Collective Movement Simulation:Methods and Applications

Machine Intelligence research 2024年第3期21卷 452-480页

作者： Hua Wang Xing-Yu Guo Hao Tao Ming-Liang Xu School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou450001China National Supercomputing Center in Zhengzhou Zhengzhou450001China China Ship Development and Design Center Wuhan430060China

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public *** this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement *** start with a discussion on certain concepts to help beginners understand it more ***,we analyze the intelligence among different collective objects and the emphasis in different ***,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback ***,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative ***,we summarize possible future research directions.

关键词： Collective movement simulation multiple objects multiple discipline simulation effect collective intelligence

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development and Evaluation of High-Density Multi-GPU Sub-System Using PCI Express Expansion Hardware

Development and Evaluation of High-Density Multi-GPU Sub-Sys...

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2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

作者： Kim, Young woo Oh, Myung-Hoon Park, Chan-Yeol ETRI Data Centric Computing System Research Section Korea Republic of KISTI Center for Development of Supercomputing System Korea Republic of

ISBN: (纸本)9781728161648

In this paper, we develop and evaluate a high-density multi-GPU hardware sub-system for high performance computing and deep learning. The high-density multi-GPU hardware is implemented as an out-of-box hardware by extending PCI Express system bus via multi-Gbps class cable assemblies from a server. The high-density multi-GPU hardware extends the PCI Express in a multiple-ways and provides multiple GPUs for a server. The high-density multi-GPU hardware is developed to fit into 21 inches OCP Open Rack Vl and V2 rack and chassis having density of 12 GPUs in 4OU (Open rack Unit) height. The HPL and deep learning applications are tested to evaluate the performance of the developed high-density multi-GPU hardware. The initial test result of HPL is shown that the maximum performance of 36.22 TFLOPS (efficiency of 63.4%) with 12 NVIDIA P100 on triple sever nodes. For deep learning application, the resnet50 result of 3, 187.2 images/sec is obtained with 12 NVIDIA P100 on single sever node with synthetic data. The experimental results show that the developed multi-GPU hardware sub-system exhibits relatively good scalability in a single node especially deep learning applications than the HPL. © 2020 IEEE.

关键词： Expansion

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SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems B...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jaume Abella Jon Perez Cristofer Englund Bahram Zonooz Gabriele Giordana Carlo Donzella Francisco J. Cazorla Enrico Mezzetti Isabel Serra Axel Brando Irune Agirre Fernando Eizaguirre Thanh Hai Bui Elahe Arani Fahad Sarfraz Ajay Balasubramaniam Ahmed Badar Ilaria Bloise Lorenzo Feruglio Ilaria Cinelli Davide Brighenti Davide Cunial Barcelona Supercomputing Center Spain Ikerlan Technology Research Centre Basque Research and Technology Alliance (BRTA) Spain RISE Research Institutes of Sweden Sweden Navinfo Europe The Netherlands AIKO s.r.l. Italy Exida Development s.r.l. Italy Exida Engineering s.r.l. Italy

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.

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A Parallel-in-time Method Based on Preconditioner for Biot’s Model

arXiv

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arXiv 2023年

作者： Zhou, Zeyuan Gu, Huipeng Ju, Guoliang Xing, Wei Faculty of Aerospace Engineering Technische Universiteit Delft Delft2629 HS Netherlands Department of Mathematics Southern University of Science and Technology Shenzhen518055 China Research and Development Department Tenfeng Shenzhen518055 China High Performance Computing Department National Supercomputing Center Shenzhen518055 China

We proposed a parallel-in-time method based on preconditioner for Biot’s consolidation model in poroelasticity. In order to achieve a fast and stable convergence for the matrix system of the Biot’s model, we design two preconditioners with approximations of the Schur complement. The parallel-in-time method employs an inverted time-stepping scheme that iterates to solve the preconditioned linear system in the outer loop and advances the time step in the inner loop. This allows us to parallelize the iterations with a theoretical parallel efficiency that approaches 1 as the number of time steps and spatial steps grows. We demonstrate the stability, accuracy, and linear speedup of our method on HPC platform through numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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development and Evaluation of High-Density Multi-GPU Sub-System Using PCI Express Expansion Hardware

Development and Evaluation of High-Density Multi-GPU Sub-Sys...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Young woo Kim Myung-hoon Oh Chan-Yeol Park Data Centric Computing System Research Section ETRI Korea Center for Development of Supercomputing System KISTI Korea

ISBN: (数字)9781728161648

ISBN: (纸本)9781728161655

关键词： Graphics processing units Hardware Deep learning Servers Bandwidth Switches Micromechanical devices

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Deep Learning for Gap Crossing Ability of Ground Vehicles

Deep Learning for Gap Crossing Ability of Ground Vehicles

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IEEE/ACM Workshop on Deep Learning on Supercomputers (DLS)

作者： Benjamin S. Parsons Jing-Ru C. Cheng Supercomputing Research Center Engineer Research & Development Center (ERDC) Vicksburg MS USA

ISBN: (纸本)9781728160122

In this work we present our results designing a deep neural network (DNN) to act as a surrogate model for costly HPC simulations. In order to determine a ground vehicle's gap crossing ability in extreme weather scenarios, several HPC simulations are currently used. Hydrologic models are first run to determine the environmental conditions over an area of interest. Once these conditions are known they are given, along with the terrain data, to a vehicle simulation which determines if a particular vehicle can cross a stream at a given point. Every point of interest must be evaluated independently, which quickly becomes infeasible for a large numbers of crossing points. In order to accelerate this phase of the process, we have created a DNN that acts as a surrogate model for the vehicle simulator. Despite several challenges converting irregular data into a form that can be used with a DNN, and incorporating scalars into the models, we were able to produce DNN models that predicted the gap crossing ability of all vehicle types with over 95% accuracy.

关键词： HPC Deep neural network Hydrologic model Gap crossing Hydrologic models High Performance Computing Ground vehicles crossing Crossing model cars

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Celebrating 10 Years of the Subseasonal to Seasonal Prediction Project and Looking to the Future

Celebrating 10 Years of the Subseasonal to Seasonal Predicti...

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作者： Woolnough, S.J. Vitart, F. Robertson, A.W. Coelho, C.A.S. Lee, R. Lin, H. Kumar, A. Stan, C. Balmaseda, M. Caltabiano, N. Yamaguchi, M. Afargan-Gerstman, H. Boult, V.L. De Andrade, F.M. Büeler, D. Carreric, A. Diaz, D.A. Campos Day, J. Dorrington, J. Feldmann, M. Furtado, J.C. Grams, C.M. Koster, R. Hirons, L. Indasi, V.S. Jadhav, P. Liu, Y. Nying’uro, P. Roberts, C.D. Rouges, E. Ryu, J. National Centre for Atmospheric Science University of Reading Reading United Kingdom European Centre lor Medium-Range Weather Forecasts Reading United Kingdom Columbia University PalisadesNY United States Cachoeira Paulista Brazil Department of Meteorology University of Reading Reading United Kingdom Environment and Climate Change Canada MontrealQC Canada National Centers for Environmental Prediction College ParkMD United States George Mason University FairfaxVA United States World Meteorological Organization Geneva Switzerland Japanese Meteorological Agency Tsukuba Japan Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Cachoeira Paulista Brazil Barcelona Supercomputing Center Barcelona Spain Santiago Chile Karbruher Institut fur Technologie Karlsruhe Germany Institute of Geography Oeschger Centre for Climate Change Research University of Bern Bern Switzerland School of Meteorology University of Oklahoma NormanOK United States Karlsruher Institut fur Technologic Karlsruhe Federal Office of Meteorology and Climatology MeteoSwiss Zurich Switzerland Global Modeling and Assimilation Office NASA/GSFC GreenbeltMD United States African Centre of Meteorological Applications for Development Niamey Niger Indian Institute of Science Education and Research Pune India Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Kenya Meteorological Department Nairobi Kenya Gwangju Institute of Science and Technology Gwangju Korea Republic of

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Future climate-driven fires may boost ocean productivity in the iron-limited North Atlantic

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Nature Climate Change 2025年 1-9页

作者： Elisa Bergas-Masso María Gonçalves Ageitos Carlos Pérez García-Pando Douglas S. Hamilton Stelios Myriokefalitakis Sagar Rathod Barcelona Supercomputing Center Barcelona Spain Universitat Politècnica de Catalunya Barcelona Spain Catalan Institution for Research and Advanced Studies Barcelona Spain Department of Marine Earth and Atmospheric Sciences North Carolina State University Raleigh NC USA Institute for Environmental Research and Sustainable Development National Observatory of Athens Athens Greece La Follette School of Public Affairs University of Wisconsin–Madison Madison WI USA Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin–Madison Madison WI USA Office of Sustainability University of Wisconsin–Madison Madison WI USA

Rapid shifts in fire regimes affect the carbon cycle by releasing carbon and nutrients such as iron (Fe), potentially enhancing marine productivity and carbon export. Here we use fire emission projections and Earth system models to examine how climate-driven changes in fire emissions may alter soluble Fe (SFe) deposition and productivity. By century’s end, climate change could increase Fe emissions from fires by 1.7–1.8 times beyond projections considering only direct human influences. Model projections show rising SFe deposition in Northern Hemisphere high latitudes under increasing socio-economic activity, potentially boosting the impact of SFe deposition on productivity in the Fe-limited North Atlantic by up to 20% annually (40% in summer), assuming stable macronutrient levels. However, declining macronutrient availability may shrink Fe-limited areas, where climate-driven fires could offset productivity losses by 7–8%. In the Southern Ocean, fossil fuel emissions primarily control SFe deposition, as reductions in anthropogenic fires counterbalance climate-driven increases.

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Local to regional-scale mechanisms behind successful climate services for agriculture in Latin America

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Climate Risk Management 2025年 49卷

作者： Diana Giraldo David Ríos Carlos Navarro-Racines Kemly Camacho Armando Martínez-Valle Steven D. Prager Diego Obando Carlos Zelaya Deissy Martínez-Baron Ángel G. Muñoz Julian Ramirez-Villegas School of Agriculture Policy and Development University of Reading Reading United Kingdom International Center for Tropical Agriculture (CIAT) Cali Colombia International Center for Tropical Agriculture (CIAT) c/o IICA Guatemala City Guatemala Independent Consultant Sulá Batsú Cooperative San José Costa Rica International Center for Tropical Agriculture (CIAT) Managua Nicaragua Bill and Melinda Gates Foundation Seattle United States International Center for Tropical Agriculture (CIAT) Tegucigalpa Honduras Department of Earth Sciences. Barcelona Supercomputing Center (BSC). Barcelona Spain International Center for Tropical Agriculture (CIAT) c/o Bioversity International Rome Italy Bioversity International Rome Italy Plant Production Systems Group Wageningen University & Research Wageningen the Netherlands

The provision of climate services (CS) has grown at an unprecedented rate over the last decade in response to climate-related risks in several sectors of the global economy; this is especially true in agriculture. Several studies document lessons learnt from (un)successful climate services, and attempt to distil these into key principles, recommendations, or requirements. However, limited systematic analysis and data on the characteristics of the CS that are conducive to success exist to date, including for agriculture. Here, we analyse the Local Technical Agroclimatic Committees (referred to here by its Spanish acronym MTAs) as a CS approach that effectively delivers information to farmers sustainably and at local scale. We propose a framework comprising sixteen metrics that help measure the effectiveness, sustainability, and scalability as key dimensions of CS success. We apply this framework to 26 MTAs across four Latin American countries, namely, Guatemala, Honduras, Nicaragua, and Colombia. The analyses revealed that the MTAs played a significant role in CS transformation pathways, producing a total of 158 outcomes (changes in behaviour of people or institutions), and involving at least 279 institutions at various levels and with diverse roles. Analyses of the sixteen metrics revealed a wide range of performance across the 26 MTAs, with nearly half of the MTAs considered to have or nearly-achieved effectiveness, sustainability, and scalability. MTAs success stems not only from an increase in numbers of farmers and locations reached but also from the evolving roles and responsibilities of a diverse ecosystem of actors that accompany enhanced capacities and tangible benefits on the ground. Based on these results, we propose key CS elements, namely, collaboration; participation; adaptability and flexibility; financial (crowd) resourcing; robust governance and strong leadership; awareness of and improvements in data availability, quality, and assurance; capacity de

关键词： Climate services Decision-making Sustainability Multi-stakeholder network Outcome Harvesting

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A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional Chinese medicines

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Science Bulletin 2021年第9期66卷 884-888,M0003页

作者： Yifei Dai Weijie Qiang Yu Gui Xue Tan Tianli Pei Kequan Lin Siwei Cai Liang Sun Guochen Ning Jianxun Wang Hongyan Guo Yimin Sun Jing Cheng Lan Xie Xun Lan Dong Wang State Key Laboratory of Southwestern Chinese Medicine Resources School of Basic Medical SciencesChengdu University of Traditional Chinese MedicineChengdu 611137China Department of Basic Medical Sciences School of MedicineTsinghua UniversityBeijing 100084China Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100193China School of Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Electronic and Computer Engineering College of EngineeringDrexel UniversityPhiladelphia 19104USA Dongli District Jinqiao Street Community Health Service Center Tianjin 300300China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing 100084China School of Life Sciences Beijing University of Chinese MedicineBeijing 100029China National Engineering Research Center for Beijing Biochip Technology Beijing 102206China State Key Laboratory of Membrane Biology School of MedicineTsinghua UniversityBeijing 100084China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310003China Medical Systems Biology Research Center School of MedicineTsinghua UniversityBeijing 100084China

The application of traditional Chinese medicine(TCM)has made great contributions to the fight against the epidemic of coronavirus disease-2019(COVID-19).Despite the remarkable therapeutic effects of TCM,the molecular mechanisms of TCM formulae inhibiting COVID-19 are still not fully ***,we cφm-bined the automated high throughput sequencing-based high throughput screening(HTS^(2))assay with bioinformatics and computer-aided drug design(CADD)to investigate the molecular mechanisms of TCM-mediated therapeutic effects on COVID-19-related cytokine storm(Fig.1a).

关键词：细胞因子风暴六君子汤理中汤诊疗方案黄连解毒汤中药提取物肺炎分子机制

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