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International Conference on High Performance Computing & Simulation (HPCS)

作者： Oyku Melikoglu Oguz Ergin Behzad Salami Julian Pavon Osman Unsal Adrian Cristal Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Computer Architecture for Parallel Paradigms Group Barcelona Supercomputing Center Barcelona Spain

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

This paper presents a deeply pipelined and massively parallel Binary Search Tree (BST) accelerator for Field Programmable Gate Arrays (FPGAs). Our design relies on the extremely parallel on-chip memory, or Block RAMs (BRAMs) architecture of FPGAs. To achieve significant throughput for the search operation on BST, we present several novel mechanisms including tree duplication as well as horizontal, duplicated, and hybrid (horizontal-vertical) tree partitioning. Also, we present efficient techniques to decrease the stalling rates that can occur during the parallel tree search. By combining these techniques and implementations on Xilinx Virtex-7 VC709 platform, we achieve up to 8X throughput improvement gain in comparison to the baseline implementation, i.e., a fully-pipelined FPGA-based accelerator.

关键词： Registers Throughput Field programmable gate arrays Vegetation Pipeline processing Binary search trees Random access memory

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Advancing IoT-Based Smart Irrigation

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IEEE Internet of Things Magazine 2019年第4期2卷 20-25页

作者： Togneri, Rodrigo Kamienski, Carlos Dantas, Ramide Prati, Ronaldo Toscano, Attilio Soininen, Juha-Pekka Conic, Tullio Salmon Getúlio Vargas Foundation (FGV) São Paulo School of Business Administration (EAESP) Federal University of ABC (UFABC) UFABC NUVEM Strategic Research Group Federal Institute of Technology of Pernambuco (IFPE) Center of Computer Science Mathematics and Cognition UFABC University of Bologna VTT Technical Research Centre of Finland School of Engineering and Architecture University of Bologna

Integrated Internet of Things (IoT) platforms are needed for realizing IoT potential in commercial-scale applications. The main challenge is to provide solution flexibility to meet custom application needs. We developed an IoT-based platform for smart irrigation, with a flexible architecture to easily connect IoT and machine learning (ML) components to build application solutions. Our architecture enables multiple and customizable analytical approaches to precision irrigation, making room for the improvement of ML approaches. Impacts on different stakeholders can be anticipated, including IoT professionals, by facilitating system deployment, and farmers, by providing cost reduction and safer crop yields. Examples are given based on pilots in Europe and Brazil. © 2018 IEEE.

关键词： Internet of things

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H2-MARL: Multi-agent reinforcement learning for Pareto optimality in hospital capacity strain and human mobility during epidemic

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Expert Systems with Applications 2025年 291卷

作者： Xueting Luo Hao Deng Jihong Yang Yao Shen Huanhuan Guo Zhiyuan Sun Mingqing Liu Jiming Wei Shengjie Zhao School of Computer Science and Technology Tongji University Shanghai 201804 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai 201804 China China Land Surveying and Planning Institute Beijing 100035 China College of Architecture and Urban Planning Tongji University Shanghai 200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing 401121 China College of Electronic and Information Engineering Tongji University Shanghai 201804 China Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou 510290 China

Effectively balancing the losses from mobility restrictions and hospital capacity strain has drawn significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics; however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different population scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can simultaneously minimize hospital capacity strain and human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications.

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Generalized Multiprocess Kbest-Based Expert System for Improved Multitemporal Evapotranspiration Forecasting in California, United States

SSRN

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SSRN 2023年

作者： Lee, Jinwook Bateni, Sayed M. Jun, Changhyun Heggy, Essam Jamei, Mehdi Kim, Dongkyun Ghafouri, Hamid Reza Deenik, Jonathan L. Department of Civil and Environmental Engineering Water Resources Research Center University of Hawaii at Manoa HonoluluHI United States Department of Civil and Environmental Engineering College of Engineering Chung-Ang University Seoul Korea Republic of Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St Peters PE Canada Department of Civil Engineering Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah Iraq Department of Civil and Environmental Engineering Hongik University Seoul Korea Republic of Department of Tropical Plant and Soil Sciences University of Hawaii at Manoa HonoluluHI United States Shahid Chamran University of Ahvaz Iran

Evapotranspiration is an essential component of the hydrological cycle. Forecasting the reference crop evapotranspiration (ETo) using a reliable and generalized framework is crucial for agricultural operations, especially irrigation. This study was aimed at evaluating the performance of a generalized multivariate-multitemporal intelligent system including the K-Best selection (KBest), multivariate variational mode decomposition (MVMD), and cascade forward neural network (CFNN) for 1-, 3-, 7-, and 10-day-ahead forecasting of the daily ETo in twelve stations of California, one of the significant regions in the U.S. The input variables included solar radiation, maximum temperature, minimum temperature, average temperature, dew point, vapor pressure, and average relative humidity. The analysis covered a span of 20 years, from 2003 to 2022. In additional to the CFNN, two other machine learning models, namely, extreme learning machine (ELM) and bagging regression tree (BRT), were integrated with various preprocessing techniques to construct three hybrid models, i.e., MVMD-KBest-CFNN, MVMD-KBest-ELM, and MVMD-KBest-BRT. Using the MVMD technique, the antecedent information of input features was factorized into the intrinsic mode functions and residuals. Subsequently, the most influential sub-components were filtered using KBest to reduce the computational cost and enhance the accuracy before inputting the machine learning models. Several statistical indices, such as the correlation coefficient (R) and root mean square error (RMSE), were used in addition to various diagnostic validation methods to assess the robustness of the hybrid frameworks and standalone models. According to the results obtained in the testing phase, averaged across all stations, the MVMD-KBest-CFNN and the MVMD-KBest-ELM models outperformed MVMD-KBest-BRT model, with R values of 0.983, 0.980, 0.977, and 0.968 for 1-, 3-, 7-, and 10-day-ahead forecasts, respectively. The corresponding RMSE values were 0.

关键词： Variational mode decomposition

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The Study of Urbanization Effect on the Land Use Changes and Urban Infrastructures Development in the Metropolitan Areas, Thailand

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IOP Conference Series: Earth and Environmental Science 2021年第1期738卷

作者： A Padon P Iamtrakul C Thanapirom Ph.d.Student Faculty of Architecture and Planning Thammasat University Pathumthani 12121 Thailand Associate Professor Director Center of Excellence in Urban Mobility Research and Innovation Faculty of Architecture andPlanning Thammasat University Pathumthani 12121 Thailand Co-advisor Spectroscopy and Sensors Research Group (SSDRG) National Electronics and Computer Technology Center (NECTEC) Pathumthani 12121 Thailand

Bangkok has expanded considerably between 1991 - 1996. Which, the key factors contributed to the expansion consisted of real estate, business development and manufacturing industry. The horizontal expansion continued until 1991, after that Bangkok has increased its vertical expansion, especially in the inner and urban areas. The increasing in term of population causes the inner zone growth with high concentration rate, however, the suburban areas are characterized as of urban sprawls with the major expansion of road network. Those connectivities cut through the heart of Bangkok with the surrounding provinces. Currently, Bangkok has been continued its expansion in all directions, with high number of private cars and less users' choices for public transportation, wich resulted in land-use changes, pollution, and traffic problems. The objective of this research is to study pattern of urban expansion and rate of changes in land use and infrastructure development by analyzing the land use changes in Bangkok and metropolitan and infrastructure development during the past 10 years, 2009 – 2019. The comparing of growth can be performed based on the study of satellite imagery, Path 129 Row 50 and Path 129 Row 51. From the result of analysis, it was found that road intersection density had an effect of the urban density areas. Finally, the Land Change Modeler (LCM) was used to explore and recommend for the appropriate ways for urban planning and development corresponding to the changes and responsive to the people in more sustainable way.

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A Cloud Environment for Ubiquitous Medical Image Reconstruction

A Cloud Environment for Ubiquitous Medical Image Reconstruct...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Estefania Serrano Javier Garcia-Blas Jesus Carretero Computer Architecture and Technology Group University Carlos III of Madrid Leganes Spain

ISBN: (纸本)9781728111421;9781728111414

The new needs that have appeared in recent years around the medical image reconstruction field have pushed a greater investment on computing and storage platforms in order to keep up with the increased requirements in performance and quality. To reduce the cost of purchasing and managing these resources novel ubiquituos services have arisen. In this work we describe and evaluate a cloud environment for ubiquitous medical image reconstruction based on Amazon EC2 and a heterogeneous Apache Spark platform. We show an architecture composed by a client application, a front-end server and an isolated cluster per application for the uninterrupted execution of the reconstruction service. We present an execution and cost model for the prediction and configuration of the cloud environment based on performance and monetary costs. The evaluation shows the best configurations in terms of execution time for different types of instances and tuning parameters. Based on these results we compute a Pareto front to study the best solutions for the configuration of the cloud environment with the objective of minimizing both execution time and costs.

关键词： Cloud computing Graphics processing units Image reconstruction Task analysis Biomedical imaging Sparks computer architecture

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Corrigendum to “Renewables but unjust? Critical restoration geography as a framework for addressing global renewable energy injustice” [Energy Res. Soc. Sci. 114 (2024) 103609]

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Energy Research & Social Science 2024年 115卷

作者： Chinedu C. Nsude Rebecca Loraamm Joshua J. Wimhurst God'sgift N.. Chukwuonye Ramit Debnath Department of Geography and Environmental Sustainability University of Oklahoma Norman OK 73019 USA Center for Peace and Development University of Oklahoma Norman OK 73019 USA South Central Climate Adaptation Science Center University of Oklahoma Norman OK 73019 USA Department of Environmental Science University of Arizona Tucson AZ 85721 USA Cambridge Zero and Computer Laboratory University of Cambridge Cambridge CB3 0HE UK Cambridge Collective Intelligence and Design Group Department of Architecture University of Cambridge CB2 1PX UK Division of Humanities and Social Sciences California Institute of Technology Pasadena CA 92115 USA

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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BENCHIP： Benchmarking Intelligence Processors

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Journal of computer Science & technology 2018年第1期33卷 1-23页

作者： Jin-Hua Tao Zi-Dong Du Qi Guo Hui-Ying Lan Lei Zhang Sheng-Yuan Zhou Ling-Jie Xu Cong Liu Hai-Feng Liu Shah Tang Allen Rush Willian Chen Shao-Li Liu Yun-Ji Chen Tian-Shi Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Intelligent Processor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Cambricon Ltd. Beijing 100190 China A libaba Infrastructure Service A libaba Group Hangzhou 311121 China Iflytek Co. Ltd. Hefei 230088 China Beijing Jingdong Century Trading Co. Ltd. Beijing 100176 China RDA Microdectronics Inc. Shanghai 201203 China Advanced Micro Devices Inc. Sunnyvale CA 94085 U.S.A.

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.

关键词： deep learning intelligence processor benchmark

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