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Correction to: Financial interbanking networks resilience under shocks propagation

Annals of Operations Research 2022年第1期332卷 1279-1279页

作者： Cerqueti, Roy Cinelli, Matteo Ferraro, Giovanna Iovanella, Antonio Department of Social and Economic Sciences Sapienza University of Rome Rome Italy GRANEM - Université d’Angers Angers France School of Business London South Bank University UK Department of Computer Science Sapienza University of Rome Rome Italy Italian National Research Council — Institute for Complex Systems Rome Italy Department of Engineering and Architecture University of Parma Parma Italy School of Economics Università degli Studi Internazionali di Roma - UNINT Rome Italy

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Communication lower bound in convolution accelerators

arXiv

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arXiv 2019年

作者： Chen, Xiaoming Han, Yinhe Wang, Yu Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping *** in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a threelevel memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%. Copyright © 2019, The Authors. All rights reserved.

关键词： Acceleration

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THz near-field imaging of extreme subwavelength metal structures

arXiv

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arXiv 2021年

作者： Chen, Xinzhong Liu, Xiao Guo, Xiangdong Chen, Shu Hu, Hai Nikulina, Elizaveta Yao, Ziheng Betchel, Hans Martin, Michael C. Carr, G. Lawrence Dai, Qing Zhuang, Songlin Hu, Qing Zhu, Yiming Hillenbrand, Rainer Liu, Mengkun You, Guanjun Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Shanghai Key Lab of Modern Optical Systems Terahertz Technology Innovation Research Institute Engineering Research Center of Optical Instrument and System Ministry of Education University of Shanghai for Science and Technology Shanghai200093 China Division of Nanophotonics CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing100190 China CIC nanoGUNE Donostia-San Sebastián20018 Spain CIC nanoGUNE UPV/EHU Donostia-San Sebastián20018 Spain IKERBARSQUE Basque Foundation of Science Bilbao48013 Spain Advanced Light Source Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Synchrotron Light Source II Brookhaven National Laboratory UptonNY11973 United States Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States

Modern scattering-type scanning near-field optical microscopy (s-SNOM) has become an indispensable tool in material research. However, as the s-SNOM technique marches into the far-infrared (IR) and terahertz (THz) regimes, emerging experiments sometimes produce puzzling results. For example, "anomalies" in the near-field optical contrast have been widely reported. In this letter, we systematically investigate a series of extreme subwavelength metallic nanostructures via s-SNOM near-field imaging in the GHz to THz frequency range. We find that the near-field material contrast is greatly impacted by the lateral size of the nanostructure, while the spatial resolution is practically independent of it. The contrast is also strongly affected by the connectivity of the metallic structures to a larger metallic "ground plane". The observed effect can be largely explained by a quasi-electrostatic analysis. We also compare the THz s-SNOM results to those of the mid-IR regime, where the size-dependence becomes significant only for smaller structures. Our results reveal that the quantitative analysis of the near-field optical material contrasts in the long-wavelength regime requires a careful assessment of the size and configuration of metallic (optically conductive) structures. Copyright © 2021, The Authors. All rights reserved.

关键词： Metals

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Health co-benefits of post-COVID-19 low-carbon recovery in Chinese cities

Nature Cities

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Nature Cities 2024年 695-705页

作者： Chenxi Lu Felix Creutzig Jiawei Hu Yingjian Huang Huibin Mo Ying Yu Yun Li Da Huo Xuanren Song Xiaoting Huang Zhu Liu Yun Sun Kai Liu Shaohui Zhang Karyn Morrissey Jinpyo Hong Zhu Deng Zhuanjia Du Mercator Research Institute on Global Commons and Climate Change Berlin Germany Sustainability Economics of Human Settlements Technical University Berlin Berlin Germany Belfer Center for Science and International Affairs Harvard Kennedy School Harvard University Cambridge MA USA Alibaba Cloud Hangzhou China Guangdong Key Laboratory for Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Data-Driven EnviroLab Department of Public Policy University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Environmental Sciences and Engineering Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC USA Faculty of Engineering Science Department of Architecture KU Leuven Leuven Belgium Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Department of Civil & Mineral Engineering University of Toronto Toronto Ontario Canada School of Environmental Science and Engineering Tianjin University Tianjin China Department of Geography King’s College London London UK Guangdong Holdings Limited Guangzhou China International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Division for Climate and Energy Policy Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark School of Engineering Brown University Providence RI USA Department of Geography University of Hong Kong Hong Kong SAR China Department of Political Science Columbia University New York NY USA

Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. However, subnational and city-level understanding of green recovery, its efficacy and its alignment with public health is poorly understood. Here we focus on post-COVID-19 low-carbon recovery—economic growth combined with reduced carbon emissions—and explore health co-benefits in Chinese cities. A novel near-real-time daily carbon emission dataset of 48 cities in China is developed, coupled with detailed health and economic municipal statistics and models. We find that, on average, six low-carbon-recovery cities, mainly megacities, saved 1.2 times as many lives per 100,000 population compared with the 42 other cities, and their annual monetary avoided premature deaths per 100,000 population was 1.5 times more than the 42 other cities. The accumulated monetary health co-benefits for low-carbon-recovery cities were US$ 4.2 billion (95% confidence interval, 2.1–6.3) during the post-COVID-19 period. We show that government spending on electric vehicles increases the likelihood of achieving low-carbon recovery in Chinese cities. Our results underscore the significant health co-benefits of low-carbon recovery, pointing to synergies between advancing local welfare and global environmental objectives.

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An improved memetic algorithm with novel level comparison for constrained optimization 2nd

An improved memetic algorithm with novel level comparison fo...

引用

2nd International Conference on Intelligent and Interactive systems and Applications, IISA 2017

作者： Qu, Xinghua Zhao, Wei Feng, Xiaoyi Bai, Liang Liu, Bo School of Computer Science & Engineering Nanyang Technological University Singapore Singapore Beijing Institute of Astronautical System Engineering Beijing100076 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319690957

Memetic algorithms combining with effective constraints handling techniques have been becoming the focus of concern as well as the subject of substantial research issue in decision-making in complex systems. As one of the most effective techniques for constraints handling, level comparison based method has been used to solve various constraint optimization problems. However, in most of the existing research, the constraint satisfaction level keeps constant or follows a fixed regulation during search, which may weaken the efficiency of relaxation of constraint violation. In this paper, we propose a TLBO based constrained optimization method, labeled as TLBO–LCSQP by incorporating a novel level comparison technique to effectively handle the constraints, and the sequential quadratic program (SQP) to enhance the searching performance. According to the simulation on a well-known constrained benchmark, the proposed TLBO–LCSQP could effectively enhance the constraints handling efficiency and greatly improve the searching ability. © 2018, Springer International Publishing AG.

关键词： Constraint handling

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On computing reduced-order bilinear models from time-domain data

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PAMM 2021年第1期21卷

作者： Ion Victor Gosea Dimitrios S. Karachalios Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

We propose a procedure for identification of reduced-order bilinear models from time-domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data corresponding to the response of the system to two differently-scaled step functions, as control inputs. Then, the Markov parameters for two time-varying linear models are extracted, and from this information, we are able to infer a reduced-order bilinear model.

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

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Federated Deep Learning for Immersive Virtual Reality over Wireless Networks

Federated Deep Learning for Immersive Virtual Reality over W...

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IEEE Global Communications Conference

作者： Mingzhe Chen Omid Semiari Walid Saad Xuanlin Liu Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Colorado Colorado Springs CO USA Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

关键词： Wireless communication Wireless sensor networks Delays Uplink Downlink Prediction algorithms Machine learning

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Industrial Recommendation of Modeling of Inverter-Based Generators for Power system Dynamic Studies with Focus on Photovoltaic

引用

IEEE Power and Energy Technology systems Journal 2018年第1期5卷 1-10页

作者： Yamashita, Koji Renner, Herwig Martinez Villanueva, Sergio Lammert, Gustav Aristidou, Petros Carvalho Martins, Jose Zhu, Lingzhi Ospina, Luis David Pabon Van Cutsem, Thierry Department of Power System System Engineering Laboratory Central Research Institute of Electric Power Industry Kanagawa248 Japan Department of Institute of Electrical Power Systems Graz University of Technology Graz8010 Austria Department of Power System Reliability Red Eléctrica de España Alcobendas28109 Spain Department of Energy Management and Power System Operation University of Kassel Kassel34125 Germany School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Department of Asset and Network Planning EDP Distribuição Lisbon1050-045 Portugal Renewable Energy Research Center China Electric Power Research Institute Nanjing210003 China Department of System Stability and Grid Integration Fraunhofer Institute for Energy Economics and Energy System Technology Kassel34119 Germany Department of Electrical Engineering and Computer Science Fund for Scientific Research University of Liège Liège4000 Belgium

Dynamic simulations have played an important role in assessing the power system dynamic studies. The appropriate numerical model is the key to obtain correct dynamic simulation results. In addition, the appropriate model including the selection of the individual model component (such as protections, controls, and capabilities) is different depending on the type of phenomena to be observed or examined. However, the proper selection of the model is not an easy task, especially for inverter-based generators (IBGs). Considerable industry experience concerning power system dynamic studies and the dynamics of the IBGs is required for the proper selection of the IBG model. The established CIGRE C4/C6.35/CIRED joint working group (JWG) has gathered a wide variety of experts, which fully cover the required industry experience. The JWG provides the guidance on the model selection for analyzing the phenomena, such as frequency deviation, large voltage deviation, and long-term voltage deviation, individually. This helps to reduce the computational burden, as well as it clarifies the required characteristics/functions that should be represented for the power system dynamic studies with the IBGs. © 2014 IEEE.

关键词： Electric inverters

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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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