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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Multipath transmission scheduling in millimeter wave cloud radio access networks

arXiv

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arXiv 2018年

作者： Chen, Xianfu Liu, Pei Liu, Hang Wu, Celimuge Ji, Yusheng VTT Technical Research Centre of Finland Department of Electrical and Computer Engineering New York University United States Department of Electrical Engineering and Computer Science Catholic University of America United States Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an "all-mmWave" cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the "all-mmWave" cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate. Copyright © 2018, The Authors. All rights reserved.

关键词： Millimeter waves

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Find Your Online Social Friends from Mobile Internet Traffic

Find Your Online Social Friends from Mobile Internet Traffic

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Yi Zhang Yuanyuan Qiao Yanting Zhang Nanfei Shu Yizhe Song Jie Yang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Technology Research Institute Aisino Corporation Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London London England

Increasingly more mobile Internet traffic is produced which contains ample personal information related to user mobility and website browsing behavior. Prior research has attempted to recommend friends based on Global Position system (GPS) in location based social networks (LBSN). However, the study of friend recommendation in general social network according to position from the base station is relatively understudied. This paper introduces a novel feature set extracted from mobile Internet traffic according to base station location and Uniform Resource Locator (URL). We train classification models using these features to predict friendship between pairs of Weibo users. Results show that both base station location and URL when acted alone can already effectively reflect friendships even in general social network. We further show that by fusing the two features together, the model obtains even better performance. Finally, we demonstrate that the location and URL features can improve prediction performance than only using the common friends.

关键词： Uniform resource locators Feature extraction Social network services Base stations Internet Global Positioning system Predictive models

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A joint learning and communications framework for federated learning over wireless networks

arXiv

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arXiv 2019年

作者： Chen, Mingzhe Yang, Zhaohui Saad, Walid Yin, Changchuan Vincent Poor, H. Cui, Shuguang Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Centre for Telecommunications Research Department of Informatics King's College London WC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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Distributed Congestion Management of Distribution Grids under Robust Flexible Buildings Operations

Distributed Congestion Management of Distribution Grids unde...

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IEEE General Meeting Power& Energy Society

作者： Sarmad Hanif Hoay Beng Gooi Tobias Massier Thomas Hamacher Thomas Reindl Renewable and sustainable energy system Technical University Munich Electrical Engineering Nanyang Technological University TUM CREATE Electrification Suite & Test Lab Electrical and Computer Engineering Chair of Renewable and Sustainable Energy Systems Solar Energy Research Institute Singapore National University of Singapore

ISBN: (纸本)9781538677049;9781538677032

Flexible demand side energy and reserve procurement has the potential to improve the overall operation of the grid. However, as argued in previous studies, this flexibility might cause congestion in distribution grids. In this paper, we improve the conventional distribution locational marginal price (DLMP) method, while integrating congestion free energy and reserve provision from buildings in distribution grids. First, robust dayahead (DA) DLMPs are calculated to account for unmodelled dynamics of flexible loads. Second, using dual decomposition, the data sharing requirements between the aggregator and the distribution system operator (DSO) are minimized. Third, a sensitivity-based real-time (RT) adjustment method is presented to remove conservatism of DA robust DLMPs. Case studies are performed on a benchmark distribution system. The numerical results show that the proposed technique efficiently handles load uncertainties and data sharing requirements, improving the practicality of the conventional DMLP method.

关键词： Buildings Solar energy Procurement Electric potential Real-time systems Benchmark testing Uncertainty

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Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning

Performance Optimization in Mobile-Edge Computing via Deep R...

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IEEE Vehicular Technology Conference

作者： Xianfu Chen Honggang Zhang Celimuge Wu Shiwen Mao Yusheng Ji Mehdi Bennis VTT Technical Research Centre of Finland College of Information Science and Electronic Engineering Zhejiang University Graduate School of Informatics and Engineering University of Electro-Communications Department of Electrical and Computer Engineering Auburn University Information Systems Architecture Research Division National Institute of Informatics Centre for Wireless Communications University of Oulu

ISBN: (纸本)9781538663592;9781538663585

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

关键词： UNLOADING task queues most economic control CCL28 gene Markov chain arithmetic Wireless networks optimal strategy state space

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A measuring method for user similarity based on interest topic

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International Journal of Performability engineering 2018年第4期14卷 691-698页

作者： Bai, Yang Deng, Guishi Zhang, Liying Wang, Yi School of System Engineering Eastern Liaoning University Dandong118003 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Department of Computer Science University of Texas at Dallas Richardson75080 United States School of Information Liaoning University Shenyang110036 China Information Center Liaoning University of Traditional Chinese Medicine Shenyang110032 China

A key problem in user relationship analysis is the identification and representation of user interest. The basis to tackle this issue is user similarity measures. In social tagging system, users collaboratively create and manage tags to annotate and categorize content for searching and recommending. Due to the contribution to reflect users' opinions and interests, tags are metadata for user similarity measures. However, there are some issues about it such as data sparseness, the user none-distinguished interest areas and relatively little consider about user influence. This article argues a similarity measure method that based on user's interest topic division. First, we construct tag clustering and divide the user community according to user interest areas. Second, we improve user similarity measurement model using social network analysis (SNA) and PageRank. Finally, the validity of the improved method about user similarity calculation is verified using *** data set. Experimental results show that the improved method gets the highestP@ N and sorting accuracy compared with the traditional tag-based user similarity. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Thesauri

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Web based testing application security system using semantic comparison method 2

Web based testing application security system using semantic...

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2nd Nommensen International Conference on Technology and engineering, NICTE 2018

作者： Iskandar, Akbar Fahlepi Tuasamu, Muhammad Resa Syamsu, Suryadi Mansyur, M. Listyorini, Tri Sallu, Sulfikar Supriyono, S. Saddhono, Kundharu Napitupulu, Darmawan Rahim, Robbi Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Negeri Makassar Makassar Indonesia Informatics Engineering Universitas Maria Kudus Central Java Indonesia Computer Information Systems Universitas Sembilan Belas November Kolaka Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Indonesian Language Education Departement Faculty of Teacher Training and Education Universitas Sebelas Maret Surakarta Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Indonesia Sekolah Tinggi Ilmu Manajemen Sukma Medan Indonesia

computer and web security system is an attempt to avoid attacks from crackers because a form and method of crime on internet network can not be predicted. Therefore, we just keep trying to use various methods in securing the system that is running. It is the same in web testing that can be accessed online by people who are not responsible from along distance to change and remove the test results or even damage the web testing system. So, the purpose of this paper is to design and implement web security testing system by using semantic comparison method to avoid structured query language injection (SQLI) attack and to do security on web testing application using Message Digest 5 encryption (MD5). Research data were obtained by using direct observation and documentation and literature review. After the design and application of the system, it can be obtained that by using semantic comparison method in web testing can prevent the occurrence of SQL injection attacks and can secure account from web testing users by using MD5. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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