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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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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach

arXiv

引用

arXiv 2018年

作者： Chen, Xianfu Zhao, Zhifeng Wu, Celimuge Bennis, Mehdi Liu, Hang Ji, Yusheng Zhang, Honggang VTT Technical Research Centre of Finland Oulu Finland Zhejiang Lab College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science Catholic University of America United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Large Scale Rollout of Smart Grid Services

Large Scale Rollout of Smart Grid Services

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Global Internet of Things Summit (GIoTS)

作者： Florian Kintzler Tobias Gawron-Deutsch Stephan Cejka Judith Schulte Mathias Uslar Eric MSP Veith Ewa Piatkowska Paul Smith Friederich Kupzog Henrik Sandberg Michelle S. Chong David Umsonst Marco Mittelsdorf Corporate Technology Siemens AG Austria Vienna Austria R&D Division Energy OFFIS e.V. - Institut fur Informatik Oldenburg Germany Center for Digital Safety Security AIT Austrian Institute of Technology Vienna Austria School of Electrical Engineering and Computer Science Department of Automatic Control KTH Royal Institute of Technology Stockholm Sweden Fraunhofer Institute for Solar Energy Systems Freiburg Germany

In the domain of energy automation, where a massive number of software-based IoT services interact with a complex dynamic system, processes for software installation and software update become more important and more complex. These processes have to ensure that the dependencies on all layers are fulfilled, including dependencies arising due to the energy system controlled by IT components being a hidden communication channel between these components. In addition, the processes have to be resilient against faults in and attacks to both the energy grid and the communication network. The ERA-Net funded project LarGo! aims at developing and testing processes for the large scale rollout of software applications in the power grid domain as well as the user domain. This article describes a work in progress and the project's roadmap to solve the technical issues. It investigates the problems that arise from the interlocking of the two networks- the power grid and the communication network. Based on this analysis a first set of requirements for a rollout process in such a Smart Grid is derived and the chosen approach to verify the resilience of the developed processes under research is described.

关键词： Smart grids Substations Software Resilience Maintenance engineering Internet of Things

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Author Correction: Enhancing untargeted metabolomics using metadata-based source annotation

引用

Nature biotechnology 2023年第11期41卷 1656页

作者： Julia M Gauglitz Kiana A West Wout Bittremieux Candace L Williams Kelly C Weldon Morgan Panitchpakdi Francesca Di Ottavio Christine M Aceves Elizabeth Brown Nicole C Sikora Alan K Jarmusch Cameron Martino Anupriya Tripathi Michael J Meehan Kathleen Dorrestein Justin P Shaffer Roxana Coras Fernando Vargas Lindsay DeRight Goldasich Tara Schwartz MacKenzie Bryant Gregory Humphrey Abigail J Johnson Katharina Spengler Pedro Belda-Ferre Edgar Diaz Daniel McDonald Qiyun Zhu Emmanuel O Elijah Mingxun Wang Clarisse Marotz Kate E Sprecher Daniela Vargas-Robles Dana Withrow Gail Ackermann Lourdes Herrera Barry J Bradford Lucas Maciel Mauriz Marques Juliano Geraldo Amaral Rodrigo Moreira Silva Flavio Protasio Veras Thiago Mattar Cunha Rene Donizeti Ribeiro Oliveira Paulo Louzada-Junior Robert H Mills Paulina K Piotrowski Stephanie L Servetas Sandra M Da Silva Christina M Jones Nancy J Lin Katrice A Lippa Scott A Jackson Rima Kaddurah Daouk Douglas Galasko Parambir S Dulai Tatyana I Kalashnikova Curt Wittenberg Robert Terkeltaub Megan M Doty Jae H Kim Kyung E Rhee Julia Beauchamp-Walters Kenneth P Wright Maria Gloria Dominguez-Bello Mark Manary Michelli F Oliveira Brigid S Boland Norberto Peporine Lopes Monica Guma Austin D Swafford Rachel J Dutton Rob Knight Pieter C Dorrestein Collaborative Mass Spectrometry Innovation Center University of California San Diego La Jolla CA USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Beckman Center for Conservation Research San Diego Zoo Wildlife Alliance Escondido CA USA. Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Division of Rheumatology Allergy & Immunology Department of Medicine University of California San Diego La Jolla CA USA. Division of Epidemiology and Community Health School of Public Health University of Minnesota Minneapolis MN USA. Department of Integrative Physiology University of Colorado Boulder Boulder CO USA. Department of Population Health Sciences University of Wisconsin-Madison Madison WI USA. Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar Puerto Ayacucho Amazonas Venezuela. Department of Pediatrics Billings Clinic Billings MT USA. Department of Animal Science Michigan State University East Lansing MI USA. Department of Pharmacology Ribeirão Preto Medicinal School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Multidisciplinary Health Institute Federal University of Bahia Vitória da Conquista Bahia Brazil. NPPNS Department of Biomolecular Sciences School of Pharmaceutical Sciences of Ribeirão Preto University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department of Internal Medicine Ribeirão Preto Medical School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department

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Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning

arXiv

引用

arXiv 2018年

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

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to 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 maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Performance optimization in mobile-edge computing via deep reinforcement learning

arXiv

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arXiv 2018年

关键词： Reinforcement learning

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Distributed Kalman Filters with state equality constraints: Time-based and event-triggered communications

arXiv

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arXiv 2017年

作者： He, Xingkang Hu, Chen Hong, Yiguang Shi, Ling Fang, Haitao Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Rocket Force University of Engineering Shaanxi China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

In this paper, we investigate a distributed estimation problem for multi-agent systems with state equality constraints (SEC). First, under a time-based consensus communication protocol, applying a modified projection operator and the covariance intersection fusion method, we propose a distributed Kalman filter with guaranteed consistency and satisfied SEC. Furthermore, we establish the relationship between consensus step, SEC and estimation error covariance in dynamic and steady processes, respectively. Employing a space decomposition method, we show the error covariance in the constraint set can be arbitrarily small by setting a sufficiently large consensus step. Besides, we propose an extended collective observability (ECO) condition based on SEC, which is milder than existing observability conditions. Under the ECO condition, through utilizing a technique of matrix approximation, we prove the boundedness of error covariance and the exponentially asymptotic unbiasedness of state estimate, respectively. Moreover, under the ECO condition for linear time-invariant systems with SEC, we provide a novel event-triggered communication protocol by employing the consistency, and give an offline design principle of triggering thresholds with guaranteed boundedness of error covariance. More importantly, we quantify and analyze the communication rate for the proposed event-triggered distributed Kalman filter, and provide optimization based methods to obtain the minimal (maximal) successive non-triggering (triggering) times. Two simulations are provided to demonstrate the developed theoretical results and the effectiveness of the filters. Copyright © 2017, The Authors. All rights reserved.

关键词： Multi agent systems

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Reference Data Based Insights Expand Understanding of Human Metabolomes

SSRN

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SSRN 2020年

作者： Gauglitz, Julia M. Bittremieux, Wout Williams, Candace L. Weldon, Kelly C. Panitchpakdi, Morgan Di Ottavio, Francesca Aceves, Christine M. Brown, Elizabeth Sikora, Nicole C. Jarmusch, Alan K. Martino, Cameron Tripathi, Anupriya Sayyari, Erfan Shaffer, Justin P. Coras, Roxana Vargas, Fernando DeRight Goldasich, Lindsay Schwartz, Tara Bryant, MacKenzie Humphrey, Gregory Johnson, Abigail J. Spengler, Katharina Belda-Ferre, Pedro Diaz, Edgar McDonald, Daniel Zhu, Qiyun Nguyen, Dominic S. Elijah, Emmanuel O. Wang, Mingxun Marotz, Clarisse Sprecher, Kate E. Robles, Daniela Vargas Withrow, Dana Ackermann, Gail Herrera, Lourdes Bradford, Barry J. Marques, Lucas Maciel Mauriz Amaral, Juliano Geraldo Silva, Rodrigo Moreira Veras, Flávio Protaso Cunha, Thiago Mattar Oliveira, Rene Donizeti Ribeiro Louzada-Junior, Paulo Mills, Robert H. Galasko, Douglas Dulai, Parambir S. Wittenberg, Curt Gonzalez, David J. Terkeltaub, Robert Doty, Megan M. Kim, Jae H. Rhee, Kyung E. Beauchamp-Walters, Julia Wright, Kenneth P. Dominguez-Bello, Maria Gloria Manary, Mark Oliveira, Michelli F. Boland, Brigid S. Lopes, Norberto Peporine Guma, Monica Swafford, Austin D. Dutton, Rachel J. Knight, Rob Dorrestein, Pieter C. Collaborative Mass Spectrometry Innovation Center University of California San Diego San DiegoCA92093 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego San DiegoCA92093 United States Department of Computer Science University of Antwerp Antwerpen2020 Belgium Reproductive Sciences Institute for Conservation Research San Diego Zoo Global EscondidoCA92027 United States Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego San DiegoCA92093 United States Division of Biological Sciences University of California San Diego San DiegoCA92093 United States Department of Pediatrics School of Medicine University of California San Diego San DiegoCA92093 United States Bioinformatics and Systems Biology Program University of California San Diego San DiegoCA92093 United States Division of Rheumatology Allergy & Immunology Department of Medicine University of California San Diego San DiegoCA92093 United States BioTechnology Institute University of Minnesota Saint PaulMN55108 United States Department of Integrative Physiology University of Colorado Boulder BoulderCO80309 United States Department of Population Health Sciences University of Wisconsin-Madison MadisonWI53726 United States Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar Amazonas Puerto Ayacucho7101 Venezuela Department of Pediatrics Wake Forest School of Medicine Winston-SalemNC27101 United States Department of Animal Science Michigan State University East LansingMI48824 United States Department of Pharmacology School of Medicine of Ribeirao Preto Center of Research in Inflammatory Diseases University of Sao Paulo SP Ribeirao PretoCEP 14049-900 - Brazil Multidisciplinary Health Institute Federal University of Bahia BA Vitória da Conquista45029094 Brazil NPPNS Department of Biomolecular Sciences School of Pharmaceutica

The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic "dark matter." © 2020, The Authors. All rights reserved.

关键词： Nucleic acids

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On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

On the nonexistence of event-based triggers that preserve Ga...

引用

American control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： State estimation Packet drops Event-based triggers State estimation Event-based Filters State variable TRIGGERS Gauss

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