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作者： Papathanassiou, Apostolos Sheen, Wern-Ho Wang, Chin-Liang Politis, Christos Next Generation and Standards Intel Client and Internet of Things Businesses and Systems Architecture Group Technology Development and Standardization Activities United States Department of Communications Engineering Georgia Institute of Technology National Chung Cheng University Atlanta United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Taiwan National Technical University of Athens School of Computer Science Kingston University United Kingdom

Members of industry and academics discussed future mobile-radio standard fifth-generation (5G) technology at the 37th Wireless World Research Forum (WWRF) meeting, held 5/7 October 2016 in Kassel, Germany. International industrial representatives and scientists from 15 countries participated in 19 plenary talks, 16 working-group sessions, and two comprehensive tutorials on key 5G technologies. The experts discussed the current status, market potentials, and technical standards of 5G technology. The program included talks on connected vehicles, mobile health services, next-generation Ethernet-based fronthaul/xhaul, the Internet of Things (IoT), and an outlook beyond 5G. Working-group sessions addressed user needs and requirements, services and devices of 5G technology, new directions in communication architectures, and technologies, including high-frequency technologies. Highlights included the talks. © 2005-2012 IEEE.

关键词： Internet of things

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Randomized transmission protocols for protection against jamming attacks in multi-agent consensus

arXiv

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arXiv 2018年

作者： Cetinkaya, Ahmet Kikuchi, Kaito Hayakawa, Tomohisa Ishii, Hideaki Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Computer Science Tokyo Institute of Technology Yokohama226-8502 Japan

Multi-agent consensus under jamming attacks is investigated. Specifically, inter-agent communications over a network are assumed to fail at certain times due to jamming of transmissions by a malicious attacker. A new stochastic communication protocol is proposed to achieve finite-time practical consensus between agents. In this protocol, communication attempt times of agents are randomized and unknown by the attacker until after the agents make their communication attempts. Through a probabilistic analysis, we show that the proposed communication protocol, when combined with a stochastic ternary control law, allows agents to achieve consensus regardless of the frequency of attacks. We demonstrate the efficacy of our results by considering two different strategies of the jamming attacker: a deterministic attack strategy and a more malicious communication-aware attack strategy. Copyright © 2018, The Authors. All rights reserved.

关键词： Jamming

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Computational representation for a simulation scenario definition language

Computational representation for a simulation scenario defin...

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AIAA Modeling and Simulation Technologies Conference, 2018

作者： Durak, Umut Jafer, Shafagh Wittman, Rob Mittal, Saurabh Hartmann, Sven Zeigler, Bernard P. Institute of Flight Systems Braunschweig38108 Germany Embry-Riddle Aeronautical University Daytona BeachFL32114-3900 United States The MITRE Corporation McLean VA22102 United States Clausthal University of Technology Clausthal-Zellerfeld38678 Germany RTSync Corp. and Arizona Center for Integrated M&S TucsonAZ85621 United States Flight Dynamics and Simulation Department of Electrical Computer Software and Systems Engineering Department Head Simulation Experimentation and Gaming Architecture Analytics Department Department of Informatics Department of Electrical and Computing Engineering

ISBN: (数字)9781624105289

ISBN: (纸本)9781624105289

The modeling and simulation community in aviation is in search of standardization in simulation scenario definition. While the System Entity Structure (SES) ontology promises an effective formal means of domain modeling, computational representation is still a key challenge to tackle for fostering wide-acceptance and standardization. This paper presents an eXtended Markup Language (XML) based approach. It first gives the relational specification and the schema definition of the SES ontology. Following a layered modeling workflow, it then presents a schema definition for the simulation scenario definition language and exemplifies its utilization. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Standardization

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Replay using recomposition: Alignment-based conformance checking in the large

Replay using recomposition: Alignment-based conformance chec...

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2017 BPM Demo Track and BPM Dissertation Award, BPM-D and DA 2017, co-located with 15th International Conference on Business Process Management, BPM 2017

作者： Lee, Wai Lam Jonathan Verbeek, H.M.W. Munoz-Gama, Jorge Van Der Aalst, Wil M.P. Sepúlveda, Marcos Department of Mathematics and Computer Science Architecture of Information Systems Group Eindhoven University of Technology Netherlands Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Chile

In the area of process mining, efficient alignment-based conformance checking is a hot topic. Existing approaches for conformance checking are typically monolithic and compute exact fitness values. One limitation with monolithic approaches is that it may take a significant amount of computation time in large processes. Alternatively, decomposition approaches run much faster but do not always compute an exact fitness value. This paper presents the tool Replay using Recomposition which returns the exact fitness value and the resulting alignments using the decomposition approach in an iterative manner. Other than computing the exact fitness value, users can configure the balance between result accuracy and computation time to get a fitness interval within set constraints, e.g., "Give me the best fitness estimation you can find within 5 minutes".

关键词： Data mining

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Modeling stated preference for mobility-on-demand transit: A comparison of machine learning and logit models

arXiv

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arXiv 2018年

作者： Zhao, Xilei Yan, Xiang Yu, Alan Van Hentenryck, Pascal H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Taubman College of Architecture and Urban Planning University of Michigan Department of Electrical Engineering and Computer Science University of Michigan

Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional logit models (e.g., multinomial logit). However, little research focuses on comparing the interpretability of machine learning with logit models. In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling. This paper aims at providing a comprehensive comparison between the two approaches by examining the key similarities and differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for travel mode choice modeling. To complement the theoretical discussions, the paper also empirically evaluates the two approaches on the stated-preference survey data for a new type of transit system integrating high-frequency fixed-route services and ridesourcing. The results show that machine learning can produce significantly higher predictive accuracy than logit models. Moreover, machine learning and logit models largely agree on many aspects of behavioral interpretations. In addition, machine learning can automatically capture the nonlinear relationship between the input features and choice outcomes. The paper concludes that there is great potential in merging ideas from machine learning and conventional statistical methods to develop refined models for travel behavior research and suggests some new research directions. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Author Correction: 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 2024年第10期21卷 1959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

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Development of a system to generate artificial ambiance based on entropy 10

Development of a system to generate artificial ambiance base...

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10th International Conference on Agents and Artificial Intelligence, ICAART 2018

作者： Ohmura, Hidefumi Shibayama, Takuro Yuasa, Masahide Hamano, Takayuki Nakagawa, Ryu Department of Information Sciences Tokyo University of Science Yamazaki 2641 Noda-shi Chiba Japan Department of Information Systems and Design Tokyo Denki University Ishizaka Hatoyama-cho Hikigun Saitama Japan Institute for Music and Acoustics ZKM Lorenzstraße 19 KarlsruheD-76135 Germany Department of Applied Computer Sciences Shonan Institute of Technology Tsujido Nishikaigan 1-1-25 Fujisawa-shi Kanagawa Japan Department of Inter Media Art Tokyo University of the Arts Omonma Toride Ibaraki5000 Japan Graduate School of Design and Architecture Nagoya City University Chikusa-ku Kitachikusa 2-1-10 Nagoya-shi Aichi Japan

ISBN: (纸本)9789897582752

Ambience is an abstract concept of sensory information. If we can control sensory information, then we will be able to somehow provide ambience as needed. We developed a controllable ambience model using the entropy of distribution functions based on this hypothesis. We adapted the model to several sensory modes, such as facial expressions, tones, and unobjective figures written in HTML and JavaScript available on any browser. We introduce these systems in this paper. © 2018 by SCITEPRESS-Science and technology Publications, Lda. All rights reserved.

关键词： Entropy

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Erratum to: Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2022年第3期2022卷 1-21页

作者： Sirunyan, A. M. Tumasyan, A. Adam, W. Ambrogi, F. Bergauer, T. Brandstetter, J. Dragicevic, M. Erö, J. Escalante Del Valle, A. Flechl, M. Frühwirth, R. Jeitler, M. Krammer, N. Krätschmer, I. Liko, D. Madlener, T. Mikulec, I. Rad, N. Schieck, J. Schöfbeck, R. Spanring, M. Spitzbart, D. Waltenberger, W. Wulz, C.-E. Zarucki, M. Drugakov, V. Mossolov, V. Suarez Gonzalez, J. Darwish, M. R. De Wolf, E. A. Di Croce, D. Janssen, X. Lauwers, J. Lelek, A. Pieters, M. Rejeb Sfar, H. Van Haevermaet, H. Van Mechelen, P. Van Putte, S. Van Remortel, N. Blekman, F. Bols, E. S. Chhibra, S. S. D’Hondt, J. De Clercq, J. Lontkovskyi, D. Lowette, S. Marchesini, I. Moortgat, S. Moreels, L. Python, Q. Skovpen, K. Tavernier, S. Van Doninck, W. Van Mulders, P. Van Parijs, I. Beghin, D. Bilin, B. Brun, H. Clerbaux, B. De Lentdecker, G. Delannoy, H. Dorney, B. Favart, L. Grebenyuk, A. Kalsi, A. K. Luetic, J. Popov, A. Postiau, N. Starling, E. Thomas, L. Vander Velde, C. Vanlaer, P. Vannerom, D. Cornelis, T. Dobur, D. Khvastunov, I. Niedziela, M. Roskas, C. Trocino, D. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Zaganidis, N. Bondu, O. Bruno, G. Caputo, C. David, P. Delaere, C. Delcourt, M. Giammanco, A. Lemaitre, V. Magitteri, A. Prisciandaro, J. Saggio, A. Vidal Marono, M. Vischia, P. Zobec, J. Alves, F. L. Alves, G. A. Correia Silva, G. Hensel, C. Moraes, A. Rebello Teles, P. Belchior Batista Das Chagas, E. Carvalho, W. Chinellato, J. Coelho, E. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. De Oliveira Martins, C. Fonseca De Souza, S. Huertas Guativa, L. M. Malbouisson, H. Martins, J. Matos Figueiredo, D. Medina Jaime, M. Melo De Almeida, M. Mora Herrera, C. Mundim, L. Nogima, H. Prado Da Silva, W. L. Sanchez Rosas, L. J. Santoro, A. Sznajder, A. Thiel, M. Tonelli Manganote, E. J. Torres Da Silva De Araujo, F. Vilela Pereira, A. Ahuja, S. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Lemos, D. S. Mercadante, P. G. Novaes, S. F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’ Moscow Russia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Joint Institute for Nuclear Research Dubna Russia Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Cairo University Cairo Egypt Academy of Scientific Research and Technolog

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Methods of obtaining modifying supplement based on calcium hydrosilicates for lime dry mixes

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IOP Conference Series: Materials Science and Engineering 2018年第1期451卷

作者： I S Pyshkina K V Zhegera G V Martyashin Department of Information-Computing Systems Penza State University of Architecture and Construction 28 Titov Str. Penza 440028 Russia Department of Quality Control and Construction Technology Penza State University of Architecture and Construction 28 Titov Str. Penza 440028 Russia Department of Computer Science Penza State University 40 Krasnaya Penza 440026 Russia

The article considers the possibility of using a supplement, based on calcium hydrosilicates (CHS), synthesized in the presence of diatomite, as a modifying additive for finishing lime dry mixes. The results of the physicochemical analysis of the supplement are given. The impact of the method of obtaining CHS supplements on the compressive strength of lime samples was researched. The X-ray phase analysis of supplements, based on calcium hydrosilicates, is presented. The technological and operational characteristics of lime finishing mixes with the use of calcium hydrosilicates, synthesized in the presence of diatomite in the their recipe, are given.

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Erratum to: Search for dark matter produced in association with a leptonically decaying Z boson in proton–proton collisions at s = 13 Te

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The European Physical Journal C 2021年第4期81卷 1-16页

作者： A. M. Sirunyan A. Tumasyan W. Adam T. Bergauer M. Dragicevic J. Erö A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko T. Madlener I. Mikulec F. M. Pitters N. Rad J. Schieck R. Schöfbeck M. Spanring S. Templ W. Waltenberger C.-E. Wulz M. Zarucki V. Chekhovsky A. Litomin V. Makarenko J. Suarez Gonzalez M. R. Darwish E. A. De Wolf D. Di Croce X. Janssen T. Kello A. Lelek M. Pieters H. Rejeb Sfar H. Van Haevermaet P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols S. S. Chhibra J. D’Hondt J. De Clercq D. Lontkovskyi S. Lowette I. Marchesini S. Moortgat A. Morton Q. Python S. Tavernier W. Van Doninck P. Van Mulders D. Beghin B. Bilin B. Clerbaux G. De Lentdecker B. Dorney L. Favart A. Grebenyuk A. K. Kalsi I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas C. Vander Velde P. Vanlaer D. Vannerom L. Wezenbeek T. Cornelis D. Dobur M. Gruchala I. Khvastunov M. Niedziela C. Roskas K. Skovpen M. Tytgat W. Verbeke B. Vermassen M. Vit G. Bruno F. Bury C. Caputo P. David C. Delaere M. Delcourt I. S. Donertas A. Giammanco V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio M. Teklishyn P. Vischia S. Wuyckens J. Zobec G. A. Alves C. Hensel A. Moraes W. L. Aldá Júnior E. Belchior Batista Das Chagas H. BRANDAO MALBOUISSON W. Carvalho J. Chinellato E. Coelho E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza J. Martins D. Matos Figueiredo M. Medina Jaime C. Mora Herrera L. Mundim H. Nogima P. Rebello Teles L. J. Sanchez Rosas A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov G. Antchev I. Atanasov R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov M. Bonchev A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov W. Fang Q. Guo H. Wang L. Yuan M. Ahmad Z. Hu Y. Wang E. Chapon G. M. Chen H. S. Chen M. Chen A. Kapoor D. Leggat H. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Department of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘K

A Correction to this paper has been published: https://***/10.1140/epjc/s10052-020-08739-5

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