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Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

Fundamental issues regarding uncertainties in artificial neural networks

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arXiv 2020年

作者： Thacker, N.A. Twining, C.J. Tar, P.D. Notley, S. Ramesh, V. Division of Informatics Imaging & Data Sciences University of Manchester United Kingdom Department of Computer Science University of Manchester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Center for Cognition and Computation Goethe-Universität Frankfurt am Main Germany

Artificial Neural Networks (ANNs) implement a specific form of multi-variate extrapolation and will generate an output for any input pattern, even when there is no similar training pattern. Extrapolations are not necessarily to be trusted, and in order to support safety critical systems, we require such systems to give an indication of the training sample related uncertainty associated with their output. Some readers may think that this is a well known issue which is already covered by the basic principles of pattern recognition. We will explain below how this is not the case and how the conventional (Likelihood estimate of) conditional probability of classification does not correctly assess this uncertainty. We provide a discussion of the standard interpretations of this problem and show how a quantitative approach based upon long standing methods can be practically applied. The methods are illustrated on the task of early diagnosis of dementing diseases using Magnetic Resonance Imaging. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrapolation

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

Prediction of fitness in bacteria with causal jump dynamic mode decomposition

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arXiv 2020年

作者： Balakrishnan, Shara Hasnain, Aqib Boddupalli, Nibodh Joshy, Dennis M. Egbert, Robert G. Yeung, Enoch Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Mechanical Engineering University of California Santa Barbara United States Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Mechanical Engineering Center for Control Dynamical Systems and Computation Biomolecular Science and Engineering University of California Santa Barbara United States

— In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Bacteria

Persistence of excitation for koopman operator represented dynamical systems

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arXiv 2019年

作者： Boddupalli, Nibodh Hasnain, Aqib Yeung, Enoch Department of Mechanical Engineering and the Center for ControlDynamical Systems and Computation University of California Santa Barbara Santa BarbaraCA93106 United States

The increase in complexity of engineered systems and naturally occurring systems, coupled with increasingly data-driven representations of these systems motivates the need for design of experiment frameworks for nonlinear system identification. In the case of a general nonlinear autonomous system (without inputs or control), designing an experiment for identification relies on the selection of persistently exciting flows and their corresponding initial conditions. This work investigates persistence of excitation conditions for discrete-time nonlinear systems that can be represented using Koopman operators. The Koopman operator presents a linear lifted representation of the evolution of a nonlinear system over a space of observable functions. We use this framework to derive spectral conditions for persistence of excitation, relating classical notions of persistence of excitation to the spectral properties of initial conditions, seen through the lens of Koopman observable functions. We illustrate these concepts on a repressilator circuit, a classical biological system from the field of synthetic biology, to motivate the proper selection of initial conditions. We show that when the rank properties of the flow's spectra are degenerate, the trained Koopman operator does not produce robust predictions on test data. Copyright © 2019, The Authors. All rights reserved.

关键词： Design of experiments

Search strategy in a complex and dynamic environment: The MH370 case

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arXiv 2020年

作者： Ivić, Stefan Crnković, Bojan Arbabi, Hassan Loire, Sophie Clary, Patrick Mezić, Igor Faculty of Engineering University of Rijeka Rijeka Croatia Department of Mathematics University of Rijeka Rijeka Croatia Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge United States Bruker Nano Surfaces Santa Barbara United States Oregon State University Corvallis United States Department of Mechanical Engineering Center for Control Dynamical Systems and Computation University of California Santa Barbara United States

Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched. Copyright © 2020, The Authors. All rights reserved.

关键词： Debris

One-hundred step measurement-based quantum computation multiplexed in the time domain with 25 MHz clock frequency

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arXiv 2020年

作者： Asavanant, Warit Charoensombutamon, Baramee Yokoyama, Shota Ebihara, Takeru Nakamura, Tomohiro Alexander, Rafael N. Endo, Mamoru Yoshikawa, Jun-ichi Menicucci, Nicolas C. Yonezawa, Hidehiro Furusawa, Akira Department of Applied Physics School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Center for Quantum Information and Control Department of Physics and Astronomy University of New Mexico AlbuquerqueNM87131 United States Centre for Quantum Computation and Communication Technology School of Science RMIT University MelbourneVIC3001 Australia

Quantum computers are known to provide algorithmic speed ups over their classical counterparts1. In recent years, approaches based on various physical systems—superconducting qubits2, ion-trap systems3, and photonic systems4,5—have been extensively explored. However, constructing devices at scale required for real-world applications is no trivial task. Among the various approaches, measurement-based quantum computation6,7 \(MBQC\) multiplexed in time domain8 is currently a promising method for addressing the need for scalability. MBQC requires two components: cluster states and programmable measurements. With time-domain multiplexing, the former has been realized on an ultra-large-scale9–12. The latter, however, has remained unrealized, leaving the large-scale cluster states unused. In this work, we make such a measurement system and use it to demonstrate basic quantum operations multiplexed in the time-domain with 25 MHz clock frequency. This programmable measurement system is a key piece for MBQC in time domain. We directly verify transformations of the input states and their nonclassicalities for single-step quantum operations and also observe multi-step quantum operations up to one hundred steps—the largest number ever observed in CV systems. Furthermore, with sufficient squeezing and bosonic qubits, cluster state and programmable measurement system in this work serve as the central components of universal fault-tolerant MBQC13–16. Our work establishes a prototype for scalable quantum computation based on CV optical systems and demonstrates a new and promising road toward full-fledged quantum computation. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

Reconstructing network structures from partial measurements

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arXiv 2020年

作者： Tyloo, Melvyn Delabays, Robin Jacquod, Philippe Department of Quantum Matter Physics University of Geneva GenevaCH-1211 Switzerland School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland Automatic Control Laboratory ETH Zürich ZürichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106-5070 United States

The dynamics of systems of interacting agents is determined by the structure of their coupling network. The knowledge of the latter is therefore highly desirable, for instance to develop efficient control schemes, to accurately predict the dynamics or to better understand inter-agent processes. In many important and interesting situations, the network structure is not known, however, and previous investigations have shown how it may be inferred from complete measurement time series, on each and every agent. These methods implicitly presuppose that, even though the network is not known, all its nodes are. Here we investigate the different problem of inferring network structures within the observed/measured agents. For symmetrically-coupled dynamical systems close to a stable equilibrium, we establish analytically and illustrate numerically that velocity signal correlators encode not only direct couplings, but also geodesic distances in the coupling network, within the subset of measurable agents. When dynamical data are accessible for all agents, our method is furthermore algorithmically more efficient than the traditional ones, because it does not rely on matrix inversion. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

Optimal price of anarchy in cost-sharing games

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arXiv 2019年

作者： Chandan, Rahul Paccagnan, Dario Marden, Jason R. Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Mechanical Engineering Center of Control Dynamical Systems and Computation UC Santa Barbara United States

The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents’ cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents’ local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions