There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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Table Of Integrals Series And Products

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Tracking and Data Relay Satellite System (TDRSS) is playing an important role in data relay for user satellites. Subject to the finite number of antenna, the non-negligible antenna slewing time, and the time-varying connectivity of inter-satellite links (ISLs), it is significantly challenging to improve the selection of antenna scheduling sequence to improve performances (e.g., higher throughput, shorter mean queue length, smaller mean scheduling number, etc). To overcome above challenges, we utilize the antenna slewing model, the track model, and the multi-queue single-server queuing model to calculate the satellite-specific attributes such as the practical antenna slewing time, the link availability period and the buffer state. Furthermore, we propose a Heuristic Algorithm based on Optimal Weight (HAOW) considering the obtained satellite-specific attributes to optimize the antenna scheduling sequence. With the optimized scheduling sequence, the network performances are analyzed by the proposed queuing model. Finally, we conduct numerous simulations for the performance comparisons of the proposed HAOW with classical scheduling algorithms.

Antenna Scheduling for Multiple User Satellites in Space Data Relay Networks

The ever-increasing demand for ubiquitous and differentiated services at anytime and anywhere emphasizes the necessity of aerospace integrated networks (AINs) which consist of multi-tiers, e.g., high-altitude platforms (HAPs) tier, unmanned aerial vehicles (UAVs) tier, low earth orbit (LEO) satellites tier, etc. for wide coverage and high capacity. However, the inherent heterogeneity, time-variability, and differentiated mobility characteristics between terrestrial networks and AINs constraint the materialization of the aforementioned widely expected potentials of AINs. Therefore, it is crucial to make a profound understanding of AINs’ impacts on pivotal models and methodologies for enabling 6G, thus providing technology reference for AINs empowering 6G. To understand the latest development and ultimately open new research niches on this significant topic, this survey is the pioneer paper to serve as a systematical and comprehensive overview in both single-tier scenarios and combining-tiers scenarios in AINs. We start with a profound discussion about the state-of-the-art potentially promising methodologies and models in the era of AINs empowering 6G from the perspective of the system architecture and networking design and enabling technologies of AINs, to enable flexible and scalable management and control of the AINs and ensure network stability. Furthermore, we make an in-depth literature overview across the network dynamics modeling, theoretical performance analysis model, and system optimization to enhance the system performance and guarantee user-on-demand services. Additionally, we highlight the ongoing research challenges and future directions with focusing on the development trend of ultra-dense satellite constellations in 6G.

Aerospace Integrated Networks Innovation for Empowering 6G: A Survey and Future Challenges

Capable of supporting the on-demand high-rate connection towards the core network, wireless millimeter wave (mmWave) backhaul becomes an appealing solution with the growing deployment of small cell base stations (BSs). However, the application of wireless backhaul would impose great limitations on the admission of BSs and the access rate, resulting in a tight coupling between backhaul and radio access network (RAN). In this light, we comprehensively investigate the performance of small cell networks (SCN) in terms of network spatial throughput (ST), supposing that backhaul is conveyed from gateways to BSs through mmWave links. Our results show that the coupling effect makes network ST in the RAN greatly dependent on gateway density $\lambda _{\mathrm{G}}$ and suffer exponential degradation with over-provision of backhaul. It manifests that, if excessive backhaul capacity is provided by deploying more gateways, the exacerbating BS-generated interference deteriorates spatial reuse gain, thereby degrading network ST. Thus, leveraging the coupling between backhaul and RAN, we further conduct a joint optimization of BS and gateway densities to improve network ST. Remarkably, the optimization could mitigate the dependence of network ST on $\lambda _{\mathrm{G}}$ and brings a 2-fold increase in network ST, which is inversely proportional to mmWave beamforming beamwidth.

Leveraging the Coupling of Radio Access Network and mmWave Backhaul Network: Modeling and Optimization

Data center networks (DCNs) are widely deployed to provide infrastructure backbone for various cloud services. Meanwhile, the emergence of Software-Defined Networking (SDN) makes it easy to monitor the global status of DCNs. In DCNs, traffic bursts or uneven traffic distribution may lead to network congestion, thus deteriorating the overall network performance. Rerouting is an effective method to mitigate network congestion by rerouting flows to paths with sufficient bandwidth. In this paper, we propose an efficient rerouting algorithm by leveraging the SDN technique. We firstly formulate the rerouting problem as a Multi-Commodity Flow (MCF) problem with the objective to minimize the maximum link utilization in the DCN. Since the problem is a NP-hard problem, we propose a heuristic algorithm that jointly considers the current path utilization and the path criticality to approach the optimal solution. Our simulation results demonstrate the superiority of the proposed algorithm in mitigating the network congestion and guaranteeing the performance of the network, over the classical Equal-Cost Multi-Path (ECMP) algorithm and Global First Fit (GFF) algorithm.

Rerouting Based Congestion Control in Data Center Networks

Compared with the small overhead of data payload in sensor network, the overhead of MAC address is significant from the point of view of energy-saving. A distributed algorithm (Virtual Grid Spatial Reusing- VGSR) for MAC address assignment is presented in this papel; which is a low energy cost algorithm and reduces the size of the fixed MAC address greatly based on the mapping of geographical position. Moreover; VGSR algorithm scales well with the network size and achieves the optimum performance by adjusting the communication range of sensor nodes. In typical scenarios, the MAC address size is 5 bits and the corresponding average size is only 3.86 bits, which outperforms other existing approaches.

Min Sheng

Papers

Antenna Scheduling for Multiple User Satellites in Space Data Relay Networks

Aerospace Integrated Networks Innovation for Empowering 6G: A Survey and Future Challenges

Leveraging the Coupling of Radio Access Network and mmWave Backhaul Network: Modeling and Optimization

Rerouting Based Congestion Control in Data Center Networks

Virtual Grid Spatial Reusing Algorithm for MAC Address Assignment in Wireless Sensor Netetwork