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

The organization of behavior: A neuropsychological theory

Neurons within human brain make use of two communication paradigms while pursuing an objective, namely, classical electromagnetic and molecular. Physiological studies revealed that communication performance between neurons, including memory formation and learning processes, highly depends on the concentration of calcium ions, whereas the intracellular calcium concentration hinges on regulation of neuron's membrane potential. Hence, the neuronal communication performance can be affected via controlled stimulation of targeted cell. In this paper we analyze the neuronal communication as potential paradigm to be applied for communication between nano-scale devices and define the stochastic spiking model, that is confined to randomness associated with neuron's firing, in order to acquire and quantify the neuron's response given specified stimulus. We also present synaptic transmission process and modifications related to memory formation and storage using existing simplified theoretical models on calcium dependent behavior and learning. Using modeling, theory, and findings presented in this paper, one can design the stimulus with adequate power spectral density in order to evoke desired synaptic modifications in terms of its strengthening and weakening. Similar approach provides a basement for future technologies and controlling method for nano-scale communication between peers.

From Nano-Scale Neural Excitability to Long Term Synaptic Modification

Multi-node leadless pacemaker system overcomes the main limitations related to lead complications of the conventional cardiac pacemaker and will thus replace them in the near future. The multiple nodes of the technology require the development of low-power, low data-rate and energy-efficient communication framework for device synchronization and bi-directional communication between them. Moreover, the nodes need to communicate with the outer peripheral devices for data telemetry, control and remote monitoring. This paper focuses on evaluation of different energy-efficient modulation schemes at 433 MHz for bi-directional communication between the nodes using homogeneous liquid phantom model of human heart and living animal experiments. In this paper, we have analyzed three simple, low-budget modulation schemes - On Off-Keying (OOK), Frequency Shift-Keying (FSK), and Gaussian Frequency Shift-Keying (GFSK). The analysis is done based on the total transmitter power required to achieve a reliable communication indicated by the minimum threshold values of bit-error rate and packet-error rate. The experiments have been conducted for three common implant communication scenarios - in-body to in-body, in-body to on-body and in-body to off-body links. For conducting the experiments, we have designed the experimental setup with electronic components and fabricated antennas. The results have shown that GFSK has the best performance among the other modulation techniques based on the total transmitter power. We also investigated higher order of the same modulation schemes – 4-FSK and 4-GFSK. The results showed that GFSK performed much better than 4-FSK and 4-GFSK. This research will be carried forward to build the entire radio frequency communication framework for the multi-node pacemaker technology.

/pdf/evaluation-of-data-telemetry-for-future-leadless-cardiac-32ddoscgwv.pdf

Evaluation of Data Telemetry for Future Leadless Cardiac Pacemaker

A Wireless Body Area Sensor Network (WBASN) is a radio-frequency (RF) based wireless networking technology that interconnects tiny nodes with sensor or actuator capabilities in, on or around a human body. Complementing the coverage of Wireless Personal Area Networks (WPANs), WBASNs target diverse applications. This paper extensively evaluates the performance of ZigBee technology for WBASN-WPAN system by analysis and computer simulations. Specifically, we configure the system deployment, network structure, applicable data rates for WBASN sensor nodes, and underlying radio parameters. Average reception ratio, throughput and latency have been evaluated for the overall system performance; fairness across sensor nodes has been examined; preliminary results regarding mobility support of ZigBee for WBASN are also shown. After examining the performance of ZigBee network with our WBASN-WPAN configurations, we come to the conclusion that serveral optimizations should be done before applying ZigBee to such a system.

/pdf/performance-analysis-of-zigbee-technology-for-wireless-body-2j9kxmurn5.pdf

Performance Analysis of ZigBee Technology for Wireless Body Area Sensor Networks

Overlay networks are becoming widely used for content delivery and file sharing services because they provide effective and reliable services by creating a virtual topology on top of existing networks. However, the management of overlay networks meet new challenges in the structure, security, services and deployment. This paper presents a survey of the overlay networks and correlated management. The management issues of two typical overlay networks: peer-to-peer and Virtual Private Network are investigated. As a conclusion, the management of overlay networks must import self-management (self-*) and intelligent strategies to deal with the complex management tasks, which are to deal with the dynamic, salability, resilient and security challenges on overlay networks.

Management of Overlay Networks: A Survey

When designing coding algorithms for the wireless endoscope, achieving good performance with very low complexity is of great importance. This is due to the fact that hardware implementation is much constrained by the small size of the endoscope capsule and available power is also very limited. In this paper, we propose a very low complexity image coder for the wireless endoscope which operates within these constraints, with special emphasis on low bit rate performance. The scheme encodes the captured images frame-by-frame using simple DPCM (Differential Pulse Coded Modulation) coding, combined with multi-rate processing, dead-zone quantization and efficient run-length coding of the quantization indices. Simulation results show that proposed coder is able to meet our objectives.

Ilangko Balasingham

Papers

From Nano-Scale Neural Excitability to Long Term Synaptic Modification

Evaluation of Data Telemetry for Future Leadless Cardiac Pacemaker

Performance Analysis of ZigBee Technology for Wireless Body Area Sensor Networks

Management of Overlay Networks: A Survey

Very low complexity low rate image coding for the wireless endoscope