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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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

Designing novel artificial intra-body networks and/or synthetic neurons, which interact with operating cells and compensate for malfunctioning cells, requires understanding and quantifying the information transfer in neural networks. However, the latter is not studied enough in the existing literature. Here we quantify the information rate transmitted between two neurons by analyzing Poisson Multiple-Input Multiple-Output (MIMO) synaptic channels. The results provided are intuitive and prove that multiple synapses working in cooperation improve the reliability of the neuron-to-neuron communication channel. The results serve as a progressive step in the evaluation of the performance of biological neural networks and the development of artificial cells and networks.

Capacity estimation in MIMO synaptic channels

Wired biomedical sensors have facilitated increasingly advanced clinical decisions support systems in specialized medical settings over the last decades. Reliable hemodynamic monitoring of cardiac and pulmonary function is mandatory for individual tailoring of treatment of critically ill patients. Sensors provide the hemodynamic parameters that reveal impending clinical problems, and initiate caregiver intervention. Biomedical sensor technologies include invasive or non-invasive sensors for intermittent or continuous monitoring of vital physiological parameters used in hemodynamic treatment at point-of-care. A hemodynamic sensor portfolio thus involves multiple sensors either attached to the patient, or embedded in biomedical devices used for treatment. The criticality of such systems is evident, as they are used for direct life support in a setting where quality, stability and continuity of real-time data is vital (Oyri et al., 2010). For the last few decades, biomedical sensors and patient monitors used in hemodynamic monitoring have been based on wired solutions. However, a digital revolution is now taking place in healthcare. Medical profiles for wireless standards, such as Bluetooth or ZigBee standards, have currently been developed and adopted by the Continua Health Alliance (Caroll et al., 2007). In the standardization bodies IEEE, ISO and CEN TC 251, improvement of care by reuse of medical device data has been addressed for many years; In particular the IEEE 1073 Standard for Medical Device Connection. A consortium of Scandinavian research institutions, technology startup companies, sensor producers, and a hospital based test facility collaborated to develop a portfolio of multiple experimental wireless sensor prototypes for a platform compliant with the X73 PoC-MDC (ISO11073/IEEE1073)(Galarraga et al., 2006) medical device communication outline (Oyri et al., 2010). Other research groups have evaluated implementations of wireless clinical alerts from pager systems (Major et al., 2002 and Reddy et al., 2005). Yao and Warren investigated how to apply the ISO/IEEE 11073 Standards to wearable home health monitoring systems (Yao & Warren., 2005). There is a demand for a point of care clinical decision support systems providing real time processing of 26

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Periodic-MAC: Improving MAC Protocols for Wireless Biomedical Sensor Networks through Implicit Synchronization

This paper investigates the advantages, disadvantages and tradeoffs of applying directive antennas to implant - on-body ultra wideband (UWB) communications. Our study concludes that directive antennas can reduce unnecessary exposition of human tissue to electromagnetic radiation, reduce exposition to narrow band interference, save energy and reduce the possibility of eavesdropping. However, when using directive antennas, receive/transmit beam direction needs to be guaranteed. We have also analyzed on-body node positioning, the best aperture angle and its tradeoffs. We further conclude that, at least, directive antennas with 180° aperture angle should be used at the on-body nodes.

On directive antennas application to implant - on-body UWB communications

This paper presents a power-saving communication system for the link from an in-body sensor to an on-body sensor over the abdominal channel in medical wireless body area networks. The idea of the work is to exploit the time diversity of the channel coding and the high dimensional signal constellation of M-ary pulse position modulation (M-PPM) to improve the system performance. In addition to the illustration of the significant performance improvement of the proposed communication system, the severe fading effect of the abdominal channel compared with the conventional Rayleigh channel is investigated through the theoretical capacity and bit error rate (BER) performance.

Coded pulse position modulation communication system over the human abdominal channel for medical wireless body area networks

Molecular communication (MC) is an interdisciplinary research area with vast potential in biomedical applications, including delivery of drugs or other therapeutic molecules in cardiovascular and other pathological conditions. The bio-molecules release modeling in MC systems is an open problem in designing the physical and chemical signal interaction level in the MC hierarchy. This also includes the release of extracellular vesicles (EVs), which are shed by all cell types and serve as nano-vehicles carrying various molecular cargo. In this paper, we propose to use engineered human induced pluripotent stem cells differentiated into atrial cardiomyocytes (ACMs) in a cardiac bio-nanonetwork and mathematically model the release of therapeutic EVs under voltage clamp. We study the stochastic nature of the proposed targeted drug delivery system by modeling the release process from engineered ACMs as a Poisson process. We model the engineered ACMs as an EV emission model, where the input signal is the voltage clamp to modulate the EV release, and the output signal is the rate of released EVs. We also study the non-linearity of the proposed EV emission system by a non-linear autoregressive exogenous model. Future drug delivery applications for cardiovascular diseases can utilize the proposed EV release modeling for engineered ACMs.

https://dl.acm.org/doi/pdf/10.1145/3477206.3477479

Ilangko Balasingham

Papers

Capacity estimation in MIMO synaptic channels

Periodic-MAC: Improving MAC Protocols for Wireless Biomedical Sensor Networks through Implicit Synchronization

On directive antennas application to implant - on-body UWB communications

Coded pulse position modulation communication system over the human abdominal channel for medical wireless body area networks

Cardiac Bio-Nanonetwork: Extracellular Vesicles Release Modeling for Engineered Stem Cell-Derived Cardiomyocyte