Computer science Books

Book SynopsisHumanless space exploration, as in the use of Rover in exploring Mars, has demonstrated the importance of human-computer communications. This book provides a comprehensive look at 'general automata' as a method of establishing the fundamentals for communication in human-computer systems (HCS).Trade Review"Essential teaching resource; exhaustive bibliography, including Koenig's 34 previously published works on GAM in HCS." (CHOICE, April 2007) "Readers have been provided with more than sufficient detail-led analysis and practical illustrations…" (Kybernetes, Volume 36, No.34, 2007)Table of ContentsPreface. 1. Introduction. 1.1 Considerations for Establishing Knowledge Structures for Computers. 1.2 Knowledge About Automata as a Subset of World Knowledge. 1.2.1 General Automata. 1.2.2 Extracting and Storing the Meanings of Sentences. 1.2.3 Associating Knowledge. 1.2.4 Establishing Conclusions and Inferences. Exercises. 2. A General Automaton. 2.1 Formal Analysis for a General Automaton. 2.1.1 General Analysis. 2.1.2 Graph Model. 2.1.3 Select Properties of the Graph Model. 2.2 An Application of the Disciplines to the Modeling of Natural Automata. 2.2.1 A Case Study. 2.2.2 Required State Changes. 2.2.3 Algorithm for Determining Required State Changes. Exercises. 3. A General Automaton: Detailed Analysis. 3.1 Distinguishable Receptors and Effectors. 3.2 Nonhomogeneous Environments. 3.3 Transformation Response Components. 3.4 Nonshared Environments Interpreted as Distinguishable. 3.4.1 Model for Performance in Both Shared and Nonshared Environments. 3.4.2 Model for Performance in Shared Environments. Exercises. 4. Processing of Knowledge About Automata. 4.1 Formulation of a Language Information Theory. 4.1.1 Class 1 Sentence. 4.1.2 Class 2 Sentence. 4.1.3 Class 3 Sentence. 4.1.4 Class 4 Sentence. 4.1.5 Class 5 Sentence. 4.1.6 Class 6 Sentence. 4.1.7 Class 7 Sentence. 4.2 Extracting and Storing the Meaning of Sentences by Computer. 4.2.1 Description of an Algorithm. 4.3 Knowledge Association. 4.3.1 Association by Combining Graphs Through Common Points. 4.3.2 Associations by Combining Graph (n + 1)-Tuples. 4.3.3 Computer Methods for Association of Knowledge. 4.4 Deductive Processes. 4.4.1 Deductive Processes Related to Association Through Common Points. 4.4.2 Deductive Processes Related to Association by Combining Graph Tuples. 4.4.3 Deductive Processes with Aristotelian Form A as a Premise. 4.5 Inferences. 4.5.1 Inferences Related to a Single Graph Tuple of Associated Knowledge. 4.5.2 Inferences Related to More than One Graph Tuple of Associated Knowledge. Exercises. 5. A General System of Interactive Automata. 5.1 Formal Analysis for a General System of Interactive Automata. 5.1.1 General Analysis. 5.1.2 Microsystem Model. 5.1.3 Macrosystem Model. 5.2 Example Applications. 5.2.1 A Two-Component System. 5.2.2 A System of Many Components. Exercises. 6. Processing of Knowledge About Systems of Automata. 6.1 A General System of Interactive Automata: Detailed Analysis. 6.1.1 The Microsystem Model. 6.1.2 The Macrosystem Model. 6.2 Knowledge Structures for Sentences Describing Systems of Interactive Automata. Exercises. 7. Changing Expressions of Knowledge for Communication from One Form and Style to Another. 7.1 Introduction. 7.2 Sets and Relations. 7.3 Establishing Open Expressions and Open Sentences. 7.4 Selecting Subsets of Open Expressions. 7.5 Applying the Results of the Above Analysis. 7.6 Summary and Conclusions. Exercises. 8. Electronic Security Through Pseudo Languages. 8.1 Introduction. 8.2 Defi nitions, Sets, and Relations. 8.3 Analysis for E-Security Through Pseudo Languages. 8.3.1 A Basic E-Security System. 8.3.2 A Two-Step Encryption System. 8.3.3 E-Signing. 8.4 Summary and Conclusions. Exercises. Appendix A: Analysis for an Effective Operation of a General Automaton. A.1 Introduction. A.2 Recursive Methods. A.3 Effective Operation Analysis. Exercises. Appendix B: Analysis for an Effective Operation of a General System of Interactive Automata. B.1 Introduction. B.2 Microsystem Graphs. B.3 Macrosystem Graphs. B.4 Example. Exercises. References. Index.

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Book SynopsisThis book is an introduction to the rudiments of Perl programming. It provides the general reader with an interest in language with the most usable and relevant aspects of Perl for writing programs that deal with language. Exposes the general reader with an interest in language to the most usable and relevant aspects of Perl for writing programs that deal with language. Contains simple examples and exercises that gradually introduce the reader to the essentials of good programming. Assumes no prior programming experience. Accompanied by exercises at the end of each chapter and offers all the code on the companion website: http://www.u.arizona.edu/~hammond Trade Review''Learning to program isn't really hard,' the author claims. Teaching good programming to linguists, however, or to arts and humanities students in general, isn’t really that easy a job either, in practice. This introductory book, clear and concise as it is, should be a helpful tool at the very first stages of such an enterprise." Kwee Tjoe Liong, Universiteit van Amsterdam "The really strong points of the book are the examples and exercises. These are almost all language-related and include useful, interesting and relevant questions and situations that the reader interested in language research will appreciate." New Zealand Studies in Applied Linguistics "Surprisingly readable...should be on the bookshelf of any discourse analysist even thinking about tinkering with using computers to automate some portion of their data analysis...the examples and exercises are excellent, as is [Hammond's] exegesis of the examples- slow without becoming tedious." Discourse StudiesTable of ContentsPreface. Acknowledgments. 1. Why Programming and Why Perl?. 2. Getting Started. 3. Basics: Control Structures And Variables. 4. Input and Output. 5. Subroutines And Modules. 6. Regular Expressions. 7. Text Manipulation. 8. HTML. 9. CGI. Appendix A. Objects. Appendix B. Tk. Appendix C. Special Variables. Appendix D. Where To Find Out More. Index.

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Book SynopsisThe construction industry is subject to more risk and uncertainty than perhaps any other industry. Yet, surprisingly, managerial techniques used to identify, analyse and respond to risk were not applied in the industry until the 80a s. Existing texts deal with the theoretical concepts of risk and the techniques that identify and manage it.Table of ContentsList of figures; List of tables; Forward; Introduction; The aim of the book; Part 1 - Putting risk into perspective:; Introduction; Risk and reward go hand in hand; Risk and contruction; Risk - another four letter word; AGAP (All goes according to plan) and WHIF (What happens if); The people, the process and the risks; Clients of the industry; Have clients' needs changed? Privately financed infrastructure projects; What do clients want?; Investment in property; Consultatns and risk; Contracting and risk; Part II The background to risk and uncertainty:; Introduction; Defining risk and uncertainty; The uncertainty of life and construction projects; Dynamic and static risk; A threat and a challenge; Some fo ther basic rules for risk taking; Risk 'Place your waterline low'; The risky shift phenomenon - what happens when groups make decisions; The risk of not risking; Risk styles; Removing ignorance - and risk; Probability; Converting uncertainty to risk; Decision-making in the construction industry; Intuition; Bias and intuition; Experts and experience; Rules of thumb; Making a model; Reacting to information; Looking at the past to forecast the future; Types of information; Building a decision model to solve a problem; Part III The risk management system: Introduction; Developing a risk management framework; Risk identification; Sources of risk; Dependent and independent risk; Risk classification; Types of risk; Impact of risk; The risk hierarchy; Risk and the general environment; The market/industry risk; The company risk; Project risk and individual risk; Consequence of risk; Risk reponse; Risk retention; Risk reduction; Risk transfer; Risk avoidance; Risk attitude; Summarising risk management; Risk management; Part IV Some of the tools and techniques of risk management: Introduction; Seeing the big picutre and tthe detail; Decision-making techniques; The risk premium; Risk-adjusted discount rate; Subjective probabilities; Decision analysis; Algorithms; Means-end chain; Decision matrix; Strategy; Decision trees; Bayesian theory; Stochastic decision tree analysis; Multi-attribute value theory; Specify the utility function; Case study; Summary; Sensitivity analysis; Spiider Diagram; Monte Carlo simulation; Portofolio theory; The aplication of portfolio analysis in the construction industry; Stochastic dominance; Cumulative distributions of illustrative portfolios; Conclusion; Part V Utility and risk attitude: Introduction; Risk exposure; Utility theory; Expected monetary value; Payoff matrix; The utility function; General types of characteristics of utility functions; The difference between EUV and EMV in practice; The use of utility theory in construction; Basic principle for the aplication of the theory; Part VI Risks and the construction project - money, time and technical risks: Introduction; Money and delivery sequence; Investment and development sequence; Cost considerations; Operational/revenue considerations; The influence of taxation; Value considerations; Design and construction sequence; Time delivery sequence; Contractors and specialist contractors; Technical delivery sequence; A case study of the technical risks faced by the building surveyor; Part VII Sensitivity analysis, breakeven analysis, and scenario analysis: Sensitivity analysis; Breakeven analysis; Scenario analysis; Sensitivity analysis - an application to life cycle costing; Part VIII Risk analysis using Monte Carlo simulation: Probability analysis - extending the sesitivity technique; How it works; Using Monte Carlo simulation in the cost planning of a building; Estimating and price prediction an overview of current practice; Cost planning and risk analysis; Interdependence of items; Risk analysis using probabilities; Risk analysis using Monte Carlo simulation; Considering some probability distributions; Comon distriubtion types; Uniform distribution; Triangular distribution; Normal distribution; A step by step approach to Monte Carlo simulation; Using Monte Carlo simulation on a live project; The result; Questions and Answers; Part IX Constracts and risk: Disagreement and conflict; The purpose of the contract; The fundamental risks - liability and responsibility; Transferring and allocating the risk in the contracts; The principles of control - the theory; The contractual links; Risk avoidance by warrannties and collateral warranties; The types of contract; Contracts and risk tactics; Part X A case study of an oil platform: A practical application of resourced schedule risk analysis; Background; The model; Comparison with deterministic plan; Data; Weather; Project variables; Processing of data; Confidence in the data; Initial results; Conclusion; References and bibliography; Index

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Book SynopsisGolumbia, who worked as a software designer for more than ten years, argues that computers are cultural all the way downthat there is no part of the apparent technological transformation that is not shaped by historical and cultural processes, or that escapes existing cultural politics.Trade ReviewThe Cultural Logic of Computation is a brilliant, audacious book. It might be described as a rollicking, East Coast version of Alan Liu's The Laws of Cool-- or one part Laws of Cool, one part Seeing Like a State, with more than a dash of Baudrillard and Virilio for brio. Golumbia's argument is that contemporary Western and Westernizing culture is deeply structured by forms of hierarchy and control that have their origins in the development and use of computers over the last 50 years. I look forward to pressing this book on friends and colleagues, starting with anyone who has ever recommended The World is Flat to me. -- Lisa Gitelman, author of Always Already New: Media, History, and the Data of CultureThe Cultural Logic of Computation is a fascinating and wise book. It takes us with great care through the history of the computational imagination and logic, from Hobbes and Leibniz to blogging and corporate practice. Its range includes the philosophy of computation, the ideology of the digital revolution, the important areas of children's education and education in general and glimpses of brilliant literary insight. Required reading for the responsible citizen. -- Gayatri Chakravorty SpivakGolumbia is no Luddite; he readily admits that computers have brought a wide range of benefits to society. His chief purpose, though, is to demonstrate that these benefits come at the cost of accepting the technophilic ideology, and changing how we perceive our own essence as human beings. -- Rob Horning * popmatters.com *A work to be read as rawly new in the brute force with which it confronts the disavowed fatal flaw in a contemporary academic disciplinary formation: here, the intractably cultural First Worldism of digital media studies...[A] meticulously crafted polemic. -- Brian Lennon * Electronic Book Review *This is a thought-provoking book, full of interesting ideas that would be valuable to teachers and researchers in the area of contemporary culture...The work should also appeal to general readers who are interested in computerization's effects on culture. -- R. Bharath * Choice *Table of Contents* The Cultural Functions of Computation Part I. Computationalism and Cognition * Chomsky's Computationalism * Genealogies of Philosophical Functionalism Part II. Computationalism and Language * Linguistic Computationalism * Computational Semantics, Digital Textuality Part III. Cultural Computationalism * Computation, Globalization, and Cultural Striation * Computationalism, Striation, and Cultural Authority Part IV. Computationalist Politics * Computationalism and Political Individualism * Computationalism and Political Authority * Epilogue: Computers Without Computationalism * Notes * References * Acknowledgments

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Book SynopsisCovers local search and its variants from both a theoretical and practical point of view. This book is suitable for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science.Trade Review"A truly remarkable and unique collection of work... Invaluable."--Informs "The world of local search has changed dramatically in the last decade and Aarts and Lenstra's book is a tribute to this development... A very useful source."--Optima

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Book SynopsisMany problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It is of interest to applied mathematicians, and computer scientists.Trade Review"This book is succinct but essentially self-contained; it includes an appendix with background material as well as an extensive bibliography. The algorithmic techniques developed may be useful anytime a model leads to a mathematical optimization problem where the domain naturally is a manifold, particularly if the manifold is a matrix manifold. The book follows the usual definition-theorem-proof style but it is not intended for traditional course work so there are no exercises. A reader with limited exposure to manifold theory and differential geometry most likely will benefit from consulting standard texts on those subjects first."--Anders Linner, Mathematical Reviews "[T]his book is succinct but essentially self-contained; it includes an appendix with background material as well as an extensive bibliography. The algorithmic techniques developed may be useful anytime a model leads to a mathematical optimization problem where the domain naturally is a manifold, particularly if the manifold is a matrix manifold. The book follows the usual definition-theorem-proof style but it is not intended for traditional course work so there are no exercises. A reader with limited exposure to manifold theory and differential geometry most likely will benefit from consulting standard texts on those subjects first."--Anders Linner, American Mathematical Society "The book is very well and carefully written. Every chapter starts with a page-long introduction clearly outlining its goals and how they are achieved together with possible relations to other chapters. I find the material very well explained and supported with appropriate examples. It is a pleasure to work with such a book."--Nickolay T. Trendafilov, Foundations of Computational MathematicsTable of ContentsList of Algorithms xi Foreword, by Paul Van Dooren xiii Notation Conventions xv Chapter 1. Introduction 1 Chapter 2. Motivation and Applications 5 2.1 A case study: the eigenvalue problem 5 2.1.1 The eigenvalue problem as an optimization problem 7 2.1.2 Some benefits of an optimization framework 9 2.2 Research problems 10 2.2.1 Singular value problem 10 2.2.2 Matrix approximations 12 2.2.3 Independent component analysis 13 2.2.4 Pose estimation and motion recovery 14 2.3 Notes and references 16 Chapter 3. Matrix Manifolds: First-Order Geometry 17 3.1 Manifolds 18 3.1.1 Definitions: charts, atlases, manifolds 18 3.1.2 The topology of a manifold* 20 3.1.3 How to recognize a manifold 21 3.1.4 Vector spaces as manifolds 22 3.1.5 The manifolds Rn x p and Rn x p 22 3.1.6 Product manifolds 23 3.2 Differentiable functions 24 3.2.1 Immersions and submersions 24 3.3 Embedded submanifolds 25 3.3.1 General theory 25 3.3.2 The Stiefel manifold 26 3.4 Quotient manifolds 27 3.4.1 Theory of quotient manifolds 27 3.4.2 Functions on quotient manifolds 29 3.4.3 The real projective space RPn x 1 30 3.4.4 The Grassmann manifold Grass(p, n) 30 3.5 Tangent vectors and differential maps 32 3.5.1 Tangent vectors 33 3.5.2 Tangent vectors to a vector space 35 3.5.3 Tangent bundle 36 3.5.4 Vector fields 36 3.5.5 Tangent vectors as derivations? 37 3.5.6 Differential of a mapping 38 3.5.7 Tangent vectors to embedded submanifolds 39 3.5.8 Tangent vectors to quotient manifolds 42 3.6 Riemannian metric, distance, and gradients 45 3.6.1 Riemannian submanifolds 47 3.6.2 Riemannian quotient manifolds 48 3.7 Notes and references 51 Chapter 4. Line-Search Algorithms on Manifolds 54 4.1 Retractions 54 4.1.1 Retractions on embedded submanifolds 56 4.1.2 Retractions on quotient manifolds 59 4.1.3 Retractions and local coordinates* 61 4.2 Line-search methods 62 4.3 Convergence analysis 63 4.3.1 Convergence on manifolds 63 4.3.2 A topological curiosity* 64 4.3.3 Convergence of line-search methods 65 4.4 Stability of fixed points 66 4.5 Speed of convergence 68 4.5.1 Order of convergence 68 4.5.2 Rate of convergence of line-search methods* 70 4.6 Rayleigh quotient minimization on the sphere 73 4.6.1 Cost function and gradient calculation 74 4.6.2 Critical points of the Rayleigh quotient 74 4.6.3 Armijo line search 76 4.6.4 Exact line search 78 4.6.5 Accelerated line search: locally optimal conjugate gradient 78 4.6.6 Links with the power method and inverse iteration 78 4.7 Refining eigenvector estimates 80 4.8 Brockett cost function on the Stiefel manifold 80 4.8.1 Cost function and search direction 80 4.8.2 Critical points 81 4.9 Rayleigh quotient minimization on the Grassmann manifold 83 4.9.1 Cost function and gradient calculation 83 4.9.2 Line-search algorithm 85 4.10 Notes and references 86 Chapter 5. Matrix Manifolds: Second-Order Geometry 91 5.1 Newton's method in Rn 91 5.2 Affine connections 93 5.3 Riemannian connection 96 5.3.1 Symmetric connections 96 5.3.2 Definition of the Riemannian connection 97 5.3.3 Riemannian connection on Riemannian submanifolds 98 5.3.4 Riemannian connection on quotient manifolds 100 5.4 Geodesics, exponential mapping, and parallel translation 101 5.5 Riemannian Hessian operator 104 5.6 Second covariant derivative* 108 5.7 Notes and references 110 Chapter 6. Newton's Method 111 6.1 Newton's method on manifolds 111 6.2 Riemannian Newton method for real-valued functions 113 6.3 Local convergence 114 6.3.1 Calculus approach to local convergence analysis 117 6.4 Rayleigh quotient algorithms 118 6.4.1 Rayleigh quotient on the sphere 118 6.4.2 Rayleigh quotient on the Grassmann manifold 120 6.4.3 Generalized eigenvalue problem 121 6.4.4 The nonsymmetric eigenvalue problem 125 6.4.5 Newton with subspace acceleration: Jacobi-Davidson 126 6.5 Analysis of Rayleigh quotient algorithms 128 6.5.1 Convergence analysis 128 6.5.2 Numerical implementation 129 6.6 Notes and references 131 Chapter 7. Trust-Region Methods 136 7.1 Models 137 7.1.1 Models in Rn 137 7.1.2 Models in general Euclidean spaces 137 7.1.3 Models on Riemannian manifolds 138 7.2 Trust-region methods 140 7.2.1 Trust-region methods in Rn 140 7.2.2 Trust-region methods on Riemannian manifolds 140 7.3 Computing a trust-region step 141 7.3.1 Computing a nearly exact solution 142 7.3.2 Improving on the Cauchy point 143 7.4 Convergence analysis 145 7.4.1 Global convergence 145 7.4.2 Local convergence 152 7.4.3 Discussion 158 7.5 Applications 159 7.5.1 Checklist 159 7.5.2 Symmetric eigenvalue decomposition 160 7.5.3 Computing an extreme eigenspace 161 7.6 Notes and references 165 Chapter 8. A Constellation of Superlinear Algorithms 168 8.1 Vector transport 168 8.1.1 Vector transport and affine connections 170 8.1.2 Vector transport by differentiated retraction 172 8.1.3 Vector transport on Riemannian submanifolds 174 8.1.4 Vector transport on quotient manifolds 174 8.2 Approximate Newton methods 175 8.2.1 Finite difference approximations 176 8.2.2 Secant methods 178 8.3 Conjugate gradients 180 8.3.1 Application: Rayleigh quotient minimization 183 8.4 Least-square methods 184 8.4.1 Gauss-Newton methods 186 8.4.2 Levenberg-Marquardt methods 187 8.5 Notes and references 188 A. Elements of Linear Algebra, Topology, and Calculus 189 A.1 Linear algebra 189 A.2 Topology 191 A.3 Functions 193 A.4 Asymptotic notation 194 A.5 Derivatives 195 A.6 Taylor's formula 198 Bibliography 201 Index 221

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Book SynopsisIntroduces the distributed control of robotic networks. This book presents a set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity. It analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation.Trade Review"This book covers its subject very thoroughly. The framework the authors have established is very elegant and, if it catches on, this book could be the primary reference for this approach. I don't know of any other book that covers this set of topics."—Richard M. Murray, California Institute of Technology"The authors do an excellent job of clearly describing the problems and presenting rigorous, provably correct algorithms with complexity bounds for each problem. The authors also do a fantastic job of providing the mathematical insight necessary for such complex problems."—Ali Jadbabaie, University of Pennsylvania"The order of presentation makes much sense, and the book thoroughly covers what it sets out to cover. The algorithms and results are presented using a clear mathematical and computer science formalism, which allows a uniform presentation. The formalism used and the way of presenting the algorithms may be helpful for structuring the presentation of new algorithms in the future."—Vincent Blondel, Université catholique de LouvainTable of ContentsPreface ix Chapter 1. An introduction to distributed algorithms 1 1.1 Elementary concepts and notation 1 1.2 Matrix theory 6 1.3 Dynamical systems and stability theory 12 1.4 Graph theory 20 1.5 Distributed algorithms on synchronous networks 37 1.6 Linear distributed algorithms 52 1.7 Notes 66 1.8 Proofs 69 1.9 Exercises 85 Chapter 2. Geometric models and optimization 95 2.1 Basic geometric notions 95 2.2 Proximity graphs 104 2.3 Geometric optimization problems and multicenter functions 111 2.4 Notes 124 2.5 Proofs 125 2.6 Exercises 133 Chapter 3. Robotic network models and complexity notions 139 3.1 A model for synchronous robotic networks 139 3.2 Robotic networks with relative sensing 151 3.3 Coordination tasks and complexity notions 158 3.4 Complexity of direction agreement and equidistance 165 3.5 Notes 166 3.6 Proofs 169 3.7 Exercises 176 Chapter 4. Connectivity maintenance and rendezvous 179 4.1 Problem statement 180 4.2 Connectivity maintenance algorithms 182 4.3 Rendezvous algorithms 191 4.4 Simulation results 200 4.5 Notes 201 4.6 Proofs 204 4.7 Exercises 215 Chapter 5. Deployment 219 5.1 Problem statement 220 5.2 Deployment algorithms 222 5.3 Simulation results 233 5.4 Notes 237 5.5 Proofs 239 5.6 Exercises 245 Chapter 6. Boundary estimation and tracking 247 6.1 Event-driven asynchronous robotic networks 248 6.2 Problem statement 252 6.3 Estimate update and cyclic balancing law 256 6.4 Simulation results 266 6.5 Notes 268 6.6 Proofs 270 6.7 Exercises 275 Bibliography 279 Algorithm Index 305 Subject Index 307 Symbol Index 313

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Book SynopsisFocusing on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). It illustrates through examples Basic gPC methods, and includes polynomial approximation theory and probability theory.Trade Review"[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter."--Peter Mathe, Mathematical ReviewsTable of ContentsPreface xi Chapter 1: Introduction 1 1.1 Stochastic Modeling and Uncertainty Quantification 1 1.1.1 Burgers' Equation: An Illustrative Example 1 1.1.2 Overview of Techniques 3 1.1.3 Burgers' Equation Revisited 4 1.2 Scope and Audience 5 1.3 A Short Review of the Literature 6 Chapter 2: Basic Concepts of Probability Theory 9 2.1 Random Variables 9 2.2 Probability and Distribution 10 2.2.1 Discrete Distribution 11 2.2.2 Continuous Distribution 12 2.2.3 Expectations and Moments 13 2.2.4 Moment-Generating Function 14 2.2.5 Random Number Generation 15 2.3 Random Vectors 16 2.4 Dependence and Conditional Expectation 18 2.5 Stochastic Processes 20 2.6 Modes of Convergence 22 2.7 Central Limit Theorem 23 Chapter 3: Survey of Orthogonal Polynomials and Approximation Theory 25 3.1 Orthogonal Polynomials 25 3.1.1 Orthogonality Relations 25 3.1.2 Three-Term Recurrence Relation 26 3.1.3 Hypergeometric Series and the Askey Scheme 27 3.1.4 Examples of Orthogonal Polynomials 28 3.2 Fundamental Results of Polynomial Approximation 30 3.3 Polynomial Projection 31 3.3.1 Orthogonal Projection 31 3.3.2 Spectral Convergence 33 3.3.3 Gibbs Phenomenon 35 3.4 Polynomial Interpolation 36 3.4.1 Existence 37 3.4.2 Interpolation Error 38 3.5 Zeros of Orthogonal Polynomials and Quadrature 39 3.6 Discrete Projection 41 Chapter 4: Formulation of Stochastic Systems 44 4.1 Input Parameterization: Random Parameters 44 4.1.1 Gaussian Parameters 45 4.1.2 Non-Gaussian Parameters 46 4.2 Input Parameterization: Random Processes and Dimension Reduction 47 4.2.1 Karhunen-Loeve Expansion 47 4.2.2 Gaussian Processes 50 4.2.3 Non-Gaussian Processes 50 4.3 Formulation of Stochastic Systems 51 4.4 Traditional Numerical Methods 52 4.4.1 Monte Carlo Sampling 53 4.4.2 Moment Equation Approach 54 4.4.3 Perturbation Method 55 Chapter 5: Generalized Polynomial Chaos 57 5.1 Definition in Single Random Variables 57 5.1.1 Strong Approximation 58 5.1.2 Weak Approximation 60 5.2 Definition in Multiple Random Variables 64 5.3 Statistics 67 Chapter 6: Stochastic Galerkin Method 68 6.1 General Procedure 68 6.2 Ordinary Differential Equations 69 6.3 Hyperbolic Equations 71 6.4 Diffusion Equations 74 6.5 Nonlinear Problems 76 Chapter 7: Stochastic Collocation Method 78 7.1 Definition and General Procedure 78 7.2 Interpolation Approach 79 7.2.1 Tensor Product Collocation 81 7.2.2 Sparse Grid Collocation 82 7.3 Discrete Projection: Pseudospectral Approach 83 7.3.1 Structured Nodes: Tensor and Sparse Tensor Constructions 85 7.3.2 Nonstructured Nodes: Cubature 86 7.4 Discussion: Galerkin versus Collocation 87 Chapter 8: Miscellaneous Topics and Applications 89 8.1 Random Domain Problem 89 8.2 Bayesian Inverse Approach for Parameter Estimation 95 8.3 Data Assimilation by the Ensemble Kalman Filter 99 8.3.1 The Kalman Filter and the Ensemble Kalman Filter 100 8.3.2 Error Bound of the EnKF 101 8.3.3 Improved EnKF via gPC Methods 102 Appendix A: Some Important Orthogonal Polynomials in the Askey Scheme 105 A.1 Continuous Polynomials 106 A.2 Discrete Polynomials 108 Appendix B: The Truncated Gaussian Model G(a?, ?ss) 113 References 117 Index 127

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Book SynopsisDescribes and explains the mathematical relationships among matrices, moments, orthogonal polynomials, quadrature rules, and the Lanczos and conjugate gradient algorithms. This book provides the mathematical background and explains the theory.Table of ContentsPreface xi PART 1. THEORY 1 Chapter 1. Introduction 3 Chapter 2. Orthogonal Polynomials 8 2.1 Definition of Orthogonal Polynomials 8 2.2 Three-Term Recurrences 10 2.3 Properties of Zeros 14 2.4 Historical Remarks 15 2.5 Examples of Orthogonal Polynomials 15 2.6 Variable-Signed Weight Functions 20 2.7 Matrix Orthogonal Polynomials 21 Chapter 3. Properties of Tridiagonal Matrices 24 3.1 Similarity 24 3.2 Cholesky Factorizations of a Tridiagonal Matrix 25 3.3 Eigenvalues and Eigenvectors 27 3.4 Elements of the Inverse 29 3.5 The QD Algorithm 32 Chapter 4. The Lanczos and Conjugate Gradient Algorithms 39 4.1 The Lanczos Algorithm 39 4.2 The Nonsymmetric Lanczos Algorithm 43 4.3 The Golub-Kahan Bidiagonalization Algorithms 45 4.4 The Block Lanczos Algorithm 47 4.5 The Conjugate Gradient Algorithm 49 Chapter 5. Computation of the Jacobi Matrices 55 5.1 The Stieltjes Procedure 55 5.2 Computing the Coefficients from the Moments 56 5.3 The Modified Chebyshev Algorithm 58 5.4 The Modified Chebyshev Algorithm for Indefinite Weight Functions 61 5.5 Relations between the Lanczos and Chebyshev Semi-Iterative Algorithms 62 5.6 Inverse Eigenvalue Problems 66 5.7 Modifications of Weight Functions 72 Chapter 6. Gauss Quadrature 84 6.1 Quadrature Rules 84 6.2 The Gauss Quadrature Rules 86 6.3 The Anti-Gauss Quadrature Rule 92 6.4 The Gauss-Kronrod Quadrature Rule 95 6.5 The Nonsymmetric Gauss Quadrature Rules 99 6.6 The Block Gauss Quadrature Rules 102 Chapter 7. Bounds for Bilinear Forms uT f(A)v 112 7.1 Introduction 112 7.2 The Case u = v 113 7.3 The Case u <> v 114 7.4 The Block Case 115 7.5 Other Algorithms for u <> v 115 Chapter 8. Extensions to Nonsymmetric Matrices 117 8.1 Rules Based on the Nonsymmetric Lanczos Algorithm 118 8.2 Rules Based on the Arnoldi Algorithm 119 Chapter 9. Solving Secular Equations 122 9.1 Examples of Secular Equations 122 9.2 Secular Equation Solvers 129 9.3 Numerical Experiments 134 PART 2. APPLICATIONS 137 Chapter 10. Examples of Gauss Quadrature Rules 139 10.1 The Golub and Welsch Approach 139 10.2 Comparisons with Tables 140 10.3 Using the Full QR Algorithm 141 10.4 Another Implementation of QR 143 10.5 Using the QL Algorithm 144 10.6 Gauss-Radau Quadrature Rules 144 10.7 Gauss-Lobatto Quadrature Rules 146 10.8 Anti-Gauss Quadrature Rule 148 10.9 Gauss-Kronrod Quadrature Rule 148 10.10 Computation of Integrals 149 10.11 Modification Algorithms 155 10.12 Inverse Eigenvalue Problems 156 Chapter 11. Bounds and Estimates for Elements of Functions of Matrices 162 11.1 Introduction 162 11.2 Analytic Bounds for the Elements of the Inverse 163 11.3 Analytic Bounds for Elements of Other Functions 166 11.4 Computing Bounds for Elements of f(A) 167 11.5 Solving Ax = c and Looking at d T/x 167 11.6 Estimates of tr(A-1) and det(A) 168 11.7 Krylov Subspace Spectral Methods 172 11.8 Numerical Experiments 173 Chapter 12. Estimates of Norms of Errors in the Conjugate Gradient Algorithm 200 12.1 Estimates of Norms of Errors in Solving Linear Systems 200 12.2 Formulas for the A-Norm of the Error 202 12.3 Estimates of the A-Norm of the Error 203 12.4 Other Approaches 209 12.5 Formulas for the l2 Norm of the Error 210 12.6 Estimates of the l2 Norm of the Error 211 12.7 Relation to Finite Element Problems 212 12.8 Numerical Experiments 214 Chapter 13. Least Squares Problems 227 13.1 Introduction to Least Squares 227 13.2 Least Squares Data Fitting 230 13.3 Numerical Experiments 237 13.4 Numerical Experiments for the Backward Error 253 Chapter 14. Total Least Squares 256 14.1 Introduction to Total Least Squares 256 14.2 Scaled Total Least Squares 259 14.3 Total Least Squares Secular Equation Solvers 261 Chapter 15. Discrete Ill-Posed Problems 280 15.1 Introduction to Ill-Posed Problems 280 15.2 Iterative Methods for Ill-Posed Problems 295 15.3 Test Problems 298 15.4 Study of the GCV Function 300 15.5 Optimization of Finding the GCV Minimum 305 15.6 Study of the L-Curve 313 15.7 Comparison of Methods for Computing the Regularization Parameter 325 Bibliography 335 Index 361

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Book SynopsisAn introduction to dynamic programming, presented by the scientist who coined the term and developed the theory in its early stages. It introduces the author's theory and furnishes a new and versatile mathematical tool for the treatment of many complex problems, both within and outside of the discipline.

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Book SynopsisTrade Review"An important contribution to the study of the history of mathematics, and any student, educator, or practitioner of mathematics or computer science, would benefit from reading this work."---Mark Causapin, MAA Reviews"In reading von Plato’s book the attention of the scholarly reader will be always captured."---L. Bellotti, History and Philosophy of Logic"This book presents an informed and informative hisotry of a crucially important part of mathematics. . . . a valuable addition to our corporate understanding."---Rob Ashmore, Mathematics Today

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Book SynopsisTrade Review"Noncooperative Game Theory offers students a fresh way of approaching engineering and computer science applications." * Mathematical Reviews *Table of ContentsPreamble xi I INTRODUCTION 1 Noncooperative Games 1.1 Elements of a Game 3 1.2 Cooperative vs. Noncooperative Games: Rope-Pulling 4 1.3 Robust Designs: Resistive Circuit 8 1.4 Mixed Policies: Network Routing 9 1.5 Nash Equilibrium 11 1.6 Practice Exercise 11 2 Policies 2.1 Actions vs. Policies: Advertising Campaign 13 2.2 Multi-Stage Games:War of Attrition 16 2.3 Open vs. Closed-Loop: Zebra in the Lake 18 2.4 Practice Exercises 19 II ZERO-SUM GAMES 3 Zero-Sum Matrix Games 3.1 Zero-Sum Matrix Games 25 3.2 Security Levels and Policies 26 3.3 Computing Security Levels and Policies with MATLAB(R) 27 3.4 Security vs. Regret: Alternate Play 28 3.5 Security vs. Regret: Simultaneous Plays 28 3.6 Saddle-Point Equilibrium 29 3.7 Saddle-Point Equilibrium vs. Security Levels 30 3.8 Order Interchangeability 32 3.9 Computational Complexity 32 3.10 Practice Exercise 34 3.11 Additional Exercise 34 4 Mixed Policies 4.1 Mixed Policies: Rock-Paper-Scissor 35 4.2 Mixed Action Spaces 37 4.3 Mixed Security Policies and Saddle-Point Equilibrium 38 4.4 Mixed Saddle-Point Equilibrium vs. Average Security Levels 41 4.5 General Zero-Sum Games 43 4.6 Practice Exercises 47 4.7 Additional Exercise 50 5 Minimax Theorem 5.1 Theorem Statement 52 5.2 Convex Hull 53 5.3 Separating Hyperplane Theorem 54 5.4 On theWay to Prove the Minimax Theorem 55 5.5 Proof of the Minimax Theorem 57 5.6 Consequences of the Minimax Theorem 58 5.7 Practice Exercise 58 6 Computation of Mixed Saddle-Point Equilibrium Policies 6.1 Graphical Method 60 6.2 Linear Program Solution 61 6.3 Linear Programs with MATLAB(R) 63 6.4 Strictly Dominating Policies 64 6.5 "Weakly" Dominating Policies 66 6.6 Practice Exercises 67 6.7 Additional Exercise 70 7 Games in Extensive Form 7.1 Motivation 71 7.2 Extensive Form Representation 72 7.3 Multi-Stage Games 72 7.4 Pure Policies and Saddle-Point Equilibria 74 7.5 Matrix Form for Games in Extensive Form 75 7.6 Recursive Computation of Equilibria for Single-Stage Games 77 7.7 Feedback Games 79 7.8 Feedback Saddle-Point for Multi-Stage Games 79 7.9 Recursive Computation of Equilibria for Multi-Stage Games 83 7.10 Practice Exercise 85 7.11 Additional Exercises 86 8 Stochastic Policies for Games in Extensive Form 8.1 Mixed Policies and Saddle-Point Equilibria 87 8.2 Behavioral Policies for Games in Extensive Form 90 8.3 Behavioral Saddle-Point Equilibria 91 8.4 Behavioral vs. Mixed Policies 92 8.5 Recursive Computation of Equilibria for Feedback Games 93 8.6 Mixed vs. Behavioral Order Interchangeability 95 8.7 Non-Feedback Games 95 8.8 Practice Exercises 96 8.9 Additional Exercises 102 III NON-ZERO-SUM GAMES 9 Two-Player Non-Zero-Sum Games 9.1 Security Policies and Nash Equilibria 105 9.2 Bimatrix Games 107 9.3 Admissible Nash Equilibria 108 9.4 Mixed Policies 110 9.5 Best-Response Equivalent Games and Order Interchangeability 111 9.6 Practice Exercises 114 9.7 Additional Exercises 116 10 Computation of Nash Equilibria for Bimatrix Games 10.1 Completely Mixed Nash Equilibria 118 10.2 Computation of Completely Mixed Nash Equilibria 120 10.3 Numerical Computation of Mixed Nash Equilibria 121 10.4 Practice Exercise 124 10.5 Additional Exercise 126 11 N-Player Games 11.1 N-Player Games 127 11.2 Pure N-Player Games in Normal Form 129 11.3 Mixed Policies for N-Player Games in Normal Form 130 11.4 Completely Mixed Policies 131 12 Potential Games 12.1 Identical Interests Games 133 12.2 Potential Games 135 12.3 Characterization of Potential Games 138 12.4 Potential Games with Interval Action Spaces 139 12.5 Practice Exercises 142 12.6 Additional Exercise 144 13 Classes of Potential Games 13.1 Identical Interests Plus Dummy Games 145 13.2 Decoupled Plus Dummy Games 146 13.3 Bilateral Symmetric Games 147 13.4 Congestion Games 148 13.5 Other Potential Games 149 13.6 Distributed Resource Allocation 150 13.7 Computation of Nash Equilibria for Potential Games 153 13.8 Fictitious Play 156 13.9 Practice Exercises 159 13.10 Additional Exercises 167 IV DYNAMIC GAMES 14 Dynamic Games 14.1 Game Dynamics 171 14.2 Information Structures 173 14.3 Continuous-Time Differential Games 175 14.4 Differential Games with Variable Termination Time 177 15 One-Player Dynamic Games 15.1 One-Player Discrete-Time Games 178 15.2 Discrete-Time Cost-To-Go 179 15.3 Discrete-Time Dynamic Programming 179 15.4 Computational Complexity 184 15.5 Solving Finite One-Player Games with MATLAB(R) 186 15.6 Linear Quadratic Dynamic Games 187 15.7 Practice Exercise 187 15.8 Additional Exercise 189 16 One-Player Differential Games 16.1 One-Player Continuous-Time Differential Games 190 16.2 Continuous-Time Cost-To-Go 191 16.3 Continuous-Time Dynamic Programming 191 16.4 Linear Quadratic Dynamic Games 195 16.5 Differential Games with Variable Termination Time 196 16.6 Practice Exercise 198 17 State-Feedback Zero-Sum Dynamic Games 17.1 Zero-Sum Dynamic Games in Discrete Time 201 17.2 Discrete-Time Dynamic Programming 203 17.3 Solving Finite Zero-Sum Games with MATLAB(R) 205 17.4 Linear Quadratic Dynamic Games 206 17.5 Practice Exercise 209 18 State-Feedback Zero-Sum Differential Games 18.1 Zero-Sum Dynamic Games in Continuous Time 214 18.2 Linear Quadratic Dynamic Games 216 18.3 Differential Games with Variable Termination Time 219 18.4 Pursuit-Evasion 220 18.5 Practice Exercise 222 References 223 Index 225

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Book SynopsisTrade Review"I want to share with everybody my enjoyment of this excellent textbook."---Narciso Marti-Oliet, European Math Society"Those teaching computer scientists who take discrete mathematics alongside other mathematics modules such as linear algebra and calculus (as is the case with the CS20 students at Harvard), and who need a book with an emphasis on proof, will likely and this book a very good choice for their students."---London Mathematical Society, Glenn Hawe

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Book SynopsisTrade Review“The Da Vinci Code has nothing on this exhaustive collection of cryptographs and codes—because these are real.”—Discover Magazine“A thoroughly engaging read.”—Brian Clegg, Popular Science“Unsolved! spans a huge arc of time and space, from Julius Caesar’s simple substitution cipher to composer Edward Elgar’s 1897 Dorabella Cipher.”—Andrew Robinson, Nature“Bauer proves an able and entertaining guide to the world of real-life ciphers, codes, and encryption. . . . Unsolved! is suited to all who enjoy the thrill of the chase.”—Peter Dabbene, Foreword Reviews“An in-depth guide to history’s greatest unsolved conundrums.”—BBC Focus“I am blown away by this book. I have never read a non-fiction book before that is so thrillingly entertaining.”—Adhemar Bultheel, European Mathematical Society

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Book SynopsisProvides a comprehensive introduction to the theory and practice of validated numerics, a field that combines the strengths of scientific computing and pure mathematics. This title features many examples, exercises, and computer labs using MATLAB/C++. It is suitable for graduate students and advanced undergraduates.Trade Review"Beyond obvious practical value, this material offers students an excellent opportunity to revisit and rethink some crucial, fundamental college mathematics." * Choice *"[T]his little book is a very important supplement to existing books on validated numerics. It is a must for researchers working in this field."---G. Alefeld, Mathematical Reviews"The book contains a lot of exercises, various small programs written in MATLAB code, and four sections with numerous problems provided for experimenting on a computer. It is written at an elementary level corresponding to its aims. But it is also a pleasure for specialists to leaf through the book."---Gunter Mayer, Zentralblatt MATH"This book is an essential resource for those entering this fast-developing field, and it is also the ideal textbook for graduate students and advanced undergraduates needing an accessible introduction to the subject." * World Book Industry *

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Book SynopsisThis is the industry's best-selling and highly-praised book on content management-revised to meet the growing needs of businesses who need to deliver content to their users, and share information internally quickly and easily.Table of ContentsForeword. Preface. Acknowledgments. Introduction. Part I: What Is Content? Chapter 1: Defining Data, Information, and Content. Chapter 2: Content Has Format. Chapter 3: Content Has Structure. Chapter 4: Functionality Is Content, Too! Chapter 5: But What Is Content Really? Part II: What Is Content Management? Chapter 6: Understanding Content Management. Chapter 7: Introducing the Major Parts of a CMS. Chapter 8: Knowing When You Need a CMS. Chapter 9: Component Management versus Composition Management. Chapter 10: The Roots of Content Management. Chapter 11: The Branches of Content Management. Part III: Doing Content Management Projects. Chapter 12: Doing CM Projects Simply. Chapter 13: Staffing a CMS. Chapter 14: Working within the Organization. Chapter 15: Getting Ready for a CMS. Chapter 16: Securing a Project Mandate. Chapter 17: Doing Requirements Gathering. Chapter 18: Doing Logical Design. Chapter 19: Selecting Hardware and Software. Chapter 20: Implementing the System. Chapter 21: Rolling Out the System. Part IV: Designing a CMS. Chapter 22: Designing a CMS Simply. Chapter 23: The Wheel of Content Management. Chapter 24: Working with Metadata. Chapter 25: Cataloging Audiences. Chapter 26: Designing Publications. Chapter 27: Designing Content Types. Chapter 28: Accounting for Authors. Chapter 29: Accounting for Acquisition Sources. Chapter 30: Designing Content Access Structures. Chapter 31: Designing Templates. Chapter 32: Designing Personalization. Chapter 33: Designing Workflow and Staffing Models. Part V: Building a CMS. Chapter 34: Building a CMS Simply. Chapter 35: What Are Content Markup Languages? Chapter 36: XML and Content Management. Chapter 37: Processing Content. Chapter 38: Building Collection Systems. Chapter 39: Building Management Systems. Chapter 40: Building Publishing Systems. Appendix: Epilogue. Index.

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Book SynopsisToday, designing a state-of-the-art circuit means knowing how to pack more and more logic on a chip. Featuring an extensive introductory material, this complete, carefully-organized guide brings you valuable information on designing modern logic circuits from gates, switches, and other basic elements to meet the rising demands on modern circuit technology. THE ESSENCE OF LOGIC CIRCUITS allows computer scientists and students to start from scratch and gain a comprehensive understanding of most important topics in the field.Table of ContentsPreface. Introduction. Boolean Algebra Applied to Logic Circuits. Designing Combinational Logic Circuits. Combinational Logic Circuits in Regular Forms. Symmetric and Iterative Circuits. Sequential Logic Circuits. Software Tools. Postscript on Professionalism. Appendix A1: Number Systems and Codes. Appendix A2: Simple Electrical Circuits. Appendix A3: MOS-Based Technologies. Appendix A4: Bipolar Families. References. Solutions to Selected Problems. Index.

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Book SynopsisIT solutions from the leading edge Information technology promises much but, as many businesses arefinding, it often fails to deliver. Representing the new wave ofthinking about I.T., this thought-provoking collection assemblesleading researchers from four continents, including Dan Robey,Robert Zmud, Claudio Ciborra and Robert Benjamin. Writing with deepknowledge of both I.T. and business, they persuasively argue forthe integration of the core business unit and the I.T. function,advocate a new role for I.T. professionals, stress the importanceof managing outcomes rather than process, and provide practicalguidelines for turning new ideas into new management practices.Trade Review"An important book for those executives looking to transition theirorganizations into the 21st Century...especially those users andproviders of information technology services. The IT-basedtransformation is the wave of the next millennium." --Carl C.Williams, Vice President of Information Technology, AmocoCorporation "Fresh approaches to some of the most vexing issues facingorganizations today....A powerful argument for a new view of therole of information technology within the business organization ofthe future." --Michael Vitale, Professor and Head, Dept. ofInformation Systems, University of Melbourne and former VicePresident, Information Technology and Corporate Services,Prudential Insurance (1988-92) "[Steps to the Future] helps us approach the impAnding third waveof major social change since farming and the industrial revolution-- namely IT&T. Not only are the difficulties and risks offailure analyzed, but ideas for new methods of organizationalapproach and finding new success parameters are documented toensure that we move toward this change with enthusiasm and hope."--Steve Burdon, Managing Director, British Telecommunications, AsiaPacific "Executives who adopt the new ideas advanced in this book willunderstand why IT must be an integral part of their organizationand how to act decisively to capture maximum business value fromit." --Neville J. Roach, Managing Director, Fujitsu AustraliaLtd. Endorsement from Tim Besley to come. Ask Nathalie to have itemailed to her. --Tim Besley, Chairman, Commonwealth Bank ofAustraliaTable of ContentsPreface. The Authors. 1. The Right Stuff: An Introduction to New Thinking About ITManagement. (Christopher Sauer, Phillp W. Yetton) Part One: The Traditional Solutions 2. False Prophecies, Successful Practice, and Future Directions inIT Management. (Phillp W. Yetton) 3. A Professional Balancing Act: Walking the Tightrope of StrategicAlignment. (Janice M. Burn) 4. The Pathology of Strategic Alignment. (Christopher Sauer, JaniceM. Burn) Part Two: Competencies of IT-Enabled Organizational Change. 5. IT-Enabled Organizational Change: New Developments of ITSpecialists. (M. Lynne Markus, Robert Benjamin) 6. At the Heart of Success: Organizationwide ManagementCompetencies. (V. Sambamurthy, Robert W. Zmund) Part Three: Process Change. 7. Against Obliteration: Reducing Risk in Business Process Change.(Robert D. Galliers) 8. The Real Event of Reengineering. (Jane Craig, Phillp W.Yetton) Part Four: New Interpretations. 9. The Paradoxes of Transformation. (Daniel Robey) 10. Joint Outcomes: The Coproduction of IT and OrganizationalChange. (Rod Coombs) 11. Improvising in the Shapeless Organization of the Future.(Claudio U. Ciborra) 12. The Paths Ahead. (Christopher Sauer, Phillp W. Yetton) Index.

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Book Synopsis'Multiliteracies for a Digital Age' serves as a guide for composition teachers to develop effective, full-scale computer literacy programs that are also professionally responsible by emphasizing different kinds of literacies. Stuart A. Selber also proposes methods for helping students move among these literacies in strategic ways.

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Book SynopsisCapturing where we are today through a tour of yesterday''s achievements and helping us better understand the evolution of computing technology, this book recounts the experiences of those who formed and functioned in the Pioneering Era. In the Beginning: Recollections of Software Pioneers records the stories of computing''s past enabling today''s professionals to improve on the realities of yesterday. The stories in this book clearly show modern concepts such as data abstraction, modularity, and structured approaches date much earlier in the field than their appearance in academic literature. These stories help capture the true evolution. The book illustrates human experiences and industry turning points through personal recollections of the pioneers themselves.

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Book SynopsisThis book on agricultural management and decision making differs from other texts which tend to describe production economics followed by the presentation of analytical approaches. Instead, the processes of agricultural production and their management are couched in terms of activity analysis, since this permits greater integration of theory with practical evaluations. Analytical tools developed in the book involve the construction of spreadsheet models, and readers are able to construct their own PC spreadsheets from the book's examples and case studies. Economic principles are presented that will assist in improving the design of agricultural processes and technologies, in guiding their appropriate combination in a business setting, and with the making of decisions through time and in recognition of noncertainty. Activity analysis models that allow the design and combination of agricultural activities to be optimized are also discussed. These include single versus multiple objective Table of Contents1: Activity analysis and the process of agricultural production 2: A spreadsheet framework for activity analysis 3: Economic principles for activity design 4: Evaluation of activity combinations 5: Determining the optimum combination of activities 6: Linear programming techniques for agricultural decision making 7: Intertemporal decision making 8: International activity analysis 9: Management and noncertainty 10: Probabilistic activity analysis

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Book SynopsisElectronic information distribution has become undeniably important in the hospitality and tourism sectors. Using a combination of narrative, analysis and case studies, this text traces the origins of electronic distribution in tourism and places current developments in context, while also looking at developing technologies and assessing their potential effect on the industry of the future. It is written from a managerial (rather than a technical) perspective, and takes an international approach with worldwide analysis and case studies encompassing Europe and the USA as well as the global marketplace. These include discussions of the distribution strategies of companies such as SABRE, Group Accor, Holiday Inn, Utell International, Best Western, as well as examinations of developing systems such as TIS, Gulliver, TravelWeb, Microsoft Expedia, Degriftour, Imminus and THG. Developments in all sectors of the tourism and hospitality industries are explored, but particular emphasis is placedTable of Contents1: Introduction: The Importance of Information 2: From Airline Reservations Systems to GDS: the Development of Global Distribution Systems 3: Case Study: SABRE 4: Hotel Central Reservation Systems 5: Case Studies: Group Accor, Holiday Inn, Utell International, Best Western 6: Distributing Small Hotel and Tourism Enterprises Electronically 7: Case Studies: TIS (Tirolean Information System), Gulliver 8: Cutting Out the Middleman! Tourism and the Internet 9: Case Studies: TravelWeb, Microsoft Expedia, Degriftour 10: What next? The future of distribution technology 11: Case Studies: Imminus, THG (The Hotel Guide)

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Book SynopsisExploring the Alternative Proteome with OpenProt and Mass Spectrometry.- Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational Proteogenomics Workflow.- Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls.- Understanding PTM Cross-Talk Through a Visualization Tool, PTMViz.- Integrating HexNAcQuest with Glycoproteomics Data Analysis Software to Distinguish HexNAc Isomers on Peptides.- UniCarb-DB, an MS/MS Experimental Glycomic Fragmentation Database.- Integration of Web-Based Tools to Visualize, Integrate, and Interpret Glycogene Expression and Glycomics Data.- PGFinder, an Open Source Software for Peptidoglycomics: The Structural Analysis of Bacterial Peptidoglycan by LC-MS.- Making MS Omics Data ML Ready: SpeCollate Protocols.- AI-Assisted Processing Pipeline to Boost Protein Isoform Detection.- Biodiversity Analysis of Metaproteomics Samples with Unipept: A Comprehensive Tutorial.- Analysis and Visualizati

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Book SynopsisIdeal for any developer familiar with, or willing to learn, the basics of quantum computing, this pocket guide quickly helps you find syntax and usage information for unfamiliar aspects of Q#. You'll explore the quantum software development lifecycle, from implementing the program to testing and debugging it to running it on quantum hardware.

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Book SynopsisThe gold standard for eBay users who want to get serious about selling Want to turn your eBay use into a steady revenue stream? Come to where everyone starts, with a copy of the latest edition of Starting an eBay Business For Dummies.Table of ContentsIntroduction 1 Part I: Getting Serious about eBay 7 Chapter 1: Launching Your Business on eBay 9 Chapter 2: The Finer Points of eBay Selling 31 Chapter 3: Cool eBay Tools 57 Chapter 4: Safe Selling Equals Profitable Sales 83 Chapter 5: Expanding Sales with an eBay Store 103 Part II: Setting Up Shop 119 Chapter 6: Finding Merchandise to Up Your Profits 121 Chapter 7: Pricing Your Items to Sell 139 Chapter 8: Establishing and Marketing Your Web Site 151 Part III: Business Is Business — Time to Get Serious 167 Chapter 9: Software Built for Online Auctions 169 Chapter 10: Dollars and Sense: Budgeting and Marketing Your Sales 185 Chapter 11: Setting Up Listings That Sell 199 Chapter 12: Providing Excellent Customer Service 219 Chapter 13: Collecting Your Money 231 Chapter 14: Shipping Your Items: Sending Them Fast and Saving Money 249 Part IV: Your eBay Back Office 263 Chapter 15: Getting Legal: Understanding Taxes and Licenses 265 Chapter 16: Savvy Record Keeping — Keeping the Tax Man at Bay 277 Chapter 17: Building an eBay Back Office 293 Part V: The Part of Tens 311 Chapter 18: Ten (or So) Sellers Who’ve Made the Jump from Hobby to Profits 313 Chapter 19: Ten Other Places to Move Your Merchandise 323 Appendix A: eBay Business Glossary 333 Appendix B: The Hows and Whys of a Home Network 339 Index 345

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Book SynopsisCompelling tips and tricks to improve your mental skills Don''t you wish you were just a little smarter? Ron and Marty Hale-Evans can help with a vast array of witty, practical techniques that tune your brain to peak performance. Founded in current research, Mindhacker features60 tips, tricks, and games to develop your mental potential. This accessible compilation helps improve memory, accelerate learning, manage time, spark creativity, hone math and logic skills, communicate better, think more clearly, and keep your mind strong and flexible.Table of ContentsIntroduction xvii Chapter 1 Memory 1 Hack 1: Remember to Remember 1 Hack 2: Build a Memory Dungeon 6 Hack 3: Mix Up Your Facts 10 Hack 4: Space Your Repetitions 14 Hack 5: Recall Long-Ago Events 18 Chapter 2 Learning 25 Hack 6: Establish Your Canon 25 Hack 7: Write in Your Books 32 Hack 8: Read at Speed 44 Hack 9: Learn by Teaching 49 Hack 10: Play the Learning Game 52 Hack 11: Pretend You’re a Grad Student 55 Hack 12: Study Kid Stuff 59 Chapter 3 Information Processing 63 Hack 13: Polyspecialize 63 Hack 14: Integrate Your Interests 67 Hack 15: Sift Your Ideas 72 Hack 16: Ask the Hive Mind 77 Hack 17: Write Magnificent Notes 83 Chapter 4 Time Management 95 Hack 18: Keep a Mental Datebook 95 Hack 19: Tell Time Who’s Boss 99 Hack 20: Meet MET 106 Hack 21: Get Control of Yourself 111 Hack 22: Locate Lost Items 121 Hack 23: Huffman-Code Your Life 126 Hack 24: Knock Off Work 129 Chapter 5 Creativity and Productivity 135 Hack 25: Manifest Yourself 135 Hack 26: Woo the Muse of the Odd 138 Hack 27: Seek Bad Examples 143 Hack 28: Turn a Job into a Game 148 Hack 29: Scrumble for Glory 160 Hack 30: Salvage a Vintage Hack 167 Hack 31: Mine the Future 174 Hack 32: Dare to Do No Permanent Damage 179 Hack 33: Make Happy Mistakes 182 Hack 34: Don’t Know What You’re Doing 187 Hack 35: Ratchet 195 Chapter 6 Math and Logic 199 Hack 36: Roll the Mental Dice 200 Hack 37: Abduct Your Conclusions 204 Hack 38: Think Clearly about Simple Errors 209 Hack 39: Notate Personally 215 Hack 40: Notate Wisely 218 Hack 41: Engineer Your Results 223 Hack 42: Enter the Third Dimension 233 Hack 43: Enter the Fourth Dimension 239 Chapter 7 Communication 263 Hack 44: Spell It Out 264 Hack 45: Read Lips 271 Hack 46: Emote Precisely 275 Hack 47: Streamline Your Shorthand 283 Hack 48: Communicate Multimodally 287 Hack 49: Mediate Your Environment 291 Chapter 8 Mental Fitness 299 Hack 50: Acquire a Taste 300 Hack 51: Try Something New Daily 305 Hack 52: Metabehave Yourself 308 Hack 53: Train Your Fluid Intelligence 315 Hack 54: Think, Try, Learn 321 Hack 55: Take the One-Question IQ Test 331 Chapter 9 Clarity 335 Hack 56: Cultivate Beginner’s Mind 336 Hack 57: Take a Semantic Pause 340 Hack 58: Retreat and Reboot 350 Hack 59: Get Used to Losing 355 Hack 60: Trust Your Intelligence (and Everyone Else’s) 359 Appendix A The Unboxed Games Manifesto 367 Appendix B 3D Visualization 369 Index 373

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Book SynopsisFeatures recent advances and new applications in graph edge coloring Reviewing recent advances in the Edge Coloring Problem, Graph Edge Coloring: Vizing''s Theorem and Goldberg''s Conjecture provides an overview of the current state of the science, explaining the interconnections among the results obtained from important graph theory studies. The authors introduce many new improved proofs of known results to identify and point to possible solutions for open problems in edge coloring. The book begins with an introduction to graph theory and the concept of edge coloring. Subsequent chapters explore important topics such as: Use of Tashkinov trees to obtain an asymptotic positive solution to Goldberg''s conjecture Application of Vizing fans to obtain both known and new results Kierstead paths as an alternative to Vizing fans Classification problem of simple graphs Generalized Trade Review “College mathematics collections need just this sort of rarity-accounts of major unsolved problems, elementary but still comprehensive. Summing Up: Recommended. Upper-division undergraduates.” (Choice, 1 September 2012) Table of ContentsPreface xi 1 Introduction 1 1.1 Graphs 1 1.2 Coloring Preliminaries 2 1.3 Critical Graphs 5 1.4 Lower Bounds and Elementary Graphs 6 1.5 Upper Bounds and Coloring Algorithms 11 1.6 Notes 15 2 Vizing Fans 19 2.1 The Fan Equation and the Classical Bounds 19 2.2 Adjacency Lemmas 24 2.3 The Second Fan Equation 26 2.4 The Double Fan 31 2.5 The Fan Number 32 2.6 Notes 39 3 Kierstead Paths 43 3.1 Kierstead's Method 43 3.2 Short Kierstead's Paths 46 3.3 Notes 49 4 Simple Graphs and Line Graphs 51 4.1 Class One and Class Two Graphs 51 4.2 Graphs whose Core has Maximum Degree Two 54 4.3 Simple Overfull Graphs 63 4.4 Adjacency Lemmas for Critical Class Two Graphs 73 4.5 Average Degree of Critical Class Two Graphs 84 4.6 Independent Vertices in Critical Class Two Graphs 89 4.7 Constructions of Critical Class Two Graphs 93 4.8 Hadwiger's Conjecture for Line Graphs 101 4.9 Simple Graphs on Surfaces 105 4.10 Notes 110 5 Tashkinov Trees 115 5.1 Tashkinov's Method 115 5.2 Extended Tashkinov Trees 127 5.3 Asymptotic Bounds 139 5.4 Tashkinov's Coloring Algorithm 144 5.5 Polynomial Time Algorithms 148 5.6 Notes 152 6 Goldberg's Conjecture 155 6.1 Density and Fractional Chromatic Index 155 6.2 Balanced Tashkinov Trees 160 6.3 Obstructions 162 6.4 Approximation Algorithms 183 6.5 Goldberg's Conjecture for Small Graphs 185 6.6 Another Classification Problem for Graphs 186 6.7 Notes 193 7 Extreme Graphs 197 7.1 Shannon's Bound and Ring Graphs 197 7.2 Vizing's Bound and Extreme Graphs 201 7.3 Extreme Graphs and Elementary Graphs 203 7.4 Upper Bounds for ÷' Depending on Ä and ì 205 7.5 Notes 209 8 Generalized Edge Colorings of Graphs 213 8.1 Equitable and Balanced Edge Colorings 213 8.2 Full Edge Colorings and the Cover Index 222 8.3 Edge Colorings of Weighted Graphs 224 8.4 The Fan Equation for the Chromatic Index X'f 228 8.5 Decomposing Graphs into Simple Graphs 239 8.6 Notes 243 9 Twenty Pretty Edge Coloring Conjectures 245 Appendix A: Vizing's Two Fundamental Papers 269 A. 1 On an Estimate of the Chromatic Class of a p-Graph 269 References 272 A.2 Critical Graphs with a Given Chromatic Class 273 References 278 Appendix B: Fractional Edge Colorings 281 B. 1 The Fractional Chromatic Index 281 B.2 The Matching Polytope 284 B.3 A Formula for X'f 290 References 295 Symbol Index 312 Name Index 314 Subject Index 318

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Book SynopsisLearn to take full advantage of search and social media for B2B marketing Business-to-business marketers have been slow to enter the online marketing arena, but now that the impact of search and social media marketing in the consumer marketplace is clearly documented, B2B marketers are ready for a complete guide to making the most of the medium.Table of ContentsIntroduction xxi Chapter 1 Understanding B2B Online Marketing 1 Why Online? 2 B2B Is Different 5 Developing Your Strategy 11 and Measurement 13 Chapter 2 Building a B2B Brand Online 17 Understanding Online and Using Digital to Build a Brand 18 Tactical Guide to B2B Branding Online 21 Go Identify Your Audience 29 Determining Your Content Strategy 32 Chapter 3 Search Engine Optimization: Outranking Your Competitors 37 What Is SEO? 38 The Wagging Tail of Keywords 41 Squeezing the Juice Out of Links 48 Designing for Optimal Results 52 Chapter 4 Using Paid Online Media in the B2B Marketplace 57 Search Engine Marketing 58 B2B Strategies for Paid Search 62 Display Advertising for B2B 69 Social Media Advertising 74 Chapter 5 Search and Social Media for Online PR 81 Overview of Traditional B2B PR 82 How Online PR Is Different 84 Three B2B Online PR Case Studies 90 Chapter 6 Social Media 101 Social Media Listening 102 Exploiting Your Resources 108 Social Engagement 115 Chapter 7 Optimizing with Metrics 121 Aligning Analytics with the Goals of Your Site 122 The Basics of Analyzing Metrics 122 Key Performance Indicators and Other Meaningful Reports .125 Wash, Rinse, and Repeat to Improve Your Site 126 Testing for Ongoing Optimization 133 Should It Stay or Should It Go? 135 Social Media Metrics 136 Chapter 8 Conversion Rate Optimization and Usability 141 Web Usability and CRO: Similarities and Differences 142 Where to Start: Stages for Usability and CRO 142 Key B2B Conversions 149 Getting Started with Usability Practices 152 Building Blocks for Usability 156 Chapter 9 Integrating Online with Offl ine Marketing 159 Can Events and Online Marketing Ever Join Forces? 160 Using Online Marketing to Track, Measure, and Understand Traditional Marketing 174 Chapter 10 Managing Your Leads: Automation and Nurturing 181 Basics of Marketing Automation 182 Lead Nurturing 183 Basics of B2B Email Marketing 198 Chapter 11 Integrating Marketing with CRM 203 Understanding Your CRM 204 Types of Data Integration 205 Marketing Automation and CRM 208 Must-Have CRM Metrics 215 Chapter 12 The Overall Marketing Mix 217 Marketing Mix Framework 218 Digital vs Traditional Investments 220 What Mix Is Most Effective? 221 Sometimes You Can't Predict the Future 227 Integrating New Forms of Marketing into the Mix 232 Glossary 241 Index 247

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Book SynopsisThe Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered around molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Topics in Volume 28 include: Free-energy Calculations with Metadynamics Polarizable Force Fields for Biomolecular Modeling Modeling Protein Folding Pathways Assessing Structural Predictions of Protein-Protein Recognition Kinetic Monte Carlo Simulation of Electrochemical Systems Reactivity and Dynamics at Liquid Interfaces Table of ContentsPreface xi List of Contributors xv Contributors to Previous Volumes xvii 1. Free-Energy Calculations with Metadynamics: Theory and Practice 1Giovanni Bussi and Davide Branduardi Introduction 1 Molecular Dynamics and Free-Energy Estimation 3 Molecular Dynamics 3 Free-Energy Landscapes 4 A Toy Model: Alanine Dipeptide 6 Biased Sampling 8 Adaptive Biasing with Metadynamics 9 Reweighting 12 Well-Tempered Metadynamics 12 Reweighting 14 Metadynamics How-To 14 The Choice of the CV(s) 15 The Width of the Deposited Gaussian Potential 17 The Deposition Rate of the Gaussian Potential 18 A First Test Run Using Gyration Radius 19 A Better Collective Variable: Φ Dihedral Angle 23 Well-Tempered Metadynamics Using Gyration Radius 24 Well-Tempered Metadynamics Using Dihedral Angle Φ 27 Advanced Collective Variables 28 Path-Based Collective Variables 30 Collective Variables Based on Dimensional Reduction Methods 32 Template-Based Collective Variables 34 Potential Energy as a Collective Variable 35 Improved Variants 36 Multiple Walkers Metadynamics 36 Replica Exchange Metadynamics 37 Bias Exchange Metadynamics 38 Adaptive Gaussians 39 Conclusion 41 Acknowledgments 42 Appendix A: Metadynamics Input Files with PLUMED 42 References 44 2. Polarizable Force Fields for Biomolecular Modeling 51Yue Shi, Pengyu Ren, Michael Schnieders, and Jean-Philip Piquemal Introduction 51 Modeling Polarization Effects 52 Induced Dipole Models 52 Classic Drude Oscillators 54 Fluctuating Charges 54 Recent Developments 55 AMOEBA 55 SIBFA 57 NEMO 58 CHARMM-Drude 58 CHARMM-FQ 59 X-Pol 60 PFF 60 Applications 61 Water Simulations 61 Ion Solvation 62 Small Molecules 63 Proteins 64 Lipids 66 Continuum Solvents for Polarizable Biomolecular Solutes 66 Macromolecular X-ray Crystallography Refinement 67 Prediction of Organic Crystal Structure, Thermodynamics, and Solubility 70 Summary 71 Acknowledgment 71 References 72 3. Modeling Protein Folding Pathways 87Clare-Louise Towse and Valerie Daggett Introduction 87 Outline of this Chapter 90 Protein Simulation Methodology 90 Force Fields, Models and Solvation Approaches 90 Unfolding: The Reverse of Folding 97 Elevated Temperature Unfolding Simulations 100 Biological Relevance of Forced Unfolding 103 Biased or Restrained MD 108 Characterizing Different States 111 Protein Folding and Refolding 115 Folding in Families 118 Conclusions and Outlook 121 Acknowledgment 122 References 122 4. Assessing Structural Predictions of Protein–Protein Recognition: The CAPRI Experiment 137Joël Janin, Shoshana J. Wodak, Marc F. Lensink, and Sameer Velankar Introduction 137 Protein–Protein Docking 138 A Short History of Protein–Protein Docking 138 Major Current Algorithms 141 The CAPRI Experiment 144 Why Do Blind Predictions? 144 Organizing CAPRI 145 The CAPRI Targets 146 Creating a Community 149 Assessing Docking Predictions 150 The CAPRI Evaluation Procedure 150 A Survey of the Results of 12 Years of Blind Predictions on 45 Targets 154 Recent Developments in Modeling Protein–Protein Interaction 160 Modeling Multicomponent Assemblies. The Multiscale Approach 160 Genome-Wide Modeling of Protein–Protein Interaction 161 Engineering Interactions and Predicting Affinity 162 Conclusion 164 Acknowledgments 165 References 165 5. Kinetic Monte Carlo Simulation of Electrochemical Systems 175C. Heath Turner, Zhongtao Zhang, Lev D. Gelb, and Brett I. Dunlap Background 175 Introduction to Kinetic Monte Carlo 176 Electrochemical Relationships 180 Applications 184 Transport in Li-ion Batteries 184 Solid Electrolyte Interphase (SEI) Passive Layer Formation 187 Analysis of Impedance Spectra 189 Electrochemical Dealloying 189 Electrochemical Cells 190 Solid Oxide Fuel Cells 193 Other Electrochemical Systems 197 Conclusions and Future Outlook 198 Acknowledgments 199 References 199 6. Reactivity and Dynamics at Liquid Interfaces 205Ilan Benjamin Introduction 205 Simulation Methodology for Liquid Interfaces 207 Force Fields for Molecular Simulations of Liquid Interfaces 207 Boundary Conditions and the Treatment of Long-Range Forces 210 Statistical Ensembles for Simulating Liquid Interfaces 213 Comments About Monte Carlo Simulations 214 The Neat Interface 214 Density, Fluctuations, and Intrinsic Structure 215 Surface Tension 221 Molecular Structure 223 Dynamics 230 Solutes at Interfaces: Structure and Thermodynamics 235 Solute Density 236 Solute–Solvent Correlations 240 Solute Molecular Orientation 242 Solutes at Interfaces: Electronic Spectroscopy 243 A Brief General Background on Electronic Spectroscopy in the Condensed Phase 243 Experimental Electronic Spectroscopy at Liquid Interfaces 245 Computer Simulations of Electronic Transitions at Interfaces 249 Solutes at Interfaces: Dynamics 253 Solute Vibrational Relaxation at Liquid Interfaces 253 Solute Rotational Relaxation at Liquid Interfaces 258 Solvation Dynamics 263 Summary 269 Reactivity at Liquid Interfaces 270 Introduction 270 Electron Transfer Reactions at Liquid/Liquid Interfaces 271 Nucleophilic Substitution Reactions and Phase Transfer Catalysis (PTC) 277 Conclusions 283 Acknowledgments 284 References 284 7. Computational Techniques in the Study of the Properties of Clathrate Hydrates 315John S. Tse Historical Perspective 315 Structures 317 The van der Waals–Platteeuw Solid Solution Theory 318 Computational Advancements 322 Thermodynamic Modelling 322 Atomistic Simulations 327 Thermodynamic Stability 344 Hydrate Nucleation and Growth 355 Guest Diffusion Through Hydrate Cages 368 Ab Initio Methods 371 Outlook 381 References 382 8. The Quantum Chemistry of Loosely-Bound Electrons 391John M. Herbert Introduction and Overview 391 What Is a Loosely-Bound Electron? 391 Scope of This Review 392 Chemical Significance of Loosely-Bound Electrons 394 Challenges for Theory 400 Terminology and Fundamental Concepts 402 Bound Anions 402 Metastable (Resonance) Anions 415 Quantum Chemistry for Weakly-Bound Anions 425 Gaussian Basis Sets 425 Wave Function Electronic Structure Methods 439 Density Functional Theory 456 Quantum Chemistry for Metastable Anions 471 Maximum Overlap Method 474 Complex Coordinate Rotation 477 Stabilization Methods 483 Concluding Remarks 495 Acknowledgments 495 Appendix A: List of Acronyms 496 References 497 Index 519

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Book SynopsisPractical, proven guidance for transforming the culture of any IT department As more and more jobs are outsourced, and the economy continues to struggle, people are looking for an alternative to the greed-driven, selfish leadership that has resulted in corporations where the workers are treated as interchangeable parts. This book shows how the human factors can be used to unlock higher returns on human capital such that workers are no longer interchangeable parts, but assets that are cared about and grown. Refreshingly innovative, Transforming IT Culture shows how neuroscientific and psychological research can be applied in the IT workplace to unleash a vast pool of untapped potential. Written by an expert on IT culture transformation Considers the widespread cultural blindness in business today, and how it can be addressed Draws on the author''s repeated success transforming IT divisions across major corporations by applying the human fTable of ContentsForeword Xi Acknowledgments Xiii Introduction 1 The Passing of an Era 2 A New Era Brings a New Focus 3 A Quick Book Tour 6 Note 10 CHAPTER 1 A Shining Light: The Blind Spot Revealed 11 A Race to the Bottom 12 Human Understanding Enters the Workplace 13 We Have Been Taught Not to See or Feel 15 Unlocking Human Potential 16 Dawn of a New Productivity Model 17 Working Social 19 The Social System Is the Factory 20 Notes 21 CHAPTER 2 Corporate America’s IT Organization: Failure Is All Too Common 23 Still Broken after All These Years 24 Unfortunately, the Truth Is Worse 29 If We Would Just Embrace and Trust Our People 30 Offshore Outsourcing: A Deeper Look 33 Notes 36 CHAPTER 3 Workers as Machines: A Social Pathology 37 He’s a Good Hand 37 The Machine Age: Still Felt Today 38 Birth of Corporate Easter Islands 41 Our Human Resource Practices Remain Primitive 41 Selectively Continue the Past; Fully Embrace the Future 43 Notes 44 CHAPTER 4 The Unseen Art and Emotion of IT: The Acme Inc.Philharmonic Orchestra: Knowledge as Notes, Leaders as Conductors, Programmers as Composers 45 A Product of Mind and Emotion 46 Limitations of Language and Our Resultant Inability to Communicate 50 Social Cohesion and Conceptual Unity 52 And the Instruments Keep Changing 56 The Encore. A Callback. Bravo! 57 Note 57 CHAPTER 5 Case Study: An Unproductive State of Mind: Toxic Leadership and Its Aftermath 59 Toxic to Competitive Advantage 64 Conclusion 64 Note 66 CHAPTER 6 What Are We Waiting For? Applied Science at Work 67 Hawthorne Studies 68 Pygmalion in the Classroom 70 Empathy, Caring, and Compassion 71 Organizational Citizenship Behavior 74 Mood Is Contagious 76 Limbic System 77 Maslow: Humanism in the Workplace 80 Working Memory 84 Mirror Neurons 85 Other Thoughts 87 Notes 87 CHAPTER 7 Empathy and Compassion: The Socially Cohesive and Resilient Organization 89 The Toxic Handler: Empathy and Compassion in Action 90 Dysfunctional Organizations Have Less Time for Compassion 92 Empathy and Compassion: A Research Perspective 93 Notes 96 CHAPTER 8 Designing a Collaborative Social System: Working Social: How the Right Culture Unlocks Productivity 97 Designing Collaborative Social Systems 98 Why a Collaborative Social System Matters 103 Notes 106 CHAPTER 9 The Social Compact: Organizational Citizenship Behavior 107 Living the Values 108 Shaping IT: One Interaction at a Time 110 Courtesy Is Contagious 111 Giving versus Getting 119 Citizenship Performance 120 Notes 122 CHAPTER 10 The Servant Leader: Prosocial and Authentic 123 Opening Night 124 Using Social Intelligence and Caring to Lead from Below 125 Conducting Styles 127 Servant Leadership in IT: Giving Credit While Silently Helping Drive Group Success 128 Academic Views 131 Moving the Group from “I Get It” to “I See It” 133 Notes 135 CHAPTER 11 Social and Emotional Intelligence:The Organizational Canvas Meets the Social Paintbrush 137 Personal and Social Competence 138 Sogence in Action 140 Understanding Expression: A Social Skill from Our Past 142 Good Vibrations: The Right Social Sentiment Energizes a Performance 144 Notes 150 CHAPTER 12 Designing an Innovative Culture 151 Talent and Mood 152 The Human Factors 153 Build a Culture of Creativity 154 CHAPTER 13 Workforce Planning: Maximizing the Productivity of Your Talent—Today and Tomorrow 157 Workforce Planning Gap 158 Goals and Process 160 Context Diagram 161 Outsourcing and Offshoring 164 Notes 172 CHAPTER 14 How to Successfully Transform Your Organization:Putting It All Together 173 High-Level Outline 175 Best Practices in Detail 185 Conclusion 187 About the author 189 Index 191

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Book SynopsisFast-Tracking Your Career provides engineers and IT professionals with a complete set of soft skills they can use to become more effective on the job and gain recognition from management and colleagues. The 11 core skills covered here are accompanied by more than 40 detailed guidelines on how to master those skills. The book offers first-rate advice on how to go about acquiring communication skills, people skills, presentation skills, time management skills, and others. Specific examples about current situations are discussed, exploring the impact of the Facebook phenomenon and the subprime mortgage crisis.Visit the author''s website for more information:www.FastTrackingCareers.comTrade Review“Whether you're an engineer, IT professional, or other technical professional, Fast-Tracking Your Career helps you advance your career by developing business and personal skills that are as sharp as your technical abilities.” (New Tech Review, 1 June 2013)Table of ContentsForeword xiii Dr. Sorel Reisman Guest Introduction i xv Dr. Simon Y. Liu Guest Introduction ii xvii Dr. Arnold "Jay" Bragg Guest Introduction iii xix Frank E. Ferrante Preface xxi Acknowledgments xxiii About the Author xxv Introduction and Summary 1 Engineers Are Potentially Better Positioned as Executives, 1 Categorization of Smart Soft Skills, 2 Rules for Mastering Smart Soft Skills, 3 Relationships among the Soft Skills, 8 PART ONE: Communications: The Absolutely Necessary Chapter 1 Communications Smart 13 Rule 1: Being always ready for elevator pitches/speeches, 14 Rule 2: Mastering a presentation by mastering the onset, 16 Rule 3: Using three diagrams to simplify complexity, 18 Rule 4: Sizing up and resonating with the audience, 20 Rule 5: Being careful of careless comments, 23 Rule 6: Using plain language, 24 Rule 7: Using jokes and self-deprecating humor, 26 PART TWO: Dealing with People: The Essential Chapter 2 People Smart 31 Rule 1: Getting accepted by accepting others fi rst, 32 Rule 2: Winning by understanding both ourselves and our counterparts, 34 Rule 3: Being aggressive by being nonaggressive, 36 Rule 4: Gaining by giving, 38 Rule 5: Successful networking by networking less, 41 Rule 6: Being heard by listening, 46 Chapter 3 Marketing Smart 49 Rule 1: Sizing up and resonating with our "customers", 51 Rule 2: Putting a positive spin on our "product", 53 Rule 3: Making a convincing presentation with a well-crafted presentation, 53 Rule 4: Inciting enthusiasm with enthusiasm, 54 A Marketing Role Model: Steve Jobs (and His Embodiment, Apple), 55 PART THREE: Dealing with the Self: The Basic Chapter 4 Work Smart 59 Rule 1: Achieving outstanding results by not seeking perfection, 60 Rule 2: Avoiding blunders of overconfi dence, 62 Rule 3: Focusing on self-examination, not on blaming others, when things gone awry, 63 Chapter 5 Time Smart 65 Rule 1: Investing time with the same zeal as venture capitalists investing money, 66 Rule 2: Killing two birds with one stone, 68 Rule 3: Minding ROI, 70 Rule 4: Making nonproductive time productive, 71 Rule 5: Turning spare time into opportunities, 73 Rule 6: Keeping the mind sharp by taking catnaps, 74 Chapter 6 Career Smart 77 Rule 1: Opting to be a big fi sh in a small pond, 78 Rule 2: Hopping to a more opportune pond at opportune moments, 80 Rule 3: Never polishing a sneaker, 84 Rule 4: Making a good lasting impression by making a good first impression, 86 PART FOUR: Dealing with the Boss: Earning Trust and Recognition Chapter 7 Job-Interview Smart 89 Rule 1: Being well prepared by collecting relevant information, 90 Rule 2: Putting a positive spin on our qualifi cations, 91 Rule 3: Preparing targeted elevator pitches/speeches, 91 Rule 4: Sizing up and resonating with the interviewer, 92 Rule 5: Winning interviewers’ confi dence in us by exhibiting confidence, 93 Rule 6: Avoiding gaffes by avoiding overconfi dence, 93 Stories of Failed Interviews, 93 A Successful Interview Story, 98 Chapter 8 Boss Smart 101 Rule 1: Winning trust by showing loyalty, 102 Rule 2: Gaining gratitude by sharing credit and taking blame, 104 Rule 3: Being astute by watching for nuances, 105 Rule 4: Being proactive and farsighted, 107 Rule 5: Showing enthusiasm for challenging assignments, 108 PART FIVE: Dealing with Staff: Inspiring Loyalty and Productivity Chapter 9 Motivating Smart 111 Rule 1: Winning loyalty by being loyal, 112 Rule 2: Getting credit by not taking credit, 114 Rule 3: Motivating by complimenting, 115 Chapter 10 Delegating Smart 117 Rule 1: Getting more done by doing less, 118 Rule 2: Delegating successfully by matching tasks with staff, 119 Rule 3: Making controversial decisions by not making them, 122 PART SIX: Being Visionary: Leading to the C-Suite Chapter 11 Beyond the Box 127 Rule 1: Examining the big picture to identify opportunities, 128 Rule 2: Forming a visionary plan, 131 Rule 3: Marketing the vision, 131 Successful Fast-Tracking Stories, 132 Final Thoughts 137 The Book's Objective, 137 "Soft Skills" and "Rules" Outside the Scope of This Book, 137 High Achievers' Soft Skills, 139 Personal Career Goals, 140 Appendix Tables for Principles, Strategies, and Rules 141 Table A.1 Principles and Strategies, 141 Table A.2 Communications Smart, 142 Table A.3 People Smart, 143 Table A.4 Marketing Smart, 144 Table A.5 Work Smart, 145 Table A.6 Time Smart, 146 Table A.7 Career Smart, 146 Table A.8 Job-Interview Smart, 147 Table A.9 Boss Smart, 148 Table A.10 Motivating Smart, 149 Table A.11 Delegating Smart, 149 Table A.12 Beyond the Box, 150 Abbreviations 151 Index 153

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Book SynopsisProvides fundamentals of reliability engineering and illustrates practical applications in the area of parallel/distributed systems (Multistage Interconnection Networks) The first part of the book (chapters 15) introduces the concept of reliability engineering, elements of probability theory, probability distributions, availability, and data analysis. The second part of the book (chapters 611) provides an overview of parallel/distributed computing, network design considerations, classification of multistage interconnection networks, network reliability evaluation methods, and reliability analysis of multistage interconnection networks including reliability prediction of distributed systems using Monte Carlo method. Fundamentals of Reliability Engineering meets the increasing demand for knowledge tools that practicing reliability professionals can use to optimize their reliability decisions. Reliability prediction is important as it determines the usabilitTable of ContentsPreface ix1 Introduction to Reliability Engineering 11.1 The Logic of Certainty 11.2 Union (OR) operation 21.3 Intersection (AND) operation 31.4 Series systems 41.5 Parallel systems 51.6 General Series-Parallel System 61.7 Active Redundancy 61.8 Standby Redundancy 71.9 Fault Tree Analysis 71.10 Minimum Cut Sets and Path Sets 9References 102 Elements of Probability Theory 112.1 Basic Rules of Probability 112.2 Cumulative Distribution Function 122.3 Probability Mass Function 122.4 Probability Density Function 122.5 Moments 132.6 Percentiles 13References 143 Probability Distributions 153.1 Binomial 163.2 Poisson 173.3 Exponential 183.4 Weibull 193.5 Normal 193.6 Lognormal 203.7 Mean Time To Failure (MTTF) 22References 234 Availability 254.1 Definition 254.2 Summary 274.3 Availability of Systems with Repair 28References 295 Data Analysis 315.1 Theoretical Model and Evidence 315.2 Censored Samples 325.3 Bayesian Theorem 33References 356 Introduction to Network Systems 376.1 Parallel Computing and Networks 386.2 Network Design Considerations 41 6.3 Classification of Interconnection Networks 45References 567 Classification of Multistage Interconnection Networks 577.1 Background 577.2 Multistage Cube Network 677.3 Extra-Stage Cube Network 707.4 Shuffle-Exchange Network 727.5 Shuffle-Exchange Network with an Additional Stage 737.6 Gamma Network 757.7 Extra-Stage Gamma Network 777.8 Dynamic Redundancy Network 787.9 Improved Enhanced Augmented Data Manipulator Network 797.10 Improved Logical Neighborhood Network 807.11 Comparison 81References 848 Network Reliability Evaluation Methods 878.1 Overview of Network Reliability 878.2 Network Model 888.3 Network Operations 898.4 Approaches for Calculating Network Reliability 898.5 Summary 99References 1009 Reliability Analysis of Multistage Interconnection Networks 1019.1 Reliability Analysis of Shuffle-Exchange Network with Minimal Extra Stages 1019.2 Terminal Reliability Improvement in Modified Shuffle-Exchange Network 1159.3 Reliability Bounds for Large MINs 121References 13210 Terminal Reliability Assessment of Gamma and Extra-Stage Gamma Networks 13310.1 Introduction 13310.2 Gamma Network 13510.3 Terminal Reliability of Gamma Network 13910.4 Extra-Stage Gamma Network 14010.5 Comparison 14610.6 Conclusions 146References 14711 Reliability Prediction of Distributed Systems Using Monte Carlo Method 14911.1 Introduction 14911.2 Reliability Parameters 15211.3 Monte Carlo Method 15311.4 Confidence Interval for Monte Carlo Point Estimate 15511.5 Numerical Results 15711.6 Conclusion 163References 164Index 167

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Book SynopsisWith recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. It is important to provide a comprehensive study on how to use such machines written by specialists of the domain. The book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. The content spans topics such as Numerical Analysis for Heterogeneous and Multicore Systems; Optimization of Communication for High Performance Heterogeneous and Hierarchical Platforms; Efficient Exploitation of Heterogeneous Architectures, Hybrid CPU+GPU, and Distributed Systems; Energy Awareness in High-Performance Computing; and Applications ofTable of ContentsContributors xxiii Preface xxvii Part I Introduction 1 1. Summary of the Open European Network for High-Performance Computing in Complex Environments 3Emmanuel Jeannot and Julius Žilinskas 1.1 Introduction and Vision 4 1.2 Scientific Organization 6 1.2.1 Scientific Focus 6 1.2.2 Working Groups 6 1.3 Activities of the Project 6 1.3.1 Spring Schools 6 1.3.2 International Workshops 7 1.3.3 Working Groups Meetings 7 1.3.4 Management Committee Meetings 7 1.3.5 Short-Term Scientific Missions 7 1.4 Main Outcomes of the Action 7 1.5 Contents of the Book 8 Acknowledgment 10 Part II Numerical Analysis for Heterogeneous and Multicore Systems 11 2. On the Impact of the Heterogeneous Multicore and Many-Core Platforms on Iterative Solution Methods and Preconditioning Techniques 13Dimitar Lukarski and Maya Neytcheva 2.1 Introduction 14 2.2 General Description of Iterative Methods and Preconditioning 16 2.2.1 Basic Iterative Methods 16 2.2.2 Projection Methods: CG and GMRES 18 2.3 Preconditioning Techniques 20 2.4 Defect-Correction Technique 21 2.5 Multigrid Method 22 2.6 Parallelization of Iterative Methods 22 2.7 Heterogeneous Systems 23 2.7.1 Heterogeneous Computing 24 2.7.2 Algorithm Characteristics and Resource Utilization 25 2.7.3 Exposing Parallelism 26 2.7.4 Heterogeneity in Matrix Computation 26 2.7.5 Setup of Heterogeneous Iterative Solvers 27 2.8 Maintenance and Portability 29 2.9 Conclusion 30 Acknowledgments 31 References 31 3. Efficient Numerical Solution of 2D Diffusion Equation on Multicore Computers 33Matjaž Depolli, Gregor Kosec, and Roman Trobec 3.1 Introduction 34 3.2 Test Case 35 3.2.1 Governing Equations 35 3.2.2 Solution Procedure 36 3.3 Parallel Implementation 39 3.3.1 Intel PCM Library 39 3.3.2 OpenMP 40 3.4 Results 41 3.4.1 Results of Numerical Integration 41 3.4.2 Parallel Efficiency 42 3.5 Discussion 45 3.6 Conclusion 47 Acknowledgment 47 References 47 4. Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience 51Natalija Tumanova and Raimondas Ciegis 4.1 Introduction 51 4.2 Formulation of the Discrete Model 53 4.2.1 The 𝜃-Implicit Discrete Scheme 55 4.2.2 The Predictor–Corrector Algorithm I 57 4.2.3 The Predictor–Corrector Algorithm II 58 4.3 Parallel Algorithms 59 4.3.1 Parallel 𝜃-Implicit Algorithm 59 4.3.2 Parallel Predictor–Corrector Algorithm I 62 4.3.3 Parallel Predictor–Corrector Algorithm II 63 4.4 Computational Results 63 4.4.1 Experimental Comparison of Predictor–Corrector Algorithms 66 4.4.2 Numerical Experiment of Neuron Excitation 68 4.5 Conclusions 69 Acknowledgments 70 References 70 Part III Communication and Storage Considerations in High-Performance Computing 73 5. An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing 75Torsten Hoefler, Emmanuel Jeannot, and Guillaume Mercier 5.1 Introduction 76 5.2 General Overview 76 5.2.1 A Key to Scalability: Data Locality 77 5.2.2 Data Locality Management in Parallel Programming Models 77 5.2.3 Virtual Topology: Definition and Characteristics 78 5.2.4 Understanding the Hardware 79 5.3 Formalization of the Problem 79 5.4 Algorithmic Strategies for Topology Mapping 81 5.4.1 Greedy Algorithm Variants 81 5.4.2 Graph Partitioning 82 5.4.3 Schemes Based on Graph Similarity 82 5.4.4 Schemes Based on Subgraph Isomorphism 82 5.5 Mapping Enforcement Techniques 82 5.5.1 Resource Binding 83 5.5.2 Rank Reordering 83 5.5.3 Other Techniques 84 5.6 Survey of Solutions 85 5.6.1 Algorithmic Solutions 85 5.6.2 Existing Implementations 85 5.7 Conclusion and Open Problems 89 Acknowledgment 90 References 90 6. Optimization of Collective Communication for Heterogeneous HPC Platforms 95Kiril Dichev and Alexey Lastovetsky 6.1 Introduction 95 6.2 Overview of Optimized Collectives and Topology-Aware Collectives 97 6.3 Optimizations of Collectives on Homogeneous Clusters 98 6.4 Heterogeneous Networks 99 6.4.1 Comparison to Homogeneous Clusters 99 6.5 Topology- and Performance-Aware Collectives 100 6.6 Topology as Input 101 6.7 Performance as Input 102 6.7.1 Homogeneous Performance Models 103 6.7.2 Heterogeneous Performance Models 105 6.7.3 Estimation of Parameters of Heterogeneous Performance Models 106 6.7.4 Other Performance Models 106 6.8 Non-MPI Collective Algorithms for Heterogeneous Networks 106 6.8.1 Optimal Solutions with Multiple Spanning Trees 107 6.8.2 Adaptive Algorithms for Efficient Large-Message Transfer 107 6.8.3 Network Models Inspired by BitTorrent 108 6.9 Conclusion 111 Acknowledgments 111 References 111 7. Effective Data Access Patterns on Massively Parallel Processors 115Gabriele Capannini, Ranieri Baraglia, Fabrizio Silvestri, and Franco Maria Nardini 7.1 Introduction 115 7.2 Architectural Details 116 7.3 K-Model 117 7.3.1 The Architecture 117 7.3.2 Cost and Complexity Evaluation 118 7.3.3 Efficiency Evaluation 119 7.4 Parallel Prefix Sum 120 7.4.1 Experiments 125 7.5 Bitonic Sorting Networks 126 7.5.1 Experiments 131 7.6 Final Remarks 132 Acknowledgments 133 References 133 8. Scalable Storage I/O Software for Blue Gene Architectures 135Florin Isaila, Javier Garcia, and Jesús Carretero 8.1 Introduction 135 8.2 Blue Gene System Overview 136 8.2.1 Blue Gene Architecture 136 8.2.2 Operating System Architecture 136 8.3 Design and Implementation 138 8.3.1 The Client Module 139 8.3.2 The I/O Module 141 8.4 Conclusions and Future Work 142 Acknowledgments 142 References 142 Part IV Efficient Exploitation af Heterogeneous Architectures 145 9. Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems 147Hamid Arabnejad, Jorge G. Barbosa, and Frédéric Suter 9.1 Introduction 148 9.1.1 Application Model 148 9.1.2 System Model 151 9.1.3 Performance Metrics 152 9.2 Concurrent Workflow Scheduling 153 9.2.1 Offline Scheduling of Concurrent Workflows 154 9.2.2 Online Scheduling of Concurrent Workflows 155 9.3 Experimental Results and Discussion 160 9.3.1 DAG Structure 160 9.3.2 Simulated Platforms 160 9.3.3 Results and Discussion 162 9.4 Conclusions 165 Acknowledgments 166 References 166 10. Systematic Mapping of Reed–Solomon Erasure Codes on Heterogeneous Multicore Architectures 169Roman Wyrzykowski, Marcin Wozniak, and Lukasz Kuczynski 10.1 Introduction 169 10.2 Related Works 171 10.3 Reed–Solomon Codes and Linear Algebra Algorithms 172 10.4 Mapping Reed–Solomon Codes on Cell/B.E. Architecture 173 10.4.1 Cell/B.E. Architecture 173 10.4.2 Basic Assumptions for Mapping 174 10.4.3 Vectorization Algorithm and Increasing its Efficiency 175 10.4.4 Performance Results 177 10.5 Mapping Reed–Solomon Codes on Multicore GPU Architectures 178 10.5.1 Parallelization of Reed–Solomon Codes on GPU Architectures 178 10.5.2 Organization of GPU Threads 180 10.6 Methods of Increasing the Algorithm Performance on GPUs 181 10.6.1 Basic Modifications 181 10.6.2 Stream Processing 182 10.6.3 Using Shared Memory 184 10.7 GPU Performance Evaluation 185 10.7.1 Experimental Results 185 10.7.2 Performance Analysis using the Roofline Model 187 10.8 Conclusions and Future Works 190 Acknowledgments 191 References 191 11. Heterogeneous Parallel Computing Platforms and Tools for Compute-Intensive Algorithms: A Case Study 193Daniele D’Agostino, Andrea Clematis, and Emanuele Danovaro 11.1 Introduction 194 11.2 A Low-Cost Heterogeneous Computing Environment 196 11.2.1 Adopted Computing Environment 199 11.3 First Case Study: The N-Body Problem 200 11.3.1 The Sequential N-Body Algorithm 201 11.3.2 The Parallel N-Body Algorithm for Multicore Architectures 203 11.3.3 The Parallel N-Body Algorithm for CUDA Architectures 204 11.4 Second Case Study: The Convolution Algorithm 206 11.4.1 The Sequential Convolver Algorithm 206 11.4.2 The Parallel Convolver Algorithm for Multicore Architectures 207 11.4.3 The Parallel Convolver Algorithm for GPU Architectures 208 11.5 Conclusions 211 Acknowledgments 212 References 212 12. Efficient Application of Hybrid Parallelism in Electromagnetism Problems 215Alejandro Álvarez-Melcón, Fernando D. Quesada, Domingo Giménez, Carlos Pérez-Alcaraz, José-Ginés Picón, and Tomás Ramírez 12.1 Introduction 215 12.2 Computation of Green’s functions in Hybrid Systems 216 12.2.1 Computation in a Heterogeneous Cluster 217 12.2.2 Experiments 218 12.3 Parallelization in Numa Systems of a Volume Integral Equation Technique 222 12.3.1 Experiments 222 12.4 Autotuning Parallel Codes 226 12.4.1 Empirical Autotuning 227 12.4.2 Modeling the Linear Algebra Routines 229 12.5 Conclusions and Future Research 230 Acknowledgments 231 References 232 Part V CPU + GPU Coprocessing 235 13. Design and Optimization of Scientific Applications for Highly Heterogeneous and Hierarchical HPC Platforms Using Functional Computation Performance Models 237David Clarke, Aleksandar Ilic, Alexey Lastovetsky, Vladimir Rychkov, Leonel Sousa, and Ziming Zhong 13.1 Introduction 238 13.2 Related Work 241 13.3 Data Partitioning Based on Functional Performance Model 243 13.4 Example Application: Heterogeneous Parallel Matrix Multiplication 245 13.5 Performance Measurement on CPUs/GPUs System 247 13.6 Functional Performance Models of Multiple Cores and GPUs 248 13.7 FPM-Based Data Partitioning on CPUs/GPUs System 250 13.8 Efficient Building of Functional Performance Models 251 13.9 FPM-Based Data Partitioning on Hierarchical Platforms 253 13.10 Conclusion 257 Acknowledgments 259 References 259 14. Efficient Multilevel Load Balancing on Heterogeneous CPU + GPU Systems 261Aleksandar Ilic and Leonel Sousa 14.1 Introduction: Heterogeneous CPU + GPU Systems 262 14.1.1 Open Problems and Specific Contributions 263 14.2 Background and Related Work 265 14.2.1 Divisible Load Scheduling in Distributed CPU-Only Systems 265 14.2.2 Scheduling in Multicore CPU and Multi-GPU Environments 268 14.3 Load Balancing Algorithms for Heterogeneous CPU + GPU Systems 269 14.3.1 Multilevel Simultaneous Load Balancing Algorithm 270 14.3.2 Algorithm for Multi-Installment Processing with Multidistributions 273 14.4 Experimental Results 275 14.4.1 MSLBA Evaluation: Dense Matrix Multiplication Case Study 275 14.4.2 AMPMD Evaluation: 2D FFT Case Study 277 14.5 Conclusions 279 Acknowledgments 280 References 280 15. The All-Pair Shortest-Path Problem in Shared-Memory Heterogeneous Systems 283Hector Ortega-Arranz, Yuri Torres, Diego R. Llanos, and Arturo Gonzalez-Escribano 15.1 Introduction 283 15.2 Algorithmic Overview 285 15.2.1 Graph Theory Notation 285 15.2.2 Dijkstra’s Algorithm 286 15.2.3 Parallel Version of Dijkstra’s Algorithm 287 15.3 CUDA Overview 287 15.4 Heterogeneous Systems and Load Balancing 288 15.5 Parallel Solutions to The APSP 289 15.5.1 GPU Implementation 289 15.5.2 Heterogeneous Implementation 290 15.6 Experimental Setup 291 15.6.1 Methodology 291 15.6.2 Target Architectures 292 15.6.3 Input Set Characteristics 292 15.6.4 Load-Balancing Techniques Evaluated 292 15.7 Experimental Results 293 15.7.1 Complete APSP 293 15.7.2 512-Source-Node-to-All Shortest Path 295 15.7.3 Experimental Conclusions 296 15.8 Conclusions 297 Acknowledgments 297 References 297 Part VI Efficient Exploitation of Distributed Systems 301 16. Resource Management for HPC on the Cloud 303Marc E. Frincu and Dana Petcu 16.1 Introduction 303 16.2 On the Type of Applications for HPC and HPC2 305 16.3 HPC on the Cloud 306 16.3.1 General PaaS Solutions 306 16.3.2 On-Demand Platforms for HPC 310 16.4 Scheduling Algorithms for HPC2 311 16.5 Toward an Autonomous Scheduling Framework 312 16.5.1 Autonomous Framework for RMS 313 16.5.2 Self-Management 315 16.5.3 Use Cases 317 16.6 Conclusions 319 Acknowledgment 320 References 320 17. Resource Discovery in Large-Scale Grid Systems 323Konstantinos Karaoglanoglou and Helen Karatza 17.1 Introduction and Background 323 17.1.1 Introduction 323 17.1.2 Resource Discovery in Grids 324 17.1.3 Background 325 17.2 The Semantic Communities Approach 325 17.2.1 Grid Resource Discovery Using Semantic Communities 325 17.2.2 Grid Resource Discovery Based on Semantically Linked Virtual Organizations 327 17.3 The P2P Approach 329 17.3.1 On Fully Decentralized Resource Discovery in Grid Environments Using a P2P Architecture 329 17.3.2 P2P Protocols for Resource Discovery in the Grid 330 17.4 The Grid-Routing Transferring Approach 333 17.4.1 Resource Discovery Based on Matchmaking Routers 333 17.4.2 Acquiring Knowledge in a Large-Scale Grid System 335 17.5 Conclusions 337 Acknowledgment 338 References 338 Part VII Energy Awareness in High-Performance Computing 341 18. Energy-Aware Approaches for HPC Systems 343Robert Basmadjian, Georges Da Costa, Ghislain Landry Tsafack Chetsa, Laurent Lefevre, Ariel Oleksiak, and Jean-Marc Pierson 18.1 Introduction 344 18.2 Power Consumption of Servers 345 18.2.1 Server Modeling 346 18.2.2 Power Prediction Models 347 18.3 Classification and Energy Profiles of HPC Applications 354 18.3.1 Phase Detection 356 18.3.2 Phase Identification 358 18.4 Policies and Leverages 359 18.5 Conclusion 360 Acknowledgements 361 References 361 19. Strategies for Increased Energy Awareness in Cloud Federations 365Gabor Kecskemeti, AttilaKertesz, Attila Cs. Marosi, and Zsolt Nemeth 19.1 Introduction 365 19.2 Related Work 367 19.3 Scenarios 369 19.3.1 Increased Energy Awareness Across Multiple Data Centers within a Single Administrative Domain 369 19.3.2 Energy Considerations in Commercial Cloud Federations 372 19.3.3 Reduced Energy Footprint of Academic Cloud Federations 374 19.4 Energy-Aware Cloud Federations 374 19.4.1 Availability of Energy-Consumption-Related Information 375 19.4.2 Service Call Scheduling at the Meta-Brokering Level of FCM 376 19.4.3 Service Call Scheduling and VM Management at the Cloud-Brokering Level of FCM 377 19.5 Conclusions 379 Acknowledgments 380 References 380 20. Enabling Network Security in HPC Systems Using Heterogeneous CMPs 383Ozcan Ozturk and Suleyman Tosun 20.1 Introduction 384 20.2 Related Work 386 20.3 Overview of Our Approach 387 20.3.1 Heterogeneous CMP Architecture 387 20.3.2 Network Security Application Behavior 388 20.3.3 High-Level View 389 20.4 Heterogeneous CMP Design for Network Security Processors 390 20.4.1 Task Assignment 390 20.4.2 ILP Formulation 391 20.4.3 Discussion 393 20.5 Experimental Evaluation 394 20.5.1 Setup 394 20.5.2 Results 395 20.6 Concluding Remarks 397 Acknowledgments 397 References 397 Part VIII Applications of Heterogeneous High-Performance Computing 401 21. Toward a High-Performance Distributed CBIR System for Hyperspectral Remote Sensing Data: A Case Study in Jungle Computing 403Timo van Kessel, NielsDrost, Jason Maassen, Henri E. Bal, Frank J. Seinstra, and Antonio J. Plaza 21.1 Introduction 404 21.2 CBIR For Hyperspectral Imaging Data 407 21.2.1 Spectral Unmixing 407 21.2.2 Proposed CBIR System 409 21.3 Jungle Computing 410 21.3.1 Jungle Computing: Requirements 411 21.4 IBIS and Constellation 412 21.5 System Design and Implementation 415 21.5.1 Endmember Extraction 418 21.5.2 Query Execution 418 21.5.3 Equi-Kernels 419 21.5.4 Matchmaking 420 21.6 Evaluation 420 21.6.1 Performance Evaluation 421 21.7 Conclusions 426 Acknowledgments 426 References 426 22. Taking Advantage of Heterogeneous Platforms in Image and Video Processing 429Sidi A. Mahmoudi, Erencan Ozkan, Pierre Manneback, and Suleyman Tosun 22.1 Introduction 430 22.2 Related Work 431 22.2.1 Image Processing on GPU 431 22.2.2 Video Processing on GPU 432 22.2.3 Contribution 433 22.3 Parallel Image Processing on GPU 433 22.3.1 Development Scheme for Image Processing on GPU 433 22.3.2 GPU Optimization 434 22.3.3 GPU Implementation of Edge and Corner Detection 434 22.3.4 Performance Analysis and Evaluation 434 22.4 Image Processing on Heterogeneous Architectures 437 22.4.1 Development Scheme for Multiple Image Processing 437 22.4.2 Task Scheduling within Heterogeneous Architectures 438 22.4.3 Optimization Within Heterogeneous Architectures 438 22.5 Video Processing on GPU 438 22.5.1 Development Scheme for Video Processing on GPU 439 22.5.2 GPU Optimizations 440 22.5.3 GPU Implementations 440 22.5.4 GPU-Based Silhouette Extraction 440 22.5.5 GPU-Based Optical Flow Estimation 440 22.5.6 Result Analysis 443 22.6 Experimental Results 444 22.6.1 Heterogeneous Computing for Vertebra Segmentation 444 22.6.2 GPU Computing for Motion Detection Using a Moving Camera 445 22.7 Conclusion 447 Acknowledgment 448 References 448 23. Real-Time Tomographic Reconstruction Through CPU + GPU Coprocessing 451José Ignacio Agulleiro, Francisco Vazquez, Ester M. Garzon, and Jose J. Fernandez 23.1 Introduction 452 23.2 Tomographic Reconstruction 453 23.3 Optimization of Tomographic Reconstruction for CPUs and for GPUs 455 23.4 Hybrid CPU + GPU Tomographic Reconstruction 457 23.5 Results 459 23.6 Discussion and Conclusion 461 Acknowledgments 463 References 463 Index 467

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Book SynopsisTable of ContentsPreface xi Acknowledgments xvi 1 Body Sensor Networks 1 1.1 Introduction 1 1.2 Background 1 1.3 Typical m‐Health System Architecture 4 1.4 Hardware Architecture of a Sensor Node 6 1.5 Communication Medium 7 1.6 Power Consumption Considerations 7 1.7 Communication Standards 8 1.8 Network Topologies 10 1.9 Commercial Sensor Node Platforms 13 1.10 Biophysiological Signals and Sensors 16 1.11 BSN Application Domains 17 1.12 Summary 20 References 20 2 BSN Programming Frameworks 25 2.1 Introduction 25 2.2 Developing BSN Applications 25 2.2.1 Application‐ and Platform‐Specific Programming 26 2.2.2 Automatic Code Generation 28 2.2.3 Middleware‐Based Programming 28 2.2.4 Programming Approaches Comparison 30 2.3 Programming Abstractions 31 2.4 Requirements for BSN Frameworks 34 2.5 BSN Programming Frameworks 37 2.5.1 Titan 38 2.5.2 CodeBlue 38 2.5.3 RehabSPOT 38 2.5.4 SPINE 39 2.5.5 SPINE2 39 2.5.6 C‐SPINE 39 2.5.7 MAPS 40 2.5.8 DexterNet 40 2.6 Summary 40 References 41 3 Signal Processing In‐Node Environment 45 3.1 Introduction 45 3.2 Background 46 3.3 Motivations and Challenges 46 3.4 The SPINE Framework 46 3.4.1 Architecture 47 3.4.2 Programming Perspective 51 3.4.3 Optional SPINE Modules 51 3.4.4 High‐Level Data Processing 52 3.4.5 Multiplatform Support 55 3.5 Summary 56 References 57 4 Task‐Oriented Programming in BSNs 59 4.1 Introduction 59 4.2 Background 60 4.3 Motivations and Challenges 60 4.3.1 Need for a Platform‐Independent Middleware 60 4.3.2 Challenges in Designing a Task‐Oriented Framework 61 4.4 SPINE2 Overview 62 4.5 Task‐Oriented Programming in SPINE2 63 4.6 SPINE2 Node‐Side Middleware 66 4.7 SPINE2 Coordinator 68 4.8 SPINE2 Communication Protocol 68 4.9 Developing Application in SPINE2 70 4.10 Summary 71 References 72 5 Autonomic Body Sensor Networks 73 5.1 Introduction 73 5.2 Background 73 5.3 Motivations and Challenges 74 5.4 State‐of‐the‐Art 75 5.5 SPINE‐*: Task‐Based Autonomic Architecture 76 5.6 Autonomic Physical Activity Recognition 81 5.7 Summary 84 References 85 6 Agent‐Oriented Body Sensor Networks 89 6.1 Introduction 89 6.2 Background 89 6.2.1 Agent‐Oriented Computing and Wireless Sensor Networks 89 6.2.2 Mobile Agent Platform for Sun SPOT (MAPS) 91 6.3 Motivations and Challenges 94 6.4 State‐of‐the‐Art: Description and Comparison 95 6.5 Agent‐Based Modeling and Implementation of BSNs 98 6.6 Engineering Agent‐Based BSN Applications: A Case Study 98 6.7 Summary 101 References 103 7 Collaborative Body Sensor Networks 107 7.1 Introduction 107 7.2 Background 108 7.3 Motivations and Challenges 109 7.4 State‐of‐the‐Art 110 7.5 A Reference Architecture for Collaborative BSNs 111 7.6 C‐SPINE: A CBSN Architecture 114 7.6.1 Inter‐BSN Communication 116 7.6.2 BSN Proximity Detection 117 7.6.3 BSN Service Discovery 118 7.6.4 BSN Service Selection and Activation 118 7.7 Summary 119 References 119 8 Integration of Body Sensor Networks and Building Networks 121 8.1 Introduction 121 8.2 Background 121 8.2.1 Building Sensor Networks and Systems 121 8.2.2 Building Management Framework 124 8.3 Motivations and Challenges 125 8.4 Integration Layers 127 8.5 State‐of‐the‐Art: Description and Comparison 129 8.6 An Agent‐Oriented Integration Gateway 130 8.7 Application Scenarios 133 8.7.1 In‐Building Physical Activity Monitoring 133 8.8 Summary 135 References 135 9 Integration of Wearable and Cloud Computing 139 9.1 Introduction 139 9.2 Background 140 9.2.1 Cloud Computing 140 9.2.2 Architectures for Sensor Stream Management 140 9.3 Motivations and Challenges 142 9.3.1 BSN Challenges 143 9.3.2 BSN/Cloud Computing Integration Challenges 144 9.4 Reference Architecture for Cloud‐Assisted BSNs 145 9.4.1 Sensor Data Collection 145 9.4.2 Sensor Data Management 147 9.4.3 Scalable Processing Framework 147 9.4.4 Persistent Storage 148 9.4.5 Decision‐Making Process 149 9.4.6 Open Standards and Advanced Visualization 149 9.4.7 Security 150 9.5 State‐of‐the‐Art: Description and Comparison 151 9.5.1 Integration of WSNs and Cloud Computing 151 9.5.2 Integration of BSNs and Cloud Computing 152 9.5.3 A Comparison 153 9.6 BodyCloud: A Cloud‐based Platform for Community BSN Applications 156 9.7 Engineering BodyCloud Applications 159 9.7.1 ECGaaS: Cardiac Monitoring 160 9.7.2 FEARaaS: Basic Fear Detection 162 9.7.3 REHABaaS: Remote Rehabilitation 165 9.7.4 ACTIVITYaaS: Community Activity Monitoring 166 9.8 Summary 171 References 171 10 Development Methodology for BSN Systems 177 10.1 Introduction 177 10.2 Background 177 10.3 Motivations and Challenges 180 10.4 SPINE‐Based Design Methodology 180 10.4.1 A Pattern‐Driven Application‐Level Design 181 10.4.2 System Parameters 183 10.4.3 Process Schema 184 10.5 Summary 186 References 186 11 SPINE‐Based Body Sensor Network Applications 187 11.1 Introduction 187 11.2 Background 187 11.3 Physical Activity Recognition 187 11.3.1 Related Work 188 11.3.2 A SPINE‐Based Activity Recognition System 189 11.4 Step Counter 191 11.4.1 Related Work 191 11.4.2 A SPINE‐Based Step Counter 192 11.5 Emotion Recognition 194 11.5.1 Stress Detection 194 11.5.1.1 Related Work 194 11.5.1.2 SPINE‐HRV: A Wearable System for Real‐Time Stress Detection 195 11.5.2 Fear Detection 197 11.5.2.1 Related Work 197 11.5.2.2 A SPINE‐Based Startle Reflex Detection System 198 11.6 Handshake Detection 200 11.6.1 Related Work 201 11.6.2 A SPINE‐Based Handshake Detection System 202 11.7 Physical Rehabilitation 205 11.7.1 Related Work 205 11.7.2 SPINE Motor Rehabilitation Assistant 206 11.8 Summary 208 References 208 12 SPINE at Work 213 12.1 Introduction 213 12.2 SPINE 1.x 213 12.2.1 How to Install SPINE 1.x 216 12.2.2 How to Use SPINE 217 12.2.3 How to Run a Simple Desktop Application Using SPINE1.3 220 12.2.4 SPINE Logging Capabilities 225 12.3 SPINE2 225 12.3.1 How to Install SPINE2 228 12.3.2 How to Use the SPINE2 API 230 12.3.3 How to Run a Simple Application Using SPINE2 232 Index 239

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Book SynopsisAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. Discusses measurement and modeling of the energTable of ContentsPreface xv Acknowledgment xvii 1 INTRODUCTION TO ENERGY EFFICIENCY IN LARGE-SCALE DISTRIBUTED SYSTEMS 1Jean-Marc Pierson and Helmut Hlavacs 1.1 Energy Consumption Status 1 1.2 Target of the Book 3 1.3 The Cost Action IC0804 4 1.3.1 Birth of the Action 4 1.3.2 Development of the Action 5 1.3.3 End and Future of the Action 10 1.4 Chapters Preview 11 Acknowledgement 12 References 12 2 HARDWARE LEVERAGES FOR ENERGY REDUCTION IN LARGE-SCALE DISTRIBUTED SYSTEMS 17Davide Careglio, Georges Da Costa, and Sergio Ricciardi 2.1 Introduction 17 2.1.1 Motivation for Energy-Aware Distributed Computing 17 2.2 Processor 19 2.2.1 Context 19 2.2.2 Advanced Configuration and Power Interface (ACPI) 20 2.2.3 Vendors 21 2.2.4 General-Purpose Graphics Processing Unit (GPGPU) 23 2.2.5 ARM Architecture 24 2.3 Memory (DRAM) 25 2.3.1 Context 25 2.3.2 Power Consumption 25 2.3.3 Energy Efficiency Techniques 26 2.3.4 Vendors 26 2.4 Disk/Flash 27 2.4.1 Spindle Speed 28 2.4.2 Seek Speed 28 2.4.3 Power Modes 29 2.4.4 Power Consumption 29 2.4.5 Solid-State Drive (SDD) 29 2.5 Fan 30 2.6 Power Supply Unit 30 2.7 Network Infrastructure 31 2.7.1 Current Scenario 31 2.7.2 New Energy-Oriented Model 32 2.7.3 Current Advances in Networking 33 2.7.4 Adaptive Link Rate (ALR) 34 2.7.5 Low Power Idle (LPI) 34 2.7.6 Energy-Aware Dynamic RWA Framework 34 2.7.7 Energy-Aware Network Attacks 35 References 36 3 GREEN WIRED NETWORKS 41Alfonso Gazo Cervero, Michele Chincoli, Lars Dittmann, Andreas Fischer, Alberto E. Garcia, Jaime Galán-Jiménez, Laurent Lefevre, Hermann de Meer, Thierry Monteil, Paolo Monti, Anne-Cecile Orgerie, Louis-Francois Pau, Chris Phillips, Sergio Ricciardi, Remi Sharrock, Patricia Stolf, Tuan Trinh, and Luca Valcarenghi 3.1 Economic Incentives and Green Tariffing 44 3.1.1 Regulatory, Economic, and Microeconomic Measures 44 3.1.2 Pricing Theory in Relation to Green Policies 46 3.1.3 COST Action Results 50 3.2 Network Components 51 3.2.1 Router 51 3.2.2 Network Interface Card 55 3.2.3 Reconfigurable Optical Add-Drop Multiplexer 56 3.2.4 Digital Subscriber Line Access Multiplexer 56 3.3 Architectures 57 3.3.1 Access Networks 57 3.3.2 Carrier Networks 58 3.3.3 Grid Overlay Networks 58 3.4 Traffic Considerations 59 3.5 Energy-Saving Mechanisms 60 3.5.1 Static Mechanisms 60 3.5.2 Dynamic Mechanisms 61 3.6 Challenges 72 3.7 Summary 72 References 73 4 GREEN WIRELESS-ENERGY EFFICIENCY IN WIRELESS NETWORKS 81Vitor Bernardo, Torsten Braun, Marilia Curado, Markus Fiedler, David Hock, Theus Hossmann, Karin Anna Hummel, Philipp Hurni, Selim Ickin, Almerima Jamakovic-Kapic, Simin Nadjm-Tehrani, Tuan Ahn Trinh, Ekhiotz Jon Vergara, Florian Wamser, and Thomas Zinner 4.1 Introduction 81 4.2 Metrics and Trade-Offs in Wireless Networks 83 4.2.1 Metrics 83 4.2.2 Energy Optimization Trade-Offs 84 4.2.3 Summary 85 4.3 Measurement Methodology 85 4.3.1 Energy Measurement Testbeds 86 4.3.2 Energy Estimation Techniques 90 4.3.3 Energy Measurements versus Estimation 97 4.3.4 Summary 99 4.4 Energy Efficiency and QoE in Wireless Access Networks 100 4.4.1 Energy Issues in Cellular Networks 100 4.4.2 Energy Efficiency and QoE in Wireless Mesh Networks 101 4.4.3 Reducing Energy Consumption of the End User Device 105 4.4.4 Energy Measurements Revealing Video QoE Issues 108 4.4.5 Energy Issues in Environmental WMNs 110 4.4.6 Summary 112 4.5 Energy-Efficient Medium Access in Wireless Sensor Networks 113 4.5.1 MaxMAC – An Energy-Efficient MAC Protocol 113 4.5.2 Real-World Testbed Experiments with MaxMAC 116 4.5.3 Summary 119 4.6 Energy-Efficient Connectivity in Ad-Hoc and Opportunistic Networks 119 4.6.1 Ad-Hoc Networking 120 4.6.2 Opportunistic and Delay-Tolerant Networking 121 4.6.3 Summary 123 4.7 Summary and Conclusions 124 References 125 5 POWER MODELING 131Jason Mair, Zhiyi Huang, David Eyers, Leandro Cupertino, Georges Da Costa, Jean-Marc Pierson, and Helmut Hlavacs 5.1 Introduction 131 5.2 Measuring Power 133 5.2.1 External Power Meters 133 5.2.2 Internal Power Meters 134 5.3 Performance Indicators 135 5.3.1 Source Instrumentation 135 5.3.2 Binary Instrumentation 136 5.3.3 Performance Monitoring Counters 136 5.3.4 Operating System Events 137 5.3.5 Virtual Machine Performance 138 5.4 Interaction between Power and Performance 138 5.4.1 Central Processing Unit (CPU) 138 5.4.2 Memory 140 5.4.3 Input/Output (I/O) 141 5.4.4 Network 141 5.4.5 Idle States 142 5.5 Power Modeling Procedure 143 5.5.1 Variable Selection 143 5.5.2 Training Data Collection 144 5.5.3 Learning from Data 145 5.5.4 Event Correlation 145 5.5.5 Model Evaluation Concepts 146 5.5.6 Power Estimation Errors 148 5.5.7 Related Work 149 5.6 Use-Cases 151 5.6.1 Applications 151 5.6.2 Single-Core Systems 152 5.6.3 Multi-core and Multiprocessor 152 5.6.4 Distributed Systems 153 5.7 Available Software 154 5.8 Conclusion 155 References 156 6 GREEN DATA CENTERS 159Robert Basmadjian, Pascal Bouvry, Georges Da Costa, László Gyarmati, Dzmitry Kliazovich, Sébastien Lafond, Laurent Lefèvre, Hermann De Meer, Jean-Marc Pierson, Rastin Pries, Jordi Torres, Tuan Anh Trinh, and Samee Ullah Khan 6.1 Introduction 160 6.2 Overview of Energy Consumption of Hardware Infrastructure in Data Center 161 6.2.1 Energy Consumption Rankings and Metrics 161 6.2.2 Processing: CPU, GPU, and memory 162 6.2.3 Storage 168 6.2.4 Communicating Elements 168 6.3 Middleware Solutions that Regulate and Optimize the Energy Consumption in Data Centers 169 6.3.1 An Overview of the Middleware 169 6.3.2 System Modeling 171 6.3.3 Control Mechanisms 172 6.3.4 A Use Case of Leveraging Energy Efficiency in Data Centers 174 6.4 Data Center Network Architectures 177 6.4.1 Architectures 177 6.4.2 Power Consumption of Data Center Architectures 181 6.4.3 Additional Proposals for Energy-Efficient Data Centers 182 6.5 Solutions for Cooling and Heat Control in Data Center 184 6.5.1 Mechanical-Based Approaches 185 6.5.2 Software-Based Approaches 187 Acknowledgments 187 References 188 7 ENERGY EFFICIENCY AND HIGH-PERFORMANCE COMPUTING 197Pascal Bouvry, Ghislain Landry Tsafack Chetsa, Georges Da Costa, Emmanuel Jeannot, Laurent Lefèvre, Jean-Marc Pierson, Frédéric Pinel, Patricia Stolf, and Sébastien Varrette 7.1 Introduction 197 7.2 Overview of HPC Components and Latest Trends Toward Energy Efficiency 198 7.2.1 Architecture of the Current HPC Facilities 198 7.2.2 Overview of the Main HPC Components 201 7.2.3 HPC Performance and Energy Efficiency Evaluation 203 7.3 Building the Path to Exascale Computing 206 7.3.1 The Exascale Challenge: Hardware and Architecture Issues 206 7.3.2 Energy Efficiency and Resource and Job Management System (RJMS) 207 7.3.3 Energy-Aware Software 210 7.3.4 A Methodology for Energy Reduction in HPC 210 7.4 Energy Efficiency of Virtualization and Cloud Frameworks over HPC Workloads 216 7.5 Conclusion: Open Challenges 221 Acknowledgments 222 References 222 8 SCHEDULING AND RESOURCE ALLOCATION 225Pragati Agrawal, Damien Borgetto, Carmela Comito, Georges Da Costa, Jean-Marc Pierson, Payal Prakash, Shrisha Rao, Domenico Talia, Cheikhou Thiam, and Paolo Trunfio 8.1 Introduction: Energy-Aware Scheduling 225 8.2 Use of Linear Programming in Energy-Aware Scheduling 226 8.2.1 Finding the Optimal Solution Using a Linear Program 226 8.2.2 Benefits and Limitations of LP 227 8.3 Heuristics in Large Instances 228 8.3.1 Energy-Aware Greedy Algorithms 229 8.3.2 Vector Packing 229 8.3.3 Improving Fast Algorithms 229 8.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 230 8.4.1 Problem Formulation 230 8.4.2 Allocation Heuristics 232 8.4.3 Results 234 8.5 Energy-Aware Task Allocation in Mobile Environments 236 8.5.1 Reference Architecture 237 8.5.2 Task Allocation Strategy 238 8.5.3 Task Allocation Algorithm 239 8.5.4 Performance Results 241 8.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 243 8.6.1 Decentralized Resources in Cloud: Overview 243 8.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 244 8.6.3 Simulation Results 245 8.6.4 Evaluation 248 8.7 Cost-Aware Scheduling with Smart Grids 248 8.7.1 Cost-Aware Scheduling 248 8.7.2 Cost-Aware Scheduling Using DE 252 8.7.3 Comparison of DE with Other Approaches 254 8.8 Heterogeneity, Cooling, DVFS, and Migration 257 8.8.1 Lever Interactions 257 8.8.2 Infrastructures 257 8.8.3 Resource Allocation as a Whole 258 8.9 Conclusions 259 References 260 9 ENERGY EFFICIENCY IN P2P SYSTEMS AND APPLICATIONS 263Simone Brienza, Sena Efsun Cebeci, Seyed-Saeid Masoumzadeh, Helmut Hlavacs, Öznur Özkasap, Giuseppe Anastasi 9.1 Introduction 264 9.2 General Approaches to Energy Efficiency 264 9.2.1 Sleep/Wakeup Approaches 264 9.2.2 Hierarchical Approaches 266 9.2.3 Resource Allocation 268 9.3 Energy Efficiency in File-Sharing Applications 269 9.3.1 Client–Server versus P2P File Sharing 269 9.3.2 Energy Efficiency in P2P File Sharing 270 9.3.3 Energy Efficiency in BitTorrent 270 9.3.4 Energy Efficiency in Other File-Sharing Protocols 279 9.4 Energy Efficiency in P2P Epidemic Protocols 280 9.5 Conclusions 282 References 283 10 TOWARD SUSTAINABILITY FOR LARGE-SCALE COMPUTING SYSTEMS: ENVIRONMENTAL, ECONOMIC, AND STANDARDIZATION ASPECTS 287Christina Herzog, Jean-Marc Pierson, and Laurent Lefèvre 10.1 Introduction 287 10.2 Green IT for Innovation and Innovation for Green IT 288 10.2.1 Defining Green IT and Its Link with Sustainability 288 10.2.2 Differences between Academia and Companies 291 10.2.3 Describing the Loop between Academia and Industry 294 10.3 Standardization Landscape in Green IT 295 10.3.1 Different Standardization Levels 296 10.3.2 Standardization Bodies 297 10.3.3 Regulations 299 10.3.4 Industry Groups and Professional Bodies 299 10.3.5 Analysis of the Standardization Actors 301 10.4 Modeling Actors of Innovation in Green IT and their Links 301 10.4.1 Researcher 301 10.4.2 Universities 302 10.4.3 Technology Transfer Office (TTO) 302 10.4.4 Industry 302 10.4.5 Funding Organization 303 10.4.6 Standardization Body 303 10.4.7 Links between Actors 303 10.4.8 Rating the Relationships between Actors 304 10.5 Using the Modeling for Deciding 306 10.5.1 Methodology to be Developed 306 10.6 Conclusion 307 Acknowledgment 307 References 307 Author Index 309 Subject Index 311

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Book SynopsisOffers a comprehensive introduction to the fundamental structures and applications of a wide range of contemporary coding operations This book offers a comprehensive introduction to the fundamental structures and applications of a wide range of contemporary coding operations. This text focuses on the ways to structure information so that its transmission will be in the safest, quickest, and most efficient and error-free manner possible. All coding operations are covered in a single framework, with initial chapters addressing early mathematical models and algorithmic developments which led to the structure of code. After discussing the general foundations of code, chapters proceed to cover individual topics such as notions of compression, cryptography, detection, and correction codes. Both classical coding theories and the most cutting-edge models are addressed, along with helpful exercises of varying complexities to enhance comprehension. Explains how to sTable of ContentsList of Figures, Tables, Algorithms and Acronyms ix Foreword xvii Introduction 1 1 Foundations of Coding 5 1.1 From Julius Caesar to Telecopy 6 1.1.1 The Source: from an Image to a Sequence of Pixels 6 1.1.2 Message Compression 7 1.1.3 Error Detection 8 1.1.4 Encryption 9 1.1.5 Decryption 9 1.1.6 Drawbacks of the Fax Code 11 1.1.7 Orders of Magnitude and Complexity Bounds for Algorithms 12 1.2 Stream Ciphers and Probabilities 15 1.2.1 The Vernam Cipher and the One-Time-Pad Cryptosystem 15 1.2.2 Some Probability 16 1.2.3 Entropy 18 1.2.4 Steganography and Watermarking 23 1.2.5 Perfect Secrecy 24 1.2.6 Perfect Secrecy in Practice and Kerckhoffs’ Principles 24 1.3 Block Ciphers, Algebra, and Arithmetic 26 1.3.1 Blocks and Chaining Modes from CBC to CTR 27 1.3.2 Algebraic Structure of Codewords 30 1.3.3 Bijective Encoding of a Block 35 1.3.4 Construction of Prime Fields and Finite Fields 43 1.3.5 Implementation of Finite Fields 52 1.3.6 Curves Over Finite Fields 57 1.3.7 Pseudo-Random Number Generators (PRNG) 62 1.4 Decoding, Decryption, Attacks 68 1.4.1 Decoding without Ambiguity 68 1.4.2 Noninjective Codes 73 1.4.3 Cryptanalysis 84 2 Information Theory and Compression 99 2.1 Information Theory 100 2.1.1 Average Length of a Code 100 2.1.2 Entropy as a Measure of the Amount of Information 101 2.1.3 Shannon’s Theorem 102 2.2 Statistical Encoding 104 2.2.1 Huffman’s Algorithm 104 2.2.2 Arithmetic Encoding 109 2.2.3 Adaptive Codes 114 2.3 Heuristics of Entropy Reduction 117 2.3.1 Run-Length Encoding (RLE) 117 2.3.2 Move-to-Front 119 2.3.3 Burrows–Wheeler Transform (BWT) 120 2.4 Common Compression Codes 122 2.4.1 Lempel–Ziv’s Algorithm and gzip Variants 123 2.4.2 Comparisons of Compression Algorithms 126 2.4.3 GIF and PNG Formats for Image Compression 127 2.5 Lossy Compression 128 2.5.1 Deterioration of Information 128 2.5.2 Transformation of Audiovisual Information 128 2.5.3 JPEG Format 129 2.5.4 Motion Picture Experts Group (MPEG) Format 133 3 Cryptology 136 3.1 General Principles 137 3.1.1 Terminology 137 3.1.2 What is the Use of Cryptography? 138 3.1.3 Main Types of Threats 139 3.2 Secret Key Cryptography 141 3.2.1 Principle of Symmetric Cryptography 141 3.2.2 Classes of Symmetric Encryption Schemes 143 3.2.3 Data Encryption Standard (DES) System 145 3.2.4 Rijndael: Advanced Encryption Standard (AES) 151 3.3 Key Exchange 161 3.3.1 Diffie–Hellman Protocol and Man-in-the-Middle Attacks 161 3.3.2 Kerberos: a Secret Key Provider 163 3.4 Public Key Cryptography 167 3.4.1 Motivations and Main Principles 167 3.4.2 Rivest–Shamir–Adleman (RSA) Encryption 169 3.4.3 El Gamal Encryption 174 3.5 Authentication, Integrity, Nonrepudiation, Signatures 175 3.5.1 Cryptographic Hash Functions 176 3.5.2 Public Key Authentication 186 3.5.3 Electronic Signatures 187 3.6 Key Management 192 3.6.1 Generation of Cryptographically Secure Bits 192 3.6.2 Public Key Infrastructure (PKI) 192 3.6.3 Securing Channels with the SSH Tool 203 4 Error Detection and Correction 209 4.1 Principle of Error Detection and Error Correction 211 4.1.1 Block Coding 211 4.1.2 A Simple Example of Parity Detection 211 4.1.3 Correction Using Longitudinal and Transverse Parity 212 4.1.4 Encoding, Decoding, and Probability of Error 213 4.1.5 Shannon’s Second Theorem 214 4.2 Error Detection by Parity – CRC Codes 218 4.2.1 Parity Check on Integers: ISBN, EAN, LUHN 218 4.2.2 Cyclic Redundancy Checks (CRC) 221 4.3 Distance of a Code 222 4.3.1 Error Correction Code and Hamming Distance 222 4.3.2 Equivalent Codes, Extended Codes, and Shortened Codes 227 4.3.3 Perfect Codes 229 4.3.4 Binary Hamming Codes 230 4.4 Linear Codes and Cyclic Codes 232 4.4.1 Linear Codes and Minimum Redundancy 232 4.4.2 Encoding and Decoding of Linear Codes 234 4.4.3 Low Density Parity Check (LDPC) Codes 238 4.4.4 Cyclic Codes 250 4.4.5 Bose–Chaudhuri–Hocquenghem (BCH) Codes 254 4.4.6 Optimal BCH Codes: Reed–Solomon Codes 256 4.5 Bursts of Errors and Interleaving 262 4.5.1 Packets of Errors 262 4.5.2 Interleaving 263 4.5.3 Interleaving with Delay and Interleaving Table 264 4.5.4 Cross-Interleaved codes 265 4.6 Convolutional Codes and Turbo Codes 268 4.6.1 Encoding by Convolution 268 4.6.2 Shortest Path Decoding 269 4.6.3 Turbo Codes 273 Compression, Encryption, Correction: As a Conclusion 277 Problem Solutions 281 Bibliography 343 Index 345

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Book SynopsisPerkovic''sIntroduction to Computing Using Python: An Application Development Focus, 2nd Editionis more than just an introduction to programming. It is an inclusive introduction to Computer Science that takes the pedagogical approach of the right tool for the job at the right moment, and focuses on application development. The approach is hands-on and problem-oriented, with practice problems and solutions appearing throughout the text. The text is imperative-first, but does not shy away from discussing objects early where appropriate. Discussions of user-defined classes and Object-Oriented Programming appear later in the text, when students have more background and concepts can be motivated. Chapters include an introduction to problem solving techniques and classical algorithms, problem-solving and programming and ways to apply core skills to application development. This edition also includes examples and practice problems provided within a greater variety of domains.Table of ContentsChapter 1. Introduction to Computer Science Chapter 2. Python Data Types Chapter 3. Imperative Programming Chapter 4. Text data, Files, and Exceptions Chapter 5. Execution Control Structures Chapter 6. Containers and Randomness Chapter 7. Namespaces Chapter 8. Object-Oriented Programming Chapter 9. Graphical User Interfaces Chapter 10. Recursion Chapter 11. The Web and Search Chapter 12. Databases and Data Processing

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Book SynopsisA comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.Trade Review"This volume will be useful to business readers interested in a high-level overview of the technologies used in Watson and how those technologies might apply to their markets." (Computing Reviews 2015) Table of ContentsIntroduction xvii Chapter 1 The Foundation of Cognitive Computing 1 Chapter 2 Design Principles for Cognitive Systems 21 Chapter 3 Natural Language Processing in Support of a Cognitive System 39 Chapter 4 The Relationship Between Big Data and Cognitive Computing 55 Chapter 5 Representing Knowledge in Taxonomies and Ontologies 71 Chapter 6 Applying Advanced Analytics to Cognitive Computing 87 Chapter 7 The Role of Cloud and Distributed Computing in Cognitive Computing 109 Chapter 8 The Business Implications of Cognitive Computing 125 Chapter 9 IBM’s Watson as a Cognitive System 137 Chapter 10 The Process of Building a Cognitive Application 157 Chapter 11 Building a Cognitive Healthcare Application 175 Chapter 12 Smarter Cities: Cognitive Computing in Government 197 Chapter 13 Emerging Cognitive Computing Areas 221 Chapter 14 Future Applications for Cognitive Computing 235 Glossary 251 Index 261

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Book SynopsisIntroducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC).Table of ContentsPreface ix Acknowledgments xiii Contributors xv I Preliminaries 1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms 3 Nasimul Noman and Hitoshi Iba 2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30 Tatsuya Akutsu 3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49 Yuji Zhang II EAs for Gene Expression Data Analysis and GRN Reconstruction 4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69 Alan Wee-Chung Liew 5 Inference of Vohradský’s Models of Genetic Networks Using a Real-Coded Genetic Algorithm 96 Shuhei Kimura 6 GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation 118 Marco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi 7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups 151 Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith 8 Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model 185 Ahsan Raja Chowdhury and Madhu Chetty III EAs for Evolving GRNs and Reaction Networks 9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213 Ibuki Kawamata and Masami Hagiya 10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240 Alexander Spirov and David Holloway 11 Evolving GRN-inspired In Vitro Oscillatory Systems 269 Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez IV Application of GRN with EAs 12 Artificial Gene Regulatory Networks for Agent Control 301 Sylvain Cussat-Blanc, Jean Disset, Stéphane Sanchez and Yves Duthen 13 Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327 Hyondong Oh and Yaochu Jin 14 Regulatory Representations in Architectural Design 362 Daniel Richards and Martyn Amos 15 Computing with Artificial Gene Regulatory Networks 398 Michael A. Lones Index 425

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Book SynopsisA computational perspective on partial order and lattice theory, focusing on algorithms and their applications This book provides a uniform treatment of the theory and applications of lattice theory.Trade Review"This nice book on lattices and their applications in computer science is written from the perspective of a computer scientist rather than a mathematician...Given its emphasis on algorithms and their complexity, it seems to be mainly intended for students of computer science and engineering. The author's approach is based on the premise that a student needs to learn the heuristics that guide the proofs, besides the proofs themselves, and to learn ways to extend and analyze theorems...One of the most important and valuable features of the book is its focus on applications of lattice theory. The author intends to treat applications on par with the theory." Altogether a "lovely book". (Mathematical Reviews/MathSciNet April 2017)Table of ContentsList of Figures xiii Nomenclature xv Preface xvii 1 Introduction 1 1.1 Introduction 1 1.2 Relations 2 1.3 Partial Orders 3 1.4 Join and Meet Operations 5 1.5 Operations on Posets 7 1.6 Ideals and Filters 8 1.7 Special Elements in Posets 9 1.8 Irreducible Elements 10 1.9 Dissector Elements 11 1.10 Applications: Distributed Computations 11 1.11 Applications: Combinatorics 12 1.12 Notation and Proof Format 13 1.13 Problems 15 1.14 Bibliographic Remarks 15 2 Representing Posets 17 2.1 Introduction 17 2.2 Labeling Elements of The Poset 17 2.3 Adjacency List Representation 18 2.4 Vector Clock Representation 20 2.5 Matrix Representation 22 2.6 Dimension-Based Representation 22 2.7 Algorithms to Compute Irreducibles 23 2.8 Infinite Posets 24 2.9 Problems 26 2.10 Bibliographic Remarks 27 3 Dilworth’s Theorem 29 3.1 Introduction 29 3.2 Dilworth’s Theorem 29 3.3 Appreciation of Dilworth’s Theorem 30 3.4 Dual of Dilworth’s Theorem 32 3.5 Generalizations of Dilworth’s Theorem 32 3.6 Algorithmic Perspective of Dilworth’s Theorem 32 3.7 Application: Hall’s Marriage Theorem 33 3.8 Application: Bipartite Matching 34 3.9 Online Decomposition of Posets 35 3.10 A Lower Bound on Online Chain Partition 37 3.11 Problems 38 3.12 Bibliographic Remarks 39 4 Merging Algorithms 41 4.1 Introduction 41 4.2 Algorithm to Merge Chains in Vector Clock Representation 41 4.3 An Upper Bound for Detecting an Antichain of Size K 47 4.4 A Lower Bound for Detecting an Antichain of Size K 48 4.5 An Incremental Algorithm for Optimal Chain Decomposition 50 4.6 Problems 50 4.7 Bibliographic Remarks 51 5 Lattices 53 5.1 Introduction 53 5.2 Sublattices 54 5.3 Lattices as Algebraic Structures 55 5.4 Bounding The Size of The Cover Relation of a Lattice 56 5.5 Join-Irreducible Elements Revisited 57 5.6 Problems 59 5.7 Bibliographic Remarks 60 6 Lattice Completion 61 6.1 Introduction 61 6.2 Complete Lattices 61 6.3 Closure Operators 62 6.4 Topped ∩-Structures 63 6.5 Dedekind–Macneille Completion 64 6.6 Structure of Dedekind--Macneille Completion of a Poset 67 6.7 An Incremental Algorithm for Lattice Completion 69 6.8 Breadth First Search Enumeration of Normal Cuts 71 6.9 Depth First Search Enumeration of Normal Cuts 73 6.10 Application: Finding the Meet and Join of Events 75 6.11 Application: Detecting Global Predicates in Distributed Systems 76 6.12 Application: Data Mining 77 6.13 Problems 78 6.14 Bibliographic Remarks 78 7 Morphisms 79 7.1 Introduction 79 7.2 Lattice Homomorphism 79 7.3 Lattice Isomorphism 80 7.4 Lattice Congruences 82 7.5 Quotient Lattice 83 7.6 Lattice Homomorphism and Congruence 83 7.7 Properties of Lattice Congruence Blocks 84 7.8 Application: Model Checking on Reduced Lattices 85 7.9 Problems 89 7.10 Bibliographic Remarks 90 8 Modular Lattices 91 8.1 Introduction 91 8.2 Modular Lattice 91 8.3 Characterization of Modular Lattices 92 8.4 Problems 98 8.5 Bibliographic Remarks 98 9 Distributive Lattices 99 9.1 Introduction 99 9.2 Forbidden Sublattices 99 9.3 Join-Prime Elements 100 9.4 Birkhoff’s Representation Theorem 101 9.5 Finitary Distributive Lattices 104 9.6 Problems 104 9.7 Bibliographic Remarks 105 10 Slicing 107 10.1 Introduction 107 10.2 Representing Finite Distributive Lattices 107 10.3 Predicates on Ideals 110 10.4 Application: Slicing Distributed Computations 116 10.5 Problems 117 10.6 Bibliographic Remarks 118 11 Applications of Slicing to Combinatorics 119 11.1 Introduction 119 11.2 Counting Ideals 120 11.3 Boolean Algebra and Set Families 121 11.4 Set Families of Size k 122 11.5 Integer Partitions 123 11.6 Permutations 127 11.7 Problems 129 11.8 Bibliographic Remarks 129 12 Interval Orders 131 12.1 Introduction 131 12.2 Weak Order 131 12.3 Semiorder 133 12.4 Interval Order 134 12.5 Problems 136 12.6 Bibliographic Remarks 137 13 Tractable Posets 139 13.1 Introduction 139 13.2 Series–Parallel Posets 139 13.3 Two-Dimensional Posets 142 13.4 Counting Ideals of a Two-Dimensional Poset 145 13.5 Problems 146 13.6 Bibliographic Remarks 147 14 Enumeration Algorithms 149 14.1 Introduction 149 14.2 BFS Traversal 150 14.3 DFS Traversal 154 14.4 LEX Traversal 154 14.5 Uniflow Partition of Posets 160 14.6 Enumerating Tuples of Product Spaces 163 14.7 Enumerating All Subsets 163 14.8 Enumerating All Subsets of Size k 165 14.9 Enumerating Young’s Lattice 166 14.10 Enumerating Permutations 167 14.11 Lexical Enumeration of All Order Ideals of a Given Rank 168 14.12 Problems 172 14.13 Bibliographic Remarks 173 15 Lattice of Maximal Antichains 159 15.1 Introduction 159 15.2 Maximal Antichain Lattice 161 15.3 An Incremental Algorithm Based on Union Closure 163 15.4 An Incremental Algorithm Based on BFS 165 15.5 Traversal of the Lattice of Maximal Antichains 166 15.6 Application: Detecting Antichain-Consistent Predicates 168 15.7 Construction and Enumeration of Width Antichain Lattice 169 15.8 Lexical Enumeration of Closed Sets 171 15.9 Construction of Lattices Based on Union Closure 174 15.10 Problems 174 15.11 Bibliographic Remarks 175 16 Dimension Theory 177 16.1 Introduction 177 16.2 Chain Realizers 178 16.3 Standard Examples of Dimension Theory 179 16.4 Relationship Between the Dimension and the Width of a Poset 180 16.5 Removal Theorems for Dimension 181 16.6 Critical Pairs in the Poset 182 16.7 String Realizers 184 16.8 Rectangle Realizers 193 16.9 Order Decomposition Method and Its Applications 194 16.10 Problems 196 16.11 Bibliographic Remarks 197 17 Fixed Point Theory 215 17.1 Complete Partial Orders 215 17.2 Knaster–Tarski Theorem 216 17.3 Application: Defining Recursion Using Fixed Points 218 17.4 Problems 226 17.5 Bibliographic Remarks 227 Bibliography 229 Index 235

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Book SynopsisProvides a comprehensive overview of wireless computing in medicine, with technological, medical, and legal advances This book brings together the latest work of leading scientists in the disciplines of Computing, Medicine, and Law, in the field of Wireless Health. The book is organized into three main sections. The first section discusses the use of distributed computing in medicine. It concentrates on methods for treating chronic diseases and cognitive disabilities like Alzheimer's, Autism, etc. It also discusses how to improve portability and accuracy of monitoring instruments and reduce the redundancy of data. It emphasizes the privacy and security of using such devices. The role of mobile sensing, wireless power and Markov decision process in distributed computing is also examined. The second section covers nanomedicine and discusses how the drug delivery strategies for chronic diseases can be efficiently improved by Nanotechnology enabled materials and devices suTable of ContentsContributors xiii Foreword xvii Preface xix PART I INTRODUCTION 1 1 Introduction to Wireless Computing in Medicine 3Amber Bhargava, Mary Mehrnoosh Eshaghian-Wilner, Arushi Gupta, Alekhya Sai Nuduru Pati, Kodiak Ravicz, and Pujal Trivedi 1.1 Introduction, 3 1.2 Definition of Terms, 5 1.3 Brief History of Wireless Healthcare, 5 1.4 What is Wireless Computing? 6 1.5 Distributed Computing, 7 1.6 Nanotechnology in Medicine, 10 1.7 Ethics of Medical Wireless Computing, 12 1.8 Privacy in Wireless Computing, 13 1.9 Conclusion, 14 References, 14 2 Nanocomputing and Cloud Computing 17T. Soren Craig, Mary Mehrnoosh Eshaghian-Wilner, Nikila Goli, Arushi Gupta, Shiva Navab, Alekhya Sai Nuduru Pati, Kodiak Ravicz, Gaurav Sarkar, and Ben Shiroma 2.1 Introduction, 17 2.2 Nanocomputing, 18 2.3 Cloud Computing, 30 2.4 Conclusion, 37 Acknowledgment, 37 References, 37 PART II PERVASIVE WIRELESS COMPUTING IN MEDICINE 41 3 Pervasive Computing in Hospitals 43Janet Meiling Wang-Roveda, Linda Powers, and Kui Ren 3.1 Introduction, 43 3.2 Architecture of Pervasive Computing in Hospitals, 45 3.3 Sensors, Devices, Instruments, and Embedded Systems, 49 3.4 Data Acquisition in Pervasive Computing, 59 3.5 Software Support for Context-Aware and Activity Sharing Services, 63 3.6 Data and Information Security, 66 3.7 Conclusion, 71 Acknowledgment, 71 References, 72 4 Diagnostic Improvements: Treatment and Care 79Xiaojun Xian 4.1 Introduction, 79 4.2 System Design, 81 4.3 Body Sensor Network, 82 4.4 Portable Sensors, 84 4.5 Wearable Sensors, 88 4.6 Implantable Sensors, 94 4.7 Wireless Communication, 95 4.8 Mobile Base Unit, 97 4.9 Conclusion and Challenges, 98 Acknowledgment, 99 References, 99 5 Collaborative Opportunistic Sensing of Human Behavior with Mobile Phones 107Luis A. Castro, Jessica Beltran-Marquez, Jesus Favela, Edgar Chavez, Moises Perez, Marcela Rodriguez, Rene Navarro, and Eduardo Quintana 5.1 Health and Mobile Sensing, 107 5.2 The InCense Sensing Toolkit, 110 5.3 Sensing Campaign 1: Detecting Behaviors Associated with the Frailty Syndrome Among Older Adults, 119 5.4 Sensing Campaign 2: Detecting Problematic Behaviors among Elders with Dementia, 123 5.5 Discussion, 131 5.6 Conclusions and Future Work, 132 References, 133 6 Pervasive Computing to Support Individuals with Cognitive Disabilities 137Monica Tentori, José Mercado, Franceli L. Cibrian, and Lizbeth Escobedo 6.1 Introduction, 137 6.2 Wearable and Mobile Sensing Platforms to Ease the Recording of Data Relevant to Clinical Case Assessment, 144 6.3 Augmented Reality and Mobile and Tangible Computing to Support Cognition, 151 6.4 Serious Games and Exergames to Support Motor Impairments, 158 6.5 Conclusions, 168 Acknowledgments, 172 References, 172 7 Wireless Power for Implantable Devices: A Technical Review 187Nikita Ahuja, Mary Mehrnoosh Eshaghian-Wilner, Zhuochen Ge, Renjun Liu, Alekhya Sai Nuduru Pati, Kodiak Ravicz, Mike Schlesinger, Shu Han Wu, and Kai Xie 7.1 Introduction, 187 7.2 History of Wireless Power, 189 7.3 Approach of Wireless Power Transmission, 191 7.4 A Detailed Example of Magnetic Coupling Resonance, 194 7.5 Popular Standards, 199 7.6 Wireless Power Transmission in Medical use, 201 7.7 Conclusion, 204 Acknowledgments, 205 References, 205 8 Energy-Efficient Physical Activity Detection in Wireless Body Area Networks 211Daphney-Stavroula Zois, Sangwon Lee, Murali Annavaram, and Urbashi Mitra 8.1 Introduction, 211 8.2 Knowme Platform, 215 8.3 Energy Impact of Design Choices, 217 8.4 Problem Formulation, 228 8.5 Sensor Selection Strategies, 232 8.6 Alternative Problem Formulation, 237 8.7 Sensor Selection Strategies for the Alternative Formulation, 241 8.8 Experiments, 244 8.9 Related Work, 254 8.10 Conclusion, 256 Acknowledgments, 257 References, 257 9 Markov Decision Process for Adaptive Control of Distributed Body Sensor Networks 263Shuping Liu, Anand Panangadan, Ashit Talukder, and Cauligi S. Raghavendra 9.1 Introduction, 263 9.2 Rationale for MDP Formulation, 265 9.3 Related Work, 268 9.4 Problem Statement, Assumptions, and Approach, 269 9.5 MDP Model for Multiple Sensor Nodes, 272 9.6 Communication, 274 9.7 Simulation Results, 276 9.8 Conclusions, 292 Acknowledgment, 294 References, 294 PART III NANOSCALE WIRELESS COMPUTING IN MEDICINE 297 10 An Introduction to Nanomedicine 299Amber Bhargava, Janet Cheung, Mary Mehrnoosh Eshaghian-Wilner, Wan Lee, Kodiak Ravicz, Mike Schlesinger, Yesha Shah, and Abhishek Uppal 10.1 Introduction, 299 10.2 Nanomedical Technology, 301 10.3 Detection, 303 10.4 Treatment, 305 10.5 Biocompatibility, 309 10.6 Power, 311 10.7 Computer Modeling, 313 10.8 Research Institutions, 315 10.9 Conclusion, 317 Acknowledgments, 317 References, 317 11 Nanomedicine Using Magneto-Electric Nanoparticles 323Mary Mehrnoosh Eshaghian-Wilner, Andrew Prajogi, Kodiak Ravicz, Gaurav Sarkar, Umang Sharma, Rakesh Guduru, and Sakhrat Khizroev 11.1 Introduction, 323 11.2 Overview of MENs, 324 11.3 Experiment 1: Externally Controlled On-Demand Release of Anti-HIV Drug Azttp Using Mens as Carriers, 325 11.4 Experiment 2: Mens to Enable Field-Controlled High-Specificity Drug Delivery to Eradicate Ovarian Cancer Cells, 331 11.5 Experiment 3: Magnetoelectric “Spin” on Stimulating the Brain, 339 11.6 Bioceramics: Bone Regeneration and MNS, 348 11.7 Conclusion, 351 References, 353 12 DNA Computation in Medicine 359Noam Mamet and Ido Bachelet 12.1 Background for the Non-Biologist, 359 12.2 Introduction, 362 12.3 In Vitro Computing, 364 12.4 Computation in Vivo, 370 12.5 Challenges, 373 12.6 Glimpse into the Future, 373 References, 374 13 Graphene-Based Nanosystems for the Detection of Proteinic Biomarkers of Disease: Implication in Translational Medicine 377Farid Menaa, Sandeep Kumar Vashist, Adnane Abdelghani, and Bouzid Menaa 13.1 Introduction, 377 13.2 Structural and Physicochemical Properties of Graphene and Main Derivatives, 379 13.3 Graphene and Derivatives-Based Biosensing Nanosystems and Applications, 382 13.4 Conclusion and Perspectives, 389 Conflict of Interest, 390 Abbreviations, 390 References, 391 14 Modeling Brain Disorders in Silicon Nanotechnologies 401Alice C. Parker, Saeid Barzegarjalali, Kun Yue, Rebecca Lee, and Sukanya Patil 14.1 Introduction, 401 14.2 The BioRC Project, 402 14.3 Background: BioRC Neural Circuits, 404 14.4 Modeling Synapses with CNT Transistors, 408 14.5 Modeling OCD with Hybrid CMOS/Nano Circuits, 410 14.6 The Biological Cortical Neuron and Hybrid Electronic Cortical Neuron, 411 14.7 Biological OCD Circuit and Biomimetic Model, 412 14.8 Indirect Pathway: The Braking Mechanism, 413 14.9 Direct Pathway: The Accelerator, 414 14.10 Typical and Atypical Responses, 415 14.11 Modeling Schizophrenic Hallucinations with Hybrid CMOS/Nano Circuits, 416 14.12 Explanation for Schizophrenia Symptoms, 416 14.13 Disinhibition due to Miswiring, 418 14.14 Our Hybrid Neuromorphic Prediction Network, 418 14.15 Simulation Results, 419 14.16 Numerical Analysis of False Firing, 421 14.17 Modeling PD with CMOS Circuits, 422 14.18 Modeling MS with CMOS Circuits, 424 14.19 Demyelination Circuit, 425 14.20 Conclusions and Future Trends, 426 References, 428 15 Linking Medical Nanorobots to Pervasive Computing 431Sylvain Martel 15.1 Introduction, 431 15.2 Complementary Functionalities, 432 15.3 Main Specifications for such Nanorobotic Agents (Nanorobots), 433 15.4 Medical Nanorobotic Agents—An Example, 436 15.5 Nanorobotic Communication Links Allowing Pervasive Computing, 438 15.6 Types of Information, 439 15.7 Medical Nanorobotic Agents for Monitoring and Early Detection, 440 15.8 Medical Nanorobotics and Pervasive Computing—Main Conditions that must be met for its Feasibility, 442 15.9 Conclusion, 443 References, 444 16 Nanomedicine’s Transversality: Some Implications of the Nanomedical Paradigm 447José J. López and Mathieu Noury 16.1 Introduction, 447 16.2 Nanomedicine’s Promises, 448 16.3 Analysing Implications of the Nanomedicine Paradigm, 451 16.4 The Molecular Underpinnings of Nanomedicine’s Transversality, 456 16.5 Nanomedicine as Predictive Medicine, 457 16.6 Nanomedicine as Personalized Medicine, 460 16.7 Nanomedicine as Regenerative Medicine, 465 16.8 Conclusion, 466 References, 468 PART IV ETHICAL AND LEGAL ASPECTS OF WIRELESS COMPUTING IN MEDICINE 473 17 Ethical Challenges of Ubiquitous Health Care 475William Sims Bainbridge 17.1 Introduction, 475 17.2 A Philosophical Framework, 478 17.3 Information Deviance, 480 17.4 The Current Frenzy, 482 17.5 Genetic Informatics, 485 17.6 Ubiquitous Information Technology, 489 17.7 Stasis versus Progress, 492 17.8 Problematic Ethics, 494 17.9 Leadership in Science and Engineering Ethics, 496 17.10 Conclusion, 498 References, 499 18 The Ethics of Ubiquitous Computing in Health Care 507Clark A. Miller, Heather M. Ross, Gaymon Bennett, and J. Benjamin Hurlbut 18.1 Introduction, 507 18.2 Ubiquitous Computing and the Transformation of Health Care: Three Visions, 511 18.3 Case Study: Cardiac Implanted Electrical Devices, 516 18.4 Ethical Reflections, 521 18.5 Conclusions: The Need for Socio-Technical Design, 534 References, 537 19 Privacy Protection of Electronic Healthcare Records in e-Healthcare Systems 541Fredrick Japhet Mtenzi 19.1 Introduction, 541 19.2 Security and Privacy Concerns of EHR in e-Healthcare Systems, 545 19.3 Privacy Laws and Regulations of EHRs, 547 19.4 Privacy of EHRs in e-Healthcare Systems, 552 19.5 Discussion and Conclusion, 558 19.6 Contributions and Future Research, 559 References, 561 20 Ethical, Privacy, and Intellectual Property Issues in Nanomedicine 567Katie Atalla, Ayush Chaudhary, Mary Mehrnoosh Eshaghian-Wilner, Arushi Gupta, Raj Mehta, Adarsh Nayak, Andrew Prajogi, Kodiak Ravicz, Ben Shiroma, and Pujal Trivedi 20.1 Introduction, 567 20.2 Ethical Issues, 568 20.3 Privacy Issues, 579 20.4 IP Issues, 590 20.5 Conclusion, 596 Acknowledgments, 596 References, 596 PART V CONCLUSION 601 21 Concluding Remarks 603Zhaoqi Chen, Mary Mehrnoosh Eshaghian-Wilner, Kalyani Gonde, Kodiak Ravicz, Rakshith Saligram and Mike Schlesinger 21.1 Wireless Computing in Health Care, 603 21.2 Nanomedicine, 606 21.3 Ethical, Privacy, and Intellectual Property Issues of Nanomedicine and Wireless Computing, 609 21.4 Conclusions, 610 Acknowledgments, 610 References, 610 Index 613

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Book SynopsisData as a Service shows how organizations can leverage data as a service by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce big data as a service' for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions Table of ContentsGuest Introduction – Sanjoy Paul xiii Guest Introduction – Christopher Surdak xv Preface (Includes the Reader’s Guide) xvii Acknowledgments xxvii Part One Overview of Fundamental Concepts 1. Introduction to DaaS 3 Topics Covered in this Chapter 3 Data-Driven Enterprise 4 Defining a Service 6 Drivers for Providing Data as a Service 7 Data as a Service Framework: A Paradigm Shift 12 2. DaaS Strategy and Reference Architecture 25 Topics Covered in this Chapter 25 Enterprise Data Strategy, Goals, and Principles 26 Critical Success Factors 28 Reference Architecture of the DaaS Framework 30 How to leverage the DaaS Reference Architecture 41 Summary 41 3. Data Asset Management 43 Topics Covered in this Chapter 43 Introduction to Major Categories of Enterprise Data 46 Transaction Data (Includes Big Data) 54 Significance of EIM in Supporting the DaaS Program 56 Role of Enterprise Data Architect 57 Summary 59 Part Two DaaS Architecture Framework and Components 4. Enterprise Data Services 63 Topics Covered in this Chapter 63 Emergence of Enterprise Data Services 64 Need for an Enterprise Perspective 65 Emergence of Enterprise Data Services 66 Publication of Enterprise Data 69 Interdependencies between DaaS, EIM, and SOA 73 Case Study: Amazon’s Adoption of Public Data Service Interfaces 76 Summary 79 5. Enterprise and Canonical Modeling 80 Topics Covered in this Chapter 80 A Model-Driven Approach Toward Developing Reusable Data Services 81 Defining a Standards-Driven Approach toward Developing New Data Services 82 Role of the Enterprise Data Model 83 Developing the Canonical Model 84 Enterprise Data Model 85 Canonical Model 85 Implementing the Canonical Model 89 Publishing Data Services with the Canonical Model as a Foundation 93 Implementing the Canonical Model in Real-life Projects 95 Data Services Roll Out and Future Releases 97 Case Study: DaaS in Real Life, Electronic-Data Interchange in U.S. Healthcare Exchanges 98 Summary 102 6. Business Glossary for DaaS 103 Topics Covered in this Chapter 103 Problem of Meaning and the Case for a Shared Business Glossary 104 Using Metadata in Various Disciplines 106 Role of an Organization’s Business Glossary 108 Enterprise Metadata Repository 113 Implementing the Enterprise Metadata Repository 115 Metadata Standards for Enterprise Data Services 116 Metadata Governance 121 Summary 121 7. SOA and Data Integration 123 Topics Covered in this Chapter 123 SOA as an Enabler of Data Integration 124 Role of Enterprise Service Bus 127 What is a Data Service? 128 Foundational Components of a Data Service 131 Service Interface 133 Major Service Categories 133 Overview of Data Virtualization 136 Consolidated Data Infrastructure Platform 143 Summary 145 8. Data Quality and Standards 146 Topics Covered in this Chapter 146 Where to Begin Data Standardization Efforts in Your Organization 150 Role of Data Discovery/Profiling to Identify DaaS Quality Issues 152 Data Quality and the Investment Paradox 156 Quality of a Data Service 157 Setting Up Standards in a DaaS Environment 158 Summary 163 Part Three DaaS Solution Blueprints 9. Reference Data Services 167 Topics Covered in this Chapter 167 Delivering Market and Reference Data Using Real-Time Data Services 169 Comparing Usage of Reference Data Against Master Data 171 Understanding Challenges of Reference Data Management 173 Other Reference Data Management Challenges 174 Role of Reference Data Standards and Vocabulary Management 177 Collaborative Reference Data Management Implementation Using Business Process Management/Workflow 180 Summary 185 10. Master Data Services 187 Topics Covered in this Chapter 187 Introduction to Master Data Services 188 Pros and Cons of Master Data Services (Virtual Master Data Management) 192 Leveraging the Golden Source to Resolve Deep-Rooted Source Differences 193 Future Trends in Master Data Management Using DaaS 194 Comparing Master Data Services Approach (Virtual) with Master Data Management Approach Involving Physical Consolidation 196 Case Study: Master Data Services for a Premier Investment Bank 197 Detailed Scope and Benefits 198 Proposed Solution Architecture for Master Data Services 199 Enterprise and Canonical Model for Master Data Management Implementation 202 Summary 208 11. Big Data and Analytical Services 210 Topics Covered in this Chapter 210 Big Data 212 Big Data Analytics 213 Relationship Between DaaS and Big Data Analytics 217 Future Impact of DaaS on Big Data Analytics 220 Extending DaaS Reference Architecture for Big Data and Cloud Services 221 Fostering an Enterprise Data Mindset 228 Case Study: Big DaaS in the Automotive Industry 231 Summary 233 Part Four Ensuring Organizational Success 12. DaaS Governance Framework 237 Topics Covered in this Chapter 237 Role of Data Governance 238 Data Governance 240 People Governance 245 Process Governance 248 Service Governance 253 Technology Governance 258 Summary 261 13. Securing the DaaS Environment 262 Topics Covered in this Chapter 262 Impact of Data Breach on DaaS Operations 263 Major Security Considerations for DaaS 264 Multilayered Security for the DaaS Environment 266 Identity and Access Management 270 Data Entitlements to Safeguard Privacy 271 Impact of Increased Privacy Regulations on Data Providers 272 Information Risk Management 273 Important Data Security and Privacy Regulations that Impact DaaS 275 Checklist to Protect Data Providers from Data Breaches 277 Summary 278 14. Taking DaaS from Concept to Reality 280 Topics Covered in this Chapter 280 Service Performance Measurement Using the Balanced Scorecard 284 Implementing the Performance Scorecard to Improve Data Services 286 Embarking on the DaaS Journey with a Vision 287 Using AGILE Principles for New Data Services Development 290 Sustaining DaaS in an Organization: How to Keep the Program Going 292 In Conclusion 295 Appendix A Data Standards Initiatives and Resources 297 Appendix B Data Privacy & Security Regulations 305 Appendix C Terms and Acronyms 309 Appendix D Bibliography 312 Index 315

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Book SynopsisThis book deals with malware detection in terms of Artificial Immune System (AIS), and presents a number of AIS models and immune-based feature extraction approaches as well as their applications in computer security Covers all of the current achievements in computer security based on immune principles, which were obtained by the Computational Intelligence Laboratory of Peking University, China Includes state-of-the-art information on designing and developing artificial immune systems (AIS) and AIS-based solutions to computer security issues Presents new concepts such as immune danger theory, immune concentration, and class-wise information gain (CIG) Table of ContentsPreface xiii About Author xxi Acknowledgements xxiii 1 Artificial Immune System 1 1.1 Introduction 1 1.2 Biological Immune System 2 1.2.1 Overview 2 1.2.2 Adaptive Immune Process 3 1.3 Characteristics of BIS 4 1.4 Artificial Immune System 6 1.5 AIS Models and Algorithms 8 1.5.1 Negative Selection Algorithm 8 1.5.2 Clonal Selection Algorithm 9 1.5.3 Immune Network Model 11 1.5.4 Danger Theory 12 1.5.5 Immune Concentration 13 1.5.6 Other Methods 14 1.6 Characteristics of AIS 15 1.7 Applications of Artificial Immune System 16 1.7.1 Virus Detection 16 1.7.2 Spam Filtering 16 1.7.3 Robots 20 1.7.4 Control Engineering 21 1.7.5 Fault Diagnosis 22 1.7.6 Optimized Design 22 1.7.7 Data Analysis 22 1.8 Summary 22 2 Malware Detection 27 2.1 Introduction 27 2.2 Malware 28 2.2.1 Definition and Features 28 2.2.2 The Development Phases of Malware 29 2.3 Classic Malware Detection Approaches 30 2.3.1 Static Techniques 31 2.3.2 Dynamic Techniques 31 2.3.3 Heuristics 32 2.4 Immune Based Malware Detection Approaches 34 2.4.1 An Overview of Artificial Immune System 34 2.4.2 An Overview of Artificial Immune System for Malware Detection 35 2.4.3 An Immune Based Virus Detection System Using Affinity Vectors 36 2.4.4 A Hierarchical Artificial Immune Model for Virus Detection 38 2.4.5 A Malware Detection Model Based on a Negative Selection Algorithm with Penalty Factor 2.5 Summary 43 3 Immune Principle and Neural Networks Based Malware Detection 47 3.1 Introduction 47 3.2 Immune System for Malicious Executable Detection 48 3.2.1 Non-self Detection Principles 48 3.2.2 Anomaly Detection Based on Thickness 48 3.2.3 Relationship Between Diversity of Detector Representation and Anomaly Detection Hole 48 3.3 Experimental Dataset 48 3.4 Malware Detection Algorithm 49 3.4.1 Definition of Data Structures 49 3.4.2 Detection Principle and Algorithm 49 3.4.3 Generation of Detector Set 50 3.4.4 Extraction of Anomaly Characteristics 50 3.4.5 Classifier 52 3.5 Experiment 52 3.5.1 Experimental Procedure 53 3.5.2 Experimental Results 53 3.5.3 Comparison With Matthew G. Schultz’s Method 55 3.6 Summary 57 4 Multiple-Point Bit Mutation Method of Detector Generation 59 4.1 Introduction 59 4.2 Current Detector Generating Algorithms 60 4.3 Growth Algorithms 60 4.4 Multiple Point Bit Mutation Method 62 4.5 Experiments 62 4.5.1 Experiments on Random Dataset 62 4.5.2 Change Detection of Static Files 65 4.6 Summary 65 5 Malware Detection System Using Affinity Vectors 67 5.1 Introduction 67 5.2 Malware Detection Using Affinity Vectors 68 5.2.1 Sliding Window 68 5.2.2 Negative Selection 68 5.2.3 Clonal Selection 69 5.2.4 Distances 70 5.2.5 Affinity Vector 71 5.2.6 Training Classifiers with Affinity Vectors 71 5.3 Evaluation of Affinity Vectors based malware detection System 73 5.3.1 Dataset 73 5.3.2 Length of Data Fragment 73 5.3.3 Experimental Results 73 5.4 Summary 74 6 Hierarchical Artificial Immune Model 79 6.1 Introduction 79 6.2 Architecture of HAIM 80 6.3 Virus Gene Library Generating Module 80 6.3.1 Virus ODN Library 82 6.3.2 Candidate Virus Gene Library 82 6.3.3 Detecting Virus Gene Library 83 6.4 Self-Nonself Classification Module 84 6.4.1 Matching Degree between Two Genes 84 6.4.2 Suspicious Program Detection 85 6.5 Simulation Results of Hierarchical Artificial Immune Model 86 6.5.1 Data Set 86 6.5.2 Description of Experiments 86 6.6 Summary 89 7 Negative Selection Algorithm with Penalty Factor 91 7.1 Introduction 91 7.2 Framework of NSAPF 92 7.3 Malware signature extraction module 93 7.3.1 Malware Instruction Library (MIL) 93 7.3.2 Malware Candidate Signature Library 94 7.3.3 NSAPF and Malware Detection Signature Library 96 7.4 Suspicious Program Detection Module 97 7.4.1 Signature Matching 97 7.4.2 Matching between Suspicious Programs and the MDSL 97 7.4.3 Analysis of Penalty Factor 98 7.5 Experiments and Analysis 99 7.5.1 Experimental Datasets 99 7.5.2 Experiments on Henchiri dataset 100 7.5.3 Experiments on CILPKU08 Dataset 103 7.5.4 Experiments on VX Heavens Dataset 104 7.5.5 Parameter Analysis 104 7.6 Summary 105 8 Danger Feature Based Negative Selection Algorithm 107 8.1 Introduction 107 8.1.1 Danger Feature 107 8.1.2 Framework of Danger Feature Based Negative Selection Algorithm 107 8.2 DFNSA for Malware Detection 109 8.2.1 Danger Feature Extraction 109 8.2.2 Danger Feature Vector 110 8.3 Experiments 111 8.3.1 Datasets 111 8.3.2 Experimental Setup 111 8.3.3 Selection of Parameters 112 8.3.4 Experimental Results 113 8.4 Discussions 113 8.4.1 Comparison of Detecting Feature Libraries 113 8.4.2 Comparison of Detection Time 114 8.5 Summary 114 9 Immune Concentration Based Malware Detection Approaches 117 9.1 Introduction 117 9.2 Generation of Detector Libraries 117 9.3 Construction of Feature Vector for Local Concentration 122 9.4 Parameters Optimization based on Particle Swarm Optimization 124 9.5 Construction of Feature Vector for Hybrid Concentration 124 9.5.1 Hybrid Concentration 124 9.5.2 Strategies for Definition of Local Areas 126 9.5.3 HC-based Malware Detection Method 127 9.5.4 Discussions 128 9.6 Experiments 130 9.6.1 Experiments of Local Concentration 130 9.6.2 Experiments of Hybrid Concentration 138 9.7 Summary 142 10 Immune Cooperation Mechanism Based Learning Framework 145 10.1 Introduction 145 10.2 Immune Signal Cooperation Mechanism based Learning Framework 148 10.3 Malware Detection Model 151 10.4 Experiments of Malware Detection Model 152 10.4.1 Experimental setup 152 10.4.2 Selection of Parameters 153 10.4.3 Experimental Results 153 10.4.4 Statistical Analysis 155 10.5 Discussions 157 10.5.1 Advantages 157 10.5.2 Time Complexity 157 10.6 Summary 158 11 Class-wise Information Gain 161 11.1 Introduction 161 11.2 Problem Statement 163 11.2.1 Definition of the Generalized Class 163 11.2.2 Malware Recognition Problem 163 11.3 Class-wise Information Gain 164 11.3.1 Definition 164 11.3.2 Analysis 166 11.4 CIG-based Malware Detection Method 170 11.4.1 Feature Selection Module 170 11.4.2 Classification Module 171 11.5 Dataset 172 11.5.1 Benign Program Dataset 172 11.5.2 Malware Dataset 172 11.6 Selection of Parameter 174 11.6.1 Experimental Setup 174 11.6.2 Experiments of Selection of Parameter 174 11.7 Experimental Results 175 11.7.1 Experiments on the VXHeavens Dataset 177 11.7.2 Experiments on the Henchiri Dataset 179 11.7.3 Experiments on the CILPKU08 Dataset 180 11.8 Discussions 180 11.8.1 The Relationship Among IG-A, DFCIG-B and DFCIG-M 181 11.8.2 Space Complexity 182 11.9 Summary 183 Index 185

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