Information theory Books

Book SynopsisWhat does it mean to be a writer today? Is writing code for an app equivalent to writing a novel? Should we change how we teach writing? Computing as Writing ponders both the implications and contradictions of the common metaphor that equates computing and writing, from "notebook" computers to "writing" code.Trade Review"Daniel Punday traces the idea—an idea that he shows to be pervasive—that to control computers we typically engage in a sort of writing. This insight informs our understanding of computation in culture and also enriches our notion of writing generally. It should, additionally, help non-programmer humanists see that, since they have learned to write, they can learn to do that specific type of writing that is known as programming."—Nick Montfort, Massachusetts Institute of Technology"In a world in which the distinction between writing and computing is increasingly blurred, Punday's volume raises some intriguing questions and offers some new ways to look at writing and computing."—CHOICETable of ContentsContentsPreface1. My Documents: Remembering the Memex2. Writing, Work, and Profession3. Programmer as Writer4. E-books, Libraries, and Feelies5. Invention, Patents, and the Technological System6. Audience Today: Between Literature and PerformanceConclusion: Invention, Creativity, and the Teaching of WritingAcknowledgmentsNotesIndex

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Book SynopsisWritten in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.Trade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsContents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.

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Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this teTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 27. Mean-Square-Error inference; 28. Bayesian inference; 29. Linear regression; 30. Kalman filter; 31. Maximum likelihood; 32. Expectation maximization; 33. Predictive modeling; 34. Expectation propagation; 35. Particle filters; 36. Variational inference; 37. Latent Dirichlet allocation; 38. Hidden Markov models; 39. Decoding HMMs; 40. Independent component analysis; 41. Bayesian networks; 42. Inference over graphs; 43. Undirected graphs; 44. Markov decision processes; 45. Value and policy iterations; 46. Temporal difference learning; 47. Q-learning; 48. Value function approximation; 49. Policy gradient methods; Author index; Subject index.

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Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbookTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 50. Least-squares problems; 51. Regularization; 52. Nearest-neighbor rule; 53. Self-organizing maps; 54. Decision trees; 55. Naive Bayes classifier; 56. Linear discriminant analysis; 57. Principal component analysis; 58. Dictionary learning; 59. Logistic regression; 60. Perceptron; 61. Support vector machines; 62. Bagging and boosting; 63. Kernel methods; 64. Generalization theory; 65. Feedforward neural networks; 66. Deep belief networks; 67. Convolutional networks; 68. Generative networks; 69. Recurrent networks; 70. Explainable learning; 71. Adversarial attacks; 72. Meta learning; Author index; Subject index.

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Book SynopsisMaster the fundamentals of digital communications systems with this accessible and hands-on introductory textbook, carefully interweaving theory and practice. The just-in-time approach introduces essential background as needed, keeping academic theory firmly linked to practical applications. The example-led teaching frames key concepts in the context of real-world systems, such as 5G, WiFi, and GPS. Stark provides foundational material on the trade-offs between energy and bandwidth efficiency, giving students a solid grounding in the fundamental challenges of designing digital communications systems. Features include over 300 illustrative figures, 80 examples, and 130 end-of-chapter problems to reinforce student understanding, with solutions for instructors. Accompanied online by lecture slides, computational MATLAB and Python resources, and supporting data sets, this is the ideal introduction to digital communications for senior undergraduate and graduate students in electrical engineering.Trade Review'This book emphasizes the fundamentals of digital communication as well as its practice. It provides examples to enhance the understanding, and the many illustrations explain the basic concepts very well. Several concepts from actual engineering practice are discussed in detail.' Ender Ayanoglu, University of California, Irvine'Wayne Stark is a widely respected researcher in digital communications, as well as a dedicated and talented teacher. This book reflects his years of experience teaching a challenging and rapidly changing subject to senior undergraduate and first-year graduate students. His choice of topics and careful balance between theory and practice ensure that this book will be a valuable resource in electrical engineering curricula for years to come.' Tom Fuja, University of Notre Dame'This self-contained book is excellent for a first course in digital communications. It strikes a perfect balance in theory, practice, and insights, so that a beginner can get a good understanding without getting lost in advanced mathematical concepts.' Sudharman K. Jayaweera, University of New Mexico'This is an extraordinary textbook on digital communication theory and practices. Key results are derived step by step, and it provides many examples and figures that help students grasp key concepts. I wish it had been available when I was a student.' Sang Wu Kim, Iowa State University'Not only is this textbook comprehensive and well written, it is mathematically rigorous. The specific numerical examples and practical applications enhance the theoretical derivations. The author does an excellent job of communicating the importance of each result, making it an appropriate textbook for senior undergraduates taking a solid course in the theory of digital communications.' Laurence B. Milstein, University of California, San Diego'I enjoyed this book's clarity and logical presentation. It is easy to read, balancing mathematical fundamentals with practical applications, problem sets, and examples. I'd be delighted to use it when teaching my undergraduate course on Communication Systems and Principles. This concise resource provides a thorough foundation on digital communication concepts, systems, and techniques, explaining communication systems in general and digital communications specifically.' Lina Mohjazi, University of Glasgow'The real jewel of the book is the introduction chapter. It lays out the most important design considerations and trade-offs at a high (but not superficial) level straightaway, serving as a roadmap to the material in the rest of the book. It is the best and most useful introduction chapter that no one should skip!' Tan F. Wong, University of Florida'This is an excellent textbook for students, communications engineers, and researchers alike. Based on many years' teaching experience, it includes detailed and illustrative examples that help students understand the fundamentals of digital communications. Professor Stark explains the trade-offs of different key parameters in digital communications, and covers state-of-the-art technologies such as LDPC codes. Each chapter contains clear goals, summaries, and useful exercises.' Xiang-Gen Xia, University of DelawareTable of ContentsContents; Preface; Acknowledgement; List of abbreviations; 1. Fundamentals of digital communications; 2. Modulation and demodulation; 3. Probability, random variables, random processes, signal bandwidth; 4. Error probability for binary signals; 5. Optimal receivers for M-ary communication; 6. Modulation techniques; 7. Wireless channels and transmission techniques; 8. Block codes; 9. Convolutional codes; Appendix A. Pseudorandom sequences; Appendix B. Trigonometric and fourier transform iIdentities; Appendix C. Finite fields and BCH codes; Appendix D. Simulation of signals and noise; References; Index.

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Book SynopsisBased on the authors' own teaching experience, this text introduces random graphs and networks, covering all the basic features before discussing the growth and structure of real-world networks. It can be used as a textbook for a one-semester course at advanced undergraduate or graduate level.Trade Review'Random Graphs and Networks: A First Course' is a wonderful textbook that covers a remarkable set of topics written by two leading experts in the field. The textbook is comprehensive and contains a wealth of theoretical preliminaries, exercises and problems, making it ideal for an introductory course or for self-study. It is the best starting point in the present textbook market for any university student interested in the foundations of network science.' Charalampos E. Tsourakakis, Boston UniversityTable of ContentsConventions/Notation; Part I. Preliminaries: 1. Introduction; 2. Basic tools; Part II. Erdos–Rényi–Gilbert Model: 3. Uniform and binomial random graphs; 4. Evolution; 5. Vertex degrees; 6. Connectivity; 7. Small subgraphs; 8. Large subgraphs; 9. Extreme characteristics; Part III. Modeling Complex Networks: 10. Inhomogeneous graphs; 11. Small world; 12. Network processes; 13. Intersection graphs; 14. Weighted graphs; References; Author index; Main index.

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Book SynopsisUncertainty Quantification (UQ) is an emerging and extremely active research discipline which aims to quantitatively treat any uncertainty in applied models. The primary objective of Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications is to present a comprehensive treatment of UQ in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields.Features First book on UQ in variational inequalities emerging from various network, economic, and engineering models Completely self-contained and lucid in style Aimed for a diverse audience including applied mathematicians, engineers, economists, and professionals from academia Includes the most recent developments on the subject which so far have only been

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Book SynopsisA concise, easy-to-read guide, introducing beginners to the engineering background of modern communication systems, from mobile phones to data storage. Assuming only basic knowledge of high-school mathematics and including many practical examples and exercises to aid understanding, this is ideal for anyone who needs a quick introduction to the subject.Trade Review'The book is nicely written, and is recommended as a textbook for a one-semester introductory course on coding and information theory.' Pushpa N. Rathie, Zentralblatt MATHTable of Contents1. Introduction Chung-Hsuan Wang; 2. Error-detecting codes Chung-Hsuan Wang; 3. Repetition and hamming codes Francis Lu; 4. Data compression: efficient coding of a random message; 5. Entropy and Shannon's source coding theorem; 6. Mutual information and channel capacity Jwo-Yuh Wu; 7. Achieving the Shannon limit by turbo coding; 8. Other aspects of coding theory Francis Lu.

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Book SynopsisProbability theory has been extraordinarily successful at describing a variety of phenomena, from the behaviour of gases to the transmission of messages, and is, besides, a powerful tool with applications throughout mathematics. At its heart are a number of concepts familiar in one guise or another to many: Gauss' bell-shaped curve, the law of averages, and so on, concepts that crop up in so many settings they are in some sense universal. This universality is predicted by probability theory to a remarkable degree. This book explains that theory and investigates its ramifications. Assuming a good working knowledge of basic analysis, real and complex, the author maps out a route from basic probability, via random walks, Brownian motion, the law of large numbers and the central limit theorem, to aspects of ergodic theorems, equilibrium and nonequilibrium statistical mechanics, communication over a noisy channel, and random matrices. Numerous examples and exercises enrich the text.Trade Review'… packs a great deal of material into a moderate-sized book, starting with a synopsis of measure theory and ending with a taste of current research into random matrices and number theory. The book ranges more widely than the title might suggest … There are numerous exercises sprinkled throughout the book. Most of these are exhortations to fill in details left out of the main discussion or illustrative examples. The exercises are a natural part of the book, unlike the exercises in so many books that were apparently grafted on after-the-fact at a publisher's insistence. McKean has worked in probability and related areas since obtaining his PhD under William Feller in 1955. His book contains invaluable insights from a long career.' John D. Cook, MAA Reviews'The scope is wide, not restricted to 'elementary facts' only. There is an abundance of pretty details … This book is highly recommendable …' Jorma K. Merikoski, International Statistical ReviewTable of ContentsPreface; 1. Preliminaries; 2. Bernoulli trials; 3. The standard random walk; 4. The standard random walk in higher dimensions; 5. LLN, CLT, iterated log, and arcsine in general; 6. Brownian motion; 7. Markov chains; 8. The ergodic theorem; 9. Communication over a noisy channel; 10. Equilibrium statistical mechanics; 11. Statistical mechanics out of equilibrium; 12. Random matrices; Bibliography; Index.

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Book SynopsisThis new edition of Wilde's popular book promises over 100 pages of new material, exercises and references. New attention is given to the derivation of the Choi-Kraus theorem for quantum channels, the CHSH game, quantum relative entropy, and sequential decoding. The text offers an ideal entry point into the topic for graduate students.Trade Review'For years, I have been hoping that somebody would write a book on quantum information theory that was clear, comprehensive, and up to date. This is that book. And the second edition is even better than the first.' Peter Shor, Massachusetts Institute of Technology'Mark M. Wilde's Quantum Information Theory is a natural expositor's labor of love. Accessible to anyone comfortable with linear algebra and elementary probability theory, Wilde's book brings the reader to the forefront of research in the quantum generalization of Shannon's information theory. What had been a gaping hole in the literature has been replaced by an airy edifice, scalable with the application of reasonable effort and complete with fine vistas of the landscape below. Wilde's book has a permanent place not just on my bookshelf but on my desk.' Patrick Hayden, Stanford University, CaliforniaReview of previous edition: '… [its] clear, thorough, and above all self-contained presentation will aid quantum information researchers in coming up to speed with the latest results in this area of the field. Meanwhile, the familiar setting and language will help classical information theorists who wish to become more acquainted with the quantum aspects of information processing … The presentation is well-structured, making it easy to jump to the desired topic and quickly determine on what that topic depends and how it is used going forward … Quantum Information Theory fills an important gap in the existing literature and will, I expect, help propagate the latest and greatest results in quantum Shannon theory to both quantum and classical researchers.' Joseph M. Renes, Quantum Information ProcessingReview of previous edition: '… a modern self-contained text … suitable for graduate-level courses leading up to research level.' Journal of Discrete Mathematical Sciences and CryptographyReview of previous edition: '… the book does a phenomenal job of introducing, developing and nurturing a mathematical sense of quantum information processing … In a nutshell, this is an essential reference for students and researchers who work in the area or are trying to understand what it is that quantum information theorists study. Wilde, as mentioned in his book, beautifully illustrates 'the ultimate capability of noisy physical systems, governed by the laws of quantum mechanics, to preserve information and correlations' through this book. I would strongly recommend it to anyone who plans to continue working in the field of quantum information.' Subhayan Roy Moulick, SIGCAT NewsTable of ContentsPreface to the second edition; Preface to the first edition; How to use this book; Part I. Introduction: 1. Concepts in quantum Shannon theory; 2. Classical Shannon theory; Part II. The Quantum Theory: 3. The noiseless quantum theory; 4. The noisy quantum theory; 5. The purified quantum theory; Part III. Unit Quantum Protocols: 6. Three unit quantum protocols; 7. Coherent protocols; 8. Unit resource capacity region; Part IV. Tools of Quantum Shannon Theory: 9. Distance measures; 10. Classical information and entropy; 11. Quantum information and entropy; 12. Quantum entropy inequalities and recoverability; 13. The information of quantum channels; 14. Classical typicality; 15. Quantum typicality; 16. The packing lemma; 17. The covering lemma; Part V. Noiseless Quantum Shannon Theory: 18. Schumacher compression; 19. Entanglement manipulation; Part VI. Noisy Quantum Shannon Theory: 20. Classical communication; 21. Entanglement-assisted classical communication; 22. Coherent communication with noisy resources; 23. Private classical communication; 24. Quantum communication; 25. Trading resources for communication; 26. Summary and outlook; Appendix A. Supplementary results; Appendix B. Unique linear extension of a quantum physical evolution; References; Index.

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Book SynopsisProbability theory has been extraordinarily successful at describing a variety of phenomena, from the behaviour of gases to the transmission of messages, and is, besides, a powerful tool with applications throughout mathematics. At its heart are a number of concepts familiar in one guise or another to many: Gauss' bell-shaped curve, the law of averages, and so on, concepts that crop up in so many settings they are in some sense universal. This universality is predicted by probability theory to a remarkable degree. This book explains that theory and investigates its ramifications. Assuming a good working knowledge of basic analysis, real and complex, the author maps out a route from basic probability, via random walks, Brownian motion, the law of large numbers and the central limit theorem, to aspects of ergodic theorems, equilibrium and nonequilibrium statistical mechanics, communication over a noisy channel, and random matrices. Numerous examples and exercises enrich the text.Trade Review'… packs a great deal of material into a moderate-sized book, starting with a synopsis of measure theory and ending with a taste of current research into random matrices and number theory. The book ranges more widely than the title might suggest … There are numerous exercises sprinkled throughout the book. Most of these are exhortations to fill in details left out of the main discussion or illustrative examples. The exercises are a natural part of the book, unlike the exercises in so many books that were apparently grafted on after-the-fact at a publisher's insistence. McKean has worked in probability and related areas since obtaining his PhD under William Feller in 1955. His book contains invaluable insights from a long career.' John D. Cook, MAA Reviews'The scope is wide, not restricted to 'elementary facts' only. There is an abundance of pretty details … This book is highly recommendable …' Jorma K. Merikoski, International Statistical ReviewTable of ContentsPreface; 1. Preliminaries; 2. Bernoulli trials; 3. The standard random walk; 4. The standard random walk in higher dimensions; 5. LLN, CLT, iterated log, and arcsine in general; 6. Brownian motion; 7. Markov chains; 8. The ergodic theorem; 9. Communication over a noisy channel; 10. Equilibrium statistical mechanics; 11. Statistical mechanics out of equilibrium; 12. Random matrices; Bibliography; Index.

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Book SynopsisNetworks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students'' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.Trade Review'A First Course in Network Science by Menczer, Fortunato, and Davis is an easy-to-follow introduction into network science. An accessible text by some of the best-known practitioners of the field, offering a wonderful place to start one's journey into this fascinating field, and its potential applications.' Albert-László Barabási, Dodge Distinguished Professor of Network Science, Northeastern University'… this textbook has finally allowed me to teach the ideal intro courses on network science, of interest to computer scientists as well as mathematicians, statisticians, economists, sociologists, and physicists.' Giancarlo Ruffo, Associate Professor of Computer Science, University of Torino'The book by Menczer, Fortunato, and Davis, A First Course in Network Science, is an amazing tour de force in bringing network science concepts to the layman. It is an extraordinary book with which to start thinking about networks that nowadays represent the linchpins of our world.' Alex Arenas, Universidad Rovira i Virgili'Buckle up! This book bounds ahead of the curve in teaching network science. Without formalism, but with remarkable clarity and insight, the authors use experiential learning to animate concepts, captivate students, and deliver skills for analyzing and simulating network data. This book will not only make students smarter, they will feel and act smarter.' Brian Uzzi, Northwestern University'If you are looking for a sophisticated yet introductory book on network analysis from a network science perspective, look no further. This is an excellent introduction that is also eminently practical, integrating exactly the right set of tools. I highly recommend it.' Stephen Borgatti, University of Kentucky'This is a book that truly takes in hand students from all backgrounds to discover the power of network science. It guides the readers through the basic concepts needed to enter the field, while providing at the same time the necessary programming rudiments and tools. Rigorous, albeit very accessible, this book is the ideal starting point for any student fascinated by the emerging field of network science.' Alessandro Vespignani, Northeastern University'We cannot make sense of the world without learning about networks. This comprehensive and yet accessible text is an essential resource for all interested in mastering the basics of network science. Indispensable for undergraduate and graduate education, the book is also a much-needed primer for researchers across the many disciplines where networks are on the rise.' Olaf Sporns, Indiana University'This is a timely book that comes from authorities in the field of Complex Networks. The book is very well written and represents the state of the art of research in the field. For these reasons, it represents both a reference guide for experts and a great textbook for the students.' Guido Caldarelli, Scuola IMT Alti Studi Lucca'Should be titled the 'Joy of Networks', clearly conveys the fun and power of the science of networks, while providing extensive hands-on exercises with network data.' David Lazer, University Distinguished Professor of Political Science and Computer and Information Science, Northeastern UniversityTable of ContentsPreface; Introduction; 1. Network elements; 2. Small worlds; 3. Hubs; 4. Directions and weights; 5. Network models; 6. Communities; 7. Dynamics; Appendix A. Python tutorial; Appendix B. NetLogo models; Bibliography; Index.

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Book SynopsisWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for gradTrade Review'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATHTable of Contents1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.

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Book SynopsisModel checking is one of the most successful verification techniques and has been widely adopted in traditional computing and communication hardware and software industries. This book provides the first systematic introduction to model checking techniques applicable to quantum systems, with broad potential applications in the emerging industry of quantum computing and quantum communication as well as quantum physics. Suitable for use as a course textbook and for self-study, graduate and senior undergraduate students will appreciate the step-by-step explanations and the exercises included. Researchers and engineers in the related fields can further develop these techniques in their own work, with the final chapter outlining potential future applications.Trade Review'This book gives a thorough account of the principles of model checking for quantum systems. It covers the basics of verifying qualitative properties such as reachability as well as quantitative properties on quantum Markov chains. This is the first comprehensive work on this young and exciting research field.' Joost-Pieter Katoen, RWTH Aachen University'The authors have been, from the start of the quantum computer science endeavour, at the forefront of research in logical methods for quantum computing. This book provides the best possible introduction to quantum model checking, by the pioneers of the field. Bob Coecke, University of Oxford'A brief final chapter offering conclusions and future prospects will be of wider interest. This work is intended as an introduction for researchers entering the field of quantum computing, and is suitable as a textbook for physics or computer science graduate students … Recommended.' M. C. Ogilvie, Choice MagazineTable of Contents1. Introduction; 2. Basics of Model Checking; 3. Basics of Quantum Theory; 4. Model Checking; 5. Model Checking Quantum Markov Chains; 6. Model Checking Super-operator-valued Markov Chains; 7. Conclusions and Prospects.

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Book SynopsisA highly interdisciplinary overview of the emerging topic of the Quantum Internet. Current and future quantum technologies are covered in detail, in addition to their global socio-economic impact. Written in an engaging style and accessible to graduate students in physics, engineering, computer science and mathematics.Trade Review'This book explores the technical and socioeconomic aspects of a future quantum internet … The volume will be a valuable acquisition for any institution supporting research in quantum computing or, more broadly, the emerging science and engineering of quantum information … Highly recommended.' M. C. Ogilvie, Choice ConnectTable of ContentsPart I. Introduction: 1. Foreword; 2. Introduction. Part II. Classical Networks: 3. Mathematical representation of networks; 4. Network topologies; 5. Network algorithms. Part III. Quantum Networks: 6. Quantum channels; 7. Optical encoding of quantum information; 8. Errors in quantum networks; 9. Quantum cost vector analysis; 10. Routing strategies; 11. Interconnecting and interfacing quantum networks; 12. Optical routers; 13. Optical stability in quantum networks. Part IV. Protocols for the Quantum Internet: 14. State preparation; 15. Measurement; 16. Evolution; 17. High-level protocols. Part V. Entanglement Distribution: 18. Entanglement – The ultimate quantum resource; 19. Quantum repeater networks; 20. The irrelevance of latency; 21. The quantum Sneakernet™. Part VI. Quantum Cryptography: 22. What is security?; 23. Classical cryptography; 24. Attacks on classical cryptography; 25. Bitcoin and the blockchain; 26. Quantum cryptography; 27. Attacks on quantum cryptography. Part VII. Quantum Computing: 28. Models for quantum computation; 29. Quantum algorithms. Part VIII. Cloud Quantum Computing: 30. The Quantum Cloud™; 31. Encrypted cloud quantum computation. Part IX. Economics and Politics: 32. Classical-equivalent computational power and computational scaling functions; 33. Per-qubit computational power; 34. Time-sharing; 35. Economic model assumptions; 36. Network power; 37. Network value; 38. Rate of return; 39. Market competitiveness; 40. Cost of computation; 41. Arbitrage-free time-sharing model; 42. Problem size scaling functions; 43. Quantum computational leverage; 44. Static computational return; 45. Forward contract pricing model; 46. Political leverage; 47. Economic properties of the qubit marketplace; 48. Economic implications; 49. Game theory of the qubit marketplace. Part X. Essays: 50. The era of quantum supremacy; 51. The global virtual quantum computer; 52. The economics of the quantum internet; 53. Security implications of the global quantum internet; 54. Geostrategic quantum politics; 55. The quantum ecosystem. Part XI. The End: 56. Conclusion. References. Index.

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Book SynopsisCommunication complexity is the mathematical study of scenarios where several parties need to communicate to achieve a common goal. This tutorial text explains fundamentals and recent developments in an accessible and illustrated form, including applications in circuit complexity, proof complexity, streaming algorithms and distributed computing.Trade Review'This looks like an essential resource for any student who wants to understand deterministic and randomized communication complexity deeply.' Scott Aaronson, University of Texas'Communication complexity is not only a beautiful and important area of the theory of computing, it is also vibrant and ever-changing. Two of the leading researchers in this area take us through a fascinating journey into the theory and applications of communication complexity and through old and new jams. I feel inspired to teach a course based on this book and help spread the word.' Omer Reingold, Stanford University, California'This book is a much-needed introductory text on communication complexity. It will bring the reader up to speed on both classical and more recent lower bound techniques, and on key application areas. An invaluable resource for anyone interested in complexity theory.' Mark Braverman, Princeton University, New Jersey'… a great book … relevant to advanced undergrads and graduate students alike, while the more advanced topics will also be of interest to researchers …' Michael Cadilhac, SIGACT News Book review column'… must-have reference for students but will be welcomed by researchers as well because it is so well-written and aptly organized … Highly recommended.' A. Misseldine, CHOICETable of ContentsPreface; Conventions and preliminaries; Introduction; Part I. Communication: 1. Deterministic protocols; 2. Rank; 3. Randomized protocols; 4. Numbers on foreheads; 5. Discrepancy; 6. Information; 7. Compressing communication; 8. Lifting; Part II. Applications: 9. Circuits and proofs; 10. Memory size; 11. Data structures; 12. Extension Complexity of Polytopes; 13. Distributed computing.

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Book SynopsisSymbolic dynamics is a mature yet rapidly developing area of dynamical systems. It has established strong connections with many areas, including linear algebra, graph theory, probability, group theory, and the theory of computation, as well as data storage, statistical mechanics, and $C^*$-algebras. This Second Edition maintains the introductory character of the original 1995 edition as a general textbook on symbolic dynamics and its applications to coding. It is written at an elementary level and aimed at students, well-established researchers, and experts in mathematics, electrical engineering, and computer science. Topics are carefully developed and motivated with many illustrative examples. There are more than 500 exercises to test the reader''s understanding. In addition to a chapter in the First Edition on advanced topics and a comprehensive bibliography, the Second Edition includes a detailed Addendum, with companion bibliography, describing major developments and new research dTable of Contents1. Shift spaces; 2. Shifts of finite type; 3. Sofic shifts; 4. Entropy; 5. Finite-state codes; 6. Shifts as dynamical systems; 7. Conjugacy; 8. Finite-to-one codes and finite equivalence; 9. Degrees of codes and almost conjugacy; 10. Embeddings and factor codes; 11. Realization; 12. Equal entropy factors; 13. Guide to advanced topics; Addendum for the second edition; Bibliography; Addendum bibliography; Notation index; Index.

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Book SynopsisEcosystems, the human brain, ant colonies, and economic networks are all complex systems displaying collective behaviour, or emergence, beyond the sum of their parts. Complexity science is the systematic investigation of these emergent phenomena, and stretches across disciplines, from physics and mathematics, to biological and social sciences. This introductory textbook provides detailed coverage of this rapidly growing field, accommodating readers from a variety of backgrounds, and with varying levels of mathematical skill. Part I presents the underlying principles of complexity science, to ensure students have a solid understanding of the conceptual framework. The second part introduces the key mathematical tools central to complexity science, gradually developing the mathematical formalism, with more advanced material provided in boxes. A broad range of end of chapter problems and extended projects offer opportunities for homework assignments and student research projects, with soluTrade Review'Henrik Jensen has produced a masterpiece - describing complexity science from the perspective of a universal theory applicable to many different subject areas, and based on fundamental theoretical principles. A clear virtue of the exposition is that many different topics relevant for complex systems are first treated in an easy-going introductory way, while concrete mathematical models and applications are then provided in the second part of the book. This is a well-thought-through textbook that presents complexity science as a whole, rather than as a collection of single topics.' Christian Beck, Queen Mary University of LondonTable of ContentsPart I. Conceptual Foundation of Complexity Science: 1. The Science of Emergence; 2. Conceptual Framework of Emergence; 3. Specific Types of Emergent Behaviour; 4. The Value of Prototypical Models of Emergence; Part II. Mathematical Tools of Complexity Science: 5. Branching Processes; 6. Statistical Mechanics; 7. Synchronisation; 8. Network Theory; 9. Information Theory and Entropy; 10. Stochastic Dynamics and Equations for the Probabilities; 11. Agent-Based Modelling; 12. Intermittency; 13. Tipping Points, Transitions and Forecasting; 14. Concluding Comments and a Look to the Future.

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Book SynopsisLearn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book''s companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplinesTrade Review'This book provides an excellent introduction to time series modelling and forecasting which are increasingly important tools in the domain of official statistics. The clear descriptions and real-life examples provided in this text make it easy to digest for those not already familiar with the topic. In addition, the exercises allow readers to develop their understanding in more depth through hands-on applications of the methods to real data using open-source tools. The inclusion of modern topics such as machine learning and artificial intelligence are a valuable addition to make the text relevant and comprehensive.' Steve Matthews, Statistics Canada'This book is a great introduction to the ideas and methods of time series data analysis. Chapter by chapter, it will show you its most valuable features, like the wealth of real examples as well as practical uses of R and graphical visualization. You will certainly enjoy this text, as it is suitable for a wide range of statistical courses.' Vera Ioudina, Texas State University'Lots of good real world examples together with the use of R helps a lot as do the nice set of exercises. In time series, it is a tricky balance between overdoing theory or just hand waving and here the author does very well. This would make a lovely course text!' Gareth Janacek, University of East Anglia'Time Series for Data Scientists' develops your intuition before walking through classical and modern time series methods in easy-to-understand terms. With each algorithm Dr. Sanchez first helps you understand the motivation behind the approach; then walks you through the formulas step-by-step, outlining what we're doing and why; she also includes R code to help you apply the techniques learned to solve real-world business problems using real-world data sets; and takes the time to show you how to interpret the output, and discuss what to try next when an initial approach doesn't quite match the trends in the data. Whether you're an undergraduate or graduate student, are curious about time series methods, are looking for a self-paced book, or a reference guide, this is a must-have.' Irina Kukuyeva, Fractional Chief Data Officer'A fine textbook for an introductory time series course aimed at undergraduates in Statistics or Data Science. The author did an excellent work in the choice of topics, covering from classical exploratory techniques to modern machine learning approaches, while keeping the level of the exposition accessible to readers with a modicum of mathematical background. To be recommended!' Giovanni Petris, University of Arkansas'This book should be a serious contender if you are looking for an introductory text for an undergraduate course in time series. It is especially suited for a course populated with students having varying degrees of mathematical skill levels. Its conversational approach to introducing time series concepts and the use of insightful examples throughout the book makes it very accessible to students who are not highly trained in abstract mathematical reasoning. Nevertheless, it does not shy away from providing the theoretical underpinnings of various time series models but does so in a manner very accessible to students. The availability of R code throughout the book is an added plus. Even if I am teaching an upper-level graduate course in time series, I would use this book as a supplement simply because of the plethora of examples and data sources it provides.' V. A. Samaranayake, Missouri University of Science and TechnologyTable of ContentsPart I. Descriptive Features of Time Series Data: 1. Introduction to time series data; 2. Smoothing and decomposing a time series; 3. Summary statistics of stationary time series; Part II. Univariate Models of Temporal Dependence: 4. The algebra of differencing and backshifting; 5. Stationary stochastic processes; 6. ARIMA(p,d,q)(P,D,Q)$_F$ modeling and forecasting; Part III. Multivariate Modeling and Forecasting: 7. Latent process models for time series; 8. Vector autoregression; 9. Classical regression with ARMA residuals; 10. Machine learning methods for time series; References; Index.

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Book SynopsisThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely from scratch based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.Trade Review'Dr Jiang has done a superb job in covering many methods, both theoretical and practical, across a broad spectrum of machine learning in this timely book. I worked closely with Dr Jiang on Bayesian speech recognition during late 90's and I have personally witnessed his excellent skills in applying machine learning to solving a wide range of practical problems. In this book, Dr Jiang has expanded his scope into a much wider set of logically organized topics in modern machine learning. The organization of the material is highly unique and cogent. A number of hot topics in machine learning, including deep learning and neural networks, are naturally incorporated in the book, which not only provides sufficient technical depth for the readers but also aligns well with popular toolkits for implementing the related machine learning methods.' Li Deng, formerly of Microsoft Corporation and Citadel LLC'It is beautifully designed, with many color images that make the complex subject matter manageable … It is a book for students and developers who are committed to specializing in ML or a specific area of ​​it.' Karl van Heijster , De Leesclub van AllesTable of Contents1. Introduction; 2. Mathematical Foundation; 3. Supervised Machine Learning (in a nutshell); 4. Feature Extraction; 5. Statistical Learning Theory; 6. Linear Models; 7. Learning Discriminative Models in General; 8. Neural Networks; 9. Ensemble Learning; 10. Overview of Generative Models; 11. Unimodal Models; 12. Mixture Models; 13. Entangled Models; 14. Bayesian Learning; 15. Graphical Models.

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Book Synopsis* Tervo first introduces the actual behaviour of specific signals and uses them to motivate presentation of mathematical concepts. * The goal is to help students who can t visualize phenomena from an equation to develop their intuition and learn to analyse signals by inspection.Table of ContentsPreface xix Acknowledgments xxiii 1 Introduction to Signals and Systems 1 1.1 Introduction 1 1.2 Introduction to Signal Manipulation 3 1.3 A Few Useful Signals 9 1.4 The Sinusoidal Signal 17 1.5 Phase Change vs. Time Shift 21 1.6 Useful Hints and Help with MATLAB 25 1.7 Conclusions 26 2 Classification of Signals 30 2.1 Introduction 30 2.2 Periodic Signals 31 2.3 Odd and Even Signals 38 2.4 Energy and Power Signals 47 2.5 Complex Signals 52 2.6 Discrete Time Signals 56 2.7 Digital Signals 58 2.8 Random Signals 58 2.9 Useful Hints and Help with MATLAB 60 2.10 Conclusions 61 3 Linear Systems 66 3.1 Introduction 66 3.2 Definition of a Linear System 67 3.3 Linear System Response Function h(t) 73 3.4 Convolution 73 3.5 Determining h(t) in an Unknown System 88 3.6 Causality 91 3.7 Combined Systems 92 3.8 Convolution and Random Numbers 94 3.9 Useful Hints and Help with MATLAB 96 3.10 Chapter Summary 97 3.11 Conclusions 97 4 The Fourier Series 101 4.1 Introduction 101 4.2 Expressing Signals by Components 102 4.3 Part One—Orthogonal Signals 106 4.4 Orthogonality 107 4.5 Part Two—The Fourier Series 118 4.6 Computing Fourier Series Components 121 4.7 Fundamental Frequency Component 123 4.8 Practical Harmonics 126 4.9 Odd and Even Square Waves 128 4.10 Gibb’s Phenomenon 131 4.11 Setting Up the Fourier Series Calculation 132 4.12 Some Common Fourier Series 136 4.13 Part Three—The Complex Fourier Series 137 4.14 The Complex Fourier Series 138 4.15 Complex Fourier Series Components 143 4.16 Properties of the Complex Fourier Series 151 4.17 Analysis of a DC Power Supply 152 4.18 The Fourier Series with MATLAB 158 4.19 Conclusions 165 5 The Fourier Transform 171 5.1 Introduction 171 5.2 Properties of the Fourier Transform 178 5.3 The Rectangle Signal 181 5.4 The Sinc Function 182 5.5 Signal Manipulations: Time and Frequency 189 5.6 Fourier Transform Pairs 198 5.7 Rapid Changes vs. High Frequencies 200 5.8 Conclusions 203 6 Practical Fourier Transforms 206 6.1 Introduction 206 6.2 Convolution: Time and Frequency 206 6.3 Transfer Function of a Linear System 210 6.4 Energy in Signals: Parseval’s Theorem for the Fourier Transform 213 6.5 Data Smoothing and the Frequency Domain 215 6.6 Ideal Filters 216 6.7 A Real Lowpass Filter 220 6.8 The Modulation Theorem 224 6.9 Periodic Signals and the Fourier Transform 230 6.10 The Analog Spectrum Analyzer 233 6.11 Conclusions 235 7 The Laplace Transform 240 7.1 Introduction 241 7.2 The Laplace Transform 241 7.3 Exploring the s-Domain 243 7.4 Visualizing the Laplace Transform 251 7.5 Properties of the Laplace Transform 267 7.6 Differential Equations 267 7.7 Laplace Transform Pairs 270 7.8 Circuit Analysis with the Laplace Transform 272 7.9 State Variable Analysis 285 7.10 Conclusions 295 8 Discrete Signals 301 8.1 Introduction 301 8.2 Discrete Time vs. Continuous Time Signals 301 8.3 A Discrete Time Signal 303 8.3.1 A Periodic Discrete Time Signal 303 8.4 Data Collection and Sampling Rate 304 8.5 Introduction to Digital Filtering 319 8.6 Illustrative Examples 328 8.7 Discrete Time Filtering with MATLAB 338 8.8 Conclusions 340 9 The z-Transform 344 9.1 Introduction 344 9.2 The z-Transform 344 9.3 Calculating the z-Transform 348 9.4 A Discrete Time Laplace Transform 356 9.5 Properties of the z-Transform 358 9.6 z-Transform Pairs 359 9.7 Transfer Function of a Discrete Linear System 359 9.8 MATLAB Analysis with the z-Transform 360 9.9 Digital Filtering—FIR Filter 366 9.10 Digital Filtering—IIR Filter 373 9.11 Conclusions 378 10 Introduction to Communications 381 10.1 Introduction 381 10.2 Amplitude Modulation 385 10.3 Suppressed Carrier Transmission 394 10.4 Superheterodyne Receiver 398 10.5 Digital Communications 402 10.6 Phase Shift Keying 407 10.7 Conclusions 409 A The Illustrated Fourier Transform 411 B The Illustrated Laplace Transform 419 C The Illustrated z-Transform 425 D MATLAB Reference Guide 431 D.1 Defining Signals 431 D.2 Complex Numbers 433 D.3 Plot Commands 434 D.4 Signal Operations 434 D.5 Defining Systems 435 D.6 Example System Definition and Test 438 E Reference Tables 440 E.2 Laplace Transform 441 E.3 z-Transform 442 Bibliography 443 Index 445

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Book SynopsisHeavy tails extreme events or values more common than expected emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engTrade Review'Heavy tailed distributions are ubiquitous in many disciplines which use probabilistic models. The book by Nair, Wierman and Zwart is a superb introduction to the topic and presents fundamental principles in a rigorous yet accessible manner. It is a must-read for researchers interested in understanding heavy tails.' R. Srikant, University of Illinois at Urbana-Champaign'As one of the people who keeps discovering heavy tails in computer systems, I'm thrilled to see a book that delves into the deeper foundations behind these ubiquitous distributions. This beautifully written book is both mathematically precise and also full of intuitions and examples which make it accessible to newcomers in the field.' Mor Harchol-Balter, Carnegie Mellon University'The book provides a fresh look at heavy-tailed probability distributions on the real line and their role in applied probability. The authors show that these distributions appear via natural algebraic operations. Their approach, towards understanding properties of these distributions, combines the key mathematical ideas alongside with informal explanations. Physical intuition is also provided, for example, the 'catastrophe/big jump principle' for heavy-tailed distributions versus the 'conspiracy principle' for light-tailed ones. The book is designed to help the practitioner and includes many interesting examples and exercises that may help to the reader to adjust and enjoy its content.' Sergey Foss, Heriot-Watt UniversityTable of ContentsCommonly used notation; 1. Introduction; Part I. Properties: 2. Scale invariance, power laws, and regular variation; 3. Catastrophes, conspiracies, and subexponential distributions; 4. Residual lives, hazard rates, and long tails; Part II. Emergence: 5. Additive processes; 6. Multiplicative processes; 7. Extremal processes; Part III. Estimation: 8. Estimating power-law distributions: Listen to the body; 9. Estimating power-law tails: Let the tail do the talking; References; Index.

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Book SynopsisUsing easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses, Trade Review'… masterfully provides a comprehensive treatment of both traditional codes as well as new and most promising coding families and decoding algorithms …' Bane Vasić, University of Arizona' an excellent, unique, and valuable contribution to the teaching of the subject.' Ian Blake, University of British Columbia'A highly readable introduction into the theory of block codes, including classical code constructions, an extensive treatment of LDPC codes, with emphasis on quasi-cyclic constructions, and an introduction to polar codes. Recommended for a beginning graduate course in coding, with enough material for either one or two semesters. Numerous examples and problems make the book very student friendly.' Daniel Costello, University of Notre Dame'The book truly explains these highly mathematical subjects to a level that can be accessed and applied with as little background in mathematics as possible. It provides step-by-step explanation of all covered topics, both more theoretical or applied, and includes sufficient illustrative examples to assist understanding.' Nikolay Yankov, zbMATHTable of ContentsPreface; Acknowledgments; 1. Coding for reliable digital information transmission and storage; 2. Some elements of modern algebra and graphs; 3. Linear block codes; 4.Binary cyclic codes; 5. BCH codes; 6. Nonbinary BCH codes and Reed-Solomon codes; 7. Finite geometries, cyclic finite geometry codes, and majority-logic decoding; 8. Reed-Muller codes; 9. Some coding techniques; 10. Correction of error-bursts and erasures; 11. Introduction to low-density parity-check codes; 12. Cyclic and quasi-cyclic LDPC codes on finite geometries; 13. Partial geometries and their associated QC-LDPC codes; 14. Quasi-cyclic LDPC codes based on finite fields; 15. Graph-theoretic LDPC codes; 16. Collective encoding and soft-decision decoding of cyclic codes of prime lengths in Galois Fourier transform domain; 17. Polar codes; Appendices.

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Book SynopsisThis book brings feminist philosophy, in the shape of feminist ethics, politics and legal theory, to an analysis of computer ethics problems including hacking, privacy, surveillance, cyberstalking and Internet dating.Trade Review'This book is highly recommended for those involved in computer ethics, both academics and practitioners, and also those involved with the social studies of science and technology more generally. However, it also deserves a much wider audience of those concerned with the continuing ubiquity of gendered inequalities.' - David Sanford Horner, Information, Communication& SocietyTable of ContentsGender and Information and Communication Technologies - It's Not for Girls Feminist Political and Legal Theory: The Public/Private Dichotomy Feminist Ethics: Ethics in a Different Voice The Rise of Computer Ethics: From Professionalism to Legislative Failures Gender and Computer Ethics: Contemporary Approaches and Contemporary Problems Internet Dating: Cyberstalking and Internet Pornography: Gender and the Gaze Hacking into Hacking: Gender and the Hacker Phenomenon Someone to Watch Over Me: Gender, Technologies and Privacy Epilogue: Feminist Cyberethics? Bibliography

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Book SynopsisThis book introduces the reader to Serres' unique manner of doing philosophy' that can be traced throughout his entire oeuvre: namely as a novel manner of bearing witness. It explores how Serres takes note of a range of epistemologically unsettling situations, which he understands as arising from the short-circuit of a proprietary notion of capital with a praxis of science that commits itself to a form of reasoning which privileges the most direct path (simple method) in order to expend minimal efforts while pursuing maximal efficiency. In Serres' universal economy, value is considered as a function of rarity, not as a stock of resources. This book demonstrates how Michel Serres has developed an architectonics that is coefficient with nature. Mathematic and Information in the Philosophy of Michel Serres acquaints the reader with Serres' monist manner of addressing the universality and the power of knowledge that is at once also the anonymous and empty faculty of incandescent, inveTrade ReviewWhat happens when we take mathematics not as the elementary basis upon which science must bloom, but as an ‘architectonics’ that unfolds the world as it informs mass, space and time? With great rigor, in content and style, Bühlmann reads the concepts that Michel Serres produced in his oeuvre through his mathematics and information theory, revealing his highly original, inclusive and affirmative philosophy of the 21st century. -- Rick Dolphijn, Associate Professor of Theories of Arts and Culture, Utrecht University, the NetherlandsThe importance of Serres’ philosophy has mostly gone unrecognized in continental philosophy, even though this philosopher had a critical influence on many of its key figures, such as Deleuze and Foucault. The dearth of informed commentary is now reduced by this scholar whose knowledge of mathematics is able to bridge both the analytical and continental traditions. -- Gregg Lambert, Dean’s Professor of Humanities, Syracuse University, USATable of ContentsForeword Chapter one: Introduction The plan of this book Chapter two: Quantum literacy Elementary indecision Communication versus production: Bearing witness, and literacy Cultivating indecision: The quantum domain’s domesticity Ciphers, zeroness, equations: Architectonics of nothing Chance-bound objects Taking ignorance into account: Quantifying strangeness Entropy and negentropy The price of information as a measure for an object’s strangeness Quantum literacy: Towards a novel theory of the subject ‘La Langue est une Puissance’ Chapter three: Chronopedia I: Counting time Meteora: The wisdom of the weather Code: A rosetta stone, a double staircase Time modelled as contemporaneity Counting time: Equinox and solstice The turning points for modelled beginnings and ends Of tables and models Sense means significance and direction Meteora A logos genuine to the world – ‘Le Logiciél Intra-Matériel’ Software, hardware Economy of maxima and minima: An anarchic logos Chapter four: Chronopedia II: Treasuring time Homothesis as the locus in quo of the universal’s presence 1st iteration (acquiring a space of possibility) 2nd iteration (learning to speak a language in which no one is native) 3rd iteration (setting the stage for thought to comprehend itself) 4th iteration (intelligence that is immanent and coextensive with the universe) 5th iteration (inventing a scale of reproduction) 6th iteration (the formula, a double-articulating application) The amorous nature of intellectual conception 1st iteration (marking all that is assumed to be constant with a cipher) 2nd iteration (confluence of multiple geneses) 3rd iteration (the residence of that which is genuinely migrational) 4th iteration (universal genitality) 5th iteration (mathematics is the circuit of cunning reason’s ruses) 6th iteration (the real as a black spectrum) Chapter five: Banking universality: The magnitudes of ageing Metaphysics The quickness of a magnanimous universe Invariance: Genericness in terms of entropy and negentropy Genuine and immanent to the all of time: Le ‘logiciel intra-matériel’ White metaphysics: How old does the world think it is? Freedom The neutral element: Materialism of identity (Pan’s) glossematics: The economy that deals with ‘purport’ Quanta of contemporaneity: Heat to incandescence, storage to bank account Quantum writing: Substitutes step in to address things themselves Chapter six: The incandescent Paraclete: Tables of plenty Equatoriality generalized Coming of age, liking sunset and sunrise How to combine precision with finesse or: euphoria contained by instruments that behave like cornucopia The (mathematical) inverse of Pantopia is not a utopia: Law in the panonymy of the whole world The objective mentality and character of instruments The vicarious order of knowledge that is authentic to the world Pan: The excitable subject of universal knowledge Generational con-sequentiality Blessed curiosity Exodic discourse Chapter seven: Sophistication and anamnesis: Retrograde movement of truth, remembering an abundant past The currency of knowledge The price of truth, and the price of information The convertibility of truth Classicism: Remembering contemporaneity Classical analysis, symbolical analysis Interlude: The Tower of Eiffel, archetypical symbol of existentialism? Building a cipher A corpus of intelligent forms The technical order of an object that is comfortable How to reason the sum total of all archetypes? Towards critique with regard to the symbolic alchemy of myth-making A realist classicism Familiarizing ourselves as strangers, native to the universe The domain of the quasi: Instructive analysis, character dispositions How can reason in general learn from singularities? Of genealogical and of tabular orders: Eating ‘next to’ (parasite) Heterogeneous scales, logistical uniformality (forms of operation) Indexical address: The referential of the centre Respecting order by challenging it Cunning ruses: The anarchic architectonic way of paying respect How to address the third-person singular? Augmentation, not authorship Anarchic civility, and the meanings of cultures Chapter eight Coda: Quantum literacy and architectonic dispositioning Architecture and philosophy Chapter zero: Instead of a conclusion: The static tripod Notes Bibliography

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Table of Contents Introduction to Backup and Recovery Backup Software Physical Backup Media Virtual Backup Media New Media Technologies Software Architectures: CommVault Software Architectures: NetBackup Application Backup Strategies Putting It All Together: Sample Backup Environments Monitoring and Reporting Summary

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Book SynopsisEntity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development.Table of Contents Getting Started with Entity Framework Entity Data Modeling Fundamentals Querying an Entity Data Model Using Entity Framework in ASP.NET Loading Entities and Navigation Properties Beyond the Basics with Modeling and Inheritance Working with Object Services Plain Old CLR Objects Using the Entity Framework in N-Tier Applications Stored Procedures Functions Customizing Entity Framework Objects Improving Performance Concurrency

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Book SynopsisBeginning Oracle SQL is your introduction to the interactive query tools and specific dialect of SQL used with Oracle Database.Table of Contents1. Relational Database Systems and Oracle2. Introduction to SQL and SQL*Plus, and SQL Developer3. Data Definition, Part I4. Retrieval: The Basics5. Retrieval: Functions6. Data Manipulation7. Data Definition, Part II8. Retrieval: Joins and Grouping9. Retrieval: Advanced Features10. Views11. Automating12. Object-Relational Features13. Appendix A – Case Tables14. Appendix B – Exercise Solutions

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Book SynopsisExpert Oracle RAC Performance Diagnostics and Tuning provides comprehensive coverage of the features, technology and principles for testing and tuning RAC databases. The book takes a deep look at optimizing RAC databases by following a methodical approach based on scientific analysis rather than using a speculative approach, twisting and turning knobs and gambling on the system.The book starts with the basic concepts of tuning methodology, capacity planning, and architecture. Author Murali Vallath then dissects the various tiers of the testing implementation, including the operating system, the network, the application, the storage, the instance, the database, and the grid infrastructure. He also introduces tools for performance optimization and thoroughly covers each aspect of the tuning process, using many real-world examples, analyses, and solutions from the field that provide you with a solid, practical, and replicable approach to tuning a RAC enTable of Contents1. Methodology2. Capacity Planning and Architecture3. Testing for Availability4. Testing for Scalability5. Real Application Testing6. Tools and Utilities7. SQL Tuning8. Parallel Query Tuning9. Tuning the Database10. Tuning Recovery11. Tuning Oracle Net12. Tuning Storage Subsystem13. Tuning Global Cache14. Tuning the Cluster Interconnect15. Optimization of Distributed Workload16. Tuning the Oracle Clusterware17. Enqueues, Waits and Latches18. Problem DiagnosticsA. The SQL Scripts Used in This BookBibliography

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Book SynopsisAn Introduction to Cryptography.- Discrete Logarithms and Diffie Hellman.- Integer Factorization and RSA.- Combinatorics, Probability and Information Theory.- Elliptic Curves and Cryptography.- Lattices and Cryptography.- Digital Signatures.- Additional Topics in Cryptography.Trade ReviewFrom the reviews: "The book is devoted to public key cryptography, whose principal goal is to allow two or more people to exchange confidential information … . The material is very well organized, and it is self-contained: no prerequisites in higher mathematics are needed. In fact, everything is explained and carefully covered … . there is abundance of examples and proposed exercises at the end of each chapter. … This book is ideal as a textbook for a course aimed at undergraduate mathematics or computer science students." (Fabio Mainardi, The Mathematical Association of America, October, 2008) "This book focuses on public key cryptography … . Hoffstein, Pipher, and Silverman … provide a thorough treatment of the topics while keeping the material accessible. … The book uses examples throughout the text to illustrate the theorems, and provides a large number of exercises … . The volume includes a nice bibliography. … Summing Up: Highly recommended. Upper-division undergraduate through professional collections." (C. Bauer, Choice, Vol. 46 (7), March, 2009) "For most undergraduate students in mathematics or computer science (CS), mathematical cryptography is a challenging subject. … it is written in a way that makes you want to keep reading. … The authors officially targeted the book for advanced undergraduate or beginning graduate students. I believe that this audience is appropriate. … it could even be used with students who are just learning how to execute rigorous mathematical proofs. … I strongly believe that it finds the right tone for today’s students … ." (Burkhard Englert, ACM Computing Reviews, March, 2009) "The exercises and text would make an excellent course for undergraduate independent study. … This is an excellent book. Hoffstein, Pipher and Silverman have written as good a book as is possible to explain public key cryptography. … This book would probably be best suited for a graduate course that focused on public key cryptography, for undergraduate independent study, or for the mathematician who wants to see how mathematics is used in public key cryptography." (Jintai Ding and Chris Christensen, Mathematical Reviews, Issue 2009 m)Table of ContentsAn Introduction to Cryptography.- Discrete Logarithms and Diffie-Hellman.- Integer Factorization and RSA.- Probability Theory and Information Theory.- Elliptic Curves and Cryptography.- Lattices and Cryptography.- Digital Signatures.- Additional Topics in Cryptology.

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Book SynopsisA prize-winning biography of one of the foremost intellects of the twentieth century: Claude Shannon, the neglected architect of the Information Age.Trade Review‘A long overdue, insightful and humane portrait of this eccentric and towering genius.’ -- Walter Isaacson, bestselling author of STEVE JOBs‘A welcome and inspiring account of a largely unsung hero - unsung because, the authors suggest, he accomplished something so fundamental that it’s difficult to imagine a world without it.' -- Kirkus Reviews‘An exceptionally elegant and authoritative portrait… Sonni and Goodman’s elucidations of Claude Shannon’s theories are gems of conciseness and clarity.' -- Sylvia Nasar, author of the bestselling A BEAUTIFUL MIND, winner of the National Books Critics Award

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Book SynopsisIn this philosophical treatment of play Kostas Axelos traces his thinking on the world deployed as play from Heraclitus through to the culmination of metaphysical philosophy with Nietzsche, Marx and Heidegger.

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Book SynopsisThe Origins of Real-Time Processing.- The Concept of Time in the Specification of Real-Time Systems.- Language-Independent Schedulability Analysis of Real-Time Programs.- Which Theory Matches Automation Engineering?.- Requirements Engineering for Real-Time and Embedded Systems.- Real-Time Programming Languages.- Comparison of Synchronization Concepts.- Real-Time Database Systems.- Microprocessor Architectures: A Basis for Real-Time Systems.- Buses in Real-Time Environments.- Distributed Systems for Real Time Applications.- Robot Programming.- Real-Time Data Processing of the Sensory Data of a Multi- Fingered Dextrous Robot Hand.- Fly-By-Wire Systems for Military High Performance Aircraft.- Artificial Intelligence Techniques in Real-Time Processing.- Recommendations for a Real-Time Systems Curriculum.Table of ContentsForeword. I: Introduction. 1. The Origins of Real-Time Processing; M. Schiebe. II: Theoretical Foundations. 2. The Concept of Time in the Specification of Real-Time Systems; B. Hoogeboom, W.A. Halang. 3. Language-Independent Schedulability Analysis of Real-Time Programs; A.D. Stoyenko. III: Models and Tools 4. Which Theory Matches Automation Engineering? Petri-Nets as a Formal Basis; E. Schnieder. 5. Requirements Engineering for Real-Time and Embedded Systems; P. Hruschka. IV: Practical Considerations. 6. Real-Time Programming Languages; W.A. Halang, K.-O. Mangold. 7. Comparison of Synchronization Concepts of Ada, Concurrent C and PEARL; K.-F. Gebhardt. 8. Real-Time Database Systems; H. Windauer. 9. Microprocessor Architectures: A Basis for Real-Time Systems; T. Bemmerl. 10. Buses in Real-Time Environments; F. Demmelmeier. 11. Distributed Systems for Real-Time Applications Using Manufacturing Automation as an Example; H. Rzehak. 12. Robot Programming; K. Fischer, B. Glavina, E. Hagg, G. Schrott, J. Schweiger, H.-J. Siegert. V: Examples for Applications. 13. Real-Time Data Processing of the Sensory Data of Multi-Fingered Dextrous Hand; A. Knoll. 14. Fly-By-Wire Systems for Military High Performance Aircraft; D. Langer, J. Rauch, M. Rößler. VI: Future Developments. 15. Artificial Intelligence Techniques in Real-Time Processing; K. Kratzer. 16. Recommendations for a Real-Time Systems Curriculum; W.A. Halang. Glossary. Index.

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Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.

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Book SynopsisTable of ContentsPart 1: Data Structures.- Chapter 1: Intro to DSA, Types and Big-O.- Chapter 2: Linear Data Structures.- Chapter 3: Tree Data Structures.- Chapter 4: Hash Data Structures.- Chapter 5: Graphs.- Part 2: Algorithms.- Chapter 6: Linear and Binary Search.- Chapter 7: Sorting Algorithms.- Chapter 8: Searching Algorithms.- Chapter 9: Clustering Algorithms.- Chapter 10: Randomness.- Chapter 11: Scheduling Algorithms.- Chapter 12: Algorithm Planning and Design.- Appendix A: Going Further.-

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Book SynopsisThis book is a complete guide to using functions in Power Query and is designed to help users of all skill levels learn and master its various functions. The Ultimate Guide to Functions in Power Query begins with an introduction to Power Query and an overview of the different types of functions available, along with detailed explanations of how to use each of them. You'll see how to leverage power functions to process and transform large datasets from various sources and learn advanced techniques such as creating custom functions and using conditional statements. The book also covers best practices for using functions, including tips on how to optimize query performance and troubleshoot common errors. Using practical example applications, Author Omid Motamedisedeh demonstrates how to optimize your data processing workflows, saving time and boosting productivity. By the end of the book, readers will have a deep understanding of Power Query functions and be ableto apply their knowledTable of ContentsChapter 1: Introduction to Power Query.- Chapter 2: Data Types.- Chapter 3: Number Functions.- Chapter 4: Text Functions.- Chapter 5: Date and Time Functions.- Chapter 6: List Functions.- Chapter 7: Record Functions.- Chapter 8: Table Functions.- Chapter 9: Extracting from Data Sources.- Chapter 10: Other Functions.

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Book SynopsisThis book explores the enduring role and intrinsic value of libraries and archives as public institutions in the digital age.Table of ContentsList of Illustrations List of Abbreviations Foreword Tami Oliphant, University of Alberta and Ali Shiri, University of Alberta Acknowledgments Introduction Patricia Demers, University of Alberta and Toni Samek, University of Alberta I. Enduring Values Libraries: Why Bother? Alice Crawford, University of St. Andrews Academic Library Spaces, Digital Culture, and Communities Guylaine Beaudry, Concordia University The Public Library’s Enduring Importance Marc Kosciejew, Western University II. Public Literacy and Private Oases Loss of the Social, Return of the Private: Acknowledging Public Failure in the Age of Boudoir Surplus Mario Hibert, University of Sarajevo Re-establishing Values, Constructing New Missions: The Value of the Modern Library in the Development of Information and Digital Literacy in Public Life Konstantina Martzoukou, Robert Gordon University III. Transformations and Resistance Libraries’ Shifting Roles and Responsibilities in the Networked Age Michael Carroll, American University Washington College of Law The Interface of the Digital Library: The Perseus Digital Library as a Case Study Geoffrey Rockwell, University of Alberta, Sarah Vela, Lisa M. Cerrato, Mihaela Ilovan, Stan Ruecker, Perseus Digital Library, and the INKE Research Group Wanderbibliotheken: Travelling Books and DIY Libraries Carolyn Guertin, Western University IV. Disciplinary and Institutional Partnerships Is Professionalism Still an Acceptable Goal for Archivists in the Global Digital Society? Richard Cox, Comcast Digital Research with All Our Senses: How the Archivist, the Historian, and the Librarian Can Work Together on the New Frontier Nigel Raab, Loyola Marymount　University The Critical, Diverse (and Sometimes Neglected) Roles of Libraries and Archives in a Museum Setting Brendan Edwards, Royal Ontario Museum V. Curation and Commons Beyond Place: Data Curation Possibilities for Post-custodial Archives and Libraries Seamus Ross, University of Toronto "The X-Files": The Truth is in the Archives, but Access is Restricted Frank J. Tough, University of Alberta Works Cited Contributors Index

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Book SynopsisPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.Trade Review“This book explains the mathematical foundations of public key cryptography in a mathematically correct and thorough way without omitting important practicalities. … I would like to emphasize that the book is very well written and quite clear. Topics are well motivated, and there are a good number of examples and nicely chosen exercises. To me, this book is still the first-choice introduction to public-key cryptography.” (Klaus Galensa, Computing Reviews, March, 2015)“This is a text for an upper undergraduate/lower graduate course in mathematical cryptography. … It is very well written and quite clear. Topics are well-motivated, and there are a good number of examples and nicely chosen exercises. … An instructor of a fairly sophisticated undergraduate course in cryptography who wants to emphasize public key cryptography should definitely take a look at this book.” (Mark Hunacek, MAA Reviews, October, 2014)Table of ContentsPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.

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Book SynopsisPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.Trade Review“This book explains the mathematical foundations of public key cryptography in a mathematically correct and thorough way without omitting important practicalities. … I would like to emphasize that the book is very well written and quite clear. Topics are well motivated, and there are a good number of examples and nicely chosen exercises. To me, this book is still the first-choice introduction to public-key cryptography.” (Klaus Galensa, Computing Reviews, March, 2015)“This is a text for an upper undergraduate/lower graduate course in mathematical cryptography. … It is very well written and quite clear. Topics are well-motivated, and there are a good number of examples and nicely chosen exercises. … An instructor of a fairly sophisticated undergraduate course in cryptography who wants to emphasize public key cryptography should definitely take a look at this book.” (Mark Hunacek, MAA Reviews, October, 2014)Table of ContentsPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.

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Book SynopsisThe modern means of communication have turned the world into an information fishbowl and, in terms of foreign policy and national security in post-Cold War power politics, helped transform international power politics.

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