Information theory Books

Book SynopsisThe phenomenal international bestseller that shows us how to stop trying to predict everything - and take advantage of uncertaintyWhat have the invention of the wheel, Pompeii, the Wall Street Crash, Harry Potter and the internet got in common? Why are all forecasters con-artists? Why should you never run for a train or read a newspaper? This book is all about Black Swans: the random events that underlie our lives, from bestsellers to world disasters. Their impact is huge; they''re impossible to predict; yet after they happen we always try to rationalize them. ''Taleb is a bouncy and even exhilarating guide ... I came to relish what he said, and even develop a sneaking affection for him as a person'' Will Self, Independent on Sunday''He leaps like some superhero of the mind'' Boyd Tonkin, IndependentTrade ReviewA fascinating study of how we are regularly taken for suckers by the unexpected * Guardian *Like the conversation of a raconteur ... hugely enjoyable - compelling * Financial Times *It has altered modern thinking * The Times *Confirms his status as a guru for every would-be Damien Hirst, George Soros and aspirant despot * Sunday Times *The Black Swan changed my view of how the world works -- Daniel Kahneman, author of Thinking, Fast and SlowGreat fun... brash, stubborn, entertaining, opinionated, curious, cajoling -- Stephen J. Dubner, co-author of FreakonomicsThe most prophetic voice of all * GQ *

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Book SynopsisProofs play a central role in advanced mathematics and theoretical computer science, yet many students struggle the first time they take a course in which proofs play a significant role. This bestselling text''s third edition helps students transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs. Featuring over 150 new exercises and a new chapter on number theory, this new edition introduces students to the world of advanced mathematics through the mastery of proofs. The book begins with the basic concepts of logic and set theory to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for an analysis of techniques that can be used to build up complex proofs step by step, using detailed ''scratch work'' sections to expose the machinery of proofs about numbers, sets, relations, and functions. Assuming no background beyond standard high school mathematics, this booTrade Review'Not only does this book help students learn how to prove results, it highlights why we care so much. It starts in the introduction with some simple conjectures and gathering data, quickly disproving the first but amassing support for the second. Will that pattern persist? How can these observations lead us to a proof? The book is engagingly written, and covers - in clear and great detail - many proof techniques. There is a wealth of good exercises at various levels. I've taught problem solving before (at The Ohio State University and Williams College), and this book has been a great addition to the resources I recommend to my students.' Steven J. Miller, Williams College, Massachusetts'This book is my go-to resource for students struggling with how to write mathematical proofs. Beyond its plentiful examples, Velleman clearly lays out the techniques and principles so often glossed over in other texts.' Rafael Frongillo, University of Colorado, Boulder'I've been using this book religiously for the last eight years. It builds a strong foundation in proof writing and creates the axiomatic framework for future higher-level mathematics courses. Even when teaching more advanced courses, I recommend students to read chapter 3 (Proofs) since it is, in my opinion, the best written exposition of proof writing techniques and strategies. This third edition brings a new chapter (Number Theory), which gives the instructor a few more topics to choose from when teaching a fundamental course in mathematics. I will keep using it and recommending it to everyone, professors and students alike.' Mihai Bailesteanu, Central Connecticut State University'Professor Velleman sets himself the difficult task of bridging the gap between algorithmic and proof-based mathematics. By focusing on the basic ideas, he succeeded admirably. Many similar books are available, but none are more treasured by beginning students. In the Third Edition, the constant pursuit of excellence is further reinforced.' Taje Ramsamujh, Florida International University'Proofs are central to mathematical development. They are the tools used by mathematicians to establish and communicate their results. The developing mathematician often learns what constitutes a proof and how to present it by osmosis. How to Prove It aims at changing that. It offers a systematic introduction to the development, structuring, and presentation of logical mathematical arguments, i.e. proofs. The approach is based on the language of first-order logic and supported by proof techniques in the style of natural deduction. The art of proving is exercised with naive set theory and elementary number theory throughout the book. As such, it will prove invaluable to first-year undergraduate students in mathematics and computer science.' Marcelo Fiore, University of Cambridge'Overall, this is an engagingly-written and effective book for illuminating thinking about and building a careful foundation in proof techniques. I could see it working in an introduction to proof course or a course introducing discrete mathematics topics alongside proof techniques. As a self-study guide, I could see it working as it so well engages the reader, depending on how able they are to navigate the cultural context in some examples.' Peter Rowlett, LMS Newsletter'Altogether this is an ambitious and largely very successful introduction to the writing of good proofs, laced with many good examples and exercises, and with a pleasantly informal style to make the material attractive and less daunting than the length of the book might suggest. I particularly liked the many discussions of fallacious or incomplete proofs, and the associated challenges to readers to untangle the errors in proofs and to decide for themselves whether a result is true.' Peter Giblin, University of Liverpool, The Mathematical GazetteTable of Contents1. Sentential logic; 2. Quantificational logic; 3. Proofs; 4. Relations; 5. Functions; 6. Mathematical induction; 7. Number theory; 8. Infinite sets.

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Book Synopsis'Timely... a crucial foundation for building a more empathetic and informed society.' - Daniel H. Pink'An important book for those who want to understand... the increasingly complex world.' - Arianna HuffingtonHow do we distinguish between fact and fiction in a post-truth world?The rise of fake news and AI-generated deep fakes have created a crisis of trust, making it more difficult than ever to know when we are being misled.Renowned social scientist Dan Ariely explores how the concept of misbelief can lead anyone to doubt established truth and embrace conspiracy theories. Drawing upon his first-hand experience of being the subject of disinformation, Ariely investigates the psychological drivers and motives behind irrational beliefs and what we do to counter them.An urgent call-to-action, Misbelief uncovers how we can stem the tide of misbelief with an empathetic and tolerant approach and restore trust in society.

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Book SynopsisTable of ContentsIntroduction 1 Part 1: Data Everywhere 5 Chapter 1: Defining Data Governance 7 Chapter 2: Exploring a World Awash in Data 23 Chapter 3: Driving Value through Data 41 Chapter 4: Transforming through Data 55 Part 2: Delivering Data Governance 75 Chapter 5: Building the Business Case for Data Governance 77 Chapter 6: Focusing on the Fundamentals of Data Governance 91 Part 3: Developing Data Governance 105 Chapter 7: Establishing Data Governance Objectives 107 Chapter 8: Identifying Data Governance Roles and Responsibilities 121 Chapter 9: Designing a Data Governance Program 139 Chapter 10: Deploying a Data Governance Program 157 Part 4: Democratizing Data 183 Chapter 11: Running a Successful Data Governance Program 185 Chapter 12: Measuring and Monitoring a Data Governance Program 209 Chapter 13: Responding to Data Governance Challenges and Risks. 227 Part 5: The Part of Tens 243 Chapter 14: Ten Data Governance Best Practices 245 Chapter 15: Ten Essential Data Governance Stakeholders 255 Index 263

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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 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 SynopsisSpatial statistics empowers you to go beyond visual analysis to answer questions confidently and make data-driven decisions.Thanks to the data and computational power now at our fingertips, data science is in every aspect of our lives. But with so many algorithms and buzzwords floating around, where do you start to solve complex problems or figure out where to go next?There has never been a more exciting time to learn about spatial statistics. Spatial statistics uses an aspect of geography that helps you quantify patterns and relationships so that you can feel confident in your analysis.Spatial Statistics Illustrated is an introductory book for learning the concepts behind the powerful spatial statistics tools in ArcGIS.With approachable explanations and uncomplicated drawings, Spatial Statistics Illustrated gives readers an accessible understanding of some of the most widely used spatial statistics methods, including how they work and when to use them. In a friendly, conversational tone, the authors share techniques that can help you explore your data in meaningful ways; quantify patterns and relationships; understand trends, and make informed, impactful decisions.This book has something for everyone analyzing data, including: seasoned data scientists looking to explore the value that spatial analysis offers GIS analysts looking to expand their spatial statistics skill set new GIS users discovering the value of spatial statistics Spatial Statistics Illustrated is a perfect complement to more traditional, technical statistics and spatial statistics texts and is also ideal as supplemental reading for academic courses.Based on the popular series of Spatial Statistics workshops presented by the authors at the annual Esri User Conference, Spatial Statistics Illustrated welcomes readers into the unparalleled world of spatial statistics.

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Book SynopsisIn this introductory textbook the author explains the key topics in cryptography. He takes a modern approach, where defining what is meant by "secure" is as important as creating something that achieves that goal, and security definitions are central to the discussion throughout.The author balances a largely non-rigorous style — many proofs are sketched only — with appropriate formality and depth. For example, he uses the terminology of groups and finite fields so that the reader can understand both the latest academic research and "real-world" documents such as application programming interface descriptions and cryptographic standards. The text employs colour to distinguish between public and private information, and all chapters include summaries and suggestions for further reading.This is a suitable textbook for advanced undergraduate and graduate students in computer science, mathematics and engineering, and for self-study by professionals in information security. While the appendix summarizes most of the basic algebra and notation required, it is assumed that the reader has a basic knowledge of discrete mathematics, probability, and elementary calculus.Trade Review“The goal of cryptography is to obfuscate data for unintended recipients. … The book is divided into four parts. … The book is very comprehensive, and very accessible for dedicated students.” (Klaus Galensa, Computing Reviews, computingreviews.com, October, 2016)“Cryptography made simple is a textbook that provides a broad coverage of topics that form an essential working knowledge for the contemporary cryptographer. It is particularly suited to introducing graduate and advanced undergraduate students in computer science to the concepts necessary for understanding academic cryptography and its impact on real-world practice, though it will also be useful for mathematicians or engineers wishing to gain a similar perspective on this material.” (Maura Beth Paterson, Mathematical Reviews, July, 2016)“This is a very thorough introduction to cryptography, aimed at lower-division undergraduates. It is an engineering textbook that uses modern mathematical terminology (such as groups and finite fields). … Bottom line: really for engineers, and a useful book if used carefully; the organization makes is easy to get overwhelmed by the background material before you get to the 'good stuff', and even the good stuff has an overwhelming amount of detail.” (Allen Stenger, MAA Reviews, maa.org, June, 2016)“This very thorough book by Smart (Univ. of Bristol, UK) is aimed at graduate students and advanced undergraduates in mathematics and computer science and intended to serve as a bridge to research papers in the field. … Summing Up: Recommended. Upper-division undergraduates through professionals/practitioners.” (C. Bauer, Choice, Vol. 53 (10), June, 2016)Table of ContentsModular Arithmetic, Groups, Finite Fields and Probability.- Elliptic Curves.- Historical Ciphers.- The Enigma Machine.- Information Theoretic Security.- Historical Stream Ciphers.- Modern Stream Ciphers.- Block Ciphers.- Symmetric Key Distribution.- Hash Functions and Message Authentication Codes.- Basic Public Key Encryption Algorithms.- Primality Testing and Factoring.- Discrete Logarithms.- Key Exchange and Signature Schemes.- Implementation Issues.- Obtaining Authentic Public Keys.- Attacks on Public Key Schemes.- Definitions of Security.- Complexity Theoretic Approaches.- Provable Security: With Random Oracles.- Hybrid Encryption.- Provable Security: Without Random Oracles.- Secret Sharing Schemes.- Commitments and Oblivious Transfer.- Zero-Knowledge Proofs.- Secure Multiparty Computation.

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Book SynopsisThe latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book''s tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencTrade Review"As expected, the quality of exposition continues to be a high point of the book. Clear explanations, nice graphical illustrations, and illuminating mathematical derivations make the book particularly useful as a textbook on information theory." (Journal of the American Statistical Association, March 2008) "This book is recommended reading, both as a textbook and as a reference." (Computing Reviews.com, December 28, 2006)Table of ContentsContents v Preface to the Second Edition xv Preface to the First Edition xvii Acknowledgments for the Second Edition xxi Acknowledgments for the First Edition xxiii 1 Introduction and Preview 1 1.1 Preview of the Book 5 2 Entropy, Relative Entropy, and Mutual Information 13 2.1 Entropy 13 2.2 Joint Entropy and Conditional Entropy 16 2.3 Relative Entropy and Mutual Information 19 2.4 Relationship Between Entropy and Mutual Information 20 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information 22 2.6 Jensen’s Inequality and Its Consequences 25 2.7 Log Sum Inequality and Its Applications 30 2.8 Data-Processing Inequality 34 2.9 Sufficient Statistics 35 2.10 Fano’s Inequality 37 Summary 41 Problems 43 Historical Notes 54 3 Asymptotic Equipartition Property 57 3.1 Asymptotic Equipartition Property Theorem 58 3.2 Consequences of the AEP: Data Compression 60 3.3 High-Probability Sets and the Typical Set 62 Summary 64 Problems 64 Historical Notes 69 4 Entropy Rates of a Stochastic Process 71 4.1 Markov Chains 71 4.2 Entropy Rate 74 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph 78 4.4 Second Law of Thermodynamics 81 4.5 Functions of Markov Chains 84 Summary 87 Problems 88 Historical Notes 100 5 Data Compression 103 5.1 Examples of Codes 103 5.2 Kraft Inequality 107 5.3 Optimal Codes 110 5.4 Bounds on the Optimal Code Length 112 5.5 Kraft Inequality for Uniquely Decodable Codes 115 5.6 Huffman Codes 118 5.7 Some Comments on Huffman Codes 120 5.8 Optimality of Huffman Codes 123 5.9 Shannon–Fano–Elias Coding 127 5.10 Competitive Optimality of the Shannon Code 130 5.11 Generation of Discrete Distributions from Fair Coins 134 Summary 141 Problems 142 Historical Notes 157 6 Gambling and Data Compression 159 6.1 The Horse Race 159 6.2 Gambling and Side Information 164 6.3 Dependent Horse Races and Entropy Rate 166 6.4 The Entropy of English 168 6.5 Data Compression and Gambling 171 6.6 Gambling Estimate of the Entropy of English 173 Summary 175 Problems 176 Historical Notes 182 7 Channel Capacity 183 7.1 Examples of Channel Capacity 184 7.1.1 Noiseless Binary Channel 184 7.1.2 Noisy Channel with Nonoverlapping Outputs 185 7.1.3 Noisy Typewriter 186 7.1.4 Binary Symmetric Channel 187 7.1.5 Binary Erasure Channel 188 7.2 Symmetric Channels 189 7.3 Properties of Channel Capacity 191 7.4 Preview of the Channel Coding Theorem 191 7.5 Definitions 192 7.6 Jointly Typical Sequences 195 7.7 Channel Coding Theorem 199 7.8 Zero-Error Codes 205 7.9 Fano’s Inequality and the Converse to the Coding Theorem 206 7.10 Equality in the Converse to the Channel Coding Theorem 208 7.11 Hamming Codes 210 7.12 Feedback Capacity 216 7.13 Source–Channel Separation Theorem 218 Summary 222 Problems 223 Historical Notes 240 8 Differential Entropy 243 8.1 Definitions 243 8.2 AEP for Continuous Random Variables 245 8.3 Relation of Differential Entropy to Discrete Entropy 247 8.4 Joint and Conditional Differential Entropy 249 8.5 Relative Entropy and Mutual Information 250 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information 252 Summary 256 Problems 256 Historical Notes 259 9 Gaussian Channel 261 9.1 Gaussian Channel: Definitions 263 9.2 Converse to the Coding Theorem for Gaussian Channels 268 9.3 Bandlimited Channels 270 9.4 Parallel Gaussian Channels 274 9.5 Channels with Colored Gaussian Noise 277 9.6 Gaussian Channels with Feedback 280 Summary 289 Problems 290 Historical Notes 299 10 Rate Distortion Theory 301 10.1 Quantization 301 10.2 Definitions 303 10.3 Calculation of the Rate Distortion Function 307 10.3.1 Binary Source 307 10.3.2 Gaussian Source 310 10.3.3 Simultaneous Description of Independent Gaussian Random Variables 312 10.4 Converse to the Rate Distortion Theorem 315 10.5 Achievability of the Rate Distortion Function 318 10.6 Strongly Typical Sequences and Rate Distortion 325 10.7 Characterization of the Rate Distortion Function 329 10.8 Computation of Channel Capacity and the Rate Distortion Function 332 Summary 335 Problems 336 Historical Notes 345 11 Information Theory and Statistics 347 11.1 Method of Types 347 11.2 Law of Large Numbers 355 11.3 Universal Source Coding 357 11.4 Large Deviation Theory 360 11.5 Examples of Sanov’s Theorem 364 11.6 Conditional Limit Theorem 366 11.7 Hypothesis Testing 375 11.8 Chernoff–Stein Lemma 380 11.9 Chernoff Information 384 11.10 Fisher Information and the Cramér–Rao Inequality 392 Summary 397 Problems 399 Historical Notes 408 12 Maximum Entropy 409 12.1 Maximum Entropy Distributions 409 12.2 Examples 411 12.3 Anomalous Maximum Entropy Problem 413 12.4 Spectrum Estimation 415 12.5 Entropy Rates of a Gaussian Process 416 12.6 Burg’s Maximum Entropy Theorem 417 Summary 420 Problems 421 Historical Notes 425 13 Universal Source Coding 427 13.1 Universal Codes and Channel Capacity 428 13.2 Universal Coding for Binary Sequences 433 13.3 Arithmetic Coding 436 13.4 Lempel–Ziv Coding 440 13.4.1 Sliding Window Lempel–Ziv Algorithm 441 13.4.2 Tree-Structured Lempel–Ziv Algorithms 442 13.5 Optimality of Lempel–Ziv Algorithms 443 13.5.1 Sliding Window Lempel–Ziv Algorithms 443 13.5.2 Optimality of Tree-Structured Lempel–Ziv Compression 448 Summary 456 Problems 457 Historical Notes 461 14 Kolmogorov Complexity 463 14.1 Models of Computation 464 14.2 Kolmogorov Complexity: Definitions and Examples 466 14.3 Kolmogorov Complexity and Entropy 473 14.4 Kolmogorov Complexity of Integers 475 14.5 Algorithmically Random and Incompressible Sequences 476 14.6 Universal Probability 480 14.7 Kolmogorov complexity 482 14.8 Ω 484 14.9 Universal Gambling 487 14.10 Occam’s Razor 488 14.11 Kolmogorov Complexity and Universal Probability 490 14.12 Kolmogorov Sufficient Statistic 496 14.13 Minimum Description Length Principle 500 Summary 501 Problems 503 Historical Notes 507 15 Network Information Theory 509 15.1 Gaussian Multiple-User Channels 513 15.1.1 Single-User Gaussian Channel 513 15.1.2 Gaussian Multiple-Access Channel with m Users 514 15.1.3 Gaussian Broadcast Channel 515 15.1.4 Gaussian Relay Channel 516 15.1.5 Gaussian Interference Channel 518 15.1.6 Gaussian Two-Way Channel 519 15.2 Jointly Typical Sequences 520 15.3 Multiple-Access Channel 524 15.3.1 Achievability of the Capacity Region for the Multiple-Access Channel 530 15.3.2 Comments on the Capacity Region for the Multiple-Access Channel 532 15.3.3 Convexity of the Capacity Region of the Multiple-Access Channel 534 15.3.4 Converse for the Multiple-Access Channel 538 15.3.5 m-User Multiple-Access Channels 543 15.3.6 Gaussian Multiple-Access Channels 544 15.4 Encoding of Correlated Sources 549 15.4.1 Achievability of the Slepian–Wolf Theorem 551 15.4.2 Converse for the Slepian–Wolf Theorem 555 15.4.3 Slepian–Wolf Theorem for Many Sources 556 15.4.4 Interpretation of Slepian–Wolf Coding 557 15.5 Duality Between Slepian–Wolf Encoding and Multiple-Access Channels 558 15.6 Broadcast Channel 560 15.6.1 Definitions for a Broadcast Channel 563 15.6.2 Degraded Broadcast Channels 564 15.6.3 Capacity Region for the Degraded Broadcast Channel 565 15.7 Relay Channel 571 15.8 Source Coding with Side Information 575 15.9 Rate Distortion with Side Information 580 15.10 General Multiterminal Networks 587 Summary 594 Problems 596 Historical Notes 609 16 Information Theory and Portfolio Theory 613 16.1 The Stock Market: Some Definitions 613 16.2 Kuhn–Tucker Characterization of the Log-Optimal Portfolio 617 16.3 Asymptotic Optimality of the Log-Optimal Portfolio 619 16.4 Side Information and the Growth Rate 621 16.5 Investment in Stationary Markets 623 16.6 Competitive Optimality of the Log-Optimal Portfolio 627 16.7 Universal Portfolios 629 16.7.1 Finite-Horizon Universal Portfolios 631 16.7.2 Horizon-Free Universal Portfolios 638 16.8 Shannon–McMillan–Breiman Theorem (General AEP) 644 Summary 650 Problems 652 Historical Notes 655 17 Inequalities in Information Theory 657 17.1 Basic Inequalities of Information Theory 657 17.2 Differential Entropy 660 17.3 Bounds on Entropy and Relative Entropy 663 17.4 Inequalities for Types 665 17.5 Combinatorial Bounds on Entropy 666 17.6 Entropy Rates of Subsets 667 17.7 Entropy and Fisher Information 671 17.8 Entropy Power Inequality and Brunn–Minkowski Inequality 674 17.9 Inequalities for Determinants 679 17.10 Inequalities for Ratios of Determinants 683 Summary 686 Problems 686 Historical Notes 687 Bibliography 689 List of Symbols 723 Index 727

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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 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 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 SynopsisA major work of feminist critical theory challenging the masculinist politics of digital media forms, practices and study.Trade Review'Furious rips beyond the vanity of know-it-all analysis to offer long-awaited new ways of thinking, feeling, and writing. Cunningly crafted by an authorial trio, it bewitches with performative feminist energies. I dare you to read it' -- Sally-Jane Norman, Founding Director of the Attenborough Centre for the Creative Arts at the University of Sussex'A rare gem of a book, Furious makes a sharp, critical feminist intervention in digital media research demonstrating the power of thinking together, going against the conventions of academic writing, and creating good trouble' -- Susanna Paasonen, co-author of 'NSFW: Sex, Humor, and Risk in Social Media''This wide-ranging and imaginative book makes a compelling case for a feminist techno-politics which challenges to the core the masculinist grip of computational culture and science. It's also a book which pays fine attention to the process of writing' -- Angela McRobbie, author of 'Be Creative: Making a Living in the New Culture Industries''A passionate guidebook to feminist theorising that refuses data as self-evident patterns and theory as beautiful abstractions, while insisting on the generative power of writing, fabulation, and future making' -- Lucy Suchman, author of 'Feminist STS and the Sciences of the Artificial''Combining clarity with rational ire, Furious insists on the power of insurgent, intersectional feminist epistemologies to disrupt, inspire, and transform. This collective feminist tour de force writes us a new course, away from the technophilic belief that technology will fix what ails the contemporary world and toward a critical and temperate utopianism' -- Carol Stabile, author of 'The Broadcast 41: Women and the Anti-Communist Blacklist'Table of ContentsSeries Preface Acknowledgements Preface 1. Feminist Futures: A Conditional Paeon for the Anything-Digital 2. Scale, Subject and Stories: Unreal Objects 3. Bland Ambition? Automation's Missing Visions 4. Driving at the Anthropocene, or, Let's Get Out of Here: How? 5. Technological Feminism and Digital Futures Bibliography Index

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Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

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Book SynopsisCryptography is now ubiquitous – moving beyond the traditional environments, such as government communications and banking systems, we see cryptographic techniques realized in Web browsers, e-mail programs, cell phones, manufacturing systems, embedded software, smart buildings, cars, and even medical implants. Today's designers need a comprehensive understanding of applied cryptography. After an introduction to cryptography and data security, the authors explain the main techniques in modern cryptography, with chapters addressing stream ciphers, the Data Encryption Standard (DES) and 3DES, the Advanced Encryption Standard (AES), block ciphers, the RSA cryptosystem, public-key cryptosystems based on the discrete logarithm problem, elliptic-curve cryptography (ECC), digital signatures, hash functions, Message Authentication Codes (MACs), and methods for key establishment, including certificates and public-key infrastructure (PKI). Throughout the book, the authors focus on communicating the essentials and keeping the mathematics to a minimum, and they move quickly from explaining the foundations to describing practical implementations, including recent topics such as lightweight ciphers for RFIDs and mobile devices, and current key-length recommendations. The authors have considerable experience teaching applied cryptography to engineering and computer science students and to professionals, and they make extensive use of examples, problems, and chapter reviews, while the book’s website offers slides, projects and links to further resources. This is a suitable textbook for graduate and advanced undergraduate courses and also for self-study by engineers.The authors' website (http://www.crypto-textbook.com/) provides extensive notes, slides, video lectures; the authors' YouTube channel (https://www.youtube.com/channel/UC1usFRN4LCMcflV7UjHNuQg) includes video lectures.Trade ReviewFrom the reviews: "The authors have succeeded in creating a highly valuable introduction to the subject of applied cryptography. I hope that it can serve as a guide for practitioners to build more secure systems based on cryptography, and as a stepping stone for future researchers to explore the exciting world of cryptography and its applications." (Bart Preneel, K.U.Leuven) "The material is very well presented so it is clear to understand. The necessary amount of mathematics is used and complete yet simple examples are used by the authors to help the reader understand the topics. ... [The authors] appear to fully understand the concepts and follow a very good pedagogical process that helps the reader not only understand the different topics but motivate you to perform some of the exercises at the end of each chapter and browse some of the reference materials. I fully recommend this book to any software developer/designer working or considering working on a project that requires security." (John Canessa) "The book presents a panoramic of modern Cryptography with a view to practical applications. ... The book is well written, many examples and figures through it illustrate the theory and the book's website offers links and supplementary information. The book also discusses the implementation in software and hardware of the main algorithms described." (Juan Tena Ayuso, Zentralblatt MATH, Vol. 1190, 2010)Table of ContentsIntroduction to Cryptography and Data Security.- Stream Ciphers.- The Data Encryption Standard (DES) and Alternatives.- The Advanced Encryption Standard (AES).- More About Block Ciphers.- to Public-Key Cryptography.- The RSA Cryptosystem.- Public-Key Cryptosystems Based on the Discrete Logarithm Problem.- Elliptic Curve Cryptosystems.- Digital Signatures.- Hash Functions.- Message Authentication Codes (MACs).- Key Establishment.

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Book SynopsisTrade Review"Distant Horizons not only proves that Ted Underwood is defining the field of cultural analytics as it emerges; it shows us why. Combining literary theory with a deep understanding of computational methods, this volume demonstrates and effectively argues that quantitative analysis is best used not to find objective truths but to explore perspectives, both historically local and theoretical. It is at once a primer for quantitative literacy and a historically sensitive exploration of gender, genre, character, and audience, putting paid once and for all to the notion that statistical methods have no place in hermeneutics."--Laura Mandell, author of Breaking the Book: Print Humanities in the Digital Age "Distant Horizons is of compelling interest to digital humanists. But its true audience is a wider society of literary and other humanities scholars spanning across fields, periods, approaches, and levels. For this larger audience, Ted Underwood goes out of his way to make distant reading accessible, inviting, and persuasive. This innovative book is the breakout work digital humanists have been waiting for, and it is positioned to be a landmark work in literary scholarship at large."--Alan Liu, author of Friending the Past: The Sense of History in the Digital Age

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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 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 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 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 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 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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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 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 SynopsisThis book constitutes the refereed post-conference proceedings of the 14th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2018, held in Ho Chi Minh City, Vietnam, in December 2018. The 13 revised full papers were carefully reviewed and selected from 28 submissions. The papers are organized thematically in tracks, starting with security and privacy, telecommunication systems and networks, networks and applications.Table of ContentsImproving Privacy for GeoIP DNS Traffic.- Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking.- 3 Throughput optimization for multirate multicasting through association control in IEEE 802.11 WLAN.- An NS-3 MPTCP Implementation.- A Novel Security Framework for Industrial IoT based on ISA 100.11a.- Social-aware Caching and Resource Sharing Optimization for Video Delivering in 5G Networks.- Energy Efficiency in QoS Constrained 60 GHz Millimeter-Wave Ultra-dense Networks.- Priority-based Device Discovery in Public Safety D2D Networks with Full Duplexing.- Modified Direct Method for Point-to-Point Blocking.- Probability in Multi-service Switching Networks with Resource Allocation Control.- Inconsistencies among Spectral Robustness Metrics.- QoS criteria for energy-aware switching networks.- Modelling Overflow Systems with Queuing in Primary.- Exploring YouTube’s CDN Heterogeneity.

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Book SynopsisThis English version of Ruslan L. Stratonovich’s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.Trade Review“The book could be useful in advanced graduate courses with students, who are not afraid of integrals and probabilities.” (Jaak Henno, zbMATH 1454.94002, 2021)Table of Contents

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Book SynopsisState-of-the-art database systems manage and process a variety of complex objects, including strings and trees. For such objects equality comparisons are often not meaningful and must be replaced by similarity comparisons. This book describes the concepts and techniques to incorporate similarity into database systems. We start out by discussing the properties of strings and trees, and identify the edit distance as the de facto standard for comparing complex objects. Since the edit distance is computationally expensive, token-based distances have been introduced to speed up edit distance computations. The basic idea is to decompose complex objects into sets of tokens that can be compared efficiently. Token-based distances are used to compute an approximation of the edit distance and prune expensive edit distance calculations. A key observation when computing similarity joins is that many of the object pairs, for which the similarity is computed, are very different from each other. Filters exploit this property to improve the performance of similarity joins. A filter preprocesses the input data sets and produces a set of candidate pairs. The distance function is evaluated on the candidate pairs only. We describe the essential query processing techniques for filters based on lower and upper bounds. For token equality joins we describe prefix, size, positional and partitioning filters, which can be used to avoid the computation of small intersections that are not needed since the similarity would be too low.Table of ContentsPreface.- Acknowledgments.- Introduction.- Data Types.- Edit-Based Distances.- Token-Based Distances.- Query Processing Techniques.- Filters for Token Equality Joins.- Conclusion.- Bibliography.- Authors' Biographies.- Index.

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Book SynopsisThis book constitutes thoroughly refereed and revised selected papers from the proceedings of 19th European Conference on Multi-Agent Systems, EUMAS 2022, held in Düsseldorf, Germany, during September 14–16, 2022.The 23 full papers included in this book were carefully reviewed and selected from 36 submissions. The book also contains 6 short summaries of talks from PhD students at the PhD day. The papers deal with current topics in the research and development of multi-agent systems.Table of ContentsEUMAS 2022 Papers.- Iterative Goal-Based Approval Voting.- Mind the Gap! Runtime Verification of Partially Observable MASs with Probabilistic Trace Expressions.- Advising Agent for Service-Providing Live-Chat Operators.- Initial Conditions Sensitivity Analysis of a Two-Species Butterfly-Effect Agent-Based Model.- Proxy Manipulation for Better Outcomes.- The Spread of Opinions via Boolean Networks.- Robustness of Greedy Approval Rules.- Using Multiwinner Voting to Search for Movies.- Allocating Teams to Tasks: An Anytime Heuristic Competence-Based Approach.- Collaborative Decision Making for Lane-Free Autonomous Driving in the Presence of Uncertainty.- Maximin Shares under Cardinality Constraints.- Welfare Effects of Strategic Voting under Scoring Rules.- Preserving Consistency for Liquid Knapsack Voting.- Strategic Nominee Selection in Tournament Solutions.- Sybil-Resilient Social Choice with Low Voter Turnout.- A Survey of Ad Hoc Teamwork Research.- Combining Theory of Mind and Abduction for Cooperation under Imperfect Information.- A Modular Architecture for Integrating Normative Advisors in MAS.- Participatory Budgeting with Multiple Resources.- A Methodology for Formalizing Different Types of Norms.- Explainability in Mechanism Design: Recent Advances and the Road Ahead.- Integrating Quantitative and Qualitative Reasoning for Value Alignment.- Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise.- PhD Day Short Papers.- Proactivity in Intelligent Personal Assistants: A Simulation-based Approach.- Stability, Fairness, and Altruism in Coalition Formation.- Pro-Social Autonomous Agents.- Axiomatic and Algorithmic Study on Different Areas of Collective Decision Making.- Participatory Budgeting: Fairness and Welfare Maximization.- Human Consideration in Analysis and Algorithms for Mechanism Design.

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Book SynopsisThis book constitutes the proceedings of the Third International Conference on Microelectronic Devices, Circuits and Systems, ICMDCS 2022, was held in Vellore, India, in August 2022.The 9 full papers and 5 short paper presented in this volume were carefully reviewed and selected from 84 submissions. The papers are organized in the following topical sections: System Level Design; Digital Design; Analog, Mixed-Signal and RF Design; and Emerging Technologies.Table of Contents​System Level Design.- Tapered Fed Modified Patch Antenna for SWB Communications using DGS.- Design of Hardware Accelerator for Facial Recognition System using Convolutional Neural Networks based on FPGA.- Digital Design.- Advanced TSV-BIST Repair Technique to target the Yield and Test challenges in 3-D Stacked IC’s.- Redundancy allocation problem evaluation using interval-based GA and PSO for multi-core system consisting of one instruction cores.- Design of Low Powered and High Speed Compressor based Multiplier.-A Route Planning for Idyllic Coverage in Sensor Networks with Efficient Area Coverage.- Low Power Mod 2 Synchronous Counter Design using Modified Gate Diffusion Input Technique.- Analog, Mixed-Signal and RF Design.- A novel blind zone free, low power phase frequency detector for fast locking of charge pump phase locked loops.- Performance Improvement of H-Shaped Antenna for Wireless Local Area Networks.- Emerging Technologies.- Real-Time Rainfall Prediction System using IoT and Machine Learning.- Performance Analysis of Image Caption Generation using Deep Learning Techniques.- The Heroes and Villains of the Mix Zone: The Preservation and Leaking of User’s Privacy in Future Vehicles.- Analysis and Design of High Speed and Low Power Finite Impulse Response Filter using Different Types of Multipliers.- MPPT using P&O algorithm for Solar-Battery powered Electric Vehicle.

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Book SynopsisThis book constitutes the refereed proceedings of the 8th International Conference on Modelling and Development of Intelligent Systems, MDIS 2022, held in Sibiu, Romania, during October 28–30, 2022.The 21 papers included in this book were carefully reviewed and selected from 48 submissions. They were organized in the following topical sections as follows: intelligent systems for decision support; machine learning; mathematical models for development of intelligent systems; and modelling and optimization of dynamic systems.Table of ContentsIntelligent Systems for Decision Support.- Effective LSTM Neural Network with Adam Optimizer for Improving Frost Prediction in Agriculture Data Stream.- Gaze Tracking: A Survey of Devices, Libraries and Applications.- Group Decision-Making Involving Competence of Experts in Relation to Evaluation Criteria: Case Study for e-Commerce Platform Selection.- Transparency and Traceability for AI-based Defect Detection in PCB Production.- Tasks Management Using Modern Devices.- Machine Learning.- A Method for Target Localization by Multistatic Radars.- Intrusion Detection by XGBoost Model Tuned by Improved Social Network Search Algorithm.- Bridging the Resource Gap in Cross-lingual Embedding Space.- Classification of Microstructure Images of Metals using Transfer Learning.- Generating Jigsaw Puzzles and an AI Powered Solver.- Morphology of Convolutional Neural Network with Diagonalized Pooling.- Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development.- Feature Selection and Extreme Learning Machine Tuning by Hybrid Sand Cat Optimization Algorithm for Diabetes Classification.- Enriching SQL-Driven Data Exploration With Different Machine Learning Models.- Mathematical Models for Development of Intelligent Systems.- Analytical Solution of the Simplest Entropiece Inversion Problem.- Latent Semantic Structure in Malicious Programs.- Innovative Lattice Sequences Based on Component by Component Construction Method for Multidimensional Sensitivity Analysis.- On an Optimization of the Lattice Sequence for the Multidimensional Integrals Connected with Bayesian Statistics.- Modelling and Optimization of Dynamic Systems.- Numerical Optimization Identification of a Keller-Segel Model for Thermoregulation in Honey Bee Colonies in Winter.- Gradient Optimization in Reconstruction of the Diffusion Coefficient in a Time Fractional Integro-Differential Equation of Pollution in Porous Media.- Flash Flood Simulation Between Slănic and Vărbilău Rivers in Vărbilău Village, Prahova County, Romania, Using Hydraulic Modeling and GIS Techniques.

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Book SynopsisThis book explains compactly, without theoretical superstructure and with as little mathematical formalism as possible, the essential concepts in the encryption of messages and data worthy of protection. The focus is on the description of the historically and practically important cipher, signature and authentication methods. Both symmetric encryption and public-key ciphers are discussed. In each case, the strategies used to attack and attempt to "crack" encryption are also discussed. Special emphasis is placed on the practical use of ciphers, especially in the everyday environment. The book is suitable for working groups at STEM schools and STEM teacher training, for introductory courses at universities as well as for interested students and adults.Table of ContentsBasics and history.- Symmetric ciphers.- Public-key ciphers.- Digital signature.

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Book SynopsisThis book is the first one that provides a solid bridge between algorithmic information theory and statistical mechanics. Algorithmic information theory (AIT) is a theory of program size and recently is also known as algorithmic randomness. AIT provides a framework for characterizing the notion of randomness for an individual object and for studying it closely and comprehensively. In this book, a statistical mechanical interpretation of AIT is introduced while explaining the basic notions and results of AIT to the reader who has an acquaintance with an elementary theory of computation.A simplification of the setting of AIT is the noiseless source coding in information theory. First, in the book, a statistical mechanical interpretation of the noiseless source coding scheme is introduced. It can be seen that the notions in statistical mechanics such as entropy, temperature, and thermal equilibrium are translated into the context of noiseless source coding in a natural manner. Then, the framework of AIT is introduced. On this basis, the introduction of a statistical mechanical interpretation of AIT is begun. Namely, the notion of thermodynamic quantities, such as free energy, energy, and entropy, is introduced into AIT. In the interpretation, the temperature is shown to be equal to the partial randomness of the values of all these thermodynamic quantities, where the notion of partial randomness is a stronger representation of the compression rate measured by means of program-size complexity. Additionally, it is demonstrated that this situation holds for the temperature itself as a thermodynamic quantity. That is, for each of all the thermodynamic quantities above, the computability of its value at temperature T gives a sufficient condition for T to be a fixed point on partial randomness.In this groundbreaking book, the current status of the interpretation from both mathematical and physical points of view is reported. For example, a total statistical mechanical interpretation of AIT that actualizes a perfect correspondence to normal statistical mechanics can be developed by identifying a microcanonical ensemble in the framework of AIT. As a result, the statistical mechanical meaning of the thermodynamic quantities of AIT is clarified. In the book, the close relationship of the interpretation to Landauer's principle is pointed out.Table of ContentsStatistical Mechanical Interpretation of Noiseless Source Coding.- Algorithmic Information Theory.- Partial Randomness.- Temperature Equals to Partial Randomness.- Fixed Point Theorems on Partial Randomness.- Statistical Mechanical Meaning of the Thermodynamic Quantities of AIT.- The Partial Randomness of Recursively Enumerable Reals.- Computation-Theoretic Clarification of the Phase Transition at Temperature T=1.- Other Related Results and Future Development.

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