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Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an intermediate user, this book will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data
This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB (R) and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
This book surveys the state-of-the-art in the theory of combinatorial games, that is games not involving chance or hidden information. Enthusiasts will find a wide variety of exciting topics, from a trailblazing presentation of scoring to solutions of three piece ending positions of bidding chess. Theories and techniques in many subfields are covered, such as universality, Wythoff Nim variations, misere play, partizan bidding (a.k.a. Richman games), loopy games, and the algebra of placement games. Also included are an updated list of unsolved problems, extremely efficient algorithms for taking and breaking games, a historical exposition of binary numbers and games by David Singmaster, chromatic Nim variations, renormalization for combinatorial games, and a survey of temperature theory by Elwyn Berlekamp, one of the founders of the field. The volume was initiated at the Combinatorial Game Theory Workshop, January 2011, held at the Banff International Research Station.
The development of business analysis as a professional discipline has extended the role of the business analyst who now needs the widest possible array of tools and the skills and knowledge to be able to use each when and where it is needed. This new edition provides 99 possible techniques and practical guidance on how and when to apply them. It complements Business Analysis also published by BCS, and offers a more detailed description of the techniques used in business analysis, together with practical advice on their application.
The award-winning author of The Music of the Primes explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human. Can a well-programmed machine do anything a human can-only better? Complex algorithms are choosing our music, picking our partners, and driving our investments. They can navigate more data than a doctor or lawyer and act with greater precision. For many years we've taken solace in the notion that they can't create. But now that algorithms can learn and adapt, does the future of creativity belong to machines, too? It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. The Pollockizer can produce drip paintings in the style of Jackson Pollock, Botnik spins off fanciful (if improbable) scenes inspired by J. K. Rowling, and the music-composing algorithm Emmy managed to fool a panel of Bach experts. But do these programs just mimic, or do they have what it takes to create? Du Sautoy argues that to answer this question, we need to understand how the algorithms that drive them work-and this brings him back to his own subject of mathematics, with its puzzles, constraints, and enticing possibilities. While most recent books on AI focus on the future of work, The Creativity Code moves us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines. It challenges us to reconsider what it means to be human-and to crack the creativity code.
This book investigates the susceptibility of intrinsic physically unclonable function (PUF) implementations on reconfigurable hardware to optical semi-invasive attacks from the chip backside. It explores different classes of optical attacks, particularly photonic emission analysis, laser fault injection, and optical contactless probing. By applying these techniques, the book demonstrates that the secrets generated by a PUF can be predicted, manipulated or directly probed without affecting the behavior of the PUF. It subsequently discusses the cost and feasibility of launching such attacks against the very latest hardware technologies in a real scenario. The author discusses why PUFs are not tamper-evident in their current configuration, and therefore, PUFs alone cannot raise the security level of key storage. The author then reviews the potential and already implemented countermeasures, which can remedy PUFs' security-related shortcomings and make them resistant to optical side-channel and optical fault attacks. Lastly, by making selected modifications to the functionality of an existing PUF architecture, the book presents a prototype tamper-evident sensor for detecting optical contactless probing attempts.
The subject of sparse matrices has its root in such diverse fields as management science, power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of sparsity is a key to solving large problems in many fields. This second edition is a complete rewrite of the first edition published 30 years ago. Much has changed since that time. Problems have grown greatly in size and complexity; nearly all examples in the first edition were of order less than 5,000 in the first edition, and are often more than a million in the second edition. Computer architectures are now much more complex, requiring new ways of adapting algorithms to parallel environments with memory hierarchies. Because the area is such an important one to all of computational science and engineering, a huge amount of research has been done in the last 30 years, some of it by the authors themselves. This new research is integrated into the text with a clear explanation of the underlying mathematics and algorithms. New research that is described includes new techniques for scaling and error control, new orderings, new combinatorial techniques for partitioning both symmetric and unsymmetric problems, and a detailed description of the multifrontal approach to solving systems that was pioneered by the research of the authors and colleagues. This includes a discussion of techniques for exploiting parallel architectures and new work for indefinite and unsymmetric systems.
Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.
This is the simplest, quickest, least technical, most affordable introduction to basic electronics. No tools are necessary--not even a screwdriver. Easy Electronics should satisfy anyone who has felt frustrated by entry-level books that are not as clear and simple as they are supposed to be. Brilliantly clear graphics will take you step by step through 12 basic projects, none of which should take more than half an hour. Using alligator clips to connect components, you see and hear immediateresults. The hands-on approach is fun and intriguing, especially for family members exploring the projects together. The 12 experiments will introduce you to switches, resistors, capacitors, transistors, phototransistors, LEDs, audio transducers, and a silicon chip. You'll even learn how to read schematics by comparing them with the circuits that you build. No prior knowledge is required, and no math is involved. You learn by seeing, hearing, and touching. By the end of Experiment 12, you may be eager to move on to a more detailed book. Easy Electronics will function perfectly as a prequel to the same author's bestseller, Make: Electronics. All the components listed in the book are inexpensive and readily available from online sellers. A very affordable kit has been developed in conjunction with the book to eliminate the chore of shopping for separate parts. A QR code inside the book will take you to the vendor's web site. Concepts include: Transistor as a switch or an amplifier Phototransistor to function as an alarm Capacitor to store and release electricity Transducer to create sounds from a timer Resistor codes A miniature light bulb to display voltage The inner workings of a switch Using batteries and resistors in series and parallel Creating sounds by the pressure of your finger Making a matchbox that beeps when you touch it And more. Grab your copy and start experimenting!
Algorithms were always an important part of many branches in the sciences. In many manuals and handbooks, algorithms of problems of computational mathematics are focused on the manual performance or by means of a calculator. In this book, descriptions of algorithms, their solutions and main characteristics are discussed. The present work is the outcome of many years of the authors' work on solving different problems and tasks from domains of instruction making, metrology, system analysis, ecology, data analysis from ecology, agriculture, medicine and creation of corresponding universal computer packages and systems.
This book introduces readers to selected issues in distributed systems, and primarily focuses on principles, not on technical details. Though the systems discussed are based on existing (von Neumann) computer architectures, the book also touches on emerging processing paradigms. Uniquely, it approaches system components not only as static constructs, but also "in action," exploring the different states they pass through. The author's teaching experience shows that newcomers to the field, students and even IT professionals can far more readily grasp the essence of distributed algorithmic structures in action, than on the basis of static descriptions.
This book considers logical proof systems from the point of view of their space complexity. After an introduction to propositional proof complexity the author structures the book into three main parts. Part I contains two chapters on resolution, one containing results already known in the literature before this work and one focused on space in resolution, and the author then moves on to polynomial calculus and its space complexity with a focus on the combinatorial technique to prove monomial space lower bounds. The first chapter in Part II addresses the proof complexity and space complexity of the pigeon principles. Then there is an interlude on a new type of game, defined on bipartite graphs, essentially independent from the rest of the book, collecting some results on graph theory. Finally Part III analyzes the size of resolution proofs in connection with the Strong Exponential Time Hypothesis (SETH) in complexity theory. The book is appropriate for researchers in theoretical computer science, in particular computational complexity.
This book constitutes the refereed proceedings of the 11th International Andrei P. Ershov Informatics Conference, PSI 2017, held in Moscow, Russia, in June 2017. The 31 full papers presented in this volume were carefully reviewed and selected from 57 submissions. The papers cover various topics related to the foundations of program and system development and analysis, programming methodology and software engineering and information technologies.
Second edition of the successful textbook which has emerged from a
lecture series. The compact introduction addresses graduate
students with a reasonably good background in physics, notably in
quantum mechanics, plus some knowledge in introductory statistical
mechanics and solid-state physics.
As computers increasingly control the systems and services we depend upon within our daily lives like transport, communications, and the media, ensuring these systems function correctly is of utmost importance. This book consists of twelve chapters and one historical account that were presented at a workshop in London in 2015, marking the 25th anniversary of the European ESPRIT Basic Research project 'ProCoS' (Provably Correct Systems). The ProCoS I and II projects pioneered and accelerated the automation of verification techniques, resulting in a wide range of applications within many trades and sectors such as aerospace, electronics, communications, and retail. The following topics are covered: An historical account of the ProCoS project Hybrid Systems Correctness of Concurrent Algorithms Interfaces and Linking Automatic Verification Run-time Assertions Checking Formal and Semi-Formal Methods Provably Correct Systems provides researchers, designers and engineers with a complete overview of the ProCoS initiative, past and present, and explores current developments and perspectives within the field.
This book provides a concise introduction to Pervasive Computing, otherwise known as Internet of Things (IoT) and Ubiquitous Computing (Ubicomp) which addresses the seamless integration of computing systems within everyday objects. By introducing the core topics and exploring assistive pervasive systems which infer their context through pattern recognition, the author provides readers with a gentle yet robust foundation of knowledge to this growing field of research. The author explores a range of topics including data acquisition, signal processing, control theory, machine learning and system engineering explaining, with the use of simple mathematical concepts, the core principles underlying pervasive computing systems. Real-life examples are applied throughout, including self-driving cars, automatic insulin pumps, smart homes, and social robotic companions, with each chapter accompanied by a set of exercises for the reader. Practical tutorials are also available to guide enthusiastic readers through the process of building a smart system using cameras, microphones and robotic kits. Due to the power of MATLAB (TM), this can be achieved with no previous programming or robotics experience. Although Pervasive Computing is primarily for undergraduate students, the book is accessible to a wider audience of researchers and designers who are interested in exploring pervasive computing further.
Although computation and the science of physical systems would appear to be unrelated, there are a number of ways in which computational and physical concepts can be brought together in ways that illuminate both. This volume examines fundamental questions which connect scholars from both disciplines: is the universe a computer? Can a universal computing machine simulate every physical process? What is the source of the computational power of quantum computers? Are computational approaches to solving physical problems and paradoxes always fruitful? Contributors from multiple perspectives reflecting the diversity of thought regarding these interconnections address many of the most important developments and debates within this exciting area of research. Both a reference to the state of the art and a valuable and accessible entry to interdisciplinary work, the volume will interest researchers and students working in physics, computer science, and philosophy of science and mathematics.
The Untold Story of Everything Digital: Bright Boys, Revisited celebrates the 70th anniversary (1949-2019) of the world "going digital" for the very first time-real-time digital computing's genesis story. That genesis story is taken from the 2010 edition of Bright Boys: The Making of Information Technology, 1938-1958, and substantially expanded upon for this special, anniversary edition. Please join us for the incredible adventure that is The Untold Story of Everything Digital, when a band of misfit engineers, led by MIT's Jay Forrester and Bob Everett, birthed the digital revolution. The bright boys were the first to imagine an electronic landscape of computing machines and digital networks, and the first to blaze its high-tech trails.
Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes-or over 50 million gigabytes-of genomic data, and they're turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O'Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You'll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra
Voronoi diagrams partition space according to the influence certain sites exert on their environment. Since the 17th century, such structures play an important role in many areas like Astronomy, Physics, Chemistry, Biology, Ecology, Economics, Mathematics and Computer Science. They help to describe zones of political influence, to determine the hospital nearest to an accident site, to compute collision-free paths for mobile robots, to reconstruct curves and surfaces from sample points, to refine triangular meshes, and to design location strategies for competing markets. This unique book offers a state-of-the-art view of Voronoi diagrams and their structure, and it provides efficient algorithms towards their computation. Readers with an entry-level background in algorithms can enjoy a guided tour of gently increasing difficulty through a fascinating area. Lecturers might find this volume a welcome source for their courses on computational geometry. Experts are offered a broader view, including many alternative solutions, and up-to-date references to the existing literature; they might benefit in their own research or application development.
In view of the growing presence and popularity of multicore and manycore processors, accelerators, and coprocessors, as well as clusters using such computing devices, the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such as Intel Xeon Phi, accelerators, such as GPUs, and clusters, as well as programming models supported on these platforms. It next introduces parallelization through important programming paradigms, such as master-slave, geometric Single Program Multiple Data (SPMD) and divide-and-conquer. The practical and useful elements of the most popular and important APIs for programming parallel HPC systems are discussed, including MPI, OpenMP, Pthreads, CUDA, OpenCL, and OpenACC. It also demonstrates, through selected code listings, how selected APIs can be used to implement important programming paradigms. Furthermore, it shows how the codes can be compiled and executed in a Linux environment. The book also presents hybrid codes that integrate selected APIs for potentially multi-level parallelization and utilization of heterogeneous resources, and it shows how to use modern elements of these APIs. Selected optimization techniques are also included, such as overlapping communication and computations implemented using various APIs. Features: Discusses the popular and currently available computing devices and cluster systems Includes typical paradigms used in parallel programs Explores popular APIs for programming parallel applications Provides code templates that can be used for implementation of paradigms Provides hybrid code examples allowing multi-level parallelization Covers the optimization of parallel programs
The essential guide to solving algorithmic and networking problems in commercial computer games, revised and extended Algorithms and Networking for Computer Games, Second Edition is written from the perspective of the computer scientist. Combining algorithmic knowledge and game-related problems, it explores the most common problems encountered in game programing. The first part of the book presents practical algorithms for solving classical topics, such as random numbers, procedural generation, tournaments, group formations and game trees. The authors also focus on how to find a path in, create the terrain of, and make decisions in the game world. The second part introduces networking related problems in computer games, focusing on four key questions: how to hide the inherent communication delay, how to best exploit limited network resources, how to cope with cheating and how to measure the on-line game data. Thoroughly revised, updated, and expanded to reflect the many constituent changes occurring in the commercial gaming industry since the original, this Second Edition, like the first, is a timely, comprehensive resource offering deeper algorithmic insight and more extensive coverage of game-specific networking problems than ordinarily encountered in game development books. Algorithms and Networking for Computer Games, Second Edition: * Provides algorithmic solutions in pseudo-code format, which emphasises the idea behind the solution, and can easily be written into a programming language of choice * Features a section on the Synthetic player, covering decision-making, influence maps, finite-state machines, flocking, fuzzy sets, and probabilistic reasoning and noise generation * Contains in-depth treatment of network communication, including dead-reckoning, local perception filters, cheating prevention and on-line metrics * Now includes 73 ready-to-use algorithms and 247 illustrative exercises Algorithms and Networking for Computer Games, Second Edition is a must-have resource for advanced undergraduate and graduate students taking computer game related courses, postgraduate researchers in game-related topics, and developers interested in deepening their knowledge of the theoretical underpinnings of computer games and in learning new approaches to game design and programming.
Cyberpsychology is an emerging area of psychological study that aims to understand and explain all facets of online behaviour. This book brings together overviews from a number of leading authorities in the field, to suggest how academic theory and research can be applied to a variety of online behaviours. Both positive and negative behaviours are considered, including topics as diverse as parenting the online child, age-related internet usage and cultural considerations in online interactions. Psychological research can no longer view online and offline worlds as different entities, but must consider online behaviours as equally distinct as offline activities. This is especially apparent when looking at online dating, the role that social networks play in organisations and online consumer behaviours, and in a consideration of the role that psychological research plays in underpinning the multi-billion pound gaming industry. Focusing on these personal applications of the Internet, insight is also offered into the role that theory and research plays in training military personnel as well as the use of psychometric testing to select and retain employees.
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