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Complexity theory aims to understand and classify computational problems, especially decision problems, according to their inherent complexity. This book uses new techniques to expand the theory for use with counting problems. The authors present dichotomy classifications for broad classes of counting problems in the realm of P and NP. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This volume presents the theory on the Boolean domain, and includes a thorough presentation of holographic algorithms, culminating in classifications of computational problems studied in exactly solvable models from statistical mechanics.
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.
Let two accomplished cyber security experts, Nick Selby and Heather Vescent, guide you through the dangers, traps and pitfalls of online life. Learn how cyber criminals operate and how you can defend yourself and your family from online security threats. From Facebook, to Twitter, to online banking we are all increasingly exposed online with thousands of criminals ready to bounce on the slightest weakness. This indispensable guide will teach you how to protect your identity and your most private financial and personal information.
Taking a highly pragmatic approach to presenting the principles and applications of chemical engineering, this companion text for students and working professionals offers an easily accessible guide to solving problems using computers. The primer covers the core concepts of chemical engineering, from conservation laws all the way up to chemical kinetics, without heavy stress on theory and is designed to accompany traditional larger core texts. The book presents the basic principles and techniques of chemical engineering processes and helps readers identify typical problems and how to solve them. Focus is on the use of systematic algorithms that employ numerical methods to solve different chemical engineering problems by describing and transforming the information. Problems are assigned for each chapter, ranging from simple to difficult, allowing readers to gradually build their skills and tackle a broad range of problems. MATLAB and Excel (R) are used to solve many examples and the more than 70 real examples throughout the book include computer or hand solutions, or in many cases both. The book also includes a variety of case studies to illustrate the concepts and a downloadable file containing fully worked solutions to the book's problems on the publisher's website. Introduces the reader to chemical engineering computation without the distractions caused by the contents found in many texts. Provides the principles underlying all of the major processes a chemical engineer may encounter as well as offers insight into their analysis, which is essential for design calculations. Shows how to solve chemical engineering problems using computers that require numerical methods using standard algorithms, such as MATLAB (R) and Excel (R). Contains selective solved examples of many problems within the chemical process industry to demonstrate how to solve them using the techniques presented in the text. Includes a variety of case studies to illustrate the concepts and a downloadable file containing fully worked solutions to problems on the publisher's website. Offers non-chemical engineers who are expected to work with chemical engineers on projects, scale-ups and process evaluations a solid understanding of basic concepts of chemical engineering analysis, design, and calculations.
Software project management is a crucial element in successful software and IT development, and requires students to develop an understanding of technical methodology and an appreciation of the many human factors that can play a part in software projects. The new fifth edition of Software Project Management has been fully revised and updated to help students to grasp these contrasting skills, and learn about new developments in the discipline. It provides both undergraduate and postgraduate students with a comprehensive introduction to software project management and has enjoyed a loyal following of users since the first edition published.
Salary surveys worldwide regularly place software architect in the top 10 best jobs, yet no real guide exists to help developers become architects. Until now. This book provides the first comprehensive overview of software architecture's many aspects. Aspiring and existing architects alike will examine architectural characteristics, architectural patterns, component determination, diagramming and presenting architecture, evolutionary architecture, and many other topics. Mark Richards and Neal Ford-hands-on practitioners who have taught software architecture classes professionally for years-focus on architecture principles that apply across all technology stacks. You'll explore software architecture in a modern light, taking into account all the innovations of the past decade. This book examines: Architecture patterns: The technical basis for many architectural decisions Components: Identification, coupling, cohesion, partitioning, and granularity Soft skills: Effective team management, meetings, negotiation, presentations, and more Modernity: Engineering practices and operational approaches that have changed radically in the past few years Architecture as an engineering discipline: Repeatable results, metrics, and concrete valuations that add rigor to software architecture
MRP is the backbone of manufacturing-so get the SAP S/4HANA details you need to maximize your system! With this comprehensive guide, set up master data and configure SAP S/4HANA with step-by-step instructions. Run classic MRP, MRP Live, or both to plan your materials; then evaluate your results using either SAP GUI transactions or the SAP Fiori apps in the MRP cockpit. From order planning and procurement proposals to stock/requirements lists and rescheduling checks, this book has your back. Configure and run classic MRP and MRP Live with SAP S/4HANA Evaluate your MRP results with classic transactions and the MRP cockpit. Get to know demand-driven MRP and long-term planning.
Prelude to Programming provides beginning students with a language-independent framework for learning core programming concepts and effective design techniques. This approach gives students the foundation they need to understand the logic behind program design and to establish effective programming skills. The Fifth Edition offers students a lively and accessible presentation as they learn core programming concepts - including data types, control structures, data files and arrays, and program design techniques such as top-down modular design and proper program documentation and style. Problem-solving skills are developed when students learn how to use basic programming tools and algorithms, which include data validation, defensive programming, calculating sums and averages, and searching and sorting lists. A copy of the RAPTOR flow-charting software is included with the Fifth Edition.
The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. * Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers * Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more * Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking * Develop your own big data strategy by accessing additional reading materials at the end of each chapter
Discover a contemporary overview of today's computer science with the best-selling INVITATION TO COMPUTER SCIENCE, 7E. This flexible, non-language-specific approach provides a solid foundation using an algorithm-driven approach that's ideal for the reader's first introduction to the field of Computer Science. Expanded chapter exercises and practice problems, feature boxes, and the latest material on emerging topics, such as privacy, drones, cloud computing, and net neutrality, connect readers with today's most current computing issues. Optional online language modules for C++, Java, Python, C#, and Ada, correspond seamlessly with this edition allowing readers to learn a programming language while expanding their understanding of concepts from the book. An optional online CourseMate (TM) offers helpful study tools, such as flashcards, quizzing, and games, as well as a new Digital Lab Manual containing 20 laboratory projects that map directly to the book. Strong visual and hands-on activities enable readers to master as well as experience the fundamentals of today's computer science.
SHORTLISTED FOR THE FT AND MCKINSEY BUSINESS BOOK OF THE YEAR AWARD 2019 NEW YORK TIMES AND SUNDAY TIMES BUSINESS BESTSELLER 'Reads more like a delicious page-turning novel...Put it on your holiday gift list for your favourite hedge-fund honcho' Bloomberg 'A compelling read' Economist 'Captivating' New York Times book review Jim Simons is the greatest moneymaker in modern financial history. His record bests those of legendary investors, including Warren Buffett, George Soros and Ray Dalio. Yet Simons and his strategies are shrouded in mystery. The financial industry has long craved a look inside Simons's secretive hedge fund, Renaissance Technologies and veteran Wall Street Journal reporter Gregory Zuckerman delivers the goods. After a legendary career as a mathematician and a stint breaking Soviet codes, Simons set out to conquer financial markets with a radical approach. Simons hired physicists, mathematicians and computer scientists - most of whom knew little about finance - to amass piles of data and build algorithms hunting for the deeply hidden patterns in global markets. Experts scoffed, but Simons and his colleagues became some of the richest in the world, their strategy of creating mathematical models and crunching data embraced by almost every industry today. As Renaissance became a major player in the financial world, its executives began exerting influence on other areas. Simons became a major force in scientific research, education and Democratic politics, funding Hilary Clinton's presidential campaign. While senior executive Robert Mercer is more responsible than anyone else for the Trump presidency - he placed Steve Bannon in the campaign, funded Trump's victorious 2016 effort and backed alt-right publication Breitbart. Mercer also impacted the success of the Brexit campaign as he made significant investments in Cambridge Anatlytica. For all his prescience, Simons failed to anticipate how Mercer's activity would impact his firm and the world. In this fast-paced narrative, Zuckerman examines how Simons launched a quantitative revolution on Wall Street, and reveals the impact that Simons, the quiet billionaire king of the quants, has had on worlds well beyond finance.
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets-Spark's core APIs-through worked examples Dive into Spark's low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark's stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
Each chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics, which are commonly asked by human-computer interaction (HCI) researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinnings for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.
As people use self-tracking devices and other digital technologies, they generate increasing quantities of personal information online. These data have many benefits, but they can also be accessed and exploited by third parties. Using rich examples from popular culture and empirical research, Deborah Lupton develops a fresh and intriguing perspective on how people make sense of and use their personal data, and what they know about others who use this information. Drawing on feminist new materialism theory and the anthropology of material culture, she acknowledges the importance of paying attention to embodied experiences, as well as discourses and ideas, in identifying the ways in which people make and enact data, and data make and enact people. Arguing that personal data are more-than-human phenomena, invested with diverse forms of vitalities, Lupton reveals significant implications for data futures, politics and ethics. Lupton's novel approach to understanding personal data will be of interest to students and scholars in media and cultural studies, sociology, anthropology, surveillance studies, information studies, cultural geography and science and technology studies.
Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying software (in Python and Perl) demonstrate the concepts.
This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search.
First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern about industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.
Exam board: AQA Level: A-level Subject: Computer Science First teaching: September 2015 First exams: Summer 2016 (AS); Summer 2017 (A-level) Strengthen your students' understanding and upgrade their confidence with our AQA Computer Science workbooks, full of self-contained exercises to consolidate knowledge and improve performance. Written by an experienced Computer Science author and teacher, these full colourworkbooks provide stimulus materials on a number of AS and A-level topics, followed by sets of questions designed to develop and test skills in the unit. * With consolidation questions to reinforce knowledge and test understanding, these workbooks will raise your students' chances of achieving the highest grades. * Helps students identify their revision needs and see how to target the top grades using online answers for each question. * Saves valuable preparation time and expense, with self-contained exercises that don't need photocopying and provide instant lesson and homework solutions for specialist and non-specialist teachers. * Encourages ongoing revision throughout the course as students progressively develop their skills in class and at home.
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
This innovative book provides a completely fresh exploration of bioinformatics, investigating its complex interrelationship with biology and computer science. It approaches bioinformatics from a unique perspective, highlighting interdisciplinary gaps that often trap the unwary. The book considers how the need for biological databases drove the evolution of bioinformatics; it reviews bioinformatics basics (including database formats, data-types and current analysis methods), and examines key topics in computer science (including data-structures, identifiers and algorithms), reflecting on their use and abuse in bioinformatics. Bringing these disciplines together, this book is an essential read for those who wish to better understand the challenges for bioinformatics at the interface of biology and computer science, and how to bridge the gaps. It will be an invaluable resource for advanced undergraduate and postgraduate students, and for lecturers, researchers and professionals with an interest in this fascinating, fast-moving discipline and the knotty problems that surround it.
Active, accessible, and assuming no prior knowledge: the ideal text for biologists encountering bioinformatics for the first time. A vast amount of biological information about a wide range of species has become available in recent years as technological advances have significantly reduced the time it takes to sequence a genome or determine a novel protein structure. This text describes how bioinformatics can be used as a powerful set of tools for retrieving and analysing this biological data, and how bioinformatics can be applied to a wide range of disciplines such as molecular biology, medicine, biotechnology, forensic science and anthropology. Fully revised and updated, the fifth edition of Introduction to Bioinformatics contains a host of new material including new content on next generation sequencing, function prediction, sequence assembly, epigenomics, the bioinformatics of gene editing, and the effects of single nucleotide variants. Written primarily for a biological audience without a detailed prior knowledge of programming, this book is the perfect introduction to the field of bioinformatics, providing friendly guidance and advice on how to use various methods and techniques. Furthermore, frequent examples, self-test questions, problems, and exercises are incorporated throughout the text to encourage self-directed learning.
Do you want reliable code for the latest methods in scientific computing? This CD-ROM contains all the source code from the new, and all previous, editions and language versions of Numerical Recipes. Included are: Numerical Recipes, Third Edition: complete source code in C++, with many brand-new routines Numerical Recipes, Second Edition: complete source code in C, Fortran 77, and Fortran 90 Numerical Recipes, First Edition: complete source code in Pascal and BASIC plus third-party ports of the code to Modula 2 and Common Lisp The CDROM also features an archive of difficult-to-find historical materials, including Baker's C Tools and More C Tools, Lau's Numerical Library in C for Scientists and Engineers, the influential NUMAL Algol 60 library from the Mathematisch Centrum in Amsterdam, and more than 250 MB of physically generated and multiply encrypted random bytes. Compatible with all computers and operating systems, the CDROM includes a Personal Single-User License that allows an individual to use the copyrighted code on any number of computers (no more than one at a time). For support or more general license information visit at www.nr.com.
A friendly introduction to the most useful algorithms written in simple, intuitive English The revised and updated second edition of Essential Algorithms, offers an accessible introduction to computer algorithms. The book contains a description of important classical algorithms and explains when each is appropriate. The author shows how to analyze algorithms in order to understand their behavior and teaches techniques that the can be used to create new algorithms to meet future needs. The text includes useful algorithms such as: methods for manipulating common data structures, advanced data structures, network algorithms, and numerical algorithms. It also offers a variety of general problem-solving techniques. In addition to describing algorithms and approaches, the author offers details on how to analyze the performance of algorithms. The book is filled with exercises that can be used to explore ways to modify the algorithms in order to apply them to new situations. This updated edition of Essential Algorithms Contains explanations of algorithms in simple terms, rather than complicated math Steps through powerful algorithms that can be used to solve difficult programming problems Helps prepare for programming job interviews that typically include algorithmic questions Offers methods can be applied to any programming language Includes exercises and solutions useful to both professionals and students Provides code examples updated and written in Python and C# Essential Algorithms has been updated and revised and offers professionals and students a hands-on guide to analyzing algorithms as well as the techniques and applications. The book also includes a collection of questions that may appear in a job interview. The book's website will include reference implementations in Python and C# (which can be easily applied to Java and C++).
As the bestselling software for professional photographers, Adobe Lightroom is a popular, fun, and highly powerful application for image organization, photo editing, and output. But with its hundreds of features and capabilities, learning Lightroom can be overwhelming for both beginner and veteran photographers. In The Indispensable Guide to Lightroom CC , Sean McCormack brings his expertise and experience as a professional photographer, Adobe Community Professional, and Lightroom Master to provide an accessible guide to learning Lightroom. In this book, Sean focuses on the fundamentals of Lightroom CC, using a hands-on learning style to carefully walk users through Lightroom's features, including file management, image editing, slideshows, printing, sharing, and much more. This book provides detailed, illustrated descriptions of the program's most important features and capabilities to get new users up and running quickly, and it covers all the new features of Lightroom CC to keep advanced users ahead of the curve. The Indispensable Guide to Lightroom CC makes learning this extensive photo-editing program simple, fun, and fast.
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