An Easy-to-Use Research Tool for Algorithm Testing and Development Before the SeqAn project, there was clearly a lack of available implementations in sequence analysis, even for standard tasks. Implementations of needed algorithmic components were either unavailable or hard to access in third-party monolithic software products. Addressing these concerns, the developers of SeqAn created a comprehensive, easy-to-use, open source C++ library of efficient algorithms and data structures for the analysis of biological sequences. Written by the founders of this project, Biological Sequence Analysis Using the SeqAn C++ Library covers the SeqAn library, its documentation, and the supporting infrastructure. The first part of the book describes the general library design. It introduces biological sequence analysis problems, discusses the benefit of using software libraries, summarizes the design principles and goals of SeqAn, details the main programming techniques used in SeqAn, and demonstrates the application of these techniques in various examples. Focusing on the components provided by SeqAn, the second part explores basic functionality, sequence data structures, alignments, pattern and motif searching, string indices, and graphs. The last part illustrates applications of SeqAn to genome alignment, consensus sequence in assembly projects, suffix array construction, and more. This handy book describes a user-friendly library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn enables not only the implementation of new algorithms, but also the sound analysis and comparison of existing algorithms. Visit SeqAn for more information.

Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material. The book first classifies scheduling problems and their complexity and then presents examples that demonstrate successful techniques for the design of efficient approximation algorithms. It also discusses classical problems, such as the famous makespan minimization problem, as well as more recent advances, such as energy-efficient scheduling algorithms. After focusing on job scheduling problems that encompass independent and possibly parallel jobs, the text moves on to a practical application of cyclic scheduling for the synthesis of embedded systems. It also proves that efficient schedules can be derived in the context of steady-state scheduling. Subsequent chapters discuss scheduling large and computer-intensive applications on parallel resources, illustrate different approaches of multi-objective scheduling, and show how to compare the performance of stochastic task-resource systems. The final chapter assesses the impact of platform models on scheduling techniques. From the basics to advanced topics and platform models, this volume provides a thorough introduction to the field. It reviews classical methods, explores more contemporary models, and shows how the techniques and algorithms are used in practice.

Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many of the existing chapters, this second edition contains four new chapters that cover external memory and parameterized algorithms as well as computational number theory and algorithmic coding theory. This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.

From finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

Philipp Meisen introduces a model, a query language, and a similarity measure enabling users to analyze time interval data. The introduced tools are combined to design and realize an information system. The presented system is capable of performing analytical tasks (avoiding any type of summarizability problems), providing insights, and visualizing results processing millions of intervals within milliseconds using an intuitive SQL-based query language. The heart of the solution is based on several bitmap-based indexes, which enable the system to handle huge amounts of time interval data.

The term "stringology" is a popular nickname for text algorithms, or algorithms on strings. This book deals with the most basic algorithms in the area. Most of them can be viewed as "algorithmic jewels" and deserve reader-friendly presentation. One of the main aims of the book is to present several of the most celebrated algorithms in a simple way by omitting obscuring details and separating algorithmic structure from combinatorial theoretical background. The book reflects the relationships between applications of text-algorithmic techniques and the classification of algorithms according to the measures of complexity considered. The text can be viewed as a parade of algorithms in which the main purpose is to discuss the foundations of the algorithms and their interconnections. One can partition the algorithmic problems discussed into practical and theoretical problems. Certainly, string matching and data compression are in the former class, while most problems related to symmetries and repetitions in texts are in the latter. However, all the problems are interesting from an algorithmic point of view and enable the reader to appreciate the importance of combinatorics on words as a tool in the design of efficient text algorithms.In most textbooks on algorithms and data structures, the presentation of efficient algorithms on words is quite short as compared to issues in graph theory, sorting, searching, and some other areas. At the same time, there are many presentations of interesting algorithms on words accessible only in journals and in a form directed mainly at specialists. This book fills the gap in the book literature on algorithms on words, and brings together the many results presently dispersed in the masses of journal articles. The presentation is reader-friendly; many examples and about two hundred figures illustrate nicely the behaviour of otherwise very complex algorithms.

Energy-Aware Memory Management for Embedded Multimedia Systems: A Computer-Aided Design Approach presents recent computer-aided design (CAD) ideas that address memory management tasks, particularly the optimization of energy consumption in the memory subsystem. It explains how to efficiently implement CAD solutions, including theoretical methods and novel algorithms. The book covers various energy-aware design techniques, including data-dependence analysis techniques, memory size estimation methods, extensions of mapping approaches, and memory banking approaches. It shows how these techniques are used to evaluate the data storage of an application, reduce dynamic and static energy consumption, design energy-efficient address generation units, and much more. Providing an algebraic framework for memory management tasks, this book illustrates how to optimize energy consumption in memory subsystems using CAD solutions. The algorithmic style of the text should help electronic design automation (EDA) researchers and tool developers create prototype software tools for system-level exploration, with the goal to ultimately obtain an optimized architectural solution of the memory subsystem.

The hybrid/heterogeneous nature of future microprocessors and large high-performance computing systems will result in a reliance on two major types of components: multicore/manycore central processing units and special purpose hardware/massively parallel accelerators. While these technologies have numerous benefits, they also pose substantial performance challenges for developers, including scalability, software tuning, and programming issues. Researchers at the Forefront Reveal Results from Their Own State-of-the-Art Work Edited by some of the top researchers in the field and with contributions from a variety of international experts, Scientific Computing with Multicore and Accelerators focuses on the architectural design and implementation of multicore and manycore processors and accelerators, including graphics processing units (GPUs) and the Sony Toshiba IBM (STI) Cell Broadband Engine (BE) currently used in the Sony PlayStation 3. The book explains how numerical libraries, such as LAPACK, help solve computational science problems; explores the emerging area of hardware-oriented numerics; and presents the design of a fast Fourier transform (FFT) and a parallel list ranking algorithm for the Cell BE. It covers stencil computations, auto-tuning, optimizations of a computational kernel, sequence alignment and homology, and pairwise computations. The book also evaluates the portability of drug design applications to the Cell BE and illustrates how to successfully exploit the computational capabilities of GPUs for scientific applications. It concludes with chapters on dataflow frameworks, the Charm++ programming model, scan algorithms, and a portable intracore communication framework. Explores the New Computational Landscape of Hybrid Processors By offering insight into the process of constructing and effectively using the technology, this volume provides a thorough and practical introduction to the area of hybrid computing. It discusses introductory concepts and simple examples of parallel computing, logical and performance debugging for parallel computing, and advanced topics and issues related to the use and building of many applications.

Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models. The first five chapters present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance. The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren't discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then surveys parallel algorithms.

Applicable to any problem that requires a finite number of solutions, finite state-based models (also called finite state machines or finite state automata) have found wide use in various areas of computer science and engineering. Handbook of Finite State Based Models and Applications provides a complete collection of introductory materials on finite state theories, algorithms, and the latest domain applications. For beginners, the book is a handy reference for quickly looking up model details. For more experienced researchers, it is suitable as a source of in-depth study in this area. The book first introduces the fundamentals of automata theory, including regular expressions, as well as widely used automata, such as transducers, tree automata, quantum automata, and timed automata. It then presents algorithms for the minimization and incremental construction of finite automata and describes Esterel, an automata-based synchronous programming language for embedded system software development. Moving on to applications, the book explores regular path queries on graph-structured data, timed automata in model checking security protocols, pattern matching, compiler design, and XML processing. It also covers other finite state-based modeling approaches and applications, including Petri nets, statecharts, temporal logic, and UML state machine diagrams.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer function, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

This book provides in-depth and wide-ranging analyses of the emergence, and subsequent ubiquity, of algorithms in diverse realms of social life. The plurality of Algorithmic Cultures emphasizes: 1) algorithms' increasing importance in the formation of new epistemic and organizational paradigms; and 2) the multifaceted analyses of algorithms across an increasing number of research fields. The authors in this volume address the complex interrelations between social groups and algorithms in the construction of meaning and social interaction. The contributors highlight the performative dimensions of algorithms by exposing the dynamic processes through which algorithms - themselves the product of a specific approach to the world - frame reality, while at the same time organizing how people think about society. With contributions from leading experts from Media Studies, Social Studies of Science and Technology, Cultural and Media Sociology from Canada, France, Germany, UK and the USA, this volume presents cutting edge empirical and conceptual research that includes case studies on social media platforms, gaming, financial trading and mobile security infrastructures.

Numerical Analysis with Algorithms and Programming is the first comprehensive textbook to provide detailed coverage of numerical methods, their algorithms, and corresponding computer programs. It presents many techniques for the efficient numerical solution of problems in science and engineering. Along with numerous worked-out examples, end-of-chapter exercises, and Mathematica (R) programs, the book includes the standard algorithms for numerical computation: Root finding for nonlinear equations Interpolation and approximation of functions by simpler computational building blocks, such as polynomials and splines The solution of systems of linear equations and triangularization Approximation of functions and least square approximation Numerical differentiation and divided differences Numerical quadrature and integration Numerical solutions of ordinary differential equations (ODEs) and boundary value problems Numerical solution of partial differential equations (PDEs) The text develops students' understanding of the construction of numerical algorithms and the applicability of the methods. By thoroughly studying the algorithms, students will discover how various methods provide accuracy, efficiency, scalability, and stability for large-scale systems.

Systematically teaches key paradigmic algorithm design methods

Provides a deep insight into randomization

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB (R) package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

This book examines the abilities of new machine learning models for predicting ore grade in mining engineering. A variety of case studies are examined in this book. A motivation for preparing this book was the absence of robust models for estimating ore grade. Models of current books can also be used for the different sciences because they have high capabilities for estimating different variables. Mining engineers can use the book to determine the ore grade accurately. This book helps identify mineral-rich regions for exploration and exploitation. Exploration costs can be decreased by using the models in the current book. In this book, the author discusses the new concepts in mining engineering, such as uncertainty in ore grade modeling. Ensemble models are presented in this book to estimate ore grade. In the book, readers learn how to construct advanced machine learning models for estimating ore grade. The authors of this book present advanced and hybrid models used to estimate ore grade instead of the classic methods such as kriging. The current book can be used as a comprehensive handbook for estimating ore grades. Industrial managers and modelers can use the models of the current books. Each level of ore grade modeling is explained in the book. In this book, advanced optimizers are presented to train machine learning models. Therefore, the book can also be used by modelers in other fields. The main motivation of this book is to address previous shortcomings in the modeling process of ore grades. The scope of this book includes mining engineering, soft computing models, and artificial intelligence.

How do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.

Offers fundamental theories and practical and more sophisticated applications of Evolutionary Computation in varied industries Provides insight into various platforms, paradigms, techniques, and tools used in Evolutionary Computation for diverse fields Presents an understanding related to optimization, performance tuning, virtualization, deployment models, and their applications Covers a variety of applications for social and essential models and is based on real life examples Useful for decision making based on optimized data through Evolutionary Computation in multi-dimensions

This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.

Few books comprehensively cover the software and programming aspects of reversible computing. Filling this gap, Introduction to Reversible Computing offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach.

Collecting scattered knowledge into one coherent account, the book provides a compendium of both classical and recently developed results on reversible computing. It explores up-and-coming theories, techniques, and tools for the application of reversible computing the logical next step in the evolution of computing systems.

The book covers theory, hardware and software aspects, fundamental limits, complexity analyses, practical algorithms, compilers, efficiency improvement techniques, and application areas. The topics span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing.

The book presents sufficient material for newcomers to easily get started. It provides citations to original articles on seminal results so that readers can consult the corresponding publications in the literature. Pointers to additional resources are included for more advanced topics. For those already familiar with a certain topic within reversible computing, the book can serve as a one-stop reference to other topics in the field.

Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers a comprehensive and up-to-date treatment of modern methods in matrix computation. It uses a unified approach to direct and iterative methods for linear systems, least squares and eigenvalue problems. A thorough analysis of the stability, accuracy and complexity of the treated methods is given.

"Numerical Methods in Matrix Computations" is suitable for use in courses on scientific computing and applied technical areas at advanced undergraduate and graduate level. A large bibliography is provided, which includes both historical and review papers as well as recent research papers. This makes the book useful also as a reference and guide to further study and research work.

This work is concerned with theoretical developments in the area of mathematical programming, development of new algorithms and software and their applications in science and industry. It aims to expose recent mathematical developments to a larger audience in science and industry.

For computer scientists, especially those in the security field, the use of chaos has been limited to the computation of a small collection of famous but unsuitable maps that offer no explanation of why chaos is relevant in the considered contexts. Discrete Dynamical Systems and Chaotic Machines: Theory and Applications shows how to make finite machines, such as computers, neural networks, and wireless sensor networks, work chaotically as defined in a rigorous mathematical framework. Taking into account that these machines must interact in the real world, the authors share their research results on the behaviors of discrete dynamical systems and their use in computer science. Covering both theoretical and practical aspects, the book presents: Key mathematical and physical ideas in chaos theory Computer science fundamentals, clearly establishing that chaos properties can be satisfied by finite state machines Concrete applications of chaotic machines in computer security, including pseudorandom number generators, hash functions, digital watermarking, and steganography Concrete applications of chaotic machines in wireless sensor networks, including secure data aggregation and video surveillance Until the authors' recent research, the practical implementation of the mathematical theory of chaos on finite machines raised several issues. This self-contained book illustrates how chaos theory enables the study of computer security problems, such as steganalysis, that otherwise could not be tackled. It also explains how the theory reinforces existing cryptographically secure tools and schemes.

This is the first comprehensive overview of the 'science of science,' an emerging interdisciplinary field that relies on big data to unveil the reproducible patterns that govern individual scientific careers and the workings of science. It explores the roots of scientific impact, the role of productivity and creativity, when and what kind of collaborations are effective, the impact of failure and success in a scientific career, and what metrics can tell us about the fundamental workings of science. The book relies on data to draw actionable insights, which can be applied by individuals to further their career or decision makers to enhance the role of science in society. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists and graduate students, policymakers, and administrators with an interest in the wider scientific enterprise.

'Work hard, have fun, make history' proclaims the slogan on the walls of Amazon's warehouses. This cheerful message hides a reality of digital surveillance, aggressive anti-union tactics and disciplinary layoffs. Reminiscent of the tumult of early industrial capitalism, the hundreds of thousands of workers who help Amazon fulfil consumers' desire are part of an experiment in changing the way we all work. In this book, Alessandro Delfanti takes readers inside Amazon's warehouses to show how technological advancements and managerial techniques subdue the workers rather than empower them, as seen in the sensors that track workers' every movement around the floor and algorithmic systems that re-route orders to circumvent worker sabotage. He looks at new technologies including robotic arms trained by humans and augmented reality goggles, showing that their aim is to standardise, measure and discipline human work rather than replace it. Despite its innovation, Amazon will always need living labour's flexibility and low cost. And as the warehouse is increasingly automated, worker discontent increases. Striking under the banner 'we are not robots', employees have shown that they are acutely aware of such contradictions. The only question remains: how long will it be until Amazon's empire collapses?