Bioinformatics is the study of biological information and biological systems - such as of the relationships between the sequence, structure and function of genes and proteins. The subject has seen tremendous development in recent years, and there are ever-increasing needs for good understanding of quantitative methods in the study of proteins. "Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis" takes the novel approach of covering both the sequence and structure analysis of proteins in one volume and from an algorithmic perspective. Provides a comprehensive introduction to the analysis of protein sequences and structures. Provides an integrated presentation of methodology, examples, exercises and applications. Emphasises the algorithmic rather than mathematical aspects of the methods described. Covers comparison and alignment of protein sequences and structures as well as protein structure prediction focusing on threading approaches. Written in an accessible yet rigorous style, suitable for biologists, mathematicians and computer scientists alike. Suitable both for developers and users of bioinformatics tools. Supported by a Web site featuring exercises, solutions, images, and computer programs.

"Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis" is ideally suited for advanced undergraduate and graduate students of bioinformatics, statistics, mathematics and computer science. It also provides an excellent introduction and reference source on the subject for practitioners and researchers.

A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.Related Link(s)

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.

This book helps readers create good VHDL descriptions and simulate VHDL designs. It teaches VHDL using selected sample problems, which are solved step by step and with precise explanations, so that readers get a clear idea of what a good VHDL code should look like. The book is divided into eight chapters, covering aspects ranging from the very basics of VHDL syntax and the module concept, to VHDL logic circuit implementations. In the first chapter, the entity and architecture parts of a VHDL program are explained in detail. The second chapter explains the implementations of combinational logic circuits in VHDL language, while the following chapters offer information on the simulation of VHDL programs and demonstrate how to define data types other than the standard ones available in VHDL libraries. In turn, the fifth chapter explains the implementation of clocked sequential logic circuits, and the sixth shows the implementation of registers and counter packages. The book's last two chapters detail how components, functions and procedures, as well as floating-point numbers, are implemented in VHDL. The book offers extensive exercises at the end of each chapter, inviting readers to learn VHDL by doing it and writing good code.

Real-world information is imperfect and is usually described in natural language (NL). Moreover, this information is often partially reliable and a degree of reliability is also expressed in NL. In view of this, the concept of a Z-number is a more adequate concept for the description of real-world information. The main critical problem that naturally arises in processing Z-numbers-based information is the computation with Z-numbers. Nowadays, there is no arithmetic of Z-numbers suggested in existing literature.This book is the first to present a comprehensive and self-contained theory of Z-arithmetic and its applications. Many of the concepts and techniques described in the book, with carefully worked-out examples, are original and appear in the literature for the first time.The book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics.

Stochastic global optimization methods and applications to chemical, biochemical, pharmaceutical and environmental processes presents various algorithms that include the genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony optimization, and cuckoo search algorithm. The design and analysis of these algorithms is studied by applying them to solve various base case and complex optimization problems concerning chemical, biochemical, pharmaceutical, and environmental engineering processes. Design and implementation of various classical and advanced optimization strategies to solve a wide variety of optimization problems makes this book beneficial to graduate students, researchers, and practicing engineers working in multiple domains. This book mainly focuses on stochastic, evolutionary, and artificial intelligence optimization algorithms with a special emphasis on their design, analysis, and implementation to solve complex optimization problems and includes a number of real applications concerning chemical, biochemical, pharmaceutical, and environmental engineering processes.

Shadow Algorithms Data Miner provides a high-level understanding of the complete set of shadow concepts and algorithms, addressing their usefulness from a larger graphics system perspective. It discusses the applicability and limitations of all the direct illumination approaches for shadow generation. With an emphasis on shadow fundamentals, the book gives an organized picture of the motivations, complexities, and categorized algorithms available to generate digital shadows. It helps readers select the most relevant algorithms for their needs by placing the shadow algorithms in real-world contexts and looking at them from a larger graphics system perspective. As a result, readers know where to start for their application needs, which algorithms to begin considering, and which papers and supplemental material should be consulted for further details.

A Thorough Overview of the Next Generation in Computing Poised to follow in the footsteps of the Internet, grid computing is on the verge of becoming more robust and accessible to the public in the near future. Focusing on this novel, yet already powerful, technology, Introduction to Grid Computing explores state-of-the-art grid projects, core grid technologies, and applications of the grid. After comparing the grid with other distributed systems, the book covers two important aspects of a grid system: scheduling of jobs and resource discovery and monitoring in grid. It then discusses existing and emerging security technologies, such as WS-Security and OGSA security, as well as the functions of grid middleware at a conceptual level. The authors also describe famous grid projects, demonstrate the pricing of European options through the use of the Monte Carlo method on grids, and highlight different parallelization possibilities on the grid. Taking a tutorial approach, this concise book provides a complete introduction to the components of the grid architecture and applications of grid computing. It expertly shows how grid computing can be used in various areas, from computational mechanics to risk management in financial institutions.

Algorithms and Theory of Computation Handbook, Second Edition: Special Topics 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 more than 15 new chapters. This edition now covers self-stabilizing and pricing algorithms as well as the theories of privacy and anonymity, databases, computational games, and communication networks. It also discusses computational topology, natural language processing, and grid computing and explores applications in intensity-modulated radiation therapy, voting, DNA research, systems biology, and financial derivatives. 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.

The 20th century saw tremendous achievements and progress in science and
technology. Undoubtedly, computers and computer-related technologies acted as
one of vital catalysts for accelerating this progress in the latter half of
the century. The contributions of mathematical sciences have been equally
profound, and the synergy between mathematics and computer science has played a
key role in accelerating the progress of both fields as well as science
and engineering.

Mathematical sciences will undoubtedly continue to play this vital role in this
new century. In particular, mathematical modeling and numerical simulation will
continue to be among the essential methodologies for solving massive and
complex problems that arise in science, engineering and manufacturing. Underpinning this all from a sound, theoretical perspective will be numerical algorithms. In recognition of this observation, this volume focuses on the
following specific topics.

(1) Fundamental numerical algorithms
(2) Applications of numerical algorithms
(3) Emerging technologies.

The articles included in this issue by experts on advanced scientific and
engineering computations from numerous countries elucidate state-of-the-art achievements in these three topics from various angles and suggest the future directions. Although we cannot hope to cover all the aspects in scientific and engineering computations, we hope that the articles will interest, inform and inspire members of the science and engineering community.

Over the past 20 years, the emergence of clone theory, hyperequational theory, commutator theory and tame congruence theory has led to a growth of universal algebra both in richness and in applications, especially in computer science. Yet most of the classic books on the subject are long out of print and, to date, no other book has integrated these theories with the long-established work that supports them.

Universal Algebra and Applications in Theoretical Computer Science introduces the basic concepts of universal algebra and surveys some of the newer developments in the field. The first half of the book provides a solid grounding in the core material. A leisurely pace, careful exposition, numerous examples, and exercises combine to form an introduction to the subject ideal for beginning graduate students or researchers from other areas. The second half of the book focuses on applications in theoretical computer science and advanced topics, including Mal'cev conditions, tame congruence theory, clones, and commutators.

The impact of the advances in universal algebra on computer science is just beginning to be realized, and the field will undoubtedly continue to grow and mature. Universal Algebra and Applications in Theoretical Computer Science forms an outstanding text and offers a unique opportunity to build the foundation needed for further developments in its theory and in its computer science applications.

The three volume set provides a systematic overview of theories and technique on social network analysis. Volume 1 of the set mainly focuses on the structure characteristics, the modeling, and the evolution mechanism of social network analysis. Techniques and approaches for virtual community detection are discussed in detail as well. It is an essential reference for scientist and professionals in computer science.

Designed for use in a second course on linear algebra, Matrix Theory and Applications with MATLAB covers the basics of the subject-from a review of matrix algebra through vector spaces to matrix calculus and unitary similarity-in a presentation that stresses insight, understanding, and applications. Among its most outstanding features is the integration of MATLAB throughout the text. Each chapter includes a MATLAB subsection that discusses the various commands used to do the computations in that section and offers code for the graphics and some algorithms used in the text.

All of the material is presented from a matrix point of view with enough rigor for students to learn to compose arguments and proofs and adjust the material to cover other problems. The treatment includes optional subsections covering applications, and the final chapters move beyond basic matrix theory to discuss more advanced topics, such as decompositions, positive definite matrices, graphics, and topology.

Filled with illustrations, examples, and exercises that reinforce understanding, Matrix Theory and Applications with MATLAB allows readers to experiment and visualize results in a way that no other text does. Its rigor, use of MATLAB, and focus on applications better prepares them to use the material in their future work and research, to extend the material, and perhaps obtain new results of their own.

This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations. Features Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management Presents various soft computing techniques Offers problems and their optimization using various soft computing techniques Discusses related software programs, with illustrations Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems

"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped-you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technology Deep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the book Inside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the reader For Python programmers with basic machine learning skills. about the author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

The three volume set provides a systematic overview of theories and technique on social network analysis. Volume 3 of the set mainly focuses on the propagation models and evolution rules of information. Information retrieval and dissemination, topic discovery and evolution, algorithms of influence maximization are discussed in detail. It is an essential reference for scientist and professionals in computer science.

Proofs 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 book will be useful to anyone interested in logic and proofs: computer scientists, philosophers, linguists, and, of course, mathematicians.

This book can be used as an experiment and reference book for algorithm design courses, as well as a training manual for programming contests. It contains 247 problems selected from ACM-ICPC programming contests and other programming contests. There's detailed analysis for each problem. All problems, and test datum for most of problems will be provided online. The content will follow usual algorithms syllabus, and problem-solving strategies will be introduced in analyses and solutions to problem cases. For students in computer-related majors, contestants and programmers, this book can polish their programming and problem-solving skills with familarity of algorithms and mathematics.

Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems contains computer-code examples for the development of genetic algorithm systems - compiling them from an array of practitioners in the field. Each contribution of this singular resource includes: unique code segments documentation description of the operations performed rationale for the chosen approach problems the code overcomes or addresses Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems complements the first two volumes in the series by offering examples of computer code. The first two volumes dealt with new research and an overview of the types of applications that could be taken with GAs. This volume differs from its predecessors by specifically concentrating on specific functions in genetic algorithms, serving as the only compilation of useful and usable computer code in the field.

Eigenvalue computations are ubiquitous in science and engineering. John Francis's implicitly shifted QR algorithm has been the method of choice for small to medium sized eigenvalue problems since its invention in 1959. This book presents a new view of this classical algorithm. While Francis's original procedure chases bulges, the new version chases core transformations, which allows the development of fast algorithms for eigenvalue problems with a variety of special structures. This also leads to a fast and backward stable algorithm for computing the roots of a polynomial by solving the companion matrix eigenvalue problem. The authors received a SIAM Outstanding Paper prize for this work. This book will be of interest to researchers in numerical linear algebra and their students.

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker