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The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations

With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. It is important to provide a comprehensive study on how to use such machines written by specialists of the domain. The book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. The content spans topics such as Numerical Analysis for Heterogeneous and Multicore Systems; Optimization of Communication for High Performance Heterogeneous and Hierarchical Platforms; Efficient Exploitation of Heterogeneous Architectures, Hybrid CPU+GPU, and Distributed Systems; Energy Awareness in High-Performance Computing; and Applications of Heterogeneous High-Performance Computing. • Covers cutting-edge research in HPC on complex environments, following an international collaboration of members of the ComplexHPC • Explains how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems • Twenty-three chapters and over 100 illustrations cover domains such as numerical analysis, communication and storage, applications, GPUs and accelerators, and energy efficiency

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.

Music is much more than listening to audio encoded in some unreadable binary format. It is, instead, an adventure similar to reading a book and entering its world, complete with a story, plot, sound, images, texts, and plenty of related data with, for instance, historical, scientific, literary, and musicological contents. Navigation of this world, such as that of an opera, a jazz suite and jam session, a symphony, a piece from non-Western culture, is possible thanks to the specifications of new standard IEEE 1599, IEEE Recommended Practice for Defining a Commonly Acceptable Musical Application Using XML, which uses symbols in language XML and music layers to express all its multimedia characteristics. Because of its encompassing features, this standard allows the use of existing audio and video standards, as well as recuperation of material in some old format, the events of which are managed by a single XML file, which is human and machine readable – musical symbols have been read by humans for at least forty centuries. Anyone wanting to realize a computer application using IEEE 1599 – music and computer science departments, computer generated music research laboratories (e.g. CCRMA at Stanford, CNMAT at Berkeley, and IRCAM in Paris), music library conservationists, music industry frontrunners (Apple, TDK, Yamaha, Sony), etc. – will need this first book-length explanation of the new standard as a reference. The book will include a manual teaching how to encode music with IEEE 1599 as an appendix, plus a CD-R with a video demonstrating the applications described in the text and actual sample applications that the user can load onto his or her PC and experiment with.

Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Stereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue. This book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.

This book describes the main classical combinatorial problems that can be encountered when designing a logistics network or driving a supply chain. It shows how these problems can be tackled by metaheuristics, both separately and using an integrated approach. A huge number of techniques, from the simplest to the most advanced ones, are given for helping the reader to implement efficient solutions that meet its needs. A lot of books have been written about metaheuristics (methods for solving hard optimization problems) and supply chain management (the field in which we find a huge number of combinatorial optimization problems) in the last decades. So, the main reason of this book is to describe how these methods can be implemented for this class of problems.

The performance of an algorithm used depends on the GNA. This book focuses on the comparison of optimizers, it defines a stress-outcome approach which can be derived all the classic criteria (median, average, etc.) and other more sophisticated. Source-codes used for the examples are also presented, this allows a reflection on the «superfluous chance,» succinctly explaining why and how the stochastic aspect of optimization could be avoided in some cases.

Introduces the topic of cloud computing with an emphasis on the trustworthiness of cloud computing systems and services This book describes the scientific basis of cloud computing, explaining the ideas, principles, and architectures of cloud computing as well the different types of clouds and the services they provide. The text reviews several cloud computing platforms, including Microsoft Azure, Amazon, Oracle, Google, HP, IBM, Salesforce, and Kaavo. The author addresses the problem of trustworthiness in cloud computing and provides methods to improve the security and privacy of cloud applications. The end-of-chapter exercises and supplementary material on the book's companion website will allow readers to grasp the introductory and advanced level concepts of cloud computing. Examines cloud computing platforms such as Microsoft Azure, Amazon, Oracle, Google, HP, IBM, Salesforce, and Kaavo Analyzes the use of aspect-oriented programming (AOP) for refactoring cloud services and improving the security and privacy of cloud applications Contains practical examples of cloud computing, test questions, and end-of-chapter exercises Includes presentations, examples of cloud projects and other teaching resources at the author’s website (http://www.vladimirsafonov.org/cloud) Trustworthy Cloud Computing is written for advanced undergraduate and graduate students in computer science, data science, and computer engineering as well as software engineers, system architects, system managers, and software developers new to cloud computing.

Advanced Graph Theory focuses on some of the main notions arising in graph theory with an emphasis from the very start of the book on the possible applications of the theory and the fruitful links existing with linear algebra. The second part of the book covers basic material related to linear recurrence relations with application to counting and the asymptotic estimate of the rate of growth of a sequence satisfying a recurrence relation.

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

The improvement of energy efficiency in electronics and computing systems is currently central to information and communication technology design; low-cost cooling, autonomous portable systems and functioning on recovered energy all need to be continuously improved to allow modern technology to compute more while consuming less. This book presents the basic principles of the origins and limits of heat dissipation in electronic systems. Mechanisms of energy dissipation, the physical foundations for understanding CMOS components and sophisticated optimization techniques are explored in the first half of the book, before an introduction to reversible and quantum computing. Adiabatic computing and nano-relay technology are then explored as new solutions to achieving improvements in heat creation and energy consumption, particularly in renewed consideration of circuit architecture and component technology. Concepts inspired by recent research into energy efficiency are brought together in this book, providing an introduction to new approaches and technologies which are required to keep pace with the rapid evolution of electronics.