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Drawing on interviews with 15 leading scientists, the authors present an unexpected vision: the future of computing is a synthesis with nature.
Tuning your database for optimal performance means more than following a few short steps in a vendor-specific guide. For maximum improvement, you need a broad and deep knowledge of basic tuning principles, the ability to gather data in a systematic way, and the skill to make your system run faster. This is an art as well as a science, and Database Tuning: Principles, Experiments, and Troubleshooting Techniques will help you develop portable skills that will allow you to tune a wide variety of database systems on a multitude of hardware and operating systems. Further, these skills, combined with the scripts provided for validating results, are exactly what you need to evaluate competing datab...
Out of their minds and the force of their imagination, men have created countless beings, from demons and monsters of legend to comic-strip characters. What if their world were real - if dragons, devils and Don Quixote hobnobbed with Dagwood Bumstead and Charlie Brown? Such a world would have its fascinations . . . and its dreadful perils - if it existed. Horton Smith found out that it did - and that he was right in the middle of it!
Join math detective in solving nearly 40 puzzles inspired by methods in computer science and mathematics. The Tower of Lego, Odd Doors Problem, Spies and Double Agents, many more. Solutions.
Aimed at both working programmers who are applying for a job where puzzles are an integral part of the interview, as well as techies who just love a good puzzle, this book offers a cache of exciting puzzles Features a new series of puzzles, never before published, called elimination puzzles that have a pedagogical aim of helping the reader solve an entire class of Sudoku-like puzzles Provides the tools to solve the puzzles by hand and computer The first part of each chapter presents a puzzle; the second part shows readers how to solve several classes of puzzles algorithmically; the third part asks the reader to solve a mystery involving codes, puzzles, and geography Comes with a unique bonus: if readers actually solve the mystery, they have a chance to win a prize, which will be promoted on wrox.com!
This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection. Included are self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis. Detailed applications are built on a solid scientific basis.
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy! gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.
Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.
This handbook provides full coverage of the most recent and advanced topics in scheduling, assembling researchers from all relevant disciplines to facilitate new insights. Presented in six parts, these experts provides introductory material, complete with tutorials and algorithms, then examine classical scheduling problems. Part 3 explores scheduling models that originate in areas such as computer science, operations research. The following section examines scheduling problems that arise in real-time systems. Part 5 discusses stochastic scheduling and queueing networks, and the final section discusses a range of applications in a variety of areas, from airlines to hospitals.