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A free ebook version of this title is available through Luminos, University of California Press’s Open Access publishing program for monographs. Visit www.luminosoa.org to learn more. How do keyboards make music playable? Drawing on theories of media, systems, and cultural techniques, Keys to Play spans Greek myth and contemporary Japanese digital games to chart a genealogy of musical play and its animation via improvisation, performance, and recreation. As a paradigmatic digital interface, the keyboard forms a field of play on which the book’s diverse objects of inquiry—from clavichords to PCs and eighteenth-century musical dice games to the latest rhythm-action titles—enter into analogical relations. Remapping the keyboard’s topography by way of Mozart and Super Mario, who head an expansive cast of historical and virtual actors, Keys to Play invites readers to unlock ludic dimensions of music that are at once old and new.
If you have a burning ambition, brilliant business idea or creative passion: What are you waiting for? Now is the time to get going! Hardly anyone gets it right the first time, but many of us are crippled by indecision and fear of failure. The desire to get it right can inhibit us from getting started. Don't wait around for everything to fall magically into place - get started, get going and get perfect later. It's the fastest route to success. In this book, Rob Moore, the bestselling author of MONEY, shows that the quickest way to perfect is starting right now and improving as you go. This book will show you how to launch your business or idea, begin the next phase of your career, and overcome self-doubt - right away. Get perfect later, get started NOW.
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Solving puzzles is fun, but the real challenge lies in creating them. "Puzzlecraft" has been a popular series in Games magazine for years, and now all the instructions have been compiled and updated (along with brand-new sample puzzles) to teach you everything you need to know to construct puzzles of all kinds, from crosswords to word searches to sudoku. Guided by Mike Selinker and Thomas Snyder, two pros who really know their stuff, you'll be making puzzles and entertaining your friends in no time.
The book at hand gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.
Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
This completely revised successor to the Handbook of Microscopy supplies in-depth coverage of all imaging technologies from the optical to the electron and scanning techniques. Adopting a twofold approach, the book firstly presents the various technologies as such, before going on to cover the materials class by class, analyzing how the different imaging methods can be successfully applied. It covers the latest developments in techniques, such as in-situ TEM, 3D imaging in TEM and SEM, as well as a broad range of material types, including metals, alloys, ceramics, polymers, semiconductors, minerals, quasicrystals, amorphous solids, among others. The volumes are divided between methods and applications, making this both a reliable reference and handbook for chemists, physicists, biologists, materials scientists and engineers, as well as graduate students and their lecturers.
Presents facts, brief stories, photos, and illustrations showing that fact can be as weird as fiction.