PDF Relationship of Language and Music, Ten Years After: Neural Organization, Cross-domain Transfer and Evolutionary Origins Download
- Author: Caicai Zhang
- Publisher: Frontiers Media SA
- ISBN: 2889768988
- Category : Science
- Languages : en
- Pages : 168
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Popular Science gives our readers the information and tools to improve their technology and their world. The core belief that Popular Science and our readers share: The future is going to be better, and science and technology are the driving forces that will help make it better.
Popular Science gives our readers the information and tools to improve their technology and their world. The core belief that Popular Science and our readers share: The future is going to be better, and science and technology are the driving forces that will help make it better.
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.
Information modeling technology--the open representation of information for database and other computing applications--has grown significantly in recent years as the need for universal systems of information coding has steadily increased. EXPRESS is a particularly successful ISO International Standard language family for object-flavored information modeling. This cogent introduction to EXPRESS provides numerous, detailed examples of the language family's applicability to a diverse range of endeavors, including mechanical engineering, petroleum exploration, stock exchange asset management, and the human genome project. The book also examines the history, practicalities, and implications of information modeling in general, and considers the vagaries of normal language that necessitate precise communication methods. This first-ever guide to information modeling and EXPRESS offers invaluable advice based on years of practical experience. It will be the introduction that students as well as information and data modeling professionals have been waiting for.
The vast majority of automatic controllers used to compensate industrial processes are of PI or PID type. This book comprehensively compiles, using a unified notation, tuning rules for these controllers proposed over the last seven decades (1935-2005). The tuning rules are carefully categorized and application information about each rule is given. The book discusses controller architecture and process modeling issues, as well as the performance and robustness of loops compensated with PI or PID controllers. This unique publication brings together in an easy-to-use format material previously published in a large number of papers and books.This wholly revised second edition extends the presentation of PI and PID controller tuning rules, for single variable processes with time delays, to include additional rules compiled since the first edition was published in 2003.
Based on a scattering theoretic approach which effectively constitutes an extension of the Dyson or Lippman-Schwinger theories, Green functions constitute the backbone of a matching analysis. This analysis is applied to a wide range of models, materials and physical problems, from electronic structure of semiconductor superlattices or phonons in metal superlattices to surface Brillouin scattering, piezoelectric surface waves or interface waves in viscoelastic fluids.