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Automata-2008
Cellular automata are regular uniform networks of locally-connected finite-state machines. They are discrete systems with non-trivial behaviour. Cellular automata are ubiquitous: they are mathematical models of computation and computer models of natural systems. The book presents results of cutting edge research in cellular-automata framework of digital physics and modelling of spatially extended non-linear systems; massive-parallel computing, language acceptance, and computability; reversibility of computation, graph-theoretic analysis and logic; chaos and undecidability; evolution, learning and cryptography. The book is unique because it brings together unequalled expertise of inter-disciplinary studies at the edge of mathematics, computer science, engineering, physics and biology.
Environmental Chemistry
This book describes advances in this new, fast developing science, which seeks to decipher fundamental mechanisms ruling the behaviour in water, soils, atmosphere, food and living organisms of toxic metals, fossil fuels, pesticides and other organic pollutants. Sections on eco-toxicology, green chemistry, and analytical chemistry round out this thorough survey of conditions and analytical techniques in an emerging specialty.
ISI Directory
None
Autonomous Horizons
Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.
Pattern Recognition and Machine Learning
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Litteratura Coleopterologica (1758-1900)
"Bibliographic references to works pertaining to the taxonomy of Coleoptera published between 1758 and 1900 in the non-periodical literature are listed. Each reference includes the full name of the author, the year or range of years of the publication, the title in full, the publisher and place of publication, the pagination with the number of plates, and the size of the work. This information is followed by the date of publication found in the work itself, the dates found from external sources, and the libraries consulted for the work. Overall, more than 990 works published by 622 primary authors are listed. For each of these authors, a biographic notice (if information was available) is given along with the references consulted"--[p. 1].
