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ISBN: 1509090460 0769527809 Year: 2007 Publisher: [Place of publication not identified] IEEE Computer Society Press
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ISBN: 1457701790 0769544118 Year: 2011 Publisher: [Place of publication not identified] IEEE
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ISBN: 0769549977 1467364665 Year: 2013 Publisher: Piscataway, NJ : IEEE,
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ISBN: 1479916714 147993626X Year: 2014 Publisher: New York : IEEE,
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ISBN: 1467382981 1467382973 Year: 2015 Publisher: Piscataway, New Jersey : IEEE,
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ISBN: 1479942634 1479942642 Year: 2014 Publisher: Los Alamitos, CA : IEEE,
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ISBN: 3939897817 Year: 2015 Publisher: Saarbrücken, Germany : Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik,
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ISBN: 1665405627 1665405635 Year: 2021 Publisher: Piscataway, New Jersey : IEEE,
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ISBN: 1108497667 9781108497664 9781108596619 Year: 2023 Publisher: Cambridge Cambridge University Press
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"Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences"--
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ISBN: 1139234757 1282390872 9786612390876 0511646445 0511804091 0511650531 0511532903 0511531990 0511533810 Year: 2009 Publisher: Cambridge : Cambridge University Press,
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This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set. The book starts with a broad introduction to the field and progresses to advanced results. Contents include: definition of Turing machines and basic time and space complexity classes, probabilistic algorithms, interactive proofs, cryptography, quantum computation, lower bounds for concrete computational models (decision trees, communication complexity, constant depth, algebraic and monotone circuits, proof complexity), average-case complexity and hardness amplification, derandomization and pseudorandom constructions, and the PCP theorem.
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