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Quelques Contributions A Lalgorithmique Distribuee
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Book Synopsis Quelques contributions à l'algorithmique distribuée by : Gabriel Antoine Louis Paillard
Download or read book Quelques contributions à l'algorithmique distribuée written by Gabriel Antoine Louis Paillard and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Ce travail présente quelques contributions en algorithmique distribuée. Premièrement , nous proposons deux algorithmes distribués du crible de la roue (qui à notre connaissance semblent les premières versions distribuées de ce crible). Ensuite, un nouvel algorithme de génération de nombres premiers en distribué est présenté ; il s'appuie sur la méthode de multiples inversions d'arêtes dans un multigraphe. Enfin, sur le thème des réseaux ad-hoc, nous traitons le problème de l'attribution de codes pour des stations appartennant à un réseau de capteurs sans fils. Un nouvel algorithme complètement distribué d'affectation de codes est introduit, ainsi que ses propriétés
Book Synopsis Persistence Theory: From Quiver Representations to Data Analysis by : Steve Y. Oudot
Download or read book Persistence Theory: From Quiver Representations to Data Analysis written by Steve Y. Oudot and published by American Mathematical Soc.. This book was released on 2017-05-17 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Persistence theory emerged in the early 2000s as a new theory in the area of applied and computational topology. This book provides a broad and modern view of the subject, including its algebraic, topological, and algorithmic aspects. It also elaborates on applications in data analysis. The level of detail of the exposition has been set so as to keep a survey style, while providing sufficient insights into the proofs so the reader can understand the mechanisms at work. The book is organized into three parts. The first part is dedicated to the foundations of persistence and emphasizes its connection to quiver representation theory. The second part focuses on its connection to applications through a few selected topics. The third part provides perspectives for both the theory and its applications. The book can be used as a text for a course on applied topology or data analysis.
Author :Neural Information Processing Systems Foundation Publisher :MIT Press ISBN 13 :0262026171 Total Pages :361 pages Book Rating :4.2/5 (62 download)
Book Synopsis Predicting Structured Data by : Neural Information Processing Systems Foundation
Download or read book Predicting Structured Data written by Neural Information Processing Systems Foundation and published by MIT Press. This book was released on 2007 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Book Synopsis Rigorous System Design by : Joseph Sifakis
Download or read book Rigorous System Design written by Joseph Sifakis and published by . This book was released on 2013-03-20 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deals with the formalization of the design of mixed hardware/software systems. It advocates rigorous system design as a model-based process leading from requirements to correct implementations and presents the current state of the art in system design, discusses its limitations and identifies possible avenues for overcoming them.
Book Synopsis Sampling in Combinatorial and Geometric Set Systems by : Nabil H. Mustafa
Download or read book Sampling in Combinatorial and Geometric Set Systems written by Nabil H. Mustafa and published by American Mathematical Society. This book was released on 2022-01-14 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the behavior of basic sampling techniques and intrinsic geometric attributes of data is an invaluable skill that is in high demand for both graduate students and researchers in mathematics, machine learning, and theoretical computer science. The last ten years have seen significant progress in this area, with many open problems having been resolved during this time. These include optimal lower bounds for epsilon-nets for many geometric set systems, the use of shallow-cell complexity to unify proofs, simpler and more efficient algorithms, and the use of epsilon-approximations for construction of coresets, to name a few. This book presents a thorough treatment of these probabilistic, combinatorial, and geometric methods, as well as their combinatorial and algorithmic applications. It also revisits classical results, but with new and more elegant proofs. While mathematical maturity will certainly help in appreciating the ideas presented here, only a basic familiarity with discrete mathematics, probability, and combinatorics is required to understand the material.
Book Synopsis An Introduction to Computational Learning Theory by : Michael J. Kearns
Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Book Synopsis Introduction to Distributed Self-Stabilizing Algorithms by : Karine Altisen
Download or read book Introduction to Distributed Self-Stabilizing Algorithms written by Karine Altisen and published by Morgan & Claypool Publishers. This book was released on 2019-04-15 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims at being a comprehensive and pedagogical introduction to the concept of self-stabilization, introduced by Edsger Wybe Dijkstra in 1973. Self-stabilization characterizes the ability of a distributed algorithm to converge within finite time to a configuration from which its behavior is correct (i.e., satisfies a given specification), regardless the arbitrary initial configuration of the system. This arbitrary initial configuration may be the result of the occurrence of a finite number of transient faults. Hence, self-stabilization is actually considered as a versatile non-masking fault tolerance approach, since it recovers from the effect of any finite number of such faults in a unified manner. Another major interest of such an automatic recovery method comes from the difficulty of resetting malfunctioning devices in a large-scale (and so, geographically spread) distributed system (the Internet, Pair-to-Pair networks, and Delay Tolerant Networks are examples of such distributed systems). Furthermore, self-stabilization is usually recognized as a lightweight property to achieve fault tolerance as compared to other classical fault tolerance approaches. Indeed, the overhead, both in terms of time and space, of state-of-the-art self-stabilizing algorithms is commonly small. This makes self-stabilization very attractive for distributed systems equipped of processes with low computational and memory capabilities, such as wireless sensor networks. After more than 40 years of existence, self-stabilization is now sufficiently established as an important field of research in theoretical distributed computing to justify its teaching in advanced research-oriented graduate courses. This book is an initiation course, which consists of the formal definition of self-stabilization and its related concepts, followed by a deep review and study of classical (simple) algorithms, commonly used proof schemes and design patterns, as well as premium results issued from the self-stabilizing community. As often happens in the self-stabilizing area, in this book we focus on the proof of correctness and the analytical complexity of the studied distributed self-stabilizing algorithms. Finally, we underline that most of the algorithms studied in this book are actually dedicated to the high-level atomic-state model, which is the most commonly used computational model in the self-stabilizing area. However, in the last chapter, we present general techniques to achieve self-stabilization in the low-level message passing model, as well as example algorithms.
Book Synopsis The Nature of Statistical Learning Theory by : Vladimir Vapnik
Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Book Synopsis The Tree and the Canoe by : Joël Bonnemaison
Download or read book The Tree and the Canoe written by Joël Bonnemaison and published by University of Hawaii Press. This book was released on 1994-01-01 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This personal observation of Tanna, an island in the southern part of the Vanuatu archipelago, presents an extraordinary case study of cultural resistance. Based on interviews, myths and stories collected in the field, and archival research, The Tree and the Canoe analyzes the resilience of the people of Tanna, who, when faced with an intense form of cultural contact that threatened to engulf them, liberated themselves by re-creating, and sometimes reinventing, their own kastom. Following a lengthy history of Tanna from European contact, the author discusses in detail original creation myths and how Tanna people revived them in response to changes brought by missionaries and foreign governments. The final chapters of the book deal with the violent opposition of part of the island population to the newly established National Unity government.
Download or read book Random Trees written by Michael Drmota and published by Springer Science & Business Media. This book was released on 2009-04-16 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide a thorough introduction to various aspects of trees in random settings and a systematic treatment of the mathematical analysis techniques involved. It should serve as a reference book as well as a basis for future research.
Book Synopsis Of Peasants, Migrants, and Paupers by : Jan Breman
Download or read book Of Peasants, Migrants, and Paupers written by Jan Breman and published by Oxford University Press, USA. This book was released on 1985 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyses the triangular relationship between migrants, local landless, and dominant landowners; shows how colonization of the tribal hinterland created mass poverty and how large farmers use culture, politics to sustain their hegemony and the tense triangular conflict.
Book Synopsis RFID Security and Privacy by : Yingjiu Li
Download or read book RFID Security and Privacy written by Yingjiu Li and published by Morgan & Claypool Publishers. This book was released on 2013-12-01 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a fast-evolving new area, RFID security and privacy has quickly grown from a hungry infant to an energetic teenager during recent years. Much of the exciting development in this area is summarized in this book with rigorous analyses and insightful comments. In particular, a systematic overview on RFID security and privacy is provided at both the physical and network level. At the physical level, RFID security means that RFID devices should be identified with assurance in the presence of attacks, while RFID privacy requires that RFID devices should be identified without disclosure of any valuable information about the devices. At the network level, RFID security means that RFID information should be shared with authorized parties only, while RFID privacy further requires that RFID information should be shared without disclosure of valuable RFID information to any honest-but-curious server which coordinates information sharing. Not only does this book summarize the past, but it also provides new research results, especially at the network level. Several future directions are envisioned to be promising for advancing the research in this area.
Book Synopsis Graph Colouring and the Probabilistic Method by : Michael Molloy
Download or read book Graph Colouring and the Probabilistic Method written by Michael Molloy and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, many major advances have been made in the field of graph coloring via the probabilistic method. This monograph, by two of the best on the topic, provides an accessible and unified treatment of these results, using tools such as the Lovasz Local Lemma and Talagrand's concentration inequality.
Book Synopsis Introduction to Scheduling by : Yves Robert
Download or read book Introduction to Scheduling written by Yves Robert and published by CRC Press. This book was released on 2009-11-18 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.The book first classifies scheduling problems and
Download or read book Sons of the Soil written by Myron Weiner and published by Princeton University Press. This book was released on 2015-03-08 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Myron Weiner's study of the relationship between internal migration and ethnic conflict in India is exceptional for two reasons: it focuses on intercultural and interstate migration throughout the nation, rather than on merely local or provincial phenomena, and it examines both the social and the political consequences of India's interethnic migrations. Professor Weiner examines selected regions of India in which migrants dominate the modern sector of the economy. He describes the forces that lead individual Indian citizens to move from one linguistic-cultural region to another in search of better opportunities, and he attempts to explain their emergence at the top of the occupational hierarchy. In addition, the author provides an account of the ways in which the indigenous ethnic groups ("sons of the soil") attempt to use political power to overcome their fears of economic defeat and cultural subordination by the more enterprising, more highly skilled, better educated migrants. In addressing the fundamental clash between the migrants' claims to equal access to their country and the claims of the local groups to equal treatment and protection by the state, Professor Weiner considers some of the ways in which government policy makers might achieve greater equality among ethnic groups without simultaneously restricting the spatial and social mobility of some of its own people. Originally published in 1978. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton
Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Book Synopsis A Guide to Algorithm Design by : Anne Benoit
Download or read book A Guide to Algorithm Design written by Anne Benoit and published by CRC Press. This book was released on 2013-08-27 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a complementary perspective to standard books on algorithms, A Guide to Algorithm Design: Paradigms, Methods, and Complexity Analysis provides a roadmap for readers to determine the difficulty of an algorithmic problem by finding an optimal solution or proving complexity results. It gives a practical treatment of algorithmic complexity and guides readers in solving algorithmic problems. Divided into three parts, the book offers a comprehensive set of problems with solutions as well as in-depth case studies that demonstrate how to assess the complexity of a new problem. Part I helps readers understand the main design principles and design efficient algorithms. Part II covers polynomial reductions from NP-complete problems and approaches that go beyond NP-completeness. Part III supplies readers with tools and techniques to evaluate problem complexity, including how to determine which instances are polynomial and which are NP-hard. Drawing on the authors’ classroom-tested material, this text takes readers step by step through the concepts and methods for analyzing algorithmic complexity. Through many problems and detailed examples, readers can investigate polynomial-time algorithms and NP-completeness and beyond.
