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Author: Gabriel Antoine Louis Paillard
Publisher:
ISBN:
Category :
Languages : fr
Pages :
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Book Description
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
Author: Gabriel Antoine Louis Paillard
Publisher:
ISBN:
Category :
Languages : fr
Pages :
Get Book Here
Book Description
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
Author: Steve Y. Oudot
Publisher: American Mathematical Soc.
ISBN: 1470434431
Category : Mathematics
Languages : en
Pages : 229
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Book Description
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: 0262026171
Category : Algorithms
Languages : en
Pages : 361
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Book Description
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Author: Joseph Sifakis
Publisher:
ISBN: 9781601986603
Category : Computers
Languages : en
Pages : 84
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Book Description
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.
Author: Nabil H. Mustafa
Publisher: American Mathematical Society
ISBN: 1470461560
Category : Mathematics
Languages : en
Pages : 251
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Book Description
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.
Author: Michael J. Kearns
Publisher: MIT Press
ISBN: 9780262111935
Category : Computers
Languages : en
Pages : 230
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Book Description
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.
Author: Karine Altisen
Publisher: Morgan & Claypool Publishers
ISBN: 1681735377
Category : Computers
Languages : en
Pages : 167
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Book Description
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.
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
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Book Description
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.
Author: Joël Bonnemaison
Publisher: University of Hawaii Press
ISBN: 9780824815257
Category : History
Languages : en
Pages : 402
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Book Description
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.
Author: Michael Drmota
Publisher: Springer Science & Business Media
ISBN: 3211753575
Category : Mathematics
Languages : en
Pages : 466
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Book Description
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.
