Ted Herman – författare

Self- governing control is a defining characteristic of autonomous computing machinery. Autonomy implies some degree of independence, and when a system''s ability to achieve its mission is independent of how it is initialized, the system is self-stabilizing. Application of self-stabilization to system and network components is motivated by core concerns of fault-tolerance in distributed systems. Self-stabilization is a solution to problems of transient memory faults and systems with dynamic reconfigurations. Research in self-stabilization explores many of the classic themes of distributed computing (distributed graph algorithms, mutual exclusion, distributed agreement). Recent papers combine self-stabilization with traditional forms of fault-tolerance, consider methodological issues for the design of self-stabilizing systems, investigate randomized techniques, and apply stabilization to new networking models. The workshop brings together concerns from theory and practice of self-stabilization.

A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python‘s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of Python, the text covers important concepts of computing, including software engineering motivation, algorithms behind syntax rules, advanced functional programming ideas, and, briefly, finite state machines.Taking a student-friendly, interactive approach to teach computing, the book addresses more difficult concepts and abstractions later in the text. The author presents ample explanations of data types, operators, and expressions. He also describes comprehensions the powerful specifications of lists and dictionaries before introducing loops and variables. This approach helps students better understand assignment syntax and iteration by giving them a mental model of sophisticated data first.Web ResourceThe book‘s supplementary website at http://functionalfirstpython.com/ provides many ancillaries, including:Interactive flashcards on Python language elements Links to extra support for each chapter Unit testing and programming exercises An interactive Python stepper tool Chapter-by-chapter points Material for lectures

A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python’s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of Python, the text covers important concepts of computing, including software engineering motivation, algorithms behind syntax rules, advanced functional programming ideas, and, briefly, finite state machines.Taking a student-friendly, interactive approach to teach computing, the book addresses more difficult concepts and abstractions later in the text. The author presents ample explanations of data types, operators, and expressions. He also describes comprehensions—the powerful specifications of lists and dictionaries—before introducing loops and variables. This approach helps students better understand assignment syntax and iteration by giving them a mental model of sophisticated data first.Web ResourceThe book’s supplementary website at http://functionalfirstpython.com/ provides many ancillaries, including: Interactive flashcards on Python language elementsLinks to extra support for each chapterUnit testing and programming exercisesAn interactive Python stepper toolChapter-by-chapter points Material for lectures

A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python''s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of

A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python''s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of

Self-stabilizationisanestablishedprincipleofmoderndistributedsystemdesign. Theadvantagesofsystemsthatself-recoverfromtransientfailures,temporary- curity attacks,and spontaneousrecon?gurationareobvious.Lessobviousis how the ambitious goal of recovering from the most general case of a transient fault, namelythatofanarbitraryinitialstate,canleadtoasimplersystemdesignthan dealing with particular cases of failures. In the area of mathematical probl- solving, Po 'lya gave the term "the inventors paradox" to such situations, where generalizing the problem may simplify the solution. The dramatic growthof d- tributed systems, peer-to-peer distribution networks, and large grid computing environments confronts designers with serious di?culties of complexity and has motivated the call for systems that self-recover, self-tune, and self-manage. The principlesofself-stabilizationcanbeusefulfor thesegoalsofautonomoussystem behavior. The Symposium on Self-Stabilizing Systems (SSS) is the main forum for - search in the area of self-stabilization. Previous Workshops on Self-Stabilizing Systems (WSS) were held in 1989, 1995, 1997, 1999, and 2001.The previous Symposium on Self-Stabilizing Systems (SSS) took place in 2003. Thirty-three papersweresubmitted toSSS2005byauthorsfromEurope(16),NorthAmerica (8), Asia (4), and elsewhere (5). From the submissions, the program committee selected 15 for inclusion in these proceedings. In addition to the presentation of these papers, the symposium event included a poster session with brief pres- tations of recent work on self-stabilization.

This book constitutes the refereed proceedings of the 6th International Symposium on Self-Stabilizing Systems, SSS 2003, held in San Francisco, CA, USA, in June 2003.The 15 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers address self-stabilization issues for various types of systems and software including communication protocols, sensor networks, biological systems, and directed networks; several new algorithms are presented.

Physicalsystemswhichrightthemselvesafterbeingdisturbedevokeourcuriosity becausewe wantto understand howsuchsystemsareableto reactto unexpected stimuli. Themechanismsareallthe morefascinatingwhensystemsarecomposed of small, simple units, and the ability of the system to self-stabilize emerges out of its components. Faithful computer simulations of such physical systems exhibit the self-stabilizing property, but in the realm of computing, particularly for distributed systems, wehavegreaterambition. We imaginethat all manner of software, ranging from basic communication protocols to high-level applications, could enjoy self-corrective properties. Self-stabilizing software o?ers a unique, non-traditional approach to the c- cial problem of transient fault tolerance. Many successful instances of modern fault-tolerant networks are based on principles of self-stabilization. Surprisingly, the most widely accepted technical de?nition of a self-stabilizing system does not refer to faults: it is the property that the system can be started in any i- tial state, possibly an “illegal state,” and yet the system guarantees to behave properly in ?nite time. This, and similar de?nitions, break many traditional approaches to program design, in which the programmer by habit makes - sumptions about initial conditions. The composition of self-stabilizing systems, initially seen as a daunting challenge, has been transformed into a mana- able task, thanks to an accumulation of discoveries by many investigators. - search on various topics in self-stabilization continues to supply new methods for constructing self-stabilizing systems, determines limits and applicability of the paradigm of self-stabilization, and connects self-stabilization to related areas of fault tolerance anddistributed computing.