Ted Herman

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