Abstract A distributed system can be characterized by the fact that the global state is distributed and that a common time base does not exist. However, the notion of time is an important concept in every day life of our decentralized \ r eal world" and helps to solve problems like getting a

We describe in this paper a method based on abstract interpretation which, from a theoretical point of view, is similar to the splitting methods proposed in [DGG93, Dam96] but the weaker abstract transition relation we use, allows us to construct automatically abstract state graphs paying a

can be divided into regions corresponding to the physio-logical states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram, " with the help of which many physiological phenomena can be summarized. A properly chosen projection from

This paper presents an algorithm by which a process in a distributed system determines a global state of the system during a computation. Many problems in distributed systems can be cast in terms of the problem of detecting global states. For instance, the global state detection algorithm helps

in the United States 1947-1974 as a byproduct of changes in such variables as labor force participation and single-adult households.

finite-state model that approximates the executable behavior of the software system of interest. Current best-practice involves handconstruction of models which is expensive (prohibitive for all but the smallest systems), prone to errors (which can result in misleading verification results

. Alice makes a joint measurement on her EPR particle and the unknown quantum system, and sends Bob the classical result of this measurement. Knowing this, Bob can convert the state of his EPR particle into an exact replica of the unknown state jOEi which Alice destroyed. Expanded version of manuscript

In this paper, we investigate the detectability problem in discrete event systems. We assume that we do not know initially which state the system is in. The problem is to determine the current and subsequent states of the system based on a sequence of observation. The observation includes partial

(or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential

of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes minimally as the range of the function changes. Through the development of good