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Gliders in one-dimensional cellular automata are compact groups of nonquiescent and non-ether patterns (ether represents a periodic background) translating along automaton lattice. They are cellular-automaton analogous of localizations or quasi-local collective excitations travelling in a spatially

In this paper we focus on the construction of the minimal deterministic finite automaton S k that recognizes the set of suffixes of a word w up to k errors. We present an algorithm that makes use of the automaton S k in order to accept in an efficient way the language of all suffixes of w up to k

. In this paper we describe the data structure of a time and space efficient string dictionary automaton, providing insertion and deletion of strings and finite state machine based substring searching. If the input alphabet is bounded and the pattern are mutually substring free an optimal worst

In order to improve our ability to simulate the complex behavior of polymers, we introduce dynamical models in the class of Cellular Automata (CA). Space partitioning methods enable us to overcome fundamental obstacles to large scale simulation of connected chains with excluded volume by parallel processing computers. A highly efficient, two-space algorithm is devised and tested on both Cellular Automata Machines (CAMs) and serial computers. Preliminary results on the static and dynamic properties of polymers in two dimensions are reported.

This is an updated version of my paper “An outline of radiatively-driven cosmology ” (Kurucz 2000). Here the Big Bang universe is replaced by a finite cellular automaton universe with no expansion (Kurucz 2006). The Big Bang is replaced by many little bangs spread throughout the universe

www.elsevier.com/locate/tcs

Abstract. This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The inductive generalisation algorithms consist of three parts. The first part describes the graphical generalisation of state transition models. State transition models are generalised by applying state mergers. The second part involves symbolic generalisation of logic programs which are embedded in each states. Plotkin’s LGG is used for symbolic generalisation of logic programs. The third part covers learning of parameters using statistics derived from the input sequences. The state transitions are unobservable in our settings. The probability distributions over the state transitions and actions are estimated using the EM algorithm. As an application of these algorithms, we learn chemical reaction rules from StochSim, the stochastic software simulator of biochemical reactions. 1

Continuum equations are derived for the large-scale behavior of a class of cellular automaton models for fluids. The cellular automata are discrete analogues of molecular dynamics, in which particles with discrete velocities populate the links of a fixed array of sites. Kinetic equations

We present the result of the article [1], in which a theoretical analysis of extension of the finite automaton built on DNA (introduced by the Shapiro team) to an arbitrary number of states and symbols is given. The Shapiro automaton action is based on cyclic cutting and ligating of DNA molecules