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Carl McTague
Researcher at University of Cambridge
Publications - 15
Citations - 350
Carl McTague is an academic researcher from University of Cambridge. The author has contributed to research in topics: Cayley plane & Cellular automaton. The author has an hindex of 5, co-authored 15 publications receiving 312 citations. Previous affiliations of Carl McTague include University of Cincinnati & Johns Hopkins University.
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The organization of intrinsic computation: complexity-entropy diagrams and the diversity of natural information processing.
TL;DR: In this article, the authors use complexity-entropy diagrams to analyze intrinsic computation in a broad array of deterministic nonlinear and linear stochastic processes, including maps of the interval, cellular automata, and Ising spin systems in one and two dimensions.
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The Organization of Intrinsic Computation: Complexity-Entropy Diagrams and the Diversity of Natural Information Processing
TL;DR: This work uses complexity-entropy diagrams to analyze intrinsic computation in a broad array of deterministic nonlinear and linear stochastic processes, including maps of the interval, cellular automata, and Ising spin systems in one and two dimensions, Markov chains, and probabilistic minimal finite-state machines.
Posted Content
Enumerating Finitary Processes
TL;DR: It is shown how to efficiently enumerate a class of finite-memory stochastic processes using the causal representation of epsilon-machines, characterized in the language of automata theory and adapted to a recent algorithm for generating accessible deterministic finite automata.
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Automated pattern detection: an algorithm for constructing optimally synchronizing multi-regular language filters
TL;DR: A finite transducer is presented that requires only a finite amount of memory, runs in linear time, and gives immediate output for each letter read; it is, moreover, the best possible finite-state approximation with these three features.
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Automated Pattern Detection--An Algorithm for Constructing Optimally Synchronizing Multi-Regular Language Filters
TL;DR: In this article, a multi-regular language filtering problem is formulated as a change-point problem, where the goal is to identify the regions of a string that belong to each cellular automata.