James P. Crutchfield

TL;DR: In this article, a synchronization analysis of exact finite-state sources was extended to nonexact sources for which synchronization occurs only asymptotically, and it was shown that the average uncertainty in the source state vanishes exponentially fast and, as a consequence, an observer's average uncertainty of predicting future output converges exponentially fast to the source entropy rate.

TL;DR: In this paper, the authors study dynamical reversibility in stationary stochastic processes from an information-theoretic perspective, and show that the computational resources necessary to generate a process in the forward and reverse temporal directions are generally not the same.

TL;DR: A new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle is proposed and the result is a timeseries which may correspond to the process of stress accretion in the earthquake cycle.

TL;DR: The evolving cellular automata (ECA) framework as mentioned in this paper is an idealized means for studying how evolution (natural or computational) can create systems that perform emergent computation, in which the actions of simple computations with local information and communication give rise to coordinated global information processing.

TL;DR: This work considers the problem of moving from less to more computationally capable classes in the search for finite descriptions of unpredictable data series and estimates optimal models within an assumed language class optimal models.