Chaotic Crystallography: How the physics of information reveals structural order in materials

We review recent progress in applying information- and computation-theoretic measures to describe material structure that transcends previous methods based on exact geometric symmetries. We discuss the necessary theoretical background for this new toolset and show how the new techniques detect and describe novel material properties. We discuss how the approach relates to well known crystallographic practice and examine how it provides novel interpretations of familiar structures. Throughout, we concentrate on disordered materials that, while important, have received less attention both theoretically and experimentally than those with either periodic or aperiodic order.Comment: 9 pages, two figures, 1 table; http://csc.ucdavis.edu/~cmg/compmech/pubs/ChemOpinion.ht

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

A semantic content analysis model for sports video based on perception concepts and finite state machines

In automatic video content analysis domain, the key challenges are how to recognize important objects and how to model the spatiotemporal relationships between them. In this paper we propose a semantic content analysis model based on Perception Concepts (PCs) and Finite State Machines (FSMs) to automatically describe and detect significant semantic content within sports video. PCs are defined to represent important semantic patterns for sports videos based on identifiable feature elements. PC-FSM models are designed to describe spatiotemporal relationships between PCs. And graph matching method is used to detect high-level semantic automatically. A particular strength of this approach is that users are able to design their own highlights and transfer the detection problem into a graph matching problem. Experimental results are used to illustrate the potential of this approac