Expanding and self-organizing 2D universe models emerging from frozen trivalent spin networks

The Self-organizing Universe

Evolution of Information and Complexity in an Ever-Expanding Universe

Using the usual definitions of information and entropy in quantum gravity and statistical mechanics and the existing views about the relation between information and complexity, we examine the evolution of complexity in an ever expanding universe.

Spin foam with topologically encoded tetrad on trivalent spin networks

We explore discrete approaches in LQG where all fields, the gravitational tetrad, and the matter and energy fields, are encoded implicitly in a graph instead of being additional data. Our graph should therefore be richer than a simple simplicial decomposition. It has to embed geometrical information and the standard model. We start from Lisi’s model. We build a trivalent graph which is an F4 la...

Rule Extraction from Self-Organizing Networks

Abstract. Generalized relevance learning vector quantization (GRLVQ) [4] constitutes a prototype based clustering algorithm based on LVQ [5] with energy function and adaptive metric. We propose a method for extracting logical rules from a trained GRLVQ-network. Real valued attributes are automatically transformed to symbolic values. The rules are given in the form of a decision tree yielding se...

Self-organizing mixture models

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps, the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a pen...