A Note on Complexity Measures for Probabilistic P Systems

In this paper we present a first approach to the definition of different entropy measures for probabilistic P systems in order to obtain some quantitative parameters showing how complex the evolution of a P system is. To achieve this, we define two possible measures, the first one to reflect the entropy of the P system considered as the state space of possible computations, and the second one to reflect the change of the P system as it evolves.Ministerio de Ciencia y Tecnología TIC2002-04220-C03-0

A Note on Complexity Measures for Probabilistic P Systems

In this paper we present a first approach to the definition of different entropy measures for probabilistic P systems in order to obtain some quantitative parameters showing how complex the evolution of a P system is. To this end, we define two possible measures, the first one to reflect the entropy of the P system considered as the state space of possible computations, and the second one to reflect the change of the P system as it evolves.Ministerio de Ciencia y Tecnología TIC2002-04220-C03-0

Approximating Non-discrete P Systems

The main goal of this paper is to propose some geometric approaches to the computations of non-discrete P systems. The behavior of this kind of P systems is similar to that of classic systems, with the difference that the contents of the membranes are represented by non-discrete multisets (the multiplicities can be non-integers) and, consequently, also the number of applications of a rule in a transition step can be non-integer.Ministerio de Ciencia y Tecnología TIC2002-04220-C03-0

Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples. Although many ways of sampling the context of a node have been proposed, the effects of the way a node is chosen have not been analyzed in depth. To fill this gap, we have re-implemented the main four word2vec inspired graph embedding techniques under the same framework and analyzed how different sampling distributions affects embeddings performance when tested in node classification problems. We present a set of experiments on different well known real data sets that show how the use of popular centrality distributions in sampling leads to improvements, obtaining speeds of up to 2 times in learning times and increasing accuracy in all cases

Evolving Creativity : An Analysis of the Creative Method in elBulli Restaurant

In this article we present an analysis of the creative method developed in the restaurant elBulli (www.elbulli.com) over the period 1987-2005. elBulli has been the 5-time recipient of the Best Restaurant in the World by Restaurant Magazine, and media, professionals and scientists have recognized the global impact of its work in the food industry over the last two decades. This impact is closely connected to the model of evolving creativity that elBulli team has implemented and refined over the years. We combine the qualitative study of documents produced by elBulli restaurant with networks analysis in order to represent a model of evolving creativity that can be applied to other domains and industries.Junta de Andalucía TIC-606

Computation in One-Dimensional Piecewise Maps

In this paper we show that the one-dimensional Piecewise Affine Maps (PAMs) are equivalent to planar Pseudo-Billiard Systems (PBSs) or so called “strange billiards”. The reachability problem for PAMs is still open, however the more general model of rational onedimensional maps is shown to be universal with undecidable reachability problem

A Virtual Laboratory for the Study of History and Cultural Dynamics

This article presents a Virtual Laboratory that enables the researcher to try hypothesis and confirm data analysis about different historical processes and cultural dynamics. This Virtual Cultural Laboratory (VCL) is developed using agent-based modeling technology. Individuals' tendencies and preferences as well as the behavior of cultural objects in the transformation of cultural information are taken into consideration. In addition, the effect of local interactions at different scales over time and space is visualized through the VCL interface. Information repositories, cultural items, borders, population size, individual' tendencies and other features are determined by the user. Finally, the researcher can also isolate specific factors whose effect on the global system might be of interest to the researcher. All the code can be found at http://projects.cultureplex.ca

The Potosí principle: religious prosociality fosters selforganization of larger commnities under extreme natural and economic conditions

We show how in colonial Potosı´ (present-day Bolivia) social and political stabil-ity was achieved through the self-organization of society through the repetition of religious rituals. Our analysis shows that the population of Potosı´ develops over the time a series of cycles of rituals and miracles as a response to social upheaval and natural disasters and that these cycles of religious performance become crucial mechanisms of cooperation among different ethnic and religious groups. Our methodology starts with a close reading and annotation of the Historia de Potosı´ by Bartolome´ Arzans. Then, we model the religious cycles of miracles and rituals and store all social and cultural information about the cycles in a multirelational graph database. Finally, we perform graph analysis through traversals queries in order to establish facts concerning social networks, historical evolution of behaviors, types of participation of miraculous characters according to dates, parts of the city, ethnic groups, etc. It is also important to note that the religious activity at the group level gave native communities a way to participate in the social life. It also guaranteed that the city performed its role as producer of silver in the global economic structure of the Spanish empire. This case proves the importance of religion as a mechanism of stability and self-organization in periods of social or political turbulence. The multidisciplinary methodology combining traditional humanistic techniques with graph analysis shows a great potential for other sociological, historical, and literary problems

A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data

Neural-symbolic approaches to machine learning incorporate the advantages from both connectionist and symbolic methods. Typically, these models employ a first module based on a neural architecture to extract features from complex data. Then, these features are processed as symbols by a symbolic engine that provides reasoning, concept structures, composability, better generalization and out-of-distribution learning among other possibilities. However, neural approaches to the grounding of symbols in sensory data, albeit powerful, still require heavy training and tedious labeling for the most part. This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex spatial sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. Following his suggestion, the model extracts atomic features from raw data by computing elemental sequential comparisons in a stream of multivariate numerical values. Higher-level constructs are built from these features by subjecting them to further comparisons in a recursive process. At any stage in the recursion, a concept structure may be obtained from these constructs and features by means of Formal Concept Analysis. Results show that the model is able to produce fairly rich yet human-readable conceptual representations without training. Additionally, the concept structures obtained through the model (i) present high composability, which potentially enables the generation of 'unseen' concepts, (ii) allow formal reasoning, and (iii) have inherent abilities for generalization and out-of-distribution learning. Consequently, this method may offer an interesting angle to current neural-symbolic research. Future work is required to develop a training methodology so that the model can be tested against a larger dataset