CADS*: Computer-Aided Development of self-* systems

We present the prototype tool CADS* for the computer-aided development of an important class of self-* systems, namely systems whose components can be modelled as Markov chains. Given a Markov chain representation of the IT components to be included into a self-* system, CADS* automates or aids (a) the development of the artifacts necessary to build the self-* system; and (b) their integration into a fully-operational self-* solution. This is achieved through a combination of formal software development techniques including model transformation, model-driven code generation and dynamic software reconfiguration.

The biology of the lichen Rhizocarpon geographicum: symbionts, growth, ecology, and lichenometry

Rhizocarpon geographicum (L.) DC. is one of the most widely distributed species of crustose lichens. This unusual organism comprises yellow-green ‘areolae’ that contain the algal symbiont which develop and grow on the surface of a non-lichenized, fungal ‘hypothallus’ that extends beyond the margin of the areolae to form a marginal ring. This species grows exceptionally slowly with annual radial growth rates (RGR) as low as 0.07 mm yr-1 and its considerable longevity has been exploited by geologists in the development of methods of dating the age of exposure of rock surfaces and glacial moraines (‘lichenometry’). Recent research has established some aspects of the basic biology of this important and interesting organism. This chapter describes the general structure of R. geographicum, how the areolae and hypothallus develop, why the lichen grows so slowly, the growth rate-size curve, and some aspects of the ecology of R. geographicum including whether the lichen can inhibit the growth of its neighbours by chemical means (‘allelopathy’). Finally, the importance of R. geographicum in direct and indirect lichenometry is reviewed.

Fitness and novelty in evolutionary art

In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.

A quantum Jensen-Shannon graph kernel using the continuous-time quantum walk

In this paper, we use the quantum Jensen-Shannon divergence as a means to establish the similarity between a pair of graphs and to develop a novel graph kernel. In quantum theory, the quantum Jensen-Shannon divergence is defined as a distance measure between quantum states. In order to compute the quantum Jensen-Shannon divergence between a pair of graphs, we first need to associate a density operator with each of them. Hence, we decide to simulate the evolution of a continuous-time quantum walk on each graph and we propose a way to associate a suitable quantum state with it. With the density operator of this quantum state to hand, the graph kernel is defined as a function of the quantum Jensen-Shannon divergence between the graph density operators. We evaluate the performance of our kernel on several standard graph datasets from bioinformatics. We use the Principle Component Analysis (PCA) on the kernel matrix to embed the graphs into a feature space for classification. The experimental results demonstrate the effectiveness of the proposed approach. © 2013 Springer-Verlag.