MoSHCA is a mHealth project designed to improve patient-doctor interaction and to promote the self-management of chronic diseases by the patients themselves. The number of people with a chronic disease is dramatically increasing worldwide. This is becoming a major obstacle for economic stability and growth and the sustainability of national health care systems. The introduction of self-management by patients with a chronic disease seems inevitable as a countermeasure against these developments. MoSHCA provides intelligent, user-friendly and secure, medical and well-being decision support through embedded software in mobile devices by utilizing specific sensors and data from customized information systems.

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Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks. The probability distribution is encoded into a Boolean normal form and compiled to a target language, in order to represent local structure expressed among conditional probabilities more efficiently. We show that further improvements are possible, by exploiting the knowledge that is lost during the encoding phase and incorporating it into a compiler inspired by Satisfiability Modulo Theories. Constraints among variables are used as a background theory, which allows us to optimize the Shannon decomposition. We propose a new language, called Weighted Positive Binary Decision Diagrams, that reduces the cost of probabilistic inference by using this decomposition variant to induce an arithmetic circuit of reduced size.

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Abstract-In this paper we propose a highly parallel GPUbased bounding algorithm for computing the exact diameter of large real-world sparse graphs. The diameter is defined as the length of the longest shortest path between vertices in the graph, and serves as a relevant property of all types of graphs that are nowadays frequently studied. Examples include social networks, webgraphs and routing networks. We verify the performance of our parallel approach on a set of large graphs comprised of millions of vertices, and using a CUDA GPU observe an increase in performance of up to 21.1× compared to a CPU algorithm using the same strategy. Based on these results, we provide a characterization of the types of graphs that are well-suited for traversal by means of our parallel diameter algorithm. We furthermore include a comparison of different GPU algorithms for single-source shortest path computations, which is not only a crucial step in computing the diameter, but also relevant in many other distance and neighborhood-based algorithms.