Thunderstriking constraints with JUPITER

We present JUPITER, a tool for analysing multi-constrained systems. JUPITER was built to explore three basic ideas. First, how to use controller synthesis so as to find the exact conditions under which a particular constraint will be satisfied. Second, how to successively refine the models used for the controller synthesis so as to obtain a series of more easily understandable and more robust controllers. Last but not least, how to structure & explain the synthesised controllers and provide hints to designers for further optimisations through the use of machine learning techniques. Thus, JUPITER can help in the design and analysis of multi-constraint systems through the automatic synthesis of control logic for certain of the constraints and the aid it provides to designers for discovering further optimisations. The controllers it synthesises can be easily implemented on top of a standard real-time OS

Single nucleotide polymorphisms from Theobroma cacao expressed sequence tags associated with witches' broom disease in cacao

In order to increase the efficiency of cacao tree resistance to witches¿ broom disease, which is caused by Moniliophthora perniciosa (Tricholomataceae), we looked for molecular markers that could help in the selection of resistant cacao genotypes. Among the different markers useful for developing marker-assisted selection, single nucleotide polymorphisms (SNPs) constitute the most common type of sequence difference between alleles and can be easily detected by in silico analysis from expressed sequence tag libraries. We report the first detection and analysis of SNPs from cacao-M. perniciosa interaction expressed sequence tags, using bioinformatics. Selection based on analysis of these SNPs should be useful for developing cacao varieties resistant to this devastating disease. (Résumé d'auteur

k-Step Relative Inductive Generalization

We introduce a new form of SAT-based symbolic model checking. One common idea in SAT-based symbolic model checking is to generate new clauses from states that can lead to property violations. Our previous work suggests applying induction to generalize from such states. While effective on some benchmarks, the main problem with inductive generalization is that not all such states can be inductively generalized at a given time in the analysis, resulting in long searches for generalizable states on some benchmarks. This paper introduces the idea of inductively generalizing states relative to $k$-step over-approximations: a given state is inductively generalized relative to the latest $k$-step over-approximation relative to which the negation of the state is itself inductive. This idea motivates an algorithm that inductively generalizes a given state at the highest level $k$ so far examined, possibly by generating more than one mutually $k$-step relative inductive clause. We present experimental evidence that the algorithm is effective in practice.Comment: 14 page