Solving Constraints in Model Transformations

Constraint programming holds many promises for model driven software development (MDSD). Up to now, constraints have only started to appear in MDSD modeling languages, but have not been properly reflected in model transformation. This paper introduces constraint programming in model transformation, shows how constraint programming integrates with QVT Relations - as a pathway to wide spread use of our approach - and describes the corresponding model transformation engine. In particular, the paper will illustrate the use of constraint programming for the specification of attribute values in target models, and provide a qualitative evaluation of the benefit drawn from constraints integrated with QVT Relations

Specializing Russian Doll Search

Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, where two consecutive subproblems differ in one variable only. We present the Specialized RDS (SRDS) algorithm, which solves the current subproblem for each value of the new variable with respect to the previous subproblem. The SRDS lower bound is superior to the RDS lower bound, which allows for a higher level of value pruning, although more work per node is required. Experimental results on random and real problems show that this extra work is often beneficial, providing substantial savings in the global computational effort

Improving Backtrack Search for Solving the TCSP 1 Abstract

In this paper, we address the task of finding the minimal network of a Temporal Constraint Satisfaction Problem (TCSP). We report the integration of three approaches to improve the performance of the exponential-time backtrack search (BT-TCSP) proposed by Dechter et al. [6] for this purpose. The first approach consists of using a new efficient algorithm ( £ STP) [21] for solving the Simple Temporal Problem (STP), an operation that must be executed at each node expansion during BT-TCSP. The second approach improves BT-TCSP itself by exploiting the topology of the temporal network. This is accomplished in three ways: finding and exploiting articulation points (AP), checking the graph for new cycles (NewCyc), and using a new heuristic for edge ordering (EdgeOrd). The third approach is a filtering algorithm, £ AC, which is used as a preprocessing step to BT-TCSP, and which significantly reduces the size of the TCSP [22]. In addition to introducing two new techniques, NewCyc and EdgeOrd, this paper discusses an extensive evaluation of the merits of the above three approaches. Our experiments on randomly generated problems demonstrate significant improvements in the number of nodes visited, constraint checks, and CPU time.

Eosinophils and disease pathogenesis

Eosinophils are granulocytic innate immune cells whose presence is conspicuous in a variety of disease states, including eosinophilic hyperproliferative and infiltrative processes, as well as conditions associated with maladaptive Th2 inflammation. This review discusses the role of eosinophils in disease pathogenesis, including a consideration of relevant eosinophil biology. Eosinophilic disease patterns of tissue infiltration are also detailed, as are candidate mechanisms by which eosinophils cause fibrosis and hypercoagulability and the importance of eosinophils in allergic inflammation. Eosinophils are unique cells in their spectrum of associated disease, with the promise of future discoveries in delineating the manner in which they contribute to disease pathogenesis

Arc consistency during search

IJCAI International Joint Conference on Artificial Intelligence137-14