Using ATL to define advanced and flexible constraint model transformations

Transforming constraint models is an important task in re- cent constraint programming systems. User-understandable models are defined during the modeling phase but rewriting or tuning them is manda- tory to get solving-efficient models. We propose a new architecture al- lowing to define bridges between any (modeling or solver) languages and to implement model optimizations. This architecture follows a model- driven approach where the constraint modeling process is seen as a set of model transformations. Among others, an interesting feature is the def- inition of transformations as concept-oriented rules, i.e. based on types of model elements where the types are organized into a hierarchy called a metamodel

Rewriting Constraint Models with Metamodels

An important challenge in constraint programming is to rewrite constraint models into executable programs calculat- ing the solutions. This phase of constraint processing may require translations between constraint programming lan- guages, transformations of constraint representations, model optimizations, and tuning of solving strategies. In this paper, we introduce a pivot metamodel describing the common fea- tures of constraint models including different kinds of con- straints, statements like conditionals and loops, and other first-class elements like object classes and predicates. This metamodel is general enough to cope with the constructions of many languages, from object-oriented modeling languages to logic languages, but it is independent from them. The rewriting operations manipulate metamodel instances apart from languages. As a consequence, the rewriting operations apply whatever languages are selected and they are able to manage model semantic information. A bridge is created between the metamodel space and languages using parsing techniques. Tools from the software engineering world can be useful to implement this framework

Automatically Discovering Hidden Transformation Chaining Constraints

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information

Solving an Air Conditioning System Problem in an Embodiment Design Context Using Constraint Satisfaction Techniques

International audienceIn this paper, the embodiment design of an air condition- ing system (ACS) in an aircraft is investigated using interval constraint satisfaction techniques. The detailed ACS model is quite complex to solve, since it contains many coupled variables and many constraints corresponding to complex physics phenomena. Some new heuristics and notions based on embodiment design knowledge, are briefly introduced to undertake some embodiment design concepts and to obtain a more relevant and more efficient solving process than classical algorithms. The benefits of using constraint programming in embodiment design are discussed and some difficulties for designers using CP tools are shortly detailed

Search Strategies for an Anytime Usage of the Branch and Prune Algorithm

International audienceWhen applied to numerical CSPs, the branch and prune algorithm (BPA) computes a sharp covering of the solution set. The BPA is therefore impractical when the solution set is large, typically when it has a dimension larger than four or five which is often met in underconstrained problems. The purpose of this paper is to present a new search tree exploration strategy for BPA that hybridizes depth-first and breadth-first searches. This search strategy allows the BPA discovering potential solutions in different areas of the search space in early stages of the exploration, hence allowing an anytime usage of the BPA. The merits of the proposed search strategy are experimentally evaluated

Rewriting Constraint Models with Metamodels

International audienceAn important challenge in constraint programming is to rewrite constraint models into executable programs calculat- ing the solutions. This phase of constraint processing may require translations between constraint programming lan- guages, transformations of constraint representations, model optimizations, and tuning of solving strategies. In this paper, we introduce a pivot metamodel describing the common fea- tures of constraint models including different kinds of con- straints, statements like conditionals and loops, and other first-class elements like object classes and predicates. This metamodel is general enough to cope with the constructions of many languages, from object-oriented modeling languages to logic languages, but it is independent from them. The rewriting operations manipulate metamodel instances apart from languages. As a consequence, the rewriting operations apply whatever languages are selected and they are able to manage model semantic information. A bridge is created between the metamodel space and languages using parsing techniques. Tools from the software engineering world can be useful to implement this framework

Ensconsin/Map7 promotes microtubule growth and centrosome separation in Drosophila neural stem cells.

International audienceThe mitotic spindle is crucial to achieve segregation of sister chromatids. To identify new mitotic spindle assembly regulators, we isolated 855 microtubule-associated proteins (MAPs) from Drosophila melanogaster mitotic or interphasic embryos. Using RNAi, we screened 96 poorly characterized genes in the Drosophila central nervous system to establish their possible role during spindle assembly. We found that Ensconsin/MAP7 mutant neuroblasts display shorter metaphase spindles, a defect caused by a reduced microtubule polymerization rate and enhanced by centrosome ablation. In agreement with a direct effect in regulating spindle length, Ensconsin overexpression triggered an increase in spindle length in S2 cells, whereas purified Ensconsin stimulated microtubule polymerization in vitro. Interestingly, ensc-null mutant flies also display defective centrosome separation and positioning during interphase, a phenotype also detected in kinesin-1 mutants. Collectively, our results suggest that Ensconsin cooperates with its binding partner Kinesin-1 during interphase to trigger centrosome separation. In addition, Ensconsin promotes microtubule polymerization during mitosis to control spindle length independent of Kinesin-1

3D steerable wavelets and monogenic analysis for bioimaging

ABSTRACT In this paper we introduce a 3D wavelet frame that has the key property of steerability. The proposed wavelet frame relies on the combination of a 3D isotropic wavelet transform with the 3D Riesz operator which brings steerability to the pyramid. The novel transform enjoys self reversibility and exact steering of the basis functions in any 3D direction by linear combination of the primary coefficients. We exploit the link between the Riesz transform and the directional Hilbert transform to define a multiresolution monogenic signal analysis in 3D which achieves multiscale AM/FM signal decomposition. We give an example of application of the 3D monogenic wavelet frame in biological imaging with the enhancement of anisotropic structures in 3D fluorescence microscopy

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers