Identikit 2: An Algorithm for Reconstructing Galactic Collisions

Using a combination of self-consistent and test-particle techniques, Identikit 1 provided a way to vary the initial geometry of a galactic collision and instantly visualize the outcome. Identikit 2 uses the same techniques to define a mapping from the current morphology and kinematics of a tidal encounter back to the initial conditions. By requiring that various regions along a tidal feature all originate from a single disc with a unique orientation, this mapping can be used to derive the initial collision geometry. In addition, Identikit 2 offers a robust way to measure how well a particular model reproduces the morphology and kinematics of a pair of interacting galaxies. A set of eight self-consistent simulations is used to demonstrate the algorithm's ability to search a ten-dimensional parameter space and find near-optimal matches; all eight systems are successfully reconstructed.Comment: 14 pages, 8 figures. Accepted for publication in MNRAS. To get a copy with high-resolution figures, use the web interface, or download the Identikit software, visit http://www.ifa.hawaii.edu/faculty/barnes/research/identikit

Un environnement de spécification et de découverte pour la réutilisation des composants logiciels dans le développement des logiciels distribués

Notre travail vise à élaborer une solution efficace pour la découverte et la réutilisation des composants logiciels dans les environnements de développement existants et couramment utilisés. Nous proposons une ontologie pour décrire et découvrir des composants logiciels élémentaires. La description couvre à la fois les propriétés fonctionnelles et les propriétés non fonctionnelles des composants logiciels exprimées comme des paramètres de QoS. Notre processus de recherche est basé sur la fonction qui calcule la distance sémantique entre la signature d'un composant et la signature d'une requête donnée, réalisant ainsi une comparaison judicieuse. Nous employons également la notion de " subsumption " pour comparer l'entrée-sortie de la requête et des composants. Après sélection des composants adéquats, les propriétés non fonctionnelles sont employées comme un facteur distinctif pour raffiner le résultat de publication des composants résultats. Nous proposons une approche de découverte des composants composite si aucun composant élémentaire n'est trouvé, cette approche basée sur l'ontologie commune. Pour intégrer le composant résultat dans le projet en cours de développement, nous avons développé l'ontologie d'intégration et les deux services " input/output convertor " et " output Matching ".Our work aims to develop an effective solution for the discovery and the reuse of software components in existing and commonly used development environments. We propose an ontology for describing and discovering atomic software components. The description covers both the functional and non functional properties which are expressed as QoS parameters. Our search process is based on the function that calculates the semantic distance between the component interface signature and the signature of a given query, thus achieving an appropriate comparison. We also use the notion of "subsumption" to compare the input/output of the query and the components input/output. After selecting the appropriate components, the non-functional properties are used to refine the search result. We propose an approach for discovering composite components if any atomic component is found, this approach based on the shared ontology. To integrate the component results in the project under development, we developed the ontology integration and two services " input/output convertor " and " output Matching "

Structured Sparsity: Discrete and Convex approaches

Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance interpretability in machine learning and statistics applications: While the ambient dimension is vast in modern data analysis problems, the relevant information therein typically resides in a much lower dimensional space. However, many solutions proposed nowadays do not leverage the true underlying structure. Recent results in CS extend the simple sparsity idea to more sophisticated {\em structured} sparsity models, which describe the interdependency between the nonzero components of a signal, allowing to increase the interpretability of the results and lead to better recovery performance. In order to better understand the impact of structured sparsity, in this chapter we analyze the connections between the discrete models and their convex relaxations, highlighting their relative advantages. We start with the general group sparse model and then elaborate on two important special cases: the dispersive and the hierarchical models. For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations. We also consider more general structures as defined by set functions and present their convex proxies. Further, we discuss efficient optimization solutions for structured sparsity problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure

Wing structure of the next-generation civil tiltrotor: From concept to preliminary design

The main objective of this paper is to describe a methodology to be applied in the preliminary design of a tiltrotor wing based on previously developed conceptual design methods. The reference vehicle is the Next-Generation Civil Tiltrotor Technology Demonstrator (NGCTR-TD) developed by Leonardo Helicopters within the Clean Sky research program framework. In a previous work by the authors, based on the specific requirements (i.e., dynamics, strength, buckling, functional), the first iteration of design was aimed at finding a wing structure with a minimized structural weight but at the same time strong and stiff enough to comply with sizing loads and aeroelastic stability in the flight envelope. Now, the outcome from the first design loop is used to build a global Finite Element Model (FEM), to be used for a multi-objective optimization performed by using a commercial software environment. In other words, the design strategy, aimed at finding a first optimal solution in terms of the thickness of composite components, is based on a two-level optimization. The first-level optimization is performed with engineering models (non-FEA-based), and the second-level optimization, discussed in this paper, within an FEA environment. The latter is shown to provide satisfactory results in terms of overall wing weight, and a zonal optimization of the composite parts, which is the starting point of an engineered model and a detailed FEM (beyond the scope of the present work), which will also take into account manufacturing, assembly, installation, accessibility and maintenance constraints