We describe the main issues found in the design of an efficient implementation, tailored to GPGPUs, of an Algebraic MultiGrid (AMG) preconditioner recently proposed by one of the authors and already available for CPU in the open-source BootCMatch code. The AMG method relies on a new approach for coarsening sparse symmetric positive definite matrices, which we refer as coarsening based on compatible weighted matching. It exploits maximum weight matching in the adjacency graph of the sparse matrix and the principle of compatible relaxation to define a pairwise aggregation of unknowns.

A general methodology, which consists in deriving two-dimensional finite-difference schemes which involve numerical fluxes based on Dirichlet-to-Neumann maps (or Steklov-Poincare operators), is first recalled. Then, it is applied to several types of diffusion equations, some being weakly anisotropic, endowed with an external source. Standard finite-difference discretizations are systematically recovered, showing that in absence of any other mechanism, like e.g.

A genuinely two-dimensional discretization of general drift-diffusion (including incompressible Navier--Stokes) equations is proposed. Its numerical fluxes are derived by computing the radial derivatives of "bubbles" which are deduced from available discrete data by exploiting the stationary Dirichlet--Green function of the convection-diffusion operator. These fluxes are reminiscent of Scharfetter and Gummel's in the sense that they contain modified Bessel functions which allow one to pass smoothly from diffusive to drift-dominating regimes.

In this poster we present some remarks about the Hilbert transform on the real line, in connection with its application in signal processing [1, 2]. References [1] M.R. Capobianco, G. Criscuolo, Convergence and stability of a new quadrature rule for evaluating Hilbert transform, Numer. Algor., 60 (2012) 579-592 [2] C. Zhou, L. Yang, Y. Liu, Z. Yang, A novel method for computing the Hilbert transform with Haar multiresolution approximation,Journal of Computational and Applied Mathematics 223 (2009), 585-597

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms.

The generalized Schur algorithm is a powerful tool allowing to compute classical decompositions of matrices, such as the QR and LU factorizations. When applied to matrices with particular structures, the generalized Schur algorithm computes these factorizations with a complexity of one order of magnitude less than that of classical algorithms based on Householder or elementary transformations.

This study shows the land cover mapping accuracy retrievable by the TASI-600 thermal airborne multispectral sensor and describes some of the classification results tested on the thermal preprocessed data for a rural area. In the paper is provided an overview of the principal TASI-600 characteristics, i.e. 32 spectral bands in the 8.0-11.5 ?m spectral range, and land cover classification performances. A full assessment of the TASI-600 spectral bands has been also obtained by ranking them in order to understanding their role in land cover classification.

We consider a general statistical linear inverse problem, where the solution is represented via a known (possibly overcomplete) dictionary that allows its sparse representation. We propose two different approaches. A model selection estimator selects a single model by minimizing the penalized empirical risk over all possible models. By contrast with direct problems, the penalty depends on the model itself rather than on its size only as for complexity penalties. A Q-aggregate estimator averages over the entire collection of estimators with properly chosen weights.