In this paper we revisit the problem of performing a QR-step on an unreduced Hessenberg matrix H when we know an "exact" eigenvalue ?0 of H. Under exact arithmetic, this eigenvalue will appear on diagonal of the transformed Hessenberg matrix H~ and will be decoupled from the remaining part of the Hessenberg matrix, thus resulting in a deflation. But it is well known that in finite precision arithmetic the so-called perfect shift can get blurred and that the eigenvalue ?0 can then not be deflated and/or is perturbed significantly.

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

Modal decomposition techniques are used to analyse the wake field past a marine propeller achieved by previous numerical simulations (Muscari et al. Comput. Fluids, vol. 73, 2013, pp. 65-79). In particular, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are used to identify the most energetic modes and those that play a dominant role in the inception of the destabilization mechanisms. Two different operating conditions, representative of light and high loading conditions, are considered.

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.

The knowledge production provided by universities is essential to sustaining a country's long-term economic growth and international competitiveness. Many nations are thus driving to create sustainable and effective funding environments. The evaluation of university knowledge, productivity and research quality becomes critical, with ever increasing share of public funding allocated on the basis of research assessment exercises.