It is commonly accepted that the GW approximation for the electron self-energy is successful for the description of the band structure of weakly to moderately correlated systems, whereas it will fail for strongly correlated materials. In the present work, we discuss two important aspects of this approximation: first, the "self-screening error," which is due to an incorrect treatment of induced exchange, and second, the atomic limit, in which, instead, correlation is directly responsible for the observed problem. Using the example of the removal of a particle from a box, we show that the self-screening error stems from the use of test charge-test charge screening and that it can be corrected by a two-point vertex contribution to the self-energy derived from time-dependent density functional theory (TDDFT). We explain why the addition of a particle, instead, requires the use of a different approximate vertex. This illustrates why the general vertex function, valid both for valence and conduction states, must be a three-point function. Moreover, we show that also the bad performance of GW in the atomic limit is due to the neglect of the vertex in the self-energy; in that case, the TDDFT-derived vertex correction is not sufficient in order to remove the error even qualitatively. We discuss the effects of the self-screening error as well as the atomic limit using GW for the exactly solvable two-site Hubbard model.

International audienceThe main contribution of our approach is to apply the Hilbert-Huang Transform (which consists of two parts: (a) Empirical Mode Decomposition (EMD), and (b) the Hilbert spectral analysis) to texture analysis. The EMD is locally adaptive and suitable for analysis of non-linear or non-stationary processes. This one-dimensional decomposition technique extracts a finite number of oscillatory components or ldquowell-behavedrdquo AM-FM functions, called Intrinsic Mode Function (IMF), directly from the data. Firstly, we extend the EMD to 2D-data (i.e. images), the so called bidimensional EMD (BEMD), the process being called 2D-sifting process. The 2D-sifting process is performed in two steps: extrema detection by neighboring window or morphological operators and surface interpolation by radial basis functions or multigrid B-splines. Secondly, we analyse each 2D-IMF obtained by BEMD by studying local properties (amplitude, phase, isotropy and orientation) extracted from the monogenic signal of each one of them. The monogenic signal is a 2D-generalization of the analytic signal, where the Riesz Transform replaces the Hilbert Transform. The performance of this texture analysis method, using the BEMD and Riesz Transform, is demonstrated with both synthetic and natural images

Because of the noncommutativity of the matrix product, the three factors into which a depolarizing Mueller matrix is decomposed, i.e., the diattenuator, the retarder, and the depolarizer, form six possible products grouped into two families, as already pointed out [J. Opt. Soc. Am. A13, 1106 (1996); Opt. Lett.29, 2234 (2004)]. We show that, apart from the generalized polar decomposition generating the first family of products, there exists a dual decomposition belonging to the second family. The mathematical procedure for this dual decomposition is given, and the symmetry existing between the two decompositions is pointed out. The choice of the most appropriate decomposition for a given practical optical arrangement is likewise discussed and illustrated by simple examples.

Most Mueller matrix imaging polarimeters implement sequential acquisition of at least 16 raw images of the same object with different incident and detected light polarizations. When this technique is implemented in vivo, the unavoidable motions of the subject may shift and distort the raw images to an extent such that the final Mueller images cannot be extracted. We describe a registration algorithm which solves this problem for the typical conditions of in vivo imaging, e.g. with spatially inhomogeneous medium to strong depolarization. The algorithm, based on the so called "optical flow," is validated experimentally by comparing the Mueller images of a pig skin sample taken in static and in dynamic conditions.