La vertu d'un LA
The virtue of an A - A fortunate hive

Assuming you have a standard Gaussian bell curve (in blue). Suppose that you want to cut it into two parts of equal areas, with an horizontal line.  Which fraction of the Gaussian peak height provides you with the red and the green curves, which sum up to the Gaussian, with equal surface integral (undr the red and the green cuves)? It turns out that, numerically, the fraction, on the y-axis, is about 0.3063622804625085, or one over 3.26410940175247 of the peak height. If one looks at the x-axis, one has to cut at +/- 1.538172262286592.\sigma, where \sigma is the usual Gaussian scale parameter. In practice, cutting the Gaussian at 3/10 of the height would be good enough, assuming sufficient, far to critical, sampling. Yet out of curiosity, i looked at several numerical constant tables, or even Plouffe's constant inverter , and did not find any of these three. So once again, the potential gaussian split constants  are: 0.3063622804625085 1.538172262286592 3.264109

Under the cryptic title dwells a nice invitation by Vincent Mazet to give a talk on a panorama of 2D wavelets at iCube-MIV : modèles, images et vision (Strasbourg University). Although the abstract is in french, slides are globish. For those who have an eye for finest details, Alfréd Haar and Frigyes Riesz , two prominent functional analysis and therefore wavelet contributors (albeit indirectly) are honored on this memorial at Szeged university. Vendredi 15 mars 2013, 10h30, A301, séminaire commun D-IRTS & École doctorale MSII Ondelettes et autres représentations bidimensionnelles, multi-échelles et géométriques pour le traitement d'images : un panorama Conférencier : Laurent Duval (IFP Energies nouvelles), avec Laurent Jacques , Caroline Chaux et Gabriel Peyré Résumé : La quête de représentations optimales en traitement d'images et vision par ordinateur se heurte à la richesse et la diversité des données bidimensionnelles. De nombreux travaux se sont cepe