Fitting 3D morphable models using implicit representations

We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1.0.

The Impact of the Sarbanes-Oxley Act on the Cost of Going Public

This paper examines the impact of the Sarbanes-Oxley Act (SOX), a legal framework intended to increase transparency and accountability of listed companies, on the cost of going public in the US. We expect SOX to increase the direct cost of going public, but decrease the underpricing because of reduced asymmetric information. Our main results corroborate these hypotheses. First, we find an increase in the cost of going public of 90 bp of gross proceeds. Second, we record a reduction in underpricing of 6 pp, which is related to a reduced offer price adjustment. This supports our hypothesis that SOX represents a mechanism to reduce asymmetric information.