Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.

How relevant is the origin of human capital for immigrant wages? Evidence from Spain

The objective of this article is to analyse the role played by the different components of human capital in the wage determination of immigrants in the Spanish labour market. Using microdata from the Encuesta Nacional de Inmigrantes, we find that human capital of immigrants acquired in Spain presents higher returns than human capital obtained in home countries, reflecting the limited international transferability of the latter. This result is reinforced by the strong heterogeneity observed in wage returns to different kinds of human capital across immigrants from different origins and, in particular, by the fact that immigrants with the higher returns to human capital acquired in their home countries are those coming from other developed countries and Latin America, the two regions more similar to Spain in terms of development and/or culture.