We construct a desingularization of the “main component” M 0 1,k (Pn, d) of the moduli space M1,k(Pn, d) of genus-one stable maps into the complex projective space Pn. As a bonus, we obtain desingularizations of certain natural sheaves over M 0 1,k (Pn, d). Such desingularizations are useful

In the CNGS (Cern Neutrino to Gran Sasso) installa-tion two magnetic lenses, namely the horn and the reflec-tor, focus the secondary beam generated in the target sta-tion. The gap between the horn and reflector is chosen to optimize a wide-band high-energy muon-neutrino beam. These two focusing elements are two coaxial lenses: the outer conductor has a cylindrical shape whereas the inner conductor consists of a sequence of conical shapes to opti-mize the focusing capacity. The evaluation of the heat load on the support structures is crucial since modifications in the elements around the horn and reflector are under way and the support structures can be adapted to the heat load found. Furthermore, the heat load in the whole horn area has been evaluated to optimize the cooling-ventilation sys-tem. The energy deposited on the horn and reflector as well as on their adjacent elements has been estimated using the FLUKA Monte Carlo package and results are presented in this paper. The FLUKA geometry input of the horn and re-flector electrical connections has been notably improved in order to accommodate the detailed striplines design to the thermal expansion.

Abstract not found

Let X be an affine toric variety under a torus T and let T be a subtorus. The generic T-orbit closures in X and their flat limits are parametrized by the main component H0 of the toric Hilbert scheme (whose existence follows from work of Haiman and Sturmfels). Further, the quotient torus T/T acts

of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes

Abstract not found

We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all

Abstract not found

Soil is a highly variable medium. There are four main ingredients (fractions) that are consistent with all types of soil: minerals, organic matter, water and air. These four fractions fall into two categories: solid (minerals and organic matter) and non-solid (water and air) (Fig. 1). The solid

Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main