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A Desingularization of the Main Component of . . .
, 2008
"... We construct a desingularization of the “main component” M 0 1,k (Pn, d) of the moduli space M1,k(Pn, d) of genusone 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 for int ..."
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We construct a desingularization of the “main component” M 0 1,k (Pn, d) of the moduli space M1,k(Pn, d) of genusone 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
MAIN COMPONENTS OF THE CNGS
"... In the CNGS (Cern Neutrino to Gran Sasso) installation two magnetic lenses, namely the horn and the reflector, focus the secondary beam generated in the target station. The gap between the horn and reflector is chosen to optimize a wideband highenergy muonneutrino beam. These two focusing elem ..."
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In the CNGS (Cern Neutrino to Gran Sasso) installation two magnetic lenses, namely the horn and the reflector, focus the secondary beam generated in the target station. The gap between the horn and reflector is chosen to optimize a wideband highenergy muonneutrino 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 optimize 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 coolingventilation system. 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 reflector electrical connections has been notably improved in order to accommodate the detailed striplines design to the thermal expansion.
The main component of the toric Hilbert scheme
 Tohoku Math. J
"... Let X be an affine toric variety under a torus T and let T be a subtorus. The generic Torbit 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 on ..."
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Cited by 2 (0 self)
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Let X be an affine toric variety under a torus T and let T be a subtorus. The generic Torbit 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
Survey on Independent Component Analysis
 NEURAL COMPUTING SURVEYS
, 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 2241 (104 self)
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of the original data. Wellknown 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
Nonlinear component analysis as a kernel eigenvalue problem

, 1996
"... 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 highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
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Cited by 1554 (85 self)
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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 highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all
On Bayesian analysis of mixtures with an unknown number of components
 INSTITUTE OF INTERNATIONAL ECONOMICS PROJECT ON INTERNATIONAL COMPETITION POLICY,&QUOT; COM/DAFFE/CLP/TD(94)42
, 1997
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WHAT IS SOIL? Main Components of Soil
"... 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 nonsolid (water and air) (Fig. 1). The solid sect ..."
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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 nonsolid (water and air) (Fig. 1). The solid
Evolutionary Computing
, 2005
"... 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 compone ..."
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Cited by 610 (35 self)
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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
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