E1 amplitudes, lifetimes, and polarizabilities of the low-lying levels of atomic ytterbium

The results of ab initio calculation of E1 amplitudes, lifetimes,and polarizabilities for several low-lying levels of ytterbium are reported. The effective Hamiltonian for the valence electrons has been constructed in the frame of CI+MBPT method and solutions of many electron equation are found.Comment: 11 pages, submitted to Phys.Rev.

Crime, Institutions and Sector-Specific FDI in Latin America

In this article, we explore how crime and institutions affect the flow of capital in the form of foreign direct investment (FDI) to Latin American and Caribbean countries in the primary, secondary and tertiary sectors during the 1996-2010 period. We use three different variables related to violent crime: homicides, crime victimization, and an index of organized crime. We find that there is a correlation between the institutional and crime variables, where the significance of institutional variables tends to disappear when the crime variables are added to the model. We find that higher crime victimization and organized crime are associated with lower FDI in the tertiary sector. We do not find that crime affects FDI inflows to Latin America in the primary and secondary sector

Feature Generation by Simple-FLDA for Pattern Recognition

Abstract—In this paper, a new feature generation method for pattern recognition is proposed, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). In a field of pattern recognition or signal processing, the principal component analysis (PCA) is popular for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigenvectors in PCA have been presented in such fields, including neural networks. Their effectiveness has been demonstrated in many applications. However, recently the FLDA has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. Generally FLDA has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is generally higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLDA is introduced and its effectiveness is demonstrated for pattern recognition problems. I