Axial-vector torsion and the teleparallel Kerr spacetime

In the context of the teleparallel equivalent of general relativity, we obtain the tetrad and the torsion fields of the stationary axisymmetric Kerr spacetime. It is shown that, in the slow rotation and weak-field approximations, the axial-vector torsion plays the role of the gravitomagnetic component of the gravitational field, and is thus responsible for the Lense-Thirring effect.

On Duffin-Kemmer-Petiau particles with a mixed minimal-nonminimal vector coupling and the nondegenerate bound-states for the one-dimensional inversely linear background

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Measurement of associated production of vector bosons and top quark-antiquark pairs in pp collisions at √s=7 TeV

The first measurement of vector-boson production associated with a top quark-antiquark pair in proton-proton collisions at √s=7 TeV is presented. The results are based on a data set corresponding to an integrated luminosity of 5.0 fb-1, recorded by the CMS detector at the LHC in 2011. The measurement is performed in two independent channels through a trilepton analysis of tt̄Z events and a same-sign dilepton analysis of tt̄V (V=W or Z) events. In the trilepton channel a direct measurement of the tt̄Z cross section σtt̄Z=0.28-0.11+0.14 (stat)-0.03+0.06 (syst) pb is obtained. In the dilepton channel a measurement of the tt̄V cross section yields σtt̄V=0.43-0.15+0.17 (stat)-0.07+0.09 (syst) pb. These measurements have a significance, respectively, of 3.3 and 3.0 standard deviations from the background hypotheses and are compatible, within uncertainties, with the corresponding next-to-leading order predictions of 0.137-0.016+0.012 and 0.306-0.053+0.031 pb. © 2013 CERN. Published by the American Physical Society.

ECLAC support for housing and population censuses in the decade starting in 2010

Includes bibliography

Does public financial support stimulate innovation and productivity? An impact evaluation

This paper contributes to the empirical literature that evaluates the effects of public financial support to innovation on innovation expenditures, innovation itself and productivity in developing countries. Propensity score matching techniques and data from Innovation Surveys are used to analyse the impacts of public financial support to innovation on Uruguayan firms. The results indicate that there is no crowding-out effect of private innovation investment by public funds and that public financial support in Uruguay seems to increase private innovation expenditures. Financial support also appears to induce increased research and development expenditures and innovative sales, with these effects being greatest for service firms. Public funds do not, however, significantly stimulate private expenditures by firms that would have carried out innovation activities even in the absence of financial support.

SPECTRUM OF STOCHASTIC ADDING MACHINES AND FIBERED JULIA SETS

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Semliki Forest Virus as a Vector: Pros and Cons for Its Use in Biopharmaceuticals Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Interface para classificação de dados por máquinas de vetores de suporte

In this project the Pattern Recognition Problem is approached with the Support Vector Machines (SVM) technique, a binary method of classification that provides the best solution separating the data in the better way with a hiperplan and an extension of the input space dimension, as a Machine Learning solution. The system aims to classify two classes of pixels chosen by the user in the interface in the interest selection phase and in the background selection phase, generating all the data to be used in the LibSVM library, a library that implements the SVM, illustrating the library operation in a casual way. The data provided by the interface is organized in three types, RGB (Red, Green and Blue color system), texture (calculated) or RGB + texture. At last the project showed successful results, where the classification of the image pixels was showed as been from one of the two classes, from the interest selection area or from the background selection area. The simplest user view of results classification is the RGB type of data arrange, because it’s the most concrete way of data acquisition

Generalized interval vector spaces and interval optimization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classification of handwriting patterns in patients with Parkinson´s disease, using a biometric sensor

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimativa da irradiação solar global pelo método de Angstrom-Prescott e técnicas de aprendizado de máquinas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)