Regular (2+1)-dimensional black holes within non-linear Electrodynamics

(2+1)-regular static black hole solutions with a nonlinear electric field are derived. The source to the Einstein equations is an energy momentum tensor of nonlinear electrodynamics, which satisfies the weak energy conditions and in the weak field limit becomes the (2+1)-Maxwell field tensor. The derived class of solutions is regular; the metric, curvature invariants and electric field are regular everywhere. The metric becomes, for a vanishing parameter, the (2+1)-static charged BTZ solution. A general procedure to derive solutions for the static BTZ (2+1)-spacetime, for any nonlinear Lagrangian depending on the electric field is formulated; for relevant electric fields one requires the fulfillment of the weak energy conditions.Comment: 5 pages, Latex, 2 figure

Conditioning of extreme learning machine for noisy data using heuristic optimization

This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data

Extreme learning machine adapted to noise based on optimization algorithms

The extreme learning machine for neural networks of feedforward of a single hidden layer randomly assigns the weights of entry and analytically determines the weights the output by means the Moore-Penrose inverse, this algorithm tends to provide an extremely fast learning speed preserving the adjustment levels achieved by classifiers such as multilayer perception and support vector machine. However, the Moore-Penrose inverse loses precision when using data with additive noise in training. That is why in this paper a method to robustness of extreme learning machine to additive noise proposed. The method consists in computing the weights of the output layer using non-linear optimization algorithms without restrictions. Tests are performed with the gradient descent optimization algorithm and with the Levenberg-Marquardt algorithm. From the implementation it is observed that through the use of these algorithms, smaller errors are achieved than those obtained with the Moore-Penrose inverse

Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography

Pathological ovary is categorized into cystic tumors, solid tumors and mixed, according to the content of the affected ovary. Accordingly, the degree of benignity or malignity thereof is established. The imaging study for the preliminary morphological assessment of PO is ultrasound, in its pelvic and transvaginal modalities, for which wellestablished criteria are available. Once the ultrasound findings suggest malignancy, complementary studies such as abdominal-pelvic tomography images and tumor markers are requested. This type of images has challenging problems called noise, artifacts and low contrast. In this paper, in order to address these problems, a computational technique is proposed to characterize a pathological ovary. To do this, a thresholding and the median and gradient magnitude filters are applied, preliminarily, to complete the preprocessing stage. Then, during the segmentation, the algorithm of region growing is used to extract the threedimensional morphology of the pathological ovary. Using this morphology, the volume of the pathological ovary is calculated and it allows selecting the surgical-medical behavior to approach this kind of ovary. The validation of the proposed technique indicates that the results are promising. This technique can be useful in the detection and monitoring the diseases linked to pathological ovary

Inferring Latent Structure From Mixed Real and Categorical Relational Data

We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix maps each row-column pair of binary feature vectors to an observed matrix element. The latent binary features of the rows are modeled via a multivariate Gaussian distribution with low-rank covariance matrix, and the Gaussian random variables are mapped to latent binary features via a probit link. The same type construction is applied jointly to the columns. The model infers latent, low-dimensional binary features associated with each row and each column, as well correlation structure between all rows and between all columns

Smarr's formula for black holes with non-linear electrodynamics

It is known that for nonlinear electrodynamics the First Law of Black Hole Mechanics holds, however the Smarr's formula for the total mass does not. In this contribution we discuss the point and determine the corresponding expressions for the Bardeen black hole solution that represents a nonlinear magnetic monopole. The same is done for the regular black hole solution derived by Ayon-Beato and Garcia, showing that in the case that variations of the electric charge are involved, the Smarr's formula does not longer is valid.Comment: 10 pages, 3 figures.Contribution to the Festscrift of Prof. A. Garci

O programa nacional de fortalecimento da agricultura familiar no Brasil: uma análise sobre a distribuição regional e setorial dos recursos.

No Brasil, as políticas públicas para o espaço rural sempre tenderam a priorizar a agricultura patronal, em detrimento dos agricultores familiares. Todavia, os estudos realizados pelos órgãos FAO - INCRA deram subsídio para a criação do Programa Nacional de Fortalecimento da Agricultura Familiar (PRONAF), resultando em um novo direcionamento dos investimentos públicos, os quais passaram a contemplar o segmento dos agricultores familiares. Entende-se o PRONAF como uma política não-compensatória, que, apesar de seus problemas, tem contribuído de fato para mudanças e melhorias no espaço agrário brasileiro. Desde sua criação no final da década de 1990, o PRONAF passou por várias mudanças em sua estrutura administrativa e operacional, a fim de alcançar seus objetivos e adequar-se face a complexa realidade social agrária brasileira. Sendo assim, o presente estudo visa discutir as ações do Estado por meio desse Programa, a partir de suas linhas de atuação, bem como analisar a distribuição de suas concessões de crédito regional e setorialmente. Assim, os procedimentos metodológicos utilizados para a realização deste trabalho compreendem pesquisa bibliográfica e documental, além de pesquisa em fontes secundárias, no intuito de obter dados e informações relevantes para a análise das relações sociais estabelecidas em meio a esse processo de concretização e espacialização desse Programa. Dentre as implicações do PRONAF pode-se notar em âmbito nacional, uma diminuição da disparidade regional brasileira, bem como a preocupação que o Programa tem demonstrado com os aspectos socioculturais locais e regionais, como forma de garantir que seus investimentos perpassem a dimensão econômica, mas valorize outras dimensões, a exemplo dos elementos culturais

Problem solving strategy in the teaching and learning processes of quantitative reasoning

The study presents an analysis of Polya's problem-solving strategy used in the training processes of quantitative reasoning competence in students of the Universidad Simón Bolívar, San José de Cúcuta, Colombia. The research was based on a descriptive design and had an intentional sample of 58 students who were studying the sciences and general competencies elective. For the collection of information, a diagnostic test (pre-test) and a final test (post-test) were applied, in order to check the incidence of the applied strategy. The results showed a significant improvement in the final results obtained by the students in each of the processes formed: interpretation, representation and modeling, and argumentation