The hierarchical model of branch planning with random coefficients

We consider the management branch model where the random resources of the subsystem are given by the exponential distributions. The determinate equivalent is a block structure problem of quadratic programming. It is solved effectively by means of the decomposition method, which is based on iterative aggregation. The aggregation problem of the upper level is resolved analytically. This overcomes all difficulties concerning the large dimension of the main problem.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Extração automática de contornos de telhados de edifícios em um modelo digital de elevação, utilizando inferência Bayesiana e campos aleatórios de Markov

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

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

Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov

This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.

Two-branes with variable tension model and the effective Newtonian constant

It is shown that, in the two brane time variation model framework, if the hidden brane tension varies according to the phenomenological Eotvos law, the visible brane tension behavior is such that its time derivative is negative in the past and positive after a specific time of cosmological evolution. This behavior is interpreted in terms of a useful mechanical system analog and its relation with the variation of the Newtonian (effective) gravitational constant is explored.

Variable parameter and double sampling (X)over-bar charts in the presence of correlation: The Markov chain approach

The general assumption under which the (X) over bar chart is designed is that the process mean has a constant in-control value. However, there are situations in which the process mean wanders. When it wanders according to a first-order autoregressive (AR (1)) model, a complex approach involving Markov chains and integral equation methods is used to evaluate the properties of the (X) over bar chart. In this paper, we propose the use of a pure Markov chain approach to study the performance of the (X) over bar chart. The performance of the chat (X) over bar with variable parameters and the (X) over bar with double sampling are compared. (C) 2011 Elsevier B.V. All rights reserved.

Circuit board accident - organizational dimension hidden by prescribed safety

This study analyzes an accident in which two maintenance workers suffered severe burns while replacing a circuit breaker panel in a steel mill, following model of analysis and prevention of accidents (MAPA) developed with the objective of enlarging the perimeter of interventions and contributing to deconstruction of blame attribution practices. The study was based on materials produced by a health service team in an in-depth analysis of the accident. The analysis shows that decisions related to system modernization were taken without considering their implications in maintenance scheduling and creating conflicts of priorities and of interests between production and safety; and also reveals that the lack of a systemic perspective in safety management was its principal failure. To explain the accident as merely non-fulfillment of idealized formal safety rules feeds practices of blame attribution supported by alibi norms and inhibits possible prevention. In contrast, accident analyses undertaken in worker health surveillance services show potential to reveal origins of these events incubated in the history of the system ignored in practices guided by the traditional paradigm.

Lobe identity in the Mongolian gerbil prostatic complex: A new rodent model for prostate study

Knowledge of structural and physiological differences among the prostatic lobes (PL) is the basis for development of experimental studies in traditional laboratory rodents. Although Mongolian gerbil reproductive organs have been increasingly investigated, its prostate structure is far from being properly known, and investigations of this organ focused on the ventral lobe (VL). Thus, the present study provides a thorough morphological description of prostatic complex in the male adult gerbil on the basis of topographic, histological, and ultrastructural analysis and ductal branching. Like other rodents, four pairs of PL were observed. However, in contrast to the rat and mouse, the VL is the least voluminous component and the dorsolateral lobe (DLL) is the most prominent and spatially isolated from remaining PL. The occurrence of a dorsal lobe (DL), hidden between bladder and insertion of seminal vesicles, has not been mentioned in previous reports with Mongolian gerbil. Collagenase digestion followed by microdissection revealed that, except for DL, which has a tubularacinar organization, all PL exhibit tubular organization and variable ductal branching. Distinct histological and ultrastructural features such as secretory epithelium, aspect of luminal secretion and stromal organization are reported for each PL and are confirmed by morphometric and stereological methods. Histological sections showed at least three intralobar segments in VL and DL. Ultrastructural analysis evidenced that, although luminal epithelial cells of PL share typical features of exocrine secretory cells, there are striking lobe phenotypical variations. Both merocrine and apocrine pathways are observed in variable rates in all PL, with the predominance of the former in the DLL and the latter in the CG. The morphological observations presented herein point to distinct structural identities for each PL, which probably reflects,specific functional compromise of seminal fluid secretion. These data also point to the gerbil as a good model for investigations concerning the regulation of prostate development and homeostasis, mainly with regard to the dorsal and dorsolateral PL.

Hidden sector effects on double Higgs production near threshold at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

THE HIGGS PORTAL and AN UNIFIED MODEL FOR DARK ENERGY and DARK MATTER

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for Dark Photons from Supersymmetric Hidden Valleys

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

Notes on the two-brane model with variable tension

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Markovian model market-Akerlof's lemons and the asymmetry of information

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.