Os salários refletem a produtividade do trabalho? Uma comparação entre o Brasil e os Estados Unidos

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThe study compares the relationship between wages and labor productivity for different categories of workers in Brazil and in the U.S. Analyses highlight to what extent the equilibrium between wages and productivity is related to the degree of economic de384629649FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO2014/09678-

Estrutura ocupacional e desigualdade socioeconômica: um estudo comparativo entre o Brasil e os Estados Unidos

CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThis paper explores how occupational structure is associated with economic inequality in Brazil in comparison to the United States. Changes in the Brazilian and American occupational structures between 1983 and 2011 are investigated in order to assess how24254229261CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOsem informaçãosem informaçãoThe authors would like to thank Capes (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and Fapesp (Fundação de Amparo à Pesquisa do Estado de São Paulo) for supporting this researc

CoRe: Color Regression for Multicolor Fashion Garments

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.Comment: 6 pages,3 figures,1 tabl

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios

Synthesis and evaluation of halogenated nitrophenoxazinones as nitroreductase substrates for the detection of pathogenic bacteria

The synthesis and microbiological evaluation of 7-, 8- and 9-nitro-1,2,4-trihalogenophenoxazin-3-one substrates with potential in the detection of nitroreductase-expressing pathogenic microorganisms are described. The 7- and 9-nitrotrihalogenophenoxazinone substrates were reduced by most Gram negative microorganisms and were inhibitory to the growth of certain Gram positive bacteria; however, the majority of Gram positive strains that were not inhibited by these agents, along with the two yeast strains evaluated, did not reduce the substrates. These observations suggest there are differences in the active site structures and substrate requirements of the nitroreductase enzymes from different strains; such differences may be exploited in the future for differentiation between pathogenic microorganisms. The absence of reduction of the 8-nitrotrihalogenophenoxazinone substrates is rationalized according to their electronic properties and correlates well with previous findings

Low Photon Count Phase Retrieval Using Deep Learning

Imaging systems' performance at low light intensity is affected by shot noise, which becomes increasingly strong as the power of the light source decreases. In this paper we experimentally demonstrate the use of deep neural networks to recover objects illuminated with weak light and demonstrate better performance than with the classical Gerchberg-Saxton phase retrieval algorithm for equivalent signal over noise ratio. Prior knowledge about the object is implicitly contained in the training data set and feature detection is possible for a signal over noise ratio close to one. We apply this principle to a phase retrieval problem and show successful recovery of the object's most salient features with as little as one photon per detector pixel on average in the illumination beam. We also show that the phase reconstruction is significantly improved by training the neural network with an initial estimate of the object, as opposed as training it with the raw intensity measurement.Comment: 8 pages, 5 figure

Do Investors Care About Biodiversity?

This paper introduces a new proprietary measure of a firm's negative impact on biodiversity, the corporate biodiversity footprint, and studies whether it is priced in the cross-section of stock returns. Using an international sample of firms, we find no evidence that the biodiversity footprint explains these returns, on average. However, event-study evidence shows that, following the UN Biodiversity Conference (COP15), which raised awareness of biodiversity issues, however, firms with larger corporate biodiversity footprints lost value. This response is consistent with investors revising their valuation of these firms downward upon the prospect that regulations to preserve biodiversity will become more stringent

How stickiness can speed up diffusion in confined systems

The paradigmatic model for heterogeneous media used in diffusion studies is built from reflecting obstacles and surfaces. It is well known that the crowding effect produced by these reflecting surfaces slows the dispersion of Brownian tracers. Here, using a general adsorption desorption model with surface diffusion, we show analytically that making surfaces or obstacles attractive can accelerate dispersion. In particular, we show that this enhancement of diffusion can exist even when the surface diffusion constant is smaller than that in the bulk. Even more remarkably, this enhancement effect occurs when the effective diffusion constant, when restricted to surfaces only, is lower than the effective diffusivity with purely reflecting boundaries. We give analytical formulas for this intriguing effect in periodic arrays of spheres as well as undulating micro-channels. Our results are confirmed by numerical calculations and Monte Carlo simulations

A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)

Computing environment is moving towards human-centered designs instead of computer centered designs and human's tend to communicate wealth of information through affective states or expressions. Traditional Human Computer Interaction (HCI) based systems ignores bulk of information communicated through those affective states and just caters for user's intentional input. Generally, for evaluating and benchmarking different facial expression analysis algorithms, standardized databases are needed to enable a meaningful comparison. In the absence of comparative tests on such standardized databases it is difficult to find relative strengths and weaknesses of different facial expression recognition algorithms. In this article we present a novel video database for Children's Spontaneous facial Expressions (LIRIS-CSE). Proposed video database contains six basic spontaneous facial expressions shown by 12 ethnically diverse children between the ages of 6 and 12 years with mean age of 7.3 years. To the best of our knowledge, this database is first of its kind as it records and shows spontaneous facial expressions of children. Previously there were few database of children expressions and all of them show posed or exaggerated expressions which are different from spontaneous or natural expressions. Thus, this database will be a milestone for human behavior researchers. This database will be a excellent resource for vision community for benchmarking and comparing results. In this article, we have also proposed framework for automatic expression recognition based on convolutional neural network (CNN) architecture with transfer learning approach. Proposed architecture achieved average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE