Combining CV and RP data: a note on the relationship between consistency and rationality

In this paper, we show that, when combining revealed (RP) and stated (SP) data, for marginal changes in quality of environmental goods, rationality implies consistency, as the consistency conditions coincide with a subset of the conditions for rationality.combined (RP and SP) individual data; rationality; data consistency

Combining Averting Behavior and Contingent Valuation Data: An Application to Drinking Water Treatment

This paper is an empirical application that combines averting behavior with contingent valuation data. Consistency tests are performed incorporating alternative heteroscedastic structures in the bivariate probit models by taking advantage of the different information content that characterizes each data source. We look at three covariates not yet examined in the literature when combining stated and revealed preferred data to explain the variance in the models: income, the bid in the contingent valuation questionnaire, and the distance between the bid and the averting expenditures with drinking water. The models estimated include between and within data sources heteroscedasticity. The results obtained allow the combination of the two data sources under a common preference structure.averting behavior, combination of data sets, consistency tests, contingent valuation, revealed preferred data

View-consistent 4D Light Field style transfer using neural networks and over-segmentation

Deep learning has shown promising results in several computer vision applications, such as style transfer applications. Style transfer aims at generating a new image by combining the content of one image with the style and color palette of another image. When applying style transfer to a 4D Light Field (LF) that represents the same scene from different angular perspectives, new challenges and requirements are involved. While the visually appealing quality of the stylized image is an important criterion in 2D images, cross-view consistency is essential in 4D LFs. Moreover, the need for large datasets to train new robust models arises as another challenge due to the limited LF datasets that are currently available. In this paper, a neural style transfer approach is used, along with a robust propagation based on over-segmentation, to stylize 4D LFs. Experimental results show that the proposed solution outperforms the state-of-the-art without any need for training or fine-tuning existing ones while maintaining consistency across LF views.info:eu-repo/semantics/acceptedVersio

ALFO: Adaptive light field over-segmentation

Automatic image over-segmentation into superpixels has attracted increasing attention from researchers to apply it as a pre-processing step for several computer vision applications. In 4D Light Field (LF) imaging, image over-segmentation aims at achieving not only superpixel compactness and accuracy but also cross-view consistency. Due to the high dimensionality of 4D LF images, depth information can be estimated and exploited during the over-segmentation along with spatial and visual appearance features. However, balancing between several hybrid features to generate robust superpixels for different 4D LF images is challenging and not adequately solved in existing solutions. In this paper, an automatic, adaptive, and view-consistent LF over-segmentation method based on normalized LF cues and K-means clustering is proposed. Initially, disparity maps for all LF views are estimated entirely to improve superpixel accuracy and consistency. Afterwards, by using K-means clustering, a 4D LF image is iteratively divided into regular superpixels that adhere to object boundaries and ensure cross-view consistency. Our proposed method can automatically adjust the clustering weights of the various features that characterize each superpixel based on the image content. Quantitative and qualitative results on several 4D LF datasets demonstrate outperforming performance of the proposed method in terms of superpixel accuracy, shape regularity and view consistency when using adaptive clustering weights, compared to the state-of-the-art 4D LF over-segmentation methods.info:eu-repo/semantics/publishedVersio

Hyperpixels: Flexible 4D over-segmentation for dense and sparse light fields

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users. A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and do not adequately deal with sparse LFs with large occlusions. Furthermore, the spatio-angular LF cues are not fully exploited in the existing methods. In this paper, the concept of hyperpixels is defined and a flexible, automatic, and adaptive representation for both dense and sparse 4D LFs is proposed. Initially, disparity maps are estimated for all views to enhance over-segmentation accuracy and consistency. Afterwards, a modified weighted K-means clustering using robust spatio-angular features is performed in 4D Euclidean space. Experimental results on several dense and sparse 4D LF datasets show competitive and outperforming performance in terms of over-segmentation accuracy, shape regularity and view consistency against state-of-the-art methods.info:eu-repo/semantics/publishedVersio

Quality of life of patients with hemophilia treated in a hematology clinic

O objetivo deste trabalho foi caracterizar a qualidade de vida de pacientes hemofílicos em acompanhamento ambulatorial em serviço especializado. Foi feita abordagem quantitativa da qualidade de vida (QV) de pacientes hemofílicos acompanhados em ambulatório de hematologia de um hemocentro regional. A coleta de dados foi realizada por meio de entrevistas utilizando-se o Whoqol-bref e questionário adicional com variáveis sociodemográficas e clínico-epidemiológicas. Para análise dos dados utilizaram-se o Epi-info 6.04d e o SPSS, cujos resultados foram expressos através de distribuição simples, medidas de tendência central e dispersão, proporções e correlação de Pearson entre facetas e domínios. Foram entrevistados 23 pacientes, com média de idade de 21 anos; todos moravam com familiares, 47,8% eram residentes na cidade sede do hemocentro. Do total, 78,3% eram solteiros, 69,6% estudavam, sendo que 45,5% possuíam o 1º grau incompleto e 82,6% não trabalhavam. A maioria (91,3%) possuía hemofilia A. Quanto à avaliação da QV, 47,8% responderam ser boa e 55% possuíam um bom nível de satisfação com a saúde. O domínio psicológico apresentou o maior escore médio e o menor foi o do domínio meio ambiente. Com esse estudo conseguiu-se salientar a magnitude de alguns problemas dos hemofílicos.The objective of this work was to characterize the quality of life of hemophilic patients being followed up in a specialized service. A cross-sectional study of hemophilic patients in a Regional Blood Bank of Brazil was carried out to evaluate their quality of life. The data were obtained by interviews employing the WHO QOL-brief questionnaire, which was analyzed using SPSS and Epi-info 6.04d computer programs. Twenty-three male patients with a mean age of 21 years old were interviewed. All reported that they live with their families, 47.8% were residents in Uberaba, 78.3% were single, 69.6% were students with 45.5% having a low level of education and 82.6% did not work. Of the 23 cases, 91.3% had hemophilia A. In respect to quality of life, 47.8% responded that their quality of life was good and 55% had a good level of satisfaction with their health. The psychological dominion presented the highest average score and the environment presented the lowest. This study highlights the magnitude of some problems of hemophilic patients

SLFS: Semi-supervised light-field foreground-background segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.info:eu-repo/semantics/acceptedVersio

Are victims also judged more positively if they say their lives are just?

Non-victims who express high versus low personal belief in a just world (PBJW) are judged as having more social value, both social utility (i.e., market value) and social desirability (i.e., affective value). Our goal was to test whether this pattern differed when the targets were presented as innocent or noninnocent victims of enduring suffering. A hundred and eighty-six participants of both sexes took part in our 2 (degree of PBJW expressed: high/low) X 3 (Target identity: innocent victim/ non-innocent victim/non-victim) between-subjects experimental study. Participants rated the targets on four measures: positive/negative social utility/desirability. Targets were judged more positively and less negatively if they expressed high versus low PBJW, regardless of their being non-victims or (non-)innocent victims. This pattern is taken as further evidence that the expression of high PBJW is a judgment norm, that is, a socially valued discourse irrespective of it being true or untrue.info:eu-repo/semantics/publishedVersio

Modifiable risk factors for 9-year mortality in older English and Brazilian adults: The ELSA and SIGa-Bagé ageing cohorts

To quantify and compare 9-year all-cause mortality risk attributable to modifiable risk factors among older English and Brazilian adults. We used data for participants aged 60 years and older from the English Longitudinal Study of Ageing (ELSA) and the Bagé Cohort Study of Ageing (SIGa-Bagé). The five modifiable risk factors assessed at baseline were smoking, hypertension, diabetes, obesity and physical inactivity. Deaths were identified through linkage to mortality registers. For each risk factor, estimated all-cause mortality hazard ratios (HR) and population attributable fractions (PAF) were adjusted by age, sex, all other risk factors and socioeconomic position (wealth) using Cox proportional hazards modelling. We also quantified the risk factor adjusted wealth gradients in mortality, by age and sex. Among the participants, 659 (ELSA) and 638 (SIGa-Bagé) died during the 9-year follow-up. Mortality rates were higher in SIGa-Bagé. HRs and PAFs showed more similarities than differences, with physical inactivity (PAF 16.5% ELSA; 16.7% SIGa-Bagé) and current smoking (PAF 4.9% for both cohorts) having the strongest association. A clear graded relationship existed between the number of risk factors and subsequent mortality. Wealth gradients in mortality were apparent in both cohorts after full adjustment, especially among men aged 60-74 in ELSA. A different pattern was found among older women, especially in SIGa-Bagé. These findings call attention for the challenge to health systems to prevent and modify the major risk factors related to non-communicable diseases, especially physical inactivity and smoking. Furthermore, wealth inequalities in mortality persist among older adults

The compound event that triggered the destructive fires of October 2017 in Portugal

Portugal is regularly affected by destructive wildfires that have severe social, economic, and ecological impacts. The total burnt area in 2017 (∼540,000 ha) marked the all-time record value since 1980 with a tragic toll of 114 fatalities that occurred in June and October events. The local insurance sector declared it was the costliest natural disaster in Portugal with payouts exceeding USD295 million. Here, the 2017 October event, responsible for more than 200,000 ha of burnt area and 50 fatalities is analyzed from a compound perspective. A prolonged drought led to preconditioned cumulative hydric stress of vegetation in October 2017. In addition, on 15 October 2017, two other major drivers played a critical role: 1) the passage of hurricane Ophelia off the Coast of Portugal, responsible for exceptional meteorological conditions and 2) the human agent, responsible for an extremely elevated number of negligent ignitions. This disastrous combination of natural and anthropogenic drivers led to the uncontrolled wildfires observed on 15 October