Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

     

Global loss of production capacity caused by the COVID-19 pandemic

The global economy faces a loss of production capacity among the most severe in at least the past half-century as a result of the recession caused by the COVID-19 pandemic. It will take several years of persistent investment efforts to restore the pre-pandemic level of capacity and put global output back to potential. By using a production function based on the incremental capital-output ratio, we estimate the loss of production capacity for the global economy and for the two main world locomotives, the U.S. and China. To do this, we estimate the production capacity implied in our current recession baseline and the capacity that would have existed in the absence of the pandemic (precrisis scenario). The difference between the two estimates is defined as the loss of capacity generated by the pandemic. The results also allow us to extract some policy implications in terms of mitigation measures and investment requirements.     

Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research

Objective

A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased.

Objective

Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo).

Methods

We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color

Results

We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and k-means clustering algorithms to determine the skin tone category of portraits.

Conclusion

After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.     

Free time,gender and the pandemic: An exploration of children's daily routines in the times of COVID-19 in Chile

This article discusses the differentiation by gender displayed by children between 8 and 12 years old on how they used their free time during the COVID-19 pandemic in Chile. This study had a qualitative approach, where 43 in-depth interviews were conducted with children from three different regions of the country, using participatory photo-elicitation as the central tool. The main results of the study show a configuration of free time based on gender stereotypes, showing that boys are the ones who most adhere to such stereotypes, triggering a crisis in the identity construction of masculinities during childhood. The study also suggests that socioeconomic and territorial differences between children configures different experiences of the use of free time.     

A weakly informative prior for Bayesian dynamic model selection with applications in fMRI

In recent years, Bayesian statistics methods in neuroscience have been showing important advances. In particular, detection of brain signals for studying the complexity of the brain is an active area of research. Functional magnetic resonance imagining (fMRI) is an important tool to determine which parts of the brain are activated by different types of physical behavior. According to recent results, there is evidence that the values of the connectivity brain signal parameters are close to zero and due to the nature of time series fMRI data with high-frequency behavior, Bayesian dynamic models for identifying sparsity are indeed far-reaching. We propose a multivariate Bayesian dynamic approach for model selection and shrinkage estimation of the connectivity parameters. We describe the coupling or lead-lag between any pair of regions by using mixture priors for the connectivity parameters and propose a new weakly informative default prior for the state variances. This framework produces one-step-ahead proper posterior predictive results and induces shrinkage and robustness suitable for fMRI data in the presence of sparsity. To explore the performance of the proposed methodology, we present simulation studies and an application to functional magnetic resonance imaging data.     

A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities

In this paper, we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible for posterior density estimation and posterior inference for high quantiles. The model works well even for small sample sizes and in the absence of prior information. We evaluate the performance of the proposed model through a simulation study. Finally, the proposed model is applied to a real environmental data.