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Introduction: Attitudes toward abortion are related to structural, cultural, and direct gender-based violence. This violence can affect women's mental, physical and reproductive health. Therefore, it is essential to know the nature of community attitudes toward abortion. Since we currently do not have an instrument that measures attitudes towards abortion in Chile, we set the objective of this study to design the Community Attitude to Abortion Scale (CAAS) and analyze its psychometric properties in a Chilean community population. Methods: This work is an instrumental design study. Using a sampling of panelists by sociodemographic quotas, we obtained a sample of 1,223 participants with a mean age of 36.7 years (SD = 13.56). Results: As a result, we obtained a scale of 18 items and two correlated factors, Autonomy and Stigma. This structure fits better as an Exploratory Structural Equations Model (ESEM). Both factors have excellent internal consistency. In addition, we obtained evidence of concurrent and discriminant validity: The scores on the factors of the Universal Religious Involvement Scale (I-E12) correlated negatively with Autonomy and positively with Stigma; participants with low levels of identification with a right-wing political orientation, with high levels of identification with a leftwing, pro-feminist, pro-LGBTQ +, and pro-euthanasia political orientation, obtained higher mean scores on Autonomy and lower on Stigma. Discussion: The CAAS is an adequate tool for use with the Chilean community population, with evidence of consistency and validity. La CAAS is the first tool to measure attitudes to abortion in this country. [ABSTRACT FROM AUTHOR]

Abortion Stigma (AS) is a negative attribute ascribed to women who terminate a pregnancy that marks her as inferior to the ideals of femininity. Based on traditional normative gender codes, religious groups are identified in the literature as the most relevant source of AS. To analyse the influence of religiosity on AS and the mediating effect of stereotyped gender attitudes, we carried out a multivariate causal quantitative study. With a community sample of 360 Chileans, we used structural equation modelling to determine that religiosity influences AS directly and indirectly through stereotyped gender attitudes. This model showed adequate fit indices and explained 69.2% of the variance. We tested a second structural equation model with a control variable, and we discuss these findings' implications for Chilean society, where religion occupies a significant place in power and decision-making centres. We propose gender equity education as a strategy to reduce AS. [ABSTRACT FROM AUTHOR]

We have experienced sociocultural changes, which have favored the development of egalitarian gender role attitudes (GRA). Nevertheless, the revolution in gender roles is incomplete, and the lack of equity in social structures limits the individual experiences in equality. In the face of this reality, the scientific community is demanding a modernized system for measuring GRA. We present adaptation, evidence of factorial and convergent validity, accuracy, ceiling and floor effect, norms of interpretation by sex, and analyses of the easiest and most difficult GRAs to modify in the Gender Role Attitudes Scale (GRAS). We used a sample of 1013 Chilean university students. The GRAS has a two-factor structure that evaluates stereotyped and transcendent attitudes of gender roles in the family, social, and work contexts. The scale presents convergent validity with a sexual double standard measure and is more reliable in the low scores of transcendence and the high scores of stereotyped attitudes. Transcendent factor had a ceiling effect, and stereotyped attitudes had a floor effect. Family gender roles are the easiest to change, and work-related roles the most difficult. The authors discuss the need for a structural change to reactivate the revolution of gender roles in Chile. [ABSTRACT FROM AUTHOR]

One‐class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. Autoencoder is the type of neural network that has been widely applied in these one‐class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD‐autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter‐free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD‐autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy. [ABSTRACT FROM AUTHOR]

In real applications learning algorithms have to address several issues such as, huge amount of data, samples which arrive continuously and underlying data generation processes that evolve over time. Classical learning is not always appropriate to work in these environments since independent and indentically distributed data are assumed. Taking into account the requirements of the learning process, systems should be able to modify both their structures and their parameters. In this survey, our aim is to review the developed methodologies for adaptive learning with artificial neural networks, analyzing the strategies that have been traditionally applied over the years. We focus on sequential learning, the handling of the concept drift problem and the determination of the network structure. Despite the research in this field, there are currently no standard methods to deal with these environments and diverse issues remain an open problem. [ABSTRACT FROM AUTHOR]

We propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in online mode. The theoretical basis of the method is given and its performance is illustrated by means of its application to different control problems showing that our proposal is able to overcome the problems generated by dynamic nature of the process or by physical changes of the system which originate important modifications in the process. A comparative experimental study is presented in order to show the more stable behavior of the proposed method in several working ranks. [ABSTRACT FROM AUTHOR]

The automatic classification of DNA microarray data is one of the hot topics in the field of bioinformatics, since it is an effective tool for the diagnosis of diseases in patients. The aim of this chapter is to present the most relevant aspects related to the classification of microarrays. We carried out an analysis of the strategies used for the classification of microarray data and a review of the main methods used in the literature. In addition, other related aspects are addressed as the reduction of dimensionality, to try to eliminate redundant information in genes, or the treatment of imbalanced data and missing of data. To conclude, we present an exhaustive review of the main scientific works in journals to show the most successful techniques applied in this discipline as well as the most used datasets to verify their effectiveness.