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Répteis da região do campus Lagoa do Sino da Universidade Federal de São Carlos, Buri, SP, Brasil
(Universidade Federal de São Carlos, 2021-01-27) São Pedro, Vinícius Avelar; Nehemy, Ibrahim Kamel Rodrigues; https://lattes.cnpq.br/7404103190291423; https://lattes.cnpq.br/9644812398421326
Triângulos perigosos: as relações entre bichas, márginals e filhas-de-santo em Maceió/AL
(Universidade Federal de São Carlos, 2021-01-27) Nascimento, Rangel Ferreira Fideles do; Leite Júnior, Jorge; https://lattes.cnpq.br/7253561448346772; https://lattes.cnpq.br/2372354860341000
The dissertation aims to understand the set of interactions established between terreiros and bocas-de-fumo located in the city of Maceió, Alagoas from two relational bundles: ritual and gender stylizations. It is anchored in an ethnography that considers special attention to the space of interlocution between bocas-de-fumo and terreiros as a result of changes in forms of governance over life in the peripheries, as well as the productive technologies generated in terreiros from the incorporation of entities and control of the circulation of information. As a background, changes in the forms of government in the peripheries of Maceio from what locally came to be called the rupture of the alliance between the criminal collectives Primeiro Comando da Capital and Comando Vermelho regarding the production of senses of masculine identification remain in abeyance. In this sense, being ethnographically inspired, it is nourished by the role of encounters and possibilities of interlocution established from the performance of different roles in interpersonal networks. Concerned with following the relational plots elaborated with queers and márginals - the research's interlocutors -, the field of concern gradually changed. In a first moment, the center of the project was to follow the battles of queers through the incorporation of female spirits and their production crossing gender stylizations as dangerous. However, following episodes and narratives involving their friendly relations made it opportune to observe plots involving men in criminal dynamics. In this way, this shift in focus led to an approach with a body of literature gathered among different concerns. More roughly, studies on gender and sexuality in terreiros; forms of government in Brazilian urban peripheries and the dialogue on illicit markets more properly related to drug trafficking. In relation to these different research contexts, the accumulation of works exploring interfaces between gender and sexuality studies and urban peripheries has sensitized the idea of the intersection of an unprecedented field of relations articulating different social landscapes. In this sense, in relation to the context of Alagoas, it is possible to argue that the idea of gender styles and rituals seems to point to how the presence of a patriarchal bias in structures of care and protection - in which the hierarchy in terreiros is situated, is founded through transitional experiences, in which subjects experience a constant immanence. Gender, in this way, articulates itself much more to the idea of an eternal tension and delimitation of another to be fought, than necessarily a stabilization of meaning.
N-BEATS-RNN: deep learning for time series forecasting
(Universidade Federal de São Carlos, 2021-01-27) Sbrana, Attilio; Rossi, André Luis Debiaso; https://lattes.cnpq.br/5604829226181486; Naldi, Murilo Coelho; https://lattes.cnpq.br/0573662728816861; https://lattes.cnpq.br/5980966794385896
This work presents N-BEATS-RNN, an extended version of an ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient weight-sharing search, to solve for an ideal Recurrent Neural Network architecture to be added to N-BEATS. We evaluated the proposed N-BEATS-RNN architecture in the widely-known M4 competition dataset, which contains 100,000 time series from a variety of sources. N-BEATS-RNN achieves comparable results to N-BEATS and the M4 competition winner while employing solely 108 models, as compared to the original 2,160 models employed by N-BEATS, when composing its final ensemble of forecasts. Thus, N-BEATS-RNN's biggest contribution is in its training time reduction, which is in the order of 9 times compared with the original ensembles in N-BEATS.