Comedian Hosts and the Demotic Turn

Podcasting is a showcase for what cultural studies scholar Graeme Turner coined “the demotic turn” or the increasing visibility of the ordinary person in the today’s media landscape. Collins argues that the emergence of a particular breed of podcasts – comedian-hosted interviews with celebrities – function in an “off-label” manner as a form of self-help or vicarious therapy. The emergence and rapid growth of this genre can attributed to three main factors: a confessional culture, the triumph of experience over expertise, and the democratization allowed by the form’s technology. She explores the link between emotional intimacy and comedy, and analyzes podcasts like Marc Maron’s WTF that are, in expression, a rejection of the pedestal version of stardom

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In the last years many populations of anurans have declined and extinctions have been recorded. They were related to environmental pollution, changes of land use and emerging diseases. The main objective of this study was to determine copper sensitivity of the anuran of the Amazon Rhinella granulosa and Scinax ruber tadpoles at stage 25 and Scinax ruber eggs exposed for 96 h to copper concentrations ranging from 15 µg Cu L-1 to 94 µg Cu L-1. LC50 at 96 h of Rhinella granulosa Gosner 25, Scinax ruber Gosner 25 and Scinax ruber eggs in black water of the Amazon were 23.48, 36.37 and 50.02 µg Cu L-1, respectively. The Biotic Ligand Model was used to predict the LC50 values for these species and it can be considered a promising tool for these tropical species and water conditions. Copper toxicity depends on water physical-chemical composition and on the larval stage of the tadpoles. The Gosner stage 19-21 (related to the appearance of external gills) is the most vulnerable and the egg stage is the most resistant. In case of contamination by copper, the natural streams must have special attention, since copper is more bioavailable.Nos últimos anos foram registrados muitas extinções e declínios de populações de anuros. Eles estavam relacionados com a poluição do ambiente, a mudanças no uso da terra e ao surgimento de doenças. O principal objetivo deste estudo foi determinar a sensibilidade dos anuros amazônicos ao cobre. Os girinos de Scinax ruber e Rhinella granulosa no estadio 25 e os ovos de Scinax ruber foram expostos por 96 horas a concentrações de cobre entre 15 µg Cu L-1 a 94 µg Cu L-1. A CL50 -96 h dos girinos de Rhinella granulosa, dos girinos de Scinax ruber e dos ovos de Scinax ruber em águas pretas da Amazônia foram 23,48; 36,37 e 50,02 µg Cu L-1, respectivamente. O modelo do ligante biótico foi usado para prever os valores de CL50 para essas duas espécies e pode ser considerado uma ferramenta promissora para essas espécies tropicais e para essas condições de água. A Toxicidade de cobre depende da composição físico-química da água e do estagio larval dos girinos. O estadio 19-21 de Gosner (relacionados ao aparecimento das brânquias externas) são os mais vulnerável e o estagio de ovo é o mais resistente. Em caso de contaminação por cobre, os igarapés naturais devem ter uma atenção especial, uma vez que o cobre é mais biodisponível nesse ambiente

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA) plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE). SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods