117 research outputs found
Comedian Hosts and the Demotic Turn
Podcasting is a showcase for what cultural studies scholar Graeme Turner coined âthe demotic turnâ or the increasing visibility of the ordinary person in the todayâs media landscape. Collins argues that the emergence of a particular breed of podcasts â comedian-hosted interviews with celebrities â function in an âoff-labelâ 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 formâs technology. She explores the link between emotional intimacy and comedy, and analyzes podcasts like Marc Maronâs 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 ”g Cu L-1 to 94 ”g 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 ”g 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 Ășltimos anos foram registrados muitas extinçÔes e declĂnios de populaçÔes 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 amazĂŽnicos 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 concentraçÔes de cobre entre 15 ”g Cu L-1 a 94 ”g Cu L-1. A CL50 -96 h dos girinos de Rhinella granulosa, dos girinos de Scinax ruber e dos ovos de Scinax ruber em ĂĄguas pretas da AmazĂŽnia foram 23,48; 36,37 e 50,02 ”g Cu L-1, respectivamente. O modelo do ligante biĂłtico foi usado para prever os valores de CL50 para essas duas espĂ©cies e pode ser considerado uma ferramenta promissora para essas espĂ©cies tropicais e para essas condiçÔes de ĂĄgua. A Toxicidade de cobre depende da composição fĂsico-quĂmica da ĂĄgua e do estagio larval dos girinos. O estadio 19-21 de Gosner (relacionados ao aparecimento das brĂąnquias externas) sĂŁo os mais vulnerĂĄvel e o estagio de ovo Ă© o mais resistente. Em caso de contaminação por cobre, os igarapĂ©s naturais devem ter uma atenção especial, uma vez que o cobre Ă© mais biodisponĂvel 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
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