Defining the nation, defining public television: discourses of publics on public service television in post-communist Latvia

Institutional and political economy approaches have long dominated the study of post-Communist public broadcasting, as well as the entire body of post-Communist media transformations research, and the enquiry into publics of public broadcasting has traditionally been neglected. Though media scholars like to talk about a deep crisis in the relationship between public broadcasters and their publics in former Communist bloc countries across Central and Eastern Europe, little has been done to understand the relationship between public broadcasters and their publics in these societies drawing on qualitative audience research tradition. Building on Hirschman’s influential theory of ‘exit, voice and loyalty’, which made it possible to see viewing choices audiences make as an act of agency, in combination with theoretical tools developed within the framework of social constructionist approaches to national imagination and broadcasting, my study focuses on the investigation of responses publics of the Latvian public television LTV have developed vis-à-vis its role as contributing to the nation-building project in this ex-Soviet Baltic country. With the help of focus groups methodology and family ethnography, the thesis aims to explore the relationship between the way members of the ethno-linguistic majority of Latvian-speakers and the sizeable ethno-linguistic minority of Russian-speakers conceptualize the public broadcaster LTV, as well as understand the concept of public broadcasting more generally, and the way they define the national ‘we’. The study concludes that what I call publics of LTV employ Hirschman’s described exit mechanism as a voice-type response. Through their rejection of public television which, for a number of complex reasons they consider to be a state broadcaster serving the interests of those in power they voice their protest against the country’s political establishment and in the case of its Russian-speaking publics also against the government’s ethno-nationalistic conception of the national ‘we’. I also find that though having exited from the public broadcaster LTV, its publics have not abandoned the idea of public broadcasting as such. At least at a normative level the public broadcasting ideals are recognized, accepted and valued, though they are not necessarily associated with the country’s de jure institutional embodiment of public broadcasting LTV. Rejection of the public television has also not made its non-loyal publics ‘less citizens’. The commercial rivals of LTV, be they national or, in the case of Russian-speaking audiences, localized transnational Russian television, have allowed their viewers to exercise citizenship and be loyal nationals day in day out in a way that is more liberal and flexible than the hegemonic form of citizenship and national imagination of the public television LTV can offer.

Ultra-deep sequencing provides insights into the virology of hepatitis C super-infections in a case of three sequential infections with different genotypes

The current epidemic of Hepatitis C infection in HIV-positive men who have sex with men is associated with increasing use of recreational drugs. Multiple HCV infections have been reported in haemophiliacs and intravenous drug users. Using ultra-deep sequencing analysis, we present the case of an HIV-positive MSM with evidence of three sequential HCV infections, each occurring during the acute phase of the preceding infection, following risk exposures. We observed rapid replacement of the original strain by the incoming genotype at subsequent time points. The impact of HCV super-infection remains unclear and UDS may provide new insights.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).