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(Dis)incentives, Features and Outcomes. The Threefold Relationship Between Deliberative Democratic Innovations and Participant’s ‘Bag of Emotions’
No full text0info:eu-repo/semantics/nonPublishe
Divergences and convergences across European musical preferences: How taste varies within and between countries
No full textWhen investigating relational structures in culture, research in Europe has often either mapped the relationship between cultural tastes in a particular context, or mapped differences in cultural tastes (measured consistently) in different countries, without assessing how these differences can vary across them. Indeed, the idea of national homology (namely that the structures of cultural capital would be fairly similar in nations across Europe) has never been really tested, probably due to a lack of cross-national research on cultural preferences. Using data from the EUCROSS survey that took place in Denmark, Germany, Italy, Romania, Spain and the UK (2012–2013, n = 6016), we first use multiple correspondence analysis to estimate the relationships between a set of items on musical tastes. We then extend this through the use of class-specific analysis, to investigate how these relationships vary in each of the six countries. Finally, we analyse the relationships between the underlying dimensions of music tastes and different components of cosmopolitanism, compared with key demographic variables. We show that the musical field significantly varies across the nations represented in the survey, demonstrating that musical preferences remain largely anchored in national contexts. Cultural preferences are shaped by historical and social dynamics specific to each country, with significant variations in the symbolic value and demographic associations of music genres.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
DA4NeRF: Depth-aware Augmentation technique for Neural Radiance Fields
No full textNeural Radiance Fields (NeRF) demonstrate impressive capabilities in rendering novel views of specific scenes by learning an implicit volumetric representation from posed RGB images without any depth information. View synthesis is the computational process of synthesizing novel images of a scene from different viewpoints, based on a set of existing images. One big problem is the need for a large number of images in the training datasets for neural network-based view synthesis frameworks. The challenge of data augmentation for view synthesis applications has not been addressed yet. NeRF models require comprehensive scene coverage in multiple views to accurately estimate radiance and density at any point. In cases without sufficient coverage of scenes with different viewing directions, cannot effectively interpolate or extrapolate unseen scene parts. In this paper, we introduce a new pipeline to tackle this data augmentation problem using depth data. We use MPEG's Depth Estimation Reference Software and Reference View Synthesizer to add novel non-existent views to the training sets needed for the NeRF framework. Experimental results show that our approach improves the quality of the rendered images using NeRF's model. The average quality increased by 6.4 dB in terms of Peak Signal-to-Noise Ratio (PSNR), with the highest increase being 11 dB. Our approach not only adds the ability to handle the sparsely captured multiview content to be used in the NeRF framework, but also makes NeRF more accurate and useful for creating high-quality virtual views.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Taal van eigen kweek: Het Nederlandse van etnische minderheden
No full textinfo:eu-repo/semantics/inPres
