Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic

Managing Overtime in China : Determinants of Overtime Culture in a Labor Intensive Manufacturing Facility in Guangdong, China : A Case Study

Master's thesis Business Administration BE501 - University of Agder 2017Konfidensiell til / confidential until 01-07-202

A new Middle Pleistocene interglacial occurrence from Ejby, Sjælland, Denmark

Despite more than a century of investigations, parts of the Quaternary stratigraphy of Denmark with their fragmented record of deposits remain ambiguous. Here we describe a newly found interglacial clay deposit from Ejby on Sjælland, Denmark, from a borehole at 55.695°N, 11.839°E (terrain elevation 5.7 m above sea level). We place the new occurrence on record and provide details of the macrofossil analysis of the sample. The clay contains remains of the present-day temperate bivalve Corbicula fluminalis and the caddis fly Hydropsyche contubernalis – both inhabiting rivers. The presence of C. fluminalis indicates that the deposit most probably is of Middle Pleistocene age, older than the last interglacial, the Eemian