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

Independent component analysis for understanding multimedia content

Abstract. This paper focuses on using independent component analysis of combined text and image data from web pages. This has potential for search and retrieval applications in order to retrieve more meaningful and context dependent content. It is demon-strated that using ICA on combined text and image features pro-vides a synergistic eect, i.e., the retrieval classication rates in-crease if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised clas-si er which works from unsupervised ICA features is invoked. In addition, we demonstrate the use of the suggested framework for automatic annotation of descriptive key words to images

Students’ pondering: An educational challenge to the arts and humanities in a market-oriented education system

The purpose of this article is to address the educational aspect of pondering in terms of the reflection and reconsideration that the arts and humanities are both known and renowned for. While arts and humanities have, for many years, been under political pressure to adopt the more solution-oriented attitude of hard science, our students were quick to adopt this trend, too, and they are requesting classes and tools for quick-fixing, rather than activities that facilitate deep learning. Instead we argue for a two-sided approach, where critical reflection through pondering is in dialogue with and mutually supportive to problem-orientation. We discuss some perspectives and possibilities of learning that can enhance the reflective dimension of academic practice among students and report on an experiment that employs learning portfolios as a student-driven tool for facilitating the reflective pondering necessary for developing a professional identity that is focussed not only on solutions and presentations, but also on a deeper understanding of issues

Infection of human mucosal tissue by Pseudomonas aeruginosa requires sequential and mutually dependent virulence factors and a novel pilus-associated adhesin

Tissue damage predisposes humans to life-threatening disseminating infection by the opportunistic pathogen Pseudomonas aeruginosa. Bacterial adherence to host tissue is a critical first step in this infection process. It is well established that P. aeruginosa attachment to host cells involves type IV pili (TFP), which are retractile surface fibers. The molecular details of attachment and the identity of the bacterial adhesin and host receptor remain controversial. Using a mucosal epithelium model system derived from primary human tissue, we show that the pilus-associated protein PilY1 is required for bacterial adherence. We establish that P. aeruginosa preferentially binds to exposed basolateral host cell surfaces, providing a mechanistic explanation for opportunistic infection of damaged tissue. Further, we demonstrate that invasion and fulminant infection of intact host tissue requires the coordinated and mutually dependent action of multiple bacterial factors, including pilus fiber retraction and the host cell intoxication system, termed type III secretion. Our findings offer new and important insights into the complex interactions between a pathogen and its human host and provide compelling evidence that PilY1 serves as the principal P. aeruginosa adhesin for human tissue and that it specifically recognizes a host receptor localized or enriched on basolateral epithelial cell surfaces