Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Computer science & Thin film. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data

Abstract: The article considers Bayesian analysis of hierarchical models for count, binomial and multinomial data using efficient MCMC sampling procedures. To this end, an improved method of auxiliary mixture sampling is proposed. In contrast to previously proposed samplers the method uses a bounded number of latent variables per observation, independent of the intensity of the underlying Poisson process in the case of count data, or of the number of experiments in the case of binomial and multinomial data. The bounded number of latent variables results in a more general error distribution, which is a negative log-Gamma distribution with arbitrary integer shape parameter. The required approximations of these distributions by Gaussian mixtures have been computed. Overall, the improvement leads to a substantial increase in efficiency of auxiliary mixture sampling for highly structured models. The method is illustrated for finite mixtures of generalized linear models and an epidemiological case study.

Recent progress in AFM molecular recognition studies.

Abstract: During the past decade, remarkable advances have been made in using atomic force microscopy (AFM) for measuring the forces and the dynamics of the interaction between individual ligands and receptors, providing fundamental insights into molecular recognition processes. In addition, affinity imaging using either adhesion force mapping or dynamic recognition force mapping has offered a means to localize specific binding sites on model and cellular surfaces. These single-molecule analyses provide novel insight into the structure-function relationships of molecular recognition systems. In this review, we describe the principles of molecular recognition studies using AFM and provide a flavor of recent progress made in the field.

Supporting Product Derivation by Adapting and Augmenting Variability Models

Abstract: Product derivation is the process of constructing products from the core assets in a product line. Guidance and support are needed to increase efficiency and to deal with the complexity of product derivation. Research has, however, devoted comparatively little attention to this process. In this paper we describe an approach for supporting product derivation. We show that variability models need to be prepared for concrete projects before they can be effectively utilized in the derivation process. Project-specific information and sales knowledge should be added and irrelevant variability should be pruned. We also present tool support and illustrate the approach using examples from ongoing research collaboration.