University of Passau

About: University of Passau is a education organization based out in Passau, Bayern, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 1543 authors who have published 4763 publications receiving 93338 citations.

Does Forced Solidarity Hamper Investment in Small and Micro Enterprises

Abstract: Sharing is a norm in many societies. We present a theoretical model on the trade-off between sharing and investment which we test on data from tailors in Burkina Faso. The empirical results support the idea that there are two behavioural patterns: entrepreneurs following an 'insurance regime' comply with sharing norms, are insured but reduce investment in their firm, whereas entrepreneurs in the 'growth regime' are not insured but take undistorted investment decisions. The choice of regime depends on the redistributive pressure, the willingness to take risk, and the return on investment.

Distortion mismatch in the quantization of probability measures

Abstract: We elucidate the asymptotics of the L^s-quantization error induced by a sequence of L^r-optimal n-quantizers of a probability distribution P on R^d when s>r. In particular we show that under natural assumptions, the optimal rate is preserved as long as s

Visual Understanding of Metabolic Pathways Across Organisms using Layout in Two and a Half Dimensions

Abstract: 1 Summary We propose a method for visualizing a set of related metabolic pathways across organisms using 21/2 dimensional graph visualization. Interdependent, twodimensional layouts of each pathway are stacked on top of each other so that biologists get a full picture of subtle and significant dierences among the pathways. The (dis)similarities between pathways are expressed by the Hamming distances of the underlying graphs which are used to compute a stacking order for the pathways. Layouts are determined by a global layout of the union of all pathway graphs using a variant of the proven Sugiyama approach for layered graph drawing. Our variant layout approach allows edges to cross if they appear in dierent graphs.

All for one or one for all? Combining heterogeneous features for activity spotting

Abstract: Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.