A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensionality, we keep numerical complexity at bay by restricting the space of solutions and by exploiting an efficient Primal-Dual formulation. Comparisons with state of the art techniques, on both synthetic and real data, show promising performances.

Online Reputation Systems and Cumulative Advantage

The study of online reputation systems and their importance for promoting trust and cooperation and, therefore, the smooth functioning of online markets has received considerable attention over the last few years. In the first part of our talk we will try to give a brief overview of the existing theoretical and empirical work in this field, summarize the main findings from this research and identify open questions where results are either controversial or do not yet exist. The second part of our talk will focus on one of these issues that deserve further research, namely the relation between online reputation systems and processes of "cumulative advantage." Cumulative advantage is the mechanism where a favorable relative position of having a good reputation becomes a resource for further relative gains. The process leads to increased status inequality and a heavily skewed distribution of number of feedbacks, i.e. the ties in the reputation network. We present empirical evidence for direct and indirect reputation effects on the micro level of an auction reputation system and discuss the distributional consequences for the market level.