Contributions in Merlin

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Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired high-resolution volume data sets have sizes in the order of tens to hundreds of GBs, and typically exhibit spatially complex internal structures. Such large structural volume data sets represent a grand challenge to be explored, analyzed and interpreted by means of interactive visualization, since the amount of data to be rendered is typically far beyond the current performance limits of interactive graphics systems. As a new approach to tackle this bottleneck problem, we employ higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and focus on, structural features in volume data. We show that TA yields a high data reduction at competitive rate distortion and that, at the same time, it provides a natural means for multiscale volume feature representation.

This paper presents a dynamic model of talent investments in a team sports league with an infinite time horizon. We show that the clubs’ investment decisions and the effects of revenue sharing on competitive balance depend on the following three factors: (i) the cost function of talent investments, (ii) the clubs’ market sizes, and (iii) the initial endowments of talent stock. We analyze how these factors interact in the transition to the steady state as well as in the steady state itself.

Reward representation in ventral striatum is boosted by perceptual novelty, although the mechanism of this effect remains elusive. Animal studies indicate a functional loop (Lisman and Grace, 2005) that includes hippocampus, ventral striatum, and midbrain as being important in regulating salience attribution within the context of novel stimuli. According to this model, reward responses in ventral striatum or midbrain should be enhanced in the context of novelty even if reward and novelty constitute unrelated, independent events. Using fMRI, we show that trials with reward-predictive cues and subsequent outcomes elicit higher responses in the striatum if preceded by an unrelated novel picture, indicating that reward representation is enhanced in the context of novelty. Notably, this effect was observed solely when reward occurrence, and hence reward-related salience, was low. These findings support a view that contextual novelty enhances neural responses underlying reward representation in the striatum and concur with the effects of novelty processing as predicted by the model of Lisman and Grace (2005).

Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.

In this report we present the theoretical foundation of TRAMP. TRAMP is a schema mapping debugging system that uses provides provenance and query support as debugging functality for schema mappings scenarios. TRAMP is an extension of Perm, a relational provenance management system developed at University of Zurich. In this report we are not focussing on the debugging functionality added by TRAMP, but instead focus on the theoretical foundation of the provenance types provided by the system. In chapter 2 we present the contribution semantics for data provenance, transformation provenance, and mapping provenance used by TRAMP. Contribution semantics define which parts of the input (in case of data provenance) and which operators of a transformation (in case of transformation provenance) belong to the provenance of an output of a transformation. Thus, contribution semantics define ``what provenance actually is''. Based on the presented contribution semantics we demonstrate in chapter 3 how provenance according to these provenance types can be computed using algebraic rewrite techniques and proof the correctness and completeness of the algorithms used to compute provenance.