Linear Dynamic Network Reconstruction from Heterogeneous Datasets

Linear Dynamic Network Reconstruction from Heterogeneous Datasets

This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiment subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all expe...

Reconstruction of Network Coded Sources From Incomplete Datasets

We investigate the problem of recovering source information from an incomplete set of network coded data with help of prior information about the sources. This problem naturally arises in wireless networks, where the number of network coded packets available at the receiver may not be sufficient for exact decoding due to channel dynamics or timing constraints, for example. We study the theoreti...

Exploring Heterogeneous Datasets from Different Searcher Perspectives

This paper demonstrates how a recently developed analytical usability evaluation method, the Sii framework, can by used to inspect semantic search interfaces for how they support people working with the large heterogeneous datasets afforded by Linked Data. To enrich the discussion, an analysis of the Tabulator browser for Linked Data is presented and discussed in terms of the workshop’s case st...

Classification of Resting State fMRI Datasets Using Dynamic Network Clusters

Resting state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating intrinsic and spontaneous brain activity. The application of univariate and multivariate methods such as multi voxel pattern analysis has been instrumental in localizing neural correlates to various cognitive states and psychiatric disease. However, many existing methods of rsfMRI analysis are insu...

Network Reconstruction of Dynamic Biological Systems