Joint Exploration and Mining of Memory-Relevant Brain Anatomic and Connectomic Patterns via a Three-Way Association Model

Early change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism. However, many existing association methods, such as sparse canonical correlation analysis (SCCA), only manage to handle two-way association and thus cannot guarantee the selection of biomarkers and associations to be memory relevant. To overcome this limitation, we propose a new outcome-relevant SCCA model (OSCCA) together with a new algorithm to enable the three-way associations among brain connectivity, anatomic structure and episodic memory performance. In comparison with traditional SCCA, we demonstrate the effectiveness of our model with both synthetic and real data from the HCP cohort

Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study

Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.FP7 EU LAC