Random Projections as Regularizers – Supplementary Material

Supplementary Material: A Hybrid Approach for Probabilistic Inference using Random Projections

Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations

The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However...

One-Dimensional Barrier Preserving Free-Energy Projections of a β-sheet Miniprotein: New Insights into the Folding Process SUPPLEMENTARY MATERIAL

Supplementary material of NSSRF: global network similarity search with subgraph signatures and its applications

This supplementary material illustrates the related descriptions, definitions, datasets, and experiment results of NSSRF. In addition, the limitation of NSSRF in terms of network size using five PPI networks to query the random forest regression (RFR) model is discussed. Moreover, the practical memory usage of NSSRF on the four real world datasets are illustrated in this supplementary manuscript.

Relaxation-Based Preprocessing Techniques for Markov Random Field Inference –Supplementary Material