Link Prediction via Matrix Completion

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms

Self-Propelled Supercapacitors for On-Demand Circuit Configuration Based on WS2 Nanoparticles Micromachines

The miniaturization of energy storage microcapacitors to develop portable electronic devices has been of high recent interest. Here, microsupercapacitors microrobot is fabricated using membrane template-assisted electrodeposition of WS2 nanoparticles (WS2NPs)/polyaniline (PANI) and platinum (Pt) layers. The microrobot navigates in the microchannel and attaches itself as part of the electrical circuit. The attached WS2NPs-PANI/Pt microrobots enhance the capacitive behavior of the circuit significantly. The results presented in this work open the door for the development of smart and miniaturized functional micromotors that are able to self-assemble to on-demand circuits.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore