Biblio

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes. The state of art solution to SMBMM is a coding scheme called polynomial sharing (PS) that was proposed by Nodehi and Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects - more efficient and secure inter-server communication, lower latency, flexible inter-server network topology, efficient batch processing, and tolerance to stragglers.

Image and video super-resolution (SR) has been explored for several decades. However, few works are integrated into practical systems for real-time image and video SR. In this work, we present a real-time deep video SpaTial Resolution UpConversion SysTem (STRUCT++). Our demo system achieves real-time performance (50 fps on CPU for CIF sequences and 45 fps on GPU for HDTV videos) and provides several functions: 1) batch processing; 2) full resolution comparison; 3) local region zooming in. These functions are convenient for super-resolution of a batch of videos (at most 10 videos in parallel), comparisons with other approaches and observations of local details of the SR results. The system is built on a Global context aggregation and Local queue jumping Network (GLNet). It has a thinner and deeper network structure to aggregate global context with an additional local queue jumping path to better model local structures of the signal. GLNet achieves state-of-the-art performance for real-time video SR.