Kai Li

TL;DR: A sketch construction algorithm is proposed, such that the weighted (and thresholded) e1 distance between two feature vectors can be estimated by the Hamming distance of their sketches, which can typically reduce the space requirement by an order of magnitude with minimal impact on similarity search quality.

TL;DR: This paper presents a framework that takes the novel view that benchmark inputs should be considered approximations of their original, full-sized inputs and offers guidelines to choose suitable simulation inputs for their scientific studies in a way that maximizes the accuracy of the simulation subject to a time constraint.

Abstract: Full correlation matrix analysis (FCMA) is an unbiased approach for exhaustively studying interactions among brain regions in functional magnetic resonance imaging (fMRI) data from human participants. In order to answer neuroscientific questions efficiently, we are developing a closed-loop analysis system with FCMA on a cluster of nodes with Intel® Xeon Phi™ coprocessors. Here we propose several ideas for data-driven algorithmic modification to improve the performance on the coprocessor. Our experiments with real datasets show that the optimized single-node code runs 5x-16x faster than the baseline implementation using the well-known Intel® MKL and LibSVM libraries, and that the cluster implementation achieves near linear speedup on 5760 cores.

TL;DR: In this article , a meta-analysis was performed to evaluate the effectiveness and safety of prophylactic central neck dissection (PCND) in patients with clinically node-negative (cN0) papillary thyroid carcinoma.

TL;DR: RT-Cloud as mentioned in this paper is a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments, which uses standardized data formats and adaptable processing streams.