A novel cascade learning strategy for training support vector machines (SVMs) is proposed to speed up the training of SVMs. The training procedure consists of three steps which are performed in a cascade way. All the subproblems are processed parallelly in each step, and non-support-vector data are filtered out step by step. The simulation results indicate that our method not only speeds up the...

Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the mod...

Challenges with surgical cricothyroidotomy on the battlefield can be attributed to limited frequency of use, procedure unfamiliarity, and limited knowledge base of anatomical landmarks of which is further heighten in the tactical environment. The objective was to identify ways to enhance the cricothyroidotomy training to minimize potential preventable procedural errors. A training review was co...

In this paper we present a novel method for 3D human body shape and pose prediction. Our work is motivated by the need to reduce our reliance on costly-to-obtain ground truth labels. To achieve this, we propose training an encoder-decoder network using a two step procedure as follows. During the first step, a decoder is trained to predict a body silhouette using SMPL [2] (a statistical body sha...