Training system

Abstract: This internationally appealing book is based on a two-year case study of a group of young people as they move through their final year of mandatory schooling and into their first year of post-16 experience. It looks at their choices, the market behaviour of local education and training providers and those who help and advise these choices. The authors show that recent and current political policies for post-16 education disadvantage, marginalise and exclude young people rather than improve their life chances. The book draws together the major issues and attempts to suggest alternative ways forward for a more inclusive post-16 education and training system.

Abstract: A method for enhancing operational efficiency of a remote vehicle using a diagnostic behavior. The method comprises inputting and analyzing data received from a plurality of sensors to determine the existence of deviations from normal operation of the remote vehicle, updating parameters in a reference mobility model based on deviations from normal operation, and revising strategies to achieve an operational goal of the remote vehicle to accommodate deviations from normal operation. An embedded simulation and training system for a remote vehicle. The system comprises a software architecture installed on the operator control unit and including software routines and drivers capable of carrying out mission simulations and training.

Abstract: In this paper, a new dance training system based on the motion capture and virtual reality (VR) technologies is proposed. Our system is inspired by the traditional way to learn new movements-imitating the teacher's movements and listening to the teacher's feedback. A prototype of our proposed system is implemented, in which a student can imitate the motion demonstrated by a virtual teacher projected on the wall screen. Meanwhile, the student's motions will be captured and analyzed by the system based on which feedback is given back to them. The result of user studies showed that our system can successfully guide students to improve their skills. The subjects agreed that the system is interesting and can motivate them to learn.

Abstract: Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9\% top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4\% accuracy. Our training system can achieve 75.8\% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7\% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems.