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A General Distributed Deep Learning Platform
Chonho Lee,Wei Wang,Meihui Zhang,Beng Chin Ooi 한국정보과학회 2016 정보과학회지 Vol.34 No.3
This article reviews Apache SINGA, a general distributed deep learning (DL) platform. The system components and its architecture are presented, as well as how to configure and run SINGA for different types of distributed training using model/data partitioning. Besides, several features and performance are compared with other popular DL tools.
A General Distributed Deep Learning Platform: A Review of Apache SINGA
Lee, Chonho,Wang, Wei,Zhang, Meihui,Ooi, Beng Chin Korean Institute of Information Scientists and Eng 2016 정보과학회지 Vol.34 No.3
This article reviews Apache SINGA, a general distributed deep learning (DL) platform. The system components and its architecture are presented, as well as how to configure and run SINGA for different types of distributed training using model/data partitioning. Besides, several features and performance are compared with other popular DL tools.
An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data
( Ran Jin ),( Gang Chen ),( Anthony K H Tung ),( Lidan Shou ),( Beng Chin Ooi ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.6
With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.
Benjamin Yong-Qiang Tan,Nicholas Jinghao Ngiam,Zi Yun Chang,Sandra Ming Yien Tan,Xiayan Shen,Shao Feng Mok,Srinivas Subramanian,Shirley Beng Suat Ooi,Adrian Chin-Leong Kee 한국의학교육학회 2019 Korean journal of medical education Vol.31 No.3
Long duty hours have been associated with significant medical errors, adverse events, and physician “burn-out”. An innovative night float (NF) system has been implemented in our internal medicine program to reduce the negative effects of long duty hours associated with conventional full-call systems. However, concerns remain if this would result in inadequate training for interns. We developed a structured questionnaire to assess junior doctors’ perceptions of the NF system compared to full calls, in areas of patient safety, medical training, and well-being. Ninety-seven (71%) of the 137 doctors polled responded. Ninety-one (94%) felt the NF system was superior to the full call system. A strong majority felt NF was beneficial for patient safety compared to full call (94% vs. 2%, p<0.001). The NF system was also perceived to reduce medical errors (94% vs. 2%, p<0.001) and reduce physician “burn-out” (95% vs. 5%, p<0.001). Beyond being a practical solution to duty-hour limitations, there was a significant perceived benefit of the NF system compared to the full call in terms of overall satisfaction, patient safety, reducing medical errors and physician “burn-out”.