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Deep learning has improved the state-of-the-art results in many domains, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of lifecycle management are largely ignored. Deep learning modeling lifecycle contains a rich set of artifacts and frequently conducted tasks, dealing with...
Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning...
Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Self-rating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification...
Heterogeneous computing is gaining attention from both industry and academia nowadays. One driving factor for heterogeneous computing is the power efficiency. GPU and FPGA have been reported to achieve much higher power efficiency over CPU on many applications. Comparisons between GPU and FPGA show different characteristics of GPU and FPGA in accelerated computing. Some tasks run better on GPU, some...
Resource management and scheduling using economic-based principles and market-oriented models have been proven to be useful for scheduling tasks in Computing Grid. However, most studies only focus on the single objective of either cost or makespan. In this paper, we consider both cost and makespan, and focus on scheduling parallel tasks from Grid users considering a commodity market. A cost and time...
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