منابع مشابه
Session 14: Government Panel
The Workshop ended with a first-time ever, hour-long Government Panel session organized at the request of the Workshop Program Committee, which wanted to hear from some of the government people present. The panel consisted of five individuals, each invited to be provocative and each given five minutes to offer his or her personal perspectives. The floor was subsequently thrown open for a genera...
متن کاملSession 14: New Directions/Applications
One of the highlights of the meeting was a presentation by Julie Payette, an astronaut for the Canadian Space Agency, who discussed the use of speech recognition in space travel. Some of the reasons why voice input and output are potentially valuable in space include the naturalness of the speech modality, the need to have hands and eyes available for performing applications tasks, and the over...
متن کاملWAVEip Final discussion session, 14
1 Introduction This final discussion session was based on feedback from Workshop participants, who were each asked to list three main issues that they felt had been important in the Workshop as a whole. Mary, Janet and Paul clustered these issues for purposes of discussion. Jen took notes (thanks Jen!), and Paul has put them together into the following semi-coherent text. The original notes (wi...
متن کامل14 Nov 08 Article on AUSSI (EER
Networks are increasingly recognized as advantageous when creating and embedding cultural change within organisations. This paper seeks to explore and problematise ideas around networks for education for sustainability (EfS), specifically in relation to the implementation of the Australian Sustainable Schools Initiative (AuSSI), a national, whole-school approach to EfS. In three Australian stat...
متن کاملSession 14 overview: Deep-learning processors
2:30 PM 14.3 A 28nm SoC with a 1.2GHz 568nJ/Prediction Sparse Deep-Neural-Network Engine with >0.1 Timing Error Rate Tolerance for IoT Applications P. N. Whatmough, Harvard University, Cambridge, MA In Paper 14.3, Harvard University presents a fully connected (FC)-DNN accelerator SoC in 28nm CMOS, which achieves 98.5% accuracy for MNIST inference with 0.36μJ/prediction at 667MHz and 0.57μJ/pred...
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ژورنال
عنوان ژورنال: Plastic and Reconstructive Surgery - Global Open
سال: 2019
ISSN: 2169-7574
DOI: 10.1097/01.gox.0000583828.08715.7f