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 کاملS2-bundles over 2-orbifolds
Let M be a closed 4-manifold with π = π1(M) 6= 1 and π2(M) ∼= Z, and let u : π → Aut(π2(M)) be the natural action. If π ∼= Ker(u) × Z/2Z then M is homotopy equivalent to the total space of an RP -bundle over an aspherical surface. We show here that if π is not such a product then M is homotopy equivalent to the total space of an S-orbifold bundle over a 2orbifold B. There are at most two such o...
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.0000582888.52139.3d