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Learning to Estimate the Composition of a Mixture with Synthetic Data

Published online by Cambridge University Press:  30 July 2021

Cuong Ly
Affiliation:
University of Utah - Scientific Computing and Imaging Institute, Salt Lake City, Utah, United States
Cody Nizinski
Affiliation:
University of Utah, United States
Clement Vachet
Affiliation:
Scientific Computing and Imaging Institute, United States
Luther McDonald
Affiliation:
University of Utah, United States
Tolga Tasdizen
Affiliation:
University of Utah - Scientific Computing and Imaging Institute, United States
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Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Heffernan, S., Ly, N., Mower, B.J., Vachet, C., Schwerdt, I.J., Tasdizen, T., McDonald IV, L.W., Identifying surface morphological characteristics to differentiate between mixtures of U3O8synthesized from ammonium diuranate and uranyl peroxide, Radiochimica Acta. 0(0) (2019). doi:10.1515/ract-2019-3140.Google Scholar
Gatys, L. A., Ecker, A. S., and Bethge, M., Texture synthesis using convolutional neural networks, Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems. (2015)pages 262270.Google Scholar
This work is supported by the Department of Homeland Security, Domestic Nuclear Detection Office, under grant Number 2015-DN-077-ARI092.Google Scholar
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