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2019 ALS User Meeting

LBNL ALS User Meeting in Oct 2019

Check our TEAM accomplishments wrt CAMERA Image Processing and DOE ASCR Early Career Research Project IDEAL

Oct 1st - check [ImageSearch] for image recommendation

  • a release of pyCBIR, a free open-source python based software compatible with the latest version of TensorFlow (r2.0)

Oct 1st - my Keynote: Latest research in machine learning with ALS

  • Chair: Kelsey Stoerzinger, Oregon State University
  • Building 50 Auditorium
  • [slides] [software]

Oct 2nd - my Talk: Workshop in Application of Machine Learning to Achieve Autonomous Experimentation

  • Organized by Carolin Sutter-Fella (CSD, LBNL) and Marcus Noack (CRD/CAMERA, LBNL)
    More The rate of scientific data acquisition is increasing at an unprecedented pace, e.g., due to faster detectors and improved computations. At the same time, in situ and high-throughput experimentation has become increasingly powerful in revealing mechanisms and synthesis-structure and structure-property relationships on multiple time and lengths scales. In this regard, efficient data evaluation requires the application of automated decision-making algorithms in order to keep pace with the data acquisition. This workshop will bring together experimentalists working on material characterization, synthesis, or high-throughput experimentation and computational researchers providing machine learning/decision-making/data-mining tools for the direct application to experiments.
  • Sessions run Wednesday morning in Room 54-130B
  • [slides] [code] [Google CO]

MRS Bulletin Materials News Podcast:

Classify nanoparticle orientation in a thin film by learning scattering patterns [podcast] with Hexemer, Ushizima and Shuai

License

The MIT License (MIT)

Copyright (c) 2019 Ushizima

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Claims

This software contains work partially sponsored by the United States. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product,process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or theRegents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.