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Dautle-Bross-1600x900.jpg

Article ID: 700924

“Model” students enjoy Argonne campus life

Argonne National Laboratory

Savanna Dautle, an intern from Rowan University in Glassboro, New Jersey, spent her summer working with assistant chemist David Bross at the U.S. Department of Energy’s (DOE) Argonne National Laboratory.

Released:
21-Sep-2018 12:05 PM EDT

Education

Embargo will expire:
25-Sep-2018 1:00 AM EDT
Released to reporters:
21-Sep-2018 5:05 AM EDT

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Albatross_and_UAV-r1_313218.jpg

Article ID: 700802

Physicists Train Robotic Gliders to Soar Like Birds

University of California San Diego

Scientists know that upward currents of warm air assist birds in flight. To understand how birds find and navigate these thermal plumes, researchers used reinforcement learning to train gliders to autonomously navigate atmospheric thermals. The research highlights the role of vertical wind accelerations and roll-wise torques as viable biological cues for soaring birds. The findings also provide a navigational strategy that directly applies to the development of UAVs.

Released:
19-Sep-2018 4:05 PM EDT
ML-Geo-Workshop1600x900.jpg

Article ID: 700752

How Machine Learning Can Sharpen Environmental Research

Argonne National Laboratory

Argonne recently hosted a workshop that brought together computational and natural scientists to discuss opportunities for applying machine learning and geospatial statistics to challenging problems in environmental research.

Released:
18-Sep-2018 5:05 PM EDT

Article ID: 700735

Argonne’s Lab-Wide Data Service

Globus

To address the challenge of streamlining data movement, storage and access, the Globus team worked with Argonne Leadership Computing Facility (ALCF) scientists to develop a lab-wide service for storing and sharing data among distributed collaborators.

Released:
18-Sep-2018 2:45 PM EDT
fi_V7-1.jpg
  • Embargo expired:
    18-Sep-2018 5:00 AM EDT

Article ID: 700545

Scientists Use Artificial Neural Networks to Predict New Stable Materials

University of California San Diego

Artificial neural networks—algorithms inspired by connections in the brain—have “learned” to perform a variety of tasks, from pedestrian detection in self-driving cars, to analyzing medical images, to translating languages. Now, researchers at the University of California San Diego are training artificial neural networks to predict new stable materials.

Released:
13-Sep-2018 7:05 PM EDT
neural-networks-gases1600x900.jpg
  • Embargo expired:
    13-Sep-2018 11:00 AM EDT

Article ID: 700373

The Next Phase: Using Neural Networks to Identify Gas-Phase Molecules

Argonne National Laboratory

Argonne scientists have developed a neural network that can identify the structure of molecules in the gas phase, offering a novel technique for national security and pharmaceutical applications.

Released:
12-Sep-2018 10:00 AM EDT
  • Embargo expired:
    12-Sep-2018 12:00 PM EDT

Article ID: 700284

Physicists Develop New Techniques to Enhance Data Analysis for Large Hadron Collider

New York University

New York University physicists have created new techniques that deploy machine learning as a means to significantly improve data analysis for the Large Hadron Collider, the world’s most powerful particle accelerator.

Released:
10-Sep-2018 4:15 PM EDT
NiZhen.jpg

Article ID: 700330

Developing “Human-Like” Control System

South Dakota State University

To explore extreme environments, machines need to think like humans. Engineers are working to solve fundamental scientific problems to make intelligent control systems possible.

Released:
11-Sep-2018 12:05 PM EDT

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