Argonne is designing and coding a “social distancing” detector using Python and OpenCV to determine the percentage of people following social distancing guidelines
Argonne is using recurrent neural networks to build forecasting models that are orders of magnitude faster than their partial differential equation-based counterparts.
An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.
From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.
A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.