As the mission space of NA-213 Office of Nuclear Detection and Deterrence continues to evolve away from traditional stationary monitoring at borders and ports, the need for a solution to maintain situational awareness is critical.
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Researchers developed an automated scanning probe microscopy (SPM) platform to rapidly find regions of interest.
As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression.
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.