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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.
![Automated Spectroscopy via Edge Computing for Smarter Instrumentation](/sites/default/files/styles/list_page_thumbnail/public/2021-07/Vasudevan%20thumbnail_260x160.jpg?h=a08abdbb&itok=3wYClzng)
Researchers developed an automated scanning probe microscopy (SPM) platform to rapidly find regions of interest.
![ExaSGD: Using High-Performance Computing to Operate Decarbonized Resilient Grid CSED ORNL Computational Sciences and Engineering](/sites/default/files/styles/list_page_thumbnail/public/2022-06/exasgd-_using_high-performance_computing_to_operate_decarbonized_resilient_grid.png?h=3c43bafe&itok=hvp7AK3a)
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.
![Variational Generative Flows for Reconstruction Uncertainty Estimation CSMD Computer Science and Mathematics Division ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/variational_generative_flows_for_reconstruction_uncertainty_estimation.png?h=429f3186&itok=Hhws1ahJ)
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.