Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative.
View Article and Find Full Text PDFMercury(II) metallation of Pseudomonas aeruginosa azurin has been characterized structurally and biochemically. The X-ray crystal structure at 1.5Å of mercury(II) metallated azurin confirms the coordination of mercury at the copper binding active site and a second surface site.
View Article and Find Full Text PDFBackground: How accurately do people perceive extreme water speeds and how does their perception affect perceived risk? Prior research has focused on the characteristics of moving water that can reduce human stability or balance. The current research presents the first experiment on people's perceptions of risk and moving water at different speeds and depths.
Methods: Using a randomized within-person 2 (water depth: 0.