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Abstract Invasion by non‐native annual grasses poses a serious threat to native vegetation in California, facilitated through interaction with wildfires. Our work is the first attempt to use the coupled fire‐atmosphere model, WRF‐Fire, to investigate how shifts from native, shrub‐dominated vegetation to invasive grasses could have affected a known wildfire event in southern California. We simulate the Mountain Fire, which burned >11,000 ha in July 2013, under idealized fuel conditions representing varying extents of grass invasion. Expanding grass to double its observed coverage causes fire to spread faster due to the lower fuel load in grasses and increased wind speed. Beyond this, further grass expansion reduces the simulated spread rate because lower heat release partially offsets the positive effects. Our simulations suggest that grass expansion may generally promote larger faster‐spreading wildfires in southern California, motivating continued efforts to contain and reduce the spread of invasive annual grasses in this region. 

Abstract While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly extract the diffusion coefficientDfrom single-molecule images, and consequently enable super-resolvedDspatial mapping. Working with single-molecule images recorded at a fixed framerate under typical single-molecule localization microscopy (SMLM) conditions, Pix2D exploits the often undesired yet evident motion blur,i.e., the convolution of single-molecule motion trajectory during the frame recording time with the diffraction-limited point spread function (PSF) of the microscope. Whereas the stochastic nature of diffusion imprints diverse diffusion trajectories to different molecules diffusing at the same givenD, we construct a convolutional neural network (CNN) model that takes a stack of single-molecule images as the input and evaluates aD-value as the output. We thus validate robustDevaluation and spatial mapping with simulated data, and with experimental data successfully characterizeDdifferences for supported lipid bilayers of different compositions and resolve gel and fluidic phases at the nanoscale. 

Because machine learning would benefit from reduced data requirements, some prior work has proposed using humans not just to label data, but also to explain those labels. To characterize the evidence humans might want to provide, we conducted a user study and a data experiment. In the user study, 75 participants provided classification labels for 20 photos, justifying those labels with free-text explanations. Explanations frequently referenced concepts (objects and attributes) in the image, yet 26% of explanations invoked concepts not in the image. Boolean logic was common in implicit form, but was rarely explicit. In a follow-up experiment on the Visual Genome dataset, we found that some concepts could be partially defined through their relationship to frequently co-occurring concepts, rather than only through labeling.