Reese Mcclain (visionpuppy2)

Observations of the nighttime thermospheric wind from two ground-based Fabry-Perot Interferometers are compared to the level 2.1 and 2.2 data products from the Michelson Interferometer Global High-resolution Thermospheric Imaging (MIGHTI) onboard National Aeronautics and Space Administration's Ionospheric Connection Explorer to assess and validate the methodology used to generate measurements of neutral thermospheric winds observed by MIGHTI. We find generally good agreement between observations approximately coincident in space and time with mean differences less than 11 m/s in magnitude and standard deviations of about 20-35 m/s. These results indicate that the independent calculations of the zero-wind reference used by the different instruments do not contain strong systematic or physical biases, even though the observations were acquired during solar minimum conditions when the measured airglow intensity is weak. We argue that the slight differences in the estimated wind quantities between the two instrument types can be attributed to gradients in the airglow and thermospheric wind fields and the differing viewing geometries used by the instruments.Deep learning based methods are routinely used to segment various structures of interest in varied medical imaging modalities. Acquiring annotations for a large number of images requires a skilled analyst, and the process is both time consuming and challenging. Our approach to reduce effort is to reduce the number of images needing detailed annotation. For intravascular optical coherence tomography (IVOCT) image pullbacks, we tested 10% to 100% of training images derived from two schemes equally-spaced image subsampling and deep-learning- based image clustering. The first strategy involves selecting images at equally spaced intervals from the volume, accounting for the high spatial correlation between neighboring images. In clustering, we used an autoencoder to create a deep feature space representation, performed k-medoids clustering, and then used the cluster medians for training. For coronary calcifications, a baseline U-net model was trained on all images from volumes of interest (VOIs) and compared with fewer images from the sub-sampling strategies. For a given sampling ratio, the clustering based method performed better or similar as compared to the equally spaced sampling approach (e.g., with 10% of images, mean F1 score for calcific class increased from 0.52 to 0.63, with equal spacing and with clustering, respectively). Additionally, for a fixed number of training images, sampling images from more VOIs performed better than otherwise. In conclusion, we recommend the clustering based approach to annotate a small fraction of images, creating a baseline model, which potentially can be improved further by annotating images selected using methods described in active learning research.Liquid-phase exfoliation is the most suitable platform for large-scale production of two-dimensional materials. One of the main open challenges is related to the quest of green and bioderived solvents to replace state-of-the-art dispersion media, which suffer several toxicity issues. Here, we demonstrate the suitability of methyl-5-(dimethylamino)-2-methyl-5-oxopentanoate (Rhodiasolv Polarclean) for sonication-assisted liquid-phase exfoliation of layered materials for the case-study examples of WS2, MoS2, and graphene. We performed a direct comparison, in the same processing conditions, with liquid-phase exfoliation using N-methyl-2-pyrrolidone (NMP) solvent. The amount of few-layer flakes (with thickness less then 5 nm) obtained with Polarclean is increased by ∼350% with respect to the case of liquid-phase exfoliation using NMP, maintaining comparable values of the average lateral size, which even reaches ∼10 μm for the case of graphene produced by exfoliation in Polarclean, and of the yield (∼40%). Correspondingly, the density of defects is reduced by 1 order of magnitude by Polarclean-assis