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  1. null (Ed.)
  2. Abstract We present near-infrared Large Binocular Telescope LMIRCam imagery of the disk around the Herbig Ae/Be star AB Aurigae. A comparison of the surface brightness at K s (2.16 μ m), H 2 O narrowband (3.08 μ m), and L ′ (3.7 μ m) allows us to probe the presence of icy grains in this (pre)transitional disk environment. By applying reference differential imaging point-spread function subtraction, we detect the disk at high signal-to-noise ratios in all three bands. We find strong morphological differences between the bands, including asymmetries consistent with the observed spiral arms within 100 au in L ′ . An apparent deficit of scattered light at 3.08 μ m relative to the bracketing wavelengths ( K s and L ′ ) is evocative of ice absorption at the disk surface layer. However, the Δ( K s − H 2 O) color is consistent with grains with little to no ice (0%–5% by mass). The Δ ( H 2 O − L ′ ) color, conversely, suggests grains with a much higher ice mass fraction (∼0.68), and the two colors cannot be reconciled under a single grain population model. Additionally, we find that the extremely red Δ ( K s − L ′ ) disk color cannot be reproduced under conventional scattered light modeling with any combination of grain parameters or reasonable local extinction values. We hypothesize that the scattering surfaces at the three wavelengths are not colocated, and that the optical depth effects in each wavelength result from probing the grain population at different disk surface depths. The morphological similarity between K s and H 2 O suggests that their scattering surfaces are near one another, lending credence to the Δ( K s − H 2 O) disk color constraint of <5% ice mass fraction for the outermost scattering disk layer. 
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  3. Abstract

    This research proposed a parallelized approach to scaling up the calculation of inundation height, the minimum sea‐level rise required to inundate a cell on a digital elevation model, which is based on Dijkstra's algorithm for shortest‐path calculations on a graph. Our approach is based on the concepts of spatial decomposition, calculate‐and‐correct, and a master/worker parallelization paradigm. The approach was tested using the U.S. Coastal Relief Model (CRM) dataset from the National Geophysical Data Center on a multicore desktop computer and various supercomputing resources through the U.S. Extreme Science and Engineering Discovery Environment (XSEDE) program. Our parallel implementation not only enables computations that were larger than previously possible, but also significantly outperforms serial implementations with respect to running time and memory footprint as the number of processing cores increases. The efficiency of the scalability seemed to be tied to tile size and flattened out at a certain number of workers.

     
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