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Figure: A general depiction of the encoder-decoder framework with an integrated CRF-RNN layer. The input data is fed into a chosen encoder, which then is upsampled by the U-Net-based decoder. Both the decoder output and the initial image serve as inputs for the CRF-RNN layer, which produces the final pixel-based prediction. (Credit: M. Avaylon, T. Perciano, Z. Bai) A 3d display of a simulated High Luminosity LHC collision event as seen by the ATLAS inner tracking detector (ITk). Image: Atlas Collaboration. Running simulations at NERSC, the research collaboration found that the effect of climate change on future storms in the San Francisco Bay Area will be significant, leading to more powerful storms unleashing substantially more water. (Credit: Brocken Inaglory via Wikimedia Commons) Osni Marques has been tapped to lead the Training and Productivity effort in for DOE's Exascale Computing Project. (Credit: Thor Swift, Berkeley Lab) Exascale Computing Project Schematic of angular states and HS-AFM snapshots of protein nanorods in their energetically preferred orientations, corresponding to specific directions of the mineral lattice. Orientational free energy landscape and heat map of relative populations at each angle determined from deep learning analysis of HS-AFM data. (Credit: Stephane A. King, PNNL) Semantic segmentation: Automated detection of dendrites (blue) and pitts (red) using Y-net, a deep-learning algorithm to automate the quality control and assessment of new battery designs that was run at NERSC on Cori and Perlmutter.
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