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https://www.selleckchem.com/products/azd5363.html Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a novel multi-scale unrolled deep learning framework which learns deep image priors through multi-scale CNN and is combined with unrolled framework to enforce data-consistency and model knowledge. Essentially, this framework combines the best of both learning paradigmsmodel-based and data-centric learning paradigms. Proposed method is verified using several experiments on numerous data sets.This study investigates the feedbacks between an interactive sea surface temperature (SST) and the self-aggregation of deep convective clouds, using a cloud-resolving model in nonrotating radiative-convective equilibrium. The ocean is modeled as one layer slab with a temporally fixed mean but spatially varying temperature. We find that the interactive SST decelerates the aggregation and that the deceleration is larger with a shallower slab, consistent with earlier studies. The surface temperature anomaly in dry regions is positive at first, thus opposing the diverging shallow circulation known to favor self-aggregation, consistent with the slower aggregation. But surprisingly, the driest columns then have a negative SST anomaly, thus strengthening the diverging shallow circulation and favoring aggregation. This diverging circulation out of dry regions is found to be well correlated with the aggregation speed. It can be linked to a positive surface pressure anomaly (PSFC), itself the consequence of SST anomalies

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