An Efficient Top-$K$  Spatial Keyword Typicality and Semantic Query

Zhang, Xiaoyan; Meng, Xiangfu; Sun, Junying; Zhang, Quangui; Pan, Li

doi:10.1109/access.2019.2941760

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…We employed a residual nonlocal attention network (RNAN) architecture [ 8 ] as the base SR model since it has shown a high performance in various vision tasks including SR with efficiency via utilization of the residual attention block (RAB) and the residual nonlocal attention block (RNAB). (Details for RNAN are described in the Experimental Section and Note S2, Supporting Information.)…”

Section: Resultsmentioning

confidence: 99%

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Accelerating AFM Characterization via Deep‐Learning‐Based Image Super‐Resolution

Kim

Lim

Kim

2021

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Atomic force microscopy (AFM) is one of the most popular imaging and characterizing methods applicable to a wide range of nanoscale material systems. However, high‐resolution imaging using AFM generally suffers from a low scanning yield due to its method of raster scanning. Here, a systematic method of data acquisition and preparation combined with a deep‐learning‐based image super‐resolution, enabling rapid AFM characterization with accuracy, is proposed. Its application to measuring the geometrical and mechanical properties of structured DNA assemblies reveals that around a tenfold reduction in AFM imaging time can be achieved without significant loss of accuracy. Through a transfer learning strategy, it can be efficiently customized for a specific target sample on demand.

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Section: Resultsmentioning

confidence: 99%

“…Deep Learning Model for SR: As a base SR model, the RNAN [8] consisting of multiple residual local and nonlocal attention modules to efficiently learn the mapping between LR and HR images. Details for the architecture and modules of RNAN are provided in Note S2 of the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%