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In detail, FCN and DeepLab showed too weak reaction to very small birds to detect them.
A few bird images over the wind turbine were detected by FCN and DeepLab, but when combined with CNN, they were missed because of low likelihood of birds.
Results indicated high selective electrode response of up to 16 times for positively charged (dimethylaminomethyl)ferrocene (FcN), and neutral ferrocenemethanol (FcMeOH), compared to the negatively charged ferrocenecarboxylic acid (FcCOOH) when the receiver electrode was maintained at negative potentials of 0.5 1.0 V relative to the feed electrode.
From Mode 2, we could find the activation of the ECN and FCN and the deactivation of the visual areas.
Expanding the number of search templates for a single compound, as was done for BGC, FCN, and TOP, indeed identified more significant known targets, supporting this hypothesis.
The GEM-PRO was utilized to predict binding sites on protein targets for known antibacterials with unknown mechanisms (028 and 2OB), binding sites on previously uncharacterized targets of well-studied antibacterials (FCN and TOP), and potential inhibitors of TrpB.
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Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes.
Eventually, we had 77 ×70 = 5390 frames for training FCNs and DeepLab.
The implementation details of FCNs and DeepLab are provided in the training section.
Except for SuperParsing, the images were cropped to 500 ×500 pixels because the original images were too large to process with FCNs and DeepLab on our GPU memory.
We implemented CNNs, FCNs, and DeepLab, as well as AdaBoost with Haar-like feature [19, 20] and SuperParsing [21] as baselines.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com