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That image was tested by his silence over the policing of the G20 protests.
The ability of this signal intensity algorithm to provide automated segmentation of both iron related contrast effects (green) and the renal arterial structures (red) from the same image was tested (Figure 3C).
Hence, firstly the rotational difference between the original image pairs was tested and then the result between the generated image and the second image was tested as well.
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Notably, only the tumor areas on a histological image were tested in the design above.
The direct neighborhood of each pixel of the image is tested.
This process is repeated for every image in the dataset so that each image is tested once.
This attack is considered in Table 3 and the watermarked image is tested under various scaling attacks with different scaling parameters.
The corresponding accuracy of every cropped image is tested.
On the next day, long-term memory (LTM) for the images was tested.
On the next day, long-term memory (LTM) for the images was tested according to a remember/know paradigm.
A dataset comprising 169 diverse retinal images was tested, and the segmentation results were assessed by a gold standard derived from the annotations of five domain experts.
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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