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This implementation makes use of the native SSE support which OpenCV offers.
I really didn't like the feature when I tested the device this week — I found it to be pretty intrusive compared to standard calling, but this implementation makes a lot more sense.
This implementation makes essential use of the structure of neural variability and leads to physiological predictions for activity in areas that combine multisensory input, such as the superior colliculus.
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All of the features described in this implementation make up a methodological framework that will render implications for engineers, physicians, patients, and policy makers.
To perform it on a commonly-used computer, the implementation makes an essential use of external memory.
However, while this kind implementation makes the software harder to remove, it is not impossible so this approach should be combined with other security features for maximum effect.
This model of partial implementation makes it difficult to draw broader conclusions about program feasibility, acceptability, and effectiveness.
This variability in interpretation and implementation makes Article 12 the most controversial provisions of the UNCRC (Lundy 2007).
But like Ikea before it, MLB's implementation makes perfect sense.
The second implementation makes a local estimation and has better performance.
Besides, their suitability for physical implementation makes CNNs very appropriate for high-speed parallel signal processing.
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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