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This discussion goes beyond the scope of this paper; so we will only mention several primary modifications.
Object localization and recognition is out of the scope of this paper, so we use AR markers for simplification.
Two-stage embedding is exploited in this paper so as to ensure the implementation of full-enclosed-based prediction.
because preprocessing, feature extracting, decoding, and N-gram counting are more complex than the functional computation in this paper, so M · c φ FUN ≪ M · c φ.
Only a few rise-to-span ratios were calculated in this paper, so the conclusion universality needs to be further verified.
All the discussion in this paper so far has been focused on a system with just a single user, where no interference from other users exist.
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In this paper, so-called one-shot optimization methods are pursued.
We describe all study endeavors in this one paper so that readers can understand the context of each sub-study and make informed comparisons.
It is difficult, if not impossible, to represent this classifier on paper so that other researchers may use it.
This sounds nice on paper, so let's hope that Renault is going to bring this technology to production vehicles.
We do not generally ask experimentalists reviewing an experimental paper to replicate the same experiments before approving a paper, so this is understandable.
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CEO of Professional Science Editing for Scientists @ prosciediting.com