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For the time-domain examples, the windowing approach has low complexity and no spectral efficiency loss.
In-lab experiments show that our approach has low performance overhead.
Before 5000 trials, the Q-learning approach has low throughput, and it almost converges after about 50000 trials, and it has the best throughput after about 100000 trials.
The first case-identification approach has high (91%) sensitivity, but low (40%) specificity, while the second case-identification approach has low (54%) sensitivity but high (87%) specificity.
As mentioned above, the simulation study by MacKinnon et al. (2002) showed that the Baron and Kenny approach has low power and therefore requires a large sample size.
A simple solution to this problem is to analyze each individual dataset separately, but this approach has low statistical power since the counts (x idj, n idj ) usually are small.
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While many false positives detected using the miRBase mapping approach have low read counts, setting a stricter threshold on the read counts could result in bona-fide miRNAs being missed.
Meanwhile, information theoretic approaches have low complexity, but they cannot cope with the dense corrupted errors beyond C/2.
Consequently, existing composition-based approaches have low binning specificity.
Q2 approaches have low power of detection in highly varying samples such as clinical data sets [ 3].
Of the three approaches, this approach has a low cost but also has the lowest chance of producing good initial designs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com