Are there types of graph substructures, other than those discussed here, that can be unambiguously inferred from P G? Are there special properties of PPI networks, other than the power-law degree distribution, of which an algorithm can take advantage to make more accurate predictions and/or provide approximation or probabilistic guarantees?
The speed and accuracy of decision-making have a well-known trading relationship: hasty decisions are more prone to errors while careful, accurate judgments take more time.
While such strategies were somewhat successful (approximately 70% correct) and can provide a preliminary representation of a problem before more abstract concepts are applied, in a number of contexts they can also take more time and be overall less accurate than formal strategies.
Take more time and you might make a wiser or more accurate decision.
Michael Milburn, professor of psychology at the University of Massachusetts, Boston, and co-author of the book "Sexual Intelligence," an examination of sexual beliefs and behaviors in America, says that the boys' take is more accurate.
To take a more accurate count, they scaled the trees and brought down all 35 ferns.
Differently, XJTCP can take a more accurate decision about the predominant condition in the network, and hence, it avoids to enter unnecessary slow start phases, with positive effects on its average delay.
To take a more accurate reading, look closely at the degree markers on the compass.
It is possible but reasonable that computation using two internal models takes more time but yields more accurate motor prediction or planning compared to the use of a single unimanual internal model.
Although the superimpositions found using the above two methods were found to be reasonably accurate, pairwise comparisons took more than a minute, which limits their large scale application.
