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A key concept underlying Deep Learning methods is distributed representations of the data, in which a large number of possible configurations of the abstract features of the input data are feasible, allowing for a compact representation of each sample and leading to a richer generalization.
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Any disagreements were resolved by a third individual, who independently extracted the data in which there was a discrepancy.
They then construct a network from the data in which each scientist is a node and links exist when one was a mentor or student of another.
In survival analysis, to model the data in which the mortality reaches a peak and then starts to decline, a model with a non-monotonic (hump-shaped) failure rate can be used.
We also annotate other information including the data in which the activity was created, its title, a description and its set of instructions for use.
A logistic function 1/(1+exp(−(x−c)/a)) was fitted to the data, in which 'a' and 'c' are free parameters that determine the slope and midpoint of the psychometric function.
We compared the fold increase of genes that had at least two tags in both the soma and germline SAGE libraries with the fold increase of the same genes in the microarray data, in which a fold increase ≥ 2 was deemed to be germline-enriched [ 11].
We need to carefully analyze tsunamis in OBTM records by correcting the effects of rapid temperature changes, especially for the MPG2 data in which a number of rapid temperature changes occur.
We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract.
Our results are also in keeping with a closely related investigation of the SEER data in which a group of 4,616 patients 35 years old or younger was compared to a group of 20,319 patients aged 50 55 years [ 21].
The method was tested with the NCEP/NCAR reanalysis data in which a quasi-balance condition exists.
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