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You can find all the datasets with the comparisons on Google Docs.
However, most of the studies mainly focused on the datasets with the single endpoints.
For all the studies reported below, we extended the datasets with pure compounds.
Clustering was observed in the datasets with urban areas representing a second order clustering effect over the watershed.
As a result, in the following LO-refinement procedure, only the datasets with SNR of 0.25 and 0.11 were tested.
Finally, we tested the optimality of the random swap using the datasets with known ground truth (S, Birch, Dim, Unbalance).
Conventional dimensionality reduction algorithms that use Gaussian maximum likelihood estimator could not handle the datasets with over 20,000 variables.
In general, these results show that the datasets with high atom type variability can still represent a challenge for the available EEM parameter sets.
We observed that the RMSE values were smaller for the datasets with smaller trees compared with the dataset with larger trees for both species.
The MLE for the datasets with magnitude thresholds M 4 and M 5 are given in Tables 1 and 2, respectively.
Due to the availability of the datasets with identified outlier edges, we generate test data by injecting random edges to real-world graphs.
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