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The merge process is the same for the two illustrations.
The role of the merge process is to reduce the complexity of the model.
In the merge process, it indicates whether two candidates for merging ellipses would result in an ellipse with high eccentricity.
Both parameters are used to decide whether to split (in the split process) or merge (in the merge process).
The split process tries to minimize the modeling error while the merge process decreases the model complexity, i.e., the number of line segments compared to the total number of points in the set.
The merge process further improves the alignment by replacing the long unaligned portions of RDP Infernal with the corresponding region from the NAST alignment.
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The number of ellipses depends on the structure of points, and it is determined by two parameters controlling both the split and the merge processes.
In the Appendix, three merged queries are presented to explain the merging process of HiveQL queries that share common input and output parts.
The merged region, thus, is shifted between those owing to the averaging strategy of the merging process.
The GreenCloud and NS2 are installed using the merging process.
For each collection involves in the merging process, the time takes to compute is increasing linearly.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com