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This algorithm starts with a high temperature.
This algorithm starts with a high initial temperature (t 1).
This algorithm starts with an initial population of feasible solutions.
This algorithm starts with an adaptation of the optical flow and includes a thresholding operation.
This algorithm starts with a PQR tree composed by a P-node containing all elements of U as its children.
This algorithm starts with an initial dictionary and initial sparse code coefficients, and then, one dictionary vector is updated at each iteration.
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To overcome this problem, the algorithm starts with previously acquired knowledge in the training phase.
In this specific study, the algorithm starts with ten thousand candidate seeds consisting of randomly chosen homologous organ groups (HOGs), for both runs.
A variation of this method was applied by Zaidi et al. [ 23], in which the algorithm starts with an oversized number of clusters to avoid misidentification of conflicting regions.
First, the algorithm starts with initial random particles.
Each iteration of the algorithm starts with setting a new value of t.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com