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The p-compact-regions problem, defined by Li, Church, and Goodchild (forthcoming) involves generating a fixed number (p) of regions from n atomic polygonal units with the objective of maximizing the compactness of each region.
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For each saccade a population of about P = 425 cells was recruited, which generated a fixed number of about 2,540 spikes.
We generate simulation data using a two-stage approach: first simulate reticulate networks, and then generate a fixed number of trees displayed in the networks according to randomly generated display vectors.
In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations.
Each 2D sampling mask was generated for each time frame and a fixed number of NPE/4 lines were sampled along the phase-encoding (PE) direction to maintain the downsampling factor of 4 (where NPE indicates the total number of PE lines).
The CPM and the HMM both assume that the observed data was generated by a hidden process consisting of a fixed number of hidden states.
For a fixed number of valid edges, we generated five random networks and five sets of random valid edges for each network.
In SLBP, a fixed number of local binary codes are generated for each pixel position.
The algorithm is terminated after a fixed number of generations.
All simulations counted a fixed number of generations.
The networks are generated by joining randomly vertices under the constraint that each vertex has a fixed number of neighbors, taken from the pre-assigned degree distribution.
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