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We illustrate this method on two models of the mitogen-activated protein kinase cascade, one with 3 uncertain parameters and one with 18 uncertain parameters.
In our experiments, we illustrate the benefits of our method on two image segmentation tasks.
We validated the performance of our method on two separate data sets from confocal and split detector AOSLO systems.
We applied our method on two exemplar datasets each containing a pair of sister fish species: Siniperca chuatsi vs. Sini.
We have also performed two case studies for this method on two shareware systems.
We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization.
We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets.
The paper provides an algorithm to implement this approach and demonstrates the method on two industrial data sets.
We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
The paper demonstrates the method on two benchmark problems, and then presents extended versions which incorporate enumerative topology.
Experimental results of tests aimed at validating, characterizing, and comparing the proposed method on two different converter architectures are presented.
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