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Finally, in order to make our methods more accessible to non-programmers, we provide a Cytoscape interface to the R implementation as a plugin, CytoCopteR.
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This would make our method likely overestimate the actual number of GSR events.
Further, to make our method be implemented easily in CAD/CAM systems, a specific implementation process is also briefly presented.
The building cube method is adopted to make our method suitable for massive parallelization systems, which can reduce the calculation and turnaround times immensely.
In this paper, we use a network to mimic this process, which has an inherent attribute to make our method get better performance.
Actually we are working to make our method as more automatic as possible, to select the best starting pixel to draw the contour.
The scalability with large motion dataset, and insensitivity with the algorithm parameters, make our method can be widely used in real-world applications.
Another advantage of our proposed method, in comparison with Ross's work, is that, here, we use an area preserving method [23] for our level set methods, which make our method robust in case of blurred images.
In Section 4, we introduce the idea of applying semi-supervised learning techniques to our datasets, in order to make our method applicable in the case where only a fraction of the training data are position-labeled.
To make our method more effective, hybrid acquisition and time-dithering sequential shooting are introduced so that clean single-shot records can be used to train the dictionary to favor the sparser representation of data to be recovered.
Its key features include ellipsoid transformation, multi-level grid partition and sliding window algorithm to make our method efficient and accurate for finding the SRAM failure probability and failure boundary in parameter space.
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