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To test the performance of the proposed edge-based adaptive method, we ran some experiments using two sets of video sequences.
To determine the reliability of each method, we ran the data of geomagnetic field measurements from the period with known storms through a test storm detector and iterated through a range of parameters unique to each method.
To evaluate normalization method, we ran the test on the tri-gram model with the normal Dice coefficient (Dice) and the improved Dice coefficient (fDice) to measure the similarity of the two sentences.
Given the random-walk nature of the method, we ran 1000 replicate runs, with a convergence limit of 0.01.
As an eighth method, we ran Clustal W (Thompson et al., 1994) on those sequence sets where this was possible and meaningful.
For each method, we ran tests on validation data, if parameter values had to be set, and all methods were evaluated using 5-fold cross-validation.
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For Avg-Feature method, we run the test for up to all frames selected from each video.
For each method, we run scaffolding using the default parameters.
To evaluate the method, we run experiments on a benchmark set proposed by Sandve et al. (2007).
To investigate the performance of the IMM normalization method, we run simulations to study the effects of RNA composition on DE analysis of RNA-seq data and compare with the TMM method.
*Graph 2 Methods: We ran a single-question survey to a 1000 respondents via Google Surveys.
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