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In Zhang's method and the edge directed methods in Figures 8(d)–8(i), the fence regions are highly improved with reduced errors.
Next, in order to quantitatively verify the performance of the proposed method and the previously reported methods in Figures 4, 5, 6, we show the structural similarity (SSIM) index [32] in Table 1.
We notice that unlike Eigenstrat, the sparse regression methods in Figures 10a and b are able to exclude SNPs that are in LD with the true causal SNP and detect the true causal SNP as the associated SNP.
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First, we illustrate the results of these methods in Figure 5.
We show the recall-precision curves of the three methods in Figure 5.
The averaged results are displayed for the four methods in Figure 8.
Finally, we justified the tracking performance of the proposed methods in Figure 12.
To evaluate the performance of different methods in NLOS conditions, we plot the CDFs of various methods in Figure 13.
The combined fairness results for GBR and NGBR flows in the proposed scheduling setup are compared with other methods in Figure 15.
For video coding application, quality-complexity comparison has been presented for different configurations and motion estimation methods in Figure 12.
We summarize the results of our methods and the MULTIPLY method, which gave the best performances among the compared methods in Figure 4, as seen in Table 3.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com