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In contrast, the remaining three methods perform relatively worse.

Consequently, depth saliency methods perform relatively worse than RGB saliency methods in terms of precision.

Meanwhile, we observe that the proposed methods perform relatively worse in hand-clapping class and running class.

As shown in Fig. 12c, in the depth saliency situation, the RGB saliency methods perform relatively worse than the RGB-D saliency methods in terms of precision.

For the proposed experiment (see Fig. 3), all methods perform relatively well at reconstructing the sparse signal from a small number of random measurements, except the ℓ 2-based ℓ 1-LS method.

The remaining pre-processing methods perform relatively similar and equally well.

The other alignment-free methods performed relatively poorly.

If only a small set of best candidate targets are to be detected, then all the methods performed relatively well in our comparisons.

When a different rate in the foreground branch was detected by the LRT, all methods performed relatively well (with some outliers, though) at detecting the direction and magnitude of the rate difference in the foreground species, that is, δ = 10 × β in the foreground species in the BRhigh10 simulations (fig. 4 and supplementary figs. S6 and S7, Supplementary Material online).

According to the Table 2, under λ Open image in new windowσ shift in the intercept, when autocorrelation is weak (ρ = 0.1), the MEWMA method performs relatively similar to MCUSUM control chart, and they also have better performance for detecting the small, moderate, and large shifts than the T2 control chart.

Although this method performs relatively better for a channel with a large delay spread, its BER performance is still far away from the perfect channel estimation.