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Quantitative and qualitative evaluations on several sequences show that our method consistently outperforms tracking methods compared.
Via intensive simulations, we show that our method consistently outperforms the existing frequentist approaches, e.g. the graphical LASSO, in the finite samples.
The results show that the proposed method consistently outperforms the conventional non-heuristic-based GA approach in terms of producing more economically designed water distribution networks.
The proposed method consistently outperforms the conventional modeled PDF approach for undersampling ratios of 0.2 or higher in both FT-MRI and CS-MRI.
Numerical experiments with 2-dimensional rough random fields, high variance and small length scale are reported, showing that the quasi-Monte Carlo method consistently outperforms the Monte Carlo method, with a smaller error and a noticeably better than O(N−1/2) convergence rate, where N is the number of samples.
Our results show that the CAWF method consistently outperforms the AWF.
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Both versions of RNLM method consistently outperform SNLM.
However, as we can see from Fig. 6, the relative performance between different classification methods showed similar tendency as in the previous set of experiments (see Fig. 5), and the proposed method consistently outperformed the other methods in all experiments.
The embedding method consistently outperformed standard procedures.
From the figures, our method consistently outperformed the private SVM at different sampling rates.
No GSA method consistently outperformed the other, and we recommend using more than one GSA method when analysing gene expression data.
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