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Experimental results show that the proposed algorithms can significantly outperform several classical online learning methods on synthetic data.
Besides, this filter has been compared with other six state-of-the-art denoising methods on synthetic data and real ultrasonic images.
Fig. 3 Comparison of different rain removal methods on synthetic rainy images.
Table 1 shows quantitative evaluation results of different methods on synthetic rainy images.
We explore the performance of these proposed methods on synthetic catalogs simulated using two different types of background rate functions.
We compare here the performances of two well-known ridge reconstruction methods, namely the Carmona and Marseille methods, on synthetic signals as well as real electrohysterogram (EHG).
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We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions).
The results of numerical simulations using both the proposed method and the alternating direction method on synthetic and real-world data sets are presented.
In this section, we present results for a series of computational experiments that demonstrate the efficacy of our proposed method on synthetic benchmark problems.
Let us remind that we use the grid (S) step 0.25 nT for calculating hourly baseline values (see "Validation of the method on synthetic data" section), whereas INTERMAGNET requires daily values.
We first evaluated our method on synthetic datasets.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com