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Quantification accuracy was compared with the standard calibration method both with phantom experiments and on patient data.
In Fig. 3, a comparison is made between reconstruction results of the SIRT-FBP method both with and without the low-frequency adjustment.
Experimental results obtained on real data collected for this specific task show the capabilities of the given method both with distributed microphone networks and with compact arrays.
This fact greatly limits the applicability of the method both with species of low specific activity and in case of surfaces of industrial specimens.
It was also seen that there is an increasing gain in the performance of our method over the prior method both with increasing SNR and with higher numbers of simultaneously supportable users K0.
In this appendix, we briefly discuss how to increase the robustness of the method, both with respect to a bad choice of the initial set (mathcal {S}_{0}) (e.g., when information about the range of the target pdf is not available) and w.r.t. the heavy tails that appear in many target pdfs.
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Thus, for these experiments, we performed a sensitivity analysis, applying our methods both with no background kernel, and with one (based on all SNPs).
The performance of the new method is compared to the fictitious domain methods both with respect to accuracy and computational cost.
With regards to real data, we used two experimental comparisons to validate our method, both designed with paired biological replicates.
In the following, we describe how synthetic control methods work both with and without the cross-validation technique.
Statistical significance (p < 0.05) was achieved on six strategies for both methods, with both methods in agreement on the significance on the top five factors.
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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