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Litvak et al. (2007) have dealt with this kind of artifacts by fitting exponential functions to the signal and by removing them.
Because this kind of artifacts has been shown to have a substantial effect on high throughput experiments and in particular with Illumina methylation arrays, we asked whether we could highlight and reduce this additional layer of confounding variability, in particular when biological factors are admixed with non-random assignment of samples to arrays or batches.
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Several full-reference metrics exist for this kind of artifact [6], while the literature is poor in no-reference ones.
Hershberg discounts this kind of artifact as a major influencer in her results.
The structuration of data of our database (e.g. numbers are seen as numeric fields) along with an intuitive user-friendly graphic interface allows users to perform many types of searches, even to exclude these kind of artifacts, and to make comparisons and calculations that are useful for data retrieval and analysis.
This kind of acquisition artifacts may deeply affect the performance of biometric systems and hence, decrease their use in real life applications.
They write, "If I point at two aeroplanes and tell you that 'aeroplanes fly', what you learn is not restricted to the particular aeroplanes you see or to the present context, but will provide you generic knowledge about the kind of artifact these planes belong to that is generalisable to other members of the category and to variable contexts…".
When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use.
To better mitigate the impact of this kind of spatially varying artifact on AWF SR, we propose employing a correlation model with a spatially varying SNR.
The twist is that the kinds of artifacts once prized only by geeks are now coveted by the rest of us too.
Given the fact that a certain type of processing, e.g., JPEG2000 coding, can introduce more than one kind of artifacts, it is imperative to have quality estimators that can assess the impact of more than one artifact.
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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