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Discover Ludwig"loss of information" is a correct and usable phrase in written English.
You can use it to refer to instances in which a person or people have lost or forgotten information. For example: "Due to the passing of time, we experienced a loss of information from our memories."
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The loss of information is inevitable in any upscaling technique.
Mike Doonesbury, for example, can be translated into words with minimal loss of information.
The loss of information from the universe would contradict a basic rule of quantum mechanics.
How many samples are required to represent a given signal without loss of information?
The arbitrary nature of the thresholds and the resulting loss of information is problematic.
Loss of information has become a severe phenomenon in many processes including state estimation.
This approach often results in loss of information that introduces errors to modelling.
To avert the loss of information, a threshold strategy (TS) was considered in some prior studies.
Different lumpings are compared using the entropy as a measure of loss of information by lumping.
The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds.
This leads to loss of information for estimating additional quantiles or performing additional applications.
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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