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We also find the same effect in 10b, 11b, 12a, and 12b.
We also find the same casualty size dependent relative bias between the two datasets at the level of months.
You'll also find the same Nintendo 3DS bundle as we saw at Sears but for $20 cheaper.
But, we also find the same thing even in choices that have nothing to do with food whether its choosing activities or colors.
We also find the same trend with respect to the resolution-dependence of the results: upon doubling the resolution, the accretion rate (langledot{M}rangle), averaged over (tin[1000,2000]), increases significantly by a factor of 1.908 and 1.843 for BHAC and HARM, respectively.
You'll find what you want to believe (self-validating) but equally, if you sat down and did the same for any day of the year, you'll also find the same level of accidents and bad events!
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We also found the same variant in other normal subjects.
I've also found the same thing in vernacular architecture -- these things, these commonalities, just emerged.
We also found the same reluctance, among many people, to prolong their lives.
We also found the same peaks in samples produced under the synthetic environment.
Finally, we also found the same reversal pattern in the ERP peaks when comparing the maximum ERP difference between perceptual and semantic object classes.
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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