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This study complements such approaches by examining the impact of a multidimensional vector that measures technological opportunity on independent inventors' decisions to patent and commercialize inventions.
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In a nutshell, the preprocessing methodology SFX is a way of transforming a two-dimensional time-series (amplitude versus time) into a multidimensional feature vector that has all the essential attributes sufficient to characterize the original time-series voice data.
A result of an expression experiment for a given sample is a (very long) vector that may be represented as a point in a multidimensional space.
One vector is very close to the vector that represents a unanimous decision.
If the edge lengths of a particular GT are divided by the relative evolution rate of the given gene, the normalized relative edge lengths form a vector that identifies a point in a multidimensional space that describes the tree shape.
For example, it has been generalized to multidimensional vector spaces.
For multidimensional vector covering we consider the case where each bin is a binary d-dimensional vector.
We consider one-dimensional and multidimensional vector covering with variable sized bins.
We first select slide window k on random matrix, calculate similarity between k multidimensional vector and k + 1 multidimensional vector, then move back a sliding window till the end of the sliding window.
The multidimensional vector of OS are selected as 〈click, post, reply〉, and 〈Y 1, Y 2, Y 3〉 in BS.
In essence, the filter corners an offending piece of text in a multidimensional vector space where there are no hiding places.
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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