Your English writing platform
Discover LudwigSuggestions(1)
Exact(19)
To identify specific genes expressed in samples transfected with ΔNp63α compared with the empty vector, we used a computer algorithm that allowed us to select genes exhibiting ≥ 3 fold changes.
For the PCA feature vector, we used the top 50 features.
The sparse vector we used in this experiment had a value of k namely, k = 40 or 60.
In order to find the local minimum and maximums of the smoothed projection vector, we used the following method.
As an acoustic feature vector, we used 32-dimensional mel-cepstral features that were calculated from the 513-dimensional WORLD [30] spectra without dynamic features.
As the acoustic feature vector, we used the 39-dimensional vector consisting of 13-dimensional mel-frequency cepstral coefficients (MFCCs) including the 0th cepstral coefficient, their derivative coefficients, and their acceleration coefficients.
Similar(41)
To obtain a smooth vector, we use an averaging filter.
For the control vector, we use 3D vectors as the velocity of each axis in (11).
In the case of GII values we use Euclidean distance and for our vector, we use cosine distance.
As the second element for the feature vector, we use the energy of edges derived by the anisotropic diffusion filter [22].
In order to avoid the minimum eigenvector being far away in signal space from the current beamforming vector, we use an incremental update that adapts the user beamforming vector in the direction of the minimum eigenvector.
Write better and faster with AI suggestions while staying true to your unique style.
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