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Decide the training set with size L centered on y(n) as shown in Fig. 4.
For either hand point, a local image patch R i of size l × l centered at p i is obtained.
(a) Decide the training set with size L centered on y(n) as shown in Fig. 4.
For example, let us consider the ideal case of a circular cylinder (with diameter D and length L) centered in a square solid of equal length, as reported in Table 3.12 of [19].
The propensity of a residue at positions i for being a part of a TMH was predicted based on a sequence segment of length L centered on i, where L is an odd number that represents the prediction window size.
We use a window with length L centered at each amino acid residue to extract features.
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f I F 2 = f I i I L center ∗ ⋯ ∗ f I i I L center ⏟ N − th order convolution (11).
As we know, the joint distribution functions shown in (28) can be calculated by P ( L center, k < K ) = P ( L center ) P ( k < K L center ) P ( L edge, k < K ) = P ( L edge ) P ( k < K L edge ) (29).
Similarly, the conditional m th moment of Y can be calculated as E [ Y m L center ] = ∫ y m f Y ( y | L center ) dy = α m M 0 ( mβ, γ ) γ 2 R mβ (19).
The distribution function of K can be expressed as P Set − B ( k < K ) = P ( L center, k < K ) + P ( L edge, k < K ) (28).
Figure 2 presents an example of a real trajectory matrix (left), the representations coefficients L (center), and the singular value distribution of the coefficients (right).
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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