Next we weighted the interaction between two proteins according to the weight function: W (x, y ) = n 1 (|| x - K x || 2 + || y - K y || 2 )+ n 2|| K x - K y || ||·|| stands for the distance metric and there are many metrics for measuring it, in this study we have used Euclidean distance.
The RT method simply shows that the noticed particularity is due to matching symmetry between the inclusion shape (through the weight function) and the material property (through the elementary operator).
The local gradient ∇ ω u(x) is defined as the vector of all partial differences ∇ ω u x, ⋅) at x: {nabla}_{omega }uleft x,yright)=left u y -u(x)right)sqrt{omega left(x,yright)}, (6)where ω(x, y) is the weight function between x and y defined based on the image u.
(Collaborative cost of a team leader) Given a relationship network of crowd workers G W, F), a crowdsourced team g of crowd workers and a team leader l, the collaborative cost of the team leader l is defined as begin{aligned} CC g,l)=sum _{win g} d w,l) end{aligned}where (din [0, 1]) is the weight function between two crowd workers.
For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss.
In this case, the weight function W between a source and detector pair is given by [ 6]: where r s, and r d are the locations of the source and detector, respectively and r is a position in the diffusive media.
For non-local means, 5 × 5 pixel image patches were used to calculate the weight function measuring similarity between patches centred at each pixel in 11 × 11 neighborhoods.
where w is the weight function.
The weight function is and satisfies, for.
Since the weight function is invariant w.r.t.
