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Recall that standard means that all Cartan matrices are equal.
When dealing with normally distributed classes, it is well known that the optimal Bayes classifier is linear only when the covariance matrices are equal.
According to the structure of array-manifold matrixA θ and time-manifoldF ϕ, the elements of the first row in these two matrices are equal to 1.
Moreover, the co-variance matrices are equal to Q=0.01I and R=I, whereas the cost matrices are Γ=1.5I and Ω=10I.
Furthermore, in scaled teleoperation there exist positive gain matrices (scaling position and force factors) that preserve its passivity only if gain matrices are equal, which is hard to find in a real system.
A Tucker‐ (N1,N) model for a N th‐order tensor X ∈ C I 1 × ⋯ × I N, with N≥N1, corresponds to the case where N-N1 factor matrices are equal to identity matrices.
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These two relations come from the fact that the Kronecker product between any two diagonal matrices is equal to the identity matrix if and only if these diagonal matrices are (scaled) identity matrices that compensate each other.
The step-up approach suggested that the matrices were unrelated, whereas the model building approach suggested that the matrices were equal (Table 4).
Finally, among months in individual red knots, the step-up approach indicated that the matrices shared all PCs but had different eigenvalues (CPC), and the model-building approach indicated that the matrices were equal (Table 5).
Thus, the particle densities of the ion exchanger and matrix are equal.
i.e., the entries of the main diagonal of a matrix are equal.
More suggestions(15)
matrices are shared
matrices are supported
matrices are integrated
matrices are associated
matrices are nonVandermonde
matrices are suitable
matrices are sparse
matrices are diagonal
matrices are symmetrical
matrices are non-invertible
matrices are common
matrices are symmetric
matrices are useful
matrices are important
matrices are identical
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Justyna Jupowicz-Kozak
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