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The separation between the modes, Φ, is calculated as a function of κ and Q.
Hence, let us focus on the effect of κ on S and N. YN uses κ and codon frequencies to estimate S and N. Since codon frequencies are constant, estimated from targeted sequences, and transitions between two codons are more likely to be synonymous especially at the third codon positions, S thus (as a function of κ and codon frequencies) is positively correlated to κ.
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We first present, in Figure 3a, results for Ψ ≡ Δ Fknotting – Δ Fknotting(0) as a function of κ for τ = 0.1, 0.4, and 0.8 kB T/σ.
We complete the derivation of our objective function as a function of κ: (6) Minimizing J creates a transformed feature space with wide label separation and therefore calibrates the quantification with the graph transition energy.
Figure 4 The value of ε κ as a function of κ.
In these experiments, we tried two different cost matrices Figure 13 Mean-squared errors as a function of κ.
Fig. 5 Spot radius R as a function of κ for a smooth Mexican hat connectivity given by (12), with parameters as in Fig. 4.
By numerically integrating ⟨(∂ V /⟩ from κ = 0, we obtain the relative free energy as a function of κ, Δ Fα = Fα – Fα(0), where α stands for either "knot" or "linear".
Overall, the shape of the MST curve we obtained as a function of κ is qualitatively similar as in [ 38] as well.
Furthermore, we also did some interesting simulations to study the change of transition paths as a function of κ (see Additional file 1 for details).
In these networks, the variance in the degree distribution, σ, varies as a function of κ while the mean degree remains constant.
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