The conditional probability of each bit in the SI can be calculated, taking into account the previously decoded bitplanes and the correlation noise model described by f F u s b k, u, v.
For our model, we define Ω o f f = { (x, y ) ∈ ℝ + 2 | x + y ≤ L } Ω o n = { (x, y ) ∈ ℝ + 2 | x + y ≥ L } with suitable parameter L. With model parameters as given in Table 1, a suitable choice which we will use in this work is L = 55.
These data were used to develop a semi-empirical model that expresses F S F sim / d f as the product of an effective sampling volume, and the effective excitation fluence and the effective escape probability within the effective sampling volume.
This could be explained by the phase error term of SAR spectrum model Φ ESAR f τ, f, r 0, i.e., Φ ESAR f τ, f, r 0 = − 4 π r 0 c α f 0 + f 2 − v c f 0 + f + c f τ 2 α v 2 − f 0 + f 2 − c f τ 2 v 2 (15) Based on the system parameters shown in Table 1, the phase error term (13) is shown in Figure 4a.
The dependence of the polymerization energy μ (polymerization energy per unit surface) on the changes in tube force is captured by the expression (Supplemental Information, Supplemental Mathematical Modeling part 4.3): (1) μ = F f 2 − F i 2 8 π 2 κ where F i = 8 pN and F f = 35 pN are the tube forces before and after Snf7 polymerization and κ = 4.8 × 10 − 20 J, is the membrane bending rigidity.
We refer to the phase error of the SAA spectrum model Φ ESAA f τ, f, r 0, which is expressed as Φ ESAA f τ, f, r 0 = − 4 π r 0 c α f 0 + f 2 − v c f 0 + f + c f τ 2 α v 2 − α f 0 + f 2 − 1 α v c f 0 + f − c f τ 2 α v 2 (17).
Model outputs of F S F ratio M C were validated by comparison with previously published fluorescence intensities over a range of background optical properties [ 18].
In order to define γ t f, we employ the probabilistic distribution map in [31]: p f (x i, t) ∝p f (Z i |M f, F ). Z i is the observation at pixel i; M f, F is the foreground model of feature f, p f (Z i |M f, F ) represents the observation likelihood of the pixel i given the foreground model M f, F of feature f.
Build a statistical model f k = f(L; α k ).
For k from 1 to K 1. Build a statistical model f k = f(L; α k ) 2.
Here, we shall call the nanoribbons as model F. The model F nanoribbon is made of the other BC2N sheet of the model II defined in[17].
