Similar to [3], we modify the classical threshold for curvelet coefficients, i.e., (sigma sqrt {2 log {left |mathcal {K}right |}}), with a weighting parameter η as follows (delta = eta sigma sqrt {2 log {left |mathcal {K}right |}}) and ({left |mathcal {K}right |}) is total number of curvelet coefficients.
The objective function J is defined as follows: Here, λ i is a weight parameter for each feature f i, trained as described below in Section 2.4.3.
The base-pairing probabilities under the mixed distribution can be defined as: (10) where w∈[0, 1] is a weight parameter (in our experiments, we fixed w=1/2).
First, we define a gain function of ŷ∈??(x) with regard to the correct secondary structure y∈𝒮(x) as follows: (1) where γ>0 is a weight parameter for base pairs, TP and TN denote the numbers of true positives (base pairs) and true negatives (non-base pairs), respectively, and I(condition) is the indicator function that takes a value of 1 or 0 depending on whether the condition is true or false.
Actually, the time step Δt 2 also controls the backward diffusion in Step 2 as the weighting parameter β, and a large Δt 2 has the risk of increasing the range of image intensity excessively, and resulting in intensity distortion.
When proteins adopt a monomer dimer equilibrium, the molecular mass, M, can be treated approximately as a weight average parameter (Mw).
The signal in DWI decays as a function of the amount of incoherent motion present in the tissue and a diffusion weighting parameter known as b-value.
As weighting parameter we chose γ1=2, γ2=1, and γ3=2.
As the weight parameter V increases, the variation of total energy consumption under different control policies are displayed in Fig. 7. 1.
In Figure 2, we see the performance of each method (in terms of AUPR) as a function of the weight parameter.
