Once obtained the linear model, our method requires to check for spatial autocorrelation.
Using a natural logarithmic model, our method first computes probabilistic light cones for TE and ATE curves.
In contrast to the previous lattice Boltzmann model, our method has a wide flexibility to select equilibrium distribution function.
The second image with complex background, comparing with the vector-valued C-V model, our method demonstrates better in visual perception.
Unlike these approaches, which is based on a joint model, our method trains two exclusive RBMs for each speaker, aiming to capture speaker-specific conversion-friendly features.
Compared with the results of Kim et al.'s model, our method yields 3, 13, and 6% gain with regard to recall, precision, and F-beta, respectively.
By combining the efficiency of low-fidelity models and the accuracy of high-fidelity models, our method exhibits fast convergence with a limited number of high-fidelity simulations.
In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle.
However, focusing on the improvement of the C-V model, our method use the local region information to replace the global region information and present an operator splitting method for implementation.
Unlike other correction methods based on B-Rep models, our method repairs parametric feature models without translating them to a B-Rep shape, and it also preserves parametric information.
