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However, the latter methods assume structural homogeneity among particles.
These latter methods appear quite robust in the experiments.
Table 1 shows the performance of the latter methods in terms of PSNR, SSIM, and VSNR.
The latter methods are referred to as volume-based methods where the entire volume is discretized.
In the latter methods, parameters of the CGs are estimated from training data.
In a second step, these latter methods are applied to engine data.
These latter methods provide more accessible opportunities to explore gene function in non-traditional model species.
Previously, the latter methods have been in the ascendancy mainly due to their numerical efficiency.
To the best of the authorsʼ knowledge, one of the latter methods is novel.
The latter methods rely on using ultra-sonic vibrations of a carrier substrate to desorb nanoparticles from the surface.
Since, in addition, the latter methods are much simpler, we use them as reference for fusion performance.
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