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Therefore, this work develops a Monte Carlo (MC) method to robustly and flexibly incorporate non-ideal conditions including arbitrary temporal, spatial, and spectral beam profiles in multi-dimension practical problems.
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Researchers have developed methods to robustly estimate heart rate (HR) by fusing information from multiple signals[8].
We present a general method to account robustly for instantaneous chemical equilibriums in reactive transport.
Our method aims to robustly establish correspondences across different subjects and simultaneously generate high-quality surface meshes without removing shape details.
A constructive method to design a robustly stabilizing controller is proposed.
In addition, we employed rank-based methods to more robustly compare expression divergence of different tissues and the dendrites.
Next, the Zomet et al. method is used to robustly detect outliers in the data.
The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate.
We have demonstrated that the proposed method is able to robustly capture the structure of the spatially variant blur of smooth PSF fields, in spite of the simplifying assumption that the local PSFs do not overlap.
Combined together with a recently developed dynamic load balancing algorithm that scales to massively parallel computing architectures, the proposed method is able to robustly compute uncertainty for highly realistic random subsurface formations that can contain a very high number (millions) of sources of uncertainty.
This is not merely a reflection of global changes in DNA methylation, as the ATRA and EB differentiation time courses induce opposite global changes; instead, this observation indicates that our method is able to robustly identify sets of genomic regions from the data that exhibit inverse trends among naive pluripotent and differentiating mouse ESCs.
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