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Data compression is not involved in those methods such that the impact of measurement noises and calculation errors cannot be mitigated.
We describe an approach to parallelize Eulerian Lagrangian localized adjoint methods such that no errors are introduced compared to the sequential case.
In this approach, the storage and CPU cost are significantly reduced for both IF and ETD methods such that the use of this type of methods becomes possible and attractive for two- or three-dimensional systems.
In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space.
The proposed method is different from previous methods such that it first transforms the ARMA spectrum estimation problem in subbands into an equivalent, multichannel prediction filtering problem, and then completely orthogonalizes the multichannel input signal using SPMLSs.
However, the results of Turk et al. (2011) were obtained using different hydrodynamical schemes for the two methods, such that it is unclear to what extent the discrepancy is related to the treatment of the optically thick cooling rate.
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We present an extension of this method, such that walls with different wettability can be included.
The foundation of SNAD is an efficient unsupervised learning method, such that it can be deployed in real systems.
It may be considered as a brownfield method such that the potential locations are selected from any existing locations.
The paper further extends the method such that the system can detect the moving direction of contact pads.
In the future, we are going to change the proposed method such that it can support all attacks by developing a blind method that uses similar idea.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com