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Overall, in our propensity score matched cost analysis we found that nosocomial multi-resistant infections add significantly to the already heavy financial burden of patients in the ICU and their providers in Asia as has been previously reported in Europe and North America [ 22- 24].
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Yeah, that match cost me a lot of energy and power.
Note that our intensity matching cost E I (d) is made up of BT.
MC-CNN uses CNN for matching cost computation, and matching is performed using Semi-Global Matching from SGM.
Image matching breaks down into three processing stages: computation of the pixel matching cost, cost aggregation and disparity calculation.
In order to estimate the dense disparity map, we combine our learning-based multilayer feature matching cost with the pixel-based intensity matching cost and hence our data term has the sum of these costs.
We propose to use a feature matching cost which is defined using the learned hierarchical features of given left and right stereo images and we combine it with the pixel-based intensity matching cost in our energy function.
Minimisation increases the current disparity until local minimum of matching cost function is found (Figure 3a, d1).
Figure 3 The result of applying both steps on the matching cost function for a given pixel.
This simple idea prevents the disparity calculation from stopping in the first local minimum of the matching cost function.
Mathematically, finding the optimal alignment functions and is equivalent to minimizing the matching cost function defined as (25).
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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