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Conditions are established for the convergence of statistics of sequences of parametric translation models to target statistics.
Two numerical examples are presented to illustrate some properties of parametric translation models and demonstrate their use in random vibration.
The tendency of translation models to do their work word by word is one of those, and can lead to serious errors.
The proposed parametric representations are memoryless transformations of sequences of parametric stationary Gaussian processes and fields, and are referred to as parametric translation models.
The methods examined are peaks-over-threshold (POT) method, the average conditional exceedance rate (ACER) method, and the translation process method with various translation models.
However, the exhaustive supervision of all translations and the use of non-incremental translation models penalizes the productivity of conventional interactive predictive systems.
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Hi5 launched a crowd-sourced translation model in spring 2008 (the site had already been professionally translated into 23 languages at that point), and even launched a crowdsourcing translation tool for OpenSocial developers last fall.
PAID was translated into a Chinese version guided by the Brislin's translation model (4, 5) (see Table S1, found in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-0768/DC1).
The training phase could be carried out without the translation model in cases where TF protein activity is regulated by post-translational modification.
In systems where TF activity is actively regulated by post-translational modification, e.g. through phosphorylation by a signalling pathway, then the above translation model would not correctly model changes in the concentration of active TF protein in the nucleus.
Furthermore, it uses the translation model instead of the affine model in (1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com