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Using the EM model, we perform runs 3 and 4 in Table 1.
Using the ES model, we perform run 1 and run 2 in Table 1.
When testing the trained model, we perform inference after providing the observed binary feature vector for one of the gesture.
Beyond developing the model, we perform an empirical investigation with the access logs of two distinct CIS.
With this model, we perform asymptotic analysis to calculate the optimal deterministic delay that can maximize the system throughput.
To assess whether the data correspond rather to a fixed or a random effects model, we perform Hausman (1978) tests.
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To test whether MRI data improve discrimination performance of this model, we performed Cox regression analyses with inclusion of each of the MRI variables and their combination.
Finally, from this ML model, we performed the discriminative training and feature transformations.
To assess the vertical resolution of the model, we performed spike tests (Fig. 6a d).
To validate our detection model, we performed experiments with three types of attacks.
To check the reliability and validity of the measurement model, we performed confirmatory factor analysis (CFA) using AMOS.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com