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The proposed rate distortion model exploits the accuracy of both power and exponential models in a wide range of target bit rates.
Since recent studies have found that the lifetimes of waking episodes are distributed as a species-invariant power law and lifetimes of non-REM sleep are distributed exponentially [29], [30], [31], we compared full behavioral arrest lifetimes to both power and exponential distributions.
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Power and exponential regression functions are fit to the data.
Rate distortion models including power model, exponential model, and the proposed combining the power and exponential models are studied.
These time-dependent functional forms are of linear, power and exponential type.
Measured data were fitted with the linear, polynomial, logarithmic, power, and exponential functions.
Continuous covariates were tried as linear, piecewise linear, power, and exponential functions, and categorical covariates were tried as linear functions.
Covariates were included in the model using different functional forms: linear, piecewise linear, power, and exponential functions.
We fit the U.S. EPA recommended models for continuous data, including Hill, linear, second-order polynomial, power, and exponential models.
The power regression best fits the data trend of ADGE microarray with the largest R2 of 0.97 among linear, logarithmic, polynomial, power and exponential.
Like at CFc, the accelerating deformations at these systems can be empirically described by both power-law and exponential growth curves (Fig. 7).
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