The phrase "adapt the importance" is not correct and may not convey a clear meaning in written English.
It could be used in contexts where one is trying to adjust or modify the significance of something, but it lacks clarity.
Example: "We need to adapt the importance of this issue to better align with our current goals."
Alternatives: "adjust the significance" or "modify the relevance."
In their recent work, Martino, Elvira et al. proposed adaptive population importance sampling [22] and gradient adaptive population importance sampling [23] that adapt the mean and the covariance of proposal Gaussian pdfs, and reported significant improvements with respect to MIS that keeps the individual proposal pdfs fixed.
IASPLS can adapt the values of the penalties according to the importance of the variables.
The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem.
By comparing the results of DDFU and DDFU (user defined η), the importance of adapting the coefficients to the scene content can be appreciated.
Another experiment demonstrated the importance of adapting the scenarios for knowledge elicitation [ 33, 34].
Previous studies show the importance of adapting the process to the work practices of the participants involved [ 25, 26].
The results obtained with the tested diet groups highlight the importance of adapting the criteria if one wishes to increase the sensitivity of exogenous testosterone detection.
However this study also demonstrates the variability between GPs, and the importance of adapting the approach of management of lifestyle risk factors to the practice and patient population.
The importance of adapting the building structure or decor was suggested by one participant who explained that colour/picture codes and signposts in multiple languages should be used.
These algorithms already highlighted the importance of adapting the input parameters to different levels of noise but still did not solve the problem of accurately detecting fixations in data sets with relatively higher levels or noise, such as those from infants.
