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We address the problem of active learning which aims to select the most representative points.
Numerical simulations are conducted on the most representative points selected using a computer experiment design method, which involves orthogonal Latin hypercube sampling and response model building.
In the present paper new closed-form expressions are given for the cancellation speeds of a generic mode, as well as for the most representative points of maximum amplitude.
Specifically, the most representative points called "exemplars" and the preferences in the source domain are considered for helping in the construction of the high-quality clustering model for insufficient target data.
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Then an optimization model is formulated to fulfill the data point reconstruction and select the most representative data points.
Quantification of phosphorylation levels for CP43, D1, D2 and LHCII proteins (Figure 3A) revealed the most representative time points for night and day at 4 am and 1 pm (time point 13 in the Figure 3A), correspondingly.
To select the most representative data points for labeling, two typical active learning methods, Transductive Experimental Design (TED) and Robust Representation and Structured Sparsity (RRSS), have been recently proposed.
It is noteworthy that our comparative analysis with the population of Australian doctors showed that the mode with the lowest response rate (online) was the most representative, confirming the point noted in the introduction, that response rate and response bias are separate issues and should both be explicitly analysed to ensure appropriate interpretation.
A cluster center can be viewed as the data point most representative of the remainder of the data points within the cluster.
Microphotographs of the most representative pulmonary lesions at each time-point are illustrated in Figure 4 as well as the total cell number and modified Ashcroft score in the aerosolized group.
The gradient generated modes are more precise for bigger sizes of PU as it has more points to approximate the most representative gradient in the PU.
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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