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Our context detection method is validated by weekly patterns of duration for different contexts obtained for sample users and at the aggregate level, as depicted in Figure 7.
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We use a multiple regression model guided by longitudinal weekly patterns of influenza virus to attribute the weekly ILI consultation incidence pattern to each influenza (sub type, while controlling for the effect of respiratory syncytial virus (RSV) epidemics.
Figure 7 Weekly patterns of duration for the different contexts.
Finally, the similarity and diversity of weekly patterns of service usages enable us to classify users into several different clusters, e.g. as characterized by the morning-type or evening-type usage patterns, except for the web and email usages.
Weekly patterns of duration aggregated over all users show the overall behavior.
Weekly patterns of app, call, and SMS usages are clustered into more than one dominant cluster.
It implies that connected users sharing more common neighbors show more similar weekly patterns of service usages.
Weekly patterns of user 81 are comparable to the temporal boundaries in terms of detected contexts, as depicted in the second row in Figure 4.
The clustering results are summarized in Table 1 and only a few weekly patterns of dominant clusters are shown in Figure 12.
However, as the characteristic daily and weekly patterns of Figure 2(b) indicate, the overall level of call activity in the network displays large variations by hour and day, and this is expected to have an effect on the aggregated networks, at least on shorter time scales.
I'm struck by how difficult the weekly pattern of life can be for students.
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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