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Fig. 1 Example sequences (pictures).
Figure 4 shows the frame-wise variation in the feature values for three example sequences.
See Fig. 2 for the response pictures for the example sequences (Fig. 1).
We investigate example sequences produced naturally by human teachers and find that humans often do not spontaneously generate optimal teaching sequences for arbitrary machine learners.
Figure 5 Example sequences from Weizmann dataset (jack, walk, wave1, skip, side, bend, p-jump, wave2, run and jump).
The HOGHOFMBH models achieve good localization performance despite being trained on a small number of example sequences.
Example sequences include (ATT 4, (TAT 4 and (ATTT 3 which recognize (7,5), (6,5) and (8,4) SWNTs, respectively.
This figure displays snapshots of the tracking process for four different time points (from left to right) in three different example sequences.
In general, the two parts of each clip were causally linked or not (Fig. 1 c or d), and the ball release or contact (kick) moment1 (Fig. 1 b) was visible or not. Figure 1 depicts example sequences.
For example sequences ATGTGGGGTAT and ATGTGGGTAT are grouped together at 0 differences.
In the Wirachsilp et al. [3] study, for example, sequences from Bangkok clustered together with sequences from other regions.
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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