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For both tasks we show possibility and impossibility results that fully characterize the graphs on which these problems can be solved.
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In conclusion, with both bisection and mental rotation tasks, we show that amusics' deficit in pitch processing does not co-occur with a deficit in spatial processing.
Furthermore, by utilizing a psychometric task, we show that DNRAb + patients exhibit a selective impairment in recalling spatial relations.
However, after the exam preparation task, we showed them their activities on the heatmap.
For both tasks, we expected participants to show a smaller boundary separation in the speed than in the accuracy condition (i.e., a speed accuracy trade-off).
When parental thresholds differ for both tasks, colonies show higher mean specialization.
Results in both tasks show the effectiveness of the DNN-based mapping.
We show experimental results for both tasks, but restrict the discussion to computation of the likelihood to simplify the presentation.
The distributions of the eye position selectivity indices (SIs) for both tasks are shown in Figure 6A,B.
The distributions of the retinal position selectivity indices (SIs) for both tasks are shown in Figure 4A,B.
We show how these two prediction tasks can be trained simultaneously, where the learned hidden variables are useful for both tasks.
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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