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In other words, the results revealed an asymmetric learning transfer between tasks, which suggests that spatial representations are hierarchically organized.
In addition, data transfer between tasks is not directly considered in most existing studies, thus being assumed as part of task execution.
This is depicted as a fundamental challenge by [127], which states that "in most studies, data transfer between tasks is not directly considered, data uploading and downloading are assumed as part of task execution".
Accordingly, if request M n is accepted, then at least one light-path l is assigned to allow data transfer between tasks t and t ′ assigned respectively to MEC-DC locations u and u ′. sumlimits_{n in N}sumlimits_{t in T_{n}} x_{u}^{t} leq M times y_{u}, ,,, u in H_{m}.
Thus, structural learning could provide a mechanism for 'learning to learn' and transfer between tasks with the same task structure.
Previous work demonstrated asymmetric transfer between tasks: training on a feature-difference task improves signal-in-noise task performance, but not vice versa [ 1, 7, 10, 13, 14].
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Dependencies between tasks imply that there can be heavy communication induced by data transfers between tasks executed on separate nodes.
Further, the macroblock size in pixels (MBPixelSize) and image size (Xres and Yres) are used to determine the data amounts transferred between tasks.
In such workflows, when scheduling tasks to distributed resources, the data transfers between tasks are not a negligible factor and may comprise a significant portion of the total execution time and cost.
On the other hand, if training improves subjects' sensitivity to particular stimulus configurations regardless of the current task demands, improvements with vertical or horizontal oriented stimuli should transfer between different tasks (dashed red line).
An example on how multi-task learning transfers knowledge between tasks is depicted in Figure 2. Figure 2 Knowledge transfer in multi-task learning.
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