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Tasks usually take a long time to execute, e.g., hours or even days, so it is not convenient to restart from scratch the execution of a task whenever the resource that is executing it fails or leave the grid.
If a resource fails during the execution of a task, this checkpoint is used by another resource to continue executing the task.
The execution of a task set repeats in cycles, while the execution and communication profile of a task set changes in time.
By contrast, the approach presented here proposes that perceptual investigations should begin with a characterisation of the underlying physical laws which govern the specific interaction of a sensor (or actuator) with its environment throughout the execution of a task.
The simulation framework being developed is based on three ingredients: (1) The ability to catch all APEs during an execution, (2) The ability to stop and then resume the execution of a task at any access point, and (3) The ability to determine the (platform-specific) execution time of the portion of code between any two consecutive access points of the same task.
The proposed simulation framework is based on three main ingredients: (1) The ability to catch all APEs during an execution, (2) The ability to stop and then resume the execution of a task at any access point, and (3) The ability to determine the (platform-specific) execution time of the portion of code between any two consecutive access points of the same task.
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There is a lively debate concerning the possibility of coexistence of the two modes of timing for the execution of a single task.
We then hypothesize about the specific role of this drug during the execution of a visuomotor task and within the sensorimotor integration process.
From the neurophysiological viewpoint, which reports brain cell recordings during the execution of a given task, neo-Heringian and neo-Helmholtzian physiologists have also provided arguments in favor of both hypotheses at the level of the brainstem premotor circuitry.
We can also consider the role as a function that one or more robots perform during the execution of a cooperative task as in [16].
Fig. 5 shows a DAG of a sample Genome workflow, which is characterized by one or more lanes, each of which starts with the execution of a fastQSplit task.
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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