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A didactic advantage is the high-level error messages generated by the compiler.
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Algorithm-based fault tolerance (ABFT) was first introduced by Huang and Abraham [1] and was directed toward detection of high-level errors because of internal processing failures.
Other dependent variables can only be divided into two categories (middle-level and high-level) because of sampling errors, thus the ordered probit model is employed.
In conclusion, it is worthwhile examining a fuller range of sample sizes when using exact methods for single-stage phase II trials, so that the smallest acceptable sample size could be chosen after allowing a slightly higher α level (error rate) than the conventional 5 or 10%, and lower power than the nominal 80%.
Because of the complex issues in estimating EA, a high level of error is expected and these errors in some cases may be beyond what we have attempted to quantify.
The ACC subserves a wide range of high-level functions including executive control, error detection, reward, anticipation, and consciousness [5], [15], [16].
Suppose that error minimization has a bell-shaped distribution, and high levels of error minimization are selectively advantageous, but not infinitely so.
This paper presents a formal verification algorithm using the Petri Net theory to detect design errors for high-level synthesis of dataflow algorithms.
These parts of the brain, which are usually used for devising and carrying out plans, high-level thinking and avoiding of errors, can be helpful when tackling complex tasks.
Other variables can only be divided into two categories (middle-level and high-level) because of the sampling errors.
Means-tested benefits are costly to administer and prone to high levels of error.
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