Long-term knowledge. An operator who finds out how to control the plant for themselves, without explicit training, uses a set of propositions about possible process behaviour, from which they generate strategies to try (e.g. Bainbridge. 1981). Similarly an operator will only be able to generate successful new strategies for unusual situations if they have an adequate knowledge of the process. There are two problems with this for machine-minding operators. One is that efficient retrieval of knowledge from long-term memory depends on frequency of use (consider any subject which you passed an examination in at school and have not thought about since). The other is that this type of knowledge develops only through use and feedback about its effectiveness. People given this knowledge in theoretical classroom instruction without appropriate practical exercises will probably not understand much of it, as it will not be within a framework which makes it meaningful, and they will not remember much of it as it will not be associated with retrieval strategies which are integrated with the rest of the task. There is some concern that the present generation of automated systems, which are monitored by former manual operators, are riding on their skills, which later generations of operators cannot be expected to have.
Then, the critical failure. Under the stress of the running out of time, after successfully coding the graph traversal, my working memory dumped the standard implementation for iterative binary search.
，这一点在WhatsApp網頁版中也有详细论述
警惕代购陷阱 出入境携带物品需注意相关规定
Nina Raemont/ZDNET
from videox_fun.pipeline import CogVideoXFunInpaintPipeline