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The rotational averaging used in the above method assumes good astigmatism correction.
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This result was already noted by Huisman [ 16] and reflects the fact that these methods assume good properties of the scale and hence, tend to incorrectly enhance its psychometric performance during imputation.
Their method assumed a good (at least 99%%) correspondence between speech and text, required manual intervention to insert text at the beginnings and endings of monologues, did not handle mispronunciations, and propagated an error in one segment to subsequent segments.
Our method assumes that the majority of the runs are good and that poor runs differ significantly in their quality descriptors from the rest.
The method assumes a turnout of approximately 50percenttomorrowow.
The method assumes approximately 50percentturnoutut in November.
First, the method assumes that an extreme point is known.
The method assumes small amplitude oscillations without flow separation.
The first method assumes a steady state.
This method assumes data are normally distributed.
Their method assumes linear relationships between sources.
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