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Single-path programming can be inefficient when the control flow is data-dependent.
It is well known that weighting in general tends to be inefficient when the weights are highly variable.
Sampling at the Nyquist rate is shown to be inefficient when the signals of interest contain only a small number of significant frequencies relative to the bandlimit [4].
Parameter estimates thus obtained are consistent irrespective of the underlying true correlation structure, but may be inefficient when the correlation structure is misspecified [ 2].
The classical approach to imputing data is imputation by mean score, which is a technique known to be inefficient when the rate of missing data is too high, as shown in our study [ 64].
In contrast to the way response strategies can be efficient as described above, response strategies can also be inefficient when the relationship between the start and target position changes and a novel path must be derived (Hartley et al., 2003; Driscoll et al., 2005).
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These methods are inefficient when the frequency of recombinants in the pool of viruses is low.
The above algorithm is inefficient when the MS is static, because the Doppler frequency shift cannot be obtained.
Typical tree algorithms provide pointers for the creation of both a node and a reference [15], which is inefficient when the CUDA technology is used.
However, the computation of SAD is inefficient when the size of image is large because the computational time is relevant to the image size.
They require channel-related information from all the candidate relay nodes, which is inefficient when the number of candidate relays is large or the time for relay selection is limited [10].
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