Sentence examples for corresponding to the features of from inspiring English sources

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The vector (Ma1, Ma2, ⋯) is corresponding to the features of the peptide p a. We use two types of datasets for evaluation of the proposed descriptors.

Similar(59)

The fingerprints corresponding to the acoustic features of each query and utterance frame were obtained from the MFCCs as described in [68].

Each SNP was mapped onto the regions corresponding to the genomic features of the NCBI gene structure, such as introns, UTRs and coding sequences (CDS).

It was observed that the peak of the accuracy was corresponding to the number of features at 25. Hence, the optimal feature subset was obtained with the first 25 features in the mRMR feature list.

We model the problem evaluating the possibility a subgraph is a true complex as a linear regression function, f (x ) = ω T ⋅ x, where f(x) is the linear function of features and ω T is the weight vector of the dimension corresponding to the number of the features.

Each image is associated with six text fields, one corresponding to the bag of features obtained from the SURF descriptor and five surrogate text representations, one for each MPEG-7 descriptor.

Additionally, the watermark is to be embedded in all areas corresponding to the extracted feature points of the normalized image in a similar fashion as in [32].

To produce the minimal p-value (the strongest T-test), the disease-related FENR profile has to display minimal scattering, thus the highest ranking belonged to the functional categories corresponding to the most generic features of chronic disease, equally displayed by all pathologies and absent in the negative random control.

Although all tests include items corresponding to the three core features of ASD (social interaction deficits, communication deficits, and repetitive behaviors), each test includes a different number of items and gives a slightly different weight to each feature.

The element of the measurement noise covariance corresponding to the depth of the feature is set to a very high value.

Thus, each verb had a score of between 0 and 15 for each semantic feature, corresponding to the number of participants who judged the feature to be relevant to the verb's meaning.

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