Sentence examples for Subtle characteristics from inspiring English sources

Now imagine that jelly infused with herbs that intensify the subtle characteristics of the wine.

Being at the same time subjects and objects of knowledge, artists, writers … and psychoanalysts are the people who still describe the subtle characteristics of memory best.

Mr. Meng described some of the subtle characteristics that mavens use to categorize their crickets: the six possible colors on the body and head, the straightness of the "fighting lines" on the head, the size of the legs and jaws.

Researchers predict that it will eventually be common practice for doctors to generate a "genomic profile" of every patient -- a precise analysis of a person's genetic makeup -- so that decisions about therapies can be based on subtle characteristics of the patient's enzyme and receptor biology.

Thomas A. Crist, an anthropologist who examined the badly decomposed remains for the Northampton County coroner's office in 2001, says she was 25 to 40 years old (most likely 30 to 35) and of European descent, though subtle characteristics of her molars also suggested an African background.

The subtle characteristics of the flow within the lattices are seen as considerable local velocity enhancements (LVE) transverse to and in the direction of superficial flow.

Such problems thus require sufficiently sophisticated methods that are able to not only capture subtle characteristic details within a song, but also to compare them with each other.

Alternatively, the DCA-SVM 's feature space contains 'de-noised' true signals with the subtle data characteristics, which avoids the global data characteristics favored decision rule inference because the subtle data characteristics are also invited in SVM hyperplane construction besides the global data characteristics.

The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier.

Interestingly, the meta-data are smoother and have values in a smaller range than the original feature for its subtle data characteristics capturing and de-noising, which reflect the true expression of the peptides/proteins at the m/z ratio better.

On the other hand, the standard SVM's feature space usually contains noises from input proteomic data, and misses the subtle data characteristics, which limit the classifier's performance and lead to a biased, global data characteristics favored decision rule.