Sentence examples for tuned patterns from inspiring English sources

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Bats rely on finely tuned patterns of airflow to keep themselves aloft, including vortices at the leading edges of the wings that enhance lift.

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Though the current version of the algorithm is tuned to patterns observed in nursing notes and discharge summaries in the MIMIC II database, the approach is general and may be customized to work on other free-text medical records.

For position tuning (Figure 3, bottom four rows), the more expressive quadratic model captures the tuning patterns better than the linear model.

The ROI analyses above demonstrate that at least 3 distinct tuning patterns exist in the data.

Similarly, the different tuning patterns cannot be explained by data smoothing or averaging, as none of the patterns is expressible as a weighted average of the other 2. As predicted, tuning curves to visual objects showed sensitivity to stimulus form throughout occipital and occipitotemporal cortical areas.

Fig. 5 a, b The absorption spectra when the periodicities of the PME patterns are tuned at normal incidence for Device A (a) and Device B (b).

Various controllers tuning methodologies have been proposed in the literature such as auto-tuning, self-tuning, pattern recognition, artificial intelligence, and optimization methods.

Tuning sampling patterns to be robust to scan-to-scan variation and to anatomic variation will improve clinical reliability.

Together, these mechanisms could be exploited in the neural tube to modify the gene expression domains at different anterior-posterior positions, thereby providing a means to generate and fine-tune different patterns of gene expression without changing the morphogen gradient itself.

TextRunner is extraction pattern-based, but rather than employing patterns tuned to a few selected relations, it uses a range of patterns obtained automatically from a syntactically parsed training corpus, weighted via Bayesian machine learning methods to extract miscellaneous relations sentence-by-sentence from text.

For velocity tuning (Figure 3, top four rows), the quadratic model captures the low-center, high-surround tuning pattern seen in many neurons, while the linear model cannot capture this pattern because it is restricted to fitting a plane in the (rate, x, y) space.

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