Sentence examples for method accurately recovers from inspiring English sources

Considering examples of both real (Enron email) and artificial (Ravasz-Barabasi) networks perturbed by noise using Monte Carlo simulations, our method accurately recovers the percentages that information will be transferred to the intended receivers.

Experiments on our collected real world images show that our method can accurately recover human body from static images with a variety of individuals, poses, backgrounds and clothing.

This demonstrates the ability of the slice based PaLS method to accurately recover 3D tubular structures even with linear approximations and limited data sets.

We find that our method can accurately recover the relevant features and reconstruct the underlying interaction kernels if a critical number of samples is available.

We demonstrate on realistically simulated data of models and real macromolecular structures that for highly distorted subtomograms, even given a small number of evenly sampled initial angles with a large interval of 60° or 45°, our method can accurately recover true transformation with very high precision.

Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our combined methods can accurately recover true transformations with a substantially higher precision than the scanning based alignment methods.

This algorithm accurately recovers the amplitude of all frequencies that contributed to the data.

All methods were able to accurately recover true SUV within 10% for spheres equal to and larger than 1 ml.

All these methods were able to accurately recover true activity within 20% for spheres equal or larger than 1 ml (Fig.  3).

All PVC methods were able to accurately recover true SUV within 20% for spheres equal to or larger than 1 ml.

Our algorithm can accurately recover the allelic dropout parameters, and an imputation strategy using the method provides an alternative to ignoring high empirical missing data rates or excluding samples and loci with large amounts of missing data.