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In this article, three useful features such as harmonic structure information, HOS and MFCCs are combined together to represent the speech signals, since harmonic structure information is robust to high-pitched sounds, HOS is robust to the Gaussian and Gaussian-like noise, and MFCCs are the important features in phoneme recognizer.
A signed weighted correlation network is attractive since it preserves sign information, is robust with respect to the soft threshold (power) parameter, and preserves the continuous nature of correlations (i.e. the result is a fully connected network in which all nodes are neighbors with one another) [ 22].
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Qualitative state representations provide soft, relative map information which is robust to metrical errors.
In both preclinical screening and mechanistic exploration, metabolic profiling can offer rapid, noninvasive toxicological information that is robust and reproducible, with little or no added technical resources to existing studies in drug metabolism and toxicity.
OPN measurements in plasma could therefore provide valuable prognostic information, which is robust, accurate and easily determined with a relatively low cost compared to other methods, which require adequate tumor tissue.
Even if it were possible to develop a tool that would capture every nuance of capacity, in every situation, there needs to be a balance between what is feasible in terms of resource investment, and gathering information that is robust and useful.
This result indicates that our method can accurately control FPs with and without using additional information, and the method is robust to irrelevant data added to the model.
However, only the vertical velocity information is not robust enough to detect the fall.
However, in [116], the authors demonstrate that the localization integrating audio and video information is more robust compared to the localization based on stand alone microphone arrays.
However, somatosensory information is more robust, even though it is not as precise as vision.
Our model therefore recovers the de novo formation of positional information from such complex GRNs and suggests that the appearance of this information is very robust.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com