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The problem is to detect these patterns reliably.
The problem is to detect these scatterers amidst their decorrelated neighbors, as spatial coherence estimation is not possible anymore.
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One of the many solutions for this problem is to detect speech-dominant regions by using a voice activity detector (VAD).
The remaining problem is to detect the moving targets.
The essence of the DAG inference problem is to detect communities of genes that span a wide range of sizes/scales and can nest hierarchically as well as share arbitrary subsets of members.
The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome).
In those business models, the most important security problem is to detect and prevent (artificially created) fraudulent events, which have no intention of generating value for advertising [3].
Thus the corresponding data mining problem is to detect a small number of cohesive, possibly overlapping clusters in the data while ignoring irrelevant data portions.
The first sub-problem is to "detect" one or more target objects at each frame.
One of the most important clinical problems is to detect early disease as well as to detect early recurrence after surgery with a curative intent.
One way around the problem is to wear an alarm bracelet that detects when something is wrong and calls an ambulance.
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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