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For a batch of query points, the main idea is to use previous queries to improve the current one.
Example trajectories are generalized by applying Gaussian process regression, using the parameters describing a task as query points into the trajectory database.
Wong et al. [10] introduce transformations of database points and query points that enable a ranking of the database points with respect to their nearness to the query points suitable for k-nearest neighbor determination.
In other words, k-skip returns more vertices residing on the shortest path of two query points when k decreases.
Distance comparisons between an encrypted query and data points are achieved using ASPE, with query points and data points being encrypted differently.
The result of the comparison is memory-efficient cache of the mean shift values for all query points speeding up the mean shift calculation.
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Point query finds FOVs that contain the user-specified query point q Fig. 6 Range query.
1. Point queries are defined as finding FOVs that contain the user-specified query point q.
An interval stabbing query determines which of the intervals in a set overlap a query point.
The properties of the query point are determined by linear regression of the stored information.
Given two data points and a query point, the cloud can determine which data point is closer to the query point.
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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