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To further speed up the collection process, we can safely use path compression as we traverse the trees, since we no longer care about the exact topology of the subtrees.
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We introduce a similarity measure with which we can make decisions on how to traverse the tree and even backtrack through the search path to find more candidate matches.
Then for each phrase, we traverse the tree starting at the root.
To estimate Pr(d|ac), we traverse the tree in the following order: O → a → c → d and get the result Pr(d|ac) = 4/7 = 0.57.
For a given PST (bar {T}) with maximum order D and the channel observations for the previous k (0≤k≤D) consecutive slots, we traverse the tree starting from the node e which denotes the empty string.
For each new element to be inserted, it is necessary to traverse the tree from the root to the leaves in order to find the leaf node with a coverage radius that encompass this element.
To compute the C value in an arbitrary region (Figure 4), we can traverse the tree and build C incrementally from the cached values of all nodes that are totally included in the query region.
Computer users can traverse the tree of life in its entirety, from its base at the domain or kingdom level to its most recent leaves at the species level.
During data entry, the user can traverse the tree to select concepts for description.
Given a second-order hierarchical clustering tree, we traverse the tree bottom up to retrieve connected network components.
Let diam(T) be the node-diameter of T, i.e. the number of steps from edge to edge required to traverse the tree.
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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