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Exact(2)
We create a similarity metric s i m q, P u ) to characterize the proximity between a query q and a user profile P u.
MPD is the minimal possible distance between a query q and ring structure C i,j.
Similar(58)
Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in.
Given two sequences of feature vectors corresponding to a query Q and an utterance U, the logarithm of the cosine distance is computed between each pair of vectors (Q[i], U[j]) to build a cost matrix as follows: d(Q[!i],U[!j])=-log frac{Q[!i]cdot U[!j] }{ |Q[!i]| cdot |U[!j]| }. (6).
Given a query (Q in mathcal{Q}) and a big graph G, the problem of querying big graphs is to compute the answers Q(G) to Q in G.
In the query-handling step, the goal is to find proteins in the database similar to a query q.
Given a query q, the retrieval method ranks the samples in an increasing order of their dissimilarities from q.
Given a query q and a similarity cutoff ϵ, instead of aligning q with all the database entries, we align it with the reference networks.
Given a query q, scores of all matching segments are computed using score s q,.) and aggregated according to the matching objects.
The MeSH document is indexed as a TF.IDF vector and the text to classify is used as a query Q.
Given a query Q of n genes, we randomly partition the set of genes into five folds.
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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