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In the setting of point measures we introduce a damped gradient that was lacking in the multidimensional case.

Second, we examined how the measures we introduce are related with the three indicators for stock market activity (price volatility, transaction volume, and the number of transactions).

The measures we introduce attempt to completely characterize the trajectory of a fly in the experimental environment, and can thus serve as the basis for a complete behavioral model of a walking fly.

The measures we introduce are based on spatial and temporal probability distributions and utilize the entire time-series trajectory of the fly, thus emphasizing the dynamic nature of locomotor behavior.

Though the C-scores derived within CADD quantify extent of pathogeneity, the derivation of these scores is distinct from the probability measures we introduce in this article.

It is important to note that when mapping a cluster to a GO term, each of the overlap measures we introduce below considers the total number of proteins annotated with that term (i.e. if that term annotates many proteins that are not part of the cluster, then the score for mapping that cluster to the term will be lower).

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Second, the topicality measure we introduce is based on the simple idea that a news article that receives more attentions from news agencies and market participants should have a larger impact on stock prices and transaction volumes.

Classification error is 1.0 minus classification accuracy, the proportion of correct decisions, a measure we introduced in Section 8.3 (page 8.3 ).

As an additional speedup measure we introduced the partition bypass method.

The semantic similarity measure we introduced was more efficient at validating text mining results from machine learning classifiers than other measures.

The DoND measure we introduced plays a key role in formulating this biological question mathematically.

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