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Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations
The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e. g., linking $P_{n}^{(\alpha, \beta)}\!\left(x\right)$ with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems.
Cost-Sensitive Learning for Predictive Maintenance
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score.
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