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GRAPHSPLINENETS differ from previ- ous neural collocation methods in two ways: first, we decrease the computational overhead by not needing to learn interpolators

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spectra of the representation matrix S¯ with the results of the Arnoldi method with double- precision and the uniform initial vector e as start vec- tor for the

As such, our article contributes to both the measurement and sentencing literature in three important ways: i) we show the importance of testing the underlying as

In addition, the rules include shares of common stock issuable pursuant to the exercise of stock options and warrants that are either immediately exercisable or e

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