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Defect integrals or local truncation errors are used as local error quantities.
This relation shows how the weighted energies of the error quantities evolve in time.
We give each of these error quantities a tuning parameter, to balance their impact on the resulting filter weights.
In the right-hand panel, the curve is bounded by the results of the regression fits and hence shows the upper limits on the error quantities associated with the random noise variances.
In Fig. 3, we plot the convergence of the error quantities from (58)–(58).
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Errors on quantities calculated from multiple measured parameters (such as kB), were calculated using propagation of errors.
Because of its nonlinearity, S is expected to be more sensitive to experimental errors than quantities that depend linearly on ρth such as magnetization.
In this article, we discuss the computational methods that allow random product selection from a combinatorial library and the statistics involved in estimating errors from quantities obtained from such samples.
Moreover, the errors in quantities of interest are smaller than those from the Strang splitting scheme indicating the accuracy gain when the reaction and transport terms are solved coupled.
Figure 12 Relative errors by quantity for storage building measurements.
Experimental results demonstrate that our solution outperforms the state-of-the-art differential privacy mechanisms in terms of security and mean absolute error for large quantities of queries.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com