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Prediction uncertainty is estimated using stacks of uncertain parameters and is explicitly minimized to produce robust and reliable solutions.
The technique is based on a novel extension of classical optimal linear estimation theory in which, in addition to error variance, Type-II conditional bias (CB) is explicitly minimized.
The primary performance index is the end-to-end Packet Delay(PD), which is modeled in this paper and is attempted to be explicitly minimized by the UTILITY, PRMPL, and DVRPLC protocols as presented in Sections 5.3, 5.4, and 5.5.
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The first formulation uses the least-squares method to explicitly minimize the displacement of the zero level set within the reinitialization.
We exploit a penalty function approach to ensure that the electrical conditions for the slow-wave lines are accordingly satisfied, apart from explicitly minimizing the area of the coupler.
We deal with the multi-objective nature of the design in WSN by explicitly minimizing message delivery latency and by imposing topology constraints that help to reduce energy consumption.
Therefore, we define the registration problem to be template free and, instead, explicitly minimize the non-rigid deformation applied to all sections.
Looking at the algorithms that compute a certain number of solutions and return the one that explicitly minimize the MEC score (MLF and SHR-three), we can observe that their performances are not among the best ones.
Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation.
Another potentially useful approach to research involving AI/AN is community-based participatory research (CBPR), as it is designed to explicitly acknowledgeand minimize the potential mistrust of outside researchers, a common problem among AI/AN communities.
We do not want to claim that this observation is necessarily true, in fact (even if not explicitly) DGS minimize the MEC score and attains good results.
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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