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By allowing the importance of drivers of diversity to vary with grain in a single model, our approach unifies disparate results from previous studies regarding environmental versus biogeographic predictors of biodiversity, and enables efficient integration of heterogeneous data.
In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable submodels by encouraging them to agree on the hidden variables.
Since design and technology parameters are input parameters of the resulting model, our approach is in particular suited for design studies.
Compared to a previous estimate of travel times in the aquifer based on a 2D hydrogeological model, our approach provides a more accurate assessment of the dynamics of nitrate concentrations in the aquifer.
Unlike previous research having mainly focused on the multiresolution representation of the final design model, our approach can carry out simultaneous and incremental multiresolution representation on the intermediate design models.
Based on this model, our approach can derive in a fully automated way complete 3D reconstructions of sensed grape clusters even for cases of partial occlusions in the process of sensor data acquisition.
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Unlike other multi-layer models, our approach explores more reasonable granularity for part detection and sophisticatedly designs part connections to model body configurations more effectively.
Moreover, it is shown that conversely to the classical models, our approach yields critical loads that depend only on rigorous well-founded mechanical hypotheses.
Starting from UML diagrams of independent web services and respective UML context models, our approach can produce a functional composite context-aware application.
To extend CHI for personalized models, our approach is built on the dictionary learning framework [35].
Like a European system, we pool our resources to share the burden of catastrophic expenses, but unlike European models, our approach doesn't cover everyone.
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CEO of Professional Science Editing for Scientists @ prosciediting.com