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This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll-up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube.
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In this paper we present three of these patterns —the Epistemic Control Loop, the MetaControl and the Deep Model Reflection patterns— that constitute the core of a pattern language for a new class of adaptive and robust control architectures for autonomous robots.
Our approach allows the application of machine learning techniques, leveraging a modeling of design patterns that is able to represent pattern instances composed of a variable number of classes.
Doesn't the complexity of POPCO models obscure the patterns that are in fact responsible for the simulation results?
We utilize a simple three-variable reaction diffusion model to study patterns that emerge beyond the onset of the (short- wave inshort- wave
Therefore, we propose a semi-formal way to describe model transformation design patterns that is independent from a specific transformation language and described in a practical way that is directly implementable by model engineers.
In this paper, we present a novel application of Markov chains to model sequential usage patterns that can be found in the change-logs of collaborative ontology-engineering projects.
However, even well designed null model tests yield patterns that could be explained by multiple processes this has been a central criticism of the community assembly null model approach [50].
This model incorporates activity patterns that may influence personal exposure to ambient pollutants.
In addition, over-fitting models may capture patterns that are specific to the data analyzed, and may thus lead to conclusions that do not reflect the population from which the data were sampled.
The results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days.
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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