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For instance, we may want to maximize visual "coverage" of a map displayed in a web application at "critical usage times" with "good" data values while working within limited bandwidth.
Contrarily to the inferencing at query time in most of existing works, our approach is designed to speed up ontology querying, which is supposed to represent the most frequent and therefore critical usage of ontologies.
So, the idea is, that creators on Openbook would be able to benefit from the social network effect via direct financial payments derived from the platform (instead of merely ad-based payments, such as are available to YouTube creators) — albeit, that's assuming reaching the necessary critical usage mass.
Due to the limitations described by the CAP theorem, it is preferable if atomic cross-shard operations – especially involving writes – in a distributed system are exceptions, permitting waiting times that exceeds the time of temporary network partitions, rather than if they are common in normal, time critical usage.
Note that even "stacking" square knots in this way won't make your knot safe for critical usage.
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This problem is particularly critical in usage scenarios involving mass media devices like Television (TV), specially taking in consideration its interactive format with all advanced features it brings.
Alas, our efforts began to stall after several months post-launch, and we were never able to scale beyond a small early adopter community and into critical, mainstream usage.
The majority of available potential scores were developed as mortality indicators and perform less well for predicting critical care usage.
And herein lies the rub: users appreciate well-written software, but ease of use and on-device navigability are critical preconditions for usage.
Consumer drones must first reach a critical mass of usage to let the tech spread beyond early adopters.
Experiments have proven that this SI estimation is acceptable for the purpose at hand since the absence of the original WZ frame (as it happens at the decoder) is more critical than the usage of a rough estimate of the real SI at the encoder, this from the noise modeling accuracy point of view.
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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