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We extensively evaluate our approach using several challenging benchmarks, and provide comparative results to related state-of-the-art techniques.
PERSPECTIVE: Systematically collecting and reporting the recommended information needed to evaluate the clinical importance of treatment outcomes of chronic pain clinical trials will allow additional validation of proposed benchmarks and provide more meaningful comparisons of chronic pain treatments.
For the sake of brevity, we focus discussion here on the results of two representative examples of datasets extracted from the MHCPEP and MHCBN benchmarks and provide the complete set of results in the supplementary materials (Data S1).
The ability to detect smaller, yet clinically significant differences between intermediate skill levels is required to establish benchmarks and provide objective feedback to the training population of residents.
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In addition, we generated 34 new CPPEC instances basing on the CPP benchmarks, and provided computational results on the two sets of 34 new CPPEC instances, and the container radii obtained are close to the published results on CPP.
Furthermore, ULIP eliminates the ≈20% outage suffered in the benchmarks, and provides throughput gains for over 80% of the MSs in the network.
The large cities, in turn, have often interest to seek also for benchmarking opportunities and provide the citizens with opportunities to choose among several service providers.
This paper focuses on the resulting software design pattern for distributed control system benchmarking and provides examples of its use in our hybrid physical/simulation environment.
This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens.
Several research groups has made contributions to test the benchmark and provided their reference solutions (Ben Gharbia and Jaffré 2014; Bourgeat et al. 2009; Marchand and Knabner 2014; Neumann et al. 2013).
What is the evidence that measuring quality, benchmarking, and providing feedback or incentives improve diabetes care?
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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