The Crises Faced by Vocational Rehabilitation: Two Radical Solutions

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The Crises Faced by Vocational Rehabilitation: Two Radical Solutions

Non

Higher State Predictions of Coaxial Rotor Helicopters Using XGBoost Gradient Boosted Trees

In this paper, the use of XGBoost™ gradient boosted trees for the prediction of on-disk velocity in a coaxial rotor helicopter is analyzed for higher state data with extremely sparse data sets. In particular, the use of these machine learning algorithms was evaluated for their prediction capabilities when intermediary state data was both reduced and withheld. This analysis showed a distinct tradeoff between model characteristics in order to produce the best performing models, as has been consistent with previous work. Additionally, it was found that these models can perform sufficiently well to predict higher harmonic solutions across the rotor disk when only trained on lower state data. This result indicates that application to finite-state inflow modeling, and in particular, higher harmonic solutions, could help to significantly reduce the associated computational cost of higher harmonic solutions

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios