Chang et al. (2010) reported several rock mechanical property datasets from this study area, consisting of three different sedimentary sequences: faulted trench deposits, highly deformed accretionary complexes, and weakly deformed fore-arc basin sediments.
One can assume that the non-unions would have had very poor mechanical properties; thus, our dataset positively skews the mechanical testing results from the rofecoxib treatment group, since non-unions were not tested.
Finally, we evaluate our algorithms on two sample datasets using Amazon Mechanical Turk, and find that our hybrid approach remains very effective even when using real-world data.
In this talk I present the Dexterity-Network (Dex-Net), a framework for generating datasets by analyzing mechanical models of contact forces and torques under stochastic perturbations across thousands of 3D object CAD models.
Consequently, Mathur et al. [ 2] generated one of the first datasets concerning the mechanical properties of intact muscle fibres of the skeletal muscle and myocardium, in comparison with endothelial cells, by using liquid-based atomic force microscopy.
Towards this aim, we assembled a large dataset comprising the mechanical properties of AM porous biomaterials with different topological designs (i.e. different unit cell types and relative densities) and material types.
For their study, they produced a new dataset using Amazon Mechanical Turk (AMT 10 in combination with TripAdvisor.
Third, data for other outcomes indicative of severity of illness were either missing in some patients (e.g., days since onset, length of hospital stay) or not available at all in the current dataset (e.g., mechanical ventilation, oxygen requirements).
The authors tested their models using the Amazon Mechanical Turk (AMT) synthetic fake reviews dataset on a real-world fake reviews dataset procured from Yelp.
Our results, on 3 real-world datasets collected with Amazons Mechanical Turk, and on 15 UCI datasets, show that our methods on average ask 1 2 orders of magnitude fewer questions than the base- line, and 4.5 44× fewer than existing active learning algorithms.
