Although machine learning algorithms have been successful when applied to several tasks, the selection of the most suitable for a given dataset is not straightforward. The recommendation of machine learning algorithms can be automated through the use of meta-learning, but this requires efficient methods for the characterizations of datasets, i.e. meta-features extraction. In this work we propose to accelerate the extraction of clustering-based meta-features on GPUs, taking advantage of the optimized libraries and API from the RAPIDS framework. We parallelized a well-known meta-feature extraction tool (MFE) via RAPIDS to accelerate the clustering meta-features extraction process. Our experiment shows that significantly less time is required to complete the extraction, up to 10x faster than the MFE implementation. These results are promising and suggest greater feasibility for large-scale experiments involving meta-learning.
Presumably an elaboration of his PhD dissertation, Pogorzelski's Virgil and Joyce: nationalism and imperialism in the Aeneid and Ulysses fills an important gap in Joyce Criticism. Robert Schork, the undisputed authority on Latin and Roman Culture in Joyce (UP of Florida, 1997), affirmed that "Latin was Joyce's first second language"(2) and verified that Virgil's work was part of his library in Trieste. With his initial help, Pogorzelski starts by tracing direct references to Virgil in Ulysses, goes on to read Virgil through Joycean lenses, and brings in "Nationalism" as tertium comparationis. He contends that Joyce uses Virgil to construct "a cultural history of Ireland through the European classical tradition" (11), and, in turn, Pogorzelski himself uses Joyce to discover a new Virgil, the Virgil of Nationalism.
Many engineering applications involve turbulent flows around bluff bodies. Because of their intrinsically unsteady dynamics, bluff body characteristic flows feature unique turbulence related phenomena, which makes their numerical modeling challenging. Accordingly, accounting for a circular bluff body flow configuration, three different turbulence modeling approaches are investigated in this work, (i) Reynolds-averaged Navier-Stokes (RANS), (ii) large eddy simulation (LES), and (iii) hybrid RANS/LES. Regarding the hybrid approaches, two variants of the detached eddy simulation (DES) one, delayed DES (DDES) and improved delayed DES (IDDES), are studied. As RANS model, the -ω is utilized here. This RANS model is also used as the background one for both DDES and IDDES. Wall-adaptive local eddy viscosity (WALE) is used in turn as the sub-grid scale (SGS) model for LES. The velocity two-point correlation function is used to assess the mesh size requirements. When compared to experimental data, the obtained numerical results indicate that RANS overestimates the recirculating bubble length by over 18% and is not capable of describing the turbulent kinetic energy and the flow anisotropy in agreement with the experimental data. In contrast, LES, DDES, and IDDES are all within 1% of the recirculating bubble length while predicting both the Reynolds stress tensor components and the corresponding flow anisotropy in agreement with the measurements. Besides, normalized anisotropy tensor invariants maxima in the shear layer were reproduced by all scale resolving models studied here, but they failed to yield the local extrema measured within the wake recirculation region. A comparative analysis of the anisotropic Reynolds stress tensor invariances underscores the adequacy of the scale resolving models.
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