Suchergebnisse

Money has always inspired obsession, both in those who amass it and in those who think about it. "Man will never be able to know what money is any more than he will be able to know what God is," wrote the French financier Marcel Labordère to his friend John Maynard Keynes. The analogy is apt. Money, like God, injects infinity into human desires. To love it is to embark on a journey without end.

This article focuses on the role of training managers (TMs) in UK participation
in WorldSkills Competitions (WSC). The TM role is outlined, according
to the perceptions of the TMs, and there is analysis of the benefits to them of participation,
as well as the barriers they face, and the benefits and barriers available
to participating further education colleges and employers. The article is based on
analysis of semi-structured interviews with almost the full cohort of UK TMs preparing
competitors for WorldSkills Brazil 2015, and concludes with reflections on
the vision and purposes of UK WorldSkills participation.

The legendary King Midas was the first Near Eastern ruler to cherish the belief that untold mineral wealth would enable him to realize all his dreams and ambitions. But just because all he touched turned into gold, he soon discovered he could no longer eat. The moral of this legend may be coming home to various regimes in oil-rich countries, which are encountering problems of agricultural decline, overrapid rural-urban migration, inflation, increased income distribution gaps, and so forth. Nowhere have the expected achievements to result from oil income been more touted than in Iran, where the "White Revolution" (later the "Shah-People Revolution") and the "Great Civilization" proclaimed from the throne since the early 1960s were not only to provide social and economic well-being for all, but also to put Iran among the world's top industrial powers before the end of the century (whether it was seriously going to be among the five top world powers varied from statement to statement, but this was said at one time).

AbstractPrincipled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repurpose denoising autoencoders for multiple imputation by treating missing values as an additional portion of corrupted data and drawing imputations from a model trained to minimize the reconstruction error on the originally observed portion. Systematic tests on simulated as well as real social science data, together with an applied example involving a large-scale electoral survey, illustrate MIDAS's accuracy and efficiency across a range of settings. We provide open-source software for implementing MIDAS.