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Turing was an exceptional mathematician with a peculiar and fascinating personality and yet he remains largely unknown. In fact, he might be considered the father of the von Neumann architecture computer and the pioneer of Artificial Intelligence. And all thanks to his machines; both those that Church called 'Turing machines' and the a-, c-, o-, unorganized- and p-machines, which gave rise to evolutionary computations and genetic programming as well as connectionism and learning. This paper looks at all of these and at why he is such an often overlooked and misunderstood figure., Author(s): Aurea Anguera [sup.2], Juan A. Lara [sup.1], David Lizcano [sup.1], María-Aurora Martínez [sup.1], Juan Pazos [sup.1], F. David de la Peña [sup.1] Author Affiliations: (1) grid.460076.3, 0000 0004 0501 [...]

Because of the complex interdependence of physics and mathematics their relation is not free of tensions. The paper looks at how the tension has been perceived and articulated by some physicists, mathematicians and mathematical physicists. Some sources of the tension are identified and it is claimed that the tension is both natural and fruitful for both physics and mathematics. An attempt is made to explain why mathematical precision is typically not welcome in physics., Author(s): Miklós Rédei [sup.1] Author Affiliations: (1) grid.13063.37, 0000 0001 0789 5319, Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, , Houghton Street, WC2A [...]

The power of the digital is changing how we understand the world and our place in it. In the autumn of 1948, Warren McCulloch, neurophysiologist, bohemian cold warrior, and a [...]

In this analysis of the risk and return of stocks in global and Chinese markets, we build a reasonably large number of models for stock selection and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques in producing stock-selection models and Markowitz-based optimization techniques in portfolio construction in a global stock universe and two Chinese stock universes. We report the results of applying a data mining corrections test to the global and Chinese stock universes. We find that (1) robust regression applications are appropriate for modeling stock returns in global and Chinese stock markets; (2) mean-variance techniques continue to produce portfolios capable of generating returns that exceed transactions costs; and (3) our global portfolio selection models pass data mining tests, such that the models produce statistically significant asset selection for global and MSCI-China universes, but not for China A-shares. History: This paper has been accepted for the special issue on Applications of Analytics and Operations Research in Big Data Analysis. Keywords: global portfolio selection * investment * optimization, In this study, we employ fundamental, price-momentum, and forecasted earnings acceleration variables and regression techniques to generate stock-selection models in global and Chinese stock universes. We test whether the stock-selection [...]

From the very earliest days of electronic computing, flowcharts have been used to represent the conceptual structure of complex software systems. In much of the literature on software development, the [...]