Discriminant parallel perceptrons

Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.With partial support of Spain’s CICyT, projects TIC 01–572, TIN2004–07676

Impact of astigmatism and high-order aberrations on subjective best focus

12 págs.; 9 figs.; 1 tab.© 2015 ARVO. We studied the role of native astigmatism and ocular aberrations on best-focus setting and its shift upon induction of astigmatism in 42 subjects (emmetropes, myopes, hyperopes, with-the-rule [WTR] and against-the-rule [ATR] myopic astigmats). Stimuli were presented in a custom-developed adaptive optics simulator, allowing correction for native aberrations and astigmatism induction (+1 D; 6-mm pupil). Best-focus search consisted on randomized-step interleaved staircase method. Each subject searched best focus for four different images, and four different conditions (with/without aberration correction, with/without astigmatism induction). The presence of aberrations induced a significant shift in subjective best focus (0.4 D; p < 0.01), significantly correlated (p = 0.005) with the best-focus shift predicted from optical simulations. The induction of astigmatism produced a statistically significant shift of the best-focus setting in all groups under natural aberrations (p = 0.001), and in emmetropes and in WTR astigmats under corrected aberrations (p < 0.0001). Best-focus shift upon induced astigmatism was significantly different across groups, both for natural aberrations and AO-correction (p < 0.0001). Best focus shifted in opposite directions in WTR and ATR astigmats upon induction of astigmatism, symmetrically with respect to the best-focus shift in nonastigmatic myopes. The shifts are consistent with a bias towards vertical and horizontal retinal blur in WTR and ATR astigmats, respectively, indicating adaptation to native astigmatism.The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. [ERC-2011- AdC 294099]. This study was supported by grants FIS2011-24637 to SM and a collaborative research project funded by Essilor International. Optometric examinations were performed in the Faculty of Optometry Clinic of the University Complutense de Madrid (Madrid, Spain). GM and MH work for Essilor International.Peer Reviewe

Automatic neural generalized font identification

The final publication is available at Springer via http://dx.doi.org/10.1007/978-1-4471-1599-1_116Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998Neural methods are gaining a steady acceptance as powerful tools in a variety of pattern detection problems, OCR certainly being one of them. The concrete implementation of these neural OCR systems is of course a well guarded corporate secret, but in broad terms it can be said that in most of the cases, multilayer perceptrons (MLPs) are used. There are several reasons for the MLPs’ success. To begin with, they are based in well understood mathematical and statistical principles and there are efficient tools and methodologies for their training and evaluation. Furthermore they have good generalization properties.With partial support of grant TIC 95-965 of Spain's CICy

Diffusion Maps for dimensionality reduction and visualization of meteorological data

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, VOL 163, (2015) DOI 10.1016/j.neucom.2014.08.090The growing interest in big data problems implies the need for unsupervised methods for data visualization and dimensionality reduction. Diffusion Maps (DM) is a recent technique that can capture the lower dimensional geometric structure underlying the sample patterns in a way which can be made to be independent of the sampling distribution. Moreover, DM allows us to define an embedding whose Euclidean metric relates to the sample's intrinsic one which, in turn, enables a principled application of k-means clustering. In this work we give a self-contained review of DM and discuss two methods to compute the DM embedding coordinates to new out-of-sample data. Then, we will apply them on two meteorological data problems that involve time and spatial compression of numerical weather forecasts and show how DM is capable to, first, greatly reduce the initial dimension while still capturing relevant information in the original data and, also, how the sample-derived DM embedding coordinates can be extended to new patterns.The authors acknowledge partial support from Spain's grant TIN2010-21575-C02-01 and the UAM{ADIC Chair for Machine Learning. The first author is also supported by an FPI{UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during her visits

Diffusion maps and local models for wind power prediction

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.With partial support from grant TIN2010-21575-C02-01 of Spain’s Ministerio de Economía y Competitividad and the UAM–ADIC Chair for Machine Learning in Modelling and Prediction. The first author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during a visit. The second author is supported by the FPU-MEC grant AP2008-00167. We also thank Red Eléctrica de España, Spain’s TSO, for providing historic wind energy dat

F-theory and Dark Energy

Motivated by its potential use as a starting point for solving various cosmological constant problems, we study F-theory compactified on the warped product $\mathbb{R}_{\text{time}} \times S^3 \times Y_{8}$ where $Y_{8}$ is a $Spin(7)$ manifold, and the $S^3$ factor is the target space of an $SU(2)$ Wess--Zumino--Witten (WZW) model at level $N$. Reduction to M-theory exploits the abelian duality of this WZW model to an $S^3 / \mathbb{Z}_N$ orbifold. In the large $N$ limit, the untwisted sector is captured by 11D supergravity. The local dynamics of intersecting 7-branes in the $Spin(7)$ geometry is controlled by a Donaldson--Witten twisted gauge theory coupled to defects. At late times, the system is governed by a 1D quantum mechanics system with a ground state annihilated by two real supercharges, which in four dimensions would appear as "$\mathcal{N} = 1/2$ supersymmetry" on a curved background. This leads to a cancellation of zero point energies in the 4D field theory but a split mass spectrum for superpartners of order $\Delta m_\text{4D} \sim \sqrt{M_\text{IR} M_\text{UV}}$ specified by the IR and UV cutoffs of the model. This is suggestively close to the TeV scale in some scenarios. The classical 4D geometry has an intrinsic instability which can produce either a collapsing or expanding Universe, the latter providing a promising starting point for a number of cosmological scenarios. The resulting 1D quantum mechanics in the time direction also provides an appealing starting point for a more detailed study of quantum cosmology.Comment: v3: 67 pages, 5 figures, reference added, typos corrected, revised analysis of superpartner masse

Journalism matters but journalists matter even more: Why Journalism Still Matters, by Michael Schudson, Cambridge, UK, Polity Press, 213 p., 22.95€, ISBN 9781509528059

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Trust Factor. The Science of Creating High-Performance Companies, by Paul Zak, AMACOM, New York, 2017, 261 pp., 22.36€, ISBN: 9780814437667

Paul J. Zak is an American neuroeconomist who explores the relationships between the brain and the economy. In particular, he is interested in knowing what physio-neural mechanisms determine or int..