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..

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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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

Convex formulation for multi-task L1-, L2-, and LS-SVMs

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific modelsWith partial support from Spain’s grant TIN2016-76406-P. Work supported also by the UAM–ADIC Chair for Data Science and Machine Learning

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

ν-SVM solutions of constrained lasso and elastic net

Many important linear sparse models have at its core the Lasso problem, for which the GLMNet algorithm is often considered as the current state of the art. Recently M. Jaggi has observed that Constrained Lasso (CL) can be reduced to an SVM-like problem, for which the LIBSVM library provides very efficient algorithms. This suggests that it could also be used advantageously to solve CL. In this work we will refine Jaggi’s arguments to reduce CL as well as constrained Elastic Net to a Nearest Point Problem, which in turn can be rewritten as an appropriate ν-SVM problem solvable by LIBSVM. We will also show experimentally that the well-known LIBSVM library results in a faster convergence than GLMNet for small problems and also, if properly adapted, for larger ones. Screening is another ingredient to speed up solving Lasso. Shrinking can be seen as the simpler alternative of SVM to screening and we will discuss how it also may in some cases reduce the cost of an SVM-based CL solutionWith partial support from Spanish government grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM; work also supported by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016 and the UAM–ADIC Chair for Data Science and Machine Learning. The first author is also supported by the FPU–MEC grant AP-2012-5163. We gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy dat

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

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

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