La emergencia de la economía híbrida como modelo de producción de contenidos en internet. El ejemplo de ninremixes.com

The culture generated within what has come to be known as the sharing economy focuses part of its debate on the future of the dissemination of knowledge and cultural industries in the digital era. This paper analyses from a multidisciplinary perspective, through the study of a specific hybrid example the remix website ninremixes.com, several economic, communicative and legal aspects fundamental to internet activist Lawrence Lessig´s proposal for a hybrid model, combining elements of the usual commercial trade of cultural property and that sharing economy.La cultura generada dentro de lo que se ha venido a llamar economía de compartición, centra parte del debate acerca del futuro de la difusión del conocimiento y de las industrias culturales en la era digital. Este artículo analiza, desde una perspectiva multidisciplinar, algunos aspectos económicos, comunicativos y legales esenciales en la propuesta del activista de internet Lawrence Lessig acerca de un modelo híbrido, que combina elementos del intercambio comercial usual de bienes culturales y de esa economía de compartición, a través del estudio de un ejemplo concreto de híbrido, la página web de remezclas ninremixes.co

Desarrollo y caracterización de helados salados con microalgas

[ES] El helado es un alimento tradicionalmente de sabor dulce, que se consume en estado congelado. Para este proyecto, se ha decidido romper los esquemas del mercado elaborando helados salados, con la particularidad de añadirles microalgas que son una fuente de vitaminas y minerales naturales y sabores vegetales. Se caracterizó el producto mediante la determinación de una serie de parámetros de calidad, como el overrun, el tiempo de caída de la primera gota y el porcentaje de derretimiento. Además, se analizaron propiedades fisicoquímicas como el pH, densidad y el color, y parámetros texturales y sensoriales. Se ensayaron con distintas formulaciones y combinaciones de proporciones de ingredientes hasta conseguir tres sabores distintos y seis prototipos finales, de los cuales tres incorporan Spirulina y tres Chlorella. Los distintos prototipos de helados conseguidos en el presente proyecto contienen propiedades refrescantes al incorporar verduras y frutas con sabores muy actuales, en sintonía con el vigente estilo de vida. Una sociedad que cada vez incorpora más verduras y frutas en su alimentación habitual. Los prototipos elaborados presentaron una textura suave en boca, con la cantidad justa de grasa para mantener su estructura el tiempo suficiente para ser consumido sin descongelar. Los colores de los distintos prototipos se ven influenciados por el tipo de microalga utilizada, siendo más verdosos-azules los elaborados con Spirulina y más verdes-amarillos los elaborados con Chlorella. Finalmente, tras el análisis sensorial de los distintos prototipos se determinó una buena aceptación del producto, pero con una baja intencionalidad de compra, como suele ocurrir con muchos productos con innovaciones radicales.[EN] Ice cream is a traditionally sweet-tasting food, which is consumed in a frozen state. For this project, it has been decided to break the market schemes by making salty ice creams, with the particularity of adding microalgae that are a source of natural vitamins and minerals and vegetable flavors. The product was characterized by the determination of a series of quality parameters, such as overrun, first dripping time and melting rate. In addition, physicochemical properties such as pH, density and color, and textural and sensory parameters were analyzed. They were tested whit different formulations and combinations of ingredient proportions until achieving three different flavors and six final prototypes, of which three incorporate Spirulina and three Chlorella. The different ice cream prototypes obtained in this project contain refreshing properties by incorporating vegetables and fruits with very current flavors, in line with the prevailing lifestyle. A society that increasingly incorporates more vegetables and fruits into their usual diet. The elaborated prototypes presented a smooth texture in the mouth, with just the right amount of fat to maintain its structure long enough to be consumed without thawing. The colors of the different prototypes are influenced by the type of microalga used, being greenish-blue those made with Spirulina and more green-yellow those made with Chlorella. Finally, after the sensory analysis of the different prototypes, a good acceptance of the product was determined, but with a low intentionality of purchase, as often happens with many products with radical innovations.García Gómez, V. (2019). Desarrollo y caracterización de helados salados con microalgas. http://hdl.handle.net/10251/119795TFG

Regularized multivariate analysis framework for interpretable high-dimensional variable selection

Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we propose a novel method that takes advantage of the,2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations.This work has been partly supported by MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R and TEC2016-75161-C2-2-R, and Comunidad de Madrid projects PRICAM P2013/ICE-2933 and S2013/ICE-2933

Nonnegative OPLS for supervised design of filter banks: application to image and audio feature extraction

Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and why filters work for audio or visual applications is a desired property, especially when energy or spectral signals are involved. In these cases, due to the nature of these signals, the nonnegativity of the filter weights is a desired property to better understand its working. Because of these two necessities, we propose different methods to reduce the dimensionality of data while the nonnegativity and interpretability of the solution are assured. In particular, we propose a generalized methodology to design filter banks in a supervised way for applications dealing with nonnegative data, and we explore different ways of solving the proposed objective function consisting of a nonnegative version of the orthonormalized partial least-squares method. We analyze the discriminative power of the features obtained with the proposed methods for two different and widely studied applications: texture and music genre classification. Furthermore, we compare the filter banks achieved by our methods with other state-of-the-art methods specifically designed for feature extraction.This work was supported in parts by the MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R, TEC2016-75161-C2-1-R, TEC2016-75161-C2-2-R, TEC2016-81900-REDT/AEI, and PRICAM (S2013/ICE-2933)

Sparse and kernel OPLS feature extraction based on eigenvalue problem solving

Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. Usually, in machine learning papers OPLS projections are obtained by solving a generalized eigenvalue problem. However, in statistical papers the method is typically formulated in terms of a reduced-rank regression problem, leading to a formulation based on a standard eigenvalue decomposition. A first contribution of this paper is to derive explicit expressions for matching the OPLS solutions derived under both approaches and discuss that the standard eigenvalue formulation is also normally more convenient for feature extraction in machine learning. More importantly, since optimization with respect to the projection vectors is carried out without constraints via a minimization problem, inclusion of penalty terms that favor sparsity is straightforward. In the paper, we exploit this fact to propose modified versions of OPLS. In particular, relying on the ℓ1 norm, we propose a sparse version of linear OPLS, as well as a non-linear kernel OPLS with pattern selection. We also incorporate a group-lasso penalty to derive an OPLS method with true feature selection. The discriminative power of the proposed methods is analyzed on a benchmark of classification problems. Furthermore, we compare the degree of sparsity achieved by our methods and compare them with other state-of-the-art methods for sparse feature extraction.This work was partly supported by MINECO projects TEC2011-22480 and PRIPIBIN-2011-1266.Publicad

Forecast-informed power load profiling: A novel approach

Power load forecasting plays a critical role in the context of electric supply optimization. The concept ofload characterization and profiling has been used in the past as a valuable approach to improve forecasting performance as well as problem interpretability. This paper proposes a novel, fully fledged theoretical framework for a joint probabilistic clustering andregression model, which is different from existing models that treat both processes independently. The clustering process is enhanced by simultaneously using the input data and the prediction targets during training. The model is thus capable of obtaining better clusters than other methods, leading to more informativedata profiles, while maintaining or improving predictive performance. Experiments have been conducted using aggregated load data from two U.S.A. regional transmission organizations, collected over 8 years. These experiments confirm that the proposed model achieves the goalsset for interpretability and forecasting performance.This work is partially supported by the National Science Foundation EPSCoR Cooperative Agreement OIA-1757207 and the SpanishMINECO grants TEC2014-52289-R and TEC2017-83838-R

Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling

[EN] The ensemble random forest filter (ERFF) is presented as an alternative to the ensemble Kalman filter (EnKF) for inverse modeling. The EnKF is a data assimilation approach that forecasts and updates parameter estimates sequentially in time as observations are collected. The updating step is based on the experimental covariances computed from an ensemble of realizations, and the updates are given as linear combinations of the differences between observations and forecasted system state values. The ERFF replaces the linear combination in the update step with a non-linear function represented by a random forest. This way, the non-linear relationships between the parameters to be updated and the observations can be captured, and a better update produced. The ERFF is demonstrated for log-conductivity identification from piezometric head observations in several scenarios with varying degrees of heterogeneity (log-conductivity variances going from 1 up to 6.25 (ln m/d)2), number of realizations in the ensemble (50 or 100), and number of piezometric head observations (18 or 36). In all scenarios, the ERFF works well, reconstructing the log-conductivity spatial heterogeneity while matching the observed piezometric heads at selected control points. For benchmarking purposes, the ERFF is compared to the restart EnKF to find that the ERFF is superior to the EnKF for the number of ensemble realizations used (small in typical EnKF applications). Only when the number of realizations grows to 500 the restart EnKF can match the performance of the ERFF, albeit at more than double the computational cost.The authors acknowledge grant PID2019-109131RB-I00 funded by MCIN/AEI/10.13039/501100011033 and project InTheMED, which is part of the PRIMA Programme supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 1923.A. Godoy, V.; Napa-García, GF.; Gómez-Hernández, JJ. (2022). Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling. Journal of Hydrology. 615:1-13. https://doi.org/10.1016/j.jhydrol.2022.12864211361