Extracting user spatio-temporal profiles from location based social networks

Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains like mobility route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim of this paper is to analyze how the spatio-temporal behavior of a large number of users in a well limited geographical area can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different behaviors that people have when living and visiting a city. The data analyzed was obtained from the public data feeds of Twitter and Instagram inside the area of the city of Barcelona for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or any general area) to discover useful profiles that can be described on terms of the city singular places and areas and their temporal relationships. These profiles can be used as a basis for making decisions in different application domains, specially those related with mobility inside and outside a city.Preprin

Single top-quark production by direct supersymmetric flavor-changing neutral-current interactions at the LHC

Production of (electrically neutral) heavy-quark pairs, such as t{\bar c} and {\bar t}c, is extremely suppressed in the SM. In supersymmetric (SUSY) theories, such as the MSSM, the number of these events can be significantly enhanced thanks (mainly) to the FCNC couplings of gluinos. We compute the efficiency of this mechanism for FCNC production of heavy quarks at the LHC. We find that \sigma (pp\to t\bar{c}+\bar{t}c) can reach 1 pb, and therefore one can expect up to 10^{5} events per 100 fb^{-1} of integrated luminosity (with no counterpart in the SM). Their detection would be instant evidence of new physics, and could be a strong indication of underlying SUSY dynamics.Comment: 6 pages, 3 figures. New references and comments added. Invited talk at the 7th International Symposium on Radiative Corrections (RADCOR 2005), Shonan Village, Japan, 200

FCNC-induced heavy-quark events at the LHC from Supersymmetry

We analyze the production and subsequent decay of the neutral Higgs bosons of the MSSM into electrically neutral quark pairs qq'=bs,tc of different flavors at the LHC and compare with the direct FCNC production mechanisms. The cross-sections are computed in the unconstrained MSSM with minimal flavor-mixing sources and taking into account the stringent bounds from radiative B-meson decays. We extend the results previously found for these FCNC processes, which are singularly uncommon in the SM. Specifically, we report here on the SUSY-EW contribution of the Higgs-mediated FCNC cross-section into bs and tc final states and the SUSY-QCD and SUSY-EW contributions to bs-production. In this way, the complete map of MSSM predictions for the qq'-pairs produced at the LHC becomes available. The upshot is that the most favorable channels are: 1) the Higgs boson FCNC decays into bs, and 2) the direct production of tc pairs, both of them at the 1 pb level and mediated by SUSY-QCD effects. If, however, the latter are suppressed, we find a small SUSY-EW yield for the tc-production through Higgs decays but, at the same time, a cross-section of 0.1-1 pb for bs-production, which implies a significant number (10^4-10^5) of bs-pairs per 100 inverse femtobarn of integrated luminosity.Comment: LaTeX, 17 pages, 4 figures, 4 tables. Extended discussion. Accepted in Phys. Lett.

Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

La prosa didáctica del Siglo de Oro, 'El Crotalón' y el diálogo.

Ce travail expose les clés et les caractéristiques du genre littéraire du dialogue, encadré dans la prose didactique du Siècle d’or espagnol, en prenant comme référence l’oeuvre de El Crótalon de Cristóbal de Villalón. En plus, pour s’expliquer à lui-même, le travail prend la forme dialoguistique

Cultura psicoterapéutica y autoayuda : el código psicológico-positivo

El presente trabajo se enmarca en el contexto general de la individualización contemporánea. Se considera que la cultura psicoterapéutica forma parte del imaginario social moderno y se está constituyendo en un lenguaje moral que va adquiriendo una progresiva legitimidad social. En primer lugar, se analizan críticamente los principales trabajos sociológicos sobre la literatura de consejos. En segundo lugar, se estudia, siguiendo a Elias, lo que llamo el «código psicológico» dentro del género de autoayuda. Se parte de que dicho género es el equivalente funcional de los manuales de comportamiento que Elias analizó.The following article has to be framed within the general context of contemporary individualization. The so-called psychotherapeutic culture is considered to be an important part of our modern social imaginary, and it is becoming a moral language with a growing social legitimacy. Firstly I analyse the main sociological perspectives on advice literature. Secondly I study, following Elias work. I investigate what I have called the psychological code within the self-help genre. In my understanding this genre is the present functional substitute of the codes of manners analysed by Elias

Go with the flow: Recurrent networks for wind time series multi-step forecasting

One of the ways of reducing the effects of Climate Change is to rely on renewable energy sources. Their intermittent nature makes necessary to obtain a mid-long term accurate forecasting. Wind Energy prediction is based on the ability to forecast wind speed. This has been a problem approached using different methods based on the statistical properties of the wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting obtaining a 12 hours ahead prediction using data from the National Renewable Energy Laboratory's WIND datasetPeer ReviewedPostprint (published version

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version