L'observació del sol a la segona meitat del segle XIX

A la segona meitat del segle xix, diversos astrònoms van observar el Sol mitjançant diverses tècniques i van determinar les coordenades de les taques. Aquestes dades van ser publicades en el seu moment, però com que formaven part de llargues taules, no han sigut emprades. En aquest article es presenta el treball de conversió de les dades a un format electrònic llegible per a ordinadors i una primera anàlisi per comprovar la seva coherènci

Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Els Asteroides de Josep Comas Solà

Through his life, Josep Comas Solà discovered several astronomical objects; variable and double stars, the atmosphere of Titan, two comets, eleven asteroids... In sixteen months, from March of 2002, members of the Agrupació Astronòmica de Sabadell were able to register all asteroids found by the barcelonian astronomer. A time later, the photometry team of the association determined the rotation period of many of them, being the first to calculate the period of 1102 Pepita

Adarules: Learning rules for real-time road-traffic prediction

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyze the situation in a commercial real-time prediction system with its current problems and limitations. We analyze issues related to the use of spatiotemporal information to reconstruct the traffic state. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic state prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network, which we call as “structure learning”. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. (Part of special issue: 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary)Peer ReviewedPostprint (published version

Communication-aware sparse patterns for the factorized approximate inverse preconditioner

The Conjugate Gradient (CG) method is an iterative solver targeting linear systems of equations Ax=b where A is a symmetric and positive definite matrix. CG convergence properties improve when preconditioning is applied to reduce the condition number of matrix A. While many different options can be found in the literature, the Factorized Sparse Approximate Inverse (FSAI) preconditioner constitutes a highly parallel option based on approximating A-1. This paper proposes the Communication-aware Factorized Sparse Approximate Inverse preconditioner (FSAIE-Comm), a method to generate extensions of the FSAI sparse pattern that are not only cache friendly, but also avoid increasing communication costs in distributed memory systems. We also propose a filtering strategy to reduce inter-process imbalance. We evaluate FSAIE-Comm on a heterogeneous set of 39 matrices achieving an average solution time decrease of 17.98%, 26.44% and 16.74% on three different architectures, respectively, Intel Skylake, Fujitsu A64FX and AMD Zen 2 with respect to FSAI. In addition, we consider a set of 8 large matrices running on up to 32,768 CPU cores, and we achieve an average solution time decrease of 12.59%.Marc Casas is supported by Grant RYC-2017-23269 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 955606. This work has been supported by the Computación de Altas Prestaciones VIII (BSC-HPC8) project. It has also been partially supported by the EXCELLERAT project funded by the European Commission’s ICT activity of the H2020 Programme under grant agreement number: 823691 and by the Spanish Ministry of Science and Innovation (Nucleate, Project PID2020-117001GB-I00).Peer ReviewedPostprint (author's final draft

Vacunación en neumococo. Actuación en la farmacia comunitaria

En la elaboración del documento han participado SEFAC, semFYC, SEMERGEN y SEPAR. El objetivo principal era definir los perfiles de paciente susceptibles prioritariamente de la vacunación frente al neumococo considerando los grupos de riesgo, las patologías concomitantes, las posibles consecuencias de una infección neumocócica y la elaboración, a su vez, de un algoritmo de vacunación en el adulto. Se han definido igualmente las indicaciones de la vacuna así como la propuesta de entrevista por parte de la farmacia comunitaria.El documento, avalado por las principales sociedades científicas médico-farmacéuticas, será de gran utilidad para el farmacéutico comunitario de cara a abordar a pacientes con mayor factor de riesgo de contraer enfermedad neumocócica. Será también una manera muy gráfica para conseguir detectar, asesorar y, llegado el caso, derivar al facultativo médico a todo este tipo de paciente

A quality control procedure for long-term series of daily precipitation data in a semiarid environment

The availability of quality precipitation records in the current climate situation is of great importance in the scientifctechnical feld but also for the public institutions that manage the meteorological networks. This work has implemented a comprehensive spatial quality control procedure in the semiarid region of Andalusia (Southern Spain), using precipitation time series from 1947 stations from three meteorological networks: Spanish Meteorological Agency (AEMET), Agroclimatic Information Network of Andalusia (RIA), and Phytosanitary Information Alert Network (RAIF). The method consists of three consecutive steps: basic, absolute, and relative quality control processes. The latter step compares data from neighboring stations taking into account their proximity, height diference, and correlation, leading to a complete evaluation of each daily value. Finally, the quality of each year at each station can be declared as acceptable, good, or excellent. The automatic weather station networks RIA and RAIF gave absolute quality index Q above 85% for almost 87% of their stations, while only 57% of AEMET network reached this percentage. However, one of the longest AEMET datasets, San Fernando-Cádiz, obtained, except for 1 year, Q values over 90% in all available years for more than a century of measurements, since 1870 until 2000. From a total of more than 15 million daily records, almost 82% was fagged as correct. Despite the limitations of Andalusia region (low density of stations and its structural water defcit), the complete quality control procedure has been satisfactorily applied. Finally, related to the number of outliers, no temporal trend was found across the region.Peer ReviewedPostprint (published version