Bartering integer commodities with exogenous prices

The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed by Nasini et al. (accepted 2014) to study pure exchange economies where discrete amounts of commodities are exchanged at fixed prices. Barter processes, consisting in sequences of elementary reallocations of couple of commodities among couples of agents, are formalized as local searches converging to equilibrium allocations. A direct application of the analyzed processes in the context of computational economics is provided, along with a Java implementation of the approaches described in this research report.Comment: 30 pages, 5 sections, 10 figures, 3 table

La cosmología evasiva de Ramon Llull

Ramon Llull fou un autor prolífic que escrigué més de 240 obres sobre els temes més diversos, però el seu interès principal era apologètic. Així, la major part de les seves obres s’orienten a la demostració racional de principis religiosos mitjançant el seu mètode peculiar anomenat art. Consegüentment, la cosmologia no apareix tematitzada en cap obra concreta, sinó que se n’ha de seguir el rastre a través de tota la producció lul·liana. A més, els diferents aspectes cosmològics evolucionen constantment al llarg de l’obra. En el present article, analitzarem aquesta evolució des de tres aspectes: l’estructura cosmològica, les relacions entre les seves parts i el procés de generació del cosmos.Ramon Llull was a prolific author who wrote more than 240 works on diverse topics, although his main interest was apologetic. Thus, most of his works are oriented towards the rational demonstration of religious principles through his peculiar method called “art”. Consequently, the topic of cosmology does not appear in any particular work but must be traced through Lull’s entire work. In addition, the various cosmological questions constantly evolve throughout the Llullian philosophy. In this article, we analyze this development from three aspects: the cosmological structure, the relationships between parts and the process of generating the cosmos.Ramon Llull fue un autor prolífico que escribió más de 240 obras sobre los temas más diversos, pero su principal interés era apologético. Así, la mayor parte de sus obras se orientan a la demostración racional de principios religiosos mediante su peculiar método denominado art. Consiguientemente, la cosmología no aparece tematizada en ninguna obra concreta, sino que hay que seguir su rastro a través de toda la producción luliana. Además, los diferentes aspectos cosmológicos evolucionan constantemente a lo largo de la obra. En el presente artículo, analizaremos esta evolución desde tres aspectos: la estructura cosmológica, las relaciones entre sus partes y el proceso de generación del cosmos

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

Contribution of thermal inertia to the interior climate of Girona Cathedral: feasibility analysis for the preservation of pieces of art through the monitoring of thermal conditions for 6 years

The purpose of this study is to determine Girona Cathedral’s thermal performance and to verify that it is suitable for containing historical pieces of art. We present the results of our analysis of the indoor thermal conditions during the period from January 2011 to December 2016. Real data were collected from strategically located probes inside the cathedral and from an outside weather station. The results were compared with an EnergyPlus computer model to verify the calculations and improve decision making. The model considers the envelope’s thermal inertia, the characteristics of the enclosure, and the passive systems for performing thermal analysis. The cathedral’s indoor environment is very stable. Because of a high capability of thermal-energy accumulation, a one-month thermal lag between indoor and outdoor temperatures exists. Furthermore, the maximum and minimum temperatures are dampened, with a two-degree variation above or below the outdoor average, depending on the season. The outdoor humidity can vary from 40% to 100% daily, whereas the indoor humidity variation is around 5%. This indoor stable condition protects the artistic objects in the building. This six-year monitoring and analysis allows for the determination that high-inertia buildings are suitable for displaying and preserving pieces of art and antique furniture, protecting them from deterioration.Peer ReviewedPostprint (published version

Les granges de pollastres i de porcs, un risc potencial d'Escherichia coli

El grup de Microbiologia Molecular del Departament de Genètica i Microbiologia de la UAB, coordinat pels professors Jordi Barbé i Montserrat Llagostera, porta força anys estudiant la problemàtica de la resistència als antibiòtics en l'àmbit de les granges de producció animal i la seva disseminació i vinculació amb la salut humana. Fruit d'aquesta trajectòria és el treball recentment publicat a la revista Applied and Environmental Microbiology i titulat "Isolation and characterization of potentically pathogenic antimicobial-resistant Escherichia coli strains from chicken and pig Farms in Spain".El grupo de Microbiología Molecular del Departamento de Genética y Microbiología de la UAB, coordinado por los profesores Jordi Barbé y Montserrat Llagostera, lleva bastantes años estudiando la problemática de la resistencia a los antibióticos en el ámbito de las granjas de producción animal y su diseminación y vinculación con la salud humana. Fruto de esta trayectoria es el trabajo recientemente publicado en la revista Applied and Environmental Microbiology y titulado "Isolation and characterization of potentically pathogenic antimicobial-resistant Escherichia coli strains from chicken and pig Farms in Spain"