Dark matter annihilation signals coming from Galactic subhaloes may account for a small fraction of unassociated point sources detected in the second Fermi-LAT Catalogue (2FGL). To investigate this possibility, we present SIBYL, a Random Forest classifier that offers predictions on class memberships for unassociated Fermi-LAT sources at high Galactic latitudes using gamma-ray features extracted from the 2FGL. SIBYL generates a large ensemble of classification trees that are trained to vote on whether a particular object is an active galactic nucleus (AGN) or a pulsar. After training on a list of 908 identified/associated 2FGL sources, SIBYL reaches individual accuracy rates of up to 97.7 per cent for AGNs and 96.5 per cent for pulsars. Predictions for the 269 unassociated 2FGL sources at |b| ≥ 10 • suggest that 216 are potential AGNs and 16 are potential pulsars (with majority votes greater than 70 per cent). The remaining 37 objects are inconclusive, but none is an extreme outlier. These results could guide future quests for dark matter Galactic subhaloes.
Abstract-The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data. The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading. Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process. To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading. However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior. In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading. The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus. Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days.
Individuals generate vast amounts of geolocated content through the use of mobile social media applications. In this context, Twitter has become an important sensor of the interactions between individuals and their environment. Building on this idea, this paper proposes the use of geolocated tweets as a complementary source of information for urban planning applications, focusing on the characterization of land use. The proposed technique uses unsupervised learning and automatically determines land uses in urban areas by clustering geographical regions with similar tweeting activity patterns. Three case studies are presented and validated for Manhattan (NYC), London (UK) and Madrid (Spain) using Twitter activity and land use information provided by the city planning departments. Results indicate that geolocated tweets can be used as a powerful data source for urban planning applications.
Abstract. The socioeconomic status of a population or an individual provides an understanding of its access to housing, education, health or basic services like water and electricity. In itself, it is also an indirect indicator of the purchasing power and as such a key element when personalizing the interaction with a customer, especially for marketing campaigns or offers of new products. In this paper we study if the information derived from the aggregated use of cell phone records can be used to identify the socioeconomic levels of a population. We present predictive models constructed with SVMs and Random Forests that use the aggregated behavioral variables of the communication antennas to predict socioeconomic levels. Our results show correct prediction rates of over 80% for an urban population of around 500,000 citizens.
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