Neural Network identification of halo white dwarfs

The white dwarf luminosity function has proven to be an excellent tool to study some properties of the galactic disk such as its age and the past history of the local star formation rate. The existence of an observational luminosity function for halo white dwarfs could provide valuable information about its age, the time that the star formation rate lasted, and could also constrain the shape of the allowed Initial Mass Functions (IMF). However, the main problem is the scarce number of white dwarfs already identified as halo stars. In this Letter we show how an artificial intelligence algorithm can be succesfully used to classify the population of spectroscopically identified white dwarfs allowing us to identify several potential halo white dwarfs and to improve the significance of its luminosity function.Comment: 15 pages, 3 postscript figures. Accepted for publication in ApJ Letters, uses aasms4.st

Event detection in location-based social networks

With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft

Towards the cloudification of the social networks analytics

In the last years, with the increase of the available data from social networks and the rise of big data technologies, social data has emerged as one of the most profitable market for companies to increase their benefits. Besides, social computation scientists see such data as a vast ocean of information to study modern human societies. Nowadays, enterprises and researchers are developing their own mining tools in house, or they are outsourcing their social media mining needs to specialised companies with its consequent economical cost. In this paper, we present the first cloud computing service to facilitate the deployment of social media analytics applications to allow data practitioners to use social mining tools as a service. The main advantage of this service is the possibility to run different queries at the same time and combine their results in real time. Additionally, we also introduce twearch, a prototype to develop twitter mining algorithms as services in the cloud.Peer ReviewedPostprint (author’s final draft

Are Americans' musical preferences more omnivores today?

Although we found a general trend favouring the omnivorousness thesis, as soon as we adjusted it to a set of structural factors and consumers’ tastes it was clear that this was caused by elitist inclusive omnivores who had increased the scope of their tastes. In general, younger cohorts were becoming less omnivorous, nevertheless, they were also becoming more educated and had greater to higher levels of inc ome, making the youth more omnivorous. As expected, upscale consumers set limits on their popular taste: musical genres, whose audiences had educational levels below the mean profile were less preferred by upscale respondents. In spite of this, as time passed, some popular brows gained social status.Symbolic consumer research, musical tastes, omnivorousness, correspondence analysis of matched matrices

SMOS: ESA'S Water Mission

Peer Reviewe

Multimedia big data computing for in-depth event analysis

While the most part of ”big data” systems target text-based analytics, multimedia data, which makes up about 2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and challenges. Multimedia Big Data Computing is the new topic that focus on all aspects of distributed computing systems that enable massive scale image and video analytics. During the course of this paper we describe BPEM (Big Picture Event Monitor), a Multimedia Big Data Computing framework that operates over streams of digital photos generated by online communities, and enables monitoring the relationship between real world events and social media user reaction in real-time. As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.Peer ReviewedPostprint (author's final draft

The effects of metallicity on the Galactic disk population of white dwarfs

It has been known for a long time that stellar metallicity plays a significant role in the determination of the ages of the different Galactic stellar populations, when main sequence evolutionary tracks are employed. Here we analyze the role that metallicity plays on the white dwarf luminosity function of the Galactic disk, which is often used to determine its age. We employ a Monte Carlo population synthesis code that accounts for the properties of the population of Galactic disk white dwarfs. Our code incorporates the most up-to-date evolutionary cooling sequences for white dwarfs with hydrogen-rich and hydrogen-deficient atmospheres for both carbon-oxygen and oxygen-neon cores. We use two different models to assess the evolution of the metallicity, one in which the adopted metallicity is constant with time, but with a moderate dispersion, and a second one in which the metallicity increases with time. We found that our theoretical results are in a very satisfactory agreement with the observational luminosity functions obtained from the Sloan Digital Sky Survey (SDSS) and from the SuperCOSMOS Sky Survey (SSS), independently of the adopted age-metallicity law. In particular, we found that the age-metallicity law has no noticeable impact in shaping the bright branch of the white dwarf luminosity function, and that the position of its cut-off is almost insensitive to the adopoted age-metallicity relationship. Because the shape of the bright branch of the white dwarf luminosity function is insensitive to the age-metallicity law, it can be safely employed to test the theoretical evolutionary sequences, while due to the limited sensitivity of the position of the drop-off to the distribution of metallicities, its location provides a robust indicator of the age of the Galactic disk.Comment: 7 pages, 5 figures, accepted for publication in A&