A Framework for Building Hypercubes Using MapReduce

The European Space Agency's Gaia mission will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), by providing unprecedented position, parallax, proper motion, and radial velocity measurements for about one billion stars. The resulting catalogue will be made available to the scientific community and will be analyzed in many different ways, including the production of a variety of statistics. The latter will often entail the generation of multidimensional histograms and hypercubes as part of the precomputed statistics for each data release, or for scientific analysis involving either the final data products or the raw data coming from the satellite instruments. In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigmbut without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission. In addition, we put forward the advantages and shortcomings of the deployment of the framework on a public cloud provider, benchmark against other popular solutions available (that are not always the best for such ad-hoc applications), and describe some user experiences with the framework, which was employed for a number of dedicated astronomical data analysis techniques workshops.

On an asymptotic formula for the maximum voltage drop in a on-chip power distribution network

We present a new asymptotic formula for the maximum static voltage in a simplified model for on-chip power distribution networks of array bonded integrated circuits. In this model the voltage is the solution of a Poisson equation in an infinite planar domain whose boundary is an array of circular pads of radius ", and we deal with the singular limit Ɛ → 0 case. In comparison with approximations that appear in the electronic engineering literature, our formula is more complete since we have obtained terms up to order Ɛ15. A procedure will be presented to compute all the successive terms, which can be interpreted as using multipole solutions of equations involving spatial derivatives of functions. To deduce the formula we use the method of matched asymptotic expansions. Our results are completely analytical and we make an extensive use of special functions and of the Gauss constant G

An integrative computational analysis provides evidence for FBN1-associated network deregulation in trisomy 21

Although approximately 50% of Down Syndrome (DS) patients have heart abnormalities, they exhibit an overprotection against cardiac abnormalities related with the connective tissue, for example a lower risk of coronary artery disease. A recent study reported a case of a person affected by DS who carried mutations in FBN1, the gene causative for a connective tissue disorder called Marfan Syndrome (MFS). The fact that the person did not have any cardiac alterations suggested compensation effects due to DS. This observation is supported by a previous DS meta-analysis at the molecular level where we have found an overall upregulation of FBN1 (which is usually downregulated in MFS). Additionally, that result was cross-validated with independent expression data from DS heart tissue. The aim of this work is to elucidate the role of FBN1 in DS and to establish a molecular link to MFS and MFS-related syndromes using a computational approach. To reach that, we conducted different analytical approaches over two DS studies (our previous meta-analysis and independent expression data from DS heart tissue) and revealed expression alterations in the FBN1 interaction network, in FBN1 co-expressed genes and FBN1-related pathways. After merging the significant results from different datasets with a Bayesian approach, we prioritized 85 genes that were able to distinguish control from DS cases. We further found evidence for several of these genes (47%), such as FBN1, DCN, and COL1A2, being dysregulated in MFS and MFS-related diseases. Consequently, we further encourage the scientific community to take into account FBN1 and its related network for the study of DS cardiovascular characteristics.

Partial correlation : Network analysis

The final year project came to us as an opportunity to get involved in a topic which has appeared to be attractive during the learning process of majoring in economics: statistics and its application to the analysis of economic data, i.e. econometrics.Moreover, the combination of econometrics and computer science is a very hot topic nowadays, given the Information Technologies boom in the last decades and the consequent exponential increase in the amount of data collected and stored day by day. Data analysts able to deal with Big Data and to find useful results from it are verydemanded in these days and, according to our understanding, the work they do, although sometimes controversial in terms of ethics, is a clear source of value added both for private corporations and the public sector. For these reasons, the essence of this project is the study of a statistical instrument valid for the analysis of large datasets which is directly related to computer science: Partial Correlation Networks.The structure of the project has been determined by our objectives through the development of it. At first, the characteristics of the studied instrument are explained, from the basic ideas up to the features of the model behind it, with the final goal of presenting SPACE model as a tool for estimating interconnections in between elements in large data sets. Afterwards, an illustrated simulation is performed in order to show the power and efficiency of the model presented. And at last, the model is put into practice by analyzing a relatively large data set of real world data, with the objective of assessing whether the proposed statistical instrument is valid and useful when applied to a real multivariate time series. In short, our main goals are to present the model and evaluate if Partial Correlation Network Analysis is an effective, useful instrument and allows finding valuable results from Big Data.As a result, the findings all along this project suggest the Partial Correlation Estimation by Joint Sparse Regression Models approach presented by Peng et al. (2009) to work well under the assumption of sparsity of data. Moreover, partial correlation networks are shown to be a very valid tool to represent cross-sectional interconnections in between elements in large data sets.The scope of this project is however limited, as there are some sections in which deeper analysis would have been appropriate. Considering intertemporal connections in between elements, the choice of the tuning parameter lambda, or a deeper analysis of the results in the real data application are examples of aspects in which this project could be completed.To sum up, the analyzed statistical tool has been proved to be a very useful instrument to find relationships that connect the elements present in a large data set. And after all, partial correlation networks allow the owner of this set to observe and analyze the existing linkages that could have been omitted otherwise.

Self-similar community structure in a network of human interactions

We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Cab45 is required for Ca(2+)-dependent secretory cargo sorting at the trans-Golgi network

Ca(2+) import into the lumen of the trans-Golgi network (TGN) by the secretory pathway calcium ATPase1 (SPCA1) is required for the sorting of secretory cargo. How is Ca(2+) retained in the lumen of the Golgi, and what is its role in cargo sorting? We show here that a soluble, lumenal Golgi resident protein, Cab45, is required for SPCA1-dependent Ca(2+) import into the TGN; it binds secretory cargo in a Ca(2+)-dependent reaction and is required for its sorting at the TGN.

Kinesin-5/Eg5 is important for transport of CARTS from the trans-Golgi network to the cell surface

Here we report that the kinesin-5 motor Klp61F, which is known for its role in bipolar spindle formation in mitosis, is required for protein transport from the Golgi complex to the cell surface in Drosophila S2 cells. Disrupting the function of its mammalian orthologue, Eg5, in HeLa cells inhibited secretion of a protein called pancreatic adenocarcinoma up-regulated factor (PAUF) but, surprisingly, not the trafficking of vesicular stomatitis virus G protein (VSV-G) to the cell surface. We have previously reported that PAUF is transported from the trans-Golgi network (TGN) to the cell surface in specific carriers called CARTS that exclude VSV-G. Inhibition of Eg5 function did not affect the biogenesis of CARTS; however, their migration was delayed and they accumulated near the Golgi complex. Altogether, our findings reveal a surprising new role of Eg5 in nonmitotic cells in the facilitation of the transport of specific carriers, CARTS, from the TGN to the cell surface.

APTIC : a social network to improve the quality of life of members of patients' associations

Peer-reviewed

Extending Learning Objects by Means of Social Networking

Learning objects have been the promise of providing people with high quality learning resources. Initiatives such as MIT Open-CourseWare, MERLOT and others have shown the real possibilities of creating and sharing knowledge through Internet. Thousands of educational resources are available through learning object repositories. We indeed live in an age of content abundance, and content can be considered as infrastructure for building adaptive and personalized learning paths, promoting both formal and informal learning. Nevertheless, although most educational institutions are adopting a more open approach, publishing huge amounts of educational resources, the reality is that these resources are barely used in other educational contexts. This paradox can be partly explained by the dificulties in adapting such resources with respect to language, e-learning standards and specifications and, finally, granularity. Furthermore, if we want our learners to use and take advantage of learning object repositories, we need to provide them with additional services than just browsing and searching for resources. Social networks can be a first step towards creating an open social community of learning around a topic or a subject. In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, with respect to the information architecture needed to capture and store the interaction between learners and resources in form of learning object metadata.

Promoting the Creation, Share and Use of OERs through Open Repositories and Social Networks

In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, including aspects related to open source technologies, promoting the use of the repository by means of social networks and helping learners to develop their own learning paths.

The building blocks of the full body ownership illusion

Previous work has reported that it is not difficult to give people the illusion of ownership over an artificial body, providing a powerful tool for the investigation of the neural and cognitive mechanisms underlying body perception and self consciousness. We present an experimental study that uses immersive virtual reality (IVR) focused on identifying the perceptual building blocks of this illusion. We systematically manipulated visuotactile and visual sensorimotor contingencies, visual perspective, and the appearance of the virtual body in order to assess their relative role and mutual interaction. Consistent results from subjective reports and physiological measures showed that a first person perspective over a fake humanoid body is essential for eliciting a body ownership illusion. We found that the illusion of ownership can be generated when the virtual body has a realistic skin tone and spatially substitutes the real body seen from a first person perspective. In this case there is no need for an additional contribution of congruent visuotactile or sensorimotor cues. Additionally, we found that the processing of incongruent perceptual cues can be modulated by the level of the illusion: when the illusion is strong, incongruent cues are not experienced as incorrect. Participants exposed to asynchronous visuotactile stimulation can experience the ownership illusion and perceive touch as originating from an object seen to contact the virtual body. Analogously, when the level of realism of the virtual body is not high enough and/or when there is no spatial overlap between the two bodies, then the contribution of congruent multisensory and/or sensorimotor cues is required for evoking the illusion. On the basis of these results and inspired by findings from neurophysiological recordings in the monkey, we propose a model that accounts for many of the results reported in the literature.

Simulació de xarxes WSN (Wireless Sensor Network) per a la detecció d'incendis

Trabajo de final de carrera enfocado a la simulación de una WSN (Wireless Sensors Networks) mediante el programa Contiki 2.7 y el SO Ubuntu. La idea global del proyecto es conseguir simular un entorno con nodos sensores y, a posteriori, comprobar su correcto funcionamiento en motas reales, comprobando los resultados obtenidos en ambos entornos. De esta manera se puede facilitar la puesta en marcha de este tipo de redes inalámbricas en una aplicación real.