Distributed and parallel sparse convex optimization for radio interferometry with PURIFY

Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity and resolution than before, computational challenges in image reconstruction need to be overcome to realize the potential of forthcoming telescopes. New methods in sparse image reconstruction and convex optimization techniques (cf. compressive sensing) have shown to produce higher fidelity reconstructions of simulations and real observations than traditional methods. This article presents distributed and parallel algorithms and implementations to perform sparse image reconstruction, with significant practical considerations that are important for implementing these algorithms for Big Data. We benchmark the algorithms presented, showing that they are considerably faster than their serial equivalents. We then pre-sample gridding kernels to scale the distributed algorithms to larger data sizes, showing application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to 50 billion visibilities, and find that the run-times for the distributed algorithms range from 100 milliseconds to 3 minutes per iteration. This work presents an important step in working towards computationally scalable and efficient algorithms and implementations that are needed to image observations of both extended and compact sources from next generation radio interferometers such as the SKA. The algorithms are implemented in the latest versions of the SOPT (https://github.com/astro-informatics/sopt) and PURIFY (https://github.com/astro-informatics/purify) software packages {(Versions 3.1.0)}, which have been released alongside of this article.Comment: 25 pages, 5 figure

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

El mercadeo para comerciantes y microempresas

En la actualidad, la finalidad e interpretación del mercadeo en el sector comercial y micro empresarial se percibe como un gasto en el proceso de desarrollo, minimizando las ganancias esperadas por los socios o dueños, sin darse cuenta, que el mercadeo es una herramienta catalizadora que bien aplicada no representa una inversión muy alta, y si, tiene como finalidad construir relaciones redituables con los clientes actuales y futuros. Pero la realidad muestra que el mercadeo es una herramienta básica para el emprendimiento en el sector real y la aplicación de este no requiere invertir grandes cantidades de dinero en el desarrollo de las dichas actividades, en este documento encontrará los pasos y la manera de aplicación del marketing en una manera fácil y práctica de aplicar mezclando los cinco pasos fundamentales del mercadeo desde la idea del negocio hasta la manera en que puede posicionarse en el mercado, dejando como inquietud asta donde está dispuesto a llegar en la aplicación de la herramient

Geometry of subduction and depth of the seismogenic zone in the Guerrero gap, Mexico

L'étude sismique de la zone côtière de Guerrero (Mexique) permet d'interpréter la géométrie de la subduction dans cette régio

Evolution of Cooperation in Mobile Populations

We consider a finite, fixed-size population of mobile cooperators and free-riders. A cooperator is an individual who, at a cost to itself, provides benefits to any and all individuals in its vicinity, whereas a free-rider does not provide any benefits and thus pays no cost. Individuals are free to move to maximize their payoff, and our model allows for the interactions among multiple individuals at the same time. Using Gillespie\u27s algorithm, we build an exact stochastic simulation of this continuous-time Markov process and find that decreasing the individuals\u27 mobility or decreasing the size of the interaction neighborhood promotes the fixation of cooperators in the population

Influenza research database: an integrated bioinformatics resource for influenza research and surveillance.

BackgroundThe recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.DesignThe Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected 'workbench' spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.ResultsTo demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.ConclusionsThe IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics

Empleo de fermentaciones secuenciales con levaduras no-Saccharomyces y aplicación de bloqueadores metabólicos para reducir el grado alcohólico en vinos

La combinación secuencial de especies no-Saccharomyces y Saccharomyces durante la fermentación y la adición de bloqueadores metabólicos como el furfural, o-vainillina, glicolaldehído y p-benzoquinona pueden resultar unas técnicas de vinificación interesantes para reducir el grado alcohólico del vino. El grado alcohólico se determinó por HPLC-IR y los azúcares residuales mediante tests enzimáticos. Las cepas de levadura 7013 (Torulaspora delbrueckii) y 938 (Schizosaccharomyces pombe) destacaron por su capacidad para reducir significativamente el grado alcohólico (reducción media del 2.1 % v/v) dando lugar a un vino seco (azúcares menor que 1.5 gl-1) en fermentación secuencial con la 7VA (Saccharomyces cerevisiae). La o-vainillina permitió una disminución en el contenido de etanol del 0.54 % v/v a dosis de 50 mg l-1, mientras que el efecto bloqueador del glicolaldehído fue más efectivo a la dosis de 200 mg l-1 con una reducción del 0.95 % v/v. Finalmente con la p-benzoquinona se logró una reducción en el grado alcohólico de hasta 0.85 % v/v