Cosmology seeking friendship with sterile neutrinos

Precision cosmology and big-bang nucleosynthesis mildly favor extra radiation in the universe beyond photons and ordinary neutrinos, lending support to the existence of low-mass sterile neutrinos. We use the WMAP 7-year data, small-scale CMB observations from ACBAR, BICEP and QuAD, the SDSS 7th data release, and measurement of the Hubble parameter from HST observations to derive credible regions for the assumed common mass scale m_s and effective number N_s of thermally excited sterile neutrino states. Our results are compatible with the existence of one or perhaps two sterile neutrinos, as suggested by LSND and MiniBooNE, if m_s is in the sub-eV range.Comment: 4 pages, 1 figure, matches version published in PR

Petuum: A New Platform for Distributed Machine Learning on Big Data

What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, allowing ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl

Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server

In last decade, data analytics have rapidly progressed from traditional disk-based processing to modern in-memory processing. However, little effort has been devoted at enhancing performance at micro-architecture level. This paper characterizes the performance of in-memory data analytics using Apache Spark framework. We use a single node NUMA machine and identify the bottlenecks hampering the scalability of workloads. We also quantify the inefficiencies at micro-architecture level for various data analysis workloads. Through empirical evaluation, we show that spark workloads do not scale linearly beyond twelve threads, due to work time inflation and thread level load imbalance. Further, at the micro-architecture level, we observe memory bound latency to be the major cause of work time inflation.Comment: Accepted to The 5th IEEE International Conference on Big Data and Cloud Computing (BDCloud 2015

Lenticular galaxies with UV-rings

By using the public UV imaging data obtained by the GALEX (Galaxy Ultraviolet Explorer) for nearby galaxies, we have compiled a list of lenticular galaxies possessing ultraviolet rings - starforming regions tightly confined to particular radial distances from galactic centers. We have studied large-scale structure of these galaxies in the optical bands by using the data of the SDSS (Sloan Digital Sky Survey): we have decomposed the galactic images into large-scale disks and bulges, have measured the ring optical colours from the residual images after subtracting model disks and bulges, and have compared the sizes of the rings in the optical light and in the UV-band. The probable origin of the outer starforming ring appearances in unbarred galaxies demonstrating otherwise the regular structure and homogeneously old stellar population beyond the rings is discussed.Comment: 9 pages plus one big colour figure in the Appendix; the slightly expanded version of the paper accepted to Astronomy Letter

Proliferating Cloud Density through Big Data Ecosystem, Novel XCLOUDX Classification and Emergence of as-a-Service Era

Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X} classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed over the last few years

Humor styles and the ten personality dimensions from the Supernumerary Personality Inventory

BACKGROUND The present study examines the relationship between humor styles and the 10 Supernumerary Personality Inventory (SPI) traits to understand how humor styles correlate with personality dimensions “beyond the Big Five” model. Humor styles and the personality dimensions of the SPI have yet to be explored. Therefore, the aim of this study is to explore how humor styles correlate with traits outside of conventional personality models, in order to better understand humor expression related to personality traits. PARTICIPANTS AND PROCEDURE The data were from 693 adult participants (135 men and 560 women) from North America. RESULTS All four humor styles positively correlated with the SPI humorousness scale. The two positive humor styles, affiliative and self-enhancing, had significant positive correlations with the egotism SPI scale. The two negative humor styles, aggressive and self-defeating, had significant positive correlations with the SPI scales of seductiveness and manipulativeness and significant negative correlations with the integrity scale from the SPI. A sub-group of the sample (n = 471) also completed a Big Five personality measure. For this sample, the variance due to the Big Five was regressed out of the SPI scales. CONCLUSIONS The correlations between the SPI residuals and the humor style scores decreased from the unaltered SPI scale scores except for the aggressive humor style correlations, which were less affected, suggesting that this dimension of humor may have some variance “beyond” the Big Five