20,486 research outputs found
QCD and Hard Diffraction at the LHC
As an introduction to QCD at the LHC I give an overview of QCD at the
Tevatron, emphasizing the high Q^2 frontier which will be taken over by the
LHC. After describing briefly the LHC detectors I discuss high mass
diffraction, in particular central exclusive production of Higgs and vector
boson pairs. I introduce the FP420 project to measure the scattered protons
420m downstream of ATLAS and CMS.Comment: To be published in Proceedings of the XIth International Conference
on Elastic and Diffractive Scattering, BLois (2005). 6 pages, 0 figure
Using Twitter to Understand Public Interest in Climate Change: The case of Qatar
Climate change has received an extensive attention from public opinion in the
last couple of years, after being considered for decades as an exclusive
scientific debate. Governments and world-wide organizations such as the United
Nations are working more than ever on raising and maintaining public awareness
toward this global issue. In the present study, we examine and analyze Climate
Change conversations in Qatar's Twittersphere, and sense public awareness
towards this global and shared problem in general, and its various related
topics in particular. Such topics include but are not limited to politics,
economy, disasters, energy and sandstorms. To address this concern, we collect
and analyze a large dataset of 109 million tweets posted by 98K distinct users
living in Qatar -- one of the largest emitters of CO2 worldwide. We use a
taxonomy of climate change topics created as part of the United Nations Pulse
project to capture the climate change discourse in more than 36K tweets. We
also examine which topics people refer to when they discuss climate change, and
perform different analysis to understand the temporal dynamics of public
interest toward these topics.Comment: Will appear in the proceedings of the International Workshop on
Social Media for Environment and Ecological Monitoring (SWEEM'16
Empirical Analysis of Time Series
Time series occur in many fields of biology, physics, chemistry, engineering. Much work has been recently performed in statistical physics using specific mathematical techniques on various time series pertaining to so-called nonlinear phenomena. Several methods, beyond the Fourier transform, are presented here. To distinguish between noise and deterministic content is the major challenge. Various phenomena are used for illustration. Some emphasis on findings and still questions will be drawn from problems in finance due to the existence (or not) of long-, medium-, short-range (power-law or not) correlations in such economic systems. The Fourier transform, the Hurst rescaled range, the instantaneous detrended fluctuations, the moving averages, and the Zipf-plots analysis methods will be recalled. They raise questions about fractional Brownian motion properties, or in sorting out correlation ranges and predictability. Among spectacular results, the possibility of crash predictions will be indicated when there is an underlying discrete scale invariance. Other time series for meteorology and electronics phenomena are also presented in order to discuss stratus cloud breaking and dielectric breakdown through avalanches for illustration purpose and to indicate that there are other widely open fields of possible investigations.time series; finance; fourier transform; Hurst exponenet; multifractal; detrended fluctuation analysis; moving average; Zipf; crashes
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Practical issues for the implementation of survivability and recovery techniques in optical networks
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