Towards a Big Crunch Dual

We show there exist smooth asymptotically anti-de Sitter initial data which evolve to a big crunch singularity in a low energy supergravity limit of string theory. This opens up the possibility of using the dual conformal field theory to obtain a fully quantum description of the cosmological singularity. A preliminary study of this dual theory suggests that the big crunch is an endpoint of evolution even in the full string theory. We also show that any theory with scalar solitons must have negative energy solutions. The results presented here clarify our earlier work on cosmic censorship violation in N=8 supergravity.Comment: 27 pages, 3 figures;v2:minor correction

A Mathematical Theory of Big Data

This article presents a cardinality approach to big data, a fuzzy logicbased approach to big data, a similarity-based approach to big data, and a logical approach to the marketing strategy of social networking services. All these together constitute a mathematical theory of big data. This article also examines databases with infinite attributes. The research results reveal that relativity and infinity are two characteristics of big data. The relativity of big data is based on the theory of fuzzy sets. The relativity of big data leads to the continuum from small data to big data, big data-driven small data analytics to become statistical significance. The infinity of big data is based on the calculus and cardinality theory. The infinity of big data leads to the infinite similarity of big data. The proposed theory in this article might facilitate the mathematical research and development of big data, big data analytics, big data computing, and data science with applications in intelligent business analytics and business intelligence

Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three gaps stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind ( http://www.dataonthemind.org ), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets

Impact of Neutrino Oscillation Measurements on Theory

Neutrino oscillation data had been a big surprise to theorists, and indeed they have ongoing impact on theory. I review what the impact has been, and what measurements will have critical impact on theory in the future.Comment: 8 pages, uses aipproc, 6 figures. Plenary talk presented at Nufact 03, 5th International Workshop on Neutrino Factories & Superbeams, Columbia University, New York, 5-11 June 200

Big data, smart cities and city planning

I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems