National Air Traffic Services

National Air Traffic Services (NATS) are concerned with ensuring low probabilities of errors in determining aircraft positions. In general, error probabilities depend on the tails of some probability distributions for which there has been no theoretical model. Analysis of radar performance is regularly undertaken by NATS to ensure radar performance is within safety limits, with the maximum range being dependent on the declared separation between aircraft. NATS brought two questions to the Study Group, involving the horizontal (azimuthal) errors in radar data and the vertical errors in altimetry system data. In both cases, NATS asked the Study Group to analyse the data and assess whether the probability distributions that are currently used are good models for the errors

"Social Selves"

In speaking of work from his photo book "American Surfaces," Stephen Shore says "...all of it was looking at the culure, the build culture." The surface of things at times expands beyong two dimensional flateness and visual aesthetic to agents involved in creating and consuming such surfaces. This expansion of two dimensional flateness to three dimensional dynamics is especially evident in cities alive with varying degerees of personhood imposed and projected upon surfaces. Image content and how images function are among questions I address in my photographic practice while taking into account theories of Martin Heidegger, W.E.B. Du Bois, Karl Marx and advertising strategies. Applying said theory to my photographic practice along with the application of paint to photographs has led to a creation of images that expands from two dimensional flatness to three dimensions in terms of form and content

Symmetry and the Origin of Mass

Fermilab High Energy Frontier experiments at the FermilabTevatron, and the LHC at CERN, will soon provide clues as to the mechanisms involved in the origin of the phenomenon of mass in nature. This is intimately tied to the fundamental symmetry principles that define modern physics, and may usher in a plethora of new elementary particles, new symmetries, and new dynamics

Exercise intensity-dependent effects of arm and leg-cycling on cognitive performance

Physiological responses to arm and leg-cycling are different, which may influence psychological and biological mechanisms that influence post-exercise cognitive performance. The aim of this study was to determine the effects of maximal and submaximal (absolute and relative intensity matched) arm and leg-cycling on executive function. Thirteen males (age, 24.7 ± 5.0 years) initially undertook two incremental exercise tests to volitional exhaustion for arm-cycling (82 ± 18 W) and leg-cycling (243 ± 52 W) for the determination of maximal power output. Participants subsequently performed three 20-min constant load exercise trials: (1) arm-cycling at 50% of the ergometer-specific maximal power output (41 ± 9 W), (2) leg-cycling at 50% of the ergometer-specific maximal power output (122 ± 26 W), and (3) leg-cycling at the same absolute power output as the submaximal arm-cycling trial (41 ± 9 W). An executive function task was completed before, immediately after and 15-min after each exercise test. Exhaustive leg-cycling increased reaction time (p 0.05). Improvements in reaction time following arm-cycling were maintained for at least 15-min post exercise (p = 0.008, d = -0.73). Arm and leg-cycling performed at the same relative intensity elicit comparable improvements in cognitive performance. These findings suggest that individuals restricted to arm exercise possess a similar capacity to elicit an exercise-induced cognitive performance benefit

Hardware as a service - enabling dynamic, user-level bare metal provisioning of pools of data center resources.

We describe a “Hardware as a Service (HaaS)” tool for isolating pools of compute, storage and networking resources. The goal of HaaS is to enable dynamic and flexible, user-level provisioning of pools of resources at the so-called “bare-metal” layer. It allows experimental or untrusted services to co-exist alongside trusted services. By functioning only as a resource isolation system, users are free to choose between different system scheduling and provisioning systems and to manage isolated resources as they see fit. We describe key HaaS use cases and features. We show how HaaS can provide a valuable, and somehwat overlooked, layer in the software architecture of modern data center management. Documentation and source code for HaaS software are available at: https://github.com/CCI-MOC/haasPartial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation award #1347525 and several commercial partners of the Mass Open Cloud who may be found at http://www.massopencloud.org.http://www.ieee-hpec.org/2014/CD/index_htm_files/FinalPapers/116.pd

Insights from structural studies of the cardiovirus 2A protein.

Cardioviruses are single-stranded RNA viruses of the family Picornaviridae. In addition to being the first example of internal ribosome entry site (IRES) utilization, cardioviruses also employ a series of alternative translation strategies, such as Stop-Go translation and programmed ribosome frameshifting. Here, we focus on cardiovirus 2A protein, which is not only a primary virulence factor, but also exerts crucial regulatory functions during translation, including activation of viral ribosome frameshifting and inhibition of host cap-dependent translation. Only recently, biochemical and structural studies have allowed us to close the gaps in our knowledge of how cardiovirus 2A is able to act in diverse translation-related processes as a novel RNA-binding protein. This review will summarize these findings, which ultimately may lead to the discovery of other RNA-mediated gene expression strategies across a broad range of RNA viruses

Learning and Inference in Massive Social Networks

Researchers and practitioners increasingly are gaining access to data on explicit social networks. For example, telecommunications and technology firms record data on consumer networks (via phone calls, emails, voice-over-IP, instant messaging), and social-network portal sites such as MySpace, Friendster and Facebook record consumer-generated data on social networks. Inference for fraud detection [5, 3, 8], marketing [9], and other tasks can be improved with learned models that take social networks into account and with collective inference [12], which allows inferences about nodes in the network to affect each other. However, these socialnetwork graphs can be huge, comprising millions to billions of nodes and one or two orders of magnitude more links. This paper studies the application of collective inference to improve prediction over a massive graph. Faced initially with a social network comprising hundreds of millions of nodes and a few billion edges, our goal is: to produce an approximate consumer network that is orders of magnitude smaller, but still facilitates improved performance via collective inference. We introduce a sampling technique designed to reduce the size of the network by many orders of magnitude, but to keep linkages that facilitate improved prediction via collective inference. In short, the sampling scheme operates as follows: (1) choose a set of nodes of interest; (2) then, in analogy to snowball sampling [14], grow local graphs around these nodes, adding their social networks, their neighbors’ social networks, and so on; (3) next, prune these local graphs of edges which are expected to contribute little to the collective inference; (4) finally, connect the local graphs together to form a graph with (hopefully) useful inference connectivity. We apply this sampling method to assess whether collective inference can improve learned targeted-marketing models for a social network of consumers of telecommunication services. Prior work [9] has shown improvement to the learning of targeting models by including social-neighborhood information—in particular, information on existing customers in the immediate social network of a potential target. However, the improvement was restricted to the “network neighbors”, those targets linked to a prior customer thought to be good candidates for the new service. Collective inference techniques may extend the predictive influence of existing customers beyond their immediate neighborhoods. For the present work, our motivating conjecture has been that this influence can improve prediction for consumers who are not strongly connected to existing customers. Our results show that this is indeed the case: collective inference on the approximate network enables significantly improved predictive performance for non-network-neighbor consumers, and for consumers who have few links to existing customers. In the rest of this extended abstract we motivate our approach, describe our sampling method, present results on applying our approach to a large real-world target marketing campaign in the telecommunications industry, and finally discuss our findings.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc