Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models

Aerodynamic characteristics at Mach numbers of 1.5, 1.8, and 2.0 of a blended wing-body configuration with and without integral canards

An exploratory, experimental, and theoretical investigation was made of a cambered, twisted, and blended wing-body concept with and without integral canard surfaces. Theoretical calculations of the static longitudinal and lateral aerodynamic characteristics of the wing-body configurations were compared with the characteristics obtained from tests of a model in the Langley Unitary Plan wind tunnel. Mach numbers of 1.5, 1.8, and 2.0 and a Reynolds number per meter of 6.56 million were used in the calculations and tests. Overall results suggest that planform selection is extremely important and that the supplemental application of new calculation techniques should provide a process for the design of supersonic wings in which spanwise distribution of upwash and leading-edge thrust might be rationally controlled and exploited

A multibeam atom laser: coherent atom beam splitting from a single far detuned laser

We report the experimental realisation of a multibeam atom laser. A single continuous atom laser is outcoupled from a Bose-Einstein condensate (BEC) via an optical Raman transition. The atom laser is subsequently split into up to five atomic beams with slightly different momenta, resulting in multiple, nearly co-propagating, coherent beams which could be of use in interferometric experiments. The splitting process itself is a novel realization of Bragg diffraction, driven by each of the optical Raman laser beams independently. This presents a significantly simpler implementation of an atomic beam splitter, one of the main elements of coherent atom optics

Artificial neural networks and player recruitment in professional soccer

The aim was to objectively identify key performance indicators in professional soccer that influence outfield players’ league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players’ (mean SD; age: 25 ± 4 yr, 1.81 ±) 90-minute performances in the English Football League. ProZone’s MatchViewer system and online databases were used to collect data on 347 indicators assessing the total number, accuracy and consistency of passes, tackles, possessions regained, clearances and shots. Players were assigned to one of three categories based on where they went on to complete most of their match time in the following season: group 0 (n = 209 players) went on to play in a lower soccer league, group 1 (n = 637 players) remained in the Football League Championship, and group 2 (n = 120 players) consisted of players who moved up to the English Premier League. The models created correctly predicted between 61.5% and 78.8% of the players’ league status. The model with the highest average test performance was for group 0 v 2 (U21 international caps, international caps, median tackles, percentage of first time passes unsuccessful upper quartile, maximum dribbles and possessions gained minimum) which correctly predicted 78.8% of the players’ league status with a test error of 8.3%. To date, there has not been a published example of an objective method of predicting career trajectory in soccer. This is a significant development as it highlights the potential for machine learning to be used in the scouting and recruitment process in a professional soccer environment

A proof of Bell\u27s inequality in quantum mechanics using causal interactions

We give a simple proof of Bell\u27s inequality in quantum mechanics which, in conjunction with experiments, demonstrates that the local hidden variables assumption is false. The proof sheds light on relationships between the notion of causal interaction and interference between particles

Quantum tunneling dynamics of an interacting Bose-Einstein condensate through a Gaussian barrier

The transmission of an interacting Bose-Einstein condensate incident on a repulsive Gaussian barrier is investigated through numerical simulation. The dynamics associated with interatomic interactions are studied across a broad parameter range not previously explored. Effective 1D Gross-Pitaevskii equation (GPE) simulations are compared to classical Boltzmann-Vlasov equation (BVE) simulations in order to isolate purely coherent matterwave effects. Quantum tunneling is then defined as the portion of the GPE transmission not described by the classical BVE. An exponential dependence of transmission on barrier height is observed in the purely classical simulation, suggesting that observing such exponential dependence is not a sufficient condition for quantum tunneling. Furthermore, the transmission is found to be predominately described by classical effects, although interatomic interactions are shown to modify the magnitude of the quantum tunneling. Interactions are also seen to affect the amount of classical transmission, producing transmission in regions where the non-interacting equivalent has none. This theoretical investigation clarifies the contribution quantum tunneling makes to overall transmission in many-particle interacting systems, potentially informing future tunneling experiments with ultracold atoms.Comment: Close to the published versio

A detector for continuous measurement of ultra-cold atoms in real time

We present the first detector capable of recording high-bandwidth real time atom number density measurements of a Bose Einstein condensate. Based on a two-color Mach-Zehnder interferometer, our detector has a response time that is six orders of magnitude faster than current detectors based on CCD cameras while still operating at the shot-noise limit. With this minimally destructive system it may be possible to implement feedback to stabilize a Bose-Einstein condensate or an atom laser.Comment: 3 pages, 3 figures, submitted to optics letter

Urban encounters: juxtapositions of difference and the communicative interface of global cities

This article explores the communicative interface of global cities, especially as it is shaped in the juxtapositions of difference in culturally diverse urban neighbourhoods. These urban zones present powerful examples, where different groups live cheek by jowl, in close proximity and in intimate interaction — desired or unavoidable. In these urban locations, the need to manage difference is synonymous to making them liveable and one's own. In seeking (and sometimes finding) a location in the city and a location in the world, urban dwellers shape their communication practices as forms of everyday, mundane and bottom-up tactics for the management of diversity. The article looks at three particular areas where cultural diversity and urban communication practices come together into meaningful political and cultural relations for a sustainable cosmopolitan life: citizenship, imagination and identity

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks