School Assignment, School Choice and Social Mobility

We estimate the chances of poor and non-poor children getting places in good schools, analysing the relationship between poverty, location and school assignment. Our dataset allows us to measure location and distance very precisely. The simple unconditional difference in probabilities of attending a good school is substantial. We run an analysis that controls completely for location, exploiting within-street variation and controlling for other personal characteristics. Children from poor families are significantly less likely to go to good schools. We show that the lower chance of poor children attending a good school is essentially unaffected by the degree of choice.School assignment, social mobility, school choice

Demo: Snap – Rapid Sensornet Deployment with a Sensornet Appstore

Despite ease of deployment being seen as a primary advantage of sensor networks, deployment remains difficult. We present Snap, a system for rapid sensornet deployment that allows sensor networks to be deployed, positioned, and reprogrammed through a sensornet appstore. Snap uses a smartphone interface that uses QR codes for node identification, a map interface for node positioning, and dynamic loading of applications on the nodes. Snap nodes run the Contiki operating system and its low-power IPv6 network stack that provides direct access from nodes to the smartphone application. We demonstrate rapid sensor node deployment, identification, positioning, and node reprogramming within seconds, over a multi-hop sensornet routing path with a WiFi-connected smartphone

Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.Comment: Published and presented in the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014

Cosmic Shear with Einstein Rings

We explore a new technique to measure cosmic shear using Einstein rings. In Birrer et al. (2017), we showed that the detailed modelling of Einstein rings can be used to measure external shear to high precision. In this letter, we explore how a collection of Einstein rings can be used as a statistical probe of cosmic shear. We present a forecast of the cosmic shear information available in Einstein rings for different strong lensing survey configurations. We find that, assuming that the number density of Einstein rings in the COSMOS survey is representative, future strong lensing surveys should have a cosmological precision comparable to the current ground based weak lensing surveys. We discuss how this technique is complementary to the standard cosmic shear analyses since it is sensitive to different systematic and can be used for cross-calibration.Comment: 4 pages, 1 figure, 1 table. ApJL accepte