The political legacy of World war I and the Easter Rising in Longford

During the Irish War of Independence, between 1919 and 1921, Longford was one of the centres of the Irish Republican Army's guerrilla campaign against British rule in Ireland. The county's emergence as a centre of republican activity appears incongruous in light of its relatively peaceful history up to that point and in view of the fact that, outside of Dublin, its neighbouring Leinster counties were not particularly strongholds of IRA resistance. The explanation for Longford's role during the crucial years of the independence campaign is to be found in the political ransformation that occurred in the county in the crucial period of turmoil encompassing World War I and the Easter Rising.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.