TREC Incident Streams: Finding Actionable Information on Social Media

The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social media-based emergency response technology. This initiative advances the state of the art in this area through an evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types, proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately 20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the 39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current state-of-the-art is insufficient for emergency responders’ requirements, particularly for rare actionable information for which there is little prior training data available

Incident Streams 2019: Actionable Insights and How to Find Them

The ubiquity of mobile internet-enabled devices combined with wide-spread social media use during emergencies is posing new challenges for response personnel. In particular, service operators are now expected to monitor these online channels to extract actionable insights and answer questions from the public. A lack of adequate tools makes this monitoring impractical at the scale of many emergencies. The TREC Incident Streams (TREC-IS) track drives research into solving this technology gap by bringing together academia and industry to develop techniques for extracting actionable insights from social media streams during emergencies. This paper covers the second year of TREC-IS, hosted in 2019 with two editions, 2019-A and 2019-B, contributing 12 new events and approximately 20,000 new tweets across 25 information categories, with 15 research groups participating across the world. This paper provides an overview of these new editions, actionable insights from data labelling, and the automated techniques employed by participant systems that appear most effective

TREC Incident Streams: Finding Actionable Information on Social Media

The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social media-based emergency response technology. This initiative advances the state of the art in this area through an evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types, proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately 20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the 39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current state-of-the-art is insufficient for emergency responders’ requirements, particularly for rare actionable information for which there is little prior training data available

Partial mixing and the formation of 13C pockets in AGB stars: effects on the s-process elements

The production of the elements heavier than iron via slow neutron captures (the s process) is a main feature of the contribution of asymptotic giant branch (AGB) stars of low mass (< 5 Msun) to the chemistry of the cosmos. However, our understanding of the main neutron source, the 13C(alpha,n)16O reaction, is still incomplete. It is commonly assumed that in AGB stars mixing beyond convective borders drives the formation of 13C pockets. However, there is no agreement on the nature of such mixing and free parameters are present. By means of a parametric model we investigate the impact of different mixing functions on the final s-process abundances in low-mass AGB models. Typically, changing the shape of the mixing function or the mass extent of the region affected by the mixing produce the same results. Variations in the relative abundance distribution of the three s-process peaks (Sr, Ba, and Pb) are generally within +/-0.2 dex, similar to the observational error bars. We conclude that other stellar uncertainties - the effect of rotation and of overshoot into the C-O core - play a more important role than the details of the mixing function. The exception is at low metallicity, where the Pb abundance is significantly affected. In relation to the composition observed in stardust SiC grains from AGB stars, the models are relatively close to the data only when assuming the most extreme variation in the mixing profile.Comment: 17 pages, 8 figures, 6 tables, accepted for publications on Monthly Notices of the Royal Astronomical Societ

Impact of in-plant HACCP implementation on food animal production in the United States

The new United States Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) regulations, known as the Final Rule on Pathogen Reduction and Hazard Analysis and Critical Control Points (HACCP) Systems, were published on July 25, 1996. (a) The rule makes clear that industry is responsible for producing and marketing products that are safe, unadulterated and properly labeled and packaged. The FSIS\u27s role is to provide regulatory oversight and enforcement of the rule in meat and poultry establishments. The final rule mandates two key strategies: (l) HACCP systems to prevent chemical, physical, and microbial hazards; and (2) new performance measures in slaughter and processing plants to target and reduce the presence of pathogenic organisms in meat and poultry products