7 research outputs found
A Study of Realtime Summarization Metrics
Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions.
The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems
Naturalistic Routing Using Inverse Reinforcement Learning
This disclosure describes techniques, referred to as naturalistic routing (NR), that improve the quality of routes found by map applications by learning from users’ real-world navigation actions, accessed with user permission. The techniques leverage the principle that users, in the aggregate, tend to travel on optimal routes to reach their destinations. A machine learning model is trained using inverse reinforcement learning and provides routes that are optimal by the users’ definition of optimality, as determined from a dataset of navigation actions
Massively Scalable Inverse Reinforcement Learning in Google Maps
Optimizing for humans' latent preferences is a grand challenge in route
recommendation, where globally-scalable solutions remain an open problem.
Although past work created increasingly general solutions for the application
of inverse reinforcement learning (IRL), these have not been successfully
scaled to world-sized MDPs, large datasets, and highly parameterized models;
respectively hundreds of millions of states, trajectories, and parameters. In
this work, we surpass previous limitations through a series of advancements
focused on graph compression, parallelization, and problem initialization based
on dominant eigenvectors. We introduce Receding Horizon Inverse Planning
(RHIP), which generalizes existing work and enables control of key performance
trade-offs via its planning horizon. Our policy achieves a 16-24% improvement
in global route quality, and, to our knowledge, represents the largest instance
of IRL in a real-world setting to date. Our results show critical benefits to
more sustainable modes of transportation (e.g. two-wheelers), where factors
beyond journey time (e.g. route safety) play a substantial role. We conclude
with ablations of key components, negative results on state-of-the-art
eigenvalue solvers, and identify future opportunities to improve scalability
via IRL-specific batching strategies
Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: insights from Spring 2020
Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions
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Development of a Novel Tablet-based Approach to Reduce HIV Stigma among Healthcare Staff in India.
Although stigma is considered to be one of the major barriers to reducing the AIDS epidemic in India, efforts to reduce stigma have not been sufficiently examined. In response, a partially computer-administered three-session stigma reduction intervention was developed and is currently being tested. This paper describes the technological design, development, implementation, and management of these in-person tablet-administered assessment and intervention sessions that are being used to evaluate the efficacy of this innovative stigma reduction intervention among nursing students and ward attendants in India
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OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic