A machine learning classifier for fast radio burst detection at the VLBA

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%–90% of the candidates, with an accuracy greater than 98%, leaving only the 10%–20% most promising candidates to be reviewed by humans

ESIP_sUASWorkshop_Flyer_2017.pdf

Workshop at the summer ESIP Meetin

Integration of sUAS imagery and atmospheric data collection for improved agricultural greenhouse gas emissions monitoring

We present on a full season of low-cost sUAS agricultural monitoring for improved GHG emissions accounting and mitigation. Agriculture contributes 10-12% of global anthropogenic GHG emissions, and roughly half are from agricultural soils. A variety of land management strategies can be implemented to reduce GHG emissions, but agricultural lands are complex and heterogenous. Nutrient cycling processes that ultimately regulate GHG emission rates are affected by environmental and management dynamics that vary spatially and temporally (e.g. soil properties, manure spreading). Thus, GHG mitigation potential is also variable, and determining best practices for mitigation is challenging, especially considering potential conflicting pressure to manage agricultural lands for other objectives (e.g. decrease agricultural runoff). Monitoring complexity from agricultural lands is critical for regional GHG accounting and decision making, but current methods (e.g., static chambers) are time intensive, expensive, and use in-situ equipment. These methods lack the spatio-temporal flexibility necessary to reduce the high uncertainty in regional emissions estimates, while traditional remote sensing methods often do not provide adequate spatio-temporal resolution for robust field-level monitoring. Small Unmanned Aerial Systems (sUAS) provide the range and the rapid response data collection needed to monitor key variables on the landscape (imagery) and from the atmosphere (CO2 concentrations), and can provide ways to bridge between in-situ and remote sensing data. Initial results show good agreement between sUAS CO2 sensors with more traditional equipment, and at a fraction of the cost. We present results from test flights over managed agricultural landscapes in Vermont, showcasing capabilities from both sUAS imagery and atmospheric data collected from on-board sensors (CO2, PTH). We then compare results from two different in-flight data collection methods: Vertical Profile and Horizontal Surveys. We conclude with results from the integration of these sUAS data with concurrently collected in-field measurements from static chambers and Landsat imagery, demonstrating enhanced understanding of agricultural landscapes and improved GHG emissions monitoring with the addition of sUAS collected data.<div><br></div><div>This poster was presented at the American Geophysical Union Fall Meeting 2017. </div

Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development

The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data&mdash;as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases&mdash;is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting&mdash;as common across the spectrum of disciplines and use cases&mdash;novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research