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

The End-of-Substructure Card for the ATLAS ITk Strip Detector: Status of the Electronics Design and Results from Recent Quality Control Tests

The silicon tracker of the ATLAS experiment will be upgraded for the upcoming High-Luminosity Upgrade of the LHC (HL-LHC). The main building blocks of the new strip tracker are modules that consist of silicon sensors and read-out ASICs, the latter hosted on hybrid PCBs. Up to 14 modules are assembled on carbon-fibre substructures, commonly named staves in the central barrel region and petals in the two end-cap regions, for mechanical support. An End-of-Substructure (EoS) card is located at the end of each substructure and facilitates the transfer of data, power, and control signals between the modules and the off-detector systems. The module front-end ASICs transfer data (up to 28 differential lines at 640 MBit/s) to low-powered GigaBit Transceivers (lpGBT) ASICs on the EoS card. The lpGBT(s) provide data serialisation and use a 10 GBit/s versatile optical link plus transceiver (VTRx+) package to transmit signals to the off-detector systems. To meet the tight integration requirements in the detector, several EoS card designs have been realised. The produced prototypes have been populated with the currently available versions of the lpGBT and VTRx+ ASICs. Here, we present the current status of the EoS cards electronic design, results from extreme temperature, magnetic field and integration tests. Additionally, we discuss the results of detailed investigations into the optical signal quality and introduce a new eye-diagram extraction tool to be used in the Quality Control (QC) procedure that aims to ensure full functionality of the EoS card throughout the entire HL-LHC operation

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit