YSE-PZ: A Transient Survey Management Platform that Empowers the Human-in-the-Loop

The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by a factor of ~20, with associated survey data, archival data, and metadata also increasing with the number of discoveries. To manage the data at this increased rate, we require new tools. Here we present YSE-PZ, a transient survey management platform that ingests multiple live streams of transient discovery alerts, identifies the host galaxies of those transients, downloads coincident archival data, and retrieves photometry and spectra from ongoing surveys. YSE-PZ also presents a user with a range of tools to make and support timely and informed transient follow-up decisions. Those subsequent observations enhance transient science and can reveal physics only accessible with rapid follow-up observations. Rather than automating out human interaction, YSE-PZ focuses on accelerating and enhancing human decision making, a role we describe as empowering the human-in-the-loop. Finally, YSE-PZ is built to be flexibly used and deployed; YSE-PZ can support multiple, simultaneous, and independent transient collaborations through group-level data permissions, allowing a user to view the data associated with the union of all groups in which they are a member. YSE-PZ can be used as a local instance installed via Docker or deployed as a service hosted in the cloud. We provide YSE-PZ as an open-source tool for the community.Comment: 23 pages, 9 figures, submitted to PAS

Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1099/thumbnail.jp

The SkyMapper Transient Survey

The SkyMapper 1.3 m telescope at Siding Spring Observatory has now begun regular operations. Alongside the Southern Sky Survey, a comprehensive digital survey of the entire southern sky, SkyMapper will carry out a search for supernovae and other transients. The search strategy, covering a total footprint area of ~2000 deg2 with a cadence of $\leq 5$ days, is optimised for discovery and follow-up of low-redshift type Ia supernovae to constrain cosmic expansion and peculiar velocities. We describe the search operations and infrastructure, including a parallelised software pipeline to discover variable objects in difference imaging; simulations of the performance of the survey over its lifetime; public access to discovered transients; and some first results from the Science Verification data.Comment: 13 pages, 11 figures; submitted to PAS

Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1202/thumbnail.jp

Sharing behavior in emergencies: An instantiation of a utility-focused prototype of a secure mobile near-real-time content device in pre-hospital and hospital settings

The implementation of healthcare information technology largely exhibits a ‘lack of fit’ with medical practice workflow, especially when data collection devices interfere with care during emergencies. Employing the design science paradigm and interpretive theory building, we examine the credibility, utility, and sharing of near-auto generated, near-real-time content regarding motor vehicle accidents. We began constructing a mobile security information model and building a mobile prototype to study the dynamics of contents sharing in the pre-hospital and hospital settings. From our focus group interviews, we learned that the most valuable feature of the prototype was the ability to capture and transmit data, audio, photo, and video contents prior to the arrival of the patient to the hospital: contents that inform clinical decisions regarding diagnostic preparedness, triaging, and therapeutic activities. We theorize that a credible content incentivizes sharing attitude and instrumental use which influence sharing behavior. We plan further observations to refine the proposition

Prevalence of Earth-size planets orbiting Sun-like stars

Determining whether Earth-like planets are common or rare looms as a touchstone in the question of life in the universe. We searched for Earth-size planets that cross in front of their host stars by examining the brightness measurements of 42,000 stars from National Aeronautics and Space Administration's Kepler mission. We found 603 planets, including 10 that are Earth size (1-2 Earth-radii) and receive comparable levels of stellar energy to that of Earth (within a factor of four). We account for Kepler's imperfect detectability of such planets by injecting synthetic planet-caused dimmings into the Kepler brightness measurements and recording the fraction detected. We find that $11\pm4$ of Sun-like stars harbor an Earth-size planet receiving between one and four times the stellar intensity as Earth. We also find that the occurrence of Earth-size planets is constant with increasing orbital period (P), within equal intervals of logP up to $\sim200$ d. Extrapolating, one finds $5.7^{+1.7}_{-2.2}$ of Sun-like stars harbor an Earth-size planet with orbital periods of 200-400 d.Comment: Main text: 6 pages, 5 figures, 1 table. Supporting information: 54 pages, 17 pages, 3 tables. Published in the Proceedings of the National Academy of Sciences available at http://www.pnas.org/cgi/doi/10.1073/pnas.131990911

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

PREM: Prestige Network Enhanced Developer-Task Matching for Crowdsourced Software Development

Many software organizations are turning to employ crowdsourcing to augment their software production. For current practice of crowdsourcing, it is common to see a mass number of tasks posted on software crowdsourcing platforms, with little guidance for task selection. Considering that crowd developers may vary greatly in expertise, inappropriate developer-task matching will harm the quality of the deliverables. It is also not time-efficient for developers to discover their most appropriate tasks from vast open call requests. We propose an approach called PREM, aiming to appropriately match between developers and tasks. PREM automatically learns from the developers’ historical task data. In addition to task preference, PREM considers the competition nature of crowdsourcing by constructing developers’ prestige network. This differs our approach from previous developer recommendation methods that are based on task and/or individual features. Experiments are conducted on 3 TopCoder datasets with 9,191 tasks in total. Our experimental results show that reasonable accuracies are achievable (63%, 46%, 36% for the 3 datasets respectively, when matching 5 developers to each task) and the constructed prestige network can help improve the matching results