24 research outputs found

    Debiased offline evaluation of recommender systems: A weighted-sampling approach

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    Offline evaluation of recommender systems mostly relies on historical data, which is often biased by many confounders. In such data, user-item interactions are Missing Not At Random (MNAR). Measures of recommender system performance on MNAR test data are unlikely to be reliable indicators of real-world performance unless something is done to mitigate the bias. One way that researchers try to obtain less biased offline evaluation is by designing new supposedly unbiased performance estimators for use on MNAR test data. We investigate an alternative solution, a sampling approach. The general idea is to use a sampling strategy on MNAR data to generate an intervened test set with less bias --- one in which interactions are Missing At Random (MAR) or, at least, one that is more MAR-like. An example of this is SKEW, a sampling strategy that aims to adjust for the confounding effect that an item's popularity has on its likelihood of being observed. In this paper, we propose a novel formulation for the sampling approach. We compare our solution to SKEW and to two baselines which perform a random intervention on MNAR data (and hence are equivalent to no intervention in practice). We empirically validate for the first time the effectiveness of SKEW and we show our approach to be a better estimator of the performance one would obtain on (unbiased) MAR test data. Our strategy benefits from high generality properties (e.g. it can also be employed for training a recommender) and low overheads (e.g. it does not require any learning)

    Tracking smell loss to identify healthcare workers with SARS-CoV-2 infection

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    Introduction Healthcare workers (HCW) treating COVID-19 patients are at high risk for infection and may also spread infection through their contact with vulnerable patients. Smell loss has been associated with SARS-CoV-2 infection, but it is unknown whether monitoring for smell loss can be used to identify asymptomatic infection among high risk individuals. In this study we sought to determine if tracking smell sensitivity and loss using an at-home assessment could identify SARS-CoV-2 infection in HCW. Methods and findings We performed a prospective cohort study tracking 473 HCW across three months to determine if smell loss could predict SARS-CoV-2 infection in this high-risk group. HCW subjects completed a longitudinal, behavioral at-home assessment of olfaction with household items, as well as detailed symptom surveys that included a parosmia screening questionnaire, and real-time quantitative polymerase chain reaction testing to identify SARS-CoV-2 infection. Our main measures were the prevalence of smell loss in SARS-CoV-2-positive HCW versus SARS-CoV- 2-negative HCW, and timing of smell loss relative to SARS-CoV-2 test positivity. SARS-CoV-2 was identified in 17 (3.6%) of 473 HCW. HCW with SARS-CoV-2 infection were more likely to report smell loss than SARS-CoV-2-negative HCW on both the at-home assessment and the screening questionnaire (9/17, 53% vs 105/456, 23%, P < .01). 6/9 (67%) of SARS-CoV-2-positive HCW reporting smell loss reported smell loss prior to having a positive SARS-CoV-2 test, and smell loss was reported a median of two days before testing positive. Neurological symptoms were reported more frequently among SARS-CoV-2-positive HCW who reported smell loss compared to those without smell loss (9/9, 100% vs 3/8, 38%, P < .01). Conclusions In this prospective study of HCW, self-reported changes in smell using two different measures were predictive of SARS-CoV-2 infection. Smell loss frequently preceded a positive test and was associated with neurological symptoms

    Gut Microbiome Dysbiosis in Antibiotic-Treated COVID-19 Patients is Associated with Microbial Translocation and Bacteremia

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    Although microbial populations in the gut microbiome are associated with COVID-19 severity, a causal impact on patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. We first demonstrate SARS-CoV-2 infection induces gut microbiome dysbiosis in mice, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, including blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19

    The James Webb Space Telescope Mission: Optical Telescope Element Design, Development, and Performance

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    The James Webb Space Telescope (JWST) is a large, infrared space telescope that has recently started its science program which will enable breakthroughs in astrophysics and planetary science. Notably, JWST will provide the very first observations of the earliest luminous objects in the Universe and start a new era of exoplanet atmospheric characterization. This transformative science is enabled by a 6.6 m telescope that is passively cooled with a 5-layer sunshield. The primary mirror is comprised of 18 controllable, low areal density hexagonal segments, that were aligned and phased relative to each other in orbit using innovative image-based wavefront sensing and control algorithms. This revolutionary telescope took more than two decades to develop with a widely distributed team across engineering disciplines. We present an overview of the telescope requirements, architecture, development, superb on-orbit performance, and lessons learned. JWST successfully demonstrates a segmented aperture space telescope and establishes a path to building even larger space telescopes.Comment: accepted by PASP for JWST Overview Special Issue; 34 pages, 25 figure

    The James Webb Space Telescope Mission

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    Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least 4m4m. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the 6.5m6.5m James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

    Real-time topic models for crisis counseling

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    ABSTRACT The proliferation of text-based crisis counseling platforms in recent months has opened an exciting opportunity for applied machine learning to (1) provide practical assistance for human counselors who provide emotional and practical support and (2) analyze counselor-caller interactions to build a landscape of the distribution of mental health issues experienced by callers on an unprecedented scale. We present Fathom, a natural language interface powered by topic models to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging. We apply a mixed-initiative labeled LDA model to analyze counselor-caller conversations and use them to power real-time visualizations aimed at mitigating counselor cognitive load. We discuss three key aspects of crisis counseling and why topic models are suitable for mining this phenomenon. We propose new variants of topic models inspired by the practical constraints posed by their real-time deployment

    Real-time topic models for crisis counseling

    No full text
    ABSTRACT The proliferation of text-based crisis counseling platforms in recent months has opened an exciting opportunity for applied machine learning to (1) provide practical assistance for human counselors who provide emotional and practical support and (2) analyze counselor-caller interactions to build a landscape of the distribution of mental health issues experienced by callers on an unprecedented scale. We present Fathom, a natural language interface powered by topic models to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging. We apply a mixed-initiative labeled LDA model to analyze counselor-caller conversations and use them to power real-time visualizations aimed at mitigating counselor cognitive load. We discuss three key aspects of crisis counseling and why topic models are suitable for mining this phenomenon. We propose new variants of topic models inspired by the practical constraints posed by their real-time deployment

    Mining Large-scale TV Group Viewing Patterns for Group Recommendation

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    We present a large-scale study of television viewing habits, focusing on how individuals adapt their preferences when consuming content in group settings. While there has been a great deal of recent work on modeling individual preferences, there has been considerably less work studying the behavior and preferences of groups, due mostly to the difficulty of data collection in these settings. In contrast to past work that has relied either on small-scale surveys or prototypes, we explore more than 4 million logged views paired with individual-level demographic and co-viewing information to uncover variation in the viewing patterns of individuals and groups. Our analysis reveals which genres are popular among specific demographic groups when viewed individually, how often individuals from different demographic categories participate in group viewing, and how viewing patterns change in various group contexts. Furthermore, we leverage this large-scale dataset to directly estimate how individual preferences are combined in group settings, finding subtle deviations from traditional preference aggregation functions. We present a simple model which captures these effects and discuss the impact of these findings on the design of group recommendation systems. 1
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