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"Smith, Travis"
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Temperature and self-reported mental health in the United States
This study estimates the association between temperature and self-reported mental health. We match individual-level mental health data for over three million Americans between 1993 and 2010 to historical daily weather information. We exploit the random fluctuations in temperature over time within counties to identify its effect on a 30-day measure of self-reported mental health. Compared to the temperature range of 60-70°F, cooler days in the past month reduce the probability of reporting days of bad mental health while hotter days increase this probability. We also find a salience effect: cooler days have an immediate effect, whereas hotter days tend to matter most after about 10 days. Using our estimates, we calculate the willingness to pay to avoid an additional hot day in terms of its impact on self-reported mental health.
Journal Article
The book of job
A modern retelling of the Frankenstein story combined with Beauty and the Beast. Violent messiahs is a genre bending, theological, sci-fi love story about criminal politics, the nature of violence and man's search for individuality.
Book
Do School Food Programs Improve Child Dietary Quality?
This paper estimates the impact of U.S. school food programs on the distribution of child dietary quality during 2005-10. The distributional approach allows one to better understand how school food impacts children prone to low-quality diets separately from those prone to higher-quality diets. Using a fixed-effects quantile estimator, I find notable heterogeneity in the general population — school food has positive impacts below the median of the dietary-quality distribution, and negative but insignificant impacts at upper quantiles. Children demonstrating substantial nutritional needs (i.e., food insecure or receiving free/reduced price meals) exhibit positive impacts at all levels of diet quality with especially high benefits at low quantiles. Although school food programs may not benefit the \"above-average\" child, they do improve the diets of the most nutritionally disadvantaged.
Journal Article
Thanos. The infinity relativity
Returning from oblivion feeling not quite complete, Thanos, with a resurrected former enemy in tow, follows a trail of clues across the universe and into the realm of Death in search of the revelatory waters of the Infinity Well.
Book
MAKING THE BLACK BOX MORE TRANSPARENT
This paper synthesizes multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications. ML has recently exploded in popularity in many fields, including meteorology. Although ML has been successful in meteorology, it has not been as widely accepted, primarily due to the perception that ML models are “black boxes,” meaning the ML methods are thought to take inputs and provide outputs but not to yield physically interpretable information to the user. This paper introduces and demonstrates multiple MIV techniques for both traditional ML and deep learning, to enable meteorologists to understand what ML models have learned. We discuss permutation-based predictor importance, forward and backward selection, saliency maps, class-activation maps, backward optimization, and novelty detection. We apply these methods at multiple spatiotemporal scales to tornado, hail, winter precipitation type, and convective-storm mode. By analyzing such a wide variety of applications, we intend for this work to demystify the black box of ML, offer insight in applying MIV techniques, and serve as a MIV toolbox for meteorologists and other physical scientists.
Journal Article
Titans. Vol. 4, Titans apart
\"While Donna Troy is kept under observation in the Justice League's watchtower to determine the link between Donna and her evil future self, Troia, the rest of the team is told to stand down and take a break. That doesn't sit well with Arsenal, though. Seemingly abandoned by his friends, Roy Harper pushes himself right to the edge to track down the source of a dangerous new street drug--even teaming up with his deadly ex, Cheshire. But while Arsenal may be in over his head, his investigation reveals a worldwide threat that even the Justice League proves unable to stop\"-- Provided by publisher.
Book
Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind
Thunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥50 kt or 25.7 m s−1) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the near-storm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (>0 is better than climatology). For all three scores, the best value occurs for the smallest distance (inside storm cell) and/or lead time (0–15 min), while the worst value occurs for the greatest distance (5–10 km outside storm cell) and/or lead time (60–90 min). The system was deployed during the 2017 Hazardous Weather Testbed.
Journal Article
Parent trap
\"As the world's deadliest mom forces Robin to make the ultimate choice between his past and future, Superboy finds himself caught in middle in this volume of Super Sons! In this exciting concluding chapter, Talia al Ghul returns for her son Damian, whom she trained from birth to be an assassin. With the evil in Robin's past finally revealed to Superboy, it might be too much for the Sons' partnership to survive...especially when the boys find out her next victim is one of the most important people in their lives!\"-- Provided by publisher.
Book
Agreement and reliability statistics for shapes
We describe a methodology for assessing agreement and reliability among a set of shapes. Motivated by recent studies of the reliability of manually segmented medical images, we focus on shapes composed of rasterized, binary-valued data representing closed geometric regions of interest. The methodology naturally generalizes to N dimensions and other data types, though. We formulate the shape variance, shape correlation and shape intraclass correlation coefficient (ICC) in terms of a simple distance metric, the Manhattan norm, which quantifies the absolute difference between any two shapes. We demonstrate applications of this methodology by working through example shape variance calculations in 1-D, for the analysis of overlapping line segments, and 2-D, for the analysis of overlapping regions. We also report the results of a simulated reliability analysis of manually delineated shape boundaries, and we compare the shape ICC with the more conventional and commonly used area ICC. The proposed shape-sensitive methodology captures all of the variation in the shape measurements, and it provides a more accurate estimate of the measurement reliability than an analysis of only the measured areas.
Journal Article