Spatiotemporal Variations in Heat-Related Health Risk in Three Midwestern U.S. Cities Between 1990 and 2010

Mortality from extreme heat is a leading cause of weather-related fatality, which is expected to increase in frequency with future climate scenarios. This study examines the spatiotemporal variations in heat-related health risk in three Midwestern cities in the United States between the years 1990 to 2010; cities include Chicago, Illinois, Indianapolis, IN, and Dayton, OH. In order to examine these variations we utilize the recently developed Extreme Heat Vulnerability Index (EHVI) that uses a principal components solution to vulnerability. The EHVI incorporates data from the U.S. Decadal Census and remotely sensed variables to determine heat-related vulnerability at an intra-urban level (census block group). The results demonstrate significant spatiotemporal variations in heat-health risk within the cities involved

Percussion Convocation

Program listing performers and works performe

Functional distinctiveness of major plant lineages

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106060/1/jec12208.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106060/2/jec12208-sup-0001-Supp_Info.pd

Synergistic roles of climate warming and human occupation in Patagonian megafaunal extinctions during the Last Deglaciation

The causes of Late Pleistocene megafaunal extinctions (60,000 to 11,650 years ago, hereafter 60 to 11.65 ka) remain contentious, with major phases coinciding with both human arrival and climate change around the world. The Americas provide a unique opportunity to disentangle these factors as human colonization took place over a narrow time frame (~15 to 14.6 ka) but during contrasting temperature trends across each continent. Unfortunately, limited data sets in South America have so far precluded detailed comparison. We analyze genetic and radiocarbon data from 89 and 71 Patagonian megafaunal bones, respectively, more than doubling the high-quality Pleistocene megafaunal radiocarbon data sets from the region.We identify a narrowmegafaunal extinction phase 12,280 ± 110 years ago, some 1 to 3 thousand years after initial human presence in the area. Although humans arrived immediately prior to a cold phase, the Antarctic Cold Reversal stadial, megafaunal extinctions did not occur until the stadial finished and the subsequent warming phase commenced some 1 to 3 thousand years later. The increased resolution provided by the Patagonian material reveals that the sequence of climate and extinction events in North and South America were temporally inverted, but in both cases, megafaunal extinctions did not occur until human presence and climate warming coincided. Overall, metapopulation processes involving subpopulation connectivity on a continental scale appear to have been critical for megafaunal species survival of both climate change and human impacts.Fil: Metcalf, Jessica L.. University of Adelaide; Australia. State University of Colorado Boulder; Estados UnidosFil: Turney, Chris. University of New South Wales; AustraliaFil: Barnett, Ross. University of Oxford; Reino Unido. Universidad de Copenhagen; DinamarcaFil: Martin, Fabiana. Universidad de Magallanes. Instituto de la Patagonia. Centro de Estudios del Hombre Austral; ChileFil: Bray, Sarah C.. University of Adelaide; Australia. University of South Australia; AustraliaFil: Vilstrup, Julia T.. Universidad de Copenhagen; DinamarcaFil: Orlando, Ludovic. Universidad de Copenhagen; DinamarcaFil: Salas-Gismondi, Rodolfo. Université de Montpellier. Institut des Sciences de l’Evolution; Francia. Universidad Nacional Mayor de San Marcos; PerúFil: Loponte, Daniel Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Medina, Matias Eduardo. Centro de Estudios Históricos ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Nigris, Mariana Eleonor. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Civalero, Maria Teresa. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernández, Pablo Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gasco, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; ArgentinaFil: Duran, Victor Alberto. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Seymour, Kevin L.. Royal Ontario Museum. Department of Natural History; CanadáFil: Otaola, Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Gil, Adolfo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; ArgentinaFil: Paunero, Rafael. Universidad Nacional de La Plata; ArgentinaFil: Prevosti, Francisco Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Bradshaw, Corey J. A.. University of Adelaide; AustraliaFil: Wheeler, Jane C.. Instituto de Investigación y Desarrollo de Camélidos Sudamericanos; PerúFil: Borrero, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Austin, Jeremy J.. University of Adelaide; AustraliaFil: Cooper, Alan. University of Adelaide; Australia. University of Oxford; Reino Unid

Enabling and Enhancing Science Exploration Across the Solar System: Aerocapture Technology for SmallSat to Flagship Missions

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PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Overcoming leakage in scalable quantum error correction

Leakage of quantum information out of computational states into higher energy states represents a major challenge in the pursuit of quantum error correction (QEC). In a QEC circuit, leakage builds over time and spreads through multi-qubit interactions. This leads to correlated errors that degrade the exponential suppression of logical error with scale, challenging the feasibility of QEC as a path towards fault-tolerant quantum computation. Here, we demonstrate the execution of a distance-3 surface code and distance-21 bit-flip code on a Sycamore quantum processor where leakage is removed from all qubits in each cycle. This shortens the lifetime of leakage and curtails its ability to spread and induce correlated errors. We report a ten-fold reduction in steady-state leakage population on the data qubits encoding the logical state and an average leakage population of less than $1 \times 10^{-3}$ throughout the entire device. The leakage removal process itself efficiently returns leakage population back to the computational basis, and adding it to a code circuit prevents leakage from inducing correlated error across cycles, restoring a fundamental assumption of QEC. With this demonstration that leakage can be contained, we resolve a key challenge for practical QEC at scale.Comment: Main text: 7 pages, 5 figure

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences