Veterans Justice Programs: Assessing Population Risks for Suicide Deaths and Attempts

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156471/2/sltb12631_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156471/1/sltb12631.pd

Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System

A computational model of open-irrigated radiofrequency catheter ablation accounting for mechanical properties of the cardiac tissue

Radiofrequency catheter ablation (RFCA) is an effective treatment for cardiac arrhythmias. Although generally safe, it is not completely exempt from the risk of complications. The great flexibility of computational models can be a major asset in optimizing interventional strategies, if they can produce sufficiently precise estimations of the generated lesion for a given ablation protocol. This requires an accurate description of the catheter tip and the cardiac tissue. In particular, the deformation of the tissue under the catheter pressure during the ablation is an important aspect that is overlooked in the existing literature, that resorts to a sharp insertion of the catheter into an undeformed geometry. As the lesion size depends on the power dissipated in the tissue, and the latter depends on the percentage of the electrode surface in contact with the tissue itself, the sharp insertion geometry has the tendency to overestimate the lesion obtained, especially when a larger force is applied to the catheter. In this paper we introduce a full 3D computational model that takes into account the tissue elasticity, and is able to capture the tissue deformation and realistic power dissipation in the tissue. Numerical results in FEniCS-HPC are provided to validate the model against experimental data, and to compare the lesions obtained with the new model and with the classical ones featuring a sharp electrode insertion in the tissue.La Caixa 2016 PhD grant to M. Leoni, and Abbott non-conditional grant to J.M. Guerra Ramo

Evolving Understanding of Antarctic Ice‐Sheet Physics and Ambiguity in Probabilistic Sea‐Level Projections

Mechanisms such as ice‐shelf hydrofracturing and ice‐cliff collapse may rapidly increase discharge from marine‐based ice sheets. Here, we link a probabilistic framework for sea‐level projections to a small ensemble of Antarctic ice‐sheet (AIS) simulations incorporating these physical processes to explore their influence on global‐mean sea‐level (GMSL) and relative sea‐level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93–243 cm) and RCP 2.6 (26–98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (versus 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea‐level observations cannot exclude future extreme outcomes. The sensitivity of post‐2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks

Reconceptualizing Context: A Multilevel Model of the Context of Reception and Second-Generation Educational Attainment

This paper seeks to return scholarly attention to a core intellectual divide between segmented and conventional (or neo-)assimilation approaches, doing so through a theoretical and empirical reconsideration of contextual effects on second-generation outcomes. We evaluate multiple approaches to measuring receiving country contextual effects and measuring their impact on the educational attainment of the children of immigrants. We demonstrate that our proposed measures better predict second-generation educational attainment than prevailing approaches, enabling a multilevel modeling strategy that accounts for the structure of immigrant families nested within different receiving contexts

Speech and Language Outcomes in Low-SES Spanish-English Bilingual Preschoolers: The Role of Maternal Education

This paper presents a longitudinal examination of Spanish and English phonological, lexical, and morpho-syntactic abilities in 20 low-SES bilingual preschoolers with mothers who had either completed primary or secondary education in Spanish in their country of origin, Mexico. We focused on the link between maternal education and the following spontaneous production measures: 1) phonological accuracy as measured by Percent of Consonants Correct-Revised, 2) lexical variety as measured by Number of Different Words, and 3) utterance length as measured by Mean Length of Utterance in words; the relation between maternal education and spontaneous production was examined both a) at preschool entry, when children were on average 3;6 and dominant in Spanish, and b) a year later, after one year of exposure to the majority language (English) and culture. The results showed that although children of more educated mothers performed significantly better on all English measures than children of less educated mothers, maternal education was not related to Spanish outcomes. The same differences persisted a year later. These results suggest that maternal education may play a different, but long-lasting role in English compared to Spanish development possibly due to language input differences attributable to distinct cultural values and practices associated with different languages