Symplectic Manifolds with Vanishing Action-Maslov Homomorphism

The action--Maslov homomorphism I\co\pi_1(\text{Ham}(X,\omega))\to\R is an important tool for understanding the topology of the Hamiltonian group of monotone symplectic manifolds. We explore conditions for the vanishing of this homomorphism, and show that it is identically zero when the Seidel element has finite order and the homology satisfies property $\mathcal{D}$ (a generalization of having homology generated by divisor classes). We use these results to show that $I=0$ for products of projective spaces and the Grassmannian of $2$ planes in \C^4.Comment: 21 pages, rewritten to remove unnecessary information and correct typographical error

Analysis and testing of a winter orographic precipitation model

Includes bibliographical references.May 1991.Figure 8 on text page 30 is missing from original.In the mid-1970's, an orographic precipitation model was developed by J. Owen Rhea in an effort to determine the ability to diagnose the effect of topography on winter precipitation for western Colorado. The model was tested for various time periods for differing wind regimes using upper air data and a fine-mesh topographic grid. The model is two-dimensional, steady state and multi-layer. Computations follow parcels at layer mid-points through topographically-induced moist adiabatic ascents and descents. The Lagrangian coordinate system allows for consideration of precipitation shadowing effects by upstream barriers. The model was originally tested for 13 winter seasons and the results were well correlated to observed values of snowpack water equivalent and spring and summer runoff. Although large discrepancies often existed between model and observations on a daily basis, the model frequency distribution of daily precipitation totals was realistic. This study attempted to update and improve the historical comparisons of model calculations to observations and also investigate the application of the model to current-season snowpack diagnosis and prediction. Model calculations were performed for the most recent 15 years of upper air data in addition to the 12 original seasons previously analyzed by Rhea (1978), and the correlation coefficients for model calculated precipitation values and the three observational types maintained good agreement throughout the 27 year historical period. Model calculations using an extended model winter season for the same 27 year period improved these comparisons for the precipitation gauges but had a slightly negative effect on the snowcourse and streamflow runoff relationships. When pre-model and post-model season observed precipitation data were included in the regression analysis for small basin streamflow runoff, some dramatic improvement in the correlations were noted in a few cases. The application of the model for "real-time" diagnosis of the seasonal snowpack was tested in the 1989-90 season and the results were comparable to the Soil Conservation Service predictions. Model calculations utilizing National Meteorological Center (NMC) gridded data as input were performed as a case study and the results were similar to the model calculations utilizing upper air data as well as to the observed precipitation values. The positive results of this study encourage further use of the model for "real-time" snowpack monitoring. Further case studies should be performed to test the model's ability as a predictive tool. The application of interfacing the model to a hydrological process model coupled with improvements such as the use of finer scale topography might further improve spring and summer runoff predictions.Sponsored by National Science Foundation - ATM-8813345 - ATM-8704776 - ATM-8519370.Sponsored by the Colorado Agricultural Experiment Station, Hydrometeorlogy - COL00113

Dataset associated with "Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation"

This dataset is for ocean surface flux diagnostic and to reproduce the analyses and figures in the manuscript "Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation". The time period is from 1998 to 2014 and focus on the ocean region between 20S-20N and 90E-180Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a latent heat flux diagnostic that depicts how latent heat fluxes vary with the near-surface specific humidity vertical gradient (dq) and surface wind speed (|V|). Compared to fluxes estimated from |V| and dq measured at tropical moorings and the COARE3.0 algorithm, tropical latent heat fluxes in the NCAR CEMS2 and DOE E3SMv1 models are significantly overestimated at |V| and dq extrema. MJO sensitivity to surface flux algorithm is tested with offline and inline flux corrections. The offline correction adjusts model output fluxes toward mooring-estimated fluxes; the inline correction replaces the original bulk flux algorithm with the COARE3.0 algorithm in atmosphere-only simulations of each model. Both corrections reduce the latent heat flux feedback to intraseasonal precipitation, in better agreement with observations, suggesting that model-simulated fluxes are overly supportive for maintaining MJO convection.Department of Energy : DE‐SC002009

Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations

The abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coherent statistical methods that incorporate measurements upon arrival and adjust the model accordingly. However, in this research area methodological developments tend to be central, with important assessments of model fidelity often taking second place. Recently, the statistical finite element method (statFEM) has been posited as a potential solution to the problem of model misspecification when the data are believed to be generated from an underlying partial differential equation system. Bayes nonlinear filtering permits data driven finite element discretised solutions that are updated to give a posterior distribution which quantifies the uncertainty over model solutions. The statFEM has shown great promise in systems subject to mild misspecification but its ability to handle scenarios of severe model misspecification has not yet been presented. In this paper we fill this gap, studying statFEM in the context of shallow water equations chosen for their oceanographic relevance. By deliberately misspecifying the governing equations, via linearisation, viscosity, and bathymetry, we systematically analyse misspecification through studying how the resultant approximate posterior distribution is affected, under additional regimes of decreasing spatiotemporal observational frequency. Results show that statFEM performs well with reasonable accuracy, as measured by theoretically sound proper scoring rules.Comment: 16 pages, 9 figures, 4 tables, submitted versio

"What's it going to be then, eh?" : tracing the English paragraph into its second century

"What's it going to be then, eh?” is borrowed from Anthony Burgess’ novel, A Clockwork Orange, This question appears at the beginning of each of the four chapters and reinforces Burgess' theme of choice. Choices are what teachers of writing will have to face as the paragraph moves into its second century; these choices will both the theory and the pedagogy of the paragraph. For over one hundred years, teachers and their students have had no real choice about what was presented in the writing class about the paragraph. Though the traditional lore of the paragraph had been challenged as early as the 1920's, this lore has remained the preeminent practice. This pedagogy, which students hear from the primary grades through their freshman year, comes from an interesting, but questionable, psychological model and from a view of language and discourse woefully uninformed

Principles of Biology I & II (GHC)

This Grants Collection for Principles of Biology I & II was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/biology-collections/1024/thumbnail.jp

Protecting patient privacy when sharing patient-level data from clinical trials

Abstract Background Greater transparency and, in particular, sharing of patient-level data for further scientific research is an increasingly important topic for the pharmaceutical industry and other organisations who sponsor and conduct clinical trials as well as generally in the interests of patients participating in studies. A concern remains, however, over how to appropriately prepare and share clinical trial data with third party researchers, whilst maintaining patient confidentiality. Clinical trial datasets contain very detailed information on each participant. Risk to patient privacy can be mitigated by data reduction techniques. However, retention of data utility is important in order to allow meaningful scientific research. In addition, for clinical trial data, an excessive application of such techniques may pose a public health risk if misleading results are produced. After considering existing guidance, this article makes recommendations with the aim of promoting an approach that balances data utility and privacy risk and is applicable across clinical trial data holders. Discussion Our key recommendations are as follows: 1. Data anonymisation/de-identification: Data holders are responsible for generating de-identified datasets which are intended to offer increased protection for patient privacy through masking or generalisation of direct and some indirect identifiers. 2. Controlled access to data, including use of a data sharing agreement: A legally binding data sharing agreement should be in place, including agreements not to download or further share data and not to attempt to seek to identify patients. Appropriate levels of security should be used for transferring data or providing access; one solution is use of a secure ‘locked box’ system which provides additional safeguards. Summary This article provides recommendations on best practices to de-identify/anonymise clinical trial data for sharing with third-party researchers, as well as controlled access to data and data sharing agreements. The recommendations are applicable to all clinical trial data holders. Further work will be needed to identify and evaluate competing possibilities as regulations, attitudes to risk and technologies evolve

Rapid Artefact Removal and H&E-Stained Tissue Segmentation

We present an innovative method for rapidly segmenting hematoxylin and eosin (H&E)-stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable artefacts such as pen marks and scanning artefacts. Our method involves taking a single-channel representation of a lowmagnification RGB overview of the WSI in which the pixel values are bimodally distributed suchthat H&E-stained tissue is easily distinguished from both background and a wide variety of artefacts. We demonstrate our method on 30 WSIs prepared from a wide range of institutions and WSI digital scanners, each containing substantial artefacts, and compare it to segmentations provided by Otsu thresholding and Histolab tissue segmentation and pen filtering tools. We found that our methodsegmented the tissue and fully removed all artefacts in 29 out of 30 WSIs, whereas Otsu thresholding failed to remove any artefacts, and the Histolab pen filtering tools only partially removed the pen marks. The beauty of our approach lies in its simplicity: manipulating RGB colour space and using Otsu thresholding allows for the segmentation of H&E-stained tissue and the rapid removal ofartefacts without the need for machine learning or parameter tuning