Effect of Mitigation Measures on the Spreading of COVID-19 in Hard-Hit States

State government-mandated social distancing measures have helped to slow down the growth of the COVID-19 pandemic in the United States. Current predictive models of the development of COVID-19, especially after mitigation efforts, are largely based on extrapolating the data from other countries. Since most states enforced stay-at-home orders towards the end of March, their effect should be reflected in the death and infection counts at the end of April. Using the data available until April 25th, we investigate the change in the infection rate due to the mitigation efforts, and project death and infection counts until September, 2020, for some of the most heavily impacted states: New York, New Jersey, Michigan, Massachusetts, Illinois and Louisiana. We find that with the current mitigation efforts five of those six states reduce their reproduction number to a value less than one, stopping the exponential growth of the pandemic. We also projected different scenarios after the mitigation is relaxed. Analysis for other states can be found at https://covid19projection.org/.Comment: 8 pages, 6 figures, 2 table

Identifying structural changes with unsupervised machine learning methods

Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems

The CTIO Prime Focus CCD: System Characteristics from 1982-1988

The CTIO Prime Focus CCD instrument with an RCA CCD was in operation at the CTIO 4-m telescope for six years between 1982-1988. A large body of literature has been published based on CCD images taken with this instrument. We review the general properties of the now-retired PFCCD system to aid astronomers in the interpretation of the photometric data in the literature.Comment: Accepted for publication in the PASP. 15 pages, AASTeX V4.0 latex format (including figures), 4 ps figures, 4 separate AASTeX V4.0 latex table

The Skills to Pay the Bills: An Evaluation of an Effort to Help Nonprofits Manage Their Finances

This study examines a Wallace Foundation-sponsored initiative aimed at improving the financial management skills and practices of 25 Chicago afterschool providers through training and coaching. Two models for this professional development were provided and each produced long-lasting improvements. Moreover, organizations receiving the less-expensive group training and coaching improved almost as much as those receiving more intensive customized coaching

Tidal Stresses and Energy Gaps in Microstate Geometries

We compute energy gaps and study infalling massive geodesic probes in the new families of scaling, microstate geometries that have been constructed recently and for which the holographic duals are known. We find that in the deepest geometries, which have the lowest energy gaps, the geodesic deviation shows that the stress reaches the Planck scale long before the probe reaches the cap of the geometry. Such probes must therefore undergo a stringy transition as they fall into microstate geometry. We discuss the scales associated with this transition and comment on the implications for scrambling in microstate geometries.Comment: 22 pages, 1 figur

Correlation between prescribing quality and pharmaceutical costs in English primary care: national cross-sectional analysis

Background Both pharmaceutical costs and quality-indicator performance vary substantially between general practices, but little is known about the relationship between prescribing costs and quality Aim To measure the association between prescribing quality and pharmaceutical costs among English general practices Design and setting Cross-sectional observational study using data from the Quality and Outcomes Framework and the Prescribing Analysis and Cost database from all 8409 general practices in England in 2005-2006 Method Correlation between practice achievement of 26 prescribing quality indicators in eight prescribing areas and related pharmaceutical costs was examined. Results There was no significant association between the overall achievement of quality indicators and related pharmaceutical costs (P= 0.399). Mean achievement of quality indicators across all eight prescribing areas was 79.0% (standard deviation 4.4%). There were small positive correlations in five prescribing areas: influenza vaccination, beta blockers, angiotensin converting enzyme inhibitors, lipid lowering, and antiplatelet treatment (all