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Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems. Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range. However, this modulation comes at the cost of a reduction in sensitivity, a blindness to petal-piston modes, and a limit to the sensor’s ability to operate at high speeds. Therefore, there is strong interest to use the PWFS without modulation, which can be enabled with nonlinear reconstructors. Here, we present the first on-sky demonstration of XAO with an unmodulated PWFS using a nonlinear reconstructor based on convolutional neural networks. We discuss the real-time implementation on the Magellan Adaptive Optics eXtreme (MagAO-X) instrument using the optimized TensorRT framework and show that inference is fast enough to run the control loop at > 2 kHz frequencies. Our on-sky results demonstrate a successful closed-loop operation using a model calibrated with internal source data that delivers stable and robust correction under varying conditions. Performance analysis reveals that our smart PWFS achieves nearly the same Strehl ratio as the highly optimized modulated PWFS under favorable conditions on bright stars. Notably, we observe an improvement in performance on a fainter star under the influence of strong winds. These findings confirm the feasibility of using the PWFS in its unmodulated form and highlight its potential for next-generation instruments. Future efforts will focus on achieving even higher control loop frequencies (> 3 kHz), optimizing the calibration procedures, and testing its performance on fainter stars, where more gain is expected for the unmodulated PWFS compared to its modulated counterpart. 

Abstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis. 

In marine ecosystems, increased global-scale transportation creates opportunities for rapid introduction of invasive parasitic species that, in some cases, result in dramatic shifts within the native communities. A lack of detailed knowledge regarding the ecology of invasive marine parasites hinders our ability to develop effective conservation strategies and avoid unforeseen ecological consequences. We examined co-infestation patterns of a highly pathogenic, introduced parasitic isopod (Orthione griffenis) and a native symbiotic clam (Neaeromya rugifera) on the North American native blue mud shrimp Upogebia pugettensis. Our comparisons included infestations of O. griffenis and N. rugifera among 447 U. pugettensis hosts over 3 study years and were designed to statistically assess whether the 2 symbionts exhibited significant associations with one another. Our results indicate that infestations by the 2 symbiont species are positively correlated, such that the presence of one symbiont is a strong, positive predictor for the presence of the other. For both symbionts, host size is an important factor that drives the observed correlation. Host sex is also influential for O. griffenis. Interestingly, even after accounting for these host attributes, the infestations by the 2 symbionts continue to correlate positively, particularly among older (second-year and beyond) symbionts, highlighting the likely influence of additional host and environmental factors in driving the symbiont correlation post-settlement. We consider potential mechanisms, including differential energetic reserves and longevities between infested and co-infested hosts, in detail. These results offer insights into the ecological drivers of symbiont co-infestation, which have important implications for understanding host-parasite interactions and future conservation measures. 

In 2018, California State University Los Angeles (Cal State LA) received a $1 M, 3-year National Science Foundation grant designed to use Big Data and geographic information system (GIS) science to promote active learning and curricular enhancements for community engagement and civic learning. We developed strategic partnerships with the City of Los Angeles Chief Data Officer, and with Community Partners’ Senior Program Director and Programs and Operations Manager. Each of these organizations offered the integrity and vitality necessary for engaging Cal State LA students and faculty, serving local nonprofits and communities, and promoting the City of Los Angeles’ data initiatives through the LA GeoHub and Open Data Portal (ArcNews 2016). This project closely aligns with the universities’ mission (Gomez et al. 2019) to transform local communities by providing underserved residents access to data and technology (Willner 2019).