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Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods. 

Unplanned intensive care units (ICU) readmissions and in-hospital mortality of patients are two important metrics for evaluating the quality of hospital care. Identifying patients with higher risk of readmission to ICU or of mortality can not only protect those patients from potential dangers, but also reduce the high costs of healthcare. In this work, we propose a new method to incorporate information from the Electronic Health Records (EHRs) of patients and utilize hyperbolic embeddings of a medical ontology (i.e., ICD-9) in the prediction model. The results prove the effectiveness of our method and show that hyperbolic embeddings of ontological concepts give promising performance. 

Deep convective transport of gaseous precursors to ozone (O₃) and aerosols to the upper troposphere is affected by liquid phase and mixed‐phase scavenging, entrainment of free tropospheric air and aqueous chemistry. The contributions of these processes are examined using aircraft measurements obtained in storm inflow and outflow during the 2012 Deep Convective Clouds and Chemistry (DC3) experiment combined with high‐resolution (dx≤3 km) WRF‐Chem simulations of a severe storm, an air mass storm, and a mesoscale convective system (MCS). The simulation results for the MCS suggest that formaldehyde (CH₂O) is not retained in ice when cloud water freezes, in agreement with previous studies of the severe storm. By analyzing WRF‐Chem trajectories, the effects of scavenging, entrainment, and aqueous chemistry on outflow mixing ratios of CH₂O, methyl hydroperoxide (CH₃OOH), and hydrogen peroxide (H₂O₂) are quantified. Liquid phase microphysical scavenging was the dominant process reducing CH₂O and H₂O₂outflow mixing ratios in all three storms. Aqueous chemistry did not significantly affect outflow mixing ratios of all three species. In the severe storm and MCS, the higher than expected reductions in CH₃OOH mixing ratios in the storm cores were primarily due to entrainment of low‐background CH₃OOH. In the air mass storm, lower CH₃OOH and H₂O₂scavenging efficiencies (SEs) than in the MCS were partly due to entrainment of higher background CH₃OOH and H₂O₂. Overestimated rain and hail production in WRF‐Chem reduces the confidence in ice retention fraction values determined for the peroxides and CH₂O.