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The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centres generate substantial amounts of multivariate time series data for these systems. Many of these cyber-physical systems (CPSs) are engineered for mission-critical tasks and are thus targets for cyber-attacks. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. Most of the current techniques also employed simple comparison between the present states and predicted normal ranges for anomaly detection, which can be inadequate given the highly dynamic behaviors of the systems. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs), using the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) as the base models (namely, the generator and discriminator) in the GAN framework to capture the temporal correlation of time series distributions. Instead of treating each data stream independently, our proposed Multivariate Anomaly Detection with GAN (MAD-GAN) framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyberintrusions compared in these complex real-world systems.

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Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

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Beta diversity (i.e. species turnover rate across space) is fundamental for understanding mechanisms controlling large-scale species richness patterns. However, the influences on beta diversity are still a matter of debate. In particular, the relative role of environmental and spatial processes (e.g. environmental niche versus dispersal limitation of species) remains elusive, and the influence of species range size has been poorly tested. Here, using distribution maps of 11 405 woody species in China (ca 9.6  10 6 km 2 ), we investigated 1) the geographical and directional patterns of beta diversity for all woody species and species with different range sizes, and 2) compared the effects of environmental and spatial processes on these patterns. Beta diversity was calculated as the decay of similarity in species composition with increasing distance. Variables representing environmental energy, water availability, climatic seasonality, habitat heterogeneity and human activities were used to evaluate the effects of environmental processes, while spatial distance was used to assess the influence of spatial processes. The results indicated significant directional patterns of beta diversity: the similarity decay along the latitudinal gradient was 1.6-2.3 times faster than that along the longitudinal gradient. Beta diversity also increased with the decrease of species range size. As compared with spatial processes, environmental processes had stronger effects on longitudinal beta diversity and on the beta diversity of widely-ranged species. This was opposite to the larger influence of spatial processes on latitudinal beta diversity and the beta diversity of narrowly-ranged species. These results suggest that the distributions of narrowly-ranged woody species in China may have not reached equilibrium with their environmental niches due to dispersal limitation induced by China's topography and/or their low dispersal ability. The projected rapid climatic changes will likely endanger such species. Species dispersal processes should be taken into account in future conservation strategies in China.

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