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Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient's mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers.This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R ( t ) . However, R ( t ) can be difficult and time consuming to compute. find protocol This paper suggests two relatively simple data-based metrics that could be used in conjunction with R ( t ) estimation and provide rapid indicators of how the epidemic's dynamic behavior is progressing. The new metrics are the epidemic rate of change (RC) and a related state-dependent response rate parameter (RP), recursive estimates of which are obtained from dynamic harmonic and dynamic linear regression (DHR and DLR) algorithms. Their effectiveness is illustrated by the analysis of COVID-19 data in the UK and Italy. The paper also shows how similar methodology, combined with the refined instrumental variable method for estimating hybrid Box-Jenkins models of linear dynamic systems (RIVC), can be used to relate the daily death numbers in the Italian and UK epidemics and then provide 15-day-ahead forecasts of the UK daily death numbers. The same approach can be used to model and forecast the UK epidemic based on the daily number of COVID-19 patients in UK hospitals. Finally, the paper speculates on how the state-dependent parameter (SDP) modeling procedures may provide data-based insight into a nonlinear differential equation model for epidemics such as COVID-19.In this paper, a new version of the well-known epidemic mathematical SEIR model is used to analyze the pandemic course of COVID-19 in eight different countries. One of the proposed model's improvements is to reflect the societal feedback on the disease and confinement features. The SEIR model parameters are allowed to be time-varying, and the ranges of their values are identified by using publicly available data for France, Italy, Spain, Germany, Brazil, Russia, New York State (US), and China. The identified model is then applied to predict the SARS-CoV-2 virus propagation under various conditions of confinement. For this purpose, an interval predictor is designed, allowing variations and uncertainties in the model parameters to be taken into account. The code and the utilized data are available on Github.Universities and companies were not prepared to the changes introduced to limit the spread of COVID-19 in Norway. Universities had to switch to online teaching overnight. There is still uncertainty how measures to control the pandemic will keep affecting universities in the short and middle term. Such measures have consequences on how to carry out research that usually relies on students, researcher and volunteers using the equipment and applications. Our group carries out research on virtual/augmented/extended reality (VR/AR/XR) for immersive training and learning. This research often involves user studies. We had established procedures on how to use the equipment, carry out demonstrations and teaching for students, teachers and visitors, develop projects as part of bachelor and master projects and test new applications with volunteers. The measures taken by authorities to control the spread of the pandemic made it difficult or unfeasible to carry out some of those activities. In this paper we describe how our group and XR lab reacted after universities were closed to students' presence in campus in March 2020. We present our actions to keep research ongoing, our evaluation of some of those actions and discuss how we had to change the way we operate our XR lab in order to continue teaching and research in the near future, under the assumption that restrictions due to the pandemic can be re-implemented at short notice. We propose procedures to run an XR lab in a manner that inspires visitors to feel safe and confident of using the equipment. Our contribution is the proposal of procedures to run an educational XR lab safely and contribute towards the conversation about how to carry out research involving users in XR under pandemic restrictions.The leaf beetle Chrysolina (Chrysolinopsis) americana (Linnaeus, 1758), commonly known as the Rosemary beetle, is native to some parts of the Mediterranean region. In the last few decades, it has expanded its distribution to new regions in the North and Eastern Mediterranean basin. Chrysolina americana feeds on plants of the Lamiaceae family, such as Rosmarinus officinalis, Lavandula spp., Salvia spp., Thymus spp. and others. Chrysolina americana is considered a pest, as many of its host plants are of commercial importance and are often used as ornamentals in house gardens and green public spaces. In this work, we report the first occurrence of C. americana in Cyprus and we present its establishment, expansion and distribution across the Island, through recordings for the period 2015 - 2020. The study was initiated from a post on a Facebook group, where the species was noticed in Cyprus for the first time, indicating that social media and citizen science can be particularly helpful in biodiversity research.
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