The paper deals with two exact mathematical models of study: A borrowed model with constant intensity of study and amodel with variable intensity of study derived by the authors are presented using data acquired in real pedagogical research. Thebounding of study performance is proved and precisely determined in dependence on personal (group) parameters of study andintensity of forgetting. The parameter of forgetting has decisive influence on total performance.

Mathematical Modelling of Study

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at this https URL.

FedML: A Research Library and Benchmark for Federated Machine Learning

Patients with rheumatic diseases are often more susceptible to different bacteria and viruses because of immune impairment, but it is not clear whether there is a higher risk of infection and a more serious course of disease for novel coronavirus (SARS-CoV-2). We performed this systematic review and meta analysis to assess the risk and clinical outcomes of COVID-19 in patients with rheumatic diseases compared with the general population. We searched PubMed, EMBASE, Scopus and Web of Science databases from January 1, 2020 to October 20, 2020 to determine epidemiological information related to patients with rheumatic diseases and COVID-19, including clear risk estimate or data that could be converted and extracted. We included 26 observational studies, totaling about 2000 patients with rheumatic diseases of whom were infected with COVID-19. Meta-analysis showed that the risk of COVID-19 infection in rheumatic patients was significantly higher than that in the general population (OR = 1.53, 95% CI 1.24-1.88, P = 0.000). In terms of hospitalization and severe clinical outcomes associated with COVID-19, we found that rheumatic patients showed similar results to the reference population (hospitalization OR = 1.36, 95% CI 0.81-2.29, P = 0.247; admitted to ICU OR = 1.94, 95% CI 0.88-4.27, P = 0.098; death OR = 1.29, 95% CI 0.84-1.97, P = 0.248). The presence of comorbidities, hypertension, lung diseases were significantly associated with the increased risk of COVID-19-related hospitalization in rheumatic patients and anti-TNF drugs were associated with lower hospitalization risk. Older age was related to severe COVID-19. Our meta-analysis indicated that rheumatic patients were at a higher risk of COVID-19 infection but might not lead to a more serious disease process.

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Risk and clinical outcomes of COVID-19 in patients with rheumatic diseases compared with the general population: a systematic review and meta-analysis.

Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data. However, heterogeneity problem and catastrophic forgetting bring distinctive challenges. First, due to non-i.i.d (identically and independently distributed) data and heterogeneous architectures, models suffer performance degradation on other domains and communication barrier with participants models. Second, in local updating, model is separately optimized on private data, which is prone to overfit current data distribution and forgets previously acquired knowledge, resulting in catastrophic forgetting. In this work, we propose FCCL (Federated CrossCorrelation and Continual Learning). For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Mean- while, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. Empirical results on various image classification tasks demonstrate the effectiveness of our method and the efficiency of modules.

Learn from Others and Be Yourself in Heterogeneous Federated Learning

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

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COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.

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Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide Emerging artificial intelligence technology is being increasingly adopted to predict stroke Because of privacy concerns, patient data are stored in distributed electronic health record (EHR) databases, voluminous clinical datasets, which prevent patient data from being aggregated and restrains AI technology to boost the accuracy of stroke prediction with centralized training data In this work, our scientists and engineers propose a privacy-preserving scheme to predict the risk of stroke and deploy our federated prediction model on cloud servers Our system of federated prediction model asynchronously supports any number of client connections and arbitrary local gradient iterations in each communication round It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting With the privacy-preserving mechanism, our federated prediction model trains over all the healthcare data from hospitals in a certain city without actual data sharing among them Therefore, it is not only secure but also more accurate than any single prediction model that trains over the data only from one single hospital Especially for small hospitals with few confirmed stroke cases, our federated model boosts model performance by 10%~20% in several machine learning metrics To help stroke experts comprehend the advantage of our prediction system more intuitively, we developed a mobile app that collects the key information of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention.

The invention provides a collaborative task prediction method and a computer readable storage medium. The method comprises the following steps: receiving sample identity information sent by a plurality of terminals, and constructing a longitudinal model based on the sample identity information through a child longitudinal model obtained by performing intermediate result interaction with the plurality of terminals; receiving a plurality of child transverse models sent by the plurality of terminals, and integrating the plurality of child transverse models to obtain a transverse model; integrating the longitudinal model and the transverse model to obtain a task prediction model, and sending the task prediction model to a plurality of terminals; obtaining to-be-predicted identity information,and sending the to-be-predicted identity information to the plurality of terminals; when a plurality of sub-prediction results returned by the plurality of terminals for the to-be-predicted identity information are received, integrating a task prediction result by using the plurality of child prediction results to complete collaborative task prediction. According to the invention, the accuracy oftask prediction can be improved.

Bo Zhao

Papers

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention.

Collaborative task prediction method and computer readable storage medium