Improving Continual Relation Extraction through Prototypical Contrastive Learning

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance

Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

Acute coronary syndrome (ACS) is a set of symptoms and signs which define a range of conditions related with the unexpected reduced blood flow to the heart. In ACS, the heart muscles cannot function properly due to the decrease of blood flow. Myocardial infarction (MI) is a condition which comes under the umbrella of acute coronary syndrome. The aim of risk stratification (RS) in ACS is to recognize patients at high risk of ischemic events. Yet, no investigative study is available to identify the patients at high risk. Therefore, to facilitate this process, it would be ideal to have a reliable and trustworthy method by the help of which the doctors can make early and easy decisions for the patient and for detecting the related disease. This research used the features of GRACE Score to RS in the ACS and presented decision support system (DSS). The concept of probabilistic approach has been used as a tool to model the identified features for decision-making (DM). This technique can be further used for DM purposes to RS in the ACS in healthcare. Furthermore, the result of the proposed method has proved closer and more reliable DM of patient and then eventually can be used for advice of medicine and rest accordingly by the doctors

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage

BackgroundPostoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP.MethodsAn internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.ResultsAmong patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively.ConclusionWe found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population

Efficacy and safety of anterior transposition of the ulnar nerve for distal humerus fractures: A systematic review and meta-analysis

BackgroundThis systematic review and meta-analysis was performed to summarize available evidence of anterior transposition of the ulnar nerve for patients with distal humerus fractures.Materials and MethodsThe databases were searched from PubMed, Cochrane, Embase, Scopus, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chongqing VIP Database (VIP), and Wan Fang Database up to June 2022. The clinical outcome included operation time, fracture healing time, hospital stays, elbow joint function, and ulnar neuritis rate. Statistical analysis was performed with Review Manager 5.3 (Cochrane Collaboration).ResultsA total of 17 studies were included (8 RCTs and 9 retrospective studies), and 1280 patients were analyzed. The results of this meta-analysis showed anterior transposition group had longer operation time (MD = 20.35 min, 95%CI: 12.56–28.14, P < 0.00001). There was no significant difference in fracture healing time (SMD = −0.50, 95%CI: −1.50–0.50, P = 0.33), hospital stays (MD = −1.23 days, 95%CI: −2.72–−0.27, P = 0.11), blood loss (MD = 2.66 ml, 95%CI: −2.45–7.76, P = 0.31), and ulnar neuritis rate (OR = 1.23, 95%CI: 0.63–2.42, P = 0.54) between two groups. Finally, elbow joint motion, elbow joint function, fracture nonunion, and post-operative infection (P > 0.05) between two groups were not significantly statistic difference.ConclusionThis meta-analysis showed that anterior transposition group is not superior to non-transposition group for patients with distal humerus fractures without ulnar nerve injury. On the contrary, non-transposition group have shorter operation time than that of anterior transposition group. Non-transposition group did not increase the post-operative ulnar neuritis rate. Therefore, both anterior transposition group and non- transposition group are the treatment options for patients with distal humerus fractures without ulnar nerve injury. Besides, these findings need to be further verified by multi-center, double-blind, and large sample RCTs

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Using machine learning for exploratory data analysis and predictive models on large datasets

Master's thesis in Computer scienceWith the advent of the era of big data, machine learning has been widely used in many technologies and industries, which is able to get computers to learn without being explicitly programmed. As one of the fields of the supervised learning, some classical types of regression models, including the linear regression, nonlinear regression and regression trees, are discussed at first. And some representative algorithms in each category and their advantages and disadvantages are also illustrated as well. After that, the data pre-processing and resampling techniques, including data transformation, dimensionality reduction and k-fold cross-validation, are explained which can be used to improve the performance of the training model. During the implementation of machine learning algorithms, three typical models (Ordinary Linear Regression, Artificial Neural Networks and Random Forest) have been implemented by the different packages in R on the given large datasets. Apart from the model training, the regression diagnostics are conducted to explain the poorly predictive ability of the simplest ordinary linear regression model. Due to the non-deterministic feature of the artificial neural network and random forest models, several small models are built on small number of samples in the dataset to get the reasonable tuning parameters, and the optimal models are chosen by the value of RMSE and R2 among several training models. The corresponding performance of the built models are quantitatively and visually evaluated in details. The quantitative and visual results of our practical implementation show the feasibility for the large datasets under the artificial neural network and random forest algorithms. Comparing with the ordinary linear regression model (RMSE = 65556.95, R2 = 0.7327), the performance of the artificial neural network (RMSE = 36945.95, R2 = 0.9151) and random forest (RMSE = 30705.78, R2 = 0.9417) models are greatly improved, but the model training process is more complex and more time-consuming. The right choice between different models relies on the characteristics of the dataset and the goal, and also depends upon the cross-validation technique and the quantitative evaluation of the models

Policy analysis of small-size households participating in pig insurance during the pig breeding transformation

PRIFPRI3DSG

Electric Vehicle Key Technology Research in China

Electrification is regarded as the mainstream for future automobile power-train in order to cope with the impending energy and environmental problems. The technology of electric vehicle (EV), including battery powered vehicle, hybrid vehicle and fuel cell vehicle, has been investigated thoroughly for more than two decades in China. Many remarkable progresses have been achieved. In this paper, the It & D of electric vehicle technology in China are introduced. Both battery and motor development for electric vehicle application are summarized. The technology trend of electric motor drive is discussed accompanying with the introduction of the practice in electric motor drive research in Chinese Academy of Sciences