In order to overcome the pandemic of COVID-19, messenger RNA (mRNA)-based vaccine has been extensively researched as a rapid and versatile strategy. Herein, we described the immunogenicity of mRNA-based vaccines for Beta and the most recent Omicron variants. The homologous mRNA-Beta and mRNA-Omicron and heterologous Ad5-nCoV plus mRNA vaccine exhibited high-level cross-reactive neutralization for Beta, original, Delta, and Omicron variants. It indicated that the COVID-19 mRNA vaccines have great potential in the clinical use against different SARS-CoV-2 variants.

Support Vector Data Description (SVDD) inherits properties of Support Vector Machines (SVM) and has become a prominent One Class Classifier (OCC). Same to standard SVM, its O(n 3 ) time and O(n 2 ) space complexities, where n is the number of training samples, have become major limitations in cases of large training datasets. As a simple and effective method, reducing the size of training dataset through reserving only samples mostly relevant to learned classifier, can be adopted to overcome the limitations. A trained SVDD enclosed decision boundary always locates on edge area of data distribution and is decided by a small subset of Support Vectors(SVs). Therefore, in this paper, we present a method based on edge detection such that edge samples mostly relevant to decision boundary can be preserved. And clustering techniques are also be applied to keep centroids representing the global distribution properties so as to avoid over-outside of decision boundary. To restrict the influences of noises, each training pattern is assigned with a weight. Experiments on real and artificial data sets prove that the classifier trained on reconstruction training set consisting of edge points and centroids can preserve performance with much faster training speed.

Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization without and with pre-training, respectively. However, they sometimes repeatedly attend to unimportant source phrases while mistakenly ignore important ones. We present new reconstruction mechanisms on two levels to alleviate this issue. The sequence-level reconstructor reconstructs the whole source document from the hidden layer of the target summary, while the word embedding-level one rebuilds the average of word embeddings of the source at the target side to guarantee that as much critical information is included in the summary as possible. Based on the assumption that inverse document frequency (IDF) measures how important a word is, we further leverage the IDF weights in our embedding-level reconstructor. The proposed frameworks lead to promising improvements for ROUGE metrics and human rating on both the CNN/Daily Mail and Newsroom summarization datasets.

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