AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model.

Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional features. For the problem of how to consider the effective fusion of multimodal data and the relevance of multimodal data in multimodal sentiment analysis, we propose an attention-based mechanism feature relevance fusion multimodal sentiment analysis model (AFR-BERT). In the data pre-processing stage, text features are extracted using the pre-trained language model BERT (Bi-directional Encoder Representation from Transformers), and the BiLSTM (Bi-directional Long Short-Term Memory) is used to obtain the internal information of the audio. In the data fusion phase, the multimodal data fusion network effectively fuses multimodal features through the interaction of text and audio information. During the data analysis phase, the multimodal data association network analyzes the data by exploring the correlation of fused information between text and audio. In the data output phase, the model outputs the results of multimodal sentiment analysis. We conducted extensive comparative experiments on the publicly available sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experimental results show that AFR-BERT improves on the classical multimodal sentiment analysis model in terms of relevant performance metrics. In addition, ablation experiments and example analysis show that the multimodal data analysis network in AFR-BERT can effectively capture and analyze the sentiment features in text and audio.

Sensitivity evaluation of 2019 novel coronavirus (SARS-CoV-2) RT-PCR detection kits and strategy to reduce false negative

An ongoing outbreak of pneumonia associated with SARS-CoV-2 has now been confirmed globally. In absence of effective vaccines, infection prevention and control through diagnostic testing and quarantine is critical. Early detection and differential diagnosis of respiratory infections increases the chances for successful control of COVID-19 disease. The nucleic acid RT-PCR test is regarded as the current standard for molecular diagnosis with high sensitivity. However, the highest specificity confirmation target ORF1ab gene is considered to be less sensitive than other targets in clinical application. In addition, a large amount of recent evidence indicates that the initial missed diagnosis of asymptomatic patients with SARS-CoV-2 and discharged patients with "re-examination positive" may be due to low viral load, and the ability of rapid mutation of coronavirus also increases the rate of false negative results. We aimed to evaluate the sensitivity of different nucleic acid detection kits so as to make recommendations for the selection of validation kit, and amplify the suspicious result to be reportable positive by means of simple continuous amplification, which is of great significance for the prevention and control of the current epidemic and the discharge criteria of low viral load patients.