Dense video caption is a task that aims to help computers analyze the content of a video by generating abstract captions for a sequence of video frames. However, most of the existing methods only use visual features in the video and ignore the audio features that are also essential for understanding the video. In this paper, we propose a fusion model that combines the Transformer framework to integrate both visual and audio features in the video for captioning. We use multi-head attention to deal with the variations in sequence lengths between the models involved in our approach. We also introduce a Common Pool to store the generated features and align them with the time steps, thus filtering the information and eliminating redundancy based on the confidence scores. Moreover, we use LSTM as a decoder to generate the description sentences, which reduces the memory size of the entire network. Experiments show that our method is competitive on the ActivityNet Captions dataset.

Vaccination traditionally has targeted infectious agents and thus has not heretofore been used to prevent neurodegenerative illness. However, amyloid β (Aβ) or tau, which can act like infectious proteins, or prions, might induce Alzheimer’s disease (AD). Furthermore, evidence suggests that traditional infectious agents, including certain viruses and bacteria, may trigger AD. It is therefore worth exploring whether removing such targets could prevent AD. Although failing to treat AD patients who already display cognitive impairment, Aβ monoclonal antibodies are being tested in pre-symptomatic, at-risk individuals to prevent dementia. These antibodies might become the first AD therapeutics. However, their high cost will keep them out of the arms of the vast majority of patients, who increasingly live in developing countries. Because vaccines produce antibodies internally at much lower cost, vaccination might be the most promising approach to reducing the global burden of dementia.

A long sequence always contains long‐term dependency problems, which leads to paragraph‐based sentiment analysis being a very challenging task and difficult to evaluate by using a simple RNN network. It is proposed in this letter to use a stacked CARU network to extract the main information in a paragraph. The resulting network also points out how to use a CNN‐based extractor to explore complete passages and capture useful features in their hidden state. In particular, instead of using the Softmax function, the Naïve‐Bayes classifier is connected to the end of the CNN‐based extractor. The proposed models also take into account the conditional independence of the observed results under the hidden variables, which aims to project features into a probability distribution appreciated for its simplicity and interpretability. The advantages of these models in sentiment analysis are empirically investigated by combining the usual classifiers with the results of GloVe embedding on the SST‐5 and IMDB datasets.