Analysis of development trend of Chinese medicine health products based on history of western health products development

Because of the rapidly rhythm of modern society,fast food and take-out become the main dining methods for most of young people,while the outside foods always have few types of foods and nutrition is not complete.Nutrient imbalances may cause many diseases such as gastrointestinal diseases,cardiovascular diseases,diabetes,obesity,and cancer.In this environment,health products have emerged.At present,there are a wide variety of health care products at home and abroad,which are broadly divided into three categories:traditional vitamins,emerging nutrient products,and extracts of natural plant active ingredients.In the early 1970 s,the sales of health products in the United States had reached 170 million dollars.At present,it has nearly 100 billion dollars,which is almost 1/3 of the total food sales,people's demand for health products is increasing rapidly.In recent years,Chinese medicine health products become more popular in western,in fact,Chinese medicine health products have a long history of application in China and have a good reputation in the folk.Obviously,Chinese medicine health products have great potential for development.So this article mainly compared the de.velopment and state of health products between China and west countries.Based on the development of health products in western countries,this article analyzes the development trend and prospects for the development of Chinese medicine health products.It is roughly divided into three parts.The first part introduces the development reasons and history of western health products development.The sec.ond part introduces the history of Chinese health products and the current situation of Chinese medi.cine health products.The third part guesses the development trend of Chinese medicine health prod.ucts and provides some development ideas.The purpose of this airticle is to provide reference and ideas for the future research and development of traditional Chinese medicine health products.

A Survey of Text Summarization Approaches Based on Deep Learning

Automatic text summarization(ATS)has achieved impressive performance thanks to recent advances in deep learning(DL)and the availability of large-scale corpora.The key points in ATS are to estimate the salience of information and to generate coherent results.Recently,a variety of DL-based approaches have been developed for better considering these two aspects.However,there is still a lack of comprehensive literature review for DL-based ATS approaches.The aim of this paper is to comprehensively review significant DL-based approaches that have been proposed in the literature with respect to the notion of generic ATS tasks and provide a walk-through of their evolution.We first give an overview of ATS and DL.The comparisons of the datasets are also given,which are commonly used for model training,validation,and evaluation.Then we summarize single-document summarization approaches.After that,an overview of multi-document summarization approaches is given.We further analyze the performance of the popular ATS models on common datasets.Various popular approaches can be employed for different ATS tasks.Finally,we propose potential research directions in this fast-growing field.We hope this exploration can provide new insights into future research of DL-based ATS.

Multi-Exposure Motion Estimation Based on Deep Convolutional Networks

In motion estimation, illumination change is always a troublesome obstacle, which often causes severely per- formance reduction of optical flow computation. The essential reason is that most of estimation methods fail to formalize a unified definition in color or gradient domain for diverse environmental changes. In this paper, we propose a new solution based on deep convolutional networks to solve the key issue. Our idea is to train deep convolutional networks to represent the complex motion features under illumination change, and further predict the final optical flow fields. To this end, we construct a training dataset of multi-exposure image pairs by performing a series of non-linear adjustments in the traditional datasets of optical.flow estimation. Our multi-exposure flow networks （MEFNet） model consists of three main components： low-level feature network, fusion feature network, and motion estimation network. The former two components belong to the contracting part of our model in order to extract and represent the multi-exposure motion features; the third component is the expanding part of our model in order to learn and predict the high-quality optical flow. Compared with many state- of-the-art methods, our motion estimation method can eliminate the obstacle of illumination change and yield optical flow results with competitive accuracy and time efficiency. Moreover, the good performance of our model is also demonstrated in some multi-exposure video applications, like HDR （high dynamic range） composition and flicker removal.