Abstract: Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis. Most of the information of the Yishui school of traditional Chinese Medicine (TCM) is stored in the form of unstructured classical Chinese text. The key information extraction of TCM texts plays an important role in mining and studying the academic schools of TCM. In order to solve these problems efficiently using artificial intelligence methods, this study constructs a word segmentation and entity relationship extraction model based on conditional random fields under the framework of natural language processing technology to identify and extract the entity relationship of traditional Chinese medicine texts, and uses the common weighting technology of TF-IDF information retrieval and data mining to extract important key entity information in different ancient books. The dependency syntactic parser based on neural network is used to analyze the grammatical relationship between entities in each ancient book article, and it is represented as a tree structure visualization, which lays the foundation for the next construction of the knowledge graph of Yishui school and the use of artificial intelligence methods to carry out the research of TCM academic schools. Key words: Natural language processing; Knowledge graph; Yi Shui school; Syntactic analysis; Traditional Chinese Medicine; 1 Introduction In the era of artificial intelligence and big data technology, the mining and utilization of ancient Chinese medicine literature knowledge is one of the important basic tasks for the inheritance and innovation and development of traditional Chinese medicine.

I used to fall asleep at night with needles in my face. One needle shallowly planted in the inner corners of each eyebrow, one per temple, one in the middle of each eyebrow above the pupil, a few by my nose and mouth. I'd wake up hours later, the hair-thin, stainless steel pins having been surreptitiously removed by a parent. Sometimes they'd forget about the treatment, and in the morning we'd search my pillow for needles. My very farsighted left eye gradually became only somewhat farsighted, and my mildly nearsighted right eye eventually achieved a perfect score at the optometrist's.

We introduce a new real-valued invariant called the natural slope of a hyperbolic knot in the 3-sphere, which is defined in terms of its cusp geometry. We show that twice the knot signature and the natural slope differ by at most a constant times the hyperbolic volume divided by the cube of the injectivity radius. This inequality was discovered using machine learning to detect relationships between various knot invariants. It has applications to Dehn surgery and to 4-ball genus. We also show a refined version of the inequality where the upper bound is a linear function of the volume, and the slope is corrected by terms corresponding to short geodesics that link the knot an odd number of times.

The British government this week unveiled plans for an ambitious AI simulator to be used to test self-driving cars. It's part of a stated mission to make the UK the world's leading destination for testing autonomous vehicles. The simulator, called OmniCAV, recreates a virtual version of 32km of Oxfordshire roads. "It's a synthetic digital model of the real world," Mark Stileman, bid manager at one of the partners involved, Ordnance Survey, told us. The OS already knows where a lot things are, and the sim adds "feature classification and retrieval" of road furnitures like gantries and white lines, and crucially, road intersections.