EduNER: a Chinese named entity recognition dataset for education research.

A high-quality domain-oriented dataset is crucial for the domain-specific named entity recognition (NER) task. In this study, we introduce a novel education-oriented Chinese NER dataset (EduNER). To provide representative and diverse training data, we collect data from multiple sources, including textbooks, academic papers, and education-related web pages. The collected documents span ten years (2012-2021). A team of domain experts is invited to accomplish the education NER schema definition, and a group of trained annotators is hired to complete the annotation. A collaborative labeling platform is built for accelerating human annotation. The constructed EduNER dataset includes 16 entity types, 11k+ sentences, and 35,731 entities. We conduct a thorough statistical analysis of EduNER and summarize its distinctive characteristics by comparing it with eight open-domain or domain-specific NER datasets. Sixteen state-of-the-art models are further utilized for NER tasks validation. The experimental results can enlighten further exploration. To the best of our knowledge, EduNER is the first publicly available dataset for NER task in the education domain, which may promote the development of education-oriented NER models.

A Variational Framework for Curve Shortening in Various Geometric Domains.

Geodesics measure the shortest distance (either locally or globally) between two points on a curved surface and serve as a fundamental tool in digital geometry processing. Suppose that we have a parameterized path γ(t)=x(u(t),v(t)) on a surface x=x(u,v) with γ(0)=p and γ(1)=q. We formulate the two-point geodesic problem into a minimization problem [Formula: see text], where H(s) satisfies and H''(s) ≥ 0 for . In our implementation, we choose H(s)=es2-1 and show that it has several unique advantages over other choices such as H(s)=s2 and H(s)=s. It is also a minimizer of the traditional geodesic length variational and able to guarantee the uniqueness and regularity in terms of curve parameterization. In the discrete setting, we construct the initial path by a sequence of moveable points {xi}i=1n and minimize ∑i=1n H(||xi - xi+1||). The resulting points are evenly spaced along the path. It's obvious that our algorithm can deal with parametric surfaces. Considering that meshes, point clouds and implicit surfaces can be transformed into a signed distance function (SDF), we also discuss its implementation on a general SDF. Finally, we show that our method can be extended to solve a general least-cost path problem. We validate the proposed algorithm in terms of accuracy, performance and scalability, and demonstrate the advantages by extensive comparisons.

Anti-osteoporosis effects and regulatory mechanism of Lindera aggregata based on network pharmacology and experimental validation.

Osteoporosis (OP) is characterized by the flaccidity of bones or bone bi-disease caused by kidney deficiency. Lindera aggregate has been used to strengthen kidney function in China for thousands of years. It has been approved by Chinese Pharmacopoeia that the root of Lindera aggregata (RLA) can replenish and tonify the kidney, which is thought to be an effective way to alleviate OP. In this study, a network pharmacology approach was applied to explore the active components and potential mechanisms of RLA in osteoporosis treatment. Then, the ethanolic extract of the root of L. aggregata (EERL) was prepared and these predicted results were validated by prednisone-induced zebrafish embryos model. Moreover, the candidate compounds were identified by UPLC-ESI-MS/MS. The anti-OP results showed that EERL could significantly reverse the bone loss of zebrafish induced by prednisone. The mRNA expressions results showed that EERL decreased osteoclast bone resorption by regulating the RANK/RANKL/OPG system. Also, it increased bone formation by regulating the gene expressions of spp1, mmp2, mmp9, runx2b, alp, and entpd5a. Our results demonstrated the reliability of the network pharmacology method, and also revealed the anti-OP effect and potential mechanism of RLA.

Geodesic Tracks: Computing Discrete Geodesics With Track-Based Steiner Point Propagation.

This article presents a simple yet effective method for computing geodesic distances on triangle meshes. Unlike the popular window propagation methods that partition mesh edges into intervals of varying lengths, our method places evenly-spaced, source-independent Steiner points on edges. Given a source vertex, our method constructs a Steiner-point graph that partitions the surface into mutually exclusive tracks, called geodesic tracks. Inside each triangle, the tracks form sub-regions in which the change of distance field is approximately linear. Our method does not require any pre-computation, and can effectively balance speed and accuracy. Experimental results show that with 5 Steiner points on each edge, the mean relative error is less than 0.3 % for common 3D models used in the graphics community. We propose a set of effective filtering rules to eliminate a large amount of useless broadcast events. For a 1000K-face model, our method runs 10 times faster than the conventional Steiner point method that examines a complete graph of Steiner points in each triangle. We also observe that using more Steiner points increases the accuracy at only a small extra computational cost. Our method works well for meshes with poor triangulation and non-manifold configuration, which often poses challenges to the existing PDE methods. We show that geodesic tracks, as a new data structure that encodes rich information of discrete geodesics, support accurate geodesic path and isoline tracing, and efficient distance query. Our method can be easily extended to meshes with non-constant density functions and/or anisotropic metrics.