A Comparison of Teaching Models in the West and in China

Models of teaching commonly used in the West and in China are analyzed and compared, using an analytical approach that systematically considers different aspects of the models. The purpose of the exploration is three-fold: (a) to create better understanding of both Chinese and Western models, for mutual insight and to strengthen the development of pedagogical theory building in China; (b) to guide a joint project between the Netherlands and China relative to the development computer-related learning resources for China; and (c) to contribute to better overall understanding of how instructional resources can be adapted for use in both Western and Chinese situations. The analysis provides a contribution for each of these goals

SPECIES: An R Package for Species Richness Estimation

We introduce an R package SPECIES for species richness or diversity estimation. This package provides simple R functions to compute point and confidence interval estimates of species number from a few nonparametric and semi-parametric methods. For the methods based on nonparametric maximum likelihood estimation, the R functions are wrappers for Fortran codes for better efficiency. All functions in this package are illustrated using real data sets.

QCD Factorization for Spin-Dependent Cross Sections in DIS and Drell-Yan Processes at Low Transverse Momentum

Based on a recent work on the quantum chromodynamic (QCD) factorization for semi-inclusive deep-inelastic scattering (DIS), we present a set of factorization formulas for the spin-dependent DIS and Drell-Yan cross sections at low transverse momentum.Comment: 12 pages, two figures include

Exploiting Sentence Embedding for Medical Question Answering

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.Comment: 8 page