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

Electrical and optical properties of fluid iron from compressed to expanded regime

Using quantum molecular dynamics simulations, we show that the electrical and optical properties of fluid iron change drastically from compressed to expanded regime. The simulation results reproduce the main trends of the electrical resistivity along isochores and are found to be in good agreement with experimental data. The transition of expanded fluid iron into a nonmetallic state takes place close to the density at which the constant volume derivative of the electrical resistivity on internal energy becomes negative. The study of the optical conductivity, absorption coefficient, and Rosseland mean opacity shows that, quantum molecular dynamics combined with the Kubo-Greenwood formulation provides a powerful tool to calculate and benchmark the electrical and optical properties of iron from expanded fluid to warm dense region

Chandra Observation of a Weak Shock in the Galaxy Cluster A2556

Based on a 21.5 ks \chandra\ observation of A2556, we identify an edge on the surface brightness profile (SBP) at about 160$h_{71}^{-1}$ kpc northeast of the cluster center, and it corresponds to a shock front whose Mach number $\mathcal{M}$ is calculated to be $1.25_{-0.03}^{+0.02}$. No prominent substructure, such as sub-cluster, is found in either optical or X-ray band that can be associated with the edge, suggesting that the conventional super-sonic motion mechanism may not work in this case. As an alternative solution, we propose that the nonlinear steepening of acoustic wave, which is induced by the turbulence of the ICM at the core of the cluster, can be used to explain the origin of the shock front. Although nonlinear steepening weak shock is expected to occur frequently in clusters, why it is rarely observed still remains a question that requires further investigation, including both deeper X-ray observation and extensive theoretical studies.Comment: 15 pages, 4 figures, accepted by Ap