End-to-End Quantum-like Language Models with Application to Question Answering

Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models

The influence of bile acids homeostasis by cryptotanshinone-containing herbs

Background: Herbs might affect the homeostasis of bile acids through influence of multiple metabolic pathways of bile acids. Aim: The present study aims to investigate the inhibition of cryptotanshinone towards the glucuronidation of LCA, trying to indicate the possible influence of cryptotanshinone-containing herbs towards the homeostasis of bile acids. Methods: The LCA-3-glucuronidation and LCA-24-glucuronidation reaction was monitored by LC-MS. Results: Initial screening showed that 100 μM of cryptotanshinone inhibited LCA-24-glucuronidation and LCA-3-glucuronidation reaction activity by 82.6% and 79.1%, respectively. This kind of inhibition behaviour exerted cryptotanshinone concentrations-dependent and LCA concentrations-independent inhibition behaviour. Conclusion: All these data indicated the possibility of cryptotanshinone’s influence towards the bile acids metabolism and homeostasis of bile acids.Keywords: herbs, lithocholic acid (LCA), homeostasisAfrican Health sciences Vol 14 No. 1 March 201

Melt compounding with graphene to develop functional, high-performance elastomers

Rather than using graphene oxide, which is limited by a high defect concentration and cost due to oxidation and reduction, we adopted cost-effective, 3.56 nm thick graphene platelets (GnPs) of high structural integrity to melt compound with an elastomer—ethylene–propylene–diene monomer rubber (EPDM)—using an industrial facility. An elastomer is an amorphous, chemically crosslinked polymer generally having rather low modulus and fracture strength but high fracture strain in comparison with other materials; and upon removal of loading, it is able to return to its original geometry, immediately and completely. It was found that most GnPs dispersed uniformly in the elastomer matrix, although some did form clusters. A percolation threshold of electrical conductivity at 18 vol% GnPs was observed and the elastomer thermal conductivity increased by 417% at 45 vol% GnPs. The modulus and tensile strength increased by 710% and 404% at 26.7 vol% GnPs, respectively. The modulus improvement agrees well with the Guth and Halpin-Tsai models. The reinforcing effect of GnPs was compared with silicate layers and carbon nanotube. Our simple fabrication would prolong the service life of elastomeric products used in dynamic loading, thus reducing thermosetting waste in the environment

Melt compounding with graphene to develop functional, high-performance elastomers

Rather than using graphene oxide, which is limited by a high defect concentration and cost due to oxidation and reduction, we adopted cost-effective, 3.56 nm thick graphene platelets (GnPs) of high structural integrity to melt compound with an elastomer—ethylene–propylene–diene monomer rubber (EPDM)—using an industrial facility. An elastomer is an amorphous, chemically crosslinked polymer generally having rather low modulus and fracture strength but high fracture strain in comparison with other materials; and upon removal of loading, it is able to return to its original geometry, immediately and completely. It was found that most GnPs dispersed uniformly in the elastomer matrix, although some did form clusters. A percolation threshold of electrical conductivity at 18 vol% GnPs was observed and the elastomer thermal conductivity increased by 417% at 45 vol% GnPs. The modulus and tensile strength increased by 710% and 404% at 26.7 vol% GnPs, respectively. The modulus improvement agrees well with the Guth and Halpin-Tsai models. The reinforcing effect of GnPs was compared with silicate layers and carbon nanotube. Our simple fabrication would prolong the service life of elastomeric products used in dynamic loading, thus reducing thermosetting waste in the environment