Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

Random lasing actions in self-assembled perovskite nanoparticles

Solution-based perovskite nanoparticles have been intensively studied in past few years due to their applications in both photovoltaic and optoelectronic devices. Here, based on the common ground between the solution-based perovskite and random lasers, we have studied the mirrorless lasing actions in self-assembled perovskite nanoparticles. After the synthesis from solution, discrete lasing peaks have been observed from the optically pumped perovskites without any well-defined cavity boundaries. The obtained quality (Q) factors and thresholds of random lasers are around 500 and 60 uJ/cm2, respectively. Both values are comparable to the conventional perovskite microdisk lasers with polygon shaped cavity boundaries. From the corresponding studies on laser spectra and fluorescence microscope images, the lasing actions are considered as random lasers that are generated by strong multiple scattering in random gain media. In additional to conventional single-photon excitation, due to the strong nonlinear effects of perovskites, two-photon pumped random lasers have also been demonstrated for the first time. We believe this research will find its potential applications in low-cost coherent light sources and biomedical detection.Comment: 16 pages, 5 figure

Investigation to the deep center related properties of low temperature grown InPBi with Hall and photoluminescence

InP1-xBix epilayers with bismuth (Bi) concentration x= 1.0% were grown on InP by gas source molecular beam epitaxy (GS-MBE) at low temperature (LT). Bi incorporation decreased the intrinsic free electron concentration of low temperature grown InP indicated by hall analysis. It is concluded that deep level center was introduced by Bi. Influence of Si doping on the InP1-xBix films Photoluminescence (PL) was investigated. N-type doping in the InP1-xBix epilayers was found to be effective at PL enhancement. Blue shift of InPBi PL emission wavelength was observed as the Si doping concentration increasing. Two independent peaks were fitted and their temperature dependence behavior was observed to be distinct obviously. Two individual radiative recombination processes were expected to be involved