With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural networkbased (NN-based) methods develop, NNbased KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the crossattention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.
The development of core-shell structures remains a fundamental challenge for pure metallic aerogels. Here we report the synthesis of Pd Au-Pt core-shell aerogels composed of an ultrathin Pt shell and a composition-tunable Pd Au alloy core. The universality of this strategy ensures the extension of core compositions to Pd transition-metal alloys. The core-shell aerogels exhibited largely improved Pt utilization efficiencies for the oxygen reduction reaction and their activities show a volcano-type relationship as a function of the lattice parameter of the core substrate. The maximum mass and specific activities are 5.25 A mg and 2.53 mA cm , which are 18.7 and 4.1 times higher than those of Pt/C, respectively, demonstrating the superiority of the core-shell metallic aerogels. The proposed core-based activity descriptor provides a new possible strategy for the design of future core-shell electrocatalysts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.