Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.Comment: submitted to ICASSP 2015 which does not perform blind review

Distinct behaviors of suppression to superconductivity in LaRu3Si2LaRu_3Si_2 induced by Fe and Co dopants

In the superconductor LaRu$_3$Si$_2$ with the Kagome lattice of Ru, we have successfully doped the Ru with Fe and Co atoms. Contrasting behaviors of suppression to superconductivity is discovered between the Fe and the Co dopants: Fe-impurities can suppress the superconductivity completely at a doping level of only 3%, while the superconductivity is suppressed slowly with the Co dopants. A systematic magnetization measurements indicate that the doped Fe impurities lead to spin-polarized electrons yielding magnetic moments with the magnitude of 1.6 $\mu_B$\ per Fe, while the electrons given by the Co dopants have the same density of states for spin-up and spin-down leading to much weaker magnetic moments. It is the strong local magnetic moments given by the Fe-dopants that suppress the superconductivity. The band structure calculation further supports this conclusion.Comment: 6 pages, 7 figure