7,078 research outputs found
Empirical information on nuclear matter fourth-order symmetry energy from an extended nuclear mass formula
We establish a relation between the equation of state (EOS) of nuclear matter
and the fourth-order symmetry energy of finite nuclei in a
semi-empirical nuclear mass formula by self-consistently considering the bulk,
surface and Coulomb contributions to the nuclear mass. Such a relation allows
us to extract information on nuclear matter fourth-order symmetry energy
at normal nuclear density from analyzing
nuclear mass data. Based on the recent precise extraction of
via the double difference of the "experimental" symmetry
energy extracted from nuclear masses, for the first time, we estimate a value
of MeV. Such a value of
is significantly larger than the predictions from
mean-field models and thus suggests the importance of considering the effects
of beyond the mean-field approximation in nuclear matter calculations.Comment: 7 pages, 1 figure. Presentation improved and discussions added.
Accepted version to appear in PL
Learning text representation using recurrent convolutional neural network with highway layers
Recently, the rapid development of word embedding and neural networks has
brought new inspiration to various NLP and IR tasks. In this paper, we describe
a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN)
with highway layers. The highway network module is incorporated in the middle
takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module
in the first stage and provides the Convolutional Neural Network (CNN) module
in the last stage with the input. The experiment shows that our model
outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment
analysis task. Besides, the analysis of how sequence length influences the RCNN
with highway layers shows that our model could learn good representation for
the long text.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieva
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