70,919 research outputs found
Vacua and Exact Solutions in Lower- Limits of EGB
We consider the action principles that are the lower dimensional limits of
the Einstein-Gauss-Bonnet gravity {\it via} the Kaluza-Klein route. We study
the vacua and obtain some exact solutions. We find that the reality condition
of the theories may select one vacuum over the other from the two vacua that
typically arise in Einstein-Gauss-Bonnet gravity. We obtain exact black hole
and cosmological solutions carrying scalar hair, including scalar hairy BTZ
black holes with both mass and angular momentum turned on. We also discuss the
holographic central charges in the asymptotic AdS backgrounds.Comment: Latex, 19 page
Integrated Node Encoder for Labelled Textual Networks
Voluminous works have been implemented to exploit content-enhanced network
embedding models, with little focus on the labelled information of nodes.
Although TriDNR leverages node labels by treating them as node attributes, it
fails to enrich unlabelled node vectors with the labelled information, which
leads to the weaker classification result on the test set in comparison to
existing unsupervised textual network embedding models. In this study, we
design an integrated node encoder (INE) for textual networks which is jointly
trained on the structure-based and label-based objectives. As a result, the
node encoder preserves the integrated knowledge of not only the network text
and structure, but also the labelled information. Furthermore, INE allows the
creation of label-enhanced vectors for unlabelled nodes by entering their node
contents. Our node embedding achieves state-of-the-art performances in the
classification task on two public citation networks, namely Cora and DBLP,
pushing benchmarks up by 10.0\% and 12.1\%, respectively, with the 70\%
training ratio. Additionally, a feasible solution that generalizes our model
from textual networks to a broader range of networks is proposed.Comment: 7 page
EMC effect in semi-inclusive deep-inelastic scattering process
By considering the -dependence of , , , ,
, , , hadron productions in charged lepton
semi-inclusive deep inelastic scattering off nuclear target (using Fe as an
example) and deuteron D target, % at GeV, we find that
and
are ideal to figure out the nuclear sea
content, which is predicted to be different by different models accounting for
the nuclear EMC effect.Comment: 21 latex pages, 15 figure
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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