14,229 research outputs found
Laplacian coefficients of unicyclic graphs with the number of leaves and girth
Let be a graph of order and let be the characteristic polynomial of its
Laplacian matrix. Motivated by Ili\'{c} and Ili\'{c}'s conjecture [A. Ili\'{c},
M. Ili\'{c}, Laplacian coefficients of trees with given number of leaves or
vertices of degree two, Linear Algebra and its Applications
431(2009)2195-2202.] on all extremal graphs which minimize all the Laplacian
coefficients in the set of all -vertex unicyclic graphs
with the number of leaves , we investigate properties of the minimal
elements in the partial set of the Laplacian
coefficients, where denote the set of -vertex
unicyclic graphs with the number of leaves and girth . These results are
used to disprove their conjecture. Moreover, the graphs with minimum
Laplacian-like energy in are also studied.Comment: 19 page, 4figure
Thermal Timescale Mass Transfer Rates in Intermediate-Mass X-ray Binaries
Thermal timescale mass transfer generally occurs in close binaries where the
donor star is more massive than the accreting star. The mass transfer rates are
usually estimated in terms of the Kelvin-Helmholtz timescale of the donor star.
But recent investigations indicate that this method may overestimate the real
mass transfer rates in accreting white dwarf or neutron star binary systems. We
have systematically investigated the thermal-timescale mass transfer processes
in intermediate-mass X-ray binaries, by calculating binary evolution sequences
with various initial donor masses and orbital periods. From the calculated
results we find that on average the mass transfer rates are lower than
traditional estimates by a factor of .Comment: 13 pages, 4 figures, and 2 tables, accepted for publication in A&
Neural Word Segmentation with Rich Pretraining
Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201
Neural Reranking for Named Entity Recognition
We propose a neural reranking system for named entity recognition (NER). The
basic idea is to leverage recurrent neural network models to learn
sentence-level patterns that involve named entity mentions. In particular,
given an output sentence produced by a baseline NER model, we replace all
entity mentions, such as \textit{Barack Obama}, into their entity types, such
as \textit{PER}. The resulting sentence patterns contain direct output
information, yet is less sparse without specific named entities. For example,
"PER was born in LOC" can be such a pattern. LSTM and CNN structures are
utilised for learning deep representations of such sentences for reranking.
Results show that our system can significantly improve the NER accuracies over
two different baselines, giving the best reported results on a standard
benchmark.Comment: Accepted as regular paper by RANLP 201
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