1,345 research outputs found
Possible open-charmed pentaquark molecule --- the bound state --- in the Bethe-Salpeter formalism
We study the -wave bound state in the Bethe-Salpeter formalism in
the ladder and instantaneous approximations. With the kernel generated by the
hadronic effective Lagrangian, two open-charmed bound states, which quantum
numbers are , and , ,
respectively, are predicted as new candidates of hadronic pentaquark molecules
in our formalism. If existing, they could contribute to the broad 3188 eV
structure near the five new narrow states observed recently by the
LHCb Collaboration.Comment: 8 pages, 4 figures, accepted by Eur. Phys. J.
Pole analysis on the hadron spectroscopy of
In this paper we study the spectroscopy in the process of
. The final state interactions of coupled channel
~-~ ~-~ are constructed
based on K-matrix with the Chew-Mandelstam function. We build the amplitude according to the Au-Morgan-Pennington method. The event
shape is fitted and the decay width of is used to
constrain the parameters, too. With the amplitudes we extract out the poles and
their residues. Our amplitude and pole analysis suggest that the
should be molecule, the could be an S-wave
compact pentaquark state, and the structure around is caused by the
cusp effect. The future experimental measurement of the decays of and would further
help to study the nature of these resonances.Comment: updated to the published versio
FCNC transitions of to neutron in Bethe-Salpeter equation approach
In a covariant quark-diquark model, we investigate the rare decay of
and in the
Bethe-Salpeter equation approach. In this model the baryons are treated as
bound states of a constituent quark and a diquark interacting via a gluon
exchange and the linear confinement. We find that the ratio of form factors
is varies from to and the branching ratios are and the branching ratio in central values of parameters.Comment: arXiv admin note: text overlap with arXiv:1911.0802
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently
2-Carboxy-1-phenylethanaminium nitrate
In the title salt, C9H12NO2
+·NO3
−, the cation and anion are linked by a bifurcated N—H⋯(O,O) hydrogen bond. The crystal packing is stabilized by intermolecular N—H⋯O, O—H⋯O and C—H⋯O hydrogen bonds, which connect neighbouring cations and anions, resulting in a two-dimensional network
Noise in Genotype Selection Model
We study the steady state properties of a genotype selection model in
presence of correlated Gaussian white noise. The effect of the noise on the
genotype selection model is discussed. It is found that correlated noise can
break the balance of gene selection and induce the phase transition which can
makes us select one type gene haploid from a gene group.Comment: 8 pages, 4 figure
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