4,990 research outputs found
Charged charmoniumlike structures in the process based on the ISPE mechanism
In 2017, the BESIII Collaboration announced the observation of a charged
charmonium-like structure in the invariant mass spectrum of
the process at different energy points,
which enables us to perform a precise study of this process based on the
initial single pion emission (ISPE) mechanism. In this work, we perform a
combined fit to the experimental data of the cross section of , and the corresponding and dipion
invariant mass spectra. Our result shows that the observed charged
charmonium-like structure in can be well
reproduced based on the ISPE mechanism, and that the corresponding dipion
invariant mass spectrum and cross section can be depicted with the same
parameters. In fact, it provides strong evidence that the ISPE mechanism can be
an underlying mechanism resulting in such novel a phenomenon.Comment: 11 pages, 8 figures and 4 table
Regge-like relation and a universal description of heavy-light systems
Using the Regge-like formula between hadron mass
and angular momentum with a heavy quark mass and a string tension
, we analyze all the heavy-light systems, i.e., mesons
and charmed and bottom baryons.Numerical plots are obtained for all the
heavy-light mesons of experimental data whose slope becomes nearly equal to 1/2
of that for light hadrons. Assuming that charmed and bottom baryons consist of
one heavy quark and one light cluster of two light quarks (diquark), we apply
the formula to all the heavy-light baryons including recently discovered
's and find that these baryons experimentally measured satisfy the
above formula. We predict the average mass values of , , ,
, , and with as 6.01, 6.13, 6.15, 3.05, 3.07,
and 3.34 GeV, respectively. Our results on baryons suggest that these baryons
can be safely regarded as heavy quark-light cluster configuration. We also find
a universal description for all the heavy-light mesons as well as baryons,
i.e., one unique line is enough to describe both of charmed and bottom
heavy-light systems. Our results suggest that instead of mass itself, gluon
flux energy is essential to obtain a linear trajectory.Comment: 10 pages, 8 figures, 5 table
Sparse Attention-Based Neural Networks for Code Classification
Categorizing source codes accurately and efficiently is a challenging problem
in real-world programming education platform management. In recent years,
model-based approaches utilizing abstract syntax trees (ASTs) have been widely
applied to code classification tasks. We introduce an approach named the Sparse
Attention-based neural network for Code Classification (SACC) in this paper.
The approach involves two main steps: In the first step, source code undergoes
syntax parsing and preprocessing. The generated abstract syntax tree is split
into sequences of subtrees and then encoded using a recursive neural network to
obtain a high-dimensional representation. This step simultaneously considers
both the logical structure and lexical level information contained within the
code. In the second step, the encoded sequences of subtrees are fed into a
Transformer model that incorporates sparse attention mechanisms for the purpose
of classification. This method efficiently reduces the computational cost of
the self-attention mechanisms, thus improving the training speed while
preserving effectiveness. Our work introduces a carefully designed sparse
attention pattern that is specifically designed to meet the unique needs of
code classification tasks. This design helps reduce the influence of redundant
information and enhances the overall performance of the model. Finally, we also
deal with problems in previous related research, which include issues like
incomplete classification labels and a small dataset size. We annotated the
CodeNet dataset with algorithm-related labeling categories, which contains a
significantly large amount of data. Extensive comparative experimental results
demonstrate the effectiveness and efficiency of SACC for the code
classification tasks.Comment: 2023 3rd International Conference on Digital Society and Intelligent
Systems (DSInS 2023
- …