1,627 research outputs found
Assignments of and baryons in the heavy quark-light diquark picture
We apply a new mass formula which is derived analytically in the relativistic
flux tube model to the mass spectra of and (\emph{Q} =
\emph{c} or \emph{b} quark) baryons. To this end, the heavy quark-light diquark
picture is employed. We find that all masses of the available and
states can be understood well. The assignments to these states do not
appear to contradict the strong decay properties. and
are assigned to the first radial excitations with .
and might be the 2\emph{P} states. The
and are the good 1\emph{D} candidates with
. is likely to be a 1\emph{D} state with . and favor the 1\emph{P}
assignments with and , respectively. We propose a search
for the state which can help to distinguish the
diquark and three-body schemes.Comment: 9 tables, more discussions and references adde
Low-lying charmed and charmed-strange baryon states
In this work, we systematically study the mass spectra and strong decays of
and charmed and charmed-strange baryons in the framework of
nonrelativistic constituent quark models. With the light quark cluster-heavy
quark picture, the masses are simply calculated by a potential model. The
strong decays are studied by the Eichten-Hill-Quigg decay formula. Masses and
decay properties of the well-established and states can be reproduced
by our method. can be assigned as a
or state. We prefer to interpret the
signal as a state although at present we cannot
thoroughly exclude the possibility that this is the same state as
. or could be
explained as the state or state,
respectively. We propose to measure the branching ratio of
in future,
which may disentangle the puzzle of this state. Our results support
as the first radial excited state of
with . The assignment of is analogous to
, \emph{i.e.}, a or
state. In addition, we predict some typical ratios
among partial decay widths, which are valuable for experimental search for
these missing charmed and charmed-strange baryons.Comment: 16 pages, 3 figures, 13 tables. Accepted by Eur. Phys. J.
Continual Learning of Natural Language Processing Tasks: A Survey
Continual learning (CL) is an emerging learning paradigm that aims to emulate
the human capability of learning and accumulating knowledge continually without
forgetting the previously learned knowledge and also transferring the knowledge
to new tasks to learn them better. This survey presents a comprehensive review
of the recent progress of CL in the NLP field. It covers (1) all CL settings
with a taxonomy of existing techniques. Besides dealing with forgetting, it
also focuses on (2) knowledge transfer, which is of particular importance to
NLP. Both (1) and (2) are not mentioned in the existing survey. Finally, a list
of future directions is also discussed
- β¦