8,937 research outputs found
Peak shifts due to rescattering in dipion transitions
We study the energy distributions of dipion transitions to
in the final state rescattering model. Since the
is well above the open bottom thresholds, the dipion transitions
are expected to mainly proceed through the real processes and . We find that the energy distributions of
markedly differ from that of . In particular, the resonance peak will be pushed up by
about 7-20 MeV for these dipion transitions relative to the main hadronic decay
modes. These predictions can be used to test the final state rescattering
mechanism in hadronic transitions for heavy quarkonia above the open flavor
thresholds.Comment: Version published in PRD, energy dependence of the total width in
Eq.(12) restored and corresponding figure changed, more discussion and
clarification adde
Multi-view Regularized Gaussian Processes
Gaussian processes (GPs) have been proven to be powerful tools in various
areas of machine learning. However, there are very few applications of GPs in
the scenario of multi-view learning. In this paper, we present a new GP model
for multi-view learning. Unlike existing methods, it combines multiple views by
regularizing marginal likelihood with the consistency among the posterior
distributions of latent functions from different views. Moreover, we give a
general point selection scheme for multi-view learning and improve the proposed
model by this criterion. Experimental results on multiple real world data sets
have verified the effectiveness of the proposed model and witnessed the
performance improvement through employing this novel point selection scheme
Distributed Learning over Unreliable Networks
Most of today's distributed machine learning systems assume {\em reliable
networks}: whenever two machines exchange information (e.g., gradients or
models), the network should guarantee the delivery of the message. At the same
time, recent work exhibits the impressive tolerance of machine learning
algorithms to errors or noise arising from relaxed communication or
synchronization. In this paper, we connect these two trends, and consider the
following question: {\em Can we design machine learning systems that are
tolerant to network unreliability during training?} With this motivation, we
focus on a theoretical problem of independent interest---given a standard
distributed parameter server architecture, if every communication between the
worker and the server has a non-zero probability of being dropped, does
there exist an algorithm that still converges, and at what speed? The technical
contribution of this paper is a novel theoretical analysis proving that
distributed learning over unreliable network can achieve comparable convergence
rate to centralized or distributed learning over reliable networks. Further, we
prove that the influence of the packet drop rate diminishes with the growth of
the number of \textcolor{black}{parameter servers}. We map this theoretical
result onto a real-world scenario, training deep neural networks over an
unreliable network layer, and conduct network simulation to validate the system
improvement by allowing the networks to be unreliable
as a molecule from the pole counting rule
A comprehensive study on the nature of the resonant structure is
carried out in this work. By constructing the pertinent effective Lagrangians
and considering the important final-state-interaction effects, we first give a
unified description to all the relevant experimental data available, including
the and invariant mass distributions from the process, the distribution from and
also the spectrum in the process.
After fitting the unknown parameters to the previous data, we search the pole
in the complex energy plane and find only one pole in the nearby energy region
in different Riemann sheets. Therefore we conclude that is of
molecular nature, according to the pole counting rule
method~[Nucl.~Phys.~A543, 632 (1992); Phys.~Rev.~D 35,~1633 (1987)]. We
emphasize that the conclusion based upon the pole counting method is not
trivial, since both the contact interactions and the explicit
exchanges are introduced in our analyses and they lead to the same
conclusion.Comment: 21 pages, 9 figures. To match the published version in PRD.
Additional discussion on the spectral density function is include
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