7,736 research outputs found
Three body radiative decay in the PQCD approach
We study the three body radiative decay by
introducing the pair distribution amplitudes (DAs) in the perturbative
QCD approach. This nonperturbative inputs, the two meson DAs, is very important
to simplify the calculations. Besides the dominant electromagnetic penguin
operator , the subleading contributions from chromomagnetic
penguin operator , quark-loop corrections and annihilation type
amplitudes are also considered. We find that the branching ratio for the decay
is about
, which is much
smaller compared with that for the decay . It is mainly
because that the former decay induces by with small CKM matrix
element being proportional to . The prediction for the direct CP
asymmetry is , which is well consistent with
the result from the U-spin symmetry approach. we also predict the decay spectrum, which exhibits a maximu at the
invariant masss around 1.95 GeV.Comment: 17 pages,6 figures, Accepted for publication in EPJ
Study of the mixing in the decays
We studied the B meson decays in the pQCD
approach beyond the leading order. With the vertex corrections and the NLO
Wilson coefficients included, the branching ratios of the considered decays are
, and with the mixing angle
, which can agree well with the data or the present
experimental upper limit within errors. So we support the opinion that
is much more favored than . Furthermore,
we also give the predictions for the polarization fractions, direct CP
violations from the different polarization components, the relative phase
angles for the considered decays with the mixing angle
and , respectively. The direct CP violations of the two charged
decays are very small ,
because there is no weak phase until up to with the
Wolfenstein parameter . These results can be tested at the
running LHCb and forthcoming Super-B experiments.Comment: 14 pages,3 figures,to appear in EPJ
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Deep neural networks are vulnerable to adversarial attacks. The literature is
rich with algorithms that can easily craft successful adversarial examples. In
contrast, the performance of defense techniques still lags behind. This paper
proposes ME-Net, a defense method that leverages matrix estimation (ME). In
ME-Net, images are preprocessed using two steps: first pixels are randomly
dropped from the image; then, the image is reconstructed using ME. We show that
this process destroys the adversarial structure of the noise, while
re-enforcing the global structure in the original image. Since humans typically
rely on such global structures in classifying images, the process makes the
network mode compatible with human perception. We conduct comprehensive
experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and
Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows
that ME-Net consistently outperforms prior techniques, improving robustness
against both black-box and white-box attacks.Comment: ICML 201
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