12,006 research outputs found
Observation of vacancy-induced suppression of electronic cooling in defected graphene
Previous studies of electron-phonon interaction in impure graphene have found
that static disorder can give rise to an enhancement of electronic cooling. We
investigate the effect of dynamic disorder and observe over an order of
magnitude suppression of electronic cooling compared with clean graphene. The
effect is stronger in graphene with more vacancies, confirming its
vacancy-induced nature. The dependence of the coupling constant on the phonon
temperature implies its link to the dynamics of disorder. Our study highlights
the effect of disorder on electron-phonon interaction in graphene. In addition,
the suppression of electronic cooling holds great promise for improving the
performance of graphene-based bolometer and photo-detector devices.Comment: 13 pages, 4 figure
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
Discovering and exploiting the causality in deep neural networks (DNNs) are
crucial challenges for understanding and reasoning causal effects (CE) on an
explainable visual model. "Intervention" has been widely used for recognizing a
causal relation ontologically. In this paper, we propose a causal inference
framework for visual reasoning via do-calculus. To study the intervention
effects on pixel-level features for causal reasoning, we introduce pixel-wise
masking and adversarial perturbation. In our framework, CE is calculated using
features in a latent space and perturbed prediction from a DNN-based model. We
further provide the first look into the characteristics of discovered CE of
adversarially perturbed images generated by gradient-based methods
\footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}.
Experimental results show that CE is a competitive and robust index for
understanding DNNs when compared with conventional methods such as
class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for
human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds
promises for detecting adversarial examples as it possesses distinct
characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal
Intervention Meets Adversarial Examples and Image Masking for Deep Neural
Networks" as the v3 official paper title in IEEE Proceeding. Please use it in
your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released
on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm
in a supersymmetric theory with an explicit R-parity violation
We studied the process in a
violating supersymmetric Model with the effects from both B- and L-violating
interactions. The calculation shows that it is possible to detect a
violating signal at the Next Linear Collider. Information about the B-violating
interaction in this model could be obtained under very clean background, if we
take the present upper bounds for the parameters in the supersymmetric interactions. Even if we can not detect a signal of in the
experiment, we may get more stringent constraints on the heavy-flavor
couplings.Comment: 16 pages, 6 figure
Decay Constants of Pseudoscalar -mesons in Lattice QCD with Domain-Wall Fermion
We present the first study of the masses and decay constants of the
pseudoscalar mesons in two flavors lattice QCD with domain-wall fermion.
The gauge ensembles are generated on the lattice with the
extent in the fifth dimension, and the plaquette gauge action at , for three sea-quark masses with corresponding pion masses in
the range MeV. We compute the point-to-point quark propagators, and
measure the time-correlation functions of the pseudoscalar and vector mesons.
The inverse lattice spacing is determined by the Wilson flow, while the strange
and the charm quark masses by the masses of the vector mesons
and respectively. Using heavy meson chiral perturbation theory
(HMChPT) to extrapolate to the physical pion mass, we obtain MeV and MeV.Comment: 15 pages, 3 figures. v2: the statistics of ensemble (A) with m_sea =
0.005 has been increased, more details on the systematic error, to appear in
Phys. Lett.
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