15,596 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
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.
Heavy Color-Octet Particles at the LHC
Many new-physics models, especially those with a color-triplet top-quark
partner, contain a heavy color-octet state. The "naturalness" argument for a
light Higgs boson requires that the color-octet state be not much heavier than
a TeV, and thus it can be pair-produced with large cross sections at
high-energy hadron colliders. It may decay preferentially to a top quark plus a
top-partner, which subsequently decays to a top quark plus a color-singlet
state. This singlet can serve as a WIMP dark-matter candidate. Such decay
chains lead to a spectacular signal of four top quarks plus missing energy. We
pursue a general categorization of the color-octet states and their decay
products according to their spin and gauge quantum numbers. We review the
current bounds on the new states at the LHC and study the expected discovery
reach at the 8-TeV and 14-TeV runs. We also present the production rates at a
future 100-TeV hadron collider, where the cross sections will be many orders of
magnitude greater than at the 14-TeV LHC. Furthermore, we explore the extent to
which one can determine the color octet's mass, spin, and chiral couplings.
Finally, we propose a test to determine whether the fermionic color octet is a
Majorana particle.Comment: 20 pages, 9 figures; journal versio
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
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