834 research outputs found
THE DIFFERENCES BETWEEN IRON AND IRON-SUBSTITUTED MANGANESE SUPEROXIDE DISMUTASE WITH RESPECT TO HYDROGEN PEROXIDE TREATMENT
Iron-substituted manganese superoxide dismutase (Fe(Mn)SOD) was produced using an in vivo preparation method. It’s an inactive enzyme in catalyzing superoxide radical dismutation owing to the mis-incorporation of Fe in the active site evolved to use Mn. To investigate the possible toxicity of human Fe(Mn)SOD proposed by Yamakura, we studied the properties of Fe(Mn)SOD upon H2O2 treatment and compared to that of FeSOD. It’s found that the responses to H2O2 treatment were different, including the changes of optical spectra, variations of active site coordination and secondary structures. Fe3+ reduction was not observed in Fe(Mn)SOD even H2O2 is believed to oxidize proteins via highly reactive intermediates including Fe and formed via Fe2+, which is true in FeSOD. What’s more, the activities of Fe(Mn)SOD and FeSOD were totally different in the ABTS assay or Amplex Red assay. These results indicated that the mechanism of peroxidase reaction of Fe(Mn)SOD is not identical to that of FeSOD
Primordial Black Hole Formation in Starobinsky's Linear Potential Model
We study the power spectrum of the comoving curvature perturbation
in the model that glues two linear potentials of different slopes, originally
proposed by Starobinsky. We find that the enhanced power spectrum reaches its
maximum at the wavenumber which is times the junction scale. The peak is
times larger than the ultraviolet plateau. We also show that its
near-peak behavior can be well approximated by a constant-roll model, once we
define the effective ultra-slow-roll -folding number appropriately by
considering the contribution from non-single-clock phase only. Such an abrupt
transition to non-attractor phase can leave some interesting characteristic
features in the energy spectrum of the scalar-induced gravitational waves,
which are detectable in the space-borne interferometers if the primordial black
holes generated at such a high peak are all the dark matter.Comment: 45 pages, 8 figure
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks
Very deep convolutional neural networks (CNNs) have been firmly established
as the primary methods for many computer vision tasks. However, most
state-of-the-art CNNs are large, which results in high inference latency.
Recently, depth-wise separable convolution has been proposed for image
recognition tasks on computationally limited platforms such as robotics and
self-driving cars. Though it is much faster than its counterpart, regular
convolution, accuracy is sacrificed. In this paper, we propose a novel
decomposition approach based on SVD, namely depth-wise decomposition, for
expanding regular convolutions into depthwise separable convolutions while
maintaining high accuracy. We show our approach can be further generalized to
the multi-channel and multi-layer cases, based on Generalized Singular Value
Decomposition (GSVD) [59]. We conduct thorough experiments with the latest
ShuffleNet V2 model [47] on both random synthesized dataset and a large-scale
image recognition dataset: ImageNet [10]. Our approach outperforms channel
decomposition [73] on all datasets. More importantly, our approach improves the
Top-1 accuracy of ShuffleNet V2 by ~2%.Comment: CVPR 2019 workshop, Efficient Deep Learning for Computer Visio
Physics-Constrained Backdoor Attacks on Power System Fault Localization
The advances in deep learning (DL) techniques have the potential to deliver
transformative technological breakthroughs to numerous complex tasks in modern
power systems that suffer from increasing uncertainty and nonlinearity.
However, the vulnerability of DL has yet to be thoroughly explored in power
system tasks under various physical constraints. This work, for the first time,
proposes a novel physics-constrained backdoor poisoning attack, which embeds
the undetectable attack signal into the learned model and only performs the
attack when it encounters the corresponding signal. The paper illustrates the
proposed attack on the real-time fault line localization application.
Furthermore, the simulation results on the 68-bus power system demonstrate that
DL-based fault line localization methods are not robust to our proposed attack,
indicating that backdoor poisoning attacks pose real threats to DL
implementations in power systems. The proposed attack pipeline can be easily
generalized to other power system tasks
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