806 research outputs found


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    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

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    We study the power spectrum of the comoving curvature perturbation R\cal R 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 π\pi times the junction scale. The peak is 2.61\sim2.61 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 ee-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

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    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

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    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