阿里巴巴集团于1999年创立,阿里巴巴集团子公司及关联公司有:阿里巴巴网络有限公司、淘宝网、淘宝商城(天猫)、一淘、支付宝、阿里云计算、中国雅虎等。

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在这项工作中,我们介绍了一系列的架构修改,旨在提高神经网络的准确性,同时保持他们的GPU训练和推理效率。我们首先演示和讨论由flops优化引起的瓶颈。然后,我们建议更好地利用GPU结构和资产的替代设计。最后,我们介绍了一种新的GPU专用模型,称为TResNet,它比以前的ConvNets具有更好的准确性和效率。使用TResNet模型,与ResNet50的GPU吞吐量相似,在ImageNet上达到80.7%的top-1精度。我们的TResNet模型也能很好地传输竞争数据集,并达到最先进的精度,如Stanford cars(96.0%)、CIFAR-10(99.0%)、CIFAR-100(91.5%)和牛津花卉(99.1%)。实现可在:这个

https://github.com/mrT23/TResNet

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As a fundamental communicative service, email is playing an important role in both individual and corporate communications, which also makes it one of the most frequently attack vectors. An email's authenticity is based on an authentication chain involving multiple protocols, roles and services, the inconsistency among which creates security threats. Thus, it depends on the weakest link of the chain, as any failed part can break the whole chain-based defense. This paper systematically analyzes the transmission of an email and identifies a series of new attacks capable of bypassing SPF, DKIM, DMARC and user-interface protections. In particular, by conducting a "cocktail" joint attack, more realistic emails can be forged to penetrate the celebrated email services, such as Gmail and Outlook. We conduct a large-scale experiment on 30 popular email services and 23 email clients, and find that all of them are vulnerable to certain types of new attacks. We have duly reported the identified vulnerabilities to the related email service providers, and received positive responses from 11 of them, including Gmail, Yahoo, iCloud and Alibaba. Furthermore, we propose key mitigating measures to defend against the new attacks. Therefore, this work is of great value for identifying email spoofing attacks and improving the email ecosystem's overall security.

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