In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Large Scale Distributed Deep Networks

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

S7200 S7200SMART SCALE库文件 （全）直接放到到软件的lib路径下就行 S7200 S7200SMART SCALE库文件 （全）直接放到到软件的lib路径下就行

CFS: A Distributed File System for Large Scale Container Platforms

对于一类具有未知时变延迟的大型系统，考虑了分散的输出反馈控制问题。 不确定的互连受系数未知的一般非线性函数限制。 每个子系统的控制方向参数都是未知的，这给分散控制器设计带来了挑战。 为了解决这个问题，我们在Nussbaum函数的帮助下提出了一种新的分散控制方案。 首先设计分散式滤波器。 通过构造Lyapunov-Krasovskii函数，我们设计了动态输出反馈控制器。 严格证明了闭环系统是渐近稳定的。 最后，进行了仿真，结果验证了所提方法的有效性。

联想黑底白字LOGO，适合改开机LOGO

MobileTouchCamera

在过去的几十年中，已经提出了大量的模型来对Internet进行建模。 但是，直到今天，仍然没有解决关于哪种模型更好地建模Internet的问题。 通过分析Internet的发展动态，我们建议在AS级别，合适的Internet模型至少应该是异构的，并且具有线性增长的机制。 更重要的是，我们表明，从工程的角度来看，拓扑特征在评估和区分Internet模型中的作用显然被高估了。 同样，我们发现分类网络不一定比分解网络更健壮，平均最短路径长度越小，不一定意味着其健壮性就越高，这与之前的观察结果有所不同。 我们的分析结果不仅对Internet有所帮助，而且对其他一般的复杂网络也有帮助。

Background: Recent advances in functional magnetic resonance imaging (fMRI) techniques make it possible to reconstruct contrast-defined visual images from brain activity. In this manner, the stimulus images are represented as the weighted sum of a set of element images with different scales. The con