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。

Energy-aware Resource Management and Green Energy Use for Large-scale Datacenters: A Survey

国外经典的关于网络测量的书籍，在sprint骨干网上做过实验

Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

2007-EMNLP-CoNLL-Large-scale named entity disambiguation based on Wikipedia data

effective large scale stereo matching，用于快速立体匹配，代码量比较大，可以直接用在工程中。

清晰 彩色 When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search.

It would be ludicrous to attempt to erect a 50 story oce building using the same materials and techniques a carpenter would use to build a single family home.

code for Large Scale Metric Learning from Equivalence Constraints