React.js(React)是 Facebook 推出的一个用来构建用户界面的 JavaScript 库。

Facebook开源了React,这是该公司用于构建反应式图形界面的JavaScript库,已经应用于构建Instagram网站及 Facebook部分网站。最近出现了AngularJS、MeteorJS 和Polymer中实现的Model-Driven Views等框架,React也顺应了这种趋势。React基于在数据模型之上声明式指定用户界面的理念,用户界面会自动与底层数据保持同步。与前面提及 的框架不同,出于灵活性考虑,React使用JavaScript来构建用户界面,没有选择HTML。Not Rest


We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chosen adaptively by an adversary that observes the outputs of the algorithm along the stream and can react in an online manner. While deterministic streaming algorithms are inherently robust, many central problems in the streaming literature do not admit sublinear-space deterministic algorithms; on the other hand, classical space-efficient randomized algorithms for these problems are generally not adversarially robust. This raises the natural question of whether there exist efficient adversarially robust (randomized) streaming algorithms for these problems. In this work, we show that the answer is positive for various important streaming problems in the insertion-only model, including distinct elements and more generally $F_p$-estimation, $F_p$-heavy hitters, entropy estimation, and others. For all of these problems, we develop adversarially robust $(1+\varepsilon)$-approximation algorithms whose required space matches that of the best known non-robust algorithms up to a $\text{poly}(\log n, 1/\varepsilon)$ multiplicative factor (and in some cases even up to a constant factor). Towards this end, we develop several generic tools allowing one to efficiently transform a non-robust streaming algorithm into a robust one in various scenarios.