论文标题
部分可观测时空混沌系统的无模型预测
Implicit Mentoring: The Unacknowledged Developer Efforts in Open Source
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
Mentoring is traditionally viewed as a dyadic, top-down apprenticeship. This perspective, however, overlooks other forms of informal mentoring taking place in everyday activities in which developers invest time and effort, but remain unacknowledged. Here, we investigate the different flavors of mentoring in Open Source Software (OSS) to define and identify implicit mentoring. We first define implicit mentoring--situations where contributors guide others through instructions and suggestions embedded in everyday (OSS) activities--through formative interviews with OSS contributors, a literature review, and member-checking. Next, through an empirical investigation of Pull Requests (PRs) in 37 Apache Projects, we build a classifier to extract implicit mentoring and characterize it through the dual lenses of experience and gender. Our analysis of 107,895 PRs shows that implicit mentoring occurs (27.41% of all PRs include implicit mentoring) and it does not follow the traditional dyadic, top-down apprenticeship model. When considering the gender of mentor-mentee pairs, we found pervasive homophily--a preference to mentor those who are of the same gender--in 93.81% cases. In the cross-gender mentoring instances, women were more likely to mentor men.