We develop tools for explicitly constructing categories enriched over generating data and that compose via ordinary scalar and matrix arithmetic arithmetic operations. We characterize meaningful size maps, weightings, and magnitude that reveal features analogous to outliers that these same notions have previously been shown to reveal in the context of metric spaces. Throughout, we provide examples of such "outlier detection" relevant to the analysis of computer programs, neural networks, cyber-physical systems, and networks of communications channels.

翻译：我们开发了工具，明确构建基于生成数据的类别，这些类别通过普通标量和矩阵算术操作进行组合。我们表征了有意义的大小映射，权重和大小，这些特征类似于先前已经在度量空间的背景下显示出来的异常值。在整个过程中，我们提供了与计算机程序、神经网络、网络物理系统和通信通道网络相关的这种“异常检测”的例子。