In a Networked Dynamical System (NDS), each node is a system whose dynamics
are coupled with the dynamics of neighboring nodes. The global dynamics
naturally builds on this network of couplings and it is often excited by a
noise input with nontrivial structure. The underlying network is unknown in
many applications and should be inferred from observed data. We assume: i)
Partial observability -- time series data is only available over a subset of
the nodes; ii) Input noise -- it is correlated across distinct nodes while
temporally independent, i.e., it is spatially colored. We present a feasibility
condition on the noise correlation structure wherein there exists a consistent
network inference estimator to recover the underlying fundamental dependencies
among the observed nodes. Further, we describe a structure identification
algorithm that exhibits competitive performance across distinct regimes of
network connectivity, observability, and noise correlation.

在网络化动力系统（NDS）中，每个节点是一个与相邻节点的动力学耦合的系统。我们提出了噪声相关性结构的可行性条件，其中存在一致的网络推断估计器以恢复观察节点之间的基本依赖关系。此外，我们描述了一种结构识别算法，该算法在网络连通性、可观测性和噪声相关性的不同情况下表现出竞争性能。

基于部分可观测和空间彩色噪声的网络动力系统推断

Inferring the Graph of Networked Dynamical Systems under Partial  Observability and Spatially Colored Noise

This paper introduces pixel-wise prediction based visual odometry (PWVO),
which is a dense prediction task that evaluates the values of translation and
rotation for every pixel in its input observations. PWVO employs uncertainty
estimation to identify the noisy regions in the input observations, and adopts
a selection mechanism to integrate pixel-wise predictions based on the
estimated uncertainty maps to derive the final translation and rotation. In
order to train PWVO in a comprehensive fashion, we further develop a data
generation workflow for generating synthetic training data. The experimental
results show that PWVO is able to deliver favorable results. In addition, our
analyses validate the effectiveness of the designs adopted in PWVO, and
demonstrate that the uncertainty maps estimated by PWVO is capable of capturing
the noises in its input observations.

本文介绍了基于像素预测的视觉里程表（PWVO）的方法，该方法通过不确定性估计识别输入观测中的噪声区域，并采用选择机制来集成基于估计的不确定性地图的像素级预测以确定最终的平移和旋转值。通过合成训练数据的数据生成工作流，全面培训 PWVO，并且实验结果表明，PWVO 能够提供良好的结果，且估计的不确定性地图能够捕获输入观测中的噪声。