The key to the effective control of a diffusion system lies in how accurately we could predict its unfolding dynamics based on the observation of its current state. However, in the real-world applications, it is often infeasible to conduct a timely and yet comprehensive observation due to resource constraints. In view of such a practical challenge, the goal of this work is to develop a novel computational method for performing active observations, termed active surveillance, with limited resources. Specifically, we aim to predict the dynamics of a large spatio-temporal diffusion system based on the observations of some of its components. Towards this end, we introduce a novel measure, the γ\boldsymbol{\gamma } value, that enables us to identify the key components by means of modeling a sentinel network with a row sparsity structure. Having obtained a theoretical understanding of the γ\boldsymbol{\gamma } value, we design a backward-selection sentinel network mining algorithm (SNMA) for deriving the sentinel network via group sparse Bayesian learning. In order to be practically useful, we further address the issue of scalability in the computation of SNMA, and moreover, extend SNMA to the case of a non-linear dynamical system that could involve complex diffusion mechanisms. We show the effectiveness of SNMA by validating it using both synthetic datasets and five real-world datasets. The experimental results are appealing, which demonstrate that SNMA readily outperforms the state-of-the-art methods.

中文翻译：

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通过群体稀疏贝叶斯学习进行主动监控


有效控制扩散系统的关键在于我们如何准确地根据对其当前状态的观察来预测其展开动力学。然而，在实际应用中，由于资源限制，往往无法进行及时、全面的观测。鉴于这样的实际挑战，这项工作的目标是开发一种新颖的计算方法，用于在有限的资源下执行主动观察，称为主动监视。具体来说，我们的目标是根据对其某些组件的观察来预测大型时空扩散系统的动力学。为此，我们引入了一种新的度量，即 γ\boldsymbol{\gamma } 值，它使我们能够通过对具有行稀疏结构的哨兵网络进行建模来识别关键组件。在获得了对 γ\boldsymbol{\gamma } 值的理论理解后，我们设计了一种后向选择哨兵网络挖掘算法（SNMA），用于通过组稀疏贝叶斯学习导出哨兵网络。为了实用，我们进一步解决了 SNMA 计算中的可扩展性问题，并将 SNMA 扩展到可能涉及复杂扩散机制的非线性动力系统的情况。我们通过使用合成数据集和五个真实数据集验证 SNMA 来展示 SNMA 的有效性。实验结果很有吸引力，这表明 SNMA 的性能明显优于最先进的方法。