TAPS：面向目标的永久采样用于图扩散
TAPS: Target-Aware Permanent Sampling for Graph Diffusion
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中文总结 AI 辅助
TAPS提出一种面向目标的永久采样图扩散框架，在降低传感器成本的同时，通过贪心规则和显式下界，以较少永久位置恢复指定区域目标，并在多国监管网络上验证了有效性。
中文摘要 AI 辅助
永久性空气质量网络安装和维护成本高昂，然而许多决策依赖于未来某一区域的暴露情况，而非完整的污染场。我们提出了面向目标的永久采样（TAPS），一种用于选择随时间重复观测的永久位置的图扩散框架，该框架可以减少不必要的传感器安装、维护和成本，同时保留未来空气质量决策所需的信息，并需使用将采用该网络的估计器进行验证。我们形式化了永久时空采样问题，并表明在无噪声模型中，恢复一个指定的目标所需的信息可能少于全状态识别所需的信息，对此我们给出了永久位置数量的显式下界。我们还推导了一个贪心规则，其边际增益分解为原始目标响应和有限更新修正。我们在加利福尼亚、加拿大和英格兰的监管空气质量网络上评估了TAPS。在每种情况下，指定的区域目标在远低于识别保留状态所需的永久位置预算下即可数值恢复，且TAPS实现了比目标加权、几何和基于设计的布局更低的正则化目标风险。一项盲前瞻性研究进一步表明，该标准可以在候选地点没有任何地面监测历史的情况下，仅使用外部环境协变量来应用。
英文摘要
Permanent air-quality networks are expensive to install and maintain, yet many decisions depend on one future regional exposure rather than the complete pollution field. We introduce Target-Aware Permanent Sampling (TAPS), a graph-diffusion framework for selecting permanent locations observed repeatedly over time, that can reduce unnecessary sensor installations, maintenance, and cost while preserving the information needed for future air-quality decisions, subject to validation with the estimator that will use the network. We formulate the permanent space-time sampling problem and show that, in the noiseless model, recovery of one prescribed target can require less information than full-state identification, for which we give an explicit lower bound on the permanent-location count. We also derive a greedy rule whose marginal gain factors into raw target response and a finite-update correction. We evaluate TAPS on regulatory air-quality networks in California, Canada, and England. In each case the prescribed regional target becomes numerically recoverable at permanent-location budgets well below those required to identify the retained state, and TAPS attains lower regularized target risk than target-weight, geometric, and design-based placements. A blind prospective study further shows that the criterion can be applied before candidate sites have any ground-monitor history, using exogenous environmental covariates alone.
发表机构
- Thomas Jefferson High School for Science and Technology（托马斯·杰斐逊科学与技术高中）
- University of Illinois Chicago（伊利诺伊大学芝加哥分校）
- NSF-Simons National Institute for Theory and Mathematics in Biology（NSF-西蒙斯生物理论与数学国家研究所）
- Mathematical Institute and Magdalen College, University of Oxford（牛津大学数学学院与莫德林学院）
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