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论文提出 ATEMPO，一种基于扩散模型的转移熵估计器，可在滞后维度和时间维度上进行摊销估计，单一拟合即可恢复完整的转移熵曲面，并在时变滞后系统、天气预测和脑机接口任务中实现更优的滞后检测与下游预测性能。
This paper introduces SpatioTemporal Causal Network Diagnostics (ST-CND), a framework that uses data-driven causal networks and dynamic mode decomposition to provide localized early warning of geographic tipping points, outperforming classical spatial indicators on synthetic and observational benchmarks.