Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change.

神经序列到序列TTS已经实现了比使用HMMS的统计语音合成的显着更好的输出质量。然而，神经TTS通常不是概率，并且使用非单调注意都会增加训练时间并引入生产中不可接受的“唠叨”的失效模式。本文展示了旧的和新的范式可以组合以获得两个世界的优势，通过用神经网络定义的自回归左右跳过隐马尔可夫模型来取代塔克罗伦2的注意力。这导致基于HMM的神经TTS模型，具有单调对准，训练，以最大化没有近似的完整序列可能性。我们讨论如何将古典和当代TTS的创新结合起来的最佳效果。最终系统比Tacotron 2较小，更简单，并学会与较少的迭代和更少的数据说话，同时在网后达到相同的自然。与Tacotron 2不同，我们的系统还允许轻松控制口语率。音频示例和代码在https://shivammehta007.github.io/neural-hmm/处获得