Learning inter-annual flood loss risk models from historical flood insurance claims.

Flooding is a natural hazard that causes substantial loss of lives and livelihoods worldwide. Developing predictive models for flood-induced financial losses is crucial for applications such as insurance underwriting. This research uses the National Flood Insurance Program (NFIP) dataset between 2000 and 2020 to evaluate the predictive skill of past data in predicting near-future flood loss risk.

Fully fluorinated non-carbon compounds NF and SF as ideal technosignature gases.

Waste gas products from technological civilizations may accumulate in an exoplanet atmosphere to detectable levels. We propose nitrogen trifluoride (NF) and sulfur hexafluoride (SF) as ideal technosignature gases. Earth life avoids producing or using any N-F or S-F bond-containing molecules and makes no fully fluorinated molecules with any element.

Neural Networks as Geometric Chaotic Maps.

The use of artificial neural networks (NNs) as models of chaotic dynamics has been rapidly expanding. Still, a theoretical understanding of how NNs learn chaos is lacking. Here, we employ a geometric perspective to show that NNs can efficiently model chaotic dynamics by becoming structurally chaotic themselves.