YANG Yuting

Ocean eddies, as a ubiquitous phenomenon of the global ocean, are extremely important for ocean energy and material exchanges. Therefore, efficient eddy detection and tracking are crucial for advancing our understanding of ocean dynamics. This work presents a framework for automatic ocean eddy detection and tracking by leveraging state-of-the-art machine learning algorithms. First, we propose a new convolutional neural network model for multieddies detection. This model is capable of extracting accurate boundary information of eddies and fitting the gap between semantic context and sea surface height (SSH). Second, a tracking algorithm is designed to track eddies lasting a number of days and provide visualization of the dynamical processes governing eddies’ movements. Finally, we have made our data set publicly available, which is named SCSE-Eddy and can be used as a benchmark to evaluate the performances of artificial intelligence (AI)-based eddy detection methods. The data set covers daily remotely sensed SSH data located in the South China Sea and its eastern sea areas over a period of 15 years. The experimental results show that our methods achieve promising performances compared to existing approaches, especially for the eddies with indistinct geographical border. We believe that this work opens a new avenue for oceanographers to better discover and understand the physical properties of ocean eddies.

Standard benefit-cost analysis often ignores distortions caused by taxation and the heterogeneity of taxpayers. In this paper, we theoretically and numerically explore the effect of imperfect taxation on the public provision of mortality risk reductions (or public safety). We show that this effect critically depends on the source of imperfection as well as on the individual utility and survival probability functions. Our simulations based on the calibration of distributional weights and applied to the COVID-19 example suggest that the value per statistical life, and in turn the optimal level of public safety, should be adjusted downwards because of imperfect taxation. However, we also identify circumstances under which this result is reversed, so that imperfect taxation cannot generically justify less public safety.

The French abstract was not provided by the author.