We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. If the inspected items are fraudulent, the officers can levy extra duties. The updated decisions are used as additional training data for successive iterations. Inspecting only the likely fraudulent items may lead to an immediate gain in revenue, yet it does not bring new insights for learning dynamic trade patterns. In contrast, including uncertain items in the inspection helps gradually acquire new knowledge that will be used as supplementary training resources to update the system. Based on multiyear customs declaration logs obtained from three countries, we demonstrate that some degree of exploration is necessary to cope with domain shifts in trade data. The results show that a hybrid strategy of jointly selecting likely fraudulent and uncertain items will eventually outperform the exploitation-only strategy.

Modern climate models offer simulation results that provide unprecedented details at the local level. However, even with powerful supercomputing facilities, their computational complexity and associated costs pose a limit on simulation resolution that is needed for agile planning of resource allocation, parameter calibration, and model reproduction. As regional information is vital for policymakers, data from coarse-grained resolution simulations undergo the process of "statistical downscaling" to generate higher-resolution projection at a local level. We present a new method for downscaling climate simulations called GINE (Geospatial INformation Encoded statistical downscaling). To preserve the characteristics of climate simulation data during this process, our model applies the latest computer vision techniques over topography-driven spatial and local-level information. The comprehensive evaluations on 2x, 4x, and 8x resolution factors show that our model substantially improves performance in terms of RMSE and the visual quality of downscaled data.

The COVID-19 pandemic left its unique mark on the twenty-first century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major initial non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.

Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current paper proposes ADAPT, an adaptive selection method that controls the balance between exploitation and exploration strategies used for customs fraud detection. ADAPT makes use of the model performance trends and the amount of concept drift to determine the best exploration ratio at every time. Experiments on data from four countries over several years show that each country requires a different amount of exploration for maintaining its fraud detection system. We find the system with ADAPT can gradually adapt to the dataset and find the appropriate amount of exploration ratio with high performance.