Study on Agricultural Product-coupled Subsidy Policy Considering Output Uncertainty and the Development of Tobacco Agriculture

Objectives: The health and well-ordered development of tobacco agriculture is very important. The incentive effects of plant-coupled subsidies and output-coupled subsidies on farming decisions with the consideration of uncertainty are investigated. The study shows that if the same unit subsidy is adopted, the incentive effect of the two policies will be determined by the expected output. When the expected output is higher, the incentive effect of the output-coupled subsidy is better than that of the plant-coupled subsidy. And when the expected output is lower, the incentive effect of the plant-coupled subsidy is better. If the implementation scheme limits the total amount of subsidies, it is better to determine subsidy policy by optimal output. The higher the optimal output is, the better the plant-coupled subsidy is. And when the optimal output is relatively low, the output-coupled subsidy shows a better incentive effect. Meanwhile, the study results also show that the incentive effects of the two coupled subsidy policies for increasing production and income are consistent, and the advantages of the policy with better incentive effects increase as the amount of subsidies increases.

Optimized Sparse Polynomial Chaos Expansion With Entropy Regularization

Sparse Polynomial Chaos Expansion(PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviate the problem of dimensionality curse. However, current sparse PCE techniques focus on choosing features with the largest coefficients, which may ignore uncertainties propagated with high order features. Hence, this paper proposes the idea of selecting polynomial chaos basis based on information entropy, which aims to retain the advantages of existing sparse techniques while considering entropy change as output uncertainty. A novel entropy-based optimization method is proposed to update the state-of-the-art sparse PCE models. This work further develops an entropy-based synthetic sparse model, which has higher computational efficiency. Two benchmark functions and a CFD experiment are used to compare the accuracy and efficiency between the proposed method and classical methods. The results show that entropy-based methods can better capture the features of uncertainty propagation, and the problem of over-fitting in existing sparse PCE methods can be avoided.

Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Modern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification—a real-world use case—and show that this yields promising results.

Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.