Uncertainty Estimation, Explanation and Reduction with Insufficient Data

Human beings have been juggling making smart decisions under uncertainties, where we manage to trade off between swift actions and collecting sufficient evidence. It is naturally expected that a generalized artificial intelligence (GAI) to navigate through uncertainties meanwhile predicting precisely. In this thesis, we aim to propose strategies that underpin machine learning with uncertainties from three perspectives: uncertainty estimation, explanation and reduction. Estimation quantifies the variability in the model inputs and outputs. It can endow us to evaluate the model predictive confidence. Explanation provides a tool to interpret the mechanism of uncertainties and to pinpoint the potentials for uncertainty reduction, which focuses on stabilizing model training, especially when the data is insufficient. We hope that this thesis can motivate related studies on quantifying predictive uncertainties in deep learning. It also aims to raise awareness for other stakeholders in the fields of smart transportation and automated medical diagnosis where data insufficiency induces high uncertainty. The thesis is dissected into the following sections: Introduction. we justify the necessity to investigate AI uncertainties and clarify the challenges existed in the latest studies, followed by our research objective. Literature review. We break down the the review of the state-of-the-art methods into uncertainty estimation, explanation and reduction. We make comparisons with the related fields encompassing meta learning, anomaly detection, continual learning as well. Uncertainty estimation. We introduce a variational framework, neural process that approximates Gaussian processes to handle uncertainty estimation. Two variants from the neural process families are proposed to enhance neural processes with scalability and continual learning. Uncertainty explanation. We inspect the functional distribution of neural processes to discover the global and local factors that affect the degree of predictive uncertainties. Uncertainty reduction. We validate the proposed uncertainty framework on two scenarios: urban irregular behaviour detection and neurological disorder diagnosis, where the intrinsic data insufficiency undermines the performance of existing deep learning models. Conclusion. We provide promising directions for future works and conclude the thesis

Safety Analyses At Signalized Intersections Considering Spatial, Temporal And Site Correlation

Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the site correlation ; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of at-fault driver or near-side were applied for disaggregated models. It shows that for the roadway and approach level models, the near-side models outperformed the at-fault driver models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The site correlation among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections

Fiscal Policy, Regional Disparity and Poverty in China: a General Equilibrium Approach

The main objective of this research is to analyze the effects of the fiscal dimension of China’s government transfer and preferential tax policy on regional income disparity and poverty reduction. Using a computable general equilibrium model with a three-region component, we find that the preferential tax policy on the eastern coastal region of China has a significant effect on household income, as well as on the FGT indicator. The simulation results suggest that tax policy is a more effective tool to counter against China’s regional disparity than government transfer.China, Regional Disparity, Fiscal Policy, Government Transfer, Preferential Policy, Poverty, CGE, FGT

Development of a radar simulator for monitoring wake vortices in rainy weather

A simulator for the evaluation of the radar signature of raindrops within wake vortices is presented. Simulated Doppler spectrum of raindrops within vortices let to think that it could be a potential criterion for identifying wake vortex hazard in rainy weather

Establishment of the liquid fluorometric method for determination of alkylresorcinols in whole wheat

Objective To determine the quality of whole wheat products, it is necessary to establish a method for determination of alkylresorcinols(ars) which are the markers of whole wheat, black wheat and other cereal products. Methods Taking wheat and black wheat as main research2o2bjects, factorial design was used to investigate the effect of extraction reagents(acetone, alcohol, and ethyl acetate), extraction methods(ultrasound for 30 min, 60 min, shaking at room temperature overnight), and sample status(granule, flour) on the determination of ars. And ars detection conditions was established by comparing chromatographic conditions and scanning fluorescence wavelengths. The ars content in some commercially available cereals were determined based on the methodological evaluation. Results Taking into account the toxicity of the reagents and efficiency, it was better that the comminuted sample was ultrasonically extracted with ethanol for 30 min before loading on the Waters CORTES-C18 column with ethanol: acetonitrile(30: 70, v: v). The fluorescence detector was set at a wavelength of 272 nm for excitation and 296 nm for emission to detect ars. The linear range was 0.050-10.0g/mL and the coefficient R2≥0.9999. RSD of precision experiment was less than 5% and the spiked recovery rates of three levels were 94.5%-104%. The total amount of ars was 47.9-54.3 mg/100g in commercially available wheat and 53.6-60.9 mg/100g in black wheat. Conclusion The liquid-fluorescence method established in this study can effectively and rapidly separate ars from wheat and black wheat and has the advantages of simple operation, high sensitivity and accuracy