DISCO Nets: DISsimilarity COefficient Networks

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks

Transportation System Performance and Traveler Behavior in the Context of a Systemwide Shock: Applications of Data Science Toward a Sustainable Future

The COVID-19 pandemic, a systemwide shock, has left a long-lasting and significant impact on transportation systems. It has contributed to a shift in travel behavior, with many people turning to work from home (WFH) and online shopping. This shift has led to a reduction in vehicular travel. However, the pandemic witnessed increased crash fatalities despite a reduction in overall crashes, disproportionately affecting disadvantaged communities (DACs). The main question arising from these pandemic-related issues is what we can learn to improve transportation systems and shape future travel behavior. Therefore, this dissertation aims to investigate how the transportation system changed during COVID-19 and explore the future implications while examining the travel behavior, technology adoption behavior, and road safety aspects in DACs compared with non-DACs during COVID-19. As such, this dissertation first explores the interaction between WFH, online shopping, and in-person shopping behaviors, revealing nuanced relationships that have evolved amidst the pandemic. Second, comprehensive safety data are utilized to dissect why crash fatalities increased during COVID-19. Third, transportation safety in DACs is investigated by leveraging safety data covering COVID-19 periods and the comprehensive DAC indicators developed by the US Department of Transportation. Fourth, DACs’ shopping behavior during COVID-19 is analyzed by focusing on the interplay of emerging online delivery components (retail, grocery, and food) and in-person activities. Finally, the study compares technology adoption behaviors between DACs and non-DACs by exploring infrastructure and socio-economic barriers. Methodologically speaking, this dissertation employs various state-of-the-art statistical and explainable artificial intelligence techniques. Overall findings indicate that compared to pre-COVID-19, the surge in WFM and e-commerce trends was associated with a substantial reduction in physical shopping trips during COVID-19. Speeding and reckless behaviors were strongly associated with the increased road fatalities. DACs experienced heightened adversity than non-DACs, associated with a higher rate of fatal crashes (an increase of 8% to 57%). Online orders were considerably less frequent in DACs than non-DACs (2% to 7%), emphasizing disparity in digital infrastructure. Additionally, technology adoption rates were significantly lower in DACs. These findings underscore the importance of better preparedness and planning for such communities to be equipped to handle future systemic shocks

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Broadband infrastructure: The regulatory framework, market transparency and risk-sharing partnerships are the key factors

Around the globe countries are attempting to forge ahead with the expansion and upgrading of advanced communications networks. In most cases they are setting very ambitious goals with regard to technology and coverage. However, the specific cost structure for broadband projects results in the private-sector-driven, competitive market for network upgrading being primarily focused on densely populated urban areas. By contrast, major progress in rolling out broadband to unserved rural areas will not be made in the foreseeable future without state subsidies. Without having to steer a course towards the return of a monopoly in the telecommunications sector, which would have a detrimental long-term impact, the public sector can in this situation promote sustainable progress in telecommunication by merging projects, entering into risk-sharing partnerships, setting realistic broadband targets, providing essential market information to market participants, offering e-government digital services itself and, on top of that, further enhancing investment incentives with a regulatory framework in a competitive environment.broadband; telecommunications; Infrastructure; investment; financing