Statistical Analysis Of The Cosmic Microwave Background

The standard ΛCDM model has successfully described the content and the evolution of the universe with predictions in impressive agreement with observations of the Cosmic Microwave Background (CMB). Yet recently major tension has emerged between results from observations of early and late cosmological time. My research focuses on applying statistical tools to analyze and quantify consistency between different data sets as well as different extension models to ΛCDM. This thesis begins with an overview of the ΛCDM model and the physics of the CMB. In the following chapters, I will present my work on examining the internal consistency of the Planck 2015 CMB temperature anisotropy power spectrum. Then I will detail the procedure and results from quantitative comparison between WMAP 9-year and Planck 2015 temperature power spectra over their common multipole range. I will also highlight the importance of examining the correlations between additional parameters when investigating extensions to the standard ΛCDM model and describe how these correlations can be quantified with simulations and Monte Carlo Markov Chain methods

Source-Free Domain Adaptation for Real-world Image Dehazing

Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains. To address these issues, we present a novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available. Specifically, we devise the Domain Representation Normalization (DRN) module to make the representation of real hazy domain features match that of the synthetic domain to bridge the gaps. With our plug-and-play DRN module, unlabeled real hazy images can adapt existing well-trained source networks. Besides, the unsupervised losses are applied to guide the learning of the DRN module, which consists of frequency losses and physical prior losses. Frequency losses provide structure and style constraints, while the prior loss explores the inherent statistic property of haze-free images. Equipped with our DRN module and unsupervised loss, existing source dehazing models are able to dehaze unlabeled real hazy images. Extensive experiments on multiple baselines demonstrate the validity and superiority of our method visually and quantitatively.Comment: Accepted to ACM MM 202

Water in Worcester A Campaign for Public Fluoridation

Water in Worcester: A Campaign for Public Fluoridation arose as a project from our sponsors, Chris Philbin, Vice President of Government Relations at UMass Memorial Health Care, and Joe O’Brien, former Worcester Mayor, Executive Director of the Environmental League of Massachusetts, and Clark University Adjunct Professor. Our goal has been to produce resources that will support the fight for fluoridation in the City of Worcester. Fluoridation has failed to pass in Worcester on four separate occasions, but Mr. Philbin and Mr. O’Brien believe that the time has come to try again. In order to fluoridate Worcester’s water supply, the municipal Board of Health would have to vote to add it. Opponents would then have the opportunity to collect signatures and make the issue into a referendum question that voters would address in the November, 2018 election. Our goal was to provide persuasive background information that would encourage the Board of Health to institute fluoridation, in addition to resources that could be used to win a referendum campaign, should it come to that

Portfolio Homogenization and Systemic Risk of Financial Network

In this paper, we argue that systemic risk should be understood from two different perspectives, the homogeneity of portfolios (or called asset homogeneity) and the contagion mechanism. The homogenization of portfolios held by different financial institutions increases the positive correlations among them and therefore the probability of simultaneous collapses of a considerable part of the network, which are prerequisites and amplifiers of contagion. We first theoretically analyze the influence of asset homogeneity on the initial risk, fragility and systemic risk of the network. Based on the theoretical predictions, we perform simulations on regular networks and Poisson random networks to illustrate the effects of portfolio homogeneity on systemic risk. It is shown that the relationship between asset homogeneity and systemic risk is not always positively related. When the network contagion is weak, then a high asset homogeneity will lead to a high systemic risk. However, if the network contagion is considerably strong, the systemic risk is quite likely to be negative related to the asset homogeneity, so that a high homogeneity will produce a low systemic risk. Moreover, networks with strong contagion and low asset homogeneity tend to have the greatest systemic risk. Results from logistic regression analysis further clarify the relationships between systemic risk and asset homogeneity

Portfolio Homogenization and Systemic Risk of Financial Network

In this paper, we argue that systemic risk should be understood from two different perspectives, the homogeneity of portfolios (or called asset homogeneity) and the contagion mechanism. The homogenization of portfolios held by different financial institutions increases the positive correlations among them and therefore the probability of simultaneous collapses of a considerable part of the network, which are prerequisites and amplifiers of contagion. We first theoretically analyze the influence of asset homogeneity on the initial risk, fragility and systemic risk of the network. Based on the theoretical predictions, we perform simulations on regular networks and Poisson random networks to illustrate the effects of portfolio homogeneity on systemic risk. It is shown that the relationship between asset homogeneity and systemic risk is not always positively related. When the network contagion is weak, then a high asset homogeneity will lead to a high systemic risk. However, if the network contagion is considerably strong, the systemic risk is quite likely to be negative related to the asset homogeneity, so that a high homogeneity will produce a low systemic risk. Moreover, networks with strong contagion and low asset homogeneity tend to have the greatest systemic risk. Results from logistic regression analysis further clarify the relationships between systemic risk and asset homogeneity