Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw data distribution is approximated by a collection of low-dimensional marginals. Differential privacy (DP) is guaranteed by introducing random noise to each low-dimensional marginal distribution. Despite its promising performance in practice, the statistical properties of marginal-based methods are rarely studied in the literature. In this paper, we study DP data synthesis algorithms based on Bayesian networks (BN) from a statistical perspective. We establish a rigorous accuracy guarantee for BN-based algorithms, where the errors are measured by the total variation (TV) distance or the $L^2$ distance. Related to downstream machine learning tasks, an upper bound for the utility error of the DP synthetic data is also derived. To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator

Emergency politics, mass sentiment and the EU during Covid

During crises, do emergency politics impair the EU polity by alienating Europeans? Recent literature suggests that executive decisions in hard times can spur negative European sentiment, increase polarisation in the public and thus create more problems than solutions. The Covid-19 pandemic offers an ideal opportunity to study this important issue. However, studying mass sentiment towards the EU is mostly constrained by imperfect survey data. We tackle this challenge with an empirical strategy that combines two original data sources: first, we use policy process analysis to identify key EU decisions; second, we leverage Twitter data to measure sentiment. As a result, we can study whether key EU decisions impacted EU sentiment and whether this impact is conditional on the level of EU competence, prior politicisation and problem pressure. We find that EU decisions impact EU sentiment positively and do not polarise it (even among highly politicised decisions). Low prior politicisation and healthcare-related decisions increase the positive impact of EU actions. There is thus no punishment of the EU for acting outside its remit. Our findings have important implications for the politics of polity maintenance in the EU

Studying bubble-particle interactions by zeta potential distribution analysis

Over a decade ago, Xu and Masliyah pioneered an approach to characterize the interactions between particles in dynamic environments of multicomponent systems by measuring zeta potential distributions of individual components and their mixtures. Using a Zetaphoremeter, the measured zeta potential distributions of individual components and their mixtures were used to determine the conditions of preferential attachment in multicomponent particle suspensions. The technique has been applied to study the attachment of nano-sized silica and alumina particles to sub-micron size bubbles in solutions with and without the addition of surface active agents (SDS, DAH and DF250). The degree of attachment between gas bubbles and particles is shown to be a function of the interaction energy governed by the dispersion, electrostatic double layer and hydrophobic forces. Under certain chemical conditions, the attachment of nano-particles to sub-micron size bubbles is shown to be enhanced by in-situ gas nucleation induced by hydrodynamic cavitation for the weakly interacting systems, where mixing of the two individual components results in negligible attachment. Preferential interaction in complex tertiary particle systems demonstrated strong attachment between micron-sized alumina and gas bubbles, with little attachment between micron-sized alumina and silica, possibly due to instability of the aggregates in the shear flow environment

Maternal Colonization With Group B Streptococcus and Serotype Distribution Worldwide: Systematic Review and Meta-analyses.

Background: Maternal rectovaginal colonization with group B Streptococcus (GBS) is the most common pathway for GBS disease in mother, fetus, and newborn. This article, the second in a series estimating the burden of GBS, aims to determine the prevalence and serotype distribution of GBS colonizing pregnant women worldwide. Methods: We conducted systematic literature reviews (PubMed/Medline, Embase, Latin American and Caribbean Health Sciences Literature [LILACS], World Health Organization Library Information System [WHOLIS], and Scopus), organized Chinese language searches, and sought unpublished data from investigator groups. We applied broad inclusion criteria to maximize data inputs, particularly from low- and middle-income contexts, and then applied new meta-analyses to adjust for studies with less-sensitive sampling and laboratory techniques. We undertook meta-analyses to derive pooled estimates of maternal GBS colonization prevalence at national and regional levels. Results: The dataset regarding colonization included 390 articles, 85 countries, and a total of 299924 pregnant women. Our adjusted estimate for maternal GBS colonization worldwide was 18% (95% confidence interval [CI], 17%-19%), with regional variation (11%-35%), and lower prevalence in Southern Asia (12.5% [95% CI, 10%-15%]) and Eastern Asia (11% [95% CI, 10%-12%]). Bacterial serotypes I-V account for 98% of identified colonizing GBS isolates worldwide. Serotype III, associated with invasive disease, accounts for 25% (95% CI, 23%-28%), but is less frequent in some South American and Asian countries. Serotypes VI-IX are more common in Asia. Conclusions: GBS colonizes pregnant women worldwide, but prevalence and serotype distribution vary, even after adjusting for laboratory methods. Lower GBS maternal colonization prevalence, with less serotype III, may help to explain lower GBS disease incidence in regions such as Asia. High prevalence worldwide, and more serotype data, are relevant to prevention efforts

Multimodal human brain connectivity analysis based on graph theory

Billions of people worldwide are affected by neurological disorders. Recent studies indicate that many neurological disorders can be described as dysconnectivity syndromes, and associated with changes in the brain networks prior to the development of clinical symptoms. This thesis presents contributions towards improving brain connectivity analysis based on graph theory representation of the human brain network. We propose novel multimodal techniques to analyze brain imaging data to better understand its structure, function and connectivity, i.e., brain connectomics. Our first contribution is towards improving parcellation, \ie brain network node definition, in terms of reproducibility, functional homogeneity, leftout data likelihood and overlaps with cytoarchitecture, by utilizing the neighbourhood information and multi-modality integration techniques. Specifically, we embed neighborhood connectivity information into the affinity matrix for parcellation to ameliorate the adverse effects of noise. We further integrate the connectivity information from both anatomical and functional modalities based on adaptive weighting for an improved parcellation. Our second contribution is to propose noise reduction techniques for brain network edge definition. We propose a matrix completion based technique to combat false negatives by recovering missing connections. We also present a local thresholding method which can address the regional bias issue when suppressing the false positives in connectivity estimates. Our third contribution is to improve the brain subnetwork extraction by using multi-pronged graphical metric guided methods. We propose a connection-fingerprint based modularity reinforcement model which reflects the putative modular structure of a brain graph. Inspired by the brain subnetwork's biological nature, we propose a provincial hub guided feedback optimization model for more reproducible subnetwork extraction. Our fourth contribution is to develop multimodal integration techniques to further improve brain subnetwork extraction. We propose a provincial hub guided subnetwork extraction model to fuse anatomical and functional data by propagating the modular structure information across different modalities. We further propose to fuse the task and rest functional data based on hypergraphs for non-overlapping and overlapping subnetwork extraction. Our results collectively indicate that combing multimodal information and applying graphical metric guided strategies outperform classical unimodal brain connectivity analysis methods. The resulting methods could provide important insights into cognitive and clinical neuroscience.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat