U.S. politics, media, and citizens are highly polarized, stipulating that society is divided between Democrats and Republicans (Hameleers, 2019). The U.S. has seen an increased political polarization over the past 25 years (Heltzel & Laurin, 2020; Westfall, Van Boven, Chambers, & Judd, 2015). Technological development in the campaign environment has fueled this political polarization (Hong & Kim, 2016). In such a polarized technological society, partisan news media cover political issues and events from their ideological perspective (Arceneaux, Johnson, & Murphy, 2012), which may affect the polarized citizens.The Republican Party is conservative, while the Democratic Party is liberal (Westfall, Van Boven, Chambers, & Judd, 2015). Each party has issue agendas that they prioritize during the campaign. When political campaigns post a message on social media, they not only post just one issue but several related issues. These interlinked issues have a networked effect on the partisan news media and the polarized citizens (McCombs, Shaw, & Weaver, 2014). How political campaigns interlinked different issue agendas during campaigns in a polarized environment has not been investigated. This study intends to see the similarities and dissimilarities between the Democratic and Republican Party issue networks using a network agenda setting theory during the 2020 U.S. presidential campaign and how they build and set networked issue agendas in the partisan news media and the polarized public on Facebook. The study uses a hybrid content analysis and network analysis of issue agendas presented by the Biden and Trump campaigns, partisan media (CNN and Fox News), and the Democratic Party and the Republican Party supporters on Facebook. Facebook posts are collected using Facebook’s CrowdTangle Search option from January 1, 2021, to November 3, 2020. This study uses a hybrid content analysis method which engages with both human coders and computational means to analyze big data sets (Guo et al., 2016). The data analysis involves measuring core-periphery block model, clique analysis, network visualization, and Quadratic Assignment Procedures (QAP). A social networking analysis software, UCINET, is used for measuring core-periphery block model, clique analysis, and QAP correlations(Borgatti, Everett, & Johnson, 2018). The scholarship of political campaign communication needs to reconnect to the ideological positions of political campaigns, partisan news media, and party supporters. This holistic study is significant in terms of better understanding the mechanism of networked agenda-setting activities of presidential campaigns in a polarized environment on Facebook. Methodologically, this study offers new techniques for investigating networked issue agendas of campaigns, news media, and citizens. It uses core-periphery block model and clique analysis as indicators of network agenda building and network agenda-setting influences. Social media practitioners like campaign managers can consider the political polarization, fragmented nature of social media, and polarized audience during political campaigning.

Let X be an Atkin-Lehner quotient of the modular curve X_0(N) whose Jacobian J_f is a simple quotient of J_0(N)^{new} over Q. We give analytic methods for determining the rational points of X using quadratic Chabauty by explicitly computing two p-adic Gross--Zagier formulas for the newform f of level N and weight 2 associated with J_f when f has analytic rank 1. Combining results of Gross-Zagier and Waldspurger, one knows that for certain imaginary quadratic fields K, there exists a Heegner divisor in J_0(N)(K) whose image is finite index in J_f(Q) under the action of Hecke. We give an algorithm to compute the special value of the anticyclotomic p-adic L-function of f constructed by Bertolini, Darmon, and Prasanna, assuming some hypotheses on the prime p and on K. This value is proportional to the logarithm of the Heegner divisor on J_f with respect to the differential form f dq/q. We also compute the p-adic height of the Heegner divisor on J_f using a p-adic Gross-Zagier formula of Perrin-Riou. Additionally, we give algorithms for the geometric quadratic Chabauty method of Edixhoven and Lido. Our algorithms describe how to translate their algebro-geometric method into calculations involving Coleman-Gross heights, logarithms, and divisor arithmetic. We achieve this by leveraging a map from the Poincaré biextension to the trivial biextension.

As a conventional transportation modality, bicycles have been gradually electrified to meet the desire for convenient and green commuting patterns, especially in developed urban areas. The electric bicycle battery pack and its management system are core elements that determine key performance metrics such as electric range and output power. With respect to electric bicycle applications, focused research on the battery, its management system, and performance has received less attention compared to other energy storage applications. In this thesis, a well-developed conversion kit produced by BionX is studied. A data collecting system is first installed to record both mechanical and electrical data, such as speed, power and voltage; this enables defining two standard riding cycles at different riding conditions. Two benchmarking tests are performed to investigate the battery life in pure electric mode and at different threshold levels of optimal assistance. A novel quadratic programming based fitting algorithm is derived and applied in both time and frequency domain parameter identification tests. The proposed algorithm is able to fit single/multiple pulses by applying a masking vector. Sensitivity study and experimental results show the high robustness and fast computation time of the approach compared to existing and commonly used methods, such as fmincon. The comparison between hybrid power pulse characterization (HPPC) and electrochemical impedance spectrum (EIS) tests are performed in terms of extracted internal resistance. A second-order RC battery model is developed using parameters extracted from HPPC tests. The model is validated by experimental riding cycles and used to generate the reference SOC profiles that are employed in a SOC estimation study. Four estimation strategies, including extended Kalman Filter (EKF), Sigma point Kalman Filter (SPKF), Cubature Kalman Filter (CKF), and joint extended Kalman Filter (JEKF), are compared systematically in terms of accuracy, robustness and computation complexity. / Thesis / Master of Applied Science (MASc)

Yes / Earthquake response spectrum is the most popular tool in the seismic analysis and design of structures. In the case of combined primary-secondary (P-S) systems, the response of the supporting P substructure is generally evaluated without considering the S substructure, which in turn is only required to bear displacements and/or forces imposed by the P substructure (¿cascade¿ approach). In doing so, however, dynamic interaction between the P and S components is neglected, and the seismic-induced response of the S substructure may be heavily underestimated or overestimated. In this paper, a novel CQC (Complete Quadratic Combination) rule is proposed for the seismic response of linear light S substructures attached to linear P substructures. The proposed technique overcomes the drawbacks of the cascade approach by including the effects of dynamic interaction and different damping in the substructures directly in the cross-correlation coefficients. The computational effort is reduced by using the eigenproperties of the decoupled substructures and only one earthquake response spectrum for a reference value of the damping ratio.

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Face recognition is a challenging problem in computer vision. Difficulties such as slight differences between similar faces of different people, changes in facial expressions, light and illumination condition, and pose variations add extra complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method. Different versions of NMF have been proposed. Wang et al. proposed the graph-based semi-supervised nonnegative learning (S2N2L) algorithm that uses labeled data in constructing intrinsic and penalty graph to enforce separability of labeled data, which leads to a greater discriminating power. Moreover the geometrical structure of labeled and unlabeled data is preserved through using the smoothness assumption by creating a similarity graph that conserves the neighboring information for all labeled and unlabeled data. However, S2N2L is sensitive to light changes, illumination, and partial occlusion. In this thesis, we propose a Semi-Supervised Half-Quadratic NMF (SSHQNMF) algorithm that combines the benefits of S2N2L and the robust NMF by the half- quadratic minimization (HQNMF) algorithm.Our algorithm improves upon the S2N2L algorithm by replacing the Frobenius norm with a robust M-Estimator loss function. A multiplicative update solution for our SSHQNMF algorithmis driven using the half- 4 quadratic (HQ) theory. Extensive experiments on ORL, Yale-A and a subset of the PIE data sets for nine M-estimator loss functions for both SSHQNMF and HQNMF algorithms are investigated, and compared with several state-of-the-art supervised and unsupervised algorithms, along with the original S2N2L algorithm in the context of classification, clustering, and robustness against partial occlusion. The proposed algorithm outperformed the other algorithms. Furthermore, SSHQNMF with Maximum Correntropy (MC) loss function obtained the best results for most test cases.