Browsing by Type "Master's thesis"

Increased consumption of virtual museum content today has fueled the development of new ways of improving the ‘museum experience.’ The sophisticated needs and expectations of the modern visitor in the pursuit of education, leisure, and adventure within museums, can be fulfilled through the application of modern-day solutions. The objective of this study is to improve the experience of paintings through providing audiences three-dimensional immersive virtual experiences. In order to achieve this goal, a case study was created to explore in depth the idea that three-dimensional interventions with interactive elements will improve experience and enhance museum engagement. A practice-based methodology was used to develop a three-dimensional virtual environment of the painting of The Goddess of the Luo by Gu Kazhi. The expertise and knowledge of the researcher concerning photo-editing software and three-dimensional game engine were instrumental in the design and implementation of the virtual environment. The results indicated that through the combination of two-dimensional and three-dimensional elements, the design of instrumental interaction and interpretive materials based on the original painting, various techniques and strategies could be applied to improve the experience of the three-dimensional animated painting. The approach described in this case study could be applied to the physical museums as a supplement to interpret the original painting, as well as being displayed on the official website of the museums.

Purpose: This project aimed to establish a method for computing 3D cardiac motion given continuous 2D-Cine MRI frames as the inputs. This approach would be useful for continuously monitoring cardiac and respiratory motion during MR-guided cardiac radiation therapy, and thus supporting radiation delivery guidance and gating.Methods: 5D-MRI datasets of seven patients, with each consisting of 3D spatial volumes of the cardiac cycle and respiratory cycle, were used for quantitative evaluation of the heart motion due to respiratory and cardiac movements. This was achieved through deformable image registration (DIR). Subsequently, principal component analysis (PCA) was performed on the computed deformation vector fields (DVF) to extract scores that effectively represent the characteristics of the DVFs. A deep learning model was then trained to predict the cardiac motion PCA scores given the inputs of 2D-Cine MRI. The predicted PCA scores were then transformed into 3D DVFs, which were then used to track 3D target motion. Results: The model’s performance was quantitatively evaluated on ground truth data that were withheld from model training. Across all 7 subjects, the average 3D DVF prediction errors for the heart region consistently remained around 0.3 ± 0.1mm. The predicted target motion, computed from the predicted DVFs, was visually evaluated, and found to be satisfactory. Conclusion: The developed method demonstrated promising potential in accurately computing and tracking real-time 3D cardiac motion given 2D-Cine MRI inputs. This approach presents a viable solution for continuously monitoring the 3D cardiac and respiratory motion of the heart during MR-guided cardiac radiation therapy.

Testing for differences within data sets is an important issue across various applications. Our work is primarily motivated by the analysis of microbiomial composition, which has been increasingly relevant and important with the rise of DNA sequencing. We first review classical frequentist tests that are commonly used in tackling such problems. We then propose a Bayesian Dirichlet-multinomial framework for modeling the metagenomic data and for testing underlying differences between the samples. A parametric Dirichlet-multinomial model uses an intuitive hierarchical structure that allows for flexibility in characterizing both the within-group variation and the cross-group difference and provides very interpretable parameters. A computational method for evaluating the marginal likelihoods under the null and alternative hypotheses is also given. Through simulations, we show that our Bayesian model performs competitively against frequentist counterparts. We illustrate the method through analyzing metagenomic applications using the Human Microbiome Project data.

Geology is a descriptive science making itself hard to provide quantification. We develop a Bayesian forward simulation approach to formulate a realistic prior model for geological images using the Approximate Bayesian computation (ABC) method. In other words, our approach aims to select a set of representative images from a larger list of complex geometrical objects and provide a probability distribution on it. This allows geologists to start contributing their perspectives to the specification of a realistic prior model. We examine the proposed ABC approach on an experimental Delta dataset and show that, on the basis of selected representative images, the nature of the variability of the Delta can be statistically reproduced by means of the IQSIM, a state-of-the-art multiple-point geostatistical (MPS) simulation algorithm. The results demonstrate that the proposed approach may have a broader spectrum of application. In addition, two different choices for the size of the prior, i.e., the number of representative images are compared and discussed.

Tremendous effort has been put into study of the etiology of complex

diseases including the breast cancer, type 2 diabetes,

cardiovascular diseases, and prostate cancers. Despite large numbers of reported disease-associated loci,

few associated loci have been replicated, and some true associations

does not belong to the group of the most significant loci

reported to be associated. We built a Bayesian hierarchical model incorporated

with SNP-level functional data that can help identify associated SNPs in pathway-wide association studies.

We applied the model to an association study for the serous invasive ovarian cancer based on the DNA repair and apoptosis pathways. We found that using our model, blocks of SNPs located in regions enriched for missense SNPs or gene inversions were more likely to be identified as candidates of the association.

As fundamental structural units of the chromatin, nucleosomes are involved in virtually all aspects of genome function. Different methods have been developed to map genome-wide nucleosome positions, including MNase-seq and a recent chemical method requiring genetically engineered cells. However, these methods are either low resolution and prone to enzymatic sequence bias or require genetically modified cells. The DNase I enzyme has been used to probe nucleosome structure since the 1960s, but in the current high throughput sequencing era, DNase-seq has mainly been used to study regulatory sequences known as DNase hypersensitive sites. This thesis shows that DNase-seq data is also very informative about nucleosome positioning. The distinctive oscillatory DNase I cutting patterns on nucleosomal DNA are shown and discussed. Based on these patterns, a Bayes factor is proposed to be used for distinguishing nucleosomal and non-nucleosomal genome positions. The results show that this approach is highly sensitive and specific. A Bayesian method that simulates the data generation process and can provide more interpretable results is further developed based on the Bayes factor investigations. Preliminary results on a test genomic region show that the Bayesian model works well in identifying nucleosome positioning. Estimated posterior distributions also agree with some known biological observations from external data. Taken together, methods developed in this thesis show that DNase-seq can be used to identify nucleosome positioning, adding great value to this widely utilized protocol.

In this paper, we develop an algorithm that utilizes a Bayesian strategy to determine a sequence of questions to play the 20 Question game. The algorithm is motivated with an application to active recommender systems. We first develop an algorithm that constructs a sequence of questions where each question inquires only about a single binary feature. We test the performance of the algorithm utilizing simulation studies, and find that it performs relatively well under an informed prior. We modify the algorithm to construct a sequence of questions where each question inquires about 2 binary features via AND conjunction. We test the performance of the modified algorithm

via simulation studies, and find that it does not significantly improve performance.

*Designated as an Exemplary Master's Project for 2014-15

This thesis develops a novel solution method for the problem of collision-free, optimal control of a robotic vehicle in an obstacle populated environment. The technique presented combines the well established approximate cell decomposition methodology with disjunctive programming in order to address both geometric and kinematic trajectory concerns. In this work, an algorithm for determining the shortest distance, collision-free path of a robot with unicycle kinematics is developed. In addition, the research defines a technique to discretize nonholonomic vehicle kinematics into a set of mixed integer linear constraints. Results obtained using the Tomlab/CPLEX mixed integer quadratic programming software exhibit that the method developed provides a powerful initial step in reconciling geometric path planning methods with optimal control techniques.

In this thesis, I probe the incremental religious and social changes in the local society that led up to the great transformations of the mid-19th century. I use the word “sleeplessness” both literally and figuratively. My protagonist, Zheng Guangzu (1776-1866), a member of a local elite from Lower Yangzi Delta, suffered from insomnia and was perturbed by the corruption of Confucianism by popular Buddhism and Taoism. These were, however, merely an interlude to the great challenge of his life, the spread of the Taiping religion, a heterodox Christian ideology that triggered the mid-19th century civil war. Through a case study, my research highlights the Confucian literati’s daily interaction with local religious practices that are alien to their cultural ethos. In doing so, I explore the diverse appeal of Confucianism to different social groups and uncover the tension between elite and popular culture. Significantly, this tension sheds light on Confucian’s responses to the Taiping. More broadly, based on my protagonist’s description of local religion, my thesis evaluates the extent of the state’s success in reaching into local society through the lens of its religious policy. Although it was the greatest patron of Confucianism, the state, I argue, exhibited an ambivalent attitude toward local cults rather than outright rejection.

Background: Global aging poses various challenges for both developing and developed countries. The proportion of older adults in developed countries is currently much higher than in developing countries. Health authorities are now confronting increasing public health problems, including a growing burden of oral diseases among older people. Although the percentage of older adults who have retained their natural teeth has increased steadily during the past decades, the number of natural teeth might still contribute vastly to older people’s quality of life. As little research has been conducted in the Southeast Asia area, this study aims to 1) investigate the association of number of natural teeth and oral pain with oral health-related quality of life; 2) discover ethnic disparities in regards to number of natural teeth and oral health-related quality of life; and 3) examine factors related to number of natural teeth retained among elderly Singaporeans.

Methods: The study was carried out in a dental clinic conducting a questionnaire survey on patients who visited the clinic. Geriatric Oral Health Assessment Index (GOHAI) was used and the GOHAI total score (ranged from 12 - 60) was measured to describe participants’ oral health-related quality of life from three perspectives: physical, social, and mental health components. Participants’ dental records were linked to the questionnaire survey and data on their dental parameters were also collected. Linear regression analysis was conducted as the main method towards all the three study aims, whereas logistic regression analysis was performed as supplementary evidence especially for Aim 1. Non-parametric statistical tests were also used in cased of normality assumption requirements throughout the analytical process.

Results: The mean GOHAI total score of the study subjects was 47.72 (SD = 7.45). The mean number of natural teeth was 18.27 (SD = 8.15). A positive association between number of natural teeth and GOHAI total score was found by the linear regression analysis (Coefficient = 0.20, 95%CI: 0.17, 0.44) and a Spearman correlation test (rho=0.22, P < 0.01). A negative association between oral pain and GOHAI total score was detected by the linear regression (Coefficient = -5.88, 95%CI: -7.86, -3.91) and a Spearman correlation test (rho = -0.40, p < 0.001) as well. In terms of factors associated with number of natural teeth, older people with more number natural teeth were found to be associated with younger age, higher educational level, and flossing at least once a day. Ethnic disparities were observed with Malays exhibited the lowest number of natural teeth and GOHAI total score, compared to Chinese and Indians.

Conclusions: The oral health-related quality of life of the investigated population was generally fair. Ethnic disparities existed in oral health-related quality of life and number of natural teeth among older adults in Singapore that Malays had adverse oral health status compared to Chinese and Indians. With more number of natural teeth retained and less oral pain, a person was more likely to have better oral health-related quality of life. Flossing at least once a day might contribute to the retention of more natural teeth.

Purpose: To develop an automatic collimator setting optimization algorithm to improve dosimetric quality of pancreas Volumetric Modulated Arc Therapy (VMAT) plans for Stereotactic Body Radiation Therapy (SBRT).

Methods: Fifty-five pancreas SBRT cases were retrospectively studied. Different from the conventional practice of initializing collimator settings manually, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions which are customized to the patient geometry. This algorithm includes two key steps: an iterative optimization algorithm via simulated annealing that generates a set of collimator settings candidates, and a scoring system that choose the final collimator settings based on organs-at-risk (OARs) sparing criteria and dose prescription. The scoring system penalizes 3 factors: 1) jaw opening ratio on Y direction to X direction; 2) unmodulated MLC area within the jaw aperture in a dynamic MLC sequence; 3) OAR shielding capability by MLC with MLC aperture control constraints. For validation, the other 16 pancreas SBRT cases were analyzed. Two dual-arc plans were generated for each validation case, an optimized plan (Planopt) and a conventional plan (Planconv). Each plan was generated by a same set of auxiliary planning structures and dose-volume-histogram (DVH) constraints in inverse optimization. Dosimetric results were analyzed and compared. All results were tested by Wilcoxon signed-rank tests.

Results: Both plan groups had no statistical differences in target dose coverage V95% (p=0.84) and Root Conformity Index (p=0.30). Mean doses of OARs were improved or comparable. In comparison with Planconv, Planopt reduced maximum dose (D0.03cc) to stomach (-49.5cGy, p=0.03), duodenum (-63.5cGy, p<0.01), and bowel (-62.5cGy, p=0.01). Planopt also showed lower modulation complexity score (p=0.02), which implies its higher modulation complexity of the dynamic MLC sequence.

Conclusions: The proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc VMAT plans in pancreas SBRT. The proposed algorithm was demonstrated with great clinical feasibility and readiness.

This thesis seeks to analyze the longest story in Ovid's Metamorphoses, tale of Alcyone and Ceyx. Despite its length, its placement within the entire work, and the presence of the work's eponymous hero, Morpheus, the Alcyone's and Ceyx' story has no major commentary in English and has earned little attention from most scholars. What has been written on it often scants the darker details of the episode, persuaded that Ovid has here sketched a portrait of an ideal, happy marriage, albeit one crossed by circumstances. In order to counterbalance this overly optimistic reading, this commentary carefully analyzes the language, motifs, and intertextual references that thread through Ovid's version of the Ceyx and Alcyone story. Particular attention has been paid to: the ambiguities in Ovid's narrative of Alcyone's attitude towards parting from her husband; the story's portrait of the gods, including Alcyone's own father, Aeolus; allusions to the earlier epic tradition (Iliad, Odyssey, and Aeneid) in the Tempest scene; and the ecphrasis of the House of Sleep, including the character of Morpheus. This analysis will show that the tale of Alcyone and Ceyx is not a simple encomium to marital fidelity, but a picture of conjugal love darker than most Ovidian scholars have suspected.

In the last two decades, the field of homogeneous gold catalysis has been

extremely active, growing at a rapid pace. Another rapidly-growing field—that of

computational chemistry—has often been applied to the investigation of various gold-

catalyzed reaction mechanisms. Unfortunately, a number of recent mechanistic studies

have utilized computational methods that have been shown to be inappropriate and

inaccurate in their description of gold chemistry. This work presents an overview of

available computational methods with a focus on the approximations and limitations

inherent in each, and offers a review of experimentally-characterized gold(I) complexes

and proposed mechanisms as compared with their computationally-modeled

counterparts. No aim is made to identify a “recommended” computational method for

investigations of gold catalysis; rather, discrepancies between experimentally and

computationally obtained values are highlighted, and the systematic errors between

different computational methods are discussed.

Purpose: To compare radiomics and deep-learning (DL) methods for predicting NSCLC surgical treatment failure. Methods: A cohort of 83 patients undergoing lobectomy or wedge resection for early-stage NSCLC from our institution was studied. There were 7 local failures and 16 non-local failures (regional and/or distant). Gross tumor volumes (GTV) were contoured on pre-surgery CT datasets after 1mm3 isotropic resolution resampling. For the radiomics analysis, 92 radiomics features were extracted from the GTV and z-score normalizations were performed. The multivariate association between the extracted features and clinical endpoints were investigated using a random forest model following 70%-30% training-test split. For the DL analysis, both 2D and 3D model designs were executed using two different deep neural networks as transfer learning problems: in 2D-based design, 8x8cm2 axial fields-of-view(FOVs) centered within the GTV were adopted for VGG-16 training; in 3D-based design, 8x8x8 cm3 FOVs centered within the GTV were adopted for U-Net’s encoder path training. In both designs, data augmentation (rotation, translation, flip, noise) was included to overcome potential training convergence problems due to the imbalanced dataset, and the same 70%-30% training-test split was used. The performances of the 3 models (Radiomics, 2D-DL, 3D-DL) were tested to predict outcomes including local failure, non-local failure, and disease-free survival. Sensitivity/specificity/accuracy/ROC results were obtained from their 20 trained versions. Results: The radiomics models showed limited performances in all three outcome prediction tasks. The 2D-DL design showed significant improvement compared to the radiomics results in predicting local failure (ROC AUC = 0.546±0.056). The 3D-DL design achieved the best performance for all three outcomes (local failure ROC AUC = 0.768 ± 0.051, non-local failure ROC AUC = 0.683±0.027, disease-free ROC AUC = 0.694±0.042) with statistically significant improvements from radiomics/2D-DL results. Conclusions: 3D-DL execution outperformed the 2D-DL in predicting clinical outcomes after surgery for early-stage NSCLC. By contrast, classic radiomics approach did not achieve satisfactory results.

This thesis evaluates the performance of several multiple imputation methods for categorical data, including multiple imputation by chained equations using generalized linear models, multiple imputation by chained equations using classification and regression trees and non-parametric Bayesian multiple imputation for categorical data (using the Dirichlet process mixture of products of multinomial distributions model). The performance of each method is evaluated with repeated sampling studies using housing unit data from the American Community Survey 2012. These data afford exploration of practical problems such as multicollinearity and large dimensions. This thesis highlights some advantages and limitations of each method compared to others. Finally, it provides suggestions on which method should be preferred, and conditions under which the suggestions hold.

This paper discusses the application of parallel programming techniques to the estimation of hidden Markov models via the use of a particle filter. It highlights how the Thrust parallel programming

language can be used to implement a particle filter in parallel. The impact of a parallel particle filter on the running times of three different models is investigated. For particle filters using a large number

of particles, Thrust provides a speed-up of five to ten times over a serial C++ implementation, which is less than reported in other research.

Synthetic data is a type of method for protecting data privacy. In the context of disseminating confidential data for public utilization, some statistical agencies employ the generation of fully synthetic datasets. This practice is applied to census and administrative records. It is important to note that many research datasets come from surveys with complex sampling methods, which is not ignorable when constructing synthetic data. The thesis presents an illustration for the comparison of three different synthetic data strategies. Each of them has different procedures to generate the synthetic data. Two of them are based on the bootstrap methods, one is Bayesian bootstrap, and the other is regular bootstrap. The third method is based on the posterior inference with pseudo-likelihood. Using simulation studies with probability proportional to size sampling, we show that all three methods can result in accurate estimates of the mean of a finite population. However, when estimating the sampling statistic's variance, only the method based on the Bayesian bootstrap method can provide an approximately unbiased estimate in these simulations.

As part of the technological growth in HR, companies are developing and adopting AI-enabled solutions for recruitment of qualified talent for a job opening. AI-enabled recruiting tools provide a variety of potential benefits to an organization: from improving overall efficiency and lowering hiring costs, to automating repetitive tasks and removing human biases. AI-enabled tools in recruiting also introduce concerns about dehumanization of the hiring process, increased discrimination, and accidental exclusion of qualified candidates. These benefits and concerns are discussed at the HR executive level in industry and in academic contexts; however, the data on the perspectives of HR practitioners is much more limited. Studies show that only 32 percent of companies include individual practitioners within the talent acquisition technology discussions. HR practitioners leverage AI-enabled tools in hiring and, therefore, should be aware of and able to mitigate potential risks of leveraging AI-enabled tools. Lack of consideration of perspectives of HR practitioners on the benefits and risks of AI-enabled tools increases the possibility of ethical concerns and legal liability for the individual companies (Nankervis, 2021). HR executives need take into consideration the perspectives of HR practitioners who work with AI-enabled tools as this awareness is likely to help the businesses successfully realize their talent management goals. This paper is based on the hypothesis that the perspectives of HR practitioners on the use of AI-enabled tools in hiring differ from the perspectives of HR executives and need to be addressed to ensure that organizations can successfully and ethically implement AI-enabled tools within organizations. Robinson 2019, states that “examination of the practitioners’ perspective [is] a valuable part of AI technology adoption, if organizations hope to have employees support and embrace the accompanying changes." This paper contributes to the examination of practitioner’s perspectives by identifying an information gap that may influence attitudes of individual HR practitioners on the use of AI-enabled recruiting tools. The paper provides additional insights into the attitudes of individual HR practitioners in the United States (U.S.) through a new small-sample survey finding. The survey findings highlight the different attitudes that individual HR practitioners have towards the use of AI-enabled recruiting tools, especially when compared with those of HR executives. This survey is an initial step for more robust research and lays the foundation for follow up research topics. Finally, the paper provides recommendations that can help organizations ethically implement AI-enabled tools by ensuring the attitudes of individual HR practitioners are taken into consideration.