Master of Arts

thesisThis thesis investigates the predicate cleft (PC) constructions in Mandarin Chinese. Cheng & Vicente conclude that the topicalized verb and the lower verb in bare PC form a long head movement relation, discarding a remnant movement analysis based on vP-external scrambling. However, to be complete, the argument also needs to consider vP-internal scrambling observed by Soh and a selective deletion analysis. I show that vP-internal scrambling cannot serve to derive a plausible remnant movement analysis; nor can a selective deletion analysis be accomplished. Long head movement is necessary to account for Mandarin bare PC. However, although this conclusion converges with cross-linguistic treatment of predicate clefts, I point out the unreliability of idiom interpretation as a diagnostic for long head movement used in several studies. Moreover, I present the puzzling restriction on the types of categories that can undergo pied-piping with the fronted verb. Last, I show that the verb doubling effect, an unresolved issue in Cheng & Vicente, can be accounted for, if the proposal on parallel chains is adopted. The necessity of a long head movement analysis supports bare phrase structure whereby head-to-spec movement is expected. In addition, it constitutes as an empirical argument against eliminating syntactic head movement. The compositionality of idiom interpretation and the restriction on full PC are worth further study

Evaluating Comparative Effectiveness of Simultaneous Liver and Kidney Transplant versus Liver Transplant Alone using Instrumental Variables

Improving the quality of medical care often requires assessment of comparative effectiveness between treatments. Although randomized controlled trials (RCTs) are considered as the gold standard for generating evidence, they may not be feasible or ethical to conduct for some comparisons. Therefore, observational studies are required to address many research questions. However, observational data may lead to a high potential for selection bias because subjects or physicians choose their treatments, which may complicate the estimation of causal effects. As one approach to overcome these issues, instrumental variables (IVs) can be used to potentially estimate unbiased causal effect in the setting of observational comparative effectiveness research. The goal of this thesis is reducing unmeasured confounding in an observational study to compare the effectiveness of simultaneous liver and kidney transplants (SLKT) versus liver-only transplants (LTA) in patients who were on the liver transplant wait list with dialysis. We hypothesize that SLKT could lower mortality by replacing both organs in the same operation. A two-stage least squares (2SLS) was used to estimate causal effects. The first stage was regressing treatment on IV and covariates to determine whether IV met the assumption of strongly predicting treatment. Then, the second stage least squares analysis was performed by regressing outcome on estimated treatment and covariates. This analysis used several strategies for formulating the IV based on geographic region, with similar results. Although our IV met the necessary assumptions, results did not show a significant causal relationship between treatment and mortality. Findings of this thesis are significant to public health because more than ten thousand patients in the US are on the liver transplant waiting list. While performing both a kidney and liver transplant in these patients may save lives, we are not aware of any other studies that evaluated this problem using IVs or other approaches that potentially account for unmeasured confounding. By evaluating the causal effects of the different transplant approaches, physician and patients can make more informed decision. The information may also be important for organ allocation strategies nationally

Firm-level performance and productivity analysis for software-as-a-service companies

Master'sMASTER OF SCIENC

Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.Comment: accepted by CVPR 202