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

Do algorithms and barriers for sparse principal component analysis extend to other structured settings?

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts

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

Master'sMASTER OF SCIENC

DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation

Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stacking generator-discriminator pairs to engage multiple adversarial training, where the text semantics used to provide generation guidance remain static across all stages. This work argues that text features at each stage should be adaptively re-composed conditioned on the status of the historical stage (i.e., historical stage's text and image features) to provide diversified and accurate semantic guidance during the coarse-to-fine generation process. We thereby propose a novel Dynamical Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under a novel single adversarial multi-stage architecture. Specifically, we design (1) Dynamic Semantic Evolution (DSE) module, which first aggregates historical image features to summarize the generative feedback, and then dynamically selects words required to be re-composed at each stage as well as re-composed them by dynamically enhancing or suppressing different granularity subspace's semantics. (2) Single Adversarial Multi-stage Architecture (SAMA), which extends the previous structure by eliminating complicated multiple adversarial training requirements and therefore allows more stages of text-image interactions, and finally facilitates the DSE module. We conduct comprehensive experiments and show that DSE-GAN achieves 7.48\% and 37.8\% relative FID improvement on two widely used benchmarks, i.e., CUB-200 and MSCOCO, respectively