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Long Day\u27s Journey into Realistic Scenic World: a Reflection on the Scenic Design for Eugene O\u27Neill\u27s Long Day\u27s Journey into Night
LONG DAY’S JOURNEY INTO REALISTIC SCENIC WORLD: A REFLECTION ON THE SCENIC DESIGN FOR EUGENE O’NEILL’S LONG DAY’S JOURNEY INTO NIGHT
MAY 2021
XINYUAN LI, B.A., SHANGHAI THEATER ACADEMY
M.F.A., UNIVERSITY OF MASSACHUSETTS AMHERST
Directed by: Professor Anya Klepikov
This thesis is a reflection of the paper project of scenic design of Long Day’s Journey Into Night by Eugene O’Neill. It’s a record tracking the entire design process from the script analysis to the final presentation and all the discovery along with the process. Successes and places need to be improved will also be included
A Decentralized Processing Schema for Efficient and Robust Real-time Multi-GNSS Satellite Clock Estimation
Real-time multi-GNSS precise point positioning (PPP) requires the support of high-rate satellite clock corrections. Due to the large number of ambiguity parameters, it is difficult to update clocks at high frequency in real-time for a large reference network. With the increasing number of satellites of multi-GNSS constellations and the number of stations, real-time high-rate clock estimation becomes a big challenge. In this contribution, we propose a decentralized clock estimation (DECE) strategy, in which both undifferenced (UD) and epoch-differenced (ED) mode are implemented but run separately in different computers, and their output clocks are combined in another process to generate a unique product. While redundant UD and/or ED processing lines can be run in offsite computers to improve the robustness, processing lines for different networks can also be included to improve the clock quality. The new strategy is realized based on the Position and Navigation Data Analyst (PANDA) software package and is experimentally validated with about 110 real-time stations for clock estimation by comparison of the estimated clocks and the PPP performance applying estimated clocks. The results of the real-time PPP experiment using 12 global stations show that with the greatly improved computational efficiency, 3.14 cm in horizontal and 5.51 cm in vertical can be achieved using the estimated DECE clock
Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model
A population-averaged additive subdistribution hazards model is proposed to
assess the marginal effects of covariates on the cumulative incidence function
and to analyze correlated failure time data subject to competing risks. This
approach extends the population-averaged additive hazards model by
accommodating potentially dependent censoring due to competing events other
than the event of interest. Assuming an independent working correlation
structure, an estimating equations approach is outlined to estimate the
regression coefficients and a new sandwich variance estimator is proposed. The
proposed sandwich variance estimator accounts for both the correlations between
failure times and between the censoring times, and is robust to
misspecification of the unknown dependency structure within each cluster. We
further develop goodness-of-fit tests to assess the adequacy of the additive
structure of the subdistribution hazards for the overall model and each
covariate. Simulation studies are conducted to investigate the performance of
the proposed methods in finite samples. We illustrate our methods using data
from the STrategies to Reduce Injuries and Develop confidence in Elders
(STRIDE) trial
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