Extrapolating Away from the Cutoff in Regression Discontinuity Designs

Canonical RD designs yield credible local estimates of the treatment effect at the cutoff under mild continuity assumptions, but they fail to identify treatment effects away from the cutoff without additional assumptions. The fundamental challenge of identifying treatment effects away from the cutoff is that the counterfactual outcome under the alternative treatment status is never observed. This paper aims to provide a methodological blueprint to identify treatment effects away from the cutoff in various empirical settings by offering a non-exhaustive list of assumptions on the counterfactual outcome. Instead of assuming the exact evolution of the counterfactual outcome, this paper bounds its variation using the data and sensitivity parameters. The proposed assumptions are weaker than those introduced previously in the literature, resulting in partially identified treatment effects that are less susceptible to assumption violations. This approach accommodates both single cutoff and multi-cutoff designs. The specific choice of the extrapolation assumption depends on the institutional background of each empirical application. Additionally, researchers are recommended to conduct sensitivity analysis on the chosen parameter and assess resulting shifts in conclusions. The paper compares the proposed identification results with results using previous methods via an empirical application and simulated data. It demonstrates that set identification yields a more credible conclusion about the sign of the treatment effect

Impact of Online Learning on International Students’ English Language Concerns

With the onset of COVID-19, U.S. universities have been forced to move many, even all, courses online. At the University of Richmond, many of our international students faced visa restrictions due to COVID and were required to stay in their countries. As a result, the majority of our international students must attend their classes remotely. International students may find language to be a challenge during online learning. The purpose of our study is to learn more about how, if at all, online classes have an impact on international students’ English language concerns

Improving Image Captioning by Leveraging Knowledge Graphs

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The results of our experiments, on several benchmark data sets such as MS COCO, as measured by CIDEr-D, a performance metric for image captioning, show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.Comment: Accepted by WACV'1

Improved constructions of permutation and multi-permutation codes correcting a burst of stable deletions

Permutation codes and multi-permutation codes have been widely considered due to their various applications, especially in flash memory. In this paper, we consider permutation codes and multi-permutation codes against a burst of stable deletions. In particular, we propose a construction of permutation codes correcting a burst stable deletion of length $s$, with redundancy $\log n+ 2\log \log n+O(1)$. Compared to the previous known results, our improvement relies on a different strategy to retrieve the missing symbol on the first row of the array representation of a permutation. We also generalize our constructions for multi-permutations and the variable length burst model. Furthermore, we propose a linear-time encoder with optimal redundancy for single stable deletion correcting permutation codes.Comment: Accepted for publication in IEEE Trans. Inf. Theor

Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection

Carbon Nanotubes Under Pressure

Graphene has been investigated intensively since its discovery in 2004, for its unique mechanical and electrical properties. Strain modi es these properties to meet speci c scienti c or technological needs. Therefore, the strain determination and monitoring are of critical application importance and contribute to the characterization and understanding of this remarkable material. However, in many cases strain cannot be directly and precisely measured. Strain is therefore related to easily-detected phonon frequency. To be speci c, researchers attribute the frequency shift of graphene in-plane vibrational mode E2g (the graphite-mode) entirely to the in-plane strain and quantify this relation via the Gr uneisen parameter and shear deformation potential. Di erent values of these parameters however have been reported by various experiments and calculations. The discrepancy comes from considering the in-plane strain contribution alone and whether this error is acceptable depends on the accuracy required in the speci c scienti c or technological problem. Chapter 2 presents our work to quantify other contributions to the graphite-mode shift under strain, namely the compression of the -electrons into the sp2 network. Calculations will use density functional theory, generalised gradient approximation for the exchangecorrelation potential, with the van der Waals interaction add-on. Carbon nanotubes can be considered as rolled-up graphene sheet. Similar to graphene, strain modi es their properties and can be determined and monitored by the graphite-mode frequency. The tube structure gives additional mechanical stability for application and meanwhile, complication in the relationship between frequency and applied strain. The thick wall tube model explains the e ect of tube diameter on this relation (Chapter 3) while more recent experiment shows the graphite-mode frequencies of tubes of similar diameter but di erent chiralities shift very di erently under pressure (Chapter 4), which is beyond current understanding. The signi cant bundling e ect is reported but not fully understood either (Chapter 5). Chapter 6 presents our attempt to describe the collapse of tubes with the atomistic re ned elastic ring model.School of Physics and Astronomy in Queen Mary, University of London and Chinese Scholarship Council