Raising students' awareness of cross-cultural contrastive rhetoric in English writing via an e-learning course

This study investigated the potential impact of e-learning on raising overseas students' cultural awareness and explored the possibility of creating an interactive learning environment for them to improve their English academic writing. The study was based on a comparison of Chinese and English rhetoric in academic writing, including a comparison of Chinese students' writings in Chinese with native English speakers' writings in English and Chinese students' writings in English with the help of an e-course and Chinese students' writings in English without the help of an e-course. Five features of contrastive rhetoric were used as criteria for the comparison. The experimental results show that the group using the e-course was successful in learning about defined aspects of English rhetoric in academic writing, reaching a level of performance that equalled that of native English speakers. Data analysis also revealed that e-learning resources helped students to compare rhetorical styles across cultures and that the interactive learning environment was effective in improving overseas students' English academic writing

A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information. We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo video can be found at https://youtu.be/Bkt53dAehj

Causal Discovery with Generalized Linear Models through Peeling Algorithms

This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments. This approach first reconstructs a super-graph to represent ancestral relationships between variables, using a peeling algorithm based on nodewise GLM regressions that exploit relationships between primary and instrumental variables. Then, it estimates parent-child effects from the ancestral relationships using another peeling algorithm while deconfounding a child's model with information borrowed from its parents' models. The article offers a theoretical analysis of the proposed approach, which establishes conditions for model identifiability and provides statistical guarantees for accurately discovering parent-child relationships via the peeling algorithms. Furthermore, the article presents numerical experiments showcasing the effectiveness of our approach in comparison to state-of-the-art structure learning methods without confounders. Lastly, it demonstrates an application to Alzheimer's disease (AD), highlighting the utility of the method in constructing gene-to-gene and gene-to-disease regulatory networks involving Single Nucleotide Polymorphisms (SNPs) for healthy and AD subjects

Characterization of Surface Urban Heat Island in the Greater Toronto Area Using Thermal Infrared Satellite Imagery

For the past decades, there have been increasing concerns about urban environmental degradation, especially under the circumstance of urbanization. This thesis compares the trends between air temperature and surface temperature, and characterizes spatial distribution and connection with relevant urban characteristics, in the Greater Toronto Area (GTA) of Ontario in the context of surface urban heat island (SUHI). The trends in annual and seasonal temperature were investigated in the GTA from 1984 to 2014. The Mann-Kendall test is used to assess the significance of the trends and the Theil-Sen slope estimator is used to identify their magnitude. Statistically significant increasing trends for annual mean temperatures are observed mainly at the urban and suburban stations. The temperature variation is consistent with the pace of urbanization, however, the choice of the stations is vital in the estimation of the UHI intensity which can overestimate or underestimate the prediction. A local scale investigation was continued by applying Landsat and ASTER thermal-band images in order to characterize SUHI intensity in the study area. Results show that strong SUHI phenomenon is mainly observed at downtown Toronto and industrial areas. As the enhancement of urbanization, tracking and monitoring of SUHI is imperative to understand the potential impact of the increased heat waves