CASE STUDY OF CHINESE IMMIGRANT CHILDREN’S BILINGUAL LITERACY (MANDARIN AND ENGLISH) PRACTICES AT HOME, AT SCHOOL, AND IN A COMMUNITY LANGUAGE SCHOOL IN SOUTHWESTERN ONTARIO CANADA

The demographic context of Canada, a multicultural and multilingual country, provides an exciting research site for the investigation of immigrant children’s bilingual literacy practices. According to Citizenship and Immigration Canada 2006, the People\u27s Republic of China has been the top source country of immigrants to Canada, and Mandarin has been reported as the top minority language since 1998. It is worthwhile to explore the bilingual literacy practices of young Chinese immigrant children in Ontario, where fifty percent of immigrants have chosen to settle. This research project consisted of a qualitative case study. Semi-structured interviews and classroom observations were employed as the main tools of data collection. Family visits were made, and artwork and documents were also collected as supplementary sources of data. Interpretational and domain analysis methods were used to analyze the data (Gall, Gall & Borg, 2007; Wolcott, 1994). Findings of the study indicate that there is a complementary learning community, including a formal public school education, supportive community language school and home literacy environments facilitating the five participating children\u27s bilingual literacy development. The public school culture, parents\u27 perceptions and support of their children’s bilingual literacy development, as well as the community Chinese language school have all influenced the five participating children\u27s biliteracy development. Based on the findings, this study makes suggestions for teachers, parents and curriculum designers. Public schools should create democratic school cultures where minority iii children\u27s culture and first languages are valued and embraced. Teachers should apply effective literacy instruction to help minority children\u27s bilingual literacy development in an authentic literacy environment. Curriculum designers should pay attention to English language learners’ cultural backgrounds and meet diverse teachers\u27 and children\u27s needs

Navigating Chinese and English Multiliteracies Across Domains in Canada: An Ethnographic Case Study of Culturally and Linguistically Diverse Children’s Literacy Practices

Increasing diversity in the globalized world challenges the field of education such as policy development and curriculum design (Suarez-Orozco & Suarez-Orozco, 2009). With more and more students speaking a home language other than English entering schools, numerous studies have examined their English language development with a focus on how they learn to read and write at home and school. However, less is known about culturally and linguistically diverse children’s literacy practices across domains. This study investigated Chinese children’s literacy practices and asked What are Chinese children’s literacy practices at school, home, and in the community? What(linguistic and sociocultural) resources do Chinese children draw upon in their literacy practices? In what ways (if any) do classroom teachers, parents, and communities support Chinese children’s literacy practices? The study took the social and cultural perspectives toward literacy with a focus on multiltieracies (the New London Group, 1996). In order to examine children’s literacy practices across domains, I employed the case study approach using ethnographic tools such as participant observations and semi-structured interviews to collect data (O’Reilly, 2005; Yin, 2005). Participants included Chinese families and teachers. Data analysis involves data triangulation, constant comparison and critical reflection. Findings of the study indicate that children’s literacy practices were directed to print literacy in adult-organized literacy events and children’s literacy practices were multimodal in children-initiated literacy events, children drew upon their social, cultural and linguistic backgrounds to explore literacies, and adults provided certain degrees of support based on their understanding and backgrounds

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Creating Love Letters to Nature: A Case Study of Children’s Multimodal Literacy Practices

Canada welcomes large numbers of immigrants each year, including children. It is certainly important to understand immigrant children’s educational experience beyond standardized tests in reading and math. This paper draws on a sociocultural approach by situating language and literacy learning in social and cultural contexts and by emphasizing the active role of learners in different contexts. Specifically, the multiliteracies framework (The New London Group, 1996) is used to understand how culturally and linguistically diverse (CLD) children choose to use different literacies and modes to make sense of their surroundings and to create artistic texts to express their understandings of nature, such as water and forests. A qualitative case study was conducted to understand five CLD children’s meaning-making process in a community setting. Data was collected through observations, informal conversations, semi-structured interviews and artifacts. The initial findings of the study indicate that CLD children are active and creative meaning-makers who select different linguistic, cultural and artistic resources as well as various modalities to effectively express their ideas and perspectives according to audience, purpose and context. The presentation discusses two nature projects and shares the artwork of the participating children to highlight a range of multilingual, multicultural and multimodal literacy practices

Accounting for spectral variability in hyperspectral unmixing using beta endmember distributions

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Alina Zare.Includes vita.Hyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across hundreds of spectral bands (Goetz, et al., 1985). The hyperspectral image yielded by such system can be represented by a three-dimensional data cube containing those radiance/reflectance values in a range of wavelengths (Landgrebe, 2002). There are two common analysis methods for hyperspectral imagery (Hu, et al., 1999): endmember estimation and hyperspectral unmixing. Endmember estimation aims at finding pure individual spectral signatures of the materials (endmembers) in the scene (Adams, et al., 1986). Hyperspectral unmixing, on the other hand, estimates the proportions of each endmember at every pixel of the image. Each pixel in the image can then be represented by endmember spectra weighted by its corresponding proportions. In order to increase the accuracy of hyperspectral unmixing, sufficient temporal and spatial spectral variability of endmembers must be taken into consideration (Roberts, et al., 1992; Roberts, et al., 1998; Bateson, et al., 2000). The most common factors contributing to spectral variability include environmental factors, such as atmospheric effects, illumination, moisture conditions, and inherent spectral variability of the material itself, such as the variations in biophysical and biochemical composition in vegetation (Song, 2005). Under such influence, the spectral signature of endmembers may vary from time to time and from pixel to pixel in the scene. In order to account for such endmember spectral variability, endmembers are regarded as either a set, or a "bundle", of individual spectra (Roberts, et al., 1998; Bateson, et al., 2000), or as a sample from a full distribution. The application of the Normal Compositional Model with Gaussian-distributed endmembers has been discussed in the literature (Eches, et al., 2010; Zare, et al., 2012). Since the domain of GaussIncludes bibliographical references (pages 110-120)

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table