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"Wang, Cangbai"
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Ambivalent Heritage: The Im/Possibility of Museumifying the Overseas Chinese in South China
The past two decades have witnessed an “overseas Chinese museum fever” across China. By commemorating heroic figures and treasuring the contributions of the overseas Chinese to the motherland, the representation of overseas Chinese in state-led museums has played an important role in promoting a “transnational nationalism” among domestic and international audiences. Using “ambivalent heritage” as a framing device and through a case study of the Chen Cihong Residence, this article discusses an underresearched aspect of museumifying the overseas Chinese, that is, issues that are unsettled and difficult to stage. By foregrounding the conflicting interpretations and uses of the Chen Cihong Residence as an ancestral house, a museum, and a tourist spot, this article raises important questions regarding the dis/continuities of national, local, and individual identities in heritagizing transnational Chinese mobilities. Additionally, it calls for a diasporic perspective on the study of cultural heritage and proposes a new insight into heritage preservation.
Journal Article
Bridging Borders in the Global City: Negotiating Sameness and Difference in Hong Kong’s Skilled Immigrants from Mainland China
Immigration labor in global cities is often framed in a dichotomy of skilled and nonskilled and explained from different perspective. Based on narratives of skilled immigrants from mainland China in postcolonial Hong Kong, this study shifts the focus of attention from generalized dissimilarities between migrant groups determined by the level of skills to commonalities of experience shaped by the broader social and cultural forces of their spatial, economic and political environments. It points to the importance of “border” in shaping the mode of incorporation of skilled migrants to localities in global city. It shows that skilled mainland immigrants in Hong Kong are deeply embedded in an overarching
xin yimin
(new immigrants) discourse according to which the Hong Kong–China border distinguishes all mainland immigrants from Hong Kong citizens regardless of the level of skills they possess. This discourse is associated with and defined by the cultural meaning of border between Hong Kong and China produced in the colonial past and reproduced in the postcolonial present. Despite being highly educated and skilled, mainland Chinese professionals experienced countless negotiation of sameness and difference in their everyday encountering localities and making place. The stories presented here ask us to rethink the assumptions informing the analytical distinctions between skilled and non-skilled and call for “unifying” skilled and non-skilled migration in global cities methodologically and theoretically.
Journal Article
Social Stratification in Chinese Societies
The annual is a venue of publication for sociological studies of Chinese societies and the Chinese all over the world. The main focus is on social transformations in Hong Kong, Taiwan, the mainland, Singapore and Chinese overseas.
eBook
Doing Families in Hong Kong
The annual is a venue of publication for sociological studies of Chinese societies and the Chinese all over the world. The main focus is on social transformations in Hong Kong, Taiwan, the mainland, Singapore and Chinese overseas.
eBook
Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations
The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.
Journal Article
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72\\% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs.
Paper