Economic Growth and Infrastructure Investments in Energy and Transportation:A Causality Interpretation of China’s Western Development Strategy

Were the large investments in energy and transportation infrastructure effective in fostering economic growth? Or did economic growth trigger these infrastructure developments? To answer these questions, we develop a simple model of production capacity constraints and use China's Western Development Strategy (WDS) as an example to investigate how the relationships among energy investment, transportation infrastructure expansion and economic growth differ in the pre- and post-WDS periods. Our Granger causality analysis uses a panel data sample for China's 30 provinces in the Western and non-Western regions for the period of 1991-2012. We find Granger causality only in the post-WDS period from transportation infrastructure expansion to economic growth and from economic growth to energy investment. These results suggest energy and transportation capacity constraints in the post-WDS period but not the pre-WDS period. Their policy implication is that China should continue its energy and transportation infrastructure investments with improved coordination.School of Accounting and Financ

Two-dimensional Laplacianfaces method for face recognition

In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the FERET and the AR face databases. The experimental results indicate that the 2D Laplacianfaces method significantly outperforms the existing 2D Eigenfaces, the 2D Fisherfaces and the 1D Laplacianfaces methods under various experimental conditions

Comparing the effects of visibility of different neighborhood greenery settings on the preference ratings and noise annoyance responses to road traffic noises

The impact of visual environment on human noise perceptions has always been under scrutiny. Two consecutive sets of laboratory experiments were performed for studying the effect of visual perceptions of different percentages of sea, greenery, and/or road views on noise-induced annoyance responses as well as preference ratings. Both experiments were carried out in a room purposely constructed inside an anechoic chamber to mimic the living room setting of a dwelling in Hong Kong. Video clips were projected consecutively onto the exterior window panel of the living room to simulate neighborhood views containing different percentages of sea, greenery and road. 82 and 58 participants were successfully administered in two experiments. Each participant was presented with 11 video clips and requested to respond to a series of questions regarding perceived noise annoyance and view preferences after presentation of individual clips. The responses collected from each experiment were employed to formulate ordered logit models to predict the probability of evoking a high annoyance response. Findings indicated that participants tended to prefer the presence of sea rather than that of either mountain or trees in views containing a trafficking road. Views containing sea would produce an attenuating effect on noise annoyance while views containing road would produce an aggravating effect. However, the size of the effects did not vary between 0% and 30% sea, or between 30% and 60% road contained in a view. Views containing dense greenery at a close distance would aggravate noise annoyance irrespective of form. However, when the percentage of greenery increased from 30% to 60%, the noise annoyance attenuating effect increased in the case of wooded mountain but decreased in the case of the more transparent tree clumps

Deceptive-NeRF: Enhancing NeRF Reconstruction using Pseudo-Observations from Diffusion Models

This paper introduces Deceptive-NeRF, a new method for enhancing the quality of reconstructed NeRF models using synthetically generated pseudo-observations, capable of handling sparse input and removing floater artifacts. Our proposed method involves three key steps: 1) reconstruct a coarse NeRF model from sparse inputs; 2) generate pseudo-observations based on the coarse model; 3) refine the NeRF model using pseudo-observations to produce a high-quality reconstruction. To generate photo-realistic pseudo-observations that faithfully preserve the identity of the reconstructed scene while remaining consistent with the sparse inputs, we develop a rectification latent diffusion model that generates images conditional on a coarse RGB image and depth map, which are derived from the coarse NeRF and latent text embedding from input images. Extensive experiments show that our method is effective and can generate perceptually high-quality NeRF even with very sparse inputs

Coronavirus-positive Nasopharyngeal Aspirate as Predictor for Severe Acute Respiratory Syndrome Mortality

Severe acute respiratory syndrome (SARS) has caused a major epidemic worldwide. A novel coronavirus is deemed to be the causative agent. Early diagnosis can be made with reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal aspirate samples. We compared symptoms of 156 SARS-positive and 62 SARS-negative patients in Hong Kong; SARS was confirmed by RT-PCR. The RT-PCR–positive patients had significantly more shortness of breath, a lower lymphocyte count, and a lower lactate dehydrogenase level; they were also more likely to have bilateral and multifocal chest radiograph involvement, to be admitted to intensive care, to need mechanical ventilation, and to have higher mortality rates. By multivariate analysis, positive RT-PCR on nasopharyngeal aspirate samples was an independent predictor of death within 30 days

Perception of urban park soundscape

A number of studies have been initiated to explore how to improve the soundscape quality in urban parks. However, good soundscape quality in parks cannot be provided without a thorough understanding of the complex relationships among sound, environment, and individuals. As acoustic comfort is considered to be an important outcome of soundscape quality, this study investigates the relative impacts of the factors influencing acoustic comfort evaluation by formulating a multivariate ordered logit model. This study also explores the inter-relationships among acoustic comfort evaluation, acceptability of the environment, and preference to stay in a park using a path model. A total of 595 valid responses were obtained from interview surveys administered in four parks in Hong Kong while objective sound measurements were carried out at the survey spots concurrently. The findings unveil that acoustic comfort evaluation, besides visual comfort evaluation of landscape, also plays an important role on users’ acceptability of the urban park environment. Compared with all the studied acoustic related factors, acoustic comfort evaluation serves as a better proxy for park users’ preference to stay in urban parks. Hearing the breeze will significantly increase the likelihood of individuals in giving high acoustic comfort evaluation. Conversely, hearing the sounds from heavy vehicles or sounds from bikes will significantly reduce the likelihood in giving a high acoustic evaluation.Department of Building Services EngineeringDepartment of Mechanical Engineerin

On the study of the effects of sea views, greenery views and personal characteristics on noise annoyance perception at homes

Noise annoyance has caused significant adverse impacts on human beings and numerous efforts have been spent on mitigating annoyance problems. Natural greenery has been shown to be able to moderate annoyance problems at home but this conclusion was drawn without properly controlling the potential confounding factors. Furthermore, few have explored the moderation effect of a sea view. Accordingly, this study formulated a multivariate model to examine the impacts of natural views as well as personal characteristics on annoyance perception. A housing estate was selected in Hong Kong as the survey site for which some of the residents were exposed to greenery views, sea views, or both from their homes. Eight hundred and sixty-one responses were collected via questionnaire surveys and analyzed using an ordered logit model. The results suggest that both a greenery view and a sea view can moderate annoyance responses. Several individual’s personal characteristics are found to affect individuals’ annoyance perception. The duration of time spent daily at home is shown to have an influence on the moderation impact exerted by a greenery view, while the age of an individual is shown to have an influence on noise moderation effect exerted by a sea view.Department of Building Services Engineerin

STATEMENT OF AUTHORSHIP

Except where reference is made in the text of the thesis, this thesis contains no material published elsewhere or extracted in whole or in part from a thesis presented by me for another degree or diploma. No other person's work has been used without due acknowledgement in the main text of the thesis. This thesis has not been submitted for the award of any other degree or diploma at any other tertiary institution

Mutual neighborhood based discriminant projection for face recognition

Linear Discriminant Analysis is optimal under the assumption that the covariance matrices of the conditional densities are normal and all identical. However, this doesn't hold for many real world applications, such as Facial Image Recognition, in which data are typically under-sampled and non-Gaussian. To address this deficiency the Non-Parametric Discriminant method has been developed, but it requires model selection to be carried out for selecting the free control parameters, making it not easy for use in practice. We proposed a method, Mutual Neighborhood based Discriminant Projection, to overcome this problem. MNDP identifies the samples that contribute most to the Baysesian errors and highlights them for optimization. It is more convenient for use than NDA and avoids the singularity problem of LDA. On facial image datasets MNDP is shown to outperform Eigenfaces and Fisherfaces under various experimental conditions