The Living Predicaments of Chinese-Australians in Brian Castro’s Birds of Passage

This thesis studies the two protagonists in Birds of Passage: Lo Yunshan and Seamus O’Young, analyzing their living predicaments and the fate of being discriminated against in Australian. With Said’s Orientalism as its guiding theory, this thesis analyzes from two perspectives: individual and society. It reveals that the essence of living predicaments of Chinese-Australians is the imbalance of relationship between man and society, man and the self. Meanwhile, the loss of discourse power leads them to be discriminated against in the whites dominated society. The aim of this thesis is to enable readers to understand the living predicaments of Chinese-Australian in different times and inspire people to care about the living conditions of Chinese-Australian in modern times

Computational Fluid Dynamic Modeling Application as a Design Tool in Air Assisted Pesticide Sprayer Development

The complex dynamic behaviors of air assisted pesticides spraying, especially inter-droplets interactions as well as effects of prevailing surrounding fluid environment before and after the spray breakup makes development of an ideal sprayer unattainable. Moreover, plants’ canopy architectures are sophisticated mainly due to variations in features’ orientation amongst species. A prior insight of the sprayer’s performance behavior at design phase can significantly help in avoiding unanticipated future failures. This situation has recently, inevitably paved way for the application of numerical analysis such as Computational Fluid Dynamic (CFD) modeling as a robust design tool. Furthermore, movement of spray droplets from the generator to the targets involve fluid flows, heat transfer and mass flow which are the principle fields in CFD simulation of transport phenomena. As the droplets travel, the surrounding environment is likely to interfere with their physical and chemical properties. The concern to fully utilize the technology has nowadays not only drawn the attention of manufacturing industry but has also captured the interests of researchers. Previous applications of CFD modeling have demonstrated its potential to ease the challenges of cost and time consumption that would have been encountered in physical experimental trials tests. Nevertheless, developing a standard ideal model still remains unattainable. Most researchers have developed simple model mainly of Lagrangian approach whose applications have primarily been on open-fields spraying despite the situation still remaining far underway. This paper gives a state-of-art review of the application of CFD modeling in air atomized pesticide spraying with an aim of highlighting future research needs. Keywords: Computational Fluid Dynamic, Air assisted sprayers, Lagrangian approach, Spray droplet

Real-time Information, Uncertainty and Quantum Feedback Control

Feedback is the core concept in cybernetics and its effective use has made great success in but not limited to the fields of engineering, biology, and computer science. When feedback is used to quantum systems, two major types of feedback control protocols including coherent feedback control (CFC) and measurement-based feedback control (MFC) have been developed. In this paper, we compare the two types of quantum feedback control protocols by focusing on the real-time information used in the feedback loop and the capability in dealing with parameter uncertainty. An equivalent relationship is established between quantum CFC and non-selective quantum MFC in the form of operator-sum representation. Using several examples of quantum feedback control, we show that quantum MFC can theoretically achieve better performance than quantum CFC in stabilizing a quantum state and dealing with Hamiltonian parameter uncertainty. The results enrich understanding of the relative advantages between quantum MFC and quantum CFC, and can provide useful information in choosing suitable feedback protocols for quantum systems.Comment: 24 page

Efficient Public Key Searchable Encryption Schemes from Standard Hard Lattice Problems for Cloud Computing

Cloud storage and computing offers significant convenience and management efficiency in the information era. Privacy protection is a major challenge in cloud computing. Public key encryption with keyword search (PEKS) is an ingenious tool for ensuring privacy and functionality in certain scenario, such as ensuring privacy for data retrieval appearing in the cloud computing. Despite many attentions received, PEKS schemes still face several challenges in practical applications, such as low computational efficiency, high end-to-end delay, vulnerability to inside keyword guessing attacks(IKGA) and key management defects in the multi-user environment. In this work, we introduce three Ring-LWE/ISIS based PEKS schemes: (1) Our basic PEKS scheme achieves high level security in the standard model. (2) Our PAEKS scheme utilizes the sender\u27s private key to generate an authentication when encrypting, which can resist IKGA. (3) Our IB-PAEKS scheme not only can resist IKGA, but also significantly reduces the complexity of key management in practical applications. Experimental results indicate that the first scheme provides lower end-to-end delay and higher computational efficiency compared to similar ones, and that our last two schemes can provide more secure properties with little additional overhead

Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections

Sparse data are common. The traditional ``handcrafted'' features are often sparse. Embedding vectors from trained models can also be very sparse, for example, embeddings trained via the ``ReLu'' activation function. In this paper, we report our exploration of efficient search in sparse data with graph-based ANN algorithms (e.g., HNSW, or SONG which is the GPU version of HNSW), which are popular in industrial practice, e.g., search and ads (advertising). We experiment with the proprietary ads targeting application, as well as benchmark public datasets. For ads targeting, we train embeddings with the standard ``cosine two-tower'' model and we also develop the ``chi-square two-tower'' model. Both models produce (highly) sparse embeddings when they are integrated with the ``ReLu'' activation function. In EBR (embedding-based retrieval) applications, after we the embeddings are trained, the next crucial task is the approximate near neighbor (ANN) search for serving. While there are many ANN algorithms we can choose from, in this study, we focus on the graph-based ANN algorithm (e.g., HNSW-type). Sparse embeddings should help improve the efficiency of EBR. One benefit is the reduced memory cost for the embeddings. The other obvious benefit is the reduced computational time for evaluating similarities, because, for graph-based ANN algorithms such as HNSW, computing similarities is often the dominating cost. In addition to the effort on leveraging data sparsity for storage and computation, we also integrate ``sign cauchy random projections'' (SignCRP) to hash vectors to bits, to further reduce the memory cost and speed up the ANN search. In NIPS'13, SignCRP was proposed to hash the chi-square similarity, which is a well-adopted nonlinear kernel in NLP and computer vision. Therefore, the chi-square two-tower model, SignCRP, and HNSW are now tightly integrated