1,145 research outputs found

    Shanghai and Globalization through the Lens of Film Noir: Lou Ye’s 2000 Film, Suzhou River

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    In the 1990s, the film industry in China decentralized with the bankruptcy of the state-owned studio system. Privatized independent film companies took over where the government had left off and a more independent film culture emerged. Although obstacles such as political censorship, financial pressures, and Hollywood infiltration were still in the way for Chinese filmmakers, privatization of the film industry was under way. As a result of this process, new film productions of controversial subject matter came into being. In 1998 one of China’s first independent film production companies—Dream Factory—was founded. Dream Factory’s first production, in association with Berlin-based German producer Philippe Bober, was the Suzhou River, directed by its founder Lou Ye.1 The 2000 film, though winning prizes at international film festivals such as the Rotterdam Film Festival and the Paris Film Festival, has been banned by the Chinese government since its production

    Random-depth Quantum Amplitude Estimation

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    The quantum amplitude estimation is a critical task in quantum computing and the foundation of quantum numerical integration. The maximum likelihood amplitude estimation (MLAE) algorithm is a practical solution to the quantum amplitude estimation problem, which has a theoretically quadratic speedup over classical Monte Carlo method. Since MLAE requires no use of the quantum Fourier transformation (QFT), it will be more likely to be widely used in the near future than QFT based algorithms. However, we find that MLAE is not unbiased due to the so-called critical points, which is one of the major causes of its inaccuracy. We propose a random-depth quantum amplitude estimation (RQAE) to avoid critical points. We also do numerical experiments to show that our algorithm is approximately unbiased and outperforms MLAE and other quantum amplitude estimation algorithms.Comment: 11 pages, 7 figure

    Quantum Amplitude Estimation with Optimized Squared Error

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    We introduce a method to optimize the error behavior of quantum amplitude estimation by optimizing the initial state of the quantum phase estimation circuit. Such optimized quantum amplitude estimation (OQAE) algorithm can achieve a standard deviation (STD) ∼2.565/L\sim 2.565/L, which overwhelms existing algorithm with an STD about >4/L>4/L, where LL is the number of oracle calls.Comment: 6 pages, 2 fiure

    Designing And Implementing An Online WebGIS-Based Decision Support System

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    This paper focuses on providing a market analysis solution through designing and implementing an online decision-support system (DSS) for businesses decision makers in Tobacco industry in China. The procedure makes use of data, information and software from Web based Geographical Information Systems (GIS) to generate online analysis, mapping and visualisation systems. These procedures are integrated and synchronised with market analysis techniques and customer relationship management (CRM) systems. By integrating these two techniques, a webGIS-based tobacco market information system is presented to demonstrate the significance of WebGIS in market analysis field. Specifically, to meet the needs of market practitioners (retailer, distributor and industry authority) in understanding the current market and sales performance, the system is designed and mainly consisted of four functional components: Communication and administration, Current market analysis, CRM (Client Relationship Management) and Sales/customer analysis, and Operational issues. From the system design and system usage perspectives, the illustration on the system architecture and the process of marketing information transmission reveals the benefits raised from this E-commerce tool to both the system users and service provider in marketing analysis. Based on this, the fusion of technology enhancement and marketing strategy in business process are called for and discussed

    CSNE: Conditional Signed Network Embedding

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    Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. These theories, however, are often inaccurate or incomplete, which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the \emph{polarity} of nodes (degree to which their links are positive) as well as signed \emph{triangle counts} (a measure of the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations

    Use of in vivo-induced antigen technology (IVIAT) for the identification of Streptococcus suis serotype 2 in vivo-induced bacterial protein antigens

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    <p>Abstract</p> <p>Background</p> <p><it>Streptococcus suis </it>serotype 2 (SS2) is a zoonotic agent that causes death and disease in both humans and swine. A better understanding of SS2-host molecular interactions is crucial for understanding SS2 pathogenesis and immunology. Conventional genetic and biochemical approaches used to study SS2 virulence factors are unable to take into account the complex and dynamic environmental stimuli associated with the infection process. In this study, <it>in vivo</it>-induced antigen technology (IVIAT), an immunoscreening technique, was used to identify the immunogenic bacterial proteins that are induced or upregulated <it>in vivo </it>during SS2 infection.</p> <p>Results</p> <p>Convalescent-phase sera from pigs infected with SS2 were pooled, adsorbed against <it>in vitro </it>antigens, and used to screen SS2 genomic expression libraries. Upon analysis of the identified proteins, we were able to assign a putative function to 40 of the 48 proteins. These included proteins implicated in cell envelope structure, regulation, molecule synthesis, substance and energy metabolism, transport, translation, and those with unknown functions. The <it>in vivo</it>-induced changes in the expression of 10 of these 40 genes were measured using real-time reverse transcription (RT)-PCR, revealing that the expression of 6 of the 10 genes was upregulated in the <it>in vivo </it>condition. The strain distribution of these 10 genes was analyzed by PCR, and they were found in the most virulent SS2 strains. In addition, protein sequence alignments of the newly identified proteins demonstrate that three are putative virulence-associated proteins.</p> <p>Conclusion</p> <p>Collectively, our results suggest that these <it>in vivo</it>-induced or upregulated genes may contribute to SS2 disease development. We hypothesize that the identification of factors specifically induced or upregulated during SS2 infection will aid in our understanding of SS2 pathogenesis and may contribute to the control SS2 outbreaks. In addition, the proteins identified using IVIAT may be useful potential vaccine candidates or virulence markers.</p

    Small area market demand prediction in the automobile industry

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    The general aim of this research is to investigate approaches to: •improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and •enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: •What are the key challenges in meso-level SAMD prediction? •What variables affect SAMD prediction? •What techniques can be used to improve SAMD prediction? •What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques’ effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology
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