Alternative Route to Strong Interaction: Narrow Feshbach Resonance

We show that a narrow resonance produces strong interaction effects far beyond its width on the side of the resonance where the bound state has not been formed. This is due to a resonance structure of its phase shift, which shifts the phase of a large number of scattering states by $\pi$ before the bound state emerges. As a result, the magnitude of the interaction energy when approaching the resonance on the "upper" and "lower" branch from different side of the resonance is highly asymmetric, unlike their counter part in wide resonances. Measurements of these effects are experimentally feasible.Comment: 4 pages, 5 figure

Do Free Users Make Seller Pay? The Asymmetry Network Effect of Free Users on Fee Sellers in Business-to-Business Electronic Platform

With the prosperity of worldwide e-commerce, platforms generally conduct premium model to convert customer assets. Extant research has investigated the network effect at e-commerce platform, but no study to date has analyzed whether the influx of free users can stimulate sellers to pay for the membership or backfire. In the context of B2B electronic market, we examine the dynamic network effect of free users in different participation quality on fee sellers with a VAR model. The results show that (1) the rise of registered regular sellers can incite more fee sellers to pay for the membership than that of regular buyers; (2) external users attracted by advertising (search advertising and social media advertising in this paper) can impact targeted internal user base. These findings emphasize more exploration should be pay attention to the engagement quality of user base in two-sided markets, and provide guidance related to advertising strategy

LIDAR data classification and compression

Airborne Laser Detection and Ranging (LIDAR) data has a wide range of applications in agriculture, archaeology, biology, geology, meteorology, military and transportation, etc. LIDAR data consumes hundreds of gigabytes in a typical day of acquisition, and the amount of data collected will continue to grow as sensors improve in resolution and functionality. LIDAR data classification and compression are therefore very important for managing, visualizing, analyzing and using this huge amount of data. Among the existing LIDAR data classification schemes, supervised learning has been used and can obtain up to 96% of accuracy. However some of the features used are not readily available, and the training data is also not always available in practice. In existing LIDAR data compression schemes, the compressed size can be 5%-23% of the original size, but still could be in the order of gigabyte, which is impractical for many applications. The objectives of this dissertation are (1) to develop LIDAR classification schemes that can classify airborne LIDAR data more accurately without some features or training data that existing work requires; (2) to explore lossy compression schemes that can compress LIDAR data at a much higher compression rate than is currently available. We first investigate two independent ways to classify LIDAR data depending on the availability of training data: when training data is available, we use supervised machine learning techniques such as support vector machine (SVM); when training data is not readily available, we develop an unsupervised classification method that can classify LIDAR data as good as supervised classification methods. Experimental results show that the accuracy of our classification results are over 99%. We then present two new lossy LIDAR data compression methods and compare their performance. The first one is a wavelet based compression scheme while the second one is geometry based. Our new geometry based compression is a geometry and statistics driven LIDAR point-cloud compression method which combines both application knowledge and scene content to enable fast transmission from the sensor platform while preserving the geometric properties of objects within a scene. The new algorithm is based on the idea of compression by classification. It utilizes the unique height function simplicity as well as the local spatial coherence and linearity of the aerial LIDAR data and can automatically compress the data to the desired level-of-details defined by the user. Either of the two developed classification methods can be used to automatically detect regions that are not locally linear such as vegetations or trees. In those regions, the local statistics descriptions, such as mean, variance, expectation, etc., are stored to efficiently represent the region and restore the geometry in the decompression phase. The new geometry-based compression schemes for building and ground data can compress efficiently and significantly reduce the file size, while retaining a good fit for the scalable "zoom in" requirements. Experimental results show that compared with existing LIDAR lossy compression work, our proposed approach achieves two orders of magnitude lower bit rate with the same quality, making it feasible for applications that were not practical before. The ability to store information into a database and query them efficiently becomes possible with the proposed highly efficient compression scheme.Includes bibliographical references (pages 106-116)

Economic returns of participation in the enclave and mainstream economy for Chinese and South Asian immigrants in Canada

Economic integration of immigrants has been studied from three theoretical perspectives: assimilation theory, social capital theory and immigrant enclave economy thesis. These theoretical perspectives differ on whether immigrants’ ethnic attachments are seen as advancing or limiting their economic interests. The enclave economy thesis suggests that immigrants benefit from enclave participation by making use of common ethnic language and cultural ties to advance their economic interests. Using individual data from the 2006 Census of Canada, this thesis investigates whether Chinese and South Asian immigrants who participate in the enclave economy have better or worse returns compared to their counterparts in the mainstream economy. There are several major general findings. First, Chinese and South Asian immigrants who immigrated to Canada at an older age, those with less human capital, and those who lived in large metropolitan centres are more likely to participate in the enclave economy. Second, the returns for Chinese and South Asian immigrants in the enclave are lower than the returns of their counterparts in the mainstream economy, but the relative enclave earnings disadvantage is smaller for self-employed than for wage workers. Third, the returns to human capital for Chinese and South Asian in the enclave tend to be lower. Fourth, when the interaction terms measuring unequal human capital returns are further controlled, there is a positive effect associated with enclave participation. Such an effect indicates unmeasured positive influences associated with enclave participation after variations in other factors and unequal returns to human capital have been controlled. The positive effect may be understood as results of ethnic solidarity and cultural attachment. At the same time, the study suggests that the enclave economy provides an alternative opportunity to some immigrants, but such an opportunity is not as good as the opportunity in the mainstream economy.

Factors Influencing the Enthusiasm to Disclose Environment Accounting Information -Econometric Analysis from SSE Data-

Protection of the environment is one of the key issues for sustainable development in China, and it must be implemented by individual companies. It is an important field of study for economists to describe and analyze the enthusiasm of individual companies to protect environment. We chose typical companies listed on the Shanghai Stock Exchange (SSE) to describe and analyze their enthusiasm for environmental protection using the Probit model, and came to the conclusion that three factors influence the disclosure of the environmental accounting information (EAI), i.e., whether the company accepting the restriction of ISO14001 standards, the rate of increase in the primary business and the proportion of national capital. This is the first research about the environmental accounting information disclosure (EAID) of Chinese companies by the method of econometrics, and it is therefore significant in terms of both theory and practice