Change Point Modeling of Covid-19 Data in the United States

To simultaneously model the change point and the possibly nonlinear relationship in the Covid-19 data of the US, a continuous second-order free knot spline model was proposed. Using the least squares method, the change point of the daily new cases against the total confirmed cases up to the previous day was estimated to be 04 April 2020. Before the point, the daily new cases were proportional to the total cases with a ratio of 0.287, suggesting that each patient had 28.7% chance to infect another person every day. After the point, however, such ratio was no longer maintained and the daily new cases were decreasing slowly. At the individual state level, it was found that most states had change points. Before its change point for each state, the daily new cases were still proportional to the total cases. And all the ratios were about the same except for New York State in which the ratio was much higher (probably due to its high population density and heavy usage of public transportation). But after the points, different states had different patterns. One interesting observation was that the change point of one state was about 3 weeks lagged behind the state declaration of emergency. This might suggest that there was a lag period, which could help identify possible causes for the second wave. In the end, consistency and asymptotic normality of the estimates were briefly discussed where the criterion functions are continuous but not differentiable (irregular)

Person ReID in Different Environment Settings Using Deep Learning Methods

University of Technology Sydney. Faculty of Engineering and Information Technology.Person Re-identification (Person ReID) is an essential research area in vision-based human image retrieval. It is a technology where the system can automatically identify the same person appearing in different camera views. Most existing works in this area focus on settings where the environment is either kept the same or has tiny fluctuation. However, it is well-known that no matter how small, the degree of environment changes may affect the robustness of a ReID algorithm significantly. Many real-world applications are required to detect the same person at a drastically different place and time, making large environment changes an unavoidable yet under-addressed problem. Hence, we want to address the problem where environment settings are different, such as illuminations, resolutions, modalities and clothing. Specifically, this thesis proposes a series of methods for environment change person ReID, summarized as follows: We proposed a Two-Stream Model which can solve the illumination adaptive person ReID problem. It can separate ReID features from lighting features to enhance ReID performance. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and is particularly potent in handling images under extremely low light. We proposed a Teacher-Student GAN model to solve the cross-modality person ReID problem. It adopts different domains and guides the ReID backbone. Unlike other GAN-based models, the proposed model only needs the backbone module at the test stage, making it more efficient and resource-saving. To showcase our model's capability, we did extensive experiments on the newly-released SYSU-MM01 and RegDB ReID benchmark and achieved superior performance to the state-of-the-art methods. We propose a novel two-stream network that can solve the cross-resolution person ReID problem. It contains a lightweight resolution association ReID feature transformation (RAFT) module and a self-weighted attention (SWA) ReID module to evaluate features under different resolutions. Comprehensive experiments on five benchmarks show the validity of our method. We design a novel unsupervised model, Syn-Person-Cluster ReID, to solve the unlabeled clothing change person ReID problem. We develop a purely unsupervised pipeline equipped with synthetic augmentation on person images and feature restriction for the same person. Extensive experiments on clothing change ReID datasets show the out-performance of our methods

Identify a Specified Fish species by the Co-occurrence and Confusion Matrix

Nowadays, invasive species threaten native species has become a global problem. Invasive species might be carrying pathogenic microorganisms, reduce biological species and even threat to human health. Therefore, in this study, we proposed a method of co-occurrence matrix to texture analysis of three species of fish. We catch the body pattern, and make a judgment based on confusion matrix. Simulation results show that three species of fish can be classified from each other reasonable.The 3rd International Conference on Industrial Application Engineering 2015, March 28-31, 2015, Kitakyushu International Conference Center, Kitakyushu, Japa

Research on the Innovation of Business Ecosystem Model in China’s 0nline Food Reservation Market at Sharing Economic Era

At the sharing economy era, the online food reservation market has experienced great changes, such as the mobilization of ordering，cooperation of logistics , diversification of revenue stream. The ordering patterns has also changed from network order to improve user experience. At present, online food reservation market has difficulties inquickly dealing with the impacts and challenges bought by external environment due to lack of coordination and sharing mechanisms and competition over cooperation among economic individuals.Based on the theory of business ecosystem, this paper focuses on the impacts and challenges brought by the sharing economic era and takes “Huijiachifan” as a case study and proposes new framework of business ecosystem model in China\u27s online food reservation market

Study on Image Segmentation in CT Metal Artifacts

Computed Tomography (CT) is one of the most important means of medical diagnosis and the quality of CT image can be seriously affected by metal artifacts. How to use CT image segmentation to extract the focused region is a classical difficult problem in this research field. According to the principle of CT reconstruction, after the medical image segmentation, projection of the metal part by compensation can improve the image quality. This paper first introduces the causes of the metal artifacts as well as the principle of CT image reconstruction. Then，it mainly discusses the simple and iterative threshold segmentation to solve metal artifacts. Corresponding comparison shows that the proposed method in this study has better segmentation effect based on the experimental results. Finally, the prospect of medical image segmentation is predicted to indicate future research work.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa

When Online Auction Meets Virtual Reality: An Empirical Investigation

The online auction is becoming increasingly popular in e-commerce, which allows to sell a product to the buyer with the highest bid. However, the lack of authentic product details for a thorough evaluation still poses challenges to its success. Recently, virtual reality (VR) is introduced to online auctions. We employ a unique dataset to investigate the effects of VR on auction outcomes and bidding activities. Results show that VR enhances buyers’ bidding competition, which in turn increases auction success and price, resulting in a competitive effect. Additionally, we find VR boosts buyers’ strategic responses to the bidding war, leading to a late-bidding effect. Findings contribute to both the theory and practice of VR and online auctions in selling houses