On Transformation Based Circular Density Estimators

Estimation of the probability density function for circular data is an important topic in statistical inference. In this thesis, I would like to introduce two transformation based methods for estimating probability density function in this context. One is derived from traditional kernel density estimator and the other one comes from the Bernstein polynomial estimator (Chaubey, 2017). We know both of the kernel density estimator (Silverman, 1986) and Bernstein polynomial estimator (Babu, Canty and Chaubey, 2002) are appropriate for the case of linear data, transformation of circular data to linear data would bring extreme simplicity to estimation of probability density function in the case of circular data by back transformation. I will conduct a simulation study to compare these methods with respect to their global and local errors. We find through our simulation study that transformed kernel density estimator has a stronger ability to alleviate the boundary problems than transformed Bernstein polynomial estimator, however, their overall performance is pretty much similar in the central part of the distribution. Therefore, in general we can say transformed kernel density estimator leads to a better method as compared to the transformed Bernstein polynomial estimator, however further research may be needed to study other transformations

Research on Tailoring Technology of Array CCD Aerial Camera Linux System

In view of the actual demand of operating system of the air plane array CCD camera, and combining with the hardware resources of the PC104 bus structure, Linux system adopted in CCD camera is cut practically, which based on the tailoring method adopting the combination of coarse-grained and fine-grained to enhance the Linux kernel preemption, improve the scheduling strategy of Linux kernel scheduler, to build a embedded system with the strong implementation capacity. The system startup and task of the response performance test in different environment shows that the cut systems is stable, reliable, and can achieve the startup time less than 5s, the performance of the task response time less than 20 millisecond

Retraction: the “other face” of research collaboration?

The last two decades have witnessed the rising prevalence of both co-publishing and retraction. Focusing on research collaboration, this paper utilizes a unique dataset to investigate factors contributing to retraction probability and elapsed time between publication and retraction. Data analysis reveals that the majority of retracted papers are multi-authored and that repeat offenders are collaboration prone. Yet, all things being equal, collaboration, in and of itself, does not increase the likelihood of producing flawed or fraudulent research, at least in the form of retraction. That holds for all retractions and also retractions due to falsification, fabrication, and plagiarism (FFP). The research also finds that publications with authors from elite universities are less likely to be retracted, which is particularly true for retractions due to FFP. China stands out with the fastest retracting speed compared to other countries. Possible explanations, limitations, and policy implications are also discussed

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs. Our code is provided at https://github.com/draym28/LSGNN.Comment: The first two authors contributed equally to this work; IJCAI2

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility. However, the integration of UAM into existing transportation systems is a complex task that requires a thorough understanding of its impact on traffic flow and capacity. In this paper, we conduct a survey to investigate the current state of research on UAM in metropolitan-scale traffic using simulation techniques. We identify key challenges and opportunities for the integration of UAM into urban transportation systems, including impacts on existing traffic patterns and congestion; safety analysis and risk assessment; potential economic and environmental benefits; and the development of shared infrastructure and routes for UAM and ground-based transportation. We also discuss the potential benefits of UAM, such as reduced travel times and improved accessibility for underserved areas. Our survey provides a comprehensive overview of the current state of research on UAM in metropolitan-scale traffic using simulation and highlights key areas for future research and development