57 research outputs found

    PDO-eS2\text{S}^\text{2}CNNs: Partial Differential Operator Based Equivariant Spherical CNNs

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    Spherical signals exist in many applications, e.g., planetary data, LiDAR scans and digitalization of 3D objects, calling for models that can process spherical data effectively. It does not perform well when simply projecting spherical data into the 2D plane and then using planar convolution neural networks (CNNs), because of the distortion from projection and ineffective translation equivariance. Actually, good principles of designing spherical CNNs are avoiding distortions and converting the shift equivariance property in planar CNNs to rotation equivariance in the spherical domain. In this work, we use partial differential operators (PDOs) to design a spherical equivariant CNN, PDO-eS2\text{S}^\text{2}CNN, which is exactly rotation equivariant in the continuous domain. We then discretize PDO-eS2\text{S}^\text{2}CNNs, and analyze the equivariance error resulted from discretization. This is the first time that the equivariance error is theoretically analyzed in the spherical domain. In experiments, PDO-eS2\text{S}^\text{2}CNNs show greater parameter efficiency and outperform other spherical CNNs significantly on several tasks.Comment: Accepted by AAAI202

    Accurate, Fast and Controllable Image and Point Cloud Registration

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    Registration is the process of establishing spatial correspondences between two objects. Many downstream tasks, e.g, in image analysis, shape animation, can make use of these spatial correspondences. A variety of registration approaches have been developed over the last decades, but only recently registration approaches have been developed that make use of and can easily process the large data samples of the big data era. On the one hand, traditional optimization-based approaches are too slow and cannot take advantage of very large data sets. On the other hand, registration users expect more controllable and accurate solutions since most downstream tasks, e.g., facial animation and 3D reconstruction, increasingly rely on highly precise spatial correspondences. In recent years, deep network registration approaches have become popular as learning-based approaches are fast and can benefit from large-scale data during network training. However, how to make such deep-learning-based approached accurate and controllable is still a challenging problem that is far from being completely solved. This thesis explores fast, accurate and controllable solutions for image and point cloud registration. Specifically, for image registration, we first improve the accuracy of deep-learning-based approaches by introducing a general framework that consists of affine and non-parametric registration for both global and local deformation. We then design a more controllable image registration approach that image regions could be regularized differently according to their local attributes. For point cloud registration, existing works either are limited to small-scale problems, hardly handle complicated transformations or are slow to solve. We thus develop fast, accurate and controllable solutions for large-scale real-world registration problems via integrating optimal transport with deep geometric learning.Doctor of Philosoph

    Water entry of slender segmented projectile connected by spring

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    An object that enters the water experiences a large impact acceleration at the initial stage of water entry, which can cause structural damage to objects that are dropped or launched into the water. To reduce the peak impact acceleration, a spring-connected segmented projectile with compressible nose was designed. Through inertial measurement unit and high-speed camera, the influence of the nose compressibility on the initial impact acceleration was qualitatively investigated. The experimental results demonstrate that the introduction of a spring between the nose and the main body of the projectile can significantly suppresses the peak acceleration during the early stage of impact (0–50 ms). Furthermore, the maximum impact acceleration experienced by the main body is only related to the maximum compression of the nose without considering the spring stiffness. In addition, using the spring exerts a slight effect on the non-dimensional pinch-off times of the cavity but increases the initial velocity required for the occurrence of cavity pinch-off events on the side of the main bod

    Development of dynamical network biomarkers for regulation in Epstein-Barr virus positive peripheral T cell lymphoma unspecified type

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    Background: This study was performed to identify key regulatory network biomarkers including transcription factors (TFs), miRNAs and lncRNAs that may affect the oncogenesis of EBV positive PTCL-U.Methods: GSE34143 dataset was downloaded and analyzed to identify differentially expressed genes (DEGs) between EBV positive PTCL-U and normal samples. Gene ontology and pathway enrichment analyses were performed to illustrate the potential function of the DEGs. Then, key regulators including TFs, miRNAs and lncRNAs involved in EBV positive PTCL-U were identified by constructing TF–mRNA, lncRNA–miRNA–mRNA, and EBV encoded miRNA–mRNA regulatory networks.Results: A total of 96 DEGs were identified between EBV positive PTCL-U and normal tissues, which were related to immune responses, B cell receptor signaling pathway, chemokine activity. Pathway analysis indicated that the DEGs were mainly enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. Based on the TF network, hub TFs were identified regulate the target DEGs. Afterwards, a ceRNA network was constructed, in which miR-181(a/b/c/d) and lncRNA LINC01744 were found. According to the EBV-related miRNA regulatory network, CXCL10 and CXCL11 were found to be regulated by EBV-miR-BART1-3p and EBV-miR-BHRF1-3, respectively. By integrating the three networks, some key regulators were found and may serve as potential network biomarkers in the regulation of EBV positive PTCL-U.Conclusion: The network-based approach of the present study identified potential biomarkers including transcription factors, miRNAs, lncRNAs and EBV-related miRNAs involved in EBV positive PTCL-U, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of EBV positive PTCL-U