Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion Network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage: https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap with arXiv:2104.1015

Vibration characteristics of mistuned multistage bladed disks of the aero-engine compressor

In order to analyze the vibration characteristics of mistuned multistage bladed disks of an aero-engine compressor, a finite element reduction model of mistuned multistage bladed disks is established based on substructure modal synthesis method. The accuracy of the substructure model was verified by comparing calculation accuracy of the substructure model and the integral model. The influence of different modal truncation numbers on the calculation results are discussed. The vibration modes of each stage of the bladed disks are obtained, the forced response is analyzed from the perspective of strain energy. The result shows that modal truncation number, rotation softening effect, and speed have significant effects on the dynamic frequency calculation results of the multistage bladed disks. The typical mode shapes of the first 200 orders of multistage bladed disks are obtained. With the increase of mistuning standard deviation, the strain energy of multistage bladed disk system decreases gradually

Identification of a peripheral blood long non-coding RNA (Upperhand) as a potential diagnostic marker of coronary artery disease

Background: Long non-coding RNAs (lncRNAs) have been confirmed to be involved in the pathologi­cal processes of multiple diseases. However, the characteristic expression of lncRNAs in peripheral blood of coronary artery disease (CAD) patients and whether some of these lncRNAs can be used as diagnostic biomarkers for CAD requires further investigation. Methods: Six healthy and CAD individuals were selected for microarray analysis, and 5 differentially expressed lncRNAs were selected and confirmed in the second cohort consisting of 30 control individu­als and 30 CAD patients with different SYNTAX scores. Upperhand were verified in the third cohort consisting of 115 controls and 137 CAD patients. Results: Thirty one lncRNAs were differentially expressed between the two groups, among whom, 25 were upregulated in the CAD group and 6 were downregulated. Four of the selected five lncRNAs were significantly upregulated in the CAD group, and Upperhand had the largest area under the curve (AUC). The diagnostic value of Upperhand was tested further, and it remained having a high diagnostic value. Conclusions: The expression level of Upperhand in peripheral blood of CAD patients is significantly higher than in control individuals, and is correlated with severity of CAD. Upperhand is a potential diagnostic biomarker of CAD, and when combined with TCONS_00029157, diagnostic value slightly increased

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions.Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training.Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification method