Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder

A Multi-Modal Attention-Based Approach for Points of Interest Detection on 3D Shapes

Identifying points of interest (POIs) on the surface of 3D shapes is a significant challenge in geometric processing research. The complex connection between POIs and their geometric descriptors, combined with the small percentage of POIs on the shape, makes detecting POIs on any given 3D shape a highly challenging task. Existing methods directly detect POIs from the entire 3D shape, resulting in low efficiency and accuracy. Therefore, we propose a novel multi-modal POI detection method using a coarse-to-fine approach, with the key idea of reducing data complexity and enabling more efficient and accurate subsequent POI detection by first identifying and processing important regions on the 3D shape. It first obtains important areas on the 3D shape through 2D projected images, then processes points within these regions using attention mechanisms. Extensive experiments demonstrate that our method outperforms existing POI detection techniques.

Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud

Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this article, we propose a novel method called "Meta-PU" to first support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model. In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.

PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.