Contrastive learning has shown impressive progress for the self-supervision based 3D point clouds feature learning. Based on the distance control in the feature space of positive and negative samples, it can obtain effective point cloud feature representations in a self-supervised manner. However, most existing contrast based point cloud learning methods only consider the feature similarity relationship between samples, (e.g., point cloud, voxel or image), which lack of explicit exploration on point cloud structure. Considering that structure is an important property of point clouds, for better feature learning, we propose a effective cross-modal contrast based method with image jigsaw (CrossCon-Jig) to better learn point cloud representations with both semantic and structural information. Specifically, our method includes intra-modal contrast of point cloud, cross-modal contrast between point cloud and rendered image, and point cloud guided image jigsaw. The intra-modal contrast and the contrast of cross-modal focus on the exploring of invariant and consistent feature representations, and image jigsaw guides the model to explore spatial structure information of point clouds. Extensive experimental tests on 3D object classification and 3D object part segmentation tasks have achieved excellent performance, demonstrating the effectiveness of the proposed method.

With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends.Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g., trading volume and position), which can potentially enhance these attention-based approaches.Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this article.Specifically, we propose a volume-aware positional attentionbased recurrent neural network (VPA-RNN) for this task.First, we propose a generic method of adding position awareness to the attention mechanism.Next, the trading volume is incorporated into the original attention distribution to form a revised distribution.To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.

With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends. Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g. trading volume and position), which can potentially enhance these attention-based approaches. Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this paper. Specifically, we propose a volume-aware positional attention-based recurrent neural network (VPA-RNN) for this task. First, we propose a generic method of adding position awareness to the attention mechanism. Next, the trading volume is incorporated into the original attention distribution to form a revised distribution. To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.