Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches

Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for MoCo v3) to achieve promising results, which is unacceptable for the general academic community and hinders the development of this topic. This work revisits the momentum-based contrastive learning frameworks and identifies the inefficiency in which two augmented views generate only one positive pair. We propose Fast-MoCo - a novel framework that utilizes combinatorial patches to construct multiple positive pairs from two augmented views, which provides abundant supervision signals that bring significant acceleration with neglectable extra computational cost. Fast-MoCo trained with 100 epochs achieves 73.5% linear evaluation accuracy, similar to MoCo v3 (ResNet-50 backbone) trained with 800 epochs. Extra training (200 epochs) further improves the result to 75.1%, which is on par with state-of-the-art methods. Experiments on several downstream tasks also confirm the effectiveness of Fast-MoCo.Comment: Accepted for publication at the 2022 European Conference on Computer Vision (ECCV 2022

Monetary policy, cash holding and corporate investment: evidence from China

This paper uses 13,766 firm-year observations between 2003 and 2013 from China to investigate the effects of monetary policy on corporate investment and the mitigating effects of cash holding. We find that tightening monetary policy reduces corporate investment while cash holdings mitigate such adverse effects. The cash mitigating role is especially significant for financially constrained firms, non-state-owned enterprises (non-SOEs) and those firms located in a less developed financial market. Cash holding also improves investment efficiency when monetary policy is tightening and tightening monetary policy enhances the ‘cash-cash flow’ sensitivity. Our empirical evidence calls for a critical evaluation on the monetary policies implemented in China which are less effective for state-owned enterprises. It also calls for a necessity for local government to further develop regional financial markets to protect vulnerable businesses, such as non-SOEs and financially constrained firms, from external shocks in order to maintain their sustainable growth and competitive advantages

Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness