Ruihan Bao

TL;DR: Li et al. as discussed by the authors proposed to incorporate the fine-grained events in stock movement prediction by using a professional finance event dictionary built by domain experts and use it to extract fine-general events automatically from finance news.

TL;DR: This work proposes a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement and shows that the category based method performs best among three grouping methods and outperforms all the baselines.

TL;DR: A VLSI-hardware-friendly action recognition algorithm using spatio-temporal motion-field patches using so-called prototype patches to recognize query actions by comparing local features in the query videos with those prototypes.

TL;DR: This work proposes a graph-based approach that can incorporate multi-view information, i.e., long-term stock trend, short-term fluctuation and sudden events information jointly into a temporal heterogeneous graph and is equipped with deep canonical analysis to highlight the correlations between different perspectives of fluctuation for better prediction.