Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To address this issue, we propose a simple but effective prior - Fourier prior to improve the generalization ability of an image rain removal model. The Fourier prior is a kind of property of rainy images. It is based on a key observation of us - replacing the Fourier amplitude of rainy images with that of clean images greatly suppresses the synthetic and real-world rain streaks. This means the amplitude contains most of the rain streak information and the phase keeps the similar structures of the background. So it is natural for single image rain removal to process the amplitude and phase information of the rainy images separately. In this paper, we develop a two-stage model where the first stage restores the amplitude of rainy images to clean rain streaks, and the second stage restores the phase information to refine fine-grained background structures. Extensive experiments on synthetic rainy data demonstrate the power of Fourier prior. Moreover, when trained on synthetic data, a robust generalization ability to real-world images can also be obtained. The code will be publicly available at https://github.com/willinglucky/ExploringFourier-Prior-for-Single-Image-Rain-Removal.

Antigen peptides that are presented by a major histocompatibility complex (MHC) and recognized by a T cell receptor (TCR) have an essential role in immunotherapy. Although substantial progress has been made in predicting MHC presentation, accurately predicting the binding interactions between antigen peptides, MHCs and TCRs remains a major computational challenge. In this paper, we propose a unified deep framework (called UniPMT) for peptide, MHC and TCR binding prediction to predict the binding between the peptide and the CDR3 of TCR β in general, presented by class I MHCs. UniPMT is comprehensively validated by a series of experiments and achieved state-of-the-art performance in the peptide–MHC–TCR, peptide–MHC and peptide–TCR binding prediction tasks with up to 15% improvements in area under the precision–recall curve taking the peptide–MHC–TCR binding prediction task as an example. In practical applications, UniPMT shows strong predictive power, correlates well with T cell clonal expansion and outperforms existing methods in neoantigen-specific binding prediction with up to 17.62% improvements in area under the precision–recall curve on experimentally validated datasets. Moreover, UniPMT provides interpretable insights into the identification of key binding sites and the quantification of peptide–MHC–TCR binding probabilities. In summary, UniPMT shows great potential to serve as a useful tool for antigen peptide discovery, disease immunotherapy and neoantigen vaccine design.

Hao Wang, Zhen Huang, Yong Dou, Yu Hong. Proceedings of the 28th International Conference on Computational Linguistics. 2020.

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document and mainly includes Emotion Cause Extraction (ECE) and Emotion Cause Pair Extraction (ECPE). Traditional ECE aims to extract the cause based on a given emotion while ECPE aims to extract both the emotion and its corresponding cause. Recently, ECEF has attracted a lot of attention and most of the advances have benefited from significant developments in deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval. The large pre-trained language model of BERT has also shown effectiveness in this field. Following the proposal of ECPE, the development of ECEF has accelerated. However, a comprehensive review of existing approaches and recent trends in the field is lacking. To address this issue, this survey presents a thorough review to summarise existing methods and recent key advances, illustrate the general technical architecture of traditional ECE, introduce several important variants, in particular ECPE, and provide a detailed comparison of several public datasets. Finally, the limitations of existing work and the prospects for further technological advances in ECEF are discussed.