Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local feature to obtain its weight score, which ignores interactions among local features. To address this, we propose an effective interaction-aware self-attention model inspired by PCA to learn attention maps. Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps. Moreover, our spatial pyramid attention is unrestricted to the number of its input feature maps so it is easily extended to a spatio-temporal version. Finally, our model is embedded in general CNNs to form end-to-end attention networks for action classification. Experimental results show that our method achieves the state-of-the-art results on the UCF101, HMDB51 and untrimmed Charades.Comment: Accepted by ECCV201

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202

CRISPR-Cas13 in malaria parasite: Diagnosis and prospective gene function identification

Malaria caused by Plasmodium is still a serious public health problem. Genomic editing is essential to understand parasite biology, elucidate mechanical pathways, uncover gene functions, identify novel therapeutic targets, and develop clinical diagnostic tools. Recent advances have seen the development of genomic diagnostic technologies and the emergence of genetic manipulation toolbox comprising a host of several systems for editing the genome of Plasmodium at the DNA, RNA, and protein level. Genomic manipulation at the RNA level is critical as it allows for the functional characterization of several transcripts. Of notice, some developed artificial RNA genome editing tools hinge on the endogenous RNA interference system of Plasmodium. However, Plasmodium lacks a robust RNAi machinery, hampering the progress of these editing tools. CRISPR-Cas13, which belongs to the VI type of the CRISPR system, can specifically bind and cut RNA under the guidance of crRNA, with no or minimal permanent genetic scar on genes. This review summarizes CRISPR-Cas13 system from its discovery, classification, principle of action, and diagnostic platforms. Further, it discusses the application prospects of Cas13-based systems in Plasmodium and highlights its advantages and drawbacks

Noise Reduction of OCT Images Based on the External Patch Prior Guided Internal Clustering and Morphological Analysis

Optical coherence tomography (OCT) is widely used in biomedical imaging. However, noise severely affects diagnosing and identifying diseased tissues on OCT images. Here, a noise reduction method based on the external patch prior guided internal clustering and morphological analysis (E2PGICMA) is developed to remove the noise of OCT images. The external patch prior guided internal clustering algorithm is used to reduce speckle noise. The morphological analysis algorithm is employed to the background for contrast enhancement. OCT images of in vivo normal skin tissues were analyzed to remove noise using the proposed method. The estimated standard deviations of the noise were chosen as different values for evaluating the quantitative metrics. The visual quality improvement includes more textures and fine detail preservation. The denoising effects of different methods were compared. Then, quantitative and qualitative evaluations of this proposed method were conducted. The results demonstrated that the SNR, PSNR, and XCOR were higher than those of the other noise-reduction methods, reaching 15.05 dB, 27.48 dB, and 0.9959, respectively. Furthermore, the presented method’s noise reduction ratio (NRR) reached 0.8999. This proposed method can efficiently remove the background and speckle noise. Improving the proposed noise reduction method would outperform existing state-of-the-art OCT despeckling methods

Block-based color correction algorithm for multi-view video coding

The color variations among different viewpoints in multi-view video sequences may deteriorate the visual quality and coding efficiency. Various color correction methods have been proposed, however, the color appearance and histogram of corrected target frames are not similar enough to the reference frames in details. Focusing on restoring more similar color, a block-based color correction algorithm is proposed. The blocks in reference frames are matched into target frames through spatial prediction, and the colorization scheme is then adopted to expand color as a coarse correction. Finally the mixture with global color transfer result yields the fine correction. The experiment results show this novel method can provide better visual effect in detail and also provide the corrected frames with histograms more similar to reference histograms.Computer Science, Software EngineeringComputer Science, Theory & MethodsEngineering, Electrical & ElectronicCPCI-S(ISTP)

Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network

Anchor Link Prediction (ALP) across heterogeneous networks plays a pivotal role in inter-network applications. The difficulty of anchor link prediction in heterogeneous networks lies in how to consider the factors affecting nodes alignment comprehensively. In recent years, predicting anchor links based on network embedding has become the main trend. For heterogeneous networks, previous anchor link prediction methods first integrate various types of nodes associated with a user node to obtain a fusion embedding vector from global perspective, and then predict anchor links based on the similarity between fusion vectors corresponding with different user nodes. However, the fusion vector ignores effects of the local type information on user nodes alignment. To address the challenge, we propose a novel type-aware anchor link prediction across heterogeneous networks (TALP), which models the effect of type information and fusion information on user nodes alignment from local and global perspective simultaneously. TALP can solve the network embedding and type-aware alignment under a unified optimization framework based on a two-layer graph attention architecture. Through extensive experiments on real heterogeneous network datasets, we demonstrate that TALP significantly outperforms the state-of-the-art methods