Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME) 2 , and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods. INDEX TERMS Micro-expression recognition, convolutional neural networks, apex frame location, spatiotemporal information.

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Speech is the most effective way for people to exchange complex information. Recognition of emotional information contained in speech is one of the important challenges in the field of artificial intelligence. To better acquire emotional features in speech signals, a parallelized convolutional recurrent neural network (PCRN) with spectral features is proposed for speech emotion recognition. First, frame-level features are extracted from each utterance and, a long short-term memory is employed to learn these features frame by frame. At the same time, the deltas and delta-deltas of the log Mel-spectrogram are calculated and reconstructed into three channels (static, delta, and delta-delta); these 3-D features are learned by a convolutional neural network (CNN). Then, the two learned high-level features are fused and batch normalized. Finally, a SoftMax classifier is used to classify emotions. Our PCRN model simultaneously processes two different types of features in parallel to better learn the subtle changes in emotion. The experimental results on four public datasets show the superiority of our proposed method, which is better than the previous works.INDEX TERMS Speech emotion recognition, parallelized convolutional recurrent neural network, convolutional neural network, long short-term memory.

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of multi-omics data generated at unprecedented scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. Resources with significant updates in the past year include BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Science Wikis (a catalog of biological knowledge wikis for community annotations) and IC4R (Information Commons for Rice). Newly released resources include EWAS Atlas (a knowledgebase of epigenome-wide association studies), iDog (an integrated omics data resource for dog) and RNA editing resources (for editome-disease associations and plant RNA editosome, respectively). To promote biodiversity and health big data sharing around the world, the Open Biodiversity and Health Big Data (BHBD) initiative is introduced. All of these resources are publicly accessible at http://bigd.big.ac.cn.