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From the perspective of the spatial domain, Graph Convolutional Network (GCN) is essentially a process of iteratively aggregating neighbor nodes. However, the existing GCNs using simple average or sum aggregation may neglect the characteristics of each node and the topology between nodes, resulting in a large amount of early-stage information lost during the graph convolution step. To tackle the above challenge, we innovatively propose an adaptive attention graph capsule network, named AA-GCN, for graph classification.

From the perspective of the spatial domain, Graph Convolutional Network (GCN) is essentially a process of iteratively aggregating neighbor nodes. However, the existing GCNs using simple average or sum aggregation may neglect the characteristics of each node and the topology between nodes, resulting in a large amount of early-stage information lost during the graph convolution step. To tackle the above challenge, we innovatively propose an adaptive attention graph capsule network, named AA-GCN, for graph classification.

Waveform optimization has recently been shown to be a key technique to boost the efficiency and range of far-field wireless power transfer (WPT). Current research has optimized transmit waveform adaptive to channel state information (CSI) and accounting for energy harvester (EH)’s non-linearity but under the assumption of linear high power amplifiers (HPA) at the transmitter. This paper proposes a channel-adaptive waveform design strategy that optimizes the transmitter's input waveform considering both HPA and EH non-linearities.

Automatic speech recognition (ASR) has suffered great performance degradation when facing low-resource languages with limited training data. In this work, we propose a series of training strategies to explore more effective data utilization for low-resource speech recognition. In low-resource scenarios, multilingual pretraining is of great help for the above purpose. We exploit relationships among different languages for better pretraining.

Automatic Speech Recognition (ASR) systems are known to exhibit difficulties when transcribing children's speech. This can mainly be attributed to the absence of large children’s speech corpora to train robust ASR models and the resulting domain mismatch when decoding children’s speech with systems trained on adult data. In this paper, we propose multiple enhancements to alleviate these issues. First, we propose a data augmentation technique based on the source-filter model of speech to close the domain gap between adult and children's speech.

Punctuation prediction is essential for automatic speech recognition (ASR). Although many works have been proposed for punctuation prediction, the on-device scenarios are rarely discussed with an end-to-end ASR. The punctuation prediction task is often treated as a post-processing of ASR outputs, but the mismatch between natural language in training input and ASR hypotheses in testing is ignored. Besides, language models built with deep neural networks are too large for edge devices.