Jun Gu

Huaiyuan Liu, Xianzhang Liu, Donghua Yang et al.

Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy. To address this problem, we propose a novel temporal dynamic graph neural network (TodyNet) that can extract hidden spatio-temporal dependencies without undefined graph structure. It enables information flow among isolated but implicit interdependent variables and captures the associations between different time slots by dynamic graph mechanism, which further improves the classification performance of the model. Meanwhile, the hierarchical representations of graphs cannot be learned due to the limitation of GNNs. Thus, we also design a temporal graph pooling layer to obtain a global graph-level representation for graph learning with learnable temporal parameters. The dynamic graph, graph information propagation, and temporal convolution are jointly learned in an end-to-end framework. The experiments on 26 UEA benchmark datasets illustrate that the proposed TodyNet outperforms existing deep learning-based methods in the MTSC tasks.

Jun Gu, Tzonelih Hwang

Recently, Tsai et al. (Laser Phys. Lett. 17, 075202, 2020) proposed a lightweight authenticated semi-quantum key distribution protocol for a quantum participant to share a secret key with a classical participant. However, this study points out that an attacker can use a modification attack to make both participants share a wrong key without being detected. To avoid this problem, an improvement is proposed here.

Jun Gu, Tzonelih Hwang

Recently, Li et al. (Int J Theor Phys: DOI: 10.1007/s10773-020-04588-w, 2020) proposed a multiparty quantum key agreement protocol via non-maximally entangled cluster states. They claimed that the proposed protocol can help all the involved participants have equal influence on the final shared key. However, this study points out a loophole that makes Li et al.'s protocol suffer from a collusion attack, i.e. several dishonest participants can conspire to manipulate the final shared key without being detected by others. To avoid this loophole, an improvement is proposed here.

Jun Gu, Tzonelih Hwang

Recently, a mutual semi-quantum key agreement protocol using Bell states is proposed by Yan et al. (Mod. Phys. Lett. A, 34, 1950294, 2019). The proposed protocol tries to help a quantum participant share a key with a classical participant who just has limited quantum capacities. Yan et al. claimed that both the participants have the same influence on the final shared key. However, this study points out that the classical participant can manipulate the final shared key by himself/herself without being detected. To solve this problem, an improved method is proposed here.

Jun Gu, Guangluan Xu, Yue Zhang et al.

Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods treat low-resolution (LR) inputs and features equally across channels, rarely notice the loss of information flow caused by the activation function and fail to leverage the representation ability of CNNs. In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images. Firstly, the channel attention mechanism is used to adaptively recalibrate the importance of each channel at the middle of the wider attention block (WAB). Secondly, we propose the Local Memory Connection (LMC) to enhance the information flow. Finally, the features within each WAB are fused to take advantage of the network's representation capability and further improve information and gradient flow. Analytic experiments on a public remote sensing data set (UC Merced) show that our WCAN achieves better accuracy and visual improvements against most state-of-the-art methods.