科研成果 by Type: Conference Paper

2021
Li B, Li S, Wang C, Fan R, Shao J, Xie G. Distributed Circle Formation Control for Quadrotors Based on Multi-agent Deep Reinforcement Learning, in 2021 China Automation Congress (CAC). IEEE; 2021:4750–4755. 访问链接
Tao Y, Zhang Z. DNC-Aided SCL-Flip Decoding of Polar Codes, in 2021 IEEE Global Communications Conference (GLOBECOM). IEEE; 2021:01–06. 访问链接
Yan P, Schroeder R. Drifting away from the mainstream: Media attention and the politics of hyperpartisan news websites, in . The 71st Annual International Communication Association (ICA) Conference.; 2021.
Chen J, Wu X, Qu T. Early Reflections Based Speech Enhancement, in 2021 4th International Conference on Information Communication and Signal Processing (ICICSP). ShangHai, China; 2021:183-187.
Tambe T, Hooper C, Pentecost L, Jia T, Yang E-Y, Donato M, Sanh V, Whatmough P, Rush A, Brooks D, et al. EdgeBERT: sentence-level energy optimizations for latency-aware multi-task NLP inference, in International Symposium on Microarchitecture (MICRO).; 2021.
Wang F, Chen J, Chen F*. Effect of carrier bandwidth on understanding mandarin sentences in simulated electric-acoustic hearing, in 22th Annual Conference of the International Speech Communication Association (INTERSPEECH). Brno, Czechia; 2021.
Fu Z(PhD student), Wang B, F C, Wu X, Chen J *. Eye gaze estimation with HEOG and Neck EMG using deep neural networks, in 29th European Signal Processing Conference (EUSIPCO). Dublin, Ireland; 2021.
Wang C, Li S, Fan R, Sun J, Shao J, Xie G. Finite-time Circle Formation Control with Collision Avoidance, in 2021 China Automation Congress (CAC). IEEE; 2021:7104–7109. 访问链接
Yang E-Y, Jia T, Brooks D, Wei G-Y. FlexACC: A programmable accelerator with application-specific ISA for flexible deep neural network inference, in International Conference on Application-specific Systems, Architectures and Processors (ASAP).; 2021.
Li S, Wang C, Xie G. Formation control of multiple nonholonomic vehicles with local measurements in 3D space, in 2021 60th IEEE Conference on Decision and Control (CDC). IEEE; 2021:7112–7117. 访问链接
Zhao H, Shi Y, Tong X, Wen J, Ying X, Zha H. G-FAN: graph-based feature aggregation network for video face recognition, in International Conference on Pattern Recognition. IEEE; 2021:1672–1678.
Yan P, Schroeder R. Globalization and anti-globalization, media trust, and populism: A comparative study of the US and Germany., in The 71st Annual International Communication Association (ICA) Conference.; 2021.
Zhang C, Gao X, Ma L, Wang Y, Wang J, Tang W. GRASP: generic framework for health status representation learning based on incorporating knowledge from similar patients, in Proceedings of the AAAI conference on artificial intelligence.Vol 35.; 2021:715–723.
Xu J, Niu Y, Wu X, Qu T. Higher order ambisonics compression method based on independent component analysis, in Audio Engineering Society Convention 150.; 2021:10456.
Zheng Y+, Zheng L+, Yu Z*, Shi B, Tian YH, Huang T. High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).; 2021:6358-6367. PDFAbstract
Fovea, located in the centre of the retina, is specialized for high-acuity vision. Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40,000 Hz, and outputs asynchronous binary spike streams. Although the temporal resolution of visual information is improved, how to reconstruct the scenes is still a challenging problem. In this paper, we present a novel high-speed image reconstruction model through the short-term plasticity (STP) mechanism of the brain. We derive the relationship between postsynaptic potential regulated by STP and the firing frequency of each pixel. By setting up the STP model at each pixel of the spiking camera, we can infer the scene radiance with the temporal regularity of the spike stream. Moreover, we show that STP can be used to distinguish the static and motion areas and further enhance the reconstruction results. The experimental results show that our methods achieve state-of-the-art performance in both image quality and computing time.
Tao Y, Zhang Z. Hima: A fast and scalable history-based memory access engine for differentiable neural computer, in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).; 2021:845–856. 访问链接
Liu Z, Li* S. Impact of Gain-Loss Message Framing on Bedtime Procrastination of College Students: From the Perspective of the Powerful Effect Theory, in 2021 ASIS&T Conference. Salt Lake, USA; 2021.
Liu X, Gui L, Tang F, Jin Y, Chen K, Lang L. Impact of Zooplankton on Underwater Wireless Optical Channel Transmission, in IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT). Nanjing, China: IEEE; 2021. 访问链接Abstract
Underwater wireless optical communication has attracted widespread attention due to its advantages of high bandwidth and low delay. Seawater environment contains different substances, which will affect the received intensity and time delay of communication. This paper proposes an adaptive Monte-Carlo method to analyze the impact of zooplankton on the received intensity, receiving time, spatial distribution of energy, transmission distance and misalignment. According to the simulation results, when seawater contains more zooplankton, the received intensity is weaker, the receiving time is longer, and the energy is more dispersed.
Yang J, Shi Y, Tong X, Wang R, Chen T, Ying X. Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation, in Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021. Association for Computational Linguistics; 2021:508–517. 访问链接
Fang W, Yu Z*, Chen Y, Masquelier T, Huang T, Tian YH*. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).; 2021:2661-2671. PDFAbstract
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.

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