The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning technologies have enabled the automatic detection of earthquakes from waveform data. In particular, various state-of-the-art deep-learning methods have been applied to this endeavour. In this study, we proposed and tested a novel phase detection method employing deep learning, which is based on a standard convolutional neural network in a new framework. The novelty of the proposed method is its separate explicit learning strategy for global and local representations of waveforms, which enhances its robustness and flexibility. Prior to modelling the proposed method, we identified local representations of the waveform by the multiple clustering of waveforms, in which the data points were optimally partitioned. Based on this result, we considered a global representation and two local representations of the waveform. Subsequently, different phase detection models were trained for each global and local representation. For a new waveform, the overall phase probability was evaluated as a product of the phase probabilities of each model. This additional information on local representations makes the proposed method robust to noise, which is demonstrated by its application to the test data. Furthermore, an application to seismic swarm data demonstrated the robust performance of the proposed method compared with those of other deep learning methods. Finally, in an application to low-frequency earthquakes, we demonstrated the flexibility of the proposed method, which is readily adaptable for the detection of low-frequency earthquakes by retraining only a local model.

由于其二进制和事件驱动的性质，尖峰神经网络（SNNS）可以在具有超高速和超低能量消耗的神经晶体装置上运行。因此，预计SNNS将具有各种应用，包括作为在边缘设备上运行的生成模型，以创建高质量图像。在这项研究中，我们用SNN构建一个变形式自动统计器（VAE）以实现图像生成。 VAE以其生成模型的稳定性而闻名;最近，其质量先进。在香草VAE中，潜伏空间表示为正态分布，并在采样中需要浮点计算。但是，在SNN中不可能，因为所有功能必须是二进制时间序列数据。因此，我们用自回归SNN模型构建了潜在空间，并从其输出中随机选择样本来对潜在变量进行采样。这允许潜在的变量遵循Bernoulli进程并允许变分学习。因此，我们构建了完全尖峰变化的自动化器，其中所有模块都是用SNN构建的。据我们所知，我们是第一个仅使用SNN层构建VAE的人。我们尝试了多个数据集，并确认它可以与传统的ANN相比产生具有相同或更好质量的图像。代码可在https://github.com/kamata1729/fullspikingvae获得

声学和视觉感测可以在人操纵时支持容器重量和其内容量的非接触式估计。但是，Opaquent和透明度（包括容器和内容的透明度）以及材料，形状和尺寸的可变性都会使这个问题具有挑战性。在本文中，我们向基准方法提出了一个开放框架，用于估计容器的容量，以及其内容的类型，质量和量。该框架包括数据集，明确定义的任务和性能测量，基线和最先进的方法，以及对这些方法的深入比较分析。使用单独的音频或音频和视觉数据的组合使用具有音频的神经网络的深度学习，用于分类内容的类型和数量，无论是独立的还是共同。具有视觉数据的回归和几何方法是优选的，以确定容器的容量。结果表明，使用仅使用Audio作为输入模块的方法对内容类型和级别进行分类，可分别获得加权平均F1-得分高达81％和97％。估计仅具有视觉视觉的近似接近和填充质量的容器容量，具有视听，多级算法达到65％的加权平均容量和质量分数。