Neural networks (NN) play a central role in modern Artificial intelligence (AI) technology and has been successfully used in areas such as natural language processing and image recognition. While majority of NN applications focus on prediction and classification, there are increasing interests in studying statistical inference of neural networks. The study of NN statistical inference can enhance our understanding of NN statistical proprieties. Moreover, it can facilitate the NN-based hypothesis testing that can be applied to hypothesis-driven clinical and biomedical research. In this paper, we propose a sieve quasi-likelihood ratio test based on NN with one hidden layer for testing complex associations. The test statistic has asymptotic chi-squared distribution, and therefore it is computationally efficient and easy for implementation in real data analysis. The validity of the asymptotic distribution is investigated via simulations. Finally, we demonstrate the use of the proposed test by performing a genetic association analysis of the sequencing data from Alzheimer's Disease Neuroimaging Initiative (ADNI).

A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean space. This kernel is built using the reservoir functional associated with a state-space representation of the Volterra series expansion available for any analytic fading memory filter. It is hence called the Volterra reservoir kernel. Even though the state-space representation and the corresponding reservoir feature map are defined on an infinite-dimensional tensor algebra space, the kernel map is characterized by explicit recursions that are readily computable for specific data sets when employed in estimation problems using the representer theorem. We showcase the performance of the Volterra reservoir kernel in a popular data science application in relation to bitcoin price prediction.

除了文献计量学之外，还有兴趣表征科学论文中思想数量的演变。调查此问题的一种常见方法是分析出版物的标题，以检测随着时间的推移词汇变化。以这样的概念，即短语或更具体的键形酶代表概念，将词汇多样性指标应用于标题的短语版本。因此，词汇多样性的变化被视为研究的指标，甚至可能扩展研究。因此，优化键形检测是该过程的重要方面。我们建议使用多个短语检测模型的目标，而不是仅一个，而是从源代码语料库中生产出更全面的钥匙串。这种方法的另一个潜在优势是，这些集合的联合和差异可能会提供自动化技术，以识别和省略非特异性短语。我们比较了几个短语检测模型的性能，分析每个短语集的输出，并使用四个通用的词汇多样性指标计算包含每个模型的键形的Corpora变体的词汇多样性。

本文介绍了一个测试台，以研究表现出群体智能的无人机（UAV）的分布式传感问题。几种智能城市应用程序，例如运输和灾难响应，需要通过一群智能和合作的无人机有效地收集传感器数据。事实证明，对于系统和严格研究而没有损害规模，现实主义和外部有效性，这通常被证明太复杂且昂贵。借助拟议的测试床，本文设置了一个垫脚石，以在小实验室空间内模仿，源自经验数据和仿真模型的大型感应区域。在此感应地图上，一群低成本的无人机可以飞行，从而可以研究各种问题，例如能源消耗，充电控制，导航和避免碰撞。分散的多代理集体学习算法（EPO）适用于无人机群智能以及对功耗测量的评估提供了概念验证，并验证了拟议的测试台的准确性。

在不确定，嘈杂或对抗性环境中学习是深度神经网络（DNN）的具有挑战性的任务。我们提出了一种在贝叶斯估计和变分推理时构建的强大学习的新理论上和有效的方法。我们制定通过DNN层层的密度传播的问题，并使用集合密度传播（ENDP）方案来解决它。ENPP方法允许我们在贝叶斯DNN的层上传播变分概率分布的片段，使得能够估计模型输出的预测分布的平均值和协方差。我们使用Mnist和CiFar-10数据集的实验表明，训练有素的模型的鲁棒性与随机噪声和对抗性攻击的稳健性显着改善。