The real-time quantification of the effect of a wireless channel on the
transmitting signal is crucial for the analysis and the intelligent design of
wireless communication systems for various services. Recent mechanisms to model
channel characteristics independent of coding, modulation, signal processing,
etc., using deep learning neural networks are promising solutions. However, the
current approaches are neither statistically accurate nor able to adapt to the
changing environment. In this paper, we propose a new approach that combines a
deep learning neural network with a mixture density network model to derive the
conditional probability density function (PDF) of receiving power given a
communication distance in general wireless communication systems. Furthermore,
a deep transfer learning scheme is designed and implemented to allow the
channel model to dynamically adapt to changes in communication environments.
Extensive experiments on Nakagami fading channel model and Log-normal shadowing
channel model with path loss and noise show that the new approach is more
statistically accurate, faster, and more robust than the previous deep
learning-based channel models.

在这篇论文中，我们提出了一种将深度学习神经网络与混合密度网络模型相结合的新方法，以得到普通无线通信系统中给定通信距离的接收功率的条件概率密度函数（PDF）。此外，我们还设计和实现了一种深度迁移学习方案，使得信道模型能够动态适应通信环境的变化。通过在 Nakagami 衰落信道模型和 Log-normal 阴影信道模型上进行广泛实验，结果表明新方法比以前基于深度学习的信道模型在统计准确性、速度和鲁棒性方面更好。

时变无线通信信道的衰落和阴影建模

Modeling of Time-varying Wireless Communication Channel with Fading and  Shadowing

Anomaly detection (AD) in images is a fundamental computer vision problem by
deep learning neural network to identify images deviating significantly from
normality. The deep features extracted from pretrained models have been proved
to be essential for AD based on multivariate Gaussian distribution analysis.
However, since models are usually pretrained on a large dataset for
classification tasks such as ImageNet, they might produce lots of redundant
features for AD, which increases computational cost and degrades the
performance. We aim to do the dimension reduction of Negated Principal
Component Analysis (NPCA) for these features. So we proposed some heuristic to
choose hyperparameter of NPCA algorithm for getting as fewer components of
features as possible while ensuring a good performance.

图像中的异常检测是一个基本的计算机视觉问题，我们提出了一种减少特征维度的算法，并利用预训练模型提取的深层特征进行异常检测，以提高性能和降低计算成本。

图像异常检测的启发式超参数选择

Heuristic Hyperparameter Choice for Image Anomaly Detection

To have a superior generalization, a deep learning neural network often
involves a large size of training sample. With increase of hidden layers in
order to increase learning ability, neural network has potential degradation in
accuracy. Both could seriously limit applicability of deep learning in some
domains particularly involving predictions of continuous variables with a small
size of samples. Inspired by residual convolutional neural network in computer
vision and recent findings of crucial shortcuts in the brains in neuroscience,
we propose an autoencoder-based residual deep network for robust prediction. In
a nested way, we leverage shortcut connections to implement residual mapping
with a balanced structure for efficient propagation of error signals. The novel
method is demonstrated by multiple datasets, imputation of high spatiotemporal
resolution non-randomness missing values of aerosol optical depth, and
spatiotemporal estimation of fine particulate matter <2.5 \mu m, achieving the
cutting edge of accuracy and efficiency. Our approach is also a general-purpose
regression learner to be applicable in diverse domains.

本文提出了一种基于自编码器的残差深度神经网络，通过利用快捷连接嵌套实现残差映射，实现了对连续变量的可靠预测，并在多个数据集上得到了较高的精度和效率。