A "wireless fingerprint" which exploits hardware imperfections unique to each
device is a potentially powerful tool for wireless security. Such a fingerprint
should be able to distinguish between devices sending the same message, and
should be robust against standard spoofing techniques. Since the information in
wireless signals resides in complex baseband, in this paper, we explore the use
of neural networks with complex-valued weights to learn fingerprints using
supervised learning. We demonstrate that, while there are potential benefits to
using sections of the signal beyond just the preamble to learn fingerprints,
the network cheats when it can, using information such as transmitter ID (which
can be easily spoofed) to artificially inflate performance. We also show that
noise augmentation by inserting additional white Gaussian noise can lead to
significant performance gains, which indicates that this counter-intuitive
strategy helps in learning more robust fingerprints. We provide results for two
different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness
of the proposed method.

本文提出使用神经网络和复合值权重学习指纹技术，用于无线安全，结果表明使用加入白高斯噪声的计数器直接策略会有显著的性能提升，同时指纹识别是一个可行的方案。

基于复数神经网络的鲁棒性无线指纹识别

Robust Wireless Fingerprinting via Complex-Valued Neural Networks

The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.

本文回顾了在心脏病学领域中使用深度学习技术的应用论文，重点讨论了在医学领域中使用深度学习的优势和局限性，并提出了一些在临床实践中最可行的方向。

心脏病学中的深度学习

Deep Learning in Cardiology

Ensuring transportation systems are efficient is a priority for modern
society. Technological advances have made it possible for transportation
systems to collect large volumes of varied data on an unprecedented scale. We
propose a traffic signal control system which takes advantage of this new, high
quality data, with minimal abstraction compared to other proposed systems. We
apply modern deep reinforcement learning methods to build a truly adaptive
traffic signal control agent in the traffic microsimulator SUMO. We propose a
new state space, the discrete traffic state encoding, which is information
dense. The discrete traffic state encoding is used as input to a deep
convolutional neural network, trained using Q-learning with experience replay.
Our agent was compared against a one hidden layer neural network traffic signal
control agent and reduces average cumulative delay by 82%, average queue length
by 66% and average travel time by 20%.

提出了一种交通信号控制系统，利用现代深度强化学习方法构建适应性交通信号控制器，通过对作为输入的交通密集信息进行 Q-learning 训练并使用经验回放，与一种单隐藏层神经网络通讯信号控制器相比，实现了平均累计延迟减少 82％，平均队列长度减少 66％，平均旅行时间减少 20％。