Broadband infrastructure owners do not always know how their customers are
connected in the local networks, which are structured as rooted trees. A recent
study is able to infer the topology of a local network using discrete time
series data from the leaves of the tree (customers). In this study we propose a
contrastive approach for learning a binary event encoder from continuous time
series data. As a preliminary result, we show that our approach has some
potential in learning a valuable encoder.

该研究旨在通过离散时间序列数据（顾客数据）推断本地网络的拓扑结构，同时提出了一种从连续时间序列数据学习二进制事件编码器的对比方法，并初步展示了该方法在学习有价值的编码器方面的潜力。

利用对比学习在根树中对连续时间序列进行二元事件编码

Encoding Binary Events from Continuous Time Series in Rooted Trees using  Contrastive Learning

Age progression/regression is a challenging task due to the complicated and
non-linear transformation in human aging process. Many researches have shown
that both global and local facial features are essential for face
representation, but previous GAN based methods mainly focused on the global
feature in age synthesis. To utilize both global and local facial information,
we propose a Global and Local Consistent Age Generative Adversarial Network
(GLCA-GAN). In our generator, a global network learns the whole facial
structure and simulates the aging trend of the whole face, while three crucial
facial patches are progressed or regressed by three local networks aiming at
imitating subtle changes of crucial facial subregions. To preserve most of the
details in age-attribute-irrelevant areas, our generator learns the residual
face. Moreover, we employ an identity preserving loss to better preserve the
identity information, as well as age preserving loss to enhance the accuracy of
age synthesis. A pixel loss is also adopted to preserve detailed facial
information of the input face. Our proposed method is evaluated on three face
aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset.
Experimental results show appealing performance of the proposed method by
comparing with the state-of-the-art.

提出了一种结合全局和局部信息的生成对抗网络 (GLCA-GAN) 方法，使用三个局部网络来处理关键局部面部区域的细微变化。在生成器中，使用全局网络生成整个人脸图像及模拟人脸的老化趋势，从而利用整个及各个局部面部信息来合成年龄，同时使用多种损失函数以保证生成的真实性和准确性。