Efficient automated detection of flux-transient, reoccurring flux-variable,
and moving objects is increasingly important for large-scale astronomical
surveys. We present braai, a convolutional-neural-network, deep-learning
real/bogus classifier designed to separate genuine astrophysical events and
objects from false positive, or bogus, detections in the data of the Zwicky
Transient Facility (ZTF), a new robotic time-domain survey currently in
operation at the Palomar Observatory in California, USA. Braai demonstrates a
state-of-the-art performance as quantified by its low false negative and false
positive rates. We describe the open-source software tools used internally at
Caltech to archive and access ZTF's alerts and light curves (Kowalski), and to
label the data (Zwickyverse). We also report the initial results of the
classifier deployment on the Edge Tensor Processing Units (TPUs) that show
comparable performance in terms of accuracy, but in a much more (cost-)
efficient manner, which has significant implications for current and future
surveys.

本文介绍了一种卷积神经网络深度学习算法（braai），用于识别天文学巡天过程中由 Zwicky 瞬变观测设备产生的真实物理事件和对象，开发者还描述了用于存档和访问 ZTF 警报和光度曲线的开源软件工具，以及用于标记数据的 Zwickyverse。同时报道了分类器部署在边缘张量处理单元（TPUs）上的初步结果展示了与准确度相抵触的更（成本）高效的状态 - 最先进的性能，这对当前和未来市场调查具有重要的影响。

利用深度学习对 Zwicky 瞬变设施进行真假分类

Real-bogus classification for the Zwicky Transient Facility using deep  learning

We present DeepStreaks, a convolutional-neural-network, deep-learning system
designed to efficiently identify streaking fast-moving near-Earth objects that
are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field,
time-domain survey using a dedicated 47 sq. deg camera attached to the Samuel
Oschin 48-inch Telescope at the Palomar Observatory in California, United
States. The system demonstrates a 96-98% true positive rate, depending on the
night, while keeping the false positive rate below 1%. The sensitivity of
DeepStreaks is quantified by the performance on the test data sets as well as
using known near-Earth objects observed by ZTF. The system is deployed and
adapted for usage within the ZTF Solar-System framework and has significantly
reduced human involvement in the streak identification process, from several
hours to typically under 10 minutes per day.

使用卷积神经网络的 DeepStreaks 系统能够高效、准确地识别和追踪 ZTF 所捕获的高速行星，有效减少人工识别的时间和准确性。

DeepStreaks：使用深度学习在 Zwicky 瞬变设施数据中识别快速运动的物体

DeepStreaks: identifying fast-moving objects in the Zwicky Transient  Facility data with deep learning

Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.

本论文介绍了一种基于卷积神经网络和生成随机几乎逼真的面部图像的方法，可以通过单张照片快速、稳健地重建面部的三维几何结构，即使在极端表情和各种光照条件下也可以成功恢复面部形状。