Although tokamaks are one of the most promising devices for realizing nuclear
fusion as an energy source, there are still key obstacles when it comes to
understanding the dynamics of the plasma and controlling it. As such, it is
crucial that high quality models are developed to assist in overcoming these
obstacles. In this work, we take an entirely data driven approach to learn such
a model. In particular, we use historical data from the DIII-D tokamak to train
a deep recurrent network that is able to predict the full time evolution of
plasma discharges (or "shots"). Following this, we investigate how different
training and inference procedures affect the quality and calibration of the
shot predictions.

通过使用历史数据训练深度循环网络来预测等离子体放电的全时演化，并研究不同的训练和推理过程对放电预测质量和校准的影响。

DIII-D 托卡马克通过深度循环网络的全景预测

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

Applying image processing algorithms independently to each frame of a video
often leads to undesired inconsistent results over time. Developing temporally
consistent video-based extensions, however, requires domain knowledge for
individual tasks and is unable to generalize to other applications. In this
paper, we present an efficient end-to-end approach based on deep recurrent
network for enforcing temporal consistency in a video. Our method takes the
original unprocessed and per-frame processed videos as inputs to produce a
temporally consistent video. Consequently, our approach is agnostic to specific
image processing algorithms applied on the original video. We train the
proposed network by minimizing both short-term and long-term temporal losses as
well as the perceptual loss to strike a balance between temporal stability and
perceptual similarity with the processed frames. At test time, our model does
not require computing optical flow and thus achieves real-time speed even for
high-resolution videos. We show that our single model can handle multiple and
unseen tasks, including but not limited to artistic style transfer,
enhancement, colorization, image-to-image translation and intrinsic image
decomposition. Extensive objective evaluation and subject study demonstrate
that the proposed approach performs favorably against the state-of-the-art
methods on various types of videos.

本文利用深度递归神经网络，结合短期、长期以及感知上的损失，为视频处理提供了一种基于帧的将原始视频和处理后的视频作为输入，生成时间上连贯的视频的方法，实现了对多种需求的处理，且无需光流计算，具有实时性能。

学习盲视频时间一致性

Learning Blind Video Temporal Consistency

The problem of video object segmentation can become extremely challenging
when multiple instances co-exist. While each instance may exhibit large scale
and pose variations, the problem is compounded when instances occlude each
other causing failures in tracking. In this study, we formulate a deep
recurrent network that is capable of segmenting and tracking objects in video
simultaneously by their temporal continuity, yet able to re-identify them when
they re-appear after a prolonged occlusion. We combine both temporal
propagation and re-identification functionalities into a single framework that
can be trained end-to-end. In particular, we present a re-identification module
with template expansion to retrieve missing objects despite their large
appearance changes. In addition, we contribute a new attention-based recurrent
mask propagation approach that is robust to distractors not belonging to the
target segment. Our approach achieves a new state-of-the-art global mean
(Region Jaccard and Boundary F measure) of 68.2 on the challenging DAVIS 2017
benchmark (test-dev set), outperforming the winning solution which achieves a
global mean of 66.1 on the same partition.

本研究提出了一种使用深度循环网络实现同时分割和追踪视频中物体的方法，结合了时间传播和重新识别模块，以及基于注意力的循环蒙版传播方法，取得了在 DAVIS 2017 基准测试（test-dev 集合）上最高的分割和边缘测量平均值（68.2），优于同一分区上获胜解决方案的全球平均值（66.1）