We consider the problem of predicting power failure cascades due to branch
failures. We propose a flow-free model based on graph neural networks that
predicts grid states at every generation of a cascade process given an initial
contingency and power injection values. We train the proposed model using a
cascade sequence data pool generated from simulations. We then evaluate our
model at various levels of granularity. We present several error metrics that
gauge the model's ability to predict the failure size, the final grid state,
and the failure time steps of each branch within the cascade. We benchmark the
graph neural network model against influence models. We show that, in addition
to being generic over randomly scaled power injection values, the graph neural
network model outperforms multiple influence models that are built specifically
for their corresponding loading profiles. Finally, we show that the proposed
model reduces the computational time by almost two orders of magnitude.

通过基于图神经网络的无流模型，我们能够预测由分支故障引起的电力故障级联，评估模型并与其他影响模型进行比较，结果表明该模型优于其他模型，并且降低计算时间近两个数量级。

基于图神经网络的电力故障级联预测

Power Failure Cascade Prediction using Graph Neural Networks

Video frame interpolation, the task of synthesizing new frames in between two
or more given ones, is becoming an increasingly popular research target.
However, the current evaluation of frame interpolation techniques is not ideal.
Due to the plethora of test datasets available and inconsistent computation of
error metrics, a coherent and fair comparison across papers is very
challenging. Furthermore, new test sets have been proposed as part of method
papers so they are unable to provide the in-depth evaluation of a dedicated
benchmarking paper. Another severe downside is that these test sets violate the
assumption of linearity when given two input frames, making it impossible to
solve without an oracle. We hence strongly believe that the community would
greatly benefit from a benchmarking paper, which is what we propose.
Specifically, we present a benchmark which establishes consistent error metrics
by utilizing a submission website that computes them, provides insights by
analyzing the interpolation quality with respect to various per-pixel
attributes such as the motion magnitude, contains a carefully designed test set
adhering to the assumption of linearity by utilizing synthetic data, and
evaluates the computational efficiency in a coherent manner.

我们提出了一个基准测试，通过利用计算它们的提交网站建立一致的误差度量，通过分析插值质量与各种像素属性（如运动幅度）的关系来提供见解，并通过利用合成数据坚持线性假设来设计精心设计的测试集，并以一致的方式评估计算效率。