Directed acyclic graph (DAG) tasks are currently adopted in the real-time
domain to model complex applications from the automotive, avionics, and
industrial domain that implement their functionalities through chains of
intercommunicating tasks. This paper studies the problem of scheduling
real-time DAG tasks by presenting a novel schedulability test based on the
concept of trivial schedulability. Using this schedulability test, we propose a
new DAG scheduling framework (edge generation scheduling -- EGS) that attempts
to minimize the DAG width by iteratively generating edges while guaranteeing
the deadline constraint. We study how to efficiently solve the problem of
generating edges by developing a deep reinforcement learning algorithm combined
with a graph representation neural network to learn an efficient edge
generation policy for EGS. We evaluate the effectiveness of the proposed
algorithm by comparing it with state-of-the-art DAG scheduling heuristics and
an optimal mixed-integer linear programming baseline. Experimental results show
that the proposed algorithm outperforms the state-of-the-art by requiring fewer
processors to schedule the same DAG tasks.

本研究设计了一种新的基于边生成调度（EGS）的有向无环图（DAG）调度框架，使用基于平凡调度性测试的调度性测试方法并结合深度强化学习算法和图表示神经网络来高效生成边缘，并通过与现有的 DAG 调度启发式算法和优化的混合整数线性规划基准进行比较，实验结果表明该算法在安排相同的 DAG 任务时需要较少的处理器。

使用深度强化学习进行 DAG 任务的边缘生成调度

Edge Generation Scheduling for DAG Tasks using Deep Reinforcement  Learning

Deep learning technique has yielded significant improvements in point cloud
completion with the aim of completing missing object shapes from partial
inputs. However, most existing methods fail to recover realistic structures due
to over-smoothing of fine-grained details. In this paper, we develop a
voxel-based network for point cloud completion by leveraging edge generation
(VE-PCN). We first embed point clouds into regular voxel grids, and then
generate complete objects with the help of the hallucinated shape edges. This
decoupled architecture together with a multi-scale grid feature learning is
able to generate more realistic on-surface details. We evaluate our model on
the publicly available completion datasets and show that it outperforms
existing state-of-the-art approaches quantitatively and qualitatively. Our
source code is available at this https URL

本文介绍了使用基于体素的网络和边缘生成（VE-PCN）来实现点云补全的方法，该方法采用多尺度网格特征学习来生成更真实的表面细节，并在公开数据集上进行了评估，表现优于现有的最先进方法。