神经图像编码现在表示现有的图像压缩方法。但是，在视频域中仍有很多工作。在这项工作中，我们提出了一部结束了学习的视频编解码器，介绍了几个建筑Noveltize以及培训Noveltizes，围绕适应和关注的概念。我们的编解码器被组织为与帧间编解码器配对的帧内编解码器。作为一种建筑新颖，我们建议培训帧间编解码器模型以基于输入视频的分辨率来调整运动估计处理。第二个建筑新奇是一种新的神经块，它将基于分裂的神经网络和Densenets的概念结合了。最后，我们建议在推理时间内过度装备一组解码器侧乘法参数。通过消融研究和对现有技术的比较，我们在编码收益方面表现出我们所提出的技术的好处。我们将编解码器与VVC / H.266和RLVC进行比较，该rlvc分别代表最先进的传统和端到端学习的编解码器，并在2021年在2021年在2021年执行端到端学习方法竞争，e2e_t_ol。我们的编解码器显然优于E2E_T_OL，并在某些设置中对VVC和RLVC有利地进行比较。

The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution.

协作推断已获得机器学习的重大研究兴趣，作为分发计算负载，减少延迟以及解决通信中隐私保护的工具。最近的协作推理框架采用了动态推理方法，例如早期外观和神经网络的运行时间分配。但是，随着机器学习框架的扩展，例如在监视应用中，需要考虑与设备故障相关的容错。本文介绍了基于正式定义的计算模型建立的Edge-Prune分布式计算框架，该框架为错误的耐受性协作推断提供了灵活的基础架构。这项工作的实验部分显示了通过协作推理可节省的推理时间的结果，呈现容错的系统拓扑，并在执行时间开销方面分析其成本。