With the development of new Internet services such as computation-intensive
and delay-sensitive tasks, the traditional "Best Effort" network transmission
mode has been greatly challenged. The network system is urgently required to
provide end-to-end transmission determinacy and computing determinacy for new
applications to ensure the safe and efficient operation of services. Based on
the research of the convergence of computing and networking, a new network
paradigm named deterministic computing power networking (Det-CPN) is proposed.
In this article, we firstly introduce the research advance of computing power
networking. And then the motivations and scenarios of Det-CPN are analyzed.
Following that, we present the system architecture, technological capabilities,
workflow as well as key technologies for Det-CPN. Finally, the challenges and
future trends of Det-CPN are analyzed and discussed.

基于计算与网络融合研究，提出了一种名为确定性计算能力网络（Det-CPN）的新网络范式，旨在为新应用提供端到端传输确定性和计算确定性，以确保服务的安全高效运行。该文章首先介绍了计算能力网络的研究进展，然后分析了 Det-CPN 的动机和场景。接着，提出了 Det-CPN 的系统架构、技术能力、工作流程和关键技术。最后，分析和讨论了 Det-CPN 面临的挑战和未来发展趋势。

确定性计算能力网络：架构、技术和前景

Deterministic Computing Power Networking: Architecture, Technologies and  Prospects

Google AlphaGos win has significantly motivated and sped up machine learning
(ML) research and development, which led to tremendous ML technical advances
and wider adoptions in various domains (e.g., Finance, Health, Defense, and
Education). These advances have resulted in numerous new concepts and
technologies, which are too many for people to catch up to and even make them
confused, especially for newcomers to the ML area. This paper is aimed to
present a clear picture of the state-of-the-art of the existing ML technologies
with a comprehensive survey. We lay out this survey by viewing ML as a MLOps
(ML Operations) process, where the key concepts and activities are collected
and elaborated with representative works and surveys. We hope that this paper
can serve as a quick reference manual (a survey of surveys) for newcomers
(e.g., researchers, practitioners) of ML to get an overview of the MLOps
process, as well as a good understanding of the key technologies used in each
step of the ML process, and know where to find more details.

本文旨在通过综合调查，以 MLOps 为过程，收集和阐述关键概念和活动及其代表作和调查，对现有机器学习技术的现状进行清晰的阐述，以便初学者（如研究人员、从业人员）了解 MLOps 过程的概述，以及在 ML 过程的每个步骤中使用的关键技术，并了解如何查找更多详细信息。

覆盖整个机器学习生命周期的 MLOps: 一项调查

MLOps Spanning Whole Machine Learning Life Cycle: A Survey

The recent proliferation of computing technologies (e.g., sensors, computer
vision, machine learning, and hardware acceleration), and the broad deployment
of communication mechanisms (e.g., DSRC, C-V2X, 5G) have pushed the horizon of
autonomous driving, which automates the decision and control of vehicles by
leveraging the perception results based on multiple sensors. The key to the
success of these autonomous systems is making a reliable decision in real-time
fashion. However, accidents and fatalities caused by early deployed autonomous
vehicles arise from time to time. The real traffic environment is too
complicated for current autonomous driving computing systems to understand and
handle. In this paper, we present state-of-the-art computing systems for
autonomous driving, including seven performance metrics and nine key
technologies, followed by twelve challenges to realize autonomous driving. We
hope this paper will gain attention from both the computing and automotive
communities and inspire more research in this direction.

本研究论文综述了自动驾驶的最新计算系统、性能指标、核心技术和挑战，并旨在吸引计算机和汽车领域的关注和更多研究。