本报告介绍了Waymo关于系统疲劳风险管理框架的建议，该框架解决了在ADS技术的公路测试期间疲劳诱导的风险的预防，监测和缓解。所提出的框架仍然可以灵活地纳入持续的改进，并受到最先进的实践，研究，学习和经验的信息（内部和外部的Waymo）。疲劳是涉及人类驾驶员的大部分公路撞车事故的公认因素，缓解疲劳引起的风险仍然是全球研究的公开关注。虽然提出的框架是专门针对SAE 4级广告技术的公路测试而设计的，但它对较低的自动化也具有含义和适用性。

We introduce the concepts of inverse solvability and security for a generic linear forward model and demonstrate how they can be applied to models used in federated learning. We provide examples of such models which differ in the resulting inverse solvability and security as defined in this paper. We also show how the large number of users participating in a given iteration of federated learning can be leveraged to increase both solvability and security. Finally, we discuss possible extensions of the presented concepts including the nonlinear case.

在单独或多任务设置中评估了当前最新的视觉和语言模型，从而忽略了持续学习（CL）任务到达时的挑战。现有的CLENG分类促进了有关调整任务和减轻“灾难性遗忘”的研究，但仅限于仅视觉和仅语言的任务。我们提出了攀登，这是研究CL设置中学习多模式任务的挑战的基准，并系统地评估上游持续学习如何迅速概括为新的多模式和单峰任务。攀登包括几种CL算法的实现以及可以在多模式和单峰任务上部署的修改视觉语言变压器（VILT）模型。我们发现，常见的CL方法可以帮助减轻多模式任务学习期间的遗忘，但不要实现交叉任务知识转移。我们设想，攀登将有助于针对这种具有挑战性的多模式环境的新的CL算法进行研究。

Adaptive partial linear beamforming meets the need of 5G and future 6G applications for high flexibility and adaptability. Choosing an appropriate tradeoff between conflicting goals opens the recently proposed multiuser (MU) detection method. Due to their high spatial resolution, nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. However, a dramatic decrease in performance can be expected in high mobility scenarios because they are very susceptible to changes in the wireless channel. The robustness of linear filters is required, considering these changes. One way to respond appropriately is to use online machine learning algorithms. The theory of algorithms based on the adaptive projected subgradient method (APSM) is rich, and they promise accurate tracking capabilities in dynamic wireless environments. However, one of the main challenges comes from the real-time implementation of these algorithms, which involve projections on time-varying closed convex sets. While the projection operations are relatively simple, their vast number poses a challenge in ultralow latency (ULL) applications where latency constraints must be satisfied in every radio frame. Taking non-orthogonal multiple access (NOMA) systems as an example, this paper explores the acceleration of APSM-based algorithms through massive parallelization. The result is a GPUaccelerated real-time implementation of an orthogonal frequency-division multiplexing (OFDM)based transceiver that enables detection latency of less than one millisecond and therefore complies with the requirements of 5G and beyond. To meet the stringent physical layer latency requirements, careful co-design of hardware and software is essential, especially in virtualized wireless systems with hardware accelerators.

我们推导了非负神经网络的固定点的存在条件，这是一个重要的研究目标，了解了涉及自动化器和循环展开技术的现代应用中神经网络的行为。特别是，我们表明，具有非负输入和非负参数的神经网络可以在非线性珀罗尼乌斯理论的框架内被识别为单调和（弱）可扩展的功能。这一事实使我们能够推导出存在非空白神经网络的非空的固定点集的条件，并且这些条件比最近使用凸分析中的参数获得的条件较弱，这通常是基于激活函数的非扩张性的假设。此外，我们证明了单调和弱可伸缩的神经网络的固定点集的形状通常是一个间隔，其为可伸缩网络的情况的点退化。本文的首席结果在数值模拟中验证，我们考虑了一种自动型型网络，首先将角度功率谱压缩在大规模的MIMO系统中，并且第二，从压缩信号重建输入光谱。