联合学习（FL）是一种新兴的隐私保留分布式学习计划。由于型号大小和频繁的模型聚集，FL受到关键通信瓶颈。已经提出了许多技术来减少通信量，包括模型压缩和量化。现有的自适应量化方案使用升高趋势量化，其中量化水平随着训练阶段而增加。在本文中，我们制定了优化给定通信量的训练收敛速率的问题。结果表明，最佳的量化水平可以由两个因素，即训练丢失和模型更新范围表示，并且优选降低量化水平而不是增加。然后，我们提出了基于训练损耗和模型范围的两个降序量化方案。实验结果表明，与当前升序量化相比，建议的方案不仅可以减少通信量，而且还可以更快地收敛。

大物体的操纵和安全地在人类附近进行安全操作的能力是通用国内机器人助手的关键能力。我们介绍了一种柔软，触觉的人形的人形机器人的设计，并展示了用于处理大物体的全身丰富的接触操作策略。我们展示了我们的硬件设计理念，用于使用软触觉传感模块，包括：（i）低成本，抗缝，接触压力定位的武器， （ii）基于TRI软气泡传感器的爪子，用于最终效应器，（III）柔顺的力/几何传感器，用于粗糙几何感测表面/胸部。我们利用这些模块的机械智能和触觉感应，为全身抓握控制进行开发和展示运动原语。我们评估硬件在实现各种大型国内物体上实现不同优势的掌握。我们的结果表明，利用富含接触的操纵策略的柔软度和触觉感应的重要性，以及与世界的全身力量控制的互动前进的道路。

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing selfdriving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world selfdriving problems, we introduce a new large-scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest cam-era+LiDAR dataset available based on our proposed geographical coverage metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open.