Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.

研究多层合作研究中的一个关键挑战是不仅需要有效合作的个人代理，而且需要与谁合作。当其他代理人隐藏的情况下，可能是错误的动机和目标时，这在局势中特别关键。社交扣除游戏提供途径来研究个人如何学习如何综合有关其他人的潜在不可靠的信息，并阐明其真正的动机。在这项工作中，我们展示了隐藏的议程，这是一个双队的社交扣除游戏，为在未知团队对齐的情况下学习学习代理的2D环境。环境承认两支球队的丰富战略。在隐藏议程中培训的强化学习代理表明，代理商可以学习各种行为，包括合作和投票，而无需以自然语言沟通。

Many state-of-the-art deep learning models for computer vision tasks are based on the transformer architecture. Such models can be computationally expensive and are typically statically set to meet the deployment scenario. However, in real-time applications, the resources available for every inference can vary considerably and be smaller than what state-of-the-art models use. We can use dynamic models to adapt the model execution to meet real-time application resource constraints. While prior dynamic work has primarily minimized resource utilization for less complex input images while maintaining accuracy and focused on CNNs and early transformer models such as BERT, we adapt vision transformers to meet system dynamic resource constraints, independent of the input image. We find that unlike early transformer models, recent state-of-the-art vision transformers heavily rely on convolution layers. We show that pretrained models are fairly resilient to skipping computation in the convolution and self-attention layers, enabling us to create a low-overhead system for dynamic real-time inference without additional training. Finally, we create a optimized accelerator for these dynamic vision transformers in a 5nm technology. The PE array occupies 2.26mm$^2$ and is 17 times faster than a NVIDIA TITAN V GPU for state-of-the-art transformer-based models for semantic segmentation.

虽然机器人提供了一个机会，为老年人和床上移动性损伤的人提供物理援助，但人们经常在床上休息，毯子覆盖着他们的大部分的毯子。为许多日常自我保健任务提供帮助，例如沐浴，敷料或守护，护理人员必须先从人体的一部分揭开毯子。在这项工作中，我们介绍了一个关于机器人床上用品操作的制定，其中一个机器人从目标身体部位揭开毯子，同时确保人体的其余部分仍然被覆盖。我们比较两种方法来优化提供具有掌握和释放点的机器人的策略，即揭示身体的目标部分：1）加强学习和2）通过优化来生成培训数据的自我监督学习。我们在物理模拟环境中培训并进行了评估，该政策包括覆盖床上模拟人类仰卧的可变形布网格。此外，我们还将模拟训练的政策转移到真正的移动操纵器，并证明它可以从躺在床上的人体模型的目标身体部位揭开毯子。源代码在线获取。

Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data. We study one such problem in the framework of distribution property testing, characterizing the number of samples required to to distinguish whether a distribution has a certain property or is far from having that property. In particular, given samples from a distribution, we seek to characterize the tail of the distribution, that is, understand how many elements appear infrequently. We develop an algorithm based on a careful bucketing scheme that distinguishes light-tailed distributions from non-light-tailed ones with respect to a definition based on the hazard rate, under natural smoothness and ordering assumptions. We bound the number of samples required for this test to succeed with high probability in terms of the parameters of the problem, showing that it is polynomial in these parameters. Further, we prove a hardness result that implies that this problem cannot be solved without any assumptions.