主动映射的传统方法专注于构建几何图。但是，对于大多数真实世界应用程序，可行的信息与环境中的语义有意义的对象有关。我们提出了一种用于主动度量语义映射问题的方法，该方法使多个异质机器人能够协作构建环境地图。这些机器人积极探索以最大程度地减少语义（对象分类）和几何（对象建模）信息中的不确定性。我们使用信息丰富但稀疏的对象模型表示环境，每个模型由基本形状和语义类标签组成，并使用大量现实世界数据在经验上表征不确定性。鉴于先前的地图，我们使用此模型为每个机器人选择动作以最大程度地减少不确定性。通过多种现实世界环境中的多机器人实验证明了我们的算法的性能。所提出的框架适用于广泛的现实问题，例如精确农业，基础设施检查和工厂中的资产映射。

The recent advent of large language models - large neural networks trained on a simple predictive objective over a massive corpus of natural language - has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training on those problems. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (GPT-3) on a range of analogical tasks, including a novel text-based matrix reasoning task closely modeled on Raven's Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing the conversion of the models from Python libraries to the FPGA chip synthesis and implementation. Then, we review the main alternatives for the hardware implementation of nonlinear activation functions. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware. The performance in Q-factor is presented for the cases of bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN equalizer, and standard 1-StpS digital back-propagation (DBP) for the simulation and experiment propagation of a single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17x70km of LEAF. The biLSTM+CNN equalizer provides a similar result to DBP and a 1.7 dB Q-factor gain compared with the chromatic dispersion compensation baseline in the experimental dataset. After that, we assess the Q-factor and the impact of hardware utilization when approximating the activation functions of NN using Taylor series, piecewise linear, and look-up table (LUT) approximations. We also show how to mitigate the approximation errors with extra training and provide some insights into possible gradient problems in the LUT approximation. Finally, to evaluate the complexity of hardware implementation to achieve 400G throughput, fixed-point NN-based equalizers with approximated activation functions are developed and implemented in an FPGA.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.

通过一系列联邦举措和命令，美国政府一直在努力确保美国在AI中的领导。这些广泛的战略文件影响了美国空军美国部（DAF）等组织。DAF-MIT AI加速器是DAF和MIT之间的一项计划，以弥合AI研究人员与DAF任务要求之间的差距。DAF-MIT AI加速器支持的几个项目正在开发公共挑战问题，这些问题解决了许多联邦AI研究的重点。这些挑战是通过公开可用的大型AI-Ready数据集，激励开源解决方案，并为可以激发进一步研究的双重使用技术创建需求信号，来针对优先事项。在本文中，我们描述了正在开发的这些公共挑战以及它们的应用如何促进科学进步。

在这项工作中，我们提出了一个端到端的异质多机器人系统框架，地面机器人能够在高空四个四极管实时创建的语义图中进行本地化，计划和导航。地面机器人在没有任何外部干预的情况下独立选择并解散目标。此外，他们通过使用语义将其本地地图与高架图匹配，执行跨视图本地化。通信主链是机会主义的，并且可以分配，使整个系统除了四型四型GPS之外没有外部基础架构，没有外部基础架构。我们通过在不同环境中的多个实验上执行不同的任务，通过执行不同的任务，对系统进行了广泛的测试。我们的地面机器人在现实世界中最少的干预和96公里的模拟无需干预即可自主行驶以上超过6公里。

筛查结肠镜检查是多种3D计算机视觉技术的重要临床应用，包括深度估计，表面重建和缺失区域检测。但是，由于难以获取地面真相数据，因此在实际结肠镜检查视频中对这些技术的开发，评估和比较仍然在很大程度上是定性的。在这项工作中，我们提出了一个带有高清临床结肠镜和高保真结肠模型的结肠镜检查3D视频数据集（C3VD），用于在结肠镜检查中进行基准计算机视觉方法。我们介绍了一种新颖的多模式2D-3D注册技术，以注册光学视频序列，并以地面真实的视图对已知3D模型的视图。通过将光学图像转换为具有生成对抗网络的深度图，并通过进化优化器对齐边缘特征来注册不同的模态。在模拟实验中，这种注册方法达到了0.321毫米的平均翻译误差，平均旋转误差为0.159度，无误地面真相可用。该方法还利用视频信息，将注册精度提高了55.6％以进行翻译，与单帧注册相比，旋转60.4％。 22个简短的视频序列被注册，以生成10,015个总帧，具有配对的地面真实深度，表面正常，光流，遮挡，六个自由度姿势，覆盖范围图和3D模型。该数据集还包括胃肠病学家与配对地面真相姿势和3D表面模型获得的筛选视频。数据集和注册源代码可在urr.jhu.edu/c3vd上获得。

平衡安全性和性能是现代控制系统设计中的主要挑战之一。此外，至关重要的是，在不诱导不必要的保守性降低绩效的情况下，确保安全至关重要。在这项工作中，我们提出了一种通过控制屏障功能（CBF）来进行安全关键控制合成的建设性方法。通过通过CBF过滤手工设计的控制器，我们能够达到性能行为，同时提供严格的安全保证。面对干扰，通过投入到国家安全的概念（ISSF）同时实现了稳健的安全性和性能。我们通过与倒置的示例同时开发CBF设计方法来采用教程方法，从而使设计过程混凝土中的挑战和敏感性。为了确定拟议方法的能力，我们考虑通过CBFS以无需拖车的8级卡车的形式来考虑通过CBF的CBF进行安全至关重要的设计。通过实验，我们看到了卡车驱动系统中未建模的干扰对CBF提供的安全保证的影响。我们表征了这些干扰并使用ISSF，生产出可靠的控制器，该控制器可以在不承认性能的情况下实现安全性。我们在模拟中评估了我们的设计，并且是在实验中首次在汽车系统上评估我们的设计。

控制屏障功能（CBF）已被证明是非线性系统安全至关重要控制器设计的强大工具。现有的设计范式不能解决理论（具有连续时间模型的控制器设计）和实践（所得控制器的离散时间采样实现）之间的差距；这可能导致性能不佳，并且违反了硬件实例化的安全性。我们提出了一种方法，通过将采样DATA对应物合成与这些基于CBF的控制器的方法，使用近似离散的时间模型和采样DATA控制屏障函数（SD-CBFS）。使用系统连续时间模型的属性，我们建立了SD-CBF与采样数据系统的实际安全概念之间的关系。此外，我们构建了基于凸优化的控制器，该控制器正式将非线性系统赋予实践中的安全保证。我们证明了这些控制器在模拟中的功效。