机器学习（ML）已用于加速基于模拟的验证中功能覆盖的进度。在先前工作中，有监督的ML算法是一种普遍的选项，用于偏向测试生成或过滤生成的测试。但是，对于缺少覆盖范围的事件，这些算法缺乏在训练阶段学习的积极示例。因此，通过算法生成或过滤的测试无法有效地填充覆盖范围。当验证大规模设计时，这更为严重，因为覆盖范围更大，功能更为复杂。本文介绍了基于神经网络（NN）的可配置的测试选择框架，该框架可以实现与随机仿真相似的覆盖率增益，而在框架的三种配置下，仿真工作却少得多。此外，该框架的性能不受受到覆盖事件的数量的限制。实验中使用了商业信号处理单元，以证明框架的有效性。与随机仿真相比，NNBNT可以减少多达53.74％的仿真时间，达到99％的覆盖率水平。

随着自主系统成为我们日常生活的一部分，确保其信任度至关重要。有许多用于证明可信赖性的技术。所有这些技术的共同点是需要阐明规格。在本文中，我们对规格进行了广泛的看法，专注于顶级要求，包括但不限于功能，安全性，安全性和其他非功能性属性。本文的主要贡献是对于与指定可信度相关的自主系统社区的一系列高级智力挑战。我们还描述了有关自主系统的许多应用程序域的独特规范挑战。

基于仿真的硬件验证的高效测试具有挑战性。使用受限的随机测试生成，可能需要数百万个测试才能实现覆盖目标。绝大多数测试并不促进覆盖率进度，但它们消耗了验证资源。在本文中，我们提出了一种混合智能测试方法，结合了两种先前已分别处理的方法，即覆盖指导的测试选择和新颖性驱动的验证。覆盖范围指导的测试选择从覆盖反馈到偏置测试的学习者学习到最有效的测试。新颖性驱动的验证学会了识别和模拟与先前刺激不同的刺激，从而减少了模拟的数量并提高了测试效率。我们讨论了每种方法的优势和局限性，并展示了我们的方法如何解决每种方法的局限性，从而导致硬件测试既高效又有效。

受约束的随机测试生成是生成基于仿真验证的刺激的最广泛采用的方法之一。随机性导致测试多样性，但是测试往往会反复行使相同的设计逻辑。约束是（通常是手动）将随机测试偏向有趣，难以触及且尚未意义的逻辑的。但是，随着验证的进行，大多数受约束的随机测试对功能覆盖率几乎没有影响。如果刺激的产生比模拟消耗的资源明显少得多，那么更好的方法涉及随机生成大量测试，选择最有效的子集，并且只能模拟该子集。在本文中，我们引入了一种新颖的方法，用于自动限制提取和测试选择。我们称之为覆盖范围的测试选择的方法基于从覆盖范围反馈中进行的监督学习。我们的方法将选择偏向于具有增加功能覆盖范围的可能性很高的测试，并优先考虑它们进行仿真。我们展示了指导的测试选择如何减少手动约束写作，优先考虑有效测试，减少验证资源消耗以及加速在大型现实生活中的工业硬件设计上的覆盖范围。

Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).

In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Resonance Imaging (CMR). While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation.We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point. We then learn the subject-specific mapping from these unique coordinates to k-space intensities using a multi-layer perceptron with frequency domain regularization. During inference, we obtain a complete k-space for Cartesian coordinates and an arbitrary temporal resolution. A simple inverse Fourier transform recovers the image, eliminating the need for density compensation and costly non-uniform Fourier transforms for non-Cartesian data. This novel imaging framework was tested on 42 radially sampled datasets from 6 subjects. The proposed method outperforms other techniques qualitatively and quantitatively using data from four and one heartbeat(s) and 30 cardiac phases. Our results for one heartbeat reconstruction of 50 cardiac phases show improved artifact removal and spatio-temporal resolution, leveraging the potential for real-time CMR.

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model that incorporates 3D atom positions and an auxiliary denoising task. The effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU acceleration, GPS++ scales to deep architectures (16 layers), training at 3 minutes per epoch, and large ensemble (112 models), completing the final predictions in 1 hour 32 minutes, well under the 4 hour inference budget allocated. Our implementation is publicly available at: https://github.com/graphcore/ogb-lsc-pcqm4mv2.

运动补偿的MR重建（MCMR）是一个强大的概念，具有巨大的潜力，由两个耦合的子问题组成：运动估计，假设已知图像和图像重建，假设已知运动。在这项工作中，我们为MCMR提出了一个基于学习的自我监督框架，以有效处理心脏MR成像中的非刚性运动腐败。与传统的MCMR方法相反，在重建之前估算运动并在迭代优化过程中保持不变，我们引入了动态运动估计过程，并将其嵌入到独立的优化中。我们建立了一个心脏运动估计网络，该网络通过小组的注册方法利用时间信息，并在运动估计和重建之间进行联合优化。在40个获得的2D心脏MR CINE数据集上进行的实验表明，所提出的展开的MCMR框架可以在其他最先进的方法失败的情况下以高加速度速率重建高质量的MR图像。我们还表明，关节优化机制对两个子任务（即运动估计和图像重建）都是互惠互利的，尤其是当MR图像高度不足时。

情绪可以提供自然的交流方式，以补充许多领域中社交机器人（例如文本和语音）现有的多模式能力。我们与112、223和151名参与者进行了三项在线研究，以调查使用情绪作为搜救（SAR）机器人的交流方式的好处。在第一个实验中，我们研究了通过机器人的情绪传达与SAR情况有关的信息的可行性，从而导致了从SAR情况到情绪的映射。第二项研究使用控制控制理论是推导此类映射的替代方法。此方法更灵活，例如允许对不同的情绪集和不同机器人进行调整。在第三个实验中，我们使用LED作为表达通道为外观受限的室外现场研究机器人创建了情感表达。在各种模拟的SAR情况下，使用这些情感表达式，我们评估了这些表达式对参与者（采用救援人员的作用）的影响。我们的结果和提议的方法提供了（a）有关情感如何帮助在SAR背景下传达信息的见解，以及（b）在（模拟）SAR通信环境中添加情绪为传播方式的有效性的证据。