共享自主权提供了一种框架，其中人类和自动化系统（例如机器人）共同控制系统的行为，使得各种应用程序能够实现有效的解决方案，包括人机交互。然而，共享自主权的具有挑战性问题是安全性，因为人类投入可能是未知的且不可预测的，这影响了机器人的安全限制。如果人类投入是通过与机器人的物理接触施加的力，它也会改变机器人的行为以保持安全性。通过提出双层控制框架，我们在实时应用中解决了分享自主权的安全问题。在第一层中，我们使用人类输入测量的历史来推断人类想要的机器人，并根据该推断定义机器人的安全约束。在第二层中，我们制定了一种快速探索的屏障对树，每个障碍对由屏障功能和控制器组成。使用这些屏障对中的控制器，机器人能够在从人体输入的干预下保持其安全操作。这一提议的控制框架允许机器人帮助人类，同时防止它们遇到安全问题。我们展示了拟议的控制框架，用于模拟双连杆机器人机器人。

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and provide theoretical insights into the design of the susceptibility metric. Finally, we show through extensive experiments on datasets with synthetic and real-world label noise that one can utilize susceptibility and the overall training accuracy to distinguish models that maintain a low memorization on the training set and generalize well to unseen clean data.

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

我们考虑涉及一组代理的在线估计问题。每个代理都可以访问（个人）流程，该过程从实数分布中生成样本，并试图估算其平均值。我们研究了某些分布具有相同均值的情况，并且允许代理人积极查询其他代理商的信息。目的是设计一种算法，该算法使每个代理都能够通过与其他代理商进行沟通来改善其平均估计。平均值的均值和分布数量尚不清楚，这使得任务是非平凡的。我们介绍了一种新颖的协作策略，以解决这个在线个性化的平均估计问题。我们分析其时间复杂性，并引入在数值实验中享有良好性能的变体。我们还将我们的方法扩展到了具有相似手段的代理商群体寻求估算其群集的平均值的环境。

现有的多方对话数据集用于核心分辨率是新生的，许多挑战仍然没有解决。我们根据电视成绩单为此任务创建了一个大规模数据集，多语言多方CoreF（MMC）。由于使用多种语言的黄金质量字幕可用，我们建议重复注释以通过注释投影以其他语言（中文和Farsi）创建银色核心数据。在黄金（英语）数据上，现成的模型在MMC上的性能相对较差，这表明MMC比以前的数据集更广泛地覆盖多方核心。在银数据上，我们发现成功使用它进行数据增强和从头开始训练，这有效地模拟了零击的跨语性设置。

网络控制系统是反馈控制系统，具有通过通信网络连接的不同位置分布的系统组件。由于通信网络是通过Internet进行的，并且存在带宽和数据包大小的限制，因此会出现网络限制。其中一些约束是时间延迟和数据包损失。这些网络限制会降低性能，甚至破坏系统的稳定。为了克服这些通信约束的不利影响，已经开发了各种方法，其中一种代表性是网络预测控制。该方法提出了一个控制器，该控制器会积极补偿网络时间延迟和数据包损耗。本文旨在实施网络预测控制系统，以通过计算机网络控制机器人组。网络延迟由预测变量解释，而使用冗余控制数据包可以减少数据包丢失的潜力。尽管延迟且数据包损失很大，但结果将显示系统的稳定性。此外，将提出对先前网络预测控制系统的改进，并显示性能的提高。最后，将研究不同系统和环境参数对控制循环的影响。

图像的持久性拓扑特性是一个附加描述符，提供了传统神经网络可能无法发现的见解。该领域的现有研究主要侧重于有效地将数据的拓扑特性整合到学习过程中，以增强性能。但是，没有现有的研究来证明引入拓扑特性可以提高或损害性能的所有可能场景。本文对拓扑特性在各种培训方案中的图像分类有效性进行了详细分析，定义为：训练样本的数量，训练数据的复杂性和骨干网络的复杂性。我们确定从拓扑功能中受益最大的场景，例如，在小数据集中培训简单的网络。此外，我们讨论了数据集的拓扑一致性问题，该问题是使用拓扑特征进行分类的主要瓶颈之一。我们进一步证明了拓扑不一致如何损害某些情况的性能。

我们使用机器学习工具来模拟电网网络中失效级联的线路交互。我们首先在初始随机故障之后收集可能的连续线路故障的模拟轨迹的数据集，并考虑模型电网中的实际约束，直到系统以稳态稳定状态。我们使用加权$ L_1 $ -Regularized基于逻辑回归的模型来查找使用成对统计数据捕获成对和潜在的高阶线路故障交互的静态和动态模型。静态模型捕获了网络稳定状态附近的故障“交互，并且动态模型在连续网络状态的时间序列中捕获失败。我们测试模型在网络中展开的失败独立轨迹，以评估其故障预测力。我们观察网络中不同线路之间的不对称，强烈的积极和负面相互作用。我们使用静态交互模型来估计级联大小的分布，并识别倾向于失败的行组，并与数据进行比较。动态交互模型在初始故障之后成功地预测了用于长持久故障传播轨迹的网络状态。