人形机器人可以在危险情况下取代人类，但大多数此类情况对他们来说同样危险，这意味着他们有很大的损害和下降的机会。我们假设人形机器人主要用于建筑物，这使它们可能靠近墙壁。为了避免跌倒，他们可以像人类那样靠在最接近的墙上，只要他们在几毫秒内找到手放手的地方。本文介绍了一种称为D-Reflex的方法，该方法学习了一个神经网络，该神经网络在墙壁方向，墙壁距离和机器人的姿势下选择此接触位置。然后，全身控制器使用此接触位置来达到稳定的姿势。我们表明，D-Reflex允许模拟的Talos机器人（1.75m，100kg，30自由度）避免了超过75％的可避免跌倒，并且可以在真正的机器人上工作。

Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior knowledge. With the embedded physical laws, PINNs enable the estimation of critical parameters, which are unobservable via physical tools, through observable variables. For example, Power Electronic Converters (PECs) are essential building blocks for the green energy transition. PINNs have been applied to estimate the capacitance, which is unobservable during PEC operations, using current and voltage, which can be observed easily during operations. The estimated capacitance facilitates self-diagnostics of PECs. Existing PINNs are often manually designed, which is time-consuming and may lead to suboptimal performance due to a large number of design choices for neural network architectures and hyperparameters. In addition, PINNs are often deployed on different physical devices, e.g., PECs, with limited and varying resources. Therefore, it requires designing different PINN models under different resource constraints, making it an even more challenging task for manual design. To contend with the challenges, we propose Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables the automated design of PINNs by combining AutoML and PINNs. Specifically, we first tailor a search space that allows finding high-accuracy PINNs for PEC internal parameter estimation. We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints. We experimentally demonstrate that AutoPINN is able to find more accurate PINN models than human-designed, state-of-the-art PINN models using fewer resources.

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select a final result. Extensive experiments on six benchmark datasets demonstrate that SEARCH not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.

深度学习技术在各种任务中都表现出了出色的有效性，并且深度学习具有推进多种应用程序（包括在边缘计算中）的潜力，其中将深层模型部署在边缘设备上，以实现即时的数据处理和响应。一个关键的挑战是，虽然深层模型的应用通常会产生大量的内存和计算成本，但Edge设备通常只提供非常有限的存储和计算功能，这些功能可能会在各个设备之间差异很大。这些特征使得难以构建深度学习解决方案，以释放边缘设备的潜力，同时遵守其约束。应对这一挑战的一种有希望的方法是自动化有效的深度学习模型的设计，这些模型轻巧，仅需少量存储，并且仅产生低计算开销。该调查提供了针对边缘计算的深度学习模型设计自动化技术的全面覆盖。它提供了关键指标的概述和比较，这些指标通常用于量化模型在有效性，轻度和计算成本方面的水平。然后，该调查涵盖了深层设计自动化技术的三类最新技术：自动化神经体系结构搜索，自动化模型压缩以及联合自动化设计和压缩。最后，调查涵盖了未来研究的开放问题和方向。

不确定性是时间序列预测任务的重要考虑因素。在这项工作中，我们专门致力于量化流量预测的不确定性。为了实现这一目标，我们开发了深层时空的不确定性定量（DeepStuq），可以估计核心和认知不确定性。我们首先利用时空模型来对流量数据的复杂时空相关性进行建模。随后，开发了两个独立的次神经网络，以最大化异质对数可能性，以估计不确定性。为了估计认知不确定性，我们通过整合蒙特卡洛辍学和平均自适应重量的重新训练方法来结合变异推理和深层结合的优点。最后，我们提出了基于温度缩放的后处理校准方法，从而提高了模型的概括能力估计不确定性。在四个公共数据集上进行了广泛的实验，经验结果表明，就点预测和不确定性量化而言，所提出的方法优于最先进的方法。

相关时间序列（CTS）预测在许多网络物理系统中起着重要作用，其中多个传感器发出捕获互连过程的时间序列。基于深度学习的解决方案，即提供最先进的CTS预测性能，采用各种时空（ST）块，能够在时间序列之间模拟时间依赖性和空间相关性。但是，仍然存在两个挑战。首先，ST-Blocks手动设计，这是耗时和昂贵的。其次，现有预测模型只需多次堆叠相同的ST块，这限制了模型潜力。为了解决这些挑战，我们提出了能够自动识别高竞争力的ST-Blocks以及使用不同拓扑连接的异构ST-Block的预测模型，而不是使用简单堆叠连接的相同的ST-Block。具体而言，我们设计微型和宏搜索空间，以模拟ST-Blocks的架构和异构ST-Block之间的连接，并且我们提供了一种能够共同探索搜索空间来识别最佳预测模型的搜索策略。关于八个常用CTS预测基准数据集的广泛实验可以证明我们的设计选择，并证明AutoCTS能够自动发现智能现有人设计型号的预测模型。这是“AutoCTS：自动相关时间序列预测”“的扩展版本，以显示在PVLDB 2022中。