扩展培训工作负载的能力是深度学习的关键性能推动者之一。主要缩放方法是基于数据并行GPU的培训，该培训已经被硬件和软件支持高效地支持高效的GPU通信，特别是通过带宽过度曝光。此支持以A价格出现：相对于其“消费者级”对应物，“云级”服务器之间存在幅度成本差异，但相对于其“消费者级”对应物，虽然服务器级和消费者级GPU可以具有类似的计算信封。在本文中，我们调查了昂贵的硬件过度控制方法是否可以通过算法和系统设计所涵盖，并提出称为CGX的框架，为通信压缩提供有效的软件支持。我们认为，在没有硬件支持的情况下，该框架能够从消费者级多GPU系统中删除通信瓶颈：在没有硬件支持的情况下：在培训现代模型和全部准确性方面时，我们的框架可以在商品上进行2-3倍的自动加速系统使用8个消费者级NVIDIA RTX 3090 GPU，并使其超越NVIDIA DGX-1服务器的吞吐量，其具有类似的峰值闪光，但是从带宽过度提供的益处。

The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversarial image perturbations. In the existing methods of generating the adversarial image perturbations, optimizations take each incoming image frame as the decision variable to generate an image perturbation. Therefore, given a new image, the typically computationally-expensive optimization needs to start over as there is no learning between the independent optimizations. Very few approaches have been developed for attacking online image streams while considering the underlying physical dynamics of autonomous vehicles, their mission, and the environment. We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations. Based on this capability level, a binary decision attack/not attack is introduced to enhance the effectiveness of the attacker. We evaluate our proposed multi-level image attack framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. The results show our method's capability to generate the image attack in real-time while monitoring when the attacker is proficient given state estimates.

This work presents an actuation framework for a bioinspired flapping drone called Aerobat. This drone, capable of producing dynamically versatile wing conformations, possesses 14 body joints and is tail-less. Therefore, in our robot, unlike mainstream flapping wing designs that are open-loop stable and have no pronounced morphing characteristics, the actuation, and closed-loop feedback design can pose significant challenges. We propose a framework based on integrating mechanical intelligence and control. In this design framework, small adjustments led by several tiny low-power actuators called primers can yield significant flight control roles owing to the robot's computational structures. Since they are incredibly lightweight, the system can host the primers in large numbers. In this work, we aim to show the feasibility of joint's motion regulation in Aerobat's untethered flights.

Flying animals, such as bats, fly through their fluidic environment as they create air jets and form wake structures downstream of their flight path. Bats, in particular, dynamically morph their highly flexible and dexterous armwing to manipulate their fluidic environment which is key to their agility and flight efficiency. This paper presents the theoretical and numerical analysis of the wake-structure-based gait design inspired by bat flight for flapping robots using the notion of reduced-order models and unsteady aerodynamic model incorporating Wagner function. The objective of this paper is to introduce the notion of gait design for flapping robots by systematically searching the design space in the context of optimization. The solution found using our gait design framework was used to design and test a flapping robot.

GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase? The main objectives for the ideal method of answering the question are: (1) empowering federated learning algorithms to reach better performance in fewer communication rounds, notably in the face of heterogeneous scenarios, and last but not least, (2) being easy to use alongside the state-of-the-art federated learning algorithms as a new module. To this end, GTFLAT models the averaging task as a strategic game among active users. Then it proposes a systematic solution based on the population game and evolutionary dynamics to find the equilibrium. In contrast with existing approaches that impose the weights on the participants, GTFLAT concludes a self-enforcement agreement among clients in a way that none of them is motivated to deviate from it individually. The results reveal that, on average, using GTFLAT increases the top-1 test accuracy by 1.38%, while it needs 21.06% fewer communication rounds to reach the accuracy.

Machine reading comprehension (MRC) is a long-standing topic in natural language processing (NLP). The MRC task aims to answer a question based on the given context. Recently studies focus on multi-hop MRC which is a more challenging extension of MRC, which to answer a question some disjoint pieces of information across the context are required. Due to the complexity and importance of multi-hop MRC, a large number of studies have been focused on this topic in recent years, therefore, it is necessary and worth reviewing the related literature. This study aims to investigate recent advances in the multi-hop MRC approaches based on 31 studies from 2018 to 2022. In this regard, first, the multi-hop MRC problem definition will be introduced, then 31 models will be reviewed in detail with a strong focus on their multi-hop aspects. They also will be categorized based on their main techniques. Finally, a fine-grain comprehensive comparison of the models and techniques will be presented.

Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 67K undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e. loop closure detection) and inter-sequence (i.e. re-localisation) place recognition. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code will be available at https://csiro-robotics.github.io/Wild-Places.

无线网络的第五生成（5G）将更加自适应和异质。可重新配置的智能表面技术使5G能够在多仪波形上工作。但是，在这样的动态网络中，特定调制类型的识别至关重要。我们提出了基于人工智能的RIS辅助数字分类方法。我们培训卷积神经网络以对数字调制进行分类。所提出的方法可以直接在接收的信号上学习并学习特征，而无需提取功能。介绍和分析了卷积神经网络学到的功能。此外，还研究了在特定SNR范围内接收信号的强大功能。发现所提出的分类方法的准确性很显着，尤其是对于低水平的SNR。

网络物理系统（CPSS）通常是复杂且至关重要的；因此，确保系统的要求，即规格，很难满足。基于仿真的CPS伪造是一种实用的测试方法，可用于通过仅要求模拟正在测试的系统来提高对系统正确性的信心。由于每个仿真通常在计算上进行密集，因此一个重要的步骤是减少伪造规范所需的仿真数量。我们研究贝叶斯优化（BO），一种样本效率的方法，它学习了一个替代模型，该模型描述了可能的输入信号的参数化与规范评估之间的关系。在本文中，我们改善了使用BO的伪造；首先采用两种突出的BO方法，一种适合本地替代模型，另一个适合当地的替代模型，利用了用户的先验知识。其次，本文介绍了伪造功能的采集函数的表述。基准评估显示，使用BO的局部替代模型来伪造以前难以伪造的基准示例的显着改善。在伪造过程中使用先验知识被证明是在模拟预算有限时特别重要的。对于某些基准问题，采集功能的选择清楚地影响了成功伪造所需的模拟数量。