班级不平衡问题是许多现实世界中的机器学习任务的固有，尤其是对于罕见的事实分类问题。尽管数据不平衡的影响和处理是广为人知的，但度量标准对阶级失衡的敏感性的幅度很少引起关注。结果，敏感的指标通常被忽略，而其敏感性可能只有边际。在本文中，我们介绍了一个直观的评估框架，该框架量化了指标对类不平衡的敏感性。此外，我们揭示了一个有趣的事实，即指标的敏感性存在对数行为，这意味着较高的失衡比与指标的较低灵敏度有关。我们的框架建立了对阶级不平衡对指标的影响的直观理解。我们认为，这可以帮助避免许多常见的错误，特别是强调和错误的假设，即在不同的级别不平衡比率下所有指标的数量都是可比的。

太阳耀斑不仅对外层空间的技术和宇航员的健康构成风险，而且还会在我们的高科技，相互联系的基础设施中造成破坏我们的生活。尽管已经提出了许多机器学习方法来改善耀斑预测，但据我们所知，它们都没有研究过异常值对可靠性和这些模型的性能的影响。在这项研究中，我们研究了异常值在多元时间序列基准数据集中的影响，即天鹅 - SF对耀斑预测模型，并检验我们的假设。也就是说，Swan-SF中存在异常值，将其删除增强了看不见的数据集上预测模型的性能。我们采用隔离森林来检测弱耀斑实例之间的异常值。使用大量污染速率进行了几项实验，这些污染速率确定了当前异常值的百分比。我们使用LimeseriessVC来评估每个数据集的实际污染质量。在我们最好的发现中，我们的真实技能统计数据增加了279％，海德克技能得分提高了68％。结果表明，如果检测到并正确删除异常值，总体上可以取得重大改进来爆发预测。

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.

Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools for a wide range of applications such as disaster response, automated mining, agriculture, military operations, search and rescue missions, and planetary exploration. Experimental validation is a requisite for CAV and AGV research, but requires a large, safe experimental environment when using full-size vehicles which is time-consuming and expensive. To address these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled vehicle platform for Connected autonomy and All-terrain Research), an open-source, cost-effective proportionally one-tenth scaled experimental vehicle platform governed by the same physics as a full-size on-road vehicle. XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed Controller (ESC) with drivers written in the new Robot Operating System (ROS 2) to facilitate experimental CAV and AGV perception, motion planning and control research, that incorporate state-of-the-art computationally expensive algorithms such as Deep Reinforcement Learning (DRL). XTENTH-CAR is designed for compact experimental environments, and aims to increase the accessibility of experimental CAV and AGV research with low upfront costs, and complete Autonomous Vehicle (AV) hardware and software architectures similar to the full-sized X-CAR experimental vehicle platform, enabling efficient cross-platform development between small-scale and full-scale vehicles.

Although extensive research in planning has been carried out for normal scenarios, path planning in emergencies has not been thoroughly explored, especially when vehicles move at a higher speed and have less space for avoiding a collision. For emergency collision avoidance, the controller should have the ability to deal with complicated environments and take collision mitigation into consideration since the problem may have no feasible solution. We propose a safety controller by using model predictive control and artificial potential function. A new artificial potential function inspired by line charge is proposed as the cost function for our model predictive controller. The new artificial potential function takes the shape of all objects into consideration. In particular, the artificial potential function that we proposed has the flexibility to fit the shape of the road structures such as the intersection, while the artificial potential function in most of the previous work could only be used in a highway scenario. Moreover, we could realize collision mitigation for a specific part of the vehicle by increasing the quantity of the charge at the corresponding place. We have tested our methods in 192 cases from 8 different scenarios in simulation. The simulation results show that the success rate of the proposed safety controller is 20% higher than using HJ-reachability with system decomposition. It could also decrease 43% of collision that happens at the pre-assigned part.

A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even though previous studies have developed accurate and robust driver's vigilance detection algorithms, these methods are still facing challenges on following areas: (a) small sample size training, (b) anomaly signal detection, and (c) subject-independent classification. In this paper, we propose a generalized few-shot model, namely EEG-Fest, to improve aforementioned drawbacks. The EEG-Fest model can (a) classify the query sample's drowsiness with a few samples, (b) identify whether a query sample is anomaly signals or not, and (c) achieve subject independent classification. The proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for 1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for 1-shot and 5-shot support samples in the SADT dataset.

从演示中学习（LFD）是一种从人提供的演示中复制和概括机器人技能的流行方法。在本文中，我们提出了一种基于优化的新型LFD方法，该方法将演示描述为弹性图。弹性图是通过弹簧网格连接的节点的图。我们通过将弹性地图拟合到一组演示中来构建技能模型。我们方法中的公式优化问题包括三个具有自然和物理解释的目标。主术语奖励笛卡尔坐标中的平方误差。第二项惩罚了导致最佳轨迹总长度的点的非等应存在分布。第三学期奖励平滑度，同时惩罚非线性。这些二次目标形成了凸问题，可以通过局部优化器有效地解决。我们研究了九种用于构建和加权弹性图并研究其在机器人任务中的性能的方法。我们还使用UR5E操纵器组在几个模拟和现实世界中评估了所提出的方法，并将其与其他LFD方法进行比较，以证明其在各种指标中的好处和灵活性。

在这项工作中，我们通过用户定义的关系网络将“社交”相互作用集成到MARL设置中，并检查代理与代理关系对新兴行为兴起的影响。利用社会学和神经科学的见解，我们提出的框架模型使用奖励共享的关系网络（RSRN）的构图代理关系，其中网络边缘的权重衡量了一项代理在成功中投入多少代理（或关心“关心） '） 其他。我们构建关系奖励是RSRN相互作用权重的函数，以通过多代理增强学习算法共同训练多代理系统。该系统的性能经过了具有不同关系网络结构（例如自我利益，社区和专制网络）的3个代理方案的测试。我们的结果表明，奖励分享关系网络可以显着影响学习的行为。我们认为，RSRN可以充当一个框架，不同的关系网络会产生独特的新兴行为，通常类似于对此类网络的直觉社会学理解。

随着物联网（IoT），边缘计算和云计算的普及，正在开发越来越多的流分析应用程序，包括在物联网传感数据之上的实时趋势预测和对象检测。一种流行的流分析类型是基于重复的神经网络（RNN）基于深度学习模型的时间序列或序列数据预测和预测。与假设数据提前可用并且不会更改的传统分析不同，流分析涉及正在连续生成的数据，并且数据趋势/分布可能会发生变化（又称概念漂移），这将导致预测/预测准确性下降时间。另一个挑战是为流分析找到最佳的资源提供，以达到良好的总体延迟。在本文中，我们研究了如何使用称为长期记忆（LSTM）的RNN模型来最佳利用边缘和云资源，以获得更好的准确性和流式分析。我们为混合流分析提出了一个新颖的边缘云集成框架，该框架支持云上边缘和高容量训练的低潜伏期推断。为了实现灵活的部署，我们研究了部署混合学习框架的不同方法，包括以边缘为中心，以云为中心和边缘云集成。此外，我们的混合学习框架可以根据历史数据进行预训练的LSTM模型，并根据最新数据定期重新训练LSTM模型的推理结果。使用现实世界和模拟流数据集，我们的实验表明，在延迟方面，提出的Edge-Cloud部署是所有三种部署类型中最好的。为了准确性，实验表明我们的动态学习方法在所有三种概念漂移方案的所有学习方法中都表现出最好的作用。

测量机器人系统的整体自主评分需要一组相关方面和系统的组合，这些方面和特征可以以不同的单位，定性和/或不和谐测量。在本文中，我们建立了现有的非语境自治框架，以衡量并结合系统的自主水平和系统的组件性能，作为整体自治分数。我们检查一些组合功能的方法，显示一些方法如何找到相同数据的不同排名，并且我们使用加权产品方法来解决此问题。此外，我们介绍了非语境自治坐标，并表示具有自主距离的系统的整体自主权。我们将我们的方法应用于一组七个无人驾驶空中系统（UAS），并获得绝对的自主评分以及与最佳系统相对得分。