无人驾驶汽车（UAV）的使用提供了各种应用程序的许多优势。但是，安全保证是广泛使用的关键障碍，尤其是考虑到无人机所经历的不可预测的操作和环境因素，这些因素很难仅在设计时间内捕获。本文提出了一种称为SAFEDRONES的新可靠性建模方法，以通过实现无人机的运行时可靠性和风险评估来帮助解决此问题。它是可执行数字可靠身份（EDDI）概念的原型实例化，该概念旨在为多机器人系统的实时，数据驱动的可靠性保证创建基于模型的解决方案。通过提供实时可靠性估算，SAFEDRONES允许无人机以自适应方式相应地更新其任务。

机器学习〜（ML）近年来在不同的应用和域上提供了令人鼓舞的结果。但是，在许多情况下，需要确保可靠性甚至安全性等质量。为此，一个重要方面是确定是否在适合其应用程序范围的情况下部署了ML组件。对于其环境开放且可变的组件，例如在自动驾驶汽车中发现的组件，因此，重要的是要监视其操作情况，以确定其与ML组件训练有素的范围的距离。如果认为该距离太大，则应用程序可以选择考虑ML组件结果不可靠并切换到替代方案，例如改用人类操作员输入。 SAFEML是一种基于培训和操作数据集的统计测试的距离测量，用于执行此类监视的模型无形方法。正确设置Safeml的限制包括缺乏用于确定给定应用程序的系统方法，需要多少个操作样本来产生可靠的距离信息以及确定适当的距离阈值。在这项工作中，我们通过提供实用方法来解决这些限制，并证明其在众所周知的交通标志识别问题中的用途，并在一个使用Carla开源汽车模拟器的示例中解决了这些局限性。

配子等合作驾驶系统，依靠沟通和信息交换，为每个特工创造情境感知。因此，控制部件的设计和性能与通信部件性能紧密耦合。车辆之间的信息流可以显着影响排的动态。因此，排列的性能和稳定性不仅取决于车辆的控制器，还取决于信息流拓扑（IFT）。 IFT可能导致某些排特性的限制，即稳定性和可扩展性。蜂窝载体 - 一切（C-V2X）已成为支持连接和自动化车辆应用的主要通信技术之一。由于数据包丢失，无线通道会创建随机链路中断和网络拓扑的变化。在本文中，我们使用一阶马尔可夫模型模拟车辆之间的通信链路，以捕获每个链路的普遍时间相关性。这些模型通过在系统设计阶段期间的通信链路更好地近似来实现性能评估。我们的方法是使用实​​验中的数据来使用马尔可夫链的分组间隙（IPG）和连续IPG状态的过渡概率矩阵来模拟分组间隙（IPG）。使用基于各种不同车辆密度和通信率的经验数据来源的模型从高保真模拟中收集训练数据。利用IPG模型，我们分析了一家车辆的平均方形稳定性，标准共识协议调整了理想的通信，并比较不同情景的性能下降。

背景：虽然卷积神经网络（CNN）实现了检测基于磁共振成像（MRI）扫描的阿尔茨海默病（AD）痴呆的高诊断准确性，但它们尚未应用于临床常规。这是一个重要原因是缺乏模型可理解性。最近开发的用于导出CNN相关性图的可视化方法可能有助于填补这种差距。我们调查了具有更高准确性的模型还依赖于先前知识预定义的判别脑区域。方法：我们培训了CNN，用于检测痴呆症和Amnestic认知障碍（MCI）患者的N = 663 T1加权MRI扫描的AD，并通过交叉验证和三个独立样本验证模型的准确性= 1655例。我们评估了相关评分和海马体积的关联，以验证这种方法的临床效用。为了提高模型可理解性，我们实现了3D CNN相关性图的交互式可视化。结果：跨三个独立数据集，组分离表现出广告痴呆症与控制的高精度（AUC $ \ GEQUQ $ 0.92）和MCI与控制的中等精度（AUC $ \约0.75美元）。相关性图表明海马萎缩被认为是广告检测的最具信息性因素，其其他皮质和皮质区域中的萎缩额外贡献。海马内的相关评分与海马体积高度相关（Pearson的r $ \大约$ -0.86，p <0.001）。结论：相关性地图突出了我们假设先验的地区的萎缩。这加强了CNN模型的可理解性，这些模型基于扫描和诊断标签以纯粹的数据驱动方式培训。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.

In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1]. More precisely, by employing oblique splits consisting in multivariate linear combination of features instead of the axis-parallel ones, we will devise an auto-encoder method through the computation of a sparse solution of a set of linear inequalities consisting of feature values constraints. The code for reproducing our results is available at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de facto standard for training neural networks without leaking sensitive information about the training data. However, applying it to models for graph-structured data poses a novel challenge: unlike with i.i.d. data, sensitive information about a node in a graph cannot only leak through its gradients, but also through the gradients of all nodes within a larger neighborhood. In practice, this limits privacy-preserving deep learning on graphs to very shallow graph neural networks. We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph. We develop three random-walk-based methods for generating such disjoint subgraphs and perform a careful analysis of the data-generating distributions to provide strong privacy guarantees. Through extensive experiments, we show that our method greatly outperforms the state-of-the-art baseline on three large graphs, and matches or outperforms it on four smaller ones.