自1970年代初以来，已经开发并改进了质谱仪和不连贯的散射雷达（MSIS）模型家族。 MSI的最新版本是海军研究实验室（NRL）MSIS 2.0经验大气模型。 NRLMSIS 2.0提供物种密度，质量密度和温度估计作为位置和空间天气条件的功能。长期以来，MSIS模型一直是研究和运营社区中的大气模型的流行选择，但与许多模型一样，并未提供不确定性估计。在这项工作中，我们开发了基于机器学习（ML）的外层温度模型，该模型可与NRLMSIS 2.0一起使用，以相对于高保真卫星密度估计值校准其。我们的模型（称为MSIS-UQ）没有提供点估计，而是输出一个分布，该分布将使用称为校准误差评分的度量进行评估。我们表明，MSIS-UQ的DEMIAS nRLMSIS 2.0导致模型和卫星密度之间的差异减少25％，并且比太空力量的高精度卫星阻力模型更接近卫星密度。我们还通过生成物种密度，质量密度和温度的高度曲线来显示模型的不确定性估计功能。这明确证明了外层温度概率如何影响NRLMSIS 2.0内的密度和温度曲线。另一项研究显示，相对于单独的NRLMSIS 2.0，迅速过冷的能力提高了，从而增强了它可以捕获的现象。

遗传算法具有独特的属性，当应用于黑匣子优化时很有用。使用选择，交叉和突变算子，可以获得候选溶液，而无需计算梯度。在这项工作中，我们研究了从遗传算法的选择机理中使用量子增强的算子获得的结果。我们的方法将选择过程描述为最小化的二元二次模型，我们使用该模型编码适合度和人群成员之间的距离，我们利用量子退火系统来为选择机制采样低能解决方案。我们在各种黑盒目标函数（包括ONEMAX函数）以及来自IOH-Profiler库中的函数进行黑盒优化的函数基准对这些量子增强算法基准针对经典算法进行基准测试。与OneMax功能上的经典相比，我们观察到平均世代相传的性能增长，以收敛到量子增强的精英选择运算符。我们还发现，具有非专业选择的量子增强选择算子在IOHProfiler库中具有适应性扰动的功能上的基准优于基准。此外，我们发现，在精英选择的情况下，量子增强的操作员在不同程度的虚拟变量和中立性方面的函数上优于经典基准。

机器学习（ML）通常被视为一种黑盒回归技术，无法提供相当大的科学见解。 ML模型是通用函数近似器，如果正确使用，则可以提供与用于拟合的地面数据集有关的科学信息。 ML比参数模型的好处是，没有预定义的基础函数限制可以建模的现象。在这项工作中，我们在三个数据集上开发了ML模型：太空环境技术（SET）高精度卫星阻力模型（HASDM）密度数据库，这是Jacchia-Bowman 2008经验热层密度模型（JB2008），Jacchia-Bowman 2008经验的空间端段匹配数据集，以及具有挑战性的Minisatellite有效载荷（Champ）的加速度计衍生的密度数据集。将这些ML模型与海军研究实验室质谱仪和不相互分的散射雷达（NRLMSIS 2.0）模型进行比较，以研究中热层中传感后冷却的存在。我们发现NRLMSIS 2.0和JB2008-ML都不能说明后冷却，因此在强烈的地磁风暴（例如2003年万圣节风暴）之后的时期内表现不佳。相反，HASDM-ML和Champ-ML确实显示了传感后冷却的证据，表明这种现象存在于原始数据集中。结果表明，根据位置和暴风雨强度，速度1-3天的密度降低可能会发生1--3天。

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.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.