使用摄像机和计算算法的生理学（例如心脏和肺）生理学的非侵入性，低成本和可扩展性测量的生命体征非常有吸引力。但是，代表各种环境，身体运动，照明条件和生理状态的各种数据是费力的，耗时且昂贵的。合成数据已被证明是机器学习的几个领域的有价值工具，但并未广泛用于摄像机测量生理状态。合成数据提供“完美”标签（例如，没有噪声且具有精确的同步），可能无法获得其他标签（例如，精确的像素级分段图），并提供了对数据集中变化和多样性的高度控制。我们提供Scamps，这是一个合成学数据集，其中包含2,800个视频（168万帧），并带有对齐的心脏和呼吸信号以及面部动作强度。 RGB框架与分割图一起提供。我们提供有关潜在波形的精确描述性统计数据，包括beat间间隔，心率变异性和脉搏到达时间。最后，我们介绍了这些合成数据和对现实世界数据集测试的基线结果培训，以说明可推广性。

基于相机的非接触式光电子溶血性描绘是指一组流行的非接触生理测量技术。目前的最先进的神经模型通常以伴随金标准生理测量的视频以监督方式培训。但是，它们通常概括域名差别示例（即，与培训集中的视频不同）。个性化模型可以帮助提高型号的概括性，但许多个性化技术仍然需要一些金标准数据。为了帮助缓解这一依赖性，在本文中，我们展示了一种名为Mobilememon的新型移动感应系统，该系统是第一个移动个性化远程生理传感系统，它利用智能手机上的前后相机，为培训产生高质量的自我监督标签个性化非接触式相机的PPG模型。为了评估MobilemeLephys的稳健性，我们使用39名参与者进行了一个用户学习，他们在不同的移动设备下完成了一组任务，照明条件/强度，运动任务和皮肤类型。我们的研究结果表明，Mobilephys显着优于最先进的设备监督培训和几次拍摄适应方法。通过广泛的用户研究，我们进一步检查了Mobilephys如何在复杂的真实环境中执行。我们设想，从我们所提出的双摄像机移动传感系统产生的校准或基于相机的非接触式PPG模型将为智能镜，健身和移动健康应用等许多未来应用打开门。

对医疗保健监控的远程工具的需求从未如此明显。摄像机测量生命体征利用成像装置通过分析人体的图像来计算生理变化。建立光学，机器学习，计算机视觉和医学的进步这些技术以来的数码相机的发明以来已经显着进展。本文介绍了对生理生命体征的相机测量综合调查，描述了它们可以测量的重要标志和实现所做的计算技术。我涵盖了临床和非临床应用以及这些应用需要克服的挑战，以便从概念上推进。最后，我描述了对研究社区可用的当前资源（数据集和代码），并提供了一个全面的网页（https://cameravitals.github.io/），其中包含这些资源的链接以及其中引用的所有文件的分类列表文章。

Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "end-to-end" models; however these methods still require several preprocessing steps. These additional operations are often non-trivial to implement making replication and deployment difficult and can even have a higher computational budget than the "core" network itself. In this paper, we propose two novel and efficient neural models for camera-based physiological measurement called EfficientPhys that remove the need for face detection, segmentation, normalization, color space transformation or any other preprocessing steps. Using an input of raw video frames, our models achieve strong performance on three public datasets. We show that this is the case whether using a transformer or convolutional backbone. We further evaluate the latency of the proposed networks and show that our most light weight network also achieves a 33% improvement in efficiency.

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.