在大脑计算机界面（BCI）研究中，记录数据耗时且昂贵，这限制了对大数据集的访问。这可能会影响BCI系统的性能，因为机器学习方法在很大程度上取决于训练数据集的大小。出现重要的问题：考虑到神经元信号特征（例如，非平稳性），我们可以通过更多数据来实现更高的解码性能来训练解码器吗？在长期BCI研究的情况下，随着时间的推移进一步改善的观点是什么？在这项研究中，我们从两个主要角度研究了长期记录对电动图像解码的影响：有关数据集大小的模型要求和患者适应的潜力。我们评估了长期BCI和四边形NCT02550522的多线性模型和两个深度学习模型（DL）模型，其中包含43个由四脑术患者执行的ECOG记录的43次会议。在实验中，参与者使用运动图像模式执行了3D虚拟手工翻译。我们设计了多个计算实验，其中增加或翻译了训练数据集，以研究模型的性能与影响记录的不同因素之间的关系。我们的分析表明，在培训数据集中添加更多数据可能不会立即提高已经包含40分钟信号的数据集的性能。与多线性模型相比，DL解码器在数据集大小上显示出类似的要求，同时证明了更高的解码性能。此外，通过在实验后面记录的相对较小的数据集获得了高解码性能，这表明运动图像模式得到改善和患者适应。最后，我们提出了UMAP嵌入和局部固有维度，以可视化数据并可能评估数据质量。

在许多在线决策过程中，要求优化代理在具有许多固有相似之处的大量替代方案之间进行选择。反过来，这些相似性意味着可能会混淆标准离散选择模型和匪徒算法的损失。我们在嵌套土匪的背景下研究了这个问题，这是一类对抗性的多臂匪徒问题，学习者试图在存在大量不同的替代方案的情况下最小化他们的遗憾，并具有嵌入式（非组合）相似性的层次结构。在这种情况下，基于指数级的蓝图（例如树篱，EXP3及其变体）的最佳算法可能会产生巨大的遗憾，因为它们倾向于花费过多的时间来探索与相似，次优成本的无关紧要的替代方案。为此，我们提出了一种嵌套的指数权重（新）算法，该算法根据嵌套的，分步选择方法对学习者的替代方案进行分层探索。这样一来，我们就获得了一系列紧密的界限，以表明学习者可以有效地解决与替代方案之间高度相似性的在线学习问题，而不会发生红色的巴士 /蓝色巴士悖论。

估计目标范围在视觉对象跟踪中构成了基本挑战。通常，跟踪器以箱子为中心，并且完全依靠边界框来定义场景中的目标。实际上，对象通常具有复杂的形状，并且与图像轴不符。在这些情况下，边界框不能提供对目标的准确描述，并且通常包含大多数背景像素。我们提出了一个以细分为中心的跟踪管道，该管道不仅会产生高度准确的分割掩码，而且还可以使用分割掩码而不是边界框来使用内部。因此，我们的跟踪器能够更好地学习目标表示形式，该目标表示明确将场景中的目标与背景内容区分开来。为了实现具有挑战性的跟踪方案的必要鲁棒性，我们提出了一个单独的实例本地化组件，该组件用于在产生输出掩码时用于调节分割解码器。我们从分段掩码中推断出一个边界框，验证我们的跟踪器在挑战跟踪数据集方面，并在LASOT上实现新的最新状态，并以69.7％的速度获得了AUC得分。由于大多数跟踪数据集不包含掩码注释，因此我们无法使用它们来评估预测的分割掩码。相反，我们在两个流行的视频对象细分数据集上验证了分割质量。

超越地球轨道的人类空间勘探将涉及大量距离和持续时间的任务。为了有效减轻无数空间健康危害，数据和空间健康系统的范式转移是实现地球独立性的，而不是Earth-Reliance所必需的。有希望在生物学和健康的人工智能和机器学习领域的发展可以解决这些需求。我们提出了一个适当的自主和智能精密空间健康系统，可以监控，汇总和评估生物医学状态;分析和预测个性化不良健康结果;适应并响应新累积的数据;并提供对其船员医务人员的个人深度空间机组人员和迭代决策支持的预防性，可操作和及时的见解。在这里，我们介绍了美国国家航空航天局组织的研讨会的建议摘要，以便在太空生物学和健康中未来的人工智能应用。在未来十年，生物监测技术，生物标志科学，航天器硬件，智能软件和简化的数据管理必须成熟，并编织成精确的空间健康系统，以使人类在深空中茁壮成长。

空间生物学研究旨在了解太空飞行对生物的根本影响，制定支持深度空间探索的基础知识，最终生物工程航天器和栖息地稳定植物，农作物，微生物，动物和人类的生态系统，为持续的多行星寿命稳定。要提高这些目标，该领域利用了来自星空和地下模拟研究的实验，平台，数据和模型生物。由于研究扩展到低地球轨道之外，实验和平台必须是最大自主，光，敏捷和智能化，以加快知识发现。在这里，我们介绍了由美国国家航空航天局的人工智能，机器学习和建模应用程序组织的研讨会的建议摘要，这些应用程序为这些空间生物学挑战提供了关键解决方案。在未来十年中，将人工智能融入太空生物学领域将深化天空效应的生物学理解，促进预测性建模和分析，支持最大自主和可重复的实验，并有效地管理星载数据和元数据，所有目标使生活能够在深空中茁壮成长。

Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of learning stochastic policies with continuous actions from the viewpoint of counterfactual risk minimization (CRM). While the CRM framework is appealing and well studied for discrete actions, the continuous action case raises new challenges about modelization, optimization, and~offline model selection with real data which turns out to be particularly challenging. Our paper contributes to these three aspects of the CRM estimation pipeline. First, we introduce a modelling strategy based on a joint kernel embedding of contexts and actions, which overcomes the shortcomings of previous discretization approaches. Second, we empirically show that the optimization aspect of counterfactual learning is important, and we demonstrate the benefits of proximal point algorithms and differentiable estimators. Finally, we propose an evaluation protocol for offline policies in real-world logged systems, which is challenging since policies cannot be replayed on test data, and we release a new large-scale dataset along with multiple synthetic, yet realistic, evaluation setups.

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds. Our theory provides theoretical guarantees and explicit bounds on the functional form of the graph Laplacian, in the case when it acts on functions defined close to singularities of the underlying manifold. We also propose methods that can be used to estimate these geometric properties of the point cloud, which are based on the theoretical guarantees.

Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous $N$-player games in literature. However, limiting applicability, existing theoretical results assume variations of a "population generative model", which allows arbitrary modifications of the population distribution by the learning algorithm. Instead, we show that $N$ agents running policy mirror ascent converge to the Nash equilibrium of the regularized game within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ samples from a single sample trajectory without a population generative model, up to a standard $\mathcal{O}(\frac{1}{\sqrt{N}})$ error due to the mean field. Taking a divergent approach from literature, instead of working with the best-response map we first show that a policy mirror ascent map can be used to construct a contractive operator having the Nash equilibrium as its fixed point. Next, we prove that conditional TD-learning in $N$-agent games can learn value functions within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ time steps. These results allow proving sample complexity guarantees in the oracle-free setting by only relying on a sample path from the $N$ agent simulator. Furthermore, we demonstrate that our methodology allows for independent learning by $N$ agents with finite sample guarantees.